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.

In January, Elon Musk’s SpaceX filed an application with the US Federal Communications Commission to launch up to one million data centers into Earth’s orbit. The goal? To fully unleash the potential of AI without triggering an environmental crisis on Earth. But could it work?

SpaceX is the latest in a string of high-tech companies extolling the potential of orbital computing infrastructure. Last year, Amazon founder Jeff Bezos said that the tech industry will move toward large-scale computing in space. Google has plans to loft data-crunching satellites, aiming to launch a test constellation of 80 as early as next year. And last November Starcloud, a startup based in Washington State, launched a satellite fitted with a high-performance Nvidia H100 GPU, marking the first orbital test of an advanced AI chip. The company envisions orbiting data centers as large as those on Earth by 2030.

Proponents believe that putting data centers in space makes sense. The current AI boom is straining energy grids and adding to the demand for water, which is needed to cool the computers. Communities in the vicinity of large-scale data centers worry about increasing prices for those resources as a result of the growing demand, among other issues.

In space, advocates say, the water and energy problems would be solved. In constantly illuminated sun-synchronous orbits, space-borne data centers would have uninterrupted access to solar power. At the same time, the excess heat they produce would be easily expelled into the cold vacuum of space. And with the cost of space launches decreasing, and mega-rockets such as SpaceX’s Starship promising to push prices even lower, there could be a point at which moving the world’s data centers into space makes sound business sense. Detractors, on the other hand, tell a different story and point to a variety of technological hurdles, though some say it’s possible they may be surmountable in the not-so-distant future. Here are four of the must-haves we’d need to make space-based data centers a reality. 

A way to carry away heat 

AI data centers produce a lot of heat. Space might seem like a great place to dispel that heat without using up massive amounts of water. But it’s not so simple. To get the power needed to run 24-7, a space-based data center would have to be in a constantly illuminated orbit, circling the planet from pole to pole, and never hide in Earth’s shadow. And in that orbit, the temperature of the equipment would never drop below 80 °C, which is way too hot for electronics to operate safely in the long term. 

Getting the heat out of such a system is surprisingly challenging. “Thermal management and cooling in space is generally a huge problem,” says Lilly Eichinger, CEO of the Austrian space tech startup Satellives.

On Earth, heat dissipates mostly through the natural process of convection, which relies on the movement of gases and liquids like air and water. In the vacuum of space, heat has to be removed through the far less efficient process of radiation. Safely removing the heat produced by the computers, as well as what’s absorbed from the sun, requires large radiative surfaces. The bulkier the satellite, the harder it is to send all the heat inside it out into space.

But Yves Durand, former director of technology at the European aerospace giant Thales Alenia Space, says that technology already exists to tackle the problem.

The company previously developed a system for large telecommunications satellites that can pipe refrigerant fluid through a network of tubing using a mechanical pump, ultimately transferring heat from within a spacecraft to radiators on the exterior. Durand led a 2024 feasibility study on space-based data centers, which found that although challenges exist, it should be possible for Europe to put gigawatt-scale data centers (on par with the largest Earthbound facilities) into orbit before 2050. These would be considerably larger than those envisioned by SpaceX, featuring solar arrays hundreds of meters in size—larger than the International Space Station.

Computer chips that can withstand a radiation onslaught

The space around Earth is constantly battered by cosmic particles and lashed by solar radiation. On Earth’s surface, humans and their electronic devices are protected from this corrosive soup of charged particles by the planet’s atmosphere and magnetosphere. But the farther away from Earth you venture, the weaker that protection becomes. Studies show that aircraft crews have a higher risk of developing cancer because of their frequent exposure to high radiation at cruising altitude, where the atmosphere is thin and less protective.

Electronics in space are at risk of three types of problems caused by high radiation levels, says Ken Mai, a principal systems scientist in electrical and computer engineering at Carnegie Mellon University. Phenomena known as single-event upsets can cause bit flips and corrupt stored data when charged particles hit chips and memory devices. Over time, electronics in space accumulate damage from ionizing radiation that degrades their performance. And sometimes a charged particle can strike the component in a way that physically displaces atoms on the chip, creating permanent damage, Mai explains.

Traditionally, computers launched to space had to undergo years of testing and were specifically designed to withstand the intense radiation present in Earth’s orbit. These space-hardened electronics are much more expensive, though, and their performance is also years behind the state-of-the-art devices for Earth-based computing. Launching conventional chips is a gamble. But Durand says cutting-edge computer chips use technologies that are by default more resistant to radiation than past systems. And in mid-March, Nvidia touted hardware, including a new GPU, that is “bringing AI compute to orbital data centers.” 

Nvidia’s head of edge AI marketing, Chen Su, told MIT Technology Review, that “Nvidia systems are inherently commercial off the shelf, with radiation resilience achieved at the system level rather than through radiation‑hardened silicon alone.” He added that satellite makers increase the chips’ resiliency with the help of shielding, advanced software for error detection, and architectures that combine the consumer-grade devices with bespoke, hardened technologies.

Still, Mai says that the data-crunching chips are only one issue. The data centers would also need memory and storage devices, both of which are vulnerable to damage by excessive radiation. And operators would need the ability to swap things out or adapt when issues arise. The feasibility and affordability of using robots or astronaut missions for maintenance is a major question mark hanging over the idea of large-scale orbiting data centers.

“You not only need to throw up a data center to space that meets your current needs; you need redundancy, extra parts, and reconfigurability, so when stuff breaks, you can just change your configuration and continue working,” says Mai. “It’s a very challenging problem because on one hand you have free energy and power in space, but there are a lot of disadvantages. It’s quite possible that those problems will outweigh the advantages that you get from putting a data center into space.”

In addition to the need for regular maintenance, there’s also the potential for catastrophic loss. During periods of intense space weather, satellites can be flooded with enough radiation to kill all their electronics. The sun has just passed the most active phase of its 11-year cycle with relatively little impact on satellites. Still, experts warn that since the space age began, the planet has not experienced the worst the sun is capable of. Many doubt whether the low-cost new space systems that dominate Earth’s orbits today are prepared for that.

A plan to dodge space debris

Both large-scale orbiting data centers such as those envisioned by Thales Alenia Space and the mega-constellations of smaller satellites as proposed by SpaceX give a headache to space sustainability experts. The space around Earth is already quite crowded with satellites. Starlink satellites alone perform hundreds of thousands of collision avoidance maneuvers every year to dodge debris and other spacecraft. The more stuff in space, the higher the likelihood of a devastating collision that would clutter the orbit with thousands of dangerous fragments.

Large structures with hundreds of square meters of solar arrays would quickly suffer damage from small pieces of space debris and meteorites, which would over time degrade the performance of their solar panels and create more debris in orbit. Operating one million satellites in low Earth orbit, the region of space at the altitude of up to 2,000 kilometers, might be impossible to do safely unless all satellites in that area are part of the same network so they can communicate effectively to maneuver around each other, Greg Vialle, the founder of the orbital recycling startup Lunexus Space, told MIT Technology Review.

“You can fit roughly four to five thousand satellites in one orbital shell,” Vialle says. “If you count all the shells in low Earth orbit, you get to a number of around 240,000 satellites maximum.”

And spacecraft must be able to pass each other at a safe distance to avoid collisions, he says. 

“You also need to be able to get stuff up to higher orbits and back down to de-orbit,” he adds. “So you need to have gaps of at least 10 kilometers between the satellites to do that safely. Mega-constellations like Starlink can be packed more tightly because the satellites communicate with each other. But you can’t have one million satellites around Earth unless it’s a monopoly.”

On top of that, Starlink would likely want to regularly upgrade its orbiting data centers with more modern technology. Replacing a million satellites perhaps every five years would mean even more orbital traffic—and it could increase the rate of debris reentry into Earth’s atmosphere from around three or four pieces of junk a day to about one every three minutes, according to a group of astronomers who filed objections against SpaceX’s FCC application. Some scientists are concerned that reentering debris could damage the ozone layer and alter Earth’s thermal balance. 

Economical launch and assembly

The longer hardware survives in orbit, the better the return on investment. But for orbital data centers to make economic sense, companies will have to find a relatively cheap way to get that hardware in orbit. SpaceX is betting on its upcoming Starship mega-rocket, which will be able to carry up to six times as much payload as the current workhorse, Falcon 9. The Thales Alenia Space study concluded that if Europe were to build its own orbital data centers, it would have to develop a similarly potent launcher. 

But launch is only part of the equation. A large-scale orbital data center won’t fit in a rocket—even a mega-rocket. It will need to be assembled in orbit. And that will likely require advanced robotic systems that do not exist yet. Various companies have conducted Earth-based tests with precursors of such systems, but they are still far from real-world use.

Durand says that in the short term, smaller-scale data centers are likely to establish themselves as an integral part of the orbital infrastructure, by processing images from Earth-observing satellites directly in space without having to send them to Earth. That would be a huge help for companies selling insights from space, as many of these data sets are extremely large, and competition for opportunities to downlink them to Earth for processing via ground stations is growing.

“The good thing with orbital data centers is that you can start with small servers and gradually increase and build up larger data centers,” says Durand. “You can use modularity. You can learn little by little and gradually develop industrial capacity in space. We have all the technology, and the demand for space-based data processing infrastructure is huge, so it makes sense to think about it.”

Smaller facilities probably won’t do much to offset the strain that terrestrial data centers are placing on the planet’s water and electricity, though. That vision of the future might take decades to come to fruition, some critics think—if it even gets off the ground at all. 

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.

Fuel prices are soaring. Plastic could be next. 

As the war in Iran continues, one of the most visible global economic ripple effects has been fossil-fuel prices. But looking ahead, further consequences could be looming for plastics. 

Plastics are made from petrochemicals, and the supply chain impacts from the conflict are starting to build up. Americans will likely feel the ripples.  

Read the full story to grasp the unpredictable impacts. 

—Casey Crownhart 

This story is from The Spark, our weekly climate newsletter. Sign up to get 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 SpaceX has filed for an IPO 
It’s set to be the largest ever, targeting a $1.75 trillion valuation. (NYT $)  
+ Which would make Elon Musk the world’s first trillionaire. (Al Jazeera) 
+ But the IPO could hinge on the success of Moon missions. (LA Times $) 
+ And the conflicts of interest are staggering. (The Next Web) 
+ Meanwhile, rivals are rising to challenge SpaceX. (MIT Technology Review)  

2 Artemis II is on its way to the Moon 
NASA successfully launched the four astronauts on its rocket yesterday. (Axios) 
+ The lunar plans could violate international law. (The Verge) 
+ But the potential scientific advances are tremendous. (Nature)  
+ Check out our roundtable on the next era of space exploration. (MIT Technology Review)  

3 Iran has struck Amazon’s cloud business in Bahrain again 
It promised to hit US companies only yesterday. (FT $) 
+ Other targets include Google, Microsoft, Apple, and Nvidia. (CNBC) 
+ AWS data centers in Bahrain were also hit last month. (Reuters $) 

4 OpenAI was secretly behind a child safety campaign group 
It pushed for age verification requirements for AI. (The San Francisco Standard $) 
+ OpenAI had backed the legislation as a compromise measure. (WSJ $) 
+ Coincidentally, Sam Altman heads a company providing age verification. (Engadget) 

5 Anthropic is scrambling to limit the Claude Code leak 
It’s trying to remove 8,000 copies of the exposed code from GitHub. (Gizmodo) 
+ An executive blamed the leak on “process errors.” (Bloomberg $) 
+ Here’s what it reveals about Anthropic’s plans. (Ars Technica) 
+ AI is making online crimes easier—and it could get much worse. (MIT Technology Review) 

6 A new Russian “super-app” aims to emulate China’s WeChat 
And give the Kremlin new surveillance powers. (WSJ $) 

7 America’s AI boom is leaving the rest of the world behind  
And it’s concentrating power and wealth in a handful of companies. (Rest of World) 

8 Chinese chipmakers have claimed nearly half the country’s market 
Nvidia’s lead is shrinking rapidly. (Reuters $) 

9 The first quantum computer to break encryption is imminent  
New research reveals how it could happen. (New Scientist) 

10 The world’s oldest tortoise has been embroiled in a crypto scam 
Reports that Jonathan died at just 194 years old are thankfully false. (Guardian) 

Quote of the day 

“Starlink is the only reason this valuation is defensible.” 

—Shay Boloor, chief market strategist at Futurum Equities, tells Reuters why SpaceX has such high hopes for its IPO. 

One More Thing 

These companies are creating food out of thin air 

Dried cells—it’s what’s for dinner. At least that’s what a new crop of biotech startups, armed with carbon-guzzling bacteria and plenty of capital, are hoping to convince us.  

Their claims sound too good to be true: they say they can make food out of thin air. But that’s exactly how certain soil-dwelling bacteria work. 

Startups are replicating the process to turn abundant carbon dioxide into nutritious “air protein.” They believe it could dramatically lower farming emissions—and even disrupt agriculture altogether. Read the full story. 

—Claire L. Evans 

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.) 

+ Need more Artemis II in your life? This site takes you inside the flight. 
+ Here’s a fascinating look at the recording errors that improved songs. 
+ Good news: the elusive Nightjar bird is making a comeback. 
+ Finally, a master chef has baked clam chowder donuts. 

As the war in Iran continues to engulf the Middle East and the Strait of Hormuz stays closed, one of the most visible global economic ripple effects has been fossil-fuel prices. In particular, you can’t get away from news about the price of gasoline, which just topped an average of $4 a gallon in the US, its highest level since 2022.

But looking ahead, further consequences for the global economy could be looming in plastics. Plastics are made using petrochemicals, and the supply chain impacts of the oil bottleneck near Iran are starting to build up. 

Plastic production accounts for roughly 5% of global carbon dioxide emissions today. And our current moment shows just how embedded oil and gas products are in our lives. It goes far beyond their use for energy. 

As I write this, I’m wearing clothes that contain plastic fibers, typing on a plastic keyboard, and looking through the plastic lenses of my glasses. It’s hard to imagine what our world looks like without plastic. And in some ways, moving away from fossil-derived plastic could prove even more complicated than decarbonizing our energy system. 

Crude oil prices have been on a roller-coaster in recent weeks, and prices have recently topped $100 a barrel.

Crude oil contains a huge range of hydrocarbons, and it’s typically refined by putting it through a distillation unit that separates the raw material into different fractions according to their boiling point. Those fractions then go on to be further processed into everything from jet fuel to asphalt binder. We’ve already seen the price spikes for some materials pulled out of crude oil, like gasoline and jet fuel.

Let’s zoom in on another component, naphtha. It can be added to gasoline and jet fuel to improve performance. It can also be used as a solvent or as a raw material to make plastics.

The Middle East currently accounts for about 20% of global naphtha production­ and supplies about 40% of the market in Asia, where prices are already up by 50% over the last month.

We’re starting to see these effects trickle down already. The price of polypropylene (which is made from naphtha and used for food containers, bottle caps, and even automotive parts) is climbing, especially in Asia.  

Typically, manufacturers have a bit of stock built up, but that’ll be exhausted soon, likely in the coming weeks. The largest supplier of water bottles in India recently announced that it would raise prices by 11% after its packaging costs went up by over 70%, according to reporting from Reuters. Toys could be more expensive this holiday season as manufacturers grapple with supply chain concerns.

Americans will likely feel these ripples especially hard if disruptions continue. The average US resident used over 250 kilograms of new plastics in 2019, according to a 2022 report from the Organization for Economic Cooperation and Development. That’s an absolutely massive number—the global average is just 60 kilograms.

The effects of higher prices for both fuels and feedstocks could compound and multiply, and alternatives aren’t widely available. Bio-based plastics made with materials like plant sugars exist, but they still make up a vanishingly tiny portion of the market. As of 2025, global plastics production totaled over 431 million metric tons per year. Bio-based and bio-degradable plastics made up about 0.5% of that, a share that could reach 1% by 2030.

Bio-based plastics are much more expensive than their fossil-derived counterparts. And many are made using agricultural raw materials, so scaling them up too much could be harmful for the environment and might compete with other industries like food production.

Recycling isn’t the easy answer either. Mechanical recycling is the current standard method used for materials like the plastics that make up water bottles and disposable coffee cups. But that degrades the materials over time, so they can’t be used infinitely. Chemical recycling has its own host of issues—the facilities that do it can be highly polluting, and today plastics that go into advanced recycling plants largely don’t actually go into new plastics.

There’s been a lot of talk in recent weeks about how this energy crisis is going to push the world more toward renewable energy. Solar panels, electric vehicles, and batteries could suddenly become more attractive as we face the drastic consequences of a disruption in the global fossil-fuel supply.

But when it comes to plastic, the future looks far more complicated. Even though the plastics industry is facing much the same disruptions as the energy sector, there aren’t the same obvious alternatives available for a transition. Our lives are tied up in plastic, with uses ranging from the essential (like medical equipment) to the mundane (my to-go coffee cup). Soon, our economy could feel the effects of just how much we rely on fossil-derived plastics, and how hard it’s going to be to replace them. 

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.

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 

Our readers recently voted humanoid robots the “11th breakthrough” to add to our 2026 list of 10 Breakthrough Technologies. Check out what else officially made the cut. 

AI benchmarks are broken. Here’s what we need instead. 

For decades, AI has been evaluated based on whether it can outperform humans on isolated problems. But it’s seldom used this way in the real world. 

While AI is assessed in a vacuum, it operates in messy, complex, multi-person environments over time. This misalignment leads us to misunderstand its capabilities, risks, and impacts. 

We need new benchmarks that assess AI’s performance over longer horizons within human teams, workflows, and organizations. Here’s a proposal for one such approach: Human–AI, Context-Specific Evaluation.  

—Angela Aristidou, professor at University College London and faculty fellow at the Stanford Digital Economy Lab and the Stanford Human-Centered AI Institute. 

MIT Technology Review Narrated: can quantum computers now solve health care problems? We’ll soon find out. 

In a laboratory on the outskirts of Oxford, a quantum computer built from atoms and light awaits its moment. The device is small but powerful—and also very valuable. Infleqtion, the company that owns it, is hoping its abilities will win $5 million at a competition.  

The prize will go to the quantum computer that can solve real health care problems that “classical” computers cannot. But there can be only one big winner—if there is a winner at all. 

—Michael Brooks 

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

The must-reads 

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

1 OpenAI just closed the biggest funding round in Silicon Valley history 
It raised $122 billion ahead of its blockbuster IPO, which is expected later this year. (WSJ $) 
+ It’s also prepping a push to “rethink the social contract.” (Vanity Fair $) 
+ Campaigners are urging people to quit ChatGPT. (MIT Technology Review)   

2 Iran has threatened to attack 18 US tech companies  
It’s eyeing their operations in the Middle East. (Politico) 
+ Targets include Nvidia, Apple, Microsoft, and Google. (Engadget) 
+ Iran struck AWS data centers earlier this month. (Reuters $) 

3 Artemis II is about to fly humans to the Moon. Here’s the science they’ll do 
Their experiments will set the stage for future explorers. (Nature) 
+ You can watch the launch attempt today. (Engadget)  

4 Putin is trying to take full control of Russia’s internet 
New outages and blockages are cutting the country off from the world. (NYT $) 
+ Can we repair the internet? (MIT Technology Review) 

5 A robotaxi outage in China left passengers stranded on highways  
Baidu vehicles froze on the streets of Wuhan. (Bloomberg $) 
+ Police are blaming a “system failure.” (Reuters $) 

6 US government requests for social media user data are soaring 
They’ve skyrocketed by 770% in the past decade. (Bloomberg $) 
+ Is the Pentagon allowed to surveil Americans with AI? (MIT Technology Review) 

7 Tesla has admitted that humans sometimes drive its robotaxis 
Remote drivers occasionally control them completely. (Wired $) 

8 A satellite-smashing chain reaction could spiral out of control 
This data visualization captures the dangers of space collisions. (Guardian) 
+ Here’s all the stuff we’ve put into space. (MIT Technology Review) 

9 Meta’s smartglasses can turn you into a creep 
According to one journalist who wore them for a month. (Guardian) 

10 A Claude Code leak has exposed plans for a virtual pet  
We could be getting a Tamagotchi for the GenAI era. (The Verge) 

Quote of the day 

“From now on, for every assassination, an American company will be destroyed.” 

—Iran’s Islamic Revolutionary Guard Corps (IRGC) threatens US tech firms in an affiliated Telegram, per CNBC. 

One More Thing 

ACKERMAN + GRUBER

How one mine could unlock billions in EV subsidies 

In a pine farm north of the tiny town of Tamarack, Minnesota, Talon Metals has uncovered one of America’s densest nickel deposits. Now it wants to begin mining the ore. 

Products made from the nickel could net more than $26 billion in subsidies through the Inflation Reduction Act (IRA), which is starting to transform the US economy. To understand how, we tallied up the potential tax credits available. Read the full story to find out what we discovered. 

—James Temple 

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 selfless group of gluttons tried to taste-test every potato chip in the world.  
+ Get romantic inspiration from these penguins’ engagement pebbles. 
+ Good news: global terrorism has hit a 15-year low. 
+ Enjoy endless new views through these windows around the world.  

When Zeus, a medical student living in a hilltop city in central Nigeria, returns to his studio apartment from a long day at the hospital, he turns on his ring light, straps his iPhone to his forehead, and starts recording himself. He raises his hands in front of him like a sleepwalker and puts a sheet on his bed. He moves slowly and carefully to make sure his hands stay within the camera frame. 

Zeus is a data recorder for Micro1, a US company based in Palo Alto, California that collects real-world data to sell to robotics companies. As companies like Tesla, Figure AI, and Agility Robotics race to build humanoids—robots designed to resemble and move like humans in factories and homes—videos recorded by gig workers like Zeus are becoming the hottest new way to train them. 

Micro1 has hired thousands of contract workers in more than 50 countries, including India, Nigeria, and Argentina, where swathes of tech-savvy young people are looking for jobs. They’re mounting iPhones on their heads and recording themselves folding laundry, washing dishes, and cooking. The job pays well by local standards and is boosting local economies, but it raises thorny questions around privacy and informed consent. And the work can be challenging at times—and weird.

Zeus found the job in November, when people started talking about it everywhere on LinkedIn and YouTube. “This would be a real nice opportunity to set a mark and give data that will be used to train robots in the future,” he thought. 

Zeus is paid $15 an hour, which is good income in Nigeria’s strained economy with high unemployment rates. But as a bright-eyed student dreaming of becoming a doctor, he finds ironing his clothes for hours every day boring. 

“I really [do] not like it so much,” he says. “I’m the kind of person that requires … a technical job that requires me to think.” 

Zeus, and all the workers interviewed by MIT Technology Review, asked to be referred to only by pseudonyms because they were not authorized to talk about their work.

Humanoid robots are notoriously hard to build because manipulating physical objects is a difficult skill to master. But the rise of large language models underlying chatbots like ChatGPT has inspired a paradigm shift in robotics. Just as large language models learned to generate words by being trained on vast troves of text scraped from the internet, many researchers believe that humanoid robots can learn to interact with the world by being trained on massive amounts of movement data. 

Editor’s note: In a recent poll, MIT Technology Review readers selected humanoid robots as the 11th breakthrough for our 2026 list of 10 Breakthrough Technologies.

Robotics requires far more complex data about the physical world, though, and that is much harder to find. Virtual simulations can train robots to perform acrobatics, but not how to grasp and move objects, because simulations struggle to model physics with perfect accuracy. For robots to work in factories and serve as housekeepers, real-world data, however time-consuming and expensive to collect, may be what we need. 

Investors are pouring money feverishly into solving this challenge, spending over $6 billion on humanoid robots in 2025. And at-home data recording is becoming a booming gig economy around the world. Data companies like Scale AI and Encord are recruiting their own armies of data recorders, while DoorDash pays delivery drivers to film themselves doing chores. And in China, workers in dozens of state-owned robot training centers wear virtual-reality headsets and exoskeletons to teach humanoid robots how to open a microwave and wipe down the table. 

“There is a lot of demand, and it’s increasing really fast,” says Ali Ansari, CEO of Micro1. He estimates that robotics companies are now spending more than $100 million each year to buy real-world data from his company and others like it.

A day in the life

Workers at Micro1 are vetted by an AI agent named Zara that conducts interviews and reviews samples of chore videos. Every week, they submit videos of themselves doing chores around their homes, following a list of instructions about things like keeping their hands visible and moving at natural speed. The videos are reviewed by both AI and a human and are either accepted or rejected. They’re then annotated by AI and a team of hundreds of humans who label the actions in the footage.

Because this approach to training robots is in its infancy, it’s not clear yet what makes good training data. Still, “you need to give lots and lots of variations for the robot to generalize well for basic navigation and manipulation of the world,” says Ansari.

But many workers say that creating a variety of “chore content” in their tiny homes is a challenge. Zeus, a scrappy student living in a humble studio, struggles to record anything beyond ironing his clothes every day. Arjun, a tutor in Delhi, India, takes an hour to make a 15-minute video because he spends so much time brainstorming new chores.

“How much content [can be made] in the home? How much content?” he says. 

There’s also the sticky question of privacy. Micro1 asks workers not to show their faces to the camera or reveal personal information such as names, phone numbers, and birth dates. Then it uses AI and human reviewers to remove anything that slips through. 

But even without faces, the videos capture an intimate slice of workers’ lives: the interiors of their homes, their possessions, their routines. And understanding what kind of personal information they might be recording while they’re busy doing chores on camera can be tricky. Reviews of such footage might not filter out sensitive information beyond the most obvious identifiers.

For workers with families, keeping private life off camera is a constant negotiation. Arjun, a father of two daughters, has to wrangle his chaotic two-year-old out of frame. “Sometimes it’s very difficult to work because my daughter is small,” he says. 

Sasha, a banker turned data recorder in Nigeria, tiptoes around when she hangs her laundry outside in a shared residential compound so she won’t record her neighbors, who watch her in bewilderment.

While the workers interviewed by MIT Technology Review understand that their data is being used to train robots, none of them know how exactly their data will be used, stored, and shared with third parties, including the robotics companies that Micro1 is selling the data to. For confidentiality reasons, says Ansari, Micro1 doesn’t name its clients or disclose to workers the specific nature of the projects they are contributing to.

“It is important that if workers are engaging in this, that they are informed by the companies themselves of the intention … where this kind of technology might go and how that might affect them longer term,” says Yasmine Kotturi, a professor of human-centered computing at the University of Maryland.

Occasionally, some workers say, they’ve seen other workers asking on the company Slack channel if the company could delete their data. Micro1 declined to comment on whether such data is deleted.

“People are opting into doing this,” says Ansari. “They could stop the work at any time.”

Hungry for data

With thousands of workers doing their chores differently in different homes, some roboticists wonder if the data collected from them is reliable enough to train robots safely. 

“How we conduct our lives in our homes is not always right from a safety point of view,” says Aaron Prather, a roboticist at ASTM International. “If those folks are teaching those bad habits that could lead to an incident, then that’s not good data.” And the sheer volume of data being collected makes reviewing it for quality control challenging. But Ansari says the company rejects videos showing unsafe ways of performing a task, while clumsy movements can be useful to teach robots what not to do.

Then there’s the question of how much of this data we need. Micro1 says it has tens of thousands of hours of footage, while Scale AI announced it had gathered more than 100,000 hours.

“It’s going to take a long time to get there,” says Ken Goldberg, a roboticist at the University of California, Berkeley. Large language models were trained on text and images that would take a human 100,000 years to read, and humanoid robots may need even more data, because controlling robotic joints is even more complicated than generating text. “It’s going to take longer than people think,” he says.

When Dattu, an engineering student living in a bustling tech hub in India, comes home after a full day of classes at his university, he skips dinner and dashes to his tiny balcony, cramped with potted plants and dumbbells. He straps his iPhone to his forehead and records himself folding the same set of clothes over and over again. 

His family stares at him quizzically. “It’s like some space technology for them,” he says. When he tells his friends about his job, “they just get astounded by the idea that they can get paid by recording chores.”

Juggling his university studies with data recording, as well as other data annotation gigs, takes a toll on him. Still, “it feels like you’re doing something different than the whole world,” he says. 

In the early days of large language models (LLMs), we grew accustomed to massive 10x jumps in reasoning and coding capability with every new model iteration. Today, those jumps have flattened into incremental gains. The exception is domain-specialized intelligence, where true step-function improvements are still the norm.

When a model is fused with an organization’s proprietary data and internal logic, it encodes the company’s history into its future workflows. This alignment creates a compounding advantage: a competitive moat built on a model that understands the business intimately. This is more than fine-tuning; it is the institutionalization of expertise into an AI system. This is the power of customization.

Intelligence tuned to context

Every sector operates within its own specific lexicon. In automotive engineering, the “language” of the firm revolves around tolerance stacks, validation cycles, and revision control. In capital markets, reasoning is dictated by risk-weighted assets and liquidity buffers. In security operations, patterns are extracted from the noise of telemetry signals and identity anomalies.

Custom-adapted models internalize the nuances of the field. They recognize which variables dictate a “go/no-go” decision, and they think in the language of the industry.

Domain expertise in action

The transition from general-purpose to tailored AI centers on one goal: encoding an organization’s unique logic directly into a model’s weights.

Mistral AI partners with organizations to incorporate domain expertise into their training ecosystems. A few use cases illustrate customized implementations in practice:

Software engineering and assisting at scale: A network hardware company with proprietary languages and specialized codebases found that out-of-the-box models could not grasp their internal stack. By training a custom model on their own development patterns, they achieved a step function in fluency. Integrated into Mistral’s software development scaffolding, this customized model now supports the entire lifecycle—from maintaining legacy systems to autonomous code modernization via reinforcement learning. This turns once-opaque, niche code into a space where AI reliably assists at scale.

Automotive and the engineering copilot: A leading automotive company uses customization to revolutionize crash test simulations. Previously, specialists spent entire days manually comparing digital simulations with physical results to find divergences. By training a model on proprietary simulation data and internal analyses, they automated this visual inspection, flagging deformations in real time. Moving beyond detection, the model now acts as a copilot, proposing design adjustments to bring simulations closer to real-world behavior and radically accelerating the R&D loop.

Public sector and sovereign AI: In Southeast Asia, a government agency is building a sovereign AI layer to move beyond Western-centric models. By commissioning a foundation model tailored to regional languages, local idioms, and cultural contexts, they created a strategic infrastructure asset. This ensures sensitive data remains under local governance while powering inclusive citizen services and regulatory assistants. Here, customization is the key to deploying AI that is both technically effective and genuinely sovereign.

The blueprint for strategic customization

Moving from a general-purpose AI strategy to a domain-specific advantage requires a structural rethinking of the model’s role within the enterprise. Success is defined by three shifts in organizational logic.

1. Treat AI as infrastructure, not an experiment.  Historically, enterprises have treated model customization as an ad hoc experiment—a single fine-tuning run for a niche use case or a localized pilot. While these bespoke silos often yield promising results, they are rarely built to scale. They produce brittle pipelines, improvised governance, and limited portability. When the underlying base models evolve, the adaptation work must often be discarded and rebuilt from scratch.

In contrast, a durable strategy treats customization as foundational infrastructure. In this model, adaptation workflows are reproducible, version-controlled, and engineered for production. Success is measured against deterministic business outcomes. By decoupling the customization logic from the underlying model, firms ensure that their “digital nervous system” remains resilient, even as the frontier of base models shifts.

2. Retain control of your own data and models. As AI migrates from the periphery to core operations, the question of control becomes existential. Reliance on a single cloud provider or vendor for model alignment creates a dangerous asymmetry of power regarding data residency, pricing, and architectural updates.

Enterprises that retain control of their training pipelines and deployment environments preserve their strategic agency. By adapting models within controlled environments, organizations can enforce their own data residency requirements and dictate their own update cycles. This approach transforms AI from a service consumed into an asset governed, reducing structural dependency and allowing for cost and energy optimizations aligned with internal priorities rather than vendor roadmaps.

3. Design for continuous adaptation. The enterprise environment is never static: regulations shift, taxonomies evolve, and market conditions fluctuate. A common failure is treating a customized model as a finished artifact. In reality, a domain-aligned model is a living asset subject to model decay if left unmanaged.

Designing for continuous adaptation requires a disciplined approach to ModelOps. This includes automated drift detection, event-driven retraining, and incremental updates. By building the capacity for constant recalibration, the organization ensures that its AI does not just reflect its history, but it evolves in lockstep with its future. This is the stage where the competitive moat begins to compound: the model’s utility grows as it internalizes the organization’s ongoing response to change.

Control is the new leverage

We have entered an era where generic intelligence is a commodity, but contextual intelligence is a scarcity. While raw model power is now a baseline requirement, the true differentiator is alignment—AI calibrated to an organization’s unique data, mandates, and decision logic.

In the next decade, the most valuable AI won’t be the one that knows everything about the world; it will be the one that knows everything about you. The firms that own the model weights of that intelligence will own the market.

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

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.

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

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

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

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

—Grace Huckins 

The Pentagon’s culture war tactic against Anthropic has backfired 

A judge has temporarily blocked the Pentagon from labeling Anthropic a supply chain risk and ordering government agencies to stop using its AI. Her intervention suggests that the feud never needed to reach such a frenzy. 

It did so because the government disregarded the existing process for such disputes—and fueled the fire on social media. Find out how it happened and what comes next. 

—James O’Donnell 

This story is from The Algorithm, our weekly newsletter giving you the inside track on all things AI. 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 California has defied Trump to impose new AI regulations 
Governor Newsom signed off on the new standards yesterday.  (Guardian) 
+ Firms seeking state contracts will need extra safeguards. (Reuters $) 
+ States are installing guardrails despite Trump’s order to stop. (NYT $)  
+ An AI regulation war is brewing in the US. (MIT Technology Review)  

2 Experiments have verified quantum simulations for the first time 
It’s a breakthrough for quantum computing applications. (Nature) 
+ Which could one day help solve healthcare problems. (MIT Technology Review) 

3 The new White House app is a security and privacy nightmare 
It extensively tracks users and relies on external code. (Gizmodo) 
+ The new app promises “unparalleled access” to Trump. (CNET) 
+ It also invites users to report people to ICE. (The Verge) 

4 Big Tech’s $635 billion AI spending faces an energy shock test 
The Middle East crisis is clouding prospects for growth. (Reuters $) 
+ Here are three big unknowns about AI’s energy burden. (MIT Technology Review) 

5 Meta and Google have been accused of breaking child safety rules 
Australia suspects they flouted a social media ban. (Bloomberg $) 
+ Indonesia is also investigating non-compliance. (Reuters $) 

6 Nebius is building a $10 billion AI data center in Finland 
The company is rapidly expanding Europe’s AI infrastructure. (CNBC) 

7 South Korea’s chipmakers’ helium stocks will last until June 
Beyond that? Who knows. (Reuters $) 
+ Shortages caused by the Iran war threaten the chip industry. (NYT $)  

8 Another Starlink satellite has inexplicably exploded  
SpaceX suffered a similar episode in December. (The Verge) 
+ We went inside Ukraine’s largest Starlink repair shop. (MIT Technology Review) 

9 Bluesky’s new AI tool is already its most blocked account—after JD Vance 
About 83 times as many users have blocked it as have followed it. (TechCrunch) 

10 An AI agent banned from Wikipedia has lashed out in angry blogs 
The bot accused its human editors of “uncivil behavior.” (404 Media)  

Quote of the day 

“Is any of this illegal? Probably not. Is it what you’d expect from an official government app? Probably not either.” 

—Security researcher Thereallo reviews the White House’s new app.

One More Thing 

CHANTAL JAHCHAN

Inside Amsterdam’s high-stakes experiment to create fair welfare AI 

When Hans de Zwart, a digital rights advocate, saw Amsterdam’s plan to have an algorithm evaluate every welfare applicant for potential fraud, he nearly fell out of his chair. He believed the system had “unfixable problems.”  

Meanwhile, Paul de Koning, a consultant to the city, was excited. He saw immense potential to improve efficiencies and remove biases. 

These opposing viewpoints epitomize a global debate about whether algorithms can ever make fair decisions that shape people’s lives. Read the full story.  

—Eileen Guo, Gabriel Geiger, and Justin-Casimir Braun 

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 newly authenticated Rembrandt had been hiding in plain sight for years. 
+ This debunking of guitar legends is musical enlightenment for strummers. 
+ Smoking into bubbles looks oddly satisfying. 
+ The man who made the front page twice exposes the thin line between heroes and villains. 

For decades, artificial intelligence has been evaluated through the question of whether machines outperform humans. From chess to advanced math, from coding to essay writing, the performance of AI models and applications is tested against that of individual humans completing tasks. 

This framing is seductive: An AI vs. human comparison on isolated problems with clear right or wrong answers is easy to standardize, compare, and optimize. It generates rankings and headlines. 

But there’s a problem: AI is almost never used in the way it is benchmarked. Although   researchers and industry have started to improve benchmarking by moving beyond static tests to more dynamic evaluation methods, these  innovations resolve only part of the issue. That’s because they still evaluate AI’s performance outside the human teams and organizational workflows where its real-world performance ultimately unfolds. 

While AI is evaluated at the task level in a vacuum, it is used in messy, complex environments where it usually interacts with more than one person. Its performance (or lack thereof) emerges only over extended periods of use. This misalignment leaves us misunderstanding AI’s capabilities, overlooking systemic risks, and misjudging its economic and social consequences.

To mitigate this, it’s time to shift from narrow methods to benchmarks that assess how AI systems perform over longer time horizons within human teams, workflows, and organizations. I have studied real-world AI deployment since 2022 in small businesses and health, humanitarian, nonprofit, and higher-education organizations in the UK, the United States, and Asia, as well as within leading AI design ecosystems in London and Silicon Valley. I propose a different approach, which I call HAIC benchmarks—Human–AI, Context-Specific Evaluation.

What happens when AI fails 

For governments and businesses, AI benchmark scores appear more objective than vendor claims. They’re a critical part of determining whether an AI model or application is “good enough” for real-world deployment. Imagine an AI model that achieves impressive technical scores on the most cutting-edge benchmarks—98% accuracy, groundbreaking speed, compelling outputs. On the strength of these results, organizations may decide to adopt the model, committing sizable financial and technical resources to purchasing and integrating it. 

But then, once it’s adopted, the gap between benchmark and real-world performance quickly becomes visible. For example, take the swathe of FDA-approved AI models that can read medical scans faster and more accurately than an expert radiologist. In the radiology units of hospitals from the heart of California to the outskirts of London, I witnessed staff using highly ranked radiology AI applications. Repeatedly, it took them extra time to interpret AI’s outputs alongside hospital-specific reporting standards and nation-specific regulatory requirements. What appeared as a productivity-enhancing AI tool when tested in a vacuum introduced delays in practice. 

It soon became clear that the benchmark tests on which medical AI models are assessed do not capture how medical decisions are actually made. Hospitals rely on multidisciplinary teams—radiologists, oncologists, physicists, nurses—who jointly review patients. Treatment planning rarely hinges on a static decision; it evolves as new information emerges over days or weeks. Decisions often arise through constructive debate and trade-offs between professional standards, patient preferences, and the shared goal of long-term patient well-being. No wonder even highly scored AI models struggle to deliver the promised performance once they encounter the complex, collaborative processes of real clinical care.

The same pattern emerges in my research across other sectors: When embedded within real-world work environments, even AI models that perform brilliantly on standardized tests don’t perform as promised. 

When high benchmark scores fail to translate into real-world performance, even the most highly scored AI is soon abandoned to what I call the “AI graveyard.” The costs are significant: Time, effort and money end up being wasted. And over time, repeated experiences like this erode organizational confidence in AI and—in critical settings such as health—may erode broader public trust in the technology as well. 

When current benchmarks provide only a partial and potentially misleading signal of an AI model’s readiness for real-world use, this creates regulatory blind spots: Oversight is shaped by metrics that do not reflect reality. It also leaves organizations and governments to shoulder the risks of testing AI in sensitive real-world settings, often with limited resources and support. 

How to build better tests 

To close the gap between benchmark and real-world performance, we must pay attention to the actual conditions in which AI models will be used. The critical questions: Can AI function as a productive participant within human teams? And can it generate sustained, collective value? 

Through my research on AI deployment across multiple sectors, I have seen a number of organizations already moving—deliberately and experimentally—toward the HAIC benchmarks I favor. 

HAIC benchmarks reframe current benchmarking in four ways: 

1.     From individual and single-task performance to team and workflow performance (shifting the unit of analysis)

2.     From one-off testing with right/wrong answers to long-term impacts (expanding the time horizon)

3.     From correctness and speed to organizational outcomes, coordination quality, and error detectability (expanding outcome measures)

4.     From isolated outputs to upstream and downstream consequences (system effects)

Across the organizations where this approach has emerged and started to be applied, the first step is shifting the unit of analysis. 

For example, in one UK hospital system in the period 2021–2024, the question expanded from whether a medical AI application improves diagnostic accuracy to how the presence of AI within the hospital’s multidisciplinary teams affects not only accuracy but also coordination and deliberation. The hospital specifically assessed coordination and deliberation in human teams using and not using AI. Multiple stakeholders (within and outside the hospital) decided on metrics like how AI influences collective reasoning, whether it surfaces overlooked considerations, whether it strengthens or weakens coordination, and whether it changes established risk and compliance practices. 

This shift is fundamental. It matters a lot in high-stakes contexts where system-level effects matter more than task-level accuracy. It also matters for the economy. It may help recalibrate inflated expectations of sweeping productivity gains that are so far predicated largely on the promise of improving individual task performance. 

Once that foundation is set, HAIC benchmarking can begin to take on the element of time. 

Today’s benchmarks resemble school exams—one-off, standardized tests of accuracy. But real professional competence is assessed differently. Junior doctors and lawyers are evaluated continuously inside real workflows, under supervision, with feedback loops and accountability structures. Performance is judged over time and in a specific context, because competence is relational. If AI systems are meant to operate alongside professionals, their impact should be judged longitudinally, reflecting how performance unfolds over repeated interactions. 

I saw this aspect of HAIC applied in one of my humanitarian-sector case studies. Over 18 months, an AI system was evaluated within real workflows, with particular attention to how detectable its errors were—that is, how easily human teams could identify and correct them. This long-term “record of error detectability” meant the organizations involved could design and test context-specific guardrails to promote trust in the system, despite the inevitability of occasional AI mistakes.

A longer time horizon also makes visible the system-level consequences that short-term benchmarks miss. An AI application may outperform a single doctor on a narrow diagnostic task yet fail to improve multidisciplinary decision-making. Worse, it may introduce systemic distortions: anchoring teams too early in plausible but incomplete answers, adding to people’s  cognitive workloads, or generating downstream inefficiencies that offset any speed or efficiency gains at the point of the AI’s use. These knock-on effects—often invisible to current benchmarks—are central to understanding real impact. 

The HAIC approach, admittedly promises to make benchmarking more complex, resource-intensive, and harder to standardize. But continuing to evaluate AI in sanitized conditions detached from the world of work will leave us misunderstanding what it truly can and cannot do for us. To deploy AI responsibly in real-world settings, we must measure what actually matters: not just what a model can do alone, but what it enables—or undermines—when humans and teams in the real world work with it.

 Angela Aristidou is a professor at University College London and a faculty fellow at the Stanford Digital Economy Lab and the Stanford Human-Centered AI Institute. She speaks, writes, and advises about the real-life deployment of artificial-intelligence tools for public good.

Earlier this month, Microsoft launched Copilot Health, a new space within its Copilot app where users will be able to connect their medical records and ask specific questions about their health. A couple of days earlier, Amazon had announced that Health AI, an LLM-based tool previously restricted to members of its One Medical service, would now be widely available. These products join the ranks of ChatGPT Health, which OpenAI released back in January, and Anthropic’s Claude, which can access user health records if granted permission. Health AI for the masses is officially a trend. 

There’s a clear demand for chatbots that provide health advice, given how hard it is for many people to access it through existing medical systems. And some research suggests that current LLMs are capable of making safe and useful recommendations. But researchers say that these tools should be more rigorously evaluated by independent experts, ideally before they are widely released. 

In a high-stakes area like health, trusting companies to evaluate their own products could prove unwise, especially if those evaluations aren’t made available for external expert review. And even if the companies are doing quality, rigorous research—which some, including OpenAI, do seem to be—they might still have blind spots that the broader research community could help to fill.

“To the extent that you always are going to need more health care, I think we should definitely be chasing every route that works,” says Andrew Bean, a doctoral candidate at the Oxford Internet Institute. “It’s entirely plausible to me that these models have reached a point where they’re actually worth rolling out.”

“But,” he adds, “the evidence base really needs to be there.”

Tipping points 

To hear developers tell it, these health products are now being released because large language models have indeed reached a point where they can effectively provide medical advice. Dominic King, the vice president of health at Microsoft AI and a former surgeon, cites AI advancement as a core reason why the company’s health team was formed, and why Copilot Health now exists. “We’ve seen this enormous progress in the capabilities of generative AI to be able to answer health questions and give good responses,” he says.

But that’s only half the story, according to King. The other key factor is demand. Shortly before Copilot Health was launched, Microsoft published a report, and an accompanying blog post, detailing how people used Copilot for health advice. The company says it receives 50 million health questions each day, and health is the most popular discussion topic on the Copilot mobile app.

Other AI companies have noticed, and responded to, this trend. “Even before our health products, we were seeing just a rapid, rapid increase in the rate of people using ChatGPT for health-related questions,” says Karan Singhal, who leads OpenAI’s Health AI team. (OpenAI and Microsoft have a long-standing partnership, and Copilot is powered by OpenAI’s models.)

It’s possible that people simply prefer posing their health problems to a nonjudgmental bot that’s available to them 24-7. But many experts interpret this pattern in light of the current state of the health-care system. “There is a reason that these tools exist and they have a position in the overall landscape,” says Girish Nadkarni, chief AI officer​ at the Mount Sinai Health System. “That’s because access to health care is hard, and it’s particularly hard for certain populations.”

The virtuous vision of consumer-facing LLM health chatbots hinges on the possibility that they could improve user health while reducing pressure on the health-care system. That might involve helping users decide whether or not they need medical attention, a task known as triage. If chatbot triage works, then patients who need emergency care might seek it out earlier than they would have otherwise, and patients with more mild concerns might feel comfortable managing their symptoms at home with the chatbot’s advice rather than unnecessarily busying emergency rooms and doctor’s offices.

But a recent, widely discussed study from Nadkarni and other researchers at Mount Sinai found that ChatGPT Health sometimes recommends too much care for mild conditions and fails to identify emergencies. Though Singhal and  some other experts have suggested that its methodology might not provide a complete picture of ChatGPT Health’s capabilities, the study has surfaced concerns about how little external evaluation these tools see before being released to the public.

Most of the academic experts interviewed for this piece agreed that LLM health chatbots could have real upsides, given how little access to health care some people have. But all six of them expressed concerns that these tools are being launched without testing from independent researchers to assess whether they are safe. While some advertised uses of these tools, such as recommending exercise plans or suggesting questions that a user might ask a doctor, are relatively harmless, others carry clear risks. Triage is one; another is asking a chatbot to provide a diagnosis or a treatment plan. 

The ChatGPT Health interface includes a prominent disclaimer stating that it is not intended for diagnosis or treatment, and the announcements for Copilot Health and Amazon’s Health AI include similar warnings. But those warnings are easy to ignore. “We all know that people are going to use it for diagnosis and management,” says Adam Rodman, an internal medicine physician and researcher at Beth Israel Deaconess Medical Center and a visiting researcher at Google.

Medical testing

Companies say they are testing the chatbots to ensure that they provide safe responses the vast majority of the time. OpenAI has designed and released HealthBench, a benchmark that scores LLMs on how they respond in realistic health-related conversations—though the conversations themselves are LLM-generated. When GPT-5, which powers both ChatGPT Health and Copilot Health, was released last year, OpenAI reported the model’s HealthBench scores: It did substantially better than previous OpenAI models, though its overall performance was far from perfect. 

But evaluations like HealthBench have limitations. In a study published last month, Bean—the Oxford doctoral candidate—and his colleagues found that even if an LLM can accurately identify a medical condition from a fictional written scenario on its own, a non-expert user who is given the scenario and asked to determine the condition with LLM assistance might figure it out only a third of the time. If they lack medical expertise, users might not know which parts of a scenario—or their real-life experience—are important to include in their prompt, or they might misinterpret the information that an LLM gives them.

Singhal, the OpenAI health lead, notes that the company’s current GPT-5 series of models, which had not yet been released when the original HealthBench study was conducted, do a much better job of soliciting additional information than their predecessors. However, OpenAI has reported that GPT-5.4, the current flagship, is actually worse at seeking context than GPT-5.2, an earlier version.

Ideally, Bean says, health chatbots would be subjected to controlled tests with human users, as they were in his study, before being released to the public. That might be a heavy lift, particularly given how fast the AI world moves and how long human studies can take. Bean’s own study used GPT-4o, which came out almost a year ago and is now outdated. 

Earlier this month, Google released a study that meets Bean’s standards. In the study, patients discussed medical concerns with the company’s Articulate Medical Intelligence Explorer (AMIE), a medical LLM chatbot that is not yet available to the public, before meeting with a human physician. Overall, AMIE’s diagnoses were just as accurate as physicians’, and none of the conversations raised major safety concerns for researchers. 

Despite the encouraging results, Google isn’t planning to release AMIE anytime soon. “While the research has advanced, there are significant limitations that must be addressed before real-world translation of systems for diagnosis and treatment, including further research into equity, fairness, and safety testing,” wrote Alan Karthikesalingam, a research scientist at Google DeepMind, in an email. Google did recently reveal that Health100, a health platform it is building in partnership with CVS, will include an AI assistant powered by its flagship Gemini models, though that tool will presumably not be intended for diagnosis or treatment.

Rodman, who led the AMIE study with Karthikesalingam, doesn’t think such extensive, multiyear studies are necessarily the right approach for chatbots like ChatGPT Health and Copilot Health. “There’s lots of reasons that the clinical trial paradigm doesn’t always work in generative AI,” he says. “And that’s where this benchmarking conversation comes in. Are there benchmarks [from] a trusted third party that we can agree are meaningful, that the labs can hold themselves to?”

They key there is “third party.” No matter how extensively companies evaluate their own products, it’s tough to trust their conclusions completely. Not only does a third-party evaluation bring impartiality, but if there are many third parties involved, it also helps protect against blind spots.

OpenAI’s Singhal says he’s strongly in favor of external evaluation. “We try our best to support the community,” he says. “Part of why we put out HealthBench was actually to give the community and other model developers an example of what a very good evaluation looks like.” 

Given how expensive it is to produce a high-quality evaluation, he says, he’s skeptical that any individual academic laboratory would be able to produce what he calls “the one evaluation to rule them all.” But he does speak highly of efforts that academic groups have made to bring preexisting and novel evaluations together into comprehensive evaluations suites—such as Stanford’s MedHELM framework, which tests models on a wide variety of medical tasks. Currently, OpenAI’s GPT-5 holds the highest MedHELM score.

Nigam Shah, a professor of medicine at Stanford University who led the MedHELM project, says it has limitations. In particular, it only evaluates individual chatbot responses, but someone who’s seeking medical advice from a chatbot tool might engage it in a multi-turn, back-and-forth conversation. He says that he and some collaborators are gearing up to build an evaluation that can score those complex conversations, but that it will take time, and money. “You and I have zero ability to stop these companies from releasing [health-oriented products], so they’re going to do whatever they damn please,” he says. “The only thing people like us can do is find a way to fund the benchmark.”

No one interviewed for this article argued that health LLMs need to perform perfectly on third-party evaluations in order to be released. Doctors themselves make mistakes—and for someone who has only occasional access to a doctor, a consistently accessible LLM that sometimes messes up could still be a huge improvement over the status quo, as long as its errors aren’t too grave. 

With the current state of the evidence, however, it’s impossible to know for sure whether the currently available tools do in fact constitute an improvement, or whether their risks outweigh their benefits.

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 Thursday, a California judge temporarily blocked the Pentagon from labeling Anthropic a supply chain risk and ordering government agencies to stop using its AI. It’s the latest development in the month-long feud. And the matter still isn’t settled: The government was given seven days to appeal, and Anthropic has a second case against the designation that has yet to be decided. Until then, the company remains persona non grata with the government. 

The stakes in the case—how much the government can punish a company for not playing ball—were apparent from the start. Anthropic drew lots of senior supporters with unlikely bedfellows among them, including former authors of President Trump’s AI policy.

But Judge Rita Lin’s 43-page opinion suggests that what is really a contract dispute never needed to reach such a frenzy. It did so because the government disregarded the existing process for how such disputes are governed and fueled the fire with social media posts from officials that would eventually contradict the positions it took in court. The Pentagon, in other words, wanted a culture war (on top of the actual war in Iran that began hours later). 

The government used Anthropic’s Claude for much of 2025 without complaint, according to court documents, while the company walked a branding tightrope as a safety-focused AI company that also won defense contracts. Defense employees accessing it through Palantir were required to accept terms of a government-specific usage policy that Anthropic cofounder Jared Kaplan said “prohibited mass surveillance of Americans and lethal autonomous warfare” (Kaplan’s declaration to the court didn’t include details of the policy). Only when the government aimed to contract with Anthropic directly did the disagreements begin. 

What drew the ire of the judge is that when these disagreements became public, they had more to do with punishment than just cutting ties with Anthropic. And they had a pattern: Tweet first, lawyer later. 

President Trump’s post on Truth Social on February 27 referenced “Leftwing nutjobs” at Anthropic and directed every federal agency to stop using the company’s AI. This was echoed soon after by Defense Secretary Pete Hegseth, who said he’d direct the Pentagon to label Anthropic a supply chain risk. 

Doing so necessitates that the secretary take a specific set of actions, which the judge found Hegseth did not complete. Letters sent to congressional committees, for example, said that less drastic steps were evaluated and deemed not possible, without providing any further details. The government also said the designation as a supply chain risk was necessary because Anthropic could implement a “kill switch,” but its lawyers later had to admit it had no evidence of that, the judge wrote.

Hegseth’s post also stated that “No contractor, supplier, or partner that does business with the United States military may conduct any commercial activity with Anthropic.” But the government’s own lawyers admitted on Tuesday that the Secretary doesn’t have the power to do that, and agreed with the judge that the statement had “absolutely no legal effect at all.”

The aggressive posts also led the judge to also conclude that Anthropic was on solid ground in complaining that its First Amendment rights were violated. The government, the judge wrote while citing the posts, “set out to publicly punish Anthropic for its ‘ideology’ and ‘rhetoric,’ as well as its ‘arrogance’ for being unwilling to compromise those beliefs.”

Labeling Anthropic a supply chain risk would essentially be identifying it as a “saboteur” of the government, for which the judge did not see sufficient evidence. She issued an order last Thursday halting the designation, preventing the Pentagon from enforcing it and forbidding the government from fulfilling the promises made by Hegseth and Trump. Dean Ball, who worked on AI policy for the Trump administration but wrote a brief supporting Anthropic, described the judge’s order on Thursday as “a devastating ruling for the government, finding Anthropic likely to prevail on essentially all of its theories for why the government’s actions were unlawful and unconstitutional.”

The government is expected to appeal the decision. But Anthropic’s separate case, filed in DC, makes similar allegations. It just references a different segment of the law governing supply chain risks. 

The court documents paint a pretty clear pattern. Public statements made by officials and the President did not at all align with what the law says should happen in a contract dispute like this, and the government’s lawyers have consistently had to create justifications for social media lambasting of the company after the fact.

Pentagon and White House leadership knew that pursuing the nuclear option would spark a court battle; Anthropic vowed on February 27 to fight the supply chain risk designation days before the government formally filed it on March 3. Pursuing it anyway meant senior leadership was, to say the least, distracted during the first five days of the Iran war, launching strikes while also compiling evidence that Anthropic was a saboteur to the government, all while it could have cut ties with Anthropic by simpler means. 

But even if Anthropic ultimately wins, the government has other means to shun the company from government work. Defense contractors who want to stay on good terms with the Pentagon, for example, now have little reason to work with Anthropic even if it’s not flagged as a supply chain risk. 

“I think it’s safe to say that there are mechanisms the government can use to apply some degree of pressure without breaking the law,” says Charlie Bullock, a senior research fellow at the Institute for Law and AI. “It kind of depends how invested the government is in punishing Anthropic.”

From the evidence thus far, the administration is committing top-level time and attention to winning an AI culture war. At the same time, Claude is apparently so important to its operations that even President Trump said the Pentagon needed six months to stop using it. The White House demands political loyalty and ideological alignment from top AI companies, But the case against Anthropic, at least for now, exposes the limits of its leverage.

If you have information about the military’s use of AI, you can share it securely via Signal (username jamesodonnell.22).

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.

After operating in secrecy for years, R3 Bio, a California-based startup, suddenly revealed last week 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 

A woman’s uterus has been kept alive outside the body for the first time 

Ten months ago, reproductive health researchers placed a freshly donated human uterus inside a new device they call “Mother.” They connected the organ to the machine’s plastic veins and arteries and pumped in modified human blood. 

The device kept the uterus alive for a day, a new feat that could lead to longer-term maintenance of wombs outside the body. Future versions of the technology could shine new light on pregnancies—and potentially even grow a human fetus. Read the full story. 

—Jessica Hamzelou 

The must-reads 

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

1 AI data centers can significantly warm up surrounding areas  
The “heat islands” may already affect 340 million people. (New Scientist) 
+ Mistral has raised $830M to build Nvidia-powered AI centers in Europe. (FT $) 
+ But nobody wants a data center in their backyard. (MIT Technology Review) 

2 Elon Musk reportedly joined Trump’s call with Modi about the Iran War 
It remains unclear what Musk was doing during the conversation. (NYT $)  
+ India has disputed the report. (Independent) 
+ The war poses a grave threat to the EV market. (Rest of World) 

3 Eli Lilly has struck a deal to bring AI-developed drugs to the market 
It’s secured a $2.75 billion drug collaboration with Insilico Medicine. (Reuters $) 
+ A I-designed compounds can kill drug-resistant bacteria. (MIT Technology Review) 

4 More and more countries are curbing children’s social media access 
Austria is the latest to pursue a ban. (Engadget) 
+ Indonesia has rolled out the first one in Southeast Asia. (DW) 
+ UK Prime Minister Keir Starmer said he will also “have to act.” (Guardian)  

5 Tech stocks just had their worst week in nearly a year 
Thanks to a combination of the Iran war and legal disputes. (CNBC) 
+ Tech insiders are split over the AI bubble. (MIT Technology Review) 

6 Meta is launching new smart glasses for prescription wearers 
It plans to debut them next week. (Bloomberg $) 

7 Taiwan is probing 11 Chinese firms for illegal poaching of tech talent 
Its semiconductors are entangled in the tensions with Beijing. (Reuters) 

8 Bluesky has built an AI app for customizing social media feeds 
It uses Anthropic’s Claude. (TechCrunch) 

9 A psychologist is making music with his brain implant 
He believes enjoyment is a prerequisite for BCI success. (Wired $) 

10 The world’s smallest QR code could store data for centuries 
It’s smaller than bacteria. (Science Daily) 

Quote of the day 

“We should be thinking about protecting young people in the digital world as opposed to protecting them from the digital world.” 

—YouTube CEO Neal Mohan gives the New York Times his take on the debate around children’s safety online. 

One More Thing 

AJ PICS / ALAMY STOCK PHOTO

AI’s growth needs the right interface 

You’d have to be pudding-brained to believe that chatbots are the best way to use computers. The real opportunity is a system built atop the visual interfaces we already know, but navigated through a natural mix of voice and touch. 

Crucially, this won’t just be a computer that we can use. It’ll be one we can break and remake to suit whatever uses we want. Instead of merely consuming technology like the gelatinous humans in Wall-E, we should be able to architect it to suit our own ends 

This idea is already lurching to life. Read the full story to find out how. 

—Cliff Kuang 

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.) 
 
+ These floating designs will elevate your perspective on architecture. 
+ Uğur Gallenkuş’s portraits of two worlds in one image beautifully build bridges. 
+ This is the anti-Karen that the world needs right now. 
+ If only we could all find a love as pure as this kitty clinging to its favorite toy. 

After operating in secrecy for years, a startup company called R3 Bio, in Richmond, California, suddenly shared details about its work last week—saying it had raised money to create nonsentient monkey “organ sacks” as an alternative to animal testing.

In an interview with Wired, R3 listed three investors: billionaire Tim Draper, the Singapore-based fund Immortal Dragons, and life-extension investors LongGame Ventures.

But there is more to the story. And R3 doesn’t want that story told.

MIT Technology Review discovered that the stealth startup’s founder John Schloendorn also pitched a startling, medically graphic, and ethically charged vision for what he’s called “brainless clones” to serve the role of backup human bodies.

Imagine it like this: a baby version of yourself with only enough of a brain structure to be alive in case you ever need a new kidney or liver.

Or, alternatively, he has speculated, you might one day get your brain placed into a younger clone. That could be a way to gain a second lifespan through a still hypothetical procedure known as a body transplant.

The fuller context of R3’s proposals, as well as activities of another stealth startup with related goals, have not previously been reported. They’ve been kept secret by a circle of extreme life-extension proponents who fear that their plans for immortality could be derailed by clickbait headlines and public backlash.

And that’s because the idea can sound like something straight from a creepy science fiction film. One person who heard R3’s clone presentation, and spoke on the condition of anonymity, was left reeling by its implications and shaken by Schloendorn’s enthusiastic delivery. The briefing, this person said, was like a “close encounter of the third kind” with “Dr. Strangelove.”

A key inspiration for Schloendorn is a birth defect in which children are born missing most of their cortical hemispheres; he’s shown people medical scans of these kids’ nearly empty skulls as evidence that a body can live without much of a brain. 

And he’s talked about how to grow a clone. Since artificial wombs don’t exist yet, brainless bodies can’t be grown in a lab. So he’s said the first batch of brainless clones would have to be carried by women paid to do the job. In the future, though, one brainless clone could give birth to another.

Last Monday, the same day it announced itself to the world in Wired, R3 sent us a sweeping disavowal of our findings. It said Schloendorn “never made any statement regarding hypothetical ‘non-sentient human clones’ [that] would be carried by surrogates.” The most overarching of these challenges was its insistence that “any allegations of intent or conspiracy to create human clones or humans with brain damage are categorically false.”

But even Schloendorn and his cofounder, Alice Gilman, can’t seem to keep away from the topic. Just last September, the pair presented at Abundance Longevity, a $70,000-per-ticket event in Boston organized by the anti-aging promoter Peter Diamandis. Although the presentation to about 40 people was not recorded and was meant to be confidential, a copy of the agenda for the event shows that Schloendorn was there to outline his “final bid to defeat aging” in a session called “Full Body Replacement.”

According to a person who was there, both animal research and personal clones for spare organs were discussed. During the presentation, Gilman and Schloendorn even stood in front of an image of a cloning needle. Pressed on whether this was a talk about brainless clones, Gilman told us that while R3’s current business is replacing animal models, “the team reserves the right to hold hypothetical futuristic discussions.”

MIT Technology Review found no evidence that R3 has cloned anyone, or even any animal bigger than a rodent. What we did find were documents, additional meeting agendas, and other sources outlining a technical road map for what R3 called “body replacement cloning” in a 2023 letter to supporters. That road map involved improvements to the cloning process and genetic wiring diagrams for how to create animals without complete brains. 

DIMITRI AGAMANOLIS, M.D. VIA WIKIPEDIA

A main purpose of the fundraising, investors say, was to support efforts to try these techniques in monkeys from a base in the Caribbean. That offered a path to a nearer-term business plan for more ethical medical experiments and toxicology testing—if the company could develop what it now calls monkey “organ sacks.” However, this work would clearly inform any possible human version. 

Though he holds a PhD, Schloendorn is a biotech outsider who has published little and is best known for having once outfitted a DIY lab in his Bay Area garage. Still, his ties to the experimental fringe of longevity science have earned him a network in Silicon Valley and allies at a risk-taking US health innovation agency, ARPA-H. Together with his success at raising money from investors, this signals that the brainless-clone concept should be taken seriously by a wider community of scientists, doctors, and ethicists, some of whom expressed grave concerns. 

“It sounds crazy, in my opinion,” said Jose Cibelli, a researcher at Michigan State University, after MIT Technology Review described R3’s brainless-clone idea to him. “How do you demonstrate safety? What is safety when you’re trying to create an abnormal human?”

Twenty-five years ago, Cibelli was among the first scientists to try to clone human embryos, but he was trying to obtain matched stem cells, not make a baby. “There is no limit to human imagination and ways to make money, but there have to be boundaries,” he says. “And this is the boundary of making a human being who is not a human being.” 

“Feasibility research”

Since Dolly the sheep was born in 1996, researchers have cloned dogs, cats, camels, horses, cattle, ferrets, and other species of mammal. Injecting a cell from an existing animal into an egg creates a carbon-copy embryo that can develop, although not always without problems. Defects, deformities, and stillbirths remain common. 

Those grave risks are why we’ve never heard of a human clone, even though it’s theoretically possible to create one. 

But brainless clones flip the script. That’s because the ultimate aim is to create not a healthy person but an unconscious body that would probably need life support, like a feeding tube, to stay alive. Because this body would share the DNA of the person being copied, its organs would be a near-perfect immunological match. 

Backers of this broad concept argue that a nonsentient body would be ethically acceptable to harvest organs from. Some also believe that swapping in fresh, young body parts—known as “replacement”—is the likeliest path to life extension, since so far no drug can reverse aging. 

And then there’s the idea of a complete body transplant. “Certainly, for the cryonics patients, that sounds like something really promising,” says Anders Sandberg, a prominent Swedish transhumanist and expert in the ethics of future technologies. He notes that many people who opt to be stored in cryonic chambers after death choose the less expensive “head only” option, so “there might be a market for having an extra cloned body.”

MIT Technology Review first approached Schloendorn two years ago after learning he’d led a confidential online seminar called the Body Replacement Mini Conference, in which he presented “recent lab progress towards making replacement bodies.” 

According to a copy of the agenda, that 2023 session also included a presentation by a cloning expert, Young Gie Chung. And there was another from Jean Hébert, who was then a professor at the Albert Einstein College of Medicine and is now a program manager at ARPA-H, where he oversees a project to use stem cells to restore damaged brain tissue. Hébert popularized the so-called replacement solution to avoiding death in a 2020 book called Replacing Aging. 

In an interview prior to joining the government in 2024, Hébert described an informal but “very collaborative” relationship with Schloendorn. The overall idea was that to stop aging, one of them would determine how to repair a brain, while the other would figure out how to create a body without one. “It’s a perfect match, right? Body, brain,” Hébert told MIT Technology Review at the time. 

Schloendorn, by working outside the mainstream, had the huge advantage of “not being bound by getting the next paper out, or the next grant,” Hébert said, adding, “It’s such a wonderful way of doing research. It’s just clean and pure.” R3 now appears on the ARPA-H website on a list of prospective partners for Hébert’s program.

In a LinkedIn message exchanged with Schloendorn that same year, he described his work as “feasibility research in body replacement.”

“We will try to do it in a way that produces defined societal benefits early on, and we need to be prepared to take no for an answer, if it turns out that this cannot be done safely,” Schloendorn wrote at the time. He declined an interview then, saying that before exiting stealth mode, he wants to be sure the benefits are “reasonably grounded in reality.”

That could prove challenging. While body-part replacement sounds logical, like swapping the timing belt on an old car, in reality there’s scant evidence that receiving organs from a younger twin would make you live any longer. 

A complete body transplant, meanwhile, would probably be fatal, at least with current techniques. In the latest test of the concept, published last July, Russian surgeons removed a pig’s head and then sewed it back on. The animal did live—breathing weakly and lapping water from a syringe. But because its spinal cord had been cut, it was otherwise totally paralyzed. (As yet, there’s no proven method to rejoin a severed spinal cord.) In an act of mercy, the doctors ended the pig’s life after about 12 hours. 

Even some of R3’s investors say the endeavor is a risky, low-odds project, on par with colonizing Mars. Boyang Wang, head of Immortal Dragons, has spoken at longevity conferences about body-swapping technology, referring to the chance that “when the time comes, you can transplant your brain into a new body.” Wang confirmed in a January Zoom call that he’d been referring to R3 and that he invested $500,000 in the company during a 2024 fundraising round.

But since making his investment, Wang says, he’s become less bullish. He now views whole-body transplant as “very infeasible, not even very scientific” and “far away from hope for any realistic application.” 

Still, he says, the investment in R3 fits with his philosophy of making unorthodox bets that could be breakthroughs against aging. “What can really move the needle?” he asks. “Because time is running out.”

Stealth mode

Clonal bodies sit at the extreme frontier of an advancing cluster of technologies all aimed at growing spare parts. Researchers are exploring stem cells, synthetic embryos, and blob-like organoids, and some companies are cloning genetically engineered pigs whose kidneys and hearts have already been transplanted into a few patients. Each of these methods seeks to harness development—the process by which animal bodies naturally form in the womb—to grow fully functional organs. 

There’s even a growing cadre of mainstream scientists who say nonsentient bodies could solve the organ shortage, if they could be grown through artificial means. Two Stanford University professors, calling these structures “bodyoids,” published an editorial in favor of manufacturing spare human bodies in MIT Technology Review last year. While that editorial left many details to the imagination, they called the idea “at least plausible—and possibly revolutionary.” 

“There are a lot of variations on this where they’re trying to find a socially acceptable form,” says George Church, a Harvard University professor who advises startups in the field. But Church says gestating an entire body is probably taking things too far, especially since nearly all patients on transplant lists are waiting for just a single organ, like a heart or kidney. 

“There’s almost no scenario where you need a whole body,” he says. “I just think even if it’s someday acceptable, it’s not a good place to start.” For the moment, Church says, brainless human bodies are “not very useful, in addition to being repulsive.”

That’s arguably why body replacement technology still feels risky to talk about, even among life-extension enthusiasts who are otherwise ready to inject Chinese peptides or have their bodies cryogenically frozen. “I think it’s exciting or interesting from a scientific perspective, but I think the world is not fully ready for it yet,” says Emil Kendziorra, CEO of Tomorrow Bio, a company in Berlin that stores bodies at -196 °C in the hope they can be restored to life in the future. 

“Everybody’s like, yeah, you know, cryopreservation makes total sense,” he says. “And then you talk about total body replacement. And then everybody’s like, Whoa, whoa, whoa.”

Even so, “replacement” technology has found a fervent base of support among a group of self-described “hardcore” longevity adherents who follow a philosophy called Vitalism, which holds that society should redirect resources toward achieving unlimited lifespans. The growing influence of this movement, achieved through lobbying, investment, recruiting, and public messaging, was detailed earlier this year in MIT Technology Review.

Last spring, during a meetup for this community, Kendziorra was among the attendees at an invite-only “Replacement Day” gathering that took place off the public schedule. It was where more radical ideas could be discussed freely, since to some in the Vitalist circle, replacing body parts has emerged as the most plausible, least expensive way to beat death. 

At least that was the conclusion of a road map for anti-aging technology produced by one Vitalist group, the Longevity Biotech Fellowship, which reckoned that a proof-of-concept human clone lacking a neocortex would cost $40 million to create—a tiny amount, relatively speaking. 

Its report cited the existence of two stealth companies working on cloning whole nonsentient bodies, although it took care not to name them. If these companies’ activities become public, “there will be a huge backlash—people will hate it,” the entrepreneur Kris Borer said while presenting the road map at a French resort last August. 

“There are a ton of dystopian movies and novels about this kind of stuff. That is why I didn’t talk about any of the companies working on it. They are trying to hide from public attention,” he said. “We have to have the angel investors and other people invest kind of in secret until things are ready.” 

Borer did say what he sees as the best way to go public: first, to slowly ease body replacement into society’s awareness by disclosing more limited aims, which will be palatable. “We are not going to start with Let’s clone you and give you a body. We are going to start with Let’s solve the organ shortage,” he said. “Eventually people will warm up to it, and then we can go to the more hardcore stuff.”

In an interview earlier this month, Borer declined to name the companies involved in his immortality road map, or to say if R3 is one of them. But we did identify one additional stealthy startup, this one focused on replacing a person’s internal organs, not the whole body. Called Kind Biotechnology, it is a New Hampshire–based company headed by the anti-aging researcher Justin Rebo, a sometime collaborator of Schloendorn’s.

WO2025260099 VIA WIPO

According to patent applications filed by the company, Rebo’s team is working to create animals with a “complete lack of ability to feel, think, or sense the environment.” Images included in the patents show mice the company produced that lack a complete brain, and others that don’t have faces or limbs. They did that by deleting genes in embryos using the gene-editing technology CRISPR with the goal of creating a “sack of organs that grows mostly on its own,” with only a minimal nervous system. A cartoon rendering submitted to the patent office shows what looks like a fleshy duffel bag connected to life support tubes. 

In an email, Rebo said his company is working on an “ethical and scalable” way to create animal organs for experimental transplant to humans. He notes that “thousands die while waiting” for an organ. 

Some of Kind’s patent applications do cover the possibility of producing these organ sacks from human cells. Rebo says that’s more of a speculative possibility. But he does see his work as part of the “replacement” approach to longevity. Firstly, that’s because a “scalable production of young, high-quality organs” would let surgeons try transplants in more types of patients, including many with heart disease in old age who aren’t candidates for a transplant now. 

“With abundant high-quality organs, replacement could become a direct form of rejuvenation by replacement of failing parts,” he says. 

And Rebo imagines that simultaneously replacing multiple internal organs (grown together in the sack) could have even broader rejuvenating effects. “Ultimately, replacing failing parts is a direct path to extending healthy human lifespan,” he says. 

Church, who agreed earlier this year to advise Kind Bio, sees this work as part of an effort to “nudge” these technologies “toward something that is more useful and more acceptable from the get-go,” he says. “And then let’s see how society responds to that—rather than jumping to the most repulsive and most useless form, which some of them seem to be aiming for.” 

“There’s one way to find out”

People who know Schloendorn describe a dynamo-like presence who is “100% dedicated” to the goal of extreme life extension. In 2006, he penned a paper in a bioethics journal outlining why the “desire to live forever” is rational, and his doctoral research at the University of Arizona was sponsored by a longevity research organization called the SENS Foundation.  

He’s also well connected. In an interview, Aubrey de Grey, the influential and controversial fundraiser and prognosticator who cofounded SENS, called Schloendorn “one of my protégés.” And around 2010, Peter Thiel reportedly invested $1.5 million in ImmunePath, a company started by Schloendorn to develop stem-cell treatments, though it soon failed. (A representative for Thiel did not respond to a request to confirm the figure.)

By 2021, Schloendorn had moved on, founding R3 Biotechnologies. He began to circulate the body replacement idea and discuss a step-by-step scheme to get there: assess techniques in the lab first, then in monkeys, and maybe eventually in humans. 

A 2023 “letter to stakeholders” signed by Schloendorn begins by saying that “body replacement cloning will require multicomponent genetic engineering on a scale that has never been attempted in primates.” Fortunately, it adds, molecular techniques for “brain knockout” are well known in mice and should also be expected to function in “birthing whole primates,” a class that includes both monkeys and humans. 

Would it work? “There’s one way to find out,” the letter says. 

Wang, the investor at Immortal Dragons, says he put money into R3 after it showed him it is possible to create mice without complete brains. “There were imperfections, but the resulting mice survived, grew up, and to me, that is a pretty strong experiment,” he says; it was evidence enough for him to fund R3’s attempt to “replicate the result in primates.” 

(In its emailed statement, R3 said the company and its founders “never produced any degree of brain alterations in any species, did not attempt to do so, did not hire another party to do so, and have no specific plans to do so in the future.” It added: “We do not work with live non-human primates.”) 

The bigger technical obstacle, though, remains the cloning. Out of 100 attempts to clone an animal, only a few typically succeed. That fact alone makes cloning a human—or a monkey—almost infeasible.

But R3 does seem to have made an effort to tackle the efficiency problem. In one document reviewed by MIT Technology Review, it claims to have implemented improvements to the basic procedure in rodents, referencing a protein, called a histone demethylase, that helps erase a cell’s genetic memory. Adding it can greatly increase the chance that the cell will form a cloned embryo after being injected into an egg in the lab.

Those molecules were used in the first successful cloning of a monkey, which occurred in 2018 in China. But it still wasn’t easy—in fact, it was a huge and costly effort to handle a crowd of monkeys in estrus and perform IVF on them. According to Michigan State’s Cibelli, monkey cloning remains nearly impossible, at least on US territory, just because it’s “unaffordable.”

Nevertheless, success in monkeys did help prove, at least biologically, that human reproductive cloning could be possible. 

The company may also have tried to tackle a second long-standing obstacle to cloning: defects in how the placenta works. Because of such problems, some cloned animals die quickly after birth.

The R3 document refers to a “birthing fix” it developed to further improve the cloning success rate. While MIT Technology Review didn’t learn what R3’s process entails, we found a reference to it on the LinkedIn page of Maitriyee Mahanta, a scientist who cosigned the 2023 letter to R3 stakeholders and is a former research assistant to Hébert. (We were unable to reach Mahanta for comment.)

Her page described her current role as “molecular lead” studying cloning, “birth rate fixing,” and cortical development using cells from nonhuman primates. Her job affiliation is given as the Longevity Escape Velocity Foundation, a nonprofit where de Grey is the president and chief science officer. But de Grey says his foundation only arranged a work visa for Mahanta as part of a partnership “with the company she actually spends her time at.”

Like several other people interviewed for this article, de Grey made a resourceful effort to avoid directly confirming the existence of R3 when we spoke, while at the same time freely discussing theoretical aspects of body cloning technology. For instance, he talked about ways to shorten the wait for your double to grow up to a size suitable for organ harvesting; a further genetic mutation could be added to cause “central precocious puberty” in the clone, he said. This condition causes a growth spurt, even pubic hair, in a toddler. 

Cloning dictators

Who would clone a body and pay to keep it alive for years, until it’s needed? The first customers for this costly technology (if it ever proves feasible) would likely be the ultra-rich or the ultra-powerful. 

Indeed, somehow the world’s top dictators seem to have gotten the memo about replacement parts. In September, a hot mic picked up a conversation between Russian president Vladimir Putin and Chinese leader Xi Jinping as they walked through Beijing with North Korean autocrat Kim Jong Un; in the exchange, the Russian speculated on life extension.  

“Biotechnology is continuously developing. Human organs can be continuously transplanted. The longer you live, the younger you become, and [you can] even achieve immortality,” Putin said through an interpreter.

“Some predict that in this century, humans will live to 150 years old,” Xi responded agreeably.

How the leaders learned of these possibilities is unknown. But scenarios involving dictators are a constant topic among body replacement enthusiasts. 

“There are companies working on this. They are in stealth—we can’t reveal too much about them—but the general concept on this is if you didn’t have any ethical qualms, you could do most of it today,” Will Harborne, the chief investment officer of LongGame Advisors, said last year, during an interview with the podcaster Julian Issa. “If you were the dictator of some country and wanted a clone of yourself, you can already go grow one. You can create a cloned embryo of yourself, you can get a surrogate to carry it to term, and you can grow [a] body until age 18 with a brain, and eventually, if you were a dictator, you could kill them and try to transplant your head on their body.”

“And now no one is suggesting you do that—it’s very unethical—but most of the technology is there,” he said. He noted that the reason for removing the cortex of a clone created for such a purpose is that “we don’t want to kill other people to live forever.” 

Harborne subsequently confirmed to MIT Technology Review that the fund invested $1 million in R3 about a year and a half ago.

In order to make the body replacement process ethical, the clone’s brain needs to be stunted so it lacks consciousness. That is where the interest in birth defects comes in. Remarkable medical scans of kids with a rare condition, hydranencephaly, show a total absence of the cerebral hemispheres. Yet if they are cared for, they may be able to live into their 20s, even though they cannot speak or engage in purposeful movement. 

The technical question, then, is how to intentionally produce such a condition in a clone. Sandberg, the futurist, says he’s visited R3’s lab, talked to Gilman, and sat through a presentation about how genetic engineering can be used to shape brain growth. Previous work has shown that by adding a toxic gene, it is possible to kill specific cell types in a growing embryo but spare others, leading to a mouse without a neocortex.

While Sandberg isn’t an expert in biotechnology, he says R3’s theory looked sensible to him. “I think it’s possible to actually prevent the development of the brain well enough that you can say ‘Yeah, there is almost certainly no consciousness here,’” Sandberg says. “Hence, there can’t be any suffering, or any individual, in a practical sense.”

“I think the overall aim—actually, it looks ethically pretty good,” he says. 

QIANG SUN AND MU-MING POO/CHINESE ACADEMY OF SCIENCES VIA AP

Yet it could be difficult to really determine where consciousness starts and ends. Under current medical standards, taking the organs of people with hydranencephaly isn’t allowed because they don’t meet the standard of brain death: They have a functioning brain stem. An even more serious problem is evidence that the brain stem alone produces a basic form of consciousness. If that is so, says Bjorn Merker, a neuroscientist who surveyed caretakers of more than a hundred children with hydranencephaly, a plan “to harvest organs from organisms modeled on this condition would be unethical.”

Of course, the most extreme version of the replacement dream isn’t just to take organs. It’s to take over the body entirely. Sergio Canavero, a controversial Italian surgeon who has proposed head and brain transplants, says he was approached for advice by Schloendorn and others a few years ago. “They told me they were looking at a head transplant on a two- or three-year-old,” he says. “I stopped short. How could you even conceive of that? The biomechanical compatibility is not there. You have to wait until at least 14. And I would say 16. It was very clear to me these guys are not surgeons—they are biologists.” 

Canavero says he’s not opposed to cloning bodies for transplant—he thinks it could work. “But if you want to use a clone,” he says, “it must be a nonsentient clone. Otherwise it’s murder, a homicide.”    

MIT Technology Review has not found any evidence that R3 has yet created an “organ sack,” much less a brainless human clone. And there are many reasons to believe their hypothetical future of “full body replacement” will never come to pass—that it is just a live-forever fantasy.

“There are so many barriers,” says Cibelli. It’s a long list: Human cloning is illegal in many countries, it’s unsafe, and few competent experts would want, or dare, to participate. And then there’s the inconvenient fact that for now, there’s no way to grow a brainless clone to birth, except in a woman’s body. Think about it, Cibelli says: “You’d have to convince a woman to carry a fetus that is going to be abnormal.”

Sandberg agrees that is where things could start to get tricky. “The problem here, of course,” he says, “is that the yuck factor is magnificent.”

“Think of this as a human body,” says Javier González.

In front of me is essentially a metal box on wheels. Standing at around a meter in height, it reminds me of a stainless-steel counter in a restaurant kitchen. It is covered in flexible plastic tubing—which act as veins and arteries—connecting a series of transparent containers, the organs of this machine.

What makes it extra special is the role of the cream-colored tub that sits on its surface. Ten months ago, González, a biomedical scientist who developed the device with his colleagues at the Carlos Simon Foundation, carefully placed a freshly donated human uterus in the tub. The team connected it to the device’s tubes and pumped in modified human blood.

The device kept the uterus alive for a day—a new feat that could represent the first step to the long-term maintenance of uteruses outside the human body. The work has not yet been published. 

The team members want to keep donated human uteruses alive long enough to see a full menstrual cycle. They hope this will help them study diseases of the uterus and learn more about how embryos burrow their way into the organ’s lining at the start of a pregnancy. They also hope that future iterations of their device might one day sustain the full gestation of a human fetus.

The machine is technically called PUPER, which stands for “preservation of the uterus in perfusion.” But González’s colleague Xavier Santamaria says the team has adopted a nickname for it: “We call it ‘Mother.’”

The organ in the machine

González and Santamaria, medical vice president of the Carlos Simon Foundation, demonstrated how the device might work when I visited the foundation in Valencia, Spain, earlier this month (although it held no organs on that day). 

Both are interested in learning more about implantation, the moment at which an embryo attaches itself to the lining of a uterus—essentially, the very first moment of pregnancy.

The foundation’s founder and director, Carlos Simon, believes it’s a sticking point in IVF: Scientists have made many improvements to the technology over the years, but the failure of embryos to implant underlies plenty of unsuccessful IVF cycles, he says. Being able to carefully study how the process works in a real, living organ might give the team a better idea of how to prevent those failures.

JESS HAMZELOU

JAVIER GONZALES/CARLOS SIMON FOUNDATION

Javier González demonstrates the perfusion machine. A previous iteration of the device kept a sheep’s uterus (right) alive for a day.

The team took inspiration from advances in technologies designed to maintain donated organs for transplantation. In recent years, researchers around the world have created devices that deliver nutrients and filter waste so that organs can survive longer after being removed from donors’ bodies.

The main goal here is to buy time. A human organ might last only a matter of hours outside the body, so a transplant may require frantic preparation for the recipient, sometimes in the middle of the night. With a little more time, doctors could find better donor-patient matches and potentially test the quality of donated organs.

This approach is called normothermic or machine perfusion, and it is already being used clinically for some liver, kidney, and heart transplants.

The team at the Carlos Simon Foundation built a similar machine for uteruses. A blood bag hangs on one side. From there, blood is ferried via plastic tubing to a pump, which functions as the heart. The pump shunts the blood through an oxygenator, which adds oxygen and removes carbon dioxide as the lungs would in a human body.

The blood is warmed and passed through sensors that monitor the levels of glucose and oxygen, along with other factors. It passes through a “kidney” to remove waste. And finally the blood reaches the uterus, hooked up to its own plastic “arteries” and “veins.” The organ itself sits at a tilt, just as in the body, and is kept in a humid environment to stay moist.

Mother’s first uterus

The team first began testing an early prototype of the device with sheep uteruses around four years ago. That meant carting the machine to an animal research center in Zaragoza, around 200 miles away. Over the course of the preliminary study, veterinary surgeons removed the uteruses of six sheep and hooked them up to the machine. They kept each uterus alive for a day, using blood from the same animals.

After the sheep experiments, the researchers carted their machine back to Valencia and modified it to achieve its current incarnation, “Mother.” They started working with a local hospital that performed hysterectomies. And in May last year, they were offered their first human uterus.

The team needed to be quick. “You need to put [the uterus in the machine] within a couple of hours, maximum, of the extraction,” says Santamaria. He and his colleagues also needed to connect the uterus’s blood vessels to the tubing delicately, taking care to avoid any blockages (clotting is a major challenge in organ perfusion). The organ was hooked up to human blood obtained from a blood bank.

It seemed to work—at least temporarily. “We kept it alive for one day,” says Santamaria.

“As a proof of concept, it is impressive,” says Keren Ladin, a bioethicist who has focused on organ transplantation and perfusion at Tufts University. “These are early days.”

It might not sound like much, but 24 hours is a long time for an organ to be out of the body. Maintaining a donated uterus for that long could expand the options for uterus transplant, a fairly new procedure offered to some people who want to be pregnant but don’t have a functional uterus, says Gerald Brandacher, professor of experimental and translational transplant surgery at the Medical University of Innsbruck in Austria.

“It is better than what we currently have, because we have only a couple of hours,” he says. So far, most uterus transplants have been planned operations involving organs from living donors. A technology like this could allow for the use of more organs from deceased donors, he says.

That work is “not in the immediate pipeline” for the team in Spain, says Santamaria. “We are working on other problems.”

Pregnancy in the lab?

Santamaria, González, and their colleagues are more interested in using sustained human uteruses for research. 

They’ve mounted a camera to a wall in the corner of the room, pointed at their machine. It allows the team to monitor “Mother” remotely, and to check if any valves disconnect. (That happened once before—a spike in pressure caused the blood bag to come loose, spilling a liter of blood on the floor, Santamaria says.)

They’d like to be able to keep their uteruses alive for around 28 days to study the menstrual cycle and disorders that affect the uterus, like endometriosis and fibroids.

It won’t be easy to maintain a uterus for that long, cautions Brandacher. As far as he knows, no one has been able to maintain a liver for more than seven days. “No studies out there … have shown 30-day survival in a machine perfusion circuit,” he says.

But it’s worth the effort. The team’s main interest is learning more about how embryos implant in the uterine lining at the start of a pregnancy. They hope to be able to test the process in their outside-the-body uteruses.

They won’t be allowed to use human embryos for this, says González—that would cross an ethical boundary. Instead, they plan to use embryo-like structures made from stem cells. The structures closely resemble human embryos but are created in a lab without sperm or eggs.

Simon himself has grander ambitions.

He sees a future in which a machine like “Mother” will be able to fully gestate a human, all the way from embryo to newborn. It could offer a new path to parenthood for people who don’t have a uterus, for example, or who are not able to get pregnant for other reasons.

He appreciates that it sounds futuristic, to say the least. “I don’t know if we will end up having pregnancies inside of the uterus outside of the body, but at least we are ready to understand all the steps to do that,” he says. “You have to start somewhere.”

Listen to the session or watch below

Whether it’s the race to find life on Mars, the campaign to outsmart killer asteroids, or the quest to make the moon a permanent home to astronauts, scientists’ efforts in space can tell us more about where humanity is headed. This subscriber-only discussion examines the progress and possibilities ahead.

Speakers: Amanda Silverman, features & investigations editor, and Robin George Andrews, award-winning science journalist and author

Recorded on March 25, 2026

Related Stories:

• America was winning the race to find Martian life. Then China jumped in.
• Inside the hunt for the most dangerous asteroid ever
• See the stunning first images from the Vera C. Rubin Observatory
• Meet the researchers testing the “Armageddon” approach to asteroid defense
• Science Writing by Robin George Andrews

Qichao Hu doesn’t mince words about how he sees the state of the battery industry. “Almost every Western battery company has either died or is going to die. It’s kind of the reality,” he says.

Hu is the CEO of SES AI, a Massachusetts-based battery company. It once had aims of making huge amounts of advanced lithium metal batteries for major industries like electric vehicles—but now the company is placing its bets on AI materials discovery.

Hu sees the pivot as an essential one. “It’s just not possible for a Western company to build a sustainable business,” he says. The company is still making some batteries, but only for smaller markets like drones rather than those that would require higher volumes, like EVs. The new focus is the company’s battery materials discovery platform—which it can either license to other battery companies or use to develop materials to sell. 

Some leading US EV battery companies have folded in recent months, and others, like SES AI, are making dramatic changes in strategy. This shift in who’s building batteries and where they’re doing it could shape the future geopolitics of energy. 

The work that would eventually evolve into SES AI began at MIT, where Hu completed his graduate research. His battery work was aimed at applications in oil and gas exploration. The industry uses sensors that go deep underground, where temperatures can top 120 °C (about 250 °F). The team hoped to develop a battery that could withstand those high temperatures and last longer on a single charge. 

The chosen technology was a solid polymer lithium metal battery. These cells use lithium metal for their anode and a polymer for their electrolyte (the material that ions move through in a battery cell). Together, these components can increase the energy density of a cell significantly, relative to the lithium-ion batteries that are common in personal devices and EVs today. (Lithium-ion batteries generally use a graphite material for their anode and a liquid for the electrolyte.)

That solid-state battery technology became the foundation of Solid Energy, a startup Hu founded that spun out from MIT in 2012 and raised its first private investment in 2013.

The team eventually realized that underground oil exploration was a small market, so after several years of operation they began to focus on electric vehicles, which were starting to come into the mainstream. After the team tweaked the chemistry to work better at lower temperatures, the company built its first pilot facility in Massachusetts and eventually another facility in Shanghai.

By 2021, the battery industry was booming, Hu recalls, and EVs were the hottest industry to be in. There was a ton of interest in next-generation battery technology from major automakers at the time, and Solid Energy started developing technology with GM, Hyundai, and Honda.

Larger vehicles, like SUVs and trucks, seemed like a good fit for next-generation batteries, Hu says. Massive vehicles like the ones Americans like to drive would need lighter batteries so they could have a reasonable range without being prohibitively heavy.

The company also shifted its chemistry focus, and in 2022 it announced a battery with a silicon anode rather than a lithium metal one. That shift could help make the battery easier to manufacture.

Since then, growth in the EV market has slowed, at least in the US, partly because of major pullbacks in funding from the Trump administration. EV tax credits for drivers, a key piece of support pushing Americans toward electric options, ended in late 2025. With the market for large electric cars in trouble, Hu says, “now we have to look at every market.”  

The AI materials discovery platform on which it’s pinning many of its hopes is called Molecular Universe. The company seeks not only to provide its software to other battery companies but also to identify new battery materials and either license them or sell them to those companies.

COURTESY OF SES AI

The platform has already identified six new electrolyte materials, according to the company. Hu says one is an additive that could help improve the lifetime of batteries with silicon anodes. 

One of the challenges with silicon anodes is that they tend to swell a lot during use, which can cause physical damage and prevent efficient charging and discharging. To address the problem, the industry typically uses a material called fluoroethylene carbonate (FEC), which can help form an elastic film on the anode so the battery can still charge effectively. That additive can degrade at high temperatures, though, producing gases that can harm a battery’s lifetime. The SES platform identified a compound that works like FEC but doesn’t release those gases.

The company’s long history and deep battery knowledge could help make its platform a useful tool, Hu says. He sees the actual model as less crucial than SES’s domain expertise and data from years of making and testing batteries. 

“By not actually making the physical battery, we’re actually able to scale and then generate revenue faster,” he says. 

But some experts are skeptical about the near-term prospects for AI materials discovery to revive the industry. “New materials development, as much as we thought that was what people wanted (and, frankly, it should be what the cell makers want)—I don’t know that that seems to be the real linchpin of the battery industry’s progress,” says Kara Rodby, a technical principal at Volta Energy Technologies, a venture capital firm that focuses on the energy storage industry.

Investors are pulling back, and a slowdown in public support is making things difficult for some parts of the battery industry, she adds: “I don’t know that the ability to discover any new material is going to unlock anything new for the battery industry at this point in time.”

Axiom Math, a startup based in Palo Alto, California, has released a free new AI tool for mathematicians, designed to discover mathematical patterns that could unlock solutions to long-standing problems.

The tool, called Axplorer, is a redesign of an existing one called PatternBoost that François Charton, now a research scientist at Axiom, co-developed in 2024 when he was at Meta. PatternBoost ran on a supercomputer; Axplorer runs on a Mac Pro.

The aim is to put the power of PatternBoost, which was used to crack a hard math puzzle known as the Turán four-cycles problem, in the hands of anyone who can install Axplorer on their own computer.

Last year, the US Defense Advanced Research Projects Agency set up a new initiative called expMath—short for Exponentiating Mathematics—to encourage mathematicians to develop and use AI tools. Axiom sees itself as part of that drive.

Breakthroughs in math have enormous knock-on effects across technology, says Charton. In particular, new math is crucial for advances in computer science, from building next-generation AI to improving internet security.

Most of the successes with AI tools have involved finding solutions to existing problems. But finding solutions is not all that mathematicians do, says Axiom Math founder and CEO Carina Hong. Math is exploratory and experimental, she says. 

MIT Technology Review met with Charton and Hong last week for an exclusive video chat about their new tool and how AI in general could change mathematics. 

Math by chatbot

In the last few months, a number of mathematicians have used LLMs, such as OpenAI’s GPT-5, to find solutions to unsolved problems, especially ones set by the 20th-century mathematician Paul Erdős, who left behind hundreds of puzzles when he died.

But Charton is dismissive of those successes. “There are tons of problems that are open because nobody looked at them, and it’s easy to find a few gems you can solve,” he says. He’s set his sights on tougher challenges—“the big problems that have been very, very well studied and famous people have worked on them.” Last year, Axiom Math used another of its tools, called AxiomProver, to find solutions to four such problems in mathematics.   

The Turán four-cycles problem that PatternBoost cracked is another big problem, says Charton. (The problem is an important one in graph theory, a branch of math that’s used to analyze complex networks such as social media connections, supply chains, and search engine rankings. Imagine a page covered in dots. The puzzle involves figuring out how to draw lines between as many of the dots as possible without creating loops that connect four dots in a row.)

“LLMs are extremely good if what you want to do is derivative of something that has already been done,” says Charton. “This is not surprising—LLMs are pretrained on all the data that there is. But you could say that LLMs are conservative. They try to reuse things that exist.”

However, there are lots of problems in math that require new ideas, insights that nobody has ever had. Sometimes those insights come from spotting patterns that hadn’t been spotted before. Such discoveries can open up whole new branches of mathematics.

PatternBoost was designed to help mathematicians find new patterns. Give the tool an example and it generates others like it. You select the ones that seem interesting and feed them back in. The tool then generates more like those, and so on.  

It’s a similar idea to Google DeepMind’s AlphaEvolve, a system that uses an LLM to come up with novel solutions to a problem. AlphaEvolve keeps the best suggestions and asks the LLM to improve on them.

Special access

Researchers have already used both AlphaEvolve and PatternBoost to discover new solutions to long-standing math problems. The trouble is that those tools run on large clusters of GPUs and are not available to most mathematicians.

Mathematicians are excited about AlphaEvolve, says Charton. “But it’s closed—you need to have access to it. You have to go and ask the DeepMind guy to type in your problem for you.”

And when Charton solved the Turán problem with PatternBoost, he was still at Meta. “I had literally thousands, sometimes tens of thousands, of machines I could run it on,” he says. “It ran for three weeks. It was embarrassing brute force.”

Axplorer is far faster and far more efficient, according to the team at Axiom Math. Charton says it took Axplorer just 2.5 hours to match PatternBoost’s Turán result. And it runs on a single machine.

Geordie Williamson, a mathematician at the University of Sydney, who worked on PatternBoost with Charton, has not yet tried Axplorer. But he is curious to see what mathematicians do with it. (Williamson still occasionally collaborates with Charton on academic projects but says he is not otherwise connected to Axiom Math.)

Williamson says Axiom Math has made several improvements to PatternBoost that (in theory) make Axplorer applicable to a wider range of mathematical problems. “It remains to be seen how significant these improvements are,” he says.

“We are in a strange time at the moment, where lots of companies have tools that they’d like us to use,” Williamson adds. “I would say mathematicians are somewhat overwhelmed by the possibilities. It is unclear to me what impact having another such tool will be.”

Hong admits that there are a lot of AI tools being pitched at mathematicians right now. Some also require mathematicians to train their own neural networks. That’s a turnoff, says Hong, who is a mathematician herself. Instead, Axplorer will walk you through what you want to do step by step, she says.

The code for Axplorer is open source and available via GitHub. Hong hopes that students and researchers will use the tool to generate sample solutions and counterexamples to problems they’re working on, speeding up mathematical discovery.

Williamson welcomes new tools and says he uses LLMs a lot. But he doesn’t think mathematicians should throw out the whiteboards just yet. “In my biased opinion, PatternBoost is a lovely idea, but it is certainly not a panacea,” he says. “I’d love us not to forget more down-to-earth approaches.”

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.

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 degrees °C for over a decade, largely undisturbed. Before he died in 2014, Coles had the brain frozen with an ambitious goal in mind: reanimation. 

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

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

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

—Jessica Hamzelou 

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 everything you need to know about the state of the industry. Take a look at this month’s edition. 
 

MIT Technology Review Narrated: how Pokémon Go is giving delivery robots an inch-perfect view of the world  

Pokémon Go was the world’s first augmented-reality megahit. Released in 2016 by Niantic, the AR twist on the juggernaut Pokémon franchise fast became a global phenomenon. “500 million people installed that app in 60 days,” says Brian McClendon, CTO at Niantic Spatial, an AI company that Niantic spun out last year.  

Now Niantic Spatial is using that vast trove of crowdsourced data to build a kind of world model—a buzzy new technology that grounds the smarts of LLMs in real environments. The firm wants to use it to help robots navigate more precisely. 

—Will Douglas Heaven 

This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we’re publishing 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 next era of space exploration 

Our footprint in the solar system is rapidly expanding. Programs to build permanent Moon bases and find life on Mars have transitioned from science fiction to active space agency missions. The scientists behind them will not only shed new light on the cosmos, but also reveal where humanity is headed. 

To examine what the future holds in store, MIT Technology Review features editor Amanda Silverman will sit down today with award-winning science journalist and author Robin George Andrews for an exclusive subscriber-only Roundtable conversation about “The Next Era of Space Exploration.” Register here to join the session at 16:00 GMT / 12:00 PM ET / 9:00 AM PT. 

The must-reads 

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

1 OpenAI is shutting down AI video generator Sora  
The app attracted at least as much controversy as acclaim. (CNBC) 
+ Closing it means saying goodbye to $1 billion from Disney. (BBC) 
+ OpenAI is cutting back on side projects ahead of an expected IPO. (WSJ $) 
+ But it’s focusing its efforts on building a fully automated researcher. (MIT Technology Review) 

2 A judge suspects the Pentagon is illegally punishing Anthropic 
She labelled the DoD’s ban “troubling.” (Bloomberg) 
+ Anthropic and the Pentagon are facing off in court. (Guardian) 
+ The DoD wants AI companies to train on classified data. (MIT Technology Review) 

3 Meta has been ordered to pay $375 million for endangering children online 
Prosecutors said the company knew it put children at risk. (Engadget) 
+ Meta is offering its top talent stock options as incentives for its AI push. (CNBC) 

4 Arm will sell its own computer chips for the first time 
It’s aimed at data centers that run AI tasks. (NYT $) 
+ Arm stock jumped 13% on the news. (CNBC) 

5 Manus’s founders have been barred from leaving China following Meta’s takeover 
Beijing is reviewing the $2 billion acquisition of the AI startup. (FT $) 

6 Baltimore has sued xAI over Grok’s fake nude images  
The chatbot allegedly violated consumer protections. (Guardian) 
+ There’s a big market for pornographic deepfakes of real women. (MIT Technology Review) 

7 NASA plans to send a nuclear-powered spacecraft to Mars in 2028 
It’ll take a payload of Ingenuity-class helicopters to the Red Planet. (NYT $) 
+ NASA also wants to put a $20 billion base on the Moon. (The Verge) 

8 A company is secretly turning Zoom meetings into AI-generated podcasts 
WebinarTV turns the calls into content without telling anyone. (404 Media) 

9 Iranian volunteers have built their own missile warning map 
It fills the gap left by Iran’s lack of a public emergency alert tool. (Wired $) 
+ Here’s where OpenAI’s tech could show up in Iran. (MIT Technology Review) 

10 A nonprofit is sending basic income payments to AI-impacted workers 
It’s starting by giving 25-50 people $1,000 per month. (Gizmodo) 

Quote of the day 

“I am first and foremost a scientist. My goal is to understand nature. But doing science is, sort of, like reading the mind of God.” 

—DeepMind CEO Demis Hassabis shares his approach to AI strategy with the FT. 

One More Thing 

EVA REDAMONTI

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.) 
 
+ Soothe subscription fatigue with this simple cancellation tool. 
+ Takashi Murakami’s reimagined Monets are pop-art magic. 
+ Jump into a rabbit hole with this app that visualizes links between Wikipedia pages. 
+ This playful lynx that snatched the top prize in a photo competition is a delight. 

Imagine telling a digital agent, “Use my points and book a family trip to Italy. Keep it within budget, pick hotels we’ve liked before, and handle the details.” Instead of returning a list of links, the agent assembles an itinerary and executes the purchase.

That shift, from assistance to execution, is what makes agentic AI different. It also changes the operating speed of commerce. Payment transactions are already clear in milliseconds. The new acceleration is everything before the payment: discovery, comparison, decisioning, authorization, and follow-through across many systems. As humans step out of routine decisions, “good enough” data stops being good enough. In an agent-driven economy, the constraint isn’t speed; it’s trust at machine speed and scale.

Automated markets already work because identity, authority, and accountability are built in. As agents transact across businesses, that same clarity is required. Master data management (MDM)—the discipline of creating a single master record—becomes the exchange layer: tracking who an agent represents, what it can do, and where responsibility sits when value moves. Markets don’t fail from automation; they fail from ambiguous ownership. MDM turns autonomous action into legitimate, scalable trust.

To make agentic commerce safe and scalable, organizations will need more than better models. They will need a modern data architecture and an authoritative system of context that can instantly recognize, resolve, and distinguish entities. It is the difference between automation that scales and automation that needs constant human correction.

The agent is a new participant

Digital commerce has long been built on two primary sides: buyers and suppliers/merchants. Agentic commerce adds a third participant that must be treated as a first-class entity: the agent acting on the buyer’s behalf.

That sounds simple until you ask the questions every enterprise will face:

• Who is the individual, across channels and devices, with enough certainty for automation?
• Who is the agent, and what permissions and limits define what it can do?
• Who is the merchant or supplier, and are we sure we mean the right one?
• Who holds liability if the agent acts with permission, but against user intent?

The practical risk is confusion. Humans, for example, can infer that “Delta” means the airline when they are booking a flight, not the faucet company. An agent needs deterministic signals. If the system guesses wrong, it either breaks trust or forces a human confirmation step that defeats the promise of speed.

Why ‘good enough’ data breaks at machine speed

Most organizations have learned to live with imperfect data. Duplicate customer records are tolerable. Incomplete product attributes are annoying. Merchant identities can be reconciled later.

Agentic workflows change that tolerance. When an agent takes action without a human checking the output, it needs data that is close to perfect, because it cannot reliably notice when data is ambiguous or wrong the way a person can.

The failure modes are predictable, and they show up in places that matter most:

• Product truth: If the catalog is inconsistent, an agent’s choices will look arbitrary (“the wrong shirt,” “the wrong size,” “the wrong material”), and trust collapses quickly.
• Payee truth: Agentic commerce expands beyond cards to account-to-account and open-banking-connected experiences, broadening the universe of payees and the need to recognize them accurately in real time.
• Identity truth: People operate in multiple contexts (work versus personal). Devices shift. A system that cannot distinguish amongst these contexts will either block legitimate activity or approve risky activity, both of which damage adoption.

This is why unified enterprise data and entity resolution move from nice to have to operationally required. The more autonomy you want, the more you must invest in modern data foundations that ensure it is safe.

Context intelligence: The missing layer

When leaders talk about agentic AI, they often focus on model capability: planning, tool use, and reasoning. Those are necessary, but they are not sufficient.

Agentic commerce also requires a layer that provides authoritative context at runtime. Think of it as a real-time system of context that can answer instantly and consistently:

• Is this the right person?
• Is this the right agent, acting within the right permissions?
• Is this the right merchant or payee?
• What constraints apply right now (budget, policy, risk, loyalty rules, preferred suppliers)?

Two design principles matter.

First, entity truth must be deterministic enough for automation. Large language models are probabilistic by nature. That is helpful for creating options for writing and drawing. It is risky for deciding where money goes, especially in B2B and finance workflows, where “probably correct” is not acceptable.

Second, context must travel at the speed of interaction and remain portable across the entire connected network value chain. Mastercard’s experience optimizing payment flows is instructive: the more services you layer onto a transaction, the more you risk slowing it down. The pattern that scales pre-resolves, curates, and packages the signal so that execution is lightweight.

This is also where tokenization is heading. Initiatives like Mastercard’s Agent Pay and Verifiable Intent signal a future in which consumer credentials, agent identities, permissions, and provable user intent are encoded as cryptographically secure artifacts — enabling merchants, issuers and platforms to deterministically verify authorization and execution at machine speed.

What leaders should do in the next 12 to 24 months

Adoption will not be uniform. Early traction will often depend less on industry and more on the sophistication of an organization’s systems and data discipline.

That makes the next two years a window for practical preparation. Five moves stand out.

1. Treat agents as governed identities, not features. Define how agents are onboarded, authenticated, permissioned, monitored, and retired.
2. Prioritize entity resolution where the cost of being wrong is highest. Start with payees, suppliers, employee-versus-personal identity, and high-volume product categories.
3. Build a reusable context service that every workflow and agent can call. Do not force each system to reconstruct identity and relationships from scratch.
4. Precompute and compress signals. Resolve and curate context upstream so that runtime decisioning stays fast and predictable.
5. Expand autonomy only as trust is earned. Build a governance framework to address disputes, keep humans in the loop for higher-risk actions, measure accuracy, and expand automation as outcomes prove reliable.

A tsunami effect across industries

Agentic AI will not be confined to shopping carts. It will touch procurement, travel, claims, customer service, and finance operations. It will compress decision cycles and remove manual steps, but only for organizations that can supply agents with clean identity, precise entity truth, and reliable context.

The winners will treat entity truth and context as core infrastructure for automation, not as a back-office cleanup project. In commerce at machine speed, trust is not a brand attribute; it is an architectural decision encoded in identity, context, and control.

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

AI is at war. Anthropic and the Pentagon feuded over how to weaponize Anthropic’s AI model Claude; then OpenAI swept the Pentagon off its feet with an “opportunistic and sloppy” deal. Users quit ChatGPT in droves. People marched through London in the biggest protest against AI to date. If you’re keeping score, Anthropic—the company founded to be ethical—is now turbocharging US strikes on Iran. 

On the lighter side, AI agents are now going viral online. OpenAI hired the creator of OpenClaw, a popular AI agent. Meta snapped up Moltbook, where AI agents seem to ponder their own existence and invent new religions like Crustafarianism. And on RentAHuman, bots are hiring people to deliver CBD gummies. The future isn’t AI taking your job. It’s AI becoming your boss and finding God.

We’re starting to give AI agents real autonomy, but are we prepared for what could happen next?

This subscriber-only eBook explores this and angles from experts, such as “If we continue on the current path … we are basically playing Russian roulette with humanity.”

by Grace Huckins June 12, 2025

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