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 gig workers who are training humanoid robots at home  When Zeus, a medical student in Nigeria, returns to his apartment from a long day at the hospital, he straps his iPhone to his forehead and records himself doing chores.  Zeus is a data recorder for Micro1, which sells the data he collects to robotics firms. As these companies race to build humanoids, videos from workers like Zeus have become the hottest new way to train them.   Micro1 has hired thousands of them in more than 50 countries, including India, Nigeria, and Argentina. The jobs pay well locally, but raise thorny questions around privacy and informed consent. The work can be challenging—and weird. Read the full story. 
—Michelle Kim  Our readers recently voted humanoid robots the “11th breakthrough” to add to our 2026 list of 10 Breakthrough Technologies. Check out what else officially made the cut. 
AI benchmarks are broken. Here’s what we need instead.  For decades, AI has been evaluated based on whether it can outperform humans on isolated problems. But it’s seldom used this way in the real world.  While AI is assessed in a vacuum, it operates in messy, complex, multi-person environments over time. This misalignment leads us to misunderstand its capabilities, risks, and impacts.  We need new benchmarks that assess AI’s performance over longer horizons within human teams, workflows, and organizations. Here’s a proposal for one such approach: Human–AI, Context-Specific Evaluation.   —Angela Aristidou, professor at University College London and faculty fellow at the Stanford Digital Economy Lab and the Stanford Human-Centered AI Institute.  MIT Technology Review Narrated: can quantum computers now solve health care problems? We’ll soon find out.  In a laboratory on the outskirts of Oxford, a quantum computer built from atoms and light awaits its moment. The device is small but powerful—and also very valuable. Infleqtion, the company that owns it, is hoping its abilities will win $5 million at a competition.   The prize will go to the quantum computer that can solve real health care problems that “classical” computers cannot. But there can be only one big winner—if there is a winner at all.  —Michael Brooks  This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we’re publishing 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 OpenAI just closed the biggest funding round in Silicon Valley history It raised $122 billion ahead of its blockbuster IPO, which is expected later this year. (WSJ $) + It’s also prepping a push to “rethink the social contract.” (Vanity Fair $) + Campaigners are urging people to quit ChatGPT. (MIT Technology Review)    2 Iran has threatened to attack 18 US tech companies  It’s eyeing their operations in the Middle East. (Politico) + Targets include Nvidia, Apple, Microsoft, and Google. (Engadget) + Iran struck AWS data centers earlier this month. (Reuters $)  3 Artemis II is about to fly humans to the Moon. Here’s the science they’ll do Their experiments will set the stage for future explorers. (Nature) + You can watch the launch attempt today. (Engadget)   4 Putin is trying to take full control of Russia’s internet New outages and blockages are cutting the country off from the world. (NYT $) + Can we repair the internet? (MIT Technology Review)  5 A robotaxi outage in China left passengers stranded on highways  Baidu vehicles froze on the streets of Wuhan. (Bloomberg $) + Police are blaming a “system failure.” (Reuters $)  6 US government requests for social media user data are soaring They’ve skyrocketed by 770% in the past decade. (Bloomberg $) + Is the Pentagon allowed to surveil Americans with AI? (MIT Technology Review) 
7 Tesla has admitted that humans sometimes drive its robotaxis Remote drivers occasionally control them completely. (Wired $)  8 A satellite-smashing chain reaction could spiral out of control This data visualization captures the dangers of space collisions. (Guardian) + Here’s all the stuff we’ve put into space. (MIT Technology Review) 
9 Meta’s smartglasses can turn you into a creep According to one journalist who wore them for a month. (Guardian)  10 A Claude Code leak has exposed plans for a virtual pet  We could be getting a Tamagotchi for the GenAI era. (The Verge)  Quote of the day  “From now on, for every assassination, an American company will be destroyed.”  —Iran’s Islamic Revolutionary Guard Corps (IRGC) threatens US tech firms in an affiliated Telegram, per CNBC.  One More Thing  ACKERMAN + GRUBER How one mine could unlock billions in EV subsidies  In a pine farm north of the tiny town of Tamarack, Minnesota, Talon Metals has uncovered one of America’s densest nickel deposits. Now it wants to begin mining the ore. 
Products made from the nickel could net more than $26 billion in subsidies through the Inflation Reduction Act (IRA), which is starting to transform the US economy. To understand how, we tallied up the potential tax credits available. Read the full story to find out what we discovered.  —James Temple  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 selfless group of gluttons tried to taste-test every potato chip in the world.  + Get romantic inspiration from these penguins’ engagement pebbles. + Good news: global terrorism has hit a 15-year low. + Enjoy endless new views through these windows around the world.  

When Zeus, a medical student living in a hilltop city in central Nigeria, returns to his studio apartment from a long day at the hospital, he turns on his ring light, straps his iPhone to his forehead, and starts recording himself. He raises his hands in front of him like a sleepwalker and puts a sheet on his bed. He moves slowly and carefully to make sure his hands stay within the camera frame.  Zeus is a data recorder for Micro1, a US company based in Palo Alto, California that collects real-world data to sell to robotics companies. As companies like Tesla, Figure AI, and Agility Robotics race to build humanoids—robots designed to resemble and move like humans in factories and homes—videos recorded by gig workers like Zeus are becoming the hottest new way to train them.  Micro1 has hired thousands of contract workers in more than 50 countries, including India, Nigeria, and Argentina, where swathes of tech-savvy young people are looking for jobs. They’re mounting iPhones on their heads and recording themselves folding laundry, washing dishes, and cooking. The job pays well by local standards and is boosting local economies, but it raises thorny questions around privacy and informed consent. And the work can be challenging at times—and weird. Zeus found the job in November, when people started talking about it everywhere on LinkedIn and YouTube. “This would be a real nice opportunity to set a mark and give data that will be used to train robots in the future,” he thought. 
Zeus is paid $15 an hour, which is good income in Nigeria’s strained economy with high unemployment rates. But as a bright-eyed student dreaming of becoming a doctor, he finds ironing his clothes for hours every day boring.  “I really [do] not like it so much,” he says. “I’m the kind of person that requires … a technical job that requires me to think.” 
Zeus, and all the workers interviewed by MIT Technology Review, asked to be referred to only by pseudonyms because they were not authorized to talk about their work. Humanoid robots are notoriously hard to build because manipulating physical objects is a difficult skill to master. But the rise of large language models underlying chatbots like ChatGPT has inspired a paradigm shift in robotics. Just as large language models learned to generate words by being trained on vast troves of text scraped from the internet, many researchers believe that humanoid robots can learn to interact with the world by being trained on massive amounts of movement data.  Editor’s note: In a recent poll, MIT Technology Review readers selected humanoid robots as the 11th breakthrough for our 2026 list of 10 Breakthrough Technologies. Robotics requires far more complex data about the physical world, though, and that is much harder to find. Virtual simulations can train robots to perform acrobatics, but not how to grasp and move objects, because simulations struggle to model physics with perfect accuracy. For robots to work in factories and serve as housekeepers, real-world data, however time-consuming and expensive to collect, may be what we need.  Investors are pouring money feverishly into solving this challenge, spending over $6 billion on humanoid robots in 2025. And at-home data recording is becoming a booming gig economy around the world. Data companies like Scale AI and Encord are recruiting their own armies of data recorders, while DoorDash pays delivery drivers to film themselves doing chores. And in China, workers in dozens of state-owned robot training centers wear virtual-reality headsets and exoskeletons to teach humanoid robots how to open a microwave and wipe down the table.  “There is a lot of demand, and it’s increasing really fast,” says Ali Ansari, CEO of Micro1. He estimates that robotics companies are now spending more than $100 million each year to buy real-world data from his company and others like it. A day in the life Workers at Micro1 are vetted by an AI agent named Zara that conducts interviews and reviews samples of chore videos. Every week, they submit videos of themselves doing chores around their homes, following a list of instructions about things like keeping their hands visible and moving at natural speed. The videos are reviewed by both AI and a human and are either accepted or rejected. They’re then annotated by AI and a team of hundreds of humans who label the actions in the footage. “There is a lot of demand, and it’s increasing really fast.” Ali Ansari, CEO of Micro1  Because this approach to training robots is in its infancy, it’s not clear yet what makes good training data. Still, “you need to give lots and lots of variations for the robot to generalize well for basic navigation and manipulation of the world,” says Ansari.

But many workers say that creating a variety of “chore content” in their tiny homes is a challenge. Zeus, a scrappy student living in a humble studio, struggles to record anything beyond ironing his clothes every day. Arjun, a tutor in Delhi, India, takes an hour to make a 15-minute video because he spends so much time brainstorming new chores. “How much content [can be made] in the home? How much content?” he says.  There’s also the sticky question of privacy. Micro1 asks workers not to show their faces to the camera or reveal personal information such as names, phone numbers, and birth dates. Then it uses AI and human reviewers to remove anything that slips through.  But even without faces, the videos capture an intimate slice of workers’ lives: the interiors of their homes, their possessions, their routines. And understanding what kind of personal information they might be recording while they’re busy doing chores on camera can be tricky. Reviews of such footage might not filter out sensitive information beyond the most obvious identifiers. For workers with families, keeping private life off camera is a constant negotiation. Arjun, a father of two daughters, has to wrangle his chaotic two-year-old out of frame. “Sometimes it’s very difficult to work because my daughter is small,” he says.  Sasha, a banker turned data recorder in Nigeria, tiptoes around when she hangs her laundry outside in a shared residential compound so she won’t record her neighbors, who watch her in bewilderment. “It’s going to take longer than people think.”Ken Goldberg, UC Berkeley While the workers interviewed by MIT Technology Review understand that their data is being used to train robots, none of them know how exactly their data will be used, stored, and shared with third parties, including the robotics companies that Micro1 is selling the data to. For confidentiality reasons, says Ansari, Micro1 doesn’t name its clients or disclose to workers the specific nature of the projects they are contributing to. “It is important that if workers are engaging in this, that they are informed by the companies themselves of the intention … where this kind of technology might go and how that might affect them longer term,” says Yasmine Kotturi, a professor of human-centered computing at the University of Maryland.
Occasionally, some workers say, they’ve seen other workers asking on the company Slack channel if the company could delete their data. Micro1 declined to comment on whether such data is deleted. “People are opting into doing this,” says Ansari. “They could stop the work at any time.”
Hungry for data With thousands of workers doing their chores differently in different homes, some roboticists wonder if the data collected from them is reliable enough to train robots safely.  “How we conduct our lives in our homes is not always right from a safety point of view,” says Aaron Prather, a roboticist at ASTM International. “If those folks are teaching those bad habits that could lead to an incident, then that’s not good data.” And the sheer volume of data being collected makes reviewing it for quality control challenging. But Ansari says the company rejects videos showing unsafe ways of performing a task, while clumsy movements can be useful to teach robots what not to do. Then there’s the question of how much of this data we need. Micro1 says it has tens of thousands of hours of footage, while Scale AI announced it had gathered more than 100,000 hours. “It’s going to take a long time to get there,” says Ken Goldberg, a roboticist at the University of California, Berkeley. Large language models were trained on text and images that would take a human 100,000 years to read, and humanoid robots may need even more data, because controlling robotic joints is even more complicated than generating text. “It’s going to take longer than people think,” he says. When Dattu, an engineering student living in a bustling tech hub in India, comes home after a full day of classes at his university, he skips dinner and dashes to his tiny balcony, cramped with potted plants and dumbbells. He straps his iPhone to his forehead and records himself folding the same set of clothes over and over again.  His family stares at him quizzically. “It’s like some space technology for them,” he says. When he tells his friends about his job, “they just get astounded by the idea that they can get paid by recording chores.” Juggling his university studies with data recording, as well as other data annotation gigs, takes a toll on him. Still, “it feels like you’re doing something different than the whole world,” he says. 

In partnership withMistral AI In the early days of large language models (LLMs), we grew accustomed to massive 10x jumps in reasoning and coding capability with every new model iteration. Today, those jumps have flattened into incremental gains. The exception is domain-specialized intelligence, where true step-function improvements are still the norm. When a model is fused with an organization’s proprietary data and internal logic, it encodes the company’s history into its future workflows. This alignment creates a compounding advantage: a competitive moat built on a model that understands the business intimately. This is more than fine-tuning; it is the institutionalization of expertise into an AI system. This is the power of customization. Intelligence tuned to context Every sector operates within its own specific lexicon. In automotive engineering, the “language” of the firm revolves around tolerance stacks, validation cycles, and revision control. In capital markets, reasoning is dictated by risk-weighted assets and liquidity buffers. In security operations, patterns are extracted from the noise of telemetry signals and identity anomalies. Custom-adapted models internalize the nuances of the field. They recognize which variables dictate a “go/no-go” decision, and they think in the language of the industry.
Domain expertise in action The transition from general-purpose to tailored AI centers on one goal: encoding an organization’s unique logic directly into a model’s weights. Mistral AI partners with organizations to incorporate domain expertise into their training ecosystems. A few use cases illustrate customized implementations in practice:
Software engineering and assisting at scale: A network hardware company with proprietary languages and specialized codebases found that out-of-the-box models could not grasp their internal stack. By training a custom model on their own development patterns, they achieved a step function in fluency. Integrated into Mistral’s software development scaffolding, this customized model now supports the entire lifecycle—from maintaining legacy systems to autonomous code modernization via reinforcement learning. This turns once-opaque, niche code into a space where AI reliably assists at scale. Automotive and the engineering copilot: A leading automotive company uses customization to revolutionize crash test simulations. Previously, specialists spent entire days manually comparing digital simulations with physical results to find divergences. By training a model on proprietary simulation data and internal analyses, they automated this visual inspection, flagging deformations in real time. Moving beyond detection, the model now acts as a copilot, proposing design adjustments to bring simulations closer to real-world behavior and radically accelerating the R&D loop. Public sector and sovereign AI: In Southeast Asia, a government agency is building a sovereign AI layer to move beyond Western-centric models. By commissioning a foundation model tailored to regional languages, local idioms, and cultural contexts, they created a strategic infrastructure asset. This ensures sensitive data remains under local governance while powering inclusive citizen services and regulatory assistants. Here, customization is the key to deploying AI that is both technically effective and genuinely sovereign. The blueprint for strategic customization Moving from a general-purpose AI strategy to a domain-specific advantage requires a structural rethinking of the model’s role within the enterprise. Success is defined by three shifts in organizational logic. 1. Treat AI as infrastructure, not an experiment.  Historically, enterprises have treated model customization as an ad hoc experiment—a single fine-tuning run for a niche use case or a localized pilot. While these bespoke silos often yield promising results, they are rarely built to scale. They produce brittle pipelines, improvised governance, and limited portability. When the underlying base models evolve, the adaptation work must often be discarded and rebuilt from scratch.In contrast, a durable strategy treats customization as foundational infrastructure. In this model, adaptation workflows are reproducible, version-controlled, and engineered for production. Success is measured against deterministic business outcomes. By decoupling the customization logic from the underlying model, firms ensure that their “digital nervous system” remains resilient, even as the frontier of base models shifts. 2. Retain control of your own data and models. As AI migrates from the periphery to core operations, the question of control becomes existential. Reliance on a single cloud provider or vendor for model alignment creates a dangerous asymmetry of power regarding data residency, pricing, and architectural updates. Enterprises that retain control of their training pipelines and deployment environments preserve their strategic agency. By adapting models within controlled environments, organizations can enforce their own data residency requirements and dictate their own update cycles. This approach transforms AI from a service consumed into an asset governed, reducing structural dependency and allowing for cost and energy optimizations aligned with internal priorities rather than vendor roadmaps. 3. Design for continuous adaptation. The enterprise environment is never static: regulations shift, taxonomies evolve, and market conditions fluctuate. A common failure is treating a customized model as a finished artifact. In reality, a domain-aligned model is a living asset subject to model decay if left unmanaged.

Designing for continuous adaptation requires a disciplined approach to ModelOps. This includes automated drift detection, event-driven retraining, and incremental updates. By building the capacity for constant recalibration, the organization ensures that its AI does not just reflect its history, but it evolves in lockstep with its future. This is the stage where the competitive moat begins to compound: the model’s utility grows as it internalizes the organization’s ongoing response to change. Control is the new leverage We have entered an era where generic intelligence is a commodity, but contextual intelligence is a scarcity. While raw model power is now a baseline requirement, the true differentiator is alignment—AI calibrated to an organization’s unique data, mandates, and decision logic. In the next decade, the most valuable AI won’t be the one that knows everything about the world; it will be the one that knows everything about you. The firms that own the model weights of that intelligence will own the market. This content was produced by Mistral AI. It was not written by MIT Technology Review’s editorial staff.

For decades, artificial intelligence has been evaluated through the question of whether machines outperform humans. From chess to advanced math, from coding to essay writing, the performance of AI models and applications is tested against that of individual humans completing tasks.  This framing is seductive: An AI vs. human comparison on isolated problems with clear right or wrong answers is easy to standardize, compare, and optimize. It generates rankings and headlines.  But there’s a problem: AI is almost never used in the way it is benchmarked. Although   researchers and industry have started to improve benchmarking by moving beyond static tests to more dynamic evaluation methods, these  innovations resolve only part of the issue. That’s because they still evaluate AI’s performance outside the human teams and organizational workflows where its real-world performance ultimately unfolds.  While AI is evaluated at the task level in a vacuum, it is used in messy, complex environments where it usually interacts with more than one person. Its performance (or lack thereof) emerges only over extended periods of use. This misalignment leaves us misunderstanding AI’s capabilities, overlooking systemic risks, and misjudging its economic and social consequences.
To mitigate this, it’s time to shift from narrow methods to benchmarks that assess how AI systems perform over longer time horizons within human teams, workflows, and organizations. I have studied real-world AI deployment since 2022 in small businesses and health, humanitarian, nonprofit, and higher-education organizations in the UK, the United States, and Asia, as well as within leading AI design ecosystems in London and Silicon Valley. I propose a different approach, which I call HAIC benchmarks—Human–AI, Context-Specific Evaluation. What happens when AI fails  For governments and businesses, AI benchmark scores appear more objective than vendor claims. They’re a critical part of determining whether an AI model or application is “good enough” for real-world deployment. Imagine an AI model that achieves impressive technical scores on the most cutting-edge benchmarks—98% accuracy, groundbreaking speed, compelling outputs. On the strength of these results, organizations may decide to adopt the model, committing sizable financial and technical resources to purchasing and integrating it. 
But then, once it’s adopted, the gap between benchmark and real-world performance quickly becomes visible. For example, take the swathe of FDA-approved AI models that can read medical scans faster and more accurately than an expert radiologist. In the radiology units of hospitals from the heart of California to the outskirts of London, I witnessed staff using highly ranked radiology AI applications. Repeatedly, it took them extra time to interpret AI’s outputs alongside hospital-specific reporting standards and nation-specific regulatory requirements. What appeared as a productivity-enhancing AI tool when tested in a vacuum introduced delays in practice.  It soon became clear that the benchmark tests on which medical AI models are assessed do not capture how medical decisions are actually made. Hospitals rely on multidisciplinary teams—radiologists, oncologists, physicists, nurses—who jointly review patients. Treatment planning rarely hinges on a static decision; it evolves as new information emerges over days or weeks. Decisions often arise through constructive debate and trade-offs between professional standards, patient preferences, and the shared goal of long-term patient well-being. No wonder even highly scored AI models struggle to deliver the promised performance once they encounter the complex, collaborative processes of real clinical care. The same pattern emerges in my research across other sectors: When embedded within real-world work environments, even AI models that perform brilliantly on standardized tests don’t perform as promised.  When high benchmark scores fail to translate into real-world performance, even the most highly scored AI is soon abandoned to what I call the “AI graveyard.” The costs are significant: Time, effort and money end up being wasted. And over time, repeated experiences like this erode organizational confidence in AI and—in critical settings such as health—may erode broader public trust in the technology as well.  When current benchmarks provide only a partial and potentially misleading signal of an AI model’s readiness for real-world use, this creates regulatory blind spots: Oversight is shaped by metrics that do not reflect reality. It also leaves organizations and governments to shoulder the risks of testing AI in sensitive real-world settings, often with limited resources and support.  How to build better tests  To close the gap between benchmark and real-world performance, we must pay attention to the actual conditions in which AI models will be used. The critical questions: Can AI function as a productive participant within human teams? And can it generate sustained, collective value?  Through my research on AI deployment across multiple sectors, I have seen a number of organizations already moving—deliberately and experimentally—toward the HAIC benchmarks I favor.  HAIC benchmarks reframe current benchmarking in four ways: 

1.     From individual and single-task performance to team and workflow performance (shifting the unit of analysis) 2.     From one-off testing with right/wrong answers to long-term impacts (expanding the time horizon) 3.     From correctness and speed to organizational outcomes, coordination quality, and error detectability (expanding outcome measures) 4.     From isolated outputs to upstream and downstream consequences (system effects) Across the organizations where this approach has emerged and started to be applied, the first step is shifting the unit of analysis.  For example, in one UK hospital system in the period 2021–2024, the question expanded from whether a medical AI application improves diagnostic accuracy to how the presence of AI within the hospital’s multidisciplinary teams affects not only accuracy but also coordination and deliberation. The hospital specifically assessed coordination and deliberation in human teams using and not using AI. Multiple stakeholders (within and outside the hospital) decided on metrics like how AI influences collective reasoning, whether it surfaces overlooked considerations, whether it strengthens or weakens coordination, and whether it changes established risk and compliance practices.  This shift is fundamental. It matters a lot in high-stakes contexts where system-level effects matter more than task-level accuracy. It also matters for the economy. It may help recalibrate inflated expectations of sweeping productivity gains that are so far predicated largely on the promise of improving individual task performance.  Once that foundation is set, HAIC benchmarking can begin to take on the element of time. 
Today’s benchmarks resemble school exams—one-off, standardized tests of accuracy. But real professional competence is assessed differently. Junior doctors and lawyers are evaluated continuously inside real workflows, under supervision, with feedback loops and accountability structures. Performance is judged over time and in a specific context, because competence is relational. If AI systems are meant to operate alongside professionals, their impact should be judged longitudinally, reflecting how performance unfolds over repeated interactions.  I saw this aspect of HAIC applied in one of my humanitarian-sector case studies. Over 18 months, an AI system was evaluated within real workflows, with particular attention to how detectable its errors were—that is, how easily human teams could identify and correct them. This long-term “record of error detectability” meant the organizations involved could design and test context-specific guardrails to promote trust in the system, despite the inevitability of occasional AI mistakes.
A longer time horizon also makes visible the system-level consequences that short-term benchmarks miss. An AI application may outperform a single doctor on a narrow diagnostic task yet fail to improve multidisciplinary decision-making. Worse, it may introduce systemic distortions: anchoring teams too early in plausible but incomplete answers, adding to people’s  cognitive workloads, or generating downstream inefficiencies that offset any speed or efficiency gains at the point of the AI’s use. These knock-on effects—often invisible to current benchmarks—are central to understanding real impact.  The HAIC approach, admittedly promises to make benchmarking more complex, resource-intensive, and harder to standardize. But continuing to evaluate AI in sanitized conditions detached from the world of work will leave us misunderstanding what it truly can and cannot do for us. To deploy AI responsibly in real-world settings, we must measure what actually matters: not just what a model can do alone, but what it enables—or undermines—when humans and teams in the real world work with it.  Angela Aristidou is a professor at University College London and a faculty fellow at the Stanford Digital Economy Lab and the Stanford Human-Centered AI Institute. She speaks, writes, and advises about the real-life deployment of artificial-intelligence tools for public good.

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. There are more AI health tools than ever—but how well do they work?  In the last few months alone, Microsoft, Amazon, and OpenAI have all launched medical chatbots.  There’s a clear demand for these tools, given how hard it is for many people to access advice through the existing medical system—and they could make safe and useful recommendations. But concerns have surfaced about how little external evaluation they undergo before being released to the public.   Read the full story to understand what’s at stake. 
—Grace Huckins  The Pentagon’s culture war tactic against Anthropic has backfired  A judge has temporarily blocked the Pentagon from labeling Anthropic a supply chain risk and ordering government agencies to stop using its AI. Her intervention suggests that the feud never needed to reach such a frenzy. 
It did so because the government disregarded the existing process for such disputes—and fueled the fire on social media. Find out how it happened and what comes next.  —James O’Donnell  This story is from The Algorithm, our weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.  The must-reads  I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.  1 California has defied Trump to impose new AI regulations Governor Newsom signed off on the new standards yesterday.  (Guardian) + Firms seeking state contracts will need extra safeguards. (Reuters $) + States are installing guardrails despite Trump’s order to stop. (NYT $)  + An AI regulation war is brewing in the US. (MIT Technology Review)   2 Experiments have verified quantum simulations for the first time It’s a breakthrough for quantum computing applications. (Nature) + Which could one day help solve healthcare problems. (MIT Technology Review)  3 The new White House app is a security and privacy nightmare It extensively tracks users and relies on external code. (Gizmodo) + The new app promises “unparalleled access” to Trump. (CNET) + It also invites users to report people to ICE. (The Verge) 

4 Big Tech’s $635 billion AI spending faces an energy shock test The Middle East crisis is clouding prospects for growth. (Reuters $) + Here are three big unknowns about AI’s energy burden. (MIT Technology Review)  5 Meta and Google have been accused of breaking child safety rules Australia suspects they flouted a social media ban. (Bloomberg $) + Indonesia is also investigating non-compliance. (Reuters $)  6 Nebius is building a $10 billion AI data center in Finland The company is rapidly expanding Europe’s AI infrastructure. (CNBC)  7 South Korea’s chipmakers’ helium stocks will last until June Beyond that? Who knows. (Reuters $) + Shortages caused by the Iran war threaten the chip industry. (NYT $)   8 Another Starlink satellite has inexplicably exploded  SpaceX suffered a similar episode in December. (The Verge) + We went inside Ukraine’s largest Starlink repair shop. (MIT Technology Review)  9 Bluesky’s new AI tool is already its most blocked account—after JD Vance About 83 times as many users have blocked it as have followed it. (TechCrunch)  10 An AI agent banned from Wikipedia has lashed out in angry blogs The bot accused its human editors of “uncivil behavior.” (404 Media)   Quote of the day 
“Is any of this illegal? Probably not. Is it what you’d expect from an official government app? Probably not either.”  —Security researcher Thereallo reviews the White House’s new app. One More Thing 
CHANTAL JAHCHAN Inside Amsterdam’s high-stakes experiment to create fair welfare AI  When Hans de Zwart, a digital rights advocate, saw Amsterdam’s plan to have an algorithm evaluate every welfare applicant for potential fraud, he nearly fell out of his chair. He believed the system had “unfixable problems.”   Meanwhile, Paul de Koning, a consultant to the city, was excited. He saw immense potential to improve efficiencies and remove biases. 

EXECUTIVE SUMMARY Earlier this month, Microsoft launched Copilot Health, a new space within its Copilot app where users will be able to connect their medical records and ask specific questions about their health. A couple of days earlier, Amazon had announced that Health AI, an LLM-based tool previously restricted to members of its One Medical service, would now be widely available. These products join the ranks of ChatGPT Health, which OpenAI released back in January, and Anthropic’s Claude, which can access user health records if granted permission. Health AI for the masses is officially a trend.  There’s a clear demand for chatbots that provide health advice, given how hard it is for many people to access it through existing medical systems. And some research suggests that current LLMs are capable of making safe and useful recommendations. But researchers say that these tools should be more rigorously evaluated by independent experts, ideally before they are widely released.  In a high-stakes area like health, trusting companies to evaluate their own products could prove unwise, especially if those evaluations aren’t made available for external expert review. And even if the companies are doing quality, rigorous research—which some, including OpenAI, do seem to be—they might still have blind spots that the broader research community could help to fill. “To the extent that you always are going to need more health care, I think we should definitely be chasing every route that works,” says Andrew Bean, a doctoral candidate at the Oxford Internet Institute. “It’s entirely plausible to me that these models have reached a point where they’re actually worth rolling out.”
“But,” he adds, “the evidence base really needs to be there.” Tipping points 
To hear developers tell it, these health products are now being released because large language models have indeed reached a point where they can effectively provide medical advice. Dominic King, the vice president of health at Microsoft AI and a former surgeon, cites AI advancement as a core reason why the company’s health team was formed, and why Copilot Health now exists. “We’ve seen this enormous progress in the capabilities of generative AI to be able to answer health questions and give good responses,” he says. But that’s only half the story, according to King. The other key factor is demand. Shortly before Copilot Health was launched, Microsoft published a report, and an accompanying blog post, detailing how people used Copilot for health advice. The company says it receives 50 million health questions each day, and health is the most popular discussion topic on the Copilot mobile app. Other AI companies have noticed, and responded to, this trend. “Even before our health products, we were seeing just a rapid, rapid increase in the rate of people using ChatGPT for health-related questions,” says Karan Singhal, who leads OpenAI’s Health AI team. (OpenAI and Microsoft have a long-standing partnership, and Copilot is powered by OpenAI’s models.) It’s possible that people simply prefer posing their health problems to a nonjudgmental bot that’s available to them 24-7. But many experts interpret this pattern in light of the current state of the health-care system. “There is a reason that these tools exist and they have a position in the overall landscape,” says Girish Nadkarni, chief AI officer​ at the Mount Sinai Health System. “That’s because access to health care is hard, and it’s particularly hard for certain populations.” The virtuous vision of consumer-facing LLM health chatbots hinges on the possibility that they could improve user health while reducing pressure on the health-care system. That might involve helping users decide whether or not they need medical attention, a task known as triage. If chatbot triage works, then patients who need emergency care might seek it out earlier than they would have otherwise, and patients with more mild concerns might feel comfortable managing their symptoms at home with the chatbot’s advice rather than unnecessarily busying emergency rooms and doctor’s offices. But a recent, widely discussed study from Nadkarni and other researchers at Mount Sinai found that ChatGPT Health sometimes recommends too much care for mild conditions and fails to identify emergencies. Though Singhal and  some other experts have suggested that its methodology might not provide a complete picture of ChatGPT Health’s capabilities, the study has surfaced concerns about how little external evaluation these tools see before being released to the public. Most of the academic experts interviewed for this piece agreed that LLM health chatbots could have real upsides, given how little access to health care some people have. But all six of them expressed concerns that these tools are being launched without testing from independent researchers to assess whether they are safe. While some advertised uses of these tools, such as recommending exercise plans or suggesting questions that a user might ask a doctor, are relatively harmless, others carry clear risks. Triage is one; another is asking a chatbot to provide a diagnosis or a treatment plan.  The ChatGPT Health interface includes a prominent disclaimer stating that it is not intended for diagnosis or treatment, and the announcements for Copilot Health and Amazon’s Health AI include similar warnings. But those warnings are easy to ignore. “We all know that people are going to use it for diagnosis and management,” says Adam Rodman, an internal medicine physician and researcher at Beth Israel Deaconess Medical Center and a visiting researcher at Google.

Medical testing Companies say they are testing the chatbots to ensure that they provide safe responses the vast majority of the time. OpenAI has designed and released HealthBench, a benchmark that scores LLMs on how they respond in realistic health-related conversations—though the conversations themselves are LLM-generated. When GPT-5, which powers both ChatGPT Health and Copilot Health, was released last year, OpenAI reported the model’s HealthBench scores: It did substantially better than previous OpenAI models, though its overall performance was far from perfect.  But evaluations like HealthBench have limitations. In a study published last month, Bean—the Oxford doctoral candidate—and his colleagues found that even if an LLM can accurately identify a medical condition from a fictional written scenario on its own, a non-expert user who is given the scenario and asked to determine the condition with LLM assistance might figure it out only a third of the time. If they lack medical expertise, users might not know which parts of a scenario—or their real-life experience—are important to include in their prompt, or they might misinterpret the information that an LLM gives them. Bean says that this performance gap could be significant for OpenAI’s models. In the original HealthBench study, the company reported that its models performed relatively poorly in conversations that required them to seek more information from the user. If that’s the case, then users who don’t have enough medical knowledge to provide a health chatbot with the information that it needs from the get-go might get unhelpful or inaccurate advice. Singhal, the OpenAI health lead, notes that the company’s current GPT-5 series of models, which had not yet been released when the original HealthBench study was conducted, do a much better job of soliciting additional information than their predecessors. However, OpenAI has reported that GPT-5.4, the current flagship, is actually worse at seeking context than GPT-5.2, an earlier version. Ideally, Bean says, health chatbots would be subjected to controlled tests with human users, as they were in his study, before being released to the public. That might be a heavy lift, particularly given how fast the AI world moves and how long human studies can take. Bean’s own study used GPT-4o, which came out almost a year ago and is now outdated.  Earlier this month, Google released a study that meets Bean’s standards. In the study, patients discussed medical concerns with the company’s Articulate Medical Intelligence Explorer (AMIE), a medical LLM chatbot that is not yet available to the public, before meeting with a human physician. Overall, AMIE’s diagnoses were just as accurate as physicians’, and none of the conversations raised major safety concerns for researchers.  Despite the encouraging results, Google isn’t planning to release AMIE anytime soon. “While the research has advanced, there are significant limitations that must be addressed before real-world translation of systems for diagnosis and treatment, including further research into equity, fairness, and safety testing,” wrote Alan Karthikesalingam, a research scientist at Google DeepMind, in an email. Google did recently reveal that Health100, a health platform it is building in partnership with CVS, will include an AI assistant powered by its flagship Gemini models, though that tool will presumably not be intended for diagnosis or treatment.
Rodman, who led the AMIE study with Karthikesalingam, doesn’t think such extensive, multiyear studies are necessarily the right approach for chatbots like ChatGPT Health and Copilot Health. “There’s lots of reasons that the clinical trial paradigm doesn’t always work in generative AI,” he says. “And that’s where this benchmarking conversation comes in. Are there benchmarks [from] a trusted third party that we can agree are meaningful, that the labs can hold themselves to?” They key there is “third party.” No matter how extensively companies evaluate their own products, it’s tough to trust their conclusions completely. Not only does a third-party evaluation bring impartiality, but if there are many third parties involved, it also helps protect against blind spots.
OpenAI’s Singhal says he’s strongly in favor of external evaluation. “We try our best to support the community,” he says. “Part of why we put out HealthBench was actually to give the community and other model developers an example of what a very good evaluation looks like.”  Given how expensive it is to produce a high-quality evaluation, he says, he’s skeptical that any individual academic laboratory would be able to produce what he calls “the one evaluation to rule them all.” But he does speak highly of efforts that academic groups have made to bring preexisting and novel evaluations together into comprehensive evaluations suites—such as Stanford’s MedHELM framework, which tests models on a wide variety of medical tasks. Currently, OpenAI’s GPT-5 holds the highest MedHELM score. Nigam Shah, a professor of medicine at Stanford University who led the MedHELM project, says it has limitations. In particular, it only evaluates individual chatbot responses, but someone who’s seeking medical advice from a chatbot tool might engage it in a multi-turn, back-and-forth conversation. He says that he and some collaborators are gearing up to build an evaluation that can score those complex conversations, but that it will take time, and money. “You and I have zero ability to stop these companies from releasing [health-oriented products], so they’re going to do whatever they damn please,” he says. “The only thing people like us can do is find a way to fund the benchmark.” No one interviewed for this article argued that health LLMs need to perform perfectly on third-party evaluations in order to be released. Doctors themselves make mistakes—and for someone who has only occasional access to a doctor, a consistently accessible LLM that sometimes messes up could still be a huge improvement over the status quo, as long as its errors aren’t too grave.  With the current state of the evidence, however, it’s impossible to know for sure whether the currently available tools do in fact constitute an improvement, or whether their risks outweigh their benefits.