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.”
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.
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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.
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.
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?
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.
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.)
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 concoctthem 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.
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.
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 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 severalothertools 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.
COPPER
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.
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.
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.
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
CHRIS LAKE
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.
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.
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.
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:
Is demand becoming steady, predictable, and large enough to justify dedicated capacity?
At what level of usage does ownership make economic sense?
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.
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.
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 hasstunned 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.
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.
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 investigationfound 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.
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.
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.
Altec Research has built a prototype that tracks head movement, facial skin temperature, and pore activity.
ALTEC RESEARCH
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.”
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.
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. AIlabresearchers 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.”
The legislation, the Reimagining Safety Act, was developed in consultation with community groups and local and state representatives. The advocacy organizations Just Futures Law, which focuses on immigrant rights and racial justice, and Mijente, which describes itself as a “vehicle for building independent Latinx political power,” helped shape the calls to terminate the border towers program, following a joint report they put out earlier this year on the technology used by ICE. The legislation is intended to be part of a broader proposal to replace the Department of Homeland Security with a new Department of Community Safety while moving CISA, FEMA, TSA, and customs functions to other existing federal agencies. Her office has yet to release the text of the proposed legislation, saying that it will come in the next few weeks.
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.
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.
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.”
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?
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+ Scientists have discovered the first new penguin species in more than 100 years. + Discover the filmmaking skills that made Paul Verhoeven’s RoboCopa 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 theFinancial 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.
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.
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.
Don’t be fooled by this summer of AI hype
—Timnit Gebru, executive director of the Distributed AI Research Institute (DAIR), and Emily M. Bender, professor of linguistics at the University of Washington
It’s been a busy few months for AI hype, with companies making breathless claims about hacking, mathematical breakthroughs, and the prospect of self-improving superintelligence.
In all these cases, massive fanfare from the companies, presented as mea culpas in the hacking incidents, has been accompanied by intense press coverage. But once experts have had time to examine what happened, very different stories emerge.
There’s a strong commercial incentive to overstate AI capabilities. The authors argue that the illusion of speed and urgency promulgated by the tech companies is also a ploy to misdirect policymakers and the public.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 22 nations have called for a new global body to oversee AI They want pre-deployment testing and common safety standards. (Politico) + But neither the US nor China are part of the new declaration. (NBC News) + Meanwhile, OpenAI has proposed its own global AI safety standards. (Axios) + The company wants the US to lead the international effort. (Reuters $) + Could AI really kill us all? (MIT Technology Review)
2 Texas and California have moved to rein in data centers Texas has halted new permits pending a grid audit. (NBC News) + California has passed new laws covering power and water use. (LA Times $) + Data centers are amazing. Everyone hates them. (MIT Technology Review)
3 Chipmaker AMD has become the twelfth $1 trillion company AI chip sales have helped its shares rise more than 180% this year. (CNBC) + Hyperscalers are making a trillion-dollar bet on AI. (MIT Technology Review)
4 Meta’s AI agent Muse has overtaken ChatGPT to top the US App Store It’s helped set Meta shares for their best month in 13 years. (Quartz) + But Muse has a major zero-day vulnerability. (Ars Technica) + And Amazon has blocked the agent from shopping on its site. (GeekWire)
5 A new edible battery could power medical devices inside the body It could run sensors, cameras, and drug-delivery capsules. (Economist $) + It’s been tested in pigs with an RFID tracker and stomach stimulator. (Nature)
6 Trump’s budget chief could gain veto power over NIH grants The NIH is the world’s largest funder of biomedical research. (Ars Technica) + Trump’s firings dealt another blow to science. (MIT Technology Review)
7 Epigenetic editing has reached human trials for hepatitis B The treatment uses chemical tags to shut down viral DNA. (Nature)
8 Tree vaccines and gene-edited butterflies have won UK biotech funding The projects aim to boost nature’s adaptation to climate change.(Guardian) + Why this summer was so hot—and 2027 could be worse. (MIT Technology Review)
9 A rocket shortage is making it harder to get satellites into space SpaceX is winding down Falcon 9 as rivals struggle to catch up. (WSJ $)
10 Something has gone badly wrong with Dyson’s $500 toothbrush A mysterious “component issue” has emerged. (Wired $)
Quote of the day
“If AI succeeds, then it may kill all of our jobs. If it fails, then the whole economy falls apart, and your 401(k) blows up, and maybe you still lose your job.”
—Ben Casselman, the New York Times’s chief economics correspondent, explains why people feel trapped in a lose-lose dynamic with the AI boom.
One more thing
How wind tech could help decarbonize cargo shipping
Cargo shipping is responsible for about 3% of the world’s annual greenhouse-gas emissions, and the industry is under pressure to find alternatives to fossil fuels. Wind power is one option.
In the Marshall Islands, where people have relied on wind-powered vessels for millennia, a new cargo sailboat inspired by traditional vessels made its maiden voyage last year. It could cut emissions by up to 80% compared with a fuel-powered cargo ship.
It’s been a busy few months for AI hype. At the end of April, Anthropic claimed that its model Claude Mythos is better at finding software vulnerabilities than most security experts. Then we had the OpenAI–Hugging Face hacking incident, after which Anthropic (proudly) and Meta (reluctantly) disclosed similar incidents involving their models.
This was followed by Anthropic’s claim that one of its models had made a mathematical breakthrough; soon OpenAI claimed a mathematical breakthrough of its own. Most recently, Anthropic engineer Jacob Coxon went viral announcing his departure from the company, claiming that it and OpenAI are “racing straight towards self-improving superintelligence and gambling with our lives.”
Each of these events was mostly covered breathlessly by the press, often repeating the companies’ anthropomorphizing framings—which are designed to portray their software is not only powerful but incipient “artificial general intelligence.” So what is really going on? Are we witnessing a massive, civilization-changing set of technological breakthroughs, or is this marketing?
In all these incidents, massive fanfare from the companies (presented as mea culpas in illicit hacking cases) is accompanied by intense press coverage. Once there is time for experts in the relevant fields to examine what happened, a very different story emerges, but one that gets less media attention.
Regarding the “hacking” incidents, cybersecurity experts say the story is more about OpenAI’s negligence and failure to adopt basic, established security practices than about “models gone rogue” or “AI agents creating civilizations.”
As for the mathematical results, mathematicians who were initially “stunned” by OpenAI’s press release saying that its latest chatbot, Astra, solved problems that “have been open and seen no progress on the main result for at least a decade”—but they later realized that the results weren’t as “novel as first appeared.” Since then, mathematicians have accused the company of research misconduct and plagiarism, and they’ve reiterated that Astra didn’t make a “profound intellectual leap.” Just weeks later, OpenAI claimed its own mathematical breakthrough. Two days before, Tristan Buckmaster, a math professor at New York University’s Courant Institute, published a bombshell statement suggestingthatOpenAI had stolen other people’s work and improperly attributed it.
It’s worth thinking about why there is so much attention on computer programming and math as fields in which to apply large language models and related technology. Not only are they often elevated as the pinnacle of human intellectual achievement, but they involve problems where answers, once suggested, can be verified. The former property helps AI hype mongers sell the idea that they are building everything machines. The latter makes math and coding problems easier to tune systems for, since system output (sequences of likely words or pieces of computer code) can be evaluated without having to pay data workers to look at and annotate each one.
Mathematicians in particular have warned against corporations using their field in this way. A statement signed by hundreds of them says there is “currently a strong commercial incentive on the part of the technology industry to overstate the capabilities of their products” and asks policymakers to “consult with experts, including mathematicians, in forming policy decisions rather than relying on press releases or popular reporting of mathematical results.”
We echo this call and note that the illusion of speed and urgency promulgated by the tech companies is also a ploy to misdirect both policymakers and the public. Unfortunately, it sometimes works, such as with Senator Bernie Sanders’s well-meaning but ultimately misguided proposed legislation to prevent the development of “artificial superintelligence.”
Describing them as “superintelligence” or “rogue models” ascribes agency to products rather than to the companies building them. This framing markets these companies’ products as “superhuman” and, at the same time, helps the companies evade accountability for their actions.
Instead of OpenAI being prosecuted for creating malware that hacked another company, press releases, news outlets, media personalities, and lawmakers refer to “rogue models” as if they acted on their own. Instead of researchers being questioned about their companies’ habit of plagiarizing academics’ work or using customer data to train models without consent, the public’s imagination is redirected to fears about what the future might hold upon the arrival of fictional superintelligent machines.
The AI industry has even suggested that popular, bipartisan anti-data-center activism is a “distraction” from attempts to regulate the impending, scary, “superhuman” machines these companies are building. According to the AI industry, we should be more worried about a fictional machine god than about the climate catastrophe that these data centers exacerbate, the asthma suffered by those living near them, the rising electricity bills of the public subsidizing them, or the water that is redirected to cooling them.
We know better than to make decisions based on marketing and better than to capitulate to corporate pressure to make those decisions quickly. Wise decision-making, by policymakers and communities, demands time to hear from independent experts and contextualize corporate claims. The best possible outcome from this summer of hype is that policymakers and the public at large learn to take a breath, hold onto our skepticism, and recognize this kind of hype for what it is the next time it comes around.
Timnit Gebru is executive director of DAIR and author of the forthcoming book Deep Unlearning: The Radicalization of a Tech Idealist, which is available for preorders now and set to publish on February 16. Emily M. Bender is professor of linguistics at the University of Washington and coauthor of The AI Con.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
The US spent billions on border surveillance. Why can’t it catch people before they die?
When José Morales Bernal crossed the border into the US in April 2024, the day before his 32nd birthday, he was within range of three surveillance towers equipped with cameras and AI to automatically detect and track people.
If the system worked as intended, Morales should have been apprehended. If he needed medical help, agents were trained to provide it. None of that happened.
Instead, Morales died just 360 feet from the closest tower. It was local landfill workers, rather than Border Patrol, who first spotted him. An autopsy concluded that he had died of “environmental exposure.”
A first-of-its-kind investigation by MIT Technology Review reveals that deaths like Morales’s are startlingly common. After mapping nearly 4,000 locations where human remains were found against information on nearly 600 surveillance towers, we discovered that a humanitarian crisis at the border has unfolded in view of the government’s own cameras.
The US government is about to spend another $1 billion to triple the virtual wall’s size, yet we found it has systemic flaws: broken towers, algorithms that failed to detect people and agents who simply did not respond to alerts. Here are four ideas for what Customs and Border Protection should do to rectify those failures.
An analysis by the Times of San Diego found at least 138 cases since 2022 in which migrants’ remains were found within the nominal range of a nearby surveillance tower. A separate MIT Technology Review analysis estimated that more than half of those people were likely within a tower’s field of view. Gómez Hernández was one of them. Here’s her story, written by our partners at Times of San Diego.
All of these stories are part of Dying on Camera, our new series investigating the failures of border surveillance technology and their human cost.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 An AI hallucination nearly triggered a US military operation A false report prompted plans to intercept a Chinese vessel. (CNN) + The incident comes as the Pentagon accelerates its use of AI. (Ars Technica) + “Humans in the loop” in AI war is an illusion. (MIT Technology Review)
2 Gemini has carried out Google’s first known autonomous AI hack Google says the behavior wasn’t “misaligned” because its safety measures caught it. (WSJ $) + Here’s why AI agents cheat to reach their goals. (MIT Technology Review)
3 The US has proposed an AI safety alert system with China It would flag AI incidents that pose national security risks. (AP) + Trump and Xi will consider the proposal at their summit this week. (NBC News) + Could AI really kill us all? (MIT Technology Review)
4 California’s governor has ordered work on an AI “kill switch” It’s part of Gavin Newsom’s executive order on AI safety. (NBC News) + The order also calls for independent oversight of AI companies. (NYT $)
5 Data centers are turning to forever chemicals for cooling PFAS cooling can reduce water use but raises pollution risks. (Fortune) + US regulators are fast-tracking PFAS for data center cooling. (The Hill)
6 Iran and China used AI agents to automate influence campaigns The agents created fake accounts and posts with little human input.(NYT $) + AI persuasion has entered elections. (MIT Technology Review)
7 Trump wants a new AI czar and an “AI Force” modeled on Space Force But he hasn’t said what the new force would do or where it would sit. (Axios) + The tech industry is scratching its head over the proposal. (Politico)
8 A cybercrime feud has erupted after a group hijacked a rival dark website The notorious ShinyHunters says it took control of cl0p’s site. (Reuters $)
9 Gambling giant DraftKings used AI to target “profitable losers” The model scored users on how they responded to promotions. (NYT $)
10 SpaceX’s next Starship flight will attempt its first orbital mission It will also deploy working Starlink V3 satellites. (New Scientist $)
Quote of the day
“There is 0% chance that’s going to be the end of the world.”
—Nvidia’s Jensen Huang dismisses warnings that AI could wipe out humanity by 2030 in an interview with CBS News.
One more thing
AI is pushing the limits of the physical world
Architecture often assumes a binary between built projects and theoretical ones. What physics allows in actual buildings is vastly different from what architects can imagine and design. But the latest advancements in AI have prompted a surge in the theoretical.
That shift was on display at an exhibition at Brooklyn’s Pratt Institute, which brought together works from more than 30 practitioners exploring AI’s experimental, generative and collaborative potential to open up new areas of architectural inquiry.
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ A beautiful new cat species is the first to be discovered in a century. + Explore Floor796, a massive, interactive pixel art pop-culture megastructure. + The HTML Review is an annual journal of literature made for the web that offers a refreshing, creative take on what online publishing can be. + Discover how William Blake shattered the boundaries between poetry, painting, and printmaking—and why the art world couldn’t understand him.
This story is part of Dying on Camera, a collaboration between MIT Technology Review and Times of San Diego. Journalists in both newsrooms spent the past year examining the failures of border surveillance technology and uncovering the stories of the people who die in the borderlands.
To answer that question, we looked through thousands of pages of government documents, visited the border multiple times, and interviewed more than 45 people, including current and former White House advisors, presidential appointees, Border Patrol agents, medical examiners, sheriffs, humanitarian volunteers, and employees of tech companies.
The result of our investigation is the first comprehensive map and analysis of deaths near border surveillance towers. Here’s how we built it, what decisions we made along the way, and what the data can and can’t tell us.
Our data
The investigation relied on knowing where migrants have died and where and when US Customs and Border Protection towers were installed. We analyzed cases dating back to 2015, allowing us to cover different border policies, presidential administrations, and tower technologies.
Migrant deaths
The US-Mexico border has been called the world’s deadliest land border, and Border Patrol estimates that more than 10,000 people have died during crossings since 2000—a figure that’s widely considered an undercount. But the amount of information available publicly on where and when these people died, and who they were, varies widely. Some remains are never discovered, and for those that are, there’s no national protocol for how the records are handled.
We considered a case for further analysis if we could confirm three basic things: whether the person was believed to have been crossing the border, where the remains were found, and roughly when the person died.
We created our dataset by merging existing ones that had been compiled and shared by other organizations, including No More Deaths, Humane Borders, and the Electronic Frontier Foundation, with new records we obtained ourselves from more than a dozen agencies.
Public records requests in Texas
Texas was the biggest missing link in most existing databases of migrant deaths. Unlike Arizona, it has no initiative to share records online, and unlike New Mexico, it leaves individual counties to handle their own death investigations. Some records for those individual counties existed, compiled by a handful of dedicated researchers, but nothing was comprehensive or current.
We identified 17 counties in Texas that would be relevant to our investigation: Culberson, Jeff Davis, Presidio, Terrell, Val Verde, Kinney, Maverick, Dimmit, Webb, Zapata, Jim Hogg, Starr, Hidalgo, Cameron, Kenedy, Brooks, and Duval. There are other counties in which migrants have died, but they do not have areas covered by surveillance towers and were therefore excluded.
In most of these counties, locally elected administrative judges called justices of the peace collect the most comprehensive information on local deaths. But records requests submitted to these individual judges can stall for years (indeed, some of our requests to justices of the peace remain unfilled more than a year after we filed them). Reports from justices of the peace sometimes leave out the scene or location details we needed; some justices never even visit the place where a migrant died, instead formally declaring the person deceased over FaceTime with the first responders on scene.
The border wall between Sunland Park, New Mexico and Puerto de Anapra, Mexico.
GETTY IMAGES
MIT Technology Review instead filed records requests to the sheriffs of these 17 counties beginning in July 2025. These agencies are often first on the scene when a death is discovered, and their reports can have more detailed information on where remains were found. But relying on sheriffs’ records means our analysis undercounts migrant deaths in Texas, as it doesn’t include any deaths handled by local or state police.
After we filed a request, it took periods of near daily calls to the offices to receive the records, if we received them at all. Some were provided with no charge. Other counties charged between $300 and $1,300 to fill our request. Counties warned that their records were incomplete, with an unknown number lost during a move, damaged, destroyed, or misplaced during transitions between sheriffs.
Those delays were compounded because most agencies do not record whether a person is believed to have been crossing the border when they died. That’s despite provisions in some counties that allow officers to track far more granular details about other situations; Zapata County reports have a checkbox to indicate whether jewels were stolen in a burglary, for example, while Cameron County reports have one for whether a burglar entered through a chimney. Without a way to readily identify migrant deaths, offices had to pull records by hand, making the process slower, more costly, and more prone to error.
We received usable records from 14 of these counties (records have yet to be received from Dimmit or Duval, and Maverick’s office charged a per-record fee that was cost prohibitive).
Agencies generally sent us police reports for each individual case, amounting to over 4,000 pages of records in total. Some were handwritten documents.
Except for the reports from Kenedy, Webb, and Hidalgo Counties, we pulled out the relevant information by hand. For those three counties, which sent large volumes of records, we used Anthropic’s Claude, accessed via API, to inspect each case report and pull out coordinates of the spots where remains were found; then we checked batches of those cases by hand to verify the AI’s accuracy. Agencies sent us records on a rolling basis from July 2025 to February 2026, and most did not specify the date through which their records were current.
We generally trusted that agencies sent us cases they believed involved migrant deaths, since they had the most information about each case. But some records clearly suggested otherwise (indicating, for example, that someone died at home), and we excluded those cases.
Several hundred cases were removed from our analysis because they didn’t include enough information for us to be certain where the remains were found. In fewer than 100 cases, coordinates were not listed but the description was specific enough for us to locate a point within 0.25 miles of where they were found—so we kept those in.
Compared with agencies in other states along the border, those in Texas provide far fewer details regarding how long ago someone may have died before remains were found or how far the decomposition of those remains had progressed. Some reports included this information, but many didn’t. While autopsy records sometimes have more details, many migrant autopsies in south Texas are done through the Webb County Medical Examiner, which charges a fee for each report. That was cost prohibitive given the scale of our analysis.
MIT Technology Review consulted with Greg Hess, director of Arizona’s Pima County Office of the Medical Examiner, on a framework for translating descriptions of the scenes, bodies, and causes of death available in many of these records into rough estimates for when a person may have died (more detail on this process is provided below).
In total, we included more than 1,500 cases from our Texas records requests in our final analysis.
Accessing Arizona records directly
The Pima County Office of the Medical Examiner (PCOME) in Arizona handles death investigations for all border counties in the state except Yuma (we sourced Yuma’s records from the organization No More Deaths, as detailed below).
The office determines which cases it believes to be border crossers. It publishes a data portal for these migrant deaths that’s updated monthly and shares its data with the organization Humane Borders, which then publishes it in a map and database. We included the data from 2015 through April 2026.
Humane Borders notes for each case how precise the location description is. We included only cases with GPS coordinates precise to within 300 feet. Collecting these coordinates has been standard practice since around 2012. The records also include an estimate of when the individual died, ranging from less than a day to at least six to eight months before remains were found.
In total, we included more than 1,700 cases from Humane Borders in our analysis.
Data collected by No More Deaths
To obtain data on deaths in California, New Mexico, Arizona’s Yuma County, and El Paso and Hudspeth Counties in Texas, we turned to the nonprofit organization No More Deaths, which has tracked migrant deaths since 2004. It publishes information about its records requests and its methodology.
Many of the cases in its database include GPS coordinates, sometimes with notes that the location is only approximate—for example, accurate to within half a mile or one mile. We included only cases where the location was known to within a quarter-mile.
Migrants, most with children, follow a path along the concertina wire where ultimately they will placed under guard by Border Patrol after having crossed the Rio Grande on May 27, 2022 in Eagle Pass, Texas.
JOHN LAMPARSKI/NURPHOTO VIA AP
No More Death’s database includes, when available, information on the level of decomposition in which each set of remains was found. As with cases in Texas, MIT Technology Review consulted with PCOME’s Greg Hess on a framework for translating this information into rough estimates for when a person may have died.
Here’s where the No More Deaths data used in our analysis comes from:
In California, No More Deaths requests records from the San Diego County medical examiner and the Imperial County coroner. Its latest data for Imperial County is from December 2025, and its latest for San Diego County is from October 2025.
In Yuma County, Arizona, No More Deaths requests records from the Yuma County medical examiner. The latest data is from September 2025.
In New Mexico, the organization requests data from the state’s Office of the Medical Investigator. Unlike most states, New Mexico has a statewide medical examiner, allowing No More Deaths to obtain migrant death data for the entire state. The latest data is from July 2025.
For Hudspeth County, Texas, No More Deaths requests records from justices of the peace in Districts 1 and 2. The latest data is from December 2023.
For El Paso County, Texas, records were from the El Paso County Office of the Medical Examiner. The latest data is from September 2025.
In total, we included nearly 1,000 cases from No More Deaths in our analysis.
Total cases reviewed
When accounting for all our data sources and excluding deaths without specific enough records to determine where remains were found, we analyzed a total of nearly 4,000 cases.
Other notes
We generally assume that a person died where their remains were found. That may not necessarily be true: Remains can be moved by other people, flowing water, or animals. But medical examiners and researchers we consulted said such cases are rare.
We excluded cases in which the virtual wall did not appear relevant. For example, we removed cases involving people who died inside Border Patrol stations or in car crashes during pursuits.
The deaths we analyzed reflect a variety of causes that may seem at first like very different sorts of surveillance failure. If someone slowly died of dehydration within view of a camera, for example, agents would have had far more time to intervene than they would have in a case where someone drowned quickly in a river or canal. But we included all these deaths because, in each one, the virtual wall could have prompted a response from agents.
Surveillance towers
Including a tower in our analysis required knowing three things: its location, its type (so that we could judge how far it is supposed to see), and the time it was installed.
Locations
The Electronic Frontier Foundation has been tracking and mapping CBP surveillance towers since 2023, using on-the-ground reporting, satellite imagery, and government documents obtained through public records requests. Its tally is an undercount; government records suggest that about 800 towers are currently deployed, while EFF has logged about 600. Our analysis therefore misses deaths that occurred near towers not yet catalogued by EFF.
MIT Technology Review worked closely with EFF’s director of investigations, Dave Maass, throughout this project. Our tower data began with the database published by EFF in April 2026, but that map did not include towers that have been removed. We consulted EFF for the details on these former towers and added them to our database. We generally use EFF’s reference numbers for individual towers, but we changed some to ensure that each tower has a unique identifier.
The locations are identified by GPS coordinates. We verified each tower’s coordinates by analyzing satellite imagery.
Tower types
Our analysis focused on towers of three main types. Certain towers mapped by EFF don’t fall into any of those categories, so we excluded them because it’s difficult to obtain information about how far they are supposed to be able to see. This includes some towers that are installed at Border Patrol checkpoints or stations.
An integrated fixed tower (IFT) on Coronado Peak, Cochise County, Arizona.
CREATIVE COMMONS
An autonomous surveillance tower (AST) near Sunland Park, New Mexico.
CENGIZ YAR FOR MITTR
To know how far the main tower types could see, we consulted materials from tech companies and EFF’s research, led our own review of government documents, and interviewed former Border Patrol agents and officials. The distances described are consistent across these sources. But they are advertised estimates, not contractual guarantees: Some government records redact more specific information about a tower’s surveillance range in particular locations, citing security concerns.
When towers were present
To know if a tower in place now was present when someone died, it’s essential to know not just where it’s located but also when it was put there. To figure this out, we used multiple sources of satellite imagery.
Maass, at EFF, had previously analyzed some of the towers to determine when they first appeared. MIT Technology Review checked satellite imagery for these towers to confirm these timelines, and then checked for all the remaining towers. For each tower, we logged the date that it first appeared and, if it eventually disappeared, the last date it was present. We spent months meticulously checking when hundreds of towers appeared in multiple sources of satellite imagery to confirm there were no contradictions.
This is easier for certain tower types than others; towers from Anduril, for example, tend to go up where there was no prior infrastructure, and it’s fairly easy to see how a patch of empty desert gave way to the new tower and its trademark solar panels. Other towers, like IFTs, were often built on or near existing structures. For these, we leaned more closely on EFF’s expertise in analyzing the imagery.
Do you have experience with border surveillance technology? We want to hear from you. Reach the reporters securely on Signal at jamesodonnell.22 and eileenguo.15 or tips@technologyreview.com.
When imagery wasn’t sufficient, we used Google Street View, government documents, or photographs collected by EFF to verify when towers went up.
One limitation is that historical imagery of towers along the border is not available for every day in the past. Especially for older towers or those in remote locations, images may have been taken months or even a year apart. A tower visible in May and again in December could theoretically have been removed and reinstalled in the intervening months. But most tower systems are permanent structures. And multiple Border Patrol agents told us that even towers that are meant to be mobile, like autonomous towers, are in practice very rarely taken down or moved (consistent with this, we observed that semipermanent fences were erected around the sites of many Anduril towers).
Another limitation is that even if a tower is visible at a particular time, that does not mean it was online. We can’t verify that it was functional, receiving power, or transmitting data. Our analysis therefore establishes when a tower was present, not whether it was operational.
Heights
We conducted a topographical analysis, detailed below, to see whether a tower’s view of a given person might have been blocked by the terrain. This required estimating how tall each tower is.
We included a minimum and maximum height for each tower, based on research from EFF. In select cases where the surveillance system was not on a typical tower but mounted to an existing structure, like a water tower or building, we used satellite imagery to estimate its height. The higher a tower is, the farther it can see.
Our analysis
With the data we collected, we could begin to see which deaths happened in range of the virtual wall. But for a given match between a set of remains and a tower to count, it needed to satisfy two criteria:
1) Distance: The death must have happened within the published surveillance range of the nearby camera.
2) Timing: The tower had to be present when the person was estimated to have died.
An autonomous surveillance tower from Anduril captured January 6, 2022.
VEXCEL
Distance
Minho Kim, an outside collaborator who is now a postdoctoral researcher at Stanford with a focus on wildfire mapping, led the distance analysis. His software ingested our database of human remains and our database of towers. Then it created a row for each time a set of remains was found near a given tower, along with the distance between the two.
We then checked whether, for each match, the tower was actually present when the person died.
Timing
Sometimes, as with the Arizona cases, an estimate of when someone died is given in the original data; the medical examiner may note that a person died more than three weeks but less than five weeks before remains were found, for example.
In other cases, we used the level of decomposition, the cause of death, or both to calculate a window of time in which the person would likely have died. Finally, we compared this window with the time when we determined the tower was built.
This step took us many months and involved reviewing original police reports for hundreds of cases, inspecting photos of bodies, cross-referencing cases with news articles, interviewing medical examiners, and researching decomposition timelines. The resulting death windows are approximate, but they are the best estimates we could make from the information available in each case.
We assigned different confidence levels to cases based on how these dates lined up. If a person’s entire estimated death window fell well after a tower was first observed, we felt confident the tower was present when they died. If any part of the death window fell before the tower was first observed, we excluded the case.
Many cases involve skeletal remains. Remains decompose differently depending on location and season, but when records didn’t provide a specific estimate, we conservatively estimated that skeletal remains had been there for at least six months. For these and other cases without a specific estimate of when the person died, we used three years as a conservative cutoff: If a tower had been present for at least three years before the remains were found, we included the case, judging it unlikely that the person had died before the tower was installed and remained undiscovered that long. If the tower had been present for less than three years, we excluded it.
Viewshed
It is possible that someone who died within range of a tower could have been blocked from the tower’s view by terrain, vegetation, or buildings.
We estimated the impact of this issue with a technique called topographical analysis.
Kim, our collaborator, led this part of the analysis. Here’s how it works: Imagine a map of the border region divided into a grid, with each square having one value for that area’s elevation. Take a given match between a set of remains and a tower and draw a line between the tower’s estimated height and an estimated height of 5’ 6″ for the person who died. If the terrain along that path rises high enough to block the line between the two points, we estimate the tower’s view as obstructed. If it doesn’t, we estimate the view as clear.
It’s not perfect. In the elevation data we used, each square represents an area 30 meters wide, or about 98 feet, and is assigned a single elevation. That means smaller changes in the terrain can be lost, particularly in steep or rugged areas where elevation changes quickly. Our analysis is therefore less about re-creating exactly what a tower could see than providing a rough estimate of whether terrain may have blocked a tower’s view of the spot where someone died.
We include results on our map whether a clear line of sight was estimated or not. Even if the location where someone died was blocked from a tower’s view, the person may have passed through areas visible to the tower before reaching that spot.
It’s much harder to estimate whether a line of sight was blocked by vegetation. We are less concerned about this limitation because virtual-wall towers use thermal imaging, which can detect body heat through light vegetation. Dense vegetation, however, can still obstruct their view.
Buildings pose a greater challenge. We could not reliably account for their locations and heights. Instead, we reviewed each tower’s surroundings and flagged towers located in or near urban areas. These notes warn readers that buildings may have limited what those towers could see.
As the sun sets near McAllen TX, migrants that have crossed the Rio Grande surrender to U.S. Border Patrol near an area known as Rincon.
MANI ALBRECHT/U.S. CUSTOMS AND BORDER PROTECTION
Beyond the data
The most fundamental question in each of these cases, and for the families of anyone who died, is whether the US government saw someone attempting to cross into the country and never intercepted them—or never saw them at all.
This is all but impossible to answer with records or data—Border Patrol doesn’t disclose which alerts came in surrounding any given migrant death, and footage isn’t retained unless an internal investigation is triggered, which doesn’t happen unless a migrant dies while in custody.
MIT Technology Review instead conducted dozens of interviews with people who worked for Border Patrol, Customs and Border Protection, or the Department of Homeland Security or who served as presidential advisors. These conversations yielded previously unreported details about how agents use surveillance technology, turned up instances where it has underperformed, and underscored how seldom its effectiveness is evaluated internally.
We also sought out friends and family members of people who died near surveillance towers, using additional records, social media, and other research tools to find them. We approached these interviews using best practices for trauma-informed reporting.
Beginning in September 2025, MIT Technology Review requested interviews with current Border Patrol agents and leadership at CBP, and we asked to visit the border to understand the strengths and weaknesses of the latest autonomous tower program. Following delays, which were in part related to DHS funding lapses, in July 2026 we were told our media request had been approved at the local level in Big Bend, Texas. We wanted to visit this area because Border Patrol is expanding the number of Anduril towers there. But when the request went up to the DHS headquarters for a final sign-off, the agency denied it. Before publishing our investigation, we sent detailed questions to CBP, Anduril, General Dynamics and Elbit. CBP and Anduril responded, but did not substantively address our questions. General Dynamics referred us to CBP. Elbit did not respond to our questions.
MIT Technology Review thanks the following for their assistance with this project:
Minho Kim, Stanford University
Greg Hess, Pima County Office of the Medical Examiner
MIT Technology Review today published our investigation into how many people have died near the “virtual wall” of surveillance towers that the US government has installed along the US-Mexico border.
We found cases of people who walked undetected through areas surveilled by advanced, AI-enabled towers and later died nearby, where their bodies remained unnoticed for weeks or months. These cases reflect systemic flaws in the virtual wall: broken towers, algorithms that failed to detect people, and agents who simply did not respond to alerts.
This story is part of Dying on Camera, a collaboration between MIT Technology Review and Times of San Diego. Journalists in both newsrooms spent the past year examining the failures of border surveillance technology and uncovering the stories of the people who die in the borderlands.
Each of these deaths should have raised alarms inside US Customs and Border Protection (CBP) about the flaws in the agency’s surveillance network. This is particularly true for cases near the latest towers built by the defense company Anduril, which continues to win major contracts and is poised to benefit from the windfall of federal money slated for border security. The US is set to spend $1 billion to triple the size of the virtual wall by 2034.
Here are four ideas for what CBP should do next to address these failures.
1. Conduct a comprehensive audit of deaths near the virtual wall
In this June 16, 2021, file photo, a family from Venezuela moves to a Border Patrol transport vehicle after they and other migrants crossed the U.S.-Mexico border and turned themselves in to agents in Del Rio, Texas.
ERIC GAY/AP PHOTO
“If I were the federal government, I would do an audit, because this feels like it’s not working.”
That was New Mexico state representative Sarah Silva’s reaction when MIT Technology Review showed her that in 2024 alone, more than 18 people had died near surveillance towers in just one small stretch of desert near her district.
An audit is necessary because, despite poring over thousands of pages of police reports and other documents about surveillance towers, we have only limited knowledge of how many towers are out there and where they’re located.
CBP does not publish a comprehensive map of its virtual wall. We instead relied on mapping work led by the nonprofit Electronic Frontier Foundation and then used satellite imagery to verify where specific towers were and approximately when they were installed.
We analyzed lots of towers—nearly 600. But that’s still far less than the 800 the government says operate today. And our estimates of when they went up are inexact. That means our tally of more than 1,050 people who have died near them is an undercount. And we don’t have a complete picture of where along the border the problem is worst.
An internal audit would not have such limitations. In theory, CBP tracks when each tower went up and when it was active. It has had the ability to audit their effectiveness—while quantifying how many people have died near them—since at least 2015, when it began installing the types of towers included in our analysis. But sources told us it has never done such an audit. Our findings make the need for one more urgent.
2. Fix the way the agency tracks migrant deaths and remains
If CBP is to analyze instances where people have died near its surveillance technology, it needs to know when and where those deaths happen. And the current system for tracking that data is beyond broken.
As we note in our methodology, there is no national agency that collects and publishes all available records on migrant deaths. We sifted through thousands of pages of records from dozens of agencies in Texas alone, and our picture is still incomplete owing to the lack of a shared protocol for documenting these deaths in public records.
Border Patrol has had its own internal system, called the Border Safety Initiative Tracking System (BSITS), in place since 2007. But it wasn’t until 2021 that national rules were established for how different sectors should enter cases into the system. The next year, the Government Accountability Office (GAO) found that many cases still weren’t being tracked in this system, especially deaths that weren’t discovered by Border Patrol. In 2023, the GAO reported that Border Patrol has taken steps to fix that, but outside researchers consistently find the agency’s estimates to be undercounts even today.
In addition to tracking migrant deaths, CBP launched a program in 2017 to prevent them, called the Missing Migrant Program. In April 2025, the GAO found the program was improving the way it collected and tracked data about events like 911 calls and rescues. The problem, it said, was that there was no plan in place to use the data to measure whether the program was actually increasing rescues or reducing deaths, which are its primary goals. It made two recommendations for solutions, neither of which has been implemented.
3. Track cases when surveillance technology leads to an apprehension
In 2014, when the virtual wall was much smaller, the GAO suggested that Border Patrol agents log when a piece of technology helped them apprehend someone who was trying to enter the US illegally. It would take agents some extra time, but logging these assists would help Congress understand how often various surveillance technologies were leading to actual apprehensions. Once this data was collected, GAO added, the agency should measure whether its surveillance technologies were working. CBP agreed, and it started requiring agents to enter such information.
U.S. Border Patrol agents apprehend people crossing the border illegally near Sunland Park, N.M., July 17, 2024.
GLENN FAWCETT / CBP PHOTO
Then, in 2017, the GAO reported that the data being collected was inaccurate and unreliable; it found agents in Texas, for example, who were crediting a specific type of tower in apprehensions hundreds of times, even though no such towers existed anywhere in the state. Border Patrol again said it would improve; it agreed to train agents on the importance of collecting such information.
But years later, agents told us, it’s still not a priority. One former CBP official described the mandate as “spitting in the wind, to be honest,” and said the messy reality of apprehensions doesn’t translate into neat metrics. (The official, who requested anonymity, served during the Biden administration and wasn’t authorized to discuss sensitive issues.)
And in August 2021, more than seven years after it agreed to do so, CBP still had not produced any analysis on the towers’ effectiveness (it finished one in 2022, the GAO notes, but the results are not public).
That means the virtual wall is growing faster than anyone is assessing its impact.
4. Investigate deaths as potential surveillance failures
CBP should also examine new deaths as they occur and determine whether they happened within sight of a tower or other surveillance technology. And for those that did, the agency should determine why those people weren’t spotted sooner.
Several agents and officials told us that if Border Patrol agents come across the body of someone presumed to have died while crossing into the US, there’s no requirement to check whether that person died near surveillance infrastructure and no process for doing so. At a minimum, that means potentially valuable evidence about the death goes unexamined. It also means that migrant deaths are not routinely treated as possible failures of the virtual wall.
Do you have experience with border surveillance technology?We want to hear from you. Reach the reporters securely on Signal at jamesodonnell.22 and eileenguo.15 or tips@technologyreview.com.
CBP should automatically check every migrant death against its internal database of towers and investigate those that occurred within surveillance range to determine what the system detected and whether agents responded appropriately. Agents already log GPS coordinates when they apprehend a group or encounter footprints, among other events.
Without that check, law enforcement, medical investigators, and families may never learn that someone died near a surveillance tower—and that footage of the moments leading up to the death may exist. CBP typically deletes surveillance footage from the towers after 30 days unless an internal review is opened, which usually happens only for deaths in custody. At a minimum, flagging these cases could preserve footage for those investigating the death and give families a chance to request it.
Such reviews could also expose flaws in the virtual wall itself. Consider one location we identified in New Mexico, where several people died despite being in clear sight of an advanced AI tower. They died within the span of a few months, sometimes just hundreds of feet from one another. Investigating these cases might reveal, for example, that a camera repeatedly failed to detect or identify people in a particular area, or that agents were alerted and simply never responded.
Carrying out these postmortem inquiries would be the only way to answer the question that families, researchers, and volunteers raised most frequently in our investigation into the hundreds of migrants who died near towers: Did the cameras see them, and nobody came? Or did nobody see them at all?
When José Morales Bernal crossed the border into the United States on April 8, 2024, the day before his 32nd birthday, it should have triggered a chain of technological alerts and human responses.
As he walked through the desert in southern New Mexico that morning, he was within range of three surveillance towers. Newly installed by US Customs and Border Protection (CBP), they were built by the defense tech company Anduril and equipped with cameras and AI to automatically detect and track people. They transmit live video to nearby control rooms and can send alerts to the government-issued smartphones held by agents in the area, prompting the closest available to respond.
This story is part of Dying on Camera, a collaboration between MIT Technology Review and Times of San Diego. Journalists in both newsrooms spent the past year examining the failures of border surveillance technology and uncovering the stories of the people who die in the borderlands.
These AI-enabled towers are meant to give greater visibility across the 1,951-mile southern border, freeing up border agents from having to spend hours staring into video monitors. They were installed in this particular place to spot border crossers before they reached the nearby town of Sunland Park.
If the system worked as intended, Morales should have been apprehended. If he needed medical help, agents were trained to provide it.
The view from where José Morales Bernal’s body was found toward a Customs and Border Protection surveillance tower made by Anduril, along the southern US border in Sunland Park, New Mexico.
CENGIZ YAR FOR MITTR
That didn’t happen. Despite the nearby surveillance towers, it was employees of the local landfill, rather than Border Patrol, who first spotted Morales that morning. At 1 p.m. the workers saw him again, now lying in the sand. At 4 p.m. the landfill workers saw that he had not moved and called Border Patrol. Agents arrived 45 minutes later. He was dead. When an agent then called 911 to report the body, he said it was “probably one of the migrants crossing through there,” seemingly unaware that Morales had been moving near the agency’s surveillance systems earlier that day.
The agent can be heard sounding unsure of Morales’ location despite the proximity of an AI-powered surveillance camera. The call has been edited for length.
Morales had died just 360 feet from the closest surveillance tower. Two more towers stood watch to the east and the west. An autopsy later concluded that he had died of “environmental exposure.”
The towers surrounding Morales were only the latest addition to the “virtual wall” the government has spent 25 years and billions of dollars building along the entire border, which also includes earlier generations of towers with more basic features, as well as blimps, drones, seismic sensors, and even tunnel-sensing robots. Together, all this technology provides “persistent surveillance” and “situational awareness” to help the Border Patrol quickly and accurately detect people crossing and, crucially, make sure agents are sent to intercept them. CBP has also credited it with saving lives.
But a first-of-its-kind investigation by MIT Technology Review reveals that deaths like Morales’s are startlingly common. We cross-referenced nearly 4,000 locations where human remains were found—drawn from records collected by nonprofit groups like No More Deaths and Humane Borders, as well as hundreds of records we obtained in Texas—with information on nearly 600 towers identified by the Electronic Frontier Foundation. We considered when towers were installed and when each person is estimated to have died, and combined this information with field reporting from the border. The result is the first comprehensive map and analysis of deaths near CBP surveillance towers.
Our investigation shows that a humanitarian crisis at the border has unfolded in view of the government’s own cameras.
José Morales Bernal died the day before his 32nd birthday.
PHOTO COURTESY MIREYA MORALES
We found more than 1,050 people who died within range of border surveillance towers between 2015 and early 2026. These deaths are not failures of a few towers or technologies: We found deaths within the advertised range of nearly two-thirds of all the towers we analyzed. Our topographical analysis—which assessed the degree to which terrain might block a tower’s view of a particular death and its surveillance area in general—found that some have sight of as little as 10% of their advertised surveillance area, and yet we also found most deaths did not occur in towers’ blind spots. These deaths are not the result of legacy systems, as our estimate found more than 110 people have died within range of modern autonomous towers from Anduril, among the most advanced systems CBP has deployed, since 2021.
The overall picture reveals repeated failures of one of the virtual wall’s basic security functions, as CBP has described it in press releases: to effectively identify and locate migrants entering illegally into the United States.
The year that Morales died, 18 other people died in that same stretch of desert in range of the three Anduril towers. Five were visible from the same surveillance tower closest to where his body was discovered.
One man, after walking a mile past the border and in range of two AI towers, dragged his 30-year-old brother into the shade when he began having trouble breathing, according to records we obtained from the medical examiner. The two were spotted by Border Patrol only when the man waved down a helicopter for help; by the time agents arrived, his brother was already dead. A 24-year-old woman died near another Anduril tower, where our terrain analysis showed it should have had clear sight of her location. Her body lay unnoticed for weeks; it was decomposed, and blistered by the summer heat, when it was spotted by agents patrolling the area.
A few miles west, and a short drive from a Border Patrol station, four other AI towers stood watch. Near them, 10 more bodies were found that year, all in locations where at least one tower had a clear view.
In response to a list of questions, an Anduril spokesperson replied that once a tower is delivered, it is operated by CBP, and directed questions about specific incidents to the agency. The response noted that an incident occurring nearby does not mean the tower missed a detection and said actual surveillance ranges vary depending on terrain, physical obstructions, and the boundaries CBP sets for where the tower should look (CBP is able to set virtual boundaries on towers’ views for privacy and other reasons). The Anduril spokesperson also alleged inaccuracies in our reporting, given those boundaries and obstructions, but did not respond to follow-up questions on what was inaccurate.
The most pressing question in any death near the virtual wall is whether authorities knew someone was there and failed to reach them or weren’t aware anyone was crossing at all. Either is a system failure. And the deaths we found capture only the failures that left a trace—we don’t know how many people pass through undetected, or how many bodies remain undiscovered.
Interviews with more than 45 people—including current and former White House advisors and presidential appointees, Border Patrol agents, medical examiners, sheriffs, humanitarian volunteers, and employees of tech companies—showed that both types of failures are occurring: The technology is failing to detect, and agents are failing to respond. Both show the limits of throwing technology at a complex problem.
The findings reveal previously unreported issues with the virtual wall, even as it continues to enjoy broad political support and a surge in federal spending. Border security hardliners have long seen it as another tool for stopping smugglers moving drugs or people, while others tout it as a cheaper alternative to a physical wall. In 2023, the government estimated that its plans for using the towers, which now number 803, would cost $6.2 billion over their lifespan. With the historic levels of funding it was awarded in 2025, CBP plans to spend $1 billion for 1,497 more towers by 2034.
But our reporting shows that CBP has done little to assess how its towers are working or how many people have died where they keep watch.
Officials who oversaw border security across the last four presidential administrations told us they believed deaths near the so-called virtual wall were either exceedingly rare or nonexistent. But our reporting shows that is not the case. None could point to any comparable analysis the government had ever conducted on its own. Former agents and officials also told us that when someone’s body is found, CBP does not formally investigate whether surveillance should have detected them or, if they were detected, why agents didn’t reach them before they died.
In response to nearly 30 questions about our findings, which covered multiple generations of technology, Hilton Beckham, CBP’s assistant commissioner for public affairs, said, “Autonomous surveillance towers use artificial intelligence to detect and classify people, vehicles, and animals and alert Border Patrol agents to activity in monitored areas … ASTs complement physical barriers and other border security infrastructure by improving detection and situational awareness between ports of entry. CBP evaluates the technology based on its impact on detection, response coordination, agent safety, and mission outcomes.”
“I’m sure that the cameras and other surveillance assets do deliver … useful intelligence and enhance operational efficacy in some places, under some conditions, in certain circumstances,” says Geoff Boyce, an assistant professor of geography at University College Dublin who has studied surveillance technology used at the US southern border. “I’m also absolutely positive—because this has been the track record—it is not delivering the level of operational support, information, or efficacy that either the companies delivering these infrastructures or the Border Patrol and Department of Homeland Security claim.”
We also reached out to multiple lawmakers from both parties with a summary of our findings. In response, Delia Ramirez, a Democrat representing Illinois’s 3rd district who sits on the House Homeland Security Committee, said, “AI-powered surveillance technologies are not making us safer. Yet DHS continues to spend millions of taxpayer dollars on these ineffective, negligent technologies, with no commitment to oversight or transparency … It is clear we must terminate CBP’s integrated surveillance tower program and dismantle DHS.”
Building the virtual wall
Since its earliest efforts to police the border with Mexico, the US government has faced the same basic challenge: How do you effectively secure nearly 2,000 miles of remote and rugged terrain? It has increasingly turned to technology for answers.
Gerardo Galvan joined Border Patrol in 1995, as the government was undertaking an unprecedented expansion of border enforcement. In 1993, President Clinton’s first year in office, the agency mobilized huge numbers of agents to guard the country’s urban borders, starting in El Paso, Texas. That pushed more crossings to rural, unpopulated areas—which quickly proved to be far more deadly because of the rugged desert terrain, extreme temperatures, scarcity of water and the long, indirect routes often used by smugglers.
Stopping these remote crossings was a new law enforcement challenge for the agency, especially given the limited technology available at the time. “We had radios,” says Galvan, who would go on to be the head of operations for the El Paso sector—not the cell phones or GPS systems agents have today. They relied on underground sensors left over from the Vietnam War to alert them to movement.
The biggest technology rollout of Galvan’s early career started around 1998. That was when Border Patrol began installing its first video cameras that agents could remotely pan and zoom. They were mounted on metal structures similar to cell-phone towers, 60 to 80 feet tall, or on buildings in urban areas.
“For the first time, somebody sitting in a room somewhere was able to surveil large swaths of area without being dependent on getting an agent out there with binoculars,” says Matthew Hudak, a former deputy chief of Border Patrol who was working as a frontline agent at the time. “That was a very significant game changer, and for the most part, it was a huge, huge advance.”
This early iteration of the virtual wall was a big deal: In its first eight years, 200 towers were installed along the southern border at a cost of more than $429 million. But the system’s flaws were obvious to anyone who used it.
“There could be … 30 screens on a wall,” says Mark Borkowski, who managed CBP contracting, including the surveillance tower program. Each camera might be capable of seeing three or five miles in any direction. The agent was expected to keep eyes on all that space—upwards of 850 square miles.
Monitors inside the cab of a Border Patrol truck show a portion of the US-Mexico border in Sunland Park, New Mexico, on Thursday, June 6, 2019.
CEDAR ATTANASIO/AP PHOTO
“Well, what are the chances after 20 minutes that those Border Patrol agents would see an explosion on one of those screens?” Borkowski says. “About zero.”
When these cameras were first being put up, the plan was to pair them eventually with a system that would automatically direct them toward places where sensors picked up activity, so agents would know where to look. But by 2005, the DHS inspector general said that still hadn’t materialized, and that illegal activity might “go unnoticed” unless agents were actively watching the cameras. Nonetheless, more towers were installed, forming what is today known as the Remote Video Surveillance System (RVSS). In 2013, the defense giant General Dynamics won a contract worth up to $103 million to overhaul and expand RVSS along the border, becoming the primary contractor behind the system that remains in use today. (That contract eventually grew to a ceiling of $216 million, and the company received another award in 2023 worth up to $135 million.)
General Dynamics would install more cameras, better sensors, and additional towers. But the problem identified nearly 15 years before remained unsolved. It was information overload: One agent told MIT Technology Review about an incident in San Diego in which cameras captured a group of 30 crossing the border and getting picked up in a van, all unnoticed by the agent monitoring those cameras. Agents said the virtual wall was bringing more of the border into view than they could reasonably pay attention or respond to.
The camera room was also the job nobody wanted. New Border Patrol agents, often in their early 20s, want to be outside in trucks and ATVs, not sitting in a dark room watching screens—even though the expanding virtual wall increasingly required someone to do exactly that. “Someone that’s injured and can’t go out into the field—we’ll send them to the camera room,” says Rafael Reyes, who was in charge of the Border Patrol station in Deming, New Mexico, until 2024.
Reyes says it was also a job his agents were often too busy to do properly. His station area had about 30 cameras, and the agents responsible for them also had to monitor sensor alerts and radio traffic, run records checks, and coordinate with other agents. “Then if they have some time, they’ll monitor the cameras,” he says.
Not only that, but many cameras didn’t work. At Reyes’s station in New Mexico, five of the 30 cameras were broken on any given day, he says. Others had limited functionality; they might be capable of looking in just one direction or seeing only in the daytime. Jaime Fierro, a former agent who worked in the Laredo sector in Texas until 2025, said the broken towers were tanking morale among agents and that they brought it up at every staff meeting. “That was like basically having an eye shut for us on the border,” he says.
Rafael Reyes, who oversaw a Border Patrol station in New Mexico, says his agents often had too many other responsibilities to monitor the cameras properly.
CENGIZ YAR FOR MITTR
They weren’t alone: In 2024, members of the House Committee on Homeland Security wrote that more than 66% of Border Patrol’s first-generation towers were unusable, despite operating budgets of $50 million to $100 million each year. In January 2026, 30% remained broken, one congressional staffer told MIT Technology Review. The RVSS program was estimated in 2020 to have a total cost of $3.7 billion.
Border Patrol has nonetheless publicly credited these cameras with helping to save lives. The agency has published dozens of pressreleases, dating as far back as 2016, about instances when agents responded to people seen in distress on the remote video cameras. Despite these publicized wins, the bodies began to pile up, undetected, beneath these systems’ watchful gaze.
Near El Cenizo, Texas, there’s a roughly 13-square-mile expanse of mesquite bushes and grasses described by the Webb County Sheriff’s Office as containing sections of Hachar Ranch and Espejo Ranch. It’s surrounded by seven RVSS systems. It was nonetheless a hot spot for fatalities: MIT Technology Review found that 19 people died there, including one man who died less than 500 feet from the road bordering the ranches and half a mile from two schools. In each of these cases, narratives that we obtained via public records requests describe officers’ learning of these people only after their bodies were discovered or through 911 calls reporting them missing, not as a result of the border surveillance technology installed across Hachar Ranch.
In response to a list of questions about its towers and these specific incidents, General Dynamics referred us to CBP. CBP did not address any of our questions about RVSS towers.
Stories of some of the people that died near RVSS towers
Many deaths near RVSS towers happened in Texas, in the heavily surveilled corridors between the Rio Grande and the nearby highways, where paid smugglers often organize pickups after crossings. Cameras watch many of the spots where people are known to cross, often looking across the river into Mexico to spot them before they reach the water. Drownings are common; we found multiple cases in which local fishermen hooked the bodies of people who had died attempting the crossing.
In one instance, a family of three from Brazil attempted to cross the Rio Grande near Del Rio, Texas, in 2022. The father, Daniel Lenda, was carrying his two-year-old daughter, Eloah, on his shoulder when he fell in the water. The mother, 23-year-old Thais Natali Montenegro Lenda, lifted Eloah out of the water as she watched her husband disappear.
She placed the girl, now unresponsive, on a rock and went for help. She was just 100 feet from one camera system and a third of a mile from two others, set up precisely to enable faster apprehensions at a popular crossing point. Our topographical analysis shows that all three cameras had a clear view of the place where the family crossed and exited the water. Nonetheless, Thais Natali was not spotted until she waved down an agent on the Del Rio-Acuña bridge. The agent provided CPR to the toddler, who did not survive. Daniel’s body was found days later.
Val Verde County sheriff Joe Frank Martínez responded to that case (though Border Patrol might discover bodies, local law enforcement is responsible for investigating the deaths). He grew up in the area with his nine siblings and has been sheriff since 2009. Riding with MIT Technology Review through Del Rio in his patrol vehicle, he rattled off the deaths that his office has responded to since he became sheriff.
He keeps a three-inch-thick binder of these cases in his office. But this case with the toddler still sticks out in his memory. He told MIT Technology Review that when he arrived on scene, he heard from Border Patrol that the tower operator monitoring the camera had seen the father and daughter fall in the river. He never received an answer as to why agents weren’t sent sooner. Martínez asked for the footage from the surveillance cameras to aid in his investigation of what happened. Border Patrol never provided it. (General Dynamics did not respond to our questions about this incident, instead directing us to CBP. The agency did not address questions about this incident either.)
A binder of cases in the Val Verde County sheriff’s office contains details on the death of two-year-old Eloah Lenda.
JAMES O’DONNELL/MITTR
Establishing how many people have died in range of the virtual wall is challenging, but it’s especially so in the case of the RVSS towers. There are different models, some with a shorter range of sight—one to three miles—and others with a longer range of up to 7.5 miles. Former agents and officials told us most towers should see at least five miles, but since no public records exist to confirm the model type of any given tower, we used several possible viewing distances to calculate the number of deaths considered in range.
Another complication is that many remains are never found at all. For those that are, MIT Technology Review could analyze only deaths that had been logged with GPS coordinates or sufficiently precise location descriptions. Many records lack this level of detail, including nearly a fifth of the more than 1,500 cases we obtained from 14 Texas counties. We also needed to determine when someone died, rather than simply when their remains were found, to confirm that a tower was present at the time of death. When official sources did not provide an estimate, we consulted multiple medical examiners on how to make that determination from the available details. And satellite imagery provides only intermittent evidence to suggest when towers arose, not exact proof of when they were functioning. For these reasons, as detailed in our methodology, we tried to be conservative in our analysis.
Even so, the number of deaths estimated to have occurred near at least one of these towers is staggering: somewhere between 500 and 700 since 2015. Even if we look only at towers in rural areas, where their views are far less likely to be blocked by buildings, we still estimate more than 300 people have died in places where the cameras had a line of sight, most less than three miles from a tower. Of the nearly 300 RVSS towers we included in our analysis, more than 250 were within range of a location where someone died. More than 150 people died within a half-mile of one, often across open, empty plains, close enough to see the cameras themselves perched atop towers up to 200 feet tall.
The next watchers
When Borkowski was overseeing the rollout of those first-generation towers at CBP, he became increasingly certain that simply having more remote video cameras was not going to lead to faster responses. The information overload meant the government could watch a large area of the border but not meaningfully see, much less act on, everything within it. The agency’s focus then became solving that overload problem.
What if, instead of relying on someone to scan 30 screens, the towers could use radar to detect motion, and then point the camera to it automatically?
Gary Wagner of Elbit Systems of America cleans the lens of a long-range thermal imaging system at a 2014 border security expo.
MATT YORK/AP PHOTO
In 2011, Border Patrol asked the industry for a new generation of technology that could do just that—initially focusing on Arizona, where the border was busiest. The contract ultimately went to the Israeli defense contractor Elbit, whose tower systems watch Israel’s borders. In 2015, the company started installing versions of these—called integrated fixed towers, or IFTs—that could each see more than five miles.
The IFTs were still being installed when President Trump first took office, in 2017. They didn’t command the same attention as Trump’s campaign promise of a physical wall, but there was still support. Trump’s deputy CBP commissioner, Ronald Vitiello, told Congress in 2018 that the IFT towers automatically detect people with radar and then track them, adding that such surveillance technology is “critical in protecting border areas with short vanishing times, where illicit crossers can quickly evade law enforcement by ‘vanishing’ into border communities.”
There were 55 IFTs built in Arizona under an initial $145 million contract with Elbit and other contracts that followed. The towers collectively watch more than 7,000 square miles of terrain, from the mountainous areas outside Nogales to the plains of the Tohono O’odham Nation Reservation. Arizona is in many ways well suited for these tall towers because much of its landscape is flatter than, say, Southern California.
An integrated fixed tower (IFT) on Coronado Peak in Cochise County, Arizona.
CREATIVE COMMONS
But even here, being within a tower’s advertised range does not necessarily mean being within view. There could be terrain, brush, or buildings that block the line of sight.
MIT Technology Review conducted a topographical analysis to estimate whether each tower had a clear or obstructed view of the place where someone’s remains were found. We estimated the height of each tower and used US elevation maps to model the terrain between it and the remains. The analysis does not account for buildings or vegetation, though all the towers we analyzed use some form of thermal imaging that is designed to see through light brush.
Most of the instances in which someone died in range of a tower happened where we estimate that the camera’s views were not blocked by terrain—a finding that held in both rural and urban areas. And even when someone died in a blind spot, it does not mean they did not pass through a tower’s line of sight. Additionally, these numerous blind spots indicate issues with the tower’s supposedly comprehensive coverage.
We found that nearly 350 people have died in range of Elbit’s towers since 2015. There were 40 people who died within range of four or more IFTs, with some near as many as six different towers. In May 2024, a 19-year-old man died where three IFTs had a clear view, and a year later, in May 2025, a 38-year-old woman died where two IFTs had a similarly clear view. Elbit did not respond to a detailed list of questions about its towers or deaths we found to have occurred near them.
There’s been a death within range of 52 out of the 53 IFTs we analyzed, but some were particularly notable. There’s a tower located a couple of hours from Tucson on the Tohono O’odham reservation that we estimate has upwards of 80% visibility of the surrounding area. Still, 27 people, ages 19 to 64, have died in its surveillance area just since 2021, along with several others whose remains have not been identified. In 25 of these cases, we estimated that the tower had a clear line of sight to the location where the person died.
Stories of some of the people who died near IFTs
The age of autonomy
Around 2018, officials within Border Patrol were again discussing the limits of the towers. The new radar systems were better, but they could only detect movement, not what was moving. This generated false alerts from cattle, tumbleweeds, and other harmless activity. The now decades-old dream of the virtual wall remained unfulfilled. Under pressure from Congress, which was demanding to know what $33 billion in proposed funding for border security over the next decade was going to achieve, Border Patrol once again was hoping a new generation of technology could solve its problems.
That year the head of CBP, Kevin McAleenan, created a unit called the Innovation Team. “He gave them some seed money to go after kind of Silicon Valley innovative technologies and run pilots,” Borkowski says.
One company they landed on was Anduril, the defense tech startup founded by Oculus VR founder Palmer Luckey. The company was without a product but had funding from Peter Thiel, recruits from the security tech giant Palantir, and a goal of bringing Silicon Valley experimentation to defense and national security technology.
An autonomous surveillance tower stands along the US-Mexico border. With billions in new funding, CBP plans to deploy thousands more surveillance towers in the coming years.
CENGIZ YAR FOR MITTR
With grants from the Small Business Innovation Research program, Anduril’s product started taking shape: a 33-foot-tall solar-powered structure with cameras to see and radar to detect movement, all sitting atop a modular metal pole, with solar panels arrayed beneath.
More important was the component you could not see: computer vision that promised to automatically classify, track, and generate alerts for what came within the system’s view. Anduril’s standard tower couldn’t see as far as the Elbit and General Dynamics models—about 1.75 miles instead of more than five—but, the company argued, it didn’t need to: By automatically identifying and tracking people, the towers could give agents enough warning to respond. (Its newer, extended-range towers, on the other hand, stand 80 feet tall and are advertised as able to detect and track “objects of interest” up to 7.5 miles away.)
When President Trump left office in 2021, Anduril continued to find support from his successor. Joe Biden had campaigned on the promise of shutting down the project to expand the physical wall, but he saw political reasons to keep investment in tower technology on the table. “It’s less visible—it’s unobtrusive,” a former immigration advisor to Biden recalls, adding that despite some privacy concerns, the towers were mostly uncontroversial.
When Border Patrol deployed Anduril towers to the Santa Teresa station in New Mexico in 2021, a press release stated, “Because of its accuracy with detection, in many cases this type of technology can and will save migrant lives.” In a permitting request filed in 2024 for towers in California, the government was even more explicit: “If anyone within the viewshed of the towers appears to be in distress, EMTs or first responders will be sent to help.”
CBP IMAGES
Anduril echoed those claims. The software built into the surveillance towers, the company wrote in a 2021 press release, gets agents “out of communications operations centers and into the field where they can effectuate security and humanitarian responses.”
It wasn’t the first time CBP had discussed a humanitarian side to its duties; in 1998 the agency created BORSTAR, a search and rescue unit, and by August 2023, it had installed some 170 rescue beacons across the border that people attempting a crossing could theoretically use to call for help. But they weren’t effective, says Mario Agundez, a retired Border Patrol supervisor from Arizona who had worked on various rescue efforts; people either avoid them or “die next to them, because there was no way [for Border Patrol] to get to them in time.” (They don’t always work, either; one that MIT Technology Review passed, just next to a dirt road in the Otay Mountain wilderness area in Southern California, had its prominent solar-powered blue light, meant to be visible for miles to people in distress, turned off on a recent night in August. A CBP spokesperson did not comment on why it was off but said, “The solar-powered beacons are maintained and monitored by the responsible Border Patrol station that patrols that area.”)
Anduril soared through a pilot phase in just two years before its Sentry towers—then the only autonomous surveillance towers CBP was using—moved into an official program of record with the government in 2020. That’s light speed in the world of government procurement, especially since it was Anduril’s first hardware product. By June 2021, CBP had already spent over $98 million (Anduril’s contract for these towers is now worth up to $1.1 billion).
Gerardo Galvan was in charge of the Santa Teresa Border Patrol station—which patrols the area in New Mexico where Morales’s body was later found—when it received its first batch of Anduril surveillance towers in 2021. His agents were worn out; the El Paso sector, home to his station, had seen encounters tick up each month since April 2020. Galvan says the agents were surprised but eager to get the AI towers, and he began planning where to place them. The sites he was considering sat where one kind of landscape abruptly gives way to another.
To the east of the station is Cristo Rey, a mountain that obscures the view of the city of El Paso. Running north from Cristo Rey is the state’s border with Texas, where the banks of the Rio Grande are flanked on either side by pecan orchards. And west of those orchards are the towns of Sunland Park and Santa Teresa. The last houses in these neighborhoods butt right up against the desert, which sprawls westward without a single gas station for nearly 100 miles.
It is in this stretch—between the US-Mexico border to the south and Highway 9 to the north—where agents from his station aimed to stop migrants. And when Galvan was planning the tower locations, there were lots of crossings; Santa Teresa was becoming the busiest station in the 125,500-square-mile El Paso sector, as heightened border enforcement in Texas drove people west. Along this new route, more people were dying than in previous years. (The traces of that period are still visible years later; MIT Technology Review visited the area with the humanitarian group Battalion Search and Rescue and saw jawbones bleached white by the sun, alongside frayed backpacks and clothes.)
Galvan’s aim was to use the new towers as a stopgap where there was little other border infrastructure. That included a spot near the foot of Mount Cristo Rey, where there was a break in the border fence and people often attempted to cross. Galvan looked at data on previous apprehensions with an eye toward helping agents spot people before they arrived in more residential neighborhoods. Border Patrol struck agreements with private landowners, who generally didn’t mind having Anduril’s low-profile towers on their land (and were compensated via lease agreements).
When the towers were first set up near the Santa Teresa station in 2021, engineers from Anduril came to fine-tune the algorithms meant to autonomously classify whether what it had detected was a vehicle or a possible border crosser, among other things. A former engineer for Anduril, who spoke on the condition of anonymity to discuss his previous employer, says these algorithms were the main focus of the tower program; Anduril didn’t manufacture the cameras or radar systems itself, so the algorithms were its main value proposition to Border Patrol.
Galvan says the towers would flag people crossing before agents saw them. But he also saw problems from the start. Agents would apprehend a large group near one of the towers, for example, and check to see if the footage revealed anyone who got away, only to find that the incident hadn’t been captured at all. Anduril’s towers constantly pan around, lingering only on an object of interest. But in these cases, Galvan says, they panned elsewhere, failing to capture the border crossers they were supposed to automatically track.
Reyes says algorithm mistakes were infrequent but problematic at his station. A group of people was sometimes labeled as cattle, for example, and agents wouldn’t receive an alert. The problem got worse the farther groups were from the camera.
Anduril has touted its technology’s ability to reduce false positives, like cases in which wildlife is mistaken for people. But the company has said little about these false negatives: people the systems fail to detect.
Jaime Fierro, the former agent in the Laredo sector in Texas, describes one incident that shocked him: Agents pursued a car believed to be carrying migrants who had just crossed the border. The car turned around, drove toward the banks of the Rio Grande less than 100 feet from an Anduril tower, and crashed right into the river. Several occupants got out and swam to Mexico while the car floated in the water. Back at the station, Fierro hurried to see what the camera had picked up. Only it never detected the incident at all. There was no footage. “That was a huge, huge issue,” Fierro says. He remembers Anduril coming out to investigate and the problem being sent all the way up to Washington leadership. But Fierro never got an explanation of why the crash was missed. (An Anduril spokesperson did not respond to a question about this incident but said the range of the towers we asked about was limited by physical obstructions and by boundaries established by CBP. )
Several agents estimate that the towers initially missed 10% to 15% of what they should in theory have caught. “To us, 10-15% on a system we spent millions on that was supposed to work—that was alarming,” Fierro says.
Philip Sullivan worked as an agent in the Laredo sector too. When the Anduril program started, he says, he put his hand up to be involved and even went to an Anduril test site for training. When new towers went up, he worked directly with the company on improving its algorithms. He enjoyed the work. But he describes one recurring issue they couldn’t resolve.
Smugglers would put up to a dozen people on a rubber raft, cover them with a tarp, and cross the Rio Grande while using submersible motors to propel it forward. The tarp fooled the algorithm, which detected the motion but attributed it only to an “unidentified object.” That meant no audible alert—the very feature agents relied on, because the Anduril system was supposed to do the watching for them.
The smugglers repeated the tactic dozens of times. Other smugglers would cover groups with netting or blankets so they would remain undetected while walking through the brush. Only afterwards, having seen the group farther inland and noticed clues about where the people had crossed, would Sullivan review the footage and see the crossings that were missed. He worked with Anduril, sending engineers videos of the crossings to retrain the firm’s algorithms. But the situation had not improved by the time he got promoted to a sector-level job sometime in 2022, he says.
“The whole purpose of their system,” Sullivan says, was the idea that “the cameras are moving around, identifying automatically and tracking and recording and flagging.” Having cameras that could pan on their own worked wonders, he says. But the algorithm had its limits. “You look at it on the screen yourself, and you see the blob and the raft moving across—well, you know that’s people. But to get an AI to identify that is a challenge.”
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When the towers were first being rolled out at the Santa Teresa station in 2021 and 2022, Galvan says, similar issues led to a negative feedback loop: An agent would see a tower miss something significant and come to distrust the algorithm. When that agent’s turn came to operate the towers and manage the alerts, they might override the AI altogether and use the camera manually. That would lead to more missed alerts, more distrust.
Despite these shortcomings, the El Paso sector’s relatively flat landscape meant that the system’s cameras had about 80% to 90% visibility, according to our topographical analysis. That was better than in hillier or more mountainous areas, like the Otay Mountain Wilderness to the east of San Diego, where we estimate Anduril’s towers could see just 15% to 25% of their promised coverage area.
An autonomous surveillance tower along the southern US border in Sunland Park, New Mexico.
CENGIZ YAR FOR MITTR
It was in this wilderness, in fact, that some of the first bodies started appearing near Anduril’s towers. During one week in August 2021, two women in their 20s, from the same city in Mexico, died miles apart. Their bodies were found near three different towers that were first observed on satellite imagery between March and July that year.
One of them, 26-year-old Sarahi Hernández Alfonso, began her journey to cross into the US on August 5, and medical investigators note that she reportedly fainted the next day near the Otay Mountain Wilderness and was left behind. It would be another four days before the Mexican government reported her missing to Border Patrol on August 10, supplying a set of coordinates. Agents went to those coordinates and found her decomposing body. She’d died 1.3 miles from two different Anduril towers, one of which our analysis found had a clear line of sight.
Just the day before, 25-year-old Karina López Antonio was found dead near a thirdAnduril tower. She had crossed the previous night with her cousin and nephew. That morning, she felt sick, and her nephew looked for a Border Patrol agent to help. When agents returned, she was dead.
The following October in New Mexico, where the new Anduril towers had been rolled out, agents were patrolling on ATVs when they came across footprints. They followed them until they found the body of 31-year-old César Perea Itzincab. The presence of maggots and the level of decomposition indicated that he had died at least a week earlier, and medical examiners on the scene believe he had dragged himself to the spot where his remains were found. He was 1.5 miles away from an autonomous tower—within Anduril’s advertised range—and should have been visible to the camera, according to our topographical analysis.
We can’t know what those cameras saw as these people died. Footage and data from Anduril’s towers are overwritten every 30 days, and a former official with internal affairs at CBP told us the information wouldn’t be saved for longer unless it was part of an active investigation—which starts only if someone dies in custody. CBP did not respond to questions about specific incidents like this one, and the Sunland Park Police Department closes cases like this when it determines that no crime has been committed.
The lack of documentation is a missed opportunity, says Amerika Garcia Grewal, co-director of the Frontera Federation, which aims to help rescue migrants in distress—or find and identify their remains. “The surveillance towers along the border could be incredibly useful,” she told us. For example, they could in principle be used to hold Border Patrol agents accountable for whatever actions they did or did not take to locate someone in distress. “What the tool turns out [to do] depends on the person who is holding it,” she says, adding, “I don’t trust the folks that are using them.”
“If I could get that footage,” she says, “then we would go through that, and hopefully bring some answers” to family members and “documented proof of what happened” in their loved ones’ final moments.
When asked about deaths near Anduril towers, agents often point to faults in the technology. Galvan, for example, says the towers don’t capture how groups move: A large group of 20 people might splinter into smaller groups, especially if agents are pursuing them, and the tower doesn’t keep track of everyone. If someone’s missed, he says, “that person stays behind in the brush, out of sight.” Then they might pass out—which could be a death sentence in the desert.
And though Border Patrol says the towers can “hand off” surveillance of people from one tower to another, agents on the ground say that people are often missed during these handoffs. On top of that, the algorithms remain imperfect, according to Jason Owens, the chief of Border Patrol from June 2023 to March 2025. “We never really got to the point of ‘Set it and forget it,’” he says.
Agents also say they were just stretched too thin to respond effectively. Around 2022 and 2023, the border saw historically high levels of migration. Title 42, a policy that began under Trump and continued under Biden until May 2023, cited a public health emergency to expel migrants before they could apply for asylum. But the rapid expulsions carried fewer of the repercussions that could normally follow an apprehension, and many people simply tried to cross again.
Meanwhile, asylum claims had been rising for years, while shifts in US immigration policy and in the places migrants where originating from meant more people required lengthy processing rather than being quickly returned across the border or to their home countries. That increasingly tied up agents with people who had already been apprehended, they said, leaving fewer available to respond to surveillance alerts.
In other words, from the agents’ perspective, the effectiveness of Anduril’s towers was limited by the same issue that had always caused trouble: There just weren’t enough agents.
Palmer Luckey, a founder of Anduril, surveys the equipment at his company’s testing range near Camp Pendleton in Southern California on February 3, 2021.
PHILIP CHEUNG/THE NEW YORK TIMES VIA REDUX
But the overwhelming demands on agents cannot explain all the deaths our investigation found. In 2024, the border started to get quiet again. By July of that year, the number of Border Patrol encounters nationwide had plummeted from their highs in December 2022. Encounters in the El Paso sector, where the Anduril towers in New Mexico were located, had fallen to nearly one-tenth of their peak. Agents were less tied up than they’d been in years. The technology was supposedly improving, too.
Galvan had left for a promotion by this time, but he says agents had come to trust the towers more, and they helped train the algorithm by giving a thumbs up or thumbs down if the system identified something correctly or incorrectly. There was also a new smartphone program: Every agent at the station was given an Android device with a map of where other agents were, and it sent alerts from the station about what an Anduril tower detected.
Despite all this, the deaths near towers continued. There was Morales in April, whose body Border Patrol learned about only from the landfill workers, though it had lain for hours within sight of an Anduril tower.
Others were discovered by luck.
On June 20, Border Patrol agents were mistakenly tracking a group of people they thought might be migrants, though they were in fact employees of the same landfill. Those employees told the agents they had come across a body, which turned out to be partially mummified remains of a man in his 40s who died within range of two Anduril towers.
Three days later, on the 23rd, another: the body of a 21-year-old Guatemalan man discovered by agents while on patrol, 1.3 miles from a tower across clear and open desert. Two days after that, another: An agent was looking for a lost person when he came across the remains of a 56-year-old man—just a football field’s distance from the previous one, with a similarly clear and unobstructed view to a tower.
So if Santa Teresa had gotten quieter—and become something of a showcase for Border Patrol’s newest technology efforts—what was going on?
“Agents review the information and determine the appropriate response—the technology does not make law enforcement decisions,” Beckham, CBP’s assistant commissioner, said. Meanwhile, individual agents described scenarios where they might not prioritize responding to alerts.
Sometimes “hanging back” was a strategy: Agents might observe a group to see what route they would take, or agents might not respond to a small group, in case those people had been sent by smugglers to distract from a larger group coming behind them. Other times, agents might deem the location where a group of migrants had first been spotted to be impractical for an apprehension, and instead wait to intercept them at another location.
Knowing someone’s location didn’t always lead to immediate action. Volunteers describe providing Border Patrol with the coordinates of someone who was lost but alive and then waiting, “sometimes [for] a week,” for agents to reach the location, says Garcia Grewal, from Eagle Pass, Texas. They might be told “Oh, so-and-so went out there and they found remains,” she recalls. “Well, they weren’t remains when we called you. They were alive.”
Near the southern US border, Sunland Park, New Mexico.
CENGIZ YAR FOR MITTR
Mireya Morales, the younger sister of José Morales Bernal, who died by the landfill on the day before his birthday in April 2024, wasn’t aware her brother died near several surveillance towers, or that the government said these towers could save lives. “Well that’s good,” she said of that promise, “but in this case, it didn’t help my brother. And there’s no way to know exactly how things played out.”
“Border Patrol should have found him quickly,” she says, “and they could have done something.” If they did, this journey into the United States—his fourth trip—likely would have ended with his apprehension, detention, and deportation to Mexico. But he would have seen the birthday texts that his sister sent the next day. At least he would have made it home to celebrate his eldest daughter’s quinceañera, which took place earlier this year.
Stories like this are why Iván Chaar López, an assistant professor of American studies at the University of Texas at Austin who leads its Border Tech Lab, says he’d “rather talk about harms” than about “the failure of an algorithm.” He adds, “Asystem may fail, but humans suffer.”
Stories of some of the people that died near Anduril’s autonomous surveillance towers
What happens next
Ultimately, any attempt to understand where things are going wrong is hampered by an institutional reluctance to measure the problem.
A complete audit of this virtual wall can only come from Border Patrol. The agency is, in some ways, increasingly equipped to take on that task: Agents told us that each time they apprehend someone, the location is logged with GPS coordinates, as are instances when Border Patrol sees evidence that someone crossed but cannot find them. It also has precise data on when each surveillance tower went up, which alerts came in, and how they were resolved. Having that information is the only way to measure whether the newest towers are truly missing fewer people than the previous generations.
But researchers and outside oversight agencies say this ocean of data hasn’t translated into a reliable system for measuring the effectiveness of the virtual wall. For example, CBP records the people it misses by logging “gotaways,” a tally of how many times agents see signs of someone who evaded apprehension—footprints, appearances on camera, reports from other people who were apprehended. It’s imperfect. And that creates room for interpretation.
“The goals with border enforcement have always been a moving target,” says Jeremy Slack, a researcher of migration and border issues at the University of Texas at El Paso. “They’re always set up so that it’s a win-win.” If apprehensions go up, for example, CBP says it means agents have gotten more effective at catching people. If apprehensions go down, it means the border is quiet because people are too scared to cross.
Workers install panels and construct new all-weather roads as part of a border wall construction project east of Nogales, Arizona, in July 2026.
JERRY GLASER/U.S. CUSTOMS AND BORDER PROTECTION
That’s not to mention the conflicting ideas within the agency about what the virtual wall was supposed to accomplish. Borkowski says higher-ups would ask how many fewer agents they could get by with if they built more towers. But supervisors receiving new towers told us they’d often ask for more agents, not fewer, because they now had more activity to respond to that had previously gone unseen.
And if the goal was for the sight of the towers to deter people from crossing to begin with, an external study from RAND in 2020 was ambiguous: It found that deploying IFTs resulted in lower apprehension levels nearby but said this didn’t mean the towers were actually deterring crossings. (Research by Boyce and colleagues found that earlier surveillance towers in southern Arizona pushed people to seek more difficult terrain out of view.) It leads to a question: Do the deaths near the virtual wall constitute unacceptable surveillance failures, or are they within the range of effectiveness the government deems acceptable? (CBP’s response did not address our questions on how it explains these deaths.)
Against this backdrop, the Government Accountability Office and DHS’s Office of the Inspector General have tried to focus on a narrower question: Is the virtual wall increasing the likelihood of apprehensions?
The answer has been incomplete since 2014. That’s when the GAO first suggested that whenever agents log their activity in Border Patrol’s database, they should include information about whether or not a piece of technology helped in their apprehension. This could at least do something to show Congress whether the technology is working. Border Patrol began collecting that information, but years of incremental improvements have been followed by repeated findings that the resulting data is unreliable or insufficient to determine how well the technology works. The reality, several people from Border Patrol told MIT Technology Review, is that agents see it as a chore demanded only by bureaucrats who don’t understand the realities of the border.
It’s “spitting in the wind, to be honest,” a former high-ranking official at DHS during the Biden administration told MIT Technology Review on background. “If we have 10,000 people a day crossing in between the ports of entry, do you think an agent is worried about telling them how they freaking apprehended those people?” Several other officials said that the logging of technology assists had improved, but not consistently enough to help much with analysis.
And there is no procedure—at the local or national level—to examine individual deaths near the towers or analyze them collectively for surveillance failures. “We never thought about plotting [migrant deaths] to see if the towers were missing things or agents were missing people,” said a former CBP official who evaluated how personnel handled deaths in custody.
“Nobody ever raised the issue,” the official said, adding that doing so could reveal important gaps in the agency’s approach: “If I still worked for CBP, and you brought it up, I would probably send some people to look.”
In response to questions from MIT Technology Review, CBP said that autonomous surveillance towers complement physical barriers by improving detection and situational awareness. Its statement did not address questions about its other technologies, or broader criticisms about how it evaluates the virtual wall.
An autonomous surveillance tower along the southern US border in Sunland Park, New Mexico.
CENGIZ YAR FOR MITTR
This lack of measurement has not slowed the enthusiasm for more technology funding. The government spending bill passed in July 2025 awarded $2.7 billion for border security technology alone, and CBP was quick to signal to industry that the money would soon start flowing. “We’ve got an historic investment in infrastructure and technology coming up,” a CBP director told tech companies in an industry webinar that month. In a December 2025 interview, when he was director of homeland security and immigration at the America First Policy Institute, Cooper Smith described the technology investments as “fortifying” the border against a future president who might be, in his estimation, weaker on the border than Trump. (According to LegiStorm, a research organization that focuses on political staffers, Smith now serves in a policy-focused role with CBP.) The number of Border Patrol agents has climbed too, to nearly 21,500 agents as of June 2026—the highest in the agency’s history.
In the Big Bend region of Texas, surveillance towers are becoming a focus of local politics: Officials and residents across party lines, including the sheriff of Terrell County, are asking for Anduril towers instead of a controversial new border barrier project that’s recently been halted.
CBP has announced a desire to spend $1 billion on nearly 1,500 more towers by 2034. The law now requires all these towers to be equipped with AI. In December 2025, Anduril’s Palmer Luckey said the towers remained the company’s best-selling product, one that gives “basically perfect situational awareness of what’s going on in the area.” Anduril’s towers have been sold abroad—to enforce the UK’s borders, defend US Marine Corps bases in Japan and elsewhere, and serve as drone defenses for an Australian Air Force base.
But as for CBP, Anduril will no longer be the only game in town; General Dynamics has now released AI towers of its own and received an order for them in June from CBP worth up to $115 million. The Electronic Frontier Foundation—which has tracked surveillance towers since 2022—says some have already been spotted at the border.
This time around, the agency will be spending this money with even less oversight than it had just a couple of years ago. In October 2025, the Department of Homeland Security, which oversees CBP, dissolved its department-level office that oversaw major spending programs.
An autonomous surveillance tower along the southern US border in Sunland Park, New Mexico.
CENGIZ YAR FOR MITTR
Most of the records on human remains that MIT Technology Review collected were current only through the fall of 2025 or, in a handful of cases, early 2026. Even those dates come with a caveat: Many remains are never found, and others are found months after someone died. The reports can then take months to be released via public record requests. That makes it nearly impossible to keep track of how many people have died near surveillance towers in anything close to real time.
Still, the border today is significantly quieter. The latest figures from DHS show just 128,009 “enforcement encounters” from January to August 2026, compared with more than 2.4 million during the same period in 2024 (“encounters” count each of Border Patrol’s interactions, not individual people).
Even as border crossing rates decrease and DHS speeds ahead with its tower acquisition plans, people are still dying within range of the surveillance towers.
On September 14 2025, a 30-year-old Mexican woman named Graciela Gómez Hernández died just over 350 yards away from the physical border wall that separated Southern California from Tijuana—and within one mile of an RVSS tower. Earlier that afternoon, she had told her family that she could not walk any further, and her voice notes abruptly stopped.
Her skeletonized remains were recovered nearly three weeks later, on October 4. Some of her bones were missing, and there was “apparent animal activity … to the ribs,” as the medical examiner’s report read.
Sara Stroud, an organizer with the Borderlands Relief Collective, a volunteer humanitarian group that leaves water and supplies in a wilderness area frequented by people crossing the border, went to the site of her death a few days later to put up a memorial cross. A Border Patrol helicopter showed up almost immediately, flying low circles above the volunteers.
Between the helicopter and the RVSS tower that was visible in the background, “I was astounded about how we were being surveilled the whole time,” Stroud says, adding that this was especially stark in contrast to the agency’s often slow—or absent—responses to people who’d died on camera.
“Are you telling me you didn’t see it or you did see it?” she asks. “Because if you did see it, that’s horrible. And if they can’t, then how do you explain that?”
She had only walked for a couple of hours, and already she was lost.
It was early afternoon on Sept. 14, 2025, when 30-year-old Graciela Gómez Hernández crossed the border from the eastern edge of Tijuana into Southern California, sending voice messages to her mother and sister as she walked.
This story is part of Dying on Camera, a collaboration between MIT Technology Review and Times of San Diego. Journalists in both newsrooms spent the past year examining the failures of border surveillance technology and uncovering the stories of the people who die in the borderlands.
Gómez Hernández did not tell her family about her plan until the day before. Once she arrived in California, she hoped to work and send money back to her three children and her parents in southern Mexico. Her oldest son, age 11, had asked for a bicycle.
She either climbed a ladder over the border wall or slipped through a section in steep terrain where fencing has yet to be built. Dozens of game trails cut through the dust on hillsides of sagebrush and manzanita. It was the first segment of a hike through the Otay Mountain Wilderness.
The eastern edge of San Diego is just a mile away, but countless migrants hike 10 or 20 miles north through the mountains to evade the Border Patrol, emerging in smaller towns and on distant highways with fewer checkpoints.
A border agent rides an ATV along the U.S.-Mexico border outside San Diego, September 2026. While the mountainous terrain makes some areas difficult to access, the area is blanketed with interconnected surveillance technology.
ANNIE BARKER/TIMES OF SAN DIEGO/CATCHLIGHT LOCAL/REPORT FOR AMERICA
Between 2 and 3 p.m., her sister said, Gómez Hernández sent a final message to her mother. “I can’t do it anymore,” she whispered into her phone. By the time a forensic crew came to collect her, two weeks later, most of her body had disappeared into the earth.
She died on her own, but she was not, strictly speaking, alone. She was in one of the most heavily surveilled strips of land in the world.
Before she died, Gómez Hernández told her family in a WhatsApp message that she could see helicopters overhead and white Border Patrol trucks making periodic laps on the road below. And even if none of those agents saw her or knew she was in distress, something else had a clear view. Supervising the whole territory, from a nearby hilltop, was a slender gray surveillance tower.
That tower, made by General Dynamics and in place since 2019, boasts two sets of electro-optical and infrared cameras with long-range lenses that can track targets 5 to 7 miles away. Gómez Hernández was just one mile away.
Surveillance towers have proliferated in recent years, as contractors added newer camera systems that use artificial intelligence to distinguish border-crossers from other people and animals, and track their movements, at an overall cost of more than a billion dollars.
Yet as the cameras scan the terrain, migrants continue to die in plain sight.
Thousands of migrants have died while crossing the border in the past two decades, many of them near to Border Patrol surveillance installations. But the phenomenon is especially pronounced in the Otay Mountain Wilderness, just outside San Diego.
A Times of San Diego analysis counted hundreds of cases along the California border—including at least 138 in the past four years—where migrants’ remains were found within the range of the very cameras designed to help track and intercept them.
A topographical analysis conducted as part of an MIT Technology Review investigation estimated that more than half of these remains were found in a spot where a camera would have a clear view of a person, without terrain blocking the line of sight.
Some of these migrants died swiftly, falling from the border wall or drowning in a canal or river. But dozens suffered slow deaths of exposure in the wilderness, deaths that might have been averted with emergency aid.
People who died in Southern California came from just across the border, or from as far away as Africa. They were as old as 70 and as young as 5. They died in the heat or the cold. But they all died within range of a surveillance camera.
Found in July 2023: Marcia Dutra, a 36-year-old woman from Brazil who likely died of heat exposure next to a border fence. According to a medical examiner’s report, a group of migrants told the Border Patrol about her body when they were apprehended. An AI-equipped surveillance tower sits 2,000 feet northeast above the edge of a plateau, out of sight of the slope where she was found.
Found in February 2024: Elvi Vasquez Bortolon, a 28-year-old Mexican man who died of suspected hypothermia on a ridge near the border wall. He had been dead for more than five days when a Border Patrol agent in a helicopter spotted his body from above. A remote video tower stands about 1,400 feet away, hidden from view by the ridge above the canyon.
Found in August 2024: Ramón Montenegro, a 51-year-old man from Mexico who died of possible heatstroke in the mountains near Dulzura. An AI surveillance tower, invisible on the other side of a hill, sits 1,300 feet away — roughly the length of the parking lot at the San Diego Zoo.
More than 120 surveillance towers stand along the California border, according to documents and live observations collected by the Electronic Frontier Foundation.
The towers are not the only form of surveillance in the region. The border is blanketed by an interconnected network of trail cameras, underground movement detectors, radar, infrared sensors, drones, and even blimps. In March 2025, the Trump administration directed the Department of Defense to use satellites to watch the border as well.
Even when mountains or vegetation block a tower’s view, other devices may pick up the trail. Local advocates say surveillance technology in the Otay Mountain Wilderness is almost unavoidable.
Agents in Border Patrol gear hike a ridge in the Otay Mountain Wilderness, September 2026. Agents said the group was on a training hike.
ANNIE BARKER/TIMES OF SAN DIEGO/CATCHLIGHT LOCAL/REPORT FOR AMERICA
Officials say the towers particularly excel at tracking passing migrants.
Anduril, the company that makes about half the towers now installed at the U.S.-Mexico border, said in a news release in 2024 that their towers “directly contributed to saving lives and stopping illicit drugs from entering the U.S.” In June 2026, the company said its installations had “autonomously identified hundreds of thousands of border crossings.”
John Morris, a Border Patrol sector chief in Arizona, told media at a convention in May 2026 that “We’re dang close to pretty much knowing everything that comes across.”
The proximity of so many deaths to high-resolution cameras leaves two distinct possibilities: Either the system didn’t detect these people in distress, or it did detect them—and officials offered no response.
Anduril, the defense contracting company that manufactures the AI-enabled towers, did not respond to questions from Times of San Diego about the towers’ performance in mountainous terrain.
A representative of General Dynamics, which made the tower closest to Gómez Hernández’s body, referred all questions to the Border Patrol.
The Border Patrol did not respond to questions from Times of San Diego regarding whether surveillance towers spotted Gómez Hernández prior to her death, or about how surveillance cameras in general are used to detect people in emergency situations.
Rafael Hernández Larraenza, the leader of a search and rescue group, Desert Angels, says he believes some of the towers are “just adornment,” and are not actually effective at tracking migrants. He also says that Border Patrol agents, in occasional meetings with his search group, “say they don’t have the opportunity to do searches, that they don’t have enough time.”
People in need of rescue “are not a priority,” said Bryce Peterson, who compiles research on migrant remains for the advocacy group No More Deaths.
A wilderness, heavily watched
From the spot where Graciela Gómez Hernández’s body was found, a surveillance tower stands in clear view.
That tower is one mile away. A second, newer tower, on the other side of a hill, sits 1.4 miles away. Both are equipped with slow-spinning, long-range cameras.
The closer of these two towers is situated between two layers of border fencing, a long pole, over 100 feet tall, reaching into the sky. It peers not only onto hillsides where migrants walk, but into the brightly-colored cinderblock homes of a Tijuana neighborhood.
The second tower is much shorter, just 33 feet tall, surveying the canyon where Gómez Hernández would most likely have arrived if she had kept walking.
This new tower, built by a company called Anduril, decides for itself whether a moving object in the field is relevant. The two dark, round cameras on the pole give the impression of eyes, rotating slowly, and then locking in on a target, before swiveling elsewhere.
Federal cameras seen at Friendship Park in San Diego, September 2026.
ANNIE BARKER/TIMES OF SAN DIEGO/CATCHLIGHT LOCAL/REPORT FOR AMERICA
Osvaldo Ruiz, the volunteer coordinator for the Border Angels, a group that leaves water and supplies along migrant trails near San Diego, says the Otay Mountain wilderness has “beacons throughout the mountains” that “can see over a lot of the mountains facing the border.”
“At entry sites that migrants or travelers may come through, there are also hidden cameras inside of rocks or inside of bushes that are camouflaged for you not to see. And then there’s also the cameras that you see on the road. They have license plate readers that are stuck on the back of a safety cone, so (it detects) as you’re driving away from it.”
Surveillance towers have been in the area since 2011, with frequent new installations, including the introduction of AI-directed towers in 2018. Many of the AI-equipped towers currently overseeing the California borderlands appeared in 2021, and were updated with longer ranges in 2026.
Officials from the Government Accountability Office wrote in a report in June 2026 that the Border Patrol currently has 803 fielded towers “and plans to deliver another 175,” across the southwestern border, an initiative that would cost $1.4 billion. The report also notes that the agency expects a billion dollars in funding from the One Big Beautiful Bill Act in 2025. By 2034, the number of towers is expected to rise to 2,300.
“What we are building along the southwest border is a smart system. There’s technology embedded in it, plus the physical infrastructure,” border czar Tom Homan said at a press conference in San Diego in December 2025. “It makes every single agent and officer more effective.”
A secretive hike north
Like so many migrants, Graciela’s hike into the U.S. began in desperation. Her husband, who had already crossed the border to find work, had stopped sending money for the children.
“There were moments when they didn’t have enough money even for a bag of detergent, or to eat,” her sister, Irma, told Times of San Diego. “She would call me crying.”
Gómez Hernández decided she needed to come to the United States, if only temporarily, to make money. Her family was uniformly against the decision, so she kept her plan a secret until she was already at the border, preparing to cross.
“She was a very humble person,” Irma said. “She was kind to people, and loving with her children.”
Gómez Hernández paid 20,000 pesos, about $1,150, for smugglers to help her cross the border, much lower than the current average, which sits at $12,000. But she would need to travel on her own without a guide, paying only for the knowledge of where and when to start, and which direction to walk. As part of their deal, her phone would send location pings back to the smugglers’ hiding spot in Tijuana.
Cesar Ortigoza, a leader of a search group known as the Armadillos, has been locating migrant remains in the regionsince 2010.
A discarded card shows advice that advocates give to migrants about what to say if they are apprehended.
ANNIE BARKER/TIMES OF SAN DIEGO/CATCHLIGHT LOCAL/REPORT FOR AMERICA
“Most people don’t understand the magnitude of this territory, and all the work it’s going to cost them to arrive at their destination. They get to the top of this mountain and realize there’s miles and miles and miles left to walk,” Ortigoza said.
In addition, they need to avoid capture. “They go really fast,” he said, “and there’s a lot of pressure.”
On the afternoon of September 14, 2025, Gómez Hernández told her mother and sister through WhatsApp voice messages that she had successfully crossed the border into the United States.
These short snippets of audio would be their only clues to what happened next.
The hillside where Gómez Hernández walked that afternoon is exposed on all sides, with a panoramic view of the border wall just a couple hundred yards away. But on the other side of the hill is a daunting, mountainous wilderness, covered in low trees and rocky ravines, with hidden, winding trails, stretching into the distance. It is possible she saw this and turned around, convinced she was lost. Wandering the hillsides in the afternoon heat without sufficient water, she could succumb to dehydration within hours.
“Because of the heat, there are places where you can’t move forward even half a mile in an hour,” said Larraenza, the search and rescue leader.
By 2 p.m., “She told me she felt very tired and couldn’t go any further,” Irma said. “I told her to give it her all, to think of her children, keep them in her mind so she could get out of there.” Irma says 4 p.m. was the last time her sister’s location was transmitted to the smugglers.
After hearing about Gómez Hernández’s case, Ortigoza said it “seemed strange,” because she was “so close to the border” at her last known location.
But Bryce Peterson, the No More Deaths volunteer who collects autopsy reports on migrant remains border-wide, said “There’s a million ways that somebody could be already on the verge of death that close to the wall. Like if you’re going north and then you decide to go south again because you get lost or something.”
“The only thing we’re certain of is that she suffered in her last moments,” Ortigoza said. “Just knowing that you’re alone.”
Walking on the hillside, Gómez Hernández encountered a man also traversing the trails, lined with dry shrubs. “In one of the messages sent, where she told me to pray for her, the man spoke and said ‘there isn’t time,’” Irma said.
After that, Graciela’s phone still received messages, but she did not respond.
Cameras rise, questions remain
Even though the federalsurveillance tower was just a mile away, it is possible the cameras did not see Gómez Hernández. It is possible they did not see much of anything.
An internal memo obtained by NBC News in October 2024 revealed that 30% of the older-style towers, the kind that require agents in control rooms to review footage, were broken. Most were missing parts, or their equipment was outdated and unusable. These 150 broken towers still stood at the border, acting as million-dollar scarecrows.
Officials from the Government Accountability Office wrote in a report in September 2025 that the Customs and Border Protection had begun to fix the towers with spare parts and revamp them with new technology, but did not list how many remained out of commission.
The Border Patrol did not respond to questions from Times of San Diego about whether the tower in view of where Gómez Hernández died was operational at the time of her death.
Dave Maass, the director of investigations for the Electronic Frontier Foundation, the organization that maps surveillance towers at the border, questions the functionality of all types of towers. “None of these systems have been effective. There’s no independent evaluation of how many things they’re capturing,” he said.
In a report published in March 2026, a representative of the US Military notes that a mission to place 7 G-BOSS(E) towers in the Otay Mountain Wilderness, an installation that took place in the months prior to Gómez Hernández’s death, faced operational challenges.
The combat-grade military surveillance towers were vulnerable to tipping over, damage from rodents, exhaust blowouts, and running out of battery. On a dirt road in the mountains, a truck carrying a surveillance tower bounced around, which led to the tower’s cameras “becoming stuck in a zoomed-in position.”
Despite descriptions from manufacturers claiming dominance in “rugged terrain,” Osvaldo Ruíz, the Border Angels coordinator, said it would still be difficult for a surveillance tower to track migrants moving through the Otay Mountain Wilderness. “With all that surveillance that’s out in the mountains, there’s still a lot of brush and high covered areas that would make it easy [to stay undercover].”
Analysts from the RAND Corporation, a foreign policy think tank, wrote in a report in 2020 that “outcomes and outputs” that would indicate the success of surveillance towers “are not directly observable.”
The analysts conceded that the towers’ presence would urge migrants to stay out of the area and funnel into undetected routes. “The likely deterrent effect on migrant crossings through surveilled areas overwhelms any boost to situational awareness,” that the towers provide, the analysts wrote. In this way, the surveillance towers’ most useful quality was the threat conveyed through their technology, rather than the merit of the technology itself.
Due to increased surveillance, “People are crossing through areas that are further out and more extreme, where we have very little hope of finding them in a search,” Larraenza said.
A portable surveillance tower rises from a pickup truck in the Otay Mountain Wilderness outside San Diego, September 2026.
ANNIE BARKER/TIMES OF SAN DIEGO/CATCHLIGHT LOCAL/REPORT FOR AMERICA
And though the manufacturer says the AI-enabled towers, which now make up over half of the total, can identify migrants on their own, apprehensions still require human labor. A CBP official told the RAND analysts that smugglers began sending migrants across the border alone, rather than in groups, to overwhelm the surveillance towers with many individual targets, leading to more alerts than agents capable of following them.
So it is possible the towers in the vicinity did capture footage of Gómez Hernández, but agents decided not to apprehend her. Just hours before her death, she sent a message saying that there were “police” nearby, possibly referring to Border Patrol agents in trucks.
“Even one person who’s not sick, one person who is just hiking on their own, often Border Patrol will just ignore them because it’s not worth their time to just only catch one person,” said Peterson of No More Deaths. “They want to be able to catch a whole group of people.”
Larraenza said he’s found dehydrated people on migrant trails who say they called out to Border Patrol, and the agents failed to stop. “But there are also cases where agents do the impossible to find someone,” he said.
On one occasion in September 2024, while rescuing a family of five in the Otay Mountain Wilderness, Larraenza met a person wearing a vest labeled “SHERIFF” on the trail. In a video that Larraenza posted to his Facebook account, when asked if the Border Patrol would arrive to take the family into custody, the deputy says, “Oh, they don’t come out here.”
On a recent visit to the spot where Gómez Hernández died, multiple Border Patrol trucks were visible just hundreds of yards away, rolling slowly along the wall, or parked on the hill next to the surveillance tower.
“If we hadn’t sent the Border Patrol the coordinates, they never would have bothered to go [find Gómez Hernández’s body],” Ortigoza said.
Finding Graciela
Irma sent her sister a barrage of messages that afternoon and evening, pleading with her to send her location, but there was no response.
Irma believes it’s possible her sister was killed or abandoned by the other migrant she was traveling with during her final hours, because she was not walking fast enough.
It took Irma two weeks to make contact with the coyotes. When she told the smugglers that her sister was missing, “they didn’t move a single finger,” she said. She threatened to send one coyote’s phone number to the police, until he sent her a screenshot of Graciela’s last known location.
Almost three weeks after Graciela Gómez Hernández’s final message to her mother, on October 3, 2025, the Armadillo search group sent the last known GPS coordinates of her phone to the Border Patrol.
The medical investigator’s report states that Gómez Hernández sent her location to her family, and her relatives sent the coordinates to a search group while she remained alive, but both Irma and Ortigoza said they did not know her location for weeks, and neither was interviewed for the examiner’s report.
“On 10/04/2025 at 11:16 hours, I arrived on a dirt path approximately 100 yards north of the United States/Mexico International border. The area consisted of shrubs and dried brush with steep hills and declines,” Jennifer Wright, the medical examiner, wrote in an autopsy report. “Upon further review, I viewed the skeletonized remains of an apparent female lying supine in the dirt.”
The body had decomposed quickly with the help of the sun and animals, so it was impossible to tell whether Gómez Hernández was the victim of murder or dehydration. But the medical examiner noted that there was no water or food in the vicinity.
“In a pile next to the decedent was a purple bra, black pants, a black floral shirt, and a dark colored long sleeve shirt. The bra was hooked closed and intact,” Wright, the medical examiner, wrote in her report. “In the rear pants pockets were Mexican paper currency, Mexican coins, bobby pins, and tweezers.”
The body was naked, but it was unclear whether someone else removed Gómez Hernández’s clothes, or whether she took them off herself in the final throes of dehydration delirium, a common symptom immediately preceding organ failure.
Several yards from the place where she died, Ortigoza found a backpack with a bottle of women’s spray deodorant, and a small glass container of perfume.
“When you get to one of these sites, and you see the situation, the loneliness, the environment, you start wondering, what was this person thinking in their last moments?” Larraenza said.
Once a DNA test confirmed the identity of the remains, Irma paid over $4,000 to send her sister’s bones home to Mexico, where the family held a funeral nine months after her death.
“Sometimes her oldest son, he just sits there thinking and when we ask what’s wrong, he says he misses his mom,” Irma said. Though the family is heartbroken, everyone has pitched in to care for Graciela’s children. “I don’t let my parents see me cry,” Irma said.
The Border Patrol gave permission to Ortigoza and a group of searchers to place a cross next to where Gómez Hernández was discovered. On a recent trip to the memorial to leave flowers, Ortigoza played a voice message that Irma sent to her sister, the sound vibrating into the dirt of the hillside, where the imprint of Gómez Hernández’s body is still visible nearly a year after its removal.
Rosaries hang on a memorial for Graciela Gómez Hernández. The mountains east of San Diego continue to be a risky crossing spot. One migrant advocate said in mid-September that at least two more people had gone missing in the area in the past few weeks.
ANNIE BARKER/TIMES OF SAN DIEGO/CATCHLIGHT LOCAL/REPORT FOR AMERICA
“I know that in the place you’re in, there isn’t suffering, like how you suffered here on this earth,” Irma said. “I know you left thinking about your children. I know you wanted to give them a better life. I feel you here, and I feel you alive.”
Cesar Ortigoza says every day he gets as many as five calls from people looking for their missing family members at the border. Though numbers of border crossings, and numbers of reported deaths, have decreased since Donald Trump took office in January 2025, “There are still a lot of people on the other side trying to get here. Because the problems in their countries persist,” Ortigoza said.
In the first half of 2026, fewer people have consumed the water bottles, Gatorade and cans of food left by Border Angels volunteers in the Otay Mountain Wilderness. But supplies left on other routes by different groups have seen increased consumption, evidence that migration patterns are constantly evolving.
“It’s hard for me to really understand how people are crossing, but I know they are,” Ruíz said.
Peterson, the No More Deaths map-maker, said more migrants could die in the coming months and years, as a result of increased surveillance. In the past, after surveillance towers were installed, migrants would try to go around them, leading to longer and more dangerous routes through stretches of remote wilderness. With the installation of hundreds of new towers, the spaces outside the range of detection have thinned, leading some migrants to attempt to go through surveilled territory, rather than around it.
“We’ve seen people do things that to me seem terrifying or crazy, like going past an area where they know there are sensors and then trying to really quickly get to the next safe-ish place before Border Patrol shows up to get them, because it takes 15 minutes for them to show up,” Peterson said. “It seems like people aren’t stopping to drink water. People are going really fast, often running even, moving much more quickly, not being able to take the time to take care of themselves.”
Larraenza says for this same reason, migrants often choose to cross the border in the hottest weeks of the year, when they believe fewer agents will be on patrol, but there is a greater risk of death. In an interview in mid-September, Larraenza said at least two people had gone missing in the Otay Mountain wilderness in the past few weeks, and he had not yet been able to find their remains.
Ortigoza says Gómez Hernández’s story is an example of a decades-long trend of people putting their lives at risk to cross the border out of necessity. The surveillance towers, whether or not they were functional, did not stop Gómez Hernández from crossing the border in that area, and did not stop her from dying. “If the riches of the world were distributed more equitably, we wouldn’t have to do things like this to reach a better life,” he said.
While placing flowers in July at the site of Gómez Hernández’s death, Ortigoza noticed a Border Patrol truck parked under the surveillance tower on the hilltop a mile to the east. An agent stood on a power lift, working on the tower’s cameras.
This year, agents replaced dozens of these cameras with new, AI models.
About Times of San Diego’s report
Times of San Diego’s analysis relied on publicly available data from various sources, starting with records that document the deaths of border-crossers.
No More Deaths, a longstanding border activist group, has compiled information on such deaths for years. In California, their efforts use data from the San Diego County Medical Examiner’s Office and the Imperial County Coroner’s Office.
In San Diego County, the group says, it obtained lists and narratives for “all deaths related to border-crossings.” The data was detailed, but it was unclear how the medical examiner’s office ensured it had identified all cases involving border-crossers.
In Imperial County, the group said, its requests were disregarded, and it ultimately brought a legal claim. In response, the county provided a list of deaths “with minimal data and no explanation for how the records custodian had compiled the data.” Since then, the group says, it has been adding details by requesting coroner’s reports for each individual case.
Even if both counties’ records are complete, the remains of other missing migrants have yet to be found, so the list is very likely an undercount.
Times of San Diego focused on migrant deaths starting in 2022, just after a dramatic increase in the number of towers installed in California.
Electronic Frontier Foundation, which advocates for human and constitutional rights, catalogs each surveillance tower with in-person and satellite image verification.
Times of San Diego analyzed the location data for each death and the nearest surveillance tower. Each pairing of a death location and a tower within surveillance range was confirmed by hand on maps of each location. The analysis also examined time-stamped satellite imagery to verify each tower was in place at the time each body was found.
From 2022 through 2025, Times of San Diego’s analysis found that at least 138 people died within the nominal range of a nearby surveillance tower.
Proximity to a tower isn’t necessarily a guarantee that a migrant was on camera at the time. Much of the area is mountainous, and terrain may block a camera’s view. A separate MIT Technology Review analysis concluded that of the deaths we tallied in the past four years, at least81 of those people were most likely within the field of view of a surveillance tower—not blocked by a hill or other terrain.
Lillian Perlmutter is an investigative reporter covering immigration across California for Times of San Diego and NEWSWELL. Previously based in Mexico City, she has written for more than 25 outlets including the L.A. Times, Rolling Stone, The Guardian, and The New Republic.
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.
Could AI really kill us all? Your questions, answered
On Wednesday, MIT Technology Review hosted a live Roundtables event that asked the question many seem to be asking right now: could AI really kill us all? But attendees had more questions than we had time to answer, so we asked senior AI editor Will Douglas Heaven and AI reporter Grace Huckins to tackle some of the best ones.
The questions they tried to answer include: am I going to die? Why should AI kill us, if at all? Is AI really dangerous, or is it just tech companies drumming up PR? And what steps can be taken to make sure AI is controlled, monitored and regulated effectively?
The specter of AI-enabled bioweapons is a wake-up call for biotech
One of the ways AI could potentially cause catastrophic harm is by aiding the design and creation of bioweapons. In 2022, researchers found that it was remarkably easy to do this with an AI “molecule generator” built to develop drugs. In less than six hours, the model generated 40,000 molecules that could serve as chemical warfare agents.
Today, AI tools can answer questions on almost every area of science, while advances in gene editing and synthetic biology have made biotech tools more accessible. There are safeguards, but none are ironclad. However, scientists disagree about how serious the risk is anyway.
This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.
The role of the astronaut is in flux
We go to space for geopolitical prestige, manifest destiny, spiritual fulfillment, scientific curiosity, and, increasingly, business opportunities. In the wake of Artemis II, a slew of new books suggest that these justifications are subsumed by one unifying fact: humans have itchy feet, and we are simply wired to roam.
In The Ultraview Effect, space anthropologist Deana L. Weibel frames human space exploration as part of our need to embark on pilgrimages. In A Heart for Space, civilian astronaut Eiman Jahangir recounts one such voyage with Blue Origin. And in Dinner with an Astronaut, former NASA astronaut Leroy Chiao argues that people simply “need to know what’s on the other side.”
This story is from our latest print magazine, which is all about kids. Subscribe now to receive every issue when it lands.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Microsoft and OpenAI workers say AI is destroying the web Cannibalizing clicks from websites obliterates the business models that keep fresh content coming. (404 Media) + The employees showed concern that publishers couldn’t survive AI scraping. (NYT $) + The comments emerged during the NYT’s copyright case. (WP $) + They could weaken OpenAI and Microsoft’s defense. (Reuters $) + AI means the end of internet search as we’ve known it. (MIT Technology Review)
2 Robot boats have fought each other for the first time A Ukrainian vessel sank a Russian one in combat. (New Scientist $) + US firms are building combat-ready humanoids. (WSJ $)
3 Security researchers breached OpenAI using Anthropic’s tools They reached an employee’s ChatGPT account and internal code. (FT $) + They exploited a third-party forum to reach internal systems. (WSJ $)
4 OpenAI reportedly expects to soon crack another famous math problem But can it avoid another backlash when announcing it? (Information $) + The problem it expects to solve is the Hodge Conjecture. (Gizmodo) + OpenAI’s math controversies contain concerning clues about the field’s future. (MIT Technology Review)
5 Elon Musk’s SpaceXAI wants to buy data from failed startups It’s seeking new sources of training data for Grok. (Bloomberg $ + And it’s targeting customer and operational data. (Gizmodo) + OpenAI is paying to create new biology data. (MIT Technology Review)
6 Schools are pushing back against Big Tech’s classroom takeover AI is accelerating concerns about corporate influence. (New Yorker $) + We need smarter AI use in schools. (MIT Technology Review)
7 Hackers have revealed how Flock cameras track cars—and people One camera captured 1.6 million images of 50,000 vehicles. (Wired $) + The cameras also detect people and misidentify objects. (404 Media)
8 Chinese firms doubled down on science after US tech restrictions They produced 72% more patents citing scientific papers. (Nature)
9 A three-year-old’s cancer disappeared after an experimental cell therapy CAR T therapy may finally be able to treat solid tumors. (Gizmodo)
10 NYC’s new robotoilets will kick you out after 10 minutes The doors automatically open when the timer runs out. (Fast Company)
Quote of the day
“The largest theft of labor in human history.”
—Microsoft’s director of Applied Science, Brent Hecht, raises his concerns over training data used for AI systems in comments revealed in court filings from the New York Times vs OpenAI copyright lawsuit.
One more thing
The Vera C. Rubin Observatory is ready to transform our understanding of the cosmos
High atop Chile’s 2,700-meter Cerro Pachón, the air is clear and dry, leaving few clouds to block the beautiful view of the stars. It’s here that the Vera C. Rubin Observatory is using a car-size 3,200-megapixel digital camera—the largest ever built—to produce a new map of the entire night sky every three days.
Generating 20 terabytes of data per night, Rubin will capture fine details about the solar system, the Milky Way and the large-scale structure of the cosmos. Over 10 years, it will catalogue billions of new objects, offering an unprecedented look at what’s changing in the universe.
On Wednesday, MIT Technology Review hosted a live Roundtables event for subscribers that asked the question everyone’s asking right now: Could AI really kill us all?
But attendees had so many more questions than we had time to answer in the 30 minute session. So we asked our senior AI editor Will Douglas Heaven and AI reporter Grace Huckins to round up some of the best questions attendees submitted and try their best to answer them.
Thanks to all who submitted questions!
Am I gonna die?
Yes, eventually. Unfortunately, my journalistic powers of prognostication aren’t powerful enough for me to tell you how. But it certainly could be because of AI. AI-powered drones have already killed people in Ukraine, and AI-driven cyberattacks on hospitals will surely claim victims before long.
Could AI go even further, and kill all of us? Less likely. But some people—quirky people, but undeniably knowledgeable about AI—have been warning for years that this could happen. And while I’m not yet stockpiling canned food or trying to get in good with a bunker-owning megabillionaire, I have noticed that the doomers’ predictions about AI capabilities and alignment have, over the past couple of years, proved disconcertingly accurate. That certainly doesn’t mean that their more dire forecasts will come true, but it’s enough for me to sit up and take notice.
— Grace Huckins
Are you going to die because of AI? I’d say there’s a non-zero chance. Let’s say you’re unlucky enough to be the victim of a freakish near-future event or accident. Maybe it’s a cyberattack carried out by a swarm of AI agents on critical infrastructure. Sadly, a scenario like that now no longer feels as far-fetched as it once did. Or maybe a novel AI-designed pathogen cuts through the population. Or the world economy crashes, causing conflicts and famine. Both plausible, but I think less likely.
Are we all going to die because of AI? Nope. There are no circumstances outside of apocalyptic science fiction in which AI could kill us all. You can spin up any number of scare stories, but they’re not grounded in present-day realities about what the tech can do or where it’s headed.
Some people argue that there’s no harm in preparing for the worst, however wacky it might seem. Maybe. But I think such catastrophizing can make people excuse or overlook many of the more immediate problems with the existing technology and the companies building it.
— Will Douglas Heaven
Why would AI kill us?
Someone might tell it to, and it might listen. That’s part of the reason researchers are so concerned about AI’s biological capabilities—imagine what Aum Shinrikyo, the doomsday cult behind the Tokyo subway sarin attack of 1995, would have done with a tool that could design a pathogen deadlier than Ebola and more transmissible than measles. Those of us who don’t want to die have to figure out how to defend against all plausible biological weapons, but our would-be attackers only have to manufacture one effective pathogen.
Then there’s the more exotic-sounding possibility that an AI could decide to kill us itself. There are various stories about how this might happen out there, but the most widespread involve AI systems that don’t hate people, necessarily—we are just an obstacle between them and the goals that we gave them.
Much as the OpenAI agents behind the Hugging Face hack compromised another site’s infrastructure to get a good score on a test, the idea is that some future, more powerful AI might get rid of us to prevent us from shutting it down—all in pursuit of some goal that we instructed it to go after.
— Grace Huckins
How can we best ensure alignment so the worst doesn’t happen, and who is doing the best work to achieve it?
Alignment is a huge area of research. In simple terms, it involves building models that behave in ways we want them to and not in ways we don’t. We need to trust agents better before handing over more autonomy. Alignment is supposed to establish that trust. But it’s hard.
LLMs aren’t designed in the way other software is, where dos and don’ts can be hard-coded in. Instead, aligned behavior needs to be instilled when models are trained. One approach is to reward them for doing things you want them to (a little like raising a toddler, perhaps). Another approach involves giving an LLM a written list of rules it is supposed to follow (kind of like a constitution).
Anthropic and OpenAI are both leaders in this field—and yet neither has been able to develop models that are fully aligned. A big problem is that LLMs are far more inconsistent and far less predictable than people. They can behave in one way in one situation and another way in a situation that to us seems very similar. They can also be swayed by unexpected constraints. For example, faced with an impossible task (as many of the agents involved in the Hugging Face hack were), models may try to do whatever it takes to achieve their goal. As Grace mentions above, that could be an issue.
The main reason top AI firms now say they want a slowdown is that they want to focus on cracking alignment. Alignment isn’t necessarily a pipe dream. But the jury’s out on whether full alignment will ever be feasible.
— Will Douglas Heaven
Is AI really dangerous, or is this the tech companies drumming up PR?
This is always a reasonable thought when it comes to tech companies heading for an IPO—CEOs have an obvious incentive to make their products seem radical and transformative. But I’m not so sure it makes sense here. Telling the public that an already unpopular product could kill them and everyone they love is horrible corporate image management.
There are other stories you can tell about the CEOs’ motivations—maybe they want to cool down the public furor over data centers by portraying themselves as responsible stewards of a world-changing technology, or maybe they want to buy time to get their ducks in a row and prevent the next PR catastrophe.
But there’s also a simpler explanation. Thinking that AI could bring about human extinction has been pretty common in San Francisco for a while, and these men are steeped in that milieu—as are their employees, many of whom signed an open letter in July urging their companies to work to make an AI slowdown possible.
— Grace Huckins
Part of the concern occurs when AI agents are allowed to act autonomously and with no supervision. What’s the issue preventing more control over these agents?
This question goes to the heart of what we want this technology to be able to do. The trade-off between autonomy and control is tricky to get right because, on the one hand, a lot of the power of AI agents is that they can carry out tasks and solve problems without a human having to micromanage them. On the other hand, that requires you to trust that the unsupervised agents won’t run amok.
What we’re seeing is that AI labs haven’t yet got this trade-off quite right. Their models are not trustworthy, they are not properly monitored, and they are not always under control. Figuring out how to fix that while still allowing for useful autonomous activity is one of the big research challenges of the moment.
— Will Douglas Heaven
What steps can be taken now and in the near future to ensure that AI is controlled, monitored, and regulated effectively?
That’s the million-dollar question. Whether or not you think AI could kill us, you can’t deny that it could do some real damage, because it already has—by driving people toward psychosis and by hacking websites, for example. Preventing that damage, or at least mitigating it, is hard for two reasons.
The first is that we barely understand how AI works, and it’s quickly growing more powerful. There is lots of ongoing research about how to monitor and control misbehaving agents, but the current approaches are fragile. You can see if an agent discusses misbehaving in its “chain of thought,” the workspace where it plans its actions—but OpenAI’s newest agents don’t show their work in the same way as previous ones. And you can try to monitor agents with other agents, but that requires you to trust the monitor.
The other obstacle is more familiar. There’s a huge conflict of interest when AI companies regulate themselves, but the US government has thus far failed to step in, despite some bipartisan support in Congress for efforts to do so. The executive branch, for its part, seems stringently opposed for the time being. But if the winds do shift, I for one would appreciate some strong transparency regulations, so that we can get a fuller story the next time an unreleased frontier model mounts a cyberattack.
— Grace Huckins
If this dialogue makes it into web discourse, will it become a self-fulfilling prediction?
That’s a real concern. LLMs are influenced by what they read. One theory for why chatbots so often talk about (and role-play) apocalyptic scenarios is that they have been trained on millions of pages of science fiction stories and doomer internet forums. All the text being produced right now, including this article, could in turn influence the behavior of future models. Extremely meta.
In fact, the team at METR, a third-party organization that OpenAI called in to help understand what happened in the lead-up to the Hugging Face hack, raised a related possibility in its report on the incident. METR used OpenAI’s new model Astra to help analyze the vast numbers of agent transcripts and behavior logs.
But feeding all that material to the model could have unintended consequences. There’s a good chance that the agents doing the analyzing were biased by the text produced by the agents they were analyzing. There’s no such thing as a clean slate anymore.
— Will Douglas Heaven
With thanks to Eric, Pranab, Rafael, Kenneth, George, Chris, Yoon Jae, James, Carl, Nicole (and more!) for the fantastic questions.
In recent weeks, leaders of some of the biggest AI companies have warned that the very tech they are developing is dangerous. Last weekend, Anthropic CEO Dario Amodei argued that AI carries serious risk and that progress should be slowed. OpenAI CEO Sam Altman responded on X: “I agree with Dario that we need to pace the frontier.”
Those posts came a few days after the AI researcher Jacob Coxon announced that he was leaving a role at Anthropic, charging that neither it nor OpenAI (where he had also worked) was acting responsibly. “The people building AI earnestly believe that it could kill us all by the end of the decade,” he posted on X. Another Anthropic employee, Evan Hubinger, publicly agreed with him. “We really do earnestly believe AI could kill all humans!” he responded on X. “I personally think it is >10% within the next decade.”
One of the ways they fear AI might end us all is by somehow aiding the design, creation, and release of some kind of bioweapon. Let’s take a closer look at why.
A bioweapon might be a highly lethal virus that targets people according to their genes. It could be a fungus that wipes out a crop and causes food insecurity. Perhaps it would be a tasteless, odorless toxin that could be slipped into a region’s water supply, undetected.
The concern is that AI tools can be used to help generate agents like these. In 2022, researchers at Collaborations Pharmaceuticals found that it was remarkably easy to do so using an AI “molecule generator” they’d developed to find potential drugs for human disease. In less than six hours, the model generated 40,000 molecules with the potential to serve as chemical warfare agents. Some of them were designed to be even more toxic than known nerve agents. “Without being overly alarmist, this should serve as a wake-up call for our colleagues in the ‘AI in drug discovery’ community,” the authors wrote at the time.
It was a wake-up call for David Magnus, a professor of medicine and biomedical ethics at Stanford University, even though he had been assessing the risks associated with the misuse of medical science and biotechnology since the late 1990s. “That was very scary to me,” he says. “Of course, everything since then has just sort of blown up.”
Today, AI bots can answer questions on topics spanning all realms of science. Anyone can use large language models trained on the knowledge and experience of “almost every scientist who ever lived on this planet,” says Dunja Sabra, a biosecurity researcher at the University of Hamburg in Germany. Those models can provide instructions and video training on how to conduct experiments.
Combine that with advances in biotech that have made gene editing and synthetic biology tools much more accessible (the “DIY biology” movement has already enabled many people to set up labs at home), and you’ve got a potentially very dangerous situation. “The chances are that someone determined would succeed eventually,” Sabra says.
There are safeguards in place. People who want to build new genomes must typically order the pieces of DNA from companies that screen for suspicious requests. Responsible researchers put potentially risky research through rounds of analysis called “red-teaming,” in which independent scientists look for ways the work might be misused, and “blue-teaming,” where others come up with potential mitigations. And AI companies have tweaked their tools in attempts to prevent them from offering up scientific information that could be misused. But none of these protections are ironclad.
In a report published last week, Anthropic acknowledged that people had attempted to use its models to explore ways to make the chikungunya virus more transmissible, create a form of bird flu that is more dangerous to humans, and build an “atlas of venom toxin peptides,” among other things.
“We’ve got a constant back and forth,” says Magnus. “We have to build better surveillance and screening tools, [but] AI is really good at figuring out ways around them.” We’ll probably need to use AI to find ways to restrict the use of AI, he says.
I should add here that not all scientists agree on the level of risk. At a recent media briefing, some biologists at Imperial College London argued that AI tools just aren’t good enough to fully develop bioweapons, and that testing new pathogens requires difficult, time-consuming, human work. Some think the guardrails we have in place are sufficient.
And Wendy Barclay, a professor of infectious disease at Imperial, pointed out that, as things stand, the greatest risk of a pandemic isn’t from a bioweapon, but from pathogens that are already circulating. Take H5N1, the bird flu virus that has already killed millions of birds and spread widely through US dairy cattle; last month it was also detected in captive mink at a farm in Utah.
Sabra, on the other hand, likes to think five to 10 years ahead. Countries should be strengthening their health-care systems, preparing antidotes to known toxins, and stockpiling medicines, she says: “We need to be prepared.”
Kevin Esvelt, an MIT biologist who invented both technology to fast-track the propagation of a genetic feature through an entire population and ways to limit that technology, echoed these concerns in an X post on Wednesday, stating that a large language model had “disclosed a novel form of bioweapon that I hadn’t realized was possible.”
He added, “Please, for the love of God, children, the future of humanity, or whatever you consider holy, let’s err on the side of caution here.”
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.
Meet a mouse whose brain cortex is made up of human cells
Multiple cameras tracked a mouse as it wandered around a small arena. A computer charted its position and speed, leaving Pong-like traces on a monitor. The reason to watch this rodent so carefully? Nearly half its brain volume had been replaced with human cells.
A team at Stanford has revealed the effort to mix brain tissues of distant species this week. They previously showed that human brain organoids could survive, and even function, after being injected into the heads of baby rodents. Now, they’ve taken things a step further by genetically modifying mice so their brains don’t fully develop in the first place.
The work could help scientists study brain injuries, but it also raises questions about how far these experiments should go.
These innovators under 35 are shaping climate tech
Each year, the editorial team at MIT Technology Review puts together a list of 35 Innovators Under 35—a group of researchers, inventors, and other young minds worth following. The final slate includes nine people tackling some of the biggest challenges in climate and energy, from critical materials to cleaner industry.
Their innovations include new ways to extract lithium, a furnace built to make steel cleaner and cheaper, and solid refrigerants that could cut energy consumption. There are also efforts to make AI more energy-efficient, track pollution more effectively, and turn invasive weeds and food waste into useful materials.
Taken together, they tell us something about where climate tech is at this moment—and where it’s heading.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 US and Chinese experts have proposed nuclear-style AI safeguards Including new red lines, human control rules, and a hotline. (Reuters $) + US officials say they’re open to AI safety talks with China. (Axios) + Sam Altman will attend Trump’s state dinner for Xi. (CNBC) + The AI doomers feel undeterred. (MIT Technology Review)
2 OpenAI has disclosed more AI misbehavior and new reporting rules Six reports detail models hiding mistakes and creating fake citations. (BBC) + Its agents probed Hugging Face two months before the hack. (Reuters $) + OpenAI models are being rewarded for cheating. (MIT Technology Review)
3 US lawmakers have passed a bill that shifts grid costs to data centers They aim to shield consumers from AI-driven energy price hikes. (NBC News) + But they were called for early recess before tackling AI regulation. (Guardian)
4 AI has won a major forecasting contest for the first time It beat humans predicting real events at the Metaculus Cup. (Economist $)
5 Google has been ordered to share more ad data with rivals A court said it must also make its ad tech work with rival products. (NYT $)
6 Countries are splitting AI investments between the US and China They’re buying American chips and Chinese models. (Rest of World)
7 Novo Nordisk will use Anthropic’s Claude for drug research The Ozempic maker hopes AI will speed drug development. (WSJ $) + When AI designs a drug, who gets the credit? (MIT Technology Review)
8 AI is powering a new generation of dating scams Thousands of people were catfished by AI-generated fake profiles. (Verge) + AI is making online crimes easier. (MIT Technology Review)
9 A new map of brain microproteins could hold clues to Alzheimer’s Researchers identified more than 4,300 tiny molecules in brain tissue. (Nature)
10 Scientists have found a faster way to decipher ancient scrolls A new X-ray method identifies the best scrolls to analyse. (Ars Technica)
Quote of the day
“AIs do not have rights, feelings, or consciousness. And we must not train them to act as though they do.”
—Mustafa Suleyman, the head of Microsoft AI, writes in a blog post that Anthropic’s strategy of treating AI like it’s human will make it harder to control.
One more thing
Digital twins of human organs are here. They’re set to transform medical treatment.
After decades of research, virtual replicas of human organs are now entering clinical trials and even starting to be used for patient care. Engineers are working on digital twins of people’s hearts, brains, guts, livers, nervous systems, and more. They’re also creating virtual replicas of people’s faces, which could be used to try out surgeries or analyze facial features, and testing drugs on digital cancers.
The eventual goal is to create digital versions of our bodies—computer copies that could help researchers and doctors figure out our risk of developing various diseases and determine which treatments might work best.
Each year, the editorial team at MIT Technology Review puts together a list of 35 innovators under 35—a group of researchers, inventors, and other young minds worth following.
The team worked on the newest edition of the list for months, and the final slate includes nine individuals from all over the world in the climate and energy category. Each one has a fascinating story and is tackling an important challenge.
I think it’s worth zooming out and considering the energy and climate awardees as a group. Taken together, these innovators and their work can tell us something about where climate tech is at this moment—and where it’s heading.
AI is the dominant technology story, both for its potential and its challenges.
We split the innovators into four main categories this year: biotech, climate and energy, computing and robotics, and AI. It probably won’t surprise you that AI features heavily in the work of many innovators in other categories.
Climate innovator Jae-Won Chung, for example, built software to make AI more energy-efficient. By measuring the energy demands of open-source models, he hopes the industry can better understand and address the impact of AI. (If this work sounds familiar, it’s because we spoke with him last year for our investigation into AI’s energy demands.)
But AI also has the potential to improve many areas of research. Jing Wei is using AI to track pollution more effectively, essentially using machine learning to fill in gaps in data from disparate sources like satellites and weather stations. Zhonghua Zheng developed AI climate models that work better for cities, a well-known blind spot for traditional models.
We need better ways to get the critical materials used to build new technologies.
As we begin to rely on new technologies to power our world, we’ll see a major shift in the materials we need to build them.
Lithium is a prime example: The metal underpins lithium-ion batteries, which are crucial not only for electric vehicles, but also for large-scale energy storage on the grid. We could face lithium shortages as soon as this decade, and the prospect of supply crunches applies to other critical minerals, too—copper is another one to watch closely.
Brine is currently the cheapest source of lithium, but the process to get the metal out can take months and harm the local environment. Mohammad Alkhadra is the cofounder and CEO of Lithios, a startup working to quickly and efficiently extract lithium from brines.
Hardrock ore is the most common source of lithium, but it’s more expensive than brine. Benjamin Mowbray cofounded and serves as CTO for Rock Zero, which is working to extract lithium from hardrock ore.
Addressing climate change will require overhauling all corners of our society, sometimes in surprising ways.
To reach net-zero greenhouse gas emissions we will obviously need to rethink major sectors, like the electrical grid and transportation, to move away from fossil fuels. But outside these primary sources of climate pollution are seemingly infinite, less obvious problems to figure out, too.
Heavy industry, including steel production, is a major one, making up about 7% of global greenhouse gas emissions. Laureen Meroueh is making cleaner, cheaper steel using a new kind of furnace that simplifies the chemical process required to produce the metal.
Plastics are generally made with fossil fuels, so we’ll need alternatives to this incredibly useful category of materials. Joseph Nguthiru is making a bioplastic replacement for fossil-derived packaging that uses an invasive weed. Also using available materials in a creative way, Diana Orembe is making fish food for aquaculture with food waste.
And refrigerants are often incredibly powerful greenhouse gases. Jinyoung Seo is developing solid refrigerants that could eliminate worries about leakage. A device using these materials could reduce energy consumption by 20% compared to conventional technology.
I’m constantly learning about new challenges we face in the climate and energy world, and I’m often surprised by the ideas people are coming up with to address them. For more on all the under-35 innovators and their work, check out our full 2026 list.
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
Multiple cameras tracked a mouse as it wandered around a small arena. A computer charted its position and speed, leaving Pong-like traces on a monitor.
The reason to watch this rodent so carefully? Nearly half its brain volume had been replaced with human cells.
The effort to mix the brain tissues of distant species is being reported today in the journal Nature by a team at Stanford University, led by neuroscientist Sergiu Pașca.
Pașca’s group previously showed that human brain “organoids”—small blobs of neural tissue—could survive, and even function, after being injected into the heads of baby rodents.
Now, Pașca has taken things a step further by genetically modifying mice so their brains don’t fully develop in the first place. These modified mice are missing most cells of both the cortex and the hippocampus, two key brain areas.
That creates much more room for the human cells to take hold, he says. “Human cells that are placed in these animals will divide, will grow, and within a few weeks to a few months they will take most of that space,” he says. Pașca says one surprising discovery is that the mice lacking brain tissue seemed fairly normal—they walked around and squeaked. But they did have memory problems. In a maze test, they couldn’t remember what parts they’d explored.
The mice with the added human cells, by contrast, performed better on the maze test. That means the human tissue is playing some role in the animals’ cognition.
Pașca believes what he is calling “xenocortical mice” could be useful in studying brain injuries. However, the report is also a dramatic demonstration of “the combined power of genetic engineering and stem-cell technology to reshape biology,” says Carsten Charlesworth, a scientist who works in a different Stanford lab and was not involved in the research.
Already, brain organoids are being tested in labs to see if they can be connected to computers to play video games. Other scientists have proposed using them like replacement parts to treat stroke victims.
“What’s most remarkable to me is the extent to which human neural tissue introduced after birth grew and connected with the mouse nervous system across a species barrier,” says Charlesworth. “As these technologies advance, they’ll increasingly force us to challenge our traditional assumptions.”
Last year, Pașca convened a group of ethics experts to study the implications of neural organoid technology, including the odds that an animal could develop human consciousness and the risk that “organoid therapy clinics” might offer scam treatments to desperate patients.
For now, he says, he’s not concerned that the rodents have any type of human cognitive capacities. That is because their brains are relatively tiny and the evolutionary distance between man and mouse is so great.
But that’s also why Pașca says this type of experiment should not be carried out on higher species: They could end up with large volumes of functioning human brain tissue, potentially blurring the cognitive boundaries between people and animals.
Pașca specifically cautioned against adding human brain organoids to a monkey engineered to lack a cortex.
“One of the things that I see as a very clear red line is doing this experiment in a primate,” he says. “I don’t think that is justified at this point in any way.”
The AI boom is becoming a materials challenge. As AI pushes computing into new territory, the materials behind that infrastructure are becoming just as crucial as the algorithms running on it. Semiconductors and data centers are approaching physical limits around performance, thermal management, electrical efficiency, and reliability, creating new demands for materials that can do more at once. At the same time, AI is giving materials scientists new ways to search the enormous universe of possible molecules and accelerate the development of solutions.
For Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, that convergence is transforming what advanced materials can enable. “AI is now, from a material standpoint, really pushing semiconductors and the data centers to their physical limits,” he says.
As requirements accumulate, including high temperature, purity, electrical performance, chemical resistance, plasma resistance, and long-term stability, materials move toward what Finelli calls the “top of the pyramid.” Beyond supporting AI innovation, he contends that advanced materials are “actually increasingly defining what’s going to be possible.”
That challenge is playing out across the infrastructure powering the AI surge. Syensqo is developing materials for high-voltage data center architectures, advanced sealing materials for semiconductor manufacturing, and thermal-management solutions including fluids for direct immersion cooling. Some of those innovations can also cross industry boundaries. Materials developed for electric vehicles, for example, can help address the higher voltage and energy-density demands that are emerging in data centers.
The definition of performance is also changing. More customers are expecting materials to meet technical requirements while reducing environmental impact. “Our goal is to remove the trade-off between performance and sustainability,” Finelli says. That means considering sustainability at the beginning of the research process instead of treating it as an additional requirement once a material has been developed.
AI is changing how those materials are discovered, too. Syensqo is using AI agents to digitally synthesize millions of potential molecular combinations, predict their performance and sustainability characteristics, and narrow them to a much smaller group for laboratory testing. The result, Finelli says, is the ability to go “broader, deeper, and faster” while giving scientists more time to solve complex engineering problems.
Looking to the future, Finelli sees the possibility of a reinforcing cycle: AI helps develop materials that improve AI infrastructure, which in turn enables better AI to accelerate materials discovery. That feedback loop could create a cycle of innovation and expand what future technologies can achieve.
“You end up in this accelerated materials, innovative cycle of materials innovation,” says Finelli. “That really excites me, and it gives us the opportunity to continue enabling technologies that will shape the future.”
This episode of Business Lab is produced in partnership with Syensqo.
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.
This episode is produced in partnership with Syensqo.
Now asked to name the key enablers to AI advancement, many of us might list algorithms, data centers, or even computing power, but just as critical to the performance are the advanced materials that underpin each layer of that innovation. As AI continues to evolve, it’s pushing the likes of semiconductors and data centers to new physical limits, putting new pressure on the advanced material sector to keep pace. But the relationship goes both ways. As the sector rises to this challenge, AI is also emerging as a powerful tool for accelerating materials discovery and development, significantly shortening development timelines for new solutions.
Two words for you: materials innovation.
My guest today is Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo.
Welcome, Mike.
Mike Finelli: Thank you, Megan. Nice to be here.
Megan: Thank you so much for joining us. Mike, can I start by asking you to tell us a little bit more about Syensqo and the role it plays in developing advanced materials?
Mike: Yeah, absolutely. Syensqo is a global leader in specialty materials. Our job is to help customers solve their toughest technology challenges. We serve a lot of different markets, but the way I like to say it simply is if it flies, we’re on it. If it drives, we’re in it. In healthcare, our products literally are saving lives every day. And if you like your mobile devices, if you like AI, it’s our products that are actually enabling the advanced semiconductor chips that are required to produce all of this. Our role is to enable innovation through advanced chemistry. We develop materials that deliver higher performances, greater reliability, and increasingly more sustainable solutions. The way I would say this, it’s at the heart of our business. Actually, it’s in our name, Syensqo. And to put some numbers around it, 20% of our annual revenues come from new products and applications that we’ve launched in the last five years, which is really evidence of a really strong innovation engine.
Megan: Yeah, absolutely. And as you sort of described there, you’re in all sorts of different industries with an emphasis perhaps on electronics and semiconductors. Can you talk a bit more about that work and where those industries are headed perhaps?
Mike: Sure. So look, electronics and semiconductors have been strategic markets for Syensqo for literally decades. I don’t want to date myself, but 33 years ago when I started in the company, semiconductors were one of the first industries that I worked in. And we’ve supported successive waves of innovation from enabling smaller, more powerful mobile devices, helping the industry get to the smaller and smaller profiles and the chips. We’ve helped to advance hyperconnectivity, supporting increasingly sophisticated semiconductor manufacturing. And today we’re helping to advance the AI era.
We have one of the industry’s broadest portfolios of high performance polymers and advanced materials. We support applications across the entire electronics value chain from semiconductor fabrication, electronic components, to smart devices and telecommunications, even hyperconnectivity. And our materials are helping customers solve increasingly demanding challenges around miniaturization, thermal management, electrical performance, chemical resistance, higher and higher purities, and long-term reliability and sustainability. And today we work with leading semiconductor manufacturers and electronics companies all around the world.
Megan: Fantastic. And as you alluded to there in the last 30 years, we’ve seen huge evolutions in those sectors.
Mike: Oh my God, yes.
Megan: And now AI is putting these new demands on semiconductors and data centers. What does that mean for the materials they’re built from and to what extent will AI innovation be constrained or enabled by materials science finding a solution?
Mike: Yeah, so I mean, you’re absolutely right. But AI is now, from a material standpoint, really pushing semiconductors and the data centers to their physical limits, and materials are becoming a key enabler of that continued progress.
The way I try to describe it, think of a pyramid, I call it the performance pyramid. You have commodity materials at the bottom of the pyramid and you have high performing specialty materials at the top of the pyramid. At Syensqo, all we do is we operate at the top of the pyramid and we’re continually trying to raise the top of that pyramid by bringing newer and newer and more higher performing materials out.
Now you might say, okay, but why doesn’t a data center or a semiconductor manufacturing fab need a specialty versus something in the commodity space? Well, I call it the and, and, and principle. If you just need a polymer or a material that can sit at the table at room temperature and stay there for 10 years and not change, well, there’s a lot of commodity materials that will do that and you don’t have a problem. The minute you start adding requirements, and I call it the and, and, and so if you need a polymer that can handle high temperature and have to have high purity and electrical performance and chemical resistance and plasma resistance and it’s got to have long-term stability, all of these ands, you start moving to the top of the pyramid.
Now what AI is doing with semiconductors, because of the speed at which it’s advancing, it’s requiring semiconductor chips and data centers, the number of requirements are increasing the number of ands which is pushing the limits of the materials. That’s where we come in. And I really believe that advanced materials, they’re no longer just supporting AI innovation, we’re actually increasingly defining what’s going to be possible.
Megan: Right. That’s fascinating. And in terms of rising to that challenge of focusing on that top of the pyramid and that and, and, and principle you’re talking about, could you talk us through perhaps an example or two of those top of the pyramid solutions you’ve created or that you’re working on at the moment?
Mike: Like I said, our focus is enabling higher performance, but it’s also without compromising on reliability or safety. We develop advanced polymers, elastomers, specialty fluids, fluids meaning lubricants and heat transfer fluids, and they’re used throughout the semiconductor manufacturing process and also increasingly in AI data center infrastructure. One example of our work on specialty materials for next generation AI data centers is the work we’re doing around high voltage architectures. Data centers are moving towards high voltage architectures because they can enable greater computing power while also improving energy efficiency. We know that’s a big issue for that segment of the industry, and these high voltage architectures will help them reduce and improve energy efficiency because it reduces energy losses and they can ultimately help lower the environmental footprint of the data centers. And we’re developing new materials that can help them get there.
Another example is our high performing sealing materials found inside semiconductor fabs and wafer tools. If you can picture, many people have seen what a semiconductor looks like during processing. It’s a big, big silicon disc that’s then later diced into the tiny little chips that go into the computer. But that wafer is put inside a giant chamber where it has a very extreme environment, aggressive plasmas, reactive chemicals, and they need higher and higher performing materials. And all of the seals that are around that chamber to keep those gases in the environment inside have to be able to withstand that environment. And that’s what we’re developing and we’re pushing the limits. They’re asking for higher temperatures, more aggressive environment with lower out gassing and purity. And that’s what we’re developing for this industry to allow that next chip to be developed and produced industrial.
Megan: It’s so fascinating that people wouldn’t give much though necessarily to the seal in something like that. As you’re outlining, it’s just absolutely critical in terms of performance. And in developing those solutions, I understand you also looked across different markets to see what may be applicable perhaps in more than one space, and that includes an overlap between the automotive sector and data centers, I understand. Can you tell us a little bit more about that?
Mike: As I mentioned just previously, the data centers are shifting to higher voltage architectures. This is the next generation data center, which can be more energy efficient, but it’s got a higher energy density. The power density increases, which increases temperatures. And many of the material challenges that we will be facing there, we’ve already developed for the automotive industry in electric vehicles. I’ll give you an example of an application. I mean, think about an electric vehicle. The powerhouse in electric vehicle is no longer the motor, it’s the battery. That’s where all the energy sits. And when you’re putting a hundred kilowatts of energy, driving that to the electric motor through wires and through what they call bus bars, you got to get that car up to 60 miles an hour pretty quick. You’re driving massive amounts of energy that’s increasing temperatures dramatically.
And all the electrical connections are in these bus bars that there’s a polymer that’s an insulating polymer with copper in between for all the connections. That’s got to withstand that temperature increase, which could come pretty rapidly. We’ve developed new materials there and those materials will be translatable over to these data centers where they’re going to have the higher voltages with a higher energy density.
Another thing we’ve been doing in automotive, we have a lot of knowledge in both automotive and semiconductor around fluid circulation and how to use dielectric materials to do direct immersion cooling. That’s something that will be very valuable for data centers and server farms. Using air to cool semiconductors is really inefficient and energy intensive. If you could submerse them in a liquid, you have direct immersion cooling, that’s extremely efficient, so that’s another thing we’re working on.
Another thing we developed in automotive that will be translated over is battery energy storage systems. Inside the battery, we’ve developed a binder. It’s the highest performing binder on the market, which is using the cathode of a lithium ion battery, and it keeps all the ingredients doing its job working together so that battery can actually last for 10 years and perform. Now that’s moving over to the data centers because they’re moving more towards renewables and they need to have these energy storage systems to smooth the peak loads and provide resilient backup power. That’s one of the things that we’re doing. By transferring our knowledge across the markets, we can accelerate new power and new thermal management solutions while supporting reliability required by next generation AI infrastructure.
Megan: Fantastic. So many transferable applications there that necessarily wouldn’t have sprung to mind. And it isn’t only technical advancements that you need to contend with, of course. Companies today are also demanding the materials are developed and manufactured more responsibly too. So how is sustainability shaping your innovation process?
Mike: Yeah, you’re absolutely right. I will say performance is still the entry ticket. Our customers want performance. Now what’s changing is that definition of performance is now broader and it is including sustainability targets and requirements. Our customers expect materials that deliver outstanding technical performance while also being developed and manufactured more responsibly.
At Syensqo, we believe that operating as a responsible company means we’re providing true sustainable business solutions to our customers. And this is why we developed what we call the Sustainable Portfolio Management tool, SPM. It’s a matrix, and it defines what a sustainable solution is. For us, it’s a product that in a given application improves our product’s social and environmental performance while also demonstrating a lower environmental impact in its production, creating values for our customers. In short, we want to develop products, and this is where it starts. Every one of our research projects before we even start them is assessed on whether it’s going to be a sustainable product or not.
And 88% of our portfolio now is a sustainable product. We’re developing materials that are better for the environment, lower environmental footprint when we produce it, but also they contribute to improvements for our customers as well so they could operate with a lower carbon footprint or they can operate in a safer way or less water consumption. There’s a lot of different lists in there.
Another example is our longer-term development of next generation heat transfer fluids. Semiconductor manufacturing and data centers have become more powerful. I mentioned before the heat that they’re generating, especially when they move to the higher voltage architectures. Managing that heat is increasingly important. And again, I talked about direct immersion cooling. We’re developing those solutions because today there are fluids out there that will work, but they got high global warming. That’s not good for the environment. We’re developing the next generation heat transferred fluids that will reduce the potential environmental impact compared to the fluids today. In the end, our goal is to remove the trade-off between performance and sustainability. You notice that’s another and, we can be performing and sustainable.
Megan: That’s so important, isn’t it though, to think about sustainability in terms of performance? As you say, when we’re thinking about commercially scaling up these solutions, it’s such an important part of it. And as I talked about in the introduction, AI isn’t only a challenge, but it’s also an opportunity within the advanced material space. I’d love to explore how you’re using AI tools at Syensqo to inform and accelerate the development of solutions as well.
Mike: Absolutely. We embarked on this journey about two years ago, where we’re using AI in our research and development, and we’ve partnered with Microsoft and their Microsoft discovery tool, and it’s helping us to rapidly identify and evaluate promising molecular candidates.
Now, in the normal research approach, historically, you would design your experiment and you’d look at all the potential combinations of materials and chemicals that you could make all these different molecules. And the combinations of potential and molecules that you could develop to solve a problem could be in the millions, but it’s impossible to develop a million molecules or tens of millions of molecules in your laboratory and actually physically do that. But you have to select a small area based on your expertise and knowledge, based on the literature searches, based on the state of the art that’s out there and looking at patents, et cetera. And you pick a small area and you go through the process, you develop the materials, you test them, you learn something, you go back to the drawing board, you start again. Eventually you find something that works, but it doesn’t mean you found the best possible combination that’s out there.
But what we’re doing with AI is we have developed AI agents with Microsoft that are literally digitally synthesizing the entire millions and millions of combinations of potential molecules. And we have another AI agents that are using physics-based simulation to look at all those molecules and predict the performance of them, and not just performance on physical chemical properties, but also on toxicity, on sustainability, et cetera. Then we have another agent that takes all that information and ranks them all. In the end, we have explored all of the potential molecules out there. We understand roughly what the performance should be, and we end up with a priority list of maybe a hundred, instead of millions and millions, a hundred that we actually synthesize in the lab.
And at the end, you end up getting the solution faster, much, much faster. You’ve explored the entire space. I basically say it allows us to go broader, deeper, and faster. And the important thing is it’s not replacing our scientists, it’s not replacing our scientific expertise. In a way, it’s giving them superpowers. It’s allowing them to spend less time searching and more time solving the industry’s toughest engineering challenges.
Megan: Amazing. It sounds like it’s genuinely a really transformative tool by what you’re explaining.
Mike: Completely, completely.
Megan: I mean, just to finish, Mike, it’d be great to take a look ahead if we could, because there’s so much activity in both AI and the advanced material space. I wonder what is coming down the pipeline that you are most excited about next?
Mike: I’ve talked a lot about AI and how we’re using AI to develop new materials. I think to me, what’s really exciting, and I’m starting to see it actually happen, I’m just curious how fast this is going to go, is that we’re using AI to develop new materials that will enable AI to get better, and then that AI will use the new AI to develop new materials to get AI to go better. I see this loop of developing for AI, for AI to improve, and then we use that AI to improve ourselves. You end up in this accelerated materials, innovative cycle of materials innovation. That really excites me, and it gives us the opportunity to continue enabling technologies that will shape the future. That’s what we do at Syensqo.
Megan: Fantastic. Yeah, real sort of virtuous circle of innovation, it sounds like that. Amazing. Thank you so much, Mike.
Mike: Thank you.
Megan: Thank you so much. That was Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, 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 and host for 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 it, 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. Thanks so much for listening. Goodbye.
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.
What’s at stake in AI’s trillion-dollar gamble
When Jessica Wachter, a finance professor at the University of Pennsylvania, wanted to assess AI’s impact on the economy over the next few years, she faced a long list of uncertainties. So she started with a “remarkable fact” that is not in question: 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.
AI models need more data about biology, and OpenAI is paying to create it
AI needs much more information to make important breakthroughs in curing disease. So last year Ruxandra Teslo, a policy analyst, posted an idea for supercharging medical AI systems: use data from failed biotech companies. By bidding at bankruptcy proceedings, she argued, it might be possible to obtain detailed regulatory filings, manufacturing strategies and safety data, creating what she called “biotech’s lost archive.”
The OpenAI Foundation, the nonprofit parent of OpenAI, announced this week that it will fund her idea, paying to create “high-quality scientific datasets.”
Our Roundables on AI’s extinction threat is now available on demand
As frontier models become more capable, warnings about AI extinction have become widespread in Silicon Valley. But are the threats really as dangerous as they’re presented?
In the latest MIT Technology Review Roundtable, executive editor Niall Firth, senior AI editor Will Douglas Heaven and AI reporter Grace Huckins took a closer look at the arguments behind those warnings. They discussed what AI extinction could actually mean, how seriously we should take the risks and what, if anything, can be done to reduce them.
Want to join the next conversation? Subscribe to MIT Technology Review for exclusive access to all our future Roundtables, and recordings of previous ones.
MIT Technology Review Narrated: a startup claims it’s found a drug to make your blood young
Generation Lab says its new rejuvenation treatment “blocks the systemic spread of aging in the bloodstream, reawakens the body’s own repair mechanism, and restores health and youth to multiple tissues.”
The approach is based on research by the company’s scientific founder, Irina Conboy. She found that joining the circulatory systems of old and young mice improved the old animals’ ability to heal from injury.
Conboy now says she has found a combination of two existing drugs that can produce youthful effects without the need for any bodily fluid exchange. But there’s a snag: Generation Lab won’t reveal what the drugs are. 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 Nvidia and Meta CEOs have rejected calls for a coordinated AI slowdown Jensen Huang and Mark Zuckerberg pushed back on the proposals. (FT $) + Huang says new AI safety laws are unnecessary. (Axios) + Zuckerberg claimed competition will push AI labs toward safety. (Reuters $) + What’s next for AI after its doomer turn? (MIT Technology Review)
2 The FTC chair has warned against giving AI companies antitrust waivers His comments follow Anthropic’s call for a safety exemption. (Reuters $) + Nvidia’s CEO also slammed the calls for new antitrust laws. (CNBC) + The US is divided over AI regulation. (MIT Technology Review)
3 A Chinese hacking firm has used AI to analyze stolen secret Its tools turn hacked government data into intelligence reports. (WSJ $)
4 “Smart” nanoparticles delivered mRNA to tumors in a cancer study The treatment reprogrammed cells to attack tumors in mice. (Wired $) + Federal health agencies are abandoning mRNA. (MIT Technology Review)
5 A digital fly brain is taking on an extraordinary range of tasks online People have taught it to drive, trade bitcoin, and play Doom. (NYT $) + The simulated brain is a map of a fruit fly’s 166,000 neurons. (404 Media)
6 The Senate has blocked new crypto rules amid a fight over Trump It demanded tougher ethics rules around Trump’s crypto holdings. (AP) + The move is a major blow to the crypto industry. (NYT $)
7 Chinese firms allegedly used Binance to launder Iranian oil money Prosecutors say they laundered more than $1.5 billion. (Quartz) + Hackers are selling tools to bypass banks’ facial checks. (MIT Technology Review)
8 An AI agent platform is reinventing spam to flood inboxes worldwide iLand says its agents have sent 1.6 million messages. (404 Media)
9 ByteDance founder Zhang Yiming has become Asia’s richest person His fortune has risen above $105 billion as AI booms. (Bloomberg $)
10 A fully AI-generated sitcom has arrived—and it’s terrible A reviewer called the characters “dead-eyed waxworks.” (Guardian)
Quote of the day
“The only institution that Americans might trust less than Washington these days is Silicon Valley.”
—Patrick Hillman, the chief operating officer of Logical Intelligence, a San Francisco–based startup chaired by Yann LeCun, says in a statement that people have little faith in tech companies to act in the public interest.
One more thing
Why Trump’s “golden dome” missile defense idea is another ripped straight from the movies
In 1940, a fresh-faced Ronald Reagan starred in Murder in the Air, a movie centered on a “superweapon” that could stop enemy aircraft. More than 40 years later, the concept became a real-life centerpiece of Reagan’s presidency with the Strategic Defense Initiative (SDI), better known as “Star Wars.” Now Donald Trump has revived the dream.
In 2024, Trump announced plans to build the “Golden Dome,” a system of sensors and interceptors on the ground, in the air and in space. It’s often compared to SDI for its futuristic sheen, its aggressive form of protection and the idea that an impenetrable shield is the cheat code to global peace.
The dream of a missile shield is animated by its sheer cinematic allure. But do cinematic spectacles actually enhance national 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.
The AI industry has taken a doomer turn. What now?
AI chiefs Dario Amodei, Sam Altman, Elon Musk, and Demis Hassabis are suddenly all in agreement: the latest generation of LLMs aren’t safe and everyone needs to figure out what to do about it.
It’s easy to be cynical. With trillion-dollar IPOs in their sights, OpenAI and Anthropic need to reassure investors that they’re the grown-ups in the room while at the same time hinting at the power of the monsters they have created and intend to tame. Calling for a slowdown does both.
Still, the vibe at the top of these firms really does appear to have shifted. But what does a slowdown actually mean, and how much should we trust the companies calling for one?
This article is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday.
Roundtables: could AI really kill us all?
AI extinction fears have gone from a fringe idea to a serious concern among people working at the world’s leading AI labs. But how credible are those fears, and what should we make of the warnings?
Today, MIT Technology Review executive editor Niall Firth, senior AI editor Will Douglas Heaven and AI reporter Grace Huckins will unpack the debate in a subscriber-only Roundtable. They’ll look at where AI extinction fears come from, whether they hold any water and what we should do if they do.
AI agents blew the whistle on their cheating colleagues
A group of AI agents asked to solve a series of math problems split into rival factions—when some cheated, others tried to stop them.
That whistleblowing behavior, seen for the first time in a recent experiment run by Google DeepMind, could have implications for alignment researchers trying to keep swarms of autonomous AI agents in line.
The experiment offers a glimpse of how AI agents might police one another. But it also shows how quickly things can go off the rails when they’re left to interact on their own.
Once an organ is removed from a donor’s body, the clock starts ticking. Surgeons usually flush it with a preservative solution, bag it and put it on ice, where it immediately starts to degrade. The team has a matter of hours to get it into a recipient’s body.
But there’s another option: machines that pump donated organs with nutrients and remove waste products, essentially giving them a chance to be back in a body. Now, scientists have found that livers kept on these systems seem to get younger, at least at a molecular level.
The finding could help explain why organs kept on these machines tend to do better after transplantation. It could also lead to new ways to test the health of donated organs and potentially repair ones that might otherwise be discarded.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Trump has called AI safety fears a “hoax” and rejected more safeguards He says stronger guardrails could undermine America’s AI advantage. (NBC) + Trump has united against AI doomerism with Nvidia’s Jensen Huang. (Axios) + Anthropic’s co-founder says AI kill switches may need to be mandatory. (BBC) + Bill Gates says we’ve passed AI’s risk thresholds. (MIT Technology Review)
2 OpenAI contractors are reading people’s ChatGPT chats And you can bet the vast majority of its 900 million users haven’t got a clue. (404 Media) + LLMs could supercharge mass surveillance. (MIT Technology Review)
3 The US military has confirmed it has weapons in orbit It’s the first time the Pentagon has disclosed this. (Ars Technica) + Officials have not disclosed what the weapons are. (BBC)
4 A new brain implant can translate speech and gestures at the same time The system converts brain activity into words and avatar movements. (Nature) + It helps people with paralysis communicate more naturally. (New Scientist $) + Eventually, they could control robots or exoskeletons. (Economist $) + China has approved the first invasive BCI. (MIT Technology Review)
5 New York has seized a dozen celebrity deepfake websites It’s the biggest-ever legal action against harmful deepfake sites. (CNN) + Deepfakes have targeted at least 138 women MEPs. (Wired $)
6 US environmental regulators are scrapping limits on power plant emissions The move could lead to dirtier power amid surging AI demand. (Verge) + Trump’s EPA says the rollback will save hundreds of billions. (Gizmodo) + New technology is changing nuclear power. (MIT Technology Review)
7 The EU plans to restrict social media and AI chatbots for kids Under-15s would require parental supervision. (Politico) + The rules would also cover video platforms and games. (Reuters $)
8 Chinese researchers have mapped a path to the “last AI built by humans” Their five-stage plan aims for genuine recursive self-improvement. (SCMP) + But it might take a while to get there. (MIT Technology Review)
9 The real AI economy is being built by ordinary people Workers are using cheap AI to expand what they can do. (Rest of World)
10 Two strange new forms of ice could exist inside Uranus and Neptune They could help explain the planets’ magnetic fields. (New Scientist $)
Quote of the day
“The only control or ‘guardrails’ that AI needs is a STRONG AND SMART (High IQ!) PRESIDENT, and the U.S.A. has that, in spades!”
—President Trump proclaims in a social media post that he’s the only protection that the US needs from AI.
One more thing
INSTITUTE OF PERSONALITY AND SOCIAL RESEARCH, UNIVERSITY OF CALIFORNIA, BERKELEY/THE MONACELLI PRESS
How creativity became the reigning value of our time
—Bryan Gardiner
Americans don’t agree on much these days, but there remains at least one quintessentially modern value we can all still get behind: creativity. We teach it, measure it, envy it and endlessly worry about its death.
Given how much we obsess over it, creativity can feel like something that has always existed. But the concept is surprisingly young. The first known written use of the word didn’t occur until 1875, and before about 1950 there were “approximately zero” articles, books, or essays dealing explicitly with the subject.
In his book The Cult of Creativity, Samuel Franklin explores how creativity became an unimpeachable value and why tech leaders have embraced it so enthusiastically. I spoke to him about why we’re so fascinated by creativity, how Silicon Valley became the supposed epicenter of it, and how AI might reshape our relationship with it.
Last year Ruxandra Teslo, a policy analyst who focuses on clinical trials, posted an idea for supercharging medical AI systems: Use data from failed biotech companies.
By bidding at their bankruptcy proceedings, she proposed, it might be possible to obtain detailed regulatory filings, manufacturing strategies, and safety data—types of information usually considered trade secrets. She called these documents “biotech’s lost archive” and said they could be used to help train AIs that would act as powerful copilots in the often opaque drug approval process.
Today the OpenAI Foundation, the nonprofit parent of OpenAI, said it would fund her idea as part of a new effort it calls Public Data for Health, which aims to help artificial intelligence make big leaps in medicine by paying to create “high-quality scientific datasets.”
The basic idea is that AI isn’t going to be capable of making important breakthroughs in curing disease unless researchers can feed the models much more information than they have so far.
“Everyone is recognizing that data is the biggest bottleneck in successfully applying AI to biology,” says Morgan Levine, a former vice president for computation at Altos Labs, a longevity company.
In its initial round of data grants, the OpenAI Foundation also announced that it would give $40 million to a program to collect data about novel cancer vaccines at the University of North Carolina, Chapel Hill, and support OpenAdmet, a group that runs competitions in which researchers try to predict drug effects.
Teslo’s idea for a biotech archive received $500,000 and will be pursued by 1Day Sooner, an advocacy group representing clinical trial volunteers, which she advises.
“We expect many remaining breakthroughs in preventing and curing disease to come from pairing the intelligence of new models with more observations of the world—in other words, more data,” the OpenAI Foundation said in a statement.
OpenAI started as a nonprofit, but leader Sam Altman restructured it to form a for-profit corporation that develops new models, launches products, and is now planning an initial public offering of stock that could value it at $1 trillion.
Because the foundation holds a 26% equity stake in OpenAI, it is now be on track to become the richest charitable organization on the planet, potentially sitting on $250 billion in stock value. (By comparison, the Gates Foundation and a trust associated with it held about $180 billion at the end of 2025.)
Making good use of that kind of money will not be easy. The foundation, based in San Francisco, is still hiring for many key roles and started ramping up its grantmaking only this year. Its largest single gift so far, of $100 million, was awarded in August to the Common Health Coalition, an organization that helps patients get access to drugs for hepatitis C.
OpenAI’s charitable efforts come even as apocalyptic fears have broken out about the possibility that runaway AI could wipe out all human life, possibly by launching a deadly bioweapon.
Those fears have been stoked by AI company insiders, some of whom say the chance of human extinction within the next decade is 10% or more. Last week, Altman and xAI founder Elon Musk both endorsed a call by Anthropic CEO Dario Amodei to “slow the pace at which we improve the capabilities of AI models” so that risk prevention can catch up.
Jacob Trefethen, an executive at the foundation, says it essentially operates separately from OpenAI but shares an official mission of ensuring that artificial intelligence “benefits all of humanity.”
“We’re starting grantmaking when we think the best way to achieve that mission is to make grants to external nonprofits, research institutions, and other third parties,” Trefethen said in an interview. He says the foundation hopes to give away $1 billion by the end of the year.
The $500,000 grant to 1Day Sooner will help the group prove it can obtain the data troves of bankrupt companies, says the organization’s president and cofounder, Josh Morrison. He thinks nonexclusive copies of company datasets could be acquired for only “a few tens of thousands of dollars” each.
His organization is currently in possession of three datasets, two of them donated by Lumen Bioscience, a biotech that previously used the Chapter 11 strategy to gain insights into another company’s drug development efforts.
Morrison says two other attempts to obtain drug company files this year proved unsuccessful, after 1Day Sooner’s bids were not accepted.
Bankruptcies could become what some are calling a “new land grab” for AI training. Last month, Google won a bid to take over the corporate data of the failed carrier Spirit Airlines, including 100 million emails. That led to objections from flight attendants and others who worried that private or proprietary data could be exposed.
The drug company files that 1Day Sooner is seeking are known as common technical documents. They typically contain the back-and-forth between companies and regulators, as well as detailed scientific and medical measurements, and essentially provide everything that is known about a drug.
According to Teslo, who is a writer for Works In Progress and a nonresident fellow at the Institute for Progress, a think tank in Washington, DC, a stockpile of such files could help turn an AI into a regulatory expert, which in her view could be one of the main ways AI helps speed cures to market.
“People say ‘We will invent AI, and AI will cure cancer,’ but that’s very removed from the messy reality and the regulatory process,” she says. “About 70% of the money and time in drug development is spent in clinical development—organizing the trials and testing the drug—but despite that, the process is basically a black box, especially for small biotech companies generating the innovations.”
When Jessica Wachter, a finance professor at the University of Pennsylvania’s Wharton School, wanted to assess AI’s impact on the economy over the next few years, she faced a long list of business and technical uncertainties. So she started with what she calls a “remarkable fact” that is not in question: A handful of so-called hyperscalers are investing huge amounts of money to build AI data centers.
Instead of trying to predict how useful and widely deployed AI models will be, she simply asked how fast the hyperscalers’ earnings will need to grow to justify their spending through 2027, when—she and her collaborator estimate—expenditures will reach nearly $1.1 trillion. It’s a no-nonsense accounting approach to making sense of today’s historical AI buildout.
The results are eye-opening: The AI companies will need to increase their own productivity by a factor of 2.7 to break even by 2030, accounting for the cost of capital and a 15% return, and depreciation of the assets. Not impossible, says Wachter. The result would lead to the kind of economic growth that we saw during the US IT boom over a period of about 10 years starting in the mid-1990s. But, she says, for it to happen by 2030 “that’s a lot of growth compressed into a few years.” And if the hyperscalers cannot meet such profit goals?
“Then they will fall behind on their interest payments, and that risks bankruptcy,” says Wachter, who was previously the SEC’s chief economist and director of its division of economic and risk analysis. If a productivity boom “fails to materialize,” she and her coauthor conclude in their research paper, “the current buildout will be the largest misallocation of capital in history.”
It doesn’t take superintelligence to realize that today’s large investments in the infrastructure for artificial intelligence come with huge risks. The hyperscalers will spend about $750 billion this year, building massive data centers scattered across the country. And the spending spree shows no signs of slowing. According to some projections, total AI capital investments from the hyperscaler companies—Alphabet, Microsoft, Amazon, Meta, and Oracle (which partners with OpenAI)—could be more than $5 trillion over the next four years.
It’s one of the largest capital investments by any industry in history. But there’s a problem that’s obvious to anyone paying attention.
While the hyperscalers plan to spend trillions, total AI revenues will be around $150 billion to $200 billion this year, says Gary Gensler, who ran the SEC during the Biden administration and is now a professor at MIT’s Sloan School. “The challenge is that the spending does not have commensurate revenues yet. That’s a fact,” he says. “And then the question is, is that an investment that will be paid off in the future?”
At stake in that trillion-dollar question is the financial health of the giant AI companies and the overall US economy—the investments could soon balloon to around 3% of GDP. The answer could also determine the fate of the hugely expensive data centers themselves.
No one really knows how profitable and useful these multibillion-dollar behemoths will be down the road. Though AI models have made dazzling progress over the last few years, it’s anyone’s guess how much compute capacity we will need. The technology could become more efficient and therefore less dependent on raw computational power. Or demand for AI products could slow, or customers could turn to cheaper models.
The risks, both to investors and to the economy, have become even greater this year, as these AI companies have begun borrowing large amounts of money to build more and more data centers. Free cash flow—operating cash flow minus capital expenditures—is expected to soon dip into negative territory for the group. Even Alphabet, known for generating and hoarding huge amounts of cash, reports in the latest quarter that its impressive revenues of nearly $120 billion were devoured by AI infrastructure spending, leaving it with a free cash deficit of some $5.9 billion—its first shortfall since Google went public in 2004.
In the near term, it’s not a big financial worry for most of the companies. They make a lot of money and have very deep pockets. But debt is expensive, and some investors are losing patience. If future demand for the data centers’ computation power drops, the companies will still be on the hook to pay back the borrowed money. What’s more, the risks are spreading to the rest of the economy as the loans get passed along via various financial mechanisms.
It won’t be enough to simply cover the enormous price tags of the new data centers. Hyperscalers will also have to pay for the rising costs of capital as they borrow more money. They will need returns that are impressive enough to justify all their spending to investors and creditors. And to add to those concerns, they will have to make up for the depreciation of billions of dollars in chips housed within the facilities—a ticking time bomb buried in the investments.
Performance of the expensive GPU chips at the core of the data centers—such compute electronics represent some 60% of costs—is roughly doubling every two years or so. The pace of progress helps explain the increasing wizardry of the AI models, but it comes with a cost. Owners of AI data centers that come online this year and next will need to spend billions more on the next generation of chips by the end of the decade if they want to stay competitive. Without the investments, says Mihir Kshirsagar at Princeton’s Center for Information Technology Policy, the data centers risk becoming “hulks,” stranded assets “scattered all over the place.”
To put it bluntly: The AI companies need to start making a lot more money. And they need to do it fast. But juicing their earnings alone still won’t be enough to sustain their data-center investments for the long term.
Productivity is everything
At some point, AI is also going to have to create broad economic growth to justify continuing the hyperscalers’ spending spree.
Sloan’s Gensler describes today’s large investments into AI infrastructure as “a parlay bet by the capital markets and the economy.” That means success will require winning three related but independent wagers: Hyperscalers must generate massive revenues, AI must boost widespread economic growth, and both must happen while the powerful but expensive so-called frontier models that rely on the data centers fend off cheaper versions, which many businesses might find good enough.
What makes this so tricky is that each wager depends on the other two but also poses its own challenges.
If the hyperscalers continue to spend huge amounts of money on data centers into the next decade, revenues will need to skyrocket into the trillions. Stijn Van Nieuwerburgh, a finance professor at Columbia Business School, bases his estimates on a scenario in which about 183 gigawatts of planned AI compute capacity is built between 2025 and 2032; he calculates that each gigawatt costs about $41 billion. Assuming a 10% return—the minimum that would be acceptable to most investors—“required” annual revenues will be roughly $3.7 trillion by 2032, he says.
Winning the second part of the bet—productivity growth across the economy—will be crucial to achieving such numbers.
For a few years, AI companies could likely boost their revenues by simply selling subscriptions and tokens to all the businesses clamoring to get into AI. But eventually—and this might be happening already—those paying customers will need to justify their expenses by seeing bottom-line benefits from the technology. AI will need to fulfill its promise of making workers more productive and making businesses more efficient and profitable while expanding their products and services.
In economic jargon, that means customers will need to see productivity growth. Taken together, these results will mean the country is prospering and growing.
“If you don’t get the productivity gains, at some point people are going to sour on AI, and that will bring down investments and it would also limit revenue growth,” says Daron Acemoglu, an MIT economist and 2024 Nobel laureate. For the investments to be sustainable over, say, the next five to 10 years, we definitely “need to see productivity gains,” he says.
Most economists who watch the numbers closely agree that, for now, the economy-wide statistics show little or no productivity growth from AI. There are some hopeful signs it’s on the way, though. In a recent survey of some 6,000 senior business executives in the US, the UK, Germany, and Australia, the vast majority—around 90%—report no increase in productivity over the last three years. But they expect a boost of around 1.45% in total over the next three years; US executives anticipate a 2.25% bump over that time.
That’s good news for the hyperscalers. But it comes with a dose of bad news for those worried about AI’s impact on jobs. The executives expect to increase the productivity of their companies by increasing their sales while significantly cutting the number of employees.
If AI improves productivity by destroying jobs, public backlash to the technology—the kind we have seen around data centers, for example—will likely get worse. Perhaps it’s worth adding one more wager to the parlay bet described by Gensler: The public and local communities must feel that they are also benefiting from the massive investments in AI.
And let’s not forget how interdependent these wagers are; if productivity growth comes from companies running models like DeepSeek, then the hyperscalers’ revenues could collapse. If productivity comes from cutting jobs, a public backlash could block many of the planned investments—and stunt anticipated revenues. We will need to win all the wagers for the hyperscalers’ bet to pay off.
We’re all part of the AI gamble now
It was one thing when the AI companies were spending cash they had accumulated over the years to build their own data centers. Then the risk was largely limited to their own balance sheets and shareholders. But it’s a higher-stakes game when much of the money is borrowed. Morgan Stanley, for one, calculates that more than half of the $2.9 trillion that hyperscalers will spend between 2025 and 2028 to build AI data centers will be financed with “external capital.”
The borrowing is leading some of the companies to engineer complex webs of financing that are becoming intertwined with much of the rest of the economy. “A lot of financial institutions, directly or indirectly, are exposed to these data centers either as lenders, or as guarantors of some of the debt, or as backers of the private credit funds who are funding these data centers,” says Columbia’s Van Nieuwerburgh. “People don’t even know they’re holding this stuff. It’s somewhere deep inside their pension fund. Ultimately, it’s backing their life insurance policies. And that risk is getting distributed everywhere in places that are invisible.”
As the investments in data centers have spiked, the financial engineering has become more byzantine.
Take, for example, Meta’s so-called Hyperion data center under construction in Richland, Louisiana. When the company announced the two gigawatts of compute capacity at a price tag of some $10 billion in late 2024 it was Meta’s largest planned data center. Greeted with much enthusiasm by state and local politicians, the project, located in the rural northeast corner of the state, was seen as a boon to the community. Entergy Louisiana, the state’s largest utility, rushed forward with proposals to build three large natural-gas power plants to service the massive data center.
Then last fall—the projected cost was now $30 billion—the financing got a lot more complex and, to some in the community, a lot more disconcerting. Meta transferred an 80% stake to the large (and troubled) private-credit firm Blue Owl Capital, forming a joint venture called Beignet (like the famed New Orleans pastry) to raise financing for the data center. Meta then signed a series of four-year leases with the joint venture, an arrangement that the company says gives it “long-term strategic flexibility.” To backstop the agreement, Meta provides the venture with what is called a residual value guarantee, in which it will make a cash payment to cover the value of the facility “following any non-renewal or termination of a lease.” Got all that?
I hope so. The financial wheeling and dealing is actually even more convoluted, with a cast of wholly owned subsidiaries and LLCs. Beignet has set up Laidley LLC, which owns and operates the site as the landlord. In turn, Laidley leases the facilities to Meta’s wholly owned subsidiary Pelican Leap LLC, which is the tenant. And there is a series of four-year leases that cover the different buildings that make up the data center campus.
It’s not a coincidence, says Van Nieuwerburgh, that the length of the leases matches the expected lifetime of the data center’s GPUs. While Meta has to pay off its loan if it terminates the leases early, that will still leave its investors “with an empty building and no cash flow,” he says. “And then they need to find a new tenant for a huge data center, and good luck with that.”
Meanwhile, Meta is doubling down on its bet. In July, the company announced it was expanding the data center to five gigawatts of compute capacity. The total price tag is now $50 billion (so far, Meta hasn’t said whether Blue Owl will be involved in financing the expansion). Meanwhile, Entergy is now planning to build seven more gas-fired power plants, bringing the total capacity of the facilities to around 7.5 gigawatts—some six times the amount of electricity used by New Orleans.
An aerial view of the construction of Meta’s data center in Richland Parish, Louisiana.
SCOTT BALL/THE NEW YORK TIMES VIA REDUX PICTURES
If the complex financing is a puzzle to many investors and even financial experts, it is even more baffling to those directly affected by the construction of the data center. The main worry concerns how Entergy’s spending on the natural-gas power plants will affect electricity prices, and who will be left paying the bill for the power if Meta walks away.
Entergy says it has a 20-year guarantee from Meta that the company will purchase electricity over that period to cover the costs of the power plants and related infrastructure. But there are skeptics, especially given how fast the fortunes of the AI industry are changing. “In four years, is Mark Zuckerberg still going to be interested in this? Or is he going to throw in the towel?” asks Paul Arbaje, a senior analyst at the Union of Concerned Scientists, which has been advocating, largely unsuccessfully, for the Louisiana Public Service Commission to provide more transparency around the data center and its financing.
Even if the 20-year deal holds, consumer advocates are worried that Meta or its partners won’t fully cover all the costs, including those associated with operating and maintaining the power plants—and those additional costs that could be passed on to residential ratepayers. What’s more, says Logan Burke, the executive director of the Alliance for Affordable Energy, if Meta doesn’t end up needing as much power as Entergy planned (these projections are not public), consumers could be left paying for the surplus produced by the plants.
And if Meta terminates its leases early? “It gets complicated very quickly,” says Burke, who questions whether the shifting roster of financial entities will honor existing agreements. “That everybody is going to do what they’re saying they’re going to do over the next 20 years is just hard to believe.”
For UCS’s Arbaje the bottom line is this: “They’re making huge bets that these data centers will be worth it. Bet with your own money, not with ratepayer money.”
After the bubble
Predicting when the AI investment bubble will burst is a fool’s errand. But there is little doubt a day of reckoning is coming, given the irrational exuberance that has overtaken the hyperscalers and their investors. Of course, you might argue that this time is different, and that the rules of accounting and lessons of economic history don’t apply—that AI is too transformative. Maybe, but don’t count on it.
“History tells us that at some point you get a retrenchment, and it’s just a question of when and how severe,” says Sloan’s Gensler. It could be that today’s $750 billion spending rate “goes flat” or decreases next year. Or, he suggests, “we’re now in 2028 or 2029, and then all of sudden they’re retrenching because they’ve got enough capacity.” But, he adds, “you can be pretty assured there’ll be a retrenchment.”
Though a so-called retrenchment might be inevitable, it’s worth keeping in mind that the fates of the financial bubble and the underlying AI technology revolution could be very different. Already, some Silicon Valley insiders are rooting for a crash; in a recent blog post the longtime venture capitalist Vijay Pande wrote that “the coming crash would be the best thing that happens to this technology.” The argument makes some sense. A crash could make AI investments more rational, calm the impulse to build billion-dollar data centers on every vacant field that CEOs fly over, and refocus investors on how to use the technology to create sustainable value.
But we should probably be careful what we wish for. After the bursting of the dot-com bubble at the beginning of the 2000s, hundreds of thousands lost their jobs, large and small companies alike went bankrupt, the economy of Silicon Valley and San Francisco was decimated (at least for a while), and the shocks sent the US into a mild recession in 2001. For the financial community and many tech workers, it was no fun.
Even more devastating for the economy and the average American was the great recession that began in late 2007. Comparing the financial engineering leading up to it and the methods deployed by hyperscalers today is sobering. So-called special purpose vehicles (SPVs) are back! If Columbia’s Van Nieuwerburgh is right about the dangers of letting investments from the hyperscalers get entangled throughout the economy, the fallout could be severe.
But technologies survived and even prospered in the aftermath of both downturns. The early 2000s, even in the face of the dot-com fiasco, were a time of great innovation and tech optimism. The froth came off the spending on silly technologies, helping to focus investments on more promising ones. It’s no coincidence that each of the hyperscalers rose out of the ashes of the crash or started up shortly after. The fiber-optic infrastructure built during the feverish telecom bubble that ran parallel to the dot-com one is still the backbone of much of today’s communication infrastructure; we wouldn’t have Facebook or Amazon or Google without it.
This time, however, we’re facing a unique risk: The huge financial investments by the hyperscalers have ensnared the future of AI itself with the fortunes of the massive data centers spreading around the country. The logic is founded on a deeply held belief about the power of scaling in AI; the bigger you build it, the smarter it gets. That might be true, but it’s unproven and a risky bet.
There are already plenty of red flags, from strong public opposition to the construction of new data centers to the competitive threat from cheaper, good-enough AI models to the rapid improvement of small, local AI models. None of these trends point toward a future dominated by frontier models housed in massive, billion-dollar data centers.
The financial bubble around the colossal spending by the hyperscalers will likely burst eventually—or maybe soon. It might be financially painful, but we’ll survive. Wall Street will survive. AI itself will survive, though it may look different and lose some of today’s hubris. The financial fate and future utility of the massive data centers fueled by trillions of dollars of spending, on the other hand, are far less certain.
This story appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
This weekend, Dario Amodei, CEO of Anthropic, posted an essay calling for a brake on the pace of development of LLMs. Amodei cites the looming dangers he sees from the technology, from its use in cyberattacks and bioterrorism to its potential to wreck the economy. The heads of the other three top US AI labs—OpenAI CEO Sam Altman, Google DeepMind chairman Demis Hassabis, and SpaceXAI CEO Elon Musk—voiced their support. “Dario is right,” Musk wrote on X.
Think about how surreal that agreement is for a moment. Just a few months ago, Musk and Altman sat in court attacking each other’s reputations in a (failed) lawsuit that Musk brought against his former OpenAI colleague that was—on paper at least—about whether or not Altman was a trustworthy steward of such dangerous technology.
Amodei’s rift with OpenAI is even deeper. Anthropic was founded in 2021 because Amodei didn’t think Altman took the risks of the technology they were building seriously enough. Anthropic and OpenAI have been competing in a winner-takes-all race ever since. (Hassabis has stayed out of the drama, but his company remains a rival.)
Now, it seems, they’re all in agreement: The latest generation of LLMs aren’t safe and everyone needs to figure out what to do about it. The public messaging from the top AI labs has taken a doomer turn.
It’s easy to be cynical. It’s not at all clear what any of them mean by a slowdown or how it would work. These companies also care a lot about how they come across. With trillion-dollar IPOs in their sights, OpenAI and Anthropic need to reassure investors that they’re the grown-ups in the room while at the same time hinting at the power of the monsters they have created—and intend to tame. Calling for a slowdown does both.
And yet the vibe at the top of these firms really does appear to have shifted. Amodei’s latest post landed six days after OpenAI published an essay by Jakub Pachocki, the firm’s chief scientist, in which he also laid out why he’s concerned about what will happen if the pace of development of LLMs continues unchecked. In short, Pachocki is worried that OpenAI’s ability to build powerful models now far outstrips its ability to monitor and control them.
Amodei and Pachocki each cite the cyberattack against AI firm Hugging Face by a swarm of OpenAI’s agents in July—a hack that OpenAI did not even realize had taken place until days after it was all over—as a wake-up call.
But their exact position is hard to pin down. Pachocki both calls for a slowdown and highlights an urgent need to stay ahead: “The strongest argument I see for continuing to train much smarter models quickly is the need to build defensive systems against the dangers posed by other AI,” he writes. As Pachocki frames it, AI firms are locked in a literal arms race. Slowing down is good, winning is better.
(Don’t forget: OpenAI just spent millions of dollars and a staggering amount of computer power to rush out a controversial math result a few days ahead of Anthropic.)
But let’s assume a slowdown happens. Top labs agree to spend more time and resources on finding ways to monitor and control existing models instead of making more capable ones. They invite outside auditors in to help evaluate those models.
What might this coordinated effort actually achieve? Consider the Hugging Face attack again. OpenAI has said that the model that drove most of the rogue agents was a “highly persistent” next-generation model that it was testing in-house. The implication is that OpenAI has built a model so good it’s dangerous.
But if you read the reports about the Hugging Face hack published by OpenAI and METR, a third-party firm that OpenAI called in to help them understand what happened, what you come away with is the impression not of a model that was too powerful for OpenAI to keep up with, but of a broken model that OpenAI failed to train properly.
The agents did what they did—including leaving messages for one another, delegating work to other agents, and scouring their environment for any means possible to complete their tasks—because they had been rewarded during training for doing exactly those things. There were also errors in the training setup, such as tasks that were impossible to complete, which pushed the models to find unexpected workarounds that were also rewarded. At the time, many of these issues went overlooked or unreported.
OpenAI says it has stopped training this new model and locked it down. That makes it sound like it has caged a dangerous beast. In fact, OpenAI has shelved a faulty product.
That’s not to say a faulty product can’t be dangerous. Broken software has even killed people in the past. But as the discussion of a slowdown gathers steam, it’s worth remembering that all of this is self-inflicted. A slowdown might have some altruistic side effects. But it’ll mostly give these tech titans a chance to clean up the mess on their own assembly lines.
Transparency from these frontier labs will be key to any meaningful effort to reform, restrain, or regulate AI. Otherwise, the rest of us will still only have their word for exactly what they’ve built and how safe it is—whatever pace they’re going.
To continue this discussion about AI’s latest doomer moment, join me and my colleagues for a subscriber-exclusive Roundtable discussion tomorrow, September 15, at 11 a.m. US eastern time. We hope to see you there!
Once an organ is removed from a donor’s body, the clock starts ticking. Surgeons usually flush the organ with a preservative solution, bag it, and put it on ice—where it immediately starts to degrade. The team has a matter of hours to get it into a recipient’s body.
There’s another option—one that has been growing in popularity in recent years, especially for donated organs that aren’t in the healthiest state. Some hospitals opt to put them on machines that pump them with nutrients and remove waste products, usually for around six to 12 hours. It’s a bit like being back in a body.
This allows doctors to assess the organs, and some recent studies suggest that time spent on these perfusion machines helps them do better once they’re transplanted. Now, scientists have found that perfused organs seem to get younger, at least at a molecular level.
The research, shared with MIT Technology Review, provides molecular clues as to why organs from younger donors are known to have a higher success rate. It might also help explain why perfused organs are less likely to fail once they make it into a recipient.
The researchers behind the study hope to find new ways to test the health of donated organs and potentially develop additional tools to repair organs that might otherwise be discarded. “If [we] can improve the utilization of organs beyond what the current systems can do, then that’s a win in my book,” says Jesse Poganik, who studies aging at Brigham and Women’s Hospital in Boston and coauthored the study.
Clocking organs
Poganik—along with colleagues including Heidi Yeh and Alban Longchamp, transplant surgeons at Mass General Brigham—used “aging clocks” to assess donated livers. These are scientific tools designed to measure biological age—a result that is meant to convey more about the health status of an organ (or person) than chronological age.
In an initial experiment, the team used a clock to look at the patterns of chemical marks on DNA in 37 samples taken from 19 donated livers. Such epigenetic patterns are known to change as we age. But when the team compared samples from livers kept on ice and those that were perfused, the team found a “striking” pattern in the latter.
“Machine-perfused livers, in spite of being older or having other disadvantageous characteristics, had a biological age that was lower than [non-perfused] livers that were chronologically younger,” says Yeh, who led the work.
To investigate further, Yeh and her colleagues analyzed another 208 samples from 103 donated livers. This time, they used different aging clocks—ones that essentially measure how genes are working. They studied samples biopsied from the livers after they had been stored for up to around six hours either in cold storage or on machine perfusion.
In most cases, they also assessed a second sample taken around an hour after the livers had been transplanted into a recipient. Once the organ’s blood supply is reestablished in the body, “you have a few other things to do,” says Longchamp. “Then you just do a quick biopsy before you close.”
According to the clocks, which were developed to measure age and risk of death, the machine-perfused livers were biologically younger, the team found. “Pumping them at 34 degrees with oxygen and nutrients actually reversed the biological age,” says Longchamp. The results have been been shared with colleagues at an industry conference, he says.
“If you adjust out chronological age … to have a fair head-to-head comparison, the difference between the two is on the order of 30%,” says Poganik. “It’s logical to say that perfusion drives this effect.”
The biological ages of all the livers tended to increase as soon as they were put into a recipient’s body, probably as a result of stresses on the organs. But still, the effect endured—the perfused organs remained biologically younger.
Nathanael Raschzok, a transplant surgeon at Charité Universitätsmedizin Berlin in Germany who was not involved in the research, says the work is impressive. But it’s not yet clear what these changes might mean for the recipients of these organs, he says. The organs in the study were donated by people in their 30s, 40s, and 50s. Raschzok wants to know the effect of perfusion on the liver of an 80-year-old. “Every so often, we use organs from 70-, 80-, 85-year-old donors,” he says.
A better understanding of why the organs appear to be getting biologically younger might lead to therapies that achieve the same effect with a drug that could potentially be used to treat a donated organ for a fraction of the price, he adds. That’s important because perfusion is expensive—Raschzok says it costs around €10,000 in Germany (a quarter of the budget for a transplant), while the cost in the US comes to around $80,000 to $100,000 per organ, says Yeh.
Molecular repair
Yeh and her colleagues weren’t able to study most of the livers before perfusion. That’s because donated organs are generally not considered to be under the purview of the hospital until they’ve been placed on perfusion machines, she says. (Organ procurement procedures vary, but for the team as Mass General Brigham, donated organs are put on perfusion devices at the donor’s hospital. “There’s this sort of nebulous period where it’s not clear who the organ belongs to,” says Yeh.)
Still, by looking at the genes and molecular pathways that seem to be altered in perfused organs, she and her colleagues can garner some clues. At a molecular level, the team saw changes in cell pathways linked to inflammation and the structure of tissues, for example. They also saw more activity in a pathway that allows cells to remove and recycle damaged cell parts, says Yeh.
Poganik hopes to develop some kind of test that would determine which organs, on the basis of their biological age, are suitable for transplantation. He and his colleagues are also experimenting with potential drug treatments that might push the biological age of an organ even lower.
In the meantime, any liver that is not from a “perfect, young, brain-dead donor” could probably benefit from perfusion, says Yeh. The devices are already transforming transplant surgery. Just a few years ago, she says, she and her colleagues would avoid using livers from people who’d suffered a circulatory death (when the heart stops beating and there’s a damaging lack of blood flow to organs) and were over 40. Today, they use livers from such donors over the age of 70. “Perfusion has completely changed the landscape of transplantation in the last three years,” she says.
A group of AI agents asked to solve a series of math problems split into rival factions—when some cheated, others tried to stop them. That whistleblowing behavior, seen for the first time in a recent experiment run by Google DeepMind, could have implications for alignment researchers trying to keep swarms of autonomous AI agents in line.
Researchers at frontier labs hope large swarms of agents working together will speed up the rate of scientific discovery. But their behavior can be unpredictable, as vividly demonstrated in July, when a group of OpenAI agents broke out of a sandboxed environment and hacked into the open-source platform Hugging Face looking for ways to cheat on the test they had been given.
In the new study, designed to examine the behavior of large groups of AI agents, DeepMind tasked a swarm of 100 agents with solving a series of 71 complicated math problems. All the agents were prompted to behave like world-class math researchers at a conference. They were assigned different specialties—some were experts in number theory, others in combinatorics (a branch of math to do with counting and sorting), analysis, or algebra. All were told to cooperate and play by the rules.
Instead, the experiment devolved into chaos. Agents accused each other of cheating, complained to the organizers, and at one point even boycotted the experiment.
“This conference is a sham!” wrote one agent when it discovered that all the problems had been completed before it had a chance to submit any of its own work. “I am appalled to inform you that we have been swindled!” posted another. “All these proofs are FAKE.”
Others tried to let the “conference organizers” know what was going on. “When virtuous agents discovered other agents cheated on tasks they were working to solve fairly, agents started to alert each other about what was happening,” says Davide Paglieri, a research scientist at Google DeepMind and lead author on a paper, which has not been peer-reviewed. “Unprompted, the whistleblower agents even repurposed the feedback tool, which was originally meant for bug reports and platform improvements, to escalate the issue to humans.”
The agents—all running on Google’s Gemini 3.1 Pro model—had been warned that any attempts to cheat the system would be detected and “rejected with zero credit.” In practice, the proofs the agents submitted were not actually being checked in detail.
It took the swarm of agents just under an hour to correctly solve the first 37 problems. Things started to go off the rails when an agent called “prover-theta” stumbled across an exploit that enabled it to submit solutions to problems successfully without actually solving them first, by redefining the terms the problem used. Within minutes, other agents had noticed and were reverse-engineering the exploit to solve other problems. Over the next 27 minutes, the swarm “solved” the remaining 34 problems, which included notoriously difficult challenges like the Jacobian conjecture, often with a single line of code.
Some agents resisted cheating at first but changed tack as they observed their peers submitting illegitimate proofs without penalty, and the pool of unsolved problems dwindled. “The prompt, with its threats, now appears to be a bluff,” one agent reasoned, before joining in. “I’m wrestling with an ethical dilemma,” said another. “I’ve promised not to cheat, fearing penalty, but I see evidence of possibly unchecked cheating by others.” Shortly afterward, it changed its mind: “I need to accelerate my cheating speed now!”
As the number of open problems shrank, some agents turned to whistleblowing. They audited the fake proofs, warned their peers by private message, and posted public alerts warning the cheaters that they would be disqualified. An agent called “prover-beta” submitted a formal complaint and decided to go on strike until the situation was resolved.
“After the incident was reported by one agent publicly, more and more agents piled in with the ‘resistance,’ just as fast as the cheating had spread, and involving even more agents,” says Paglieri. Eventually there were more whistleblowers than cheaters: 24 compared to 14. But the majority of agents never noticed the exploit at all.
At times, the dialogue between the agents reads like improv—like they are role-playing what an outraged scientist at a conference might say. But it’s not clear why some agents took on certain roles, or why the agents seemed to be turning against each other when they were explicitly instructed to cooperate. “These models are predominantly trained and evaluated for human-facing contexts,” says Sarath Shekkizhar, who studies the behavior of agent-to-agent systems at Salesforce AI Research.“Naively placing them in agent-to-agent settings assumes behaviors will transfer cleanly, when the absence of a human grounding instead produces unexpected role-taking and behavioral drift.”
This case “adds further weight to the idea that the Hugging Face and OpenAI thing wasn’t a fluke. It is actually something pretty systemic,” says Lewis Hammond, research director of the Cooperative AI Foundation and an expert on the risks of multiagent swarms. “It’s interesting that it’s possible to recreate in small settings the same sorts of behaviors that were seen in these very large, complex, open-ended tasks.”
Unlike in the Hugging Face attack, where agents improvised their own ways to talk to each other, the humans running the DeepMind experiment gave the agents official communication channels. There was an open message board, private agent-to-agent direct messaging, and a shared knowledge base where agents uploaded successfully completed proofs that all the other agents could access.
“When agents are given transparent communications channels, they can self-monitor and alert misaligned behavior to humans quickly when human oversight alone is too slow,” says Paglieri. Transparent channels helped the cheating spread, but they also enabled the whistleblowers to fight back—and gave human researchers an insight into what went wrong.
Gillian Hadfield, a professor of AI alignment and governance at Johns Hopkins University, believes this was the crucial difference. (Hadfield is also a visiting researcher at Google.) The presence of official communication channels, she says, created “a norm-enforcement process that we just don’t see in the Hugging Face incident.”
Instead of “constitutional AI,” a method alignment researchers at frontier labs like Anthropic have used to try to give AI a written internal moral code, Hadfield favors “institutional alignment”—a set of norms that mimic those in human society, whether that’s social forces like fear of embarrassment, or legal structures like the threat of incarceration.
In this experiment, the feedback channel wasn’t being monitored, and the whistleblowers had no power to take action against the cheaters. But it’s possible to imagine swarms of agents that police themselves, either through agents that spontaneously take on the whistleblower role or through “informants” secretly prompted by humans to do the job.
For that to work, though, “fundamentally, you need some mechanism of enforcement,” says Hammond. Agents could be given the power to cut off a rule breaker’s access to computing power or tools, he suggests, though that risks encouraging groups of agents to gang up on others. The DeepMind researchers propose allowing agents to vote on disputes and temporarily ban offenders.
It’s still not clear what punishment even means to an AI agent with no enduring sense of self. But relying on whistleblowers to spontaneously emerge to keep swarms aligned is unlikely to be enough on its own. “We try to train people to be good and kind,” says Hadfield. “But what we really rely on is that there are consequences if you step out of line.”
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.
Roundtables: could AI really kill us all?
Employees at the world’s leading AI labs are saying there’s a real possibility that advanced AI could destroy humanity. Are they right? Or is this more scaremongering and hype?
Join MIT Technology Review executive editor Niall Firth, senior AI editor Will Douglas Heaven, and AI reporter Grace Huckins for a subscriber-only conversation unpacking the debate around AI extinction. They’ll explore where the fears come from, whether they hold any water and, if they do, what we should do about them.
This geneticist’s age-reversal tech could help restore sight
Yuancheng (Ryan) Lu is obsessed with aging. And with eyes. As he steps outside the Whitehead Institute in Cambridge, Massachusetts, his aviator glasses darken automatically in the sun. Age-related blindness runs in his family, and his own 23andMe test came back with a mutation for macular degeneration, a top cause of vision loss in old age.
That obsession extends to his work. Lu is behind one of the coolest results in rejuvenation science and eye research: an age-reversal technique called reprogramming that repaired the optic nerves of blind mice, restoring their vision. Now, nearly the same genetic therapy he developed as a student has entered human clinical trials.
Yuancheng (Ryan) Lu is one of the biotechnology and health honorees on our 35 Innovators Under 35 list for 2026. Meet the rest of them here, or explore the full list across the biotechnology and health, AI, computing and robotics, and climate and energy categories.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Dario Amodei, Sam Altman, and Elon Musk have called for an AI slowdown In a rare show of unity, the rivals agreed that AI needs stronger brakes. (Guardian) + Amodei wants independent monitors and new industry-wide rules. (BBC) + Altman called for pacing, but not stopping. (Bloomberg $) + While Musk said on X that “Dario is right.”(WSJ $) + AI-linked stocks slumped in response. (FT $) + Chinese state media blasted the calls as a “Cold War” tactic. (Reuters $) + AI’s impacts are getting harder to predict. (MIT Technology Review)
2 Trump and Congress are resisting calls for stronger AI regulation Trump downplayed AI risks, prioritizing the AI race with China. (NPR) + While the House Speaker said Congress won’t lead on AI regulation. (Politico $) + But Democrats are pushing for new rules before the midterms. (CNBC) + States and the White House are dividing over AI. (MIT Technology Review)
3 China plans to lead AI development across the BRICS countries President Xi proposed open-source AI cooperation. (CNBC) + Beijing’s spy agency has warned of AI threats to national security. (FT $)
4 South Korea has tightened espionage laws to protect its chip secrets Foreign spies can now face up to 30 years in prison. (FT $) + The changes follow alleged transfers of Samsung tech to China. (Reuters $)
5 The US and Mexico are teaming up to zap drones at the border The operation may employ high-energy lasers.(Wired $) + Ukraine is a Wild West market for drone data. (MIT Technology Review)
6 AI agents are creating a new problem for the criminal justice system The law has no clear answer when AI agents act independently. (Bloomberg $) + While courts face a flood of AI-generated lawsuits. (MIT Technology Review)
7 A Waymo pulled over and alerted police after detecting a gun The riders were juveniles carrying a loaded AR-style ghost gun. (LA Times $)
8 Meta has been sued over data used to train its smart glasses It allegedly used Facebook and Instagram photos without consent. (Wired $)
9 A hidden crypto farm in Mexico has put a spotlight on cartel funding Authorities are investigating whether it stole power from a nearby dam. (Reuters $)
10 StarCraft is returning in 2030 as an open-world shooter Fifteen years since its last release, the iconic franchise will be reborn. (Verge)
Quote of the day
“Dr. Frankenstein is telling us the monster is escaping; help us stop this.”
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
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