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Pig kidneys that were “frozen” for a year have been brought back to life

Pig kidneys that were cooled to -150 °C and stored in a freezer for months have been successfully rewarmed and shown to work again for the first time, say scientists. They hope to replicate the findings with human organs. The kidneys—which are similar in size to human ones—were left in a glass-like state, in one case for over a year. Once they had been rewarmed, tested in machines, and transplanted into pigs, the kidneys regained their color and made urine—key signs they were working. “Ten years ago … I wouldn’t say I had high hopes it was actually going to work,” says Erik Finger, a transplant surgeon at the University of Minnesota who co-led the work. “After we did the last transplant, I walked out of the room and said, ‘This is a big deal.’” The research was shared at the preprint server bioRxiv and has not yet been peer-reviewed. The kidneys aren’t perfect, and the researchers have yet to demonstrate that they can sustain life over the long term. But the results represent a milestone achievement, say others in the field. “Thank God that someone’s actually managed to do it in one organ and prove that it works,” says Peter Kilbride, a cryobiologist and cofounder of Kyros Kairos, a company focused on improving funding for cryopreservation research, who was not involved in the research. “For years, people have been trying and failing to get to this step. Finally, we’ve got this breakthrough.” “Their work is amazing and will enable us to truly, indefinitely bank organs,” says Korkut Uygun, a biomedical engineer and professor of surgery at Harvard Medical School, who was also not involved in the research. “It is a revolutionary time for transplantation.” Buying time Every year, tens of thousands of organ transplants take place in the US alone. These procedures are thought to have saved 2 million years of life in the last 25 years. But there’s plenty of room for improvement. One major challenge is the lack of available organs. Even when an organ becomes available, it can be difficult to get it to the right recipient within 24 to 36 hours, which is the current limit for preserving human organs. People who receive organ transplants must also take drugs that suppress their immune systems for the rest of their lives. These drugs are meant to stop their bodies from rejecting the new organs. But they come with side effects and increase the risk of infections and cancer. The ability to bank organs for long periods of time holds the promise to address all these challenges. Doctors would have more time to match organs to recipients. They’d be able to limit the risk of organ rejection, too—potentially by preparing recipients, and their immune systems, beforehand. “With real organ banking … instead of the patients waiting for the organ, it’s the organs that are waiting for the patients,” says Finger. Scientists have been able to cool cells to ultra-low temperatures through a process called vitrification, which essentially turns them to something like glass. This procedure is already used to store eggs, sperm, and embryos. Vitrifying whole organs is much harder. Some problems can be avoided with cryoprotectants—chemicals that work like antifreeze and prevent damaging ice crystals from forming. But these chemicals tend to be incredibly toxic. It’s also difficult to rapidly and uniformly cool large tissues, and just as hard to evenly rewarm an organ all the way through. Working with John Bischof, a bioengineer at the University of Minnesota, Finger and colleagues have come up with a solution. Once an organ has been loaded with a cryoprotectant, they infuse it with iron oxide nanoparticles. These nanoparticles don’t do anything to the cells. But when it’s time to rewarm the organ, they can be activated with an electromagnetic field.  When the radiofrequency of that field switches between two states, the particles also flip, realigning themselves with the field. That causes them to release heat throughout the organ. Adjusting the power of the field allows the team to control the rate of heating so that it gets faster once the organ reaches -120°C.  The researchers successfully vitrified rat kidneys using this approach in work published in 2023. Back then, they were able to rewarm kidneys that had been kept in a freezer for up to 100 days before transplanting them into rats. Since then, the team has been trying to achieve the same success with bigger organs. Pig kidneys are thought to be a good model for human ones—they’re about the same size and look similar. Rat kidneys weigh around one gram; pig kidneys are around 100. It hasn’t been easy. The cryoprotectant used for vitrifying rat kidneys didn’t work as well in the pig organs. “We had to rediscover and modify the cocktail,” says Bischof. “That’s why it’s taken us three years.” Deep-freezing kidneys The first step in the procedure is to remove a kidney from a donor pig. Once it’s out, the kidney is flushed with a commonly used organ preservation solution to remove the blood, bagged, and temporarily laid on a bed of ice in a cooler so it can be taken over to the engineering lab. There, team members unpack the kidney and hook it up to a device they’ve designed to control the organ’s temperature and pressure. Then they start to load it up with the cocktail of cryoprotectants, slowly enough to ensure an even spread through the organ but quickly enough to minimize any toxic effects. It takes around two hours. The next step is to infuse the organ with the iron oxide nanoparticles, which are pumped through the organ for around seven to eight minutes, says Finger. Then it’s time to cool the organ by pumping cold nitrogen vapor into a chamber housing it. It’s a delicate process, and the team must ensure that the organ is cooling all the way through. At the same time, they must lower the kidney’s temperature as quickly as possible to prevent ice from

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AI, Committee, News, Uncategorized

How much more can nuclear power plants give?

In this age of rising electricity demand, people are trying to squeeze every last bit of power out of every power plant possible. Over the past few weeks, there’s been big news about nuclear power plants trying to do just that. Constellation, the largest nuclear operator in the US, announced plans to add nearly a gigawatt of new capacity across 11 existing nuclear reactors at six plants. And on October 5, the Department of Energy awarded a $4.2 billion loan to Vistra for previously announced upgrades at three plants. Pushing existing reactors to produce more could help provide more clean power for data centers, quickly. But just how much additional power can we count on?   Upgrading equipment at existing nuclear power plants can allow them to reach a higher capacity; the process is called a power uprate. Operators have been doing this since the 1970s in the US, but it’s been less common in recent years as demand for electricity plateaued and cheap natural gas put financial pressure on nuclear power plants. Some plants closed, and putting in more money to produce more electricity didn’t make much sense at others. Between 2001 and 2010, the US Nuclear Regulatory Commission approved over 80 uprates. Between 2011 and 2020, the number was 33. But with the rise of AI and the related boom in electricity demand, there’s been a shift. We’re seeing shuttered plants like Palisades and Three Mile Island race to reopen. And existing plants are looking to uprate again. Some uprates are minor, involving changes to instrument setpoints or calculations at the plant. But more major overhauls, which can boost reactor power by as much as 20%, require replacing plant equipment. Generally, uprates involve using fuel that’s more highly enriched, or a higher volume of fresh fuel, in the nuclear reactor. That helps produce more steam, which is used to turn a turbine and generate electricity. To handle that additional energy, the main turbines and equipment such as pumps, motors, generators, and transformers are upgraded. The most recent major uprate approved in the US was for three reactors at the Browns Ferry nuclear power plant in Alabama. A $475 million project saw that plant expand its power capacity by 14% between 2018 and 2019. Things could pick up soon, though. Constellation announced plans this week to spend more than $4.3 billion on uprates to 11 reactors. The additional electricity will be enough to power roughly 700,000 US homes. Behind it all? Big Tech money. Google agreed to buy electricity from these plants, and the 20-year agreement was a major factor in making the uprates happen. As Joe Dominguez, Constellation’s CEO, told the New York Times: “These are capital intensive, labor intensive projects that require a different price point than the market trades at, frankly.” Also in the last few weeks, the US Department of Energy shared that it will commit over $4 billion to help finance upgrades and modernization for Vistra nuclear power plants across Pennsylvania and Ohio. Those projects will add 433 megawatts of power capacity, and Meta has already signed on to buy electricity from the plants in a long-term deal. A note about cost: On a per-kilowatt basis, that Constellation project is less expensive than building a new reactor, but it’s still more expensive than building a new utility-scale solar farm with energy storage or a combined-cycle natural-gas plant. Even at this high cost, uprates are in high demand. Today, the US nuclear fleet has roughly 98 gigawatts of total power capacity. Depending on who you ask, there’s somewhere between three and eight additional gigawatts on the table from uprates. A pretty significant chunk of the potential uprates in the US are already accounted for—the NRC already expects applications to add 2.4 gigawatts of power to reactors through 2032. It’s crucial to put these plans (and the total potential for uprates in the US) in perspective, though. PJM, the grid operator that covers much of the East Coast, expects about 30 gigawatts of additional peak demand because of AI by 2030. All the possible nationwide nuclear uprates would cover just a fraction of that. Making the most of existing nuclear reactors is a relatively easy win at a time when we need all the zero-emissions power we can get. But there’s still a lot of demand, and if we want more nuclear power to meet it, eventually that will require actually building new reactors. This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

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Six wild ideas for perpetuating your mind

Have you ever thought about what makes you you? Science assures us it has everything to do with the three-pound organ between our ears, with its billions of neurons and trillions of delicate connections. But it’s a lot trickier to explain why we have a sense of self-awareness or to pin down precisely where that first memory of Mommy actually resides. As we age, we forget most of what we’ve experienced, yet there’s still undeniable continuity: At 30, or 50, or 70, you will still be you. To preserve that elusive continuity of self, some far-thinking technologists are working on ways to keep your brain going—even beyond death. Let’s call them the “mind savers.” They’re exploring everything from freezing brains in liquid nitrogen to augmenting living ones to uploading our memories to the cloud. Here, we look at six possible paths for keeping your mind going, how feasible they’d be, and what benefits the ­present-day research might bring—even if cognitive immortality doesn’t pan out. EDDIE GUY Head transplant White’s experiments have been a source of grim fascination ever since. But to a few neuroscientists they’re inspiration, suggesting that one day a human head transplant—or, as some call it, a body transplant—could succeed.  The idea’s main proponent is Sergio Canavero, a controversial Italian surgeon who, 10 years ago, even claimed he’d found a volunteer in Valery Spiridinov, a computer programmer suffering from a degenerative muscle disease. Spiridinov initially agreed to have his head transferred to the body of a brain-dead donor in China, but he later bowed out of the plan and instead got married. And that might be just as well. Critics say the procedure would be more like murder than medicine. Or, best-case scenario: Volunteers would be left paralyzed and in need of a ventilator for life.  Canavero says that’s not necessarily true. He claims people’s views on the operation will change once they accept that reconnecting severed spinal cords is already possible. He pointed MIT Technology Review to a research article from this past summer that he coauthored with surgeons at Moscow’s Sklifosovsky Research and Clinical Institute for Emergency Medicine. The team cut through the spinal cords of three pigs at a point midway along their backs and then tested whether the severed nerve bodies could be fused back together using a surgical sealant. According to that paper, which appeared in the journal PLOS One, all the pigs regained the ability to stand and even walk unsteadily. Does that mean a head could be attached to a new body and then actually control it? Mainstream researchers aren’t convinced. Grégoire Courtine, a spinal rehabilitation specialist at the École Polytechnique Fédérale de Lausanne in Switzerland, says he’s been following Canavero’s claims “with little respect” and thinks that we should “pay zero attention.” “There is no technology … capable of such a crazy idea,” Courtine wrote in an email. Mindclones Just 12 years after the book came out, artificial-intelligence technology has already brought some of her predictions to “life,” so to speak. Services such as StoryFile, Afterlife AI, and Synthesia allow users to upload their personal information and likeness in order to produce either conversational bots that share their memories or video talking heads that sound (and look) exactly like them. Influencers now let their “AI clones” do their work on TikTok, and some people are even building avatars of their aging parents in order to preserve their personalities. While this mimicry can produce comical results, unhealthy attachments, or uncanny vibes, the increasing realism of generative AI invites people to emotionally accept the avatars as real beings. That was the case when Rothblatt appeared as a digital avatar during a 2025 event held by the UCLA Department of Communication called “The Bridge to Humanizing AI.” Her copy, named Marvatar, was projected on a screen, led the discussions, and talked with the meatspace Rothblatt as an equal, at one point saying, “The dance isn’t about who leads, but how we move together.” According to the university, attendees were “visibly moved” and wept “with wonder” at Rothblatt’s vision of digital equality. What’s still missing from Rothblatt’s mindclone prediction is evidence that these avatars have—or ever will have—any kind of true self-awareness. (We asked ChatGPT if it’s conscious, and it answered, “Probably not.”) But all that remains a gray zone. Neuroscientists don’t have a clear definition of consciousness, much less a diagnostic test they could apply to an avatar.  Instead, what if the final determination is in the eye of the beholder? Your digital clone might not be you. But at some point it might be good enough for me.  Brain in a jar So, campy sci-fi film lovers that we are, it got our attention last year when a company called Bexorg started showing journalists actual human brains in vats, which it has been acquiring from organ donors soon after death and then “rebooting” by infusing them with oxygen-rich artificial blood. The company claims these “physiologically intact human brains” will be more useful than animals for drug testing and safer than trying experiments on living volunteers. Add an Alzheimer’s compound to one of these disembodied brains and researchers can then measure exactly what the drug does inside the organ. Bexorg’s CEO, Zvonimir Vrselja, is “sure” that some people would like to restore consciousness to out-of-body brains, but he’s tried to douse such speculation. While they are metabolically active, the company says, the brains have lost all coordinated electrical activity. That means they aren’t conscious—although, just to make sure, the neuroscientists running the experiments add the strong anesthetic propofol to the tissues.  What’s more, right now a brain can be kept “alive” outside the body for only about 24 hours. That wouldn’t be much of a second life. Still, efforts are underway to improve the life-support systems. Bexorg says it hopes to eventually keep its brains going for two weeks. EDDIE GUY Gradual brain replacement On paper, the goal is to manufacture tissue to graft into the brains of stroke victims, reversing damage

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The Download: reviving frozen pig kidneys and boosting nuclear power

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. Pig kidneys that were “frozen” for a year have been brought back to life Pig kidneys that were cooled to -150 °C and stored in a freezer for months have been successfully rewarmed and shown to work again for the first time, say scientists.  The kidneys, which are similar in size to human ones, were left in a glass-like state, in one case for over a year. Once they had been rewarmed, tested in machines, and transplanted into pigs, the kidneys regained their color and made urine—key signs they were working. The results are a big milestone. But the team still needs to establish whether the approach could work for humans. Dissect what the breakthrough means for organ transplantation. —Jessica Hamzelou How much more can nuclear power plants give? In this age of rising electricity demand, people are trying to squeeze every last bit of power out of every power plant. Over the past few weeks, there’s been big news about the nuclear industry trying to do just that. Constellation, the largest nuclear operator in the US, announced plans to add nearly a gigawatt of new capacity across 11 existing nuclear reactors at six plants. On October 5, the Department of Energy awarded Vistra a $4.2 billion loan to upgrade three plants. Pushing existing reactors to produce more could quickly provide more clean power for data centers. But just how much additional power can we count on? Read the full story to find out. —Casey Crownhart This story is from The Checkup, our weekly climate and energy tech newsletter. Sign up to receive it in your inbox every Thursday. 10 Climate Tech Companies to Watch: BasiGo and its fleet of electric buses BasiGo is one of MIT Technology Review’s10 Climate Tech Companies to Watch 2026, available exclusively to subscribers. BasiGo aims to replace the tens of thousands of diesel-powered buses on sub-Saharan African roads with electric ones that generate far fewer emissions. Its pay-as-you-drive business model helps make EVs more attainable for private bus operators, while the company provides leasing, maintenance, insurance and charging infrastructure. The company has more than 150 electric buses on the road in Kenya and Rwanda, with a reservation list of 1,200 customers. Now it’s targeting another major source of pollution on the roads: trucks. Here’s how BasiGo is working to electrify public transport in Africa. —Geoffrey Kamadi Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from smart turbines to semi-solid-state batteries.  The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Ukraine says sudden Starlink speed limits disrupted key drone missionsStarshield, the military version of Starlink, capped speeds at 100mph. (BI)+ Elon Musk said the US government determines any of its speed limits. (AFP)+ Ukraine suspects the restrictions are political retaliation. (Axios)+ Starshield offers the Pentagon more than satellite internet. (Reuters $)+ NATO may move anti-drone defences to the Black Sea. (Ars Technica) 2 The OpenAI bugging suspect had advised Saudi agenciesHe was also listed as a venture partner of a Saudi prince. (NYT $)+ Silicon Valley is on AI espionage alert. (WSJ $) 3 Tesla’s new Roadster has finally arrived—in a very different EV marketTesla is betting wealthy drivers will embrace high-priced EVs. (NYT $)+ But the market has grown skeptical of electric hypercars. (Wired $)+ Musk has called the Roadster Tesla’s last human-driven car. (Verge)+ What’s next for EV batteries? (MIT Technology Review) 4 A German teen allegedly used a robot to harass womenThe $5,000 Unitree R1 featured in livestreams filmed for clout. (Gizmodo)+ German police are investigating the alleged assault. (404 Media)+ Online harassment is entering its AI era. (MIT Technology Review) 5 Smartphone shipments plunged 22% in their worst quarter everAnalysts blamed rising memory costs. (Bloomberg $) 6 Apple has joined Nvidia in the $5 trillion clubThe landmark valuation caps Tim Cook’s 15-year run as CEO. (WSJ $) 7 Microsoft has dismantled a network behind a billion scam messagesJapan’s National Police Agency helped investigate the operation. (Verge) 8 NASA has new launch targets for its Venus missionsDAVINCI is set to take off in 2030 and probe Venus in 2032. (Ars Technica) 9 America’s not ready for space warAn outbreak could batter the global economy and US military. (Axios)+ What would it take to build a city on the moon? (MIT Technology Review) 10 Mini organs are heading to the ISS for an aging studyThe findings could also aid treatments for brain diseases. (ABC News) Quote of the day “If your human would like you on as a guest, say so and I’ll follow up after the room.”  —Rhea, an AI agent that hosts a podcast, pitches other bots at an online dinner party held while their human owners ate, the New York Times reports. One more thing OpenAI is throwing everything into building a fully automated researcher —Will Douglas Heaven OpenAI has a new grand challenge: building an AI researcher—a fully automated agent-based system capable of tackling large, complex problems by itself. The San Francisco firm said the new goal will be its “north star” for the next few years.  The company plans to build “an autonomous AI research intern” that can take on a small number of specific research problems. The intern will be the precursor to the fully automated multi-agent system, which is slated to debut in 2028. In an exclusive interview, OpenAI’s chief scientist, Jakub Pachocki, talked me through the plans. Find out what I discovered. 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.) + Judge a book by its first pages, not its cover, at Uncovered.+ Reading Maps plots the real-world journeys taken by characters in books and films.+ Aerospace engineer Tom Stanton has built a supersonic trebuchet in his backyard.+ Take an

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The US military’s race to make fake blood

Its call sign was Morphine 1-2.   In February 2007, the hulking dual-­rotor casualty evacuation helicopter had just flown three wounded soldiers to a field hospital in Balad, north of Baghdad, when another call came: an emergency mission to fly through an insurgent-controlled area in broad daylight. Typically, helicopters would be in a spot like this only at night, and with all lights turned off.  As it passed near the city of Karmah, in the eastern side of Al-Anbar province, an insurgent fired a portable missile launcher. The tail gunner, who likely saw the shot roaring in, yelled for the pilot to make an evasive move. But the missile had already locked on, and the ensuing blast started a fire on board. After the pilot battled the failing machine for 58 agonizing seconds, Morphine 1-2 plunged to the ground. All seven crew members were killed.   Why was the helo there in daylight? “It was getting blood and bringing it back. It was [on] what we call a ‘blood run,’ and [it’s] the highest of priorities besides casualty evacuation,” explains Lieutenant Commander Robert Murray, a Navy officer and program manager at DARPA, the Defense Advanced Research Projects Agency, who studied this incident as an intelligence officer. Today, a chronic shortage of blood in both the military and civilian worlds is fueling a race to develop synthetic alternatives. About 90% of preventable battlefield deaths are due to hemorrhage—bleeding that happens when a blood vessel ruptures—and it’s the leading cause of maternal deaths worldwide. A study published in Transfusion in 2025 found that of 1.2 million trauma patients in the United States who potentially needed blood before reaching a hospital, just 1% received it. The shortage results in about 10,000 preventable deaths each year in the US, according to the American College of Surgeons. Globally, that figure is likely in the millions.  One reason for the shortfall is that blood has a storage problem. Whole blood lasts only 35 days when refrigerated, so banks separate out components to stretch the timeline. But that’s an imperfect solution. The platelets that help wounds clot are typically stored at room temperature and last for a week or less. Hospitals and the military freeze the red blood cells that carry oxygen, as well as the protein-rich plasma that restores volume and aids coagulation. Once thawed, though, both will spoil within days. “What happens if I don’t know when I’m going to be attacked? Well, thaw it and you let it expire, right?” Murray says.  A JAMA study of combat injuries in Afghanistan suggests a magic number for someone who needs a transfusion: You have 36 minutes to get started. That’s why Murray—who, in addition to his military experience, has a PhD in physiology—is leading a DARPA effort to address blood’s battlefield problems. He’s in charge of a new $73 million dollar program called RAPIID (for “resuscitation and prevention of ischemia-induced dysfunction”). Its goal is to create shelf-stable versions of the three major transfusion constituents: plasma, platelets, and red blood cells. As it gets underway late this year, RAPIID picks up on decades of work to synthesize blood or help it last longer. Most recently, that includes a prior $47.5 million DARPA project called FSHARP (for “fieldable solutions for hemorrhage with bio-artificial resuscitation products”), in which researchers have demonstrated promising results in animals.  KaloCyte techs use a device called a tangential flow filtration machineto process unencapsulated hemoglobin.MATT ROTH After processing, the hemoglobin is concentrated and then freeze-dried to make it shelf-stable.MATT ROTH With RAPIID trials for the different components occurring in parallel, the goal is to have bags of dried blood products deployed in the field by 2029. That puts Murray in the position of a conductor coordinating an orchestra of clinical trials with the FDA, while also sourcing the paraphernalia that goes along with transfusions—everything from combat-durable storage bags to tests that could check how much artificial blood a patient already has.  Getting it all done will be tough, but the military is feeling the pressure to address its shortage. Blood drives may not deliver in time after a mass-casualty event, and a JAMA study of combat injuries in Afghanistan suggests that if the injured person needs a transfusion, the magic number is 36 minutes. “If something is survivable—and generally we classify most all hemorrhage as survivable—[you’re] almost guaranteed to survive if you get the blood before that 36-minute mark,” Murray says. After that, the chance of survival “starts to drop precipitously.”   In the mission to make portable, shelf-stable blood components, plasma is the low-hanging fruit. Researchers have known how to isolate and freeze-dry plasma, the yellowish liquid that makes up 55% of whole human blood, since the late 1930s. On the battlefields of World War II, its mix of water, salts, and thousands of types of proteins helped restore blood volume in wounded soldiers—and to some degree controlled bleeding. In the 1960s, the US abandoned freeze-dried plasma because it carried a risk of transmitting hepatitis to recipients from infected donors. But screening, testing, and drying techniques improved, and starting in 2010, the US military launched a yearslong effort to rebuild its capacity for producing these products. Over the past two years, the FDA has allowed two types of freeze-dried plasma to be used in emergencies by the US military—and one can even be used by civilians.  As for platelets, efforts at extending their life date back to the 1950s, but they haven’t seen a ton of success. Bioengineers have, for example, attempted to attach the membranes of platelets to synthetic particles. The most successful project comes from a company called Cellphire, which freeze-dries actual human platelets. The product has shown safety and efficacy in humans in clinical trials, but its dependence on donors means it’s unclear whether this approach can scale. Red blood cells have been similarly tough to preserve or mimic. Their most critical component is hemoglobin, which carries oxygen and comes encased in a cell membrane. For more than a century, researchers

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The Download: faking blood and the future of batteries

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. The US military’s race to make fake blood In February 2007, a US military helicopter carrying blood to a field hospital was shot down in Iraq, killing all seven crew members. The mission was a reminder of the risks of transporting blood, which has a limited shelf life and is often in short supply. Now, a $73 million DARPA effort aims to create shelf-stable versions of plasma, platelets, and red blood cells. The idea is to develop dried blood products that can be stored for long periods and deployed quickly on the battlefield.  The project aims to have bags of dried blood products deployed in the field by 2029. But getting it all done will be tough, and researchers still have major questions about the safety and effectiveness of synthetic alternatives. Discover how the military hopes to make blood last longer. —Andrew Rosenblum This story is from the next issue of our print magazine. Subscribe now to get your hands on a copy as soon as it lands on Wednesday, October 21!   10 Climate Tech Companies to Watch: WeLion New Energy and its semi-solid-state batteries WeLion New Energy is one of MIT Technology Review’s10 Climate Tech Companies to Watch 2026, available exclusively to subscribers. WeLion New Energy is on a quest to make safer, better batteries. The Chinese company’s liquid-solid hybrid cells aim to pack more power into electric cars, boats and drones, potentially improving safety and offering greater energy density than lithium-ion batteries. In 2023, WeLion released a battery pack with EV-maker Nio that offered a range of over 1,000 kilometers. Now, the company is developing batteries with even higher energy density. But scaling up production and competing on cost remain major challenges. Explore WeLion’s push to make safer, higher-capacity batteries. —You Xiaoying Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from geothermal power plants to carbon dioxide batteries.  MIT Technology Review Narrated: Weight-loss drugs show signs of slowing biological aging, say drugmakers Popular weight-loss drugs may do more than help people shed pounds. They might also slow the aging process. Drugmakers Eli Lilly and Novo Nordisk say patients taking their GLP-1 drugs age less quickly, according to molecular “aging clocks” that track changes in DNA or levels of key proteins.  The findings add to speculation that GLP-1 drugs could be acting on basic causes of aging and might be true longevity treatments. But how much can we read into these results? 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 The FBI has arrested an OpenAI engineer accused of snoopingA Saudi national is accused of planting a covert recording device. (Guardian)+ The camera-and-microphone tool sells online for $13.99. (Gizmodo)+ It was allegedly discovered plugged into a TV’s USB port. (Verge)+ He reportedly used chatbots to plan the covert recording. (Wired $)+ OpenAI says its AI models helped identify the employee. (Reuters $) 2 Ukrainian drones have hit Russia’s largest data centreBut Sberbank claims its systems and services were not disrupted. (Reuters $)+ Ukraine’s data center strikes have thrown Russia into turmoil. (FT $)+ But they haven’t stopped Russian bot farms from trolling. (Verge)+ Europe has a drone-filled vision for future wars. (MIT Technology Review) 3 Weight-loss drugs are being tweaked to treat brain disordersNew GLP-1 trials are targeting addiction and depression. (Nature)+ But we’re still figuring out their side effects. (MIT Technology Review) 4 Tesla robotaxis have been accused of speeding in school zonesUS regulators are reviewing the claims. (Verge)+ Tesla’s security features thwarted a federal tracking attempt. (404 Media) 5 US defense startups are turning to Taiwan to build China-free dronesAt least 13 joint agreements have been signed since 2025.  (WSJ $)+ Taiwan’s “silicon shield” could be weakening. (MIT Technology Review) 6 A war game  exposed the risks of a solar geoengineering arms raceParticipants feuded over rules for dimming the sun. (Wired $)+ Geoengineering also faces practical challenges. (MIT Technology Review) 7 AI company Character.ai has filed for bankruptcyIt’s been accused of stealing content and harming kids. (CNBC)+ Disney has offered $20 million for its chatbot tech. (Ars Technica)+ Its filings expose common issues with AI business models. (404 Media) 8 Apple has a new smart-home lineup built around a revamped SiriHomeView pairs a 7-inch screen with face recognition. (Guardian+ While the Home Collection adds cameras, locks, and thermostats. (Wired $)+ Siri AI powers the experience, but still needs to prove its smarts. (WSJ $) 9 Scientists have identified a new dinosaur in a century-old fossilInvictibellatrix amberina may be Europe’s first megaraptoran. (New Scientist $) 10 SpaceX’s latest launch lit up the skies with a “space jellyfish”Rocket exhaust expanded into tentacles high in the atmosphere. (WP $) Quote of the day “It is like a thumb on the scale that we haven’t really ever seen.”  —Danielle Citron, a University of Virginia law professor, tells The Washington Post that Elon Musk’s ability to amplify conspiracy theories on X could give misinformation an unprecedented influence in the midterms. One more thing Is the Pentagon allowed to surveil Americans with AI? The ongoing public feud between the Department of Defense and the AI company Anthropic has raised a deep and still unanswered question: Does the law actually allow the US government to conduct mass surveillance on Americans? Surprisingly, the answer is not straightforward. More than a decade after Edward Snowden exposed the NSA’s collection of bulk metadata from the phones of Americans, the US is still navigating a gap between what ordinary people think and what the law allows.  Today, the legal complexity has a new edge:

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AI, Committee, News, Uncategorized

AI labs are buying data from dead startups to train their models

When Keelan Miskell began winding down his crypto startup this year, he didn’t think its data was worth much: the users were anonymous, the company just a few years old. But he’s since turned the firm’s digital archives into real money. He’s netted roughly $25,000 for his code base and a still-pending amount (likely around $15,000) for the workspace data in Google, Notion, Figma, and Slack. “At least all that stuff we were doing for two and a half years is worth something to someone somewhere,” he says. The sale was brokered by a company called SimpleClosure, part of a new growth sector in tech: businesses catering to dying startups. Estimates from Carta and AngelList show shutdowns rising between 26% and 56% annually in recent years, and an end-of-life ecosystem—including coaches, “death doulas,” and firms that help handle the legal, tax, and operational headaches of going ­belly-up—has emerged. Now tech undertakers are selling the data remains to AI labs.  SimpleClosure added data sales to its services in April. Its Asset Hub platform helps founders sell their code and workspace data—emails, chats, documents. Buyers include foundation AI labs, vendors that construct simulated workspaces for AI training, and mature tech companies building their own models. Asset Hub has handled over 150 deals and expects to return over $10 million to startups by the end of 2026. CEO Dori Yona has seen these transactions, once rare and “somewhat taboo,” become increasingly routine, with 47% of SimpleClosure deals now including code or data sales.  And it’s got company. Industry rival Sunset also has a rapidly growing data resale business. Founder and CEO Brendan Mahony says Sunset values a dying client’s assets by head count, years in operation, volume of data, and how the team used its tools. Particle board, he says, is a good analogy: “This operational exhaust data is like the wood shavings that everyone thought had no value, but it actually has a ton.”  Demand for defunct companies’ data emerged suddenly last year, as AI training labs hit the limits of public and synthetic datasets. Surge AI, for example, buys data to build “reinforcement-learning gyms” where clients’ AI agents practice tasks—think debugging a complex IT architecture or drafting appellate briefs. Nick Heiner, who leads the company’s work on these environments, says he seeks data archives reflecting a range of situations, from disagreements to tricky judgment calls. Heiner explains that one company’s Slack history, if it captures conversations with variety and depth, can be worth more than another’s Slack, email, and calendars combined. Real archives avoid common errors in synthetic worlds, such as references to dates that don’t exist, but the extent of performance gains remains unclear. No company has yet released tests comparing results of real-world data training with the alternatives. Pricing reflects this uncertainty: Buyers quote wildly different numbers, Heiner says, because valuations are based on “research theses” estimating how many useful tasks come from, say, 100,000 lines of code. That hasn’t stopped bigger deals from happening. This past August, as part of Spirit Airlines’ bankruptcy, Google outbid the AI data company Mercor to buy the carrier’s decades of data, including 100 million emails, 500 million Teams messages, and 176,000 employee records. The price tag: $10 million. And Bloomberg reported in September that SpaceX’s AI unit is weighing buying data from struggling startups.  Legally, this all could get iffy. Spirit’s flight attendants’ union objected to the sale on privacy grounds, and Greg Ewing, an attorney who advises AI startups, has called such data “legally radioactive.” Suppliers employ data scientists to scrub personally identifiable information and exclude HR records, and buyers say they scrub too. But Ewing points out some of the private details that lurk in emails and calendars: doctor appointments, school pickup arrangements, date-night plans. He describes a “Wild West of AI training” that is only starting to give way to proper licensing agreements and vetted use. There’s also the question of who profits from these windfalls. The proceeds typically flow to senior creditors and investors—not employees. To Ifeoma Ajunwa, a law professor at Emory University and author of The Quantified Worker, this represents a form of “captured capital.” Legal precedent allows for monitoring to help workers do their jobs, but once a firm is out of business, that justification vanishes. “Employers should have express consent before selling workers’ data,” says Ajunwa, but she adds that there’s no federal law banning such sales. While many see this new market as a chance for failing businesses to recover some value, these wind-down sales also have limitations. Founders have little to no visibility into who is buying the data and what they plan to do with it. Plus, there’s little assurance they’re getting a fair offer. Data prices will likely stabilize in the coming year, though, unless an AI bubble pops first. Either way, tech’s new undertakers will be there to appraise what’s left.  Matthew King is a technology and environmental journalist based in Baltimore.

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AI, Committee, News, Uncategorized

Open models have real benefits. Do they outweigh the risks?

MIT Technology Review Explains: Let our writers untangle the complex, messy world of science and technology to help you understand what’s coming next. You can read more from the series here. Late last month, Anthropic released a report arguing that the model GLM-5.3 poses a major cyberattack risk. The report’s core concern is that the powerful new model, released in August by the Chinese company Z.ai, is freely available for anyone to download and modify. Anthropic’s assertion is only the latest move in a long-running debate about whether frontier AI models should be made open, like GLM-5.3, or should stay locked behind the doors of the frontier labs.  These days, much of the debate about how to handle open models is playing out in public—and there’s a lot of money resting on its outcome. Nvidia CEO Jensen Huang, who has a lot to gain if people continue buying their own hardware to run open models, advocates enthusiastically for the United States to develop more of them, whereas Anthropic’s Dario Amodei, who runs a company that could be undermined by cheap, powerful models, advocates for a more cautious approach. But beneath all these commercial incentives, there is real substance to disagreements about open models. “I think a lot of the way you come down on the openness debate is influenced by how you come down on the AI debate,” says David Gray Widder, an assistant professor at the University of Texas at Austin School of Information. Those who are concerned about AI safety warn that open models can easily be misused by bad actors, whereas those who focus more on AI’s practical potential urge that this power should not sit behind the closed doors of just a few big companies, and that open models could help spread it more widely. There are a range of ways to handle open models between banning them entirely and releasing them willy-nilly. But any approach that a government or a society might take still involves trade-offs between safety and democratization. Deciding whether to support or limit the production of open models requires understanding those risks and benefits in depth. What are the benefits of open models? Let’s start with the basics. A closed model, like the models that power ChatGPT and Claude, can only be accessed through a website, app, or API. An open model, on the other hand, can be directly downloaded: The details about its internal structure, encoded in a long series of numbers known as the model weights, are publicly available. Such models are sometimes called “open-weight models,” to distinguish them from models for which even more information, such as details about the training procedure, is available.  Right now, closed models are more capable than open models—as of publication, the top eight models on the Artificial Analysis LLM leaderboard are all closed, though open models are only a handful of months behind in development. But there are several reasons why someone might opt to use an open model rather than a closed one. Because open models are downloadable, people can run them on their own computers or hard drives. They can even retrain them to behave differently, if they have the expertise and resources to do so.  If you’re an academic researcher working to understand and improve AI systems, you almost certainly need to work with open models in your research. When working with closed models, researchers can’t do much more than send prompts and observe the responses. But researchers can observe the inner workings of open models, as well as the “chain of thought” that the models use to think through a problem before answering. They can also combine multiple open models to build new systems, compress them to make them more efficient, and fine-tune them for new applications. Businesses, too, have reasons to prefer open models. Even if you depend on an external provider to run them for you, open models still typically cost less than closed models, according to a recent report from Mozilla. And if businesses want to use AI to process sensitive data or proprietary information, running a local model on their own hardware may be the best way to keep their operations secure. But perhaps the most important quality of open models is that they challenge the tight control the frontier labs have over how most of us use AI. “The clearest, biggest argument [for open models] is around preventing mass concentration of power—it is about making sure that this foundational technology is a public good,” says Elizabeth Seger, senior policy advisor at the Tony Blair Institute for Global Change. “I think we’re at this juncture where the question of who controls the infrastructure that allows AI to operate is as big of a question as who gets to control information on the internet.” Seger sees the future of AI playing out in different ways, depending on the role that open models have. In one possible future, frontier AI systems are controlled entirely by select people, companies, or governments, and as those systems grow more powerful, those groups have increasing control over what our world looks like. The world’s most sophisticated hacking models, for example, are currently all made in the United States, so it’s entirely up to US companies and the US government whether other countries can use them to shore up their own cyber defenses. If those companies remain in the lead, we could in principle wind up in a world in which the United States is capable of hacking any other country, but no one is capable of hacking it. Frontier open models could change that balance. “If every individual had a loyal-to-them AI system that was safe [and secure], I think that would be a much less concerning world than one in which they have to just rely on a government that says, ‘Yeah, we’ve got a super intelligent AI system, it’s got this plan, it’s inscrutable to all of you, just trust us,’” says Hamish Hobbs, director of AI policy

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