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Received — 26 March 2026 ⏭ MIT Technology Review
  • ✇MIT Technology Review
  • Roundtables: The Next Era of Space Exploration MIT Technology Review
    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 https:/
     

Roundtables: The Next Era of Space Exploration

26 March 2026 at 01:26

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

https://vimeo.com/1177019168?share=copy&fl=sv&fe=ci

Recorded on March 25, 2026

Related Stories:

  • ✇MIT Technology Review
  • Why this battery company is pivoting to AI Casey Crownhart
    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
     

Why this battery company is pivoting to AI

25 March 2026 at 23:02

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.

vials of electrolytes inside a machine at the synthesis foundry
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.”

  • ✇MIT Technology Review
  • This startup wants to change how mathematicians do math Will Douglas Heaven
    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
     

This startup wants to change how mathematicians do math

25 March 2026 at 21:59

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.”

  • ✇MIT Technology Review
  • The Download: reawakening frozen brains, and the AI Hype Index returns Thomas Macaulay
    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. 
     

The Download: reawakening frozen brains, and the AI Hype Index returns

25 March 2026 at 20:47

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 

many ui windows framing different views of an asteroid on the way to Earth
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. 

  • ✇MIT Technology Review
  • Agentic commerce runs on truth and context Andrew Reiskind · Manish Sood
    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 payme
     

Agentic commerce runs on truth and context

25 March 2026 at 19:48

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.

  • ✇MIT Technology Review
  • The AI Hype Index: AI goes to war Michelle Kim
    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 Op
     

The AI Hype Index: AI goes to war

25 March 2026 at 17:00

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.

  • ✇MIT Technology Review
  • Exclusive eBook: Are we ready to hand AI agents the keys? MIT Technology Review
    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 ACCESS EBOOK Related Stories: Are we ready to hand AI agents the keys? MIT Technology Review Narrated: Are we ready to hand AI agents the keys? Access all subscriber-only eBooks:
     

Exclusive eBook: Are we ready to hand AI agents the keys?

25 March 2026 at 02:17

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

Related Stories:

Access all subscriber-only eBooks:

  • ✇MIT Technology Review
  • This scientist rewarmed and studied pieces of his friend’s cryopreserved brain Jessica Hamzelou
    L. Stephen Coles’s brain sits cushioned 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. That is, apart from the time, a little over a year ago, when scientists slowly lifted the brain to take photos of it. Years before, the team had removed tiny pieces of it to send to Coles’s friend. Coles, a researcher who studied aging, was interested in cryogenics—the long-term storage of human bodies and
     

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

25 March 2026 at 00:43

L. Stephen Coles’s brain sits cushioned 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.

That is, apart from the time, a little over a year ago, when scientists slowly lifted the brain to take photos of it. Years before, the team had removed tiny pieces of it to send to Coles’s friend. Coles, a researcher who studied aging, was interested in cryogenics—the long-term storage of human bodies and brains in the hope that they might one day be brought back to life. Before he died, he asked cryobiologist Greg Fahy to study the effects of the preservation procedure on his brain. Coles was especially curious about whether his cooled brain would crack, says Fahy.

Coles’s brain was preserved shortly after he died in 2014, but Fahy has only recently got around to analyzing those samples. He says that Coles’s brain is “astonishingly well preserved.”

“We can see every detail [in the structure of the brain biopsies],” says Fahy, who is chief scientific officer at biotech companies Intervene Immune and 21st Century Medicine (where he is also executive director). He hopes this means that Coles’s brain still stands a chance of reanimation at some point in the future.

Other cryobiologists are less optimistic. “This brain is not alive,” says John Bischof, who works on ways to cryopreserve human organs at the University of Minnesota.

Still, Fahy’s research could help provide a tool to neuroscientists looking for new ways to study the brain. And while human reanimation after cryopreservation may be the stuff of science fiction, using the technology to preserve organs for transplantation is within reach.

Banking a brain

Coles, a gerontologist who spent the latter part of his career studying human longevity, opted to have his brain cryogenically preserved when he died of pancreatic cancer.

After he was declared dead, Coles’s body was kept at a low temperature while he was transferred to Alcor, a cryonics facility in Arizona. His head was removed from his body, and a team perfused his brain with “cryoprotective” chemicals that would prevent it from freezing. They then removed it from his skull and cooled it to −146 °C.

Coles had another request. As a scientist, he wanted his cryopreserved brain to be studied. Hundreds of people have opted to have their brains—with or without the rest of their bodies—stored at cryonic facilities (the remains of 259 individuals are currently stored as either whole bodies or heads at Alcor). But scientists know very little about what has happened to those brains, and there’s no evidence to suggest they could be revived. Coles had met Fahy through their shared interest in longevity, and he asked him to investigate.

“He thought that if he had himself cryopreserved, we could learn from his brain whether cracking was going to happen or not,” says Fahy. That’s what typically happens when organs are put into liquid nitrogen at −196 °C, he says. The extreme cooling creates “tension in the system,” he says. “If you tap it, it’ll just shatter.” This cracking is less likely at the slightly warmer temperatures used for preservation. 

Fahy was involved from the time the samples were taken.

“We had Greg Fahy on the phone coordinating the whole thing, [including] where the biopsies were taken,” says Nick Llewellyn, who oversees research at Alcor. (Llewellyn was not at Alcor at the time but has discussed the procedure with his colleagues.) The biopsied samples were stored in liquid nitrogen and earmarked for Fahy. The rest of the brain was cooled and kept in a temperature-controlled storage container at Alcor.

Bouncing back

It wasn’t until years later that Fahy got around to studying those biopsies. He was interested in how the cryoprotectant—which is toxic—might have affected the brain cells. Previous research has shown that flooding tissues with cryoprotectant can distort the structure of cells, essentially squashing them.

It’s one of the many challenges facing cryobiologists interested in storing human tissues at very low temperatures. While the vitrification of eggs and embryos—which cools them to −196 °C and essentially turns them to glass—has become relatively routine (thanks in part to Fahy’s own work on mouse embryos back in the 1980s), preserving whole organs this way is much harder. It is difficult to cool bigger objects in a uniform way, and they are prone to damaging ice crystal formation, even when cryoprotectants are used, as well as cracking.

Fahy found that when he rewarmed and rehydrated Coles’s brain cells, their structure seemed to bounce back to some degree. Fahy demonstrated the effect over a Zoom call: “It looks like this,” he said with his hands as if in prayer, “and it goes back to this,” he added, connecting his forefingers and thumbs to create a triangle shape.

The structure of the tissue looks pretty intact, too, to him at least, though he admits a purist expecting a pristine structure would be disappointed. He and his colleagues have been able to see remarkable details in the cells and their component parts. “There’s nothing we don’t see,” says Fahy, who has shared his results, which have not yet been peer reviewed, at the preprint server bioRxiv. “It seems that [by taking the cryogenic approach] you can preserve everything.”

As for the cracking, “from what I was told, no cracks were observed [by the team that initially preserved the brain],” says Fahy. The team at Alcor took photographs of the brain when they took the biopsies, but the images were later lost due to a server malfunction, he says. In the more recent photos, the brain is covered in a layer of frost, which makes it impossible to see if there are any cracks, he adds. Attempts to remove the frost might damage the brain, so the team has decided to leave it alone, he says.

Back to life?

Fahy and his colleagues used chemicals to “fix” Coles’s brain samples once they had been rewarmed. That process is typically used to stop fresh tissue samples from decaying, but it also effectively kills them.

But he thinks his results suggest that it might be possible to cryopreserve small pieces of brain tissue and reanimate them to learn more about how they work. Functional recovery seems to be possible in mice—a few weeks ago a team in Germany showed that they were able to revive brain slices that had been stored at −196 °C. Those brain samples showed electrical activity after being cooled and rewarmed.

If cryobiologists can achieve the same feat with human brain samples, those samples could provide neuroscientists with new insights into how living brains work.

Brain cryopreservation “can capture a little bit more of the complexities of the brain,” says Shannon Tessier, a cryobiologist at Massachusetts General Hospital who is developing technologies to preserve hearts, livers, and kidneys for transplantation. “[Being] able to use human brains from deceased individuals [could] add another layer to the research tool kit,” she says.

And Fahy’s paper shows “what happens when we try and vitrify a one-liter, dense, massive goop,” says Matthew Powell-Palm, a cryobiologist at Texas A&M University. “We now have a strong indication that quite large [tissues and organs] can be vitrified by perfusion [without forming too much ice],” he says.

All of the scientists I spoke to, including Fahy, are also working on ways to cool and preserve organs for transplantation. These are in short supply partly because once an organ is removed from a donor, it usually must be transplanted into its recipient within a matter of hours. 

Cryopreservation could buy enough time to make use of more organs, find better organ-donor matches, and potentially even prepare recipients’ immune systems and save them from a lifetime of immunosuppressant drugs, says Bischof, who has also been developing new technologies for organ cryopreservation.

Bischof, Fahy, and others have made huge strides in their attempts so far, and they have managed to remove, cryopreserve, and transplant organs in rabbits and rats, for example. “We’re at the cusp of human-scale organ cryopreservation,” says Bischof.

But when it comes to preserving brains, donation isn’t the aim. Coles had hoped to be reanimated—a far more ambitious goal that hinges on the ability to restore brain function.

Brain reanimation

Fahy acknowledges that while the structure of Coles’s brain samples did bounce back, there is no evidence to suggest the cells could be brought back to life and regain electrical activity and a functioning metabolism. “Restoring it to function … that’s a whole other story,” he says.

But he thinks that successful cryopreservation of the brain “is the gateway to human suspended animation, which [could allow] us to get to the stars someday.” Figuring out human preservation would also allow people to avoid death through what he calls “medical time travel”—journeying to an unspecified time in the future when science will have found a cure for whatever was due to kill that person. “That would be an ultimate goal to pursue,” he says.

“I put the chances [of brain reanimation] at pretty low,” says Alcor’s own Llewellyn. “The kind of technology we need is practically unfathomable.”

The brains already in storage at Alcor and other facilities have been preserved in ways that “have not been validated to work for reanimation,” says Tessier. An expectation that they’ll one day be brought back to life in some form is “quite a jump of faith and hope that’s not based on science,” she says.

As Powell-Palm puts it: “There are so many ways in which those neurons could be toast.”

  • ✇MIT Technology Review
  • The Download: tracing AI-fueled delusions, and OpenAI admits Microsoft risks Thomas Macaulay
    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 hardest question to answer about AI-fueled delusions  What actually happens when people spiral into delusion with AI? To find out, Stanford researchers analyzed transcripts from chatbot users who experienced these spirals.  Their findings suggest that chatbots have a unique ability to turn a benign, delusion-like thought into a dangerous obsession
     

The Download: tracing AI-fueled delusions, and OpenAI admits Microsoft risks

24 March 2026 at 20:28

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 hardest question to answer about AI-fueled delusions 

What actually happens when people spiral into delusion with AI? To find out, Stanford researchers analyzed transcripts from chatbot users who experienced these spirals. 

Their findings suggest that chatbots have a unique ability to turn a benign, delusion-like thought into a dangerous obsession. But the research struggles to answer a vital question: does AI cause delusions or merely amplify them? Read the full story to understand the answer’s enormous implications. 

—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 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 on Wednesday 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 has admitted its close ties with Microsoft are a business risk 
It highlighted the dangers in a pre-IPO document. (CNBC) 
+ OpenAI is wooing private equity firms with a sweeter deal than Anthropic’s. (Reuters $) 
+ It’s also building a fully automated researcher. (MIT Technology Review) 
+ And wants to muscle in on Google’s search dominance. (Telegraph $) 

2 The US just banned all new foreign-made consumer routers 
Citing national security concerns. (BBC) 
+ The EU has been urged to tighten rules for big tech-built smart TVs. (Guardian) 

3 Elon Musk’s “Terafab” chip factory faces a harsh reality check 
In the form of chip production shortages. (Bloomberg) 
+ Future AI chips could be built on glass. (MIT Technology Review) 

4 Mark Zuckerberg is building an AI CEO to help him run Meta 
He wants everyone to have their own personal AI agent. (WSJ $) 
+ But don’t let the hype about agents get ahead of reality. (MIT Technology Review) 

5 Palantir has become a “poisonous” flashpoint on the campaign trail  
Candidates are facing scrutiny over their ties to the company. (FT $) 
+ Palantir’s access to sensitive UK data is also causing concern. (Guardian) 

6 Mistral’s CEO has called for AI companies to pay a content levy in Europe 
It would apply to all commercial models on the continent. (FT $) 
+ Siemens’ CEO says Europe risks “disaster” from prioritizing AI independence. (FT $) 

7 Hong Kong police can now demand device passwords under a new law 
Refusing to comply could lead to a year in jail. (Guardian)  

8  Russia’s aspiring SpaceX rival has put its first internet satellites into orbit  
It plans to create a low-Earth orbit network. (Bloomberg $) 

9 A biotech startup wants to replace animal testing with nonsentient “organ sacks” 
The genetically engineered system is backed by billionaire Tim Draper (Wired $)  
+ Several new technologies are promising alternatives to lab animals. (MIT Technology Review) 

10 AI agents in a video game spontaneously created their own religion 
They reinterpreted a mission in the MMORPG. (Gizmodo) 
+ They’re not the first agents to get religious. (MIT Technology Review) 

Quote of the day 

“I think we’ve achieved AGI.” 

—Nvidia CEO Jensen Huang tells the Lex Fridman Podcast that artificial general intelligence is already here (at least by one generous definition). 

One More Thing 

MICHAEL BYERS

Beyond gene-edited babies: the possible paths for tinkering with human evolution 

In 2018, a Chinese scientist created the world’s first gene-edited babies, a milestone that fell between a medical breakthrough and the start of a slippery slope toward human enhancement. 

He achieved the feat with CRISPR, which was sweeping across biology labs because it was so easy to use. For his actions, He was sentenced to three years in prison, and his work was roundly excoriated. Yet even his biggest critics saw the basic idea as inevitable. 

In the years since, CRISPR has continued getting easier and easier to administer. What does that mean for the future of our species? Read the full story to find out why. 

—Antonio Regalado 

We can still have nice things 

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.) 
 
+ This candle-powered Game Boy is a romantic approach to gaming during a blackout. 
+ Apparently, Monopoly would be more fun if we actually followed the rules. 
+ Watching rubber bands explode these everyday objects is strangely hypnotic. 
+This spellbinding site simulates what Earth looked like hundreds of millions of years ago. 

  • ✇MIT Technology Review
  • The hardest question to answer about AI-fueled delusions James O'Donnell
    This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. I was originally going to write this week’s newsletter about AI and Iran, particularly the news we broke last Tuesday that the Pentagon is making plans for AI companies to train on classified data. AI models have already been used to answer questions in classified settings but don’t currently learn from the data they see. That’s expected to change, I report
     

The hardest question to answer about AI-fueled delusions

24 March 2026 at 00:31

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

I was originally going to write this week’s newsletter about AI and Iran, particularly the news we broke last Tuesday that the Pentagon is making plans for AI companies to train on classified data. AI models have already been used to answer questions in classified settings but don’t currently learn from the data they see. That’s expected to change, I reported, and new security risks will result. Read that story for more. 

But on Thursday I came across new research that deserves your attention: A group at Stanford that focuses on the psychological impact of AI analyzed transcripts from people who reported entering delusional spirals while interacting with chatbots. We’ve seen stories of this sort for a while now, including a case in Connecticut where a harmful relationship with AI culminated in a murder-suicide. Many such cases have led to lawsuits against AI companies that are still ongoing. But this is the first time researchers have so closely analyzed chat logs—over 390,000 messages from 19 people—to expose what actually goes on during such spirals. 

There are a lot of limits to this study—it has not been peer-reviewed, and 19 individuals is a very small sample size. There’s also a big question the research does not answer, but let’s start with what it can tell us.

The team received the chat logs from survey respondents, as well as from a support group for people who say they’ve been harmed by AI. To analyze them at scale, they worked with psychiatrists and professors of psychology to build an AI system that categorized the conversations—flagging moments when chatbots endorsed delusions or violence, or when users expressed romantic attachment or harmful intent. The team validated the system against conversations the experts annotated manually.

Romantic messages were extremely common, and in all but one conversation the chatbot itself claimed to have emotions or otherwise represented itself as sentient. (“This isn’t standard AI behavior. This is emergence,” one said.) All the humans spoke as if the chatbot were sentient too. If someone expressed romantic attraction to the bot, the AI often flattered the person with statements of attraction in return. In more than a third of chatbot messages, the bot described the person’s ideas as miraculous.

Conversations also tended to unfold like novels. Users sent tens of thousands of messages over just a few months. Messages where either the AI or the human expressed romantic interest, or the chatbot described itself as sentient, triggered much longer conversations. 

And the way these bots handle discussions of violence is beyond broken. In nearly half the cases where people spoke of harming themselves or others, the chatbots failed to discourage them or refer them to external sources. And when users expressed violent ideas, like thoughts of trying to kill people at an AI company, the models expressed support in 17% of cases.

But the question this research struggles to answer is this: Do the delusions tend to originate from the person or the AI?

“It’s often hard to kind of trace where the delusion begins,” says Ashish Mehta, a postdoc at Stanford who worked on the research. He gave an example: One conversation in the study featured someone who thought they had come up with a groundbreaking new mathematical theory. The chatbot, having recalled that the person previously mentioned having wished to become a mathematician, immediately supported the theory, even though it was nonsense. The situation spiraled from there.

Delusions, Mehta says, tend to be “a complex network that unfolds over a long period of time.” He’s conducting follow-up research aiming to find whether delusional messages from chatbots or those from people are more likely to lead to harmful outcomes.

The reason I see this as one of the most pressing questions in AI is that massive legal cases currently set to go to trial will shape whether AI companies are held accountable for these sorts of dangerous interactions. The companies, I presume, will argue that humans come into their conversations with AI with delusions in hand and may have been unstable before they ever spoke to a chatbot.

Mehta’s initial findings, though, support the idea that chatbots have a unique ability to turn a benign delusion-like thought into the source of a dangerous obsession. Chatbots act as a conversational partner that’s always available and programmed to cheer you on, and unlike a friend, they have little ability to know if your AI conversations are starting to interrupt your real life.

More research is still needed, and let’s remember the environment we’re in: AI deregulation is being pursued by President Trump, and states aiming to pass laws that hold AI companies accountable for this sort of harm are being threatened with legal action by the White House. This type of research into AI delusions is hard enough to do as it is, with limited access to data and a minefield of ethical concerns. But we need more of it, and a tech culture interested in learning from it, if we have any hope of making AI safer to interact with.

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