Every year the World Health Organization publishes a global health statistics report. It features the numbers behind world health trends and, importantly, assesses whether we’re on track to reach ambitious goals set in 2015. It’s a bit like a health grade.
The 2026 report was published on Wednesday. And the results aren’t looking brilliant. While we are seeing some improvements, they are uneven, and they’re far too slow.
The targets themselves are part of the United Nations’ Sustainable Development Goals, a sprawling and ambitious plan focused on improving life around the world. The 17 goals were set to tackle poverty and climate change and to boost education, gender equality, health, and well-being, among many other quality of life issues. Those targets were meant to be met by 2030.
Perhaps they were a little too ambitious. Here are the numbers and statistics that stood out to me on this year’s world health report card.
1.3 million new cases of HIV in 2024
Before the SDGs, there were the Millennium Development Goals. One MDG target was to halt and reverse the spread of HIV—and that target was exceeded by 2015. Back then, we were considered on track to “end the AIDS epidemic by 2030.”
How depressing, then, to see that in 2024 there were an estimated 1.3 million new cases of HIV. That’s 40% lower than the figure from 2010. But it’s still 1.3 million additional people with HIV. The SDG target is to reduce HIV incidence by 90% by 2030—we’re not likely to meet it.
10.7 million new cases of TB
The picture is even bleaker for tuberculosis, which ranks 10th on the WHO’s list of top global causes of death. The goal was to reduce cases by 80% between 2015 and 2030. So far, cases have only fallen by a measly 12%. And when you break the change down by region, the Americas saw an increase of 13%
An 8.5% rise in malaria cases
And then there’s malaria, the mosquito-borne disease with a 7% fatality rate. The European region has been free of malaria since 2015, but the disease is a significant concern in many countries in the Global South, particularly in Africa. The goal was to lower rates by 90% between 2015 and 2030. In 2024, there were an estimated 282 million cases of malaria globally—representing an 8.5% increase in incidence rates.
Antimalarial drug resistance is a major challenge here—forms of the malaria virus that are resistant to drugs have been confirmed or suspected in eight countries in Africa, according to a separate WHO report. Mosquitoes that are resistant to commonly used insecticides are present in nine African countries. And climate change, which can alter mosquito habitats, may be making things worse.
42.8 million children are wasting
We’re not meeting child health targets, either. Take malnutrition, for example. As of 2024, the global prevalence of wasting in children was 6.6%—that’s a staggering 42.8 million children who are literally wasting away because of a lack of adequate food. On the other end of the spectrum, 5.5% of children are now considered overweight. Both figures were meant to be below 5% by 2030, which now seems unlikely.
Vaccination rates are dropping in the Americas
Progress in improving childhood vaccination coverage has stalled. Globally, an estimated 76% of children are getting their second dose of a measles vaccine—a figure far below the the approximately 95% needed to prevent outbreaks. The Americas currently has lower rates of vaccine coverage for three of the four “core” vaccines than it did in 2015.
This is partly due to a lack of investment, says Goodarz Danaei, an epidemiologist at the Harvard T.H. Chan School of Public Health. “But now we have a misinformation campaign going around vaccines that makes it worse,” he adds.
And of course the pandemic affected progress toward health goals in more direct ways: 7 million people died of covid-19. The WHO report estimates that, for each of these, there were an additional two “excess” deaths related to the pandemic, due to disruptions in health care, for example. That puts the total figure at 22.1 million pandemic-related deaths.
A woman dies every two minutes from “maternal causes”
Maternal mortality rates fell by about 40% between 2020 and 2023. But today’s rate equates to 712 maternal deaths every single day. That’s one every two minutes. The WHO report notes that we’d have to reduce the mortality rate by almost 15% per year in order to meet the 2030 target. This seems incredibly unlikely, particularly given the recent decimation of US funding for global aid programs, which is expected to result in thousands of additional maternal deaths.
Progress has also slowed in reducing the risk of death from noninfectious diseases like cancer, diabetes and cardiovascular disease. “Overall, neither the world nor any WHO region is currently on track to meet the 2030 SDG target,” the report states.
2.1 billion people struggle to afford health care
Despite plans to make health care more affordable, a significant chunk of the population is being pushed into poverty by health-care costs. In 2022, 2.1 billion people faced financial hardship due to health spending—and 1.6 billion of them were living in or had been pushed into poverty.
Across the board, there have been some important improvements in global health. But the achievements have not gone far enough. “The good news is that there is progress,” says Danaei. “But as always, the glass is half empty.”
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.
In a dimly lit bedroom, a frightened young woman is thrown onto a bed by a tall, muscular man. He grabs her hand, and flame-like vines crawl across her body, fusing with her flesh. She levitates, then drops. A dragon-shaped tattoo appears across her chest.
“Two months,” the man says. “Give me an heir, or I will eat you.”
The scene is from Carrying the Dragon King’s Baby, one of the many hundreds of short dramas that appear on apps like DramaWave and ReelShort. There’s just something about this one that isn’t quite right. The lighting may be glossy and cinematic, but the show has an odd visual texture like something between a movie and a video game cutscene.
That’s because Carrying the Dragon King’s Baby is part of a new trend for making these shows entirely with AI: no actors, camera operators, cinematographers, or CGI specialists required.
China’s short drama industry has boomed since its launch, in 2018. These ultrashort, melodramatic, and often smutty shows are designed for smartphone viewing, with episodes often running just one or two minutes long: Viewers can finish an entire series in as little as 30 minutes to an hour. The films are made for endless scrolling, packed with emotional confrontations and melodramatic plot twists. The trend’s growth is driven by apps that bombard TikTok, Instagram, and Facebook with cliffhanger-heavy ads designed to lure viewers into buying subscriptions. In 2024, China’s short drama market reached roughly $6.9 billion in revenue, surpassing the country’s annual box office earnings for the first time.
Since 2022, Chinese short drama companies have aggressively expanded overseas, translating existing hits and producing localized series featuring local actors. Globally, short drama apps have approached a billion cumulative downloads. The United States is the biggest market outside of China, providing around 50% of the revenue, according to research firm DataEye.
Now the industry is reinventing itself. Chinese short drama companies—already masters of low-budget, algorithmically optimized entertainment—are embracing generative AI to produce content faster and cheaper than ever. An average of 470 AI-generated short dramas were released every day in January, according to DataEye. Short-drama companies like Kunlun Tech are ramping up AI productions, shrinking film crews, and reorganizing the labor pipeline from the ground up. For some studios, AI has moved from being a supporting tool to providing the backbone of production itself.
Infinite stories, infinite tropes
Short dramas are already famously low-budget. But AI has made them dramatically cheaper to mass-produce, helping to accelerate the entire process—and save money. Production timelines have collapsed. Conceptualization, script writing, casting, shooting, and editing used to take three to four months. With AI, the process can now take less than a month, says Tang Tang, vice president at short-drama platform FlexTV. Producing a short drama in North America once cost roughly $200,000, but AI can cut that cost by 80% to 90%, according to Tang.
After expanding into the US market, Chinese short drama companies largely followed the same playbook they used in China: Buy traffic aggressively on TikTok, Facebook, and YouTube; offer a handful of free episodes; then charge viewers to unlock the rest inside the companies’ apps. Decisions about what to produce next are often driven less by creative instinct than by performance data. “We look at what themes, plotlines, and writers resonate with audiences, then quickly adjust,” says Tang.
The industry operates at a relentless pace. “Everyone expects quick returns,” Tang says. “In China, if a series doesn’t break even within a month, the industry considers it a failure.”
As a result, screenwriters who spoke with MIT Technology Review said platforms often categorize projects using highly specific keywords that encompass everything from genre and setting to plot structure, such as “campus romance,” “gang rivalry,” “enemies to lovers,” or “rags to riches.” Recently, one of the most popular genres has been “reborn revenge,” a fantasy trope in which a wronged protagonist is miraculously reborn and given a chance to change their fate.
“You kind of have to keep the emotional intensity extremely high throughout the show, using the same plot devices over and over again: sudden deaths, betrayals, physical violence, huge confrontations,” says Phoenix Zhu, a freelance short drama screenwriter based in Suzhou. “It’s common to sacrifice narrative logic for shock value, because otherwise people are more likely to scroll away.”
Those simple tropes have made the format particularly compatible with AI-generated production. Earlier this year, FlexTV halted all traditionally shot productions and shifted entirely to AI-generated dramas. Kunlun Tech, the parent company of drama apps DramaWave and FreeReels, began producing AI-generated short dramas in 2025 and now offers more than 1,000 AI titles on its platforms. StoReels, another popular short drama company targeting a global audience, has said it aims to produce 100 AI-generated dramas per month.
“People’s attention spans are getting shorter, and serialized drama naturally has to get shorter,” says Han “Daniel” Fang, the CEO of Kunlun Tech. Fang told MIT Technology Review that the company is not going to stop investing in traditionally shot short dramas with real actors. But the company is expanding AI-generated productions and gradually increasing their share on its platforms as a low-cost way to experiment with new genres, themes, and ideas. “We want to bring the amount of AI work to 20% of the platform,” Fang says.
The format is also rapidly growing overseas. Research firm Omdia estimates that the global microdrama market reached $11 billion in 2025 and will grow to $14 billion by the end of 2026. The United States is expected to generate $1.5 billion in revenue in that market this year.
“No one comes to short dramas expecting high art,” says investor Shangguan Hong, former partner of Legend Capital. “The short-drama industry already stands out from traditional TV and filmmaking by being real-time and data-driven. AI only furthers that logic. In a sense, short drama is perfectly compatible with AI.”
Inside the content machine
The industry’s AI revolution is already changing the type of roles required to make short dramas.
Phoenix Zhu graduated from college in 2024 with a degree in philosophy. After months of rejections from traditional media and film studios, she eventually found work writing scripts for short dramas. “It was a very difficult job market for young people,” Zhu says. “I couldn’t afford to be picky about what I wrote.”
To support herself, Zhu worked a string of part-time jobs, including as a barista, a flower seller, and an event coordinator, while taking freelance writing gigs online for advertising and education companies. In April 2025, she sold her first short-drama script for around 20,000 yuan (approximately $2,945). More commissions followed, and she thought her career was finally beginning to pick up.
Then AI arrived. Two projects already in the contract stage were abruptly canceled, Zhu says. Rates across the industry began falling. The raises she expected as she gained more experience never materialized.
Still, writers like Zhu have been among the less disrupted workers in the industry. Many production roles on traditional filming sets have disappeared almost entirely from AI-generated productions.
“We could shrink the production team down to around 10 people,” says Tang, vice president at FlexTV. Like many companies in the industry, FlexTV relies primarily on Chinese writers and production teams, even for shows featuring non-Chinese characters and targeting overseas audiences. The reason is not just lower costs, Tang says, but also that Chinese writers better understand the pacing and narrative rhythm of short dramas.
Instead of camera crews, lighting technicians, makeup artists, and visual effects teams, AI productions now rely on smaller groups consisting largely of producers, writers, AI directors, and “AI asset curators.”
An AI asset curator translates scripts into prompts and generates reference images of characters, costumes, and scenes for AI video models to follow. MIT Technology Review found hundreds of job listings for the role on Chinese job sites, many requiring little prior industry experience beyond familiarity with AI tools.
“The technology has improved enormously just in the past few months,” says Hanzhong Bai, an AI short-drama producer based in Beijing. Bai says it is common for AI asset curators to use prompts like “combine the faces of these celebrities I like” when generating characters. Studios typically use a mix of tools, including Google’s image-generation model Nano Banana, ByteDance’s Seedance, and Kuaishou’s Kling.
For producers like Bai, AI also makes it economically viable to produce genres that were previously too expensive for short dramas, especially fantasy series requiring elaborate visual effects, costumes, or makeup. “We’ll see many more dragon and mermaid shows for exactly this reason,” Bai says.
The compressed production cycle has also changed the writing process itself. Writers once had two to three months to finish a script. Now, Zhu says, platforms often expect delivery within a month. Scripts can also be rougher and more flexible, since scenes, visuals, and even plot details can be changed later through prompts.
As a result, writers increasingly have to write for AI models as much as for human audiences. Zhu says she now has to describe scenes with far greater visual specificity, effectively taking on responsibilities once handled by cinematographers or visual effects teams.
“Before AI, writing ‘He gave her a cold stare’ might have been enough,” Zhu says. “Now I might need to write, ‘Cold beams of light shot out from his eyes.’”
Fang of Kunlun Tech believes the future quality of AI-generated short dramas is ultimately a numbers game. “Good ideas and good writing still stand out,” Fang says. “The quality [of AI short drama] will improve simply because more people with strong ideas will be able to make their shows.”
In the final week of the Musk v. Altman trial, lawyers traded blows over Elon Musk’s and OpenAI CEO Sam Altman’s credibility. Altman was grilled on his alleged history of lying and self-dealing involving companies that do business with OpenAI. But he fired back, painting Musk as a power-seeker who wanted to control the development of artificial general intelligence (AGI)—powerful AI that can compete with humans on most cognitive tasks.
As evidence of their commitment to AI safety, OpenAI brought out a golden trophy of a donkey’s ass that was gifted to an employee after he was called a “jackass” for standing up to Musk’s plans to race toward AGI.
Lawyers for both sides also presented their closing arguments, floating unflattering mugshot-style photos of Musk and Altman next to each other on a giant screen. Musk’s lawyer Steven Molo argued that Altman and OpenAI president Greg Brockman broke their promise to use money Musk donated to maintain OpenAI as a nonprofit that develops AI for the benefit of humanity. Instead, they created a for-profit subsidiary that made them extraordinarily wealthy.
OpenAI’s lawyer Sarah Eddy argued that Altman and Brockman never promised to keep OpenAI a nonprofit. She added that even though it’s been restructured, OpenAI remains a nonprofit dedicated to developing AI safely.
She claimed that Musk sued too late—and that his real motive is to sabotage a competitor to his own AI company, xAI, which he launched in 2023.
Musk is asking the court to unwind the 2025 restructuring that converted OpenAI’s for-profit subsidiary into a public benefit corporation and to remove Altman and Brockman from their roles. He is also seeking as much as $134 billion in damages from OpenAI and Microsoft, to be awarded to OpenAI’s nonprofit.
The jury will begin deliberating on Monday and deliver an advisory verdict as soon as next week. The jury verdict is not binding on the judge, who will decide the case.
If the judge rules in Musk’s favor, it could upend OpenAI’s race toward an IPO at a valuation approaching $1 trillion. Meanwhile, xAI is expected to go public as a part of Musk’s rocket company SpaceX as early as June, at a target valuation of $1.75 trillion.
Musk the power-seeker, Altman the liar.
In the first week of the trial, Musk said he was suing to save OpenAI’s mission to build AI safely for the benefit of humanity. This week, Altman denied Musk was a paladin of AI safety and painted him as a power-seeker who wanted to control OpenAI.
Altman told the jury that in 2017, when Musk and other cofounders were discussing creating a for-profit arm, they asked Musk what would happen to his control over such an entity if he died. “Maybe the control of OpenAI should pass to my children,” Musk said, according to Altman.
Musk’s lawyer shot back, grilling Altman on his alleged history of lying. He pointed out that OpenAI’s former executives Ilya Sutskever and Mira Murati, and former board members Helen Toner and Tasha McCauley, all testified that Altman had lied to them. In 2023, Altman was briefly fired as CEO over the alleged behavior.
Molo also pressed Altman about his personal investments in startups that do business with OpenAI. Altman testified that he tried to steer OpenAI to buying power from the nuclear energy company Helion Energy, a third of which he owns.
(Last Friday, the US House oversight committee launched an investigation into Altman’s potential conflicts of interest. Attorneys general from more than a half-dozen states called for the Securities and Exchange Commission to review them.)
During his closing statement, Molo put Altman’s credibility on the stand again. “Imagine that you’re on a hike, and you come upon one of those wooden bridges that you see on a trail, and it’s over a gorge,” he said. “A woman standing by the entry to the bridge says, ‘Don’t worry—the bridge is built on Sam Altman’s version of the truth.’ Would you walk across that bridge?”
Altman, who sat behind his lawyers, looked up uneasily every time his name was mentioned.
During her closing argument, Eddy fired back. Musk “never cared about the nonprofit structure,” she said. “What he cared about was winning.”
Musk, though, was absent. Despite the judge’s order that he remain available, he flew to China with President Trump.
Did Altman promise to keep OpenAI a nonprofit?
During her closing argument, Eddy argued that no testimony or evidence showed any conditions on Musk’s donations, or any promises made by Altman and Brockman to keep the company a nonprofit. “No commitments or promises were made. No restrictions were placed on Mr. Musk’s donations,” she said.
Eddy added that it was evident Musk wasn’t truly committed to keeping OpenAI a nonprofit. She noted that in 2017, he tried to create a for-profit subsidiary and fought a bitter battle with Altman and Brockman to have control over it.
“I was not opposed to there being a small for-profit that provides funding to the nonprofit,” Musk told the jury earlier in the trial, “as long as the tail didn’t wag the dog.”
Eddy then argued that Musk sued too late, filing in 2024 after the statutes of limitations on his claims ran out. In 2019, OpenAI created a for-profit subsidiary, under which employees and investors received a capped return on their investment.
But Musk testified that he discovered OpenAI had abandoned its nonprofit mission only in 2022, when Microsoft was preparing to invest $10 billion in OpenAI—a deal that closed in 2023. “I was disturbed to see OpenAI with a $20B valuation,” he texted Altman after reading the news. “This is a bait and switch.”
Musk told the jury that the $20 billion valuation made him realize “the for-profit is the tail wagging the dog.”
“The 2023 deal was different,” Molo hammered home during his closing argument.
Is OpenAI still a nonprofit committed to its mission?
A central question raised in the last week of trial was whether OpenAI remains a nonprofit committed to developing AGI safely for the benefit of humanity. Eddy, the OpenAI lawyer, argued that the nonprofit still controls the for-profit and seeks to “help AGI turn out well for humanity.” “The OpenAI nonprofit is the best-resourced nonprofit in the world,” thanks to the for-profit, she added.
Molo countered that while the OpenAI’s nonprofit nominally controls the company, it does not do so in practice. OpenAI’s nonprofit and for-profit are controlled by the same people—seven of the nonprofit’s eight board members are on the for-profit’s board. The nonprofit hired employees only a month before the trial started and does work only in grant-making rather than AI research.
Molo played a video interview of Altman saying that the nonprofit board’s failure to fire him in 2023 was “its own kind of governance failure.”
“We’re left with this nonprofit that doesn’t have any voice,” Jill Horwitz, a law professor at Northwestern University who studies nonprofits, told MIT Technology Review. “It doesn’t have much money, and OpenAI doesn’t think it has any obligation to fund it. It barely has a staff,” she says. “It’s unclear how on earth the nonprofit is supposed to exercise its duties and control the entire company.”
Civil society groups and policymakers have spoken out against OpenAI’s restructuring over the years. So has Musk, although his own stake in the AI race makes him a dubious champion for the public interest.
“The public interest in the nonprofit loses, no matter who wins or loses this trial,” says Horwitz.
Jackass for AI safety
Despite US District Judge Yvonne Gonzalez Rogers’s warning during the first week that this trial was not about AI safety, the issue stole the show again. Throughout the trial, the lawyers from both sides traded barbs over the safety track records of ChatGPT (which has allegedly caused teen suicides) and Grok (which has flooded X with porn).
On the last day of testimony, OpenAI’s lawyer Bradley Wilson handed the judge a small golden trophy of a donkey’s ass, inscribed: “Never stop being a jackass for safety.”
The trophy belonged to Joshua Achiam, OpenAI’s chief futurist. He testified that he’d warned, when Musk announced in 2018 that he was leaving OpenAI to race toward building AGI, that speed could compromise safety. Musk snapped and called him a “jackass,” said Achiam. His colleagues, including Dario Amodei, now CEO of Anthropic, gave him the trophy to enshrine the diss.
“I don’t want it,” said the judge. The shenanigans spilled out into the street too. In front of the Oakland courthouse, a protester paraded around wearing a costume of Musk holding a bag of ketamine and driving a Cybertruck. Another held a photo of Sam Altman and a poster reading, “Stop AGI or we’re all gonna die.”
When Jennifer got a job doing research for a nonprofit in 2023, she ran her new professional headshot through a facial recognition program. She wanted to see if the tech would pull up the porn videos she’d made more than 10 years before, when she was in her early 20s. It did in fact return some of that content, and also something alarming that she’d never seen before: one of her old videos, but with someone else’s face on her body.
“At first, I thought it was just a different person,” says Jennifer, who is being identified by a pseudonym to protect her privacy.
But then she recognized a distinctly garish background from a video she’d shot around 2013, and she realized: “Somebody used me in a deepfake.”
Eerily, the facial recognition tech had identified her because the image still contained some of Jennifer’s features—her cheekbones, her brow, the shape of her chin. “It’s like I’m wearing somebody else’s face like a mask,” she says.
“It’s like I’m wearing somebody else’s face like a mask.”
Conversations about sexualized deepfakes—which fall under the umbrella of nonconsensual intimate imagery, or NCII—most often center on the people whose faces are featured doing something they didn’t really do or on bodies that aren’t really theirs. These are often popular celebrities, though over the past few years more people (mostly women and sometimes youths) have been targeted, sparking alarm, fear, and even legislation. But these discussions and societal responses usually are not concerned with the bodies the faces are attached to in these images and videos.
As Jennifer, now 37 and a psychotherapist working in New York City, says: “There’s never any discussion about Whose body is this?”
For years, the answerhas generally been adult content creators. Deepfakes in fact earned their name back in November 2017, when someone with the Reddit username “deepfakes” uploaded videos showing faces of stars like Scarlett Johansson and Gal Gadot pasted onto porn actors’ bodies. The nonconsensual use of their bodies “happens all the time” in deepfakes, says Corey Silverstein, an attorney specializing in the adult industry.
But more recently, as generative AI has improved, and as “nudify” apps have begun to proliferate, the issue has grown far more complicated—and, arguably, more dangerous for creators’ futures.
Porn actors’ bodies aren’t necessarily being taken directly from sexual images and videos anymore, or at least not in an identifiable way. Instead, they are inevitably being used as training data to inform how new AI-generated bodies look, move, and perform. This threatens the livelihood and rights of porn actors as their work is used to train AI nudes that in turn could take away their business. And that’s not all: Advancements in AI have also made it possible for people to wholly re-create these performers’ likenesses without their consent, and the AI copycats may do things the performers wouldn’t do in real life. This could mean their digital doubles are participating in certain sex acts that they haven’t agreed to do, or even perpetrating scams against fans.
Adult content creators are already marginalized by a society that largely fails to protect their safety and rights, and these developments put them in an even more vulnerable position. After Jennifer found the deepfake featuring her body, she posted on social media about the psychological effects: “I’ve never seen anyone ask whether that might be traumatic for the person whose body was used without consent too. IT IS!” Several other creators I spoke with shared the mental toll that comes with knowing their bodies have been used nonconsensually, as well as the fear that they’ll suffer financially as other people pirate their work. Silverstein says he hears from adult actors every day who “are concerned that their content is being exploited via AI, and they’re trying to figure out how to protect it.”
One law professor and expert in violence against women calls these creators the “forgotten victims” of NCII deepfakes. And several of the people I spoke with worry that as the US develops a legal framework to combat nonconsensual sexual content online, adult actors are only at risk of further injury; instead of helping them, the crackdown on deepfakes may provide a loophole through which their content and careers could be stripped from the internet altogether.
How deepfakes cause “embodied harms”
During his preteen years in the 1970s, Spike Irons, now a porn actor and president of the adult content platform XChatFans, was “in love” with Farrah Fawcett. Though Fawcett did not pose nude, Jones managed to get his hands on what looked like pictures of her naked. “People were cutting out faces and pasting them on bodies,” Irons says. “Deepfakes, before AI, had been going around for quite a while. They just weren’t as prolific.”
The early public internet was rife with websites capitalizing on the idea that you could use technology to “see” celebrities naked. “People would just use Microsoft Paint,” says Silverstein, the attorney. It was a simple way to mash up celebrities’ faces with porn.
People later used software like Adobe After Effects or FakeApp, which was designed to swap two individuals’ faces in images or videos. None of these programs required serious expertise to alter content, so there was a low barrier to entry. That, plus the wealth of porn performers’ videos online, helped make face-swap deepfakes that used real bodies prevalent by the 2010s. When, later in the decade, deepfakes of Gal Gadot and Emma Watson caused something of a broader panic, their faces were allegedly swapped onto the bodies of the porn actors Pepper XO and Mary Moody, respectively.
But it wasn’t just high-profile actors like them whose bodies were being used. Jennifer was “a very minor performer,” she says. “If it happened to me, I feel like it could happen to anybody who’s shot porn.” Since he started his practice in 2006, Silverstein says, “numerous clients” have reached out to report “This is my body on so-and-so.”
Both people whose faces appear in NCII deepfakes and those whose bodies are used this way can feel serious distress. Experts call this type of damage “embodied harms,” says Anne Craanen, who researches gender-based violence at the UK’s Institute for Strategic Dialogue, an organization that analyzes extremist content, disinformation, and online threats.
The term reflects the fact that even though the content exists in the virtual realm, it can cause physiological effects, including body dysmorphia. The face-swapped entity occupies the uncanny valley, distorting self-perception. After discovering their faces in sexual deepfakes, many people feel silenced, experts told me; they may “self-censor,” as Craanen puts it, and step back from public-facing life. Allison Mahoney, an attorney who works with abuse survivors, says that people whose faces appear in NCII can experience depression, anxiety, and suicidal ideation: “I’ve had multiple clients tell me that they don’t sleep at night, that they’re losing their hair.”
Independent creators aren’t just “having sex on camera.” For someone to rip off their work “for their own entertainment or financial gain fucking sucks.”
Though the impact on people whose bodies are used hasn’t been discussed or studied as often, Jennifer says that “it’s just a really terrible feeling, knowing that you are part of somebody else’s abuse.” She sees it as akin to “a new form of sexual violence.”
The uncertainty that comes with not being aware of what your body is doing online can be highly unsettling. Like Jennifer, many adult actors don’t really know what’s out there. But some devoted followers know the actors’ bodies well—often recognizing tattoos, scars, or birthmarks—and “very quickly they bring [deepfakes] to the adult performer’s attention,” says Silverstein. Or performers will stumble upon the content by chance; some 20 years ago, for instance, the first such client to tell Silverstein her body was being used in a deepfake happened to be searching Nicole Kidman online when she found that one of the results showed Kidman’s face on her porn. “She was devastated, obviously, because they took her body,” he says, “and they were monetizing it.”
Otherwise, this imagery may be found by an organization like Takedown Piracy, one of several copyright enforcement companies serving adult content creators. US copyright violations can be challenging to prove if someone’s body lacks distinguishing features, says Reba Rocket, Takedown Piracy’s chief operating and marketing officer. But Rocket says her team has added digital fingerprinting technology to clients’ material to help flag and remove problematic videos, often finding them before clients realize they’re online.
By capturing “tens of thousands of tiny little visual data points” from videos, digital fingerprinting creates unique corresponding files that can be used to identify them, Rocket says—kind of like an invisible watermark. The prints remain even if pirates alter the videos or replace performers’ faces. Takedown Piracy has digitally fingerprinted more than half a billion videos and the organization has gotten 130 million copyrighted videos taken down from Google alone (though, of those videos, Rocket hasn’t tracked how many of these specifically include someone else’s face on a performer’s body).
Besides copyright, a range of legal tools can be used to try to combat NCII, says Eric Goldman, a law professor at Santa Clara University. For example, victims can claim invasion of privacy. But using these tools isn’t particularly straightforward, and they may not even apply when it comes to someone’s body. If there aren’t, for instance, unique markers indicating that a body in a deepfake belongs to the person who says it does, US law “doesn’t really treat [this content] as invasion of privacy,” Goldman says, “because we don’t know who to attribute it to.”
In a 2018 study that reviewed “judicial resolution” of cases involving NCII, Goldman found that one successful way plaintiffs were able to win cases was to assert “intentional affliction of emotional distress.” But again, that hinges on the ability to clearly identify the person in the content. Relevant statutes, he adds, might also require “intent to harm the individual,” which may be hard to show for people whose bodies alone are featured.
“AI girls will do whatever you want”
In the last few years, Silverstein says, it’s become less and less common to see the bodies of real adult content creators in deepfakes, at least in a way that makes them clearly identifiable.
Sometimes the bodies have been manipulated using AI or simpler editing tools. This can be as basic as erasing a birthmark or changing the size of a body part—minor edits that make it impossible to identify someone’s image beyond a reasonable doubt, so even porn actors who can tell that an altered image used their body as a base won’t get very far in the legal realm. “A lot of people are like, That looks like my body,” says Silverstein, but when he asks them how, they’ll reply, It just does.
At the same time, other users are now creating NCII with wholly AI-generated bodies. In “nudify” apps, anyone with a minimal grasp of technology can upload a photo of someone’s clothed body and have it replaced with a fake naked one. “So [much] of this content being created is just someone’s face on an AI body,” Silverstein says.
Such apps have drawn a ton of attention recently, in incidents from Grok’s “nudifying” minors to Meta’s running ads for—and then suing—the nudify app Crushmate. But there’s been relatively little attention paid to the content being used to train them. They almost certainly draw on the more than 10,000 terabytes of online porn, and performers have virtually zero recourse.
One reason is that creators aren’t able to demonstrate with any certainty that their content is being used to train AI models like those used by nudify apps. “These things are all a black box,” says Hany Farid, a professor at the University of California, Berkeley, who specializes in digital forensics. But “given the ubiquity” of adult content, he adds, it’s a “reasonable assumption” that online porn is being used in AI training.
“It’s just not at all difficult to come up with pornographic data sets on the internet,” says Stephen Casper, a computer science PhD student at MIT who researches deepfakes. What’s more, he says, plenty of shadowy online communities provide “user guides” on how to use this data to train AI, and in particular programs that generate nudes.
It’s not certain whether this activity falls within the US legal definition of “fair use”—an issue that’s currently being litigated in several lawsuits from other types of content creators—but Casper argues that even if it does, it’s ethically murky for porn created by consenting adults 10 years ago to wind up in those training data sets. When people “have their stuff used in a way that doesn’t respect or reflect reasonable expectations that they had at that time about what they were creating and how it would be used,” he says, there’s “a legitimate sense in which it’s kind of … nonconsensual.”
Adult performers who started working years ago couldn’t possibly have consented to AI anything; Jennifer calls AI-related risks “retroactively placed.” Contracts that porn actors signed before AI, adds Silverstein, might provide that “the publisher could do anything with the content using technology that now exists or here and after will be discovered.” That felt more innocuous when producers were talking about the shift from VHS to DVD, because that didn’t change the content itself, just the way it was conveyed. It’s a far different prospect for someone to use your content to train a program to create new content … content that could replace your work altogether.
Of course, this all affects creators’ bottom line—not unlike the way Google’s AI overviews affect revenue for online publishers who’ve stopped getting clicks when people are content with just reading AI-generated summaries. Performers’ “concern is … it’s another way to pirate [their] content,” says Rocket.
After all, independent creators aren’t just “having sex on camera,” as the adult content creator Allie Eve Knox puts it. They’re paying for filming equipment and location rentals, and then spending hours editing and marketing. For someone to then rip off and distort that content “for their own entertainment or financial gain,” she says, “fucking sucks.”
KIM HOECKELE
Tanya Tate, a longtime adult content creator, tells me about another highly unsettling AI-created situation: She was recently chatting with a fan on Mynx, a sexting app, when he asked her if she knew him. She told him no, and “his eyes just started watering,” Tate says. He was upset because he thought she did know him. Turns out he’d sent $20,000 to a scammer who’d used an AI-generated deepfake of Tate to seduce him.
Several men, Tate subsequently learned, had been scammed by an AI version of her, and some of them began blaming her for their losses and posting false statements about her online. When she reported one particularly aggressive harasser to the police, they told her he was exercising his “freedom of speech,” she says. Rocket, too, is familiar with situations where AI is used to take advantage of fans. “The actual content creator will get nasty emails from these people who’ve been scammed,” she says.
Other porn actors say they fear that their likenesses have been used without consent to do other things they wouldn’t do. One, Octavia Red, tells me she doesn’t do anal scenes, “but I’m sure there’s tons of deepfake anal videos of me that I didn’t consent to.” That could cost her, she fears, if viewers choose to watch those videos instead of subscribing to her websites. And it could cause fans to develop false expectations about what kind of porn she’ll create.
“I saw one AI creator saying, ‘Well, AI girls will do whatever you want. They don’t say no,’” says Rocket. “That horrifies me … especially if they’re training those AI models on real people. I don’t think they understand the damage to mental health or reputation that that can create. And once it’s on the internet, it’s there forever.”
Efforts to “scrub adult content from the internet”
As AI technology improves, it’s increasingly difficult for people to discern any type of real video from the best AI-generated ones on their own. In one 2025 study, UC Berkeley’s Farid found that participants correctly identified AI-generated voices about 60% of the time (not much better than random chance), while advances like false heartbeats make AI-generated humans tougher than ever to spot.
Nevertheless, most lawyers and legal experts I spoke with said copyright laws are still adult performers’ best bet in the US legal system, at least for getting their face-swapped content taken down. For his clients, Silverstein says, he tries to figure out the content’s origins and then issue takedown requests under the Digital Millennium Copyright Act, a 1998 law that adapted copyright law for the internet era. “Even recently, I had a performer who has an insanely well-known tattoo,” he says, and with a DMCA subpoena he managed to identify the poster of the content, who voluntarily removed it.
But this way of working is becoming increasingly rare.
These days it’s nearly “impossible,” Silverstein says, to determine who produced a deepfake, because many platforms that host pirated content operate facelessly. They’re also often based in places that “don’t really care about US law when it comes to copyrights,” says Rocket—places like Russia, the Seychelles, and the Netherlands.
While governments in the EU, the UK, and Australia have said they will ban or restrict access to nudify apps, it’s not an easily executed proposition. As Craanen notes, when app stores remove these services, they often simply reappear under different names, providing the same services. And social platforms where people share NCII deepfakes, argues Rocket, are slacking in getting them removed. “It’s endless, and it’s ridiculous, because places like Twitter and Facebook have the same technology we do,” Rocket says. “They can identify something as an infringement instantly, but they choose not to.”
(An Apple spokesperson, Adam Dema, said in an email that “’nudification’ apps are against our guidelines” in the app store, and it has “proactively rejected many of these apps and removed many others,” flagging a reporting portal for users. A Google spokesperson emailed, “Google Play does not allow apps that contain sexual content,” noting that the company takes “proactive steps to detect and remove apps with harmful content” and has suspended hundreds of apps for violating its policy. A Meta spokesperson shared a blog post about actions that company has taken against nudify apps but did not respond to follow-up questions about copyrighted material. X did not respond to a request for comment.)
As porn performers are forced to navigate AI-related threats, the only current federal law to address deepfakes may not help them much—and could even make matters worse. The Take It Down Act, which became US law last year, criminalizes publishing NCII and requires websites to remove it within 48 hours. But, as Farid notes, people could weaponize the measure by reporting porn that was made legally and with consent and claiming that it’s NCII. This could result in the content’s removal, which would hurt the performers who made it. Santa Clara’s Goldman points to Project 2025, the Heritage Foundation’s policy blueprint for the second Trump administration, which aims to wipe porn from the web. The Take It Down Act, he argues, “allows for the coordinated effort to scrub adult content from the internet.”
US lawmakers have a history of hurting sex workers in their attempts to regulate explicit content online. State-level age verification laws are an example; visitors can pretty easily get around these measures, but they can still result in reduced revenue for adult performers (because of lower traffic to those sites and the high price of age-checking services they have to purchase).
“They’re always doing something to fuck with the porn industry, but not in a way that actually helps sex workers,” says Jennifer. “If they do something, they’re taking away your income again—as opposed to something like giving you more rights to your image, [which] would be tremendously helpful.”
But as generative AI plays an increasingly large role in NCII deepfakes, the types of images to which adult performers have rights moves deeper into a gray area. Can actors lay claim to AI images likely trained on their bodies? How about AI-generated videos that impersonate them, like the one that tricked Tanya Tate’s fan?
The biggest challenge will be creating “legitimate, effective laws that will absolutely protect content creators from abusing their likeness to train and create AI,” Rocket says. “Absent that, we’re just going to have to keep pulling content down from the internet that’s fake.”
In the meantime, a few porn actors tell me, they’re trying to take advantage of copyright laws that weren’t really made for them; they’ve signed with platforms that host their AI-generated duplicates, with whom fans pay to chat, in part so they’ll have contracts that protect ownership of their AI likenesses. When I spoke with the actor Kiki Daire in September 2025 for a story on adult creators’ “AI twins,” she said she “own[ed] her AI” because she’d signed a contract with Spicey AI, a site that hosted AI duplicates of adult performers. If another company or person created her AI-generated likeness, she added, “I have a leg to stand on, as far as being able to shut that down.”
Even this, though, is not a sure thing; Spicey AI, for instance, shut down several months after I spoke with Daire, so it’s unlikely that her contract would hold. And when I spoke in October with Rachael Cavalli, another adult actor who had signed with an AI duplicate site in hopes it’d help protect her AI image, she admitted, “I don’t have time to sit around and look for companies that have used my image or turned something into a video that I didn’t actually do … it’s a lot of work.” In other words, having rights to your AI image on paper doesn’t make it easier to track down all the potentially infinite breaches of those rights online.
If she’d known what she knows about technology today, Jennifer says, she doesn’t think she would have done porn. The risks have increased too much, and too unpredictably. She now does in-person sex work; it’s “not necessarily safer,” she says, “but it’s a different risk profile that I feel more equipped to manage.”
Plus, she figures AI is unlikely to replace in-person sex workers the way it could porn actors: “I don’t think there’s going to be stripper robots.”
Jessica Klein is a Philadelphia-based freelance journalist covering intimate partner violence, cryptocurrency, and other topics.
The Tesla Semi has officially arrived. The company recently released a photo of the first vehicle rolling off its new full-scale production line.
This moment has been nearly a decade in the making: The company first announced the truck in late 2017. And now we’ve got final battery specs, official prices, and big news about big orders.
The Semi is a relatively affordable electric semitruck with pretty impressive performance. It also comes at a moment when Tesla has lost its grip on the global electric-vehicle market. Let’s talk about what’s new with the Tesla Semi and why this could be a breakout moment for electric trucking.
Medium- and heavy-duty vehicles, like buses and semitrucks, make up a small fraction of vehicles on the road but contribute an outsize fraction of pollution, including both carbon dioxide emissions and other pollutants like nitrogen oxides (NOx) and small particles. Globally, trucks and buses represent about 8% of total vehicles on the road, but they create 35% of carbon dioxide emissions from road transport.
Tesla’s latest addition to its vehicle lineup, the Class 8 Semi, could be part of the solution to cleaning up this polluting sector. (I’ll note here that I briefly interned at Tesla in 2016. I don’t have any ties to or financial interest in the company today.)
In November 2017, Elon Musk took to the stage at a lavish event in LA to announce the Semi. At that event, Musk promised a truck that could go from zero to 60 miles per hour in five seconds, could achieve a range of 500 miles, and would come with thermonuclear-explosion-proof glass. (Remember the era before the Twitter takeover and DOGE, when this was what Musk was known for? A simpler time.)
Soon after the unveiling, major corporations including Walmart put in early orders for Tesla Semis. Deliveries were expected in 2019.
That deadline obviously didn’t work out. The date was pushed back several times, and Tesla did start delivering a small number of pilot trucks, beginning in 2022. But this year, things got more serious, with the company releasing its final production specifications in February and rolling its first Semi off its high-volume production line in late April.
And last week, WattEV announced an order of 370 Tesla Semis. WattEV offers electric freight operations, essentially providing trucks as a service to companies so they don’t have to purchase their own or supply their own charging infrastructure. The company will pay over $100 million for the new trucks, and the first 50 should be delivered this year, with the full fleet expected by the end of 2027. Those trucks will be supported by megawatt-charging systems located in Oakland, Fresno, Stockton, and Sacramento.
With the factory up and running and a huge order on the books, it feels as if the Tesla Semi has truly arrived. And some of Musk’s claims from 2017 ring true: The base model has a range of about 320 miles, and the long-range version about 480 miles (quite close to his 500-mile claim).
Delivering this much range for this big truck means a whopping battery. The base model Tesla Semi battery pack has a usable capacity of 548 kilowatt-hours, according to a document filed with the California Air Resources Board (CARB). But the battery is even more massive in the long-range version, which boasts a whopping 822 kilowatt-hour battery. Compare these to the Tesla Model 3, which typically comes with a 64 kilowatt-hour pack.
I reached out to Tesla to confirm the battery size and ask other questions for this article; the company didn’t respond.
These trucks cost quite a bit more than they were expected to in 2017. At that time, the expected price was $150,000 for the base model and $180,000 for the long-range. Today, Tesla is pricing the trucks at $260,000 and $300,000, respectively, according to documentation filed with CARB.
That’s considerably more expensive than the median diesel truck being sold today, which rang in at $172,500 for the 2025 model year, according to research from the International Council on Clean Transportation. But it’s much cheaper than similar battery-electric trucks available today, where the median is about $411,000.
And in California, where companies can get vouchers that cover $120,000 towards the purchase price of an electric truck, the Tesla Semi is competitive right away, especially since electric trucks tend to be much cheaper to run and maintain than diesel ones.
Over the years, it wasn’t always clear that the Tesla Semi would ever actually hit the roads. (At that same 2017 event, Musk announced a new Roadster sports car, and that’s nowhere to be seen.) So it’s encouraging to see the factory starting up, and a large order that looks like it could lend this project some commercial momentum.
Tesla had a massive impact on the electric vehicle market, and if it can scale production and support charging infrastructure, it could help do the same for trucking.
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
A big deal: Varda Space Industries says it has signed a pharmaceutical company as a commercial customer, marking what could be a landmark moment for in-orbit manufacturing.
Space as a lab: The bet is that microgravity causes drug molecules to crystallize into atomic arrangements impossible on Earth, potentially unlocking new versions of existing medicines.
Economics favor drugs: At $7,000 per kilogram to reach orbit, space manufacturing is impractical for most industries — but blockbuster drugs can be worth over $100 million per kilogram, making them a rare exception to the brutal math of rocket launches.
Still more experiment than factory: Despite the excitement, no product has ever been manufactured in space, brought back, and sold on Earth.
Varda Space Industries, a startup that’s been pitching its ability to perform drug experiments in space, says it has signed up the pharmaceutical company United Therapeutics in what may be remembered as a notable step toward in-orbit manufacturing.
The idea of building things in outer space for use on Earth has so far been explored mostly on board the International Space Station, and only in small-scale experiments backed by governments.
But Varda, based in El Segundo, California, is now telling drug companies it has a practical, and repeatable, way to produce novel molecules in microgravity.
“This is the first commercial path to products made in space,” says Michael Reilly, Varda’s chief strategy officer.
The scientific idea is that chemical mixtures have different properties under weightless conditions. For instance, water will hang together in a wiggly sphere, since without gravity, surface tension is the strongest force present.
The plan is to launch versions of United Therapeutics’ drugs into orbit, where they can be allowed to form solid crystals. The hope is that in microgravity, they’ll take on atomic arrangements not seen on Earth, possibly leading to new versions with improved stability or other valuable properties.
United is led by CEO Martine Rothblatt, who worked on early telecommunications satellites. Since then, she’s built a multibillion-dollar health franchise with a succession of drugs to treat a lung disease called pulmonary arterial hypertension, which her daughter suffers from, and a subsidiary developing genetically modified pigs as a source of organs for transplantation.
Rothblatt says space could be the next step if orbital conditions permit United to identify “even more amazing” versions of its drugs.
Space to reformulate
Pharmaceutical companies often try to keep their blockbuster franchises alive by creating improved versions of drugs or reformulating them—for example, making the switch from a pill to an inhaled version, as United has done with some of its products. Doing so can keep imitators at bay and create extra decades of patent protection.
Assisting drugmakers are specialist companies, such as Halozyme and MannKind, that earn profits by helping to reformulate other companies’ drugs, often taking a royalty on future sales.
That’s the business Varda has been trying to break into—by using excursions into space instead of nebulizers, patches, or nanoparticles. The company was formed in 2021 by Delian Asparouhov, a partner at Peter Thiel’s Founders Fund, along with Will Bruey, a former avionics engineer with Elon Musk’s SpaceX who is now Varda’s CEO.
The pair’s bet is that space manufacturing will become viable once rocket launches become frequent enough—and cheap enough—to support a business model in which raw materials are sent into orbit, processed, and then returned to Earth in a new form.
And that’s starting to happen. To get into space, Varda has been purchasing rides from SpaceX—which now launches a rocket every two or three days, usually a reusable Falcon 9.
Those rockets have a nose cone, or payload fairing, about the size of a moving truck that gets filled with satellites or instruments, which are then released into orbit.
Starting in 2023, Varda began sending up small satellites that have a boulder-size capsule attached. The capsule contains equipment to carry out experiments, and it can detach and fall back to Earth, entering the atmosphere at a speed of around Mach 25 before slowing via air resistance and eventually drifting to land with a parachute. (Varda lands its craft in the Australian outback.)
That speedy reentry has also drawn interest from the US military, including the Air Force, which has paid Varda to fly instruments and take measurements relevant to hypersonic missile technology. Of the six craft Varda has paid to put into orbit so far, half have been dedicated to military research and half carried drug-related demonstrations.
At Varda, such “dual use” of technology is accepted as part of being in the space business, which remains reliant on government support. The company’s founders say Varda may be the only company that employs hypersonic engineers and pharmaceutical chemists under the same roof.
At Varda’s headquarters, drug samples are loaded into a spinning arm that creates extra-high g-forces. While that’s the opposite of microgravity, increased weight can provide clues into whether a drug will act differently under new conditions.
COURTESY VARDA
Launching industries
Actual space manufacturing still remains mostly an aspirational project. In 2021, Jeff Bezos, after his first trip aloft in a rocket, suggested that polluting industries should be moved beyond the atmosphere. “We need to take all heavy industry, all polluting industry, and move it into space. And keep Earth as this beautiful gem of a planet that it is,” he told MSNBC.
Weight is the big obstacle to such dreams. It still costs around $7,000 to launch a single kilogram of payload into orbit, which makes it impractical to, say, send cotton into space to be dyed there, or even to launch the acids and solvents needed to make a semiconductor chip.
But drugs may be among the few exceptions to this economic rule, since pound for pound, they can be as valuable as rare radioactive isotopes and fine-cut diamonds.
For instance, just one kilogram of the weight-loss drug Ozempic is worth more than $100 million at retail. (The reason your Ozempic bill is only $1,000 a month is that minute quantities of the active ingredient are present in the shots.)
That’s why Varda thinks it may eventually be able to manufacture drugs in orbit. However, its effort with United is more of a flying experiment to learn whether the company’s lung medicines will crystallize differently in microgravity.
The terms of the deal between Varda and United aren’t public, and the companies haven’t said which specific drugs the collaboration will study. But Rothblatt did confirm that United is paying Varda to help it identify new crystal forms of its drugs (also called polymorphs), which it hopes could have improved properties.
“One has to do the experiment to find out if that is so. The first part of the experiment is to see what polymorphs of these molecules can be made without the influence of gravity,” she says. “Then, once we have those polymorphs, we will test them.”
There is good evidence that crystals form differently in space. For instance, in 2017 the pharmaceutical giant Merck sent samples of its cancer immunotherapy drug Keytruda to the International Space Station, where it was found to form crystals of a single size. On Earth, the drug tended to form two different sizes at once.
That experiment offered clues for how to formulate the drug as a shot instead of administering it intravenously. Still, when Merck introduced a Keytruda injection last year, it ended up using a different approach. That means there’s still no straight-line connection between orbital discoveries and any drug here on Earth. Actual space factories are another step further from reality.
“We’ve been learning from space for years, but I can’t name anything manufactured in space, brought down to Earth, and sold,” says Reilly. “So that is a first—or it will be a first.”
Reilly says that Varda anticipates launching United Therapeutics’ drugs into orbit sometime early next year.
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: 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, Futurismfound that if you promptedxAI’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 multiplestudies 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.
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.
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.
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.
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.
