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. We’re in a new era of AI-driven scams When ChatGPT was released in late 2022, it showed how easily generative AI could create human-like text. This quickly caught the eye of cybercriminals, who began using LLMs to compose malicious emails. Since then, they’ve adopted AI for everything from turbocharged phishing and hyperrealistic deepfakes to automated vulnerability scans. Many organizations are now struggling to cope with the sheer volume of cyberattacks. AI is making them faster, cheaper, and easier to carry out, a problem set to worsen as more cybercriminals adopt these tools—and their capabilities improve. Read the full story on how AI is reshaping cybercrime. —Rhiannon Williams
“Supercharged scams” is one of the 10 Things That Matter in AI Right Now, our essential guide to what’s really worth your attention in the field. Subscribers can watch an exclusive roundtable unveiling the technologies and trends on the list, with analysis from MIT Technology Review’s AI reporter Grace Huckins and executive editors Amy Nordrum and Niall Firth. Healthcare AI is here. We don’t know if it actually helps patients. Doctors are using AI to help them with notetaking. AI-based tools are trawling through patient records, flagging people who may require certain support or treatments. They are also used to interpret medical exam results and X-rays.
A growing number of studies suggest that many of these tools can deliver accurate results. But there’s a bigger question here: Does using them actually translate into better health outcomes for patients? We don’t yet have a good answer—here’s why. —Jessica Hamzelou The story is from The Checkup, our weekly newsletter that gives you the latest from the worlds of health and biotech. Sign up to receive it in your inbox every Thursday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 DeepSeek has unveiled its long-awaited new AI modelThe Chinese company has just launched preview versions of DeepSeek-V4. (CNN)+It says V4 is the most powerful open-source platform. (Bloomberg $) + And rivals top closed-source models from OpenAI and DeepMind. (SCMP)+ The model is adapted for Huawei chip technology. (Reuters $)2 More countries are curbing children’s social media accessNorway is set to enforce the latest ban. (Reuters $)+ The Philippines could follow soon. (Bloomberg $)+ Americans are pushing to get AI out of schools. (The New Yorker)3 The US has accused China of mass AI theft as tensions riseA White House memo claims Chinese firms are exploiting American models. (BBC)+ Beijing calls the accusations “slander.” (Ars Technica)4 OpenAI set itself apart from Anthropic by widely releasing its new modelIt’s releasing GPT-5.5 to all ChatGPT users, despite cybersecurity concerns. (NYT $)+ OpenAI says the new model is better at coding and more efficient. (The Verge)5 Meta is cutting 10% of jobs to offset AI spendingRoughly 8,000 layoffs are set to be announced on May 20. (QZ)+ Anti-AI protests are growing. (MIT Technology Review)6 Palantir is facing a backlash from employeesThanks to its work with ICE and the Trump administration. (Wired $)+ Surveillance tech is reshaping the fight for privacy. (MIT Technology Review)7 The era of free access to advanced AI is coming to an endAI labs are under mounting pressure to start turning profits. (The Verge)8 Elon Musk’s feud with Sam Altman is heading to court The case has already revealed several unflattering secrets. (WP $)9 A new movement is encouraging people to ditch their smartphones for a month“Month Offline” is like a Dry January for smartphones. (The Atlantic)10 Spotify has revealed its most-streamed music of the last 20 yearsFeaturing Taylor Swift, Bad Bunny, and The Weeknd. (Gizmodo)  Quote of the day “We want a childhood where children get to be children. Play, friendships, and everyday life must not be taken over by algorithms and screens.”  —Norwegian Prime Minister Jonas Gahr Store announces age restrictions for social media.

One More Thing NASA/JPL-CALTECH VIA WIKIMEDIA COMMONS; CRAFT NASA/JPL-CALTECH/SWRI/MSSS; IMAGE PROCESSING: KEVIN M. GILL The search for extraterrestrial life is targeting Jupiter’s icy moon Europa As astronomers have discovered more about Europa over the past few decades, Jupiter’s fourth-largest moon has excited planetary scientists interested in the geophysics of alien worlds.  All that water and energy—and hints of elements essential for building organic molecules —point to an extraordinary possibility. In the depths of its ocean, or perhaps crowded in subsurface lakes or below icy surface vents, Jupiter’s big, bright moon could host life.  To find further evidence, NASA is now searching for signs of alien existence on Europa. Read the full story on the mission.

I don’t need to tell you that AI is everywhere. Or that it is being used, increasingly, in hospitals. Doctors are using AI to help them with notetaking. AI-based tools are trawling through patient records, flagging people who may require certain support or treatments. They are also used to interpret medical exam results and X-rays. A growing number of studies suggest that many of these tools can deliver accurate results. But there’s a bigger question here: Does using them actually translate into better health outcomes for patients? We don’t yet have a good answer.
That’s what Jenna Wiens, a computer scientist at the University of Michigan, and Anna Goldenberg of the University of Toronto, argue in a paper published in the journal Nature Medicine this week. Wiens tells me she has spent years investigating how AI might benefit health care. For the first decade of her career she tried to pitch the technology to clinicians. Over the last few years, she says, it’s as though “a switch flipped.” Health-care providers not only appear much more interested in the promise of these technologies, they have also begun rapidly deploying them.
The problem is that many providers aren’t rigorously assessing how well they actually work. Take “ambient AI” tools, for example. Also known as AI scribes, they “listen” to conversations between doctors and patients, then transcribe and summarize them. Multiple tools are available, and they are already being widely adopted by health-care providers. A few months ago, a staffer at a major New York medical center who develops AI tools for doctors told me that, anecdotally, medics are “overjoyed” by the technology—it allows them to focus all their attention on their patients during appointments, and it saves them from a lot of time-consuming paperwork. Early studies support these anecdotes and suggest that the tools can reduce clinician burnout. That’s all well and good. But what about patient health outcomes? “[Researchers] have evaluated provider or clinician and patient satisfaction, but not really how these tools are affecting clinical decision-making,” says Wiens. “We just don’t know.” The same holds true for other AI-based technologies used in health-care settings. Some are used to predict patients’ health trajectories, others to recommend treatments. They are designed to make health care more effective and efficient. But even a tool that is “accurate” won’t necessarily improve health outcomes. AI might speed up the interpretation of a chest X-ray, for example. But how much will a doctor rely on its analysis? How will that tool affect the way a doctor interacts with patients or recommends treatment? And ultimately: What will this mean for those patients? The answers to those questions might vary between hospitals or departments and could depend on clinical workflows, says Wiens. They might also differ between doctors at various stages of their careers. Take the AI scribes, as another example. Some research on AI use in education suggests that such tools can impact the way people cognitively process information. Could they affect the way a doctor processes a patient’s information? Will the tools affect the way medical students think about patient data in a way that impacts care? These questions need to be explored, says Wiens. “We like things that save us time, but we have to think about the unintended consequences of this,” she says.

In a study published in January 2025, Paige Nong at the University of Minnesota and her colleagues found that around 65% of US hospitals used AI-assisted predictive tools. Only two-thirds of those hospitals evaluated their accuracy. Even fewer assessed them for bias. The number of hospitals using these tools has probably increased since then, says Wiens. Those hospitals, or entities other than the companies developing the tools, need to evaluate how much they help in specific settings. There’s a possibility that they could leave patients worse off, although it’s more likely that AI tools just aren’t as beneficial as health-care providers might assume they are, says Wiens. “I do believe in the potential of AI to really improve clinical care,” says Wiens, who stresses that she doesn’t want to stop the adoption of AI tools in health care. She just wants more information about how they are affecting people. “I have to believe that in the future it’s not all AI or no AI,” she says. “It’s somewhere in between.” This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. 

Codex can automatically run tasks on a schedule.This makes Codex proactive. Instead of waiting for you to come back and ask for an update, Codex can return at the scheduled time, do the work, and surface the result for you to review.This is useful for recurring work, like preparing for the day, reviewing what changed, checking for updates, summarizing recent activity, or creating a weekly report.For example, you might use a thread automation to:Write a weekly review every FridayCreate a morning brief from yesterday’s workSummarize new files added to a folderClean up a weekly data exportCheck for missing or inconsistent informationCreate a recurring project status updateSome automations can also return to the same conversation and continue from the context already there. That is especially useful when you want Codex to pick up an ongoing task instead of starting fresh each time.A good automation is specific, repeatable, and easy to review.For example:Try itEvery Monday morning, return to this conversation and help me plan the week based on my current notes, drafts, and priorities.Try itEvery Friday, review my recent work and write a short summary of what I finished this week, what is still open, and what needs attention next.Note: If you’re running Codex locally, automations work best when your laptop is awake and Codex is running.Start by chatting back and forth with Codex to zero in on the exact kind of behavior and output you’re looking for. Once Codex understands exactly what you need, turn that task into an automation.

Decoupled DiLoCo is not only more resilient to failures, but is also practical for executing production-level, fully distributed pre-training. We successfully trained a 12 billion parameter model across four separate U.S. regions using 2-5 Gbps of wide-area networking (a level relatively achievable using existing internet connectivity between datacenter facilities, rather than requiring new custom network infrastructure between facilities). Notably, the system achieved this training result more than 20 times faster than conventional synchronization methods. This is because our system incorporates required communication into longer periods of computation, avoiding the “blocking” bottlenecks where one part of the system must wait for another.Driving the evolution of AI training infrastructureAt Google, we take a full-stack approach to AI training, spanning hardware, software infrastructure and research. Increasingly, gains are coming from rethinking how these layers fit together.Decoupled DiLoCo is one example. By enabling training jobs at internet-scale bandwidth, it can tap any unused compute wherever it sits, turning stranded resources into useful capacity.Beyond efficiency and resilience, this training paradigm also unlocks the ability to mix different hardware generations, such as TPU v6e and TPU v5p, in a single training run. This approach not only extends the useful life of existing hardware, but also increases the total compute available for model training. In our experiments, chips from different generations running at different speeds still matched the ML performance of single-chip-type training runs, ensuring that even older hardware can meaningfully accelerate AI training.What’s more, because new generations of hardware don’t arrive everywhere all at once, being able to train across generations can alleviate recurring logistical and capacity bottlenecks.As we push the frontiers of AI infrastructure today, we’re continuing to explore approaches to resilient systems needed to unlock the next generation of AI.

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. Introducing: the Nature issue When we talk about “nature,” we usually mean something untouched by humans. But little of that world exists today.  From microplastics in rainforest wildlife to artificial light in the Arctic Ocean, human influence now reaches every corner of Earth. In this context, what even is nature? And should we employ technology to try to make the world more “natural”?   In our new Nature issue, MIT Technology Review grapples with these questions. We investigate birds that can’t sing, wolves that aren’t wolves, and grass that isn’t grass. We look for the meaning of life under Arctic ice, within ourselves, and in the far future on a distant world, courtesy of new fiction by the renowned author Jeff VanderMeer. 
Together, these stories examine how technology has altered our planet—and how it might be used to repair it. Subscribe now to read the full print issue. What’s next for large language models? After ChatGPT launched in late 2022, the OpenAI chatbot became an everyday everything app for hundreds of millions of people. It led to LLMs being heralded as the new future. The entire tech industry was consumed by the inferno, with companies racing to spin up rival products.
But what’s the next big thing after LLMs? More LLMs—but better. Let’s call them LLMs+. Find out how they’re set to become cheaper, more efficient, and more powerful. —Will Douglas Heaven LLMs+ is on our list of the 10 Things That Matter in AI Right Now, MIT Technology Review’s guide to what’s really worth your attention in the busy, buzzy world of AI. We’ll be unpacking one item from the list each day here in The Download, so stay tuned. Will fusion power get cheap? Don’t count on it. Fusion power could provide a steady, zero-emissions source of electricity in the future—if companies can get plants built and running. But a new study published in Nature Energy suggests that even if that future arrives, it might not come cheap. The research team aimed to improve predictions of fusion’s future price by estimating the technology’s experience rate—the percentage by which its cost declines every time capacity doubles. Their findings offer new clues on the technology’s path to deployment. Read the full story. —Casey Crownhart This story is from The Spark, our weekly climate newsletter. Sign up to receive it in your inbox every Wednesday. The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Trump signaled he’s open to reversing the Anthropic banWhat that really means in practice remains to be seen. (Reuters $)+ Anthropic says there’s no “kill switch” for its AI. (Axios)+ “Humans in the loop” in AI warfare is an illusion. (MIT Technology Review) 2 SpaceX plans to manufacture its own GPUsTo support the company’s growing AI ambitions. (Reuters $)+ Musk is shifting SpaceX’s focus from Mars to AI ahead of its IPO. (NYT $)+ SpaceX and Tesla may be on a collision course. (FT $)3 Chinese tech giant Tencent has unveiled its first flagship AI modelA former OpenAI researcher is at the helm. (SCMP)+ Chinese open models are spreading fast. (MIT Technology Review)4 High earners are racing ahead on AI, deepening workplace dividesThe division in adoption risks widening inequality. (FT $)+ Startups are bragging they spend more on AI than staff. (404 Media)5 Thousands of Samsung workers are demanding a new share of AI profitsChip-division employees want 15% of the operating profit. (Bloomberg $)+ Here’s why opinion on AI is so divided. (MIT Technology Review)6 AI is helping mediocre Korean hackers steal millionsThey’re vibe coding their malware. (Wired $)+ AI is making online crimes easier. (MIT Technology Review)7 Kalshi suspended three political candidates for betting on their own racesIncluding a Democrat and a Republican running for Congress. (CNN)+ And an independent candidate who said he did it to make a point. (Gizmodo)+ Lawmakers argue that prediction markets are a loophole for gambling. (NPR)8 A ping-pong robot is beating elite human players for the first timeThe Sony AI system was trained with reinforcement learning. (New Scientist)+ Just days earlier, a humanoid smashed the human half-marathon record. (AP)9 Crypto scammers are luring ships into the Strait of HormuzBy falsely promising safe passage. (Ars Technica)10 ‘Age tech’ could help us grow old comfortably at homeApps, wearables, and remote monitoring could fill caregiving gaps. (NYT $) Quote of the day “It’s a hallucinogenic business plan.” —Ross Gerber, the chief executive of Gerber Kawasaki, an investment firm that owns SpaceX shares, tells the New York Times that he’s unimpressed by Musk’s changing goals for the aerospace company.  One More Thing AP PHOTO/LINDSEY WASSON This grim but revolutionary DNA technology is changing how we respond to mass disasters After hundreds went missing in Maui’s deadly fires, victims were identified with rapid DNA analysis—an increasingly vital tool for putting names to the dead in mass-casualty events. The technology helped identify victims within just a few hours and bring families some closure more quickly than ever before. But it also previews a dark future marked by the rising frequency of catastrophic events.
Find out how this forensic breakthrough is preparing us for a more volatile world. —Erika Hayasaki We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)+ This fascinating dive into botanical history reveals the origins of the first true plants.+ Here’s how to use Google’s reference desk to find what ordinary search engines miss.+ Watch duct tape get deconstructed to reveal the physics behind its legendary stickiness.+ When Radiohead covers Joy Division, the result is a beautiful intersection of two legendary musical eras.

Fusion power could provide a steady, zero-emissions source of electricity in the future—if companies can get plants built and running. But a new study suggests that even if that future arrives, it might not come cheap. Technologies tend to get less expensive over time. Lithium-ion batteries are now about 90% cheaper than they were in 2013. But historically, different technologies tend to go through this curve at different rates. And the cost of fusion might not sink as quickly as the prices of batteries or solar. It’s tricky to make any predictions about the cost of a technology that doesn’t exist yet. But when there’s billions of dollars of public and private funding on the line, it’s worth considering what assumptions we’re making about our future energy mix and its cost. One crucial measure is a metric called experience rate—the percentage by which an energy technology’s cost declines every time capacity doubles. A higher figure means a quicker price drop and better economic gains with scaling.
Historically, the experience rate is 12% for onshore wind power, 20% for lithium-ion batteries, and 23% for solar modules. Other energy technologies haven’t gotten cheap quite as quickly—fission is at just 2%. In the new study, published in Nature Energy, researchers aimed to improve predictions of fusion’s future price by estimating the technology’s experience rate. The team looked at three key characteristics that can correlate with experience rate: unit size, design complexity, and the need for customization. The larger and more complex a technology is, and/or the more it needs to be customized for different use cases, the lower the experience rate.
The researchers interviewed fusion experts, including public-sector researchers and those working at companies in the private sector. They had the experts evaluate fusion power plants on those characteristics and used that info to predict the experience rate. (One note here: The study focused only on magnetic confinement and laser inertial confinement, two of the leading fusion approaches, which together receive the vast majority of funding today. Other approaches could come with different cost benefits.) Fusion plants will likely be relatively large, similar to other types of facilities (like coal and fission power plants) that rely on generating heat. They will probably need less customization than fission plants—largely because regulations and safety considerations should be simpler—but more than technologies like solar panels. And as for complexity, “there was almost unanimous agreement that fusion is incredibly complex,” says Lingxi Tang, a PhD candidate in the energy and technology policy group at ETH Zurich in Switzerland and one of the authors of the study. (Some experts said it was literally off the scale the researchers gave them.) The final figure the researchers suggest for fusion’s experience rate is between 2% and 8%, meaning it will see a faster price reduction than nuclear power but not as dramatic an improvement as many common energy technologies being deployed today. That means that it would take a lot of deployment—and likely quite a long time—for the price of building a fusion reactor to drop significantly, so electricity produced by fusion plants could be expensive for a while. And it’s a much slower rate than the 8% to 20% that many modeling studies assume today. “On the whole, I think questions should be raised about current investment levels in fusion,” Tang says. (The US allocated over $1 billion to fusion in the 2024 fiscal year, and private-sector funding totaled $2.2 billion between July 2024 and July 2025.) “If you’re talking about decarbonization of the energy system, is this really the best use of public money?” But some experts say that looking to the past to understand the future of energy prices might be misleading.“It’s a good exercise, but we have to be humble about how much we don’t know,” says Egemen Kolemen, a professor at the Princeton Plasma Physics Laboratory. In 2000, many analysts predicted that solar power would remain expensive—but then production exploded and prices came crashing down, largely because China went all in, he says. “People weren’t exactly wrong then,” he adds. “They were just extrapolating what they saw into the future.” How fast prices drop depends on regulations, geopolitical dynamics, and labor cost, he says: “We haven’t built the thing yet, so we don’t know.” This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.