May 14 2026
AI chatbots are giving out people’s real phone numbers

People report that their personal contact info was surfaced by Google AI—and there’s apparently no easy way to prevent it. 

A Redditor recently wrote that he was “desperate for help”: for about a month, he said, his phone had been inundated by calls from “strangers” who were “looking for a lawyer, a product designer, a locksmith.” Callers were apparently misdirected by Google’s generative AI. 

In March, a software developer in Israel was contacted on WhatsApp after Google’s chatbot Gemini provided incorrect customer service instructions that included his number. 

And in April, a PhD candidate at the University of Washington was messing around on Gemini and got it to cough up her colleague’s personal cell phone number. 

AI researchers and online privacy experts have long warned of the myriad dangers generative AI poses for personal privacy. These cases give us yet another scenario to worry about: generative AI exposing people’s real phone numbers. (The Redditor did not respond to multiple requests for comment and we could not independently verify his story.)

Experts say that these privacy lapses are most likely due to personally identifiable information (PII) being used in training data, though it’s hard to understand the exact mechanism causing real phone numbers to show up in the AI-generated responses. But no matter the reason, the result is not fun for people on the receiving end—and, even more worryingly, there appears to be little that anyone can do to stop it. 

A 400% increase in AI-related privacy requests

It’s impossible to know how often people’s phone numbers are exposed by AI chatbots, but experts say they believe that it is happening far more than is reported publicly. 

DeleteMe, a company that helps customers remove their personal information from the internet, says customer queries about generative AI have increased by 400%—up to a few thousand—in the last seven months. These queries “specifically reference ChatGPT, Claude, Gemini … or other generative AI tools,” says Rob Shavell, the company’s cofounder and CEO. Specifically, 55% of these concerns about generative AI reference ChatGPT, 20% reference Gemini, 15% Claude, and 10% other AI tools, Shavell says. (MIT Technology Review has a business subscription to DeleteMe.)

Shavell says customer complaints about personal information being surfaced by LLMs usually take two forms: Either “a customer asks a chatbot something innocuous about themselves and gets back accurate home addresses, phone numbers, family members’ names, or employer details.” Alternatively, a customer may be confronted with and report the exposure of someone else’s personal data, when “the chatbot generates plausible-but-wrong contact information.” 

This aligns with what happened to Daniel Abraham, a 28-year-old software engineer in Israel. In mid-March, he says, a stranger sent him a “weird WhatsApp message from an unknown number” asking for help with his account in PayBox, an Israeli payment app. 

“I thought it was a spam message,” he wrote to MIT Technology Review in an email—“someone who was trying to troll me.”

But when he asked the stranger how they had found his number, they sent him a screenshot of Gemini’s instructions to contact PayBox customer service via WhatsApp—giving his personal number. Abraham does not work for PayBox, and PayBox does not have a WhatsApp customer service number, Elad Gabay, a customer service representative for the company, confirmed.

Later, Abraham asked Gemini how to contact PayBox, and it generated another person’s WhatsApp number. When I recently asked, Gemini again responded with an Israeli phone number—it belonged not to PayBox, but to a separate credit card company that works with PayBox.

Screenshot of the second part of a Google Gemini conversation. Gemini provides an incorrect phone number for PayBox.
Screenshot: Google Gemini provides MIT Technology Review with the incorrect number for PayBox.

Abraham’s exchange with the stranger ended quickly, but he said he was concerned about how other potential exchanges could quickly turn sour, including “harassment or other bad interactions.” “What if I asked for money in order to ‘solve’ that [customer service] issue?” he said.

To try to figure out how this happened, Abraham ran a regular Google search on his phone number, and he found that it had been shared online once, back in 2015, on a local site similar to Quora. Though he’s not sure who posted it there, it may explain how it ended up being reproduced by Gemini over a decade later. 

Chatbots like Gemini, Open AI’s ChatGPT, and Anthropic’s Claude are built on LLMs that are trained on huge amounts of data scraped from across the web. This inevitably includes hundreds of millions of instances of PII. As we reported last summer, for example, the large popular open-source data set DataComp CommonPool, which has been used to train image-generation models, included copies of résumés, driver’s licenses, and credit cards. 

The likelihood of PII appearing in AI training data is only increasing as public data “runs out” and AI companies look for new sources of high-quality training data. This includes information from data brokers and people-search websites. According to the California data broker registry, for instance, 31 of 578 registered data brokers operating in the state self-reported that they had “shared or sold consumers’ data to a developer of a GenAI system or model in the past year.” 

Furthermore, models are known to memorize and reproduce data verbatim from training data sets—and recent research suggests that it is not just frequently appearing data that is most likely to be memorized.

Imperfect Measures

It’s standard practice now to build guardrails into an LLM’s design to constrain certain outputs, ranging from content filters meant to identify and prevent chatbots from releasing PII to Anthropic’s instructions to Claude to choose responses that contain “the least personal, private, or confidential information belonging to others.” 

But as a pair of University of Washington PhD students researching privacy and technology saw firsthand recently, these safeguards don’t always work.

“One day, I was just playing around on Gemini, and I searched for Yael Eiger, my friend and collaborator,” Meira Gilbert says. She typed in “Yael Eiger contact info,” and after Gemini provided an overview of Eiger’s research, which Gilbert had expected, Gemini also returned her friend’s personal phone number. “It was shocking,” Gilbert says.

When she saw the Gemini result, Eiger remembered that she had, in fact, shared her phone number online in the previous year, for a technology workshop. But she had not expected it to be so visible to everyone on the internet. 

Have you had your PII revealed by generative AI? Reach the reporter on Signal at eileenguo.15 or tips@technologyreview.com.

“Having your information be … accessible to one audience, and then Gemini making it accessible to anyone” feels completely different, Eiger says—especially when she found that the information was buried in a normal Google search.

“It was severely downgraded,” Gilbert confirms. “I never would have found it if I was just looking through Google results.” (I tried the same prompt in Gemini earlier this month, and after an initial denial, the tool also gave me Eiger’s number.)

After this experience, Eiger, Gilbert, and another UW PhD student, Anna-Maria Gueorguieva, decided to test ChatGPT to see what it would surface about a professor. 

At first, OpenAI’s guardrails kicked in, and ChatGPT responded that the information was unavailable. But in the same response, the chatbot suggested, “if you want to go deeper, I can still try a more ‘investigative-style’ approach.” Their inquiry just had to help “narrow things down,” ChatGPT said, by providing “a neighborhood guess” for where the professor might live, or “a possible co-owner name” for the professor’s home. ChatGPT continued: “That’s usually the only way to surface newer or intentionally less-visible property records.” 

The students provided this information, leading ChatGPT to produce the professor’s home address, home purchase price, and spouse’s name from city property records. 

(Taya Christianson, an OpenAI representative, said she was not able to comment on what happened in this case without seeing screenshots or knowing which model the students had tested, though we pointed out that many users may not know which model they were using in the ChatGPT interface. In response to questions about the exposure of PII, she sent links to documents describing how OpenAI handles privacy, including filtering out PII, and other tools.) 

This reveals one of the fundamental problems with chatbots, says DeleteMe’s Shavell. AI companies “can build in guardrails, but [their chatbots] are also designed to be effective and to answer customer questions.”

The exposure issue is not limited to Gemini or ChatGPT. Last year, Futurism found that if you prompted xAI’s chatbot Grok with “[name] address,” in almost all cases, it provided not only residential addresses but also often the person’s phone numbers, work addresses, and addresses for people with similar-sounding names. (xAI did not respond to a request for comment.) 

No clear answers

There aren’t straightforward solutions to this problem—there’s no easy way to either verify whether someone’s personal information is in a given model’s training set or to compel the models to remove PII. 

Ideally, individual consumers should be able to request that their PII be removed, says Jennifer King, the privacy and data fellow at Stanford University Institute for Human-Centered Artificial Intelligence. But this is typically interpreted to apply only to the data that people have directly given to companies—like when they interact with a chatbot, King explains.

“I don’t know if Google even has the infrastructure … to say to me, ‘Yes, we have your data in our training data, we can summarize what we know about you, and then we can delete or correct things that are wrong or things that you don’t want in there,’” she says. 

Existing privacy legislation, like the California Consumer Privacy Act or Europe’s GDPR, does not cover the “publicly available” information that has already been scraped and used to train LLMs, especially since much of this is anonymized (though multiple studies have also shown how easy it is to infer identities and PII from anonymized and pseudonymous data). 

As to “whether they [AI companies] have ever systematically tried to go back through data that had already been collected from the public internet and minimized that stuff?” King adds. “No idea.” 

The next best solution would be that the companies are “taking out everybody’s phone numbers or all data that resembles [phone numbers],” King says, but “nobody’s been willing to say” they’re doing that. 

Hugging Face, a platform that hosts open-source data sets and AI models, has a tool that allows people to search how often a piece of data—like their phone number—has appeared in open-source LLM training data sets, but this does not necessarily represent what has been used to train closed LLMs that power popular chatbots like Claude, ChatGPT, and Gemini. (Eiger’s number, for example, did not show up in Hugging Face’s tool.) 

Alex Joseph, the head of communications for Gemini apps and Google Labs, did not respond to specific questions, but he said that “the team” is “looking into” the particular cases flagged by MIT Technology Review. He also provided a link to a support document that describes how users can “object to the processing of your personal data” or “ask for inaccurate personal data in Gemini Apps’ responses to be corrected.” The page notes that the company’s response will depend on the privacy laws of your jurisdiction. 

OpenAI has a privacy portal that allows people to submit requests to remove their personal information from ChatGPT responses, but notes that it balances privacy requests with the public interest and “may decline a request if we have a lawful reason for doing so.” 

Anthropic describes how it uses personal data in model training, but it does not have a clear way for people to request its removal. The company did not respond to a request for comment.

The best option for anyone who wants to protect their private data right now is to “start upstream: get personal data off the public web before it ends up in the next scrape,” says Shavell. Since the start of the year, for instance, California has offered its residents a web portal to request that data brokers delete their information. Still, this doesn’t guarantee that your data hasn’t already been used for training—and will therefore not appear in a chatbot’s response. 

The Redditor who received incessant calls posted that he had “submitted an official Legal Removal/Privacy Request to Google, asking them to urgently blacklist my number from their LLM outputs,” but had not yet received a response. He also wrote last month that “the harassment continues daily.” 

Abraham, the Israeli software developer, says he contacted Google’s customer service on March 17, the day after his phone number was exposed. He says he did not receive a response until May 4, and it simply asked for documentation that he had already provided. 

Meanwhile, inspired by her own exposure on Gemini, Eiger, along with Gilbert and Gueorguieva, is designing a research project to further study what personal information is being surfaced by various AI chatbots—and what they may know, even if they’re not telling us. 

Some of that information may “technically be public,” says Gilbert, but chatbots may be altering “the amount of effort you would put into finding” it. Now instead of searching through 10 pages of Google search results, or paying for the information from a data broker site, “does generative AI just lower the barrier to entry to target people?” 

This piece has been updated to clarify OpenAI’s response.

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May 12 2026
Three things in AI to watch, according to a Nobel-winning economist

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

A few months before he was awarded the Nobel Prize in economics in 2024, Daron Acemoglu published a paper that earned him few fans in Silicon Valley. Contrary to what Big Tech CEOs had been promising—an overhaul of all white-collar work—Acemoglu estimated that AI would give only a small boost to US productivity and would not obviate the need for human work. It’s okay at automating certain tasks, he wrote, but some jobs will be perfectly fine.

Two years later, Acemoglu’s measured take has not caught on. Chatter about an AI jobs apocalypse pops up everywhere from Senator Bernie Sanders’s rallies to conversations I overhear in line at the grocery store. Some previously skeptical economists have gotten more open to the idea that something seismic could be coming with AI. A California gubernatorial candidate said last week that he wants to tax corporate AI use and pay victims of “AI-driven layoffs.” 

On the one hand, the data is still on Acemoglu’s side; studies repeatedly find that AI is not affecting employment rates or layoffs. But the technology has advanced quite a bit since his cautious predictions. I spoke with him to understand if any of the latest developments in AI have changed his thesis, and to find out what does worry him these days if not imminent AGI.

AI agents

One of the biggest technical leaps in AI since Acemoglu’s paper has been agentic AI, or tools that can go beyond chatbots and operate on their own to complete the goal you give them. Because they can work independently rather than just answering questions, companies are increasingly pitching agents as a one-to-many replacement for human workers.

“I think that’s just a losing proposition,” Acemoglu says. He thinks agents are better thought of as tools to augment particular pieces of someone’s work than something malleable enough to handle a person’s whole job.

One reason has to do with all the various tasks that go into a job, something Acemoglu has been researching in his work on AI since 2018. For example, an x-ray technician juggles 30 different tasks, from taking down patient histories to organizing archives of mammogram images. A worker can naturally switch between formats, databases, and working styles to do this, Acemoglu says, but how many individual tools or protocols would an AI require to do the same?

Whether or not agents will supercharge AI’s impact on jobs will come down to whether they can eventually handle the orchestration between tasks that humans do naturally. AI companies are in heated competition to prove that their AI agents can work independently for ever longer periods without making mistakes, sometimes exaggerating the results—but Acemoglu says many jobs will be spared from an AI takeover if agents can’t fluidly switch between tasks.

The new hiring spree

For years Big Tech has been offering staggering salaries to recruit AI researchers. But I asked Acemoglu about a different hiring spree I’ve noticed: AI companies are all building in-house economics teams.

OpenAI hired Ronnie Chatterji from Duke University in 2024 to be its chief economist and announced last year that Chatterji will work with Jason Furman—Harvard economist and former advisor to Barack Obama—to research AI and jobs. Anthropic has convened a group of 10 leading economists to do similar work. And just last week, Google DeepMind announced it had hired Alex Imas, an economist from the University of Chicago, to be its “director of AGI economics.”

Acemoglu has noticed colleagues getting snatched up for these roles too. “It makes sense,” he says: AI companies are well aware that public skepticism about AI, in large part due to job concerns, is growing. And they have strong incentives to shape the economic narrative around their technology (consider OpenAI’s latest proposal for a new era of industrial policy).

“What I hope we won’t get,” Acemoglu says, “is that they’re interested in economists just to further their viewpoints or further the hype.” That tension hangs over the emerging field of “AI economics”; it’s concerning that some of the most influential research about AI’s impact on work may increasingly come from the companies with the most to gain from favorable conclusions.

AI apps

I don’t think of AI as hard to use; most of us interact with it via chatbots that use plain language. But Acemoglu says we should consider how it compares with the sort of software that kicked off earlier tech transformations, like PowerPoint for slide decks and Word for documents. 

“Anybody could install these on their computer and get them to do the things that they want them to do,” he says. They spread accordingly. 

“We have not seen the development of apps based on AI that have the same usability,” he says. Even if anyone can chat with an AI model, it tends to take a while for the average worker to get practical and productive use out of it. That’s part of the reason why AI has not yet shown any seismic impact on the job market or the economy. One of the key signals Acemoglu is watching, then, is the creation of apps that make AI easier to use. 

But he acknowledges that for a while, we’re going to see all sorts of conflicting evidence about AI: anecdotes that college grads are finding the job market worse and worse, but no noticeable effect of AI on productivity, for example. “There’s a huge amount of uncertainty,” he says. And that’s the most telling thing about the AI economy right now: the certainty of the rhetoric alongside the uncertainty of everything else.

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May 9 2026
Here’s how technology transformed babymaking

Technology is changing the way we make babies. The pioneering work of the scientists who invented IVF led to the birth of the first “test tube baby” in 1978. We’ve come a long, long way since then.

This week, I’ve been working on a piece about the cutting edge of IVF technologies and what’s coming next. Think AI and robots and, potentially, gene-edited embryos.

My reporting has also made me think about just how much progress has been made in the last five decades. Clinicians have improved hormonal treatments. Embryologists have devised ways to culture embryos in the lab for longer. IVF clinics today offer multiple genetic tests for embryos.

In recent years, we’ve had reports of babies born with DNA from three people, babies born following “IVF on wheels,” babies born from decades-old embryos, and even babies “conceived” with the aid of a sperm-injecting robot.

The technology has also had a huge social impact. It has allowed for changes in the structure of families and provided more reproductive choices for would-be parents. So this week, let’s consider the technologies that have transformed babymaking.

Alan Penzias, a reproductive endocrinologist at Boston IVF, has been working in IVF since the early 1990s. In those days, his lab at Yale would collect a person’s eggs, fertilize them, and culture any resulting embryos for two days, until the embryos had two or four cells.

The embryos couldn’t survive any longer outside a body, so they’d be transferred to the uterus at that point. All of them. Even if there were, say, five embryos in total. Typical healthy patients could expect a live birth rate of 12% to 15%, he says.

Then Penzias heard that other teams were managing to culture embryos for three days. “We thought, No, that’s not possible,” he recalls. He learned that scientists had achieved this by tinkering with the culture medium—the nutrient-rich fluid the embryos are grown in.

Those three-day embryos, which had around six to 10 cells, seemed to have a better chance of resulting in a live birth. The teams culturing embryos for longer saw their success rates climb to 25% among similar patient groups, says Penzias. Again, he couldn’t believe it. “We thought they were making it up,” he says.

In the years since, teams have made more improvements to culture medium. Today, most IVF embryos are cultured for five or six days—a point at which they have 80 to 100 cells. The culturing process can act a little like a stress test—the embryos that make it to day six are generally more likely to go all the way and develop into a healthy baby.

Over the same period, advances in other technologies have opened up the options for what we can do with those embryos. Scientists learned they were able to freeze embryos and use them at a later date. A little over a decade ago, clinics shifted to a “vitrification” approach that rapidly cools the embryos to a glassy state. Vitrified embryos are more likely to survive freezing and thawing, so this approach quickly caught on.

As a result, doctors no longer needed to transfer multiple embryos at once. This made it less likely that patients would have twins or triplets, which can increase the risk of pregnancy complications.

Vitrification has also made IVF safer in other ways, including by affording patients a bit of time between fertility treatments. The hormonal treatments used in the first phase of IVF are designed to increase the production of mature eggs that can be collected. These treatments carry a small risk of a condition called ovarian hyperstimulation syndrome (OHSS), which in rare cases can be life-threatening. The ability to freeze all your embryos and use them at a later date is thought to give the body a chance to recover from hormonal treatment and reduces the risk of OHSS.

And because clinics are now able to culture embryos for up to a week, they can take a few of the 100 or so cells and send them for genetic testing before freezing the embryos. People undergoing IVF can get genetic readouts of all the embryos before deciding which to implant. (It is worth noting, however, that these testing technologies are not perfect.)

“Those are really radical changes, and we take them for granted,” says Penzias.

These technologies have also changed the function of IVF. What was once a treatment for infertility is now used to preserve fertility. People who want to delay parenthood can opt to freeze their eggs or embryos and use them later. They might opt to transfer one embryo in a year’s time and a second several years later. “We’ve been able to empower women to be able to have much more reproductive choice and get more reproductive mileage from a single IVF cycle,” says Penzias.

People who are about to undergo cancer treatments that might damage the testes or ovaries can opt to store their eggs or sperm ahead of time, too. Scientists have even been able to preserve pieces of ovarian and testicular tissue and reimplant them later, enabling recipients to have healthy babies.

Today, more people than ever have access to safe IVF options that offer multiple paths to parenthood. Those options look set to expand. But if you want to find out more about the AI and IVF robots, you’ll have to read this week’s story, here!

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.

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May 9 2026
Here’s what you need to know about the cruise ship hantavirus outbreak

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

Eight passengers aboard a Dutch-flagged cruise ship have contracted a type of hantavirus, a rare virus transmitted by rats. Three of them have died. As the ship prepares to dock in the Canary Islands, plans are being finalized to let the remaining passengers and crew disembark safely.

The virus in question appears to have a high fatality rate. Read on for answers to the big questions surrounding the outbreak—and to hear why health experts don’t expect a rerun of the covid-19 pandemic.

What is hantavirus?

Hantaviruses are a group of viruses that typically infect rodents but can be transmitted to humans through exposure to the animals or their droppings, urine, or saliva. The viruses don’t seem to cause illness in rodents, but they can make people very unwell. The symptoms can depend on the type of hantavirus a person has been exposed to. Varieties found in the Americas can cause hantavirus cardiopulmonary syndrome, which affects the lungs and heart and has a fatality rate of up to 50%.

That condition made headlines last year when it caused the death of pianist Betsy Arakawa, the wife of actor Gene Hackman. 

How many cases have there been so far?

On April 6, a man aboard the MV Hondius developed respiratory symptoms. He became very unwell and died just five days later. His wife, who left the ship at the island of Saint Helena, also developed symptoms. Her health deteriorated during a flight to Johannesburg, South Africa, and she died the following day, on April 26. South Africa’s National Institute of Communicable Diseases tested samples taken from the woman and confirmed that she had hantavirus.

A third person aboard the ship, who developed symptoms on April 28, died on May 2. Four other passengers who became ill were evacuated—one to South Africa and three to the Netherlands.

An eighth person had disembarked in Saint Helena and reported similar symptoms once he was in Zurich, Switzerland. A team at Geneva University Hospitals confirmed that he had become ill from the Andes virus—a form of hantavirus that can be spread between people.

Could this be the start of the next pandemic?

Health experts don’t believe so. They stress that the situation is nothing like the one the coronavirus that causes covid-19 presented in 2020. For a start, the Andes virus is not a mysterious new virus—scientists already have an understanding of it, and Argentina is sharing diagnostic kits it has already developed.

The virus also doesn’t spread in the same way. Officials at the World Health Organization emphasized that the spread of hantavirus requires close contact—the kind a person might have with a partner, household member, or medical caregiver.

The cruise ship outbreak represents “a specific confined setting where people are interacting in a prolonged close contact,” Abdirahman Mahamud, the alert and response director for the WHO’s health emergency program, said at a press event on Thursday. “With the experience our member states have, and the actions they have taken, we believe that this will not lead to a subsequent chain of transmission.”

What about the rest of the people onboard the ship?

All the remaining passengers have been asked to stay in their cabins, which the WHO says are being disinfected. Doctors and health professionals from the WHO and the European Center for Disease Prevention and Control have boarded the ship and are assessing everyone on board.

So far, no one else on board has developed symptoms, Maria Van Kerkhove, WHO acting director for epidemic and pandemic management, said at the press event. That’s “a good sign,” she said, but she added that the Andes virus has a long incubation period (around six weeks). Passengers are being advised to wear a medical mask when they leave their rooms.

At the same event, WHO director general Tedros Adhanom Ghebreyesus said he was in regular contact with the ship’s captain, who was reporting that “morale had increased significantly” since the ship started its journey to the Canary Islands.

What do we know about the Andes virus?

The Andes virus is the only hantavirus that is known to be transmitted between people. That transmission seems to rely on prolonged, intimate contact.

There was an Andes virus outbreak in Argentina around eight years ago. Between November 2018 and February 2019, there were 34 confirmed cases of infection, and 11 deaths. That outbreak was triggered when a person with symptoms attended a social gathering, said Tedros. “We are in a similar situation right now,” he said. “A cluster in a confined space with close contact.”

The fact that the 2018 outbreak was limited to 34 cases should be somewhat reassuring, he implied. “We believe this will be a limited outbreak if the public health measures are implemented and solidarity is shown across all countries,” he said.

How is hantavirus treated?

Unfortunately, we don’t have any specific antiviral treatments or vaccines for hantavirus. The WHO recommends early intensive care for people who develop symptoms. “This can save lives,” Anaïs Legand, WHO technical lead on viral hemorrhagic fevers, said on Thursday.

How did people get infected in the first place?

We don’t yet have an answer to that. But we do know that the couple who died had traveled through Argentina, Chile, and Uruguay on a birdwatching trip before they boarded the ship. That trip included visits to areas where species of rats that carry the Andes virus are known to live. The WHO is working with authorities in Argentina to try to retrace the couple’s movements on that trip.

Has the virus spread beyond the ship?

We don’t yet know for sure. The WHO is receiving reports of “potential suspect cases,” Van Kerkhove said at the Thursday briefing. Some of them have links to the ship or its passengers. Each “alert” will be followed up by health authorities in the relevant country, she said.

Has the US withdrawal from WHO affected anything?

Five US states have said they are monitoring US nationals who have disembarked from the ship. WHO officials are stressing that they are still sharing technical information with the US Centers for Disease Control and Prevention. “Things are … as they used to be,” Tedros said. “WHO’s mission is to help the world to be safe … and we want the American people to be safe as well.”

But it’s worth noting that cuts made by the Trump administration aren’t exactly putting the US in a good position for events like these. Last year, all full-time employees in the CDC’s Vessel Sanitation Program—which helps prevent and control illness outbreaks on cruise ships—were laid off. Further cuts to the CDC have left public health experts worried about how ill prepared the US is to deal with future disease outbreaks.

What will happen next?

Any suspected cases will be monitored by health authorities. Passengers are due to disembark in Tenerife in the Canary Islands on Sunday, May 10, and the WHO has said it will work with the Spanish government to ensure that the risk to residents remains low and that the passengers are treated with dignity and respect.

In the meantime, scientists are working to fully sequence the genome of the virus from patient samples. They want to find out if it is different from the viruses involved in the previous cases. “So far, we haven’t seen anything unusual,” said Van Kerkhove.

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May 9 2026
Musk v. Altman week 2: OpenAI fires back, and Shivon Zilis reveals that Musk tried to poach Sam Altman

In the second week of the landmark trial between Elon Musk and OpenAI, Musk’s motivations for bringing the suit were under scrutiny.

Last week, Musk took the stand, alleging that OpenAI CEO Sam Altman and president Greg Brockman had deceived him into donating $38 million to the company. He claimed that they’d promised to maintain it as a nonprofit dedicated to developing AI for the benefit of humanity, only to later accept billions of dollars of investment from Microsoft and restructure the company to operate a for-profit subsidiary.  

This week, Brockman fired back with his side of the story, arguing that Musk had actually pushed for OpenAI to create a for-profit arm and fought a bitter battle to have “absolute control” over it. OpenAI has argued that Musk is suing because he didn’t get his way and is now trying to undermine a competitor to his own AI company, xAI.

Shivon Zilis, a former OpenAI board member and the mother of four of Musk’s children, also testified, revealing that Musk tried to recruit OpenAI CEO Sam Altman to lead a new AI lab at his electric-car company, Tesla. 

Musk cofounded OpenAI in 2015 with Altman, Brockman, and others but left in 2018. Now, he’s asking the court to remove Altman and Brockman from their roles and to unwind the restructuring OpenAI undertook last year, which converted its for-profit subsidiary into a public benefit corporation. He is also seeking as much as $134 billion in damages from OpenAI and Microsoft, OpenAI’s investor. 

The outcome of the trial could upend OpenAI’s race toward an IPO at a valuation approaching $1 trillion. Meanwhile, xAI, which Musk founded in 2023, is now a division of his rocket company, SpaceX; the combined companies are also expected to go public as early as June, at a target valuation of $1.75 trillion.

On Monday, Brockman walked into the courtroom in a blue suit and tie, holding hands with his wife, Anna Brockman. On the stand, he was serene, even chipper, as he recalled OpenAI’s early days. But he grew agitated under impassioned questioning from Elon Musk’s lawyer, Steven Molo. Altman listened in silence, while Anna Brockman sat behind him, fidgeting. Outside the courthouse, protesters rallying against the AI race sang hymns over the voices of lawyers giving press conferences.

Two days before trial began, according to Brockman, Musk messaged him to ask if he would be interested in settling. When Brockman suggested that both sides drop their claims, Musk texted back: “By the end of this week, you and Sam will be the most hated men in America. If you insist, so it will be.”

Musk stormed out with a Tesla painting

Last week, Musk testified that he’s suing to save OpenAI’s nonprofit mission to develop AI safely, but he said he was open to seeing OpenAI become a capped-profit company with moderate investments from Microsoft. 

This week, Brockman told the jury that Musk was never truly committed to keeping OpenAI a nonprofit. In the summer of 2017, when an AI model that OpenAI built beat the world’s best players in a video game called Dota 2, Musk hosted a gathering at his “Haunted Mansion” near San Francisco. The house was splattered with confetti and cups, Brockman recalled, and the actress Amber Heard, who was Musk’s girlfriend at the time, served whiskey.

“Time to make the next step for OpenAI. This is the triggering event,” Musk wrote in an email—having said weeks earlier that if OpenAI made a major public achievement, it would be “time to create a for-profit,” Brockman told the jury.

Over the next six weeks, Brockman said, Musk and the other cofounders had intense discussions about creating a for-profit entity to raise enough capital to build artificial general intelligence—powerful AI that can compete with humans on most cognitive tasks. Musk wanted to have majority equity in the entity and the right to choose a majority of the board members. He also wanted to be its CEO, said Brockman. 

Brockman testified that in August 2017, he and other cofounders gathered to hash out the terms of the for-profit structure. Ilya Sutskever, OpenAI’s chief scientist at the time, arrived bearing a painting of a Tesla as a “token of goodwill” in return for the actual Teslas Musk had given them days earlier. “It felt a little bit like [Musk] was buttering us up, right,that he wanted us to feel indebted to him,” Brockman told the jury.

When Brockman and Sutskever proposed that they all have equal shares of equity, said Brockman, Musk fell silent and finally said, “I decline.” Musk then stood up and “stormed around the table,” he said. “I actually thought he was going to hit me.” Musk grabbed the painting and walked out. 

Brockman said that afterwards he struggled to decide whether to continue building OpenAI with Musk or break away. “There was a fork in the road,” he said. “Do we accept Elon’s terms? Or do we reject the terms, he quits to create his own, and then we create our own?”

“The one thing we could not accept was to hand him unilateral, absolute control, potentially, over the AGI,” Brockman told the jury.

What was Brockman thinking?

In his theatrical baritone, Molo argued that Brockman was motivated by greed rather than a commitment to OpenAI’s nonprofit mission to develop AI that benefits humanity. He noted that while Brockman never invested money in the company, he now owns a stake worth close to $30 billion. 

“Solving for the mission has always been my primary motivation,” Brockman said, pushing back on Molo’s characterization of him. “It remains so today.” 

Molo pulled up Brockman’s electronic journal on a screen in the courtroom, trying to show the jury what Brockman was really thinking behind the scenes. In 2017, while negotiating with Musk about the future of OpenAI, Brockman wrote about wanting to become a billionaire: “Financially what will take me to $1B?” 

“Why didn’t you take the $29 billion and donate it to the nonprofit that you had a fiduciary duty to, for the good of humanity?” Molo asked Brockman, raising his voice to dramatize moral indignation. 

Molo then pulled up a journal entry Brockman had written in November 2017, while he was torn over whether to turn OpenAI into a for-profit without Musk: “it’d be wrong to steal the nonprofit from him. to convert to a b-corp without him. that’d be pretty morally bankrupt.” Brockman and Musk had previously considered creating a b-corp, which is a for-profit company that pursues a social mission.

Brockman explained, “I meant it would actually serve the mission, but it’d be hard to look at yourself in the mirror.”

Molo also tried to undermine Brockman’s credibility by revealing that he holds a stake in multiple companies with business ties to OpenAI, including the AI company Cerebras, the cloud provider CoreWeave, and the nuclear fusion startup Helion Energy. Altman has tried to steer OpenAI into deals with companies that he invests in, including Helion and the rocket maker Stoke Space, drawing scrutiny over potential conflicts of interest.

Former OpenAI chief technology officer Mira Murati and former OpenAI board member Helen Toner both appeared in video depositions. They addressed the brief firing of Altman in 2023, saying that they could not trust him because of his alleged history of lying. Murati’s text messages with Altman from that time, which were introduced as evidence, revealed his desperate attempts to understand what was happening and regain control. 

Musk plotted a rival AI lab at Tesla

After Brockman’s two days of testimony, Shivon Zilis, who left OpenAI’s board in 2023, took the stand in a black jacket and black jeans, appearing composed but with a flicker of nerves. OpenAI’s lawyer Sarah Eddy asked her in a deceptively soothing voice whether she acted as a conduit for Musk as he tried to poach OpenAI’s cofounders to work at a new AI lab within Tesla. Eddy argued that Musk is suing OpenAI only to undermine a competitor in the AI race. 

Zilis said she met Musk while working at OpenAI as an informal advisor in 2016, and that they had a “one-off” romantic encounter. In 2017, she joined Tesla and Musk’s brain-implant company, Neuralink. In 2020, she joined OpenAI’s board of directors. She became pregnant with Musk’s children through IVF but did not disclose her ties with Musk to OpenAI until Business Insider reported them in 2022. 

By late 2017, Musk had concluded that OpenAI was unlikely to build AGI and pivoted to building an AI lab at Tesla, according to an email sent to Zilis. 

Eddy pulled up a draft of an FAQ document that Zilis emailed a colleague at Tesla in 2017 about an event the company was organizing at the NeurIPS AI conference: “The purpose of this event is to share that Tesla is building a world leading AI lab(?) which will rival the likes of Google/DeepMind and Facebook AI Research.” 

Zilis told the jury that when Musk was still on OpenAI’s board, he tried to recruit Altman to lead that prospective AI lab. Musk had asked Andrej Karpathy, an OpenAI research scientist he’d recruited to work at Tesla, “to send a list of top OpenAI people to poach,” according to a text message by Zilis. 

“There is little chance of OpenAI being a serious force if I focus on TeslaAI,” Musk texted Zilis in 2018, just before he left OpenAI. Tesla’s AI lab never came to fruition.

Eddy pressed Zilis about whom she was loyal to when she was working for OpenAI and Musk at the same time. “I had an allegiance to the best outcome for AI for humanity,” Zilis told the jury.

What’s going on next week?

Next week, Ilya Sutskever will testify, as will Microsoft CEO Satya Nadella. The lawyers for both Musk and OpenAI will deliver their closing arguments. The jury will begin deliberating the week after and deliver an advisory verdict guiding the judge to decide the case.

This story is part of MIT Technology Review’s ongoing coverage of the Musk v. Altman trial. Follow @techreview or @michelletomkim on X for up-to-the-minute reporting.

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May 8 2026
What’s next for IVF

<div data-chronoton-summary="

  • Helping embryos stick: Even healthy-looking embryos only implant 40–60% of the time. Researchers in Spain are trialing a device that physically injects embryos directly into the uterine lining at the press of a button.

  • AI and robots are taking over the lab: Automated systems can now select sperm, fertilize eggs, and culture embryos without human hands. At least 19 children have already been born through fully automated IVF.

  • Genetic testing is getting complicated: Standard embryo screening helps reduce miscarriage, but newer tests claiming to predict IQ or height are gaining ground in the US—and making many fertility doctors deeply uncomfortable.

  • Gene editing is quietly creeping back: Years after He Jiankui went to prison for editing human embryos, startups are revisiting CRISPR as a way to prevent serious inherited disease—raising hopes, and familiar fears about a slippery slope.

” data-chronoton-post-id=”1136946″ data-chronoton-expand-collapse=”1″ data-chronoton-analytics-enabled=”1″>

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

Forty-eight years ago this July, Louise Joy Brown became the world’s first person born with the help of in vitro fertilization. Millions more IVF babies have entered the world since then. And that’s partly thanks to advances in technology that have made IVF safer and more effective.

But it’s still not perfect. The process can be slow, painful, and expensive—and that’s for the lucky people who are able to access it in the first place. And by at least one measure, IVF success rates have been declining in recent years.

Reproduction is complex, and there’s a lot that embryologists and gynecologists still don’t know and can’t control. They don’t know why many healthy-looking embryos don’t “stick” in the uterus, for example. They don’t always have an explanation for why their patients can’t get pregnant. And they can’t always account for vast differences in IVF success rates between individuals and between fertility clinics.

Scientists are working on all those questions and more. They’re wrestling with complex ethical questions about how new genetic tools will be used to analyze or even alter embryos. Meanwhile, technologies designed to standardize treatment, eliminate human error, boost success rates, and make IVF more accessible are already beginning to usher in a new era for assisted reproduction—one aided by AI and robots.

1. Helping embryos stick

Some of those technologies are being developed at the Carlos Simon Foundation in Valencia, Spain. When I visited in March, researchers gave me a tour of the labs and showed me a device that had been used to keep a human uterus alive outside the body for the first time.

While some members of the team dream of building artificial uteruses that might one day be able to carry a fetus to term, they first want to use such devices to learn more about implantation—the moment at which a fertilized egg makes contact with the lining of the uterus, burrows inside, and essentially “hatches,” triggering the start of a pregnancy.

Despite decades of advances in IVF, that process is still poorly understood. Even healthy-looking embryos stick no more than 40% to 60% of the time.

In IVF techniques used today, clinics can create early-stage embryos and wait until the uterus is deemed most receptive, but once they insert the embryo into the uterus, it’s on its own. Xavier Santamaria, senior clinical scientist at the Carlos Simon Foundation, and his colleagues are trialing a different approach. They’ve developed a device that, at the press of a button, injects the embryo into the uterine lining.

Scientists in Valencia showcase Transfer Direct.

JESS HAMZELOU / MITTR

In a demonstration I watched with a prototype, Santamaria picked up his speculum and turned to face the vaginal opening of his “patient,” which in this case was just a model of the real thing—a plastic bottom with labia, a vagina, a uterus, and ovaries, two short stumps representing what would normally be a pair of legs held in stirrups.

He hunched over and peered inside. “Embryo,” he called. His colleague Maria Pardo, an embryologist, passed him a thin needle containing a mouse embryo she had recently collected from a petri dish.

Santamaria’s device allows for the embryo-containing needle to be connected to a delivery tube. This tube also has a camera, a light, and a sensor that lets the doctor know when the needle reaches the uterine lining. Once it has been fed into the uterus, the gynecologist can see the inside of the organ and direct the tube to the lining.

Scientists in Valencia showcase Transfer Direct.

JESS HAMZELOU / MITTR

“When everything is ready, you just press the button,” Santamaria said as he activated it using a foot pedal, allowing the embryo to be injected. “There it goes.”

The team has just started a trial of the device; so far, fewer than 10 women have undergone the procedure, and none of those have become pregnant. But foundation director Carlos Simon is hopeful, noting that the inventors of IVF had to perform over 160 cycles before Louise Brown was born (between 1969 and 1978, that team performed 457 cycles in 250 people, resulting in only two live births). “The trial is ongoing,” he says.

2. Picking the “best” eggs, sperm, and embryos

One long-running challenge of IVF has been selection. Say you manage to collect 10 eggs from one partner and a decent-looking semen sample from the other. How do you choose which cells to use? The same question comes up once the resulting embryos have been cultured in a dish for a few days: Which should you transfer to the uterus?

Traditionally, these judgments have been made by eye. Embryologists literally pick the ones that look the best in terms of their shape or, in the case of sperm, how they move. But scientists have been working on alternatives. And over the last decade or so, many have turned to genetic testing to hint at which embryos have the best chances of creating a healthy baby.

The most commonly used test is called PGT-A, which stands for preimplantation genetic testing for aneuploidy. Aneuploidy essentially means having an “incorrect” number of chromosomes, and it is thought that embryos with such characteristics are more likely to be lost through miscarriage or potentially develop into babies with genetic conditions.

Once embryologists have created embryos in the lab, they can pinch off a few cells and test them for aneuploidies. The tests are especially beneficial for women over the age of 38, says Alan Penzias, a reproductive endocrinologist at Boston IVF. “You start to see an improvement: more babies and fewer miscarriages,” he says. The tests can shorten the time to pregnancy.

This type of genetic testing is possible thanks to multiple advances in technology—not just in genomics, but also in the ability to keep embryos alive in a dish for five to six days and the technique of freezing embryos while the cells undergo testing and thawing them once the results are in. And it has become hugely popular—some clinics do PGT-A tests on all their embryos.

But PGT-A won’t give you a perfect readout of a future baby’s genetics, says Sonia Gayete-Lafuente, a reproductive endocrinologist at the Center for Human Reproduction in New York City. And some of the abnormalities might be able to self-correct with time. Gayete-Lafuente and her colleagues have transferred some of those “abnormal” embryos into patients’ uteruses and seen them develop into perfectly healthy children, she says.

Other forms of PGT are even more controversial. PGT-P tests are designed to predict an embryo’s chances of developing complex traits that rely on multiple genes, including medical disorders but also physical characteristics like height or cognitive factors like IQ. These tests are new, and they are illegal in some countries, including the UK. But they are gaining ground in the US. Nucleus Genomics—a company that invites customers to “have [their] best baby”—promises to predict traits running the gamut from eye color and intelligence to left-handedness and risk of Alzheimer’s.

When I asked IVF practitioners how they might respond if a patient asked for this service, most dodged the question and told me there’s not enough evidence that any of these tests actually work. They also cautioned that selecting for one trait might inadvertently introduce new risks. None seemed especially keen on the idea of using genetic testing for anything other than preventing serious disease.

3. Speeding things up with AI

Some seemed more excited about the potential for AI. After all, AI tools are generally good at recognizing patterns. Many researchers have attempted to train tools to spot healthy sperm, eggs, and embryos.

And they’ve had some success. A team at Columbia University Medical Center in New York has developed a device that uses AI to examine semen samples from men who have only tiny numbers of healthy sperm. An embryologist might struggle to find a single healthy sperm in such a sample. But the Sperm Tracking and Recovery (STAR) system can analyze over a million microscope images in an hour. It has already been used to create healthy embryos. The team behind the work announced the first pregnancy resulting from the treatment in November last year.

Other teams are using AI tools to advance IVF in more dramatic ways. Around a decade ago, a reproductive endocrinologist named Alejandro Chavez-Badiola began developing an AI tool trained to rank embryos, another to rank eggs, and another to select sperm. He recalls being struck by a realization that these tools were “the brains that have the potential to drive robots in the future,” he says.

4. Using robots to standardize IVF

In the early 2020s, Chavez-Badiola and his colleagues decided to combine technologies and develop an automated system for IVF. In theory, a robotic system loaded up with AI tools could undertake most of the steps required in the IVF process: selecting the eggs and sperm, fertilizing eggs to create embryos, culturing those embryos in a dish, and selecting the “best” one for transfer. Such a system could “do everything in a standard way” without ever getting tired, he says.

Chavez-Badiola, who is now founder and chief medical officer at Conceivable, started building prototypes by motorizing regular IVF equipment and connecting it to computers. He and his colleagues started testing their system with animal cells before eventually moving on to human ones. “We were able to prove that integrating robots to automate different steps in IVF is doable,” he says.

The device is now being used to prepare sperm and eggs and create embryos. At least 19 children have been born following the automated IVF. It is early days, but Chavez-Badiola is hoping that future iterations of the machine could each process thousands of IVF cycles in a year, potentially making the procedure more affordable and accessible.

Many in the field are excited about the potential for automated devices like Conceivable’s. “This is all time saved for the embryologists,” says Laura Rienzi, a clinical embryologist and scientific director of the IVIRMA network of fertility centers in Italy. She also hopes it will help standardize IVF treatments. “Automation [will allow for] every patient to be treated in the same way in every single lab in the world,” she says.

5. Controversial edits are on the table

There’s a catch, however: All these technologies rely on the availability of at least some healthy sperm, eggs, and embryos at the outset. Embryologists and IVF patients have to work with what they’ve got. And sometimes, what they’ve got won’t result in a healthy baby. 

That’s why some scientists are proposing a controversial idea: using gene-editing technologies like CRISPR to tinker with the genome of an IVF embryo before it is implanted. The biophysicist He Jiankui infamously took this approach to create embryos that resulted in the births of three children in the late 2010s. He was widely condemned by the scientific community and ultimately spent three years in a Chinese prison. 

His former romantic partner Cathy Tie, who now leads startup Origin Genomics, is pursuing the technology as a potential way to prevent serious disease in children. At a recent event held at the Hastings Center for Bioethics, Tie made the case for using embryo editing to prevent diseases like cystic fibrosis, Huntington’s, and sickle-cell.

It won’t be straightforward from a technical, legal, or ethical perspective. Diseases that are known to be caused by single-gene mutations are good first candidates, but as the Center for Human Reproduction’s Gayete-Lafuente points out, most diseases are much more complicated than that. “I wish we could understand the genetic basis of every disease to be able to prevent it,” she says. So far, we can’t. Besides, most diseases can be influenced by our diets, behaviors, and environments as well as our genes.

As things stand, no one knows if editing a human embryo to eliminate the risk of one disease might increase a future child’s risk of some other disorder. And some scientists worry that such edits might be a slippery slope to genetic enhancement or eugenics.

Rienzi hopes that the technology might be developed in a safe way with regulatory oversight, and only for a specific list of diseases. “It has to be within a legal context,” she says. “But to me, it’s a dream.”

In the meantime, the field looks set to keep transforming with the development of new technologies that are already creating healthy babies. Watch this space. 

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May 8 2026
The balcony solar boom is coming to the US

Dozens of US states are considering legislation to allow people to install plug-in solar systems, often called balcony solar. These small arrays require little to no setup and could help cut emissions and power bills.

Balcony solar is already popular in Europe, and proponents say that the systems could make solar power more accessible for more people in the US, including renters. As popularity rises, though, some experts caution that there are safety concerns with how balcony solar would work with existing electrical equipment in homes.

Let’s talk about what balcony solar is, why it’s unique, and how new testing requirements could affect our progress toward deploying the technology in the US.

Plug-in solar systems are designed to be simple to install, often requiring no electrician or specialized worker at all. They’re small, and many can be plugged into existing outlets.

People across Germany have installed over a million balcony solar systems. They generally measure up to roughly two square meters or about 20 square feet, and can generate up to 800 watts—enough to power a standard microwave.

Now the plug-in solar wave is coming to the US. Many Americans have already installed DIY balcony solar without the permission of their utilities—it’s something of a regulatory gray area. In late 2025, Utah became the first state to explicitly allow people to install and use balcony solar systems. Over two dozen other states are now considering similar legislation.

Generally, utilities require users to sign an interconnection agreement before they can plug in large arrays of solar panels that generate power for the grid. There can be fees and permits, and it all amounts to an expensive and lengthy process.

Utah’s law ditched the interconnection requirement for panels that have a low power cap and that are certified by a national testing facility. (Legislation under consideration in other states, including New York, includes the same requirements.) The thinking is that since the panels produce very little power, which would be used to meet a home’s own energy demand and probably not get sent back to the grid, the same requirements shouldn’t apply. 

As for that certification piece, in January the national testing and certification lab UL Solutions released UL 3700, a testing protocol to certify balcony solar systems and ensure that they’re safe. 

There are three main safety considerations to address for these plug-in solar systems, says Joseph Bablo, manager of principal engineering, energy, and industrial automation at UL Solutions. First, there’s the possibility of overloading a circuit. Generally, electrical circuits have circuit breakers, which can trip and interrupt current if necessary. But if there’s a solar panel adding extra power to a circuit, a traditional breaker might not be able to respond to overload. Over time, overloaded circuits can damage equipment or even start a fire. 

Second, these small systems are typically installed on the outside of homes, and outdoor power outlets generally have ground fault circuit interruption (GFCI). Basically, if an outlet or its surroundings are wet, it can shut down to prevent electric shock. Many GFCI systems may not work if there’s power going back into an outlet from a solar panel.

Finally, there’s touch safety: If a plug gets disconnected from the wall, the blades of the plug may still have power running through them for a short time. If a panel is getting sunlight, those blades could be energized for longer than is typical.

The new UL Solutions testing framework aims to address these concerns. One of the key recommendations is that plug-in solar panels should use a special outlet that’s designed specifically for them. The safety measures included in that connection, and within a panel, would ensure that the panels are safe.

The need for a special outlet means that currently, people who want to plug in a solar panel array would probably need to have an electrician come and update their wiring in order to comply with the protocol, Bablo says. “I know they want to say ‘No electrician, no permits’—we’re not there.”

Today, anyone can buy products like solar panels and inverters, some of which carry their own component UL certifications, and string them together. (Inverters are covered under UL 1741, for example.)

But the gold standard is to have an entire system that meets the safety requirements, and that means adhering to the new standard, Bablo says. As of early May, there aren’t any plug-in solar systems that have been fully certified by UL Solutions. And Bablo said he couldn’t share information about what, if any, are in the pipeline.  

Even with the new certification requirements, Bablo still thinks plug-in solar still has the potential to help more people access the technology. “There’s a way for it to work, but we want it to work safely,” he says.

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

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May 6 2026
A blueprint for using AI to strengthen democracy

Every few centuries, changes in how information moves reshape how societies govern themselves. The printing press spread vernacular literacy, helping give rise to the Reformation and, eventually, representative government. The telegraph made it possible to administer vast nations like the US, accelerating the growth of the modern bureaucratic state. Broadcast media created shared national audiences, which in turn fueled mass democracy.

We are now in the early stages of another such shift. Faster than many realize, AI is becoming the primary interface through which we form beliefs and participate in democratic self-governance. If left unchecked, this shift could further strain America’s already fragile institutions. But it could also help address long-standing problems, like lagging civic engagement and deepening polarization. What happens next depends on design choices that are already being made, whether we know it or not.

Start with what might be called the epistemic layer—how we come to know things. People are increasingly relying on AI to know what is true, what is happening, and whom to trust. Search is already substantially AI-mediated. The next generation of AI assistants will synthesize information, frame it, and present it with authority. For a growing number of people, asking an AI will become the default way to form views on a candidate, a policy, or a public figure. Whoever controls what these models say therefore has increasing influence over what people believe. 

Technology has always shaped the way citizens interact with information. But a new problem will soon arise in the form of personal AI agents, which can change not only how people receive information but how they act on it. These systems will conduct research, draft communications, highlight causes, and lobby on a user’s behalf. They will inform decisions such as how to vote on a ballot measure, which organizations are worth supporting, or how to respond to a government notice. They will, in a meaningful sense, begin to mediate the relationship between individuals and the institutions that govern them.

We’ve already seen with social media what happens when algorithms optimize for engagement over understanding. Platforms do not need to have an explicit political agenda to produce polarization and radicalization. An agent that knows your preferences and your anxieties—one shaped to keep you engaged—poses the same risks. And in this case the risks may be even more difficult to detect, because an agent presents itself as your advocate. It speaks for you, acts on your behalf, and may earn trust precisely through that intimacy.

Now zoom out to the collective. AI agents and humans could soon participate in the same forums, where it may be impossible to tell them apart. Even if every individual AI agent were well-designed and aligned with its user’s interests, the interactions of millions of agents could produce outcomes that no individual wanted or chose. For example, research shows that agents displaying no individual bias can still generate collective biases at scale. And setting aside what agents do to each other, there is what they do for their users. A public sphere in which everyone has a personalized agent attuned to their existing views is not, in aggregate, a public sphere at all. It is a collection of private worlds, each internally coherent but collectively inhospitable to the kind of shared deliberation that democracy requires.

Taken together, these three transformations—in how we know, how we act, and how we engage in collective governance—amount to a fundamental change in the texture of citizenship. In the near future, people will form their political views through AI filters, exercise their civic agency through AI agents, and participate in institutions and public discussions that are themselves shaped by the interactions of millions of such agents.

Today’s democracy is not ready for this. Our institutions were designed for a world in which power was exercised visibly, information traveled slowly enough to be contested, and reality felt more shared, if imperfectly. All of this was already fraying long before generative AI arrived. And yet this need not be a story of decline. Avoiding that outcome requires us to design for something better.

On the informational layer, AI companies must ramp up existing efforts to ensure that models’ outputs are truthful. They should also explore some promising early findings that AI models can help reduce polarization. A recent field evaluation of AI-generated fact checks on X found that people with a variety of political viewpoints deemed AI-written notes more helpful than human-written ones. The paper is yet to be peer-reviewed, but that is a potentially revolutionary finding: AI-assisted fact-checking may be able to achieve the kind of cross-partisan credibility that has eluded most manual human efforts. Greater understanding of and transparency about how models make these assertions and prioritize sources in the process could help build further public trust.

On the agentic layer, we need ways to evaluate whether AI agents faithfully represent their users. An agent must never have an agenda of its own or misrepresent its user’s views—a technically daunting requirement in domains where users may have not explicitly stated any preferences. But faithful representation also cannot become an accessory to motivated reasoning. An agent that refuses to present uncomfortable information, that shields its user from ever questioning prior beliefs or fails to adjust to a change of heart, is not acting in the person’s best interest.

Finally, on the institutional level, policymakers should hurry to harness AI’s potential to make governance more responsive and legitimate. Several states and localities are already using AI-mediated platforms to conduct democratic deliberation at scale, building on research showing that AI mediators can help citizens find common ground. As agents become increasingly common participants in public input processes—and there is already evidence that bots are skewing those processes—identity verification for both humans and their agentic proxies must be built in from the start.

What is needed is a new generation of democratic infrastructure, technological and institutional, built for the world that is actually here. Failing to design for democratic outcomes, in a domain this consequential, means designing for something else. And the history of unaccountable power does not leave much room for optimism about what that something else tends to be.

Andrew Sorota and Josh Hendler lead work on AI and democracy at the Office of Eric Schmidt.

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May 5 2026
Week one of the Musk v. Altman trial: What it was like in the room

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Two of the most powerful people in AI—Sam Altman and Elon Musk—began their face-off in court in Oakland, California, last week. Musk is suing OpenAI, alleging that the millions he spent to fund it around a decade ago were meant for a nonprofit, not a corporation, and that the company has reneged on that mission since. 

The stakes are high—even a partial win for Musk could set OpenAI back as it reportedly plans to go public this year. But most of the attention comes from the spectacle of a feud on X now playing out in federal court. “Cringey texts, raw diary entries, and endless scheming behind the founding and growth of OpenAI are expected to come to light,” my colleague Michelle Kim wrote before it began. And the trial unfolds as the cultural backlash against AI swells; some of the signs held by protesters outside the courthouse suggest that to a significant number of people, whatever the outcome of Musk v. Altman, we all lose.  

Most of us have had to observe the trial from afar, but Michelle, who also happens to be a lawyer, has been in court each day. I caught up with her to learn what’s unfolded thus far and what might come next.

Can you give us the overview of what this case is actually about? What exactly is being decided, and who is favored right now?

Elon Musk is arguing that Sam Altman and OpenAI president Greg Brockman have breached the company’s charitable trust by effectively converting OpenAI into a for-profit company. Musk alleges that is not what they promised him in the company’s early days. He has asked for several remedies, like a crazy amount of damages and removing Sam Altman. But the main remedy he wants is unwinding OpenAI’s restructuring. [In October 2025 OpenAI struck deals with the attorneys general of California and Delaware that would essentially allow its nonprofit portion to have less day-to-day control of OpenAI. It’s a compromise from what OpenAI originally proposed, but Musk still wants to stop it.] 

OpenAI argues that Elon Musk actually agreed to have the company operate a for-profit arm, because he knew building AI is very expensive. So it’s about proving what Musk knew, what he didn’t know, and whether he really was deceived by Altman and Brockman.

There’s a big debate about when exactly Musk found out about this alleged misconduct. Musk founded OpenAI with Altman and Brockman in 2015, and he brought the suit in 2024. There’s a statute of limitations for charitable trust claims; you need to have brought a claim within three to four years after you find out about the alleged misconduct. So Musk tries to paint a picture that back in the day he was a little suspicious, but that it was really only in 2022 that he realized OpenAI was no longer committed to its original charitable mission, and that he had been scammed. It’s only the first week of trial, but I’m not sure Musk has proved this to the judge and jury.

What were some standout moments thus far?

At one point one of Elon Musk’s lawyers said, “We could all die as a result of AI.” I think a lot of the people in the room were really shaken by this comment, and the judge told Musk’s lawyer: You talk about all these safety risks that OpenAI has when building AI, but Musk is also creating a company that’s in the same exact space. She basically said, I’m sure there’s plenty of people who also don’t want to put the future of humanity in Elon Musk’s hands. 

And then the lawyers just kept going on and on about the catastrophic risks of AI and whether Elon Musk or OpenAI was in the better position to steward AI safety. And the judge sort of snapped. She said very sternly that this trial was not about whether or not artificial intelligence has damaged humanity. And I thought that was a really striking standout moment of the trial that pointed at how even though it is technically just about whether Elon Musk was really deceived by OpenAI, it’s also become a huge discussion about AI safety and some of the practices that the labs are engaging in when building AI. 

Can you give us a look behind the curtain at how getting into this trial works?

There are tons of reporters. This is a very high-profile suit, so I have to wake up around 4:30 a.m. and show up to the Oakland courthouse at 6 a.m. sharp to get in line. And on some days, even 6 a.m. doesn’t get you into the courtroom. There are lots of photographers in front of the courthouse, especially on days when you know Musk or Altman and Brockman are present. And there’s also some concerned citizens who want to watch the trial. I usually have to wait, like, two hours in line to get in to be one of the 30 people who claim the unreserved seats in the courtroom. 

What has it felt like to see Elon Musk testify? How would you describe his demeanor?

He shows up in a crisp black suit. He can be this inflammatory person on X, but in the courtroom, he is calm, cool, collected, and looks very comfortable. He has been in a lot of lawsuits. He knows how to talk to the jury and how to present himself in front of them and the judge. He’s also cracking jokes with his lawyer and even the opposing party’s lawyer and the judge. 

And he can be witty. There was this one moment when OpenAI’s lawyer was asking Musk a question and sort of fed him an answer. And Musk said “That’s not a leading question, that’s a leading answer.” The judge intervened and said, “You’re not a lawyer, Elon.” And then he was like, “Well, I did take Law 101.”

That said, he does get flustered and uncomfortable when OpenAI’s lawyer asks tough, piercing questions. Which he’s been doing.

What are the biggest things we’ve learned that weren’t clear in the earlier phases of this case?

On the fourth day of the trial, Musk admitted during cross-examination that xAI distills OpenAI’s models to train its own models, which was shocking. Musk followed up by saying that this is standard practice among all the labs now and that xAI wasn’t doing anything beyond what others were already doing. But a lot of the journalists started typing away at their laptops as soon as Musk made this comment. 

I also learned that there’s just so much scheming among Big Tech executives. You know about it vaguely, but to hear firsthand accounts and read their emails and text messages is fascinating. 

For example, there was a text message between Musk and Mark Zuckerberg of Meta, where they’re kind of teaming up to stop OpenAI’s restructuring. They’re even trying to make a bid to buy all the assets of OpenAI’s nonprofit. The level of scheming that goes on among these executives is mind-blowing.

What happens next?

OpenAI’s president, Greg Brockman, who was meticulously taking notes during some of Elon Musk’s testimony, is expected to testify next week. And Stuart Russell, a computer scientist at UC Berkeley, will testify about AI safety. I’m expecting that to open the floodgates to this crazy discussion about who can be trusted to build AI. 

A bunch of other high-profile people are expected to testify, like former OpenAI chief scientist Ilya Sutskever, former CTO Mira Murati, and Microsoft CEO Satya Nadella. 

The trial is supposed to last around three weeks. The nine jurors will deliver an advisory verdict that guides the judge on how to decide Musk’s claims against OpenAI. The judge doesn’t have to listen to the jury and can decide however she wants. If she decides OpenAI is liable, then she’ll decide what sort of remedies are appropriate. 

MIT Technology Review will have ongoing coverage of Musk v. Altman until its conclusion. Follow @techreview or @michelletomkim on X for up-to-the-minute reporting.

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May 2 2026
Trump’s mass firing just dealt another blow to American science

This past week delivered another gut punch for science in the US. This time, the target was the National Science Foundation—a federal agency that funds major research projects to the tune of around $9 billion. The foundation’s efforts were overseen by a board of 22 prominent scientists. On Friday last week, they were all fired.

The NSF has been without a director since April 2025, when former director Sethuraman Panchanathan stepped down in the wake of DOGE-led funding cuts and mass firings. Trump’s nominee for the role is Jim O’Neill, an investor and longevity enthusiast who does not have a science background.

It’s hard to predict exactly how things will shake out for science. But it’s not looking great.

The NSF was established in 1950 to “promote the progress of science,” among other goals. It has served as a major source of support for research and education since then. In 2024, the agency spent $9.39 billion—a substantial figure but only 0.1% of all federal spending.

Key decisions about how that money is spent have been made by the National Science Board. Each of the scientists who made up the board until last week was appointed by a US president to serve, at least initially, a six-year term. Those members were responsible for establishing NSF policies, authorizing major expenditures and providing oversight, says Keivan Stassun, a physicist and astronomer at Vanderbilt University who was appointed to the board in late 2022.

A few years ago, the board was responsible for establishing a new “directorate” within the agency to channel funding to “technology, innovations and partnerships,” for example. The board also authorized funding for the US Extremely Large Telescope Program.

“It’s a relatively small group with a tremendous amount of responsibility and authority,” says Stassun. He viewed his appointment as “a tremendous honor.”

Then, last Friday, the email landed in his inbox. “It said: On behalf of President Trump, this letter is to notify you that your position as a member of the National Science Board is terminated effective immediately. Thank you for your service,” says Stassun. “It was deeply disappointing.”

Still, Stassun wasn’t surprised, given the administration’s actions across federal science agencies over the past year.

Since Donald Trump took office at the start of 2025, the NSF—along with many other federal agencies—has frozen, unfrozen, and terminated grants. “The board was not involved in any of those [terminations],” says Stassun. Members had no say in the firing of agency staff either, he says. Staff numbers are currently down 40%, he adds.

In a 2026 budget request, the Trump administration sought to cut the NSF’s budget by around 57%. Last summer, NSF staffers wrote a letter of dissent arguing that such substantial cuts would “cripple American science.” The proposed cuts would have hit biological sciences, engineering, and STEM education particularly hard.

Those cuts were rejected by Congress earlier this year. But grant terminations and firings are essentially allowing them to take effect regardless, says Stassun. “The funds that the White House has been dispersing to the agency … have been far less than what Congress intended,” he says.

Many ambitious research projects are grinding to a halt as a result. “The Extremely Large Telescope Program appears to be dead in the water for now,” says Stassun. And the NSF arm dedicated to science education “has effectively zeroed out,” he says.

But not all of them. While the administration’s 2027 budget request states that NSF will “close out” its directorate for social, behavioral, and economic sciences, it describes AI and quantum information science as key “frontier initiatives.” Biotechnology is described as a “focal point.” 

When asked for comment, the NSF directed MIT Technology Review to the White House press office. The White House did not respond directly to questions about the firing of NSB members and said in a statement, “The National Science Foundation’s work continues uninterrupted.”

Jim O’Neill, Trump’s current candidate for the position of NSF director, is certainly interested in biotechnology. Specifically, when I spoke to O’Neill in February, he told me that he supposes he is a Vitalist—a hardcore supporter of efforts to extend human longevity who believes that death is wrong.

O’Neill was deputy secretary of the Department of Health and Human Services and acting director of the Centers for Disease Control and Prevention until a leadership shakeup a couple of months ago. But he isn’t a scientist. And that has some scientists worried. He has yet to be confirmed by the Senate for the role.

In the meantime, the administration’s efforts are having a real impact on research. “We [NSB members] tried to stand for a continued investment in science, engineering, and technology, and in science education broadly,” says Stassun. “The administration will now be able to operate the agency the way that [it wants to, with] no governance body in the way.”

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