In a bid toward greater transparency, the Food and Drug Administration sent reminder letters to more than 2,200 companies and researchers that they are required to report clinical trial results to a federal government database or they may face fines.
FDA officials disclosed that an internal analysis found results were not submitted for nearly 30% of studies that were “highly likely” to fall under mandatory reporting requirements. The agency also noted that the letters were sent to companies and researchers associated with more than 3,000 registered trials, some of which were publicly funded.
In explaining its move, the regulator acknowledged a long-standing complaint from researchers who have argued that without access to specific data, trial results cannot be easily duplicated, which inhibits greater understanding of how medicines might work. They also contend this can adversely affect treatment decisions and health care costs.
If you’re following AI news, you’re probably getting whiplash. AI is a gold rush. AI is a bubble. AI is taking your job. AI can’t even read a clock. The 2026 AI Index from Stanford University’s Institute for Human-Centered Artificial Intelligence, AI’s annual report card, comes out today and cuts through some of that noise.
Despite predictions that AI development may hit a wall, the report says that the top models just keep getting better. People are adopting AI faster than they picked up the personal computer or the internet. AI companies are generating revenue faster than companies in any previous technology boom, but they’re also spending hundreds of billions of dollars on data centers and chips. The benchmarks designed to measure AI, the policies meant to govern it, and the job market are struggling to keep up. AI is sprinting, and the rest of us are trying to find our shoes.
All that speed comes at a cost. AI data centers around the world can now draw 29.6 gigawatts of power, enough to run the entire state of New York at peak demand. Annual water use from running OpenAI’s GPT-4o alone may exceed the drinking water needs of 12 million people. At the same time, the supply chain for chips is alarmingly fragile. The US hosts most of the world’s AI data centers, and one company in Taiwan, TSMC, fabricates almost every leading AI chip.
The data reveals a technology evolving faster than we can manage. Here’s a look at some of the key points from this year’s report.
The US and China are nearly tied
In a long, heated race with immense geopolitical stakes, the US and China are almost neck and neck on AI model performance, according to Arena, a community-driven ranking platform that allows users to compare the outputs of large language models on identical prompts. In early 2023, OpenAI had a lead with ChatGPT, but this gap narrowed in 2024 as Google and Anthropic released their own models. In February 2025, R1, an AI model built by the Chinese lab DeepSeek, briefly matched the top US model, ChatGPT. As of March 2026, Anthropic leads, trailed closely by xAI, Google, and OpenAI. Chinese models like DeepSeek and Alibaba lag only modestly. With the best AI models separated in the rankings by razor-thin margins, they’re now competing on cost, reliability, and real-world usefulness.
The index notes that the US and China have different AI advantages. While the US has more powerful AI models, more capital, and an estimated 5,427 data centers (more than 10 times as many as any other country), China leads in AI research publications, patents, and robotics.
As competition intensifies, companies like OpenAI, Anthropic, and Google no longer disclose their training code, parameter counts, or data-set sizes. “We don’t know a lot of things about predicting model behaviors,” says Yolanda Gil, a computer scientist at the University of Southern California who coauthored the report. This lack of transparency makes it difficult for independent researchers to study how to make AI models safer, she says.
AI models are advancing super fast
Despite predictions that development will plateau, AI models keep getting better and better. By some measures, they now meet or exceed the performance of human experts on tests that aim to measure PhD-level science, math, and language understanding. SWE-bench Verified, a software engineering benchmark for AI models, saw top scores jump from around 60% in 2024 to almost 100% in 2025. In 2025, an AI system produced a weather forecast on its own.
“I am stunned that this technology continues to improve, and it’s just not plateauing in any way,” says Gil.
However, AI still struggles in plenty of other areas. Because the models learn by processing enormous amounts of text and images rather than by experiencing the physical world, AI exhibits “jagged intelligence.” Robots are still in their early days and succeed in only 12% of household tasks. Self-driving cars are farther along: Waymos are now roaming across five US cities, and Baidu’s Apollo Go vehicles are shuttling riders around in China. AI is also expanding into professional domains like law and finance, but no model dominates the field yet.
But the way we test AI is broken
These reports of progress should be taken with a grain of salt. The benchmarks designed to track AI progress are struggling to keep up as models quickly blow past their ceilings, the Stanford report says. Some are poorly constructed—a popular benchmark that tests a model’s math abilities has a 42% error rate. Others can be gamed: when models are trained on benchmark test data, for example, they can learn to score well without getting smarter.
Because AI is rarely used the same way it’s tested, strong benchmark performance doesn’t always translate to real-world performance. And for complex, interactive technologies such as AI agents and robots, benchmarks barely exist yet.
AI companies are also sharing less about how their models are trained, and independent testing sometimes tells a different story from what they report. “A lot of companies are not releasing how their models do in certain benchmarks, particularly the responsible-AI benchmarks,” says Gil. “The absence of how your model is doing on a benchmark maybe says something.”
AI is starting to affect jobs
Within three years of going mainstream, AI is now used by more than half of people around the world, a rate of adoption faster than the personal computer or the internet. An estimated 88% of organizations now use AI, and four in five university students use it.
It’s early days for deployment, and AI’s impact on jobs is hard to measure. Still, some studies suggest AI is beginning to affect young workers in certain professions. According to a 2025 study by economists at Stanford, employment for software developers aged 22 to 25 has fallen nearly 20% since 2022. The decline might not be pinned on AI alone, as broader macroeconomic conditions could be to blame, but AI appears to be playing a part.
Employers say that hiring may continue to tighten. According to a 2025 survey conducted by McKinsey & Company, a third of organizations expect AI to shrink their workforce in the coming year, particularly in service and supply chain operations and software engineering. AI is boosting productivity by 14% in customer service and 26% in software development, according to research cited by the index, but such gains are not seen in tasks requiring more judgment. Overall, it’s still too early to understand the bigger economic impact of AI.
People have complicated feelings about AI
Around the world, people feel both optimistic and anxious about AI: 59% of people think that it will provide more benefits than drawbacks, while 52% say that it makes them nervous, according to an Ipsos survey cited in the index.
Notably, experts and the public see the future of AI very differently, according to a Pew survey. The biggest gap is around the future of work: While 73% of experts think that AI will have a positive impact on how people do their jobs, only 23% of the American public thinks so. Experts are also more optimistic than the public about AI’s impact on education and medical care, but they agree that AI will hurt elections and personal relationships.
Among all countries surveyed, Americans trust their government least to regulate AI appropriately, according to another Ipsos survey. More Americans worry federal AI regulation won’t go far enough than worry it will go too far.
Governments are struggling to regulate AI
Governments around the world are struggling to regulate AI, but there were some minor successes last year. The EU AI Act’s first prohibitions, which ban the use of AI in predictive policing and emotion recognition, took effect. Japan, South Korea, and Italy also passed national AI laws. Meanwhile, the US federal government moved toward deregulation, with President Trump issuing an executive order seeking to handcuff states from regulating AI.
Despite this federal action, state legislatures in the US passed a record 150 AI-related bills. California enacted landmark legislation, including SB 53, which mandates safety disclosures and whistleblower protections for developers of AI models. New York passed the RAISE Act, requiring AI companies to publish safety protocols and report critical safety incidents.
But for all the legislative activity, Gil says, regulation is running behind the technology because we don’t really understand how it works. “Governments are cautious to regulate AI because … we don’t understand many things very well,” she says. “We don’t have a good handle on those systems.”
With thousands of illegal e-cigarettes for sale in the U.S., both the Trump and Biden administrations have vowed to crack down on the illicit fruit- and candy-flavoredvapes that hold particular appeal to minors. But a new government report suggests law enforcement efforts by the Department of Justice lag far behind the scope of the problem.
Most DOJ enforcement actions between fiscal year 2022 and fiscal year 2025 — 50 out of a total of 88 — were to add the names of remote e-cigarette sellers to a list of unauthorized businesses, according to the report from the Government Accountability Office. The second-most common type of enforcement actions (20 out of 88) noted in the report were injunctions to stop legal violations.
Zac Jiwa, a federal Medicare official, delivered a eulogy of sorts at a Thursday Medicare event highlighting the successes of the Health Tech Ecosystem initiative.
The eulogy’s subject? The clipboard.
For the past eight months, hundreds of health tech companies have been working to meet goals set out by the federal government to make patient records more portable, create systems that import patients’ data into providers’ electronic health records systems, and stand up various patient apps. The idea is to make filling out a stack of paperwork at every doctor’s visit, on that ubiquitous clipboard, a thing of the past.
Background: Youth should be partners in the development and dissemination of health information created for their demographic. The Manaora Rangatahi (youth) Guidelines comprise 10 eating and 10 wellbeing messages that were cocreated with rangatahi Māori (Māori youth) in Hawke’s Bay, New Zealand, and then disseminated through a digital media campaign. Objective: This study aimed to present the process evaluation of the co-design and implementation of the pilot digital media dissemination campaign of the Manaora Rangatahi Guidelines. Methods: The 17 rangatahi who were involved in the cocreation of the guidelines codeveloped a dissemination plan, filmed video clips for each of the 20 messages, and supported the 20-week digital media campaign. The codevelopment process over 4 wānanga (workshops) is described and critiqued, and the implementation was assessed using the data analytics from Instagram and TikTok, the main social media platforms for dissemination. The rangatahi participated in a short postcampaign review survey. Results: The dissemination plan included a website for the messages, a campaign name and logo, apparel with messaging and QR codes, and the design of a digital media campaign featuring the participating rangatahi. Video clips of each of the guideline messages featuring the rangatahi were professionally developed with support from 3 Māori influencers and a video production company. The 10 phases of the campaign involved the release of 2 messages (one eating and one wellbeing) every fortnight over 20 weeks. Instagram and TikTok analytics showed that the campaign achieved >1.48 million impressions and >19,000 engagement actions (eg, likes, comments, and sharing). The mean engagement rate (Instagram 6.2%, TikTok 1.2%) was greater than or similar to the platforms’ medians across all industries (0.36% and 1.73%, respectively). Various paid promotion strategies boosted the number of impressions, and paying one of the influencers to promote the messages in phase 8 created a more than 10-fold increase in impressions on Instagram. The estimated cost of the overall campaign was NZ $125,000 (US $72,500). The majority (about 60%) of rangatahi felt the campaign was successful and engaging. Conclusions: Rangatahi have expert knowledge in how to disseminate messages to their peers. They successfully co-designed and pilot-tested the implementation of a low-cost digital media campaign using peer-to-peer messaging and videos, which achieved substantial reach. The dissemination reach was good, but was significantly influenced by paid promotions. The cost per thousand impressions was equivalent to or better than much larger government-funded health promotion social media campaigns targeting youth. The development and dissemination of eating and well-being messages aimed at youth should ideally involve partnering with the target audience to enhance the relevance and reach of the messages. A well-funded dissemination campaign with paid promotions through influencers and digital platforms could expect to achieve substantial awareness of the messages.
You’re reading the web edition of D.C. Diagnosis, STAT’s twice-weekly newsletter about the politics and policy of health and medicine. Sign up here to receive it in your inbox on Tuesdays and Thursdays.
In which RFK Jr. says the “government lies to us” while sitting in front of an HHS seal. Send news tips and podcast recommendations to John.Wilkerson@statnews.com or John_Wilkerson.07 on Signal.
First, control health care costs
Last month, a group of 12 Senate Democrats proposed a framework for rebuilding the health care system. The idea was to spur input from others ahead of when Democrats might get a chance to act on those plans.
On Thursday, the federal government closes the letter-of-intent window for MAHA ELEVATE, a $100 million initiative to fund “functional or lifestyle medicine” interventions for Medicare beneficiaries. The stated goal is to test evidence-based approaches to chronic disease prevention alongside conventional care. The unstated risk is that it will open a federal funding pipeline to interventions that sound integrative but can’t survive contact with a plausibility filter.
I’m a palliative care physician. I spend my days managing pain, breathlessness, nausea, and the existential weight of serious illness. My field should be cheering this investment in whole-person care. Instead, I’m watching it with one eye on the evidence and the other on a pattern I’ve seen up close: When uncertainty is high and emotions are higher, sectarian certainty moves in fast.
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.
Desalination plants in the Middle East are increasingly vulnerable
As the conflict in Iran has escalated, a crucial resource is under fire: the desalinization technology that supplies water in the region.
President Donald Trump has threatened to destroy “possibly all desalinization plants” in Iran if the Strait of Hormuz is not reopened. The impact on farming, industry, and—crucially—drinking in the Middle East could be severe. Find out why.
—Casey Crownhart
This story is part of MIT Technology Review Explains, our series untangling the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.
AI is changing how small online sellers decide what to make
For small entrepreneurs, deciding what to sell and where to make it has traditionally been a slow, labor-intensive process. Now that work is increasingly being done by AI.
Tools like Alibaba’s Accio compress weeks of product research and supplier hunting into a single chat. Business owners and e-commerce experts say they’re making sourcing more accessible—and slashing the time from product idea to launch.
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
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 Anthropic’s new model found security problems in every OS and browser Claude Mythos has been heralded as a cybersecurity “reckoning.” (The Verge) + Anthrophic is limiting the rollout over hacking fears. (CNBC) + It’s also launching a project that lets Mythos flag vulnerabilities. (Gizmodo) + Apple, Google, and Microsoft have joined the initiative. (ZDNET)
2 Iranian hackers are targeting American critical infrastructure Their focus is on energy and water infrastructure.(Wired) + They’re targeting industrial control devices. (TechCrunch)
3 Google’s AI Overviews deliver millions of incorrect answers per hour Despite a 90% accuracy rate. (NYT $) + AI means the end of internet search as we’ve known it. (MIT Technology Review)
4 Elon Musk is trying to oust OpenAI CEO Sam Altman in a lawsuit As remedies for Altman allegedly defrauding him. (CNBC) + Musk wants any damages given to OpenAI’s nonprofit arm. (WSJ $)
5 ICE has admitted it’s using powerful spyware The tools that can intercept encrypted messages. (NPR) + Immigration agencies are also weaponizing AI videos. (MIT Technology Review)
6 Greece has joined the countries banning kids from social media Under-15s will be blocked from 2027. (Reuters) + Australia introduced the world’s first social media ban for children. (Guardian) + Indonesia recently rolled out the first one in Southeast Asia. (DW) + Experts say they’re a lazy fix. (CNBC)
7 Intel will help Elon Musk build his Terafab in Texas They aim to manufacture chips for AI projects. (Engadget) + Musk says it will be the largest-ever semiconductor factory. (Engadget) + Future AI chips could be built on glass. (MIT Technology Review)
8 TikTok is building a second billion-euro data center in Finland It’s moving data storage for European users. (Reuters) + Finland has become a magnet for data centers. (Bloomberg $) + But nobody wants one in their backyard. (MIT Technology Review)
9 Plans for Canada’s first “virtual gated community” have sparked a row The AI-powered surveillance system has divided neighbors. (Guardian) + Is the Pentagon allowed to surveil Americans with AI? (MIT Technology Review)
10 The high-tech engineering of the “space toilet” has been revealed Artemis II is the first mission to carry one around the world. (Vox)
Quote of the day
“This case has always been about Elon generating more power and more money for what he wants. His lawsuit remains nothing more than a harassment campaign that’s driven by ego, jealousy and a desire to slow down a competitor.”
—OpenAI criticizes Musk’s legal action in an X post.
One More Thing
USWDS
Inside the US government’s brilliantly boring websites
You may not notice it, but your experience on every US government website is carefully crafted.
Each site aligns an official web design and a custom typeface. They aim to make government websites not only good-looking but accessible and functional for all.
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ Rejoice in the splendor of the “Earthset” image captured by Artemis II. + Meet the fearless cat chasing off bears. + This document vividly explains what makes the octopus so unique. + Revealed: the rhythmic secret that makes emo music so angsty.
WASHINGTON — An influential progressive think tank is proposing that Democrats assert more federal government control over hospitals and private insurers to make health care more affordable.
Democrats have set out to gather ideas for overhauling the health care system in preparation for potentially regaining control of Congress and the presidency. Democrats see themselves in a “blue sky moment” following Republicans’ big cuts to health care funding, and they want to give themselves plenty of time to devise a fresh approach so they’re ready to strike if and when they get the chance.
For the second time in two years, a bill is moving through the Colorado legislature that would exempt orphan drugs, which are used to treat rare diseases, from pricing caps that might be pursued by the state’s Prescription Drug Affordability Board — a panel whose work is being closely watched elsewhere in the country.
The effort reflects concerns that patients may lose access to these drugs if pharmaceutical companies halt sales of such treatments in the state. But opponents argue exemptions would unnecessarily extend to numerous big-selling medicines for common conditions that — thanks to regulatory endorsements — also happen to have an orphan designation.
As a result, consumer advocates complain the maneuver would only increase the risk that countless patients could have trouble paying for a wide variety of medicines. They further argue that the legislation would preserve profits for drug companies at the expense of the state government — and its taxpayers — as it tries to cope with budgetary strains.
