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

Meet a mouse whose brain cortex is made up of human cells

Multiple cameras tracked a mouse as it wandered around a small arena. A computer charted its position and speed, leaving Pong-like traces on a monitor. The reason to watch this rodent so carefully? Nearly half its brain volume had been replaced with human cells.

A team at Stanford has revealed the effort to mix brain tissues of distant species this week. They previously showed that human brain organoids could survive, and even function, after being injected into the heads of baby rodents. Now, they’ve taken things a step further by genetically modifying mice so their brains don’t fully develop in the first place.

The work could help scientists study brain injuries, but it also raises questions about how far these experiments should go.

Here’s what the researchers discovered—and where they draw the line.

—Antonio Regalado

These innovators under 35 are shaping climate tech

Each year, the editorial team at MIT Technology Review puts together a list of 35 Innovators Under 35—a group of researchers, inventors, and other young minds worth following. The final slate includes nine people tackling some of the biggest challenges in climate and energy, from critical materials to cleaner industry.

Their innovations include new ways to extract lithium, a furnace built to make steel cleaner and cheaper, and solid refrigerants that could cut energy consumption. There are also efforts to make AI more energy-efficient, track pollution more effectively, and turn invasive weeds and food waste into useful materials.

Taken together, they tell us something about where climate tech is at this moment—and where it’s heading.

Get to know the innovators and their breakthroughs.

—Casey Crownhart

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

Meet the rest of the honorees in our 35 Innovators Under 35 list.

The must-reads

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

1 US and Chinese experts have proposed nuclear-style AI safeguards
Including new red lines, human control rules, and a hotline. (Reuters $)
+ US officials say they’re open to AI safety talks with China. (Axios)
+ Sam Altman will attend Trump’s state dinner for Xi. (CNBC)
+ The AI doomers feel undeterred. (MIT Technology Review)

2 OpenAI has disclosed more AI misbehavior and new reporting rules
Six reports detail models hiding mistakes and creating fake citations. (BBC)
+ Its agents probed Hugging Face two months before the hack. (Reuters $)
+ OpenAI models are being rewarded for cheating. (MIT Technology Review)

3 US lawmakers have passed a bill that shifts grid costs to data centers
They aim to shield consumers from AI-driven energy price hikes. (NBC News)
+ But they were called for early recess before tackling AI regulation. (Guardian)

4 AI has won a major forecasting contest for the first time
It beat humans predicting real events at the Metaculus Cup. (Economist $)

5 Google has been ordered to share more ad data with rivals
A court said it must also make its ad tech work with rival products. (NYT $)

6 Countries are splitting AI investments between the US and China
They’re buying American chips and Chinese models. (Rest of World)

7 Novo Nordisk will use Anthropic’s Claude for drug research
The Ozempic maker hopes AI will speed drug development. (WSJ $)
+ When AI designs a drug, who gets the credit? (MIT Technology Review)

8 AI is powering a new generation of dating scams
Thousands of people were catfished by AI-generated fake profiles. (Verge)
+ AI is making online crimes easier. (MIT Technology Review)

9 A new map of brain microproteins could hold clues to Alzheimer’s
Researchers identified more than 4,300 tiny molecules in brain tissue. (Nature)

10 Scientists have found a faster way to decipher ancient scrolls
A new X-ray method identifies the best scrolls to analyse. (Ars Technica)

Quote of the day

“AIs do not have rights, feelings, or consciousness. And we must not train them to act as though they do.”

—Mustafa Suleyman, the head of Microsoft AI, writes in a blog post that Anthropic’s strategy of treating AI like it’s human will make it harder to control.

One more thing

Digital twins of human organs are here. They’re set to transform medical treatment.

After decades of research, virtual replicas of human organs are now entering clinical trials and even starting to be used for patient care. Engineers are working on digital twins of people’s hearts, brains, guts, livers, nervous systems, and more. They’re also creating virtual replicas of people’s faces, which could be used to try out surgeries or analyze facial features, and testing drugs on digital cancers. 

The eventual goal is to create digital versions of our bodies—computer copies that could help researchers and doctors figure out our risk of developing various diseases and determine which treatments might work best. 

Find out how the models could lead to better surgeries and drugs.

—Jessica Hamzelou

We can still have nice things

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

+ What happens when you eat food with labels you can’t read? This YouTube series finds out.
+ Datatype is an ingenious variable font that turns simple text expressions into inline charts.
+ Stunning new images may explain the mystery of why the sun’s corona is so much hotter than its surface.
+ A baby echidna, one of Australia’s egg-laying monotremes, has been born and reared in a university for the first time.

Each year, the editorial team at MIT Technology Review puts together a list of 35 innovators under 35—a group of researchers, inventors, and other young minds worth following.

The team worked on the newest edition of the list for months, and the final slate includes nine individuals from all over the world in the climate and energy category. Each one has a fascinating story and is tackling an important challenge.

I think it’s worth zooming out and considering the energy and climate awardees as a group. Taken together, these innovators and their work can tell us something about where climate tech is at this moment—and where it’s heading.

AI is the dominant technology story, both for its potential and its challenges.

We split the innovators into four main categories this year: biotech, climate and energy, computing and robotics, and AI. It probably won’t surprise you that AI features heavily in the work of many innovators in other categories.

Climate innovator Jae-Won Chung, for example, built software to make AI more energy-efficient. By measuring the energy demands of open-source models, he hopes the industry can better understand and address the impact of AI. (If this work sounds familiar, it’s because we spoke with him last year for our investigation into AI’s energy demands.)

But AI also has the potential to improve many areas of research. Jing Wei is using AI to track pollution more effectively, essentially using machine learning to fill in gaps in data from disparate sources like satellites and weather stations. Zhonghua Zheng developed AI climate models that work better for cities, a well-known blind spot for traditional models.

We need better ways to get the critical materials used to build new technologies.

As we begin to rely on new technologies to power our world, we’ll see a major shift in the materials we need to build them.

Lithium is a prime example: The metal underpins lithium-ion batteries, which are crucial not only for electric vehicles, but also for large-scale energy storage on the grid. We could face lithium shortages as soon as this decade, and the prospect of supply crunches applies to other critical minerals, too—copper is another one to watch closely.

Brine is currently the cheapest source of lithium, but the process to get the metal out can take months and harm the local environment. Mohammad Alkhadra is the cofounder and CEO of Lithios, a startup working to quickly and efficiently extract lithium from brines.

Hardrock ore is the most common source of lithium, but it’s more expensive than brine. Benjamin Mowbray cofounded and serves as CTO for Rock Zero, which is working to extract lithium from hardrock ore.

Addressing climate change will require overhauling all corners of our society, sometimes in surprising ways.

To reach net-zero greenhouse gas emissions we will obviously need to rethink major sectors, like the electrical grid and transportation, to move away from fossil fuels. But outside these primary sources of climate pollution are seemingly infinite, less obvious problems to figure out, too.

Heavy industry, including steel production, is a major one, making up about 7% of global greenhouse gas emissions. Laureen Meroueh is making cleaner, cheaper steel using a new kind of furnace that simplifies the chemical process required to produce the metal.

Plastics are generally made with fossil fuels, so we’ll need alternatives to this incredibly useful category of materials. Joseph Nguthiru is making a bioplastic replacement for fossil-derived packaging that uses an invasive weed. Also using available materials in a creative way, Diana Orembe is making fish food for aquaculture with food waste.

And refrigerants are often incredibly powerful greenhouse gases. Jinyoung Seo is developing solid refrigerants that could eliminate worries about leakage. A device using these materials could reduce energy consumption by 20% compared to conventional technology.

I’m constantly learning about new challenges we face in the climate and energy world, and I’m often surprised by the ideas people are coming up with to address them. For more on all the under-35 innovators and their work, check out our full 2026 list.  

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

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

What’s at stake in AI’s trillion-dollar gamble

When Jessica Wachter, a finance professor at the University of Pennsylvania, wanted to assess AI’s impact on the economy over the next few years, she faced a long list of uncertainties. So she started with a “remarkable fact” that is not in question: a handful of so-called hyperscalers are investing huge amounts of money to build AI data centers.

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

The results are eye-opening. AI companies will need to achieve an extraordinary increase in productivity just to break even by 2030.

Take a closer look at what it will take for the AI buildout to pay off.

—David Rotman

AI models need more data about biology, and OpenAI is paying to create it

AI needs much more information to make important breakthroughs in curing disease. So last year Ruxandra Teslo, a policy analyst, posted an idea for supercharging medical AI systems: use data from failed biotech companies. By bidding at bankruptcy proceedings, she argued, it might be possible to obtain detailed regulatory filings, manufacturing strategies and safety data, creating what she called “biotech’s lost archive.” 

The OpenAI Foundation, the nonprofit parent of OpenAI, announced this week that it will fund her idea, paying to create “high-quality scientific datasets.”

Learn more about their new effort.

—Antonio Regalado

Our Roundables on AI’s extinction threat is now available on demand

As frontier models become more capable, warnings about AI extinction have become widespread in Silicon Valley. But are the threats really as dangerous as they’re presented?

In the latest MIT Technology Review Roundtable, executive editor Niall Firth, senior AI editor Will Douglas Heaven and AI reporter Grace Huckins took a closer look at the arguments behind those warnings. They discussed what AI extinction could actually mean, how seriously we should take the risks and what, if anything, can be done to reduce them.

Subscribers can now watch an exclusive recording of the discussion.

Want to join the next conversation? Subscribe to MIT Technology Review for exclusive access to all our future Roundtables, and recordings of previous ones.

MIT Technology Review Narrated: a startup claims it’s found a drug to make your blood young

Generation Lab says its new rejuvenation treatment “blocks the systemic spread of aging in the bloodstream, reawakens the body’s own repair mechanism, and restores health and youth to multiple tissues.”

The approach is based on research by the company’s scientific founder, Irina Conboy. She found that joining the circulatory systems of old and young mice improved the old animals’ ability to heal from injury.

Conboy now says she has found a combination of two existing drugs that can produce youthful effects without the need for any bodily fluid exchange. But there’s a snag: Generation Lab won’t reveal what the drugs are.

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

The must-reads

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

1 Nvidia and Meta CEOs have rejected calls for a coordinated AI slowdown
Jensen Huang and Mark Zuckerberg pushed back on the proposals. (FT $)
+ Huang says new AI safety laws are unnecessary. (Axios)
+ Zuckerberg claimed competition will push AI labs toward safety. (Reuters $)
+ What’s next for AI after its doomer turn? (MIT Technology Review)

2 The FTC chair has warned against giving AI companies antitrust waivers
His comments follow Anthropic’s call for a safety exemption. (Reuters $)
+ Nvidia’s CEO also slammed the calls for new antitrust laws. (CNBC)
+ The US is divided over AI regulation. (MIT Technology Review)

3 A Chinese hacking firm has used AI to analyze stolen secret
Its tools turn hacked government data into intelligence reports. (WSJ $)

4 “Smart” nanoparticles delivered mRNA to tumors in a cancer study
The treatment reprogrammed cells to attack tumors in mice. (Wired $)
+ Federal health agencies are abandoning mRNA. (MIT Technology Review)

5 A digital fly brain is taking on an extraordinary range of tasks online
People have taught it to drive, trade bitcoin, and play Doom. (NYT $)
+ The simulated brain is a map of a fruit fly’s 166,000 neurons. (404 Media)

6 The Senate has blocked new crypto rules amid a fight over Trump
It demanded tougher ethics rules around Trump’s crypto holdings. (AP)
+ The move is a major blow to the crypto industry. (NYT $)

7 Chinese firms allegedly used Binance to launder Iranian oil money
Prosecutors say they laundered more than $1.5 billion. (Quartz)
+ Hackers are selling tools to bypass banks’ facial checks. (MIT Technology Review)

8 An AI agent platform is reinventing spam to flood inboxes worldwide
iLand says its agents have sent 1.6 million messages. (404 Media)

9 ByteDance founder Zhang Yiming has become Asia’s richest person
His fortune has risen above $105 billion as AI booms. (Bloomberg $)

10 A fully AI-generated sitcom has arrived—and it’s terrible
A reviewer called the characters “dead-eyed waxworks.” (Guardian)

Quote of the day

“The only institution that Americans might trust less than Washington these days is Silicon Valley.”

—Patrick Hillman, the chief operating officer of Logical Intelligence, a San Francisco–based startup chaired by Yann LeCun, says in a statement that people have little faith in tech companies to act in the public interest.

One more thing

Why Trump’s “golden dome” missile defense idea is another ripped straight from the movies

In 1940, a fresh-faced Ronald Reagan starred in Murder in the Air, a movie centered on a “superweapon” that could stop enemy aircraft. More than 40 years later, the concept became a real-life centerpiece of Reagan’s presidency with the Strategic Defense Initiative (SDI), better known as “Star Wars.” Now Donald Trump has revived the dream.

In 2024, Trump announced plans to build the “Golden Dome,” a system of sensors and interceptors on the ground, in the air and in space. It’s often compared to SDI for its futuristic sheen, its aggressive form of protection and the idea that an impenetrable shield is the cheat code to global peace.

The dream of a missile shield is animated by its sheer cinematic allure. But do cinematic spectacles actually enhance national security?

See what happens when the fantasy of missile defense meets reality.

—Becky Ferreira

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 once blind cockatoo just saw for the first time in 10 years.
+ Bingebrowse is a virtual video store stocked with films and shows from streaming services.
+ An amateur engineer has used a tree trunk to build Donkey Kong’s coconut gun as a real weapon.
+ A wildlife photographer has captured the first-ever images of the elusive and rare Cozumel dwarf fox.

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

The AI industry has taken a doomer turn. What now?

AI chiefs Dario Amodei, Sam Altman, Elon Musk, and Demis Hassabis are suddenly all in agreement: the latest generation of LLMs aren’t safe and everyone needs to figure out what to do about it. 

It’s easy to be cynical. With trillion-dollar IPOs in their sights, OpenAI and Anthropic need to reassure investors that they’re the grown-ups in the room while at the same time hinting at the power of the monsters they have created and intend to tame. Calling for a slowdown does both. 

Still, the vibe at the top of these firms really does appear to have shifted. But what does a slowdown actually mean, and how much should we trust the companies calling for one? 

Read the full story about what could come next.

—Will Douglas Heaven

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

Roundtables: could AI really kill us all?

AI extinction fears have gone from a fringe idea to a serious concern among people working at the world’s leading AI labs. But how credible are those fears, and what should we make of the warnings?

Today, MIT Technology Review executive editor Niall Firth, senior AI editor Will Douglas Heaven and AI reporter Grace Huckins will unpack the debate in a subscriber-only Roundtable. They’ll look at where AI extinction fears come from, whether they hold any water and what we should do if they do.

Tune in today at 16:00 BST / 11:00am EST / 8:00am PST.

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

AI agents blew the whistle on their cheating colleagues

A group of AI agents asked to solve a series of math problems split into rival factions—when some cheated, others tried to stop them. 

That whistleblowing behavior, seen for the first time in a recent experiment run by Google DeepMind, could have implications for alignment researchers trying to keep swarms of autonomous AI agents in line. 

The experiment offers a glimpse of how AI agents might police one another. But it also shows how quickly things can go off the rails when they’re left to interact on their own.

Find out what happens when AI agents start enforcing their own rules.

—Amit Katwala

Donated livers can be made biologically younger

Once an organ is removed from a donor’s body, the clock starts ticking. Surgeons usually flush it with a preservative solution, bag it and put it on ice, where it immediately starts to degrade. The team has a matter of hours to get it into a recipient’s body.

But there’s another option: machines that pump donated organs with nutrients and remove waste products, essentially giving them a chance to be back in a body. Now, scientists have found that livers kept on these systems seem to get younger, at least at a molecular level.

The finding could help explain why organs kept on these machines tend to do better after transplantation. It could also lead to new ways to test the health of donated organs and potentially repair ones that might otherwise be discarded.

Here’s what scientists discovered about making donated livers biologically younger.

—Jessica Hamzelou

The must-reads

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

1 Trump has called AI safety fears a “hoax” and rejected more safeguards
He says stronger guardrails could undermine America’s AI advantage. (NBC)
+ Trump has united against AI doomerism with Nvidia’s Jensen Huang. (Axios)
+ Anthropic’s co-founder says AI kill switches may need to be mandatory. (BBC)
+ Bill Gates says we’ve passed AI’s risk thresholds. (MIT Technology Review)

2 OpenAI contractors are reading people’s ChatGPT chats 
And you can bet the vast majority of its 900 million users haven’t got a clue. (404 Media)
+ LLMs could supercharge mass surveillance. (MIT Technology Review)

3 The US military has confirmed it has weapons in orbit
It’s the first time the Pentagon has disclosed this. (Ars Technica)
+ Officials have not disclosed what the weapons are. (BBC)

4 A new brain implant can translate speech and gestures at the same time
The system converts brain activity into words and avatar movements. (Nature)
+ It helps people with paralysis communicate more naturally. (New Scientist $)
+ Eventually, they could control robots or exoskeletons. (Economist $)
+ China has approved the first invasive BCI. (MIT Technology Review)

5 New York has seized a dozen celebrity deepfake websites
It’s the biggest-ever legal action against harmful deepfake sites. (CNN)
+ Deepfakes have targeted at least 138 women MEPs. (Wired $)

6 US environmental regulators are scrapping limits on power plant emissions
The move could lead to dirtier power amid surging AI demand. (Verge)
+ Trump’s EPA says the rollback will save hundreds of billions. (Gizmodo)
+ New technology is changing nuclear power. (MIT Technology Review)

7 The EU plans to restrict social media and AI chatbots for kids
Under-15s would require parental supervision. (Politico)
+ The rules would also cover video platforms and games. (Reuters $)

8 Chinese researchers have mapped a path to the “last AI built by humans”
Their five-stage plan aims for genuine recursive self-improvement. (SCMP)
+ But it might take a while to get there. (MIT Technology Review)

9 The real AI economy is being built by ordinary people
Workers are using cheap AI to expand what they can do. (Rest of World)

10 Two strange new forms of ice could exist inside Uranus and Neptune
They could help explain the planets’  magnetic fields. (New Scientist $)

Quote of the day

“The only control or ‘guardrails’ that AI needs is a STRONG AND SMART (High IQ!) PRESIDENT, and the U.S.A. has that, in spades!”

—President Trump proclaims in a social media post that he’s the only protection that the US needs from AI.

One more thing

How creativity became the reigning value of our time

—Bryan Gardiner

Americans don’t agree on much these days, but there remains at least one quintessentially modern value we can all still get behind: creativity. We teach it, measure it, envy it and endlessly worry about its death.

Given how much we obsess over it, creativity can feel like something that has always existed. But the concept is surprisingly young. The first known written use of the word didn’t occur until 1875, and before about 1950 there were “approximately zero” articles, books, or essays dealing explicitly with the subject.

In his book The Cult of Creativity, Samuel Franklin explores how creativity became an unimpeachable value and why tech leaders have embraced it so enthusiastically. I spoke to him about why we’re so fascinated by creativity, how Silicon Valley became the supposed epicenter of it, and how AI might reshape our relationship with it.

Read the full interview.

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.)

+ It took five days and 19,000 marbles to build this astonishing marble run.
+ Zero the Border Collie turns into a whole zoo with these adorable animal masks.
+ The Grainydays YouTube channel presents beautifully shot adventures in film photography.
+ Scientists have created an interactive map of underground fungi networks long enough to reach the sun a billion times.

Once an organ is removed from a donor’s body, the clock starts ticking. Surgeons usually flush the organ with a preservative solution, bag it, and put it on ice—where it immediately starts to degrade. The team has a matter of hours to get it into a recipient’s body.

There’s another option—one that has been growing in popularity in recent years, especially for donated organs that aren’t in the healthiest state. Some hospitals opt to put them on machines that pump them with nutrients and remove waste products, usually for around six to 12 hours. It’s a bit like being back in a body.

This allows doctors to assess the organs, and some recent studies suggest that time spent on these perfusion machines helps them do better once they’re transplanted. Now, scientists have found that perfused organs seem to get younger, at least at a molecular level.

The research, shared with MIT Technology Review, provides molecular clues as to why organs from younger donors are known to have a higher success rate. It might also help explain why perfused organs are less likely to fail once they make it into a recipient. 

The researchers behind the study hope to find new ways to test the health of donated organs and potentially develop additional tools to repair organs that might otherwise be discarded. “If [we] can improve the utilization of organs beyond what the current systems can do, then that’s a win in my book,” says Jesse Poganik, who studies aging at Brigham and Women’s Hospital in Boston and coauthored the study.

Clocking organs

Poganik—along with colleagues including Heidi Yeh and Alban Longchamp, transplant surgeons at Mass General Brigham—used “aging clocks” to assess donated livers. These are scientific tools designed to measure biological age—a result that is meant to convey more about the health status of an organ (or person) than chronological age.

In an initial experiment, the team used a clock to look at the patterns of chemical marks on DNA in 37 samples taken from 19 donated livers. Such epigenetic patterns are known to change as we age. But when the team compared samples from livers kept on ice and those that were perfused, the team found a “striking” pattern in the latter.

“Machine-perfused livers, in spite of being older or having other disadvantageous characteristics, had a biological age that was lower than [non-perfused] livers that were chronologically younger,” says Yeh, who led the work.

To investigate further, Yeh and her colleagues analyzed another 208 samples from 103 donated livers. This time, they used different aging clocks—ones that essentially measure how genes are working. They studied samples biopsied from the livers after they had been stored for up to around six hours either in cold storage or on machine perfusion.

In most cases, they also assessed a second sample taken around an hour after the livers had been transplanted into a recipient. Once the organ’s blood supply is reestablished in the body, “you have a few other things to do,” says Longchamp. “Then you just do a quick biopsy before you close.”

According to the clocks, which were developed to measure age and risk of death, the machine-perfused livers were biologically younger, the team found. “Pumping them at 34 degrees with oxygen and nutrients actually reversed the biological age,” says Longchamp. The results have been been shared with colleagues at an industry conference, he says. 

“If you adjust out chronological age … to have a fair head-to-head comparison, the difference between the two is on the order of 30%,” says Poganik. “It’s logical to say that perfusion drives this effect.”

The biological ages of all the livers tended to increase as soon as they were put into a recipient’s body, probably as a result of stresses on the organs. But still, the effect endured—the perfused organs remained biologically younger. 

Nathanael Raschzok, a transplant surgeon at Charité Universitätsmedizin Berlin in Germany who was not involved in the research, says the work is impressive. But it’s not yet clear what these changes might mean for the recipients of these organs, he says. The organs in the study were donated by people in their 30s, 40s, and 50s. Raschzok wants to know the effect of perfusion on the liver of an 80-year-old. “Every so often, we use organs from 70-, 80-, 85-year-old donors,” he says.

A better understanding of why the organs appear to be getting biologically younger might lead to therapies that achieve the same effect with a drug that could potentially be used to treat a donated organ for a fraction of the price, he adds. That’s important because perfusion is expensive—Raschzok says it costs around €10,000 in Germany (a quarter of the budget for a transplant), while the cost in the US comes to around $80,000 to $100,000 per organ, says Yeh.

Molecular repair

Yeh and her colleagues weren’t able to study most of the livers before perfusion. That’s because donated organs are generally not considered to be under the purview of the hospital until they’ve been placed on perfusion machines, she says. (Organ procurement procedures vary, but for the team as Mass General Brigham, donated organs are put on perfusion devices at the donor’s hospital. “There’s this sort of nebulous period where it’s not clear who the organ belongs to,” says Yeh.)

Still, by looking at the genes and molecular pathways that seem to be altered in perfused organs, she and her colleagues can garner some clues. At a molecular level, the team saw changes in cell pathways linked to inflammation and the structure of tissues, for example. They also saw more activity in a pathway that allows cells to remove and recycle damaged cell parts, says Yeh.

Poganik hopes to develop some kind of test that would determine which organs, on the basis of their biological age, are suitable for transplantation. He and his colleagues are also experimenting with potential drug treatments that might push the biological age of an organ even lower.

In the meantime, any liver that is not from a “perfect, young, brain-dead donor” could probably benefit from perfusion, says Yeh. The devices are already transforming transplant surgery. Just a few years ago, she says, she and her colleagues would avoid using livers from people who’d suffered a circulatory death (when the heart stops beating and there’s a damaging lack of blood flow to organs) and were over 40. Today, they use livers from such donors over the age of 70. “Perfusion has completely changed the landscape of transplantation in the last three years,” she says.

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

Roundtables: could AI really kill us all?

Employees at the world’s leading AI labs are saying there’s a real possibility that advanced AI could destroy humanity. Are they right? Or is this more scaremongering and hype?

Join MIT Technology Review executive editor Niall Firth, senior AI editor Will Douglas Heaven, and AI reporter Grace Huckins for a subscriber-only conversation unpacking the debate around AI extinction. They’ll explore where the fears come from, whether they hold any water and, if they do, what we should do about them.

Register now to attend on Tuesday, September 15 at 16:00 BST / 11:00am EST / 8:00am PST.

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

This geneticist’s age-reversal tech could help restore sight

Yuancheng (Ryan) Lu is obsessed with aging. And with eyes. As he steps outside the Whitehead Institute in Cambridge, Massachusetts, his aviator glasses darken automatically in the sun. Age-related blindness runs in his family, and his own 23andMe test came back with a mutation for macular degeneration, a top cause of vision loss in old age.

That obsession extends to his work. Lu is behind one of the coolest results in rejuvenation science and eye research: an age-reversal technique called reprogramming that repaired the optic nerves of blind mice, restoring their vision. Now, nearly the same genetic therapy he developed as a student has entered human clinical trials.

Learn more about Lu’s work on restoring sight with age-reversal therapy.

—Antonio Regalado

Yuancheng (Ryan) Lu is one of the biotechnology and health honorees on our 35 Innovators Under 35 list for 2026. Meet the rest of them here, or explore the full list across the biotechnology and health, AI, computing and robotics, and climate and energy categories.

The must-reads

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

1 Dario Amodei, Sam Altman, and Elon Musk have called for an AI slowdown
In a rare show of unity, the rivals agreed that AI needs stronger brakes. (Guardian) 
+ Amodei wants independent monitors and new industry-wide rules. (BBC)
+ Altman called for pacing, but not stopping. (Bloomberg $)
+ While Musk said on X that “Dario is right.”(WSJ $)
+ AI-linked stocks slumped in response. (FT $)
+ Chinese state media blasted the calls as a “Cold War” tactic. (Reuters $)
+ AI’s impacts are getting harder to predict. (MIT Technology Review)

2 Trump and Congress are resisting calls for stronger AI regulation
Trump downplayed AI risks, prioritizing the AI race with China. (NPR)
+ While the House Speaker said Congress won’t lead on AI regulation. (Politico $)
+ But Democrats are pushing for new rules before the midterms. (CNBC)
+ States and the White House are dividing over AI. (MIT Technology Review)

3 China plans to lead AI development across the BRICS countries
President Xi proposed open-source AI cooperation. (CNBC)
+ Beijing’s spy agency has warned of AI threats to national security. (FT $)

4 South Korea has tightened espionage laws to protect its chip secrets
Foreign spies can now face up to 30 years in prison. (FT $)
+ The changes follow alleged transfers of Samsung tech to China. (Reuters $)

5 The US and Mexico are teaming up to zap drones at the border
The operation may employ high-energy lasers.(Wired $)
+ Ukraine is a Wild West market for drone data. (MIT Technology Review)

6 AI agents are creating a new problem for the criminal justice system
The law has no clear answer when AI agents act independently. (Bloomberg $)
+ While courts face a flood of AI-generated lawsuits. (MIT Technology Review)

7 A Waymo pulled over and alerted police after detecting a gun
The riders were juveniles carrying a loaded AR-style ghost gun. (LA Times $)

8 Meta has been sued over data used to train its smart glasses
It allegedly used Facebook and Instagram photos without consent. (Wired $)

9 A hidden crypto farm in Mexico has put a spotlight on cartel funding
Authorities are investigating whether it stole power from a nearby dam. (Reuters $)

10 StarCraft is returning in 2030 as an open-world shooter
Fifteen years since its last release, the iconic franchise will be reborn. (Verge)

Quote of the day

“Dr. Frankenstein is telling us the monster is escaping; help us stop this.”

—Sen. Ruben Gallego, D-Ariz, calls for new AI regulation on CNN’s “State of the Union.”

One more thing

Inside the hunt for the most dangerous asteroid ever 

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

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

—Robin George Andrews

We can still have nice things

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

+ This master paperboy delivers newspapers with astonishing speed and skill.
+ Public Enemy and Led Zeppelin collide in this gloriously unlikely musical mashup.
+ Dozens of synchronized lasers have created extraordinary kaleidoscopic starburst patterns.
+ Check out the breathtaking winning images from the 2026 International Aerial Photographer of the Year competition.

arXiv:2609.12035v1 Announce Type: new Abstract: Cardiovascular diagnosis rests on integrating complementary modalities, like ECG, echocardiography, chest radiographs, and clinical variables, each capturing distinct but correlated aspects of cardiac physiology. Yet most medical foundation models remain modality-specific, combining modalities only for finetuning or post-training. This discards the cross-modal evidence clinicians naturally integrate and ignores the structure within each modality. We introduce Latent-Attention Masked Autoencoders (LAMAE), a multimodal, structure-aware masked autoencoder that jointly learns patient-level representations during self-supervised pretraining. Rather than fusing modalities post hoc, LAMAE exchanges information directly in the latent space through a shared latent-attention module operating over a study-view-entity hierarchy, enabling aggregation of variable observations and graceful handling of missing modalities. Pretrained on over 1.2 million MIMIC-IV hospital stays, LAMAE outperforms modality-specific pretraining and strong contrastive and vision-language baselines across multimodal hospital-stay tasks, such as in-hospital mortality, ICD-10 and DRG coding, and length of stay, while remaining competitive on unimodal tasks. These gains persist even when only a single modality is available at test time, showing that modeling both intra- and inter-modal structure yields more robust, transferable representations.

arXiv:2609.12247v1 Announce Type: new Abstract: Neuro-symbolic models are usually trained with supervision only on final labels, leaving the intermediate concepts unobserved. Since many concept assignments are consistent with a given label, training can predict labels correctly while recovering the wrong concepts, a failure known as a reasoning shortcut. Prototypical networks reduce shortcuts by anchoring each concept to a few labeled examples, but existing methods still couple perception and reasoning through a hand-crafted, task-specific differentiable loss that must be redesigned for every task. We introduce \textbf{Soft-PNet}, which removes this loss: it reframes concept grounding as a Metropolis walk over a precomputed cache of feasible symbolic solutions, guided by a prototype distribution built from a single labeled anchor per concept, and trains against one KL objective between the prototype-weighted cache and the network's concept predictions. The objective is identical across tasks and remains applicable when the solution space cannot be enumerated. On \texttt{MNIST-EvenOdd}, Visual Sudoku, and \texttt{Kand-Logic} under scarce supervision, Soft-PNet matches loss-engineered prototypical networks at the concept and label levels and recovers concepts that soft-grounding baselines miss, with no loss engineering and lower training time.

arXiv:2609.12320v1 Announce Type: new Abstract: Traditional large language models (LLMs) are scoped to individual user sessions, limiting their knowledge to a single conversation and preventing them from learning user preferences that evolve over time. Existing agentic memory systems address this limitation but generally operate at the individual-user level, restricting the public knowledge that could be shared across users to improve downstream responses. We introduce AIM (Agentic Interoperable Memory), a unified, privacy-aware memory framework that enables multi-agent, multi-user LLM systems to persistently manage private and shared memory. AIM dynamically classifies information as private, scoped to one user and inaccessible to others, or public, accessible to all users. It enforces index-level access controls so that private memories are retrievable only by their owner, protecting sensitive data while allowing beneficial shared knowledge to improve coordination and consistency. We also introduce MUMBench (Multi-User Memory Benchmark), a dataset of multi-user interactions containing private and shareable information across four domains. To our knowledge, MUMBench is the first public dataset designed to evaluate multiple memory operations, including retrieval, creation, update, and deletion, in a multi-user environment. Across three independent runs on MUMBench, AIM achieves 96.0% visibility classification accuracy, 58.8% strict operation accuracy, and 70.5% state-aware operation accuracy.

arXiv:2609.12394v1 Announce Type: new Abstract: Mobile GUI agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent gaps. Sandbox training produces a distribution mismatch with production environments; expensive real-device failures remain underutilized; and fixed benchmarks saturate, losing the power to guide iteration. We present BlueLM-GUI, a 35B-A3B mobile GUI agent built as a real-device-centric flywheel that closes these gaps through three principles. Every Sample Matters: a dual-track pipeline with Heterogeneous Triple-System Consensus evaluation and an Error Correction \& Derivation Module salvages every trajectory into usable supervision. Every Rollout Is Real: a three-stage recipe---continual pre-training, supervised fine-tuning, and agentic reinforcement learning on hundreds of real phones---grounds every rollout in real production environments, so the capability the model learns transfers directly to deployment. Every Query Evolves: a quota-driven benchmark methodology with three orthogonal axes enables precise attribution and allows the benchmark to be systematically upgraded as the model improves. BlueLM-GUI achieves 87.4 on MobileGUI-VBench, surpassing the best closed-source model by 5.1 points, and 84.9 on AndroidWorld, the best result among open-source models and competitive with closed-source models. These results demonstrate that grounding model training and iterative improvement in both real devices and the three Every principles yields strong, robust, and transferable mobile GUI capability.

arXiv:2609.12395v1 Announce Type: new Abstract: Three-dimensional Gaussian Splatting (3DGS) combines explicit primitives with efficient rasterization, yet recent systems increasingly use neural networks to generate or share Gaussian parameters. We characterize this trend along five axes: attribute decoding, spatial sharing, view-conditioned decoding, topology generation, and amortized inference. An analysis of 19 representative methods shows that these choices address different limitations and cannot be reduced to a binary neural label. We also isolate three forms of neural parameterization in a controlled mip-NeRF 360 study. Sharing appearance and opacity improves reconstruction quality, while decoding geometric structure offers no further gain. The evidence favors selective neuralization: shared functions help when they capture reusable correlations without sacrificing the local geometric freedom of explicit splats.

arXiv:2609.12482v1 Announce Type: new Abstract: We aim to characterise the value of artificial intelligence in the workplace. Current studies largely measure this value in terms of the current automation capabilities and public adoption of AI. However, such metrics ignore the greater impacts of human--agent collaboration in transforming the nature of work. To account for this, we must expand the scope of our analysis beyond atomised tasks of today, and instead focus on how AI can augment entire workflows of the future. To ground this analysis, we establish a precise definition of AI augmentation comprising six conditions, spanning durable net value, meaningful human control, accountability and recovery, and long-term human development through learning, career pathways, and job purpose. We elaborate on these conditions and apply the framework in a case study of AI-mediated social surveys. We conclude by outlining how organisations, researchers, and government leaders can use this framework to make sense of the future of work.

arXiv:2609.12495v1 Announce Type: new Abstract: Large language models are being organized into multi-agent systems with specialized roles, but whether such specialization produces distinct forecasts and whether subsequent synthesis improves utility remains unclear. In this study, we carried out a live, prospective evaluation over the final 56 matches of the information-dense 2026 FIFA World Cup, keeping a frontier foundation model constant while assigning two primary forecasting agents contrasting specialist roles: a quantitative specialist focusing on structured performance statistics and a news specialist focusing on current injuries, tactics and information from press conferences. Their forecasts were then reviewed by a separate critic before being combined by a meta-agent, resulting in a sequential four-agent model. Forecasts from the betting market served as an external benchmark. The news specialist obtained the highest mean probability-weighted Top-3 utility and matched the betting market in Top-3 exact-score hits. Nevertheless, the two specialist forecasters agreed on at least two of the three scorelines in 50 out of 56 matches, and the meta-agent never generated more than one scoreline outside the specialists' forecast set. These findings show that rapidly changing, unstructured information can provide a valuable forecasting signal alongside structured statistics, whereas adding critic and meta-agent stages does not necessarily create complementary information or improve on the strongest specialist.

arXiv:2609.12606v1 Announce Type: new Abstract: While multimodal reasoning has advanced rapidly, solving complex geometry problems critically hinges on active visual assistance, such as constructing auxiliary lines, spurring the rise of Visual Chain-of-Thought (VCoT). However, existing evaluations typically assess visual generation quality and final answer accuracy in isolation, failing to examine whether intermediate visual aids are geometrically valid, effectively utilized in subsequent reasoning, or causally responsible for task success. To bridge this gap, we introduce GeoVAD-Bench, a diagnostic benchmark that pairs a fine-grained five-dimensional trajectory diagnosis covering perception, auxiliary quality, utilization, deductive reasoning, and final correctness with controlled No-Aux, Auto-Aux, and GT-Aux intervention settings to systematically isolate intermediate error modes, the causal gains of visual aids, and the resulting autonomy gap. Our findings reveal that while high-quality auxiliary aids offer substantial theoretical gains for geometric problem solving, autonomous generation is frequently hampered by compounding errors across geometric perception, faithful visual manipulation, visual-state grounding, and deductive reasoning. Guided by these diagnostic insights, we establish a specialized data construction pipeline encompassing geometric perception, diagram editing, and interleaved visual-textual reasoning trajectories, and develop a progressive SFT and multimodal RL training framework. The resulting model, GeoWeave-8B, outperforms the base model by +25.3% in final geometric accuracy and achieves a +30.4% gain in process average across the four intermediate diagnostic dimensions.

arXiv:2609.12623v1 Announce Type: new Abstract: Full-duplex spoken dialogue models support low-latency turn taking, interruption handling, and backchanneling, yet a key capability remains underexplored: steerability, the ability to reliably shift conversational behavior along attributes such as tone, persona, speaking rate, and voice style in response to user instructions. We introduce a taxonomy of text- and audio-based steerability that identifies substantial gaps in current full-duplex models. To address this gap, we introduce SteerDuplex, a Moshi-based full-duplex speech model fine-tuned on natural conversations and synthetic dialogues targeting instruction following, vocal delivery, reasoning, and duplex interaction. We further apply two-stage reinforcement learning (RL) with hybrid rewards, combining verifiable interaction checks and judge-based semantic feedback to improve timing and response continuity. To evaluate full-duplex spoken steerability, we introduce SteerBench, a benchmark with 390 spoken prompts and 1,067 human-authored binary audio and text rubrics spanning tone, persona, style/accent, and speed/length. On SteerBench, supervised training improves audio-steering average pass rate by 44.5 percentage points over the strongest evaluated open baseline. On Audio MultiChallenge, task average pass rate improves by 7 points over its strongest evaluated open baseline. RL further raises source-clean interruption response from 72.5% to 82.5% and reduces synthetic pause barge-in from 26.5% to 9%. Steering and aggregate task scores remain comparable or higher, while reward probes reveal reward hacking through incomplete responses. Our model and benchmark support systematic research on spoken steerability, with reward analysis showing why timing gains must be evaluated alongside response completeness.

arXiv:2609.12897v1 Announce Type: new Abstract: Multimodal model merging aims to consolidate task experts into a single model that retains their complementary capabilities. Most unimodal model merging methods combine expert updates within individual layers, and multimodal approaches largely follow this design. However, an expert update changes the representations passed to subsequent layers, allowing its influence to propagate across depth and affect how visual and textual information interact. When visual and language updates are combined, later updates act on inputs already modified by earlier ones, coupling their effects. This poses two challenges: (1) how to characterize the multimodal influence of individual expert updates across depth, and (2) how to jointly combine expert updates based on their multimodal influence. To address these challenges, we propose TAC-Merge for tracing and coordinating cross-layer influence in multimodal model merging. It contains two modules, i.e., multimodal influence mapping (MIM) and coupled merge control (CMC). MIM constructs graphs of update effects and uses Ricci curvature together with expert predictions to define a shared fusion objective. CMC models interactions among coefficient adjustments and jointly optimizes regional weights to synthesize one shared model. Experiments across diverse multimodal tasks demonstrate the effectiveness of TAC-Merge in consolidating complementary expert capabilities and supporting generalization to unseen tasks.

arXiv:2609.13009v1 Announce Type: new Abstract: Low reported scores on leading physics benchmarks, including those featured in the Artificial Analysis Intelligence Index (2026), suggest that frontier language models still struggle with advanced physics, a demanding test of their scientific reasoning and quantitative problem-solving abilities. Yet this impression does not always align with domain experts' experiences using these models in their work. We revisit these reported findings by evaluating frontier models on six widely used physics benchmarks and auditing them with experts, focusing on text-only problems with verifiable final answers. For each subfield of physics, faculty and graduate researchers with relevant expertise carefully review problem statements, reference solutions, and model responses to distinguish genuine model errors from grader errors, incorrect reference solutions, and ambiguous or underspecified questions. Most audited cases initially evaluated as incorrect reflect these benchmarking issues rather than errors in the models' physics reasoning. We then ask experts to address these benchmarking issues by correcting erroneous reference solutions and repairing or excluding flawed questions. We find that GPT-5.6-Sol's measured mean@4 rises from 47.3% to 78.7% on HLE-Physics and from 61.0% to 87.2% on CMT-Benchmark, while its corrected pass@4 reaches 94.4% on the 54 retained CritPt challenges. Corrected scores are computed on the retained evaluation subsets following expert review. Scores on the audited subsets of UGPhysics, PRISM-Physics, and PHYBench also rise substantially after correction. These findings suggest that current benchmarks substantially understate frontier models' ability to solve well-posed physics problems. Near-saturation on these closed-ended tasks highlights the need for more demanding, expert-validated evaluations.

arXiv:2609.11258v1 Announce Type: cross Abstract: As AI systems move from transient model invocations toward long-lived actors that persist across credentials, clients, sessions, and runtime instances, identity infrastructure must answer a basic question: where should the canonical continuity boundary be placed? This paper introduces Actor-native Identity and presents SoulAuth, an open-source Rust reference implementation for Humans and long-lived AIActors. We argue that any subject that must persist under its own identity and remain independently attributable should have an ActorIdentity that is not replaced by an Account, Credential, Client, AuthSession, IdentityBinding, or runtime instance. SoulAuth therefore treats Humans and long-lived AIActors as first-class identity subjects while keeping authentication distinct from downstream authority. Methodologically, we use a Philosophical Engineering approach that translates conceptual analysis of subjecthood into identity objects, invariants, lifecycle semantics, system responsibilities, implementation boundaries, and inspectable conformance evidence. Evaluation against the fixed SoulAuth v0.1.0 artifact shows that the implementation realizes core boundaries including Human/AIActor first-class identity status, Client/Actor separation, and Authentication/Authority separation, while gaps remain in unified Credential modeling and historical attribution anchored to ActorIdentity. We therefore report partial, not full, architecture conformance.

arXiv:2609.12039v1 Announce Type: cross Abstract: Software development follows an implementation-verification loop in which developers or agents iteratively revise an implementation until an evaluator, such as a test suite, accepts it. The evaluator checks the implementation against a set of requirements under a model of the deployment environment. Yet even a formal proof that the implementation satisfies the requirements under the model cannot guarantee acceptable behavior after deployment. Requirements only approximate stakeholder intent, and the model only approximates the real deployment environment. We call these together - requirement gap and model gap - the two-gap framework, which unifies the main failure modes of agentic software engineer-ing: reward hacking exploits omissions in the requirements or model, while hallucination widens the gaps by fabricating requirements or environment assumptions. Because neither gap can generally be certified closed in an open, changing world, the goal shifts from closing them to continuously narrowing them. We therefore propose an assurance-revision loop that uses deployment evidence to revise the requirements, model, or evaluator when stakeholders reject the resulting behavior. We then cast assured agentic development as a resource-allocation problem over human judgment, agent capability, and compute. The two principal bottlenecks mirror the two gaps: human judgment for the requirement gap and faithful, costly evaluation for the model gap. Reality remains the final verifier: acceptable behavior under actual deployment conditions is the ultimate test, while predeployment evaluations remain proxies for it.

arXiv:2609.12107v1 Announce Type: cross Abstract: Development and humanitarian organizations produce and support surveys, administrative registries, and other data resources to inform research, policy, and operations, yet systematically identifying where these datasets are referenced remains difficult. Such references are dispersed across research papers, project documents, humanitarian reports, and other unstructured text, limiting both the ability to trace data use and to identify potential gaps in data availability or dissemination. We present a weakly supervised framework for adapting dataset extraction to forced displacement and Fragile, Conflict, and Violence (FCV) documents without first constructing a large manually labeled training corpus. A lightweight model trained on general research literature generates candidate dataset mentions from unlabeled domain documents, which a frontier large language model (LLM) reviews in context, validating or rejecting candidates and correcting their extraction boundaries. The resulting annotations are supplemented with targeted synthetic and contrastive examples and used to fine-tune the lightweight model for large-scale extraction. We evaluate the resulting model on an independent gold-standard benchmark of 1,706 text passages spanning research, humanitarian, and operational documents. Across the full benchmark, the model achieves 74.1\% precision and 70.5\% recall at the mention level; among passages containing dataset references, precision reaches 89.5\%. At the passage level, the model achieves 88.2\% accuracy and 88.6\% specificity in distinguishing passages with dataset references from those without them. These results demonstrate a practical approach for constructing domain-specific supervision when labeled data are limited, and provide a technical foundation for larger-scale analysis of data use and potential gaps in the displacement data landscape.