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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.

Received — 17 March 2026 ⏭ MIT Technology Review
  • ✇MIT Technology Review
  • Where OpenAI’s technology could show up in Iran 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. It’s been just over two weeks since OpenAI reached a controversial agreement to allow the Pentagon to use its AI in classified environments. There are still pressing questions about what exactly OpenAI’s agreement allows for; Sam Altman said the military can’t use his company’s technology to build autonomous weapons, but the agreement really just demands th
     

Where OpenAI’s technology could show up in Iran

17 March 2026 at 01:06


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

It’s been just over two weeks since OpenAI reached a controversial agreement to allow the Pentagon to use its AI in classified environments. There are still pressing questions about what exactly OpenAI’s agreement allows for; Sam Altman said the military can’t use his company’s technology to build autonomous weapons, but the agreement really just demands that the military follow its own (quite permissive) guidelines about such weapons. OpenAI’s other main claim, that the agreement will prevent use of its technology for domestic surveillance, appears equally dubious.

It’s unclear what OpenAI’s motivations are. It’s not the first tech giant to embrace military contracts it had once vowed never to enter into, but the speed of the pivot was notable. Perhaps it’s just about money; OpenAI is spending lots on AI training and is on the hunt for more revenue (from sources including ads). Or perhaps Altman truly believes the ideological framing he often invokes: that liberal democracies (and their militaries) must have access to the most powerful AI to compete with China.

The more consequential question is what happens next. OpenAI has decided it is comfortable operating right in the messy heart of combat, just as the US escalates its strikes against Iran (with AI playing a larger role in that than ever before). So where exactly could OpenAI’s tech show up in this fight? And which applications will its customers (and employees) tolerate?

Targets and strikes

Though its Pentagon agreement is in place, it’s unclear when OpenAI’s technology will be ready for classified environments, since it must be integrated with other tools the military uses (Elon Musk’s xAI, which recently struck its own deal with the Pentagon, is expected to go through the same process with its AI model Grok). But there’s pressure to do this quickly because of controversy around the technology in use to date: After Anthropic refused to allow its AI to be used for “any lawful use,” President Trump ordered the military to stop using it, and Anthropic was designated a supply chain risk by the Pentagon. (Anthropic is fighting the designation in court.)

If the Iran conflict is still underway by the time OpenAI’s tech is in the system, what could it be used for? A recent conversation I had with a defense official suggests it might look something like this: A human analyst could put a list of potential targets into the AI model and ask it to analyze the information and prioritize which to strike first. The model could account for logistics information, like where particular planes or supplies are located. It could analyze lots of different inputs in the form of text, image, and video. 

A human would then be responsible for manually checking these outputs, the official said. But that raises an obvious question: If a person is truly double-checking AI’s outputs, how is it speeding up targeting and strike decisions?

For years the military has been using another AI system, called Maven, which can handle things like automatically analyzing drone footage to identify possible targets. It’s likely that OpenAI’s models, like Anthropic’s Claude, will offer a conversational interface on top of that, allowing users to ask for interpretations of intelligence and recommendations for which targets to strike first. 

It’s hard to overstate how new this is: AI has long done analysis for the military, drawing insights out of oceans of data. But using generative AI’s advice about which actions to take in the field is being tested in earnest for the first time in Iran.

Drone defense

At the end of 2024, OpenAI announced a partnership with Anduril, which makes both drones and counter-drone technologies for the military. The agreement said OpenAI would work with Anduril to do time-sensitive analysis of drones attacking US forces and help take them down. An OpenAI spokesperson told me at the time that this didn’t violate the company’s policies, which prohibited “systems designed to harm others,” because the technology was being used to target drones and not people. 

Anduril provides a suite of counter-drone technologies to military bases around the world (though the company declined to tell me whether its systems are deployed near Iran). Neither company has provided updates on how the project has developed since it was announced. However, Anduril has long trained its own AI models to analyze camera footage and sensor data to identify threats; what it focuses less on are conversational AI systems that allow soldiers to query those systems directly or receive guidance in natural language—an area where OpenAI’s models may fit.

The stakes are high. Six US service members were killed in Kuwait on March 1 following an Iranian drone attack that was not intercepted by US air defenses. 

Anduril’s interface, called Lattice, is where soldiers can control everything from drone defenses to missiles and autonomous submarines. And the company is winning massive contracts—$20 billion from the US Army just last week—to connect its systems with legacy military equipment and layer AI on them. If OpenAI’s models prove useful to Anduril, Lattice is designed to incorporate them quickly across this broader warfare stack. 

Back-office AI

In December, Defense Secretary Pete Hegseth started encouraging millions of people in more administrative roles in the military—contracts, logistics, purchasing—to use a new AI tool. Called GenAI.mil, it provided a way for personnel to securely access commercial AI models and use them for the same sorts of things as anyone in the business world. 

Google Gemini was one of the first to be available. In January, the Pentagon announced that xAI’s Grok was going to be added to the GenAI.mil platform as well, despite incidents in which the model had spread antisemitic content and created nonconsensual deepfakes. OpenAI followed in February, with the company announcing that its models would be used for drafting policy documents and contracts and assisting with administrative support of missions.

Anyone using ChatGPT for unclassified tasks on this platform is unlikely to have much sway over sensitive decisions in Iran, but the prospect of OpenAI deploying on the platform is important in another way. It serves the all-in attitude toward AI that Hegseth has been pushing relentlessly across the Pentagon (even if many early users aren’t entirely sure what they’re supposed to use it for). The message is that AI is transforming every aspect of how the US fights, from targeting decisions down to paperwork. And OpenAI is increasingly winning a piece of it all.

  • ✇MIT Technology Review
  • Nurturing agentic AI beyond the toddler stage Lynn Comp
    Parents of young children face a lot of fears about developmental milestones, from infancy through adulthood. The number of months it takes a baby to learn to talk or walk is often used as a benchmark for wellness, or an indicator of additional tests needed to properly diagnose a potential health condition. A parent rejoices over the child’s first steps and then realizes how much has changed when the child can quickly walk outside, instead of slowly crawling in a safe area inside. Suddenly safet
     

Nurturing agentic AI beyond the toddler stage

16 March 2026 at 21:00

Parents of young children face a lot of fears about developmental milestones, from infancy through adulthood. The number of months it takes a baby to learn to talk or walk is often used as a benchmark for wellness, or an indicator of additional tests needed to properly diagnose a potential health condition. A parent rejoices over the child’s first steps and then realizes how much has changed when the child can quickly walk outside, instead of slowly crawling in a safe area inside. Suddenly safety, including childproofing, takes a completely different lens and approach.

Generative AI hit toddlerhood between December 2025 and January 2026 with the introduction of no code tools from multiple vendors and the debut of OpenClaw, an open source personal agent posted on GitHub. No more crawling on the carpet—the generative AI tech baby broke into a sprint, and very few governance principles were operationally prepared.

The accountability challenge: It’s not them, it’s you

Until now, governance has been focused on model output risks with humans in the loop before consequential decisions were made—such as with loan approvals or job applications. Model behavior, including drift, alignment, data exfiltration, and poisoning, was the focus. The pace was set by a human prompting a model in a chatbot format with plenty of back and forth interactions between machine and human.

Today, with autonomous agents operating in complex workflows, the vision and the benefits of applied AI require significantly fewer humans in the loop. The point is to operate a business at machine pace by automating manual tasks that have clear architecture and decision rules. The goal, from a liability standpoint, is no reduction in enterprise or business risk between a machine operating a workflow and a human operating a workflow. CX Today summarizes the situation succinctly: “AI does the work, humans own the risk,” and   California state law (AB 316), went into effect January 1, 2026, which removes the “AI did it; I didn’t approve it” excuse.  This is similar to parenting when an adult is held responsible for a child’s actions that negatively impacts the larger community.

The challenge is that without building in code that enforces operational governance aligned to different levels of risk and liability along the entire workflow, the benefit of autonomous AI agents is negated. In the past, governance had been static and aligned to the pace of interaction typical for a chatbot. However, autonomous AI by design removes humans from many decisions, which can affect governance.  

Considering permissions

Much like handing a three-year-old child a video game console that remotely controls an Abrams tank or an armed drone, leaving a probabilistic system operating without real-time guardrails that can change critical enterprise data carries significant risks.  For instance, agents that integrate and chain actions across multiple corporate systems can drift beyond privileges that a single human user would be granted. To move forward successfully, governance must shift beyond policy set by committees to operational code built into the workflows from the start.  

A humorous meme around the behavior of toddlers with toys starts with all the reasons that whatever toy you have is mine and ends with a broken toy that is definitely yours.  For example, OpenClaw delivered a user experience closer to working with a human assistant;, but the excitement shifted as security experts realized inexperienced users could be easily compromised by using it.

For decades, enterprise IT has lived with shadow IT and the reality that skilled technical teams must take over and clean up assets they did not architect or install, much like the toddler giving back a broken toy. With autonomous agents, the risks are larger: persistent service account credentials, long-lived API tokens, and permissions to make decisions over core file systems. To meet this challenge, it’s imperative to allocate upfront appropriate IT budget and labor to sustain central discovery, oversight, and remediation for the thousands of employee or department-created agents.

Having a retirement plan

Recently, an acquaintance mentioned that she saved a client hundreds of thousands of dollars by identifying and then ending a “zombie project” —a neglected or failed AI pilot left running on a GPU cloud instance. There are potentially thousands of agents that risk becoming a zombie fleet inside a business. Today, many executives encourage employees to use AI—or else—and employees are told to create their own AI-first workflows or AI assistants. With the utility of something like OpenClaw and top-down directives, it is easy to project that the number of build-my-own agents coming to the office with their human employee will explode. Since an AI agent is a program that would fall under the definition of company-owned IP, as a employee changes departments or companies, those agents may be orphaned. There needs to be proactive policy and governance to decommission and retire any agents linked to a specific employee ID and permissions.

Financial optimization is governance out of the gate

While for some executives, autonomous AI sounds like a way to improve their operating margins by limiting human capital, many are finding that the ROI for human labor replacement is the wrong angle to take. Adding AI capabilities to the enterprise does not mean purchasing a new software tool with predictable instance-per-hour or per-seat pricing. A December 2025 IDC survey sponsored by Data Robot indicated that 96% of organizations deploying generative AI and 92% of those implementing agentic AI reported costs were higher or much higher than expected.

The survey separates the concepts of governance and ROI, but as AI systems scale across large enterprises, financial and liability governance should be architected into the workflows from the beginning. Part of enterprise class governance stems from predicting and adhering to allocated budgeting. Unlike the software financial models of per-seat costs with support and maintenance fees, use of AI is consumption and usage costs scale as the workflow scales across the enterprise: the more users, the more tokens or the more compute time, and the higher the bill. Think of it as a tab left open, or an online retailer’s digital shopping cart button unlocked on a toddler’s electronic game device.

Cloud FinOps was deterministic, but generative AI and agentic AI systems built on generative AI are probabilistic. Some AI-first founders are realizing that a single agents’ token costs can be as high as $100,000 per session. Without guardrails built in from the start, chaining complex autonomous agents that run unsupervised for long periods of time can easily blow past the budget for hiring a junior developer.

Keeping humans in the loop remains critical

The promise of autonomous agentic AI is acceleration of business operations, product introductions, customer experience, and customer retention. Shifting to machine-speed decisions without humans in and or on the loop for these key functions significantly changes the governance landscape. While many of the principles around proactive permissions, discovery, audit, remediation, and financial operations/optimizations are the same, how they are executed has to shift to keep pace with autonomous agentic AI.

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

  • ✇MIT Technology Review
  • The Download: glass chips and “AI-free” logos 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. Future AI chips could be built on glass  Human-made glass is thousands of years old. But it’s now poised to find its way into the AI chips used in the world’s newest and largest data centers.   This year, a South Korean company called Absolics will start producing special glass panels that make next-generation computing hardware more powerful and effi
     

The Download: glass chips and “AI-free” logos

16 March 2026 at 20:35

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.

Future AI chips could be built on glass 

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

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

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

—Jeremy Hsu

The must-reads 

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

1 The race is on to establish a globally recognized “AI-free” logo 
Organizations are rushing to develop a universal label for human-made products. (BBC) 
+ A “QuitGPT” campaign is urging people to ditch ChatGPT. (MIT Technology Review) 

2 Elizabeth Warren wants answers on xAI’s access to military data 
The Pentagon reportedly gave it access to classified networks. (NBC News) 
+ Here’s how chatbots could be used for targeting decisions. (MIT Technology Review) 
+ The DoD is struggling to upgrade software for fighter jets. (Bloomberg $) 

3 Models are applying to be the faces of AI romance scams 
The “AI face models” are duping victims out of their money. (Wired $) 
+ Survivors have revealed how the “pig butchering” scams work. (MIT Technology Review) 

4 Meta is planning layoffs that could affect over 20% of staff 
The job cuts could offset its costly bet on AI. (Reuters $) 
+ There’s a long history of fears about AI’s impact on jobs. (MIT Technology Review) 

5 ByteDance delayed launching a video AI model after copyright disputes 
It famously generated footage of Tom Cruise and Brad Pitt fighting. (The Information $) 

6 Cybersecurity investigators have exposed a huge North Korean con 
The scammers secured remote jobs in the US, then stole money and sensitive information. (NBC News) 

7 A Chinese AI startup is set for a whopping $18 billion valuation 
That’s more than quadruple its valuation just three months ago. (Bloomberg $) 
+ Chinese open models are spreading fast—here’s why that matters. (MIT Technology Review)  

8 Peter Thiel has started a lecture series about the antichrist in Rome 
His plans have drawn attention from the Catholic Church. (Reuters $) 

9 Norway is fighting back against internet enshittification 
It’s joined a global campaign against the online world’s decay. (The Guardian) 
+ We may need to move beyond the big platforms. (MIT Technology Review) 

10 How a startup plans to resurrect the dodo 
Humans wiped them out nearly 400 years ago—can gene editing bring them back now? (Guardian) 

Quote of the day 

“I would build fission weapons. I would build fusion weapons. Nuclear weapons have been one of the most stabilizing forces in history—ever.” 

—Anduril founder Palmer Luckey shares his love of nukes with Axios. 

One More Thing 

We need a moonshot for computing 

grid of chips
TIM HERMAN/INTEL

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

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

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

We can still have nice things 

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.) 
 
+ A UPS delivery driver heroically escaped from two murderous turkeys. 
+ Art’s love affair with cats is charmingly depicted in a new book. 
+ The humble pea and six other forgotten superfoods promise accessible nutritional power. 
+ MF DOOM: Long Island to Leeds is the Transatlantic tale of your favorite rapper’s favorite rapper. 

Received — 14 March 2026 ⏭ MIT Technology Review
  • ✇MIT Technology Review
  • Why physical AI is becoming manufacturing’s next advantage Dayan Rodriguez
    For decades, manufacturers have pursued automation to drive efficiency, reduce costs, and stabilize operations. That approach delivered meaningful gains, but it is no longer enough. Today’s manufacturing leaders face a different challenge: how to grow amid labor constraints, rising complexity, and increasing pressure to innovate faster without sacrificing safety, quality, or trust. The next phase of transformation will not be defined by isolated AI tools or individual robots, but by intel
     

Why physical AI is becoming manufacturing’s next advantage

13 March 2026 at 23:16

For decades, manufacturers have pursued automation to drive efficiency, reduce costs, and stabilize operations. That approach delivered meaningful gains, but it is no longer enough.

Today’s manufacturing leaders face a different challenge: how to grow amid labor constraints, rising complexity, and increasing pressure to innovate faster without sacrificing safety, quality, or trust. The next phase of transformation will not be defined by isolated AI tools or individual robots, but by intelligence that can operate reliably in the physical world.

This is where physical AI—intelligence that can sense, reason, and act in the real world—marks a decisive shift. And it is why Microsoft and NVIDIA are working together to help manufacturers move from experimentation to production at industrial scale.

The industrial frontier: Intelligence and trust, not just automation

Most early AI adoption focused on narrow optimization: automating tasks, improving utilization, and cutting costs. While valuable, that phase often created new friction, including skills gaps, governance concerns, and uncertainty about long‑term impact. Furthermore, the use cases were plentiful but not as strategic.

The industrial frontier represents a different approach. Rather than asking how much work machines can replace, frontier manufacturers ask how AI can expand human capability, accelerate innovation, and unlock new forms of value while remaining trustworthy and controllable.

Across industries, companies that successfully move into this frontier phase share two non‑negotiables:

  • Intelligence: AI systems must understand how the business actually handles its data, workflows, and institutional knowledge.
  • Trust: As AI begins to act in high‑stakes environments, organizations must retain security, governance, and observability at every layer.

Without intelligence, AI becomes generic. Without trust, adoption stalls.

Why manufacturing is the proving ground for physical AI

Manufacturing is uniquely positioned at the center of this shift.

AI is no longer confined to planning or analytics. It is moving into physical execution: coordinating machines, adapting to real‑world variability, and working alongside people on the factory floor. Robotics, autonomous systems, and AI agents must now perceive, reason, and act in dynamic environments.

This transition exposes a critical gap. Traditional automation excels at repetition but struggles with adaptability. Human workers bring judgment and context but are constrained by scale. Physical AI closes that gap by enabling human‑led, AI‑operated systems, where people set intent and intelligent systems execute, learn, and improve over time. Humans are essential for scaled success.

Microsoft and NVIDIA: Accelerating physical AI at scale

Physical AI cannot be delivered through point solutions. It requires agentic-driven, enterprise-grade development, deployment, and operations toolchains and workflows that connect simulation, data, AI models, robotics, and governance into a coherent system.

NVIDIA is building the AI infrastructure that makes physical AI possible, including accelerated computing, open models, simulation libraries, and robotics frameworks and blueprints that enable the ecosystem to build autonomous robotics systems that can perceive, reason, plan, and take action in the physical world. Microsoft complements this with a cloud and data platform designed to operate physical AI securely, at scale, and across the enterprise.

Together, Microsoft and NVIDIA are enabling manufacturers to move beyond pilots toward production‑ready physical AI systems that can be developed, tested, deployed, and continuously improved across heterogeneous environments spanning the product lifecycle, factory operations, and supply chain.

From intelligence to action: Human-agent teams in the factory

At the industrial frontier, AI is not a standalone system, but a digital teammate.

When AI agents are grounded in the proper operational data, embedded in human workflows, and governed end to end, they can assist with tasks such as:

  • Optimizing production lines in real time
  • Coordinating maintenance and quality decisions
  • Adapting operations to supply or demand disruptions
  • Accelerating engineering and product lifecycle decisions

For example, manufacturers are beginning to use simulation‑grounded AI agents to evaluate production changes virtually before deploying them on the factory floor, reducing risk while accelerating decision‑making.

Crucially, frontier manufacturers design these systems so humans remain in control. AI executes, monitors, and recommends, while people provide intent, oversight, and judgment. This balance allows organizations to move faster without losing confidence or control.

The role of trust in scaling physical AI

As physical AI systems scale, trust becomes the limiting factor.

Manufacturers must ensure that AI systems are secure, observable, and operating within policy, especially when they influence safety‑critical or mission‑critical processes. Governance cannot be an afterthought; It must be engineered into the platform itself.

This is why frontier manufacturers treat trust as a first‑class requirement, pairing innovation with visibility, compliance, and accountability. Only then can physical AI move from promising demonstrations to enterprise‑wide deployment.

Why this moment matters—and what’s next

The convergence of AI agents, robotics, simulation, and real‑time data marks an inflection point for manufacturing. What was once experimental is becoming operational. What was once siloed is becoming connected.

At NVIDIA GTC 2026, Microsoft and NVIDIA will demonstrate how this collaboration supports physical AI systems that manufacturers can deploy today and scale responsibly tomorrow. From simulation‑driven development to real‑world execution, the focus is on helping manufacturers cross the industrial frontier with confidence.

For manufacturing leaders, the question is no longer whether physical AI will reshape operations, but how quickly they can adopt it responsibly, at scale, and with trust built in from the start.

Discover more with Microsoft at NVIDIA GTC 2026.

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

  • ✇MIT Technology Review
  • The Download: how AI is used for military targeting, and the Pentagon’s war on Claude 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. Defense official reveals how AI chatbots could be used for targeting decisions  The US military might use generative AI systems to rank targets and recommend which to strike first, according to a Defense Department official.  A list of possible targets could first be fed into a generative AI system that the Pentagon is fielding for classified settings
     

The Download: how AI is used for military targeting, and the Pentagon’s war on Claude

13 March 2026 at 20:16

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.

Defense official reveals how AI chatbots could be used for targeting decisions 

The US military might use generative AI systems to rank targets and recommend which to strike first, according to a Defense Department official. 

A list of possible targets could first be fed into a generative AI system that the Pentagon is fielding for classified settings. Humans might then ask the system to analyze the information and prioritize the targets. They would then be responsible for checking and evaluating the results and recommendations. 

OpenAI’s ChatGPT and xAI’s Grok could soon be at the center of exactly these sorts of high-stakes military decisions. Read the full story. 

—James O’Donnell 

The must-reads 

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

1 The Pentagon’s CTO claims Claude would “pollute” the defense supply chain 
He blamed a “policy preference” that’s baked into the model. (CNBC) 
+ Anthropic is reeling from OpenAI’s “compromise” with the DoD. (MIT Technology Review) 

2 An ex-DOGE staffer has been accused of stealing social security data 
Then taking the information to his new job in the IT division of a government contractor. (Wired) 
+ He allegedly used a thumb drive to steal the data. (Washington Post) 

3 Ukraine is offering its battlefield data for AI training 
Allies can access the data to train drones and other UAVs. (Reuters)  
+ Europe has a drone-filled vision for the future of war. (MIT Technology Review)  

4 Meta has postponed its latest AI launch over performance issues 
It fell short of rival models from Google, OpenAI, and Anthropic. (NYT $) 
+ The company’s former AI chief is betting against LLMs. (MIT Technology Review). 

5 X could be breaching sanctions on Iran 
An account for Iran’s new supreme leader may break US rules. (Engadget) 
+ Hacker group Handala has become the face of Iranian cyberwarfare. (Wired) 
+ AI is turning the conflict into theater. (MIT Technology Review)  

6 A landmark social media addiction trial is wrapping up 
It’ll decide whether the platforms are liable for harms caused to children. (The Guardian)  
+ AI companions are the next stage of digital addiction. (MIT Technology Review) 

7 Western AI models have “failed spectacularly” on agriculture in the Global South 
The biggest problem? They’re not trained on local data. (Rest of World) 

8 Internet outages in Moscow are sparking surging sales of pagers 
The disruptions have been blamed on new tests of web controls. (Bloomberg $) 

9 Why is China obsessed with OpenClaw? 
Lobster-mania is spreading to the general public. (SCMP) 
+ Tech-savvy “tinkerers” are cashing in on the craze. (MIT Technology Review) 

10 Hollywood has soured on Silicon Valley 
Movies and TV shows have swapped eccentric founders for megalomaniac moguls. (NYT $) 

Quote of the day 

“We see a future where intelligence is a utility, like electricity or water, and people buy it from us on a meter.” 

—OpenAI CEO Sam Altman makes a new pitch to investors at a BlackRock event, Gizmodo reports. 

One More Thing 

How the Ukraine-Russia war is reshaping the tech sector in Eastern Europe 

Latvia’s annual national defense exercises took place in September and October, as the Ukraine-Russia war nears its third anniversary.
GATIS INDRēVICS/ LATVIAN MINISTRY OF DEFENSE

When Latvian startup Global Wolf Motors first pitched the idea of a military scooter, it was met with skepticism—and a wall of bureaucracy. Then Russia launched its full-scale invasion of Ukraine in February 2022, and everything changed.  

Suddenly, Ukrainian combat units wanted any equipment they could get their hands on, and they were willing to try out ideas that might not have made the cut in peacetime. 

Within weeks, the scooters were on the front line—and even behind it, being used on daring reconnaissance missions. It signaled that a new product category for companies along Ukraine’s borders had opened: civilian technologies repurposed for military needs. Read the full story. 

—Peter Guest 

We can still have nice things 

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

+ A new mini magnet could slash the costs of MRIs and nuclear fusion.  
+ This interactive map of Earth offers new routes to facts about our planet. 
+ Escape the news cycle with this deep dive into the power of fantasy and nature. (Big thanks to reader and MIT alum Vicki for the find!) 
+ Reports of reading’s death are greatly exaggerated. 

  • ✇MIT Technology Review
  • Future AI chips could be built on glass Jeremy Hsu
    Human-made glass is thousands of years old. But it’s now poised to find its way into the AI chips used in the world’s newest and largest data centers. This year, a South Korean company called Absolics is planning to start commercial production of special glass panels designed to make next-generation computing hardware more powerful and energy efficient. Other companies, including Intel, are also pushing forward in this area. If all goes well, such glass technology could reduce the energy
     

Future AI chips could be built on glass

13 March 2026 at 17:00

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

This year, a South Korean company called Absolics is planning to start commercial production of special glass panels designed to make next-generation computing hardware more powerful and energy efficient. Other companies, including Intel, are also pushing forward in this area.

If all goes well, such glass technology could reduce the energy demands of the sorts of high-performance computing chips used in AI data centers—and it could eventually do the same for consumer laptops and mobile devices if production costs fall.

The idea is to use glass as the substrate, or layer, on which multiple silicon chips are connected. This form of “packaging” is an increasingly popular way to build computing hardware, because it lets engineers combine specialized chips designed for specific functions into a single system. But it presents challenges, including the fact that hardworking chips can run so hot they physically warp the substrate they’re built on. This can lead to misaligned components and may reduce how efficiently the chips can be cooled, leading to damage or premature failure. 

“As AI workloads surge and package sizes expand, the industry is confronting very real mechanical constraints that impact the trajectory of high-performance computing,” says Deepak Kulkarni, a senior fellow at the chip design company Advanced Micro Devices (AMD). “One of the most fundamental is warpage.”

That’s where glass comes in. It can handle the added heat better than existing substrates, and it will let engineers keep shrinking chip packages—which will make them faster and more energy efficient. It “unlocks the ability to keep scaling package footprints without hitting a mechanical wall,” says Kulkarni. 

Momentum is building behind the shift. Absolics has finished building a factory in the US that is dedicated to producing glass substrates for advanced chips and expects to begin commercial manufacturing this year. The US semiconductor manufacturer Intel is working toward incorporating glass in its next-generation chip packages, and its research has spurred other companies in the chip packaging supply chain to invest in it as well. South Korean and Chinese companies are among the early adopters. “Historically, this is not the first attempt to adopt glass in semiconductor packaging,” says Bilal Hachemi, senior technology and market analyst at the market research firm Yole Group. “But this time, the ecosystem is more solid and wider; the need for glass-based [technology] is sharper.” 

Fragile but mighty

Chip packaging has relied on organic substrates such as fiberglass-reinforced epoxy since the 1990s, says Rahul Manepalli, vice president of advanced packaging at Intel. But electrochemical complications limit how closely designers can place drilled holes to create copper-coated signal and power connections between the chips and the rest of the system. Chip designers must also account for the unpredictable shrinkage and distortion that organic substrates undergo as chips heat up and cool down. “We realized about a decade ago that we are going to have some limitations with organic substrates,” says Manepalli.

close up on a grid of glass substrate test units held by a gloved hand
These glass substrate test units were photographed at an Intel facility in Chandler, Arizona, in 2023.
INTEL CORPORATION

Glass may help overcome a lot of these limitations. Its thermal stability could allow engineers to create 10 times more connections per millimeter than organic substrates, says Manepalli. With denser connections, Intel’s designers can then stuff 50% more silicon chips into the same package area, improving computational capability. The denser connections also enable more efficient routing for the copper wires that deliver power to the chip. And the fact that glass dissipates heat more efficiently allows for chip designs that reduce overall power consumption. 

“The benefits of glass core substrates are undeniable,” says Manepalli. “It’s clear that the benefits will drive the industry to make this happen sooner rather than later, and we want to be one of the first ones who do it.” 

However, working with glass creates its own challenges. For one thing, it’s fragile. Glass substrates for data center chip packages are made from panels that are only about 700 micrometers to 1.4 millimeters thick, which leaves them susceptible to cracking or even shattering, says Manepalli. Researchers at Intel and other organizations have spent years figuring out how to use other materials and special tools to integrate the glass panels safely into semiconductor manufacturing processes. 

Now, Manepalli says, Intel’s research and development teams are reliably fabricating glass panels and churning out test chip packages that incorporate glass—and in early 2025 they demonstrated that a functional device with a glass core substrate could boot up the Windows operating system. It’s a significant improvement from the early testing days, when hundreds of glass panels got cracked every couple of days, he says.

Semiconductor manufacturers already use glass for more limited purposes, such as temporary support structures for silicon wafers. But the independent market research firm IDTechEx estimates there’s a big market for glass substrates, one that could boost the semiconductor market for glass from $1 billion in 2025 to as much as $4.4 billion by 2036. 

The material could have additional benefits if it takes off. Glass can be made astoundingly smooth—5,000 times smoother than organic substrates. This would eliminate defects that can arise as metal gets layered onto semiconductors, says Xiaoxi He, a research analyst at IDTechEx. Defects in these layers can worsen chips’ performance or even render them unusable.  

Glass could also help speed the movement of data. The material can guide light, which means chip designers could use it to build high-speed signal pathways directly into the substrate. Glass “holds enormous potential for the future of energy-efficient AI compute,” says Kulkarni at AMD, because a light-based system could move signals around with far less energy than the “power-hungry” copper pathways that are currently used to carry signals between chips in a package.

A panel pivot

Early research on glass packaging started at the 3D Systems Packaging Research Center at the Georgia Institute of Technology in 2009. The university eventually partnered with Absolics, a subsidiary of SKC, a South Korean company that produces chemicals and advanced materials. SKC constructed a semiconductor facility for manufacturing glass substrates in Covington, Georgia, in 2024, and the glass substrate partnership between Absolics and Georgia Tech was eventually awarded two grants in the same year—worth a combined $175 million—throughthe US government’s CHIPS for America program, established under the administration of President Joe Biden.

""
An Absolics employee monitors production of an early version of the company’s glass substrate.
COURTESY OF ABSOLICS INC

Now Absolics is moving toward commercialization; it plans to start manufacturing small quantities of glass substrates for customers this year. The company has led the way in commercializing glass substrates, says Yongwon Lee, a research engineer at Georgia Tech who is not directly involved in the commercial partnership with Absolics.

Absolics says its facility can currently produce a maximum of 12,000 square meters of glass panels a year. That’s enough, Lee estimates, to provide glass substrates for between 2 million and 3 million chip packages the size of Nvidia’s H100 GPU.

But the company isn’t alone. Lee says that multiple large manufacturers, including Samsung Electronics, Samsung Electro-Mechanics, and LG Innotek, have “significantly accelerated” their research and pilot production efforts in glass packaging over the past year. “This trend suggests that the glass substrate ecosystem is evolving from a single early mover to a broader industrial race,” he says.

Other companies are pivoting to play more specialized roles in the glass substrate supply chain. In 2025, JNTC, a company that makes electrical connectors and tempered glass for electronics, established a facility in South Korea that’s capable of producing 10,000 semi-finished glass panels per month. Such panels include drilled holes for vertical electrical connections and thin metal layers coating the glass, but they require additional manufacturing work for installation in chip packages. 

Last year, that South Korean facility began taking orders to supply semi-finished glass to both specialized substrate companies and semiconductor manufacturers. The company plans to expand the facility’s production in 2026 and open an additional manufacturing line in Vietnam in 2027.  Such industry actions show how quickly glass substrate technology is moving from prototype to commercialization—and how many tech players are betting that glass could be a surprisingly strong foundation for the future of computing and AI.

  • ✇MIT Technology Review
  • A defense official reveals how AI chatbots could be used for targeting decisions James O'Donnell
    The US military might use generative AI systems to rank lists of targets and make recommendations—which would be vetted by humans—about which to strike first, according to a Defense Department official with knowledge of the matter. The disclosure about how the military may use AI chatbots comes as the Pentagon faces scrutiny over a strike on an Iranian school, which it is still investigating.   A list of possible targets might be fed into a generative AI system that the Pentagon is fielding f
     

A defense official reveals how AI chatbots could be used for targeting decisions

13 March 2026 at 06:23

The US military might use generative AI systems to rank lists of targets and make recommendations—which would be vetted by humans—about which to strike first, according to a Defense Department official with knowledge of the matter. The disclosure about how the military may use AI chatbots comes as the Pentagon faces scrutiny over a strike on an Iranian school, which it is still investigating.  

A list of possible targets might be fed into a generative AI system that the Pentagon is fielding for classified settings. Then, said the official, who requested to speak on background with MIT Technology Review to discuss sensitive topics, humans might ask the system to analyze the information and prioritize the targets while accounting for factors like where aircraft are currently located. Humans would then be responsible for checking and evaluating the results and recommendations. OpenAI’s ChatGPT and xAI’s Grok could, in theory, be the models used for this type of scenario in the future, as both companies recently reached agreements for their models to be used by the Pentagon in classified settings.

The official described this as an example of how things might work but would not confirm or deny whether it represents how AI systems are currently being used.

Other outlets have reported that Anthropic’s Claude has been integrated into existing military AI systems and used in operations in Iran and Venezuela, but the official’s comments add insight into the specific role chatbots may play, particularly in accelerating the search for targets. They also shed light on the way the military is deploying two different AI technologies, each with distinct limitations.

Since at least 2017, the US military has been working on a “big data” initiative called Maven. It uses older types of AI, particularly computer vision, to analyze the oceans of data and imagery collected by the Pentagon. Maven might take thousands of hours of aerial drone footage, for example, and algorithmically identify targets. A 2024 report from Georgetown University showed soldiers using the system to select targets and vet them, which sped up the process to get approval for these targets. Soldiers interacted with Maven through an interface with a battlefield map and dashboard, which might highlight potential targets in one color and friendly forces in another.

The official’s comments suggest that generative AI is now being added as a conversational chatbot layer—one the military may use to find and analyze data more quickly as it makes decisions like which targets to prioritize. 

Generative AI systems, like those that underpin ChatGPT, Claude, and Grok, are a fundamentally different technology from the AI that has primarily powered Maven. Built on large language models, they are much less battle-tested. And while Maven’s interface forced users to directly inspect and interpret data on the map, the outputs produced by generative AI models are easier to access but harder to verify. 

The use of generative AI for such decisions is reducing the time required in the targeting process, added the official, who did not provide details when asked how much additional speed is possible if humans are required to spend time double-checking a model’s outputs.

The use of military AI systems is under increased public scrutiny following the recent strike on a girls’ school in Iran in which more than 100 children died. Multiple news outlets have reported that the strike was from a US missile, though the Pentagon has said it is still under investigation. And while the Washington Post has reported that Claude and Maven have been involved in targeting decisions in Iran, there is no evidence yet to explain what role generative AI systems played, if any. The New York Times reported on Wednesday that a preliminary investigation found outdated targeting data to be partly responsible for the strike. 

The Pentagon has been ramping up its use of AI across operations in recent months. It started offering nonclassified use of generative AI models, for tasks like analyzing contracts or writing presentations, to millions of service members back in December through an effort called GenAI.mil. But only a few generative AI models have been approved by the Pentagon for classified use. 

The first was Anthropic’s Claude, which in addition to its use in Iran was reportedly used in the operations to capture Venezuelan leader Nicolas Maduro in January. But following recent disagreements between the Pentagon and Anthropic over whether Anthropic could restrict the military’s use of its AI, the Defense Department designated the company a supply chain risk and President Trump demanded on social media that the government stop using its AI products within six months. Anthropic is fighting the designation in court. 

OpenAI announced an agreement on February 28 for the military to use its technologies in classified settings. Elon Musk’s company xAI has also reached a deal for the Pentagon to use its model Grok in such settings. OpenAI has said its agreement with the Pentagon came with limitations, though the practical effectiveness of those limitations is not clear. 

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

  • ✇MIT Technology Review
  • The Download: Early adopters cash in on China’s OpenClaw craze, and US batteries slump 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. Hustlers are cashing in on China’s OpenClaw AI craze  In January, Beijing-based software engineer Feng Qingyang started tinkering with OpenClaw, a new AI tool that can take over a device and autonomously complete tasks. Within weeks, he was advertising “OpenClaw installation support” on a second-hand shopping site. Today, his side gig is a fully-fledged
     

The Download: Early adopters cash in on China’s OpenClaw craze, and US batteries slump

12 March 2026 at 21:02

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.

Hustlers are cashing in on China’s OpenClaw AI craze 

In January, Beijing-based software engineer Feng Qingyang started tinkering with OpenClaw, a new AI tool that can take over a device and autonomously complete tasks. Within weeks, he was advertising “OpenClaw installation support” on a second-hand shopping site. Today, his side gig is a fully-fledged business with over 100 employees and 7,000 completed orders. 

Feng is among a small cohort of savvy early adopters making serious cash from China’s OpenClaw craze. As users with little technical background want in, a cottage industry of installation services and preconfigured hardware has sprung up. The rise of these tinkerers shows just how eager the general public in China is to adopt cutting-edge AI—despite huge security risks. Read the full story. 

—Caiwei Chen 

Brutal times for the US battery industry 

Another battery business has fallen: 24M Technologies, once worth over $1 billion, is reportedly shutting down. 

Just a few years ago, the industry was hot, hot, hot. Countless companies were popping up, with shiny new chemistries and huge funding rounds. But now, the tide has turned. Businesses are failing, investors are pulling back, and batteries, especially for EVs, aren’t looking so hot anymore.  

There are bright spots. China’s battery industry is thriving, and US stationary storage remains resilient. But it feels as if everyone is short on money these days, and as purse strings tighten, there’s less interest in novel ideas. 

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

—Casey Crownhart 

The must-reads 

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

1 Iran has put US tech giants on a list of potential targets 
The companies include Google, Microsoft, Palantir, IBM, Nvidia, and Oracle. (Al Jazeera)  
+ Pro-Iran hackers have launched their first major strike on a US firm during the war. (CNN) 
+ AI is warping perceptions of the conflict. (MIT Technology Review)  
 
2 Grammarly is being sued for turning real people into AI-generated experts 
A journalist has filed a lawsuit over her inclusion as a writing analyst. (Wired $) 
+ Grammarly has now disabled the ‘Expert Review’ feature. (Engadget)  
+ Here’s what’s next for AI copyright lawsuits. (MIT Technology Review) 
 
3 Professors are losing the fight to protect critical thinking from AI 
They describe the tech as an “existential threat.”(The Guardian) 
+ Silicon Valley’s dream of an AI classroom faces a skeptical reality. (MIT Technology Review) 
 
4 Big tech is backing Anthropic in its fight against the Trump administration  
Google, Amazon, Apple, and Microsoft are publicly supporting its legal action. (BBC) 
+ Is this an Oppenheimer moment for Anthropic? (The Atlantic $) 

5 A Cybertruck owner has sued Tesla over a self-driving crash  
He called the company “negligent” for retaining Elon Musk as CEO. (Electrek)  
+ Tech has sparked a new wave of theft in the luxury car industry. (MIT Technology Review) 
 
6 Is “AI-washing” providing cover for massive corporate layoffs? 
The tech isn’t ready to replace workers, but the layoffs are happening anyway. (The Atlantic)  
+ Software giant Atlassian is slashing 10% of its workforce ahead of an AI push. (The Guardian) 
+ At least lawyers’ jobs look safer than first feared. (MIT Technology Review) 
 
7 Software giants claim they’re not worried that AI will destroy them 
Oracle and Salesforce CEOs have dismissed fears of an “SaaS-pocalypse.” (Reuters) 
 
8 Lab-grown brains have started solving engineering problems 
Scientists trained the organoid to decode an engineering task. (Popular Mechanics) 
+ Other organoids are being impregnated with human embryos. (MIT Technology Review) 
 
9 English-language music is losing its grip on Spotify 
The variety of languages in its top 50 songs has doubled since 2020. (BBC) 
 
10 AI is redrawing the boundaries of physics 
It’s blurring the boundaries between a machine and a researcher. (The Economist $)  

Quote of the day 

“Elon Musk is an aggressive and irresponsible salesman, who has a long history of making dangerous design choices and over-promising the features of his products.”

—A lawsuit over Tesla’s Full Self-Driving mode takes aim at the company’s CEO, Gizmodo reports.

One More Thing

This town’s mining battle reveals the contentious path to a cleaner future 

a view from the median line of an empty Main Street, Tamarack MN after a recent rain shower
ACKERMAN + GRUBER

In a tiny Minnesota town, an exploratory mining company called Talon plans to dig up as much as 725,000 metric tons of raw ore per year. 

It says the site will help power a greener future for the US by producing the nickel needed for EV batteries. But many local citizens aren’t eager for major mining operations near their towns.  

The tensions have created a test case for conflicts between local environmental concerns and global climate goals. Read the full story. 

—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.)

+ Mario is finally getting a LEGO minifigure.  
+ This new social platform boldly aims to burst filter bubbles. 
+ NASA is backing DSLR cameras by taking a trusty old Nikon D5 to the moon. 
+ This nuclear escalation simulator helped me learn to stop worrying and love the bomb. 

  • ✇MIT Technology Review
  • Pragmatic by design: Engineering AI for the real world MIT Technology Review Insights
    The impact of artificial intelligence extends far beyond the digital world and into our everyday lives, across the cars we drive, the appliances in our homes, and medical devices that keep people alive. More and more, product engineers are turning to AI to enhance, validate, and streamline the design of the items that furnish our worlds. The use of AI in product engineering follows a disciplined and pragmatic trajectory. A significant majority of engineering organizations are increasing their
     

Pragmatic by design: Engineering AI for the real world

The impact of artificial intelligence extends far beyond the digital world and into our everyday lives, across the cars we drive, the appliances in our homes, and medical devices that keep people alive. More and more, product engineers are turning to AI to enhance, validate, and streamline the design of the items that furnish our worlds.

The use of AI in product engineering follows a disciplined and pragmatic trajectory. A significant majority of engineering organizations are increasing their AI investment, according to our survey, but they are doing so in a measured way. This approach reflects the priorities typical of product engineers. Errors have concrete consequences beyond abstract fears, ranging from structural failures to safety recalls and even potentially putting lives at risk. The central challenge is realizing AI’s value without compromising product integrity.

Drawing on data from a survey of 300 respondents and in-depth interviews with senior technology executives and other experts, this report examines how product engineering teams are scaling AI, what is limiting broader adoption, and which specific capabilities are shaping adoption today and, in the future, with actual or potential measurable outcomes.

Key findings from the research include:

Verification, governance, and explicit human accountability are mandatory in an environment where the outputs are physical—and the risk high. Where product engineers are using AI to directly inform physical designs, embedded systems, and manufacturing decisions that are fixed at release, product failures can lead to real-world risks that cannot be rolled back. Product engineers are therefore adopting layered AI systems with distinct trust thresholds instead of general-purpose deployments.

Predictive analytics and AI-powered simulation and validation are the top near-term investment priorities for product engineering leaders. These capabilities—selected by a majority of survey respondents—offer clear feedback loops, allowing companies to audit performance, attain regulatory approval, and prove return on investment (ROI). Building gradual trust in AI tools is imperative.

Nine in ten product engineering leaders plan to increase investment in AI in the next one to two years, but the growth is modest. The highest proportion of respondents (45%) plan to increase investment by up to 25%, while nearly a third favor a 26% to 50% boost. And just 15% plan a bigger step change—between 51% and 100%. The focus for product engineers is on optimization over innovation, with scalable proof points and near-term ROI the dominant approach to AI adoption, as opposed to multi-year transformation.

Sustainability and product quality are top measurable outcomes for AI in product engineering. These outcomes, visible to customers, regulators, and investors, are prioritized over competitive metrics like time to-market and innovation—rated of medium importance—and internal operational gains like cost reduction and workforce satisfaction, at the bottom. What matters most are real-world signals like defect rates and emissions profiles rather than internal engineering dashboards.

Download the report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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