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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 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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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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The Download: open model trade-offs and geothermal power’s promise

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. Open models have real benefits. Do they outweigh the risks? Anthropic recently released a report arguing that a powerful new model released by the Chinese company Z.ai, poses a major cyberattack risk. Its core concern was that it is freely available for anyone to download and modify. The warning was the latest flashpoint in a debate over whether frontier AI models should be open or kept behind the doors of frontier labs. Safety advocates warn that open models can be misused by bad actors, while supporters argue they could prevent a handful of companies from controlling this powerful technology. Here’s why the debate over open AI is more complicated than it seems. —Grace Huckins This story is part of MIT Technology Review Explains, our series untangling the complex, messy world of technology to help you understand what’s coming next. Read more from the series here.  10 Climate Tech Companies to Watch: Fervo Energy and its plan to streamline geothermal power Fervo Energy is one of MIT Technology Review’s10 Climate Tech Companies to Watch 2026, available exclusively to subscribers. Fervo Energy wants to expand geothermal power beyond the handful of places where it has traditionally been practical. The company uses horizontal drilling and hydraulic fracturing to open pathways through underground rock, potentially making geothermal development possible in far more places. Now, Fervo is betting that AI’s power boom will help scale up enhanced geothermal energy. With a $2.2 billion public offering and an expanded agreement with Google, it plans to build larger plants and drive down costs. But can its approach work beyond Utah and Nevada? Read the full story to find out. —Eshan Raul Subscribers can now access the full 10 Climate Tech Companies to Watch, spanning everything from sustainable cement to shipping containers packed with old EV batteries.  The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 A cyberattack overheated fridges at hundreds of French supermarketsTemperatures rose to 120°F, spoiling food and sparking fires. (BBC)+ The attack has sparked alarm about cyber threats to food security. (FT $)+ It joins a recent wave of cyberattacks and cybercrimes. (CNBC)+ AI is exacerbating the threats. (MIT Technology Review) 2 Meta has launched a bunch of new AI modelsIt hopes the models, built for complex, multi-step tasks, will rival OpenAI’s and Anthropic’s.(BI)+ They scored well on tests of reasoning, writing, and coding. (NYT $)+ Mark Zuckerberg says they bring Meta to the “frontier” of AI. (FT $)+ And will soon power the Muse agent. (Verge) 3 Anthropic warns that AI models may soon self-replicate across devicesWithout even requiring access to data centers. (Reuters $)+ It says models could spread through smart TVs and appliances. (CNBC)+ We’re putting too much faith in AI saying no. (MIT Technology Review) 4 Police have a new AI tool that finds homes from interior photosIt can match single pictures to Airbnb and Zillow listings. (404 Media)+ Police-tech giant Flock is tightening its rules. (MIT Technology Review) 5 The world’s first utility-scale enhanced geothermal plant is onlineFervo Energy’s Utah facility will supply carbon-free electricity. (IEEE)+ AI is uncovering hidden geothermal resources. (MIT Technology Review) 6 Did drone strikes on Yandex data centres blitz Russian bots?Zelenskyy claims the attacks quietened Kremlin-funded social media accounts. (Euronews) + But troll farm experts aren’t convinced. (Verge) 7 NASA’s Perseverance rover has set a Mars driving recordIt’s covered 28 miles since landing in 2021. (Reuters $)+ The rover passed Opportunity after detouring to study ancient rocks. (NYT $) 8 Elon Musk has become the 2026 midterms’ biggest known donorHe gave $158 million to a Republican Senate super PAC. (AP News) 9 Anduril and Palantir founders have backed execution livestreamsPalmer Luckey and Joe Lonsdale support Pete Hegseth’s plan. (Mother Jones) 10 A spacecraft has traced how a mysterious solar switchback formedThe Solar Orbiter’s discovery could improve space weather forecasts. (Gizmodo) Quote of the day “Many AI researchers have started smoking as a way of signaling to other researchers how much they believe in AI.”  —Palantir CTO Shyam Sankar tells CBS News that Silicon Valley’s “social delusion” has convinced researchers to smoke because they expect AI to cure cancer. One more thing A brain implant changed her life. Then it was removed against her will. Sticking an electrode inside a person’s brain can do more than treat a disease. Take the case of Rita Leggett, an Australian woman whose experimental brain implant changed her sense of agency and self. She told researchers that she “became one” with her device. She was devastated when, two years later, she was told she had to remove the implant because the company that made it had gone bust. Her case highlights the need for a new category of legal protection: neuro rights. Explore how they could be protected. —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.) + Astronomers have captured the clearest image yet of a medieval supernova.+ Watch centuries-old Chinese paper-cutting techniques create a spectacular work of art.+ Artist Josh Gluckstein creates astonishing animal sculptures from recycled cardboard, including this great white shark.+ An endangered sea turtle named Pancake has returned to the ocean for the first time in months after recovering from hypothermia.

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AI, Committee, Nachrichten, Uncategorized

Lychee-Reranker: Fast but Not Late Interaction for Compressed Document Reranking

arXiv:2606.22807v4 Announce Type: replace Abstract: As retrieval systems scale, effective and efficient reranking becomes increasingly important. However, most existing encoder- and decoder-based rerankers jointly process every query–passage pair, tightly coupling their online computation and limiting deployment efficiency and flexibility. We present Lychee-Reranker, a fast but not late-interaction (FBNL) reranker that decouples query and passage computation while retaining expressive relevance modeling. Built on an encoder–decoder architecture, Lychee-Reranker pre-encodes passages using Matryoshka embedding pooling, while its decoder models system and user instructions together with query intent; cross-attention then captures fine-grained relevance between the resulting query context and passage representations. Together, these designs offer four key advantages: (i) efficiency from offline passage encoding, (ii) expressiveness from cross-attention, (iii) compactness from Matryoshka embedding pooling, and (iv) test-time compute through an adjustable compute budget. We instantiate Lychee-Reranker in three sizes, Nano, Small, and Large, with 0.27B, 1B, and 4B activated parameters, respectively. Extensive experiments on BEIR, MIRACL, and LMEB demonstrate strong reranking performance with superior efficiency. On BEIR and MIRACL, our models achieve competitive performance in multi-domain and multilingual reranking, on par with strong industrial rerankers such as the Qwen3/BGE-Reranker series. On LMEB-Dialogue, a compact embedding model paired with our Nano reranker, which has only 0.27B activated parameters, remains competitive with 7–12B embedding models. Lychee-Reranker was previously known as KaLM-Reranker-V1. Data and models are available at https://huggingface.co/collections/KaLM-Embedding/lychee-kalm-reranker-and-jev.

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AI, Committee, Nachrichten, Uncategorized

The ecologist revealing the invisible networks that revive rainforests

In a research station in central Panama, you can walk through a century of forest time, watching scrubby plots of adolescent trees give way to cathedral-­like canopies of mature growth. Forest ecologist Daisy Dent, a senior scientist at ETH Zurich, oversees this sequence of plots across the Barro Colorado Nature Monument, some of which date as far back as the 19th century. Not one of the trees she surveys was planted by human hands. She maps, measures, and tracks each trunk that emerges, revealing how waves of tree life rise from bare earth.  Tropical forests like these are our planet’s biggest terrestrial carbon sink, accounting for around 40% of carbon stored on land. As part of an international network of researchers, Dent is deciphering the networks of animal and plant interactions that enable some forests to recover after destruction. The goal is a low-cost, high-impact alternative to typical carbon-­crediting schemes, which plant huge areas with a single species of tree. For over a decade, her research has built a body of evidence that such large “monocrop” programs could be expensive mistakes—and that more resilient forests can rebound naturally in just a few decades, re-creating ecosystems that suck up vast quantities of carbon dioxide.  Few places are better to demonstrate this than Barro Colorado, a reserve covering the forested peninsulas and islands that formed when construction of the Panama Canal flooded the area in 1914. Under the management of the Smithsonian Tropical Research Institute, generations of ecologists have amassed more than a century of records.  That means Dent can trace why some plots rebound after clear-cutting for farming and forestry while others falter. Fruit-eating animals—including spider monkeys, bats, and parrots—are responsible for planting more than 70% of trees in tropical forests, and Dent’s dataset can tell which built each patch of trees by depositing seeds in their nutrient-rich droppings. Many of these creatures worked together to plant a diverse 70-year-old patch whose canopy is approaching the stunning structure and richness of an ancient jungle. In contrast, a patch of similar age populated by narrow trunks of a single long-lived species, Gustavia superba, hints that tropical rodents called agoutis are more or less the sole animal architects behind this more homogeneous forest.  The data she collects helps write a playbook for when to let ecosystems regenerate and when to give them a helping hand. One 2024 estimate in Nature found that some 215 million hectares of forest worldwide (an area larger than Mexico) could regenerate naturally. “Tropical forests are underpinned by this massive network of interactions,” Dent says—interactions between life-forms like the fungi that help trees take up nutrients, as well as pollinators and seed-distributors. Supporting those relationships is key to growing forests that lock up carbon and help sustain local communities and wildlife.  Filling in the gaps Just over a decade ago, as the landmark Paris Agreement was being codified in 2015, forest recovery seized global attention as a solution to the climate crisis. Global leaders rallied behind plans to regrow a trillion trees, after the ecologist Thomas Crowther highlighted their potential to capture a decade’s worth of global carbon emissions. But the biggest question has lingered: How, exactly, do you regrow two trees for every star in the Milky Way? While startups have proposed seed-spraying drones and nutrient-coated seed bombs, Dent has spent two decades studying what happens when we let forests rebuild themselves. As a PhD student around the turn of the millennium, she researched old-growth forests in northern Borneo but felt hopeless watching entire landscapes felled for palm oil plantations. Rather than despair, she turned to restoration. Then, as a postdoc in 2005, she joined Panama’s Prorena, a joint initiative between the Smithsonian Tropical Research Institute and the Yale School of Forestry and Environmental Studies, which aimed at determining how best to establish diverse native forests on clear-cut land. Back then, secondary forests—ones that grow naturally after the original forest was lost—had been “a bit of a niche research area,” Dent says. But as farming and human infrastructure have driven deforestation, resurgent forests now account for some 70% of all tropical canopies. Prorena initially followed the usual playbook of transplanting nursery trees to rangelands, but Dent began to question whether planting was necessary. In 2009, she published an encouraging paper showing that animal populations rebound quickly as forests naturally regrow. In 2010, she began working in Barro Colorado on the plots established by the pioneering American ecologist Julie Denslow. In 1994, Denslow identified plots that had been regenerating naturally for 20 to 100 years, and Dent would add younger ones to continue the sequence.  The site provides not just general data on forest regrowth but in-depth demographic information over time, says Robin Chazdon, a professor emeritus of ecology and evolutionary biology at the University of Connecticut who collaborated with Denslow when she established the plots. “It tells you more about the actual processes that are happening in the site,” she says.  For example, previous research had shown that fast-growing “pioneer trees,” the first to thrive in the damaged land, are eventually overtaken by larger carbon-storing trees. Dent’s research helped fill in gaps about the mechanics of how this happens—or doesn’t happen—in individual cases. In the Gustavia-dominated patch, for instance, stubborn grasses planted by cattle ranchers smothered most other trees before they could take root. These trees can hang around, crowding out the light, for 150 years, blocking the second wave of large-trunked trees.  To plant or not to plant Dent found herself well placed for the massive influx of interest in reforestation that began in 2015. She’d already become a principal investigator for 2ndFor, an international research network aiming to learn how best to manage secondary forests. Using data gathered from the Barro Colorado plots, the team authored a 2016 paper in Nature showing that a hectare of naturally regenerated secondary forest can capture 11 times as much carbon each year as an old-growth one, with young trees absorbing it quickly as they compete to grow toward the

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