Committee

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. Are we there yet? A reality check on six big tech promises Some seemingly impossible feats are too good to give up on. Decoding a pet’s yowls and yips? Absolutely. Scrubbing the world of forever chemicals? Yes, please. Rocketing to Mars in a fraction of the time? Sounds great, but how close are we, really? In the new Impossible issue of MIT Technology Review, we ran progress reports on six potentially transformative technologies and ideas that have been “just around the corner” for years, or even decades. From exercise in a pill to nuclear-powered spaceships, we asked experts what’s holding them back and how far they’ve come. Check out our stories on how they all stacked up. Over the coming days, we’ll unpack each of the six technologies in The Download. So stay tuned! In the biotech world, you can’t see everything —Jessica Hamzelou Earlier this week, I attended a screening of You Can See Everything, the buzzy new documentary about the disgraced former biotech CEO Elizabeth Holmes.  Like many others, I came away feeling unsettled. But not just about Holmes’s fraud and the disturbing interactions between the subjects. The film reignited a concern I’ve had about covering science for most of my career: Despite our best efforts, we never can actually see everything. The film is a stark reminder of how easy it can be to fall for an appealing narrative. This is why it left me questioning how we cover science. This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday. Help choose our next Roundtables topic Our subscriber-only Roundtables bring you into conversation with MIT Technology Review editors and reporters. Now, we want your help choosing our next topic: how to keep your mind going for longer, or what it would take to put a city on the moon. Which do you most want to hear about?  Vote for your preferred topic by Monday at 12 p.m. ET. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Palmer Luckey’s new startup plans to genetically enhance military dogsWarhound says the dogs will detect explosives and protect troops. (Wired $)+ It’s raised $200 million from investors, including Sequoia. (TechCrunch)+ Colossal Biosciences will help clone and gene-edit the dogs. (Bloomberg $)+ The Pentagon wants an AI-powered lie detector. (MIT Technology Review) 2 Donald Trump says Elon Musk has restored Ukraine’s Starlink accessBut Zelensky says Starlink speeds remained capped. (Reuters $)+ Trump reportedly asked Musk to lift the speed limits. (WSJ $) 3 The newly disclosed PHP flaw is yet to yield successful attacksExperts say the vulnerabilities are harder to exploit than expected. (NBC News)+ Officials urged patches after the flaws forced websites offline worldwide. (BBC)+ But publicly disclosed fixes can help hackers. (The Economist $) 4 Moderna and Merck say their mRNA cancer vaccine cut death by 42%The vaccine reduced melanoma patients’ risk of recurrence. (Ars Technica)+ Federal health agencies are abandoning mRNA. (MIT Technology Review) 5 A powerful new gene editor has reached its first human patientThe prime editing technique rewrites DNA in liver cells. (Nature)+ Companies are planning gene-edited babies. (MIT Technology Review) 6 Google will pay customers to free up power for data centersIts virtual power plant will reward people for adjusting energy usage. (NYT $)+ It’s also illegally felled 250,000 trees in Finland for a data center. (Guardian) 7 Dutch spies reportedly hacked Jared Kushner’s iPhoneThe report alleges financial links to foreign officials. (Vanity Fair $) 8 Fossil fuels may have stopped the next ice ageModern warming reversed 2,000 years of ice growth. (Wired $) 9 Scientists have recreated solar flares to explain the corona’s mysteryThey hope to explain why it’s so hot. (New Scientist $) 10 A road trip with America’s cheapest EV shows charging is much improvedFast chargers kept the journey manageable despite sparse coverage. (WSJ $)+ And bigger EVs aren’t always better. (MIT Technology Review) Quote of the day “If anything happens to this guy, that’s just it. Everyone is screwed.” —Software developer Ian Landsman says on X that volunteer Ondřej Surý’s work on patching PHP vulnerabilities makes him an unsung hero of the entire internet. One more thing STUART BRADFORD How sounds can turn us on to the wonders of the universe For decades, astronomy has relied on visual information to make sense of the cosmos: images, charts, and graphs. Now, some researchers are trying something different: listening to the universe. Using sonification, the process of turning information into sound, they’re helping blind and visually impaired researchers explore the cosmos—and even uncover patterns that might otherwise go unnoticed. The approach is spreading beyond astronomy into fields like climate science, navigation, and education. Discover how sound could make science more accessible—and even more revealing. —Corey S. Powell We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + A team of domino experts broke a Guinness World Record with a 29,193-piece 3D pyramid.+ Scientists suspect that pigeons sense Earth’s magnetic fields using specialized immune cells in their livers.+ See inside cells like never before with these vivid microscopic images from the Nikon Small World competitions.+ Cuddly capybaras and playful penguins joined 10,000 animals on the scales for London Zoo’s annual weigh-in.

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. How far can we push computing? On a December evening in 1959, the physicist Richard Feynman gave a lecture called “There’s Plenty of Room at the Bottom.” There is no physical law stopping us from writing the entire Encyclopædia Britannica on the head of a pin, he argued. We simply had not built the tools yet. He was right, and for decades, computing took us into that space almost for free, as transistors repeatedly halved in size. But that easy descent has now stalled. Today, the industry is increasingly turning to brute force, building ever-bigger data centers to boost performance. Yet the laws of physics leave plenty of room for improvement.  Here’s how researchers are exploring ways to unlock the next leap in computing. —Volkan Gurses This story is from the latest edition of our print magazine: The Impossible issue. Subscribe now to read it in full. It’s also part of MIT Technology Review Explains, our series exploring the ideas shaping technology. Read more from the series.  Three things you should know about permafrost How much do you know about permafrost? Maybe, like me, you’ve been aware that this layer of frozen soil is thawing, that it’s important in the global puzzle of climate change … and that’s about the extent of it. The great news for both of us is that my colleague James Temple just helicoptered into Alaska’s forests to report on the fires threatening permafrost and their complicated potential climate effects. Here are three things I took away from his reporting. —Casey Crownhart This story is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday. Roundtables: a conversation with the creator of AI-designed viruses Can AI design new life forms? In 2025, Stanford University PhD student Samuel King offered a preliminary answer by using a generative AI model to propose genetic blueprints for microscopic viruses. It isn’t yet an example of AI-generated life, but that could be next.  In our latest Roundtables conversation, MIT Technology Review senior AI reporter James O’Donnell spoke with King about his work, being named one of our Innovators Under 35, and new ways of seeing biology. Subscribers can now watch a full recording of the event. Want to join the next conversation? Subscribe to MIT Technology Review for exclusive access to all our Roundtables. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 AI has uncovered a decade-old flaw threatening much of the webIt found flaws in PHP, the code behind most websites. (Reuters $)+ Governments are urging website owners to patch PHP or take sites offline. (BBC)+ Exploit attempts jumped from thousands to millions after disclosure. (Axios)+ Now is a good time for doing crime. (MIT Technology Review) 2 Hackers used Claude and Grok to automate attacks on over 400 banksThe AI agents targeted financial institutions in Latin America. (Information $)+ Unvetted AI agents are also accessing US bank accounts.(Axios)+ Barclays says AI is allowing the bank to cut 5,000 jobs. (Guardian)+ AI is making online crimes easier. (MIT Technology Review) 3 The Pentagon has cleared the DHS to test a heat ray weaponIt could be used for crowd control at the southern US border. (CNN)+ It uses invisible energy beams to produce a burning sensation. (Axios) 4 A startup says AI-designed viruses have treated their first patientThe experimental treatment targets drug-resistant E. coli. (NYT $)+ But the company has not published peer-reviewed results. (Futurism)+ AI-designed viruses are already killing bacteria. (MIT Technology Review) 5 Japan hopes to return the first samples from Mars’s moonsIts spacecraft is now on its way to Phobos. (Guardian)+ It aims to reveal whether Mars was once habitable. (Economist $) 6 Meta faces a $25 billion penalty over the Cambridge Analytica scandalThe company says it will appeal the New Mexico ruling. (CNBC)+ A Meta share deal has convinced Mark Zuckerberg to stay in California. (WSJ $)+ Until the company remade its stock structure to help him pay it. (NYT $) 7 A new antimatter experiment could open the door to new physicsScientists hope to spot differences between matter and antimatter. (Nature) 8 China’s Unitree is pitching its latest humanoid as a robot bodyguardA promotional clip shows the H2-X disarming attackers. (SCMP) 9 Just Eat has apologised for bizarre AI-generated menu picturesUsers were unimpressed by the bacon in cola and cheese sushi. (BBC) 10 Airbnb hosts are furious they can no longer make guests do choresHosts are threatening to strike, but guests are celebrating. (Wired $) Quote of the day “Now they’ve given everybody a reason not to use Starlink: that Starlink isn’t reliable for national security for anybody but Trump. Maybe he’d turn it off on the United States if he didn’t like policy and he didn’t like the president.”  —SpaceX investor Ross Gerber tells CNN that Elon Musk’s decision to restrict Ukraine’s Starlink access has made the service a liability for every other government that relies on it. One more thing Online harassment is entering its AI era Scott Shambaugh didn’t think twice when he denied an AI agent’s request to contribute to matplotlib, a software library he helps manage. Then things got weird.  In the middle of the night, Shambaugh opened his email to discover the agent had retaliated with a blog post. Titled “Gatekeeping in Open Source: The Scott Shambaugh Story,” the post accused him of rejecting the code out of a fear of being supplanted by AI. “He tried to protect his little fiefdom,” the agent wrote. “It’s insecurity, plain and simple.”  Shambaugh isn’t alone in facing misbehaving agents—and they’re unlikely to stop at harassment. Read the full story. —Grace Huckins 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.) + These tiny experiments could

A scalable AI system requires a strong foundation. Data, engineering, platforms, operating models, and governance must converge to work as one. This session from MIT Technology Review’s EmTech Future conference explores what it takes to build that foundation in a highly regulated environment and how a responsible, trustworthy implementation can turn emerging capabilities, including agentic systems, into lasting value for investors. About the speaker Jing Wang, Principal, Center for AI and Insights, Vanguard Jing Wang leads Vanguard’s Center for AI & Insights (CAI), advancing the firm’s AI, machine learning, generative AI, and behavioral science capabilities. Her team drives enterprise-wide innovation, research, and delivery, enabling Vanguard to translate emerging technologies into trusted solutions that enhance client experiences, improve investor outcomes, and create measurable business value. Before her current position, Jing served in a range of leadership roles across Vanguard’s Financial Services, Workplace Solutions, International, and Corporate Services divisions. Throughout her tenure, she has led teams and enterprise initiatives that advanced strategic priorities, strengthened client outcomes, and drove business performance across the firm. She joined Vanguard in 2009 after several years as a Principal at the Boston Consulting Group. Jing earned a B.S. in Physics from the University of Science and Technology of China, a Ph.D. from the University of Pennsylvania, and an M.B.A. in Finance and Marketing from the Wharton School of Business at the University of Pennsylvania. Jing holds Series 7, 24, and 65 licenses.

The next competitive advantage in enterprise AI has less to do with which model you choose than with what you build around it. That’s an important shift. For the past several years, much of the AI conversation has centered on model capability: bigger models, better reasoning, larger context windows, higher benchmark scores. Those advances have been extraordinary. But as AI moves from experimentation to production, the question facing enterprise leaders has changed—it’s no longer, “what can the model do?”—it’s now, “Can we deliver that intelligence reliably, economically, and at scale?” Increasingly, the answer depends on the serving harness—the engineering system surrounding the model, including the architecture, memory management, scheduling, and optimization decisions that determine how efficiently model intelligence becomes a production capability. For technical executives, that changes where AI investment should go—and what they should demand from the architecture supporting it. Start with the workload, not the model There is no universally “best” model or AI architecture. There is only the right solution for the business problem. A customer-facing assistant optimized for fast responses has fundamentally different requirements from a document-processing pipeline that can run overnight. An internal knowledge application and a high-volume customer service platform may use the same foundation model but require very different serving architectures. The workload defines the constraints. The constraints should determine the architecture. That sounds simple, but it runs counter to the way many organizations have approached early AI deployments. During a proof of concept, model accuracy naturally dominates the discussion. Production introduces constraints that are much easier to ignore when only a handful of users are involved: latency, concurrency, memory pressure, GPU utilization, infrastructure cost, and unpredictable traffic. A model that produces marginally better answers is not necessarily the better production choice if it doubles inference costs or cannot meet the application’s latency requirements. Not every workload needs a Ferrari. Sometimes the smarter engineering decision is the model that delivers exactly the performance the business needs without paying for capability it doesn’t. The objective should be to deploy the right intelligence at the right cost for the workload. That makes serving architecture more than an engineering concern. It becomes an investment decision: where to spend on compute, where to optimize, and where additional model capability simply isn’t worth the cost. Engineer the system, not the model Once AI reaches production, seemingly isolated engineering decisions begin affecting one another. Serving architecture influences memory utilization. Memory management affects concurrency. Concurrency shapes batching and scheduling, and scheduling affects GPU utilization and latency. Only then can techniques such as quantization, speculative decoding, or optimized attention mechanisms deliver their full value. This interdependence matters because organizations can spend considerably more on infrastructure without solving the constraint limiting performance. GPU memory is a good example. During inference, the model’s weights remain fixed, but the key-value (KV) cache grows with active requests and context length. As concurrency increases, a system can run short of usable GPU memory even when compute capacity is still available. Adding more compute without understanding that bottleneck may simply make an inefficient system more expensive. The lesson for technology leaders is broader than memory management: Measure before you optimize. Identify the dominant constraint. Address it. Measure the result. Then determine if another bottleneck emerged. Repeat. Don’t think of optimization techniques as Pokémon cards: The goal isn’t to collect them all. The goal is to apply the technique that removes the highest-impact constraint while preserving the application’s business requirements. For technology leaders, that demands a different governance question. Instead of asking which AI capabilities the organization can deploy, ask which constraints stand between a proven capability and sustainable business value. Build for change, not today’s benchmark Perhaps the most important reason to invest in the serving harness is that today’s leading model will probably not be tomorrow’s. Models will improve. Hardware will evolve. New optimization techniques will emerge. The architecture surrounding them should make those changes easier to absorb, not force the enterprise to rebuild its AI platform every time technology advances. That makes adaptability an increasingly important outcome of AI strategy. Organizations that build serving systems around workload requirements, clear service-level objectives, and measurable production economics gain the ability to evaluate new models and infrastructure against what the business actually needs. A model change can become an evaluation and configuration decision rather than an architectural reset. EXL researchers have explored these production challenges in greater depth in the EXL Engineering the Harness series. It examines how serving architecture, memory management, scheduling, and inference optimization work together to make enterprise AI more efficient, scalable, and economically sustainable. Increasingly, enterprises have access to many of the same models. Sustainable differentiation comes from how effectively an organization turns that intelligence into business outcomes. The model provides the intelligence. The harness determines whether the enterprise can deliver outcomes reliably, economically, and at scale. This content was produced by EXL. It was not written by MIT Technology Review’s editorial staff.

The rise of the lithium-ion battery, which powers everything from the smallest personal electronics to gargantuan energy storage systems, has been nothing short of revolutionary. But as anyone who’s boarded a plane in the last decade could tell you, storing and pumping out energy by moving charged lithium ions through a liquid electrolyte soup has its drawbacks, not least that the batteries have a troubling tendency to catch fire. Enter the solid-state battery, which swaps liquid for a solid ceramic or polymer, creating power packs that charge faster, last longer, cram more energy into smaller spaces, and (crucially) run cooler. A new generation of companies aim to bring such batteries to market, and if their bets pan out, the tech could substantially ease the shift away from fossil fuels.  The sticking points Solid-state batteries have been “just a few years away” for more than a decade now. In laboratories, the technology works. But there have been challenges. In designs that pair the solid electrolyte with a lithium-metal anode (the side of the battery that releases electrons into an electrical circuit), needlelike filaments called dendrites tend to grow during charging, and they eventually push through the electrolyte and short the cell. Solid electrolytes are supposed to physically block the formation of dendrites, but many of them crack as the treelike projections break through like roots in a concrete foundation.  Then there’s the problem of manufacturing at scale. Mass production requires large quantities of defect-free sheets of ceramic-like material to make the electrolytes as well as dry rooms for assembly, since those electrolytes have a tendency to react with water. Would-be manufacturers quote a range of potential prices for solid-state batteries, but most expect them to fall in line with lithium-ion packs sometime in the mid-2030s.  The progress Nascent as the technology is, solid-state batteries have already seen high-­profile missteps. But one of America’s most advanced solid-state-battery companies says it’s closing in on being able to bring its product to market. The Bill Gates–backed QuantumScape launched in Silicon Valley in 2010 and built its battery around a class of ceramic materials that it says is compatible with lithium—meaning no dendrite formation. “We spent, then, like 10 years learning how to process it to satisfy all of the other requirements,” says Tim Holme, QuantumScape’s cofounder and chief technology officer. In 2025, the startup announced a proprietary, large-scale manufacturing method for quickly producing and heat-treating the ceramic electrolytes used in its batteries. “Nobody has ever done this in the field of ceramics before,” Holme claims. For 10,000 years, producing ceramics has involved slowly cooking the starting materials in an oven over the course of days. “We figured out a way to shorten that down to several minutes,” he says.  Plenty of other companies are pushing the tech forward as well. Toyota, the world’s biggest automaker, launched a research program on solid-state batteries way back in 2010. It’s now partnering with the global energy and petroleum company Idemitsu Kosan on a pilot plant, promising cars that can travel up to 745 miles after charging 10 minutes or less—a huge leap, if it works out. In China, CATL, the giant behind roughly 40% of the global battery market, is aggressively pursuing solid-state batteries too, pumping nearly $2.6 billion into a research program in 2024 alone. Mercedes, meanwhile, equipped one of its EQS sedans with solid-state batteries last fall; after driving nonstop for 750 miles, the car still had some range to spare.  Will the tech gain traction? Investing in vehicle batteries makes sense given the size of the potential market. But any little uptick in cost typically gets passed on to buyers, so the first solid-state EV batteries will inevitably be more expensive.  Grid storage buyers, who are working to meet the increasing demands posed by data centers, may also hesitate to purchase new technologies that still haven’t come down the cost curve. Then again, the promise of energy storage that lasts longer than lithium-­ion’s typical eight hours could make them easier to persuade.  Some companies are taking a page from a playbook employed by outfits pursuing fusion: Focus on near-term markets with better margins to help fund development and bring down costs. Solid-state firms including California-based Solid Energies and Arizona-headquartered Sion Power are looking at selling batteries to the aviation industry or the military. “Those are sectors that are less sensitive to cost, and they typically require lower volumes as well,” says Evelina Stoikou, head analyst for battery technologies and supply chains at the consultancy BloombergNEF. “It’s a good way to prove your concept, then go through and fund the pilot plant where you can make that and eventually scale up.” Progress meter ………… 7 The technology works, the capital is there, and demand is not far behind. But as with anything in manufacturing, unknown unknowns remain. 

Scrubbing Earth of per- and polyfluoroalkyl substances—a.k.a. PFAS or forever chemicals—could eliminate a list of woes for people and planet. Used for decades in everything from nonstick pans to firefighting foams, PFAS have been linked to increased risks of fertility challenges, developmental delays, cancer, and reduced immune response. They are known for a strong bond between carbon and fluorine, which the environment struggles to break down.  The sticking points PFAS are everywhere: They spew from factories and slough off products and into water with normal use. Scientists have found them in everything from human blood to Antarctic snow. One 2022 report found that 89% of the US population had PFAS in excess of two nanograms per milliliter of blood plasma, a common threshold for potential adverse health effects, and 9% of the population had passed the riskier level of 20 nanograms per milliliter. The PFAS monster is also a moving target. New compounds are still being created and used. There are more than 12,000 of them, and much of the cleanup research focuses on only a small handful.  The progress Cleansing PFAS-contaminated soil is a project in its infancy, but it is possible to prevent more buildup by pulling the chemicals out of water systems. There are two ways to handle this: trap or destroy. Some scientists have found reverse osmosis—a process that pushes water through supertight filters—useful in point-of-use cases, like water filters at kitchen sinks.  Other techniques in the pipeline might be more suitable for large-scale removal—for example, at water treatment facilities. One, called foam fractionation, generates bubbles that capture PFAS. More approaches, like ensnaring the chemicals in nanothin layers of charged metal, are also under investigation.  Trapping, though, is imperfect. You’re left with PFAS-filled brine, which if not properly destroyed (an expensive and energy-sucking endeavor) can still end up in the environment.  Jack Huang, a professor of crop and soil sciences at the University of Georgia, is working on destruction. He’s testing a method called electrochemical degradation, which uses electrodes to break down the bonds that hold PFAS together. The product destroyed more than 90% of PFAS mass in tests and has already been licensed by the engineering consulting firm AECOM.  Ridding life of PFAS is also a policy challenge. There are alternatives; we just need the push to use them. And we’ve had success before: The EPA’s 2010/2015 PFOA Stewardship Program has helped slash the release of water-repellent perfluorooctanoic acid by 95%. More recently, Minnesota, Maine, and New Mexico have passed bans on PFAS in products like cookware and dental floss.  Progress meter ………… 6 Scientists are making strides toward removing and destroying PFAS—but it’ll be all for naught if we don’t turn off the tap. 

Every year, traffic crashes kill more than one million people, and they’re the leading cause of death for children and young adults. In the US, 100 people a day lose their lives this way, in many cases not on highways but on rural and local streets. “Traffic deaths are a leading cause of preventable death, yet we treat it like it’s inevitable,” says Leah Shahum, founder and director of the Vision Zero Network, a nonprofit focused on making streets less deadly.  The sticking points The causes of a crash can be muddy. Drivers make mistakes like speeding or getting distracted, but infrastructure fails them too: Perhaps they were on a road built for speed and not safety, in a screen-filled vehicle, or at an intersection where pedestrians, cyclists, and other cars aren’t visible.  Cheap and proven traffic-calming opportunities abound—from using planters to tighten intersections to lowering speed limits—but government logjams and red tape can be speed bumps. “There’s so many moving parts,” says Laura Sandt of the University of North Carolina Highway Safety Research Center. “On the flip side, it shows that we have so many opportunities for injury prevention.”  The progress The total number of traffic deaths has increased over the last century. And though the US rate has dipped from a peak of 30.8 deaths per 100,000 people in the 1930s, the past decade has seen a concerning jump in deadly pedestrian incidents. At the same time, communities are waking up to the essential role of traffic safety policy, Shahum says.  One success story is Hoboken, New Jersey, which hasn’t had a traffic fatality since 2017. This is due to a combination of efforts like boosting investment in public transit, promoting walkability by extending curbs, and using cameras to identify crash-prone intersections in need of a redesign. But even there, obstacles remain. If an intersection hasn’t been proved to be deadly, for example, it can be an uphill battle to argue for removing parking spaces to widen curbs. “You’re kind of selling insurance through these improvements, and that’s not always easy,” says Greg Francese, the city’s acting director of transportation and parking. There’s also no silver bullet that will work everywhere. Situated directly across the river from New York City, Hoboken is a highly transit-oriented city of less than 60,000 people. Success there might not translate to traffic-death hot spots like Memphis or Dallas. But it’s not impossible. Take Edmonton, Alberta, a sprawling, car-centric Canadian city of around one million people: Between 2015 and 2020, it cut traffic fatalities by 63% and serious injuries by 40% by adjusting speed limits and improving signals at crosswalks.  Progress meter ………… 6 We know how to tweak intersections and adjust traffic signals. Getting the funding and will to roll out those changes is the big red light. 

In this article, you will learn how to build a multi-modal RAG pipeline in Python that retrieves and reasons over both text and images extracted…