Chinese tech workers are starting to train their AI doubles–and pushing back

Tech workers in China are being instructed by their bosses to train AI agents to replace them—and it’s prompting a wave of soul-searching among otherwise enthusiastic early adopters. 

Earlier this month a GitHub project called Colleague Skill, which claimed workers could use it to “distill” their colleagues’ skills and personality traits and replicate them with an AI agent, went viral on Chinese social media. Though the project was created as a spoof, it struck a nerve among tech workers, a number of whom told MIT Technology Review that their bosses are encouraging them to document their workflows in order to automate specific tasks and processes using AI agent tools like OpenClaw or Claude Code. 

To set up Colleague Skill, a user names the coworker whose tasks they want to replicate and adds basic profile details. The tool then automatically imports chat history and files from Lark and DingTalk, both popular workplace apps in China, and generates reusable manuals describing that coworker’s duties—and even their unique quirks—for an AI agent to replicate. 

Colleague Skill was created by Tianyi Zhou, who works as an engineer at the Shanghai Artificial Intelligence Laboratory. Earlier this week he told Chinese outlet Southern Metropolis Daily that the project was started as a stunt, prompted by AI-related layoffs and by the growing tendency of companies to ask employees to automate themselves. He didn’t respond to requests for further comment.

Internet users have found humor in the idea behind the tool, joking about automating their coworkers before themselves. However, Colleague Skill’s virality has sparked a lot of debate about workers’ dignity and individuality in the age of AI.

After seeing Colleague Skill on social media, Amber Li, 27, a tech worker in Shanghai, used it to recreate a former coworker as a personal experiment. Within minutes, the tool created a file detailing how that person did their job. “It is surprisingly good,” Li says. “It even captures the person’s little quirks, like how they react and their punctuation habits.” With this skill, Li can use an AI agent as a new “coworker” that helps debug her code and replies instantly. It felt uncanny and uncomfortable, Li says. 

Even so,  replacing coworkers with agents could become a norm. Since OpenClaw became a national craze, bosses in China have been pushing tech workers to experiment with agents. 

Although AI agents can take control of your computer, read and summarize news, reply to emails, and book restaurant reservations for you, tech workers on the ground say their utility has so far proven to be limited in business contexts. Asking employees to make manuals describing the minutiae of their day-to-day jobs the way Colleague Skill does is one way to help bridge that gap. 

Hancheng Cao, an assistant professor at Emory University who studies AI and work, believes that companies have good reasons to push employees to create work blueprints like these, beyond simply following a trend. “Firms gain not only internal experience with the tools, but also richer data on employee know-how, workflows, and decision patterns. That helps companies see which parts of work can be standardized or codified into systems, and which still depend on human judgment,” he says.

To employees, though, making agents or even blueprints for them can feel strange and alienating. One software engineer, who spoke with MIT Technology Review anonymously because of concerns about their job security, trained an AI (not Colleague Skill) on their workflow and found that the process felt reductive—as if their work had been flattened into modules in a way that made them easier to replace. On social media, workers have turned to bleak humor to express similar feelings. In one comment on Rednote, a user wrote that “a cold farewell can be turned into warm tokens,” quipping that if they use Colleague Skill to distill their coworkers into tasks first, they themselves might survive a little longer.

The push for creating agents has also spurred clever countermeasures. Irritated by the idea of reducing a person to a skill, Koki Xu, 26 an AI product manager in Beijing, published an “anti-distillation” skill on GitHub on April 4. The tool, which took Xu about an hour to build, is designed to sabotage the process of creating workflows for agents. Users can choose between light, medium, and heavy sabotage modes depending on how closely their boss is observing the process, and the agent rewrites the material into generic, non-actionable language that would produce a less useful AI stand-in. A video Xu posted about the project went viral, drawing more than 5 million likes across platforms.

Xu told MIT Technology Review that she has been following the Colleague Skill trend from the start and that it has made her think about alienation, disempowerment, and broader implications for labor. “I originally wanted to write an op-ed, but decided it would be more useful to make something that pushes back against it,” she says.

Xu, who has undergraduate and master’s degrees in law, said the trend also raises legal questions. While a company may be able to argue that work chat histories and materials created on a work laptop are corporate property, a skill like this can also capture elements of personality, tone, and judgment, making ownership much less clear. She said she hopes Colleague Skill prompts more discussion about how to protect workers’ dignity and identity in the age of AI. “I believe it’s important to keep up with these trends so we (employees) can participate in shaping how they are used,” she says. Xu herself is an avid AI adopter, with seven OpenClaw agents set up across her personal and work devices.

Li, the tech worker in Shanghai, says her company has not yet found a way to replace actual workers with AI tools, largely because they remain unreliable and require constant supervision. “I don’t feel like my job is immediately at risk,” she says. “But I do feel that my value is being cheapened, and I don’t know what to do about it.”

Colossal Biosciences said it cloned red wolves. Is it for real?

If you want to capture something wolflike, it’s best to embark before dawn.

So on a morning this January, with the eastern horizon still pink-hued, I drove with two young scientists into a blanket of fog. Forty miles to the west, the industrial sprawl of Houston spawned a golden glow. Tanner Broussard’s old Toyota Tacoma bumped over the levee-top roads as killdeer, flushed from their rest, flew across the beams of his headlights. 

Broussard peered into the darkness, looking for traps. “I have one over here,” he said, slowing slightly. A master’s student at McNeese State University, he was quiet and contemplative, his bearded face half-hidden under a black ball cap. “Nothing on it,” he said, blandly. The truck rolled on.

Wolves and their relations—dogs, jackals, coyotes, and so on—are classed in the family Canidae, and the canid that dominated this landscape in eastern Texas was once the red wolf. But as soon as white settlers arrived on the continent, Canis rufus found itself under siege. The war on wolves “lasted 200 years,” federal researchers once put it, in a surprisingly evocative report. “The wolf lost.” By 1980, the red wolf was declared extinct in the wild, its population reduced to a small captive breeding population.

Still, for decades afterward, people noted that strange wolflike creatures persisted along the Gulf Coast. Finally, in 2018, scientists confirmed that some local coyotes were more than coyotes: They were taller, long-legged, their coats shaded with hints of cinnamon. These animals contained relict red wolf genes. They became known as the ghost wolves.

Broussard grew up in southwest Louisiana, watching coyotes trot across his parents’ ranch. The thrilling fact that these might have been not just coyotes but something more? That reset a rambling academic career. In 2023, Broussard had recently returned to college after a seven-year pause, and his budding obsession with wolves narrowed his focus. Before he finished his bachelor’s degree, he began to supply field data to a prominent conservation nonprofit.

a wolf pup chews on a terrycloth toy
The American red wolf, Canis rufus, is the most endangered wolf species in the world. This pup is one of four animals said to be clones of this native North American species.
COURTESY OF COLOSSAL BIOSCIENCES

Then, last year, just before he began his master’s studies, he woke to disconcerting news. A startup called Colossal Biosciences claimed to have resuscitated the dire wolf, a large canid that went extinct more than 10,000 years ago. Pundits debated the utility of the project and whether the clones—technically, gray wolves with some genetic tweaks—could really be called dire wolves. But what mattered to Broussard was Colossal’s simultaneous announcement that it had cloned four red wolves.  

“That surprised pretty much everybody in the wolf community,” Broussard said as we toured the wildlife refuge where he’d set his traps. The Association of Zoos and Aquariums runs a program that sustains red wolves through captive breeding; its leadership had no idea a cloning project was underway. Nor did ecologist Joey Hinton, one of Broussard’s advisors, who had trapped the canids Colossal used to source the DNA for its clones. Some of Hinton’s former partners were collaborating with the company, but he didn’t know that clones were on the table.

There was already disagreement among scientists about the entire idea of de-extinction. Now Colossal had made these mystery clones, whose location was kept secret. Even the purpose of the clones was murky to some scientists; just how they might restore red wolf populations was unclear. 

Red wolves had always been a contentious species, hard for scientists to pin down. The red wolf research community was already marked by the inevitable interpersonal tensions of a small and passionate group. Now Colossal’s clones became one more lightning rod. Perhaps the most curious question, though, was whether the company had cloned red wolves at all. 


You can think of the red wolf as the wolf of the East—an apex predator that once roamed the forests and grasslands and marshes everywhere from Texas to Illinois to New York. Smaller than a gray wolf (though a good bit larger than a coyote), this was a sleek beast, with, according to one old field guide, a “cunning fox-like appearance”: long body, long legs; clearly built to run across long distances. Its coat was smooth and flat and came in many colors: a reddish tone that comes out in the right light, yes, but also, despite the name, white and gray and, in certain regions and populations, an ominous all black.

We know these details thanks to a few notes from early naturalists. As writer Andrew Moore writes in his new book, The Beasts of the East, by the time a mammalogist decided to class these eastern wolves as a standalone species in the 1930s, the red wolf had been extirpated from the East Coast and was rapidly dwindling across its range. Working with remnant skulls and other specimens, the mammalogist chose the name red wolf—which was later enshrined with the Latinate Canis rufus—because that’s what these wolves were called in the last place they survived. 

The looming extinction of the red wolf turned out to be a good thing for coyotes. Canis latrans is a distant relative of wolves that split away from a common ancestor thousands of years ago and might be considered, as one canid biologist put it to me, the “wolf of the Anthropocene.” Their smaller size means they need less food and can survive in smaller and more fragmented territory, the kind that modern humans tend to build. 

The last red wolves, which lived in Louisiana and Texas, decided a strange and smaller mate was preferable to no mate at all.

Red wolves had kept coyotes out of eastern America, outcompeting them for prey. Now, as the wolves declined, the coyotes began to slip in. The last red wolves, which lived in Louisiana and Texas, decided a strange and smaller mate was preferable to no mate at all. Soon the territory became a genetic jumble, home to both wolves and coyotes and hybrids that, after several generations of intermixing, came in every shade between. Scientists call such a population a “hybrid swarm,” and it poses a genetic threat to the declining species: As more coyotes poured east, and as all the canids kept interbreeding, there would be nothing that was “purely” wolf. 

Ron Wooten surveys a location on the edge of Galveston Island State Park in Texas. In 2016, Wooten’s photographs of oversized local coyotes got the attention of Joey Hinton, then a postdoctoral researcher at the University of Georgia.
TRISTAN SPINSKI

For years, no one seemed to notice. Perhaps trappers in the region mistook the new hybrids for wolves—or were happy to take the higher bounty that a wolf pelt earned. Finally, though, by the 1960s, as the concept of endangered species first emerged, biologists began to worry for the disappearing wolf. 

The best solution they could come up with was a program of mass extermination. Over several years, trappers rounded up hundreds of canids in Texas and Louisiana. Those deemed true red wolves (on the basis of their howls and skull shape) were whisked away to breed in captivity. Most of the rest were euthanized. In 1980, the red wolf was declared extinct in the wild. To put it plainly: The red wolf was wiped out intentionally, in a roundabout effort to keep it alive.

Just 14 individuals survived this gauntlet; today’s wolves descend from 12 of those. They became the ark, the source material for the few hundred red wolves that live today. There are about 280 in the “Species Survival Plan” population, living in captivity, and another 30 or so that roam a federal refuge in coastal North Carolina, and that the government deems “nonessential” and “experimental.” According to the US Fish and Wildlife Service, to be classified as a representative of the protected entity known as Canis rufus, an animal must trace at least 87.5% of its lineage to the 12 founders. 

The scientist who led this trapping-and-breeding program understood that the federal government would be narrowing the red wolf’s gene pool precipitously—so much so that the result could be an entirely new species. None of those notably black wolves persisted in the new population, for example. But what other choice existed? A new kind of wolf, free of the taint of the invading coyote, seemed better than no wolf at all.


After I learned about Colossal’s clones, I decided to travel to eastern Texas. The clones were hidden away on an unnamed refuge, but on this coastline, I might be able to at least see the animals that provided their genetic material. I arrived in the small town of Winnie on a balmy afternoon in January and met up with Broussard and another graduate student, Patrick Cunningham, at a Tex-Mex joint to discuss the challenges of studying red wolves.

“We don’t have a good reference genome,” Cunningham said. We can collect DNA from the descendants of the 12 founders, but not from the countless wolves that had been killed. It’s difficult to extract usable DNA from old samples. So our picture of what the species used to look like is limited. 

Studies of the genes we do have, meanwhile, have proved controversial. When a Princeton geneticist named Bridgett vonHoldt dug into the genome of the Species Survival Plan population, she found little about their DNA that could set them apart from other wolflike American canids. In 2016, in a paper in Science Advances, vonHoldt and her coauthors wondered if there ever really was a separate southern wolf species. Perhaps the 12 founders were just coyotes injected with some smaller portion of wolf.

It’s long been clear that North America’s soup of Canis genes is something less like a family tree and more like a river—one that’s broken by islands and sandbars into many braided channels that split and merge and re-split.

Her paper called for complex new interpretations of the Endangered Species Act. We should, she wrote, focus less on species and more on the function a group of animals performs. The red wolves deserved protection, then, as creatures that filled the same role as truly endangered wolves and carried some of their genetics. Nonetheless, for Canis rufus, the timing of the paper was bad news.

The red wolves roaming that federal reserve in North Carolina are supposed to be a first step toward the species’ return to the wild. But some locals never liked the idea of living alongside wolves. By 2016, state officials had turned against the recovery program and were requesting its termination. The wild population, which had included as many as 120 a few years earlier, was falling. But the US Fish and Wildlife Service had paused further releases of wolves. Now a group of scientists, led by vonHoldt, was saying that the red wolf showed “a lack of unique ancestry.” Why spend money, some people wondered, on a species that does not exist? 

Part of the problem was that the concept of a “species” is less sturdy than your high school biology teacher might have led you to believe. The most familiar definition is that a species consists of animals that can produce fertile offspring. But that’s a rule various species of canids violate all the time; it’s long been clear that North America’s soup of Canis genes is something less like a family tree and more like a river—one that’s broken by islands and sandbars into many braided channels that split and merge and re-split.

VonHoldt suggested that the modern red wolf is a channel in that river, part wolf and part coyote, that appeared surprisingly recently. But a year after her study came out, other researchers claimed that her data, if interpreted differently, could suggest that the red wolf braid had emerged tens of thousands of years ago, meaning this was a species that had long been on its own evolutionary journey. 

These nuances were confusing for the policymakers who oversaw actual, living animals. “Congress was just like, ‘What is going on?’” Cunningham said. “‘Why is there not just a simple explanation for what this thing is?’”

Given the policy implications, the National Academies of Science, Engineering, and Medicine tasked a panel of scientists with finding that simple answer. Their report, published in 2019, declared that the red wolf is, by virtue of its appearance and seemingly long-standing isolated population, a species. As their study got underway, though, a new question was arising: What to make of the strange canids on the Gulf Coast, those today called the ghost wolves?


The path to that name began in 2008, when a photographer from Galveston Island, Texas, grew obsessed with the oversized local coyotes. He began to take photos of the packs, which he distributed to scientists, seeking answers: What were they? By 2016, the photos had reached Joey Hinton, then a postdoctoral researcher at the University of Georgia.

Hinton had spent more than a decade trapping wolves and coyotes in North Carolina, and his work has always focused on live animals, especially visual ways to distinguish red wolves and coyotes. So he was a good choice for helping the photographer, Ron Wooten, figure out the status of the canids. In his freezer Wooten also had tissue samples he’d collected from road-killed coyotes. These could be used by a geneticist to give a fuller picture of the canids’ ancestry. So vonHoldt was brought in too. The result was a 2018 paper, with Hinton as a coauthor, that identified the Galveston Island canids as at least part red wolf.

These canids were not, to be clear, actual red wolves; no canid on the Gulf Coast is descended from the government’s 12 canonical founders, so under current policy, none can be officially classified as a wolf. Subsequent studies have found that, on average, the ancestry of the region’s canids is less than half red wolf, and often far less. In scientific terms, the red wolf had introgressed into the Gulf Coast population—its genes had leaked across the species boundary and lodged themselves in a different population.

Hinton, vonHoldt, and their coauthors also noted the presence of what they called “ghost alleles”—DNA sequences unknown in any other named species. The Occam’s razor assumption was that, in these already wolfy coyotes, these sequences likely represented Canis rufus genetics that had not been captured in the sweep of the marsh that yielded the Species Survival Plan population. Since so much of the red wolf gene pool had been lost, these genes seemed to be a potential resource for the species—a way to expand its diversity. When the New York Times covered this discovery a few years later, the headline popularized the “ghost wolf” moniker that has proved so indelible. 

As it happened, a separate team, focused on canids in and around federally protected marsh in Louisiana, published a similar paper in 2018, at nearly the same time. The twin discoveries raised new questions—What should we make of these creatures, the latest branch in the canid river? What do they mean for the wolves in North Carolina?—and helped researchers secure new funding.

In 2020, vonHoldt and Kristin Brzeski, a former postdoc under vonHoldt and now a professor at Michigan Technological University, launched what they called the Gulf Coast Canine Project. Brzeski, who led the field work, hired Hinton to do much of the canid trapping and sample collection. In 2022, vonHoldt, Hinton, and Brzeski were all coauthors of another paper that identified even more red-wolf-descended canids in Louisiana and noted a positive correlation between red wolf ancestry and body mass—the more red wolf genes, the bigger the animal. The paper also suggested that given this newly discovered reservoir of red wolf DNA, “genomic technologies” could prove useful in the long-term survival of the species.

Bridgett vonHoldt (left) and Kristin Brzeski (center) visit a location where canids have been spotted with an animal control worker.
TRISTAN SPINSKI

VonHoldt and Brzeski eventually conceived of an ambitious project. They hoped that by carefully matching the most wolf-­descended canids and breeding them together, over three generations they’d increase the proportion of red wolf genes—de-introgression. “I’m expecting, based on these pairings of animals, that I can stitch together the puzzle pieces,” vonHoldt told me recently. “We are very likely to get puppies each generation that are higher and higher red wolf content”—enough wolf content, she hopes, to eventually win her permission to breed the resulting animals with the Species Survival Plan population of red wolves. They’d essentially be adding a new founder to the limited lineage.

Hinton told me he felt he’d been kept in the dark about the de-introgression idea. He was also worried, he says, to learn that Colossal Biosciences hovered in the background. (In a draft proposal for the project, vonHoldt indicated that Colossal would be in charge of “live capture.”) Hinton says he was not comfortable collecting materials for a for-profit company that has to keep its shareholders happy. 

Hinton says he reached out to state and federal officials and found they knew little about the project. (The US Fish and Wildlife Service declined to make anyone available for an interview for this story, and the Louisiana Department of Wildlife and Fisheries did not reply to requests for comment.) He knew the group’s next phone call would be difficult, and indeed it was. He wound up speaking one-on-one with vonHoldt for at least half an hour.

“We didn’t reach an agreement,” he says. After the call, he sent her a text: He was exiting the project. He believes that had Colossal not been involved, they’d all still be working as a team. Both vonHoldt and Brzeski declined to comment on what felt to them like a matter of interpersonal relationships rather than a scientific dispute. “There were challenges over time, and the tone and manner of the interactions became increasingly difficult to navigate productively,” Brzeski said in an email. 


Colossal was cofounded in 2021 by George Church, an eminent Harvard geneticist who, thanks to investors, could finally embark on a long-discussed dream. He wanted to make de-extinction a reality—using CRISPR gene-editing technology to, say, turn a modern elephant into something like the extinct woolly mammoth. The concept has drawn skepticism from the beginning—at best it would only be possible to make something like a woolly mammoth. Was there any point to that? Some scientists note that genes alone do not teach an animal how to exist in the world; indeed, since social structures affect how genes are expressed, an animal without parents may not effectively fill its ecological niche.

Less reproachable, though, was Colossal’s interest in partnering with scientists who, like vonHoldt and Brzeski, focus on extant species that are endangered. This gave more heft to Colossal’s gee-whiz de-extinction projects: They would, along the way, supply technology that could save our natural world.

For red wolves, such technologies could offer a quick way to expand the limited gene pool. Through genetic engineering, Colossal could take clones of the Gulf Coast canids and tune up the wolf, tune down the coyote. It would be a high-tech shortcut past vonHoldt and Brzeski’s careful breeding program. “You can do the same thing much more precisely, much more quickly, much more efficiently, in vitro,” says Matt James, Colossal’s chief animal officer and the executive director of the Colossal Foundation, the company’s nonprofit arm. VonHoldt notes that the old-fashioned approach, with breeding, means she has to take a few individual canids out of the wild, into captivity—never ideal but, in her view, a worthwhile price for progress. The advantage of cloning, which Colossal has managed to do with blood samples alone, is that the wild canid populations can be kept intact. 

VonHoldt has always been an advocate for wolves. Indeed, when she hypothesized that the red wolf had hybrid origins, in 2016, she’d framed it as an argument for protecting the gray wolf, which the federal government was considering removing from the Endangered Species List. (In short: If all wolves were one wolf, then it was undeniable that the species’ range had contracted precipitously.) But she’d grown frustrated with the federal government’s efforts to restore the red wolf, which after half a century had seen few meaningful successes, she says. 

VonHoldt joined Colossal’s scientific advisory board in 2023. “I love the bold, the shock and awe,” she told me, explaining her decision. She saw the fact that Colossal sparked controversy as an asset, given the problems she sees in conservation: “Get something out there. Start pushing buttons and start forcing these conversations,” she says. The red wolf was akin to a terminal patient who was ready to accept any and all therapies, however experimental. Why not embrace biotech? 

She also notes that the federal budget for endangered species conservation is incredibly limited. Rely only on that money and “we can kiss our world goodbye,” she said in an e-mail. The $100 million raised by the Colossal Foundation is essential, then, she says. As for the samples the team had collected on the Gulf Coast, she says, limited freezer space is often devoted to animals that are officially categorized as threatened or endangered, which the Gulf Coast canids are not. Colossal could take the samples, and the team passed them along to the company.

Dr. Joey Hinton
Ecologist Joey Hinton trapped the canids that Colossal Biosciences used to source the DNA for its clones. He dismisses the clones as a way for the company to earn headlines and attract funding.
RICH SAAL

It was Hinton—a source for a former story—who first alerted me to Colossal’s work on red wolves; he described vonHoldt and Brzeski’s de-introgression project, which won federal funding in late 2024, as nefarious-sounding work to “disappear” canids off the Gulf Coast. But he did not have all the details of the project, which had changed after he left the team. He suggested they’d be “just throwing animals together,” whereas vonHoldt described a careful program of observing the canids in the wild so she could determine which acted most wolflike, findings she’d cross-­reference with their genetic data.

 Colossal did not wind up participating in the de-­introgression project. But the company is doing work on the red wolf that ­vonHoldt views as complementary: Its scientists are assembling a “pangenome” of North American canids by studying samples pulled from museums, universities, zoos, and other institutions. This data set is expected to clarify both what genetic sequences are shared across the entire canid family and what snippets differ in certain populations. The hope is that this will provide a clearer picture of the red wolf in its early days, before the coyotes arrived and the gene pool narrowed. That might shift what Colossal’s James calls the government’s arbitrary definition of the red wolf, to encompass more of the species’ full former diversity. 

The pangenome, then, might allow vonHoldt’s de-­introgressed canids, descended from the Gulf coast canids, to qualify as actual red wolves. Indeed, James suggested to me that more information about historic red wolves might force the government to take a new look at the Gulf Coast canids; some individuals might have high enough red wolf ancestry to be classified as red wolves. (“That has management implications that terrify state and federal government,” he added.)

hair in Zip-Loc bags on a metal tray
Blood and tissue samples collected by the Galveston Island Humane Society from canid roadkill will be shipped to Princeton University for DNA analysis.
TRISTAN SPINSKI

The purpose of vonHoldt’s de-introgression project is to bring back certain lost red wolf genes—to create a whole new wolf lineage. But she has also pushed against the idea of “genetic purity,” which she thinks limits what we protect with conservation laws; she told me emphasizing it reminds her of the human history of eugenics and “makes every part of my soul hurt.” She cares less about what species are out there, in the landscape, than what ecological function the animals play, and she sees coyotes and red wolves as closely related animals that may have a role to play in one another’s future survival.


As for Colossal’s clones, even vonHoldt seems to describe them as something less than a conservation breakthrough. They are a “proof of principle that we, collectively, as a scientific community, know how to do it,” she told me. If an urgent need arises to clone red wolves, the groundwork has been laid. 

Hinton, meanwhile, is one of several scientists I spoke with who were skeptical Colossal was doing good science, given that so much is conducted behind closed doors. He implied that the clones were nothing but an empty showpiece, a way to earn headlines and attract funders. “The work is anything but symbolic,” James responded via e-mail. “It expands the genetic toolkit available for critically endangered species, demonstrates scalable approaches to biodiversity restoration, and contributes directly to preserving imperiled lineages.” He noted that Colossal had intentionally decided to avoid the “snail’s pace” of the peer review process and suggested that the skepticism from scientists may actually be a “panicked response to being outpaced.”

Until some evidence confirms that the Gulf Coast canids—the source material for the clones—are red wolves, they can’t legally be classified as such for federal conservation purposes. Nonetheless, Colossal’s press release claimed that the company had “birthed two litters of cloned red wolves, the most critically endangered wolf in the world.” On the same day that press release dropped, Colossal’s CEO and cofounder, Ben Lamm, appeared on The Joe Rogan Experience and claimed that he had offered to create hundreds of red wolves for the federal government to use in recovery—for free! He was miffed when the government, under the Biden administration, replied that it wanted to spend several years and many millions of dollars to study the potential for cloning before it would take any action. (The company has gotten more traction with the Trump administration, Lamm said.)

When I first spoke to James at Colossal, he said that he was “cognizant” of the concerns over the names and labels and that the company’s own materials described the clones as “red ‘ghost’ wolves.” He suggested that if anyone assumed the clones were actual red wolves, that was because journalists had failed to grasp the nuances of the science. But this phrase appears so late in a long document that it was cut off in some versions. Later, over email, James indicated that further analysis had convinced him that what the company had created were red wolves, and that anyone who disagreed either could not grasp the science or is “so ideologically opposed to Colossal’s conservation revolution that they are willing to compromise their scientific integrity.”

VonHoldt has had her own issues with the company’s communications; she told me it was “stressful” when Lamm described the clones as red wolves—which, she notes, “federally, they’re not.” But she values the company’s work, she says, and “the thing that I value the most is shaking things up.” People are paying attention to red wolves. If it’s hard to decide what to call the animals on the Gulf Coast—where some heavily wolfy animals live alongside others that are more coyote—that’s just proof that our concept of a “species” does not capture the complex realities on the ground. 


In 2025, the same year as Colossal’s wolf announcement, Hinton launched the Texas-Louisiana Canid Project. He’s working in partnership with Broussard, the master’s student at McNeese, in slightly different territory from vonHoldt and Brzeski—and focusing more on the animals’ appearance and behavior than their genes. The Gulf Coast canids are stable and faring better than the North Carolina red wolves, and his hope is that if we learn why they’ve been successful for so many years, we might be able to help the official red wolf population, which is only just limping along. 

a wolf crosses a road outside of the city
Galveston locals hope that the presence of these remarkable creatures—red wolves or not—might rein in the rapid development of the island’s last stands of green.
TRISTAN SPINSKI

I had planned to join Hinton in the field, but by the time I was able to visit, he’d had to go home to his family. So I joined Broussard on his last days trapping in Texas that season. Before I’d left for Winnie, I’d told my friends I’d be out chasing the last surviving red wolves. But there, on the Gulf Coast, I came to understand that this was just as much a story about coyotes.

That’s what Broussard and Cunningham both called the creatures. Hinton does too; he considers the animals to be a specific “ecotype” of coyote, featuring an injection of wolf DNA that has helped them adapt to the local marshes. 

At vonHoldt’s behest, I drove an hour down the coast to Galveston Island, where she and Brzeski began working with the island’s animal control department; when locals find a coyote, the animal is captured so its blood can be collected and a GPS collar fitted on its neck. A small group of locals who support the project have come to call themselves the “ghost wolf team.” They hoped that the presence of these remarkable creatures might rein in the rapid development of the island’s last stands of green. Still, the people I spoke to in Galveston conceded that the animals were, if special, nonetheless a form of coyote. 

VonHoldt describes Galveston Island as a potential model for what conservation could look like in the future. Top-down recovery hasn’t been working, but helping more places fall in love with their local animals might. And for that to happen, we need to stop obsessing over whether or not something is a “pure” wolf. What matters, she argues, is that an animal is doing what a larger predator does in an ecosystem. She embraces the “ghost wolf” name because, more than “Gulf Coast canid,” it makes clear that there’s something special on the coast—something worth protecting. 

Her vision is enticing: Focus on function over purity. Let evolution proceed. Stop protecting the wolf of the past and consider the wolf of the future. Such rapid genetic exchange may be necessary to help predators adapt to a hotter, increasingly shattered world, she says. 

If we throw out the concept of “endangered species,” will we really protect “endangered functions” instead?

Then again, we already know what’s adapted to the world we’re building: coyotes. The argument against genetic purity can sound like giving up on wolves entirely, with the possible exception of whatever specimens we produce in cloning facilities. And there is the matter of politics: If we throw out the concept of “endangered species,” will we really protect “endangered functions” instead? Under an administration already rolling back environmental protections, the likeliest outcome may be protecting nothing at all.

I tried in Galveston, too, to see the coyotes. Ron Wooten, the local resident who helped alert scientists to this population, dropped some pins on a map, pointing me toward several likely spots. That evening, after the sun set, I chose a quiet road that passed through marshes until it reached the island’s eastern beach. It was mating season, Wooten had noted. The animals should be on the move, he said; look to the bushes. As I drove up and down the road, my headlights revealed only empty darkness. No coyote. No wolf. Fitting, perhaps—isn’t absence the essence of a ghost? But whether this was a good omen was less clear. As individuals, these animals do best by avoiding us humans. As a group, their survival—like the survival of the red wolves—depends on our knowing that they are here, and were here, and deciding that is reason enough to care.

In Winnie the next morning, I went out one last time with Broussard, and we struck out again. With no coyotes in his traps and the new semester looming, he decided to take down his game cameras. Back at the hotel, I caught at least an image of what I’d been chasing: In black and white, the animals were appropriately silver, spectral, dashing across the midnight fields. In one clip, a canid paused and howled. “That’s super cool,” Broussard said quietly, as an echoing, interweaving chorus responded from somewhere deeper in the marsh. 

Boyce Upholt is a journalist based in New Orleans and founding editor of Southlands, a magazine about Southern nature. 

The case for fixing everything

The handsome new book Maintenance: Of Everything, Part One, by the tech industry legend Stewart Brand, promises to be the first in a series offering “a comprehensive overview of the civilizational importance of maintenance.” One of Brand’s several biographers described him as a mainstay of both counterculture and cyberculture, and with Maintenance, Brand wants us to understand that the upkeep and repair of tools and systems has profound impact on daily life. As he puts it, “Taking responsibility for maintaining something—whether a motorcycle, a monument, or our planet—can be a radical act.”

Radical how? This volume doesn’t say. In an outline for the overall work, Brand says his goal is to “end with the nature of maintainers and the honor owed them.”

The idea that maintainers are owed anything, much less honor, might surprise some readers. Actually, maintenance and repair have been hot topics in academia since the mid-2010s. I played some role in that movement as a cofounder of the Maintainers, a global, interdisciplinary network dedicated to the study of maintenance, repair, care, and all the work that goes into keeping the world going.

Brand is right, too, that maintainers haven’t gotten the laurels they deserve. Over the past few decades, scholars have shown that work from oiling tools to replacing worn parts to updating code bases all tends to be lower in status than “innovation.” Maintenance gets neglected in many organizational and social settings. (Just look at some American infrastructure!) And as the right-to-­repair movement has shown, companies in pursuit of greater profits have frequently locked us out of being able to do repairs or greatly reduced the maintainable life of their products. It’s hard to think of any other reason to put a computer in the door of a refrigerator.

Some of Brand’s earlier work helped inspire those insights. But his new book makes me think he doesn’t see things that way. For Brand, maintenance seems to be a solitary act, profound but more about personal success and fulfillment than tending to a shared world or making it better.


Born in 1938, Brand is 87 years old. A sense hangs over the book—with its battles against corrosion, rust, and decay, with its attempts to keep things going even as they inevitably falter—of someone looking over life and pondering its end. Maintenance: Of Everything connects to every stage of Brand’s life. It’s worth reviewing where it falls in that arc. Brand has always been interested in tools and fixing things, but rarely has he focused on the systems that need the most care. 

More than a half-century ago, Brand was a member of the Merry Pranksters, a countercultural, LSD-centered hippie collective famously led by Ken Kesey, the author of One Flew Over the Cuckoo’s Nest. In 1966, Brand co-produced the Trips Festival, where bands like the Grateful Dead and Big Brother and the Holding Company performed for thousands amid psychedelic light shows.

Brand’s Whole Earth Catalog had a vision that might feel progressive, but its libertarian, rugged-individualist philosophy of remaking civilization alone stood in contrast to more collective social change movements.

In some ways, the Trips Festival set a paradigm for the rest of his life’s work. Brand’s biographers have described him as a network celebrity—someone who got ahead by bringing people together, building coalitions of influential figures who could boost his signal. As Kesey put it in 1980, “Stewart recognizes power. And cleaves to it.” 

Brand applied this network logic to the undertaking he will always be best remembered for: the Whole Earth Catalog. First published in 1968 and aimed at hippies and members of the nascent back-to-the-land movement, the publication had the motto “Access to tools.” Its pages were full of Quonset huts, geodesic domes, solar panels, well pumps, water filters, and other technologies for life off the grid. It was a vision that might feel progressive or left-leaning, but the libertarian, rugged-individualist philosophy of eschewing corrupt systems and remaking civilization alone stood in contrast to the more collective movements pushing for deep social change at the time—like civil rights, feminism, and environmentalism.

That vision also led straight to the empowerment that came with new digital tools, and to Silicon Valley. In 1985, Brand published the Whole Earth Software Catalog, the last of the series, and also cofounded the WELL—the Whole Earth ’Lectronic Link, a pioneering online community famous for, among other things, facilitating the trade of Grateful Dead bootlegs. He also wrote a hagiographic book about the MIT Media Lab, known for its corporate-sponsored research into new communications tech. “The Lab would cure the pathologies of technology not with economics or politics but with technology,” Brand wrote. Again, not collective action, not policymaking: tools. And Brand then cofounded the Global Business Network, a group of pricey consulting futurists that further connected him to MIT, Stanford, and the Valley. Brand had literally helped bring about the modern digital revolution.

His attention then turned toward its upkeep. Brand’s 1994 book, How Buildings Learn: What Happens After They’re Built, argued against high-modernist architectural ideas. Nearly all buildings eventually get remade, he argued, but he especially favored cheap, simple structures that inhabitants could easily retool to suit changing needs. In some ways, Brand was recapitulating the liberated—or libertarian—philosophy of the Whole Earth Catalog: People can remake their world, if they have access to tools. In a chapter titled “The Romance of Maintenance,” he asked readers to see the beauty, value, and occasional pleasures of fixer-uppers of all kinds.

This chapter was a touchstone for many of us in the academic subfield of maintenance studies. Researchers in disciplines like history, sociology, and anthropology, as well as artists and practitioners in fields like libraries, IT, and engineering, all started trying to understand the realities and, yes, romance of maintenance and repair. Brand joined and contributed to Listservs, attended conferences, chatted with intellectual leaders. So it’s a bit uncharitable when he writes that his new book is “the first to look at maintenance in general.” He knows better. The real question, though, is what his work has to teach us that others have not said before. In this first volume, the answer is unclear.


Maintenance: Of Everything, Part One is an odd book. If so much of Brand’s thinking has been about access to tools, he now asks, in a more extended way: How are our tools maintained? But where Brand began his career with a catalogue, in this volume we get … what? A digest? An almanac? An encyclopedia? Its form and riotous variety fit no genre easily. 

The book has two chapters. The first, “The Maintenance Race,” recounts the story of three men who took part in the Golden Globe, a round-the-world race for solo sailors held in 1968. Each of the sailors, Brand explains, had a different philosophy of maintenance. One neglected it and hoped for the best. He died. Another thought of and prepared for everything in advance, and while he didn’t win the race, he completed it and once held the record for the “world’s longest recorded nonstop solo sailing voyage.” The final sailor won and did so through heroic acts of perseverance; his style was “Whatever comes, deal with it,” Brand explains. Structured like a fairy tale and unremittingly romantic, the story—like most of the anecdotes in the book—focuses on the derring-do of vigorous white guys. The strategy is no secret. Brand’s outline explains: “Start with a dramatic contest of maintenance styles under life-critical conditions—a true story told as a fable.” This myth is meant to inspire. 

The second chapter, “Vehicles (and Weapons),” is over 150 pages long. It has five sections, multiple subsections, five subsections designated “digressions,” one called a “subdigression,” two “postscripts,” and several “footnotes” that are not footnotes in a formal sense but, rather, further addenda. At times, it all feels like notes for a future work. Brand makes no apology for the book’s woolliness. “All I can offer here,” he writes, “is to muse across a representative of maintenance domains and see what emerges.” Perhaps the most charitable reading of the potpourri is that it represents the return of a Merry Prankster, offering us a riotous varied light show. It’s a good book to leave on a table and occasionally open to a random page for entertainment. But it often seems as if it does not know what it wants to say or be. 

“Vehicles (and Weapons)” begins by paraphrasing two famous works of maintenance philosophy, Robert M. Pirsig’s Zen and the Art of Motorcycle Maintenance and Matthew B. Crawford’s Shop Class as Soulcraft. Maintenance involves both “problem finding” and “problem solving.” While much repair work is marked by anxiety, impatience, and boredom, it also offers positive values and outcomes. “Motorcycle maintainers take heart from what they repair for—the glory of the ride,” Brand writes. 

The beauty and triumph of cheapness is a running theme throughout the work, harking back to How Buildings Learn. Henry Ford’s Model T won out over early electric vehicles and hugely expensive luxury vehicles like Rolls-Royce’s Silver Ghost because it was cheap and easier to maintain. The three most popular cars in human history—the Ford Model T, the Volkswagen Bug, and the Lada “Classic” from Russia—all privileged cheapness, “retained their basic design for decades, and … invited repair by the owner.” Or, to be fair, maybe demanded it? For every hobbyist who delighted in being able to self-reliantly keep a VW running, there must have been thousands who appreciated how cheap it was and hated that it broke a lot. Brand never points to social research, like surveys, that might help us know people’s feelings on such matters.

Other sections recount how Americans created interchangeable parts (enabling not only cheap mass production but also easy maintenance), examine how maintenance works with assault rifles and in war, and track the history of technical manuals from the early modern period to the age of YouTube. These stories are solid, but they’re also well known to students of technology, and nearly all are recycled from the work of others, featuring many large block quotes. The volume breaks little new ground. 

Brand treats maintenance as an unalloyed good. But the field of maintenance studies has moved on, burrowing into the domain’s ironies, complexities, and difficulties. A simple example: In most cases, it is environmentally far better to retire and recycle an internal-combustion vehicle and buy an electric one than to keep the polluting beast going forever. Maintaining a gas-guzzler or a coal-­burning power plant isn’t a radical act but a regressive one. Also, maintenance can become a life-breaking burden on the poor, and it falls inequitably on the shoulders of women and people of color. Keeping existing systems going can be a way of avoiding tough, necessary change—like making technological systems more accessible for people with disabilities. In this volume, Brand is uninterested in such difficult trade-offs. He avoids any question of how politics shapes these issues, or how they shape politics.

This avoidance comes out most clearly in a section of “Vehicles (and Weapons)” that talks about Elon Musk—a character of “unique mastery,” Brand informs us. He tells us that Bill Gates once shorted Tesla’s stock, only to lose $1.5 billion. The lesson is clear: Elon won. 

In what political and social vision is money the best way to keep the score? Brand rightly points out that electric vehicles have fewer moving parts and, in that sense, are more maintainable than internal-combustion vehicles. He celebrates Musk most of all because his products “have all proven to be game changers in part because they combine ingenious design with surprisingly low cost.” Again, it’s Brand’s “cheap, available tools” hypothesis. But there’s a real superficiality and lack of follow-through in thinking here: Teslas remain luxury vehicles whose sales have slumped since federal tax subsidies disappeared. The company has faced several right-to-repair lawsuits; there’s even a law review article on the topic. Musk is in no sense a maintenance hero. Yet Brand writes that with his companies, “Musk may have done more practical world saving than any other business leader of his time.” By the time Brand was writing this book, the controversies surrounding Musk for at least flirting with antisemitism, racism, sexism, authoritarianism, and more were quite clear. About this, the book says not a word.

book cover
Maintenance: Of Everything, Part One
Stewart Brand
STRIPE PRESS, 2026

For sure, Brand needn’t agree with Musk’s critics, but failing to even broach the subject is tone deaf and out of touch. Others have argued that Silicon Valley’s “Move fast and break things” mentality undermines healthy maintenance. Brand doesn’t raise the idea—even to dismiss it. 

It could be that with Maintenance: Of Everything, Part One Brand is just getting going; that in subsequent volumes he’ll have something more coherent to say; that he’ll raise really hard questions and try to answer them. But given his track record, we might reasonably doubt it. Kesey said Brand cleaves to power; he certainly doesn’t question it. 

Lee Vinsel is an associate professor of science, technology, and society at Virginia Tech and host of Peoples & Things, a podcast about human life with technology.

How robots learn: A brief, contemporary history

Roboticists used to dream big but build small. They’d hope to match or exceed the extraordinary complexity of the human body, and then they’d spend their career refining robotic arms for auto plants. Aim for C-3P0; end up with the Roomba. 

The real ambition for many of these researchers was the robot of science fiction—one that could move through the world, adapt to different environments, and interact safely and helpfully with people. For the socially minded, such a machine could help those with mobility issues, ease loneliness, or do work too dangerous for humans. For the more financially inclined, it would mean a bottomless source of wage-free labor. Either way, a long history of failure left most of Silicon Valley hesitant to bet on helpful robots.

That has changed. The machines are yet unbuilt, but the money is flowing: Companies and investors put $6.1 billion into humanoid robots in 2025 alone, four times what was invested in 2024. 

What happened? A revolution in how machines have learned to interact with the world. 

Imagine you’d like a pair of robot arms installed in your home purely to do one thing: fold clothes. How would it learn to do that? You could start by writing rules. Check the fabric to figure out how much deformation it can tolerate before tearing. Identify a shirt’s collar. Move the gripper to the left sleeve, lift it, and fold it inward by exactly this distance. Repeat for the right sleeve. If the shirt is rotated, turn the plan accordingly. If the sleeve is twisted, correct it. Very quickly the number of rules explodes, but a complete accounting of them could produce reliable results. This was the original craft of robotics: anticipating every possibility and encoding it in advance.

Around 2015, the cutting edge started to do things differently: Build a digital simulation of the robotic arms and the clothes, and give the program a reward signal every time it folds successfully and a ding every time it fails. This way, it gets better by trying all sorts of techniques through trial and error, with millions of iterations—the same way AI got good at playing games.

The arrival of ChatGPT in 2022 catalyzed the current boom. Trained on vast amounts of text, large language models work not through trial and error but by learning to predict what word should come next in a sentence. Similar models adapted to robotics were soon able to absorb pictures, sensor readings, and the position of a robot’s joints and predict the next action the machine should take, issuing dozens of motor commands every second.

This conceptual shift—to reliance on AI models that ingest large amounts of data—seems to work whether that helpful robot is supposed to talk to people, move through an environment, or even do complicated tasks. And it was paired with other ideas about how to accomplish this new way of learning, like deploying robots even if they aren’t yet perfect so they can learn from the environment they’re meant to work in. Today, Silicon Valley roboticists are dreaming big again. Here’s how that happened. 


Jibo

A movable social robot carried out conversations long before the age of LLMs.

An MIT robotics researcher named Cynthia Breazeal introduced an armless, legless, faceless robot called Jibo to the world in 2014. It looked, in fact, like a lamp. Breazeal’s aim was to create a social robot for families, and the idea pulled in $3.7 million in a crowdsourced funding campaign. Early preorders cost $749.

The early Jibo could introduce itself and dance to entertain kids, but that was about it. The vision was always for it to become a sort of embodied assistant that could handle everything from scheduling and emails to telling stories. It earned a number of devoted users, but ultimately the company shut down in 2019.

A crowdfunding campaign started in 2014 and drew 4,800 Jibo preorders.
COURTESY OF MIT MEDIA LAB

In retrospect, one thing that Jibo really needed was better language capabilities. It was competing against Apple’s Siri and Amazon’s Alexa, and all those technologies at the time relied on heavy scripting. In broad terms, when you spoke to them, software would translate your speech into text, analyze what you wanted, and create a response pulled from preapproved snippets. Those snippets could be charming, but they were also repetitive and simply boringdownright robotic. That was especially a challenge for a robot that was supposed to be social and family oriented. 

What has happened since, of course, is a revolution in how machines can generate language. Voice mode from any leading AI provider is now engaging and impressive, and multiple hardware startups are trying (and failing) to build products that take advantage of it. 

But that comes with a new risk: While scripted conversations can’t really go off the rails, ones generated by AI certainly can. Some popular AI toys have, for example, talked to kids about how to find matches and knives. 


Dactyl

A robot hand trained with simulations tries to model the unpredictability and variation of the real world.

By 2018, every leading robotics lab was trying to scrap the old scripted rules and train robots through trial and error. OpenAI tried to train its robotic hand, Dactyl, virtuallywith digital models of the hand and of the palm-size cubes Dactyl was supposed to manipulate. The cubes had letters and numbers on their faces; the model might set a task like “Rotate the cube so the red side with the letter O faces upward.”

Here’s the problem: A robotic hand might get really good at doing this in its simulated world, but when you take that program and ask it to work on a real version in the real world, the slight differences between the two can cause things to go awry. Colors might be slightly different, or the deformable rubber in the robot’s fingertips could turn out to be stretchier than it was in simulation.

a Dactyl robot hand holds a Rubix cube
Dactyl, part of OpenAI’s first attempt at robotics, was trained in simulation to solve Rubik’s Cubes.
COURTESY OF OPENAI

The solution is called domain randomization. You essentially create millions of simulated worlds that all vary slightly and randomly from one another. In each one the friction might be less, or the lighting more harsh, or the colors darkened. Exposure to enough of this variation means the robots will be better able to manipulate the cube in the real world. The approach worked on Dactyl, and one year later it was able to use the same core techniques to do something harder: solving Rubik’s Cubes (though it worked only 60% of the time, and just 20% when the scrambles were particularly hard). 

Still, the limits of simulation mean that this technique plays a far smaller role today than it did in 2018. OpenAI shuttered its robotics effort in 2021 but has recently started the division up againreportedly focusing on humanoids. 


RT-2

Training on images from across the internet helps robots translate language into action.

Around 2022, Google’s robotics team was up to some strange things. It spent 17 months handing people robot controllers and filming them doing everything from picking up bags of chips to opening jars. The team ended up cataloguing 700 different tasks.

The point was to build and test one of the first large-scale foundation models for robotics. As with large language models, the idea was to input lots of text, tokenize it into a format an algorithm could work with, and then generate an output. Google’s RT-1 received input about what the robot was looking at and how the many parts of the robotic arm were positioned; then it took an instruction and translated it into motor commands to move the robot. When it had seen tasks before, it carried out 97% of them successfully; it succeeded at 76% of the instructions it hadn’t seen before. 

a robot at a table of small toys
The model RT-2, for Robotic Transformer 2, incorporated internet data to help robots process what they were seeing.
COURTESY OF GOOGLE DEEPMIND

The second iteration, RT-2, came out the following year and went even further. Instead of training on data specific to robotics, it went broad: It trained on more general images from across the internet, like the vision-language models lots of researchers were working on at the time. That allowed the robot to interpret where certain objects were in the scene.

“All these other things were unlocked,” says Kanishka Rao, a roboticist at Google DeepMind who led work on both iterations. “We could do things now like ‘Put the Coke can near the picture of Taylor Swift.’” 

In 2025, Google DeepMind further fused the worlds of large language models and robotics, releasing a Gemini Robotics model with improved ability to understand commands in natural language. 


RFM-1

An AI model that allows robotic arms to act like coworkers.

In 2017, before OpenAI shuttered its first robotics team, a group of its engineers spun out a project called Covariant, aiming to build not sci-fi humanoids but the most pragmatic of all robots: an arm that could pick up and move things in warehouses. After building a system based on foundation models similar to Google’s, Covariant deployed this platform in warehouses like those operated by Crate & Barrel and treated it as a data collection pipeline. 

By 2024, Covariant had released a robotics model, RFM-1, that you could interact with like a coworker. If you showed an arm many sleeves of tennis balls, for example, you could then instruct it to move each sleeve to a separate area. And the robot could respondperhaps predicting that it wouldn’t be able to get a good grip on the item and then asking for advice on which particular suction cups it should use. 

This sort of thing had been done in experiments, but Covariant was launching it at significant scale. The company now had cameras and data collection machines in every customer location, feeding back even more data for the model to train on.

a warehouse robot arm lifts object with many suckers to place in a bin
A Covariant robot demonstrates “induction”—the common warehouse task of placing objects on sorters or conveyors.
COURTESY OF COVARIANT

It wasn’t perfect. In a demo in March 2024 with an array of kitchen items, the robot struggled when it was asked to “return the banana” to its original location. It picked up a sponge, then an apple, then a host of other items before it finally accomplished the task. 

It “doesn’t understand the new concept” of retracing its steps, cofounder Peter Chen told me at the time. “But it’s a good exampleit might not work well yet in the places where you don’t have good training data.”

Chen and fellow founder Pieter Abbeel were soon hired by Amazon, which is currently licensing Covariant’s robotics model (Amazon did not respond to questions about how it’s being used, but the company runs an estimated 1,300 warehouses in the US alone). 


Digit

Companies are putting this humanoid to the test in real-world settings.

The new investment dollars flowing to robotics startups are aimed largely at robots shaped not like lamps or arms but like people. Humanoid robots are supposed to be able to seamlessly enter the spaces and jobs where humans currently work, avoiding the need to retool assembly lines to accommodate new shapes such as giant arms. 

It’s easier said than done. In the rare cases where humanoids appear in real warehouses, they’re often confined to test zones and pilot programs. 

Digit humanoid robot putting a plastic bin on a conveyor belt
Amazon and other companies are using Digit to help move shipping totes.
COURTESY OF AGILITY ROBOTICS

That said, Agility’s humanoid Digit appears to be doing some real work. The designwith exposed joints and a distinctly unhuman headis driven more by function than by sci-fi aesthetics. Amazon, Toyota, and GXO (a logistics giant with customers like Apple and Nike) have all deployed itmaking it one of the first examples of a humanoid robot that companies see as providing actual cost savings rather than novelty. Their Digits spend their days picking up, moving, and stacking shipping totes.

The current Digit is still a long way from the humanlike helper Silicon Valley is betting on, though. It can lift only 35 pounds, for exampleand every time Agility makes Digit stronger, its battery gets heavier and it has to recharge more often. And standards organizations say humanoids need stricter safety rules than most industrial robots, because they’re designed to be mobile and spend time in proximity to people. 

But Digit shows that this revolution in robot training isn’t converging on a single method. Agility relies on simulation techniques like those OpenAI used to train its hand, and the company has worked with Google’s Gemini models to help its robots adapt to new environments. That’s where more than a decade of experiments have gotten the industry: Now it’s building big.

The noise we make is hurting animals. Can we learn to shut up?

When the covid-19 pandemic started, Jennifer Phillips thought about the songs of the sparrows.

They were easier to hear, because the world had suddenly become quieter. Car traffic plummeted as people sheltered at home and shifted to remote work. Air travel collapsed. Cities—normally filled with the honking, screeching, engine-gunning riot of transportation—became as silent as tombs.

For years, Phillips has studied how animals react to “anthropogenic noise,” or the racket created by human activity. Most animals really don’t like it, she and her colleagues have learned. Animals constantly listen to the world around them: They’re on the alert for the rustle of approaching predators, or a mating call from a member of their species. As human society has expanded—with sprawling cities, industrial mines, and roads crisscrossing the world—it has gotten noisier too, and animals have trouble hearing one another.

Noise is invisible; there’s no billowing smokestack, no soiled waterway. We just got used to it as it vibrated in the background.

Phillips and her colleagues had spent time in the 2010s in San Francisco recording the sound of white-crowned sparrows in the Presidio. It’s a park that is half peaceful nature and half automobile noise, since it’s filled with thick clumps of trees and grassy fields but also has two highways that slice through it, feeding onto the Golden Gate Bridge. In past recordings, starting in the 1950s, sparrows had sung with complex and lower-pitched melodies and three major “dialects.” But by the 2010s, traffic in the Presidio had exploded, and the hubbub was so loud that the birds began to sing with faster trills—and at a higher pitch—so their fellows could hear them. The two quietest dialects were either dead or on their way to extinction.

They’re “screaming at the top of their lungs,” says Phillips. “They really can’t hear the lower frequencies when the traffic noise is present.” Urban noise can even change birds’ bodies; they get thinner and more stressed out. Their mating calls aren’t as effective, because female birds, as researchers have found, generally don’t enjoy high-pitched, high-volume shouting. (It makes them wonder if the males are unhealthy.) The noise can increase bird-on-bird conflict, because when birds can’t hear warning cries they accidentally stumble into enemy territory. Perhaps worst of all, in situations like these biodiversity takes a hit: Entire species that can’t handle urban clamor simply head out of town and never come back.

But as the sudden, eerie silence of the pandemic descended, Phillips sat at home thinking, It’s really quiet. And then she wondered: Would the Presidio birds now be able to hear each other better?

She raced over to the park and started recording. Sure enough, the park was seven decibels quieter—a huge drop. (That’s like the difference between the noise of the average home and whispering.)

And remarkably, the researchers found that the songs of the white-crowned sparrows had transformed. They were singing more quietly, with a richer range of frequencies. A bird could be heard twice as far as before. And the mating calls had gotten more sultry.

“They could sing a higher performance, basically a sexier song, but not have to scream it so loud,” Phillips says. 

It was as if time had been reversed and all the damage abruptly repaired. And it proved what Phillips and her peers have been increasingly documenting: that anthropogenic noise is the newest form of pollution we need to tackle. The noise of our relentlessly on-the-move industrial society affects all life on Earth, wildlife and humans, in ways we’re just beginning to grasp. Yet strategies such as electrification and clever urban design could help. As the Presidio showed, noise can vanish overnight—once we figure out how to shut up.

Hidden impacts

Many forms of pollution are obvious to us humans. Dumping toxic goo into lakes? Sure, that’s bad. Coal smokestacks pumping soot and carbon dioxide, plastic bags and sea nets choking whales—we now understand that these, too, are problems. Even an idea as gauzy as light pollution has penetrated the public consciousness to some extent, since it’s why city dwellers can’t see many stars, and we’ve heard it confuses migratory birds.

But noise, mostly from transportation, took longer to hit our radar. This is partly because it’s invisible; there’s no billowing smokestack, no soiled waterway. We just got used to it as it vibrated in the background.

sparrow perched on a branch, singing
Sparrows in San Francisco’s Presidio began to sing with faster trills—and at a higher pitch—so their fellows could hear them over the noise of nearby traffic.
GETTY IMAGES
hummingbird in flight
The black-chinned hummingbird seems to prefer noisy areas, fledging more chicks than the same species does in quieter areas.
MDF/WIKIMEDIA COMMONS

There were a few studies in the ’70s and ’80s showing that animals were upset by our noise. But the field really began to take off in the ’00s, in part because digital technology made it easier to record long swathes of sound out in nature and analyze them. One early salvo came from the biologist Hans Slabbekoorn, who was studying doves in the city of Leiden and irritatedly noticed that he could rarely get a clean recording because of the background noise. Sometimes he’d see the doves’ throats moving as they cooed but couldn’t hear them. “If I’m having difficulty hearing them,” he thought, “what about them?”

So he and a colleague started recording ambient sound levels in different parts of Leiden. Some were quiet residential areas, which registered a soothing 42 decibels, and others were noisy intersections or areas near highways, which reached 63 decibels, about as loud as background music. Sure enough, he found that birds in the noisy areas were singing at a higher pitch.

Over the next two decades, research in the field bloomed. Noise, the scientists found, has a few common ill effects on animals. It disrupts communication, certainly. But it also generally stresses them, reducing everything from their body weight to their receptivity to mating calls. If an animal nests closer to a road, its reproduction rates can go down; eastern bluebirds, for example, produce fewer fledglings. Truly cacophonous noise—like planes taking off at a nearby airport—can cause hearing loss in birds. And animals can wind up becoming less aware of threats from predators. They’ll wander closer to danger, because they can’t hear it coming. (And sometimes they’ll do the opposite: They’ll develop a rageaholic hair-­trigger temper, because they’re constantly on high alert and regard everything as a threat.) 

Even in deep rural areas, where things are normally pretty quiet, highways can disrupt wildlife—the noise carries far into the fields nearby. Fraser Shilling, a biologist at the University of California, Davis, has stood up to half a mile from rural highways and recorded sound as loud as 60 decibels, which is at least 20 decibels higher than you’d typically find in the wilderness. “The motorcycles and the 18-wheelers are really the ones that project a lot of noise,” he told me. 

Above 55 decibels, many skittish animals get into a fight-or-flight panic. The prevalence of bobcats—an endangered species famously rattled by noise—“starts dropping off the cliff,” says Shilling. Above 65, “you’re really starting to exclude almost all wildlife.”

And that’s not even the upper limit of what wildlife is exposed to. There are roughly a half-million natural-gas wells around the US, and piercingly loud compressors are used to shoot water down into most of them. Up close, the compressors can kick out 95 decibels, a sound as loud as a subway train; at one Wyoming gas well the sound still registered around 48 decibels nearly a quarter-mile away.

Historically, it wasn’t always easy to prove that noise was causing whatever problems the animals were experiencing. Maybe it was other factors; maybe animal populations reduce near a road because some are hit by vehicles? 

But several clever experiments have proved that noise—and noise alone—can disrupt wildlife. One was the “phantom road” experiment by the conservation scientist Jesse Barber and his team, then at Boise State University. They went out to a quiet, uninhabited area of the Boise foothills in Idaho, far away from any roads. In this valley in the mountains, thousands of migratory birds stop on their way south each year; they’ll gorge themselves on cherry bushes, gaining weight for the next days of flying. The researchers strapped 15 pairs of speakers to Douglas fir trees, in a half-kilometer line. Then they blasted recordings of highway noise. They played the noise for four days and then turned it off for four days. Then they observed thousands of birds, capturing many to measure their body mass.

The noise truly rattled the birds. When the sound was turned on, nearly a third left the area. Those that stuck around ate less: While birds should be heavier after a day of foraging, these ones didn’t gain much. The noise seemed to have so interrupted their feeding that they weren’t packing on the weight needed for their migratory trip.

Other, similarly nifty A/B tests followed. One was led by David Luther, a biologist at George Mason University (who also worked with Phillips on the covid-19 study in San Francisco). In 2015, these researchers took 17 white-crowned sparrows at birth and raised them in a lab. To teach them their species’ songs, they played the nestlings recordings of adult sparrows singing, at low and high pitches. Six of the nestlings heard the songs without any interference; with the other half, the researchers played the sounds of city noise at the same time.

The results were stark. The lucky birds that were spared the traffic noise learned to perform the quieter, sweeter, more complex songs. But the birds that had traffic noise blasted learned only the higher, faster, more stressed-out songs. From the cradle, noise changed the way they communicated.

Humans hate noise too

You can’t pull the same experiment with humans, raising them in a lab to see how noise affects them. (Not ethically, anyway.) But if we could, we’d likely find the same thing. We, too, are animals—and it appears that we suffer in similar ways from anthropogenic noise, even though we’re the ones creating it.

The sound of traffic is correlated with lousy sleep, higher blood pressure, more heart disease, and higher stress.

Stacks of research in the last few decades have found that noise—most often, as with wildlife, the sound of traffic—is correlated with lousy sleep, higher blood pressure, more heart disease, and higher stress. A Danish study followed almost 25,000 nurses for years and found that an additional 10 decibels hit them hard; over a 23-year period they had an 8% higher rate of death, plus higher rates of nearly every bad thing that could happen to you: cancers, psychiatric problems, strokes. (They controlled for other malign health influences.) As you’d probably predict by now, children fare badly too. When Barcelona researchers followed almost 3,000 elementary school kids for a year, they found that those in noisier schools performed worse on assessments of working memory and ability to pay attention.

“We think of ourselves as being ‘used to it,’” says Gail Patricelli, a professor of evolution and ecology at the University of California, Davis. “We’re not as used to it as we think we are.”

It’s also true that there’s a trade-off. Many people understand that noise from cities and highways is aggravating, but we tolerate it because we get benefits along with the hassles. Cities are crammed with jobs and connections and dating opportunities; cars and trucks bring us the things we need and increase our personal mobility.

It turns out that animals make a similar calculus. Some species appear to benefit in certain ways from proximity to noise, so they move toward it. 

Clinton Francis, a biologist at California Polytechnic State University, and a team studied bird populations near noisy gas wells in rural New Mexico. Most species avoided the riot of the well pumps. But Francis was surprised to find that some hummingbirds and finches preferred it, and by one important measure they thrived: They were nesting more in the noisy areas than in the quieter areas. Additionally, several species had more success at fledging chicks in noisier locations.

What was going on? It’s likely that the noise makes it harder for predators to hear the birds and hunt down their nests. “It’s essentially a predator shield,” Francis says. Since his research found that predators can cause as much as 76% of failures of eggs to produce healthy offspring, that’s a significant survival advantage.

Cities can offer the same protections to certain species. Consider the case of Flaco, a Eurasian eagle-owl that escaped from the Central Park Zoo in February of 2023 and found he was in a terrific place to hunt. The incessant traffic ought to have caused him trouble. “An owl like this is among the most vulnerable species to intrusions from noise pollution. They’re listening for extremely faint signals or cues that their prey provide,” Francis notes. But New York has its compensations, because prey animals abound. They’re also naïve and unguarded, never expecting an owl with a six-foot wingspan to swoop down and devour them.

EDDIE GUY

Granted, these upsides don’t cancel out the negatives. Human noise may shield some birds from predators, but in other ways it leaves them faintly miserable, with high levels of stress hormones and lower weight. 

Worse, the species that manage to thrive in cities or near highways are often the same ones all over the country.  And they represent only a minority of species; most are driven further away, with less and less land to live on as civilization spreads ever outward. 

“Overall, it’s kind of a nightmare for diversity,” says Luther.

How to silence the world

In the early ’00s, the village of Alverna in the Netherlands began to get louder. A major intercity road cut straight through the town, and traffic had gone up by two-thirds in the previous decade. Facing complaints about the din, the town offered to put up some 13-foot walls on either side of the route. Residents hated the idea. Who wants to look out the window at massive walls?

So instead town planners redesigned the road in subtle ways. They lowered it by half a meter, slightly blocking the tire sounds. They built wedges that rise up three feet on either side, and surfaced them with attractive antique stone; that blocked even more sound. They planted sound-absorbing trees. And as a final coup de grâce, they reduced the speed limit from about 50 to 30 miles per hour. When a car is moving slowly, the engine is producing most of the roar—but once it’s going 45 mph or faster, the rumble of tires on the pavement takes over and is much louder. Each intervention had only a small effect, but cumulatively they made the road a blessed 10 decibels quieter.

This tale illustrates one curious upside of noise. Compared with other forms of pollution, it can be ended quickly. Toxic pollutants or CO2 can hang around for tens of thousands of years; the microplastics in your pancreas are probably never coming out. But with noise, the instant you reduce the source, the benefits are immediate. 

Plus, most of what works is “not rocket science,” Shilling says. A tall wall at the side of a highway will cut noise by 10 decibels; fill a double-sided wall with rubble and it’s even better. That could cut the traffic noise to below 55 decibels, he notes, which would help particularly skittish forms of wildlife. Walls can block animal movement, though, so in animal-heavy areas it’s better to build berms—small hills on either side of a highway. Areas of high ecological importance could be prioritized to keep costs down. 

“If there’s a great chunk of wetland habitat and it’s the only one around for 50 miles in any direction? Well, then we should build noise walls around it,” he says. We should also build overpasses and underpasses to help animals get around. And to quiet the din of gas wells out in the countryside, states could require companies to build walls around them. (They’ll likely only do that, though, when human neighbors complain or launch lawsuits; animals don’t have lawyers.)

Cities, too, can learn to shut up, as Alverna proved. At the most ambitious, some have buried noisy highways that once cut through the downtown core. Boston put a massive elevated highway underground in its “Big Dig”; in Slabbekoorn’s hometown of Amstelveen—a suburb of Amsterdam—they’re currently enclosing the A9 highway in a tunnel and turning the surface into a verdant park with new buildings. “That’s amazing, getting back a lot of the space as well,” he says. 

Granted, this sort of reengineering can be brutally expensive, which is why politicians blanch when they’re asked to reduce road noise. The Big Dig cost $15 billion, and with interest up to $24 billion. When I mentioned cost to Shilling, he sighed. “It’s not as expensive as a B-1 bomber or tax cuts for rich people,” he says. “Environmental stuff is considered expensive just because our expectations are low, not because we can’t afford to do it.”

There are cheaper and more politically palatable fixes, though. Reducing urban speed limits is one; Paris recently cut the top speed on its ring roads from 70 to 50 kilometers per hour (43 to 31 mph), and noise at night went down by an average 2.7 decibels—a noticeable drop. Planting more trees and vegetation all around roads and cities can cut a few decibels more, and residents love it. 

Growing adoption of electricity would also bring down the volume. “Electric vehicles of all kinds have the potential to make a big difference,” Patricelli says; when the light turns green and an EV next to you accelerates away, it’s up to 13 decibels quieter than a comparable gas-­powered vehicle. These benefits won’t be felt as much on highways, because EVs still make tire noise at high speeds. But in the slower stop-and-go traffic of urban life, they are far more pleasant to the ears, both animal and human. Indeed, the electrification of everything that currently uses a gas-powered motor will make urban life quieter. Cities like Alameda, California, and Alexandria, Virginia, are increasingly banning gas-powered leaf blowers and lawn mowers, which operate at hair-raising volume while electric ones whisper along. 

We’ve engineered a civilization that roars, but the next phase is making it purr. The animals will thank us. 

Clive Thompson is a science and technology journalist based in New York City.

The quest to measure our relationship with nature

As a movement, environmentalism has been pretty misanthropic. Understandably so—we humans have done some destructive things to the ecosystems around us. In the 21st century, though, mainstream conservation is learning that humans can be a force for good. Foresters are turning to Indigenous burning practices to prevent wildfires. Biologists are realizing that flower-dotted meadows were ancient food-production landscapes that need harvesting or they’ll disappear. And the once endangered peregrine falcon now thrives in part thanks to nesting sites on skyscrapers and abundant urban prey: rats. 

For decades (two, but that counts), I’ve been writing about how humans aren’t metaphysically different from any other species on Earth. Conservation can’t only be about fencing people out of protected areas. A lot of the time the real trick is not to withdraw from “nature” but to get better at being part of it. 

Still, I recognize that living in harmony with nature sounds like a mushy idea. I was therefore stoked to participate in a meeting in Oxford, UK, that sought to build more precise tools to assess human-nonhuman relationships. Scientists have invented lots of measurements of environmental destruction, from parts per million of carbon dioxide to extinction rates to “planetary boundaries.” These have their uses, but they engage people mostly through dread. Why not invent metrics, we thought, that would engage people’s hopes and dreams? 

It was harder than I expected. How do you quantify how good people in any given nation are at living with other Earthlings? Some of the metrics the group proposed seemed to me to be too similar to the older, more adversarial approach. Why tally the agricultural land use per person, for example? Environmentalists have typically seen farms as the opposite of nature, but they’re also potential sites for both edible and inedible biodiversity. Some of us were keen on satellite imagery to calculate things like how close people live to green space. But without local information, you can’t prove that people can actually access that space.

Eventually the 20 or so scientists, authors, and philosophers who met in Oxford settled on three basic questions. First, is nature thriving and accessible to people? We wanted to know if humans could engage with the world around them. Second, is nature being used with care? (Of course, “care” could mean lots of things. Is it just keeping harvests under maximum sustainable yield? Or does it require a completely circular economy?) And third, is nature safeguarded? Again, not easy to assess. But if we could roughly measure each of these three things, the numbers could combine into an overall score for the quality of a human-nature relationship. 

We published our ideas in Nature last year. Though they weren’t perfect, green-space remote sensing and agricultural footprint calculations made the cut. Since then, a team in the United Nations Human Development Office has continued that work, planning to debut a Nature Relationship Index (NRI) later this year alongside the 2026 Human Development Report. Everyone loves a ranked list; we hope countries will want to score well and will compete to rise to the top. 

Pedro Conceição, lead author of the Human Development Report, tells me that he wants the new index to shift how countries see their environmental programs. (He wouldn’t give me spoilers as to the final metrics, but he did tell me that nothing from our Nature paper made it in.) The NRI, Conceição says, will be critical for “challenging this idea that humans are inherent destroyers of nature and that nature is pristine.” Narratives around constraints, limits, and boundaries are polarizing instead of energizing, he says. So the NRI isn’t about how badly we are failing. It speaks to aspirations for a green, abundant world. As we do better, the number goes up—and there is no limit. 

Emma Marris is the author of Wild Souls: Freedom and Flourishing in the Non-Human World.

Is carbon removal in trouble?

Last week, news outlets reported that Microsoft was pausing carbon removal purchases. It was something of a bombshell.

The thing is, Microsoft is the carbon removal market. The company has single-handedly purchased something like 80% of all contracted carbon removal. If you’re looking for someone to pay you to suck carbon dioxide out of the atmosphere, Microsoft is probably who you’re after.

The company has said that it is not permanently ending its carbon removal purchases (though it didn’t directly answer further questions about this apparent pause). But with this flurry of news, there’s a lot of fear in the industry—so, it’s worth talking about the state of carbon removal, and where Big Tech companies fit in.

Carbon removal aims to reliably pull carbon dioxide out of the atmosphere and permanently store it. There’s a wide range of technologies in this space, including direct air capture (DAC) plants, which usually use some kind of sorbent or solvent to pull carbon dioxide from the air. Another important method is bioenergy with carbon capture and storage (BECCS), in which biomass like trees or waste-derived biofuels are burned for energy, and scrubbing equipment captures the greenhouse gases.

There was a huge boom of interest in carbon removal technologies in the first half of this decade. One UN climate report in 2022 found that nations may need to remove up to 11 billion metric tons of carbon dioxide every year by 2050 to keep warming to 2 °C above preindustrial levels.

One nagging problem is that the economics here have always been tricky. There’s a major potential public good to pulling carbon pollution out of the atmosphere. The question is, Who will pay for it?

So far, the answer has been Microsoft. The company is by far the largest buyer of carbon removal contracts, and it’s the only purchaser that has made megatonne-scale purchases, says Robert Höglund, cofounder of CDR.fyi, ​​a public-benefit corporation that analyzes the carbon removal sector. “Microsoft has had a huge importance, especially for getting large-scale projects off the ground and showing there is demand for large deals,” Höglund said via email.

Microsoft has pledged to become carbon-negative by 2030 and to remove the equivalent of its historic emissions by 2050. Progress on actually cutting emissions has been tough to achieve though—in the company’s latest Environmental Sustainability Report, published in June 2025, it announced emissions had risen by 23.4% since 2020.

On April 10, Heatmap News reported that Microsoft staff had told suppliers and partners that it was pausing future purchases of carbon removal, though it wasn’t clear whether the company would increase support for existing projects, or when purchases might resume. Bloomberg reported a similar story the next day. In one instance, Microsoft employees said that the decision was related to financial considerations, one source told Bloomberg. 

In a statement in response to written questions, Microsoft said that it was not permanently closing its carbon removal program. “At times we may adjust the pace or volume of our carbon removal procurement as we continue to refine our approach toward sustainability goals. Any adjustments we make are part of our disciplined approach—not a change in ambition,” Microsoft Chief Sustainability Officer Melanie Nakagawa said in the statement.

Whatever, exactly, is happening behind the scenes, many in the industry are nervous, says Wil Burns, Co-Director of the Institute for Responsible Carbon Removal at American University. People viewed the company as the foundational supporter of carbon removal, he adds.

“This pause—whether it’s short term or whatever it is—the way it’s been rolled out is extremely irresponsible,” Burns says. The vast majority of firms looking to get carbon removal contracts are probably seeking Microsoft deals. So, while Microsoft has every right to change its plans, the company needs to be open with the industry now, he adds.

“I don’t think you can hold yourself out as the paragon of fostering carbon removal and then treat a nascent industry that disrespectfully,” Burns says.

Carbon removal companies were already in turmoil in the US, particularly because of recent policy shifts: Funding has been cut back, and recent changes at the Environmental Protection Agency were aimed at the government’s ability to target carbon pollution.

Now, if the largest corporate backer is shifting plans or taking a significant pause, things could get rocky.

Depending on the extent of this pause, the industry may need to survive on smaller purchases and hope for support from governments and philanthropy, Höglund says. But for carbon removal to truly scale, we need policymakers to create mandates so that emitters are responsible for either storing the carbon dioxide they produce or paying for it, Burns says.

“Maybe the upside of this is Microsoft has sent a wake-up call, that you just can’t rely on the kindness of strangers to make carbon removal scale.”

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

Why having “humans in the loop” in an AI war is an illusion

The availability of artificial intelligence for use in warfare is at the center of a legal battle between Anthropic and the Pentagon. This debate has become urgent, with AI playing a bigger role than ever before in the current conflict with Iran. AI is no longer just helping humans analyze intelligence. It is now an active player—generating targets in real time, controlling and coordinating missile interceptions, and guiding lethal swarms of autonomous drones.

Most of the public conversation regarding the use of AI-driven autonomous lethal weapons centers on how much humans should remain “in the loop.” Under the Pentagon’s current guidelines, human oversight supposedly provides accountability, context, and nuance while reducing the risk of hacking.

AI systems are opaque “black boxes”

But the debate over “humans in the loop” is a comforting distraction. The immediate danger is not that machines will act without human oversight; it is that human overseers have no idea what the machines are actually “thinking.” The Pentagon’s guidelines are fundamentally flawed because they rest on the dangerous assumption that humans understand how AI systems work.

Having studied intentions in the human brain for decades and in AI systems more recently, I can attest that state-of-the-art AI systems are essentially “black boxes.” We know the inputs and outputs, but the artificial “brain” processing them remains opaque. Even their creators cannot fully interpret them or understand how they work. And when AIs do provide reasons, they are not always trustworthy.

The illusion of human oversight in autonomous systems

In the debate over human oversight, a fundamental question is going unasked: Can we understand what an AI system intends to do before it acts?

Imagine an autonomous drone tasked with destroying an enemy munitions factory. The automated command and control system determines that the optimal target is a munitions storage building. It reports a 92% probability of mission success because secondary explosions of the munitions in the building will thoroughly destroy the facility. A human operator reviews the legitimate military objective, sees the high success rate, and approves the strike.

But what the operator does not know is that the AI system’s calculation included a hidden factor: Beyond devastating the munitions factory, the secondary explosions would also severely damage a nearby children’s hospital. The emergency response would then focus on the hospital, ensuring the factory burns down. To the AI, maximizing disruption in this way meets its given objective. But to a human, it is potentially committing a war crime by violating the rules regarding civilian life. 

Keeping a human in the loop may not provide the safeguard people imagine, because the human cannot know the AI’s intention before it acts. Advanced AI systems do not simply execute instructions; they interpret them. If operators fail to define their objectives carefully enough—a highly likely scenario in high-pressure situations—the “black box” system could be doing exactly what it was told and still not acting as humans intended.

This “intention gap” between AI systems and human operators is precisely why we hesitate to deploy frontier black-box AI in civilian health care or air traffic control, and why its integration into the workplace remains fraught—yet we are rushing to deploy it on the battlefield.

To make matters worse, if one side in a conflict deploys fully autonomous weapons, which operate at machine speed and scale, the pressure to remain competitive would push the other side to rely on such weapons too. This means the use of increasingly autonomous—and opaque—AI decision-making in war is only likely to grow.

The solution: Advance the science of AI intentions

The science of AI must comprise both building highly capable AI technology and understanding how this technology works. Huge advances have been made in developing and building more capable models, driven by record investments—forecast by Gartner to grow to around $2.5 trillion in 2026 alone. In contrast, the investment in understanding how the technology works has been minuscule.

We need a massive paradigm shift. Engineers are building increasingly capable systems. But understanding how these systems work is not just an engineering problem—it requires an interdisciplinary effort. We must build the tools to characterize, measure, and intervene in the intentions of AI agents before they act. We need to map the internal pathways of the neural networks that drive these agents so that we can build a true causal understanding of their decision-making, moving beyond merely observing inputs and outputs. 

A promising way forward is to combine techniques from mechanistic interpretability (breaking neural networks down into human-understandable components) with insights, tools, and models from the neuroscience of intentions. Another idea is to develop transparent, interpretable “auditor” AIs designed to monitor the behavior and emergent goals of more capable black-box systems in real time.  

Developing a better understanding of how AI functions will enable us to rely on AI systems for mission-critical applications. It will also make it easier to build more efficient, more capable, and safer systems.

Colleagues and I are exploring how ideas from neuroscience, cognitive science, and philosophy—fields that study how intentions arise in human decision-making—might help us understand the intentions of artificial systems. We must prioritize these kinds of interdisciplinary efforts, including collaborations between academia, government, and industry.

However, we need more than just academic exploration. The tech industry—and the philanthropists funding AI alignment, which strives to encode human values and goals into these models—must direct substantial investments toward interdisciplinary interpretability research. Furthermore, as the Pentagon pursues increasingly autonomous systems, Congress must mandate rigorous testing of AI systems’ intentions, not just their performance.

Until we achieve that, human oversight over AI may be more illusion than safeguard.

Uri Maoz is a cognitive and computational neuroscientist specializing in how the brain transforms intentions into actions. A professor at Chapman University with appointments at UCLA and Caltech, he leads an interdisciplinary initiative focused on understanding and measuring intentions in artificial intelligence systems (ai-intentions.org).

No one’s sure if synthetic mirror life will kill us all

For four days in February 2019, some 30 synthetic biologists and ethicists hunkered down at a conference center in Northern Virginia to brainstorm high-risk, cutting-­edge, irresistibly exciting ideas that the National Science Foundation should fund. By the end of the meeting, they’d landed on a compelling contender: making “mirror” bacteria. Should they come to be, the lab-created microbes would be structured and organized like ordinary bacteria, with one important exception: Key biological molecules like proteins, sugars, and lipids would be the mirror images of those found in nature. DNA, RNA, and many other components of living cells are chiral, which means they have a built-in rotational structure. Their mirrors would twist in the opposite direction. 

Researchers thrilled at the prospect. “Everybody—everybody—thought this was cool,” says John Glass, a synthetic biologist at the J. Craig Venter Institute in La Jolla, California, who attended the 2019 workshop and is a pioneer in developing synthetic cells. It was “an incredibly difficult project that would tell us potentially new things about how to design and build cells, or about the origin of life on Earth.” The group saw enormous potential for medicine, too. Mirror microbes might be engineered as biological factories, producing mirror molecules that could form the basis for new kinds of drugs. In theory, such therapeutics could perform the same functions as their natural counterparts, but without triggering unwelcome immune responses. 

After the meeting, the biologists recommended NSF funding for a handful of research groups to develop tools and carry out preliminary experiments, the beginnings of a path through the looking glass. The excitement was global. The National Natural Science Foundation of China funded major projects in mirror biology, as did the German Federal Ministry of Research, Technology, and Space.

By five years later, in 2024, many researchers involved in that NSF meeting had reversed course. They’d become convinced that in the worst of all possible futures, mirror organisms could trigger a catastrophic event threatening every form of life on Earth; they’d proliferate without predators and evade the immune defenses of people, plants, and animals. 

“I wish that one sunny afternoon we were having coffee and we realized the world’s about to end, but that’s not what happened.”

Kate Adamala, synthetic biologist, University of Minnesota

Over the past two years, they’ve been ringing alarm bells. They published an article in Science in December 2024, accompanied by a 299-page technical report addressing feasibility and risks. They’ve written essays and convened panels and cofounded the Mirror Biology Dialogues Fund (MBDF), a broadly funded nonprofit charged with supporting work on understanding and addressing the risk. The issue has received a blaze of media attention and ignited dialogues among not only chemists and synthetic biologists but also bioethicists and policymakers.  

What’s received less attention, however, is how we got here and what uncertainties still remain about any potential threat. Creating a mirror-life organism would be tremendously complicated and expensive. And although the scientific community is taking the alarm seriously, some scientists doubt whether it’s even possible to create a mirror organism anytime soon. “The hypothetical creation of mirror-­image organisms lies far beyond the reach of present-day science,” says Ting Zhu, a molecular biologist at Westlake University, in China, whose lab focuses on synthesizing mirror-image peptides and other molecules. He and others have urged colleagues not to let speculation and anxiety guide decision-making and argued that it’s premature to call for a broad moratorium on early-stage research, which they say could have medical benefits. 

But the researchers who are raising flags describe a pathway, even multiple pathways, to bringing mirror life into existence—and they say we urgently need guardrails to figure out what kinds of mirror-biology research might still be safe. That means they’re facing a question that others have encountered before, multiple times over the last several decades and with mixed results—one that doesn’t have a neat home in the scientific method. What should scientists do when they see the shadow of the end of the world in their own research? 

Looking-glass life

The French chemist and microbiologist Louis Pasteur was the first to recognize that biological molecules had built-in handedness. In the late 19th century, he described all living species as “functions of cosmic asymmetry.” What would happen, he mused, if one could replace these chiral components with their mirror opposites? 

Scientists now recognize that chirality is central to life itself, though no one knows why. In humans, 19 of the 20 so-called “standard” amino acids that make up proteins are chiral, and all in the same way. (The outlier, glycine, is symmetrical.) The functions of proteins are intricately tied to their shapes, and they mostly interact with other molecules through chiral structures. Almost all receptors on the surface of a cell are chiral. During an infection, the immune system’s sentinels use chirality to detect and bind to antigens—substances that trigger an immune response—and to start the process of building antibodies. 

By the late 20th century, researchers had begun to explore the idea of reversing chirality. In 1992, one team reported having synthesized the first mirror-image protein. That, in turn, set off the first clarion call about the risk: In response to the discovery, chemists at Purdue University pointed out, briefly, that mirror-life organisms, if they escaped from a lab, would be immune to any attack by “normal” life. A 2010 story in Wired highlighting early findings in the area noted that if a such a microbe developed the ability to photosynthesize, it could obliterate life as we know it. 

The synthetic biology community didn’t seriously weigh those threats then, says David Relman, a specialist who bridges infectious disease and microbiology at Stanford University and a trailblazer in studying the gut and oral microbiomes. The idea of a mirror microbe seemed too far beyond the actual progress on proteins. “This was almost a solely theoretical argument 20 years ago,” he says. 

Now the research landscape has changed. 

Scientists are quickly making progress on mirror images of the machinery cells use to make proteins and to self-replicate. Those components include DNA, which encodes the recipes for proteins; DNA polymerases, which help copy genetic material; and RNA, which carries recipes to ribosomes, the cell’s protein factories. If researchers could make self-replicating mirror ribosomes, then they would have an efficient way to produce mirror proteins. That could be used as a biological manufacturing method for therapeutics. But embedded in a self-­replicating, metabolizing synthetic cell, all these pieces could give rise to a mirror microbe. 

When synthetic biologists convened in Northern Virginia in 2019, they didn’t recognize how quickly the technology was advancing, and if they saw a threat at all, it may have been obscured by the blinding appeal of pushing the science forward. What’s become apparent now, says Glass, is that scientists in different disciplines, all related to mirror life, were largely unaware of what other scientists had been doing. Chemists didn’t know that synthetic biologists had made so much progress on creating mirror cells with natural chirality from scratch. Biologists didn’t appreciate that chemists were building ever-larger mirror macromolecules. “We tend to be siloed,” Glass says. And nobody, he says, had thought to seriously examine the immune system concerns that had already been raised in response to earlier work. “There was not an immunologist or an infectious disease person in the room,” Glass says, reflecting on the 2019 meeting. “I may have come closest, given that I work with pathogenic bacteria and viruses,” he adds, but his work doesn’t address how they cause infections in their hosts.

on the left, a hand with petri dish and the same image inverted on the right

GETTY IMAGES

These scientists also didn’t know that around the same time as their meeting, another conversation about mirror life was happening—a darker dialogue that was as focused on danger as it was on discovery. Starting around 2016, researchers with a nonprofit called Open Philanthropy had begun compiling research files on catastrophic biological risks. The organization, which rebranded as Coefficient Giving in 2025, funds projects across a range of focus areas; it adheres to a divisive philanthropic philosophy called effective altruism, which advocates giving money to projects with the highest potential benefit to the most people. While that might not sound objectionable, critics point out that the metrics devotees use to gauge “effectiveness” can prioritize long-term solutions while neglecting social injustices or systemic problems. 

Someone in Open Philanthropy’s bio­security group had suggested looking into the risks posed by mirror life. In 2019 the organization began funding research by Kevin Esvelt, who leads the Sculpting Evolution group at the MIT Media Lab, on biosecurity issues, including mirror life. He began reading up to see whether mirror life was something to worry about.

Esvelt made waves in 2013 for pioneering the use of CRISPR to develop a gene drive, a technology that could spread genetic changes introduced into a living organism through a whole population. Researchers are exploring its use, for example, to make mosquitoes hostile to the parasite that causes malaria—and, as a result, lower their chance of spreading it to humans. But almost immediately after he developed the tool, Esvelt argued against using it for profit, at least until proper safeguards could be set and its use in fighting malaria had been established. “Do you really have the right to run an experiment where if you screw up, it affects the whole world?” he asked, in this magazine, in 2016. At the Media Lab, Esvelt leads efforts to safely develop gene drives that can be deployed locally but prevented from spreading globally. 

Esvelt says he’s often thinking about the security risks posed by self-sustaining genetically engineered technologies, and research led him to suspect that the threat of mirror organisms hadn’t been seriously interrogated. The more he learned about microbial growth rates, predator-prey and microbe-microbe interactions, and immunology, the more he began to worry that mirror organisms, if impervious to the innate defenses of natural ones, could cause unstoppable infections in the event that they escaped the lab. 

Even if the first experimental iteration of such a germ were too fragile to survive in the environment or a human body, Esvelt says, it would be a light lift to genetically engineer new, more resilient versions with existing technology. Even worse, he says, the results could be weaponized. The possible path from 2019 to global annihilation seemed almost too direct, he found. 

But he wasn’t an expert in all the scientific fields involved in research on mirror life, so he started making calls. He first described his concerns to Relman one night in February 2022, at a restaurant outside Washington, DC. Esvelt hoped Relman would tell him he was wrong, that he’d missed something over the years of gathering data. Instead, he was troubled. 

The concern spreads

When Relman returned to California, he read more about the technology, the risks, and the role of chirality in the immune system and the environment. And he consulted experts he knew well—ecologists, other microbiologists, immunologists, all of them leaders in their fields—in an attempt to assuage his concerns. “I was hoping that they’d be able to say, I’ve thought about this, and I see a problem with your logic. I see that it’s really not so bad,” he says. “At every turn, that did not happen. Something about it was new to every person.” 

The concern spread. Relman worked with Jack Szostak, a professor of chemistry at the University of Chicago, and a group of researchers to see if it was possible to make an argument that mirror life wasn’t going to wipe out humanity. Included in that group was Kate Adamala, a synthetic biologist at the University of Minnesota. She was a natural choice: Adamala had shared the initial grant from the NSF, in 2019, to explore mirror-life technologies. 

She also became convinced the risk was real—and was dumbfounded that she hadn’t seen it earlier. “I wish that one sunny afternoon we were having coffee and we realized the world’s about to end, but that’s not what happened,” she says. “I’m embarrassed to admit that I wasn’t even the one that brought up the risks first.” Through late 2023 and early 2024, the endeavor began to take on the form of a rigorous scientific investigation. Experts were presented with a hypothesis—namely, that if mirror cells were built, they would pose an existential threat—and asked to challenge it. The goal was to falsify the hypothesis. “It would be great if we were wrong,” says Vaughn Cooper, a microbiologist at the University of Pittsburgh and president-elect of the American Society for Microbiology. 

Relman says that as the chemists and biologists learned more about one another’s work and began to understand what immunologists know about how living things defend themselves, they started to connect the dots and see an emerging picture of an unstoppable synthetic threat.

Some scientists have pushed back against the doomsday scenario, suggesting that the case against mirror life offers an “inflated view of the danger.”

Timothy Hand, an immunologist at the University of Pittsburgh who hadn’t participated in the 2019 NSF meeting, wasn’t initially worried when he heard about mirror life, in 2024. “The mammalian immune system has this incredible capability to make antibodies against any shape,” he says. “Who cares if it’s a mirror?” But when he took a closer look at that process, he could see a cascade of potential problems far upstream of antibody production. Start with detection: Macrophages, which are cells the immune system uses to identify and dispatch invaders, use chiral sensing receptors on their surfaces. The proteins they use to grab on to those invaders, too, are chiral. That suggests the possibility that an organism could be infected with a mirror organism but not be able to detect it or defend against it. “The lack of innate immune sensing is an incredibly dangerous circumstance for the host,” Hand says.

By early 2024, Glass had become concerned as well. Relman and James Wagstaff, a structural biologist from Open Philanthropy, visited him at the Venter Institute to talk about the possibility of using synthetic cell technology—Glass’s specialty—to build mirror life. “At first I thought, This can’t be real,” Glass says. They walked through arguments and counterarguments. “The more this went on, the more I started feeling ill,” he says. “It made me realize that work I had been doing for much of the last 20 years could be setting the world up for this incredible catastrophe.” 

In the second half of 2024, the growing group of scientists assembled the report and wrote the policy forum for Science. Relman briefed policymakers at the White House and members of the national security community. Researchers met with the National Institutes of Health and the National Science Foundation. “We briefed the United Nations, the UK government, the government of Singapore, scientific funding organizations from Brazil,” says Glass. “We’ve talked to the Chinese government indirectly. We were trying to not blindside anybody.” 

A year and a half on, the push has had an impact. UNESCO has recommended a precautionary global moratorium on creating mirror-life cells, and major philanthropic organizations that fund science, including the Alfred P. Sloan Foundation, have announced they will not finance research leading to a mirror microorganism. The Bulletin of the Atomic Scientists highlighted considerations about mirror life in its most recent report on the Doomsday Clock. In March, the United Nations Secretary-General’s Scientific Advisory Board issued a brief highlighting the risks—noting, for example, that recent progress on building mirror molecules could reduce the cost of creating a mirror microbe. 

“I think no one really believes at this stage that we should make mirror life, based on the evidence that’s available,” says James Smith, the scientist who leads the MBDF, the nonprofit focused on assessing the risks of mirror life, which is funded by Coefficient Giving, the Sloan Foundation, and other organizations. The challenge now, Smith says, is for scientists to work with policymakers and bioethicists to figure out how much research on mirror life should be permitted—and who will enforce the rules.

Drawing the line

Not everyone is convinced that mirror organisms pose an existential threat. It’s difficult to verify predictions about how mirror microbes would fare in the immune system—or the larger world—without running experiments on them. Some scientists have pushed back against the doomsday scenario, suggesting that the case against mirror life offers an “inflated view of the danger.” Others have noted that carbohydrates called glycans already exist in both left- and right-handed forms—even in pathogens—and the immune system can recognize both of them. Experiments focused on interactions between the immune system and mirror molecules, they say, could help clarify the risks of mirror organisms and reduce uncertainty. 

Even among those convinced that the worst-case scenario is possible, researchers still disagree over where to draw the line. What inquiries should be allowed and what should be prohibited?

Andy Ellington, a biotechnologist and synthetic biologist at the University of Texas at Austin, doesn’t think mirror organisms will come to fruition anytime soon. Even if they do, he isn’t sure they will pose a threat. “If there is going to be harm done to the human race, this is about position 382 on my list,” he says. But at the same time, he says it’s a complicated issue worth studying more, and he wants to see the conversations continue: “We’re operating in a space where there’s so much unknown that it’s very difficult for us to do risk assessment.” 

Even among those convinced that the worst-case scenario is possible, researchers still disagree over where to draw the line. What inquiries should be allowed and what should be prohibited? 

Adamala, of the University of Minnesota, and others see a natural line at ribosomes, the cellular factories that transform chains of amino acids into proteins. These would be a critical ingredient in creating a self-replicating organism, and Adamala says the path to getting there once mirror ribosomes are in place would be pretty straightforward. But Zhu, at Westlake, and others counter that it’s worth developing mirror ribosomes because they could possibly produce medically useful peptides and proteins more efficiently than traditional chemical methods. He sees a clear distinction, and a foundational gap, between that kind of technology and the creation of a living synthetic organism. “It is crucial to distinguish mirror-image molecular biology from mirror-image life,” he says. That said, he points out that many synthetic molecules and organisms containing unnatural components, including but not limited to the mirror-image subset, might pose health risks. Researchers, he says, should focus on developing holistic guidelines to cover such risks—not just those from mirror molecules. 

Even if the exact risk remains uncertain, Esvelt remains more convinced than ever that the work should be paused, perhaps indefinitely. No one has taken a meaningful swing at the hypothesis that mirror life could wipe out everything, he says. The primary uncertainties aren’t around whether mirror life is dangerous, he points out; they have more to do with identifying which bacterium—including what genes it encodes, what it eats, how it evades the immune system’s sentinels—could lead to the most serious consequences. “The risk of losing everything, like the entire future of humanity integrated over time, is not worth any small fraction of the economy. You just don’t muck around with existential risk like that,” he says. 

In some ways, scientists have been here before, working out rules and limits for research. Two years after the start of the covid-19 pandemic, for example, the World Health Organization published guidelines for managing risks in biological research. But the history is much deeper: Horrific episodes of human experimentation led to the establishment of institutional review boards to provide ethical oversight. In the early 1970s, in response to concerns over lab-acquired infections and growing use of biological warfare, the US Centers for Disease Control and Prevention established biohazard safety levels (BSLs), which govern work on potentially dangerous biological experiments.

And in 1975—at the dawn of recombinant DNA research, which allows researchers to put genetic material from one organism into another—geneticists met at the Asilomar conference center in Pacific Grove, California, to hammer out rules governing the work. There were concerns over what would happen if some virus or bacterium, genetically engineered to have traits that would make it particularly dangerous for people, escaped from a lab. Scientists agreed to self-imposed restrictions, like a moratorium on research until new safety guidelines were in place. As a result of the meeting, in June 1976 the NIH issued rules that, among other things, categorized the risks associated with rDNA experiments and aligned them with the newly adopted BSL system.

Asilomar is often hailed as a successful model for scientific self-governance. But that perception reflects a tendency to recall the meeting through a nostalgic haze. “In fact, it was incredibly messy and human,” says Luis Campos, a historian of science at Rice University. Equally brilliant Nobelists argued on either side of the question of whether to rein in rDNA research. Technical discussions dominated; talks about who would be affected by the technology were missing. The meeting didn’t start establishing guidelines, says Campos, until the lawyers mentioned liability and lab leaks. 

For now it’s unclear whether these examples of self-­governance, which arose from the demonstrated risks of existing technologies, hold useful lessons for the mirror-life community. Three competing images of the future are coming into focus: Mirror life might not be possible, it might be possible but not threatening, or it might be possible and capable of obliterating all life on Earth. 

Scientists may be censoring themselves out of fear and speculation. To some, shutting down the work seems necessary and urgent; to others, it is unnecessarily limiting. What’s clear is that the question of what to do about mirror life has been both illuminating and disorienting, pushing scientists to interrogate not only their current research but where it might lead. This is uncharted territory. 

Stephen Ornes is a science writer based in Nashville, Tennessee.

Correction (April 15): An earlier version of this article incorrectly stated that David Relman briefed the National Security Agency. Relman says he briefed members of the national security community.

Cyberscammers are bypassing banks’ security with illicit tools sold on Telegram

<div data-chronoton-summary="

  • A growing black market: Scammers are buying tools advertised on Telegram that trick banks’ facial recognition checks, letting them access accounts using photos, deepfakes, or virtual cameras instead of live video.
  • The stakes are enormous: Crypto scams stole an estimated $17 billion in 2025 alone, and virtual-camera attacks were 25 times more common in 2024 than the year before.
  • Banks are aware, but holes remain: Major institutions like Binance, BBVA, and Revolut acknowledge the problem but won’t confirm its scale. Experts warn that the most successful attacks may never be detected at all.
  • Regulators are scrambling to keep up: New laws in Thailand and warnings from US financial regulators signal growing pressure on the industry, but researchers say determined scammers will keep adapting.

” data-chronoton-post-id=”1135898″ data-chronoton-expand-collapse=”1″ data-chronoton-analytics-enabled=”1″>

From inside a money-laundering center in Cambodia, an employee opens a popular Vietnamese banking app on his phone. The app asks him to upload a photo associated with the account, so he clicks on a picture of a 30-something Asian man.

Next, the app requests to open the camera for a video “liveness” check. The scammer holds up a static image of a woman bearing no resemblance to the man who owns the account. After a 90-second wait—as the app tells him to readjust the face inside the frame—he’s in. 

The exploit he’s demonstrating, in a video shared with me by a cyberscam researcher named Hieu Minh Ngo, is possible thanks to one of a growing range of illicit hacking services, readily available for purchase on Telegram, that are designed to break “Know Your Customer” (KYC) facial scans.

These banking and crypto safeguards are supposed to confirm that an account belongs to a real person, and that the user’s face matches the identity documents that were provided to open the account. But scammers are bypassing them in order to open mule accounts and launder money. Rather than using a live phone camera feed for a liveness check, the hacks typically deploy a tool known as a virtual camera. Users can replace the video stream with other videos or photos—depicting a real or deepfake person or even an object.

As financial institutions enact enhanced security measures aimed at stopping cyberscammers, these workarounds are the latest round in the cat-and-mouse game between criminal operators and the financial services industry.

Over the course of a two-month investigation earlier this year, MIT Technology Review identified 22 Chinese-, Vietnamese-, and English-language public Telegram channels and groups advertising bypass kits and stolen biometric data. The software kits use a variety of methods to compromise phone operating systems and banking applications, claiming to enable users to get around the compliance checks imposed by financial institutions ranging from major crypto exchanges such as Binance to name-brand banks like Spain’s BBVA. 

“Specializing in bank services—handling dirty money,” reads the since-deleted Telegram bio of the program used by the Cambodian launderer, complete with a thumbs-up emoji. “Secure. Professional. High quality.” Some of the channels and groups had thousands of subscribers or members, and many posted bullet points listing their services (“All kinds of KYC verification services”; “It’s all smooth and seamless”) alongside videos purporting to show successful hacks. 

Telegram says that after reviewing the accounts, it removed them for violating its terms of service. But such online marketplaces proliferate easily, and multiple channels and groups advertising similar tools remain active.

Banks and butchers

The rise in KYC bypasses has occurred alongside an expansion of a global industry in “pig-butchering” cyberscams. Crypto platforms and banks around the world are facing increasing scrutiny over the flow of illegally obtained money, including profits from such scams, through their platforms. This has prompted tightened banking regulations in countries such as Vietnam and Thailand, where governments have increased customer verification and fraud monitoring requirements and are pushing for stronger anti-money-laundering safeguards in the crypto industry.

Chainalysis, a US blockchain analysis firm, estimates that around $17 billion was stolen in 2025 in crypto scams and fraud, up from $13 billion in 2024. The United Nations Office on Drugs and Crime, meanwhile, warned in a recent report that the expansion of Asian scam syndicates in Africa and the Pacific has helped the industry “dramatically scale up profits.”

That combination of factors—more scrutiny, but also more revenue—has vaulted KYC bypasses to the center of the online marketplace for cyberscam and casino money launderers. Although estimates vary, cybersecurity researchers say these kinds of attacks are rising: The biometrics verification company iProov estimated that virtual-camera attacks were more than 25 times as common worldwide 2024 than in 2023, while Sumsub, a company providing KYC services, reported that “sophisticated” or multi-step fraud attempts, including virtual-camera bypasses, almost tripled last year among its clients. 

Three financial institutions that were named as targets on such Telegram channels—the world’s largest crypto exchange, Binance, as well as BBVA and UK-based Revolut—told me they’re aware of such bypasses and emphasize that they’re an industry-wide challenge. A spokesperson from Binance said it has “observed attempts of this nature to circumvent our controls,” adding that “we have successfully prevented such attacks and remain confident in our systems.”  BBVA and Revolut also declined to comment on whether their safeguards had been breached.

It’s difficult to estimate success rates, because companies may not be aware of bypasses—or report them—until later. “What’s important is what we don’t see,” Artem Popov, Sumsub’s head of fraud prevention products, told me, referring to attacks that go undetected. “There’s always part of the story where it might be completely hidden from our eyes, and from the eyes of any company in the industry, using any type of KYC provider.”

How criminals navigate a compliance maze 

Advertisements for the exploits appear simple enough, but on the back end, building a successful bypass is complex and often involves multiple methods. Some channels offer to jailbreak a physical phone so that scammers can trigger the use of a virtual camera (VCam) instead of the built-in one whenever they’d like. Other hacks inject code known as a “hooking framework” into a financial institution’s app that triggers the VCam to open. Either way, VCams can be used to dupe KYC safeguards with images or videos that replace genuine, live video of the account’s owner.

Sergiy Yakymchuk, CEO of Talsec, a cybersecurity company that primarily serves financial institutions, reviewed details from the Telegram channels identified by MIT Technology Review and says they are consistent with successful tactics used against his banking and crypto clients. His team received help requests from banks and exchanges for roughly 30 VCam-based hacks over the past year, up from fewer than 10 in 2023. 

Increasingly, hackers compromise both the phone itself and the code of the financial institutions’ apps before feeding the virtual camera a mix of stolen biometrics and deepfakes, Yakymchuk says.

“Some time ago, it was enough to decompile the app of a bank and distribute this on Telegram, and that was everything you needed,” he says. “Now it’s not enough, because you have KYC—and more and more things are needed.”

For money launderers, KYC bypasses have “become essential for everything right now—because scam compounds need to move money,” says Ngo, the researcher who shared the demo video. A convicted former hacker who became a cybersecurity advisor for the Vietnamese government, Ngo now runs an anti-scam nonprofit and helps law enforcement investigate money laundering. 

He describes how the process works in the case of pig-butchering scams: Funds originating with victims are received into bank accounts controlled or rented by a money-laundering network, known colloquially as “water houses.” Money launderers use KYC bypasses to access the accounts and quickly redistribute the profits before converting them into digital assets—typically in the form of the stablecoin Tether, a type of cryptocurrency that is pegged to the US dollar.

These transactions often happen in seconds, under tightly orchestrated management. “They know, very clearly, the flow of how the banks verify or authenticate accounts,” Ngo says. 

A cat-and-mouse game 

The growth of cyberscam money laundering has led to heightened scrutiny of financial institutions. In 2023, Binance pleaded guilty in US federal courts to operating without anti-money-laundering safeguards. Donald Trump pardoned former Binance CEO Chaopeng Zhao last October.

Recent analysis from the International Consortium of Investigative Journalists found that after Zhao’s guilty plea, more than $400 million continued to move to Binance from Huione Group, a Cambodia-based firm that the US sanctioned after the Treasury Department deemed it a “critical node” for money laundering in pig-butchering scams.

Binance says it has “state-of-the-art security systems” that prevented billions in fraud losses and that the company processed more than 71,000 law enforcement requests in 2025.

But John Griffin, a finance and blockchain expert at the University of Texas at Austin, does not think the exchanges are sufficiently secure. “Even though they have all this press about ‘Oh, yes, we’ve changed this and that’—well, the proof is in the pudding. The criminals are still using your exchange,” Griffin told me of the industry at large. “So there must be holes.” (Binance says it “objects to the dubious findings” of Griffin’s work tracking the flow of criminal profits across exchanges like Binance, Huobi, OKX, and Tokenlon, calling it “misleading at best and, at worst, wildly inaccurate.”)

Binance also pointed out that some purported bypass services are themselves scams, casting doubt on whether successful bypasses are as widespread as the Telegram marketplace may suggest. Engaging with such services “exposes individuals to significant security risks,” a spokesperson said. “Even where access appears to be granted, accounts are often already restricted by internal detection and compliance controls, rendering them nonfunctional for trading or withdrawals.”

Regulators around the world are trying to catch up. In Thailand, where citizens’ bank accounts regularly serve as money mules for cyberscams based in neighboring Myanmar and Cambodia, new legislation has enhanced KYC monitoring, limited daily transactions, and strengthened oversight bodies’ ability to suspend accounts. The US money-laundering regulator, the Financial Crimes Enforcement Network, issued a warning against KYC deepfakes and the use of VCams in late 2024, encouraging platforms to track broader transaction patterns to identify money laundering.

For scammers, any new security or reporting requirements will make bypasses harder, but “it’s not going to stop them,” Ngo says. “It’s just a matter of time.”