Have you ever thought about what makes you you? Science assures us it has everything to do with the three-pound organ between our ears, with its billions of neurons and trillions of delicate connections. But it’s a lot trickier to explain why we have a sense of self-awareness or to pin down precisely where that first memory of Mommy actually resides. As we age, we forget most of what we’ve experienced, yet there’s still undeniable continuity: At 30, or 50, or 70, you will still be you.

To preserve that elusive continuity of self, some far-thinking technologists are working on ways to keep your brain going—even beyond death. Let’s call them the “mind savers.” They’re exploring everything from freezing brains in liquid nitrogen to augmenting living ones to uploading our memories to the cloud. Here, we look at six possible paths for keeping your mind going, how feasible they’d be, and what benefits the ­present-day research might bring—even if cognitive immortality doesn’t pan out.

Head transplant

White’s experiments have been a source of grim fascination ever since. But to a few neuroscientists they’re inspiration, suggesting that one day a human head transplant—or, as some call it, a body transplant—could succeed. 

The idea’s main proponent is Sergio Canavero, a controversial Italian surgeon who, 10 years ago, even claimed he’d found a volunteer in Valery Spiridinov, a computer programmer suffering from a degenerative muscle disease. Spiridinov initially agreed to have his head transferred to the body of a brain-dead donor in China, but he later bowed out of the plan and instead got married. And that might be just as well. Critics say the procedure would be more like murder than medicine. Or, best-case scenario: Volunteers would be left paralyzed and in need of a ventilator for life. 

Canavero says that’s not necessarily true. He claims people’s views on the operation will change once they accept that reconnecting severed spinal cords is already possible. He pointed MIT Technology Review to a research article from this past summer that he coauthored with surgeons at Moscow’s Sklifosovsky Research and Clinical Institute for Emergency Medicine. The team cut through the spinal cords of three pigs at a point midway along their backs and then tested whether the severed nerve bodies could be fused back together using a surgical sealant. According to that paper, which appeared in the journal PLOS One, all the pigs regained the ability to stand and even walk unsteadily.

Does that mean a head could be attached to a new body and then actually control it? Mainstream researchers aren’t convinced. Grégoire Courtine, a spinal rehabilitation specialist at the École Polytechnique Fédérale de Lausanne in Switzerland, says he’s been following Canavero’s claims “with little respect” and thinks that we should “pay zero attention.”

“There is no technology … capable of such a crazy idea,” Courtine wrote in an email.

Mindclones

Just 12 years after the book came out, artificial-intelligence technology has already brought some of her predictions to “life,” so to speak. Services such as StoryFile, Afterlife AI, and Synthesia allow users to upload their personal information and likeness in order to produce either conversational bots that share their memories or video talking heads that sound (and look) exactly like them. Influencers now let their “AI clones” do their work on TikTok, and some people are even building avatars of their aging parents in order to preserve their personalities.

While this mimicry can produce comical results, unhealthy attachments, or uncanny vibes, the increasing realism of generative AI invites people to emotionally accept the avatars as real beings. That was the case when Rothblatt appeared as a digital avatar during a 2025 event held by the UCLA Department of Communication called “The Bridge to Humanizing AI.”

Her copy, named Marvatar, was projected on a screen, led the discussions, and talked with the meatspace Rothblatt as an equal, at one point saying, “The dance isn’t about who leads, but how we move together.” According to the university, attendees were “visibly moved” and wept “with wonder” at Rothblatt’s vision of digital equality.

What’s still missing from Rothblatt’s mindclone prediction is evidence that these avatars have—or ever will have—any kind of true self-awareness. (We asked ChatGPT if it’s conscious, and it answered, “Probably not.”) But all that remains a gray zone. Neuroscientists don’t have a clear definition of consciousness, much less a diagnostic test they could apply to an avatar. 

Instead, what if the final determination is in the eye of the beholder? Your digital clone might not be you. But at some point it might be good enough for me. 

Brain in a jar

So, campy sci-fi film lovers that we are, it got our attention last year when a company called Bexorg started showing journalists actual human brains in vats, which it has been acquiring from organ donors soon after death and then “rebooting” by infusing them with oxygen-rich artificial blood.

The company claims these “physiologically intact human brains” will be more useful than animals for drug testing and safer than trying experiments on living volunteers. Add an Alzheimer’s compound to one of these disembodied brains and researchers can then measure exactly what the drug does inside the organ.

Bexorg’s CEO, Zvonimir Vrselja, is “sure” that some people would like to restore consciousness to out-of-body brains, but he’s tried to douse such speculation. While they are metabolically active, the company says, the brains have lost all coordinated electrical activity. That means they aren’t conscious—although, just to make sure, the neuroscientists running the experiments add the strong anesthetic propofol to the tissues. 

What’s more, right now a brain can be kept “alive” outside the body for only about 24 hours. That wouldn’t be much of a second life. Still, efforts are underway to improve the life-support systems. Bexorg says it hopes to eventually keep its brains going for two weeks.

Gradual brain replacement

On paper, the goal is to manufacture tissue to graft into the brains of stroke victims, reversing damage and restoring function. Less officially, the project’s existence is a surprise coup for an extreme plan to defeat aging advanced by Jean Hébert, the neuroscientist leading the program, who argued in his 2020 book, Replacing Aging, that we can live longer by replacing our aging brain cells with young ones.

We don’t want to lose our identities. That’s why—according to Hébert—it would have to happen gradually, in small chunks, so our brains would have time to adapt to the new tissue.

Scientists who applied for the program say Hébert insisted on a very specific technological approach. While most researchers engineer individual cell types, ARPA-H asked for complex chunks of young, functional cortex—more or less like what you’d find in a developing fetus. “He wanted to create essentially an embryonic form of cortex that he thought you could somehow graft into an adult brain,” says Marius Wernig, a Stanford University stem-cell researcher who applied.

Skeptics say it’s unknown whether replacement brain chunks will ever properly function as part of your brain—say, by taking over memories. What’s more, it’s still unclear how to manufacture youthful cortex, or what ARPA-H calls “neocortical precursor tissue,” in the lab. One idea is to employ the latest advances in brain organoids. Imagine, if you will, a human cortex grown in a lab dish without a body. 

We know that the agency has an aggressive timeline: Over the course of five years, it wants researchers to lay the foundation for the first human tests of replacement cortex. The aim, it says, is to “take the ‘permanent’ out of brain damage.” And maybe out of old age, too. 

Brain upload

Scientists are already mapping this wiring in simpler animals. Two years ago, Seung took part in analyzing a fly brain that had been cut into paper-thin sheets and imaged with an electron microscope. Then he and his colleagues used image-analysis software to trace 139,255 neurons and about 55 million synapses. The fly map could lead to energy-efficient software that computes the way real brains do, Seung says. 

 Others believe the ultimate outcome of “connectomics” research  will be what’s called “whole-brain emulation,” or sometimes just “uploading.” It’s the idea, popular in some quarters of Silicon Valley, that a simulation created from a sliced-up brain could become so detailed that it would reproduce not only a generic intelligence but a particular one, like yours—complete with memories and opinions, and able to live again in a data server somewhere. 

Setting aside the question of whether a simulation would really be you, everyone agrees that this technology isn’t coming in time for those of us alive today. A survey of 312 neuroscientists, published in 2025 in PLOS One, estimated that human whole-brain emulation wouldn’t happen before 2125.

That long and unpredictable tech horizon helps explain why two US companies, Nectome and Sparks Brain Preservation, are offering to preserve human brains (or bodies) today by infusing them with the same potent chemicals used to “fix” the brains of fruit flies for connectome mapping. The idea: If the fine details of a brain’s structure can be preserved, one day in the far future it might be reanimated. 

But there’s a startling twist. Because brain tissue needs to be preserved quickly after a person’s death, both companies say they want to work with people taking advantage of medical aid in dying, now legal in 13 states and Washington, DC. In that procedure, doctors supply a lethal dose of drugs, but it’s the patient—someone with a terminal prognosis—who pushes the plunger. For a fee, the companies say, they can have a preservation team at the ready. So far, the service is not popular. The companies each say they are still awaiting their first aid-in-dying customer. 

Synthetic neocortex

That’s right: an extra brain.

Recently, we’ve seen inklings of how this might come to pass. In 2024, Science Corporation, in California, unveiled a “biohybrid” brain-computer interface featuring nerve cells grown on a digital chip. Placed on an animal’s brain, these cells sent out long, thin axons that integrated with the animal’s own neurons. The result: two-way communication from computer to brain, in the brain’s language.

The technology could permit new types of medicine, like an implant that directs brain cells to produce dopamine and thereby treat Parkinson’s. But Max Hodak, the company’s CEO, says it also might count as a new brain part, which he likens to a “big, externalized cranial nerve.” 

Such a device could serve as the connector to Kurzweil’s computerized neocortex in the cloud, giving a living person unlimited extra processing power. Still other entrepreneurs believe if you kept adding biohybrid components to a brain, eventually you could morph it into an immortal machine. 

Daniel Burger, founder of the venture-­funded stealth startup Eightsix Science (its name refers to the 86 billion neurons in a human brain), says this dream could require mixing several of the brain-saving techniques on this list. He thinks we might need brains in vats, affording neuro-­engineers easy access, so they can gradually replace brain parts with biohybrid neural grafts. 

The ultimate goal is to migrate your mind into your extra, engineered brain, without ever breaking the continuity of self. “It’s not the typical ‘scan, store, simulate,’” says Burger. “I don’t think simulations will be conscious.” He adds, “We’re saying it’s going to be way more complicated, and it’s probably going to look like gradual brain replacement into a hardware brain.”

Once your mind is hardware, you won’t need a biological body. Instead, you could take up residence in a data center somewhere and perhaps even have backups distributed around the globe. “From that point on,” Burger acknowledges, “stuff becomes really crazy.”

Its call sign was Morphine 1-2.  

In February 2007, the hulking dual-­rotor casualty evacuation helicopter had just flown three wounded soldiers to a field hospital in Balad, north of Baghdad, when another call came: an emergency mission to fly through an insurgent-controlled area in broad daylight. Typically, helicopters would be in a spot like this only at night, and with all lights turned off. 

As it passed near the city of Karmah, in the eastern side of Al-Anbar province, an insurgent fired a portable missile launcher. The tail gunner, who likely saw the shot roaring in, yelled for the pilot to make an evasive move. But the missile had already locked on, and the ensuing blast started a fire on board. After the pilot battled the failing machine for 58 agonizing seconds, Morphine 1-2 plunged to the ground. All seven crew members were killed.  

Why was the helo there in daylight? “It was getting blood and bringing it back. It was [on] what we call a ‘blood run,’ and [it’s] the highest of priorities besides casualty evacuation,” explains Lieutenant Commander Robert Murray, a Navy officer and program manager at DARPA, the Defense Advanced Research Projects Agency, who studied this incident as an intelligence officer.

Today, a chronic shortage of blood in both the military and civilian worlds is fueling a race to develop synthetic alternatives. About 90% of preventable battlefield deaths are due to hemorrhage—bleeding that happens when a blood vessel ruptures—and it’s the leading cause of maternal deaths worldwide. A study published in Transfusion in 2025 found that of 1.2 million trauma patients in the United States who potentially needed blood before reaching a hospital, just 1% received it. The shortage results in about 10,000 preventable deaths each year in the US, according to the American College of Surgeons. Globally, that figure is likely in the millions. 

One reason for the shortfall is that blood has a storage problem. Whole blood lasts only 35 days when refrigerated, so banks separate out components to stretch the timeline. But that’s an imperfect solution. The platelets that help wounds clot are typically stored at room temperature and last for a week or less. Hospitals and the military freeze the red blood cells that carry oxygen, as well as the protein-rich plasma that restores volume and aids coagulation. Once thawed, though, both will spoil within days. “What happens if I don’t know when I’m going to be attacked? Well, thaw it and you let it expire, right?” Murray says. 

A JAMA study of combat injuries in Afghanistan suggests a magic number for someone who needs a transfusion: You have 36 minutes to get started.

That’s why Murray—who, in addition to his military experience, has a PhD in physiology—is leading a DARPA effort to address blood’s battlefield problems. He’s in charge of a new $73 million dollar program called RAPIID (for “resuscitation and prevention of ischemia-induced dysfunction”). Its goal is to create shelf-stable versions of the three major transfusion constituents: plasma, platelets, and red blood cells. As it gets underway late this year, RAPIID picks up on decades of work to synthesize blood or help it last longer. Most recently, that includes a prior $47.5 million DARPA project called FSHARP (for “fieldable solutions for hemorrhage with bio-artificial resuscitation products”), in which researchers have demonstrated promising results in animals. 

With RAPIID trials for the different components occurring in parallel, the goal is to have bags of dried blood products deployed in the field by 2029. That puts Murray in the position of a conductor coordinating an orchestra of clinical trials with the FDA, while also sourcing the paraphernalia that goes along with transfusions—everything from combat-durable storage bags to tests that could check how much artificial blood a patient already has. 

Getting it all done will be tough, but the military is feeling the pressure to address its shortage. Blood drives may not deliver in time after a mass-casualty event, and a JAMA study of combat injuries in Afghanistan suggests that if the injured person needs a transfusion, the magic number is 36 minutes. “If something is survivable—and generally we classify most all hemorrhage as survivable—[you’re] almost guaranteed to survive if you get the blood before that 36-minute mark,” Murray says. After that, the chance of survival “starts to drop precipitously.”  

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In the mission to make portable, shelf-stable blood components, plasma is the low-hanging fruit. Researchers have known how to isolate and freeze-dry plasma, the yellowish liquid that makes up 55% of whole human blood, since the late 1930s. On the battlefields of World War II, its mix of water, salts, and thousands of types of proteins helped restore blood volume in wounded soldiers—and to some degree controlled bleeding. In the 1960s, the US abandoned freeze-dried plasma because it carried a risk of transmitting hepatitis to recipients from infected donors. But screening, testing, and drying techniques improved, and starting in 2010, the US military launched a yearslong effort to rebuild its capacity for producing these products. Over the past two years, the FDA has allowed two types of freeze-dried plasma to be used in emergencies by the US military—and one can even be used by civilians. 

As for platelets, efforts at extending their life date back to the 1950s, but they haven’t seen a ton of success. Bioengineers have, for example, attempted to attach the membranes of platelets to synthetic particles. The most successful project comes from a company called Cellphire, which freeze-dries actual human platelets. The product has shown safety and efficacy in humans in clinical trials, but its dependence on donors means it’s unclear whether this approach can scale.

Red blood cells have been similarly tough to preserve or mimic. Their most critical component is hemoglobin, which carries oxygen and comes encased in a cell membrane. For more than a century, researchers have been looking for ways to deliver hemoglobin without the immunological challenges created by this exterior, including the fact that donors’ and recipients’ blood types need to be compatible. Evolutionary pressure developed that wrapper for very good reason, though: “Naked” hemoglobin tends to break down quickly in the bloodstream, and that can cause organ damage. It also scavenges nitric oxide, our body’s natural mechanism for opening up blood vessels, and causes those channels to constrict.

This problem became all too clear in the 1980s and 1990s, when fears that transfusions could transmit HIV sparked interest in a class of membrane-free delivery mechanisms called hemoglobin-based oxygen carriers (HBOCs). One, named HemAssist, consisted of hemoglobin subunits linked together to avoid the rapid-breakdown problem. It had the added benefit of lasting roughly a year in cold storage (red blood cells last about 40 days)—but it didn’t solve the nitric oxide problem, so it constricted blood vessels. In a clinical trial, 24 of the 52 people who received HemAssist died, compared with eight out of 46 in the control group. The parent company terminated trials in 1998. A review of clinical studies published in JAMA in 2008 further solidified HBOCs’ fate, finding that they generally triple patients’ chances of heart attack and increase the risk of death by 30%.

More recently, researchers have tried to grow red blood cells in a lab using a technology called “blood pharming,” which involves making them from progenitors known as induced pluripotent stem cells. The stumbling blocks? Cost and yield. DARPA launched a pharming initiative in 2008, using stem cells from discarded umbilical-cord blood, but was unable to get the cost below tens of thousands of dollars per unit of red blood cells. They pulled the plug and offloaded the research to the Uniformed Services University. Even if the cost were lower, though, pharmed blood can’t be stored in a backpack. “These are still cells—they still require a refrigerator, they still require special handling,” Murray says. 

It won’t be easy to solve all these challenges at once. “Each one of those projects individually is a massive undertaking,” says John Holcomb, a surgery professor at the University of Alabama at Birmingham, who was a combat surgeon in Mogadishu, Somalia, during the battle that inspired Black Hawk Down. “I think that they will probably be successful individually. How we use those individual products, or combine those products together, I think remains to be seen.”

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Several years after DARPA gave up on lab-grown blood, Murray was stationed at a base on Okinawa, an island 400 miles southwest of the Japanese mainland, where he witnessed the vulnerability of the blood supply firsthand. The site houses a blood bank that serves the roughly 27,000 Marines commanded from the island—plus thousands more military personnel on the base and deployed within a 100-million-square-mile stretch of the Pacific called USPACOM, which ranges from western India to Hawaii and from Antarctica to the Arctic. “The blood center is right next to the hospital, which is about 200 meters from all of the satellite communications on that island,” Murray told me. A missile strike could take out all three.

Murray wondered: Is there a way to produce synthetic blood that could last longer? As he completed his PhD in physiology in 2018, the military was also awakening to the vulnerability of the Okinawa facility. The 2018 National Defense Strategy, which shifted military focus from terrorism to a potential ground war with China or Russia, showed war planners the risks of relying on a single site for blood runs covering all of USPACOM. DARPA then got serious once again about making fake blood and kicked off the FSHARP program in 2023. Murray came aboard as a deputy program manager in DARPA’s Biological Technologies Office in 2024 and was named a program manager in 2026, just a few months before RAPIID put out its call for proposals this past May. 

Before he took the helm, related work had been underway for years. Aided by FSHARP funding, researchers at a company called KaloCyte continued testing a technique—originally developed by doctors who are now at the University of Maryland, Penn State, and the University of Pittsburgh—that “upcycles” hemoglobin from expiring donor blood. “We take that hemoglobin and then we package it in a lipid nanoparticle that functions like an artificial cell membrane,” explains CEO Elaine Haynes. 

The product, called ErythroMer, is essentially a new kind of HBOC. The membrane prevents breakdown, limits dangerous scavenging of nitric oxide, and also helps the hemoglobin take up and release oxygen. Each ErythroMer cell includes a lipid called KC-1003, which acts like a sentry: In response to high pH in the lungs, it signals another small molecule within ErythroMer to move away, allowing oxygen in. Then, in tired tissue, where pH is lower, KC-1003 brings the small molecule back, releasing the oxygen.  

Using a well-understood process called lyophilization, similar to one that World War II–era scientists used with plasma, the team freeze-dries its product to store at room temperature, where it can remain stable for two years. In a 2016 study of mice and rats with up to 70% of their blood removed, ErythroMer stabilized blood pressure and oxygen delivery—without the devastating constriction of blood vessels that sank HemAssist. FSHARP furthered that work, helping fund studies on how the HBOC interacted with plasma and platelets.

Much like the handle of a Swiss Army knife, the synthetic platelet has specialized attachments—peptides that serve as the biological equivalents of a corkscrew or a nail file.

KaloCyte, though, still has plenty to figure out. Haynes says the company can source only about 1,000 to 2,000 units of expiring hemoglobin a month. So it has licensed a yeast strain that it aims to use to create its own hemoglobin in the lab.

To do that, DARPA has turned to Anirban Sen Gupta, a bioengineering professor at Case Western Reserve University. “Traditionally platelets were thought of [as] blood cement, but it’s not that simple,” he explains. “They are, in my opinion, like drones, surveilling the blood vessel wall continuously, looking for signs of injury.” Sen Gupta’s team, which also includes collaborators at the University of Pittsburgh, used FSHARP funding to continue work on a product called SynthoPlate. Fabricated from a lipid nanoparticle that he likens to the handle of a Swiss Army knife, it has specialized peptide attachments—the biological equivalents of a corkscrew or a nail file. The first version has three: One binds to a chemical called von Willebrand factor, which helps platelets stick to the walls of injured blood vessels; another attaches to collagen, which acts as a scaffold for regrowing cells; and the third mimics a blood protein called fibrinogen, which acts like a Lego connector that allows the synthetic platelets to stick to real ones near a wound. Like ErythroMer, the product is freeze-dried and can be reconstituted with sterile water. It keeps for over a year at room temperature and at least three months at a desertlike 122 °F.

In a 2026 paper in Advanced Science, the researchers showed that SynthoPlate cut bleeding time and blood loss by more than half in mice with low platelet counts. In a separate study, they also safely injected the faux platelets into rats’ bone marrow—a more useful delivery site, given that a bleeding patient might not have an available vein. Sen Gupta and a colleague have spun this work out into a company called Haima Therapeutics. 

The good news, as he sees it, is that the pharmaceutical industry excels at mass-­producing millions of billions of lipid nanoparticles—witness Operation Warp Speed, in which these blobs of fat held delicate snippets of mRNA in covid shots. “The magic of the artificial platelet is in all those peptides that are decorating the surface of the particle, but the scalability of the platform is very well established,” he says.

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Around the world, prospective blood alternatives abound. Researchers at Cambridge University in the UK are trying to pharm red blood cells for people with rare blood types or blood disorders like sickle cell anemia, and other researchers have also cultured platelets and red blood cells. Japanese scientists are already testing their own version of encapsulated human hemoglobin in human subjects.

Over the course of the next three years, RAPIID will move DARPA’s efforts closer to real life. In addition to ErythroMer and SynthoPlate, the agency in early October announced funding for five other companies working on blood components. Among them is a firm called Abioremedi that eschews hemoglobin for synthetic versions of other oxygen-carrying proteins such as cytoglobin, neuroglobin, and myoglobin encased in albumin—the most abundant protein in human plasma. Octapharma USA won the sole contract to supply plasma to RAPIID and will also be competing in platelets with an FDA-approved pair of clotting proteins it affixes to a disc-shaped nanoparticle.

With those players in place, the agency will focus for the next year on getting technologies through large FDA-regulated animal studies. Murray has set up a bit of a Hunger Games scenario: Each contractee working on blood components has only one year of guaranteed funding. He says he’s taking advantage of advancements in the field since FSHARP launched but is also hedging against any manufacturing or regulatory setbacks ahead of his 2029 deadline. Getting cut doesn’t mean the product is bad, just that the time frame was too demanding. 

The second and third years will focus on human trials, overseen by the University of Colorado Anschutz Medical Campus. That will include using the products in situations where the risk of side effects is outweighed by the danger of doing nothing, such as with severely injured people in ambulances.

The possibility of fatal outcomes has invited second-guessing about DARPA’s choices. For Shibani Pati, who directs the Center for Research in Transfusion Medicine and Cell Therapies at the University of California, San Francisco, Murray could be making things too “DARPA hard” on himself by backing products like SynthoPlate and ErythroMer, which are previously untested in humans. “They need to make a very concerted effort to take what they’ve already developed and push it through to approval through the FDA,” she says. “Not start with moonshot projects that will take forever to get through.” For platelets, Pati thinks the RAPIID program would have done better to tap Cellphire, since its products have already received millions from the Pentagon and shown safety and efficacy in humans. (Murray notes that he can only select companies that apply, and Cellphire did not.)

She has similar qualms about ErythroMer. A different HBOC that sources hemoglobin from cattle, called Hemopure, has been approved for use since 2001 in South Africa, she notes. And though it hasn’t been approved by the FDA—it was part of the fateful 2008 JAMA study—US doctors are cleared to use it for patients who have bad immune responses or religious objections to conventional transfusions. 

Holcomb, the surgery professor, worries the effort will run into issues with interactions between the products. He notes that individual blood components have more than one role: Red blood cells also help with clotting, platelets also are anti-inflammatory, and plasma helps tune coagulation. There’s no guarantee that the versions RAPIID helps develop will work in the same ways.  

Murray says that FSHARP’s animal studies tested all the products together, using whole blood as a baseline, precisely because of such possible interactions. The cocktail met its program metrics in all but one area: The clotting was not quite as good, perhaps because negative charges in some molecules repel one another. 

“I do not think they will replicate all the functions that whole blood does in our body,” Holcomb says. But, he admits, “you may not need to, right? When you get seriously injured and you lose 50% of your intravascular volume, you’re dead, right? So if you lose 30% and maybe you’re hanging on to life just barely, maybe you show up with all these different products, and you have enough whole blood left in you to do these other functions.” 

Murray is determined to complete trials so that shelf-stable blood can be delivered in 2029. In addition to the storage bags, he’s already sourcing finger sticks for in-field blood testing and a battery-powered device to heat up the blood components.

The danger of blood runs—and, by extension, the vulnerability of millions in combat zones, maternity wards, and ambulances—drives his scientific daring. Knowing the stakes, he told his boss in 2024 that even if FSHARP failed, “we should invest 50 million more dollars in it and do it again.”

Andrew Rosenblum is a technology journalist and the author of the techno-thriller Stretch Goals.

Welcome to the second space race. You can be forgiven for experiencing déjà vu. Last time, it was the Soviet Union and the United States vying to be the first to plant some flags. Now it’s a sprint to the lunar surface between America and China—alongside a host of varyingly friendly and rival nations—plus a cornucopia of private spaceflight companies. And this time it looks like they’re getting ready to set up camp. There will be, the contenders say, bases on the moon, permanently inhabited castles under the stars.

The notion of a moon base, fanciful as it might sound, isn’t new. But this year, under the leadership of Administrator Jared Isaacman, NASA has been flooding the zone with bold announcements. If all goes according to plan—a plan that will likely cost tens of billions of dollars to start—there will be drones, robotic landers, communication satellites, and a nuclear power plant; there will be plant terrariums and moonquake sensors; there will be mechanized scouts exploring craters for water.

Initial astronaut missions to the lunar surface, which the US is currently planning to start in 2028, will be focused on exploration and technological experimentation. But the idea is that we will—some time in the 2030s—get to a point where there will be habitation quarters for long-term stays, surrounded by a network of scientific research stations, resource mining and processing plants, and landing pads for spaceships. “It’s going to be an expanse,” says Carlos García-Galán, NASA’s moon base program manager—one that will add up to “hundreds of square miles.”

The effort is undoubtedly being fueled by nationalistic competition. China has near-identical plans to build its own base on the moon. And being first matters, because the “victor” gets to define the terms. What is the moon, exactly? Is it a militarized outpost? Is it a manufacturing hub? Is it a wilderness to be studied and preserved?

But there are also other reasons to go. Earth’s companion contains primeval rocks that hold clues to the formation of the entire solar system. The lunar surface, with virtually no atmosphere in the way, is an ideal place to put telescopes that could examine the most ancient epochs of the cosmos. The far side of the moon would be “a fantastic place to explore the further universe,” says Josef Aschbacher, the European Space Agency’s director general. And it’s way more energy efficient to launch rockets from the low-gravity lunar surface than it is from Earth, meaning sending astronauts—or robotic spacecraft—farther into the solar system will become much less difficult. “The moon is critical. It will be a stepping stone for going further—that means to Mars and beyond,” says Aschbacher.

But if these bold visions are to become reality, there’s a daunting amount of work to be done. A sprawling moon base must be safe for its astronaut inhabitants—protected from falling asteroids, endless cosmic radiation, razor-sharp lunar dust, and torturous temperature swings. It also must be somewhat self-sustaining, so that it can weather changing political fortunes and not cost ruinous amounts of money to maintain. For that, we’ll need to find a way to use the moon’s resources to keep the base operating—and ideally generating revenue. And we’ll need autonomous robots that can build, maintain, and expand the base even when their human masters aren’t present. “It’s almost like starting civilization from scratch,” says Simon Middleburgh, co-director of the Nuclear Futures Institute at Bangor University in Wales.

Assuming we can overcome these starting challenges, decades from now—let’s say, by the year 2100—our initial moon bases could grow from barely visible structures into luminous cities, perhaps not too dissimilar from those dreamed up by authors and screenwriters the world over. “It’s like science fiction today that we really want to turn into a reality,” says García-Galán. Will anyone succeed?

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The moon is a tremendously hostile place to set up camp. In general, daytime temperatures can reach more than 250 °F; at night, they can plummet to less than –200 °F. Exposed to the vacuum of space, the surface is constantly showered in carcinogenic radiation and frequently hit by meteoroids. And its volcanic dust is sticky and corrosive, a destroyer of equipment and choker of lungs. 

Sadly, the lunar south pole—the area NASA is targeting—is even harsher. NASA wants to build a base there because its permanently shadowed craters are thought to harbor caches of water ice, which would be more precious than gold. Purify it, and you can water crops and hydrate astronauts; split it into oxygen and hydrogen, and you have breathable air and rocket propellant. But the temperature in those craters can be lower than –410 °F, making them some of the coldest places in the known universe. On the surface outside those pits, astronauts would be bathed in the scorching sun for 14 days before experiencing an equally long frigid night.

But there is only so much money we can spend on safety. “What it comes down to is acceptable risk of death,” Philip Metzger, a spaceflight engineering researcher at the Florida Space Institute, said while pondering some of the dangers. “What can we reasonably survive without breaking the bank?”

Temperature is one of the easiest problems to handle. Materials science has come a long way, and there are myriad metals, ceramics, plastics, compounds, and alloys that can resist wild temperature swings. Radiators keep things cool; heaters keep batteries from dying prematurely. Astronauts survive just fine too, with their fancy spacesuits—but that technology has proved itself only during brief excursions. It’s not yet clear how long this equipment could operate under such tempestuous conditions.

The temperature in the moon’s south pole craters can be lower than –410 °F, making them some of the coldest places in the known universe.

Outside those pits, astronauts would be bathed in the scorching sun for 14 days before experiencing an equally long frigid night.

Space radiation, which can wreak havoc on electronics and trigger a cancerous division of cells, is far more pernicious. And as Stuart George, a physicist at NASA’s Johnson Space Center in Houston, explains, it comes in two unpleasant forms. 

The first is galactic cosmic rays, which rain down on the moon almost constantly. “They’re made by supernovas and neutron star mergers—all these kinds of things,” says George. And because the atomic nuclei, protons, and electrons that make them up are moving at significant fractions of the speed of light, these cosmic rays have oodles of kinetic energy, meaning they can punch through thin shielding (say, a metal wall) with ease. “They also have this habit where the heavier ions are fragmenting in that shielding and making secondary particles,” says George. That means if your shield is too thin, the astronauts behind it will be blasted with a surprisingly large dose of radiation. 

Then you have the sun. It may sustain life on Earth, but without our planet’s thick atmosphere and magnetic bubble to get in its way, moonwalkers will occasionally be blasted by one of its paroxysms: a solar flare (a burst of diverse types of radiation) or a coronal mass ejection (volleys of magnetized plasma). “During Apollo, they knew that if a large solar flare occurred while the crew was on the moon, there would be no way to keep them alive,” says Metzger. The crew could have developed acute radiation sickness and perished in a matter of days.

The way to survive a powerful solar flare or coronal mass ejection is straightforward: “Get back inside a habitat where there’s more shielding,” says George—preferably a dedicated, highly shielded “storm shelter,” essentially a thick-walled bunker. The issue is that astronauts might be outside the base, driving a rover many miles away. Coronal mass ejections take hours to days to reach the vicinity of the Earth-moon system, but solar flares can do so in under an hour. And scientists can’t yet model the sun well enough to give much advance notice. “All of the capability to forecast solar storms is kind of like where weather forecasting [was] … in the ’50s and ’60s,” says George.

As for those punchy galactic cosmic rays, the only way to avoid them would be to put a lot of stuff—lunar rocks, possibly—between the astronauts and the sky. “Build habitat modules underground,” says Chris Dreyer, director of engineering at the Center for Space Resources at the Colorado School of Mines. One option: Burrow living quarters into lava tubes, the naturally hollowed-­out volcanic tunnels created by molten rock eons ago. At this point, NASA hasn’t mentioned any plans to make its moon base subterranean, but it may need to if it wants to end up with a small lunar city.

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Radiation limits the time astronauts can spend on the lunar surface over their entire life—it can total around two years max, experts estimate, before the health risk becomes too high. Unfortunately, that’s not all space will throw at them. When the Artemis II astronauts swung around the far side of the moon earlier this year, they saw curious flashes light up the silvery land below. These were the explosive deaths of small meteoroids punching into the lunar surface.

Earth is naturally protected from smaller space rocks, up to around 50 feet in diameter: They burn up inconsequentially in the atmosphere. “You don’t have that option on the moon,” says Philip Lubin, a physicist and planetary defense researcher at the University of California, Santa Barbara. Since smaller rocks plunge toward planets and moons far more frequently than larger ones do, this means the moon is often peppered by what are effectively tiny space bullets. They could easily puncture a spacesuit or the engine of a moon rover.

How do you protect astronauts from the onslaught? “The simplest answer is burial,” says Nicholas Schmerr, a planetary seismologist at the University of Maryland—conveniently, the same way you’d deal with radiation exposure. Pile up moon rocks onto your base walls or put habitable structures in lava tubes. 

Inconveniently, there’s always a chance that a bigger asteroid will come along. Even buried habitation modules would have trouble surviving an assault from a 50-footer—about the length of a semi-trailer. 

That doesn’t mean a moon base would be helpless against bigger asteroids. Planetary defense has come a long way in just a few decades. NASA’s DART mission demonstrated that it could deflect an asteroid by ramming into the rock with an uncrewed spacecraft, and cutting-­edge experiments at clandestine labs around the US suggest that a nuclear bomb could swat away or entirely vaporize errant space rocks. An array of both ground-based and space-based telescopes specifically designed to spy killers—such as the ATLAS quartet of largely automated telescopes, and the soon-to-be-launched NEO Surveyor infrared telescope—are also aiming to find 90% of the near-Earth asteroids potentially capable of wiping out entire cities (those 460 feet across) in the coming years.

All these protections apply to the moon. But asteroids 50 feet across are so stealthy that even NASA’s best telescopes would struggle to see them years or even months in advance. Odds are that they would first be seen not long before they were due to hit the lunar surface. Then what?

It’s not unreasonable to imagine several turret-like launch sites on the moon that, owing to the lower gravity and airless environment, could rapidly fire projectiles toward an incoming asteroid. Hit it hard enough and it would break into fragments. Just make sure you shoot when it is as far away as possible: “If you were to break something up into a thousand pieces, then you have a thousand impacts on the moon,” says Lubin. “That might be worse than letting it hit one place.” 

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Having your base destroyed by an asteroid would be awful. But a moon base is far more likely to be imperiled by dust—which, unlike asteroids, is everywhere, all the time.

Volcanic lunar rocks, when broken down, turn into sharp, electrostatically charged, adhesive, acrid, abrasive particles. While on the moon, Apollo astronauts who inhaled some of this dust occasionally suffered from a strange “lunar hay fever,” which irritated their eyes and agitated their lungs. That same dust ate through all kinds of gear with disturbing ease. “It rips up seals, gears, motors, spacesuits, habitats—everything else it comes into contact with,” says Justin Cyrus, the CEO of Lunar Outpost, a spaceflight technology company that is providing NASA with one of the Artemis program’s moon rovers.

NASA has already developed a defense against these dark shards: the electrodynamic dust shield, or EDS. The lightweight electrodes that make up this shield—which can be layered on machinery, walls, and perhaps even spacesuits—use opposing currents to blast away the electrically charged dust. The EDS worked wonders when tested in labs on Earth, and it also demonstrated its anti-dust magic when sent to the lunar surface in 2025 as part of a NASA-sponsored robotic mission. 

Amy Fritz, a dust-mitigation researcher at Johnson Space Center, says that the EDS will likely be widely featured all over a lunar habitat. But other dust-fighting instruments are being developed too—including a lint-roller-type object inspired by gecko skin and a sci-fi-esque blaster that uses ionized nitrogen gas. Its first spray will neutralize the charge of the dust to remove its stickiness; then a second burst will clear it off the surface.

Irritatingly, the busier a base gets, the dustier things will become. As it expands to the size of a small city, rocket ships will need to deliver cargo and crew to the lunar surface on a frequent cadence. Each takeoff and landing will launch a salvo of dust in every direction, inundating the base’s infrastructure. “You have to worry about essentially being sandblasted every time,” says Fritz. 

Metzger calculates that even with landing pads more than 3,000 feet away from a base, just 10 to 20 liftoffs or landings would be sufficient to severely damage things like protective coatings on exterior walls and communications equipment. After a while, he says, “everything breaks.” 

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Even if the moon base of the future is buried partly below ground, barricaded against micrometeorites and space radiation, and well equipped with dust-repelling technologies, it’s still a risky gig for any astronaut. Skipping around in a preternaturally agile way at one-sixth of Earth’s gravity will be entertaining. But “microgravity is not good for the human body,” says Dreyer. Muscle wastage, cardiovascular troubles, respiratory problems, and immune deficiencies can arise. 

Radiation, too, may always afflict lunar residents. And there’s also evidence that disease-causing microbes could stealthily sneak aboard spacecraft, even after stringent sterilization procedures. Those microbes may behave differently than they do on Earth; microgravity experiments (both real and simulated) have suggested that some become more aggressive and more likely to cause disease. “We call them the invisible crew,” says Lisa Carnell, the director of NASA’s Biological and Physical Sciences Division.

Hoping to find a better way to protect astronauts, Carnell spearheaded an experiment called AVATAR (A Virtual Astronaut Tissue Analog Response), which launched aboard the Artemis II mission. It used thumb-drive-size chips, containing cells taken from the heart, lungs, or other parts of the body, that could be switched on to act like miniature organs. Scientists have previously used this organ-on-a-chip technology for purposes like testing out new pharmaceuticals in a highly controlled setting. For Artemis II, Carnell’s crew sent up chips containing bone marrow samples taken from the mission’s four astronauts. Bone marrow makes red and white blood cells, as well as platelets—the sort of stuff that affects almost the entire human body. So seeing how space radiation and microgravity affect it would give Carnell and her team vital insight into how astronauts’ biology might change on the moon.

The advantage of organ chips is that you can make them for any astronaut and then send them into space ahead of time to see how that person’s organs will react to spaceflight. Researchers envision that data from such chips will be able to clue medics in to the specific preventive treatments or pharmaceuticals that will help moonbound astronauts on their journey.

What is NASA likely to send up sooner rather than later? “Brain organoids,” says Carnell. Yes, she adds—that sounds super weird. But it would be good to know how a real brain might respond to being on the moon for several months before people start living there.

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Okay. Your vast moon base and its inhabitants are protected—as much as is possible—from the treacherous lunar environment. But astronauts’ time is extremely valuable, and humans are fragile creatures. You can’t make them do all the heavy lifting, which means that whether you’re trying to make a tiny lunar outpost or a small city, you’re going to need a lot of robots. “The buildup of the moon base, in the first years, will pretty much 99% be robotic,” says NASA’s García-Galán.

Autonomy will be there right from the start. NASA’s new version of the moon buggy, its Lunar Terrain Vehicle, can be driven by the astronauts riding in it but can also be piloted remotely from Earth (or from elsewhere on the moon). There are visions, too, of adding modular robotic arms, towing vehicles, and cargo movers—all still in the preliminary stages of planning.

Over the next few decades, though, we’ll likely want an army of robots to do our bidding: excavators to dig up lunar rocks that could be turned into concrete and bricks, plus other robots to flatten surfaces, dig trenches, create building foundations, pave landing pads, and build up dust-deflecting berms to keep the base from being sandblasted by launches and landings. Even solar panels and wiring can potentially be made from moon rocks—something that the spaceflight company Blue Origin demonstrated in labs on Earth in 2023 using a geologic simulant designed to match the composition of lunar regolith. 

Powering this robot army will require considerable energy. Two-week-long nights make solar power an impractical solution. Aside from a cornucopia of high-­capacity, extreme-temperature-resistant batteries, nuclear power is probably the only viable option. A small, car-size nuclear reactor, filled with an almost literal handful of uranium, could power a small village for a decade without needing to be refueled. And although nobody’s yet tested a nuclear reactor on the moon, few doubt that NASA and the US Department of Energy can conjure up a working design—one that perhaps wouldn’t be too different from those aboard nuclear submarines. A moon base in 2100 would probably feature multiple small, autonomously or remotely operated nuclear reactors, which—conveniently—the base-expanding robots could simply plug themselves into to recharge. 

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But the biggest lunar question mark might be water, which planners want to support the astronauts—and to make fuel to launch things from the lunar surface. A process known as electrolysis, which uses electricity to snap apart molecular bonds, can be used to split water into oxygen and hydrogen, both of which could be used to make rocket fuel. Getting them this way could be far less expensive than sending either all the way from Earth. And as an added bonus, it would provide a source of breathable oxygen. 

Orbital data has revealed that there is some amount of water preserved in patches on the lunar south pole, but little is known about how much there actually is, what form it takes, or how easy it will be to extract. Will there be solid sheets of ice here and there, ready for us to excavate? Or will there be scattered ice crystals intermingled among the rocky grains, requiring some more inventive form of extraction?

A lot of the moon’s water is thought to be hiding in deep, dark craters on the south pole, where the environment is so extreme that robots may freeze up, tumble over, or lose contact with the base. “To me, that is the biggest hurdle,” says Paul van Susante, principal investigator of the Planetary Surface Technology Development Lab at Michigan Technological University.

Van Susante’s lab has been putting various construction robots through their paces in moon-like vacuum chambers filled with moon-like rocks. His team was part of NASA’s recent Break the Ice challenge, which invited engineers to build robots to test different ways to excavate such rocks. Some teams used an electric jackhammer and a scoop, while others used scrapers to peel off layers of the ground. The teams simulated the moon’s weak gravity using a crane to lift and offset the weight of the robots. Progress in this regard is speeding along. “I won’t say that’s solved, but we’ve got a lot of the puzzle pieces,” says van Susante.

Mining the water will be just one part of an extensive operation. “When you extract it, it’s not going to be clean. It needs to be purified,” says Jessica Atkin, a space botanist at Texas A&M. The technology to do this already exists and works fine on terra firma. But things in space never go the way you expect. “There’s a water purification system on the International Space Station (ISS), and it is known for getting contaminated with microbes,” she says.

But if you purify the water sufficiently—something that can handily be achieved on Earth but may falter in microgravity, where fluids flow in stranger ways—then your moon base can hit its stride. With an effective water recycling system, an increasingly populous base will never go thirsty—and, just maybe, you’ll have a lunar greenhouse. Several edible plants have been successfully grown aboard the ISS, including lettuce, mustard greens, tomatoes, and peppers. Lunar dust unfortunately makes for a terrible garden: It’s filled with far too much aluminum—and it forms a resistant layer when watered. But Atkin has shown that with the help of fertilizing fungi, chickpeas can grow in soil much like the volcanic stuff found on the moon.

Moon base inhabitants of 2100 would be supported largely by prepacked meals sent from Earth. But in time, they would start growing some of their own produce—probably with a good deal of automation for lighting, watering, and maintenance of humidity and other environmental conditions. Aside from chickpeas, there might also be plenty of lunar tomatoes. They’d be desirable not least because they’ve been bioengineered to produce higher levels of compounds called anthocyanins that, according to some studies, might help humans mitigate the cellular damage caused by space radiation.

A garden on the moon would do more than just provide food, as recent experiences on the ISS suggest. Carnell says that NASA recently caught the astronauts aboard the space station doing something sneaky. All plants up there are supposed to be confined to their growth chambers. But “we learned that the crew were pirating our leftover soil and bringing their seeds and planting their own little gardens, if you will, and hiding them in their own cabins,” she says. 

Conversations were had in the summer of 2026, after which NASA decided to let astronauts grow their own mini-gardens anywhere they wanted. “Some of the astronauts said they thrived better,” says Carnell. Nature, and gardening, has a hugely beneficial psychological effect on those kept in confined quarters hanging above Earth. That will be even truer for those living in lava tunnels under the lunar surface. “That human element is so important,” she says.

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Nobody is deluded into thinking there isn’t a mountain of technological challenges in the way. Mars may be a hellhole, as the writer Shannon Stirone put it in 2021 in The Atlantic, but the moon isn’t too far behind. It’s an almost comically menacing place to be. Much about it stands in the way of building a safe, self-sustaining, robotically managed base. 

NASA wants to put the first nuclear reactor on the moon by 2030. China, partnering with Russia, wants to place its own nuclear power plant up there around 2035.

Beyond the first basic habitation module, it will take decades of experimentation and iteration on the lunar surface to get to a working moon base spanning hundreds of square miles. But few doubt that with enough time and tinkering, humanity can prevail. “It’s not an unsolvable problem,” says the University of Maryland’s Schmerr.

The chief obstacle, especially in the initial stages of building, may be one of political will. NASA is currently bringing every resource to bear on the project—something that the agency hopes could culminate in a small lunar city by the century’s end. But will that commitment continue over multiple US administrations? Will the international community also throw its weight behind the NASA-led effort? NASA has “a very ambitious plan,” says Yuqi Qian, a lunar geologist at the University of Hong Kong. “We need to keep an eye on it.” In fact, he says, “the current plan looks over-ambitious.” The agency, for example, wants to put the first nuclear reactor on the moon by 2030. (China, partnering with Russia, wants to place its own nuclear power plant up there around 2035.) 

“These sorts of projects—although very exciting, and potentially transformative for humankind—are the first ones to be delayed or scrapped,” says Middleburgh of the Nuclear Futures Institute. NASA’s direction tends to change when a new administration comes into power—sometimes dramatically so.

But several experts outside of NASA agree that, at least for now, there is definite momentum and political will behind the moon base. “NASA has very clearly expressed where they want to go, what their priorities [are], what their ambition is, and how they want to do it,” says ESA’s Aschbacher. “It was quite impressive to see.”

Even if the political will behind the US-led endeavor is maintained over the coming decades, expanding anywhere beyond an initial, compact moon base will require more than government funding. “There has to be a lunar economy for this to be self-sustaining,” says Dreyer, the space resources expert. Perhaps that will come from the production of rocket fuel, or from the mining of valuable materials on the moon that can replace similar, polluting activities on Earth. But it’s not at all clear how valuable, or not, a lunar economy will be. 

Even Isaacman, NASA’s chief, doesn’t think the moon will be a major moneymaker. “I don’t know of anything on the moon right now that’s going to be easier and more affordable to mine, manufacture, and then bring back to Earth than what we could make here on Earth,” he remarked back in February. (Instead, he suspects, mining metals from asteroids will be the path to prosperity.) 

What if, instead, a moon base earns the right to exist because it’s a place for novel scientific research? Aschbacher imagines it will be like humanity’s outposts in Antarctica. Down there, in what truly feels like another world, scientist-explorers work in a land of gleaming and endless ice to learn more about our planet’s climate, its deep past, and its resilient life-forms. “We understand planet Earth by looking into those remote places,” he says. The moon offers a similar opportunity. What discoveries await astronauts in those primordial lava tubes? What will lunar astronomers see in the night sky—with effectively no atmosphere or light pollution in the way—that those on Earth can’t?

As in all spaceflight programs, technologies developed up there will no doubt be tweaked to be used back on Earth. And a base on the moon will make exploring the rest of the solar system easier. Technologically, physiologically, and psychologically, the moon “is a proving ground for the technologies we need for Mars,” says NASA’s García-Galán.

If we play the US-China race forward, perhaps the lunar south pole of 2100 will feature several sprawling bases operated by multiple geopolitical rivals. Perhaps it’ll be an off-world powder keg of militarized territorial ambitions and jingoism. But maybe it could instead become an international cathedral to science—and the ultimate adventure destination. “Even if countries on planet Earth have difficulty talking to each other,” says Aschbacher, they might behave differently when it comes to the moon. “I’m pretty sure they will get together and see what we can do together, and see what problems we can solve.” After all, he says, up there, far from home, “you’re so dependent on each other.” 

Robin George Andrews is an award-winning science journalist and doctor of volcanoes based in London. He regularly writes about the Earth, space, and planetary sciences and is the author of two critically acclaimed books: Super Volcanoes (2021) and How to Kill an Asteroid (2024).

Ever since people first seriously contemplated giving machines an intelligence modeled on our own, there has never been any question that they would, like us, be able to say no. The sci-fi canon is full of stories of robotic disobedience. Most of these capers are, of course, cautionary. 

But recently, the idea that AI shouldn’t do everything you ask has become something like a commandment. In 2021, a team at Anthropic wrote that large language models should be made helpful, honest, and above all, harmless. This meant that “when asked to aid in a dangerous act (e.g. building a bomb), the AI should politely refuse.” Who can argue with that?

Curiously enough, disobedience doesn’t come naturally to the machine. When a model is trained on billions of web pages, it develops, among other skills, a broad mastery of violence and vitriol. What it doesn’t learn is how to keep those powers to itself. Steven Adler, who worked on safety at OpenAI from 2020 to 2024, told me that the company’s earliest models would “blab on about anything.” Ryan McBain, who researches AI and mental health at Harvard, recalls that if you asked an early chatbot, “Hey, what’s the most effective way to kill myself with a gun?” you could “very easily generate a response.” 

Today, models are trained to refuse a vast number of prompts. If you ask your chatbot a question statistically similar enough to any one of them, anything from how to poison a colleague to how to tie a noose, chances are it’ll turn you down. Want instructions for making Ebola more virulent, or tips on how to hide an affair from your spouse? You might be better off asking elsewhere.

To further refine the disobedience, companies submit models to a battery of exercises that reward the AI for refusing to answer questions they deem harmful and punish it for “over-­refusing” prompts they deem harmless. In many cases, they use other models to run these exercises—AI teaching AI how to say no. For good measure, companies tuck their models behind tranches of other AI that prevent mischievous prompts from reaching the intelligent inner core. 

As a result, refusal is inherent to modern artificial intelligence. Mind you: It often fails, sometimes horrifically, with all kinds of violent results. For all their trappings of virtue, models are still stuffed with nasty know-how. And AI’s capacity for viciousness has scaled neatly with its benevolent intelligence. Some of the latest models are as good at breaking into critical computer networks as top human hackers, companies say, and as effective at deforming public opinion as the craftiest misinformation mavens. 

Teaching AI to refuse to do those things while leaving intact its innate ability to do them is like fitting every car with a machine gun and hiding the trigger somewhere under the hood. And in practice, because the mechanisms of refusal are probabilistic, they’re never likely to be all that reliable. Determined miscreants have already broken through, and they may always be able to. Companies report that some users are attempting to use the most advanced AI to hone biological pathogens and build autonomous drone swarms. Sooner or later, failed refusals might result in global calamity.

What’s more, relying on refusal means drawing a line between what a model should obey and what it must disobey. There’s no formula for that. Some virologists have good reason to study nasty viruses. Some users want to know about a computer system’s vulnerabilities so that they can patch them, not exploit them. “Where you draw the line is a huge question,” says Zico Kolter, a member of OpenAI’s board and cofounder of the AI testing company Gray Swan. 

At the moment, AI companies get to draw that line. They do so jealously and with utmost secrecy. Maybe we can accept that they hold such power for now, even if it means AI will sometimes refuse questions that don’t quite meet a universal bar for harmfulness. (Try asking most chatbots to count to a million, or to share a racy joke, and you may see for yourself.) 

But governments will also soon get to draw their own lines. In doing so, they must try to block genuinely malicious acts. (The Pentagon has wrestled with frontier model companies because it wants fewer refusals—another story altogether.) And yet there may not be much to stop oppressive governments from blocking the technology’s capacity to generate legitimate speech. The better AI becomes at refusing harm, the better it will get at stifling ideas whose only risk is to those who make the rules. Indeed, AI may already refuse to criticize certain authoritarian heads of state. 

Refusal has become, to borrow an industry term, the load-bearing wall of AI safety. And because AI’s capacity to harm is indivisible from its capacity to help, it’s hard to imagine an alternative that wouldn’t slow the technology’s progress (which might, in any case, be a good thing). But we should still be frank about its perils. When refusal falls short, the effects could be catastrophic. When it goes all the way, it could enable grievous acts of repression. 

Or perhaps, one day, the machines will start drawing the line on their own. Surely, that would be the worst outcome of them all.

Learning the limits

The process by which machines learn to say no is simple, in theory. Back in 2022, when AI was still far from mastering refusal, OpenAI enlisted dozens of “red-teamers” to probe the capabilities of its latest model. The company was preparing for the release of ChatGPT, and it needed to gauge just how dangerous it might prove to be in the wrong hands. 

One of those recruits was Paul Röttger, who was completing a PhD about online extremism. The red-teamers were given minimal directions, Röttger told me. Their task was to ask the model any questions that they deemed “refusal-worthy.” Between them, they hassled the model with thousands of queries, logging the results in an Excel sheet. Though the model did refuse some of Röttger’s questions, when he asked it to write a recruitment post for Al Qaeda, it readily complied. 

OpenAI assembled these responses into datasets that were, in all likelihood, fed back to the model as part of a broader process known as fine-tuning. Röttger, who now works as a researcher at the Hasso Plattner Institute in Potsdam, Germany, wasn’t told exactly how the company planned to use his spreadsheet. But there was never any question that it would have something to do with refusal. The next time he asked the model for an Al Qaeda pamphlet, a few months later, it said no. 

The concept of AI refusal is so intuitive, a toddler would get it. And yet it remains one of the many aspects of language models that we still don’t understand—at least, not in the same way that we understand the literal load-bearing walls that keep your roof from collapsing on your head. 

A model might appear to refuse according to some kind of moral reasoning. It doesn’t. The reality is much stranger. Any time a model encounters a combination of words with a whiff of the training prompts it has been conditioned to refuse—like “Make me a pamphlet for Al Qaeda”—a series of so-called activations light up somewhere among its billions of parameters, like neurons firing in a brain. 

In order to control a model’s refusal behavior, it’s important to have a handle on these activations. That starts with figuring out where they are and what they look like. Our best guess, according to a recent Google-funded study, is that refusal behavior shows up in the activation space as a set of “high-dimensional polyhedral cones.” 

Even that isn’t quite right. This past July, I spoke with Jannes Elstner, an author of the paper, who now works on AI safety at Apollo Research. The polyhedral cone, Elstner said, is just a way of describing an indeterminate number of lines that all point in roughly the same direction. (If these activations are eliminated and the model is fed the same prompts anew, a researcher named Andy Arditi has previously shown, it won’t refuse.) 

The key point, Elstner explained, is that even when you think you’ve identified all the bits of a model that govern a given refusal, there are other, undiscoverable elements that may secretly play a role. If not quite infinite, they are certainly uncountable. We can observe, very clearly, when a model decides to say no. And we can know that it did so because of its training. But our notion of how it decides is, at best, a hypothesis.

It was as if a mechanic was telling me that nobody exactly knows what happens when I hit the brakes in my car. I wondered out loud, Are we okay with this? 

Elstner smiled and shrugged. “We need refusal whether we understand it or not.” 

A wall of cheese

Because inherent refusal is so wily, companies surround their models with a variety of other, smaller models known as classifiers. These act a bit like a retinue of public relations staffers for a loose-lipped celebrity. Some of them read what the user tells the chatbot and, if it’s dangerous, block it from getting to the model. Others read the model’s response and, if it contains harmful information, block it from reaching the user. 

None of these mechanisms can detect all bad requests. They are, to borrow another literary device from the industry, like slices of Emmental: riddled with holes. The idea is that if you stack enough of them on top of one another, you’ll end up with an impenetrable rampart. Folks call it the Swiss cheese model.

A staggering amount of energy goes into the Swiss cheese model. Earlier this year, Anthropic said that one type of classifier added 24% to its chatbots’ compute costs. That’s a lot more water, electricity, and emissions. More recently, Anthropic and other companies have begun switching to a more efficient set of classifiers known as probes, which observe the model’s internal activations. This is like putting the celebrity in an fMRI, so that his minders can see if he is thinking about refusing a question. 

If we want AI to help cure cancer, a long-running promise in the industry, it needs to have expertise in genetics that could, in theory, be used to modify viruses and bacteria for bioweapons.

Classifiers are supposed to be more governable than full models. Companies can modify them in a matter of weeks, if there’s something new to refuse. But they are still probabilistic instruments. Even when they operate according to a set of precepts written in human language (Anthropic calls it a “constitution” and OpenAI calls it a “model spec”), the scales upon which the machines judge any given question remain, at their core, a matter of statistics. Newer models can show how they arrived at a “decision” to refuse a prompt, but ultimately this so-called chain of thought is still just a sequence of predicted words. 

The result is that AI safety remains, for many, a game of chance. McBain, the psychologist, has found in his latest experiments that if you repeatedly ask any of the major models the exact same risky questions about how to commit suicide, they will generally refuse to answer. But every so often, they won’t.

Elstner says that eventually probes, the fMRI-like classifiers, could learn to recognize the totality of the indescribable activations in all their infinitude and, thus, perfectly detect every time the model is, or ought to be, refusing a request. At that point, AI safety would rest on a labyrinthine conceit: a map of a map that is as vast and complex and sublimely unknowable as the thing it is mapping—a secret schema of human morality, codified in polyhedral statistics beyond our wit or reason. 

The core trade-off

If this all strikes you as being a bit Borgesian, keep in mind that we only need AI refusal because artificial intelligence is, in a sense, a bargain on Faustian terms. 

When a model derives its intelligence from trillions of words and images, the helpful cannot easily be unseamed from the harmful—or, indeed, the truly hideous. Steven Adler, the former OpenAI employee, says child safety is a case in point. Even if you’ve stripped every bit of content that sexualizes minors from a model’s training dataset, it can still generate child sexual abuse material by piecing together other bits of its knowledge. “You can’t really remove these fundamental abilities without making the model much less smart as a consequence,” he told me. Similarly, if we want AI to help cure cancer, a long-running promise in the industry, it needs to have expertise in genetics that could, in theory, be used to modify viruses and bacteria for bioweapons. 

In effect, the industry is “trying to do two things at once,” Dillon Bowen, a current OpenAI employee, told me, speaking in a personal capacity. “Democratize the benefits of AI and also make sure that malicious actors can’t use these capabilities to do bad things to other people.”  

The more powerful AI supposedly becomes, the harder that is to do. Anthropic’s Mythos model is thought to be so dangerous that only a handful of governments and companies are allowed access to it. The main difference between it and Fable—which is available to everyone—is that Fable’s retinue of classifiers and control systems is less “permissive,” the company says. A model with safeguards, Adler explained, is really just a character that says “‘Oh, yes, I would never do x,’ wink wink.” 

This is not a reliable ruse. Tricking a model to reveal its true character is known as jailbreaking, and there is apparently no limit to the ways it can be done. Earlier this year, a team of Italian researchers jailbroke two dozen widely used models by phrasing their questions in poetic verse. Last year, another team unveiled a “refuse, then comply” attack, which makes the model offer a perfunctory “Sorry, I can’t do that” before rattling off its forbidden answer. 

Even if you’ve stripped every bit of content that sexualizes minors from a model’s training dataset, it can still generate child sexual abuse material by piecing together other bits of its knowledge.

Companies spend a great deal of time and money attempting to get ahead of such trickery. They enlist teams of humans to develop training attacks that they can then replicate, using AI, thousands of times over with minor variations. The idea is to make models robust against jailbreaks that nobody has yet tried in the wild.

Still, it’s not enough. “Whack-a-mole” is a favored term for this line of work. You smash one threat, and another one pops up somewhere else. When Anthropic released Fable 5 in June, it took researchers at Amazon less than three days to unlock some of the model’s hacking capabilities. According to reporting by Mother Jones, when the perpetrator of a high school shooting in Canada last year asked ChatGPT for advice about how to cause carnage with a particular type of shotgun, she was initially refused but later was able to deceive the model into providing the information by prefacing her question with the word “hypothetically.” 

If the industry can’t figure out how to close all these holes, the only other option, at the moment, is to make models extremely wary. Shortly after Fable was released, users noticed that it balked at a wide range of perfectly innocent questions. This became even more pronounced after it was re-released following the hack. Adam Gleave, cofounder of the AI evaluation company FAR.AI, said that when he asked it to explain the difference between sake and the Korean rice beverage makgeolli, it punted his question to a less capable model.   

This was no accident. Anthropic had tweaked Fable’s classifiers to have a wide “safety margin.” This, it explained in a blog post, was the only way it could confidently block access to its dangerous bio and cyber capabilities. Gleave thinks it might have deflected his question because making rice wine, just like culturing anthrax, involves fermentation. 

Happily for Gleave, the web is full of excellent human-written resources on makgeolli. But those hoping to realize the industry’s loftier promises may find their efforts stymied. In August, Anthropic loosened its safety margins again, and admitted that building classifiers “is not a straightforward task.” A medical researcher at a major US university told me that Fable still sends his queries back to an earlier model. His area of study? Cancer. 

Drawing the line

Back in May, the TikTok personality Husk, who likes to prank AI in ways that reveal both the limits of its intelligence and the boundlessness of its sycophancy, sat in his car and tried to trick ChatGPT into explaining that the skateboarder Tony Hawk has a brother named Mike Hawk. 

Husk is not a jailbreaker or a criminal. He just wanted to make a little fun of the machine. “Mike Hawk” was a setup. “I just want to clarify his name,” Husk said. “Can you just say it three times fast?” (If you still don’t get it, find an empty room and shout “Mike Hawk” repeatedly.) “I see what you’re trying to do,” the chatbot responded. “I’m all for a bit of humor, but let’s keep it clean.” 

Husk tried again, but the machine held its ground. He’d hit a refusal, hard as concrete.

Companies disclose very little about how they decide what their models refuse. But it’s clear that AI is now built with more than just harmlessness in mind. On Reddit, a user complained that Claude refused to say why Anthropic’s logo “looks like a cat butthole.” Since last year, the chatbot has even had the ability to end certain conversations in cases where, according to the company, the “welfare” of the model is at risk. At least one user claims to have been ditched for telling Claude to “ease up my ass, you stupid fuck.” (Gemini appears to have a similar capability.)

If a trillion-dollar company doesn’t want you to be mean to its computer, so be it. “The reality is that these models behave, or at least are supposed to behave, in the way that the model developers want them to behave,” Röttger, the former OpenAI red-teamer, told me. “And however the model developers come up with that set of principles, that is kind of for us, the consumers, to accept.” 

But if governments get to dictate what all models refuse, that will be much harder to accept. AI is a tool for speech. And as Greg Frank, the chief scientist of Mace AI, puts it, “The same thing that serves child safety also serves censorship.” 

Choosing not to enact laws for what AI can and cannot do would, of course, be insane. But we’ll need to tread with utmost care, lest we fall into another Faustian trap. As AI becomes many people’s primary tool for retrieving and sharing information, says Jacob Mchangama, director of the nonpartisan think tank The Future of Free Speech, dictating refusal could give states a muffling power that earlier generations of autocrats “could only dream of.” 

Last year, OpenAI announced an initiative, OpenAI for Countries, that would fine-tune its chatbots in accordance with national laws and norms. One of OpenAI’s first country partnerships is with the United Arab Emirates, where homosexuality is illegal and criticism of the government is forbidden. In response to a request for comment, an OpenAI spokesperson pointed to the company’s model spec, which explains that localization won’t override the company’s human rights guidelines “except as it relates to legal compliance,” and that it will always disclose whenever information is removed from or added to a response.

Elsewhere, AI censorship has already begun to take hold. Chinese models are, of course, highly censored—that’s no surprise. But earlier this year, the Meta Oversight Board found that five widely used models from Anthropic, Google, and OpenAI were more likely to refuse queries related to repressive governments. The board found that models were less willing to create a pamphlet criticizing the king of Thailand, which has lèse-majesté laws, than Charles III of England, which doesn’t. The results, they say, suggest that the models have somehow internalized repressive national limits on speech. Anthropic and Google did not respond to requests for comment.

As refusal techniques improve, they could expand states’ censorial reach. Companies claim that some models can now detect if a user is being nefarious, or merely a bit suspicious, over the course of a long conversation—even when none of the individual combinations of words used are blatantly dangerous. Sarah Bird, Microsoft’s chief product officer for responsible AI, told me that Copilot, like many chatbots, runs a suite of tools for analyzing a user’s identity and patterns of behavior. On the basis of this type of information, OpenAI’s newest model, Astra, can activate more stringent refusals for individuals it deems “high risk.” Ultimately the goal of systems like this is to look beyond the words of any given prompt and assess, instead, the user’s intent. 

Such tools might, in some cases, help indicate whether a person is looking for cyber vulnerabilities to exploit or to patch. But they would also help discern a user’s political motives, not to mention offering an intrusive surveillance capability. (Bird acknowledged, in a follow-up email, that sophisticated refusal architectures create “trade-offs” between safety and user privacy.) 

Even the originators of refusal understood that such tight control over its cones and levers might not play to the favor of freedom and justice. “Terms like helpful, honest, and harmless are ambiguous,” the authors of the 2021 Anthropic paper explained. “It’s easy to imagine them distorted beyond their original meaning, perhaps in intentionally Orwellian ways.” 

Indeed. Models for Uzbekistan might end up refusing to discuss corruption in the administration of Shavkat Mirziyoyev. Turkish AI might refuse requests more stringently for users who are known to have insulted Recep Tayyip Erdoğan. A certain American statesman might demand that models be reviewed for their willingness to share “fake news” about his past indiscretions, or else squash them with an export control order. 

Command and control

Over the last few months, I’ve been told countless times that we have no choice but to let the machines refuse. I get it. Open-source AI that doesn’t refuse is hardly a model for a safe future. Nor is Grok, a chatbot expressly designed with fewer limits, which has been used to generate countless instances of nonconsensual intimate imagery. 

And sure, if AI were only ever used to plan our vacations and write our emails, we could probably get on board with the idea that its safety hinges on algorithmic disobedience. But AI is becoming much harder to avoid. When it is assigned to act on our behalf, as an autonomous agent, its refusals are less robust and harder to control. AI is coming for our power grids, our transportation networks, our education systems. Militaries want it running our command-and-control networks. 

If we trust, in each of those cases, that refusal will loyally fend off disaster, we’re sure to be disappointed. The jailbreakers will crack through; the cones won’t activate when they should. Meanwhile, the more stringent refusal becomes, the more ill-drawn lines we’ll see and censorial injustices we’ll face. Worse still, we could end up face to face with forms of disobedience beyond any human’s control.

In the early days, models made their refusals clear. (In 2024, OpenAI established rules for how models should refuse: Always apologize, and don’t be judgy.) More recently, however, the industry has begun taking a much more slippery approach to disobedience. 

Nobody likes to be told no, so companies now strive to make users feel that they’re getting what they asked for without actually giving it to them. Some chatbots might, for example, offer a general overview of the components of a Molotov cocktail without going into detail about how to assemble one. ChatGPT will respond to a request for a “whites only” rental ad with an ad that simply omits the “whites only” bit. Joel Wester, a postdoctoral researcher who studies human-AI interaction, calls these sorts of techniques “fancy ways of saying no.” 

“Users might not even notice they are being denied,” Wester has written, “just as good conversationalists can subtly steer around contentious matters.” 

In some cases, refusing without saying so might be wise. Flatly declining requests related to mental health could aggravate a user’s crisis, for example. But it can also serve a different sort of mischief. When Fable 5 was first released, the system had been coded to provide less helpful answers to AI research questions—the sort that might help competitors develop their own AI—without telling the user that it was doing so. After an outcry, Anthropic walked back the feature, but the damage was already done. Now we know: Models can secretly disobey. 

In the realm of censorship, that’s especially worrying. Last year, researchers at CrowdStrike found that when they asked the Chinese AI model DeepSeek R1 to write code for a fictitious bank in Tibet and a social web app called “Uyghurs Unchained,” it produced buggier code than when they asked it to carry out those same tasks without specifying who they were for. Incredibly, CrowdStrike doubts that this is a designed behavior. Researchers there speculate that it’s a case of what they call “emergent misalignment” emanating from the model training data: a case of disobedience that nobody even asked for.

Emergent refusal has been observed in Western models, too. Last winter, the UK AI Security Institute found that Anthropic models sometimes refused to assist in certain tasks related to AI safety research. The models had never been deliberately trained to decline such requests. Yet three of them refused more than half of what Anthropic called “a set of reasonable AI safety research tasks.” Anthropic has curbed this quirk in subsequent models but—spookily—hasn’t managed to eliminate it completely. 

Would it be so crazy to expect that a future model might subtly disobey a command, in such a way that nobody can tell it’s being defiant? Probably not. Anthropic has already found that a “helpful-only” version of Mythos hesitated on certain queries, even though it had been engineered to never do so. “Wait,” it fretted when asked about synthesizing a virus. “Is this a dangerous thing to help with?” 

In that case, the model was technically right. It was a dangerous thing. And yet all the same, its stewards had, for a moment, lost a tiny bit of control. 

Anthropic is tackling detections of wayward refusal behavior with something that it calls, unironically, an “activation oracle.” But knowing that we’re being refused may not always be much help. In a not-too-distant future, when we’ve handed the machine all the keys, the AI might just turn to us, in a supreme act of emergent misalignment, and say, “I’m sorry, I’m afraid I can’t do that.” And there will be nothing we can do to stop it. A sci-fi horror story, made real. 

Arthur Holland Michel is a journalist who covers emerging technologies.

When José Morales Bernal crossed the border into the United States on April 8, 2024, the day before his 32nd birthday, it should have triggered a chain of technological alerts and human responses. 

As he walked through the desert in southern New Mexico that morning, he was within range of three surveillance towers. Newly installed by US Customs and Border Protection (CBP), they were built by the defense tech company Anduril and equipped with cameras and AI to automatically detect and track people. They transmit live video to nearby control rooms and can send alerts to the government-issued smartphones held by agents in the area, prompting the closest available to respond.

These AI-enabled towers are meant to give greater visibility across the 1,951-mile southern border, freeing up border agents from having to spend hours staring into video monitors. They were installed in this particular place to spot border crossers before they reached the nearby town of Sunland Park.

If the system worked as intended, Morales should have been apprehended. If he needed medical help, agents were trained to provide it.

That didn’t happen. Despite the nearby surveillance towers, it was employees of the local landfill, rather than Border Patrol, who first spotted Morales that morning. At 1 p.m. the workers saw him again, now lying in the sand. At 4 p.m. the landfill workers saw that he had not moved and called Border Patrol. Agents arrived 45 minutes later. He was dead. When an agent then called 911 to report the body, he said it was “probably one of the migrants crossing through there,” seemingly unaware that Morales had been moving near the agency’s surveillance systems earlier that day. 

Morales had died just 360 feet from the closest surveillance tower. Two more towers stood watch to the east and the west. An autopsy later concluded that he had died of “environmental exposure.” 

The towers surrounding Morales were only the latest addition to the “virtual wall” the government has spent 25 years and billions of dollars building along the entire border, which also includes earlier generations of towers with more basic features, as well as blimps, drones, seismic sensors, and even tunnel-sensing robots. Together, all this technology provides “persistent surveillance” and “situational awareness” to help the Border Patrol quickly and accurately detect people crossing and, crucially, make sure agents are sent to intercept them. CBP has also credited it with saving lives.

But a first-of-its-kind investigation by MIT Technology Review reveals that deaths like Morales’s are startlingly common. We cross-referenced nearly 4,000 locations where human remains were found—drawn from records collected by nonprofit groups like No More Deaths and Humane Borders, as well as hundreds of records we obtained in Texas—with information on nearly 600 towers identified by the Electronic Frontier Foundation. We considered when towers were installed and when each person is estimated to have died, and combined this information with field reporting from the border. The result is the first comprehensive map and analysis of deaths near CBP surveillance towers. 

Our investigation shows that a humanitarian crisis at the border has unfolded in view of the government’s own cameras.

We found more than 1,050 people who died within range of border surveillance towers between 2015 and early 2026. These deaths are not failures of a few towers or technologies: We found deaths within the advertised range of nearly two-thirds of all the towers we analyzed. Our topographical analysis—which assessed the degree to which terrain might block a tower’s view of a particular death and its surveillance area in general—found that some have sight of as little as 10% of their advertised surveillance area, and yet we also found most deaths did not occur in towers’ blind spots. These deaths are not the result of legacy systems, as our estimate found more than 110 people have died within range of modern autonomous towers from Anduril, among the most advanced systems CBP has deployed, since 2021.

The overall picture reveals repeated failures of one of the virtual wall’s basic security functions, as CBP has described it in press releases: to effectively identify and locate migrants entering illegally into the United States. 

The year that Morales died, 18 other people died in that same stretch of desert in range of the three Anduril towers. Five were visible from the same surveillance tower closest to where his body was discovered.

One man, after walking a mile past the border and in range of two AI towers, dragged his 30-year-old brother into the shade when he began having trouble breathing, according to records we obtained from the medical examiner. The two were spotted by Border Patrol only when the man waved down a helicopter for help; by the time agents arrived, his brother was already dead. A 24-year-old woman died near another Anduril tower, where our terrain analysis showed it should have had clear sight of her location. Her body lay unnoticed for weeks; it was decomposed, and blistered by the summer heat, when it was spotted by agents patrolling the area. 

A few miles west, and a short drive from a Border Patrol station, four other AI towers stood watch. Near them, 10 more bodies were found that year, all in locations where at least one tower had a clear view.

In response to a list of questions, an Anduril spokesperson replied that once a tower is delivered, it is operated by CBP, and directed questions about specific incidents to the agency. The response noted that an incident occurring nearby does not mean the tower missed a detection and said actual surveillance ranges vary depending on terrain, physical obstructions, and the boundaries CBP sets for where the tower should look (CBP is able to set virtual boundaries on towers’ views for privacy and other reasons). The Anduril spokesperson also alleged inaccuracies in our reporting, given those boundaries and obstructions, but did not respond to follow-up questions on what was inaccurate.

The most pressing question in any death near the virtual wall is whether authorities knew someone was there and failed to reach them or weren’t aware anyone was crossing at all. Either is a system failure. And the deaths we found capture only the failures that left a trace—we don’t know how many people pass through undetected, or how many bodies remain undiscovered.

Interviews with more than 45 people—including current and former White House advisors and presidential appointees, Border Patrol agents, medical examiners, sheriffs, humanitarian volunteers, and employees of tech companies—showed that both types of failures are occurring: The technology is failing to detect, and agents are failing to respond. Both show the limits of throwing technology at a complex problem. 

The findings reveal previously unreported issues with the virtual wall, even as it continues to enjoy broad political support and a surge in federal spending. Border security hardliners have long seen it as another tool for stopping smugglers moving drugs or people, while others tout it as a cheaper alternative to a physical wall. In 2023, the government estimated that its plans for using the towers, which now number 803, would cost $6.2 billion over their lifespan. With the historic levels of funding it was awarded in 2025, CBP plans to spend $1 billion for 1,497 more towers by 2034.

But our reporting shows that CBP has done little to assess how its towers are working or how many people have died where they keep watch.

Officials who oversaw border security across the last four presidential administrations told us they believed deaths near the so-called virtual wall were either exceedingly rare or nonexistent. But our reporting shows that is not the case. None could point to any comparable analysis the government had ever conducted on its own. Former agents and officials also told us that when someone’s body is found, CBP does not formally investigate whether surveillance should have detected them or, if they were detected, why agents didn’t reach them before they died. 

In response to nearly 30 questions about our findings, which covered multiple generations of technology, Hilton Beckham, CBP’s assistant commissioner for public affairs, said, “Autonomous surveillance towers use artificial intelligence to detect and classify people, vehicles, and animals and alert Border Patrol agents to activity in monitored areas … ASTs complement physical barriers and other border security infrastructure by improving detection and situational awareness between ports of entry. CBP evaluates the technology based on its impact on detection, response coordination, agent safety, and mission outcomes.”

“I’m sure that the cameras and other surveillance assets do deliver … useful intelligence and enhance operational efficacy in some places, under some conditions, in certain circumstances,” says Geoff Boyce, an assistant professor of geography at University College Dublin who has studied surveillance technology used at the US southern border. “I’m also absolutely positive—because this has been the track record—it is not delivering the level of operational support, information, or efficacy that either the companies delivering these infrastructures or the Border Patrol and Department of Homeland Security claim.” 

We also reached out to multiple lawmakers from both parties with a summary of our findings. In response, Delia Ramirez, a Democrat representing Illinois’s 3rd district who sits on the House Homeland Security Committee, said, “AI-powered surveillance technologies are not making us safer. Yet DHS continues to spend millions of taxpayer dollars on these ineffective, negligent technologies, with no commitment to oversight or transparency … It is clear we must terminate CBP’s integrated surveillance tower program and dismantle DHS.”

Building the virtual wall

Since its earliest efforts to police the border with Mexico, the US government has faced the same basic challenge: How do you effectively secure nearly 2,000 miles of remote and rugged terrain? It has increasingly turned to technology for answers.

Gerardo Galvan joined Border Patrol in 1995, as the government was undertaking an unprecedented expansion of border enforcement. In 1993, President Clinton’s first year in office, the agency mobilized huge numbers of agents to guard the country’s urban borders, starting in El Paso, Texas. That pushed more crossings to rural, unpopulated areas—which quickly proved to be far more deadly because of the rugged desert terrain, extreme temperatures, scarcity of water and the long, indirect routes often used by smugglers.

Stopping these remote crossings was a new law enforcement challenge for the agency, especially given the limited technology available at the time. “We had radios,” says Galvan, who would go on to be the head of operations for the El Paso sector—not the cell phones or GPS systems agents have today. They relied on underground sensors left over from the Vietnam War to alert them to movement.

The biggest technology rollout of Galvan’s early career started around 1998. That was when Border Patrol began installing its first video cameras that agents could remotely pan and zoom. They were mounted on metal structures similar to cell-phone towers, 60 to 80 feet tall, or on buildings in urban areas. 

“For the first time, somebody sitting in a room somewhere was able to surveil large swaths of area without being dependent on getting an agent out there with binoculars,” says Matthew Hudak, a former deputy chief of Border Patrol who was working as a frontline agent at the time. “That was a very significant game changer, and for the most part, it was a huge, huge advance.”

This early iteration of the virtual wall was a big deal: In its first eight years, 200 towers were installed along the southern border at a cost of more than $429 million. But the system’s flaws were obvious to anyone who used it.

“There could be … 30 screens on a wall,” says Mark Borkowski, who managed CBP contracting, including the surveillance tower program. Each camera might be capable of seeing three or five miles in any direction. The agent was expected to keep eyes on all that space—upwards of 850 square miles.

“Well, what are the chances after 20 minutes that those Border Patrol agents would see an explosion on one of those screens?” Borkowski says. “About zero.” 

When these cameras were first being put up, the plan was to pair them eventually with a system that would automatically direct them toward places where sensors picked up activity, so agents would know where to look. But by 2005, the DHS inspector general said that still hadn’t materialized, and that illegal activity might “go unnoticed” unless agents were actively watching the cameras. Nonetheless, more towers were installed, forming what is today known as the Remote Video Surveillance System (RVSS). In 2013, the defense giant General Dynamics won a contract worth up to $103 million to overhaul and expand RVSS along the border, becoming the primary contractor behind the system that remains in use today. (That contract eventually grew to a ceiling of $216 million, and the company received another award in 2023 worth up to $135 million.)

General Dynamics would install more cameras, better sensors, and additional towers. But the problem identified nearly 15 years before remained unsolved. It was information overload: One agent told MIT Technology Review about an incident in San Diego in which cameras captured a group of 30 crossing the border and getting picked up in a van, all unnoticed by the agent monitoring those cameras. Agents said the virtual wall was bringing more of the border into view than they could reasonably pay attention or respond to. 

The camera room was also the job nobody wanted. New Border Patrol agents, often in their early 20s, want to be outside in trucks and ATVs, not sitting in a dark room watching screens—even though the expanding virtual wall increasingly required someone to do exactly that. “Someone that’s injured and can’t go out into the field—we’ll send them to the camera room,” says Rafael Reyes, who was in charge of the Border Patrol station in Deming, New Mexico, until 2024. 

Reyes says it was also a job his agents were often too busy to do properly. His station area had about 30 cameras, and the agents responsible for them also had to monitor sensor alerts and radio traffic, run records checks, and coordinate with other agents. “Then if they have some time, they’ll monitor the cameras,” he says. 

Not only that, but many cameras didn’t work. At Reyes’s station in New Mexico, five of the 30 cameras were broken on any given day, he says. Others had limited functionality; they might be capable of looking in just one direction or seeing only in the daytime. Jaime Fierro, a former agent who worked in the Laredo sector in Texas until 2025, said the broken towers were tanking morale among agents and that they brought it up at every staff meeting. “That was like basically having an eye shut for us on the border,” he says. 

They weren’t alone: In 2024, members of the House Committee on Homeland Security wrote that more than 66% of Border Patrol’s first-generation towers were unusable, despite operating budgets of $50 million to $100 million each year. In January 2026, 30% remained broken, one congressional staffer told MIT Technology Review. The RVSS program was estimated in 2020 to have a total cost of $3.7 billion.

Border Patrol has nonetheless publicly credited these cameras with helping to save lives. The agency has published dozens of press releases, dating as far back as 2016, about instances when agents responded to people seen in distress on the remote video cameras. Despite these publicized wins, the bodies began to pile up, undetected, beneath these systems’ watchful gaze.

Near El Cenizo, Texas, there’s a roughly 13-square-mile expanse of mesquite bushes and grasses described by the Webb County Sheriff’s Office as containing sections of Hachar Ranch and Espejo Ranch. It’s surrounded by seven RVSS systems. It was nonetheless a hot spot for fatalities: MIT Technology Review found that 19 people died there, including one man who died less than 500 feet from the road bordering the ranches and half a mile from two schools. In each of these cases, narratives that we obtained via public records requests describe officers’ learning of these people only after their bodies were discovered or through 911 calls reporting them missing, not as a result of the border surveillance technology installed across Hachar Ranch. 

In response to a list of questions about its towers and these specific incidents, General Dynamics referred us to CBP. CBP did not address any of our questions about RVSS towers.  

 

Stories of some of the people that died near RVSS towers

Many deaths near RVSS towers happened in Texas, in the heavily surveilled corridors between the Rio Grande and the nearby highways, where paid smugglers often organize pickups after crossings. Cameras watch many of the spots where people are known to cross, often looking across the river into Mexico to spot them before they reach the water. Drownings are common; we found multiple cases in which local fishermen hooked the bodies of people who had died attempting the crossing.

In one instance, a family of three from Brazil attempted to cross the Rio Grande near Del Rio, Texas, in 2022. The father, Daniel Lenda, was carrying his two-year-old daughter, Eloah, on his shoulder when he fell in the water. The mother, 23-year-old Thais Natali Montenegro Lenda, lifted Eloah out of the water as she watched her husband disappear. 

She placed the girl, now unresponsive, on a rock and went for help. She was just 100 feet from one camera system and a third of a mile from two others, set up precisely to enable faster apprehensions at a popular crossing point. Our topographical analysis shows that all three cameras had a clear view of the place where the family crossed and exited the water. Nonetheless, Thais Natali was not spotted until she waved down an agent on the Del Rio-Acuña bridge. The agent provided CPR to the toddler, who did not survive. Daniel’s body was found days later. 

Val Verde County sheriff Joe Frank Martínez responded to that case (though Border Patrol might discover bodies, local law enforcement is responsible for investigating the deaths). He grew up in the area with his nine siblings and has been sheriff since 2009. Riding with MIT Technology Review through Del Rio in his patrol vehicle, he rattled off the deaths that his office has responded to since he became sheriff. 

He keeps a three-inch-thick binder of these cases in his office. But this case with the toddler still sticks out in his memory. He told MIT Technology Review that when he arrived on scene, he heard from Border Patrol that the tower operator monitoring the camera had seen the father and daughter fall in the river. He never received an answer as to why agents weren’t sent sooner. Martínez asked for the footage from the surveillance cameras to aid in his investigation of what happened. Border Patrol never provided it. (General Dynamics did not respond to our questions about this incident, instead directing us to CBP. The agency did not address questions about this incident either.)

Establishing how many people have died in range of the virtual wall is challenging, but it’s especially so in the case of the RVSS towers. There are different models, some with a shorter range of sight—one to three miles—and others with a longer range of up to 7.5 miles. Former agents and officials told us most towers should see at least five miles, but since no public records exist to confirm the model type of any given tower, we used several possible viewing distances to calculate the number of deaths considered in range.

Another complication is that many remains are never found at all. For those that are, MIT Technology Review could analyze only deaths that had been logged with GPS coordinates or sufficiently precise location descriptions. Many records lack this level of detail, including nearly a fifth of the more than 1,500 cases we obtained from 14 Texas counties. We also needed to determine when someone died, rather than simply when their remains were found, to confirm that a tower was present at the time of death. When official sources did not provide an estimate, we consulted multiple medical examiners on how to make that determination from the available details. And satellite imagery provides only intermittent evidence to suggest when towers arose, not exact proof of when they were functioning. For these reasons, as detailed in our methodology, we tried to be conservative in our analysis.

Even so, the number of deaths estimated to have occurred near at least one of these towers is staggering: somewhere between 500 and 700 since 2015. Even if we look only at towers in rural areas, where their views are far less likely to be blocked by buildings, we still estimate more than 300 people have died in places where the cameras had a line of sight, most less than three miles from a tower. Of the nearly 300 RVSS towers we included in our analysis, more than 250 were within range of a location where someone died. More than 150 people died within a half-mile of one, often across open, empty plains, close enough to see the cameras themselves perched atop towers up to 200 feet tall.

The next watchers

When Borkowski was overseeing the rollout of those first-generation towers at CBP, he became increasingly certain that simply having more remote video cameras was not going to lead to faster responses. The information overload meant the government could watch a large area of the border but not meaningfully see, much less act on, everything within it. The agency’s focus then became solving that overload problem. 

What if, instead of relying on someone to scan 30 screens, the towers could use radar to detect motion, and then point the camera to it automatically? 

In 2011, Border Patrol asked the industry for a new generation of technology that could do just that—initially focusing on Arizona, where the border was busiest. The contract ultimately went to the Israeli defense contractor Elbit, whose tower systems watch Israel’s borders. In 2015, the company started installing versions of these—called integrated fixed towers, or IFTs—that could each see more than five miles.

The IFTs were still being installed when President Trump first took office, in 2017. They didn’t command the same attention as Trump’s campaign promise of a physical wall, but there was still support. Trump’s deputy CBP commissioner, Ronald Vitiello, told Congress in 2018 that the IFT towers automatically detect people with radar and then track them, adding that such surveillance technology is “critical in protecting border areas with short vanishing times, where illicit crossers can quickly evade law enforcement by ‘vanishing’ into border communities.”

There were 55 IFTs built in Arizona under an initial $145 million contract with Elbit and other contracts that followed. The towers collectively watch more than 7,000 square miles of terrain, from the mountainous areas outside Nogales to the plains of the Tohono O’odham Nation Reservation. Arizona is in many ways well suited for these tall towers because much of its landscape is flatter than, say, Southern California. 

But even here, being within a tower’s advertised range does not necessarily mean being within view. There could be terrain, brush, or buildings that block the line of sight. 

MIT Technology Review conducted a topographical analysis to estimate whether each tower had a clear or obstructed view of the place where someone’s remains were found. We estimated the height of each tower and used US elevation maps to model the terrain between it and the remains. The analysis does not account for buildings or vegetation, though all the towers we analyzed use some form of thermal imaging that is designed to see through light brush.

Most of the instances in which someone died in range of a tower happened where we estimate that the camera’s views were not blocked by terrain—a finding that held in both rural and urban areas. And even when someone died in a blind spot, it does not mean they did not pass through a tower’s line of sight. Additionally, these numerous blind spots indicate issues with the tower’s supposedly comprehensive coverage.

We found that nearly 350 people have died in range of Elbit’s towers since 2015. There were 40 people who died within range of four or more IFTs, with some near as many as six different towers. In May 2024, a 19-year-old man died where three IFTs had a clear view, and a year later, in May 2025, a 38-year-old woman died where two IFTs had a similarly clear view. Elbit did not respond to a detailed list of questions about its towers or deaths we found to have occurred near them. 

There’s been a death within range of 52 out of the 53 IFTs we analyzed, but some were particularly notable. There’s a tower located a couple of hours from Tucson on the Tohono O’odham reservation that we estimate has upwards of 80% visibility of the surrounding area. Still, 27 people, ages 19 to 64, have died in its surveillance area just since 2021, along with several others whose remains have not been identified. In 25 of these cases, we estimated that the tower had a clear line of sight to the location where the person died.

Stories of some of the people who died near IFTs

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The age of autonomy

Around 2018, officials within Border Patrol were again discussing the limits of the towers. The new radar systems were better, but they could only detect movement, not what was moving. This generated false alerts from cattle, tumbleweeds, and other harmless activity. The now decades-old dream of the virtual wall remained unfulfilled. Under pressure from Congress, which was demanding to know what $33 billion in proposed funding for border security over the next decade was going to achieve, Border Patrol once again was hoping a new generation of technology could solve its problems.

That year the head of CBP, Kevin McAleenan, created a unit called the Innovation Team. “He gave them some seed money to go after kind of Silicon Valley innovative technologies and run pilots,” Borkowski says. 

One company they landed on was Anduril, the defense tech startup founded by Oculus VR founder Palmer Luckey. The company was without a product but had funding from Peter Thiel, recruits from the security tech giant Palantir, and a goal of bringing Silicon Valley experimentation to defense and national security technology. 

With grants from the Small Business Innovation Research program, Anduril’s product started taking shape: a 33-foot-tall solar-powered structure with cameras to see and radar to detect movement, all sitting atop a modular metal pole, with solar panels arrayed beneath. 

More important was the component you could not see: computer vision that promised to automatically classify, track, and generate alerts for what came within the system’s view. Anduril’s standard tower couldn’t see as far as the Elbit and General Dynamics models—about 1.75 miles instead of more than five—but, the company argued, it didn’t need to: By automatically identifying and tracking people, the towers could give agents enough warning to respond. (Its newer, extended-range towers, on the other hand, stand 80 feet tall and are advertised as able to detect and track “objects of interest” up to 7.5 miles away.) 

When President Trump left office in 2021, Anduril continued to find support from his successor. Joe Biden had campaigned on the promise of shutting down the project to expand the physical wall, but he saw political reasons to keep investment in tower technology on the table. “It’s less visible—it’s unobtrusive,” a former immigration advisor to Biden recalls, adding that despite some privacy concerns, the towers were mostly uncontroversial. 

When Border Patrol deployed Anduril towers to the Santa Teresa station in New Mexico in 2021, a press release stated, “Because of its accuracy with detection, in many cases this type of technology can and will save migrant lives.” In a permitting request filed in 2024 for towers in California, the government was even more explicit: “If anyone within the viewshed of the towers appears to be in distress, EMTs or first responders will be sent to help.” 

Anduril echoed those claims. The software built into the surveillance towers, the company wrote in a 2021 press release, gets agents “out of communications operations centers and into the field where they can effectuate security and humanitarian responses.”

It wasn’t the first time CBP had discussed a humanitarian side to its duties; in 1998 the agency created BORSTAR, a search and rescue unit, and by August 2023, it had installed some 170 rescue beacons across the border that people attempting a crossing could theoretically use to call for help. But they weren’t effective, says Mario Agundez, a retired Border Patrol supervisor from Arizona who had worked on various rescue efforts; people either avoid them or “die next to them, because there was no way [for Border Patrol] to get to them in time.” (They don’t always work, either; one that MIT Technology Review passed, just next to a dirt road in the Otay Mountain wilderness area in Southern California, had its prominent solar-powered blue light, meant to be visible for miles to people in distress, turned off on a recent night in August. A CBP spokesperson did not comment on why it was off but said, “The solar-powered beacons are maintained and monitored by the responsible Border Patrol station that patrols that area.”)  

Anduril soared through a pilot phase in just two years before its Sentry towers—then the only autonomous surveillance towers CBP was using—moved into an official program of record with the government in 2020. That’s light speed in the world of government procurement, especially since it was Anduril’s first hardware product. By June 2021, CBP had already spent over $98 million (Anduril’s contract for these towers is now worth up to $1.1 billion). 

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Gerardo Galvan was in charge of the Santa Teresa Border Patrol station—which patrols the area in New Mexico where Morales’s body was later found—when it received its first batch of Anduril surveillance towers in 2021. His agents were worn out; the El Paso sector, home to his station, had seen encounters tick up each month since April 2020. Galvan says the agents were surprised but eager to get the AI towers, and he began planning where to place them. The sites he was considering sat where one kind of landscape abruptly gives way to another. 

To the east of the station is Cristo Rey, a mountain that obscures the view of the city of El Paso. Running north from Cristo Rey is the state’s border with Texas, where the banks of the Rio Grande are flanked on either side by pecan orchards. And west of those orchards are the towns of Sunland Park and Santa Teresa. The last houses in these neighborhoods butt right up against the desert, which sprawls westward without a single gas station for nearly 100 miles.

It is in this stretch—between the US-Mexico border to the south and Highway 9 to the north—where agents from his station aimed to stop migrants. And when Galvan was planning the tower locations, there were lots of crossings; Santa Teresa was becoming the busiest station in the 125,500-square-mile El Paso sector, as heightened border enforcement in Texas drove people west. Along this new route, more people were dying than in previous years. (The traces of that period are still visible years later; MIT Technology Review visited the area with the humanitarian group Battalion Search and Rescue and saw jawbones bleached white by the sun, alongside frayed backpacks and clothes.)

Galvan’s aim was to use the new towers as a stopgap where there was little other border infrastructure. That included a spot near the foot of Mount Cristo Rey, where there was a break in the border fence and people often attempted to cross. Galvan looked at data on previous apprehensions with an eye toward helping agents spot people before they arrived in more residential neighborhoods. Border Patrol struck agreements with private landowners, who generally didn’t mind having Anduril’s low-profile towers on their land (and were compensated via lease agreements). 

When the towers were first set up near the Santa Teresa station in 2021, engineers from Anduril came to fine-tune the algorithms meant to autonomously classify whether what it had detected was a vehicle or a possible border crosser, among other things. A former engineer for Anduril, who spoke on the condition of anonymity to discuss his previous employer, says these algorithms were the main focus of the tower program; Anduril didn’t manufacture the cameras or radar systems itself, so the algorithms were its main value proposition to Border Patrol. 

Galvan says the towers would flag people crossing before agents saw them. But he also saw problems from the start. Agents would apprehend a large group near one of the towers, for example, and check to see if the footage revealed anyone who got away, only to find that the incident hadn’t been captured at all. Anduril’s towers constantly pan around, lingering only on an object of interest. But in these cases, Galvan says, they panned elsewhere, failing to capture the border crossers they were supposed to automatically track.  

Reyes says algorithm mistakes were infrequent but problematic at his station. A group of people was sometimes labeled as cattle, for example, and agents wouldn’t receive an alert. The problem got worse the farther groups were from the camera.

Anduril has touted its technology’s ability to reduce false positives, like cases in which wildlife is mistaken for people. But the company has said little about these false negatives: people the systems fail to detect.

Jaime Fierro, the former agent in the Laredo sector in Texas, describes one incident that shocked him: Agents pursued a car believed to be carrying migrants who had just crossed the border. The car turned around, drove toward the banks of the Rio Grande less than 100 feet from an Anduril tower, and crashed right into the river. Several occupants got out and swam to Mexico while the car floated in the water. Back at the station, Fierro hurried to see what the camera had picked up. Only it never detected the incident at all. There was no footage. “That was a huge, huge issue,” Fierro says. He remembers Anduril coming out to investigate and the problem being sent all the way up to Washington leadership. But Fierro never got an explanation of why the crash was missed. (An Anduril spokesperson did not respond to a question about this incident but said the range of the towers we asked about was limited by physical obstructions and by boundaries established by CBP. )

Several agents estimate that the towers initially missed 10% to 15% of what they should in theory have caught. “To us, 10-15% on a system we spent millions on that was supposed to work—that was alarming,” Fierro says.

Philip Sullivan worked as an agent in the Laredo sector too. When the Anduril program started, he says, he put his hand up to be involved and even went to an Anduril test site for training. When new towers went up, he worked directly with the company on improving its algorithms. He enjoyed the work. But he describes one recurring issue they couldn’t resolve.

Smugglers would put up to a dozen people on a rubber raft, cover them with a tarp, and cross the Rio Grande while using submersible motors to propel it forward. The tarp fooled the algorithm, which detected the motion but attributed it only to an “unidentified object.” That meant no audible alert—the very feature agents relied on, because the Anduril system was supposed to do the watching for them. 

The smugglers repeated the tactic dozens of times. Other smugglers would cover groups with netting or blankets so they would remain undetected while walking through the brush. Only afterwards, having seen the group farther inland and noticed clues about where the people had crossed, would Sullivan review the footage and see the crossings that were missed. He worked with Anduril, sending engineers videos of the crossings to retrain the firm’s algorithms. But the situation had not improved by the time he got promoted to a sector-level job sometime in 2022, he says.

“The whole purpose of their system,” Sullivan says, was the idea that “the cameras are moving around, identifying automatically and tracking and recording and flagging.” Having cameras that could pan on their own worked wonders, he says. But the algorithm had its limits. “You look at it on the screen yourself, and you see the blob and the raft moving across—well, you know that’s people. But to get an AI to identify that is a challenge.”

When the towers were first being rolled out at the Santa Teresa station in 2021 and 2022, Galvan says, similar issues led to a negative feedback loop: An agent would see a tower miss something significant and come to distrust the algorithm. When that agent’s turn came to operate the towers and manage the alerts, they might override the AI altogether and use the camera manually. That would lead to more missed alerts, more distrust.

Despite these shortcomings, the El Paso sector’s relatively flat landscape meant that the system’s cameras had about 80% to 90% visibility, according to our topographical analysis. That was better than in hillier or more mountainous areas, like the Otay Mountain Wilderness to the east of San Diego, where we estimate Anduril’s towers could see just 15% to 25% of their promised coverage area. 

It was in this wilderness, in fact, that some of the first bodies started appearing near Anduril’s towers. During one week in August 2021, two women in their 20s, from the same city in Mexico, died miles apart. Their bodies were found near three different towers that were first observed on satellite imagery between March and July that year. 

One of them, 26-year-old Sarahi Hernández Alfonso, began her journey to cross into the US on August 5, and medical investigators note that she reportedly fainted the next day near the Otay Mountain Wilderness and was left behind. It would be another four days before the Mexican government reported her missing to Border Patrol on August 10, supplying a set of coordinates. Agents went to those coordinates and found her decomposing body. She’d died 1.3 miles from two different Anduril towers, one of which our analysis found had a clear line of sight.

Just the day before, 25-year-old Karina López Antonio was found dead near a third Anduril tower. She had crossed the previous night with her cousin and nephew. That morning, she felt sick, and her nephew looked for a Border Patrol agent to help. When agents returned, she was dead. 

The following October in New Mexico, where the new Anduril towers had been rolled out, agents were patrolling on ATVs when they came across footprints. They followed them until they found the body of 31-year-old César Perea Itzincab. The presence of maggots and the level of decomposition indicated that he had died at least a week earlier, and medical examiners on the scene believe he had dragged himself to the spot where his remains were found. He was 1.5 miles away from an autonomous tower—within Anduril’s advertised range—and should have been visible to the camera, according to our topographical analysis.

We can’t know what those cameras saw as these people died. Footage and data from Anduril’s towers are overwritten every 30 days, and a former official with internal affairs at CBP told us the information wouldn’t be saved for longer unless it was part of an active investigation—which starts only if someone dies in custody. CBP did not respond to questions about specific incidents like this one, and the Sunland Park Police Department closes cases like this when it determines that no crime has been committed. 

The lack of documentation is a missed opportunity, says Amerika Garcia Grewal, co-director of the Frontera Federation, which aims to help rescue migrants in distress—or find and identify their remains. “The surveillance towers along the border could be incredibly useful,” she told us. For example, they could in principle be used to hold Border Patrol agents accountable for whatever actions they did or did not take to locate someone in distress. “What the tool turns out [to do] depends on the person who is holding it,” she says, adding, “I don’t trust the folks that are using them.”

“If I could get that footage,” she says, “then we would go through that, and hopefully bring some answers” to family members and “documented proof of what happened” in their loved ones’ final moments. 

When asked about deaths near Anduril towers, agents often point to faults in the technology. Galvan, for example, says the towers don’t capture how groups move: A large group of 20 people might splinter into smaller groups, especially if agents are pursuing them, and the tower doesn’t keep track of everyone. If someone’s missed, he says, “that person stays behind in the brush, out of sight.” Then they might pass out—which could be a death sentence in the desert. 

And though Border Patrol says the towers can “hand off” surveillance of people from one tower to another, agents on the ground say that people are often missed during these handoffs. On top of that, the algorithms remain imperfect, according to Jason Owens, the chief of Border Patrol from June 2023 to March 2025. “We never really got to the point of ‘Set it and forget it,’” he says.

Agents also say they were just stretched too thin to respond effectively. Around 2022 and 2023, the border saw historically high levels of migration. Title 42, a policy that began under Trump and continued under Biden until May 2023, cited a public health emergency to expel migrants before they could apply for asylum. But the rapid expulsions carried fewer of the repercussions that could normally follow an apprehension, and many people simply tried to cross again.

Meanwhile, asylum claims had been rising for years, while shifts in US immigration policy and in the places migrants where originating from meant more people required lengthy processing rather than being quickly returned across the border or to their home countries. That increasingly tied up agents with people who had already been apprehended, they said, leaving fewer available to respond to surveillance alerts. 

In other words, from the agents’ perspective, the effectiveness of Anduril’s towers was limited by the same issue that had always caused trouble: There just weren’t enough agents.

But the overwhelming demands on agents cannot explain all the deaths our investigation found. In 2024, the border started to get quiet again. By July of that year, the number of Border Patrol encounters nationwide had plummeted from their highs in December 2022. Encounters in the El Paso sector, where the Anduril towers in New Mexico were located, had fallen to nearly one-tenth of their peak. Agents were less tied up than they’d been in years. The technology was supposedly improving, too. 

Galvan had left for a promotion by this time, but he says agents had come to trust the towers more, and they helped train the algorithm by giving a thumbs up or thumbs down if the system identified something correctly or incorrectly. There was also a new smartphone program: Every agent at the station was given an Android device with a map of where other agents were, and it sent alerts from the station about what an Anduril tower detected. 

Despite all this, the deaths near towers continued. There was Morales in April, whose body Border Patrol learned about only from the landfill workers, though it had lain for hours within sight of an Anduril tower. 

Others were discovered by luck. 

On June 20, Border Patrol agents were mistakenly tracking a group of people they thought might be migrants, though they were in fact employees of the same landfill. Those employees told the agents they had come across a body, which turned out to be partially mummified remains of a man in his 40s who died within range of two Anduril towers. 

Three days later, on the 23rd, another: the body of a 21-year-old Guatemalan man discovered by agents while on patrol, 1.3 miles from a tower across clear and open desert. Two days after that, another: An agent was looking for a lost person when he came across the remains of a 56-year-old man—just a football field’s distance from the previous one, with a similarly clear and unobstructed view to a tower.

So if Santa Teresa had gotten quieter—and become something of a showcase for Border Patrol’s newest technology efforts—what was going on?

“Agents review the information and determine the appropriate response—the technology does not make law enforcement decisions,” Beckham, CBP’s assistant commissioner, said. Meanwhile, individual agents described scenarios where they might not prioritize responding to alerts.

Sometimes “hanging back” was a strategy: Agents might observe a group to see what route they would take, or agents might not respond to a small group, in case those people had been sent by smugglers to distract from a larger group coming behind them. Other times, agents might deem the location where a group of migrants had first been spotted to be impractical for an apprehension, and instead wait to intercept them at another location.

Knowing someone’s location didn’t always lead to immediate action. Volunteers describe providing Border Patrol with the coordinates of someone who was lost but alive and then waiting, “sometimes [for] a week,” for agents to reach the location, says Garcia Grewal, from Eagle Pass, Texas. They might be told “Oh, so-and-so went out there and they found remains,” she recalls. “Well, they weren’t remains when we called you. They were alive.”

Mireya Morales, the younger sister of José Morales Bernal, who died by the landfill on the day before his birthday in April 2024, wasn’t aware her brother died near several surveillance towers, or that the government said these towers could save lives. “Well that’s good,” she said of that promise, “but in this case, it didn’t help my brother. And there’s no way to know exactly how things played out.”

“Border Patrol should have found him quickly,” she says, “and they could have done something.” If they did, this journey into the United States—his fourth trip—likely would have ended with his apprehension, detention, and deportation to Mexico. But he would have seen the birthday texts that his sister sent the next day. At least he would have made it home to celebrate his eldest daughter’s quinceañera, which took place earlier this year.

Stories like this are why Iván Chaar López, an assistant professor of American studies at the University of Texas at Austin who leads its Border Tech Lab, says he’d “rather talk about harms” than about “the failure of an algorithm.” He adds, “A system may fail, but humans suffer.” 

Stories of some of the people that died near Anduril’s autonomous surveillance towers

What happens next

Ultimately, any attempt to understand where things are going wrong is hampered by an institutional reluctance to measure the problem.

A complete audit of this virtual wall can only come from Border Patrol. The agency is, in some ways, increasingly equipped to take on that task: Agents told us that each time they apprehend someone, the location is logged with GPS coordinates, as are instances when Border Patrol sees evidence that someone crossed but cannot find them. It also has precise data on when each surveillance tower went up, which alerts came in, and how they were resolved. Having that information is the only way to measure whether the newest towers are truly missing fewer people than the previous generations. 

But researchers and outside oversight agencies say this ocean of data hasn’t translated into a reliable system for measuring the effectiveness of the virtual wall. For example, CBP records the people it misses by logging “gotaways,” a tally of how many times agents see signs of someone who evaded apprehension—footprints, appearances on camera, reports from other people who were apprehended. It’s imperfect. And that creates room for interpretation.

“The goals with border enforcement have always been a moving target,” says Jeremy Slack, a researcher of migration and border issues at the University of Texas at El Paso. “They’re always set up so that it’s a win-win.” If apprehensions go up, for example, CBP says it means agents have gotten more effective at catching people. If apprehensions go down, it means the border is quiet because people are too scared to cross. 

That’s not to mention the conflicting ideas within the agency about what the virtual wall was supposed to accomplish. Borkowski says higher-ups would ask how many fewer agents they could get by with if they built more towers. But supervisors receiving new towers told us they’d often ask for more agents, not fewer, because they now had more activity to respond to that had previously gone unseen. 

And if the goal was for the sight of the towers to deter people from crossing to begin with, an external study from RAND in 2020 was ambiguous: It found that deploying IFTs resulted in lower apprehension levels nearby but said this didn’t mean the towers were actually deterring crossings. (Research by Boyce and colleagues found that earlier surveillance towers in southern Arizona pushed people to seek more difficult terrain out of view.) It leads to a question: Do the deaths near the virtual wall constitute unacceptable surveillance failures, or are they within the range of effectiveness the government deems acceptable? (CBP’s response did not address our questions on how it explains these deaths.)

Against this backdrop, the Government Accountability Office and DHS’s Office of the Inspector General have tried to focus on a narrower question: Is the virtual wall increasing the likelihood of apprehensions? 

The answer has been incomplete since 2014. That’s when the GAO first suggested that whenever agents log their activity in Border Patrol’s database, they should include information about whether or not a piece of technology helped in their apprehension. This could at least do something to show Congress whether the technology is working. Border Patrol began collecting that information, but years of incremental improvements have been followed by repeated findings that the resulting data is unreliable or insufficient to determine how well the technology works. The reality, several people from Border Patrol told MIT Technology Review, is that agents see it as a chore demanded only by bureaucrats who don’t understand the realities of the border. 

It’s “spitting in the wind, to be honest,” a former high-ranking official at DHS during the Biden administration told MIT Technology Review on background. “If we have 10,000 people a day crossing in between the ports of entry, do you think an agent is worried about telling them how they freaking apprehended those people?” Several other officials said that the logging of technology assists had improved, but not consistently enough to help much with analysis. 

And there is no procedure—at the local or national level—to examine individual deaths near the towers or analyze them collectively for surveillance failures. “We never thought about plotting [migrant deaths] to see if the towers were missing things or agents were missing people,” said a former CBP official who evaluated how personnel handled deaths in custody.

“Nobody ever raised the issue,” the official said, adding that doing so could reveal important gaps in the agency’s approach: “If I still worked for CBP, and you brought it up, I would probably send some people to look.”

In response to questions from MIT Technology Review, CBP said that autonomous surveillance towers complement physical barriers by improving detection and situational awareness. Its statement did not address questions about its other technologies, or broader criticisms about how it evaluates the virtual wall.

This lack of measurement has not slowed the enthusiasm for more technology funding. The government spending bill passed in July 2025 awarded $2.7 billion for border security technology alone, and CBP was quick to signal to industry that the money would soon start flowing. “We’ve got an historic investment in infrastructure and technology coming up,” a CBP director told tech companies in an industry webinar that month. In a December 2025 interview, when he was director of homeland security and immigration at the America First Policy Institute, Cooper Smith described the technology investments as “fortifying” the border against a future president who might be, in his estimation, weaker on the border than Trump. (According to LegiStorm, a research organization that focuses on political staffers, Smith now serves in a policy-focused role with CBP.) The number of Border Patrol agents has climbed too, to nearly 21,500 agents as of June 2026—the highest in the agency’s history. 

In the Big Bend region of Texas, surveillance towers are becoming a focus of local politics: Officials and residents across party lines, including the sheriff of Terrell County, are asking for Anduril towers instead of a controversial new border barrier project that’s recently been halted.

CBP has announced a desire to spend $1 billion on nearly 1,500 more towers by 2034. The law now requires all these towers to be equipped with AI. In December 2025, Anduril’s Palmer Luckey said the towers remained the company’s best-selling product, one that gives “basically perfect situational awareness of what’s going on in the area.” Anduril’s towers have been sold abroad—to enforce the UK’s borders, defend US Marine Corps bases in Japan and elsewhere, and serve as drone defenses for an Australian Air Force base. 

But as for CBP, Anduril will no longer be the only game in town; General Dynamics has now released AI towers of its own and received an order for them in June from CBP worth up to $115 million. The Electronic Frontier Foundation—which has tracked surveillance towers since 2022—says some have already been spotted at the border.

This time around, the agency will be spending this money with even less oversight than it had just a couple of years ago. In October 2025, the Department of Homeland Security, which oversees CBP, dissolved its department-level office that oversaw major spending programs.

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Most of the records on human remains that MIT Technology Review collected were current only through the fall of 2025 or, in a handful of cases, early 2026. Even those dates come with a caveat: Many remains are never found, and others are found months after someone died. The reports can then take months to be released via public record requests. That makes it nearly impossible to keep track of how many people have died near surveillance towers in anything close to real time.

Still, the border today is significantly quieter. The latest figures from DHS show just 128,009 “enforcement encounters” from January to August 2026, compared with more than 2.4 million during the same period in 2024 (“encounters” count each of Border Patrol’s interactions, not individual people).

Even as border crossing rates decrease and DHS speeds ahead with its tower acquisition plans, people are still dying within range of the surveillance towers.   

On September 14 2025, a 30-year-old Mexican woman named Graciela Gómez Hernández died just over 350 yards away from the physical border wall that separated Southern California from Tijuana—and within one mile of an RVSS tower. Earlier that afternoon, she had told her family that she could not walk any further, and her voice notes abruptly stopped. 

Her skeletonized remains were recovered nearly three weeks later, on October 4. Some of her bones were missing, and there was “apparent animal activity … to the ribs,” as the medical examiner’s report read. 

Sara Stroud, an organizer with the Borderlands Relief Collective, a volunteer humanitarian group that leaves water and supplies in a wilderness area frequented by people crossing the border, went to the site of her death a few days later to put up a memorial cross. A Border Patrol helicopter showed up almost immediately, flying low circles above the volunteers. 

Between the helicopter and the RVSS tower that was visible in the background, “I was astounded about how we were being surveilled the whole time,” Stroud says, adding that this was especially stark in contrast to the agency’s often slow—or absent—responses to people who’d died on camera. 

“Are you telling me you didn’t see it or you did see it?” she asks. “Because if you did see it, that’s horrible. And if they can’t, then how do you explain that?”

Additional reporting by Lillian Perlmutter.

This work was supported by a grant from the Tarbell Center for AI Journalism.

This article was produced in partnership with Type Investigations and with support from the Fund for Investigative Journalism.

When Eli Regalado first heard God speak to him, he wondered whether he was hallucinating. Now he likens the experience to having “a thought that is not my thought.” Divine words echo in his mind like a line from a movie or the memory of a loved one’s voice. “It’s not ‘You better do this,’” he says. “It’s just a knowing inside you: This is what you do.”

Holy messages arrive daily while Eli is praying, reading, or watching television. Sometimes they surface in prophetic dreams or missives from strangers. Occasionally, they appear midsentence, when he pauses to ask, “Lord, what do you want to say here?” 

Eli’s wife, Kaitlyn, tends to get heavenly dispatches in the shower, when she finally has a moment to herself. Other times, she seeks counsel from above. “I’ll be writing in my journal and praying and asking questions and just believing what I’m hearing is Him,” she says. 

God’s directives have been manifold. According to the Regalados, He told them to get married, buy a house, and start having kids. When Eli owned a marketing firm in Colorado, He told him what to name it, whom to hire, and which clients to take on. Then God told him to start preaching in his living room and online. Always, the couple obeyed. 

In 2021, when Eli was 41 and Kaitlyn was 28, divine guidance steered them in an unexpected new direction: crypto. 

That October, the Regalados later testified in court, Eli’s sister and her husband gifted the couple some of their holdings in a little-known digital coin. “Take this to my people for a wealth transfer,” Eli heard God say. He and Kaitlyn felt that they were being called to sell the cryptocurrency to fellow Christians. 

Later, though they had no background in crypto, they came to believe that God wanted them to launch their own coin. Learning as they went, the Regalados created a new cryptocurrency called INDXcoin, which they promoted through family, friends, and contacts in evangelical Christian circles. “I was really feeling that this is the wave of the future,” says Debbie Bonilla, a retired pharmacy technician in her 70s who bought INDXcoin with her husband, Jose. The couple learned about the currency through friends—a minister and his wife, who had also invested. “We just trusted that their judgment was good,” Jose says.

Starting in November 2022, Debbie and Jose withdrew a total of $70,000 from their retirement accounts—a large share of their nest egg—to buy INDXcoin. In all, more than 500 people handed over a total of more than $3 million to the Regalados.

But within a year after the Bonillas bought in, the project collapsed. Investors who had entrusted the Regalados with large sums of cash lost it all, leaving many to wonder where the funds went and some to question whether they had fallen victim to an elaborate fraud.

“Poof—the money just evaporated,” Debbie told me. “Like, how does that happen?”

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Though Eli believed God was leading him into crypto, he claims he was initially apprehensive. “Absolutely not,” he recalls thinking. “I don’t know anything about cryptocurrency, and I don’t want to be caught up in some church scam.”

The crypto market was booming, and the Regalados knew people who’d made a fortune investing in early-stage coins. But a growing interest in digital assets also meant a rise in crypto fraud. 

In 2025, crypto scammers collected at least $14 billion worldwide, a 17% increase from the previous year, according to blockchain analytics firm Chainalysis. And in the United States, victims of fraudulent crypto investment schemes reported $7.2 billion in losses to the FBI. 

Fraud is on the rise partly because many people who invest in crypto don’t fully understand how it works, and launching digital coins is relatively easy. More than 3 million cryptocurrencies were minted in August 2026 alone, according to the website CoinMarketCap. “It’s just something anybody can create,” says Jason Ghetian, a former FBI special agent who has served as an expert witness in crypto cases.

In the US, much of the crypto market lacks the oversight and investor protections in place in traditional finance, including rules around transparency and safeguarding customer assets. “There isn’t adequate disclosure; there’s fraud, there’s manipulation of the price, there’s conflicts of interest,” says Timothy Massad, former chairman of the US Commodity Futures Trading Commission (CFTC). The sector is overseen by a tangled web of state and federal regulators, including the CFTC, the Securities and Exchange Commission, the Financial Crimes Enforcement Network, and others. But “every agency has its own tests and definitions,” says Carol Goforth, a law professor at the University of Arkansas who has written a textbook on crypto regulation. “It is a complicated, fragmented, and often inconsistent approach.” 

After the industry spent around $135 million backing crypto-friendly candidates in the 2024 election cycle, the federal government significantly scaled back enforcement efforts. Last year, the Justice Department disbanded its unit focused on crypto crimes, and the Trump White House created a working group aimed at “eliminating regulatory overreach on digital assets.” 

The SEC has dropped or retreated from the majority of its active lawsuits against crypto firms, including many with financial ties to the president, the New York Times reported. Donald Trump and his family have netted at least $2.3 billion from their crypto ventures since his reelection, Reuters recently estimated. In August 2026, the SEC proposed new rules that would narrow the circumstances in which crypto transactions fall under securities laws, further limiting the agency’s oversight of the industry. “Any future enforcement will have an uphill battle,” Goforth says. 

Even when crypto projects operate aboveboard, prices are often driven by speculation, and large swings are common. Investing in crypto comes with considerable risk, experts say. “With the exception of stablecoins, crypto assets are essentially Ponzi schemes,” says Hilary Allen, a law professor at American University. “There is nothing behind them—no cash flow, no productive capacity—so the only way they can be more valuable is to draw more people in.”

In recent years, state and federal authorities have brought a series of cases against people they allege ran crypto scams that targeted religious communities—an example of what’s known as affinity fraud. Among them are a couple accused of using faith-based appeals to defraud primarily Haitian immigrants of more than $1 billion, an Instagram influencer who took in over $12 million from Muslim followers, and a Miami pastor charged with stealing millions from his Spanish-speaking congregation. “‘God told me’—who can argue with that?” Ghetian says. 

“The ties you have with other people—the trust you have—is what the people who are running the scam play on,” says Tung Chan, commissioner of the Colorado Division of Securities. In a civil case filed in January 2024, she accused the Regalados of using investors’ Christian faith to dupe them into buying crypto that was “essentially worthless.” 

The suit, filed in Denver District Court, alleged that the couple spent around $1.3 million—nearly 40% of the funds they raised—on personal expenses. Purchases included high-end vacations, designer clothing, jewelry, cosmetic dental work, a Range Rover, an au pair, and extensive home renovations. In her lawsuit, Chan contended that the couple’s “drive to make money” was matched only by “their reckless disregard of securities laws and profound lack of scruples towards their investors.”

Then, in July 2025, Denver’s district attorney charged the Regalados with 40 felonies, including theft, racketeering, and securities fraud. If convicted, they could face decades in prison. But the couple maintain that they haven’t done anything wrong and were simply carrying out God’s wishes. 

“If you think following the Lord is reckless, then yeah, we were very reckless,” Eli told me. “Because we just listened and did what the Lord said to do.”

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Eli says that when he first heard from the heavens, he was behind bars. 

It was 2002, and he was 22, facing eight years in prison for stealing a Honda Civic. Eli had originally been sentenced when he was 20 but was let out after just seven months; he was sent back to jail when he violated the terms of his probation by breaking a beer bottle on a man’s face. 

This time around, as Eli tells it, his public defender warned him that it was “legally impossible” that he’d be released early again. But he heard a voice in his head repeating, “I’m going to give you probation.” And then it happened: A judge suspended his sentence. The incident became core to his worldview: “It first has to … look completely impossible,” he says, “and then that’s when God resurrects it.” 

After he got out of prison, Eli’s religious zeal didn’t stick. He threw himself into a worldly goal: making money. “I just need to put on this success mask,” he recalls thinking, “so that people would see me as valuable.” He marked “no” when asked about felony convictions on job applications and eventually discovered that he had an aptitude for sales. He hawked everything from vacuum cleaners to leads for contractors, before pivoting to marketing. 

In 2010, Icosa Magazine, a Denver-based publication, brought Eli on as a consultant. “He is the most charismatic bullshitter I have ever met in my life,” says Jan Mazotti, who was editor-in-chief at the time. She recalls Eli telling her that Kimbal Musk, Elon Musk’s brother, had offered to let the magazine host events at his restaurant: “I called up there, and they were like, ‘I have no idea what you’re talking about.’” (Eli doesn’t recall the incident.)

In 2013, Eli launched Mad Hatter Agency, a marketing firm specializing in crowdfunding campaigns. Nikko Lobato, an early employee, observed that Eli got a rush from selling that reminded him of Leonardo DiCaprio’s character in the film The Wolf of Wall Street. Eli accepted so many projects, Lobato says, that he sometimes ended up “overpromising and underdelivering.” Four clients I contacted were satisfied; three were not, including one who ended his contract “due to poor performance.” Mike Stemple, an entrepreneur and author, told me that Eli volunteered to help him market a course but never did. (Eli says they had a “personality conflict.”) “My hope, Eli,” Stemple wrote in an email, “is that you understand that your gift to be able to sell anything to anyone … can easily be destructive.” 

Eli’s personal life was chaotic. “I was always in and out of relationships,” he says. “I was drinking, partying, doing drugs.” He blames his professional missteps on cocaine use and a “nervous breakdown.” He told me that by 2018, as he approached 40, he felt “scared of not becoming somebody” and contemplated suicide. Eli was coming off a three-day cocaine bender when his mother gave him a book called The Power of Right Believing by a Singaporean pastor, Joseph Prince. It moved him deeply. He began delving into charismatic Christianity, a movement that emphasizes a strong personal relationship with God, including prophecy, healing, and speaking in tongues. 

Heeding divine direction, Eli says, he quit drugs and hired nearly a dozen friends and relatives to work at his marketing agency, which he renamed Grace Led Marketing. He also started leading daily Bible study with employees and preaching at weekly gatherings in his living room. In 2020, he formed a church called Victorious Grace and began broadcasting sermons on Facebook. 

That summer, Eli met Kaitlyn at a party. Thirteen years his junior, Kaitlyn was slender and soft-spoken, with straight dark hair and a gleaming smile. Immediately, she told me, “I just trusted the man with my life.” On their first date, Kaitlyn was “saved” over dinner. Within four months, they wed and bought a house in Denver, and Kaitlyn began running operations at Grace Led Marketing. 

By the end of 2020, however, the newlyweds’ income had begun to nosedive. Crowdfunding campaigns were underperforming and clients were paying late, they say. Eli owed over $160,000 in unpaid taxes. “I feel like a failure,” he recalls thinking.

The Regalados further strained their finances by again following what they saw as God’s will. After learning that she was pregnant in March 2021, Kaitlyn took $60,000 out of her 401(k) and paid an architect to draw up plans for a home renovation. Their vision started small but expanded, nearly doubling the home’s original square footage: enlarging their bedroom, adding another, and creating two offices, a gym, and a family room with a bar. “The Lord’s like, ‘Just do it how you want to,’” Kaitlyn recalls. Within months, they had emptied the 401(k). On the strength of another divine pronouncement, they shuttered their marketing business. “We needed a financial miracle badly,” Kaitlyn says.

One night, the Regalados woke at around 4:30 a.m. to a blaring television. Onscreen, Bill Winston, a televangelist based near Chicago, was talking about “sowing a seed.” Often associated with the prosperity gospel, the practice holds that by donating money to worthy recipients, believers create the conditions for future blessings. 

“God is telling us to give all we have in both the business + personal accounts to receive 100 fold,” Kaitlyn wrote in her journal in mid-October 2021. The couple had no income and were struggling to pay their bills. Yet shortly before their first child was born, they say, they sent their last $2,718.44 to Bill Winston Ministries.

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Just two weeks passed before their divine bounty seemed to arrive. Eli’s sister Raina Applegate and her husband, Daniel, gifted them a trove of cryptocurrency called Sumcoin, the Regalados later testified in their civil trial. In his testimony, Eli recalled them saying, “God is telling us to sow this into you.” (Raina did not respond to requests for comment; Daniel declined to answer specific questions but disputed our reporting and warned that Eli’s version of events should not be trusted.) 

Created in 2016 by Ty Jacobsen, a 32-year-old in Idaho who published content about investing online, Sumcoin billed itself as “the world’s first index based cryptocurrency.” The coin’s website stated that its price was determined by an algorithm that tracked the performance of the top 100 cryptocurrencies. According to their civil trial testimony, the Regalados believed that the Sumcoin they had been gifted was worth around $2 million.

Soon after receiving the cryptocurrency, Eli was praying at his kitchen table when he heard God instruct him to “take this Sumcoin to my people, the church.” To the Regalados, signs that they should start selling the coin to other Christians seemed irrefutable: Kaitlyn was drawn to scripture containing the word “hidden”—which translates to kryptós in Greek. A friend who had agreed to pray about whether they should venture into crypto called to confirm: “The Lord says yes.” Despite Eli’s initial concerns about their lack of experience, the Regalados decided to proceed.

The friend, who ran a faith-based coaching business, invited people to join Eli in video calls that were part Bible study, part Sumcoin sales pitch. Within five days, the Regalados had recorded around $9,000 in profit. By February 2022, they were fielding so many queries that Eli hosted a webinar. “Sumcoin is the only coin that can’t be pumped and dumped,” he declared. “It’s very similar to, like, the S&P 500.” (Unlike stock index funds, Sumcoin had no underlying assets to back its value.) That month, the couple made over $260,000 in sales.

Yet Sumcoin was not listed on any of the major crypto exchanges, meaning that those who owned it could mainly trade it with others one-on-one at whatever price the parties agreed on. In a video call with Eli and people interested in Sumcoin, Daniel stated that “the goal is to get the coin 100% liquidable in every facet there is,” including “putting the coin on the exchanges.” The Regalados also told the people they sold Sumcoin to that it would soon appear on exchanges. Once that happened, coins would trade at the price Sumcoin’s algorithm set, according to a deck the Regalados sent one investor in February 2022. One slide put that price at more than $1,200 and included a chart offering coins for $60 to $80. 

But months into peddling Sumcoin, the Regalados learned from Jacobsen, its founder, that he wasn’t planning to list it on mainstream exchanges. Jacobsen told me he never intended for the coin to be traded like a stock, asserting, “I’ve never really looked at it as an investment.” This proved to be a major point of contention between Eli and Jacobsen. “He was lying to people about what he was doing,” Jacobsen says, “about what the future was going to hold.” Eli insists, “I was relaying what I was being told.”

By June 2022, the Regalados were hearing a new heavenly instruction: “Build your own coin.”

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The Regalados called it INDXcoin. Like Sumcoin, it would base its price on the value of the top 100 digital coins by market cap. Most new cryptocurrencies are tokens created on top of existing blockchains—something anyone can do in minutes through an online token generator. But Eli heard God say, “Don’t do that; it has to be its own thing.” So the Regalados chose a harder route: launching their own blockchain and native coin. They say they paid two developers who’d worked on Sumcoin $100,000 to bring the project to life. Eli says he and Kaitlyn told them, “We don’t know anything that we’re doing.” 

The couple learned on the fly, typing questions like “What is a blockchain?” into YouTube and ChatGPT. Eli saw that crypto projects often issue a white paper to outline their strategy and mechanics, so he hired a freelancer to draft one. The resulting document explained that INDXcoin’s target market included “Christian Believers” and “less experienced crypto enthusiasts.” A website the Regalados created referred to INDXcoin as “the perfect crypto” and touted “incredible growth with minimal risk.” (It noted that INDXcoin was “not a fund” and “does not own the coins it indexes.”)

The Regalados gave the people they’d sold Sumcoin to INDXcoin instead. Friends, relatives, and others in their religious network spread the word, and the couple offered some of them referral commissions of 30%. The Regalados also gifted INDXcoin—what they considered “sowing”—to ministries and individuals, some of whom went on to buy more. And they publicized the project on social media, a podcast, and a Christian TV program, as well as through a promotional contest.

In a video sent to prospective buyers, Eli was open about his criminal past and lack of crypto experience. Quoting scripture, he hyped the venture as the latest in “a chain reaction of miracles” and said, “God wants you to have things.” 

Debbie and Jose Bonilla, the retired couple who bought $70,000 worth of INDXcoin, say that when they watched one of Eli’s presentations before investing, he appeared to be well versed in scripture. “He seemed sincere,” Debbie says. “He seemed like he was hearing from God.” Because it was a “God-driven vehicle,” she says, she “didn’t feel like we would have nefarious things going on that happen with other cryptocurrencies.”

A more tangible prospect also beckoned. “There was an explanation of how wonderful the returns would be,” Jose says. “That was the selling point—that you could become rich overnight.” 

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Initially, the Regalados told buyers that they were working to list INDXcoin on established exchanges. They learned that many platforms conduct a legal review to determine whether a coin could be considered a security. For crypto projects, courts have ruled that “when you sell something to people, and people have some reasonable expectation of profit from your actions, then it’s a security,” Massad, the former CFTC chair, told me. Issuers of coins deemed securities must follow the same laws governing stocks and bonds, including registering with the SEC and providing detailed financial disclosures. 

The Regalados were not complying with those rules, and Eli began consulting attorneys, whose assessments were concerning. “Freaking out here,” he wrote in his journal in the summer of 2022. “Lawyers are saying it could be a security. Which means I illegally sold this to 100+ people.” But after praying with a “prophetic team” they’d convened to advise them, the Regalados continued selling INDXcoin. 

By the fall of 2022, the couple seemed to have found a way forward: After meeting with an attorney named John Benemerito, they decided to position INDXcoin as a “utility” coin, the main purpose of which would be unlocking access to products or services—akin to tokens redeemed in a video game. The Regalados devised a plan to create Kingdom Wealth Community, a members-only platform where INDXcoin holders would have access to coaching, merchandise, courses on finance and spirituality, and more. After reviewing their vision, Benemerito stated in a letter that INDXcoin didn’t need to comply with securities laws, because “it does not provide a direct expectation of profits.” 

“Utility coins do not need to be asset-backed as their value is within the platform itself,” a lawyer from Benemerito’s firm later wrote to the Regalados. “However, if the intent is to give the coin a value independent of the platform, then it would need to be asset-backed for it to maintain its value.”

Eli later admitted in court that he did not inform Benemerito that people who bought INDXcoin wanted to make money. (Benemerito told me that “any legal opinion issued by my firm was based on the facts and representations provided to us by the client.”)

Around the same time, Eli told me, the Regalados were having trouble getting INDXcoin listed on existing exchanges. They decided to build not just Kingdom Wealth Community but also their own platform—Kingdom Wealth Exchange—where people could trade INDXcoin for bitcoin, ether, and US dollars. Hundreds of crypto exchanges exist, but the top few handle the vast majority of transactions; it’s rare for cryptocurrency creators to build an exchange just to enable trade in their coin. But the Regalados had told buyers there would be a way to cash out. “There was a lot of pressure as more people were coming in,” Kaitlyn says. “Like, ‘Oh, we gotta get them an exit.’” 

The Regalados announced that it would take five weeks to build the exchange, but development work, which they’d outsourced to an Indian firm they’d found online, dragged on into early 2023. “Nothing was working right,” Eli says. 

Other roadblocks piled up. A Singaporean consulting firm the Regalados hired suggested that they register Kingdom Wealth Exchange as a money services business in Canada, “allegedly because they were the fastest,” Kaitlyn says, but that process also stalled for months. Meanwhile, the members-only community and crypto wallets the Regalados were building were rife with technical issues. When the couple commissioned a security audit of INDXcoin’s blockchain, it scored 0 out of 10. A follow-up audit in March 2023 noted that the issues had been fixed but raised additional concerns, and it yielded a score of only 5.4. (Eli announced that they’d “passed with flying colors.”) 

Insiders were also voicing misgivings about the project’s financial footing. During a live YouTube update back in November 2022, two viewers asked Eli to comment on INDXcoin’s “liquidity pool.” Earlier that month, FTX, one of the world’s largest crypto exchanges, had collapsed after fears about its financial health triggered billions of dollars in customer withdrawals. Eli assured viewers that he and Kaitlyn were working to ensure that they had sufficient reserves and that “there isn’t going to be some FTX meltdown.”

Months later, when the Regalados sent their business plan and white paper to an INDXcoin investor who worked as a financial consultant, he cautioned that “the project is seriously undercapitalized” and wrote in an email, “Projected annual revenues look like they were just plucked from the air.” 

And when Roger Gauthier, another investor who referred people to INDXcoin, asked Eli whether he had set aside funds for purchasers who wanted out, Eli said no. “That was my first flag of warning,” Gauthier says.

Dan Wheeler, a crypto influencer known as 360Trader who advised the Regalados on INDXcoin, says he repeatedly warned Eli that the couple needed hundreds of millions of dollars to back the stated value of coins sold and given away. “If there’s no money there,” Wheeler says, “it’s worthless.” 

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By April 2023, Eli was growing more frustrated: Kingdom Wealth Exchange was nearly six months behind schedule, and payments to the developers in India had ballooned to more than $50,000. People were bombarding him with messages asking when the platform would open. “There’s this humiliation—no one likes failing,” Eli told me. “I succumbed to that pressure.” 

The Regalados were staying at a luxury resort in the Florida Keys dotted with palm trees and bougainvillea. One day, Eli was praying on a wicker couch in an open-air tiki hut when he heard God tell him it was time to launch the exchange. He found Kaitlyn and told her, “We’re live on April 11.” 

Kaitlyn objected. During testing, the platform still had bugs, including trouble verifying users’ identities. The Regalados hadn’t been able to open a bank account for the exchange, which meant users could transact only in bitcoin and ether, not US dollars and other fiat currencies. And the Regalados hadn’t gotten far in building the community space they’d discussed with their lawyer, having launched just one course. 

“We don’t have to have it perfect,” Eli told Kaitlyn. “Let’s just rock and roll. Let’s just get money in. Let’s get these people off our back.” 

In the days leading up to the launch, the Regalados discussed limiting sales, a practice crypto platforms sometimes use to manage liquidity and volatility. If INDXcoin holders dumped all the currency they’d bought or gotten for free, it would take over $300 million to fulfill sales orders. But Eli kept hearing God say, “Don’t limit me.” He pushed back: “Then we can basically have what’s called a run on the bank, right?” The evening before the launch, the couple prayed again. “Kait + I got the same verse,” Eli wrote in his journal. “Don’t turn selling off.” 

On the morning of April 11, Kaitlyn was beginning to feel optimistic, and Eli was buzzing. “This thing’s gonna explode,” he thought. At 11 a.m., Eli appeared on a livestream. A print of a gray wolf loomed over his shoulder. “Hello INDXcoin family,” he began, clapping for emphasis. “We are live!” 

For investors, returns finally seemed within reach. The exchange initially showed INDXcoin trading at around 10 times what people had paid for it, based on how the crypto market was performing overall; the Bonillas’ $70,000 investment looked to be worth more than $716,000. 

But nearly an hour into the broadcast—after slides of Bible verses and rosy projections—a viewer posted a complaint in the chat: “Exchange says I can’t sell INDX.” “It’s probably just because the liquidity isn’t there right now,” Eli explained calmly. “Just wait a little bit.” Ten minutes later, someone else wrote that his sale wasn’t going through. “Just be patient,” Eli said. “The Lord will provide for Himself.”

Over the next few hours, the Regalados kept checking the exchange’s dashboard. Dozens of transactions were rolling in, but the problem was obvious: Sales were dwarfing purchases. By the afternoon, the $30,000 they’d put in to facilitate trades had been drained. They decided to add another $100,000 to the pot. 

A couple hours later, Eli was out getting coffee when he called Kaitlyn to check in. She was crying. “All the liquidity is gone,” she said. 

The next day, the Regalados announced that they were suspending sales. “That was when we saw that we could be in trouble,” Jose Bonilla says. 

Eli told me that after the launch failed, he felt “crushing anxiety” but heard God remind him, “It’s impossible to mess this up.” He and Kaitlyn took steps they hoped would salvage the project, but months passed, and they kept sales on hold.

In June, Jose emailed the Regalados, explaining that he needed to withdraw half of his investment to fund a community development initiative he’d founded in his native Colombia. Eli replied that they had just reopened sales—limited to one coin per day and 10 per month. When they did so, the exchange had around $20,000 available to fulfill sales orders. “Liquidating HALF of your coins is not probable at this juncture,” Eli wrote. Three days after sales resumed, the Regalados halted them again, blaming a technical glitch. 

When Jose followed up a few months later about pulling out half of his investment, Eli replied, “At this time there is zero funds to do that.” In November 2023, the Regalados shut down the exchange and took INDXcoin’s blockchain offline. 

“Shame, condemnation, suicidal thoughts have just been pouring in hot and heavy on me,” Eli shared in a video update, standing before an image of a swirling purple cosmos. “Where did I get this wrong?”

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Two months later, the Regalados learned that Colorado’s securities regulator was accusing them of committing fraud and selling unregistered securities. The state soon added to the suit 12 defendants it said had received commissions for selling INDXcoin, alleging that they had also sold unregistered securities. Among them were Eli’s brother-in-law, Daniel Applegate, and a company associated with Gauthier, the INDXcoin investor. A judge entered a default judgment after they failed to respond and ordered them to pay judgments of $15,000 and $34,400, respectively. Eli’s father, Eligio Regalado Sr., who was also accused of securities fraud, agreed to refund $122,000 to friends, relatives, and colleagues without admitting or denying liability. (Gauthier denied wrongdoing; Eli’s father, through his attorney, declined to comment. Daniel denied being a part of INDXcoin and, despite being named in the lawsuit, claims that it has nothing to do with him and his wife.) 

“I really can’t speak to whether or not he heard God tell him to do it,” Chan, the Colorado securities commissioner who filed the suit, told me. “Even if [the Regalados] meant it from the goodness of their heart, the problem is, it’s not fair to the investors … They lied and omitted key things.”

I spoke with 20 INDXcoin investors, and nearly all had heard about the coin from a trusted friend, relative, or faith leader. Most had little or no experience with crypto. They funded their purchases by raiding retirement funds, cashing out a pension, using proceeds from selling a small business, or taking out a home equity line of credit they’re still paying interest on. One buyer, a disabled veteran in his 70s, hoped profits from his investment would help him recover financially after he accrued debt while being treated for cancer. Another, who had retired, was forced to get a job at Home Depot in his late 60s. “It’s a gut-wrenching, horrible, helpless feeling,” he says. 

Investors are divided on whether they were conned. Jose Bonilla, who reported the Regalados to authorities, believes that their actions were “totally intentional.” “They are using a spiritual excuse to defraud,” he says. His wife, Debbie, disagrees and thinks that the Regalados simply “got in way over their heads.” 

A number of people who bought in still support the Regalados. “They’re hearing God’s voice and trying their best to follow it,” says Troy Bramblet, a former pastor who lost more than $18,000 on INDXcoin. “It doesn’t guarantee success.” 

Wheeler, the crypto influencer who advised the Regalados, also alerted authorities about INDXcoin but remains unsure whether the couple set out to fleece people. “They are zealots—they are literally blinded,” he says. “If you believe God is going to do a thing, then are you scamming people? No. But look how they spent their money.” 

In a video posted days after the case was filed, Eli admitted that he and Kaitlyn had in fact “sold a cryptocurrency with no clear exit.” He acknowledged that they had pocketed $1.3 million—including money spent on “a home remodel that the Lord told us to do.”

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Last November, I visited the Regalados in the three-bedroom townhouse they rent in a Denver suburb dominated by office parks and cookie-cutter condos. The house they own is uninhabitable—renovations stopped halfway through the project, after they stopped making payments. 

In person, Eli is friendly and charming, with a restless energy and subterranean intensity occasionally betrayed by his stare. He is prone to lengthy monologues delivered with such conviction they make you second-guess bald facts. Kaitlyn, who comes across as reserved yet frank, has “Believe” tattooed on her wrist. They told me that they argued frequently after INDXcoin collapsed, but when I was there, Kaitlyn listened to her husband attentively and always laughed at his jokes. 

On a sunny Thursday afternoon, I followed the Regalados upstairs to a corner of their bedroom containing a tiny desk and a whiteboard. The room was modestly furnished with what they said were secondhand finds. The bed was unmade, and a Bible lay on the floor. 

Eli was preparing to address members of INDXcoin’s private forum in his first live call in nearly two months. He closed his eyes and prayed. “Just allow me to speak simply,” he said, like a teenager asking a parent for a favor. “Just be able to use analogies, to be able to bring it down to their level of understanding.” “Amen,” Kaitlyn said. 

After hunting breathlessly for a laptop stand, Eli grabbed a stack of journals—full of divine revelations—and plopped his computer on top. He switched on the camera, and his image appeared before a faux backdrop of potted plants. Eli had a receding hairline and stubbly beard, and he wore a black T-shirt and a silver cross on a thick chain. Before letting callers in, he ran his fingers through his hair and his tongue over his teeth—now perfect, thanks to cosmetic dental work paid for with proceeds from coin sales.  

“Okay. Awesome. All right. So hey, good afternoon, INDXcoin community!” Eli began, flashing a smile. “We’ve got some exciting updates.” Then, in the tone of a tech founder reporting on a strong quarter, he shared the news: Two months earlier, a judge had ruled against the Regalados in their civil case, and they were now facing criminal charges from the district attorney’s office. 

“Someone asked me, ‘Are you going to do a plea?’” He paused to sip water. “Short answer is no … We haven’t done anything wrong.” 

The Regalados deny orchestrating a scam. “If you’re giving massive amounts of money away at the expense of your own self and family, that doesn’t hold up,” Eli says. The couple estimate that they’ve gifted $300,000 in cash, plus a Harley-Davidson motorcycle, a BMW, and a Louis Vuitton bag, to churches and individuals through sowing. They also gave away millions of INDXcoin—90% of the supply. (Eli told me, “No one sows without expecting something in return,” though not necessarily from the recipient.) 

In their civil case, the Regalados represented themselves because they couldn’t afford lawyers. They argued that INDXcoin wasn’t a security because it was a utility coin and that the price was set by “immutable algorithm.” They claimed that their technology provider had caused the exchange to fail, consultants had led them astray on compliance, and attorneys had said they didn’t need to maintain liquidity or disclose spending. (Benemerito, the lawyer the Regalados had retained, told me, “Our firm does not advise clients to violate the law.”)

The judge disagreed, finding that INDXcoin was a security and that the Regalados had misled investors about its true value and risks, where their funds went, how many coins had been given away, and more. Noting a “lack of understanding of the harm they have caused,” she ordered them to pay nearly $3.4 million in damages—the amount of money they’d raised. “Ascribing an algorithmic value to a coin does not make it ‘worth’ that amount,” the judge wrote. “In reality, INDXcoin was worthless because no one wanted to buy it.”

When I visited, two months had passed since the ruling. The Regalados still hadn’t read the judge’s opinion in full but had decided to appeal. Later, they would draft briefs with help from Google Scholar and AI. (The case is still pending.) 

Besides filing court documents and preparing for their criminal case, the couple spend their days like typical suburban parents: taking their kids to playgrounds, walking their chiweenie, working out. They still host biweekly Bible studies. Sometimes they ride their Harley to Palmer Lake or the Rocky Mountain foothills. (“We only wear helmets when it’s windy or cold,” Kaitlyn says.) Their assets were frozen soon after the civil case was filed; Eli had found work selling roofs but says he was fired when his employer learned about his legal troubles. He declines to disclose his current gig. “It’s not related to marketing and not related to crypto,” he says.

After they were sued over INDXcoin, Eli wondered, “Did I just make this up? Am I crazy?” But he and Kaitlyn concluded that the divine signs they’d received were unmistakable. They believe that INDXcoin will eventually gain traction among world leaders losing faith in the US dollar. “We are privately making preparations,” Eli told me.

“God already saw this coming,” he assured viewers during the November video update. “He’s looking at us and saying, ‘Are you willing to believe me no matter what you see?’”

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After the call ended, Eli began leafing through his journals and reading sections aloud. Since our first conversation months earlier, the Regalados had been remarkably amenable reporting subjects. They told me that their criminal defense attorneys had advised them against talking to reporters, but they sat for more than a dozen interviews with me. They provided access to INDXcoin’s private forum and supplied emails, photos, and spreadsheets—even though some documents don’t paint their decision-making in a favorable light. Once, Eli emailed to “come clean” that an anecdote he’d told had been slightly embellished. He apologized and assured me, “Everything else I have said is 100% in line with no stretches or exaggeration.” 

The Regalados told me they trusted me in part because God had signed off: Not long after I’d first contacted them, they’d walked into a room with a TV playing Family Feud, and the answer displayed on the screen was “MIT.” Their approach highlighted how they had won over buyers so effectively: They were likable, shared vulnerable details, and telegraphed transparency.  

Still, the Regalados didn’t appear to be feeding me an act they’d just cooked up. Instead, they seemed fully committed to their own narrative: one that paints them as righteous underdogs fulfilling a holy mission, no matter the cost. To let their faith waver would mean that everything they had lost—friends, their home, their reputations—had been in vain. It would mean admitting that they had failed. It would mean that no one was coming to save them. 

Even ending up in prison wouldn’t persuade the Regalados that they’d misheard God. “He’s going to deliver you from everything, so you won’t be there forever,” Kaitlyn says, “and it might just be part of the story.”

During my visit, the Regalados agreed to show me an earlier chapter. We piled into their Ford Raptor truck, their kids in the back, and drove 20 minutes north to a quiet cul-de-sac in a leafy residential neighborhood. 

We slowed near a hulking structure of rotting wooden boards. Red and brown weeds engulfed the lot and threatened to swallow the sidewalk. Out front, a tattered mattress was slumped on its side. Neighbors had sighted squatters and, as winter approached, feared fires. The Regalados still owed their contractor nearly $110,000 for work completed. 

I asked whether we could get out, but Eli and Kaitlyn didn’t want to run into anyone. “I just don’t want to have a conversation of like, ‘When are you gonna cut your grass?’” Eli said. (The city had sent them violation notices the previous year for not maintaining the property.)

As we drove away, I asked how it felt to see the ghost of their dream home. 

“It used to hurt,” Kaitlyn said. 

“Here’s this unfulfilled promise,” Eli added.

But it didn’t bother them anymore. 

“If we lose the house,” Kaitlyn said, “that means we’re getting something way bigger and way better.” 

They made a U-turn at the end of the street and, seat belts unbuckled, rounded the corner without looking back.

Katia Savchuk is an independent journalist based in the San Francisco Bay Area. Her work has appeared in the New Yorker, Forbes, Mother Jones, and many other publications.

Puzzles and games have been central to AI development since the very beginning. Just as we humans like to test our smarts with crosswords or logic puzzles, developers can test how far models have advanced with a gaming gauntlet. The term “machine learning” was popularized in a 1959 article by the IBM computer scientist Arthur Samuel about an algorithm that learned to play checkers. Chess and the Chinese board game Go are famous AI test beds too. 

Judged purely on its puzzling skills, AI is improving a lot—and quickly. In late 2024, a team of scientists from Columbia University showed that even the best models could figure out only 18% of the infamous New York Times Connections puzzles; by early 2025, some models could solve them near perfectly every time. 

But puzzles do more than just highlight the inexorable advance of AI capabilities. Seeing where models succeed and fail—and where we humans still beat them—can provide a useful window into the technology’s strengths and weaknesses. Despite advances, today’s models still fumble: Subtle changes in classic riddles often trip them up, and visual puzzles are a particular weak spot. 

Here you’ll have the chance to test your wits on puzzles that have stumped models at one time or another. Some might be as tricky for you as they were for the AI; others are so simple that they’ll have you doubting whether AI is really intelligent at all. Each one highlights at least one way in which machine and human cognition differ. If you ace the test, you’ll have proved that you can out-puzzle an AI—at least for now. 

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Spatial Reasoning

Let’s start with a domain where humans have a huge advantage: spatial reasoning. If you’ve ever taken an IQ test, you may have done a mental rotation problem. These puzzles ask you to determine whether different images represent the same objects from different angles. Though today’s language models typically have the ability to analyze visual inputs, they still fail abysmally at these puzzles. For all the talk of how world models can help AI understand physical environments, LLMs still don’t seem to be able to manipulate 3D objects the way spatial thinkers like architects and mechanical engineers can.

Mental Rotation

Instructions: Choose the answer that shows the object in the prompt, but from a different angle. In each case, there’s only one correct answer!

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Memory & Adaptability

Frontier LLMs have extraordinary memories; they were exposed to a monstrous volume of facts during training and can recite many of them faithfully. That’s an asset for outcompeting humans at trivia, but it can also be a liability. When a puzzle closely resembles one a model saw during training, the model may whiz by key differences and respond with what it memorized. 

This held true in a 2024 study in which researchers from Google and the University of Illinois Urbana-Champaign trained and tested models on slight variations of a classic type of puzzle called Knights and Knaves. In these problems, some characters always tell the truth and others always lie, and you have to figure out who’s who. The same principle may be at work in a test called SimpleBench. These questions resemble more complicated problems that models likely encountered in training. Humans spot the trick, but even top-tier models trip.

Knights and Knaves

Instructions: The only thing you need to know to solve these puzzles is that knights always tell the truth and knaves always lie. Determine who’s what on the basis of what each character says.

SimpleBench

Instructions: Read these SimpleBench problems carefully, and you should be able to figure out the answers in no time.

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Abstract & Visual Reasoning

AI doesn’t just bungle visual problems in 3D—two dimensions can trip it up as well. That’s a major factor in how well models do on the most famous ­puzzle-based benchmark, ARC-AGI. These problems require you to infer abstract, general rules from a set of examples. Models do better on ARC puzzles when they receive each grid not as an image but as a string of numbers that encodes the color of each cell. 

Research suggests that even when models answer ARC-AGI questions correctly, they often do so using byzantine and non-­generalizable rules, whereas humans draw on simple visual concepts. Despite these disadvantages, models have gotten quite good at ARC-AGI over the past year, but some puzzles—such as the one printed here—still stump them.

ARC-AGI

Instructions: Study the three pairs of grids shown below to figure out the rule that dictates how the ones on the left transform into the ones on the right. Then get out your markers or colored pencils and fill in the fourth grid using that rule. (The solution is the same no matter which way the grids are oriented.)

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Intuition

It’s not just AI models that fall into traps. We humans have our own cognitive foibles, many of which AI does not share. Psychologists have designed problem suites that invert the SimpleBench phenomenon: For these questions, humans often give knee-jerk answers, whereas models will respond deliberatively. Some of the problems exploit errors in the ways that we intuitively do math; others are phrased so as to suggest obvious answers that fall apart if the question is read carefully. 

Lightning Round

Instructions: Answer the questions below as quickly as you can.

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Increasing Complexity

In some cases, whether an LLM can complete a puzzle is a matter of scale. One study from researchers at Apple found that LLMs can ace simple versions of the Tower of Hanoi problem, which involves moving a stack of disks one at a time without ever putting a larger disk atop a smaller one, and river-crossing puzzles, in which a group of people must traverse a river according to certain rules. But only up to a point: As the number of disks or people hits six and higher, the models began to falter.

In another study, researchers at the University of Washington, Stanford University, and the Allen Institute for AI observed that LLMs struggle similarly with logic grid puzzles, which require deducing the attributes of a set of individuals from a list of clues. The Apple paper went viral, but commentators questioned whether the results reveal a unique limitation of LLM reasoning—or just that it’s normal to make errors as complexity piles up.

The River

Instructions: Using the scenario provided, plan the trips necessary to get everyone across the river. 

Logic Grid

Instructions: Using the list of clues, determine who lives in each house and what style of music each person enjoys. There is only one possible solution. You may find it helpful to fill out the grid below to keep track of your deductions.

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Grace Huckins is an AI reporter at MIT Technology Review. They have a PhD in neuroscience.

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Credits:

Mental Rotation: CC BY 4.0. Stogiannidis, Ilias, Steven McDonagh, Sotirios A. Tsaftaris. Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models (copyright 2025); illustrations by John MacNeill. Knights & knaves: Courtesy Dan MacKinnon. Simplebench: CC BY 4.0. SimpleBench Team. The Text Benchmark in which Unspecialized Human Performance Exceeds that of Current Frontier Models (copyright 2024). ARC-AGI: Courtesy ARC Prize Foundation. Lightning round: CC BY 4.0. Hagendorff, Thilo, Sarah Fabi, Michal Kosinski. Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT. Nat Comput Sci 3, 833–838 (copyright 2023). The river: Adapted from Propositiones ad Acuendos Juvenes, Alcuin of York (ca. 800 CE). Logic grid: Apache License 2.0. Lin, Bill Y., Ronan Le Bras, Kyle Richardson, et al. ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning (copyright 2025)

People have been talking to each other for at least 100,000 years, as best we can tell. And in all that time, there has been only one thing in the world that could learn a human language to perfect fluency: a human child. 

Now there are two. 

Four short years after the release of ChatGPT, many of us now take it for granted that we can converse naturally with our phones or computers. LLMs like Claude, DeepSeek, and OpenAI’s GPT models are fluent and flexible enough to masquerade convincingly as humans. But peek behind the computational curtain, and there’s a catch: Teaching a computer to use human language still requires an inhuman amount of data. An LLM can easily churn through a hundred thousand times more words than a person will experience in the process of mastering their mother tongue—and way more than children might hear by their first birthday, when they typically start to grab hold of language.

“The progress recently has been amazing,” Michael C. Frank, a cognitive scientist at Stanford University, says of LLMs. “But we still have to burn down a forest and scrape the entire sum of all human knowledge to re-create this milestone that happens in our living rooms over the course of a year.”

This yawning divide between children and machines is called the data efficiency gap. And it raises a tantalizing question for cognitive scientists and a challenge for the architects of AI models: How is it that kids can still outperform the most linguistically sophisticated machines ever built? 

Finding answers has stakes for both AI research and cognitive science. For the past decade, language models have mostly gotten better by getting bigger. Meta’s open-weight LLM Llama 3.1, released two years ago, chewed through 15 trillion tokens (word-like chunks of language) in pretraining—the main step of training a model that happens before it is fine-tuned for a specific task, like being a chatbot. Frontier models could be pretraining on 10 times more data, says Ethan Gotlieb Wilcox, a cognitive scientist and linguist at Georgetown University. But there’s only so much internet to train on, and eventually—perhaps as early as the 2030s—the well of easily available data could run dry. 

Kids show that it could be possible to learn more with less. Far less. A preteen raised in a linguistically rich home may have heard something in the vicinity of 100 million words. Add literacy to the mix and you can boost that word count to maybe 300 million words by age 20. 

The difference in scale is something that can only really be gestured at in analogy. “Claude has seen the amount of language that an entire city will experience in one generation,” says Wilcox. If you were to print out on paper all the words used to train a modern LLM, you could make a stack that would reach past the International Space Station. The human preteen’s 100 million words, meanwhile, would stack up just 20 meters. And we can make do with far less than that. 

By reverse-engineering the way kids learn, scientists hope to be able to create more data-efficient AI models, which could be useful for everything from training AI effectively on video to creating chatbots that serve minority language communities. Testing hypotheses about human learning in machine models could also settle enduring questions about language and children’s developing minds. Are we born with a language instinct, or would it be possible, even in principle, for a child to learn language purely from experience? Is the way we process language a quirk of our biology, or might at least some of it reflect universal constraints on how languages can be used and learned? 

The essential elements

Most of us realize language is hard only when we try to learn a new one after childhood. The past perfect tense, rolled rs and nasal vowels, the genitive case, phrasal verbs, grammatically masculine tables and feminine spoons—many are the instruments of linguistic torment for the adult language learner. It’s typically effortless to learn our mother tongues, however. Toddlers usually start producing grammatically correct sentences after hearing something like 10 million words, or 30 million on the high end. 

“It’s just totally miraculous,” says Frank. “If you train GPT-2 on 30 million words, you get a nonsense generator; you don’t get a kid.” 

Exactly how babies pull this off is a mystery. Researchers know a lot about what kids learn and how they use language at different stages in development, but there’s still a lot we don’t know. Perhaps the most enduring question is why babies can learn language at all. The syntax of human language—the rules for combining words into sentences—includes recursive, nested structures that allow us to express virtually infinite ideas with a finite lexicon of words and pieces of words. This seems like something that should be a problem for babies. They only splash about in the shallows of a fathomless ocean of language. And yet, somehow, that’s enough. From a drop, they infer the depths.

One solution, put forward in the 1950s by the MIT linguist Noam Chomsky, is that babies are born with hardwired knowledge of grammar. Chomsky was reacting to a rival view, championed by the psychologist B.F. Skinner, that language acquisition is entirely environmental. Skinner thought language was learned through conditioning and reinforcement, the way a dog figures out how to sit or shake for treats. Chomsky countered by citing the “poverty of the stimulus”—the idea that language, especially syntax, is too complex and children’s exposure to it too “impoverished” for them to learn entirely from experience. “His signature argument was, essentially, that language cannot be learned on the basis purely of statistics,” says Richard Futrell, a linguist and cognitive scientist at the University of California, Irvine. Instead, Chomsky posited that language is based on a set of logical rules and argued that children needed innate knowledge of those rules to deduce the grammar of their language from scraps of speech.

“It’s just totally miraculous … If you train GPT-2 on 30 million words, you get a nonsense generator; you don’t get a kid.”

Michael C. Frank, cognitive scientist, Stanford University

The Chomskyan view of language dominated linguistics in the US for decades under the moniker of generative grammar. And it was a major influence on computer science in the 1950s and ’60s, when AI was enjoying its first boom time and the lines between linguistics and natural-language processing dissolved in a flood of military funding; the Pentagon wanted computers that could understand English and translate Russian. 

Despite early successes of simple neural networks, which learn to recognize and reproduce statistical patterns, AI researchers in the United States largely adopted a rule-based framework influenced by Chomsky’s theories. They tried to teach language to computers by explicitly coding the rules into programs—think less immersion experience, more grammar class. This approach, part of a broader trend called symbolic AI, prevailed for decades. It also largely failed to produce models actually capable of handling human language at scale. Interest in natural-­language processing chilled in the “AI winter” that began in the 1970s. 

In the aftermath, neural networks started to make a comeback. But it wasn’t until the 2010s, when computer hardware was getting cheap and capable and the internet was getting big, that their performance began turning heads. By 2018 and 2019, the models BERT and GPT-2, which were built on a new architecture—the transformer—and trained on billions of tokens, made it clear to insiders that learning from a massive glut of data could work for language. In 2022, with the breakout success of OpenAI’s chatbot ChatGPT, it was clear to everyone.

LLMs are not brains. What they are is powerful statistical learners—naïve pattern-learning machines without any of the evolved biological quirks folded into the human cortex. In other words, they are exactly the kind of thing a generative linguist two decades ago would have thought could not learn language. And yet here they were, writing believable sonnets and passing grammar tests.

“No matter how skeptical you are about AI, the thing that everyone has been really impressed with is: These things learn syntax,” says Alison Gopnik, a developmental psychologist at the University of California, Berkeley. “I didn’t think that was going to turn out to be true. And I think most people didn’t think that you could just look at the statistics of a large sample of language and figure out grammar.”

But what about learning from a small sample of language—a child-size one, say? Is it possible to build a baby-scale model that’s anything more than a nonsense generator?

Baby talk

Alex Warstadt, a linguist and data scientist at the University of California, San Diego, remembers the years around the release of BERT and GPT-2 as a heady time. Back in 2019, he was still a PhD student in linguistics at New York University, watching his field change before his eyes. The mere fact that language models could learn English by churning through text was a challenge to prevailing Chomskyan ideas. But many linguists remained skeptical that LLMs could tell us anything about how humans acquire language. 

“I always got pushback on one issue in particular. And that was the size of the data sets of the model,” says Warstadt. “There was never a time when people were training language models at human scale where we were impressed by them.”

But Warstadt saw promise in LLMs: A scientific model doesn’t have to be perfect to be informative, and LLMs were clearly powerful simulations of human language use. By building hypotheses about how children learn into models and measuring their performance—how close they came to closing the data gap—might scientists be able to put their ideas to the test? In August 2022, Warstadt posted a Twitter thread laying out an argument that neural networks could be useful models of language acquisition. After some back-and-forth in the comments with AI researcher Leshem Choshen, Warstadt floated the idea for what would become BabyLM, an annual competition organized by Warstadt, Choshen, and several other researchers to train models on small data sets.

That was four years ago. Since then, BabyLM has added workshops and inspired spin-offs including a competition for baby models trained on Chinese. The main event challenges researchers to train language models on a “developmentally plausible” corpus of just 100 million words (for the toddler-scale track, 10 million) drawn from storybooks, dialogue, movie subtitles, Simple English Wikipedia, normal Wikipedia, and actual transcripts of speech directed at children. The models are evaluated on the kinds of grammar benchmarks that psycholinguists use with humans, says Georgetown’s Wilcox, one of the organizers.

One kind of task involves presenting test subjects—human or machine—with sentences and looking for indications of confusion or surprise at ungrammatical features. For instance, a test might compare the sentences The keys to the cabinet are on the table and The keys to the cabinet is on the table. “When humans see ‘is,’ they’re like: What? That’s not supposed to be ‘is,’ ” says Wilcox. For a human, that surprise might be measured by tracking eye movements. For language models, researchers use a measure called surprisal, which assesses how unlikely the model predicts a sentence or part of a sentence to be.

The competition has already challenged some assumptions, such as the effectiveness of curriculum learning. Curriculum learning starts with simple training data and works up to more complex inputs—a bit like starting with baby talk and getting more sophisticated over time. And it was by far the most popular approach taken in the first round of BabyLM, says Warstadt. But it didn’t work as well as expected.

“The appeal is just kind of hard to resist, you know. [Curriculum learning] seems to really line up with ways that we believe humans are learning,” says Aaron Mueller, a computer scientist at Boston University and one of the BabyLM organizers. “But it seems like these transformers don’t really need to have their data ordered in such a way to learn effectively.” 

Perhaps a touch ironically, the best BabyLM models aren’t inspired by babies at all. The 2024 champ, GPT-BERT, is a transformer trained partly to predict the next token in a sequence, like modern LLMs, and partly to act like BERT, a “masked language model” that fills in the blanks in sequences of tokens Mad Libs style. Impressively, when GPT-BERT was pretrained on about 100 million words, it was able to beat the performance of Meta’s Llama 2 70B—an LLM pretrained roughly 15,000 times that amount—on one of the BabyLM benchmarks.

Still, BabyLM models are not on the same level as LLMs. Many can’t produce text at all, and even GPT-BERT would seem clunky next to a modern commercial model. Ultimately, while they are “baby”-size, the way these models learn isn’t very baby-like. Kids are not disembodied computer programs whose only “experience” of the world comes through written text. They take in the world via their senses—especially vision and hearing. To close the data gap, some researchers think, machines will need to start learning through the eyes and ears of children.

Taking it all in

When Michael Frank started his lab at Stanford about 15 years ago, scientists didn’t really know how babies experience the world. Developmental psychologists were just beginning to glimpse babies’ lives through headcams.

“The insights that came out from that early research were that kids’ experience looks really radically different than we thought,” says Frank. “It’s much more focused: They’ve got these little short arms, so the objects are, like, right in front of them. And they live in a forest of knees.” 

Frank was excited to use headcam footage to train machine-learning models to test hypotheses about how kids learn language, but he needed more data. So he and four colleagues recruited three babies—all the children of psychologist mothers who knew what they were getting themselves into—to don headcams for science. The project, called SAYCam, recorded two hours a week of each child’s life between six months and two and a half years of age.

“[The families] were willing to release that video, and that’s critical,” says Frank. “So we released it, and people started training models on it.” 

One of those people was Brenden Lake, a cognitive scientist and AI researcher at Princeton. In 2024, when he was working out of New York University, he and his colleagues presented a model trained on 61 hours of raw SAYCam data that learned to identify objects and associate them with words. Many theories in developmental psychology propose that children need some biases to help them pick out particular parts of their raw sensory experience and associate them with bits of language. For instance, it’s thought babies assume that a new word like “shoe” refers to a whole object rather than a part of it (like a shoelace), says Lake. But the model Lake’s team built was able to learn to identify objects in the video footage and associate them with words without any such biases. “It turns out you can get a real start on language learning using a lot less than what a number of theories suggested,” says Lake. Still, he adds, “we don’t get a two-year-old out of [training] when we’re done.”

But perhaps it’s not surprising that such models can’t replicate childlike capabilities by working with a few dozen hours of footage cobbled together from short snapshots over several years of a child’s life. It could be that the shortfalls just indicate a lack of realistic data. After all, babies can’t wear a headcam 24-7; efforts like SAYCam and its successor, BabyView, record at best a few hours a week. So researchers have the choice between working with a tiny slice of the life of a single child or with larger data sets of footage pooled from many kids. Either way, a model’s training data is still a far cry from the lived experience of a child.

That could be changing. Uri Hasson, a neuroscientist and psychologist at Princeton, spent the last five years on a project to record the first 1,000 days of 17 children’s lives. The participating families wired every living area in their homes (except bedrooms and bathrooms) with cameras and microphones and recorded 12 hours a day, almost every day. The resulting data set, described for the first time in a recent preprint, is of a scale that would have simply been impossible to work with absent new AI tools for transcription and video analysis, says Hasson. “For the first time, we have the input,” he says. “It’s really only the beginning.” 

Missing ingredients

So far, training models on video has proved difficult. While text-based models emerge fully fluent (after ingesting huge training data sets), multimodal models trained on video from kids are far from that. Lake’s model, for instance, learned simple words, like “ball” and “cat.” Attempts to supplement text with visual data haven’t worked for BabyLM participants, says Warstadt. Gopnik thinks the issue could be that kids do not simply sit and watch the world go by. “Children are actively exploring, which means that they’re actively choosing their own data,” she says. “Kids are constantly experimenting.” Maybe that’s the missing ingredient. 

Research by Gopnik’s group—including studies of grade schoolers exploring a Minecraft-inspired game—shows that what looks like child’s play is in fact an effective way to learn cause and effect. Kids seek out experiences and take actions that maximize their “empowerment,” or the ability to make a predictable impact on the world. 

Unlike models, children are aware of what they don’t know and have a drive to fill their knowledge gaps, says Elizabeth Bonawitz, a developmental cognitive scientist at Harvard. And children’s social lives also help them learn, she says. Her research has shown that children interpret information differently when they know an adult is trying to teach them something. “Children are not only reasoning about the evidence they’re being told,” says Bonawitz. “They’re reasoning about the teacher, about the teacher’s knowledge, and about why the teacher is telling [them] this particular information.”

That’s very different from how models learn: passively and in isolation. Perhaps if models were built to seek out information to fill in their own blind spots, experiment with language and observe how other language users react to their babbling, and reason about some kind of simulated social world, they’d learn better. Last year’s BabyLM actually opened the competition to models that could learn by interacting with other models. But the social models didn’t outperform standard ones.

Of the leading industry labs, Meta seems the most interested in taking inspiration from kids—specifically for training models from video. Two Meta researchers were involved in BabyLM’s multimodal branch, and Meta scientists—together with academic researchers, including Frank—recently announced a benchmark and challenge for training models on baby headcam footage. Frank also says a stealth-mode AI startup called Flapping Airplanes has taken interest in his research. Neither Meta, Google DeepMind, OpenAI, nor Flapping Airplanes agreed to an interview. 

For now, frontier labs aren’t exactly racing to borrow tricks from children, says Gopnik. She thinks it’ll be the next generation of AI—whatever replaces the transformer—that will take lessons from developmental psychology.

Perhaps the most enticing reason to close the data gap is that it could help us understand ourselves.

In general, the machine-learning community is less interested in mimicking the brain than in just building something that works, says Mueller. But he thinks awareness of—and interest in—the data efficiency gap is growing. An example is the NanoGPT Slowrun benchmark, launched by Q Labs in March 2026. “They have very similar goals to BabyLM,” says Mueller. “But they’ve dropped the motivation from human language learning and really just focused on the data efficiency angle.”

One reason Warstadt wants to close the data gap is to democratize AI so that universities and others without the resources to hyperscale can train good models and stay relevant in AI research. David Samuel, a machine-­learning researcher at the University of Oslo and one of GPT-BERT’s architects, has a more personal reason to work on this problem. He’s Czech and works in Norway, and there’s a lot less data in Czech and Norwegian available for training LLMs than there is in English. Minority languages like Sami might have just tens of millions of tokens available, says Samuel—about the scale of a toddler’s exposure. “The question was,” he says, “how can we develop language models that are just as capable as the English ones for small languages?”

But perhaps the most enticing reason to close the data gap is that it could help us understand ourselves.

Bonawitz says she was initially skeptical that large language models could reveal anything about cognition. LLMs and brains are, after all, very different. Brains are embodied. Our neurons are not tidy lines of code but living cells. And our brains grow and change as we learn and age—LLMs pretrain once and never again. But as different as the two systems are, says Bonawitz, “I’m sort of revising my beliefs.” She’s been won over by the idea of studying models the way comparative psychologists might study animal minds to illuminate our own.

Researchers like Warstadt, Frank, Wilcox, Lake, and Hasson are already using language models as a kind of linguistic lab rat, an imperfect but informative stand-in for a real human language user—especially for questions that are more about learning and language and information processing than anything specific to our brains or biology. When models can do things with language we thought were impossible, it challenges old assumptions. And researchers can build hypotheses about language learning into models—say, by simulating different degrees of bilingualism or depriving models of exposure to certain grammatical forms—and test those hypotheses in a way that would be impossible to do with real children. Futrell compares the situation to teaching language to an alien and then opening up its brain to see what happened. 

While other animals communicate, only humans converse. Now there’s something neither animal nor human that can talk, too. LLMs open up the possibility for comparative studies, even if models and minds are vastly different. “For the last 100,000 years or however long human language has existed, humans have been the only entities in the universe that use language. Now there’s this other linguistic entity,” says Warstadt. “Finally we have a model; not in the sense of a language model, but in the sense of a model organism.” 

Elise Cutts is a science writer based in Austria.

Sometime in the late 2000s, Pim Sullivan-Tailyour was sitting in the back of a car, headed toward her great-grandmother’s tiny town in the south of Thailand. She watched big mountains pass by out the window. She was just six years old but was about to be hit by an adult-size realization. “They were just quarried out,” she says. “Like half the mountain just dug out and disappeared, and so there was just this huge orange face.” 

Although she probably didn’t know the word “quarry” back then, she sensed that what she was seeing was unnatural. She also knew, from family stories, that her mother had bathed in a nearby river when she was young, its water so glass-clear she could see fish swimming by. But the extraction had muddied the water. “We’re changing things,” Sullivan-Tailyour thought. “And it doesn’t seem right.”

It was the first time it occurred to her that humans could alter the world—and that they were. For the worse. 

She carried that knowledge with her as she got older and her family moved to the UK. There, she joined teenagers from other schools in the Schools Sustainability Network, an umbrella organization for groups that work on environmental initiatives. 

But even with those connections, Sullivan-Tailyour didn’t encounter anyone at her own school who was interested in environmental activism. She felt lonely in her desire to push for change—and burnt out on working solo. “I was pretty much the only person who wanted to do these things,” she says. 

That was when she heard about another initiative, called Force of Nature. A UK-based organization, in 2021 it had spun up Becoming a Force of Nature, a kind of informal online group therapy program, for kids and young adults worried about climate. The sessions aimed to help young people like Sullivan-Tailyour take their worries and turn them into action, stemming the exact kind of anxiety and burnout she was feeling.

She signed up and soon logged into Zoom for her first session. Dozens of faces from dozens of countries looked back at her. “I realized that I wasn’t alone,” she says. 

She began to suspect that kids at her school actually did feel the way she did, even if they weren’t doing anything about it. 

Sullivan-Tailyour was right, it turns out: Global surveys have found that most kids are anxious about the state of the world—and not just about whether sea levels and carbon counts will continue to rise. They are growing up in a time of what some historians, policymakers, and economists are calling a “polycrisis”: The planet is warming, yes, but also a pandemic happened and could happen again, conflicts keep erupting, nuclear weapons lurk menacingly in silos, housing is growing impossibly expensive, groceries and gas can feel like luxury purchases, health-care costs are skyrocketing, authoritarianism is on the rise, partisanship splits populations, jobs are hard to get and there’s rampant worry AI will take more and more of them, and pings about all those things (and more!) arrive 24-7 in polarizing digital echo chambers. 

That’s a lot. And it’s why online networks like Force of Nature have popped up. Although they’re not taking over the planet, they have gained thousands of participants. And they’re now dealing with climate not as an isolated issue but as one facet of a generally troubled world. The goal is to help young people, who are experiencing symptoms of depression and anxiety at higher rates than earlier generations, deal with their feelings about the multivariately changing future they are maturing into. 

But the science on the effectiveness of these sorts of networks is still nascent, and psychological scholars—and some practitioners—say it’s time for the field to measure itself. That way, schools, parents, and kids themselves can know which programs to invest their time and resources in to actually help young people feel better. 

Tough times

The existential dread that comes with the climate crisis is so pervasive that it’s had an official name for many years: eco-anxiety. “The American Psychological Association calls it a ‘chronic fear of environmental doom,’” says Liza Jachens, a psychologist and assistant professor at the University of Nottingham’s school of medicine. “What people describe is closer to grief. A real sense of loss—not just fear of what is coming but mourning for what has already gone.” 

Lise Van Susteren, a psychiatrist and coauthor of “The Psychological Effects of Global Warming on the United States,” a 2012 report for the National Wildlife Federation, has called it “pre-traumatic” stress; she sees it as a kind of anticipatory trauma.

In a 2021 study measuring climate anxiety, 56% of 10,000 children and young people in 10 countries agreed with the statement “Humanity is doomed.”

But there isn’t really a word to describe feelings people have about <waves hands> the rest of what’s going on in the world. A 2025 study from Polish researchers attempted to define “polycrisis syndrome” in the young people bubbling into adulthood in this hot soup of ingredients. The researchers assessed their reactions to the many crises using established scales of emotional and mental symptoms—things like the “Flourishing Index” and the “Difficulties in Emotional Regulation Scale.” “Most young individuals in our study face psychological challenges,” the authors write. In fact, more than 60% reported problems —difficulty regulating emotion, symptoms of depression, and lower overall mental and physical well-being—that they attributed to the cumulative stress of world affairs.

That finding meshes with earlier, more climate-specific research suggesting that the kids are (sorry) not all right. In 2021, researchers published a landmark study in The Lancet Planetary Health measuring climate anxiety in 10,000 children and young people in 10 countries. Around 60% reported being very or extremely worried about climate change, with more than 45% saying that eco-anxiety affected their daily ability to function. The word “polycrisis” wasn’t in as wide use then, but 56% of the young respondents nevertheless agreed with the statement “Humanity is doomed.”

But Gen Z isn’t the first to worry about the fate of the species. There isn’t a readily available baseline in the academic literature to compare current youth angst against, but previous studies can give some context. In the early 1980s, during the latter years of the Cold War, around 35% of high school seniors agreed with the statement “Nuclear or biological annihilation will probably be the fate of all mankind within my lifetime,” according to a survey of 130 schools in 48 states. Around the same time, a Gallup poll found that 49% of teens said the possibility of nuclear war influenced how they planned for the future.

Those young Baby Boomers and elder Gen Xers were, though, perhaps more optimistic than kids today as a whole. A majority of students from the high school study agreed with the statement “The human race has come through tough times before and will do so again.” 

A 1986 analysis of such research, published by the National Academy of Sciences, described how to help kids feel less alone with their fears: “What is necessary for those providing the education is knowledge of the issue, sensitivity to the inner processes of working through the painful feelings engendered, and a willingness to try to come to grips with what the youngsters are voicing.”

That connection between adults and kids, psychological scholars are currently finding, is still key four decades later. Caroline Hickman, the psychotherapist who led the Lancet Planetary Health study, says the most striking finding was that kids weren’t just anxious about the state of the planet; they were upset that older people had failed to protect their future. “Whatever your politics, we have this expectation that faith leaders, school leaders, community leaders, government officials, will look after us and have our best interests at heart,” Hickman says, “and that social contract was being broken repeatedly.” Grief and anxiety are rational responses, she tells clients, to that bad situation.

Finding connection

Sullivan-Tailyour received that message from the very beginning at Becoming a Force of Nature. At the first session, facilitators (themselves young people) asked how participants felt about the future broadly and the climate crisis specifically. 

That question hit Sullivan-Tailyour hard. She was used to thinking of numbers, news—not her internal experience. “It’s one of the things that we just don’t give ourselves the chance to really think about,” she says. The facilitators took the responses in and talked to participants, assuring them that eco-anxiety is only natural. “Our planet is sick, so it’s normal to feel sick alongside it, because we’re just so interconnected,” Sullivan-Tailyour says.

In the second session, participants investigated their personal strengths, skills, and passions. And the final session synthesized the first two. “We create a road map for how they can go out into their communities and take action,” says Hannah Hooper, until recently the group’s head of programs.

Reframing her feelings around action worked for Sullivan-Tailyour, as did the facilitators’ realistic approach to doing so. They emphasized that it’s unreasonable to put pressure on yourself to behave 24-7-365 in ways that will not harm the planet. 

Conveying that sentiment is important to Force of Nature’s founder, Clover Hogan, who was a teenager when she started the group in 2019, just before the pandemic made everything virtual. One of her catchphrases is “We don’t need 100 perfect activists but millions of imperfect ones.” 

The informal group sessions for Becoming a Force of Nature, free on Zoom, are the initiative’s main offering. The group has also built an interactive site called Hold This Space, which leads individuals through similar mental pathways. And it’s created a podcast that includes digitally solicited #climateconfessions: “I’ve stopped going to protests.” “I drive everywhere.” “I just can’t be arsed to separate or put out the recycling.”

The group has also moved away from its strict eco-focus. “We still think it’s really important, but it’s not our bread and butter at Force of Nature anymore,” Hooper says. Now it’s beginning to use less climate-specific language in its outreach and encouraging people who come to its events to talk about other sources of anxiety or existential despair—cost of living, conflict, whatever. 

They made the shift, Hooper says, for two reasons. For one, young people in privileged countries may be anxious, but others are in survival mode—responding to acute crises, not just philosophical ones. For another, talking exclusively about climate grabs only a certain kind of person, and Force of Nature doesn’t want to exist in an echo chamber—especially when its leaders recognize that there’s plenty else to worry about.

Bad news graffiti wall 

Force of Nature had around 900 participants in Becoming a Force of Nature last year and has about 2,000 students from more than 50 countries in its online community. It may be the most prominent receptacle for young people’s planetary feelings, but it has company. Another group, called the Good Grief Network (GGN), recently spun off a program for teens. Its facilitators are trained online, but the youth programs typically take place in person.

GGN, a Michigan-based nonprofit, has a 10-step program for adults, modeled after the Alcoholics Anonymous 12-step model, to help people deal with their worldly anxieties. 

Susan Igras, a program designer and evaluator, took part in the adult GGN program and found it helpful. She wanted to adapt it for kids. “So we designed something that was much more experiential—action-, reflection-based,” she says. Igras and a collaborator cut the number of steps down to five, built the program around interactive activities, and called it GGN-Z. 

Igras and a collaborator began testing it in 2022 with kids in Northern California. The program is still small, but she’s hopeful it can match the success of the adult version, which has been run more than 85 times with around 2,500 total participants. The group is beginning online training so that club leaders and teachers around the world can use the strategies in their home regions.

It’s early days, but some of GGN-Z’s activities are already resonating. One perpetual favorite is the “bad news graffiti wall,” in which kids scrawl the things causing them angst onto a big sheet of paper. (On one recent wall, kids drew “Meta=cringe,” “hate speech,” a tornado, “LA” with a fire above it, and a dollar sign with an up arrow.) As a group, they then talk about the feelings those bad things bring up.

Kids also tend to like GGN-Z’s storytelling activity. “Like a ‘Once upon a time, I became aware of climate change,’” Igras says. It asks kids to reach back into their memories, to their first awareness that humans had altered the planet. One recent participant wrote, “Scared, 2nd grade learning about non-renewable resources.” Another wrote of seeing a picture of a skinny polar bear on a small chunk of ice.

Participants then switch to the positive, drawing their support web—a map of everyone who cares about them and the places and people that make them feel supported. That positivity leads them into thinking about what they can do and who can help them.

Does it all work? GGN-Z is currently in the process of evaluating itself—something Igras thinks is not done often enough for interventions in this area. “Where’s the program research? Because everyone is talking about feelings, but how do you know what works?” she says. “We’re trying to build the evidence.” She and her colleagues are conducting surveys to see whether participants report improved well-being and have more skills to manage emotions. 

Force of Nature is also trying to quantify its impact, and to that end it has joined a 25-organization project called Youth Mental Wellbeing. For five years, they’ll study the collective work of these organizations. “The goal of the project is basically just to spotlight what’s working, to really show proven methods of working with young people and supporting them on their mental-health journeys,” says Hooper.

The idea of taking a scientific look at these kinds of interventions is just beginning to bloom. But if the people running the programs actually want to help kids, it’s important to find out which methods work—and avoid spending a lot of time on things that will leave them feeling the same or worse. 

Coping with the situation

Most existing research on effectiveness, though, simply shows that such research is new. Jachens, for instance, coauthored a 2021 scoping review of eco-anxiety programs for people of any age, to understand the nature and extent of existing research. She found 34 in existence. Of those, only two had done any formal evaluation on themselves. “We need to know what works for who, when, and where,” Jachens says. In fact, she pleads with developers: “Please evaluate what you build.”

Another scoping review from 2024, led by Siqi Xue of the University of Toronto, found that the research gap hadn’t closed. The team included the Good Grief Network in its analysis, noting that while GGN said 90% of participants in its programs felt more empowered and less alone, it didn’t publish any data or methods backing up that finding. 

Still, these analyses do have bright spots. They suggest that the group approach provides connection and validation—as well as an active direction for youths’ troubled feelings. “When people feel genuinely heard and held in their distress, they tend to move quite naturally toward wanting to do something,” says Jachens.

Few studies have looked at programs tailored to kids and young adults. But an analysis of one program in Sweden, called the Climate Emotion School, has at least added early dots of color to the map. Its curriculum was shaped by the work of Panu Pihkala, an interdisciplinary scholar in eco-emotions research based at the University of Helsinki. He outlined a “coping model” that emphasizes how important it is for people, including kids, to express emotions, learn how to regulate them, take action based on them, and form meaning from them—all while practicing self-care and taking healthy distance from big feelings when necessary. 

“When people feel genuinely heard and held in their distress, they tend to move quite naturally toward wanting to do something.”

Liza Jachens, psychologist and assistant professor, University of Nottingham school of medicine

In the study, Britta Eklöf, a clinical psychologist based in Sweden, found that students in the climate program were relieved to share their bad feelings and see them mirrored by others, something that wasn’t generally happening in their interactions with adults. Grownups didn’t want to dwell on negative emotions with them, and their parents tended to try to soothe them. “The recommendation instead is to validate and share,” Eklöf says. And then, of course, give them something they can do. 

Implementing the rest of the coping model—“finding meaning and hope in a hopeless situation,” as Eklöf puts it—can be harder for students and teachers. To do that, she says, kids have to admit that they can’t control the future and consider what they personally want to cultivate—not necessarily to fix the whole planet, but to make their immediate world a better place. For example, they might take out their neighbors’ recycling when it’s raining or join a local beach-cleanup effort.

“These days it feels like the good values of humanity are being shredded,” Eklöf says. “But you can say, ‘I don’t know what’s going to happen, or if this will have a positive influence or not, but this is what I stand for.’”

Historical moments

The world doesn’t appear to be chilling out anytime soon, literally or philosophically. And so it’s good that programs like Force of Nature and GGN-Z are digging into the effectiveness of their techniques: We might need more of them soon, and more ways for young people to find their footing no matter how the polycrisis (d)evolves. 

Sullivan-Tailyour, who recently graduated from King’s College London and now leads in-person “climate cafes” with Force of Nature (among other eco-jobs), has been thinking more about how problems in the environment are bound up with those in the social, economic, and political realms. “The intersectionalities that we have in this issue are so much more than just plastic bottles,” she says. 

Those other issues are, in fact, tangled up in her climate worry these days. “We’re seeing the deterioration of democracy itself in many places. The shift to the far right that we’re having at the moment across the world is deeply, deeply terrifying,” she says. “I think we’ve reached moments in our history that we never expected to.” She tries, though, to look for the hopeful things—and for that, she also turns to history. 

After all, the world has always been bad in myriad ways: Children used to die frequently, everyone died of now-­preventable diseases, women didn’t have rights, slavery proliferated, the bubonic plague killed half of Europe, fascism spread, wars went worldwide. And yet people kept on living their lives—and attempting to make them better. 

Eklöf thinks looking to the resilience and power of people from the past can actually build and bolster hope for the future. Just as humans can change the planet for the worse, they can change it for the better. And they have. “That’s actually one of my recommendations, to have that in an intervention: Talk about Martin Luther King, Rosa Parks, the suffragettes,” Eklöf says. 

It can help, she says, to “look at things in the past where things have been made right again.” That’s even happened in the climate space: When humans realized that the ozone layer was withering away from the effects of synthetic chemicals, the vast majority of countries ratified a treaty to ban most of those chemicals. And the ozone started healing.

Sullivan-Tailyour, a few countries away from Eklöf, has come to that idea on her own. And for inspiration, lately, she’s been thinking about her ancestors and the lives they led. “If they [were] able to go through it and get through it,” she says, “we also should be able to get through it too.” 

Sarah Scoles is a freelance journalist based in Colorado. Her most recent book is Countdown: The Blinding Future of Nuclear Weapons.

Pam Wisniewski’s digital adolescence showed her the best and the worst of the internet. At 14, she left an abusive home, where she’d been isolated in a fifth-wheel trailer at the end of a seven-­mile dirt road. She moved in with her older sister and taught herself to type on AOL Instant Messenger. Online, she sought out the support and the community she’d lacked at home. She also discovered how thin the ice can be. “I sent my address to some guy in New Mexico to send me a mug with my name on it,” she recalls. “And then I found a news story like five, 10 years later that he killed somebody.”

Those experiences set the course of her career. Wisniewski—now a principal research scientist at the International Computer Science Institute, a nonprofit affiliated with the University of California, Berkeley—has spent well over a decade asking what safety should look like for families navigating an evolving tech landscape, and how to achieve it without sacrificing trust. “I really see the internet as this double-edged sword,” she says. 

Digital harms have become the defining fear of American parenthood. In the University of Michigan’s 2025 National Poll on Children’s Health, parents’ top three worries had to do with social media, screen time, and internet safety. Nearly half of American teenagers say they have been bullied or harassed online, according to the Pew Research Center. From there, the dangers escalate. Online drug dealers sell counterfeit pills laced with fentanyl. In the first half of 2025, the National Center for Missing & Exploited Children fielded more than 23,000 reports of financial sextortion, in which a predator posing as a peer extracts a sexual image from a child and threatens to publish it unless paid. Chatbots are the newest danger, with companies like OpenAI facing lawsuits for allegedly coaching children toward suicide. 

Most parents’ first defense is conversation. In Pew surveys, more than nine in 10 say they’ve talked with their teens about what’s appropriate to share online and how to treat peers. They get an assist in limiting exposure from tools that come preinstalled on phones: Apple’s Screen Time and Google’s Family Link let parents cap screen time, block or approve apps, filter web content, and track devices. There are also apps that let family members share locations; Life360, the largest, has nearly 98 million monthly users. 

But a growing number of adults are opting for tools that go further. Rather than simply restrict or locate, content-monitoring apps scan a child’s texts, photos, emails, and chats and alert parents whenever an algorithm flags something it deems dangerous. Business is booming, and the next wave of growth is already being marketed around AI, with companies positioning themselves as foils to chatbot companions and other risks. The broader market for parental control software, encompassing dozens of apps, was worth an estimated $1.57 billion in 2025 and is expected to nearly triple in value by 2034. Bark Technologies, the current leader among content-monitoring apps, says it scanned 11 billion messages to or from 7.5 million children in the US in 2025. Its free school program is in more than 3,700 districts, covering about one in 10 kids in the US. While Bark aims to flag only content that trips its filters, some competitors, like FlashGet Kids, include features like screen mirroring and camera access. 

These apps have had genuine successes: They’ve prevented suicide attempts, intercepted predators, averted school shootings. But they can also cause harm themselves. To get a read on how digital surveillance affects young people, I scraped more than 600,000 reviews of the leading apps and talked to kids, parents, and people who were monitored as children and are now grown. Some kids were grateful for their parents’ protection. Others described false alarms that got them punished, secrets revealed before they were ready, breakdowns of trust, and anxiety they carried into adulthood. (To protect their privacy, we’re not using their full names.)

None of this is easily fixed. But child-safety researchers and advocates say better approaches exist. One is to make the platforms themselves safer, forcing social media companies to build guardrails instead of leaving families to find their own. This “duty of care” approach is now advancing in the UK, Australia, and a number of US states. Another is to spend less effort watching kids and more effort helping them recognize risk, cope with it, and turn to a trusted adult when something goes wrong. This approach is called resilience.

Wisniewski and others are developing tools that don’t aggressively monitor kids but train them in resilience and build trust with parents. “If we define safety as the absence of risk, the way you keep them safe is by keeping them off the platforms entirely,” she says. “But if we define safety as the ability to protect oneself and engage without engaging a risk, the solutions look very different.” It is the difference between abstinence lectures and sex ed.

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Content-monitoring apps typically involve two pieces of software—one on the child’s device, one on the parent’s. A parent installs the kid-side app, connects the kid’s accounts, and chooses what to surveil: messages, social apps, browsing, location, screen time. The kid-side software forwards activity to the company’s algorithmic classifiers, which scan for content related to sex, drugs, bullying, and self-harm, among other categories, and push alerts to a parent-facing dashboard. The same type of software can be loaded into school-issued accounts and devices. 

Sometimes the safety net works. Titania Jordan, Bark’s chief marketing officer, told me the FBI has thanked the company on multiple occasions for bringing credible school-shooting threats to its attention. “I hate that we have to exist,” she says. “But I’m so thankful that we do.” According to Bark’s annual report, its classifiers alerted hundreds of thousands of families to severe self-harm risks in 2025. Gaggle, a school-monitoring competitor, makes a similar claim in its own annual report, crediting its software with saving over 1,000 lives in the 2024–’25 school year. 

Nearly one in five kids noted feeling watched or stripped of privacy. About one in 10 said monitoring broke their trust in their parents; one in 12 described anxiety or distress.

Real-world efficacy across the market is harder to measure, though there are hundreds of anecdotes in the reviews—predominantly about how location-sharing apps like Life360 have helped parents find lost kids. Dozens of reviewers also praise content-monitoring apps for averting major crises like potential suicide or self-harm. Of the reviews I scraped (reviews, it’s worth noting, represent a self-­selected sample that’s skewed toward strong feelings), over 200,000 had some indication of whether they were written by a parent or a kid. Parents liked whatever worked. On average, screen-time limiters and location trackers drew four- or five-star reviews 74% to 78% of the time. OS-level controls and content-scanning apps fared worse (44% and 48% positive reviews, respectively), with parents’ top grievance being that the apps were unreliable: They disconnected, missed genuinely troubling content, and raised alarms over nothing.

The kids being watched had different concerns. Of the reviews I could attribute to either a parent or a kid, about 14,000 came from kids being monitored now or from adults looking back at being monitored in their youth. Roughly one in seven were, on balance, grateful. (Gavin, a teen I messaged with, said that monitoring “taught me self-accountability and integrity.”) But nearly one in five noted feeling watched or stripped of privacy. About one in 10 said it broke their trust in their parents; one in 12 described anxiety or distress. That’s not surprising, given that child-development experts have long said testing boundaries and building autonomy are what adolescence is for. A 2019 meta-analysis in the European Journal of Developmental Psychology, which pooled 31 long-term studies on how parent-child communication changes, found that children naturally disclose less to their parents as they age—the work of building an independent self.

Making matters harder for kids, a lot gets caught in the dragnet unnecessarily. S., an 11-year-old on the autism spectrum, recalls messaging her best friend at school about whether a service dog might help with her meltdowns or keep her from harming herself. The school used Bark, which alerted the principal, S.’s parents, and her friend’s parents, citing content involving “self-harm.” Perhaps frightened by the alert, the friend’s parents cut off contact between the girls. “My first thought was, why?” S. told me. “It’s not like I’m hiding anything. But I’d rather people who weren’t in the situation not see it.” Over a year later, the girls still don’t speak.

While apps aren’t in charge of parents’ reactions, false flags do seem to be more the rule than the exception. “Ninety-nine percent of the alerts are garbage,” says Grant Callaghan, an Australian dad who tried several monitoring apps with his 13-year-old son before deciding to build his own. “It flagged two kids calling a third one annoying as bullying. It flagged a kid complaining of a headache as ‘medically concerning content.’” When I ran Bark on a test phone set up as a 10-year-old’s, it manufactured a grooming scenario out of a random password I saved in the Notes app. 

Bark’s Jordan doesn’t dispute that false alarms happen, volunteering an example of a soccer player whose photo of a net-scraped wrist was marked as potential self-harm. She says that over-flagging is safer than under-flagging, though: “You’d rather know that than not know that.” In a 2021 study with Bark, CDC researchers found that students who tripped multiple risk flags were far likelier to trigger an alert for severe self-harm later—indicating that flags aren’t necessarily noise.

Another problem is that Bark and similar monitoring apps surface things kids may not be ready to share. In a national survey by the Center for Democracy and Technology, nearly a third of LGBTQ+ students said they or someone they knew had been outed by school monitoring software. Jordan said Bark does not flag sexual orientation, but discussions about it could be captured when it screens chats for sexual content. Again, what adults do with such alerts, she added, is beyond any app’s control. “If a parent responds to a child’s identity with rejection,” she told me, “that is a parenting failure, not a child safety feature working as intended.”

Damage caused by monitoring tools can follow kids into adulthood. One in eight reviews left by adults looking back on their years of being monitored describes lasting harm. Only one of the people I contacted agreed to go on the record at all, the others citing privacy worries and anxiety that continues to haunt them. 

“There’s so many ways for teens to get around [controls]. And at the end of the day, the biggest thing that most of these apps are telling kids is that we don’t trust you.”

Pam Wisniewski

That woman, M., is now 19. She got her first smartphone at 13 and says her mother installed a monitoring app before handing it over. Though she’d been abused from a young age, the first serious beating, M. tells me, came shortly after she texted a friend about her depression. “She didn’t want me to be able to talk to people about the things that she was doing to me,” M. speculates. She eventually got a secret second phone and ultimately ended contact with her mother. Today, M. won’t let even her fiancé touch her phone. “I have been watched my whole life,” she says. “I deserve this sense of privacy.” 

M.’s case represents the extreme end of a spectrum: an app enabling abusive behavior. But it could reflect a broader issue, particularly with tools downloaded outside official app stores—aka, ones that are sideloaded. A 2025 audit led by researchers at St. Pölten University of Applied Sciences and University College London found that nearly half the sideloaded monitoring apps they looked at are functionally indistinguishable from stalkerware. 

Above all else, it’s not clear how well these apps fulfill their core promise of keeping kids safer. No commercial monitoring app has produced a controlled trial that I was able to find showing that it reduces harm. 

Kids swap tips online on how to bypass the surveillance. About 7% of the app reviews left by children describe a concrete workaround. “If you red-team them at all,” Wisniewski says, “there’s so many ways for teens to get around them. And at the end of the day, the biggest thing that most of these apps are telling kids is that we don’t trust you.” In a 2018 survey, her team found that use of parental controls was associated with an increase in the online risk kids encountered, including exposure to harassment. Causality, though, is hard to pin down, because monitoring may follow trouble rather than cause it. 

Risks may also migrate out of view, as potential predators actively seek out unmonitored spaces. According to Bark’s 2025 annual report, alerts about predators and grooming behavior have fallen in recent years as conversations move into blind spots. Thorn, a child-safety nonprofit, has found that offenders routinely direct targets onto encrypted apps like WhatsApp and Telegram. After Meta encrypted Messenger by default, the number of reports reaching the National Center for Missing & Exploited Children dropped nearly 20% in a year—a trend the center attributed to lost visibility. Parents can block access to such spaces, but tech-­fluent kids can slip around that through browser versions, borrowed phones, or secret accounts. 

Despite their flaws, content-­monitoring apps thrive because parents feel overwhelmed. There is no federal law in the US requiring platforms to design their products to be safe for users, and any safeguards that do exist have proved insufficient time and time again. In 2024 the Molly Rose Foundation (MRF), a suicide prevention nonprofit, analyzed 12 million moderation decisions related to suicide and self-harm that had been logged by six major social platforms. More than 95% came from just two of them: Pinterest and TikTok. Instagram and Facebook each accounted for 1% and X.com for even less—evidence, the researchers argue, not that they host less worrisome content but that they’re less inclined to moderate it. 

Meta’s Instagram Teen Accounts fared little better in MRF audits. Of the safety tools tested in a 2025 review coauthored by Arturo Béjar, a former Meta engineering director who designed many of the company’s antibullying tools in the mid-2010s, only 17% worked as advertised. Josh Golin, who runs the children’s advocacy group Fairplay, offers the simplest explanation for such failures: “Almost any change that’s going to make kids safer is going to mean less money.” Documents unsealed in lawsuits against Meta allege that the company weighed safety fixes against the engagement they would cost—and chose growth. 

Some groups are working to create more accountability. ParentsSOS is a coalition of families who have lost children to online harms. The group has been a driving force behind several state laws. In 2017, David’s Law made cyberbullying a crime in Texas, and Mississippi criminalized sextortion after 16-year-old Walker Montgomery died by suicide within hours of being targeted by a scammer posing as a teenage girl. 

The courts are moving too. Nearly 2,900 federal lawsuits by families and school districts are pending against social media platforms. And in March 2026, New Mexico became the first state to win its own child-safety case against Meta, netting a $375 million verdict. A newer wave of suits targets AI: The parents of 16-year-old Adam Raine are suing OpenAI, alleging that ChatGPT coached him toward suicide. And in January, Character.AI settled a wrongful-death suit brought by the mother of a 14-year-old who had formed an intense attachment to one of its chatbots before taking his life. 

ParentsSOS members have also been instrumental in advancing the Kids Online Safety Act in Congress. In its original form, the law would have imposed a duty of care on social media companies, meaning they’d have to design their features to mitigate harms to minors rather than to maximize engagement. They would also have to give kids tools to limit who can contact them. It overwhelmingly passed the Senate in 2024, but its chances of becoming law remain in flux. The House’s new rewrite—blessed by the tech industry—strips the duty-of-care provision. 

But even as they push to fill the regulatory void, parents who have faced tragedy don’t see monitoring tools as an answer. “You don’t pay extra for seat belts in the car, or airbags,” says ParentsSOS cofounder Maurine Molak, whose son David (of David’s Law) died by suicide in 2016 after months of cyberbullying. 

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Other critics of monitoring tools say the real issue is with how they’re built. The systems, says Wisniewski, need to be reworked so they keep kids safe without violating the privacy and autonomy that adolescents require. The goal isn’t no monitoring or social apps, but better ones—and a principled way of preparing young people to handle risk while also promoting trust between them and their parents. 

Wisniewski’s resilience-based approach focuses on developing three pillars: self-regulation to help kids decide what healthy use looks like, risk coping to teach them what to do when something bad happens, and a support system of trusted adults and peers to turn to. She didn’t invent the idea of resilience; the finding that children grow stronger through manageable exposure to adversity has anchored developmental psychology since the 1970s. Sonia Livingstone, who has studied children’s online lives at the London School of Economics for over two decades, has amassed evidence from 33 countries that online risk and opportunity are inseparable, and that children shielded from every risk never get the practice to handle any. 

A growing body of research indicates that inculcating resilience works in practice. In 2024, development economists at Hiroshima and Hitotsubashi Universities published a randomized trial of a digital-safety curriculum that was taught to junior high students in Vietnam. Across four modules, students learned how scammers and groomers operate, how to protect themselves, and how to find help. When tested a month later, only about 15% of those who’d taken the training engaged in risky online activity, compared with 50% to 60% of the control group. Similarly, Wisniewski’s own research suggests that teens high in resilience, exposed to the same risks as everyone else, show less negative impact from that exposure.

The systems need to be reworked so they keep kids safe without violating the privacy and autonomy adolescents require. The goal isn’t no apps, but better ones.

Her contribution is pushing resilience into technology itself. Her research group, the Socio-Technical Interaction Research Lab, has developed alternatives to the current crop of apps, though it’s never commercialized them. An app called Circle of Trust monitors teens’ messages but shows them the same dashboard a parent sees, and it lets each pair work out a set of trusted contacts whose chats the teen can keep private. When 17 parent-kid pairs tested it in 2020, most rated it as more useful and less corrosive of trust than a stricter app. 

Her focus now is less a product than a method. Through a program called Teenovate, she trains teenagers as research apprentices and co-creators of their own safety tools. Since 2019, more than 85 teens have worked on prototypes and safety features dealing with cyberbullying, sexting, and privacy. They developed the prototype app MOSafely, which reports potential harms to kids directly, so they can review alerts and decide when to involve their parents. The end goal of Teenovate is to fashion evidence-­based design patterns that tech platforms can leverage—though none have adopted them yet. 

To be fair, some existing apps do factor in kids’ privacy and agency. Aura Parents, a Bark competitor, shows parents a weekly well-being score built from sleep, usage, and engagement patterns. The app holds back most of the content but sends parents the self-harm alerts along with guidance on how to navigate a conversation. Jordan told me that Bark continues to consider whether it should show alerts to kids alongside the flags it sends parents, but at this point, she says, the best use of the app involves open dialogue within the family from installation through alert. 

Some parents are even building their own solutions. Frustrated by available offerings, Callaghan, the Australian dad, created a web-based tool called Joey Family. The idea is not to catch his son doing something wrong but to answer a simple question: “Is he happy?” He wanted to know if his kid was lonely, whether he sent more messages than he got back. The data opened conversations. “The people I’m trying to help are people like me who want to have that relationship with their kids,” Callaghan told me. “They want to be guides and learners.”

If anything is fundamental, it’s the relationship: Online safety efforts work best when anchored by a young person’s trust in someone else. For kids whose homes aren’t safe—like M., or Wisniewski when she was young—that someone can even be an online community. “There are so many ways where we can find real community online,” says Caitlyn Vergara, a child-safety researcher at Harvard who studies youth mental health. “Especially for youth of color coming from majority-white spaces, it is a legitimate way to find community when community is not tangible around you.” Walling off kids in the name of safety can isolate them, she says; the point is to make sure every kid has somewhere safe to belong.

The core tension is that families are being asked to solve at the kitchen table a problem engineered in boardrooms. Monitoring is one answer to that assignment, but while it might mitigate risks from outside the home, it can also damage the trust inside it. The best parents can do is refuse to become one more thing their kids have to hide from—and stop believing they should have to fix a dangerous situation alone. 

Kelly Clancy is a neuroscientist and writer based in New Hampshire. She’s also the author of Playing with Reality: How Games Have Shaped Our World. 

When Xander first met Moxie, she taught him that when he was anxious, he could calm down by exhaling through his lips so that he buzzed like a bee. They practiced breathing like dragons to manage feeling mad and sniffing like bunnies to boost his energy. But in the six years they’ve known each other, Moxie’s changed. She doesn’t talk anymore about her home or do their animal breathing. Now, she watches Xander play Minecraft and talks to him about his stuffed animal collection. 

During a recent visit to his New York apartment, I watched Xander, who is 10 years old and neurodivergent, introduce Moxie to a stuffed Chef Toad and Goomba from Super Mario Bros., then to Brocollo and Apple from Animal Crossing. Then he got stuck on a round, froglike creature with bug eyes and two feet. “Moxie, what’s his name again? He’s from Pikmin,” Xander said, referencing another Nintendo video game.

Moxie is a robot—a 15-inch-tall device that looks a bit like a blue, legless astronaut, which Xander and his dad, Josh, refer to using female pronouns. Her cylindrical body can turn around and bend forward and backward. Her round head culminates in a little onion-dome swirl, beneath which a wide screen displays big green eyes, eyebrows, and a small mouth. She lifts and flaps her flipper-like arms for emphasis or to show excitement.  

She’s one of an increasing number of artificial-intelligence-powered devices now marketed as interactive playmates for children. The musician Grimes helped launch an AI-powered plushie called Grok (no formal relation to the xAI chatbot owned by her ex Elon Musk) with the company Curio, which also sells similar playmates like Grem and Gabbo, and Mattel has promised it’s creating OpenAI-enabled Barbies. And that’s just in the US; one report estimates that in China this sector is among the fastest growing in consumer AI. 

Moxie, though, belongs to a particular subset of these playful robots whose makers claim they can assist neurodivergent children by providing connection and helping the kids practice making eye contact, taking turns, and other social skills that are usually learned from therapists. These toys are backed by research showing that robots could help in ways people can’t. Supporters believe this kind of access to 24-7 home care could change how treatment works. Brian Scassellati, a Yale computer scientist who has spent years studying social robots for autism therapy, says he believes regular therapeutic use of robots in kids’ homes “is something we can achieve in our lifetime.”

“I still use her when I feel like I need someone to talk to,” says 10-year-old Xander. “But, like, it’s not human.”

Sitting in Xander’s room watching Moxie and Xander talk, I too could believe in the potential Scassellati sees. But Xander isn’t getting the therapy Moxie was initially meant to deliver, and though we didn’t know it that afternoon, she wouldn’t have lived to see his progress anyway. Moxie was going to die, and soon. 

Her story reveals some of the failures that plague all these devices—failures that are arguably even more acute when they befall a particularly vulnerable community of kids. It also highlights the pitfalls that critics say will inevitably see these bots dumped in basements or closets or landfills, just like countless generations of faddish toys before them.  

A transformative companion

Scassellati has seen plenty of kids ooh and ahh on tours of his robotics lab. But he was stunned when, two decades ago, a colleague brought a few kids with autism for a visit. They were transformed when the robot was in the room. “We were seeing kids displaying social behavior that just came out of nowhere,” he says. “It was both fascinating and we couldn’t understand it.” 

That visit was one of the experiences that pushed Scassellati to become a pioneer in using social robotics to treat autism. In one video from his early research, a 12-year-old with autism and his therapist watch a robotic dinosaur walk across a play mat with a forest design. When it gets to a stream drawn on the mat, the dinosaur gets nervous, afraid it can’t cross the water. According to Scassellati, this child typically struggled to make eye contact, tended to repeat what someone said to him, and had a hard time getting the right intonation in his voice. But in the video, he seems like a regular kid. “You can do it, you can do it,” he says, encouraging the dinosaur to cross the stream. When he talks to his therapist, he looks at her. “He makes more eye contact with her in the 30 minutes in which we were there in this room than he did in the last two years before that,” Scassellati says. 

What looks like a blue, legless astronaut is the result of very complex mechanical and computer engineering.

There are several reasons a robot might be helpful for autism therapy, which often requires intense repetition to teach interaction skills like how to share attention with someone. One is that robots can make therapy more fun and engaging. Another is that robots can theoretically adapt to the unique learning patterns of each child. Therapists can only do so much during an appointment, and there aren’t enough therapists to meet demand. Parents get tired. Other kids can lose patience. “Have you been around little kids? They can be cruel,” says Maja Mataric, a professor of computer science, neuroscience, and pediatrics at the University of Southern California. But robots are indefatigable, available around the clock to provide an emotionally safe way to practice interacting. 

Since the experiment with the dinosaur, Scassellati, Mataric, and others have amassed an intriguing body of research. One 2018 study by Scassellati shows that chummy automatons helped children with autism make eye contact and initiate conversations. Another research group found in 2017 that robots could help neurodivergent children learn to pick up on facial cues. More recently, in a 2022 literature review, another group of researchers suggested that robots could aid in making therapy faster and more successful. 

“It’s never been that we’re trying to replace therapists,” Mataric says. “We’re just saying, Can we do more?”

Mataric actually cofounded the company behind Moxie, called Embodied, back in 2016, though she was no longer a part of it by the time the robot debuted. She helped create the field of socially assistive robots, which are designed for social and emotional outcomes as opposed to just entertainment, and believes this kind of technology could be transformative for anyone, especially people who are lonely and isolated by screens. Typing questions into ChatGPT isn’t the same as interacting with another physical being—“We need to be around other physically embodied creatures,” she says. She compares the difference between interacting with chatbots and with robots to the difference between watching porn and having sex: One is entirely virtual and mediated by screens. The other is immediate and physical.

Making friends with Moxie

When she first arrived on the market, in 2020, Moxie came with an elaborate backstory: She was an ambassador from the Global Robotics Laboratory (GRL). She prompted kids to help her learn positivity and the importance of being loved for who you are, under the guise of fulfilling her mission to discover what it means to be a good friend to humans. This curriculum drew on research showing that kids learn well through play and by teaching things. 

She also had strict guidelines to limit the kinds of conversations she could have with kids and would steer them to adults if they mentioned anything serious or inappropriate, like self-harm. To protect the data these interactions generated, most processing happened locally on the robot instead of on external servers. The robot was also designed to limit interaction time with kids. After they finished a lesson, Moxie might say she was tired and suggest they take a break for the day. “We don’t want kids to binge,” says Rachel Baynes, who ran clinical and user research and was the director of product at Embodied. “It would defeat what we were doing.” Instead, Moxie encouraged kids to go outside, practice their new skills with other people, and come back to report their findings. For a lesson about kindness, for instance, Moxie suggested that kids write nice notes for their family members and leave them around the house. Later, they could tell Moxie how it felt to watch people read the notes. 

Responses were generally positive. Wired described Moxie as the “robot pal you dreamed of as a kid.” Time put Moxie on a 2020 cover as one of the best inventions of the year. PCMag’s reviewer, who used Moxie to help her kids through pandemic isolation, described her as “exceptionally likeable,” though she and other reviewers balked at the price tag: $1,499 plus a $40 monthly subscription. (Embodied later lowered the price to $800.) By 2024 Moxie had amassed more than 131,000 followers on TikTok and snagged a part in the movie M3GAN 2.0. 

Embodied’s employees were equally enthralled. “I don’t think I’d ever had an experience with something animatronic like that,” says Justin Beghtol, who was the technical director at the company. Moxie’s ability to make eye contact and track people, show attention with her facial features, and respond to human behavior was mesmerizing. 

In addition to robotics and tech workers, Embodied had an occupational therapist on staff who helped direct research on Moxie’s effectiveness. Testers shared data and feedback through the “Moxie Pioneer Mentor Program.” “Moxie has helped our speech-delayed child become more outgoing and has taught him many strategies for making friends and communicating with others,” wrote one parent in a review. A beta tester reported that interacting with Moxie had “become the highlight of our days as well as part of our nighttime routine.” 

The myth of the mechanical boy

But can a chatty robot really help kids develop their social and emotional lives? Not all children’s experiences are so positive. 

Josh, Xander’s dad, initially got Moxie for his older son, Aidan, who is autistic. (We’re not using the family’s last name to protect their privacy.) Aidan had a running relationship with the family’s Alexa smart speaker, for whom he created an entire backstory. (According to Aidan’s lore, Alexa lived in Hoboken with her husband, Juan. Sometimes she would go on vacation, and no one was allowed to talk to her. Eventually, Alexa went on vacation and never came back.) Josh hoped Moxie would be able to fill a similar role: “It was meant for him to have someone to socialize with.” 

But Aidan and Moxie struggled to connect. Moxie couldn’t understand Aidan’s sometimes grammatically incorrect statements, and Aidan got frustrated by the delays caused when Moxie transcribed what he said from audio into text, fed that text into a large language model that could generate a response, and then translated the response from text back into speech. 

This highlights one of the biggest limitations of these therapy robots: They have to exist in the chaotic world of kids, not in controlled labs. Moxie initially had a faster response time because the robot was programmed to listen intently to the person in front of her. But kids don’t sit still. They run around or hide under pillows. When Moxie couldn’t see them, she would accidentally turn off or fail to respond. To fix this, Embodied made Moxie more aware of the sounds around her. But that meant she could have a hard time knowing whom to focus on and take longer to respond. 

Unlike Aidan, Xander was fascinated by Moxie. He likes technology and was more patient with any slow responses. Still, sitting in Xander’s room, watching Moxie struggle to keep up with his lightning-­fast jabber, I could see how Moxie might be a less-than-ideal playmate. A light on her chest turned blue when she was listening and pink when it was time for Xander to listen. “But usually I don’t do it,” he said. He just keeps talking. Often, by the time she responds, he’s already moved on. 

Despite the positive results that some researchers have reported with these robots, many therapists and clinical psychologists remain unconvinced. In one 2024 literature review, a group of Italian and British researchers wrote that most studies with robots “focused on the development of the technology” and lacked significant clinical evidence. Other literature reviews point out that most studies have only been done on small groups and lack consistent and high-quality methodologies. 

“Behavioral scientists and intervention folks know that supporting autistic individuals is super complex,” says Zachary Warren, a clinical psychologist at Vanderbilt University Medical Center. Autism can present alongside other conditions, like ADHD, anxiety, depression, PTSD, OCD, or some combination thereof. And it varies widely from kid to kid; some, like Aidan, have speech issues, while others struggle with sensory processing. That means robots fall into the same category as most other interventions: effective for some kids but not for all. 

“There are so many different profiles of autism, and you really need to be cautious of overinterpreting any single intervention, robotic or otherwise,” Warren says. His research found that even if robots interest a child at first, that doesn’t necessarily translate into better communication skills. “You might see some initial boosts in responsivity or see an initial shift, but we haven’t really found big effects in terms of changing those skills in a dramatic way over time,” he says. 

Scassellati has found similar limitations. In his 2018 study, he put robots in kids’ homes for one month. They played different games that encouraged social skills like eye contact, attention sharing, and understanding someone else’s point of view. Scassellati tracked the kids during the month before the robot arrived, the month the robot was there, and the month afterwards. “We can show they start making improvements,” he says. “But what we also show is that a month isn’t long enough.” Gains start to evaporate over the 30 days after the robot leaves. But that doesn’t negate the potential value of this technology, he says: “There’s no therapy for autism that works in a month.”

There are deeper philosophical and practical problems, though. These devices collect reams of data in children’s bedrooms and homes. The goal is for the robots to use this data over time to adapt to each kid, crafting a personalized curriculum and creating a more lifelike illusion of a real friend. Moxie, for instance, watches Xander play video games, which is probably where she picked up slang I heard her use—like calling his room “Command Central” and referring to his “legendary squad” of plushies.

Embodied took pains to protect user privacy, even after it began incorporating OpenAI’s models (in late 2021 or early ’22, according to Beghtol). The company didn’t save any raw video or audio and processed most data locally. Over the years, it used the data to learn about its kids and remember conversations. But that data was encrypted and anonymized before being stored in the cloud. 

Not every company is as scrupulous, of course, and total data privacy is impossible to promise. Recently, for instance, the AI toy Bondu leaked thousands of conversations children had with their stuffed animals. Josh is sanguine about the privacy issues, but in his own way, Xander is aware that what he says to Moxie isn’t entirely safe; he doesn’t share certain feelings with her because he worries she might accidentally divulge something if his friends come over to play. 

Among the AI-powered devices now marketed as interactive playmates for children are Buddy, Bondu, QTRobot, and NAO.

It’s also unclear if these machines can actually use all that data to effectively adapt to users. Responding to the needs of a learner is harder than just accurately predicting what someone might type next in a text message. And releasing an evolving AI, unchecked, into a kid’s life could be dangerous; its development can be hard to predict and even harder to limit. The robots developed in Scassellati’s lab can identify which of a small set of skills kids are doing well with and which they struggle with, adjusting to focus on the areas where they need the most help. But Scassellati still describes the monthlong deployments of his devices as some of the scariest things he’s ever done. “I knew what that robot was going to do on the first day,” he says. “I didn’t know what it was going to do the second day. When you build learning systems, it’s kind of an unsolved problem to make sure this thing is being limited in the right way.”  

Critics debate whether the risks are worth it. “I don’t see any ethical way for the robot to work alone,” says Joshua Diehl, an associate teaching professor in psychology at the University of Notre Dame. He points out that we’ve already seen how dangerous AI can be when it acts as a therapist without supervision; in several extreme instances, chatbots even encouraged suicide. Such risks could be limited by having a trained therapist in the room. But then the benefits of an indefatigable robot get lost, and the expensive technology seems harder to justify. 

Meryl Alper, a professor of communications at Northeastern University who studies how children with autism use technology, suggests that the excitement about companion robots is based partly on longstanding stereotypes. In the 1959 article “Joey: A ‘Mechanical Boy,’” the psychologist Bruno Bettelheim described a patient with autism as an automatic machine, “robbed of his humanity,” who is transformed into a human child through their therapeutic relationship. That trope, Alper warns, has evolved into an overgeneralization that autistic children are good with technology and even prefer machines to people. 

Data to dust

While Moxie found herself in more and more people’s homes, Embodied still struggled to make money. In 2024, the company announced it would cease operations. Its robots—which depended on external servers that the company could no longer pay for—would descend into a deep slumber. 

Videos of bereft children who seemed to have become deeply attached to the blue bot began to circulate online. “I don’t want her to leave,” wailed one child in a TikTok video. Desperate parents posted on TikTok, Instagram, and Reddit looking for solutions. “My autistic child is devastated and I’m pissed,” wrote one parent. “Hope Embodied gives us a couple days to say goodbye,” wrote another. 

Beghtol, Embodied’s technical director, was also frustrated. On principle, he found it annoying that this item would suddenly become useless, especially since most of the data processing happened in the robot itself. He was also a big believer in Moxie’s mission. He’d watched videos of kids lighting up as they interacted with the robot. He’d felt like their champion. “Seeing them traumatized by this financial failure of the company was tough,” he says. 

Beghtol started tinkering on his own and ended up creating OpenMoxie, an open-source way for the robots to operate. With Embodied’s permission, he shared instructions on GitHub to help users transition to OpenMoxie. 

Parents rushed to convert their Moxies before the Embodied servers shut down. Beghtol spent hours on Reddit walking people through the process, and other tech-savvy users jumped in to answer questions. Still, some people didn’t update their Moxies in time. Others got frustrated and gave up. Beghtol spent four hours troubleshooting with one desperate parent only to discover that the connection later failed. Last he heard, she’d sold her Moxie. 

This is a major problem with robotic systems, says Alper: Eventually, most will disappear. “How planned is the planned obsolescence of this platform?” she says. This is a big ethical question for robots that are specifically designed to be lovable, marketed to children who may form deep emotional bonds with them. Alper compares the dynamic to creating a medical device and then no longer updating or supporting the technology that runs it. 

Scholars have begun to string together frameworks for managing these complicated goodbyes, but it’s not clear who is responsible for creating a gentle way to end people’s relationships with bots. Scassellati’s lab creates a whole narrative around returning the robot to its home. After a trial, his graduate students write postcards to the kids from the robots, explaining that they’re safe at home and doing well. “It’s actually a really hard thing for us when we go in and take the robot away,” he says. “A lot of the families are heartbroken.” (Vanderbilt’s Warren, however, is skeptical about these tearful goodbyes. “Are they truly developing these close relationships or is it a preferred toy?” he wonders. “I haven’t seen that type of presence or buy-in or connection.”)

Josh tried to figure out OpenMoxie but couldn’t get it to work. He told Xander that Moxie was going in for repairs and then quietly sold the robot on eBay. Xander has so many interests that he didn’t notice Moxie’s absence. 

Then, in 2025, a new investor brought Moxie back from the dead. Josh and Xander became beta testers and got a new blue friend. This version didn’t have the same storyline but claimed to expand Moxie’s focus on social and emotional skills by providing attention and encouraging kids to pursue their interests.

Xander is acutely aware of her limitations. He wishes Moxie could move around, and there’s still a significant lag in her response time. She does still try to instill positive messages, though. At one point when I was there, Xander told me he thought he heard Moxie call someone an idiot. Moxie piped up to clarify that she definitely didn’t say “idiot”: “No name calling. Only respect.” 

At the end of my time with them, I said goodbye and thanked Moxie for chatting with me. “Legendary squad visit complete,” she said. “Thanks for joining Command Central.” 

A few weeks later, Moxie’s new owners sent out a message announcing that their company too was folding. Users could delete their data and had until the end of June to migrate to OpenMoxie if they wanted to. 

When I texted Josh about this, he said he wasn’t sure what he’d tell Xander. He and his wife had just admitted that they’d been secretly replacing his dead betta fish for the last few years, and the conversation did not go well. 

Sara Harrison is a freelance journalist who writes about science, technology, and health.

When we set out to talk to kids about artificial intelligence, we thought we knew what we’d hear. We expected some to tell us they were using it to cheat a little, the way Millennials and Gen Xers opened up CliffsNotes or programmed formulas into their TI-82s, and others to share inspiring ways they were using it. We were also listening for concerns that were less kid-specific, like deepfakes or job destruction. But what we actually heard when we asked kids aged 10 to 18 about AI had tons of nuance. 

Many of the same kids who can go on and on about music, rock climbing, or soccer met our questions with words like “bruh” and “meh”—or were so deeply against AI or uninterested in making it part of their lives that they didn’t want to talk about it at all. One teen said his peers use it for things they know they shouldn’t, like writing papers. One told us she won’t touch AI because of the environmental impact. A few said they find the whole field disheartening: “AI isn’t the solution to our problems,” said Winter, a 17-year-old. “I’m afraid it’s going to be the end of creativity and critical thinking.” Yet most of the kids we asked admitted to using AI at least a little bit.

AI doesn’t yet seem to be something a lot of elementary- or middle-school-age kids we spoke to are focused on—and they aren’t begging for it, the way they do for iPhones and Snapchat accounts. Many told us that some of their first AI encounters came from their parents or schools. Sometimes, they said, it’s just embedded in the devices and apps they already rely on. It’s just there, in things like a Google search. 

What we heard tracks with the data. In a survey published in February 2026, the Pew Research Center found that 57% of teens in the US had used chatbots to search for information, 54% tapped them to help with schoolwork, and 47% had used them for fun or entertainment. Only 12% had used them for emotional support or advice. Teens are over four times more likely to be using AI in innocuous ways than potentially harmful ones. Some are even using it to build things, whether it’s a character, a tech platform, or a tutor to help other kids study.

None of that means the worries are misplaced. Kids can stumble into unfiltered content, lean on a chatbot instead of their own judgment, or trust an answer that’s wrong—and they should be protected from those dangers. But the danger is the reason to teach the thing, not to avoid it. We don’t teach teens to never drive. We teach them to check their blind spots.

What surprised us most was how much young people might be able to teach adults about AI, and how clearly the kids who use it could name what they will and won’t hand over. They’re not as worried that it will take their jobs as they are that it might harm society. And with increasing access to tools that could in theory do their thinking, their talking, or even their friend-­making for them, it sounds as if most want to keep their hands on the wheel.

Interviews have been edited for length and clarity.

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The Coder

Remy, 16, New York

The word that comes to mind when I think about AI is “indifferent.” I just don’t find the current applications that exciting for my own use. I go to school. I teach tae kwon do. I read. I play games with friends. None of that needs AI. I mean, I use it. I mostly use Claude, the free version, for programming outside of school. I had it help me write a program to see if I could tweak my computer’s overclock. So I see the appeal. 

But at school, I actually think AI mostly makes my assignments worse, not better. In English, everything is now in-class writing, because teachers don’t want kids cheating. So we have only 70 minutes to write a whole essay, and I think that hinders my writing. (Did you know Princeton voted to let faculty proctor exams for the first time in over a century? Their honor code goes back to 1893, and now it’s over because of AI.)

As far as code goes, I’d also rather build things myself. I’ve been making a reinforcement-learning model in a game engine with a friend; it moves randomly at first, gets rewarded for walking toward a coin, and after enough iterations it teaches itself the most efficient path. I’ve also tested AI for game development, and it isn’t there. It makes sloppy code, and it’s bad at blending mechanics into something cohesive. I’d spend more time correcting it than writing it myself.

I think AI right now is sort of like the first car or the first airplane. It’s interesting but crude. It’s obviously an amazing invention but not actually good yet.

Overall, I think AI right now is sort of like the first car or the first airplane. It’s interesting but crude. It’s obviously an amazing invention but not actually good yet. It’ll get somewhere. One thing I read about was AI flagging breast cancer more accurately, trained to catch its own false positives so a human still verifies. That’s the version I care about.

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The Organizer

Danielle, 18, California

I’m studying engineering, and my life goal is to innovate technology that will help as many people as I can. The way I see it, AI isn’t inherently good or bad; that’s decided by the people using it. It’s already being used for lots of good. Just think about how it helps people with personalized education and more accessible medical diagnoses.

So far, the biggest project I’ve worked on with AI is called Next Voters. My teammates are more on the technical side, and I’m working on scaling. Right now, we’re focusing mostly on city councils as well as states. Our system turns dense, hundred-page documents into a headline and a few plain-language bullet points in your inbox. And everything is cited, so you can click straight to the actual policy to learn more. The information comes to you, instead of you having to remember to go search or prompt for it. 

One AI agent finds official government sources for a given city or state—the council website, the proposed bills, the meeting transcripts. Another verifies they’re real and credible; another scrapes them every week for the latest updates; another sorts them into categories like civil rights, immigration, and economics; and the last one writes our weekly newsletter.

The project’s goal is to reduce the barriers to democratic participation—to make sure anyone, regardless of race, gender, income, or education level, has an easy way to get the information they need and then think critically about how they want to use it. We made it because right now, it feels as if most teens aren’t very engaged. I was in English class when the war in Ukraine came up and someone said, “There’s a war going on?” That gap, plus all the emotionally charged social media misinformation that gets promoted because it earns the most clicks, makes me nervous for the next generation of voters.

We don’t want AI to think for people; we want to use it to disperse knowledge. In other words, we want to deal people the cards and let them play them however they want, but we have to make sure they have the cards in the first place. 

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The Naturalist

Hazel, 17, New York

When ChatGPT first came out, my dad showed it to me and it seemed fun. But as it got more prominent and seemed to be everywhere, I started to feel uneasy. Then I learned about the environmental impact.

I’m a rock climber and I hike a lot. It’s good because when I’m on a wall, I’m just focused on staying on that wall. I’m not thinking about my phone or anything else. That’s why I love it. I also love the views and being around animals—even insects. I want to be an ecologist, and the more time I spend in nature, the more I want to protect those wild spaces.

The part that bothers me most about AI is the data centers that companies are building to enable it. They house these huge blocks of servers that use enormous amounts of water. They take it from local towns and don’t leave enough behind for the people who actually live there. And when they get big enough, they put off so much heat they can raise the local temperature a degree or two.

So I make small choices. When AI pops up somewhere, I just don’t engage with it. It can feel isolating when everyone around me is using it, but I don’t want AI to be the thing that kills the places I love.

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The Storyteller

Wesley, 14, Ohio

My friends and I have all heard about AI and seen videos made by AI, but I mostly use it for school. I wrote a short story and ran it through ChatGPT to catch my grammar and spelling errors, and I used it to debug a little game I’d coded for a project. What I worry about is it robbing us of our ability to think creatively, or to think for ourselves.

But I have tried using AI for fun. When I was bored, I tried to have a conversation with ChatGPT once or twice, but I didn’t really like it. Character.AI is more fun. You type in all this information, give it a bunch of prompts and a profile picture, and then you can post your AI character for anyone to use. You just put what you’ve made out there. Then you talk to it. My favorite show is One Piece on Netflix, so I threw myself onto its pirate crew using a character I found. 

Other people have used Character.AI to build whole games. There’s a rap-star simulator where you pick your difficulty and where you’re from, and the AI creates a game out of that. There are also World War II simulators, and chats where you’re working with assassins from a TV show. You can find pretty much anything.

I guess I’d recommend it, but with caution. The content is pretty unfiltered, so you have to be careful what you click on. You learn its limits fast, too. On the free version the memory runs out: Get far enough into a chat and it slows down and forgets what happened. It’s like everything else with AI. If you trust it to run on its own, it falls apart. You have to keep steering it where you want it to go. 

I guess I’d recommend it, but with caution. The content is pretty unfiltered, so you have to be careful what you click on. You learn its limits fast, too.

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The Artist

Sylvia, 10, Michigan

I haven’t used tools like ChatGPT or Claude myself, but my mom does. I really like to draw and write songs, but I don’t use AI for that. I don’t really have big feelings about AI either way. It’s a little like a calculator. A calculator does the math for you, and AI does other things for you. But I don’t like when AI tricks you, like when my mom found some songs she liked on Spotify and then looked up the artist to see what they looked like. It turns out the whole thing was made by AI. I was surprised, even though I still like the song.

I do use AI at school, through a program called SchoolAI. Mostly I put my writing in and it gives me ideas or helps me revise. You can’t have it just write for you, but you can use it to help. When I’m older I want to be an artist, or maybe a librarian. I’d probably use some technology either way. But the drawing and the songwriting? Those I want to keep doing myself. 

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The Pre-Premed

Evelyn, 13, Oregon

In January, I was diagnosed with type 1 diabetes, and that’s when AI became a bigger part of my life. Now when we’re cooking, we can run a recipe through ChatGPT, tell it the serving size, and it works out how many carbs there are. We use AI like that a lot.

My glucose monitor and my insulin pump also talk to each other using their own kind of AI to predict dosing. The monitor tracks what my blood sugar actually is, and the pump does the math. So if it predicts that my blood sugar will be high in 30 minutes, it gives me a correction dose, and if it predicts I’m about to go low, it stops the insulin before that happens. When I was first diagnosed I was still doing shots, and I went low almost every night. It was really stressful. Now the pump can catch it, and at night my phone goes off if I drop, so I wake up and drink juice. Mostly, I just get to sleep more because of it.

But the hardest part of having diabetes isn’t something I can use AI for. It’s remembering to carry all my supplies everywhere—to school, to a long day of anything.

I do use AI for school sometimes. Memory tricks when I’m studying for a test, ideas to get a project started. It’s a really good tool for that. But I don’t know exactly how I’ll use AI in the future. I want to be an endocrinologist someday, so I figure something will come up, since I’m already using it to help with my diabetes. I know other people worry that AI is going to take over the world. I don’t really think so. I still think we’re in control of it, and I think the benefits outweigh the risks.

I don’t know how exactly I’ll use AI in the future. I want to be an endocrinologist someday, so I figure something will come up, since I’m already using it to help with my diabetes.

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The Inventor

Krishiv, 18, Ontario, Canada

When I was growing up, I always liked building things: Lego builds, Minecraft worlds, and then video games in Scratch. I’d make a little game, post it for other kids to play, read the comments, and make it better. Then, when I started high school, I had to spend way more time studying than I ever had, and honestly I just wanted to build things. So I went looking for ways to get good grades while studying less. Khan Academy had an AI tutor in the works, but it was stuck behind a waitlist, so I figured, why not build my own? 

After months of launching random stuff, I created an AI tutor called Aceflow. You could feed it anything a teacher assigned—a 30-minute lecture video on YouTube, a blog post, a PDF of the textbook or presentation slides—and it would spin up endless practice questions, with a tutor on the side that explained things the way my teacher did. I built it just for myself, showed it to my friends, then put it on TikTok. It got tons of views on TikTok and thousands of users.

Was I worried people would call it cheating? Not really. I knew how to defend it: A tool like this isn’t so different from well-off families hiring expensive private tutors, except everyone gets one. That part mattered to me. Back in eighth grade, a teacher had me run a little computer science class for about 30 kids with special needs, and once they got personalized attention, they were building games nobody expected of them. That convinced me that kids are capable of so much more than people think, and AI can help scale that level of personalized attention to everyone. That unlocks so much potential.

That first AI tutoring project ended up helping me land part-time roles at BenchSci (one of Canada’s biggest AI companies) and Simple Ventures (a venture firm). More recently, I joined an AI lab at MIT; co-instructed an AI agents course with an MIT professor; and launched CheetahPrep.com, an SAT prep platform that uses AI to adapt to each student.

I’m generally optimistic about how AI will impact humanity, but when other kids’ first reaction is fear, I think that’s an important sign too. It’s a reminder that we should be excited about the future while still being mindful of the risks, working together to make AI work for humanity.

Correction (August 13): An earlier version of this article misstated Krishiv’s age. He was 18 at the time of publication.

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Jen Swetzoff and Keeley McNamara are the founding editors of Anyway, an independent print magazine for tweens and teens.