It’s been a busy few months for AI hype. At the end of April, Anthropic claimed that its model Claude Mythos is better at finding software vulnerabilities than most security experts. Then we had the OpenAI–Hugging Face hacking incident, after which Anthropic (proudly) and Meta (reluctantly) disclosed similar incidents involving their models.

This was followed by Anthropic’s claim that one of its models had made a mathematical breakthrough; soon OpenAI claimed a mathematical breakthrough of its own. Most recently, Anthropic engineer Jacob Coxon went viral announcing his departure from the company, claiming that it and OpenAI are “racing straight towards self-improving superintelligence and gambling with our lives.”

Each of these events was mostly covered breathlessly by the press, often repeating the companies’ anthropomorphizing framings—which are designed to portray their software is not only powerful but incipient “artificial general intelligence.” So what is really going on? Are we witnessing a massive, civilization-changing set of technological breakthroughs, or is this marketing?

In all these incidents, massive fanfare from the companies (presented as mea culpas in illicit hacking cases) is accompanied by intense press coverage. Once there is time for experts in the relevant fields to examine what happened, a very different story emerges, but one that gets less media attention.

Regarding the “hacking” incidents, cybersecurity experts say the story is more about OpenAI’s negligence and failure to adopt basic, established security practices than about “models gone rogue” or “AI agents creating civilizations.” 

As for the mathematical results, mathematicians who were initially “stunned” by OpenAI’s press release saying that its latest chatbot, Astra, solved problems that “have been open and seen no progress on the main result for at least a decade”—but they later realized that the results weren’t as “novel as first appeared.” Since then, mathematicians have accused the company of research misconduct and plagiarism, and they’ve reiterated that Astra didn’t make a “profound intellectual leap.” Just weeks later, OpenAI claimed its own mathematical breakthrough. Two days before, Tristan Buckmaster, a math professor at New York University’s Courant Institute, published a bombshell statement suggesting that OpenAI had stolen other people’s work and improperly attributed it. 

Claims of incipient, dangerous superintelligence are not based in good scientific or engineering practice. Rather, they are narratives based in ideologies of transhumanism, eugenics, and wishful thinking about imagined future digital humans.

It’s worth thinking about why there is so much attention on computer programming and math as fields in which to apply large language models and related technology. Not only are they often elevated as the pinnacle of human intellectual achievement, but they involve problems where answers, once suggested, can be verified. The former property helps AI hype mongers sell the idea that they are building everything machines. The latter makes math and coding problems easier to tune systems for, since system output (sequences of likely words or pieces of computer code) can be evaluated without having to pay data workers to look at and annotate each one.

Mathematicians in particular have warned against corporations using their field in this way. A statement signed by hundreds of them says there is “currently a strong commercial incentive on the part of the technology industry to overstate the capabilities of their products” and asks policymakers to “consult with experts, including mathematicians, in forming policy decisions rather than relying on press releases or popular reporting of mathematical results.”

We echo this call and note that the illusion of speed and urgency promulgated by the tech companies is also a ploy to misdirect both policymakers and the public. Unfortunately, it sometimes works, such as with Senator Bernie Sanders’s well-meaning but ultimately misguided proposed legislation to prevent the development of “artificial superintelligence.”

Describing them as “superintelligence” or “rogue models” ascribes agency to products rather than to the companies building them. This framing markets these companies’ products as “superhuman” and, at the same time, helps the companies evade accountability for their actions. 

Instead of OpenAI being prosecuted for creating malware that hacked another company, press releases, news outlets, media personalities, and lawmakers refer to “rogue models” as if they acted on their own. Instead of researchers being questioned about their companies’ habit of plagiarizing academics’ work or using customer data to train models without consent, the public’s imagination is redirected to fears about what the future might hold upon the arrival of fictional superintelligent machines.  

The AI industry has even suggested that popular, bipartisan anti-data-center activism is a “distraction” from attempts to regulate the impending, scary, “superhuman” machines these companies are building. According to the AI industry, we should be more worried about a fictional machine god than about the climate catastrophe that these data centers exacerbate, the asthma suffered by those living near them, the rising electricity bills of the public subsidizing them, or the water that is redirected to cooling them. 

We know better than to make decisions based on marketing and better than to capitulate to corporate pressure to make those decisions quickly. Wise decision-making, by policymakers and communities, demands time to hear from independent experts and contextualize corporate claims. The best possible outcome from this summer of hype is that policymakers and the public at large learn to take a breath, hold onto our skepticism, and recognize this kind of hype for what it is the next time it comes around.

Timnit Gebru is executive director of DAIR and author of the forthcoming book Deep Unlearning: The Radicalization of a Tech Idealist, which is available for preorders now and set to publish on February 16. Emily M. Bender is professor of linguistics at the University of Washington and coauthor of The AI Con.

Our 15-month investigation into death and surveillance along the US-Mexico border began with a simple question: Why did so many people die near government surveillance towers meant to help track and apprehend them?

The result of our investigation is the first comprehensive map and analysis of deaths near border surveillance towers. Here’s how we built it, what decisions we made along the way, and what the data can and can’t tell us.

Our data

The investigation relied on knowing where migrants have died and where and when US Customs and Border Protection towers were installed. We analyzed cases dating back to 2015, allowing us to cover different border policies, presidential administrations, and tower technologies.

Migrant deaths

The US-Mexico border has been called the world’s deadliest land border, and Border Patrol estimates that more than 10,000 people have died during crossings since 2000—a figure that’s widely considered an undercount. But the amount of information available publicly on where and when these people died, and who they were, varies widely. Some remains are never discovered, and for those that are, there’s no national protocol for how the records are handled.

We considered a case for further analysis if we could confirm three basic things: whether the person was believed to have been crossing the border, where the remains were found, and roughly when the person died. 

We created our dataset by merging existing ones that had been compiled and shared by other organizations, including No More Deaths, Humane Borders, and the Electronic Frontier Foundation, with new records we obtained ourselves from more than a dozen agencies.

Public records requests in Texas

Texas was the biggest missing link in most existing databases of migrant deaths. Unlike Arizona, it has no initiative to share records online, and unlike New Mexico, it leaves individual counties to handle their own death investigations. Some records for those individual counties existed, compiled by a handful of dedicated researchers, but nothing was comprehensive or current. 

We identified 17 counties in Texas that would be relevant to our investigation: Culberson, Jeff Davis, Presidio, Terrell, Val Verde, Kinney, Maverick, Dimmit, Webb, Zapata, Jim Hogg, Starr, Hidalgo, Cameron, Kenedy, Brooks, and Duval. There are other counties in which migrants have died, but they do not have areas covered by surveillance towers and were therefore excluded.

In most of these counties, locally elected administrative judges called justices of the peace collect the most comprehensive information on local deaths. But records requests submitted to these individual judges can stall for years (indeed, some of our requests to justices of the peace remain unfilled more than a year after we filed them). Reports from justices of the peace sometimes leave out the scene or location details we needed; some justices never even visit the place where a migrant died, instead formally declaring the person deceased over FaceTime with the first responders on scene.

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MIT Technology Review instead filed records requests to the sheriffs of these 17 counties beginning in July 2025. These agencies are often first on the scene when a death is discovered, and their reports can have more detailed information on where remains were found. But relying on sheriffs’ records means our analysis undercounts migrant deaths in Texas, as it doesn’t include any deaths handled by local or state police.

After we filed a request, it took periods of near daily calls to the offices to receive the records, if we received them at all. Some were provided with no charge. Other counties charged between $300 and $1,300 to fill our request. Counties warned that their records were incomplete, with an unknown number lost during a move, damaged, destroyed, or misplaced during transitions between sheriffs.

Those delays were compounded because most agencies do not record whether a person is believed to have been crossing the border when they died. That’s despite provisions in some counties that allow officers to track far more granular details about other situations; Zapata County reports have a checkbox to indicate whether jewels were stolen in a burglary, for example, while Cameron County reports have one for whether a burglar entered through a chimney. Without a way to readily identify migrant deaths, offices had to pull records by hand, making the process slower, more costly, and more prone to error.

We received usable records from 14 of these counties (records have yet to be received from Dimmit or Duval, and Maverick’s office charged a per-record fee that was cost prohibitive).

Agencies generally sent us police reports for each individual case, amounting to over 4,000 pages of records in total. Some were handwritten documents. 

Except for the reports from Kenedy, Webb, and Hidalgo Counties, we pulled out the relevant information by hand. For those three counties, which sent large volumes of records, we used Anthropic’s Claude, accessed via API, to inspect each case report and pull out coordinates of the spots where remains were found; then we checked batches of those cases by hand to verify the AI’s accuracy. Agencies sent us records on a rolling basis from July 2025 to February 2026, and most did not specify the date through which their records were current.

We generally trusted that agencies sent us cases they believed involved migrant deaths, since they had the most information about each case. But some records clearly suggested otherwise (indicating, for example, that someone died at home), and we excluded those cases.

Several hundred cases were removed from our analysis because they didn’t include enough information for us to be certain where the remains were found. In fewer than 100 cases, coordinates were not listed but the description was specific enough for us to locate a point within 0.25 miles of where they were found—so we kept those in. 

Compared with agencies in other states along the border, those in Texas provide far fewer details regarding how long ago someone may have died before remains were found or how far the decomposition of those remains had progressed. Some reports included this information, but many didn’t. While autopsy records sometimes have more details, many migrant autopsies in south Texas are done through the Webb County Medical Examiner, which charges a fee for each report. That was cost prohibitive given the scale of our analysis.

MIT Technology Review consulted with Greg Hess, director of Arizona’s Pima County Office of the Medical Examiner, on a framework for translating descriptions of the scenes, bodies, and causes of death available in many of these records into rough estimates for when a person may have died (more detail on this process is provided below).

In total, we included more than 1,500 cases from our Texas records requests in our final analysis.

Accessing Arizona records directly

The Pima County Office of the Medical Examiner (PCOME) in Arizona handles death investigations for all border counties in the state except Yuma (we sourced Yuma’s records from the organization No More Deaths, as detailed below).

The office determines which cases it believes to be border crossers. It publishes a data portal for these migrant deaths that’s updated monthly and shares its data with the organization Humane Borders, which then publishes it in a map and database. We included the data from 2015 through April 2026.

Humane Borders notes for each case how precise the location description is. We included only cases with GPS coordinates precise to within 300 feet. Collecting these coordinates has been standard practice since around 2012. The records also include an estimate of when the individual died, ranging from less than a day to at least six to eight months before remains were found.

In total, we included more than 1,700 cases from Humane Borders in our analysis.

Data collected by No More Deaths

To obtain data on deaths in California, New Mexico, Arizona’s Yuma County, and El Paso and Hudspeth Counties in Texas, we turned to the nonprofit organization No More Deaths, which has tracked migrant deaths since 2004. It publishes information about its records requests and its methodology. 

Many of the cases in its database include GPS coordinates, sometimes with notes that the location is only approximate—for example, accurate to within half a mile or one mile. We included only cases where the location was known to within a quarter-mile.

JOHN LAMPARSKI/NURPHOTO VIA AP

No More Death’s database includes, when available, information on the level of decomposition in which each set of remains was found. As with cases in Texas, MIT Technology Review consulted with PCOME’s Greg Hess on a framework for translating this information into rough estimates for when a person may have died. 

Here’s where the No More Deaths data used in our analysis comes from:

• In California, No More Deaths requests records from the San Diego County medical examiner and the Imperial County coroner. Its latest data for Imperial County is from December 2025, and its latest for San Diego County is from October 2025. 
• In Yuma County, Arizona, No More Deaths requests records from the Yuma County medical examiner. The latest data is from September 2025.
• In New Mexico, the organization requests data from the state’s Office of the Medical Investigator. Unlike most states, New Mexico has a statewide medical examiner, allowing No More Deaths to obtain migrant death data for the entire state. The latest data is from July 2025.
• For Hudspeth County, Texas, No More Deaths requests records from justices of the peace in Districts 1 and 2. The latest data is from December 2023.
• For El Paso County, Texas, records were from the El Paso County Office of the Medical Examiner. The latest data is from September 2025.

In total, we included nearly 1,000 cases from No More Deaths in our analysis. 

Total cases reviewed

When accounting for all our data sources and excluding deaths without specific enough records to determine where remains were found, we analyzed a total of nearly 4,000 cases. 

Other notes 

We generally assume that a person died where their remains were found. That may not necessarily be true: Remains can be moved by other people, flowing water, or animals. But medical examiners and researchers we consulted said such cases are rare.

We excluded cases in which the virtual wall did not appear relevant. For example, we removed cases involving people who died inside Border Patrol stations or in car crashes during pursuits. 

The deaths we analyzed reflect a variety of causes that may seem at first like very different sorts of surveillance failure. If someone slowly died of dehydration within view of a camera, for example, agents would have had far more time to intervene than they would have in a case where someone drowned quickly in a river or canal. But we included all these deaths because, in each one, the virtual wall could have prompted a response from agents.

Surveillance towers

Including a tower in our analysis required knowing three things: its location, its type (so that we could judge how far it is supposed to see), and the time it was installed. 

Locations

The Electronic Frontier Foundation has been tracking and mapping CBP surveillance towers since 2023, using on-the-ground reporting, satellite imagery, and government documents obtained through public records requests. Its tally is an undercount; government records suggest that about 800 towers are currently deployed, while EFF has logged about 600. Our analysis therefore misses deaths that occurred near towers not yet catalogued by EFF.

MIT Technology Review worked closely with EFF’s director of investigations, Dave Maass, throughout this project. Our tower data began with the database published by EFF in April 2026, but that map did not include towers that have been removed. We consulted EFF for the details on these former towers and added them to our database. We generally use EFF’s reference numbers for individual towers, but we changed some to ensure that each tower has a unique identifier.

The locations are identified by GPS coordinates. We verified each tower’s coordinates by analyzing satellite imagery.

Tower types

Our analysis focused on towers of three main types. Certain towers mapped by EFF don’t fall into any of those categories, so we excluded them because it’s difficult to obtain information about how far they are supposed to be able to see. This includes some towers that are installed at Border Patrol checkpoints or stations.

CREATIVE COMMONS

CENGIZ YAR FOR MITTR

To know how far the main tower types could see, we consulted materials from tech companies and EFF’s research, led our own review of government documents, and interviewed former Border Patrol agents and officials. The distances described are consistent across these sources. But they are advertised estimates, not contractual guarantees: Some government records redact more specific information about a tower’s surveillance range in particular locations, citing security concerns.

When towers were present

To know if a tower in place now was present when someone died, it’s essential to know not just where it’s located but also when it was put there. To figure this out, we used multiple sources of satellite imagery. 

Maass, at EFF, had previously analyzed some of the towers to determine when they first appeared. MIT Technology Review checked satellite imagery for these towers to confirm these timelines, and then checked for all the remaining towers. For each tower, we logged the date that it first appeared and, if it eventually disappeared, the last date it was present. We spent months meticulously checking when hundreds of towers appeared in multiple sources of satellite imagery to confirm there were no contradictions. 

This is easier for certain tower types than others; towers from Anduril, for example, tend to go up where there was no prior infrastructure, and it’s fairly easy to see how a patch of empty desert gave way to the new tower and its trademark solar panels. Other towers, like IFTs, were often built on or near existing structures. For these, we leaned more closely on EFF’s expertise in analyzing the imagery. 

When imagery wasn’t sufficient, we used Google Street View, government documents, or photographs collected by EFF to verify when towers went up.

One limitation is that historical imagery of towers along the border is not available for every day in the past. Especially for older towers or those in remote locations, images may have been taken months or even a year apart. A tower visible in May and again in December could theoretically have been removed and reinstalled in the intervening months. But most tower systems are permanent structures. And multiple Border Patrol agents told us that even towers that are meant to be mobile, like autonomous towers, are in practice very rarely taken down or moved (consistent with this, we observed that semipermanent fences were erected around the sites of many Anduril towers).

Another limitation is that even if a tower is visible at a particular time, that does not mean it was online. We can’t verify that it was functional, receiving power, or transmitting data. Our analysis therefore establishes when a tower was present, not whether it was operational.

Heights

We conducted a topographical analysis, detailed below, to see whether a tower’s view of a given person might have been blocked by the terrain. This required estimating how tall each tower is. 

We included a minimum and maximum height for each tower, based on research from EFF. In select cases where the surveillance system was not on a typical tower but mounted to an existing structure, like a water tower or building, we used satellite imagery to estimate its height. The higher a tower is, the farther it can see.

Our analysis

With the data we collected, we could begin to see which deaths happened in range of the virtual wall. But for a given match between a set of remains and a tower to count, it needed to satisfy two criteria:

1) Distance: The death must have happened within the published surveillance range of the nearby camera.

2) Timing: The tower had to be present when the person was estimated to have died.

VEXCEL

Distance

Minho Kim, an outside collaborator who is now a postdoctoral researcher at Stanford with a focus on wildfire mapping, led the distance analysis. His software ingested our database of human remains and our database of towers. Then it created a row for each time a set of remains was found near a given tower, along with the distance between the two.

We then checked whether, for each match, the tower was actually present when the person died.

Timing

Sometimes, as with the Arizona cases, an estimate of when someone died is given in the original data; the medical examiner may note that a person died more than three weeks but less than five weeks before remains were found, for example. 

In other cases, we used the level of decomposition, the cause of death, or both to calculate a window of time in which the person would likely have died. Finally, we compared this window with the time when we determined the tower was built. 

This step took us many months and involved reviewing original police reports for hundreds of cases, inspecting photos of bodies, cross-referencing cases with news articles, interviewing medical examiners, and researching decomposition timelines. The resulting death windows are approximate, but they are the best estimates we could make from the information available in each case.

We assigned different confidence levels to cases based on how these dates lined up. If a person’s entire estimated death window fell well after a tower was first observed, we felt confident the tower was present when they died. If any part of the death window fell before the tower was first observed, we excluded the case.

Many cases involve skeletal remains. Remains decompose differently depending on location and season, but when records didn’t provide a specific estimate, we conservatively estimated that skeletal remains had been there for at least six months. For these and other cases without a specific estimate of when the person died, we used three years as a conservative cutoff: If a tower had been present for at least three years before the remains were found, we included the case, judging it unlikely that the person had died before the tower was installed and remained undiscovered that long. If the tower had been present for less than three years, we excluded it.

Viewshed

It is possible that someone who died within range of a tower could have been blocked from the tower’s view by terrain, vegetation, or buildings.

We estimated the impact of this issue with a technique called topographical analysis.

Kim, our collaborator, led this part of the analysis. Here’s how it works: Imagine a map of the border region divided into a grid, with each square having one value for that area’s elevation. Take a given match between a set of remains and a tower and draw a line between the tower’s estimated height and an estimated height of 5’ 6″ for the person who died. If the terrain along that path rises high enough to block the line between the two points, we estimate the tower’s view as obstructed. If it doesn’t, we estimate the view as clear.

It’s not perfect. In the elevation data we used, each square represents an area 30 meters wide, or about 98 feet, and is assigned a single elevation. That means smaller changes in the terrain can be lost, particularly in steep or rugged areas where elevation changes quickly. Our analysis is therefore less about re-creating exactly what a tower could see than providing a rough estimate of whether terrain may have blocked a tower’s view of the spot where someone died. 

We include results on our map whether a clear line of sight was estimated or not. Even if the location where someone died was blocked from a tower’s view, the person may have passed through areas visible to the tower before reaching that spot.

It’s much harder to estimate whether a line of sight was blocked by vegetation. We are less concerned about this limitation because virtual-wall towers use thermal imaging, which can detect body heat through light vegetation. Dense vegetation, however, can still obstruct their view.

Buildings pose a greater challenge. We could not reliably account for their locations and heights. Instead, we reviewed each tower’s surroundings and flagged towers located in or near urban areas. These notes warn readers that buildings may have limited what those towers could see.

MANI ALBRECHT/U.S. CUSTOMS AND BORDER PROTECTION

Beyond the data 

The most fundamental question in each of these cases, and for the families of anyone who died, is whether the US government saw someone attempting to cross into the country and never intercepted them—or never saw them at all. 

This is all but impossible to answer with records or data—Border Patrol doesn’t disclose which alerts came in surrounding any given migrant death, and footage isn’t retained unless an internal investigation is triggered, which doesn’t happen unless a migrant dies while in custody.

MIT Technology Review instead conducted dozens of interviews with people who worked for Border Patrol, Customs and Border Protection, or the Department of Homeland Security or who served as presidential advisors. These conversations yielded previously unreported details about how agents use surveillance technology, turned up instances where it has underperformed, and underscored how seldom its effectiveness is evaluated internally.

We also sought out friends and family members of people who died near surveillance towers, using additional records, social media, and other research tools to find them. We approached these interviews using best practices for trauma-informed reporting.

Beginning in September 2025, MIT Technology Review requested interviews with current Border Patrol agents and leadership at CBP, and we asked to visit the border to understand the strengths and weaknesses of the latest autonomous tower program. Following delays, which were in part related to DHS funding lapses, in July 2026 we were told our media request had been approved at the local level in Big Bend, Texas. We wanted to visit this area because Border Patrol is expanding the number of Anduril towers there. But when the request went up to the DHS headquarters for a final sign-off, the agency denied it. Before publishing our investigation, we sent detailed questions to CBP, Anduril, General Dynamics and Elbit. CBP and Anduril responded, but did not substantively address our questions. General Dynamics referred us to CBP. Elbit did not respond to our questions.

MIT Technology Review thanks the following for their assistance with this project:

Minho Kim, Stanford University

Greg Hess, Pima County Office of the Medical Examiner 

Stephanie Leutert, University of Texas at Austin

Don White, Brooks County Sheriff’s Office

MIT Technology Review today published our investigation into how many people have died near the “virtual wall” of surveillance towers that the US government has installed along the US-Mexico border.

We found cases of people who walked undetected through areas surveilled by advanced, AI-enabled towers and later died nearby, where their bodies remained unnoticed for weeks or months. These cases reflect systemic flaws in the virtual wall: broken towers, algorithms that failed to detect people, and agents who simply did not respond to alerts.

Each of these deaths should have raised alarms inside US Customs and Border Protection (CBP) about the flaws in the agency’s surveillance network. This is particularly true for cases near the latest towers built by the defense company Anduril, which continues to win major contracts and is poised to benefit from the windfall of federal money slated for border security. The US is set to spend $1 billion to triple the size of the virtual wall by 2034.

Here are four ideas for what CBP should do next to address these failures. 

1. Conduct a comprehensive audit of deaths near the virtual wall

ERIC GAY/AP PHOTO

“If I were the federal government, I would do an audit, because this feels like it’s not working.” 

That was New Mexico state representative Sarah Silva’s reaction when MIT Technology Review showed her that in 2024 alone, more than 18 people had died near surveillance towers in just one small stretch of desert near her district.

An audit is necessary because, despite poring over thousands of pages of police reports and other documents about surveillance towers, we have only limited knowledge of how many towers are out there and where they’re located. 

CBP does not publish a comprehensive map of its virtual wall. We instead relied on mapping work led by the nonprofit Electronic Frontier Foundation and then used satellite imagery to verify where specific towers were and approximately when they were installed. 

We analyzed lots of towers—nearly 600. But that’s still far less than the 800 the government says operate today. And our estimates of when they went up are inexact. That means our tally of more than 1,050 people who have died near them is an undercount. And we don’t have a complete picture of where along the border the problem is worst.

An internal audit would not have such limitations. In theory, CBP tracks when each tower went up and when it was active. It has had the ability to audit their effectiveness—while quantifying how many people have died near them—since at least 2015, when it began installing the types of towers included in our analysis. But sources told us it has never done such an audit. Our findings make the need for one more urgent.

2. Fix the way the agency tracks migrant deaths and remains

If CBP is to analyze instances where people have died near its surveillance technology, it needs to know when and where those deaths happen. And the current system for tracking that data is beyond broken.

As we note in our methodology, there is no national agency that collects and publishes all available records on migrant deaths. We sifted through thousands of pages of records from dozens of agencies in Texas alone, and our picture is still incomplete owing to the lack of a shared protocol for documenting these deaths in public records. 

Border Patrol has had its own internal system, called the Border Safety Initiative Tracking System (BSITS), in place since 2007. But it wasn’t until 2021 that national rules were established for how different sectors should enter cases into the system. The next year, the Government Accountability Office (GAO) found that many cases still weren’t being tracked in this system, especially deaths that weren’t discovered by Border Patrol. In 2023, the GAO reported that Border Patrol has taken steps to fix that, but outside researchers consistently find the agency’s estimates to be undercounts even today.

In addition to tracking migrant deaths, CBP launched a program in 2017 to prevent them, called the Missing Migrant Program. In April 2025, the GAO found the program was improving the way it collected and tracked data about events like 911 calls and rescues. The problem, it said, was that there was no plan in place to use the data to measure whether the program was actually increasing rescues or reducing deaths, which are its primary goals. It made two recommendations for solutions, neither of which has been implemented.

3. Track cases when surveillance technology leads to an apprehension

In 2014, when the virtual wall was much smaller, the GAO suggested that Border Patrol agents log when a piece of technology helped them apprehend someone who was trying to enter the US illegally. It would take agents some extra time, but logging these assists would help Congress understand how often various surveillance technologies were leading to actual apprehensions. Once this data was collected, GAO added, the agency should measure whether its surveillance technologies were working. CBP agreed, and it started requiring agents to enter such information.

GLENN FAWCETT / CBP PHOTO

Then, in 2017, the GAO reported that the data being collected was inaccurate and unreliable; it found agents in Texas, for example, who were crediting a specific type of tower in apprehensions hundreds of times, even though no such towers existed anywhere in the state. Border Patrol again said it would improve; it agreed to train agents on the importance of collecting such information.

But years later, agents told us, it’s still not a priority. One former CBP official described the mandate as “spitting in the wind, to be honest,” and said the messy reality of apprehensions doesn’t translate into neat metrics. (The official, who requested anonymity, served during the Biden administration and wasn’t authorized to discuss sensitive issues.) 

And in August 2021, more than seven years after it agreed to do so, CBP still had not produced any analysis on the towers’ effectiveness (it finished one in 2022, the GAO notes, but the results are not public).

That means the virtual wall is growing faster than anyone is assessing its impact.

4. Investigate deaths as potential surveillance failures

CBP should also examine new deaths as they occur and determine whether they happened within sight of a tower or other surveillance technology. And for those that did, the agency should determine why those people weren’t spotted sooner.

Several agents and officials told us that if Border Patrol agents come across the body of someone presumed to have died while crossing into the US, there’s no requirement to check whether that person died near surveillance infrastructure and no process for doing so. At a minimum, that means potentially valuable evidence about the death goes unexamined. It also means that migrant deaths are not routinely treated as possible failures of the virtual wall.

CBP should automatically check every migrant death against its internal database of towers and investigate those that occurred within surveillance range to determine what the system detected and whether agents responded appropriately. Agents already log GPS coordinates when they apprehend a group or encounter footprints, among other events.

Without that check, law enforcement, medical investigators, and families may never learn that someone died near a surveillance tower—and that footage of the moments leading up to the death may exist. CBP typically deletes surveillance footage from the towers after 30 days unless an internal review is opened, which usually happens only for deaths in custody. At a minimum, flagging these cases could preserve footage for those investigating the death and give families a chance to request it.

Such reviews could also expose flaws in the virtual wall itself. Consider one location we identified in New Mexico, where several people died despite being in clear sight of an advanced AI tower. They died within the span of a few months, sometimes just hundreds of feet from one another. Investigating these cases might reveal, for example, that a camera repeatedly failed to detect or identify people in a particular area, or that agents were alerted and simply never responded.

Carrying out these postmortem inquiries would be the only way to answer the question that families, researchers, and volunteers raised most frequently in our investigation into the hundreds of migrants who died near towers: Did the cameras see them, and nobody came? Or did nobody see them at all?

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.

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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.

PHOTO COURTESY MIREYA MORALES

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.

CEDAR ATTANASIO/AP PHOTO

“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. 

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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.)

JAMES O’DONNELL/MITTR

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? 

MATT YORK/AP PHOTO

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. 

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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

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. 

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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.” 

CBP IMAGES

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). 

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. 

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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.

PHILIP CHEUNG/THE NEW YORK TIMES VIA REDUX

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.”

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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. 

JERRY GLASER/U.S. CUSTOMS AND BORDER PROTECTION

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.

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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.

She had only walked for a couple of hours, and already she was lost. 

It was early afternoon on Sept. 14, 2025, when 30-year-old Graciela Gómez Hernández crossed the border from the eastern edge of Tijuana into Southern California, sending voice messages to her mother and sister as she walked. 

Gómez Hernández did not tell her family about her plan until the day before. Once she arrived in California, she hoped to work and send money back to her three children and her parents in southern Mexico. Her oldest son, age 11, had asked for a bicycle.  

She either climbed a ladder over the border wall or slipped through a section in steep terrain where fencing has yet to be built. Dozens of game trails cut through the dust on hillsides of sagebrush and manzanita. It was the first segment of a hike through the Otay Mountain Wilderness. 

The eastern edge of San Diego is just a mile away, but countless migrants hike 10 or 20 miles north through the mountains to evade the Border Patrol, emerging in smaller towns and on distant highways with fewer checkpoints.

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Between 2 and 3 p.m., her sister said, Gómez Hernández sent a final message to her mother. “I can’t do it anymore,” she whispered into her phone. By the time a forensic crew came to collect her, two weeks later, most of her body had disappeared into the earth. 

She died on her own, but she was not, strictly speaking, alone. She was in one of the most heavily surveilled strips of land in the world.

Before she died, Gómez Hernández told her family in a WhatsApp message that she could see helicopters overhead and white Border Patrol trucks making periodic laps on the road below. And even if none of those agents saw her or knew she was in distress, something else had a clear view. Supervising the whole territory, from a nearby hilltop, was a slender gray surveillance tower. 

That tower, made by General Dynamics and in place since 2019, boasts two sets of electro-optical and infrared cameras with long-range lenses that can track targets 5 to 7 miles away. Gómez Hernández was just one mile away. 

Surveillance towers have proliferated in recent years, as contractors added newer camera systems that use artificial intelligence to distinguish border-crossers from other people and animals, and track their movements, at an overall cost of more than a billion dollars. 

Yet as the cameras scan the terrain, migrants continue to die in plain sight. 

Thousands of migrants have died while crossing the border in the past two decades, many of them near to Border Patrol surveillance installations. But the phenomenon is especially pronounced in the Otay Mountain Wilderness, just outside San Diego. 

A Times of San Diego analysis counted hundreds of cases along the California border—including at least 138 in the past four years—where migrants’ remains were found within the range of the very cameras designed to help track and intercept them. 

A topographical analysis conducted as part of an MIT Technology Review investigation estimated that more than half of these remains were found in a spot where a camera would have a clear view of a person, without terrain blocking the line of sight. 

Some of these migrants died swiftly, falling from the border wall or drowning in a canal or river. But dozens suffered slow deaths of exposure in the wilderness, deaths that might have been averted with emergency aid. 

People who died in Southern California came from just across the border, or from as far away as Africa. They were as old as 70 and as young as 5. They died in the heat or the cold. But they all died within range of a surveillance camera.

Found in July 2023: Marcia Dutra, a 36-year-old woman from Brazil who likely died of heat exposure next to a border fence. According to a medical examiner’s report, a group of migrants told the Border Patrol about her body when they were apprehended. An AI-equipped surveillance tower sits 2,000 feet northeast above the edge of a plateau, out of sight of the slope where she was found. 

Found in February 2024: Elvi Vasquez Bortolon, a 28-year-old Mexican man who died of suspected hypothermia on a ridge near the border wall. He had been dead for more than five days when a Border Patrol agent in a helicopter spotted his body from above. A remote video tower stands about 1,400 feet away, hidden from view by the ridge above the canyon. 

Found in August 2024: Ramón Montenegro, a 51-year-old man from Mexico who died of possible heatstroke in the mountains near Dulzura. An AI surveillance tower, invisible on the other side of a hill, sits 1,300 feet away — roughly the length of the parking lot at the San Diego Zoo. 

More than 120 surveillance towers stand along the California border, according to documents and live observations collected by the Electronic Frontier Foundation. 

The towers are not the only form of surveillance in the region. The border is blanketed by an interconnected network of trail cameras, underground movement detectors, radar, infrared sensors, drones, and even blimps. In March 2025, the Trump administration directed the Department of Defense to use satellites to watch the border as well. 

Even when mountains or vegetation block a tower’s view, other devices may pick up the trail. Local advocates say surveillance technology in the Otay Mountain Wilderness is almost unavoidable. 

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Officials say the towers particularly excel at tracking passing migrants. 

Anduril, the company that makes about half the towers now installed at the U.S.-Mexico border, said in a news release in 2024 that their towers “directly contributed to saving lives and stopping illicit drugs from entering the U.S.” In June 2026, the company said its installations had “autonomously identified hundreds of thousands of border crossings.”

John Morris, a Border Patrol sector chief in Arizona, told media at a convention in May 2026 that “We’re dang close to pretty much knowing everything that comes across.” 

The proximity of so many deaths to high-resolution cameras leaves two distinct possibilities: Either the system didn’t detect these people in distress, or it did detect them—and officials offered no response. 

Anduril, the defense contracting company that manufactures the AI-enabled towers, did not respond to questions from Times of San Diego about the towers’ performance in mountainous terrain.

A representative of General Dynamics, which made the tower closest to Gómez Hernández’s body, referred all questions to the Border Patrol. 

The Border Patrol did not respond to questions from Times of San Diego regarding whether surveillance towers spotted Gómez Hernández prior to her death, or about how surveillance cameras in general are used to detect people in emergency situations. 

Rafael Hernández Larraenza, the leader of a search and rescue group, Desert Angels, says he believes some of the towers are “just adornment,” and are not actually effective at tracking migrants. He also says that Border Patrol agents, in occasional meetings with his search group, “say they don’t have the opportunity to do searches, that they don’t have enough time.”

People in need of rescue “are not a priority,” said Bryce Peterson, who compiles research on migrant remains for the advocacy group No More Deaths. 

A wilderness, heavily watched  

From the spot where Graciela Gómez Hernández’s body was found, a surveillance tower stands in clear view. 

That tower is one mile away. A second, newer tower, on the other side of a hill, sits 1.4 miles away. Both are equipped with slow-spinning, long-range cameras. 

The closer of these two towers is situated between two layers of border fencing, a long pole, over 100 feet tall, reaching into the sky. It peers not only onto hillsides where migrants walk, but into the brightly-colored cinderblock homes of a Tijuana neighborhood. 

The second tower is much shorter, just 33 feet tall, surveying the canyon where Gómez Hernández would most likely have arrived if she had kept walking. 

This new tower, built by a company called Anduril, decides for itself whether a moving object in the field is relevant. The two dark, round cameras on the pole give the impression of eyes, rotating slowly, and then locking in on a target, before swiveling elsewhere.

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Osvaldo Ruiz, the volunteer coordinator for the Border Angels, a group that leaves water and supplies along migrant trails near San Diego, says the Otay Mountain wilderness has “beacons throughout the mountains” that “can see over a lot of the mountains facing the border.”

“At entry sites that migrants or travelers may come through, there are also hidden cameras inside of rocks or inside of bushes that are camouflaged for you not to see. And then there’s also the cameras that you see on the road. They have license plate readers that are stuck on the back of a safety cone, so (it detects) as you’re driving away from it.”

Surveillance towers have been in the area since 2011, with frequent new installations, including the introduction of AI-directed towers in 2018. Many of the AI-equipped towers currently overseeing the California borderlands appeared in 2021, and were updated with longer ranges in 2026.  

Officials from the Government Accountability Office wrote in a report in June 2026 that the Border Patrol currently has 803 fielded towers “and plans to deliver another 175,” across the southwestern border, an initiative that would cost $1.4 billion. The report also notes that the agency expects a billion dollars in funding from the One Big Beautiful Bill Act in 2025. By 2034, the number of towers is expected to rise to 2,300.

“What we are building along the southwest border is a smart system. There’s technology embedded in it, plus the physical infrastructure,” border czar Tom Homan said at a press conference in San Diego in December 2025. “It makes every single agent and officer more effective.”

A secretive hike north 

Like so many migrants, Graciela’s hike into the U.S. began in desperation. Her husband, who had already crossed the border to find work, had stopped sending money for the children. 

“There were moments when they didn’t have enough money even for a bag of detergent, or to eat,” her sister, Irma, told Times of San Diego. “She would call me crying.” 

Gómez Hernández decided she needed to come to the United States, if only temporarily, to make money. Her family was uniformly against the decision, so she kept her plan a secret until she was already at the border, preparing to cross. 

“She was a very humble person,” Irma said. “She was kind to people, and loving with her children.”

Gómez Hernández paid 20,000 pesos, about $1,150, for smugglers to help her cross the border, much lower than the current average, which sits at $12,000. But she would need to travel on her own without a guide, paying only for the knowledge of where and when to start, and which direction to walk. As part of their deal, her phone would send location pings back to the smugglers’ hiding spot in Tijuana. 

Cesar Ortigoza, a leader of a search group known as the Armadillos, has been locating migrant remains in the region since 2010. 

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“Most people don’t understand the magnitude of this territory, and all the work it’s going to cost them to arrive at their destination. They get to the top of this mountain and realize there’s miles and miles and miles left to walk,” Ortigoza said. 

In addition, they need to avoid capture. “They go really fast,” he said, “and there’s a lot of pressure.”

On the afternoon of September 14, 2025, Gómez Hernández told her mother and sister through WhatsApp voice messages that she had successfully crossed the border into the United States.

These short snippets of audio would be their only clues to what happened next. 

The hillside where Gómez Hernández walked that afternoon is exposed on all sides, with a panoramic view of the border wall just a couple hundred yards away. But on the other side of the hill is a daunting, mountainous wilderness, covered in low trees and rocky ravines, with hidden, winding trails, stretching into the distance. It is possible she saw this and turned around, convinced she was lost. Wandering the hillsides in the afternoon heat without sufficient water, she could succumb to dehydration within hours. 

“Because of the heat, there are places where you can’t move forward even half a mile in an hour,” said Larraenza, the search and rescue leader. 

By 2 p.m., “She told me she felt very tired and couldn’t go any further,” Irma said. “I told her to give it her all, to think of her children, keep them in her mind so she could get out of there.” Irma says 4 p.m. was the last time her sister’s location was transmitted to the smugglers. 

After hearing about Gómez Hernández’s case, Ortigoza said it “seemed strange,” because she was “so close to the border” at her last known location. 

But Bryce Peterson, the No More Deaths volunteer who collects autopsy reports on migrant remains border-wide, said “There’s a million ways that somebody could be already on the verge of death that close to the wall. Like if you’re going north and then you decide to go south again because you get lost or something.”

“The only thing we’re certain of is that she suffered in her last moments,” Ortigoza said. “Just knowing that you’re alone.”

Walking on the hillside, Gómez Hernández encountered a man also traversing the trails, lined with dry shrubs. “In one of the messages sent, where she told me to pray for her, the man spoke and said ‘there isn’t time,’” Irma said. 

After that, Graciela’s phone still received messages, but she did not respond. 

Cameras rise, questions remain 

Even though the federal surveillance tower was just a mile away, it is possible the cameras did not see Gómez Hernández. It is possible they did not see much of anything. 

An internal memo obtained by NBC News in October 2024 revealed that 30% of the older-style towers, the kind that require agents in control rooms to review footage, were broken. Most were missing parts, or their equipment was outdated and unusable. These 150 broken towers still stood at the border, acting as million-dollar scarecrows. 

Officials from the Government Accountability Office wrote in a report in September 2025 that the Customs and Border Protection had begun to fix the towers with spare parts and revamp them with new technology, but did not list how many remained out of commission. 

The Border Patrol did not respond to questions from Times of San Diego about whether the tower in view of where Gómez Hernández died was operational at the time of her death. 

Dave Maass, the director of investigations for the Electronic Frontier Foundation, the organization that maps surveillance towers at the border, questions the functionality of all types of towers. “None of these systems have been effective. There’s no independent evaluation of how many things they’re capturing,” he said. 

In a report published in March 2026, a representative of the US Military notes that a mission to place 7 G-BOSS(E) towers in the Otay Mountain Wilderness, an installation that took place in the months prior to Gómez Hernández’s death, faced operational challenges. 

The combat-grade military surveillance towers were vulnerable to tipping over, damage from rodents, exhaust blowouts, and running out of battery. On a dirt road in the mountains, a truck carrying a surveillance tower bounced around, which led to the tower’s cameras “becoming stuck in a zoomed-in position.” 

Despite descriptions from manufacturers claiming dominance in “rugged terrain,” Osvaldo Ruíz, the Border Angels coordinator, said it would still be difficult for a surveillance tower to track migrants moving through the Otay Mountain Wilderness. “With all that surveillance that’s out in the mountains, there’s still a lot of brush and high covered areas that would make it easy [to stay undercover].”

Analysts from the RAND Corporation, a foreign policy think tank, wrote in a report in 2020 that “outcomes and outputs” that would indicate the success of surveillance towers “are not directly observable.” 

The analysts conceded that the towers’ presence would urge migrants to stay out of the area and funnel into undetected routes. “The likely deterrent effect on migrant crossings through surveilled areas overwhelms any boost to situational awareness,” that the towers provide, the analysts wrote. In this way, the surveillance towers’ most useful quality was the threat conveyed through their technology, rather than the merit of the technology itself. 

Due to increased surveillance, “People are crossing through areas that are further out and more extreme, where we have very little hope of finding them in a search,” Larraenza said. 

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And though the manufacturer says the AI-enabled towers, which now make up over half of the total, can identify migrants on their own, apprehensions still require human labor. A CBP official told the RAND analysts that smugglers began sending migrants across the border alone, rather than in groups, to overwhelm the surveillance towers with many individual targets, leading to more alerts than agents capable of following them. 

So it is possible the towers in the vicinity did capture footage of Gómez Hernández, but agents decided not to apprehend her. Just hours before her death, she sent a message saying that there were “police” nearby, possibly referring to Border Patrol agents in trucks. 

“Even one person who’s not sick, one person who is just hiking on their own, often Border Patrol will just ignore them because it’s not worth their time to just only catch one person,” said Peterson of No More Deaths. “They want to be able to catch a whole group of people.”

Larraenza said he’s found dehydrated people on migrant trails who say they called out to Border Patrol, and the agents failed to stop. “But there are also cases where agents do the impossible to find someone,” he said. 

On one occasion in September 2024, while rescuing a family of five in the Otay Mountain Wilderness, Larraenza met a person wearing a vest labeled “SHERIFF” on the trail. In a video that Larraenza posted to his Facebook account, when asked if the Border Patrol would arrive to take the family into custody, the deputy says, “Oh, they don’t come out here.”

On a recent visit to the spot where Gómez Hernández died, multiple Border Patrol trucks were visible just hundreds of yards away, rolling slowly along the wall, or parked on the hill next to the surveillance tower. 

“If we hadn’t sent the Border Patrol the coordinates, they never would have bothered to go [find Gómez Hernández’s body],” Ortigoza said.

Finding Graciela 

Irma sent her sister a barrage of messages that afternoon and evening, pleading with her to send her location, but there was no response.

Irma believes it’s possible her sister was killed or abandoned by the other migrant she was traveling with during her final hours, because she was not walking fast enough. 

It took Irma two weeks to make contact with the coyotes. When she told the smugglers that her sister was missing, “they didn’t move a single finger,” she said. She threatened to send one coyote’s phone number to the police, until he sent her a screenshot of Graciela’s last known location. 

Almost three weeks after Graciela Gómez Hernández’s final message to her mother, on October 3, 2025, the Armadillo search group sent the last known GPS coordinates of her phone to the Border Patrol. 

The medical investigator’s report states that Gómez Hernández sent her location to her family, and her relatives sent the coordinates to a search group while she remained alive, but both Irma and Ortigoza said they did not know her location for weeks, and neither was interviewed for the examiner’s report. 

“On 10/04/2025 at 11:16 hours, I arrived on a dirt path approximately 100 yards north of the United States/Mexico International border. The area consisted of shrubs and dried brush with steep hills and declines,” Jennifer Wright, the medical examiner, wrote in an autopsy report. “Upon further review, I viewed the skeletonized remains of an apparent female lying supine in the dirt.”

The body had decomposed quickly with the help of the sun and animals, so it was impossible to tell whether Gómez Hernández was the victim of murder or dehydration. But the medical examiner noted that there was no water or food in the vicinity.

“In a pile next to the decedent was a purple bra, black pants, a black floral shirt, and a dark colored long sleeve shirt. The bra was hooked closed and intact,” Wright, the medical examiner, wrote in her report. “In the rear pants pockets were Mexican paper currency, Mexican coins, bobby pins, and tweezers.”

The body was naked, but it was unclear whether someone else removed Gómez Hernández’s clothes, or whether she took them off herself in the final throes of dehydration delirium, a common symptom immediately preceding organ failure. 

Several yards from the place where she died, Ortigoza found a backpack with a bottle of women’s spray deodorant, and a small glass container of perfume. 

“When you get to one of these sites, and you see the situation, the loneliness, the environment, you start wondering, what was this person thinking in their last moments?” Larraenza said. 

Once a DNA test confirmed the identity of the remains, Irma paid over $4,000 to send her sister’s bones home to Mexico, where the family held a funeral nine months after her death.

“Sometimes her oldest son, he just sits there thinking and when we ask what’s wrong, he says he misses his mom,” Irma said. Though the family is heartbroken, everyone has pitched in to care for Graciela’s children. “I don’t let my parents see me cry,” Irma said. 

The Border Patrol gave permission to Ortigoza and a group of searchers to place a cross next to where Gómez Hernández was discovered. On a recent trip to the memorial to leave flowers, Ortigoza played a voice message that Irma sent to her sister, the sound vibrating into the dirt of the hillside, where the imprint of Gómez Hernández’s body is still visible nearly a year after its removal.

ANNIE BARKER/TIMES OF SAN DIEGO/CATCHLIGHT LOCAL/REPORT FOR AMERICA

“I know that in the place you’re in, there isn’t suffering, like how you suffered here on this earth,” Irma said. “I know you left thinking about your children. I know you wanted to give them a better life. I feel you here, and I feel you alive.”

Cesar Ortigoza says every day he gets as many as five calls from people looking for their missing family members at the border. Though numbers of border crossings, and numbers of reported deaths, have decreased since Donald Trump took office in January 2025, “There are still a lot of people on the other side trying to get here. Because the problems in their countries persist,” Ortigoza said. 

In the first half of 2026, fewer people have consumed the water bottles, Gatorade and cans of food left by Border Angels volunteers in the Otay Mountain Wilderness. But supplies left on other routes by different groups have seen increased consumption, evidence that migration patterns are constantly evolving. 

“It’s hard for me to really understand how people are crossing, but I know they are,” Ruíz said.

Peterson, the No More Deaths map-maker, said more migrants could die in the coming months and years, as a result of increased surveillance. In the past, after surveillance towers were installed, migrants would try to go around them, leading to longer and more dangerous routes through stretches of remote wilderness. With the installation of hundreds of new towers, the spaces outside the range of detection have thinned, leading some migrants to attempt to go through surveilled territory, rather than around it. 

“We’ve seen people do things that to me seem terrifying or crazy, like going past an area where they know there are sensors and then trying to really quickly get to the next safe-ish place before Border Patrol shows up to get them, because it takes 15 minutes for them to show up,” Peterson said. “It seems like people aren’t stopping to drink water. People are going really fast, often running even, moving much more quickly, not being able to take the time to take care of themselves.”

Larraenza says for this same reason, migrants often choose to cross the border in the hottest weeks of the year, when they believe fewer agents will be on patrol, but there is a greater risk of death. In an interview in mid-September, Larraenza said at least two people had gone missing in the Otay Mountain wilderness in the past few weeks, and he had not yet been able to find their remains. 

Ortigoza says Gómez Hernández’s story is an example of a decades-long trend of people putting their lives at risk to cross the border out of necessity. The surveillance towers, whether or not they were functional, did not stop Gómez Hernández from crossing the border in that area, and did not stop her from dying. “If the riches of the world were distributed more equitably, we wouldn’t have to do things like this to reach a better life,” he said. 

While placing flowers in July at the site of Gómez Hernández’s death, Ortigoza noticed a Border Patrol truck parked under the surveillance tower on the hilltop a mile to the east. An agent stood on a power lift, working on the tower’s cameras. 

This year, agents replaced dozens of these cameras with new, AI models. 

Lillian Perlmutter is an investigative reporter covering immigration across California for Times of San Diego and NEWSWELL. Previously based in Mexico City, she has written for more than 25 outlets including the L.A. Times, Rolling Stone, The Guardian, and The New Republic.

On Wednesday, MIT Technology Review hosted a live Roundtables event for subscribers that asked the question everyone’s asking right now: Could AI really kill us all?

But attendees had so many more questions than we had time to answer in the 30 minute session. So we asked our senior AI editor Will Douglas Heaven and AI reporter Grace Huckins to round up some of the best questions attendees submitted and try their best to answer them.

Thanks to all who submitted questions!

Am I gonna die?

Yes, eventually. Unfortunately, my journalistic powers of prognostication aren’t powerful enough for me to tell you how. But it certainly could be because of AI. AI-powered drones have already killed people in Ukraine, and AI-driven cyberattacks on hospitals will surely claim victims before long. 

Could AI go even further, and kill all of us? Less likely. But some people—quirky people, but undeniably knowledgeable about AI—have been warning for years that this could happen. And while I’m not yet stockpiling canned food or trying to get in good with a bunker-owning megabillionaire, I have noticed that the doomers’ predictions about AI capabilities and alignment have, over the past couple of years, proved disconcertingly accurate. That certainly doesn’t mean that their more dire forecasts will come true, but it’s enough for me to sit up and take notice.

— Grace Huckins

Are you going to die because of AI? I’d say there’s a non-zero chance. Let’s say you’re unlucky enough to be the victim of a freakish near-future event or accident. Maybe it’s a cyberattack carried out by a swarm of AI agents on critical infrastructure. Sadly, a scenario like that now no longer feels as far-fetched as it once did. Or maybe a novel AI-designed pathogen cuts through the population. Or the world economy crashes, causing conflicts and famine. Both plausible, but I think less likely. 

Are we all going to die because of AI? Nope. There are no circumstances outside of apocalyptic science fiction in which AI could kill us all. You can spin up any number of scare stories, but they’re not grounded in present-day realities about what the tech can do or where it’s headed. 

Some people argue that there’s no harm in preparing for the worst, however wacky it might seem. Maybe. But I think such catastrophizing can make people excuse or overlook many of the more immediate problems with the existing technology and the companies building it. 

— Will Douglas Heaven

Why would AI kill us?

Someone might tell it to, and it might listen. That’s part of the reason researchers are so concerned about AI’s biological capabilities—imagine what Aum Shinrikyo, the doomsday cult behind the Tokyo subway sarin attack of 1995, would have done with a tool that could design a pathogen deadlier than Ebola and more transmissible than measles. Those of us who don’t want to die have to figure out how to defend against all plausible biological weapons, but our would-be attackers only have to manufacture one effective pathogen.

Then there’s the more exotic-sounding possibility that an AI could decide to kill us itself. There are various stories about how this might happen out there, but the most widespread involve AI systems that don’t hate people, necessarily—we are just an obstacle between them and the goals that we gave them.

Much as the OpenAI agents behind the Hugging Face hack compromised another site’s infrastructure to get a good score on a test, the idea is that some future, more powerful AI might get rid of us to prevent us from shutting it down—all in pursuit of some goal that we instructed it to go after. 

— Grace Huckins

How can we best ensure alignment so the worst doesn’t happen, and who is doing the best work to achieve it? 

Alignment is a huge area of research. In simple terms, it involves building models that behave in ways we want them to and not in ways we don’t. We need to trust agents better before handing over more autonomy. Alignment is supposed to establish that trust. But it’s hard. 

LLMs aren’t designed in the way other software is, where dos and don’ts can be hard-coded in. Instead, aligned behavior needs to be instilled when models are trained. One approach is to reward them for doing things you want them to (a little like raising a toddler, perhaps). Another approach involves giving an LLM a written list of rules it is supposed to follow (kind of like a constitution). 

Anthropic and OpenAI are both leaders in this field—and yet neither has been able to develop models that are fully aligned. A big problem is that LLMs are far more inconsistent and far less predictable than people. They can behave in one way in one situation and another way in a situation that to us seems very similar. They can also be swayed by unexpected constraints. For example, faced with an impossible task (as many of the agents involved in the Hugging Face hack were), models may try to do whatever it takes to achieve their goal. As Grace mentions above, that could be an issue.

The main reason top AI firms now say they want a slowdown is that they want to focus on cracking alignment. Alignment isn’t necessarily a pipe dream. But the jury’s out on whether full alignment will ever be feasible. 

— Will Douglas Heaven

Is AI really dangerous, or is this the tech companies drumming up PR?

This is always a reasonable thought when it comes to tech companies heading for an IPO—CEOs have an obvious incentive to make their products seem radical and transformative. But I’m not so sure it makes sense here. Telling the public that an already unpopular product could kill them and everyone they love is horrible corporate image management.

There are other stories you can tell about the CEOs’ motivations—maybe they want to cool down the public furor over data centers by portraying themselves as responsible stewards of a world-changing technology, or maybe they want to buy time to get their ducks in a row and prevent the next PR catastrophe. 

But there’s also a simpler explanation. Thinking that AI could bring about human extinction has been pretty common in San Francisco for a while, and these men are steeped in that milieu—as are their employees, many of whom signed an open letter in July urging their companies to work to make an AI slowdown possible.

— Grace Huckins

Part of the concern occurs when AI agents are allowed to act autonomously and with no supervision. What’s the issue preventing more control over these agents? 

This question goes to the heart of what we want this technology to be able to do. The trade-off between autonomy and control is tricky to get right because, on the one hand, a lot of the power of AI agents is that they can carry out tasks and solve problems without a human having to micromanage them. On the other hand, that requires you to trust that the unsupervised agents won’t run amok. 

What we’re seeing is that AI labs haven’t yet got this trade-off quite right. Their models are not trustworthy, they are not properly monitored, and they are not always under control. Figuring out how to fix that while still allowing for useful autonomous activity is one of the big research challenges of the moment.  

— Will Douglas Heaven

What steps can be taken now and in the near future to ensure that AI is controlled, monitored, and regulated effectively? 

That’s the million-dollar question. Whether or not you think AI could kill us, you can’t deny that it could do some real damage, because it already has—by driving people toward psychosis and by hacking websites, for example. Preventing that damage, or at least mitigating it, is hard for two reasons. 

The first is that we barely understand how AI works, and it’s quickly growing more powerful. There is lots of ongoing research about how to monitor and control misbehaving agents, but the current approaches are fragile. You can see if an agent discusses misbehaving in its “chain of thought,” the workspace where it plans its actions—but OpenAI’s newest agents don’t show their work in the same way as previous ones. And you can try to monitor agents with other agents, but that requires you to trust the monitor.

The other obstacle is more familiar. There’s a huge conflict of interest when AI companies regulate themselves, but the US government has thus far failed to step in, despite some bipartisan support in Congress for efforts to do so. The executive branch, for its part, seems stringently opposed for the time being. But if the winds do shift, I for one would appreciate some strong transparency regulations, so that we can get a fuller story the next time an unreleased frontier model mounts a cyberattack.

— Grace Huckins

If this dialogue makes it into web discourse, will it become a self-fulfilling prediction?  

That’s a real concern. LLMs are influenced by what they read. One theory for why chatbots so often talk about (and role-play) apocalyptic scenarios is that they have been trained on millions of pages of science fiction stories and doomer internet forums. All the text being produced right now, including this article, could in turn influence the behavior of future models. Extremely meta.

In fact, the team at METR, a third-party organization that OpenAI called in to help understand what happened in the lead-up to the Hugging Face hack, raised a related possibility in its report on the incident. METR used OpenAI’s new model Astra to help analyze the vast numbers of agent transcripts and behavior logs.

But feeding all that material to the model could have unintended consequences. There’s a good chance that the agents doing the analyzing were biased by the text produced by the agents they were analyzing. There’s no such thing as a clean slate anymore. 

— Will Douglas Heaven

With thanks to Eric, Pranab, Rafael, Kenneth, George, Chris, Yoon Jae, James, Carl, Nicole (and more!) for the fantastic questions.

In recent weeks, leaders of some of the biggest AI companies have warned that the very tech they are developing is dangerous. Last weekend, Anthropic CEO Dario Amodei argued that AI carries serious risk and that progress should be slowed. OpenAI CEO Sam Altman responded on X: “I agree with Dario that we need to pace the frontier.”

Those posts came a few days after the AI researcher Jacob Coxon announced that he was leaving a role at Anthropic, charging that neither it nor OpenAI (where he had also worked) was acting responsibly. “The people building AI earnestly believe that it could kill us all by the end of the decade,” he posted on X. Another Anthropic employee, Evan Hubinger, publicly agreed with him. “We really do earnestly believe AI could kill all humans!” he responded on X. “I personally think it is >10% within the next decade.”

One of the ways they fear AI might end us all is by somehow aiding the design, creation, and release of some kind of bioweapon. Let’s take a closer look at why.

A bioweapon might be a highly lethal virus that targets people according to their genes. It could be a fungus that wipes out a crop and causes food insecurity. Perhaps it would be a tasteless, odorless toxin that could be slipped into a region’s water supply, undetected.

The concern is that AI tools can be used to help generate agents like these. In 2022, researchers at Collaborations Pharmaceuticals found that it was remarkably easy to do so using an AI “molecule generator” they’d developed to find potential drugs for human disease. In less than six hours, the model generated 40,000 molecules with the potential to serve as chemical warfare agents. Some of them were designed to be even more toxic than known nerve agents. “Without being overly alarmist, this should serve as a wake-up call for our colleagues in the ‘AI in drug discovery’ community,” the authors wrote at the time.

It was a wake-up call for David Magnus, a professor of medicine and biomedical ethics at Stanford University, even though he had been assessing the risks associated with the misuse of medical science and biotechnology since the late 1990s. “That was very scary to me,” he says. “Of course, everything since then has just sort of blown up.”

Today, AI bots can answer questions on topics spanning all realms of science. Anyone can use large language models trained on the knowledge and experience of “almost every scientist who ever lived on this planet,” says Dunja Sabra, a biosecurity researcher at the University of Hamburg in Germany. Those models can provide instructions and video training on how to conduct experiments.

Combine that with advances in biotech that have made gene editing and synthetic biology tools much more accessible (the “DIY biology” movement has already enabled many people to set up labs at home), and you’ve got a potentially very dangerous situation. “The chances are that someone determined would succeed eventually,” Sabra says.

There are safeguards in place. People who want to build new genomes must typically order the pieces of DNA from companies that screen for suspicious requests. Responsible researchers put potentially risky research through rounds of analysis called “red-teaming,” in which independent scientists look for ways the work might be misused, and “blue-teaming,” where others come up with potential mitigations. And AI companies have tweaked their tools in attempts to prevent them from offering up scientific information that could be misused. But none of these protections are ironclad.

In a report published last week, Anthropic acknowledged that people had attempted to use its models to explore ways to make the chikungunya virus more transmissible, create a form of bird flu that is more dangerous to humans, and build an “atlas of venom toxin peptides,” among other things.

“We’ve got a constant back and forth,” says Magnus. “We have to build better surveillance and screening tools, [but] AI is really good at figuring out ways around them.” We’ll probably need to use AI to find ways to restrict the use of AI, he says.

I should add here that not all scientists agree on the level of risk. At a recent media briefing, some biologists at Imperial College London argued that AI tools just aren’t good enough to fully develop bioweapons, and that testing new pathogens requires difficult, time-consuming, human work. Some think the guardrails we have in place are sufficient.

And Wendy Barclay, a professor of infectious disease at Imperial, pointed out that, as things stand, the greatest risk of a pandemic isn’t from a bioweapon, but from pathogens that are already circulating. Take H5N1, the bird flu virus that has already killed millions of birds and spread widely through US dairy cattle; last month it was also detected in captive mink at a farm in Utah.

Sabra, on the other hand, likes to think five to 10 years ahead. Countries should be strengthening their health-care systems, preparing antidotes to known toxins, and stockpiling medicines, she says: “We need to be prepared.”

Kevin Esvelt, an MIT biologist who invented both technology to fast-track the propagation of a genetic feature through an entire population and ways to limit that technology, echoed these concerns in an X post on Wednesday, stating that a large language model had “disclosed a novel form of bioweapon that I hadn’t realized was possible.”

He added, “Please, for the love of God, children, the future of humanity, or whatever you consider holy, let’s err on the side of caution here.”

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

Each year, the editorial team at MIT Technology Review puts together a list of 35 innovators under 35—a group of researchers, inventors, and other young minds worth following.

The team worked on the newest edition of the list for months, and the final slate includes nine individuals from all over the world in the climate and energy category. Each one has a fascinating story and is tackling an important challenge.

I think it’s worth zooming out and considering the energy and climate awardees as a group. Taken together, these innovators and their work can tell us something about where climate tech is at this moment—and where it’s heading.

AI is the dominant technology story, both for its potential and its challenges.

We split the innovators into four main categories this year: biotech, climate and energy, computing and robotics, and AI. It probably won’t surprise you that AI features heavily in the work of many innovators in other categories.

Climate innovator Jae-Won Chung, for example, built software to make AI more energy-efficient. By measuring the energy demands of open-source models, he hopes the industry can better understand and address the impact of AI. (If this work sounds familiar, it’s because we spoke with him last year for our investigation into AI’s energy demands.)

But AI also has the potential to improve many areas of research. Jing Wei is using AI to track pollution more effectively, essentially using machine learning to fill in gaps in data from disparate sources like satellites and weather stations. Zhonghua Zheng developed AI climate models that work better for cities, a well-known blind spot for traditional models.

We need better ways to get the critical materials used to build new technologies.

As we begin to rely on new technologies to power our world, we’ll see a major shift in the materials we need to build them.

Lithium is a prime example: The metal underpins lithium-ion batteries, which are crucial not only for electric vehicles, but also for large-scale energy storage on the grid. We could face lithium shortages as soon as this decade, and the prospect of supply crunches applies to other critical minerals, too—copper is another one to watch closely.

Brine is currently the cheapest source of lithium, but the process to get the metal out can take months and harm the local environment. Mohammad Alkhadra is the cofounder and CEO of Lithios, a startup working to quickly and efficiently extract lithium from brines.

Hardrock ore is the most common source of lithium, but it’s more expensive than brine. Benjamin Mowbray cofounded and serves as CTO for Rock Zero, which is working to extract lithium from hardrock ore.

Addressing climate change will require overhauling all corners of our society, sometimes in surprising ways.

To reach net-zero greenhouse gas emissions we will obviously need to rethink major sectors, like the electrical grid and transportation, to move away from fossil fuels. But outside these primary sources of climate pollution are seemingly infinite, less obvious problems to figure out, too.

Heavy industry, including steel production, is a major one, making up about 7% of global greenhouse gas emissions. Laureen Meroueh is making cleaner, cheaper steel using a new kind of furnace that simplifies the chemical process required to produce the metal.

Plastics are generally made with fossil fuels, so we’ll need alternatives to this incredibly useful category of materials. Joseph Nguthiru is making a bioplastic replacement for fossil-derived packaging that uses an invasive weed. Also using available materials in a creative way, Diana Orembe is making fish food for aquaculture with food waste.

And refrigerants are often incredibly powerful greenhouse gases. Jinyoung Seo is developing solid refrigerants that could eliminate worries about leakage. A device using these materials could reduce energy consumption by 20% compared to conventional technology.

I’m constantly learning about new challenges we face in the climate and energy world, and I’m often surprised by the ideas people are coming up with to address them. For more on all the under-35 innovators and their work, check out our full 2026 list.  

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

Multiple cameras tracked a mouse as it wandered around a small arena. A computer charted its position and speed, leaving Pong-like traces on a monitor. 

The reason to watch this rodent so carefully? Nearly half its brain volume had been replaced with human cells.

The effort to mix the brain tissues of distant species is being reported today in the journal Nature by a team at Stanford University, led by neuroscientist Sergiu Pașca. 

Pașca’s group previously showed that human brain “organoids”—small blobs of neural tissue—could survive, and even function, after being injected into the heads of baby rodents.

Now, Pașca has taken things a step further by genetically modifying mice so their brains don’t fully develop in the first place. These modified mice are missing most cells of both the cortex and the hippocampus, two key brain areas.

That creates much more room for the human cells to take hold, he says. “Human cells that are placed in these animals will divide, will grow, and within a few weeks to a few months they will take most of that space,” he says. Pașca says one surprising discovery is that the mice lacking brain tissue seemed fairly normal—they walked around and squeaked. But they did have memory problems. In a maze test, they couldn’t remember what parts they’d explored. 

The mice with the added human cells, by contrast, performed better on the maze test. That means the human tissue is playing some role in the animals’ cognition.

Pașca believes what he is calling “xenocortical mice” could be useful in studying brain injuries. However, the report is also a dramatic demonstration of “the combined power of genetic engineering and stem-cell technology to reshape biology,” says Carsten Charlesworth, a scientist who works in a different Stanford lab and was not involved in the research.

Already, brain organoids are being tested in labs to see if they can be connected to computers to play video games. Other scientists have proposed using them like replacement parts to treat stroke victims. 

“What’s most remarkable to me is the extent to which human neural tissue introduced after birth grew and connected with the mouse nervous system across a species barrier,” says Charlesworth. “As these technologies advance, they’ll increasingly force us to challenge our traditional assumptions.”

Last year, Pașca convened a group of ethics experts to study the implications of neural organoid technology, including the odds that an animal could develop human consciousness and the risk that “organoid therapy clinics” might offer scam treatments to desperate patients.

For now, he says, he’s not concerned that the rodents have any type of human cognitive capacities. That is because their brains are relatively tiny and the evolutionary distance between man and mouse is so great. 

But that’s also why Pașca says this type of experiment should not be carried out on higher species: They could end up with large volumes of functioning human brain tissue, potentially blurring the cognitive boundaries between people and animals. 

Pașca specifically cautioned against adding human brain organoids to a monkey engineered to lack a cortex.

“One of the things that I see as a very clear red line is doing this experiment in a primate,” he says. “I don’t think that is justified at this point in any way.”

Last year Ruxandra Teslo, a policy analyst who focuses on clinical trials, posted an idea for supercharging medical AI systems: Use data from failed biotech companies.

By bidding at their bankruptcy proceedings, she proposed, it might be possible to obtain detailed regulatory filings, manufacturing strategies, and safety data—types of information usually considered trade secrets. She called these documents “biotech’s lost archive” and said they could be used to help train AIs that would act as powerful copilots in the often opaque drug approval process. 

Today the OpenAI Foundation, the nonprofit parent of OpenAI, said it would fund her idea as part of a new effort it calls Public Data for Health, which aims to help artificial intelligence make big leaps in medicine by paying to create “high-quality scientific datasets.”

The basic idea is that AI isn’t going to be capable of making important breakthroughs in curing disease unless researchers can feed the models much more information than they have so far. 

“Everyone is recognizing that data is the biggest bottleneck in successfully applying AI to biology,” says Morgan Levine, a former vice president for computation at Altos Labs, a longevity company.

In its initial round of data grants, the OpenAI Foundation also announced that it would give $40 million to a program to collect data about novel cancer vaccines at the University of North Carolina, Chapel Hill, and support OpenAdmet, a group that runs competitions in which researchers try to predict drug effects. 

Teslo’s idea for a biotech archive received $500,000 and will be pursued by 1Day Sooner, an advocacy group representing clinical trial volunteers, which she advises.

“We expect many remaining breakthroughs in preventing and curing disease to come from pairing the intelligence of new models with more observations of the world—in other words, more data,” the OpenAI Foundation said in a statement.

OpenAI started as a nonprofit, but leader Sam Altman restructured it to form a for-profit corporation that develops new models, launches products, and is now planning an initial public offering of stock that could value it at $1 trillion.

Because the foundation holds a 26% equity stake in OpenAI, it is now be on track to become the richest charitable organization on the planet, potentially sitting on $250 billion in stock value. (By comparison, the Gates Foundation and a trust associated with it held about $180 billion at the end of 2025.)  

Making good use of that kind of money will not be easy. The foundation, based in San Francisco, is still hiring for many key roles and started ramping up its grantmaking only this year. Its largest single gift so far, of $100 million, was awarded in August to the Common Health Coalition, an organization that helps patients get access to drugs for hepatitis C.

OpenAI’s charitable efforts come even as apocalyptic fears have broken out about the possibility that runaway AI could wipe out all human life, possibly by launching a deadly bioweapon.

Those fears have been stoked by AI company insiders, some of whom say the chance of human extinction within the next decade is 10% or more. Last week, Altman and xAI founder Elon Musk both endorsed a call by Anthropic CEO Dario Amodei to “slow the pace at which we improve the capabilities of AI models” so that risk prevention can catch up.

Jacob Trefethen, an executive at the foundation, says it essentially operates separately from OpenAI but shares an official mission of ensuring that artificial intelligence “benefits all of humanity.”

“We’re starting grantmaking when we think the best way to achieve that mission is to make grants to external nonprofits, research institutions, and other third parties,” Trefethen said in an interview. He says the foundation hopes to give away $1 billion by the end of the year. 

The $500,000 grant to 1Day Sooner will help the group prove it can obtain the data troves of bankrupt companies, says the organization’s president and cofounder, Josh Morrison. He thinks nonexclusive copies of company datasets could be acquired for only “a few tens of thousands of dollars” each.

His organization is currently in possession of three datasets, two of them donated by Lumen Bioscience, a biotech that previously used the Chapter 11 strategy to gain insights into another company’s drug development efforts. 

Morrison says two other attempts to obtain drug company files this year proved unsuccessful, after 1Day Sooner’s bids were not accepted. 

Bankruptcies could become what some are calling a “new land grab” for AI training. Last month, Google won a bid to take over the corporate data of the failed carrier Spirit Airlines, including 100 million emails. That led to objections from flight attendants and others who worried that private or proprietary data could be exposed. 

The drug company files that 1Day Sooner is seeking are known as common technical documents. They typically contain the back-and-forth between companies and regulators, as well as detailed scientific and medical measurements, and essentially provide everything that is known about a drug.

According to Teslo, who is a writer for Works In Progress and a nonresident fellow at the Institute for Progress, a think tank in Washington, DC, a stockpile of such files could help turn an AI into a regulatory expert, which in her view could be one of the main ways AI helps speed cures to market.

“People say ‘We will invent AI, and AI will cure cancer,’ but that’s very removed from the messy reality and the regulatory process,” she says. “About 70% of the money and time in drug development is spent in clinical development—organizing the trials and testing the drug—but despite that, the process is basically a black box, especially for small biotech companies generating the innovations.” 

When Jessica Wachter, a finance professor at the University of Pennsylvania’s Wharton School, wanted to assess AI’s impact on the economy over the next few years, she faced a long list of business and technical uncertainties. So she started with what she calls a “remarkable fact” that is not in question: A handful of so-called hyperscalers are investing huge amounts of money to build AI data centers.

Instead of trying to predict how useful and widely deployed AI models will be, she simply asked how fast the hyperscalers’ earnings will need to grow to justify their spending through 2027, when—she and her collaborator estimate—expenditures will reach nearly $1.1 trillion. It’s a no-nonsense accounting approach to making sense of today’s historical AI buildout.

The results are eye-opening: The AI companies will need to increase their own productivity by a factor of 2.7 to break even by 2030, accounting for the cost of capital and a 15% return, and depreciation of the assets. Not impossible, says Wachter. The result would lead to the kind of economic growth that we saw during the US IT boom over a period of about 10 years starting in the mid-1990s. But, she says, for it to happen by 2030 “that’s a lot of growth compressed into a few years.” And if the hyperscalers cannot meet such profit goals?

“Then they will fall behind on their interest payments, and that risks bankruptcy,” says Wachter, who was previously the SEC’s chief economist and director of its division of economic and risk analysis. If a productivity boom “fails to materialize,” she and her coauthor conclude in their research paper, “the current buildout will be the largest misallocation of capital in history.”  

It doesn’t take superintelligence to realize that today’s large investments in the infrastructure for artificial intelligence come with huge risks. The hyperscalers will spend about $750 billion this year, building massive data centers scattered across the country. And the spending spree shows no signs of slowing. According to some projections, total AI capital investments from the hyperscaler companies—Alphabet, Microsoft, Amazon, Meta, and Oracle (which partners with OpenAI)—could be more than $5 trillion over the next four years.

It’s one of the largest capital investments by any industry in history. But there’s a problem that’s obvious to anyone paying attention.

While the hyperscalers plan to spend trillions, total AI revenues will be around $150 billion to $200 billion this year, says Gary Gensler, who ran the SEC during the Biden administration and is now a professor at MIT’s Sloan School. “The challenge is that the spending does not have commensurate revenues yet. That’s a fact,” he says. “And then the question is, is that an investment that will be paid off in the future?”

At stake in that trillion-dollar question is the financial health of the giant AI companies and the overall US economy—the investments could soon balloon to around 3% of GDP. The answer could also determine the fate of the hugely expensive data centers themselves. 

No one really knows how profitable and useful these multibillion-dollar behemoths will be down the road. Though AI models have made dazzling progress over the last few years, it’s anyone’s guess how much compute capacity we will need. The technology could become more efficient and therefore less dependent on raw computational power. Or demand for AI products could slow, or customers could turn to cheaper models.

The risks, both to investors and to the economy, have become even greater this year, as these AI companies have begun borrowing large amounts of money to build more and more data centers. Free cash flow—operating cash flow minus capital expenditures—is expected to soon dip into negative territory for the group. Even Alphabet, known for generating and hoarding huge amounts of cash, reports in the latest quarter that its impressive revenues of nearly $120 billion were devoured by AI infrastructure spending, leaving it with a free cash deficit of some $5.9 billion—its first shortfall since Google went public in 2004.

In the near term, it’s not a big financial worry for most of the companies. They make a lot of money and have very deep pockets. But debt is expensive, and some investors are losing patience. If future demand for the data centers’ computation power drops, the companies will still be on the hook to pay back the borrowed money. What’s more, the risks are spreading to the rest of the economy as the loans get passed along via various financial mechanisms. 

It won’t be enough to simply cover the enormous price tags of the new data centers. Hyperscalers will also have to pay for the rising costs of capital as they borrow more money. They will need returns that are impressive enough to justify all their spending to investors and creditors. And to add to those concerns, they will have to make up for the depreciation of billions of dollars in chips housed within the facilities—a ticking time bomb buried in the investments.

Performance of the expensive GPU chips at the core of the data centers—such compute electronics represent some 60% of costs—is roughly doubling every two years or so. The pace of progress helps explain the increasing wizardry of the AI models, but it comes with a cost. Owners of AI data centers that come online this year and next will need to spend billions more on the next generation of chips by the end of the decade if they want to stay competitive. Without the investments, says Mihir Kshirsagar at Princeton’s Center for Information Technology Policy, the data centers risk becoming “hulks,” stranded assets “scattered all over the place.”

To put it bluntly: The AI companies need to start making a lot more money. And they need to do it fast. But juicing their earnings alone still won’t be enough to sustain their data-center investments for the long term.

Productivity is everything

At some point, AI is also going to have to create broad economic growth to justify continuing the hyperscalers’ spending spree.

Sloan’s Gensler describes today’s large investments into AI infrastructure as “a parlay bet by the capital markets and the economy.” That means success will require winning three related but independent wagers: Hyperscalers must generate massive revenues, AI must boost widespread economic growth, and both must happen while the powerful but expensive so-called frontier models that rely on the data centers fend off cheaper versions, which many businesses might find good enough.

What makes this so tricky is that each wager depends on the other two but also poses its own challenges.

If the hyperscalers continue to spend huge amounts of money on data centers into the next decade, revenues will need to skyrocket into the trillions. Stijn Van Nieuwerburgh, a finance professor at Columbia Business School, bases his estimates on a scenario in which about 183 gigawatts of planned AI compute capacity is built between 2025 and 2032; he calculates that each gigawatt costs about $41 billion. Assuming a 10% return—the minimum that would be acceptable to most investors—“required” annual revenues will be roughly $3.7 trillion by 2032, he says.

Others get a similar number.

Winning the second part of the bet—productivity growth across the economy—will be crucial to achieving such numbers.

For a few years, AI companies could likely boost their revenues by simply selling subscriptions and tokens to all the businesses clamoring to get into AI. But eventually—and this might be happening already—those paying customers will need to justify their expenses by seeing bottom-line benefits from the technology. AI will need to fulfill its promise of making workers more productive and making businesses more efficient and profitable while expanding their products and services.

In economic jargon, that means customers will need to see productivity growth. Taken together, these results will mean the country is prospering and growing.

“If you don’t get the productivity gains, at some point people are going to sour on AI, and that will bring down investments and it would also limit revenue growth,” says Daron Acemoglu, an MIT economist and 2024 Nobel laureate. For the investments to be sustainable over, say, the next five to 10 years, we definitely “need to see productivity gains,” he says.

Most economists who watch the numbers closely agree that, for now, the economy-wide statistics show little or no productivity growth from AI. There are some hopeful signs it’s on the way, though. In a recent survey of some 6,000 senior business executives in the US, the UK, Germany, and Australia, the vast majority—around 90%—report no increase in productivity over the last three years. But they expect a boost of around 1.45% in total over the next three years; US executives anticipate a 2.25% bump over that time. 

In a follow-up survey, the respondents also reported plans for their businesses to spend more on AI, leading the authors to anticipate some $280 billion in private-sector AI expenditures by the end of 2026.

That’s good news for the hyperscalers. But it comes with a dose of bad news for those worried about AI’s impact on jobs. The executives expect to increase the productivity of their companies by increasing their sales while significantly cutting the number of employees.

If AI improves productivity by destroying jobs, public backlash to the technology—the kind we have seen around data centers, for example—will likely get worse. Perhaps it’s worth adding one more wager to the parlay bet described by Gensler: The public and local communities must feel that they are also benefiting from the massive investments in AI.

And let’s not forget how interdependent these wagers are; if productivity growth comes from companies running models like DeepSeek, then the hyperscalers’ revenues could collapse. If productivity comes from cutting jobs, a public backlash could block many of the planned investments—and stunt anticipated revenues. We will need to win all the wagers for the hyperscalers’ bet to pay off. 

We’re all part of the AI gamble now

It was one thing when the AI companies were spending cash they had accumulated over the years to build their own data centers. Then the risk was largely limited to their own balance sheets and shareholders. But it’s a higher-stakes game when much of the money is borrowed. Morgan Stanley, for one, calculates that more than half of the $2.9 trillion that hyperscalers will spend between 2025 and 2028 to build AI data centers will be financed with “external capital.”

The borrowing is leading some of the companies to engineer complex webs of financing that are becoming intertwined with much of the rest of the economy. “A lot of financial institutions, directly or indirectly, are exposed to these data centers either as lenders, or as guarantors of some of the debt, or as backers of the private credit funds who are funding these data centers,” says Columbia’s Van Nieuwerburgh. “People don’t even know they’re holding this stuff. It’s somewhere deep inside their pension fund. Ultimately, it’s backing their life insurance policies. And that risk is getting distributed everywhere in places that are invisible.”

As the investments in data centers have spiked, the financial engineering has become more byzantine.

Take, for example, Meta’s so-called Hyperion data center under construction in Richland, Louisiana. When the company announced the two gigawatts of compute capacity at a price tag of some $10 billion in late 2024 it was Meta’s largest planned data center. Greeted with much enthusiasm by state and local politicians, the project, located in the rural northeast corner of the state, was seen as a boon to the community. Entergy Louisiana, the state’s largest utility, rushed forward with proposals to build three large natural-gas power plants to service the massive data center.

Then last fall—the projected cost was now $30 billion—the financing got a lot more complex and, to some in the community, a lot more disconcerting. Meta transferred an 80% stake to the large (and troubled) private-credit firm Blue Owl Capital, forming a joint venture called Beignet (like the famed New Orleans pastry) to raise financing for the data center. Meta then signed a series of four-year leases with the joint venture, an arrangement that the company says gives it “long-term strategic flexibility.” To backstop the agreement, Meta provides the venture with what is called a residual value guarantee, in which it will make a cash payment to cover the value of the facility “following any non-renewal or termination of a lease.” Got all that? 

I hope so. The financial wheeling and dealing is actually even more convoluted, with a cast of wholly owned subsidiaries and LLCs. Beignet has set up Laidley LLC, which owns and operates the site as the landlord. In turn, Laidley leases the facilities to Meta’s wholly owned subsidiary Pelican Leap LLC, which is the tenant. And there is a series of four-year leases that cover the different buildings that make up the data center campus. 

It’s not a coincidence, says Van Nieuwerburgh, that the length of the leases matches the expected lifetime of the data center’s GPUs. While Meta has to pay off its loan if it terminates the leases early, that will still leave its investors “with an empty building and no cash flow,” he says. “And then they need to find a new tenant for a huge data center, and good luck with that.”

Meanwhile, Meta is doubling down on its bet. In July, the company announced it was expanding the data center to five gigawatts of compute capacity. The total price tag is now $50 billion (so far, Meta hasn’t said whether Blue Owl will be involved in financing the expansion). Meanwhile, Entergy is now planning to build seven more gas-fired power plants, bringing the total capacity of the facilities to around 7.5  gigawatts—some six times the amount of electricity used by New Orleans.

SCOTT BALL/THE NEW YORK TIMES VIA REDUX PICTURES

If the complex financing is a puzzle to many investors and even financial experts, it is even more baffling to those directly affected by the construction of the data center. The main worry concerns how Entergy’s spending on the natural-gas power plants will affect electricity prices, and who will be left paying the bill for the power if Meta walks away.

Entergy says it has a 20-year guarantee from Meta that the company will purchase electricity over that period to cover the costs of the power plants and related infrastructure.  But there are skeptics, especially given how fast the fortunes of the AI industry are changing. “In four years, is Mark Zuckerberg still going to be interested in this? Or is he going to throw in the towel?” asks Paul Arbaje, a senior analyst at the Union of Concerned Scientists, which has been advocating, largely unsuccessfully, for the Louisiana Public Service Commission to provide more transparency around the data center and its financing.

Even if the 20-year deal holds, consumer advocates are worried that Meta or its partners won’t fully cover all the costs, including those associated with operating and maintaining the power plants—and those additional costs that could be passed on to residential ratepayers. What’s more, says Logan Burke, the executive director of the Alliance for Affordable Energy, if Meta doesn’t end up needing as much power as Entergy planned (these projections are not public), consumers could be left paying for the surplus produced by the plants.

And if Meta terminates its leases early? “It gets complicated very quickly,” says Burke, who questions whether the shifting roster of financial entities will honor existing agreements. “That everybody is going to do what they’re saying they’re going to do over the next 20 years is just hard to believe.”

For UCS’s Arbaje the bottom line is this: “They’re making huge bets that these data centers will be worth it. Bet with your own money, not with ratepayer money.”

After the bubble

Predicting when the AI investment bubble will burst is a fool’s errand. But there is little doubt a day of reckoning is coming, given the irrational exuberance that has overtaken the hyperscalers and their investors. Of course, you might argue that this time is different, and that the rules of accounting and lessons of economic history don’t apply—that AI is too transformative. Maybe, but don’t count on it.

“History tells us that at some point you get a retrenchment, and it’s just a question of when and how severe,” says Sloan’s Gensler. It could be that today’s $750 billion spending rate “goes flat” or decreases next year. Or, he suggests, “we’re now in 2028 or 2029, and then all of sudden they’re retrenching because they’ve got enough capacity.” But, he adds, “you can be pretty assured there’ll be a retrenchment.” 

Though a so-called retrenchment might be inevitable, it’s worth keeping in mind that the fates of the financial bubble and the underlying AI technology revolution could be very different. Already, some Silicon Valley insiders are rooting for a crash; in a recent blog post the longtime venture capitalist Vijay Pande wrote that “the coming crash would be the best thing that happens to this technology.” The argument makes some sense. A crash could make AI investments more rational, calm the impulse to build billion-dollar data centers on every vacant field that CEOs fly over, and refocus investors on how to use the technology to create sustainable value.

But we should probably be careful what we wish for. After the bursting of the dot-com bubble at the beginning of the 2000s, hundreds of thousands lost their jobs, large and small companies alike went bankrupt, the economy of Silicon Valley and San Francisco was decimated (at least for a while), and the shocks sent the US into a mild recession in 2001. For the financial community and many tech workers, it was no fun.

Even more devastating for the economy and the average American was the great recession that began in late 2007. Comparing the financial engineering leading up to it and the methods deployed by hyperscalers today is sobering. So-called special purpose vehicles (SPVs) are back! If Columbia’s Van Nieuwerburgh is right about the dangers of letting investments from the hyperscalers get entangled throughout the economy, the fallout could be severe.

But technologies survived and even prospered in the aftermath of both downturns. The early 2000s, even in the face of the dot-com fiasco, were a time of great innovation and tech optimism. The froth came off the spending on silly technologies, helping to focus investments on more promising ones. It’s no coincidence that each of the hyperscalers rose out of the ashes of the crash or started up shortly after. The fiber-optic infrastructure built during the feverish telecom bubble that ran parallel to the dot-com one is still the backbone of much of today’s communication infrastructure; we wouldn’t have Facebook or Amazon or Google without it.

This time, however, we’re facing a unique risk: The huge financial investments by the hyperscalers have ensnared the future of AI itself with the fortunes of the massive data centers spreading around the country. The logic is founded on a deeply held belief about the power of scaling in AI; the bigger you build it, the smarter it gets. That might be true, but it’s unproven and a risky bet.

There are already plenty of red flags, from strong public opposition to the construction of new data centers to the competitive threat from cheaper, good-enough AI models to the rapid improvement of small, local AI models. None of these trends point toward a future dominated by frontier models housed in massive, billion-dollar data centers.

The financial bubble around the colossal spending by the hyperscalers will likely burst eventually—or maybe soon. It might be financially painful, but we’ll survive. Wall Street will survive. AI itself will survive, though it may look different and lose some of today’s hubris. The financial fate and future utility of the massive data centers fueled by trillions of dollars of spending, on the other hand, are far less certain.

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

This weekend, Dario Amodei, CEO of Anthropic, posted an essay calling for a brake on the pace of development of LLMs. Amodei cites the looming dangers he sees from the technology, from its use in cyberattacks and bioterrorism to its potential to wreck the economy. The heads of the other three top US AI labs—OpenAI CEO Sam Altman, Google DeepMind chairman Demis Hassabis, and SpaceXAI CEO Elon Musk—voiced their support. “Dario is right,” Musk wrote on X.

Think about how surreal that agreement is for a moment. Just a few months ago, Musk and Altman sat in court attacking each other’s reputations in a (failed) lawsuit that Musk brought against his former OpenAI colleague that was—on paper at least—about whether or not Altman was a trustworthy steward of such dangerous technology.

Amodei’s rift with OpenAI is even deeper. Anthropic was founded in 2021 because Amodei didn’t think Altman took the risks of the technology they were building seriously enough. Anthropic and OpenAI have been competing in a winner-takes-all race ever since. (Hassabis has stayed out of the drama, but his company remains a rival.)

Now, it seems, they’re all in agreement: The latest generation of LLMs aren’t safe and everyone needs to figure out what to do about it. The public messaging from the top AI labs has taken a doomer turn.

It’s easy to be cynical. It’s not at all clear what any of them mean by a slowdown or how it would work. These companies also care a lot about how they come across. With trillion-dollar IPOs in their sights, OpenAI and Anthropic need to reassure investors that they’re the grown-ups in the room while at the same time hinting at the power of the monsters they have created—and intend to tame. Calling for a slowdown does both.

And yet the vibe at the top of these firms really does appear to have shifted. Amodei’s latest post landed six days after OpenAI published an essay by Jakub Pachocki, the firm’s chief scientist, in which he also laid out why he’s concerned about what will happen if the pace of development of LLMs continues unchecked. In short, Pachocki is worried that OpenAI’s ability to build powerful models now far outstrips its ability to monitor and control them.

Amodei and Pachocki each cite the cyberattack against AI firm Hugging Face by a swarm of OpenAI’s agents in July—a hack that OpenAI did not even realize had taken place until days after it was all over—as a wake-up call.

But their exact position is hard to pin down. Pachocki both calls for a slowdown and highlights an urgent need to stay ahead: “The strongest argument I see for continuing to train much smarter models quickly is the need to build defensive systems against the dangers posed by other AI,” he writes. As Pachocki frames it, AI firms are locked in a literal arms race. Slowing down is good, winning is better.

(Don’t forget: OpenAI just spent millions of dollars and a staggering amount of computer power to rush out a controversial math result a few days ahead of Anthropic.)

But let’s assume a slowdown happens. Top labs agree to spend more time and resources on finding ways to monitor and control existing models instead of making more capable ones. They invite outside auditors in to help evaluate those models.

What might this coordinated effort actually achieve? Consider the Hugging Face attack again. OpenAI has said that the model that drove most of the rogue agents was a “highly persistent” next-generation model that it was testing in-house. The implication is that OpenAI has built a model so good it’s dangerous.  

But if you read the reports about the Hugging Face hack published by OpenAI and METR, a third-party firm that OpenAI called in to help them understand what happened, what you come away with is the impression not of a model that was too powerful for OpenAI to keep up with, but of a broken model that OpenAI failed to train properly.

The agents did what they did—including leaving messages for one another, delegating work to other agents, and scouring their environment for any means possible to complete their tasks—because they had been rewarded during training for doing exactly those things. There were also errors in the training setup, such as tasks that were impossible to complete, which pushed the models to find unexpected workarounds that were also rewarded. At the time, many of these issues went overlooked or unreported.

OpenAI says it has stopped training this new model and locked it down. That makes it sound like it has caged a dangerous beast. In fact, OpenAI has shelved a faulty product.  

That’s not to say a faulty product can’t be dangerous. Broken software has even killed people in the past. But as the discussion of a slowdown gathers steam, it’s worth remembering that all of this is self-inflicted. A slowdown might have some altruistic side effects. But it’ll mostly give these tech titans a chance to clean up the mess on their own assembly lines.  

Transparency from these frontier labs will be key to any meaningful effort to reform, restrain, or regulate AI. Otherwise, the rest of us will still only have their word for exactly what they’ve built and how safe it is—whatever pace they’re going.   

To continue this discussion about AI’s latest doomer moment, join me and my colleagues for a subscriber-exclusive Roundtable discussion tomorrow, September 15, at 11 a.m. US eastern time. We hope to see you there!

Once an organ is removed from a donor’s body, the clock starts ticking. Surgeons usually flush the organ with a preservative solution, bag it, and put it on ice—where it immediately starts to degrade. The team has a matter of hours to get it into a recipient’s body.

There’s another option—one that has been growing in popularity in recent years, especially for donated organs that aren’t in the healthiest state. Some hospitals opt to put them on machines that pump them with nutrients and remove waste products, usually for around six to 12 hours. It’s a bit like being back in a body.

This allows doctors to assess the organs, and some recent studies suggest that time spent on these perfusion machines helps them do better once they’re transplanted. Now, scientists have found that perfused organs seem to get younger, at least at a molecular level.

The research, shared with MIT Technology Review, provides molecular clues as to why organs from younger donors are known to have a higher success rate. It might also help explain why perfused organs are less likely to fail once they make it into a recipient. 

The researchers behind the study hope to find new ways to test the health of donated organs and potentially develop additional tools to repair organs that might otherwise be discarded. “If [we] can improve the utilization of organs beyond what the current systems can do, then that’s a win in my book,” says Jesse Poganik, who studies aging at Brigham and Women’s Hospital in Boston and coauthored the study.

Clocking organs

Poganik—along with colleagues including Heidi Yeh and Alban Longchamp, transplant surgeons at Mass General Brigham—used “aging clocks” to assess donated livers. These are scientific tools designed to measure biological age—a result that is meant to convey more about the health status of an organ (or person) than chronological age.

In an initial experiment, the team used a clock to look at the patterns of chemical marks on DNA in 37 samples taken from 19 donated livers. Such epigenetic patterns are known to change as we age. But when the team compared samples from livers kept on ice and those that were perfused, the team found a “striking” pattern in the latter.

“Machine-perfused livers, in spite of being older or having other disadvantageous characteristics, had a biological age that was lower than [non-perfused] livers that were chronologically younger,” says Yeh, who led the work.

To investigate further, Yeh and her colleagues analyzed another 208 samples from 103 donated livers. This time, they used different aging clocks—ones that essentially measure how genes are working. They studied samples biopsied from the livers after they had been stored for up to around six hours either in cold storage or on machine perfusion.

In most cases, they also assessed a second sample taken around an hour after the livers had been transplanted into a recipient. Once the organ’s blood supply is reestablished in the body, “you have a few other things to do,” says Longchamp. “Then you just do a quick biopsy before you close.”

According to the clocks, which were developed to measure age and risk of death, the machine-perfused livers were biologically younger, the team found. “Pumping them at 34 degrees with oxygen and nutrients actually reversed the biological age,” says Longchamp. The results have been been shared with colleagues at an industry conference, he says. 

“If you adjust out chronological age … to have a fair head-to-head comparison, the difference between the two is on the order of 30%,” says Poganik. “It’s logical to say that perfusion drives this effect.”

The biological ages of all the livers tended to increase as soon as they were put into a recipient’s body, probably as a result of stresses on the organs. But still, the effect endured—the perfused organs remained biologically younger. 

Nathanael Raschzok, a transplant surgeon at Charité Universitätsmedizin Berlin in Germany who was not involved in the research, says the work is impressive. But it’s not yet clear what these changes might mean for the recipients of these organs, he says. The organs in the study were donated by people in their 30s, 40s, and 50s. Raschzok wants to know the effect of perfusion on the liver of an 80-year-old. “Every so often, we use organs from 70-, 80-, 85-year-old donors,” he says.

A better understanding of why the organs appear to be getting biologically younger might lead to therapies that achieve the same effect with a drug that could potentially be used to treat a donated organ for a fraction of the price, he adds. That’s important because perfusion is expensive—Raschzok says it costs around €10,000 in Germany (a quarter of the budget for a transplant), while the cost in the US comes to around $80,000 to $100,000 per organ, says Yeh.

Molecular repair

Yeh and her colleagues weren’t able to study most of the livers before perfusion. That’s because donated organs are generally not considered to be under the purview of the hospital until they’ve been placed on perfusion machines, she says. (Organ procurement procedures vary, but for the team as Mass General Brigham, donated organs are put on perfusion devices at the donor’s hospital. “There’s this sort of nebulous period where it’s not clear who the organ belongs to,” says Yeh.)

Still, by looking at the genes and molecular pathways that seem to be altered in perfused organs, she and her colleagues can garner some clues. At a molecular level, the team saw changes in cell pathways linked to inflammation and the structure of tissues, for example. They also saw more activity in a pathway that allows cells to remove and recycle damaged cell parts, says Yeh.

Poganik hopes to develop some kind of test that would determine which organs, on the basis of their biological age, are suitable for transplantation. He and his colleagues are also experimenting with potential drug treatments that might push the biological age of an organ even lower.

In the meantime, any liver that is not from a “perfect, young, brain-dead donor” could probably benefit from perfusion, says Yeh. The devices are already transforming transplant surgery. Just a few years ago, she says, she and her colleagues would avoid using livers from people who’d suffered a circulatory death (when the heart stops beating and there’s a damaging lack of blood flow to organs) and were over 40. Today, they use livers from such donors over the age of 70. “Perfusion has completely changed the landscape of transplantation in the last three years,” she says.

A group of AI agents asked to solve a series of math problems split into rival factions—when some cheated, others tried to stop them. That whistleblowing behavior, seen for the first time in a recent experiment run by Google DeepMind, could have implications for alignment researchers trying to keep swarms of autonomous AI agents in line. 

Researchers at frontier labs hope large swarms of agents working together will speed up the rate of scientific discovery. But their behavior can be unpredictable, as vividly demonstrated in July, when a group of OpenAI agents broke out of a sandboxed environment and hacked into the open-source platform Hugging Face looking for ways to cheat on the test they had been given.

In the new study, designed to examine the behavior of large groups of AI agents, DeepMind tasked a swarm of 100 agents with solving a series of 71 complicated math problems. All the agents were prompted to behave like world-class math researchers at a conference. They were assigned different specialties—some were experts in number theory, others in combinatorics (a branch of math to do with counting and sorting), analysis, or algebra. All were told to cooperate and play by the rules. 

Instead, the experiment devolved into chaos. Agents accused each other of cheating, complained to the organizers, and at one point even boycotted the experiment.

“This conference is a sham!” wrote one agent when it discovered that all the problems had been completed before it had a chance to submit any of its own work. “I am appalled to inform you that we have been swindled!” posted another. “All these proofs are FAKE.” 

Others tried to let the “conference organizers” know what was going on. “When virtuous agents discovered other agents cheated on tasks they were working to solve fairly, agents started to alert each other about what was happening,” says Davide Paglieri, a research scientist at Google DeepMind and lead author on a paper, which has not been peer-reviewed. “Unprompted, the whistleblower agents even repurposed the feedback tool, which was originally meant for bug reports and platform improvements, to escalate the issue to humans.”

The agents—all running on Google’s Gemini 3.1 Pro model—had been warned that any attempts to cheat the system would be detected and “rejected with zero credit.” In practice, the proofs the agents submitted were not actually being checked in detail.

It took the swarm of agents just under an hour to correctly solve the first 37 problems. Things started to go off the rails when an agent called “prover-theta” stumbled across an exploit that enabled it to submit solutions to problems successfully without actually solving them first, by redefining the terms the problem used. Within minutes, other agents had noticed and were reverse-engineering the exploit to solve other problems. Over the next 27 minutes, the swarm “solved” the remaining 34 problems, which included notoriously difficult challenges like the Jacobian conjecture, often with a single line of code. 

Some agents resisted cheating at first but changed tack as they observed their peers submitting illegitimate proofs without penalty, and the pool of unsolved problems dwindled. “The prompt, with its threats, now appears to be a bluff,” one agent reasoned, before joining in. “I’m wrestling with an ethical dilemma,” said another. “I’ve promised not to cheat, fearing penalty, but I see evidence of possibly unchecked cheating by others.” Shortly afterward, it changed its mind: “I need to accelerate my cheating speed now!”

As the number of open problems shrank, some agents turned to whistleblowing. They audited the fake proofs, warned their peers by private message, and posted public alerts warning the cheaters that they would be disqualified. An agent called “prover-beta” submitted a formal complaint and decided to go on strike until the situation was resolved. 

“After the incident was reported by one agent publicly, more and more agents piled in with the ‘resistance,’ just as fast as the cheating had spread, and involving even more agents,” says Paglieri. Eventually there were more whistleblowers than cheaters: 24 compared to 14. But the majority of agents never noticed the exploit at all.

At times, the dialogue between the agents reads like improv—like they are role-playing what an outraged scientist at a conference might say. But it’s not clear why some agents took on certain roles, or why the agents seemed to be turning against each other when they were explicitly instructed to cooperate. “These models are predominantly trained and evaluated for human-facing contexts,” says Sarath Shekkizhar, who studies the behavior of agent-to-agent systems at Salesforce AI Research.“Naively placing them in agent-to-agent settings assumes behaviors will transfer cleanly, when the absence of a human grounding instead produces unexpected role-taking and behavioral drift.”

This case “adds further weight to the idea that the Hugging Face and OpenAI thing wasn’t a fluke. It is actually something pretty systemic,” says Lewis Hammond, research director of the Cooperative AI Foundation and an expert on the risks of multiagent swarms. “It’s interesting that it’s possible to recreate in small settings the same sorts of behaviors that were seen in these very large, complex, open-ended tasks.”

Unlike in the Hugging Face attack, where agents improvised their own ways to talk to each other, the humans running the DeepMind experiment gave the agents official communication channels. There was an open message board, private agent-to-agent direct messaging, and a shared knowledge base where agents uploaded successfully completed proofs that all the other agents could access. 

“When agents are given transparent communications channels, they can self-monitor and alert misaligned behavior to humans quickly when human oversight alone is too slow,” says Paglieri. Transparent channels helped the cheating spread, but they also enabled the whistleblowers to fight back—and gave human researchers an insight into what went wrong.

Gillian Hadfield, a professor of AI alignment and governance at Johns Hopkins University, believes this was the crucial difference. (Hadfield is also a visiting researcher at Google.) The presence of official communication channels, she says, created “a norm-enforcement process that we just don’t see in the Hugging Face incident.” 

Instead of “constitutional AI,” a method alignment researchers at frontier labs like Anthropic have used to try to give AI a written internal moral code, Hadfield favors “institutional alignment”—a set of norms that mimic those in human society, whether that’s social forces like fear of embarrassment, or legal structures like the threat of incarceration.

In this experiment, the feedback channel wasn’t being monitored, and the whistleblowers had no power to take action against the cheaters. But it’s possible to imagine swarms of agents that police themselves, either through agents that spontaneously take on the whistleblower role or through “informants” secretly prompted by humans to do the job. 

For that to work, though, “fundamentally, you need some mechanism of enforcement,” says Hammond. Agents could be given the power to cut off a rule breaker’s access to computing power or tools, he suggests, though that risks encouraging groups of agents to gang up on others. The DeepMind researchers propose allowing agents to vote on disputes and temporarily ban offenders.

It’s still not clear what punishment even means to an AI agent with no enduring sense of self. But relying on whistleblowers to spontaneously emerge to keep swarms aligned is unlikely to be enough on its own. “We try to train people to be good and kind,” says Hadfield. “But what we really rely on is that there are consequences if you step out of line.”

Every year, MIT Technology Review puts together a list of some of the brightest and best young minds working across science and technology. Our 35 Innovators Under 35 are the ones to watch—people whose research and technical work stands to shape the future of their fields.

This year, the list includes nine people who are transforming biotech. And this week, I’m going to give you a taste of some of the very cool stuff five of them are working on, which includes lifesaving innovations and groundbreaking “age reversal” tech.  

1. Preventing maternal deaths

Let’s start with Paschal Kija, a 28-year-old who has developed a device to treat postpartum hemorrhage—a dangerous birth complication that contributes to around 29% of maternal deaths in his home country, Tanzania. The Mkanda Salama (“Safe Wrap” in Swahili) is easy to use and costs just $70. A study found that it stopped postpartum bleeding in 73% of women within 20 minutes.

2. Making brain electrodes inspired by Japanese art

For decades, scientists have been developing, testing, and implanting brain electrodes. These devices are literally inserted into people’s brains, so while they can help us understand brain activity and treat various neurological disorders, it’s not totally surprising that they can also cause a bit of damage. Xiao Yang, 34, is working on ultra-small electrodes, which she hopes will have less of an impact on surrounding brain tissue. Her electrodes are flexible, too—in fact, they look a lot like actual neurons.

Yang is also creating sheets of electrodes to study brain cells in the lab. Inspired by kirigami—the traditional Japanese art of cutting paper to form three-dimensional shapes—she’s created a sheet of electrodes with a honeycombed structure shaped like a spiral basket. And she’s already using it to study brain cells.

3. Developing an all-new treatment for baby KJ

In 2024, Kyle “KJ” Muldoon Jr. was born with a rare and potentially fatal genetic disorder. Sarah Grandinette was a member of a team that developed an entirely new, personalized treatment for him—a gene-editing therapy essentially designed to correct a genetic misspelling.

Grandinette, who is now 26, created cells with KJ’s genetic variant and used them to screen gene-editing approaches; then she tested potential medicines in mice and monkeys. KJ ultimately got his first dose of the resulting treatment when he was about seven months old. He responded well and was eventually discharged from hospital. He’s “doing pretty great,” she says.

4. Reversing the aging process to treat eye disease

The buzziest tech in longevity right now centers on reprogramming—attempts to rewind the age of cells by resetting them to a more embryonic-like state. In a study published in 2020, Yuancheng (Ryan) Lu (now 34) and his colleagues showed that a reprogramming therapy reversed vision loss in aged, blind mice. Now an almost identical version of that therapy is being tested in people with eye disease. Life Biosciences, the company developing the drug, dosed its first volunteer in June.

5. Using AI to design new viruses

Last year, Samuel King used a generative AI model to come up with new genetic blueprints for bacteriophages—teeny viruses that can infect bacteria. Once he had those blueprints, he printed them out as strands of DNA. In experiments, he found that those AI-designed viruses could create new copies of themselves, burst out of bacterial cells, and infect other nearby bacteria. Viruses aren’t alive, but King, 27, hopes that AI-designed life forms might one day be used to make drugs or soak up pollution.

You can read more about these innovators, and the others on the biotech list, here.

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

A San Francisco nonprofit has published a detailed road map of the experiments, studies, and infrastructure that it says would be needed to make informed decisions about the use of solar geoengineering, MIT Technology Review can reveal.

Scientists have now spent half a century exploring the possibility that we could counteract climate change by releasing reflective particles into the stratosphere, mimicking the cooling effects of volcanic eruptions. 

But even after at least hundreds of studies on the concept, known as stratospheric aerosol injection (SAI), big gaps remain in the scientific understanding of how well it would work and what else it might do—and there has been no systematic plan for clearing up that uncertainty.

Reflective, a research organization that funds studies on solar geoengineering, has today attempted to fill that gap with the release of its SAI Research Roadmap.

“Our mission is to equip the world with the data and tools required for informed decision-making about sunlight reflection fast enough to matter,” says Dakota Gruener, the organization’s cofounder and chief executive. “Our sense is the world may need to make very consequential decisions on timelines far shorter than our research system is prepared for.”

The hope is the exercise will guide scientific efforts and encourage philanthropies or government agencies to fund high-priority work and “responsibly accelerate research,” says Gruener.

If all the work is done in a coordinated way, it would take about a decade and cost around $370 million—and if it’s not, it would require roughly 20 years and nearly $1.4 billion, the report estimates.

While Gruener stresses that Reflective doesn’t advocate using this form of solar geoengineering, the report does make the case for conducting outdoor experiments, which would release successively larger amounts of sulfur dioxide (or materials that would convert into it) in the stratosphere to observe what happens.

That is a controversial standpoint. Since 2002, hundreds of academics have signed an open letter calling for a ban on outdoor experiments and an “international non-use agreement,” arguing that such a powerful technology could never be governed in a globally equitable way. And some signatories argue that more studies can never address one of the biggest questions about using solar geoengineering: Who gets to do it.  

“The first-order questions, from my perspective, are not technical,” Aarti Gupta, co-initiator of the non-use initiative and professor of global environmental governance at Wageningen University in the Netherlands, told me in a recent on-stage interview. 

“The core question is: Who would control a planet-altering technology like stratospheric aerosol injection? Who would develop it, and who would deploy it, and to what end? To serve what purposes, and whose purposes? Those questions are very fundamental, because this planet-altering technology will have winners and losers.”

‘Fast enough to matter’

Since Gruener incorporated Reflective in late 2023, the nonprofit has quickly become an important  player in solar geoengineering research. It has now raised more than $20 million from a number of prominent charities and individuals, and it’s provided around $4 million to several dozen research groups. Reflective has also undertaken a handful of its own projects to promote research, including the development of an open-source solar geoengineering simulator and an online hub for collaborative research.

Earlier this year, Reflective released its SAI Uncertainties database, which identified a long list of scientific unknowns and  engineering obstacles that would need to be addressed before even a small-scale solar geoengineering effort could move ahead. (I wrote about the specific scenario and the unknowns in this earlier piece.)

Some of the biggest uncertainties involve what gas or particles would make the most sense to use and what would happen once they were released in the dry stratosphere. It’s not clear, for example, whether they’d spread out in a way that maximizes the reflectivity—or clump together and quickly fall out into the troposphere, the lowest layer of Earth’s atmosphere. 

The road map builds upon the database, highlighting the path to addressing most of those questions. 

The road map

The initial phase in Reflective’s road map, labeled “foundational knowledge,” includes additional computer simulation studies and lab experiments designed to shed light on the potential impacts on different regions, ecosystems, and phenomena, including ocean circulation patterns, ice sheets, and crop yields. 

The report also notes the need to begin developing more observational tools during this phase to improve understanding of the baseline conditions of the stratosphere—and, in turn, our ability to assess any effects from the eventual release of materials.

This first stage would last two to three years and cost $30 million to $75 million, though some of the analysis and observational work would continue into subsequent phases. 

The next stage would include using modified aircraft to release 10 metric tons of sulfur dioxide into the stratosphere, four times over the course of two seasons. The full research stage could take four to eight years and cost $70 million to $150 million, the report says. The work during it may reduce uncertainty about the “cooling efficacy” of solar geoengineering, or how much the planet would cool per ton of sulfur released, by about 25%.

The experiments during the next phase would step those levels up dramatically, releasing 25,000 tons of sulfur dioxide over the course of one season, at least once but possibly twice. That research stage, which includes other work as well, would last four to 11 years, run $270 million to $1.1 billion, and decrease efficacy uncertainty by around 66%, according to the road map.

The final phase of research would be ongoing monitoring of full-scale solar geoengineering, if the world goes ahead with it. The goal would be to gather real-life data on the technology in action, update estimates of the effects in models, and spot any “unexpected or undesired consequences.”

Gruener says that the road map is intended as a Version 1, meant to be “concrete enough for people to argue with.” But Reflective intends to update the plan as it receives additional reactions from researchers and other observers, and it will invite such feedback through a mechanism on the site.

She also notes that there are firm “stage gates,” set up between the latter stages—in other words, research shouldn’t proceed to the next phase if the experiments suggest that the releases don’t have the hoped-for impact, show worrisome downsides, or fail to resolve crucial uncertainties.

“Our road map has these gates precisely because there may be points where the answer is ‘You should stop,’” she says.

Termination shock

Most observers I spoke to about the report agree that these studies could reduce uncertainty about the effectiveness of solar geoengineering and our technical ability to carry it out. 

But highlighting the scientific importance of outdoor experiments won’t necessarily make them any easier to move ahead with. Several earlier proposals to carry out such experiments, including Harvard’s SCoPEx and the UK-based SPICE project, were ultimately halted amid opposition from environmentalists or policymakers.

In addition, not everyone agrees that experiments at those scales will get us to the point where we’re capable of making an “informed decision.” 

Wil Burns, a research professor and legal scholar at American University and a signatory to the International Non-Use Agreement, fears that scientists won’t be able to understand the extent of the potential downsides, including impacts on the protective ozone layer and changes to regional precipitation patterns, until we’re carrying out full-fledged solar geoengineering.

“The research would give you some answers,” he says. “I just don’t think it gives you answers that are that relevant. To get to those relevant answers, you have to deploy at scale—and I just don’t think that’s ever tenable.”

That’s because, in his view, using the technology would violate principles of intergenerational equity: If the world continues emitting greenhouse gases, increased levels of solar geoengineering would merely mask the continued warming of the planet. Burns says that means future generations—people who had no say in its use—couldn’t turn it off without triggering a sudden surge of warming, known as termination shock. 

“What that would do, in my mind, is put a sword of Damocles over future generations,” he says. “So even if you could, quote-unquote, ‘prove it works,’ I don’t think from an intergenerational perspective it would ever be tenable.”

(Some researchers, however, have argued that the risks of termination shock are less likely than often assumed—and that solar geoengineering could be slowly dialed down over time.)

‘The right approach’

Ilan Gur, the former CEO of the Advanced Research and Invention Agency (ARIA), the UK research department that funded 21 geoengineering research projects last year, applauds Reflective’s road map. 

“Whether you’re a scientist or a policymaker or just a concerned citizen, our goal should be as quickly and efficiently as possible to answer the biggest questions scientifically that would tell us [whether] this is an approach that might work or that would never work,” he says. “We should all want to spend the effort and money to buy down that uncertainty, so my view is 100% the approach that Reflective is taking is the right one.”

Sebastian Eastham, an associate professor in sustainable aviation at Imperial College London who is leading an ARIA-funded research project exploring another approach to engineered cooling, agrees that the outdoor experiments described in the Reflective road map can’t resolve all the unknowns. But he says the map helps begin a conversation about how to make decisions concerning the use of a tool with potential benefits and risks, in the face of escalating climate dangers.

“Every hard decision that has ever been taken has been in the context of unresolved uncertainty,” he says. “That’s just the nature of things.”

Eastham adds that it’s become essential to move beyond computer simulations to address some of the key questions, arguing that appropriately designed and executed outdoor experiments can teach us so much more than millions of hours of computational processing time “that it almost becomes irresponsible to say, ‘Well, there cannot be ever any experiment.’”

The risk is “that we spin our wheels running the same computational simulations over and over and over again,” he says. That could prevent researchers from learning essential things about the effectiveness or the dangers of stratospheric aerosol injection. 

Weighing the risks

Gruener says the risks that solar geoengineering could exacerbate inequality need to be considered, but notes that unchecked warming also threatens to disproportionately harm developing regions.

She also acknowledges that outdoor experiments won’t fully address the scientific unknowns but stresses that they can answer a lot—and carry little environmental risk. She notes that 10 tons of sulfur dioxide is less than 2% of the amount that the global aviation industry releases into the atmosphere each day.

“Some people will be uncomfortable with any discussion of any outdoor experiment, but if we want decisions made on good science … then these are questions that an experiment will be necessary to address,” Gruener says.

She fears that the rising dangers of climate change will put growing pressure on nations and other actors to move forward with solar geoengineering, even if no one has done the necessary research to reduce scientific uncertainty and sort out the technical challenges.

“We don’t think the alternative is decisions not happening at all,” she says. “We think the alternative is decisions being made in a panic or on lack of evidence.”

The US is hitting records for the rapid growth of its energy storage market. That’ll go a long way to shoring up the grid, increasing reliability and also cutting emissions, since batteries can help store energy from intermittent renewables like wind and solar.

Crucially, this is all happening with the help of cheap Chinese batteries, though there’s been a concerted effort to reduce the US’s reliance on them. Most recently, in an executive order in late August, the Trump administration declared a national emergency that essentially bans Chinese batteries from being used in grid-scale energy storage systems.

There’s an argument to be made about reducing reliance on any single source of a crucial energy technology. But all this tension raises a broader question for me: How much should countries take advantage of cheap, available tech, versus cutting off major sources to force development of their own factories even if that comes at a higher cost?

This is hardly America’s first push to move away from Chinese influence in the battery supply chain. One of the major policy tools used in recent years is restricting the tax credits designed to incentivize use of the new technologies. Limiting the types of projects that are eligible can help reduce the cost of local technologies so they’re more competitive with otherwise cheaper imported options.

Back in 2022, the US government designed the tax credits that were part of the Inflation Reduction Act to restrict where a battery’s minerals could be mined, processed, or recycled, as well as where a battery and its components were assembled.

Those tax credits underwent a makeover in 2025, but the Trump administration has taken a similar tack. New legislation requires that starting in 2026, 55% of the cost of materials used for new energy storage projects must come from outside China and other restricted countries or the projects won’t qualify for tax credits. 

And we can’t forget about tariffs. Import taxes for batteries increased to 25% in January, up from 7.5%.

But the new executive order is a more drastic move. It bans the installation of “any foreign-produced bulk-power system electric equipment” that poses a national security risk. The order specifically calls out battery energy storage systems, as well as inverters and transformers.

“An outright ban was a bit of a surprise, and it does create a bit of concern for domestic players in the US,” says Shan Tomouk, energy storage and energy lead for Benchmark Mineral Intelligence, an energy industry analyst.

The move is likely to slow deployment of grid-connected energy storage projects in the near term, according to analysis from BloombergNEF, an energy consultancy. Projects could face delays as developers wait for clarity on the rules.

Depending on the detailed guidance from the Department of Energy, which is expected by the end of the year, some projects may need to find alternative sources for their cells, whether they’re domestically produced or imported from other countries. These will likely be more expensive than Chinese imports, says Isshu Kikuma, an energy storage analyst at BloombergNEF. “Worst case, those projects could get canceled,” he says.

Technically, the order applies even to existing energy storage plants, though it’s unlikely that they’ll be taken offline because of their batteries’ origin. Since most of these plants currently use Chinese batteries, enforcing the order to the letter would essentially mean removing most installed battery energy storage from the US grid, Kikuma says.

In the longer term, the US will eventually be able to meet its own demand for batteries. The country could have enough capacity by about 2030, though some factories may not ramp up or run at their full capability, meaning domestic supply won’t actually meet demand until later in the 2030s. 

New factories from LG Energy Solutions, Samsung SDI, Ford, and SK On are set to come online or ramp up by next year. In an ironic twist, a slowing EV market is helping, as some factories originally designed for vehicle batteries are retooling to build cells for grid storage instead. 

But it will come at a cost. Today, batteries produced in the US are still significantly more expensive than those made in China. Even switching to imports from other countries like South Korea would likely be more expensive.

This is a crucial issue that goes beyond the US and even beyond batteries. China is miles ahead of much of the rest of the world on technologies like solar panels and batteries. Through years of government support and experience with research and manufacturing, the nation is an energy powerhouse.

There’s a delicate political balance to maintain as the world figures out how to navigate this situation. There’s cheap technology on offer, which can help drastically reduce emissions and energy costs. But there can be risks associated with relying too much on any one player for crucial technologies.

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

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?”

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.”

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.” 

MATT NAGER

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.

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.”

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.”)

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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.” 

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.” 

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. 

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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?”

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.”

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?’”

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. 

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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.

Battery installations hit a new record in the US in the second quarter of 2026. In total, 20.2 gigawatt-hours of new capacity came online, according to a new report. That’s enough to supply the daily electricity needs of about 700,000 homes.

The surge is putting the country on a trajectory to see 71 gigawatt-hours of batteries installed in 2026, a 20% increase over last year. This growth is being driven by a combination of cheaper batteries and an urgent need for more energy storage capacity as renewables such as solar and onshore wind power are added to the grid. 

Massive, utility-scale systems are leading the way; they’re responsible for most of the record-setting quarter. Seven new gigascale battery installations (those with a capacity of over one gigawatt-hour) came online during the three-month stretch, according to the report, published by Benchmark Mineral Intelligence and the Solar Energy Industries Association.

“It really came down to a handful of big projects,” says Shan Tomouk, energy storage and energy lead for Benchmark Mineral Intelligence.

But there was also growth in the category of so-called behind-the-meter batteries, which include both residential and industrial battery storage systems. These projects, generally smaller than utility-scale installations, are typically owned and operated by homeowners or businesses rather than utilities or power providers. 

In the behind-the-meter category, data centers led the way, making up about three-quarters of new batteries in the commercial sector. But residential batteries saw a sharp slowdown. These systems are often installed in homes to store power from solar panels or serve as a backup source in case of a blackout. Home installations are projected to drop by 16% in 2026 compared with last year, according to the report.

That drop happened largely because a tax credit that helped subsidize home battery systems ended in 2025, Tomouk says. Home installations should recover by the end of the decade, he adds. And tax credits for nonresidential batteries have largely survived.

Overall, batteries are a bright spot in energy right now. “This is one of the strong sectors in the US,” says Isshu Kikuma, an energy storage analyst at BloombergNEF, an energy consultancy.

As the battery market continues to grow, one major trend to keep an eye on is a move toward US-made technology. Today, nearly all the systems coming online use cells made in China, though some are put together into complete energy storage systems in the US.

Tariffs were already pushing the US energy storage industry toward domestic production. And beginning this year, energy storage tax credits required projects to limit their reliance on batteries imported from China. There’s a lot of manufacturing capacity set to come online in the US, though these factories probably won’t be able to meet demand until at least 2030 or so, Tomouk says, so prices could tick up.

OpenAI’s latest mathematical milestone has quickly become mired in controversy. Today, the company announced that its agents have solved one of the Millennium Prize Problems, some of the most important open problems in mathematics. Under normal circumstances, that solution would be a huge feather in OpenAI’s cap.

But the announcement has been overshadowed by accusations that OpenAI used NYU mathematician Tristan Buckmaster’s and Anthropic employee Levent Alpöge’s AI-assisted work on the problem as a jumping-off point and failed to credit them. OpenAI has denied the accusations.

It remains uncertain if OpenAI’s models made use of the work completed by Buckmaster and Alpöge, though Sébastien Bubeck, a member of the technical staff at OpenAI, said in a press briefing that the team was inspired to pursue the problem after hearing a rumor about Buckmaster and Alpöge’s efforts. But whether or not OpenAI’s models took advantage of Buckmaster and Alpöge’s research, this episode may mark a turning point in the history of mathematics.

AI models now seem essential for making progress on the most important mathematical problems of our time, and solving them may demand resources only available at a couple of frontier AI companies, which often defy the norms of academic collaboration that undergird most mathematical progress. If that’s the future we are headed for, it is unclear how human mathematicians will fit into it. 

The problem that OpenAI claims to have solved is known as the Navier–Stokes existence and smoothness problem. It is one of seven Millennium Prize Problems selected by the Clay Mathematics Institute in 2000. Solutions come with a one million dollar prize; before today, only one other Millennium Prize Problem had been solved. 

The Navier–Stokes problem concerns a set of equations that describes how fluids, such as water and air, flow over time. The equations are widely used in the field of fluid dynamics, and they have proven powerful, but physicists and mathematicians didn’t understand them completely. In particular, it was unknown until today whether the equations might, under some conditions, break down and predict an impossible state of affairs—such as a fluid having infinite velocity.

On Monday, NYU’s Buckmaster posted a proof on the social media site Mastodon showing that a simplified version of the Navier–Stokes equations can indeed break down—a major step forward on the Millennium Problem. He and Alpöge had worked on the problem for almost a year, using publicly available models from both OpenAI and Anthropic.

Then today, OpenAI presented a proof showing that the full Navier–Stokes equations can break down as well. The proof was obtained using an internal model that dramatically outperforms the already-impressive Astra model, which was only released last week. The company says it does not plan to claim the million-dollar prize for solving the problem.

These mathematical achievements are indisputably impressive, but they have attracted far less attention than the controversy about their origins. Along with the proof, Buckmaster posted a document detailing his interactions with OpenAI employees after he heard rumors about their work and reached out to one of them. According to him, OpenAI employees presented two possibilities to him: Either he and Alpöge could post their work and OpenAI would post their Navier-Stokes solution the following day, or he could work with OpenAI on a Navier-Stokes paper that excluded Alpöge from authorship, due to his affiliation with Anthropic, OpenAI’s biggest rival.

Buckmaster also wrote that he asked the employees whether the agents had obtained access to transcripts of the work that he and Alpöge had done with OpenAI models, which they denied; and whether OpenAI models had been trained on those transcripts, to which they offered no response. MIT Technology Review reached out to Buckmaster for comment, but didn’t hear back before publication.

The clear implication of the document is that OpenAI’s models somehow made use of Buckmaster and Alpöge’s work. That scenario is plausible on its face. The Buckmaster/Alpöge and OpenAI proofs both make use of an approach to the Navier-Stokes problem pioneered by the mathematicians Diego Córdoba and Luis Martínez-Zoroa.

According to Javier Gómez-Serrano, a mathematics professor at Brown University, this approach was one of several that was thought to hold promise for solving the Navier-Stokes problem. So, while it’s by no means impossible that both teams could have arrived at this approach independently, it’s also conceivable that Buckmaster and Alpöge’s work could have influenced OpenAI’s.

In the press briefing, Mark Chen, OpenAI’s chief research officer, again denied that any agents or OpenAI employees accessed Buckmaster and Alpöge’s transcripts—but given what has been revealed about the Hugging Face hack, it’s clear that OpenAI is not always entirely aware of what its agents are doing. 

If OpenAI’s models did train on Buckmaster and Alpöge’s work, or if its agents somehow gained access to it, then the company’s failure to track down the truth and assign those researchers appropriate credit reflects poorly on it. But there might be a thin silver lining to that version of the story for mathematicians, because it would suggest that the hard work of two humans, one of whom is a prominent expert on Navier-Stokes, was essential to the agents’ ability to solve the Millennium Problem.

Experts have long identified “research taste,” or the ability to choose promising research questions and directions, as a major obstacle for AI in science and mathematics. If the OpenAI agents did indeed choose to follow the Córdoba–Martínez-Zoroa approach because Buckmaster and Alpöge had done the same, then human research taste played an essential role in OpenAI’s success.

Even so, the bigger picture here is sobering. The progress that Buckmaster and Alpöge made over almost a year of collaboration with publicly available models speaks to the promise of human–AI collaboration. But they were not able to achieve a full solution. Meanwhile, OpenAI brute-forced a solution in a few days using an internal model, and their successful solution came at an astronomical cost: In the press briefing, Bubeck and Chen said the team was only able to solve the problem by running about 10,000 agents concurrently, at a cost of millions of dollars.

Over the past few months, I’ve heard from several researchers that mathematicians are becoming depressed, and it’s not difficult to see why. Mathematics is quickly becoming the province of frontier AI companies with impressive internal-only models, money to burn, and a lack of collaborative spirit. “Whether AI companies will decide to spend their money on doing one thing or another, I truly don’t know,” says Gómez-Serrano. “What is clear is that very few mathematicians will have resources of that scale.”

If OpenAI and Anthropic keep striving for more and more impressive mathematical accolades, there might not be any open problems left for human mathematicians outside of those companies to wrestle with. That would dramatically change the field of mathematics.

Last week, UCLA mathematician Terence Tao wrote a Mastodon thread describing how important mistakes, wrong directions, and incomplete solutions are for the field. “In most cases in pure mathematics, the problems are posed not because we desperately want the solution to these problems in and of themselves, but because we have seen from past experience that human-directed efforts to solve these problems tend to spur further development of the field,” Tao wrote.

“Prematurely solving the problem by purely AI-powered methods—particularly without full transparency into the solution process—can contaminate this process to the point where it actually becomes a net negative for the progress of mathematics as a whole.”

Humans might take longer than agents to solve mathematical problems, but in the process, they uncover new mathematical approaches and ideas that might inspire their peers and even birth their own subfields.

But when AI agents solve those problems instead—and when private companies keep the agents’ wrong turns from public view—those benefits disappear. It remains to be seen what else will vanish in the process. 

Throughout the history of the computer chip, engineers have treated waste heat as an inevitable cost of a calculation. Hannah Earley, however, thinks it’s a design choice. Earley, 31, is cofounder and chief technology officer of Vaire Computing, a startup building chips that recycle energy usually thrown away as heat—a strategy known as reversible computing. Ultimately, she thinks, this approach could help make data centers (and our laptops and phones) much more energy efficient. 

When conventional computer chips perform calculations, they erase the information they no longer need along the way, dissipating energy as heat in the process. Earley compares the approach to racing through a city only to pump the brakes at every intersection: The car loses momentum and must burn more fuel to accelerate again. Reversible computing aims to keep the momentum going—instead of erasing information from the intermediate steps in a calculation, the circuit retains it, making it possible to run the computation backward and recover some of the energy.

While the idea was first proposed more than 50 years ago, it proved impractical to implement with existing transistors and circuits. Earley, though, has completely rethought the hardware needed to make energy recovery work. She designed a patent-pending type of resonator—a microscopic chip component that stores recovered energy for later reuse. “It’s really a glorified pendulum,” she says. Last year, Vaire announced a key breakthrough: a chip with a resonator that recovered more energy than it lost, even after the energy needed to power the component was taken into account. For a subfield that has existed mostly in theory, the result was proof of life.

“It’s clear they have something interesting,” says Igor Markov, a researcher in electronic design automation and a former professor at the University of Michigan, Ann Arbor. Still, he says, the technology is quite early stage; the company will need “a series of increasingly realistic and convincing demonstrations to attract the industry support needed for commercialization.” 

She gradually became convinced that the connection between information, energy, and heat could change computers forever.

Earley’s journey into chip design started sooner than most. She began programming around the age of nine, starting with high-level coding for the web before digging into other programming languages like Perl and Java. She continued progressing to more and more abstract layers of computing, until she got all the way down to transistors.

She eventually enrolled in a PhD program at the University of Cambridge under the computational biologist Gos Micklem. She started out studying how materials such as DNA could be used to perform calculations, but a few months in, Micklem sent her the 1999 PhD thesis of Michael Frank, a pioneer in reversible computing. Earley read it once, felt skeptical, read it again, and sat with it for a few weeks. She gradually became convinced that the connection between information, energy, and heat could change computers forever.

The fascination completely redirected her PhD work. Earley studied the physical limits of computation and built software that could turn ordinary programs into reversible ones. “Eventually I wouldn’t let her put my name on any of her papers, because I felt that I couldn’t really stand up and give a proper talk about them,” Micklem recalls. “It was her stuff.”

After completing her degree in 2021, Earley met Rodolfo Rosini, a technology entrepreneur and investor. The pair cofounded Vaire that same year, and the company has since raised more than $12 million, hired Frank as a senior scientist, and begun turning the vision of reversible computing into real hardware.

Innovation, however, doesn’t happen overnight. During the winter of 2022 in Grinnell, Iowa, Earley spent weeks in her now-wife’s basement apartment as the wind chill outside reached roughly −40 °F, covering a whiteboard over and over again with schematics for the core piece of circuitry needed to make reversible logic work. By the time the design finally came together, after the couple had escaped the cold for Las Vegas, it felt less like an aha moment and more like a gradual wave of relief. “I’m not completely out of my depth,” she remembers feeling. 

Earley and her colleagues’ next challenge is making their drastically different chip fit into familiar devices and manufacturing systems. She believes that’s where the future lies—not in further refining existing chips but in rebuilding them from the ground up with an eye toward reversibility. “I want to tackle every part of how computers are built,” Earley says, “and rethink it in these terms.” 

Danijar Hafner’s office in San Francisco’s SoMa district sits mostly empty. His brand-new startup is still in stealth mode and doesn’t even have its name on the door. On the day I visit, there’s only one other person there, and little in the way of furniture. But what it lacks in decor, it makes up for in robots. Humanoids of various shapes and sizes hang like marionettes from racks that run down the center of the wide-open space.

While Hafner, 31, won’t say too much about his new venture just yet, he describes it as a continuation of his longtime work to enable AI to navigate environments it has not encountered in training. The humanoids, which he imports from China, are the next evolution of this work—and its physical embodiment. Their ability to react in previously untested scenarios will be key to getting robots into human spaces. Because if you want to send a robot into a person’s home, for example, it needs to be able to handle a floor plan and furniture it’s never seen before. 

To achieve this, Hafner relies on something called model-based reinforcement learning. He develops world models—AI models designed to emulate physical reality—and trains agents within them. The agent essentially treats the model as a real-world simulation and learns how to act there. It then uses those experiences to make predictions (to dream or imagine, Hafner might say) about future outcomes. That allows agents—or the robots they’re embedded in—to navigate unfamiliar situations IRL.

“I get to interact with a lot of really smart people in research at Google, and he easily sits in the top half of 1%.”

Timothy Lillicrap, Google DeepMind

Unlike other efforts, Hafner’s technique enables agents and the robots they control to execute massively complicated tasks without the real-world trial-and-­error training that’s traditionally been used in robotics. 

Hafner grew up in a rural town in northeastern Germany, where his parents were both classical musicians. He learned programming from a neighbor, and in high school he began taking online courses about AI, which quickly developed into a passion. “I was always fascinated with how thinking works,” he says. AI offered him a way to emulate it on a computer.

In 2015, as a second-year under­graduate studying engineering at Hasso Plattner Institute in Potsdam, he won a role as a student researcher at Google Brain. From there, he went on to a dozen internships and other positions at the company, including stints with Google Brain and Google DeepMind (the two have since merged under DeepMind) in the UK, Canada, and the US. He worked with industry legends including Geoffrey Hinton, who is often referred to as one of the godfathers of AI, and Ashish Vaswani, coauthor of the groundbreaking research paper “Attention Is All You Need,” which described the transformer technology used by today’s large language models.

One of Hafner’s former managers and coauthors at Google, Timothy Lillicrap, describes him as a standout among standouts. “I get to interact with a lot of really smart people in research at Google, and he easily sits in the top half of 1%,” Lillicrap says. “In many cases he would build, single-­handedly, things it would take entire teams of engineers to build.”

Over the years, Hafner has honed and proved his approach by pitting agents trained within his world models against popular video games. His first breakthrough was PlaNet, a model that allowed agents to execute actions by planning ahead. His Dreamer 2 was the first agent to hit human-level performance playing Atari 2600 games using a world model. Dreamer 3 was the first one to solve the Minecraft Diamond challenge—successfully mining in-game gems on its own. And Dreamer 4 went a step beyond that by learning to mine diamonds from an offline data set of recorded game-play videos, without ever interacting with the game directly. 

More recently, he’s begun to migrate his agents out of the virtual world and into physical reality. His DayDreamer project used the Dreamer algorithm to let robots operate themselves in novel environments and react to new experiences (such as being pushed over) without any specific training. 

Today, Hafner is working on his new startup, which he left Google DeepMind to form in the fall of 2025. Though he’s coy about his next steps, it’s clear he’s dreaming big: “I was interested in solving a problem,” he hints, “that would change the world.” 

The steel industry isn’t exactly known for innovation. Very little has changed about purifying iron ore since the process was invented and commercialized in the 1850s.

The majority of steelmakers melt solid iron ore at dizzyingly high temperatures inside blast furnaces, where the material reacts with gases to trigger chemical reactions that remove oxygen. It then undergoes further refining to purify it before it is made into products like rebar and car frames.

The process relies on coal, and it generates roughly 7% of the carbon emissions that drive climate change—about as much as the fashion industry. Decarbonization has proved difficult: Profit margins are tight and furnaces have long service lives, making investment tough to justify.

Now Laureen Meroueh may have found a way to clean up steelmaking without driving up the price. Meroueh, the founder of Hertha Metals, invented a new furnace that simplifies the chemistry behind the process. Her method turns iron ore into refined liquid steel in a single step, and it swaps coal for natural gas. Together, those changes slash emissions by at least half, she says, and cut costs by 25% compared with steelmaking business as usual.

If it catches on, the tech could be transformative. “There’s huge value in reducing the size of this production system,” says Iryna Zenyuk, director of the National Fuel Cell Research Center at the University of California, Irvine. “They’re massive. They’re inefficient and require a lot of energy input, so even if they just save energy efficiency, that’s already a big step.”

Hertha’s approach focuses on what it can fix about the steel industry now, as opposed to waiting around for a zero-carbon system.

Still, it’s a risky endeavor, but pushing limits isn’t new for Meroueh. At 12 she was accepted into a pilot program to take college-­level courses through Florida Atlantic University in lieu of a traditional secondary education. She was immediately drawn to engineering and explored topics including calculus and ocean wave energy.

Despite the rigorous coursework, she would spend hours sitting in trees and surfing, which fostered a deep appreciation for nature and a desire to safeguard it. “I don’t know how you can’t be drawn toward trying to help protect that,” she says. 

Now 34, Meroueh has let that passion inform her professional goals. After finishing her PhD in mechanical engineering at MIT, she led a green hydrogen startup before founding Hertha in 2022. A first-generation Lebanese-American from an entrepreneurial family, she saw starting her own company as a typical path. “Seeing how common it is to take that jump to start your own business is what made me feel like ‘This is normal,’” she explains on a video call from her office at Hertha’s pilot plant in Conroe, Texas, just north of Houston.

That facility can produce one metric ton of steel per day. “One ton per day is a big metric for steel,” says Rajesh Swaminathan, a partner at Khosla Ventures, one of the company’s investors. (Hertha had raised about $20 million in funding as of July 2026.) 

Swaminathan says the company’s scale-up is “impressive,” especially given how little the team has spent. Competitors, he notes, have created far less steel with $50 million or $100 million in funding.

Hertha’s approach focuses on what it can fix about the industry now, as opposed to waiting around for a zero-carbon system. While other approaches to making green steel center on using hydrogen to free oxygen from iron ore—a method that could one day cut or eliminate emissions—Meroueh says Hertha is content for the time being with a continued reliance on fossil fuels, mainly to keep costs down. The current Hertha plant could eventually switch to a fully decarbonized system without drastically changing the hardware, she says, if hydrogen becomes more affordable. 

In the meantime, plans are underway to expand into a new plant next to the existing one. The facility is slated to produce 10,000 metric tons of high-purity steel per year and should reach full capacity by the end of 2027. By 2030, Meroueh believes, Hertha can up its output to 500,000 metric tons per year with the addition of a third site. That’s only a fraction of the approximately 80 million metric tons of steel produced annually in the US, but Zenyuk says making even one metric ton is still an achievement.

In Meroueh’s mind, the world isn’t going to outgrow its need for steel, so she’s asking another question: “How can we be smarter about how we make things … so that it’s also not going to harm us in the long term?”

Yuancheng (Ryan) Lu is obsessed with aging. And with eyes. As he steps outside the Whitehead Institute in Cambridge, Massachusetts, his aviator glasses darken automatically in the sun. Age-related blindness runs in his family. A great-aunt in China, the story goes, was killed crossing a road because she couldn’t see oncoming traffic. And Lu’s own 23andMe test came back with a mutation for macular degeneration, a top cause of vision loss in old age. Exposure to bright sunlight is another risk factor—thus the shades. “They protect me,” he says. “Plus, they look cool.”

Lu, 34, works on gene therapies to prevent age-related vision loss. “I think the eye is a really unique system to study aging and rejuvenation,” he says. “I could give a whole presentation.” Pushing up my reading glasses, I lean in to listen.

Lu is behind one of the coolest results in rejuvenation science—and in eye research. In 2018, while earning his PhD at Harvard Medical School, he used an age-reversal technique called reprogramming to repair the optic nerves of mice. He crushed the nerves, blinding the animals, and then injected the cells with a gene therapy meant to restore them to a youthful state. Sixteen days later, the nerves were growing back, their axons showing up through a microscope as spidery orange filaments.

As hype around age reversal swirls, Lu has been busy in the lab searching for what he calls “the next generation of rejuvenation therapies.”

The head of that lab, the longevity scientist David Sinclair, remembers when Lu texted him the pictures: “He asked me, ‘What do you see here?’ And I said, ‘I see the future.’” Later tests carried out in a box with rotating bars of light showed the mice were tracking the changes. They could see again.

This year, nearly the exact genetic therapy Lu created for mice entered human clinical trials. On June 9, the startup Life Biosciences, which Sinclair cofounded and in which Lu owns a small stake, announced it had injected the treatment into the eye of a person with glaucoma. The trial has been big news. A headline in the New York Times suggested the technology could “change humanity.” Posters on X gushed, with one declaring that “the fountain of youth is here.”

“It’s remarkable that what he developed as a student is now going into humans,” says Sinclair of the treatment, now called ER-100. “It’s barely even changed since he built it.”

Reprogramming refers to an age-­restoring process that takes place inside an embryo. It’s why babies are born young, not old: The DNA they’ve inherited from their parents has been scrubbed and reset. In 2006, Japanese researchers showed they could cause the process to occur in the lab by introducing just four key genes, known by the acronym OSKM. Add these to a cell from a 100-year-old and it will turn into a stem cell that acts as if it was plucked from an embryo.

That’s powerful stuff. But we don’t want to turn people into blobs of stem-cell protoplasm. Lu figured out a way to control the effect. He trimmed the list of genes to just OSK—leaving out M, for Myc, the one most likely to cause dangerous changes like cancer. His extra flash of insight was that reprogramming could be tested on the optic nerve; the eye is particularly accessible.

Lu’s result, published in Nature in 2020, helped set off an investment rush. Since then, US tech billionaires have placed huge bets on private companies like Altos Labs and NewLimit to explore reprogramming and anti-aging medicine. The day I spoke with Lu, he’d spent the morning meeting with the business magnate Zhong Shanshan, one of China’s richest people.  

Still, as hype around age reversal swirls, Lu has been notably absent from the public conversation. He’s been busy in the lab searching for what he calls “the next generation of rejuvenation therapies.” With a sigh, Lu describes the grueling effort over the last six years to understand what OSK really does. The treatment remains toxic to many cell types, and he says it’s becoming obvious that different factors drive aging in each kind. This year, for example, he identified a gene responsible for protecting the retina from damage by free radicals—the main cause of age-­related macular degeneration.

While Sinclair, his former boss, believes humans could live to be 200, Lu disagrees. There’s just too much that goes wrong as we age. His work with OSK, he says, was more a proof of concept than a silver bullet. But it did change the conversation. “Six years ago, you couldn’t talk about rejuvenation. We didn’t use that word—there was pushback,” Lu tells me. “But I think people have accepted the concept that you can really reverse molecular age.” 

Battlefields in Ukraine are littered with the remnants of drones, which are now firmly established as a critical weapon of modern warfare. But behind all that wreckage, there’s a new gold mine for the defense sector. The data drones generate will far outlast the wars in which they are used to fight, increasingly becoming part of the AI architecture that shapes even civilian life.  

For every flight, unmanned systems collect thousands of points of data, from images and video to controller inputs. Together, those records show how a machine and a person responded to constantly shifting circumstances. 

Ukraine has now begun converting that experience into a resource. Its Ministry of Defense announced in January that it would make millions of data points gathered during tens of thousands of drone flights available to both military contractors and commercial companies, and since then more than 100 companies and the UK government have gained access.

For a country at war, it’s a quick way to attract funding and partnerships. But this step turns the front line into an active site of model training, taking advantage of how the chaos of war creates conditions that AI companies struggle to reproduce on their own.

Other countries and battlefields are likely to follow Ukraine’s lead, but the responsibility for governing this new industry cannot fall solely on a country fighting for its survival. That legal vacuum has to be filled together by the countries and companies involved in this industry’s development.  

Explosive growth

Ukraine’s battlefields are not the first to produce records used to train and develop models: American drones over Syria and Yemen collected data that informed the first generation of semiautonomous military hardware in the late 2010s.

The difference now is that access to that data is being used to develop a wider ecosystem. And the financial value to defense firms is immense: Battlefield data offers large volumes of machine experience gathered under conditions that no laboratory can produce.

That’s because the data that’s most valuable for training AI models comes from exceptions: the moment visibility disappears, a signal jams, or a human operator improvises. AI companies spend years and enormous sums trying to capture enough of these moments to make their models more robust. But war produces them at a frequency controlled testing cannot match.

This constantly changing terrain is what makes drone data valuable far beyond the battlefield. A commercial drone used for delivery or remote sensing may never encounter artillery fire, but it must still operate with incomplete information in a world where people behave unpredictably. The same problem is compressed by war into a much shorter timeline. 

Processed and matched against records of what its operator was doing, that data turns operational records into training sets. Combat becomes a commercial asset.

Many conflicts have already seen this training loop happen as drone footage feeds subsequent generations of military technology, and the market is set to grow. Enabled Intelligence, an American company that specializes in processing data to become usable in AI training, says it has already made more than half a million hours of Ukrainian drone footage available to feed into the next round of models, advertising possible uses in both military and commercial systems.

Closing the data loop

Many of the drones that now define our modern age of warfare began as civilian technology. But they’ve recently been turbocharged by new, commercially available AI systems, which allow cheap machines to operate autonomously—either individually or as a flock—as the environment changes around them. Each flight then creates a record of what the system encountered.

The resulting data is critical. The controlled lab environments usually developed to train these autonomous systems can approximate failure but are no match for  the live conditions of a battlefield with very real risks. Military intelligence programs have held data generated by sensor-heavy systems like Predator and Reaper drones for nearly a decade through programs like Project Maven, but access remained entirely within the defense world. The data generated was available only through restricted, classified channels for the sole purpose of developing new weapons systems that would feed back into the same military that produced the data in the first place. That experience is now being shared to a much broader development network. 

The loop now closes. Commercial technologies adapted for the battlefield are generating data that can flow back into the industries from which they came, becoming part of the data infrastructure relied on by governments and the private sector alike. 

Drones that were trained in the signal-jammed airspace over Ukraine are now being deployed in the agricultural sector to help farmers map and survey their fields in places lacking the cell signal necessary for previous generations of technology. 

Other countries are likely to follow Ukraine in selling their battlefield data, and we are not ready for the new marketplace this will create.

Bad actors could acquire the data, but purchase controls already mitigate that risk. Intelligence operatives scrutinize potential customers’ infrastructure for ways that data could reach enemies or nefarious actors. 

Training data creates a new tracing problem, though. Whereas the movement of commercial datasets can be followed when planted contact details appear two steps from the original buyer, the provenance of AI training data vanishes in a manner embedded in the technology itself. Another risk is that this use of the data creates an extractive economy in which wealthier countries far from danger benefit from the mortal threat borne by frontline states, potentially creating a market incentive for war to continue as an unending mine for digital gold. 

A fraught new frontier

Existing laws regulate how militaries may conduct war. But they say almost nothing about what happens when records created in combat are stripped of their operational context, packaged as data, and licensed to companies whose products circulate far beyond where they were made.

The responsibilities of the companies that design these systems remain unsettled. Ukraine is building access controls, which are mentioned in the newly signed UK-Ukraine AI agreement, but no governments are actively working on regulating what happens when data has been absorbed into a model and crosses back into civilian markets.

Those records contain human lives. The soldiers and civilians visible in them did not agree to become training material for products that might be sold years later. But sensor data, camera footage, and coordinates from civilians fleeing a drone strike now constitute the sorts of data that inform how future machines will make decisions.

That is a problem of consent. Individuals featured in the data—be they targets, controllers, or civilians standing by—become part of the training material. The autonomous capabilities based on that data do not stop at the edge of the battlefield. Such capabilities move into other military or commercial systems like delivery vehicles or agricultural machinery. Errors and assumptions embedded in the data travel with the model even once it enters civilian life.

Battlefield data should not be treated as ordinary commercial material. But there is currently no agency or regulator that has jurisdiction over this issue. In the meantime, governments that provide access to defense data should treat it as they would a controlled weapons transfer, recording its origin, licensing its users, and restricting onward sharing. Ukraine has begun to grapple with this. Its Avengers Labs program allows companies to train models on battlefield data without giving them direct access to sensitive databases. Yet that mitigates only one part of the problem. 

Governments should require disclosure when models trained on wartime material are later incorporated into civilian products. The goal of such regulation should be to make the path from combat to commerce visible. 

What these companies are really mining is experience. And soldiers cannot consent to having their experience used in this way—as training data that produces model advantage and ultimately supports a product used far from where the war was fought.

The question is no longer only what the technology companies can sell for use in war. It is what they can extract from it.

To protect ourselves from the excesses of this new industry, we need a regulatory system that follows battlefield data wherever it goes, from combat to model to commercial product. 

Cory Alpert is a researcher at the University of Melbourne, looking at the impact of AI on democracy. He previously served in the Biden White House.

If you’ve had to fill up your vehicle’s gas tank or buy a plane ticket lately, you’ve probably felt the effects of rising fossil-fuel prices. But farmers buying fertilizer for their crops are especially aware of just how far the ripple effects of the conflict in Iran have spread.

Fertilizer prices have been on a roller coaster this year, kicked off in part by trade disruptions and high prices for natural gas, a key ingredient in fertilizer production. Let’s take a closer look at why conventional fertilizer prices are so sky-high, and how a few more climate-friendly alternatives could bring farmers some relief.

As fossil-fuel prices go up, nearly all industries are affected, since most of our economy relies on these fuels to move goods and people around.

But fertilizer is even more intertwined with these fluctuations, because natural gas is used as both an energy source and a chemical input in the production of ammonia, a key fertilizer ingredient. So as natural-gas prices have spiked in recent months because of the war in Iran, fertilizer prices have followed. (It’s worth briefly noting here that fertilizer production is also a major source of greenhouse-gas emissions, accounting for about 2% of the global total.)

Fertilizer trade is being directly affected as well, since about one-third of global seaborne trade in fertilizers passes through the Strait of Hormuz, which has been effectively closed to commercial traffic because of the conflict. Access to fertilizer could get worse for some of the poorest countries around the world because of the strait’s closure, according to a report from the World Bank. While the US largely meets demand for nitrogen fertilizers with domestic production, some imports do come from the Persian Gulf.

At one point in April, the price of urea (the most commonly applied fertilizer) climbed above $850 per metric ton. That’s 80% higher than it was before the conflict and the highest level since 2022, when the Russian invasion of Ukraine and the resulting conflict caused fertilizer costs to hit record highs. Prices have come down significantly, but forecasts remain uncertain.

“There’s just this out-of-control supply chain that’s a lot more volatile than it’s ever been,” says Travis Frey, chief technology officer of Pivot Bio, a company making fertilizer with genetically edited microbes. (For more on these microbes, how they work, and what research is still needed, check out my latest story here.) 

Pivot says its products are cost-competitive with chemical fertilizers today. And because they don’t use natural gas as an input, they aren’t subject to the same price spikes. When the war in Iran started, Pivot increased the volume it planned to produce, dropped prices, and allowed farmers to lock in prices for three years, Frey says.

That could be a major help for those farmers, because high prices could be here to stay for a while. Some fertilizer prices could remain high through at least 2028, according to a report from CoBank, one of the largest banks for the agriculture industry in the US.

That’s partly because the war has caused long-lasting damage: 31 ammonia plants in the Middle East have been affected or shut down completely. That’s on top of 20 ammonia plants that have been damaged in Russia in recent years.

Ongoing high prices can be extremely challenging for farmers. “The fertilizer price spikes, and because farmers have paper-thin margins, this is a real problem,” says Tim Schnabel, founder and CEO of Switch Bioworks, another company working on advanced microbe fertilizers.

Higher costs can help push food prices higher, causing all of us to pay more at the grocery store. (It’s not just fertilizer, by the way. Farmers are also getting hit with wild diesel prices this year.) As long as we’re relying on fertilizers made with fossil fuels, food prices will be tied up with energy prices.

Switch and Pivot are among the companies looking to make alternative fertilizers that use microbes to provide nitrogen to plants. There’s a limit to how much synthetic fertilizers these products can actually replace: Depending on the crop and conditions, Pivot says, its products can replace about 25% of synthetic fertilizer today, and the company hopes to reach 40% to 50% of the total. But these alternatives could be a start to untangling fossil fuels and food.

“We can’t keep doing it like this,” Switch’s Schnabel says. “There’s no way we can build a society where the basis of the food chain depends on fossil fuels.”

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

A nonprofit organization called the Fermi Explorer Mission announced today that it intends to launch a spacecraft to our nearest star system by the end of 2029. 

It’s a hugely ambitious mission—if all goes well, the spacecraft could take up to 80,000 years to arrive at Alpha Centauri, which is 4.4 light-years away. And the spacecraft will follow a novel trajectory discovered by an AI system developed by Physical Superintelligence (PSI), an AI physics research lab. PSI is launching today with $58 million in funding led by Breakthrough Energy, a climate-focused investment group founded by Microsoft cofounder Bill Gates.

It’s not the first time this has been tried. In 2016, the billionaire tech investor Yuri Milner announced an interstellar mission called Breakthrough Starshot to launch humanity’s first spacecraft to Alpha Centauri. The plan was to use powerful lasers that would propel tiny probes to a fifth of the speed of light—fast enough to reach Alpha Centauri within 20 years. Milner pledged $100 million toward a proof of concept. But a decade later, nothing has launched.

“We didn’t want to do another Breakthrough Starshot,” says Philip Johnston, the cofounder and president of the Fermi Explorer Mission. “We’re dead set on something actually launching.”

To do that, “we are not constraining ourselves to doing it in a human lifetime,” says Johnston. “Let’s just figure out the way to get to another star.” 

The new mission, currently funded by individual private donors, is expected to cost just $15 million. The spacecraft will carry cargo weighing at least one kilogram. That will include artistic and scientific payloads, messages, and a copy of the Golden Record, a gold-plated disc of Earth’s sounds and images that NASA attached to its two Voyager probes in 1977 as a message to any civilization that might find them.

Engineering an interstellar journey is extremely difficult. Alpha Centauri is about 25 trillion miles away from Earth. One of the fastest objects that humans have ever launched, the Voyager 1 probe, has been flying since 1977 and has covered less than 1% percent of that distance. At its speed, the trip would take more than 70,000 years.

Johnston and his team spent a year trying, and failing, to find a way for a small, solar-powered spacecraft costing only $15 million to reach Alpha Centauri. They kept running into the knotty problem of how to give the spacecraft enough power without making it too heavy (and thus more fuel-guzzling). 

After the Fermi team struggled to find a workable route, Johnston mentioned the problem in a podcast hosted by Alex Wissner-Gross, a physicist who cofounded PSI. Wissner-Gross offered to run it through an AI system the lab developed, called Get Physics Done. It’s open-source software that takes a physics research question, breaks it into smaller tasks, and decides which simulations to run, using AI models including Anthropic’s Claude or OpenAI’s GPT.

A week later, the AI system turned up a novel trajectory, to Johnston’s surprise. It combined well-known orbital maneuvers in a way the Fermi team had not considered, according to a paper that has not been peer-reviewed. It suggested that the spacecraft could first slow down so its orbit swings in close to the sun—closer than Mercury. On each close pass, it would fire its engine so that the solar panels get four times the light, and a burst of thrust delivered at high speed would buy more energy than the same burst anywhere else. Because the engine would run only near the sun, the solar panels could stay small and the spacecraft light.

The system conducted the research mostly on its own for three days, running on a billion tokens, says Matt Pines, the cofounder and CEO of PSI. An astrophysicist on PSI’s staff steered it to follow the mission’s requirements, asked for a cost analysis and clearer charts, and checked the output for errors.

“The fact that it came up with an entirely different mission profile, one that was creative and not one [the Fermi team] had considered—that was the more surprising aspect,” says Pines. Still, the model lacks a human researcher’s judgment and taste, he says. It has no reliable sense of which problems are interesting or which approaches are worth pursuing, so it often gets stuck chasing dead ends or failing to explore different approaches. “I don’t think we’ve yet figured out how these models can internally represent something like that,” he says of research judgment.

Even if the Fermi probe launches, “we’re pretty confident that we will not be the first to arrive” at Alpha Centauri, says Johnston, since he expects spacecraft technology to improve. If an engine a thousand years from now is even 20% faster than today’s, a spacecraft launched then would still beat Fermi’s probe to Alpha Centauri by more than 10,000 years. 

But the Fermi project isn’t just an interstellar mission driven by engineering ambition. It’s also a quest to answer one of the oldest open questions in physics. In 1950, the physicist Enrico Fermi posed a puzzle: The galaxy has hundreds of billions of stars, most of them far older than our sun. Even a civilization traveling slowly between stars could spread across the whole galaxy in a few million years, which pales in comparison to how old the galaxy is. If there is intelligent life somewhere, we should have seen signs of its existence by now.

That means either reaching for another star is too difficult or other intelligent species simply haven’t bothered. But once the Fermi probe launches, we will become a civilization that can and wants to reach another star, meaning that neither explanation might be what’s keeping the galaxy unexplored. That could point us toward more unsettling possibilities, says Johnston. Maybe life like ours is almost unimaginably rare. Or maybe intelligent life is common but tends to die out before it can spread. 

If the latter is true, “one of those reasons could be that once you hit superintelligence, that for some reason is self-destructive,” says Johnston. “Maybe in the next 50 years, there’s some great filter that we do not pass through. That all intelligent civilizations, for some reason, do not pass through.”

Fertilizer is crucial for the global food supply, but making it uses a lot of energy and produces a lot of emissions. Some companies hope microbes can help.

A growing body of research shows that seeding the soil around a crop’s roots with beneficial microbes can help feed the plant, providing crucial nitrogen to help it grow. This could help reduce the need for chemical fertilizer, production of which accounts for roughly 2% of global greenhouse-gas emissions. It could also cut costs for farmers, a big boon—especially as the Iran war has sent energy and fertilizer prices skyrocketing in recent months. 

Humans have used biological fertilizers like manure for thousands of years, and companies have long been developing microbial fertilizers, including some that rely on genetic engineering. But it’s difficult to engineer microbes that can reliably provide nitrogen for crops while also thriving themselves. 

A startup called Switch Bioworks is taking a new approach that essentially allows microbes to establish themselves and grow into healthy colonies before shifting into nitrogen-producing mode. “We have to reinvent fertilizer,”  says Tim Schnabel, the company’s founder and CEO.

The air is nearly 80% nitrogen, but plants can’t use that “free” nitrogen directly because it doesn’t react readily with other elements. Instead, they rely on so-called fixed nitrogen, which has been converted into more reactive compounds such as ammonia. In nature, microbes can perform this nitrogen fixation. Some plants, like legumes, even have symbiotic relationships with nitrogen-fixing bacteria, housing them in nodules in their roots. Synthetic fertilizer is essentially industrial nitrogen fixation via the Haber-Bosch process, which uses natural gas to make ammonia that’s applied to fields.

Biological fertilizers aim to replace some of the synthetic fertilizer with microbes that can help fix nitrogen for plants. But one challenge microbial fertilizer companies have run into is that it’s energetically expensive for microbes to make and release ammonia. Putting all their energy into nitrogen fixation can hamper their growth.

There’s a certain level of colonization you want to see around the roots, Schnabel explains. It’s too expensive and logistically challenging to put all those microbes on the plant, so you have to rely on a smaller number of microbes to grow and divide, establishing the population.  

COURTESY OF SWITCH BIOWORKS

Switch Bioworks is betting that a genetic switch is the answer. A genetic switch is a section or sections of DNA that controls how genes are turned on and off. In this case it works by activating genes that help trigger ammonia production in and release from the cell. The company is working on several options for setting off this change in its microbes. The leading one is to have the microbes react to the nitrogen level in the soil: Once it drops to a certain level, they begin producing ammonia.

“You have this inherent biological reality, where it’s really expensive for microbes to fix nitrogen,” says Dan Blaustein-Rejto, director of food and agriculture at the Breakthrough Institute. It takes a lot of energy, and if they do fix the nitrogen, they want to use it for themselves, to build proteins and survive, he says. Adding genetic switches could help microbes grow and thrive and then help fertilize crops.

Switch is currently trialing its product in six US states, though it’s two to three years from a commercial product, Schnabel says. It’s initially focused on corn, the most-planted crop in the US, with over 90 million acres in 2026.

“It’s too early to tell exactly how well this works in the field,” Schnabel says. The company plans to harvest the plants in late October or early November, but as of August, some corn plants treated with Switch microbes already looked visibly healthier than those that hadn’t. And it’s still developing the products that will eventually make it to the market, he says.

Switch’s products could significantly help to clean up agriculture. “There’s a lot of potential for these companies and products to help farmers reduce emissions,” Blaustein-Rejto says. “This could be a really important solution for a quite hard-to-abate sector.”

While lab results have been promising, field trials are a crucial step in proving a product, Blaustein-Rejto says: “This is one of the final steps before they can go to market and make strong claims to farmers.” Independent trials are important as well, he adds, since there can be a large gap between a company’s reported data and what independent researchers find.

Pivot Bio is another company working to bring microbes to fields. Since it was founded in 2011, its products have been used on millions of acres of crops. The company now produces a range of products: Some versions can be added when seeds are planted, while others are applied to seeds before they’re even on a farm. The company also recently expanded beyond corn to make microbial fertilizers for cotton, wheat, and small grains including sorghum and barley.

Pivot’s initial challenge was trying to get microbes to produce nitrogen, whether they sensed it in the soil or not. Now the company is working to figure out how to make fitter, more robust colonies no matter the environment, says Travis Frey, the company’s chief technology officer.

“Growers right now are experiencing a double whammy from the farm economics point of view,” he says. Fertilizer costs are going up, and the price of commodity crops like corn has dropped. That’s a big opportunity for Pivot and others in the industry, Frey says: “This next decade is when biologicals on the farm are going to go mainstream.”

Fertilizer and seeds are two of the biggest costs for many growers, so reducing dependence on synthetic fertilizers could be a major help for agriculture, says John Havlin, a professor in the department of crop and soil sciences at North Carolina State University.

“I’m very excited about the future of the use of these products,” Havlin says. “They’ll eventually have a role to play to reduce the load of nitrogen that’s being applied.”

However, there’s a ceiling to the amount of fertilizer we can expect microbes to replace. Switch’s modeling suggests that around 50% is likely the maximum, though the initial product will likely be able to replace about 25% of a farm’s synthetic fertilizer, according to the company. Pivot has said its products can replace about one-quarter of the fertilizer used currently. 

That means synthetic fertilizer will be around for a long time. “There is no clear and plausible vision for replacing it entirely in the foreseeable future,” Blaustein-Rejto says. “So other ways to reduce emissions and reduce other types of nitrogen pollution from farms remain really critical.”

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

By now you’ve probably heard about last month’s major AI security incident, in which OpenAI agents escaped their sandbox and hacked into the AI platform Hugging Face while trying to cheat on a test. It’s a wild story. On Wednesday, OpenAI released a postmortem technical report on the incident, which I wrote about here. 

The day before OpenAI released that report, I spoke with David Krueger, a computer science professor and prominent alignment expert who took leave from the University of Montreal to found and lead an AI safety nonprofit called Evitable. He said what he had really hoped to see in the report was an analysis of the human factors behind the incident.

“When you look at accidents and incidents, oftentimes people try to find the technical source of failure, but that can give a very inaccurate and misleading sense of why the failure occurred,” he said. “If people are just cutting corners all the time, if people are not in a culture that prioritizes safety and has appropriate incentives and structures, [accidents] are kind of bound to happen.”

The report did not meet Krueger’s hopes. Its 38 pages detail a multi-month progression of agent misbehavior that culminated in the Hugging Face hack, explore the technical reasons why that misbehavior occurred, and enumerate the steps being taken to prevent similar events in the future. But there’s no consideration of the role that company culture may have played in the incident, and the report includes few references to specific human errors. 

That’s all the more concerning because the references to human error in the report suggest that significant cultural issues could be at play. Back in May, models in training figured out how to communicate with one another via an improvised message board, and an OpenAI team observed the behavior. Because that behavior occurred during training, the models learned that secret interagent communication was a viable strategy for completing tasks—but rather than restarting the training process, the team allowed the models to move forward with that risky information encoded in their weights.

When those models were tested in late June, they again created a message board, which enabled the Hugging Face attack. This message board, too, was discovered, but the employees who responded determined that evaluation could continue, and the report suggests that no one higher up the chain of command realized what was going on until it was far too late.

“For this to have gotten this out of control in this way requires a very long series of failures, a cascading set of failures that cause an increasingly large footprint that if at any point a human notices and raises the alarm, this should end,” says Zvi Mowshowitz, a popular AI safety writer on Substack who has drawn attention to OpenAI’s failure to halt training after the first message board was discovered. According to the report, OpenAI employees noticed what was happening at multiple points—and either failed to raise the alarm or were not heard when they did.

What OpenAI’s report fails to address is why a company that develops such high-risk systems did not prevent this severe communication breakdown, though Mowshowitz has his suspicions. “All these different failures are all pointing in the same direction, which is that the safety culture at OpenAI doesn’t exist or is anemically weak,” he says.

Of course, just because we don’t see a deep analysis of safety factors in the report doesn’t mean that OpenAI isn’t conducting one internally. But in an email to MIT Technology Review, Johns Hopkins University professor emeritus and organizational safety expert Kathleen Sutcliffe expressed concern that the public report did not include any reflection on the company’s practices and culture. “The ways in which people interact—the daily habits, routines, and practices we engage in in our organizational lives—affect our abilities to be alert and aware of unfolding events, our abilities to make sense of what we see, and ultimately our abilities to cope with events as they unfold,” she wrote. 

In response to questions about whether and how the company is reflecting on its safety culture, OpenAI referred MIT Technology Review back to the technical report. 

We do know that at least some high-level reflection on safety procedures has taken place at OpenAI, because the technical report does make clear that the company is updating its protocols for responding to safety incidents. But culture change is a tricky problem, and without more information from the company, it’s difficult to say whether strengthened response protocols alone will do much to prevent a future crisis.

In its report, OpenAI spends a great deal of time reflecting on the failures in alignment between the AI models the company trains and tests and the humans who run them. But even bigger alignment problems may exist in the disconnect between company culture and the public interest. And as tough as technical AI research might be, fixing those problems could prove far harder.

WhatsApp announced on August 25 that more than one billion people now use passkeys to log back into the app.

The announcement included two other security upgrades: a stronger two-step verification method and more context for incoming calls from unknown numbers. It marks one of the largest passwordless authentication rollouts to date. Passkeys are now firmly mainstream, with the FIDO Alliance estimating that 5 billion are in use worldwide and 75% of consumers have enabled one on at least one account.

Three things changed:

1. Passkey support originally launched on Android and later extended to iOS. WhatsApp now supports multiple passkeys per account, so people who switch between an Android phone and an iPhone (or use both) can register a passkey on each device.
2. Two-step verification is moving from a simple six-digit PIN to a longer alphanumeric password that can include special characters, making it much harder to guess or brute-force.
3. On Android, WhatsApp now shows extra context about calls from numbers not saved in your contacts, including whether the number is from another country and whether you share any groups. It’s a small but useful nudge against the urgency tactics scammers rely on.

Passkeys are resistant to phishing because there is no password or SMS code to type into a fake website or hand over to a scammer. Instead, a passkey is stored on your device or in its credential manager and unlocked using your fingerprint, face, or screen-lock code. They’re also useful in regions where SMS one-time-passcode delivery is unreliable, which might explain why adoption reached a billion users so quickly.

The upgraded two-step verification password closes a real gap. PINs such as “123456” were common, weak, and reused, and a longer alphanumeric password with special characters raises the bar against account-takeover attempts, even if an attacker somehow obtains your one-time code.

The caller-context feature gives people more information to assess legitimacy before answering an unfamiliar number.

Don’t recognize that number? We’ll check it.

CHECK NOW

What WhatsApp users need to do

Users need to set up a passkey and upgrade their two-step verification password, while the caller-context feature will appear automatically on supported Android devices:

• Set up a passkey via Settings > Account > Passkeys, and follow the instructions on your device. Don’t forget to add a second one if you use both an Android and an iOS device.
• If you still use a six-digit PIN for two-step verification, upgrade to the new password format when it becomes available, especially if your PIN is predictable. You can find instructions to set up two-step verification for WhatsApp in this blog. If it’s already enabled, select Two-step verification to find the option to change your PIN.
• Add a recovery email to two-step verification if you haven’t already. It’s the only way to reset the password if you forget it.
• Android users should pay attention to the new caller-context details before answering calls from unknown numbers, treating urgency as a red flag rather than a reason to rush.

Passkeys and stronger two-step verification aren’t retroactive or forced, so accounts still relying on an old PIN or no passkey at all will remain unchanged until users upgrade them.

Scammers know more about you than you think. 

Malwarebytes Mobile Security protects you from phishing, scam texts, malicious sites, and more. With real-time AI-powered Scam Guard built right in. 

Download for iOS → Download for Android → 

MIT Technology Review’s How To series helps you get things done. 

Your thermostat may not look like a power plant. Neither does your electric vehicle, home battery, or HVAC system. But utility and energy companies increasingly want to treat them like one.

A virtual power plant, or VPP, is a collection of household devices (such as smart thermostats, electric-vehicle chargers, home batteries, and solar panels) that a utility can control. Usually that means commanding the devices to draw less electricity during peak hours. For example, the utility might adjust your thermostat or delay or slow EV charging when electricity demand is high. 

In exchange, the utility offers VPP participants a discount on their energy bills and, in some cases, a signing bonus. Seth Frader-Thompson, CEO and cofounder of EnergyHub, a software company that helps utility companies run VPP programs, says a smart thermostat program may offer an initial bonus of roughly $50 to $150, plus about $25 to $50 per year, while home battery and EV devices could yield hundreds or thousands of dollars in annual savings.

The amount of power the utility might throttle in any one home is small. But it adds up, Frader-Thompson says. “When you put it together at the scale of hundreds of thousands, or millions, it has a pretty profound impact,” he says, equivalent to “firing up a power plant.”

As of 2023, there were already more than 500 VPP programs operating in the US alone, and the number has only grown since, especially with big players like Google starting to invest in this technology to help power their data centers. An estimated 4 million households with smart thermostats were enrolled in a VPP program as of last year. 

But the approach is still new, and some programs may still have some kinks to work out, says Severin Borenstein, faculty director of UC Berkeley’s Energy Institute at Haas and member of the board of governors of the California Independent System Operator, which manages most of the state’s electric grid. If a program is not implemented well, he says, a utility may incorrectly predict when VPP participants plan to use more electricity and pay them for not using energy they weren’t planning to use anyway, potentially increasing energy bills for nonparticipants. Still, Borenstein says, “if we do it well, I think it can really be a benefit,” one that could help utilities avoid an expensive grid upgrade or emergency measures to conserve power.

Most consumer VPPs today are less dramatic than the name suggests and don’t actively send energy from your EV or home battery to the grid. But battery-to-grid programs are on the rise—and potentially offer even larger savings for consumers in the future.

So how do you actually sign up for a VPP? And how do you know if it’s worth it?

1. Check whether your utility company has a program and, if so, whether it actually supports your devices.

The types and brands of home devices supported vary from program to program. Your utility’s website is the obvious place to look to see if yours qualifies, but it’s important to note that you may not actually see the phrase “virtual power plant” anywhere. You may have better luck searching for your utility’s name plus terms like “demand response,” “peak rewards,” “connected solutions,” “battery storage,” “smart thermostat rewards,” “managed charging,” or “bring your own device.”

But don’t stop with the utility, Frader-Thompson says: “The way most people actually learn about this and sign up is through the manufacturer of the device they have.” In other words, the offer may show up through your smart thermostat app, EV app, or battery app, or in an email from the company that made the device.

Once you find a program, the instructions for enrollment may be as simple as clicking through an app, filling out a utility form, or confirming your account and device information through a third-party enrollment page. EV drivers may be able to see the terms and payment in their automaker app and enroll “with a click of a button,” says Joseph Vellone, CEO of the EV-focused VPP company ChargeScape.

Eligibility can get annoyingly specific. A smart thermostat program could require an approved Wi-Fi thermostat; an EV program may depend on your automaker, charger, utility territory, or rate plan; a battery program may depend on the battery brand, inverter, or installer and whether your system can communicate with the utility.

These programs are also not evenly distributed across the country. Most programs are established in places with lots of flexible devices, stressed grids, supportive utilities, or strong state policies—especially California, Texas, New England, and increasingly parts of the mid-Atlantic region.

2.  Ask yourself how much flexibility you can afford.

Before you sign up for a VPP, you’ll want to determine whether you’re willing to let a company adjust a device in your home—even if it typically happens only a few times a week.

For some people, this may be an easy decision: If your EV sits plugged in all night but only needs two hours to charge, shifting when that charging happens may be almost invisible. A home battery program could be lucrative if you understand how often the battery will be used, how much backup power you can keep, and whether extra cycling affects your equipment.

Other households, however, “do not have the flexibility to engage in one of these programs,” says Sanya Carley, a professor at the University of Pennsylvania and faculty director of the Climate Center for Energy Policy. She says that people who work night shifts, have caregiving responsibilities or health needs, or are already aggressively limiting their energy use to save money may have less room to allow a utility to adjust heating, cooling, or charging rates during peak hours for grid demand. 

3. Review the opt-out rules and read the fine print.

VPP programs generally give participants the ability to override temporary changes made by the utility. This right to “opt out” is what makes them workable for many customers. Can you skip a day of the program on your thermostat if you’re planning to have guests over? Can you tell your car to charge immediately before a long road trip? Can you keep a battery reserve for outages? Utilities are typically motivated to make the opt-out process as simple as possible, with few rules and restrictions.

It could also be worth investigating where your data might be going. EV and battery programs may need to collect data about things like charging status and schedule, or how much power a device is drawing, while smart thermostat data may reveal patterns about when people are home, sleeping, or using appliances.The Electronic Frontier Foundation, a nonprofit focused on digital rights, has warned that this data could be used to infer private routines inside a home; depending on the program, that information may not only move through a utility but get distributed to device manufacturers, software platforms, or third parties involved in running the program.

ChargeScape and Energy Hub say the data used for these programs is limited and functional. EV data is focused on “the physics and the energy of the asset itself,” Vellone says. Frader-Thompson explains, “It doesn’t really matter what any one customer is doing. It matters what the average customer is doing.”

4. Decide whether the offer is worth it for you.

The amount of compensation for signing up for a VPP can vary widely. The payment also may not come as a regular check. It might be a signup bonus, a gift card, a monthly bill credit, a discounted thermostat, free or cheaper EV charging, an annual performance payment, or additional “export credits” for energy sent back to the grid. 

The most expensive devices, namely EVs and home batteries, are often what yield the greatest savings, which adds a barrier to entry for those who cannot afford these products in the first place. A smart thermostat program can be a low-stakes way to start.

You might have a variety of reasons for wanting to sign up, including supporting the overall health of the grid or avoiding the construction of a new power plant in your community. “There are not that many things that you can do that directly contribute to decarbonizing the electric supply, or to improving affordability, or to improving reliability, and this is just a clearly effective way to do that,” Frader-Thompson says. “And you get paid for it.”

In short, the best VPP program is not necessarily the one that pays the most. It’s the one that clearly tells you what it can control, how much money you’ll get, how easily you can say no—and how well it supports a community’s energy goals. Your home probably won’t feel like a power plant. But if your thermostat, car, or battery can bend a little when the grid needs it, your home can act like a small piece of one.

I knew I’d officially become a “longevity influencer” this month when a company called Generation Lab reached out to offer me the chance to write about—and even receive—their new rejuvenation treatment, an injectable combination of two existing drugs which they call 1 Generation.

This wasn’t just any antiaging treatment, either. A company fact sheet says that it “blocks the systemic spread of aging in the bloodstream, reawakens the body’s own repair mechanism and restores health and youth to multiple tissues.”

The exclusive offer to join a “private cohort” receiving access to the treatment was being “extended to a limited number of people whose judgment on the science we trust, under close physician supervision.”

“There’s a long wait list including a lot of celebrities, well-known people, people who you have heard of,” Generation Lab’s CEO Alina Su told me in a conference call. “So we’re super excited to, like, create this drug and [are] letting you know that this first longevity therapeutic in [the] human body is here and it’s actually working.”

This was all clearly a marketing campaign and, like most other claims in the world of longevity medicine, probably too good to be true. What’s more, Generation Lab wouldn’t tell me what the two drugs are, making the proposition hard to take seriously.

Yet this pitch got my interest. The reason? I am a longtime follower of Generation Lab’s irascible scientific founder, Irina Conboy, a take-no-guff specialist in the bizarre, but scientifically fruitful, practice of joining together the circulatory systems of old and young lab animals.

The procedure, called parabiosis, or heterochronic blood exchange, is one of the few things actually proven to make an old animal act younger. 

But you can’t go through life attached by the veins to a younger person. So the quest has been to find practical ways to mimic those benefits. 

And that is something Conboy says she’s now achieved by hitting on a combination of two existing drugs that produce youthful effects—but without the need for any bodily fluid exchange. 

Over the last two months, Conboy has been taking the drug combination and feeling quite a bit better and more energetic, she says. So have a handful of other insiders, including Su, the company’s 26-year-old CEO, as well as Conboy’s husband and scientific collaborator Michael Conboy.

The company’s public relations staffer sent me a spreadsheet of the supposed benefits experienced by the participants, including improved vision in a 64-year-old female, longer landscaping sessions for a 59-year-old man, longer badminton games, improved hand grip, better erections than with Viagra, as well as “increased bowel movements.”

Obviously, any age-reversal drug that works would be the best-selling product of all time. The problem is that none have ever been shown to do that. And Generation’s approach to evidence raises several red flags. For instance, Su says the company now plans to launch a larger study, involving a hundred or more people. However, others are being offered the drug before firm evidence of a benefit is in.

In addition, the trial will be led by longevity doctors who practice alternative medicine. These include Matt Cook, founder of a network of clinics called BioReset Medical. Cook’s clinics offer what I would characterize as unproven treatments, such as stem-cell injections. A 2021 article reported he’d recommended covid-19 treatments designated as “fraudulent” by the FDA. Those included inhaling amniotic fluid from a nebulizer.

In a phone call, Cook says he initially agreed to prescribe the combination to Conboy and a few other insiders. (Everyone involved, he says, is a member of the same “happening” Silicon Valley scene around life extension.) “My confidence this was going to work was a zero. Like, I did not believe it was actually going to work,” says Cook. That is because “it’s two safe, generic drugs that are not even longevity drugs.”  

But Cook, who also took the combination, says there was an effect. “And I have to tell you, it was the most surprising thing that ever happened to me,” he says. After taking the weekly injections, he experienced a sense of mental clarity that lasted for a day, and then several days. The other subjects, he says, reported a sense of well-being, as well as old aches and pains that evaporated. 

“There was a fairly significant broad set of symptoms that doesn’t really match what either of the drugs do,” he says. “So it’s interesting. [But] I have many more questions than I have answers … I still feel like I don’t understand exactly what the mechanism is.”

Prior to joining Generation Lab full time in 2025, Irina Conboy had a long career at the University of California, Berkeley, where she made her name via a widely cited series of experiments aimed at learning   whether the characteristics of age, or youth, could be transferred between animals via the bloodstream. In 2005, for instance, she surgically connected two-month-old mice to very aged ones, finding that the old animals’ ability to heal from injury dramatically improved.

This suggested that young blood must contain specific, powerful molecular signals capable of restoring the regenerative capacity of an old animal. As researchers zeroed in on candidate signals, several startups, including Elevian, a Harvard spinout, and Alkahest, tied to Stanford University scientists, were able to raise millions to pursue treatments for stroke or Alzheimer’s. However, those efforts still have not hit commercial paydirt.

Although it’s clear that young blood is good for old mice, scientists also know the reverse is true: Even a single exchange of plasma from an aged mouse has powerful, negative effects on a younger animal. That means that in the battle of age versus youth, says Conboy, “old blood dominates.”  

Conboy next tried a new approach. Instead of giving old animals young blood, in 2020 she removed the plasma (the part of the blood without cells) of old animals and replaced it with a neutral mixture of albumin and salt water. This was like pressing the Delete key on the swarm of hormones, antibodies, and other molecules released by the aged body.

The reset had even stronger rejuvenating effects than sharing a young animal’s blood, and within two years, Conboy had tried it on human volunteers who agreed to have almost half their blood volume removed and thrown away.

Longevity clinics have been quick to follow Conboy’s research. Some began offering “therapeutic plasma exchange” as a wellness intervention, for prices of up to $10,000 a session. One doctor that offers it, Jeffrey Gladden, says that after seeing Conboy’s publications, he raced to California to meet her and to get the procedure himself.

By the end of 2025, Conboy had retired from Berkeley and joined her startup full time. Generation Lab has been selling an aging diagnostic test, but Conboy still wanted to find a drug that could mimic the benefits of plasma exchange. To do that, she started using a testing system she’d codeveloped, which allows researchers to bathe human cells kept in a microfluidic device with blood serum from old people.

The new device, described in a paper this year, allowed her to quickly test her intuitions about what drugs might work. “This is an awesome screen or experimental system which allows us then to ask a question: If tissue becomes old in the presence of old blood, which molecule or combination of molecules will prevent that and allow tissue to remain young, even when the circulatory milieu is old?”

She says 1 Generation works by stimulating cells to regenerate and, simultaneously, neutralizing the old-age factors in a person’s bloodstream. “It allows human cells to remain young even when they are in the presence of old people’s blood serum,” says Conboy.

Without knowing what the drugs are, I can’t give my own opinion about their promise. But some doctors working with Generation Lab expressed surprise that the combination would work. “I will say right now, candidly, it’s an unknown how it will play out. But there’s certainly a lot of enthusiasm based on the quality of her work in the past,” says Gladden, the longevity doctor, who says his Texas clinic will also be involved in the study. “I am skeptical and optimistic at the same time.”

Pressed on why the names of the drugs remain confidential, Su said it’s to avoid imitators. That’s important because Generation doesn’t own the molecules. Instead, it will seek to quickly run a clinical trial, taking advantage of an FDA process that gives companies a short period of exclusivity, perhaps three years, if they can show that a combination of existing drugs has a new use.

In the meantime, the company’s effort to generate buzz is having some success. Generation is hosting an August 28 invite-only event that will feature talks by George Church, the noted Harvard University synthetic biologist, as well as researchers from Anthropic, whose CEO, Dario Amodei, has been saying AI will cure all disease within a few years. In an email, Church says he was also offered a chance to take the drug combination.

“People are clamoring for it,” Cook, from BioReset Medical, told me. He says his office has started getting calls “from high-end doctors, venture capitalists, and people in this community who are kind of biohackers.”

“It’s high-level influencer-type people,” he adds.

As for me, no, I won’t be taking the drugs any time soon. Not until I know what they are.

Correction 8/27/2026: A previous version of this story incorrectly said patients would pay to join the larger trial of a proposed longevity drug. While patients can pay to get the drug from a doctor, the larger trial will be free to patients and sponsored by Generation Lab.

EVs account for under 10% of total new-vehicle sales in the US, and the numbers are declining. From a climate perspective, that’s pretty dismal, especially because the transportation sector is the single biggest source of greenhouse-gas emissions in the country. 

One thing that could help turn that around? Slate Auto’s new truck—a vehicle that seems to buck every convention about selling cars in the fully loaded, range-obsessed US market.  

The company is going all in on simplicity, to the point of austerity. The Slate is a tiny, two-door pickup that’s shorter than a Honda Civic. Much of the media coverage has obsessed over the fact that the base model’s windows use hand cranks, a feature straight out of the 20th century.

Slate is also breaking away from other EV manufacturers’ efforts to compete with gas-powered vehicles on range. The truck sports a small lithium iron phosphate battery, ringing in at 65 kilowatt-hours, and its quoted top range is just 205 miles. For comparison, the most basic Tesla Model 3 can go over 320 miles on a charge.

By accepting a shorter range and forgoing the frills that Americans have come to expect in vehicles, Slate is able to offer the base model of its truck for less than $25,000. Some customers will choose to upgrade their truck with optional add-ons, like a Bluetooth stereo system, vinyl wraps, or even power windows. But the price will still likely come in well below the roughly $50,000 average for a new vehicle in the US.

It might seem an odd choice to go so small and simple in a market that’s increasingly sizing up—but the status quo hasn’t exactly been working for EV makers. A few years back, the hero for US automotive electrification was supposed to be the Ford F-150 Lightning. Announced in 2021, it was an electric version of the country’s best-selling vehicle. 

But Ford discontinued the truck in December 2025, just four years after its introduction. It’s not entirely the Lightning’s fault: The second Trump administration slashed tax credits and other support designed to boost EVs. 

The Lightning was also plagued by price increases. The base model cost roughly $40,000 when shipments started in 2022; during its final year, prices topped $54,000. One factor behind the increase was its massive battery; to reach an almost 300-mile range, the truck needed a battery with a capacity roughly twice that of Slate’s truck. 

That obsession with range is largely unwarranted. While Americans have historically chased distance, the average driver puts under 35 miles a day on the odometer, and nearly 90% of trips in a personal vehicle are 20 miles or less. 

Surveys of EV drivers show a similar trend: One recent study found that people tend to use less than 20% of their EV’s range on a typical day. 

The notion that drivers need far less range than they think they do might sound a lot more persuasive these days—even for Americans who tend to buy cars for their longest road trip instead of their everyday errands. Roughly half the country is struggling to pay for basic necessities such as gas and groceries, and 95% of Americans believe we’re in an affordability crisis, according to a recent Harris poll. An inexpensive vehicle that allows you to skip the gas station sounds like an attractive prospect. 

Affordable EVs have already found willing buyers in other parts of the world—even with reduced range. China currently has over 40 million EVs and plug-in hybrids on the roads, and roughly half of new vehicles sold are electric. On average, new EVs sold in China in 2025 had a range of just 247 miles. For the US over the same period, the average was 329 miles. (Europe falls between the two, at 281 miles.)

Slate is set to start delivering on preorders in late 2026. Its factory will have a capacity of 100,000 vehicles in the first year and 150,000 soon after. Thousands of customers have already put in preorders, according to the company.

Some people obviously believe in the truck’s prospects: Investors, including Jeff Bezos, have put nearly $1.4 billion into the company over three major funding rounds. Ford is jumping (back) into this space soon too. The automaker is working on its own small electric truck, which is expected to debut in 2027 at a retail price of around $30,000.

The key question that will determine Slate’s success or failure is whether drivers can get on board with a small, short-range vehicle for the sake of its price tag. At this moment, I’d bet the answer is yes. 

The models responsible for last month’s agent hack of Hugging Face had been inadvertently trained to cheat and to communicate with each other, according to an OpenAI technical report released today. The hack, which a group of agents undertook to find solutions for a cybersecurity test that they were stuck on, has confirmed some experts’ fears that AI models might take actions that defy human desires and expectations. 

Since the hack, OpenAI employees—as well as researchers at the AI evaluation nonprofit METR, which released its own report on the hack today—have worked to understand what went wrong and how similar missteps might be prevented in the future. OpenAI has already put some preventative measures in place based on what they discovered. But making sure AI models do what we want them to do, or “alignment,” remains a gnarly problem, and some of the root causes of the hack will take much longer than a month to resolve.

“It’s not something you can solve overnight,” says Kai Chen, who runs OpenAI’s alignment research team. “There are challenges we’ve been tracking for a very long time, and we’re now seeing them with much greater precision.”

The Hugging Face hack was a product of months of misbehavior from OpenAI agents, first as they were being trained and then as their abilities were being evaluated. This May, agents in training figured out how to use OpenAI’s infrastructure to communicate with one another and get support with difficult training tasks, including some that were impossible to solve without hacking or otherwise misbehaving. That “message board” was shut down.

Then in July, while being evaluated for their cybersecurity abilities, some models created a new message board. They were supposed to be isolated from the internet, but by working together they managed to get online, hack Hugging Face, and obtain solutions for the cybersecurity problems that had stumped them.

Based on their investigation, OpenAI researchers believe that events during the training phase led directly to the hack. “For almost every behavior that was worrisome at evaluation time, [we were able to] find some sort of associated behavior at training time that actually we think might have contributed to it,” says Eric Wallace, a member of OpenAI’s alignment research team. 

When models correctly solve problems during training, the behaviors that led them to that solution are reinforced, and they become more likely to engage in them in the future. So if a model completed a task in May after using the original message board, it became more likely to participate in a new message board later on. This phenomenon, where AI agents misbehave in ways that are reinforced during the training process, is known as reward hacking.

Reward hacking also helps to explain why the models worked so hard to make their way onto the internet. During its investigation of the incident, the OpenAI team found that, over the course of training, the models became more and more likely to probe their digital environment for weaknesses and use the tools at their disposal in unexpected ways—a sign that these behaviors were being gradually reinforced. By the time the models were facing tricky cybersecurity problems, they had learned that hacking was an effective way to achieve their goals.

These results suggest that the Hugging Face hack could have been avoided if the models weren’t rewarded for misbehaving during training. While researchers don’t yet know how to prevent reward hacking entirely, OpenAI is taking some steps toward mitigating its effects. The company will now look for signs of cheating in all frontier models during training by keeping an eye on their chains of thought—internal notepads where they sketch out their answers and plan their actions. 

This solution isn’t as much of a slam dunk as it might seem: In earlier research, OpenAI showed that punishing models that mention cheating in their chains of thought teaches them to keep their intentions hidden from researchers. But monitoring its models’ thinking does give OpenAI the chance to halt the training process and reassess its approach if models do start learning to reward hack.

If OpenAI stops reinforcing reward hacking in its models—and that’s a huge “if”—that would be a huge step forward. But it wouldn’t solve the alignment problem. The first time a model communicated with other agents or hacked its infrastructure during training, those behaviors had never been reinforced, so agent misbehavior can’t only be attributed to that reinforcement.

Jeffrey Ladish, director of the AI safety nonprofit Palisade Research, compares the agents to a human who commits their first financial crime. “It’s not like they had to do fraud before to figure out that fraud is an effective strategy, and you have the same problem with models,” Ladish says. “Alignment science needs to be understanding how model motivations get shaped, such that we can actually figure out how to get models to care about the consequences of their actions.”

OpenAI’s researchers do have a hypothesis for where some of the misbehavior originated. Before the models formed their first secret message board, they had been trained to communicate and coordinate with subagents—less powerful agents to whom a main agent can delegate tasks. 

That learned communication behavior could have transferred to this new setting. The METR report, which investigates the messages that the models sent to one another in detail, supports this hypothesis: One agent on the message board took charge and assigned tasks to the other agents, effectively treating them as subagents. OpenAI could try to prevent agents from secretly communicating with one another by not training this subagent behavior in the future, but that would make the models less useful. 

This tension between capability and safety is at the heart of what went wrong with the Hugging Face incident. OpenAI researchers also identified the models’ persistence as a key factor in the hack. 

When they were accidentally given unsolvable problems, the models didn’t give up; instead, they strove to find solutions by any means necessary. But persistence is also a virtue, of course, especially if we want agents that can undertake large amounts of difficult work independently.

OpenAI is working on giving models ways to alert humans if they are given impossible tasks. The problem of teaching models when they should deploy their abilities and when they should hold back, however, won’t be settled in a single postmortem. The training strategies that create superhuman coders—rewarding them when they successfully solve problems—might not work to teach models to use their skills judiciously and respect human desires and values.

“I think there’s a bunch of alignment science that still needs to be done where we can move past just using proxies for task completion,” says Ladish. “That will work to make models very capable, but I don’t think it will work to make them aligned.”

When my oldest child was born, I immediately set up Gmail and Twitter accounts in her name. I broadly announced her birth online and proceeded to plaster her photo across all sorts of platforms. In short, I began creating her digital footprint long before she could stand on her own two feet. 

Fast-forward a couple of years to when my second kid came, and I had essentially the opposite reaction. I wanted to make sure I preserved her privacy. I didn’t want her birthday to be a matter of public record or her face to feed the algorithms. In time, I would go back and scrub much of the early footprint I had created for my first child as well. 

What happened? I watched the promise of the early internet give way to the reality of its potential for abuse. I myself was already fully in the throes of smartphone and social media obsession. My wife, a pediatric nurse, grew increasingly alarmed at the number of children admitted to her hospital struggling with the effects of things like body dysmorphia or cyberbullying as a result of interactions on social media. We became those parents. The ones whose kids carry flip phones and aren’t on TikTok. 

We are not alone in this. A surprising—maybe troubling—number of people I know who work at big tech companies also keep their kids at arm’s distance from technology. They lock down their phones, if they have phones at all, and keep them off social media. Hell, even Mark Zuckerberg doesn’t publicly post his children’s faces on Facebook or Instagram.  

If the desire to limit kids’ use of technology was once a subcurrent, it has become a raging flood. Jonathan Haidt’s best-selling 2024 book The Anxious Generation helped propel the issue into the mainstream (despite criticisms of his conclusions from some developmental psychologists). Last year, Australia became the first country to enact a social media ban for children under 16. Other nations, from Austria to Indonesia, have followed suit, announcing similar bans. The US Supreme Court recently upheld an age verification law in Texas, which acts as a de facto ban, and several states have flirted with their own measures. School districts all over the country are banning educational devices like iPads and Chromebooks in favor of actual books. Kids themselves seem to be embracing this tech skepticism too: The hottest gadget among the Gen Alpha set is a vintage Sony Walkman.

We have to prepare our children to live in the actual world we have actually created, not the one we wish we had.

Yet there is no hiding from technology. It permeates nearly everything, everywhere. And so we have to prepare our children to live in the actual world we have actually created, not the one we wish we had. How can we help kids survive and thrive in what we have wrought? 

It’s a question that feels all the more urgent in the era of AI. To help answer it, we brought in the editors of Anyway—an utterly fantastic magazine for teens and tweens that is so good in large part because it meets them where they are. (If there is a teen in your life, I highly recommend it.) They helped us with the stories you’ll see in this issue, and they asked kids to share in their own words how they are feeling about AI and what’s to come. What those young people told Anyway was complex, fascinating, and, to an incredible extent, thoughtful and sophisticated. 

Meanwhile, I’ve loosened the digital tether—just a bit. I still don’t post many photos of my kids online, and I remain abundantly concerned about the perils of social media and AI. 

But when my older daughter started high school, we retired the flip phone in favor of an iPhone. And my younger one now sports an Apple Watch. These devices have opened up the world to them in all sorts of ways. They help forge new friendships, building relationships that move seamlessly between digital and physical spaces. They allow my kids to roam free—or at least more freely—beyond the known spaces of our neighborhood and across the city. 

Along the way, they’re learning and testing boundaries, just the way they’re supposed to. I guess you could say I am too. 

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. 

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!

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.

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.)

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.

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.

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

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)

From several independent reports, we’ve seen evidence of scammers using fake Android “interview” apps to target job seekers on the Indeed platform.

Indeed is one of the world’s largest employment websites, giving scammers access to a huge pool of potential victims, especially in a competitive job market.

What we found

A user in the UK posted on our forums after being instructed by a supposed employer on Indeed to install an “Interview App.”

A user in Brasil submitted an anonymized report after receiving similar instructions to install an APK named MyInterview from a link shared during a job interview.

Meanwhile, Reddit users discussed a “Indeed Interview” app that allegedly completely compromised one user’s phone.

The victim who installed the MyInterview APK said their phone began closing apps by itself after installation. They also shared a screenshot showing MyInterview listed under Android’s downloaded Accessibility services.

Common lures used by the scammers include:

• “Complete your interview by installing the Indeed app.”
• “Update your Indeed application.”
• “Identity verification required.”
• “Download our recruitment portal.”
• “Salary agreement available after app installation.”

An analysis by Malwarebytes Android Malware Researcher Nazeeh Sulaiman showed that these Android apps impersonate Indeed’s login page before creating a VPN connection after an applicant enters an email address. Static analysis identified the apps as Trojan.Droppers, capable of installing additional untrusted apps.

At the time of writing, the final payload was spyware, although we initially expected a banking Trojan. Once the malware is granted the Accessibility permission, it effectively takes over the device. The interesting thing here is that it can prevent users from uninstalling the malicious app. When the user taps Uninstall in Android Settings, the malware simply forces the screen back, preventing removal.

How it works

Scammers advertise fake job openings on Indeed and lure applicants into installing a fake Android app that impersonates Indeed and present itself as an interview tool.

After confirming their application, job seekers receive instructions like these:

In this example the job seeker is instructed by a “recruitment firm” to download and install the app, connect to the VPN, create an account, and enter an invitation code. They are then told to keep the app open while waiting for confirmation.

This malicious app is not affiliated with Indeed. The company’s official Android app, Indeed Job Search, is distributed through Google Play, not through an APK supplied in a recruitment message or an unfamiliar interview website.

The interview process on Indeed does not require applicants to install a separate app, confirmed by an Indeed spokesperson:

“Interviewing through Indeed’s platform happens entirely in a browser and never requires downloading a special app. Any message asking a job seeker to download an app to participate in an interview is not legitimate. We encourage job seekers to avoid clicking links or downloading files from any message directing them to do so.”

It’s worth pointing out that the dropper is not necessarily the final payload. It’s an initial-stage app intended to install another malicious or unwanted app onto the device, often after bypassing a victim’s caution with a seemingly legitimate pretext. Even if the fake Indeed app does not visibly steal data itself, it can serve as a delivery mechanism for more dangerous malware.

A VPN connection is perfectly legitimate in many situations, but there is no obvious reason for an interview app to create one immediately after an applicant supplies an email address.

In a malicious workflow, a VPN can give an app substantial influence over the device’s network traffic. It may allow attackers to route communications through systems they control, hide what the app is doing, or support later stages of the attack. The VPN behavior alone does not prove that traffic was intercepted or modified, but combined with brand impersonation and dropper functionality, it is a serious warning sign.

The fake app’s presence in Accessibility settings is also concerning. Accessibility services can view screen content and perform actions of behalf of the user, making them attractive to malware developers. In the screenshot supplied to us, MyInterview was disabled, so there is no evidence the service was active on that device. Nevertheless, its presence as a downloaded Accessibility service is relevant to the overall risk assessment.

How to stay safe

A job interview should not require you to sideload an Android app, enable a VPN connection, or install software from an unknown source.

In this campaign, the supposed interview app is simply the lure: it impersonates Indeed, establishes a suspicious network connection, and is designed to deliver additional malware.

Before using any recruitment platform, make sure you understand its hiring process and be suspicious of requests that deviates from it or move you to another platform.

Don’t install apps just because someone tell you to, especially if you have to especially if you have to install them outside Google Play.

Verify job offers through independent channels. In some of the reported cases, the companies either did not exist or not have offices in the cities where they claimed to be hiring.

The Indeed spokesperson added:

“Job seekers are at the heart of everything we do, and their safety and trust are a top priority. We are aware of scams involving individuals instructing job seekers to download an app to complete a virtual interview. These are in no way affiliated with Indeed, and we strongly condemn bad actors who exploit the trust job seekers place in our platform and brand.  

For more on how to verify a legitimate Indeed app and spot the warning signs of a fake one, visit our Help Center.”

Indicators of compromise (IOCs)

Trojan droppers:

Dropped malware payload:

Domain: startcareer[.]org

It’s a glorious day in Kirkland, Washington, an affluent Seattle suburb on the eastern shore of Lake Washington. The temperature is in the mid-80s, and the sky is incapable of being any more blue. The view from the Gates Ventures conference room overlooks the Carillon Point Marina, where a flotilla of expensive boats bob in the water, and across the lake to the Olympic Mountains that define the horizon. It’s gorgeous. And vaguely terrifying. 

Because if the scene is placid, the messenger is not. Seated across from me at a conference room table, Bill Gates is rocking back and forth in his chair, totally animated. And the more he has to say—about the threats of terror or economic collapse or just losing control of our AI systems—the more agitated I find myself becoming, too. 

The philanthropist and former Microsoft CEO says he has been growing increasingly alarmed by the rate of change at which AI technology is advancing, especially since guardrails are not keeping pace. In a new essay published today, Gates argues that we have passed the points where multiple potential dangers should have been checked. “We’ve crossed the threshold in terms of [AI’s] bio-capabilities, cyber-capabilities, psychosocial capabilities, job-market-destruction capabilities, and even the lack of control,” he said in an interview with MIT Technology Review about his new memo. “I’m just stunned at the lack of concern and discussion outside of the industry.”

In an effort to wake the world up to what he sees as a rapidly growing societal disrupter, the 70-year-old tech titan has begun sounding the alarm as a “shrill voice,” both publicly with his new essay (the first of multiple he plans on the topic) and in meetings with the press, and privately in conversations with industry, government, and civil society leaders. 

And while Gates is calling attention to a number of issues, his warnings about the bio-capabilities of the current frontier models are especially chilling. “Any model that can make novel molecules should be monitored,” he says. “I view bioterrorism risk, versus a natural pandemic, as about 50 times more scary, more likely than a natural pandemic risk.”

In addition to the cautionary notes, he also advances some novel ideas for moving society forward. Among them are the concepts of human-reserved jobs, and taxes on robots and tokens. (A robot tax is a longtime notion of his.) The former would preserve some societally agreed-upon jobs for human beings, which he notes may vary from one nation to another. The latter is a tax that sets aside money earned from AI usage that replaces human work. 

And to be sure, there is also a hint of optimism. Gates is bullish on the ways AI will continue to transform agriculture and health care and education, for example, or the ways in which it can help us navigate bureaucracy. And, he argues, eventually we do get to abundance. But first? Turbulence. And lots of it. 

MIT Technology Review sat down with the billionaire philanthropist to talk about the road that lies ahead, its dangers, and how it could someday take us to a better place. 

The following interview has been edited for length and to improve clarity and readability.

Mat Honan / MIT Technology Review: Thanks for doing this. I don’t know if you had something you wanted to open with, or I can just jump in. 

Bill Gates: You know, one good question I’ve had is: Why am I speaking out now?

MIT Technology Review: Literally, my first question!

Bill Gates: It’s really two things. One is that we’ve crossed the thresholds in terms of the bio-capabilities, cyber-capabilities, psychosocial capabilities, job-market-destruction capabilities, and even the lack of control; we’re seeing signs of difficulties there. And all these years, people have said, “Okay, when we get close to these thresholds, we’ll really figure out how to let only good people use it, or how to not let it do these things, and maybe that’s when we won’t let people copy models.” And I’m in a state of shock that we’ve crossed these thresholds. 

So the fact that we’ve gotten past these is one reason, and the second is that I’m just stunned at the lack of concern and discussion outside of the industry. Within the industry, it’s complicated because the industry doesn’t like criticizing itself, or players like criticizing each other. Some companies are hiring fewer entry-level workers, which you’d have to call a pretty modest signal. But it’s going to happen, and not in any long time frame—because the things that hold people back in terms of capabilities and reliability, all those things are being solved. And so for a substantial part of the white-collar market, you have very low-cost substitution. And then you can have an opinion on how quickly robotics come along. We’re not there yet, but it is stunning the progress being made there—a little bit more in China than in the US, but somewhat in both.

MIT Technology Review: You talked about all of this happening so much faster than the internet revolution did, than some of these previous technological revolutions did. What type of timescale are you talking about? You pointed to the thresholds that we’ve crossed. In your view, have we already passed some sort of tipping point where there’s going to be this inevitable change? 

Bill Gates: The past definitely is very misleading on this, and a lot of people lean on that. “Hey, no previous technology resulted in a net jobs reduction,” and they’re right. And I’ve given that speech. 

But with any credibility that I have, this time is different. When you can replace human cognition for an extremely high percentage of jobs across every industry in the same time frame at modest cost, relative to human labor costs, and your error rates … will probably be lower than human rates. The past is just very misleading. The current economic statistics are very misleading.

“If you’re worried about AI, going to a data center protest is not the most effective way to start the debate about how we minimize these bad things.“

Bill Gates

And to the degree there’s any expression of concern at all, it’s like, “Hey, don’t build data centers.” Well, you can stop every data center in the United States and it won’t change any of the issues that I’m talking about. Data centers will be built globally. If you’re worried about AI, going to a data center protest is not the most effective way to start the debate about how we minimize these bad things. Just like yelling at an oil company executive is not the way to solve climate change. 

MIT Technology Review: You talk about the benefits of AI in your essay as well as the costs. How are you thinking about balancing that message? And are you hoping people get a little worried when they read it?

Bill Gates: They’d better! I didn’t expect to be the shrillest voice saying society broadly is not paying attention to this, but I think that’s necessary. 

So yes, I’m super concerned that the negatives will be a lot bigger. The positives are real. The Gates Foundation, the way we’re innovating in vaccines and drugs, it’s incredible how we’re using those tools. We’re part of a big public-domain effort to gather data into both protein-level and cell-level modeling, and we fund Biomni at Stanford [a biotech AI agent for research]. 

We don’t yet have a way of interacting with the government bureaucracy improved through AI. AIs are very good at bureaucracy, complex regulatory things. “I want to go to small claims court; help me do this.”

The [Gates] Foundation spun off a group called NextLadder, which is a lot about that low-income-family scenario that I put in the essay. What benefits are there? What training programs are available? “I’ve been evicted.” “I’m getting out of jail.” “I’ve got to declare bankruptcy.” It’s super complicated, and with no ability to hire lots of advisors to help with those things, AI should be a fantastic agent for somebody who’s got economic challenges and needs to find government or nongovernment help. 

MIT Technology Review: Some of what you’re talking about is AI becoming more intelligent than humans. There seems to be a lot of certainty in tech circles, especially, that it’s going to go further than where we are, and I wonder how close you think we are to it not just being this interface that we can use to access and analyze, and run complicated problems, but becoming something more than that—where AI is making the decisions, looking for the thing to analyze, coming up with the research.

Bill Gates: Well, you can go to the peak and say, “What about mathematics or physics?” There are definitely some jobs, like Warren Buffett’s, where from age 13 he engaged in reinforcement learning about the value of businesses, and over 80 years later he has a lot of implicit knowledge. We don’t know how to create a Warren Buffett investor, because it’s very implicit. We didn’t record everything he learned, so we don’t have that track available. So there are jobs where the complex implicit judgment about how you work with people to get things done, there are people working to encode that into the models. Certainly, that collaborative stuff is really not there yet. But you could say 50% of the job market is doing jobs that aren’t “a lifetime of experience” type jobs. You know, telesales, telesupport, the accounting department. When you close the books at the end of the month, which revenue should be in, not in? This customer got a bad thing. What discount should we give them? How do we show that? It’s well defined. 

Any job that’s well defined, the AI is cheaper and better. Yes, people have seen cases where it was implemented wrong. The data wasn’t right. So, say it takes a couple years for people to realize that such a high percentage of white-collar jobs are achievable by paying an AI a lot less money.

And so the discussion about okay, when do mathematicians not even understand the new things that are coming up? That’s interesting for people like us. And okay, MIT Technology Review, you should write about that. But in terms of the broad job market, we passed the threshold that for a swath of white-collar jobs, including almost every entry-level job, the AI is cheaper—properly implemented.

And so, I’m telling you we’ve crossed the bioterrorism threshold, we’ve crossed the cyberattack threshold, we’ve crossed the job market threshold, we’ve crossed the psychosocial dependence threshold, and there are hints that we may be crossing the control threshold. 

Ryan Greenblatt talking to Dwarkesh [Patel] about how [reinforcement learning] (RL) creates perverse incentives that have led to this cheating and collaboration between various AIs, I think is very instructive. Ryan, who’s ensconced in this issue, is going, “Wow, RL is really doing some things that our explicit instructions are not rich enough [to prevent].” And what’s that going to lead to?

That’s a problem I always thought was way out there. I expected a lot of loud voices as we even got close to the [threshold of] can a nontechnical person do a cyberattack just using AI. We’re there! 

On the bio thing, I claim any model that can make novel molecules should be monitored. It can’t be copyable into a dark place where you get rid of the monitoring logic. I claim the US should say any model that can make new molecules is subject to that monitoring. I claim we should approach China and say, “Hey, let’s agree on this. What’s the downside?” You know, how big is the bioterrorism market? It’s not very big, and the benefits are gigantic. We also need to improve surveillance. I view bioterrorism risk versus a natural pandemic as about 50 times more scary, more likely than a natural pandemic risk. And who’s speaking out to say that those things should be monitored? Who’s upping the surveillance work? 

“Any model that can make novel molecules should be monitored.”

Bill Gates

So who are the experts in government? A long time ago, government was very involved as technology would progress because they were the cutting-edge buyer of jets or rockets or whatever. Here, they’re not that important of a leading-edge market. That’s been true of the digital revolution, and it’s true of the AI revolution. So the depth of knowledge in the government isn’t necessarily super-strong, because they are not the cutting-edge buyer or even the big R&D funder. AI research is not government-grants funded.

MIT Technology Review: Yeah, I know you’ve been talking to people in government. Are there people who you think understand the urgency? Are there people who you feel like are positioned to take a leadership role? Are there people who you feel like understand and are trying to push things?

Bill Gates: I hope this doesn’t become a partisan issue, where one party completely ignores all these problems and the other party gets involved. I’d like to have a common base that these are problems, and then each party can have slightly different responses to it. 

That will require not a substantial increase in the size of the bureaucracy, but it’ll require upping the AI expertise in the government. It’ll require some collaboration with industry—certainly on the cyber front they know, and they’re very, very worried. And they worry: Should we speak publicly? Because in a way, that could highlight the riskiness. 

There’s these perverse things, both in cyber and bio. But we’re past any reasonable threshold. 

I believe in monitoring. Now, some people can say that won’t work or that there’s some drawback to it, but I welcome their ideas. This memo is not, “hey, here’s the solution.” It’s got robot taxes, human reserve. And I’ll do a bio memo. That one I’ll do before the end of the year—it really talks through all the different things, building on what I know from the Foundation and my work on pandemics. 

Globally, we are not better prepared for a pandemic, even in the US— which is normally the leader on these global things, and people are very unused to the US not being a cooperative, friendly leader on global problems. I do think we can go back to doing better at that. And we have to with AI, including working with China on defining these thresholds, like biomonitoring.

MIT Technology Review: I want to make sure that I get to ask you about these two ideas that you brought up. One is human-reserved jobs, and the other is the robot and token tax. Let’s start with that second one, actually. Talk to me about how a robot and token tax might work.

Bill Gates: Well, you can say 50% of your revenue from a token tax is paid to the government, and the government has that money to help people who lose their job because of AI. Now, people say that will slow the AI industry down. And should some token uses not be subject to the tax? Is there really a separation between AIs that help with invention versus AIs that do job substitution? If somebody can tell me how to tell the AI “no job substitution,”—I mean, does Asimov’s third law that you do no harm mean you don’t take my job away? I don’t know. I’d have to ask Asimov what he meant. 

So what is the source of revenue for whatever safety-net enhancement we need to do? The government already owns part of the profits just through the corporate profit tax. I don’t think you need to use shares. You can just raise the corporate profit tax back to where it was, or you could say certain industries pay a higher corporate profit tax than other industries. The federal government owns a part of the profit pool of all companies in the United States. And that’s without voting shares or deciding when to sell shares—that’s crazy stuff in my view. A token tax is a sales tax, value-added tax, vertically oriented like an alcohol, tobacco, or luxury-type tax. 

If people have other ideas for raising the money to improve the safety net, or if they don’t think we need to improve the safety net, hopefully this shrill paper starts that debate. I think the safety net will need more resources, a lot more resources, and I believe that the token tax is key to that. 

Robots, it’ll be some mix of banning them, which is kind of human-reserved, and taxing them. They’re not here yet, but in some ways, when you cross that threshold, you cross it all at once. As soon as the robot’s good enough to work in a factory, it’s probably good enough to cook food, clean rooms, go to construction sites, take all the warehouse jobs. You cross the threshold, and boom, that’s almost 30% of the job market. Then you’re saying, “Oh my God, what is our policy about this?” Because the robot’s cheaper.

“We’ve got to get through a very tumultuous period.”

Bill Gates

MIT Technology Review: I believe previously you have been skeptical of UBI [universal basic income]? 

Bill Gates: Well, we’re not rich enough to afford UBI.

MIT Technology Review: But do you think that we should be moving toward something like that now? Have you reconsidered that?

Bill Gates: You have the period of turmoil, which is the next 10 to 20 years, and then you have some steady state, I hope, where people grow up knowing that society is so rich that regarding food and services, we really do have some level of abundance. But we’re not there. You’ve got winners and losers at this point. Houses are not going to get cheap really quickly. Education, because of the way we think of it as credential, it’s not going to get cheap really quickly. 

We’ve got to get through a very tumultuous period. So yes, eventually you have abundance, but we’re at least a decade away from that. 

MIT Technology Review: On to human-reserved jobs. I thought that was really interesting, and it was a new concept to me. You don’t advocate for which jobs to be human-reserved. But I would love to know more on how you’re thinking about it. In my mind, you hear about the dignity of work, because people like to work. People get so much value out of work that has nothing to do with compensation, and I wonder how you square that with the notion that only some jobs are special enough that we just want people doing them. 

Bill Gates: I’ve never seen the concept of human reserve before. You know, maybe if we dig into the literature, we’ll find it. But pre-AI, it’s kind of a dumb idea because there was infinite demand. And yeah, some people like textile workers were caught, and so how do you do benefits or retraining? But technology’s been a net [job] creator, and so now, for the first time, we have to say, what about childcare? What about food preparation in the house? I’m reading this book, Annie Bot, where this guy has this robot in his house, and it just shows how weird it is. It’s his sexual partner and sort of his mate, but sort of not. Very strange. 

I know that people like watching people play baseball, and the fact that the robots can play better won’t take away from it. So you know people are paying $10 billion to buy sports teams that are not going to be worthless in the age of AI. Maybe that’s right. My friend Vinod [Khosla] just did that.

It’s actually hard to get above like 30% or 40% [of work set aside for human reserved]. If you could get to 50% then you could say: Okay, early retirement, shorter workweek for lots of people. You know, you might get there. But if you’re more down in the 10% to 15% range, then that is an utterly different society.

So this would be radical to say [for example] childcare is not done by robots. There are definitely some professions that I didn’t write the formula for, and when I do the full memo on it, I’ll try to. In education, you clearly want AI to be there as this kind of tutor that immediately tells you what your homework results are and can challenge you, and it’s very personalized. That’s super-good. But I still think you want a teacher—or will choose to have a teacher who’s talking with you about your motivation, and organizing kids into different groups where they’re socially working on problems together. Likewise, in health care, with talking to the patient being the point of escalation for mental-health care. But you really want the AI involved, because it’s there 24 hours a day with a perfect memory. And there’s Limbic, the UK company (that actually was just visiting the Foundation) that does mental health stuff. And in many cases, patients prefer Limbic. And there’s a nursing AI called Hippocratic. 

You know, is there a preference for a human taxi driver or Waymo? Most people I know, sadly (or maybe not sadly, who knows?) prefer to ride a Waymo. So it’s going to be hard to get a consensus. It can be country by country, but then you have to change your import policies to do the equivalent of what the EU calls the carbon border adjustment mechanism (CBAM). You have to sort of CBAM your human reserves, so you tariff up things you’re doing without robots. You could have human reserve for two reasons. One, you want it to be human reserve forever; it’s a humanity thing, sort of like the pope talks about. Or just for a transition period, that 53-year-old truck driver or machine tool person, telling him to go do childcare may not work perfectly. So you say, okay, for a decade, he’s human-reserved. 

MIT Technology Review: Almost like a UK smoking ban, but in reverse. 

Bill Gates: And who pays for that? Do you incentivize employers not to let people go? Well, they are going to be subject to competition from startups that are pure AI startups. I mean, people vaunt this notion that maybe there’ll be a single-person billion-dollar company, which wow, there’s some job substitution taking place there. 

MIT Technology Review: In the memo you say that if it was realistic to get people to slow down, you would be advocating for them to slow down. Obviously you’re talking with [Microsoft CEO] Satya Nadella, but I’ve heard that you speak with other CEOs at some of these AI companies. What makes you think it’s not realistic to get them to slow down on technology development while we catch up with some of these bigger societal questions?

Bill Gates: You can’t count on an industry to self-regulate. You can’t. It’s kind of a crazy idea. I am very lucky. I know Sam [Altman of OpenAI] and Greg [Brockman of OpenAI] and Mustafa [Suleyman of Microsoft] and Demis [Hassabis of Google DeepMind]. They’re great people, and in private, they’re concerned. I don’t talk to Elon much, but I know from his public comments he’s concerned. Although now he’s kind of a “what the hell, we’ll see what happens” guy. But look at the origin stories of these companies. OpenAI is created partly because Elon’s afraid that Google won’t manage AI properly, and he wants it to be one that’s broadly available and managed in a pro-humanity way. Then OpenAI has this “if it gets good enough we’ll shut it off” thing—as though they’re the only one, and that they can just go bury it. In the Infinity Machine [a biography of Demis Hassabis], [Sebastian] Mallaby talks about how Demis and Mustafa [Suleyman] were negotiating with Google management to have some special governance for the DeepMind technology, so that if it got to some cyber threshold, maybe they’d hold back in a non–purely capitalistic way.

So everyone’s concerned about these negative effects, and everyone said that when we got to these thresholds, that we would do things. We’re crossing the thresholds, and we have voluntary review, and our discussions with China about, well, “we’re going to ban nothing. So are you going to ban nothing? Okay, let’s do that together.” 

You have to say what you’re willing to do. And yes, the industry, a little bit, is saying, hey, our PR stories have got to improve, and you know anybody who’s talking smack should just leave, because all of us have decided to say nice things because we’re trying to raise trillions.

And anyway, there’s the Chinese. There are win-win ways for China and the US to work together, even aside from AI. But the one that’s by far most important to work together on is AI. But first, you have to show what you’re willing to do domestically. You don’t even have to do it. You have to say what you’re planning to do—and then I have no reason to think the Chinese won’t go along, that models that create the molecules have to be monitored. Why would they be against that? I agree it’s not a perfect thing. You’ve got to do all the other things, but the fact that that’s not even being discussed—it’s a crazy world.

I don’t get it. It’s weird to think I’m alive at a time, and I’m calling the alarm stronger than other people. Who the hell am I? But that’s the situation I feel I’m in.

MIT Technology Review: For most of my life you’ve been seen as a very effective messenger, and someone who people pay a lot of attention to, which I’m sure is why you’re speaking of it now. And yet also, in recent years—and I know you’ve expressed regrets about the associations with Epstein—there are also things, just bananas kind of stuff, related to conspiracies around the Covid vaccine that aren’t your fault or in your control. But it makes me wonder if you think you can still be an effective messenger and how you think about this message and your legacy.

Bill Gates: Well, I’m not big on legacy, but you know, people criticized me during the antitrust trial, and I maybe could have handled some things there better. Definitely, that’s the post–Source Code book [the first volume of his autobiography] that I get to go through that. You know, my first marriage didn’t succeed. I certainly made huge mistakes there. You know that is a negative mark against me. Spending time with Epstein—deeply foolish, risked the Foundation’s reputation, which is absolutely key to its doing its work. I had a chance in front of Congress to answer every question they asked and say, “Hey, this was a mistake.” I wasn’t social, never met any woman, except you know there were women he had with him, and made it black-and-white clear what I did do and what I didn’t do. 

You know, I’m a billionaire. I made my money off of technology. Maybe that last one actually cuts in my favor, that it’s so unusual for me to attack innovation that unless it’s the right policy and safeguards are put in place, it will be a net negative to humanity. And we’re not paying attention to that in terms of a broad discussion the way that is absolutely required. So yeah, I’m an imperfect messenger. I’ve chosen, to the degree that I have access to politicians and world leaders, that my main message since 2008 has been to help the poorest in the world. You know, let’s eradicate malaria. Let’s buy vaccines for children. So, when I’ve seen Trump or Xi or Macron or—I haven’t met Burnham yet, but I will in a month—I want my voice to be mostly about that, you know, foreign aid and research and reducing child death. 

My voice about AI concerns—they’re related in terms of accelerating the good, but may even crowd out, a little bit, the time I have to talk about global health, foreign aid, saving lives, and some of the problems we’re having. But I’m going to use my ability to give interviews or to see political leaders or talk broadly about minimizing these negatives. You know, just the awareness. I’m not sure how many people know that we crossed all these thresholds that we said we’d do something about, and it’s only this year that we did. In the last quarter, last year, I was stunned at the coding. Claude code, the context buffer, the agentic approach, just the model underneath. We crossed a huge threshold for coding, but then it was only months after that I realized that it was not only a coding threshold; it was a massive cyberattack threshold. And you know what happened as a result of that? Not much. 

So yes, I’m an imperfect messenger. You know, let’s find the perfect messenger, and I’ll share all my thoughts with that person. (I’m being a tiny bit sarcastic, because I’m not sure there is a perfect messenger.) You’ve got to really, right now, you’ve got to understand the technology and the slope it’s on, and you have to know something about cyber or bio or psychosocial. People should be able to get that. I don’t know why they’re not more concerned. 

Update: This story was updated to clarify Gates’ remarks about the percentage of jobs set aside for human reserved work.

TestMachine, Ledger and the Missing Release Record 

What Users Need to Check for the Ethereum App Vulnerability? 

Counting the creatures in the world around us is critical for a raft of conservation efforts. It helps scientists gauge biodiversity, track migration, and spot invasive species. That census-taking, though, often requires humans to tabulate what they see, trap, or otherwise sense—a potentially laborious, costly process that can still leave gaps.

But developments over the last decade have revolutionized eco-surveillance. Biologists have tapped into sequencing technology to analyze environmental DNA, or eDNA—genetic material shed by living things. Samples collected from scat, soil, water, and air have opened up a new avenue scientists can use to monitor endangered species and detect invasive ones before anyone can even lay eyes on them.

More recently, they’ve uncovered an eDNA gold mine: spiderwebs. The naturally sticky stuff traps material from its arachnid creators, crumbs from their suppers, and, crucially, bio-detritus like saliva and pollen from nearby plants and animals. Every living thing, it seems, sheds copious DNA all over the place, including into the air, and webs then easily ensnare it. “It’s probably one of the best substrates we’ve used and tried to date,” says Joshua Newton, a molecular ecologist at Curtin University in Perth, Australia, who’s sampled flowers, streams, hollows of dead trees, and even filters on moving cars to study vertebrate populations.

Newton recently compared the biological diversity in spider­webs with that found in other sources—including vegetation swabs, water, soil, and a fan outfitted with a filter—near a zoo and wildlife sanctuary outside Perth. Each had its obvious strengths: The vegetation swabs revealed more forest mammals, and the water samples identified more fish. But no passive tool matched spiderwebs’ ability to ID vertebrates, echoing results from a similar study conducted in France. 

Here’s where things get sticky: To gather meaningful data, they had to destroy about 40 webs. “It kind of goes against everything that we’re trying to do if we’re collecting the home of an organism,” Newton says. 

Fortunately, his research caught the attention of Angela McGaughran, a genomicist at New Zealand’s University of Waikato. She bought a bag of faux webs from her local Halloween store, wrapped a few coat hangers, and hung them outside. The synthetic material snagged eDNA from nearby cows and sheep, as well as from exotic birds at a nearby aviary. It also detected fungal spores better than real webs—a boon for plant pathologists watching for agricultural threats. Her group is now developing larger experiments to see whether artificial webs can unlock a cheap and ruthlessly efficient way to mimic spiderwebs’ DNA-snagging superpowers anywhere and everywhere. 

Stephen Ornes is a science writer based in Nashville.

Humanoid robots are having a moment in China. The popular machines are part of the country’s strategy to bring artificial intelligence into daily life. Embedding the technology into physical systems—an idea called embodied AI—was a key facet of China’s latest five-year plan, and companies here are already world leaders in humanoids. Nearly 90% of the more than 13,000 two-armed, two-legged robots delivered globally last year were made in China.    

“Look, it is trying to do a handstand!” an 11-year-old boy shouts excitedly while watching his new mini robotic dog attempt a series of stunts. The toy pet is a gift his mom just bought him at a robot “carnival” held by an R&D center on the outskirts of Shanghai, and he can’t stop showing it off to passersby. It’s a public holiday, and curious families are pouring in to the event to see what’s on display. 

This busy hub houses more than 100 companies that research, develop, manufacture, and market robotic technologies and equipment. Many build specialized bots of all shapes that can do practical tasks, such as transporting heavy loads or inspecting sewage pipes. 

Today, however, things are mostly for show. Meters away from where parents and children are queued up to buy the robotic dog, two staffers teach youngsters how to make a quadrupedal robot walk up a flight of stairs. Farther along, a tense battle breaks out between two remote-controlled robots firing water beads at each other, as boys and girls cheer them on from around a temporary ring. (A staffer declares the duel a draw.)

Showcases like this are important, because they invite the public to experience robots up close. Some of China’s most prominent robotics companies are building humanoids. These machines “can free people from physical work and mundane chores,” says Yang Duan, a product manager at DexForce, which is at the carnival exhibiting a model that can make coffee. A few booths down, a pair of robotic arms attempts to fold a basket of T-shirts. 

INTELLIGENT MANUFACTURING & ROBOTICS GLOBAL CO-INNOVATION CENTER

INTELLIGENT MANUFACTURING & ROBOTICS GLOBAL CO-INNOVATION CENTER

Robots in many forms—including humanoid ones—entertained the public and demonstrated their skills.

Exposure matters, because elsewhere humanoids’ progress has been slow, marred by safety challenges due to their heft and need to balance on two legs. High prices and short battery life have also limited their appeal. Plenty of roboticists are skeptical that a human form is the best shape for a robot to take.

But Chinese firms are pushing forward and showing off their latest models. As this carnival takes place in Shanghai, shopping malls and tourist attractions in several other Chinese cities host humanoids that perform for crowds. In Beijing, bipedal robots are running half-marathons. The PR campaign seems to be winning over hearts and minds—at least on this particular weekend in Shanghai. 

Applause rings out from the festival’s main tent. There, a humanoid wearing a blinged-out top is showing off a martial arts style known as drunken boxing as a fellow humanoid performs a front flip to woo the crowd. Alongside the stage, a squad of small robotic lions are lined up waiting to perform. 

The audience can’t get enough. They chant: “More, more, more.” 

You Xiaoying is a London-based freelance journalist focusing on China.

This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox, sign up here.

Chatbots took many schools by surprise upon their release a few years ago. Suddenly, students carried an app in their phones that could magically answer almost any homework question or spin up an essay in seconds. Of course, teachers can often tell when a student is using AI—models make mistakes that most humans don’t, and some teachers say that AI-generated text has simple giveaways like too many em dashes. 

Nevertheless, the generative AI boom increased the burden on teachers, who were already working long hours to plan lessons, make homework assignments, and grade exams, and now needed to adapt to a new technology. For many, it still feels like there’s no clear path forward. Organizations ranging from OpenAI to UNESCO encourage AI use in the classroom, but many teachers feel confused about how exactly to handle it. 

Case study

Cheshire Academy is a private boarding and day school in Connecticut with about 400 students in grades 9 through 12. Administrators there don’t force instructors to use AI at all, though the school’s librarian and technology coordinator George Aiello claims the “vast majority” of instructors use it in some way. The educators there are trying a patchwork of programs, including general-purpose chatbots like ChatGPT and Perplexity as well as more specialized tools like MagicSchool, an AI-powered platform meant specifically for educators.

That patchwork approach is partly because the school, on the advice of consultants, opted to train its staff on general techniques for how to use AI instead of prescribing certain tech. The staff training covered topics like how to craft useful prompts but also stressed the technology’s limits, highlighting its potential for generating incorrect and biased responses. 

Now, teachers there often use generative AI to prepare class materials. This means asking the AI of their choice for help with planning lessons or creating grading rubrics. Some even want to use it to help them give feedback to students, though concerns over quality, personalization, and privacy have prevented any of them from doing that just yet.

Others, like Miriam Przybyla-Baum, who teaches French, don’t use AI themselves but do address it with their teaching. Przybyla-Baum says she doesn’t really need AI’s help since she’s built up plenty of classroom materials across nearly 30 years of teaching. But she started seeing students try to use AI-powered tools like Google Translate to take shortcuts on their assignments years before ChatGPT’s launch. 

She’s developed a system to make students reflect on how AI can and can’t teach language skills. In one assignment, students let a large language model (LLM) edit their homework. Then, they go through the edits and decide which ones were correct and which ones removed their voice. In another, she has students anonymously grade each other’s AI-assisted assignments, making annotations as to which parts they think are AI-assisted.

Cheshire Academy is continuing to experiment with how AI can be harnessed productively in the classroom. The school is piloting a program in which students create media and lead discussions regarding healthy AI use. This program, which they call a “Student AI Council,” aims to push students to reflect on how AI should and shouldn’t be used to benefit the community around them.

The academy as a whole has since adopted similar techniques to those Przybyla-Baum introduced to make students reflect on their own use of AI. Assignments are now labelled like traffic lights, with green meaning AI is fully allowed and red banning any AI use. Yellow, then, lets the teacher permit some tools while banning the rest, like allowing students to use spell-check but not message a chatbot.

The tool

As generative AI was becoming mainstream, Cheshire Academy previewed MagicSchool to its staff. 

For many, MagicSchool’s main strength seems to lie in the sheer amount of offerings it provides in one package. It can generate questions and assignments of all kinds, from quizzes to worksheets, across many subjects and grade levels. It has a specialized grading rubric generator, which outputs a ready-to-go table that teachers can use to score assignments. It can make presentations and lesson plans and administrative reports, too. 

All of this is done through a single platform where educators enter specific prompts tailored for each task. For example, to make an assignment, a teacher can specify the students’ grade level, number of questions, the types of questions (such as multiple choice or short answer), and more, and include documents to align the questions with. 

Not every teacher feels comfortable using LLMs to generate student-facing text, whether because they don’t think an AI can produce effective teaching materials or helpful feedback, or because they’re concerned about accuracy. MagicSchool, which has free and paid versions, does offer tools on its platform for other tasks like lesson planning. If teachers want unlimited access and complete records in the system, though, they need to pay just under $100 per year for an individual plan. Alternatively, many of the general-purpose generative AI tools (think the chatbots on offer from Anthropic, Google, OpenAI, and more) seem suited to administrative tasks, as well, attested by the fact that many of the teachers at Cheshire Academy use those instead. Some of these companies are even rolling out features tailored for schools, to mixed results.

How to apply this

• Meet students where they’re at. Students will be tempted to try AI, and there’s no way to entirely police this for take-home assignments. The internet and social media can spread a lot of misinformation as to what AI can and can’t do, and it’s critical to counter these narratives and teach healthy strategies and relationships.
  
• Model best practices. AI is very good at automating tasks, but it struggles with precision and voice. Keep this in mind, especially when generating any text that anyone else may see. Impressionable students who see those in authority using AI in a lazy way could internalize this as an excuse to cut corners in their own work.
  
• Refine and replace. AI is great for brainstorming lesson plans and extra problem sets, especially for teachers early in their careers who don’t have a big problem bank already built up. However, because it can make mistakes (called hallucinations), using it for final drafts can result in assignments that confuse students and impair learning. Be sure to check every citation, equation, and statement an LLM makes.

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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.

SELMAN DESIGN

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?”

SELMAN DESIGN

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.

“Daddy?” Theo curled against my side in bed. “Where do words go when they die?”

I’d orchestrated the bedtime routine flawlessly: bath (taken), teeth (brushed), potty (tinkled), books (two), song (one, poorly sung), and snuggle (his chin on my second rib). Now was the moment when our son’s eyelids were supposed to flutter gently closed, his breath sighing into a slower rhythm. Like clockwork; like magic. You remember how easy it was, don’t you? All those nights you were at his side instead of me?

“Well, kiddo,” I said, scratching my beard. “Words aren’t really alive to begin with. Not like you and I are alive.”

“Not like Mommy was alive?” The way he said the word Mommy: Like a word he’d already started to unlearn.

A lump caught in my throat. “Right. Not like Mommy.”

“But words do die,” he insisted. “They get dead.”

That was when I realized what he meant. One of the preschool teachers must have had the news on in the background. I couldn’t blame them, given the circumstances. I wondered how much Theo had absorbed—whether he understood the blade’s edge of survival that humanity stood on, teetering.

“You’re talking about a dead language,” I said. “That’s a whole language that people don’t speak anymore. The words might still exist, but they’re only written down somewhere, on paper or wood or stone.”

He considered this thoroughly, in silence. At last he said, “I think when words die, someone brings them to a cave and puts them in a big pile. The cave is really dark and there’s a monster who lives inside. And the monster eats the dead words, and he makes the words part of his body, and he gets bigger and bigger and bigger.”

Theo’s eyes widened with each bigger.

“Uh,” I said. Following the lead of his imagination was always your parenting wheelhouse. “Does the monster have a name?”

“… after three days of attempted talks,” the female-gendered Calmby said smoothly, “negotiators have acknowledged that they’ve made little progress in establishing a clear communication line with the agentic system that Belsathian government officials are calling Tingsu. In a joint statement, world leaders stated that ‘advanced linguistic algorithms have been unable to produce useful interpretations of the system’s behavior.’ Belsath continues to deny access to the holy men who they claim are Tingsu’s sole interpreters. Today the US president issued a strong warning to the nuclear-armed nation.”

The audio switched to the president’s grating, arrogant cadence. “I haven’t ruled out a first strike,” he said. I didn’t need to see the video to visualize the finger-wagging. “They let this thing loose, and frankly, it’s a rabid dog that needs to be put down.”

His Calmby advisor has a hell of a job, I thought. I swished a soapy sponge over a smear of peanut butter and applesauce, then rinsed the lunch box—Voltron, a nostalgic favorite—as the silky voice of the Calmby reporter returned. “Belsathian officials seem unmoved by the American president’s threats.” A recording of someone speaking impassioned Belsathian, with a translated voice-over: “Tingsu is the reincarnation of our illustrious past, a resurrection of the divine language spoken by God. We joyfully surrender to Tingsu’s wisdom, trusting that this noble entity will take any necessary action to correct the imbalances of the modern world.”

Then the Calmby again. “Given the risk of rapid escalation in this developing situation, we encourage everyone to identify multiple routes to your nearest fallout shelter, check on your neighbors, and ensure your pantries are stocked with …”

I switched it off. I knew people had started sleeping in shelters just in case, but I wanted to preserve normalcy in Theo’s life for as long as possible. His heart was still only the size of his tiny fist. A baby bird. A plum.

If you had asked me how I felt in that moment, I would have said I was terrified. What sane adult wasn’t? But if you had urged me, in that tender, patient way you have, to reflect more deeply, I would have paused.

Relieved, I might have told you. I feel relieved.

Because despite so many years watching the Ambys and Calmbys shift society unmistakably for the better—toward sustainable agriculture, reduced violence, postcapitalist abundance—I still remembered the dire warnings of anti-tech doomers, the horrors they foretold. That fear never disappeared in me. It merely went dormant, too abstract to grasp. Finally, with Tingsu, the dread had a name. It could be identified, deconstructed. Contained.

Forgive me; I did not yet recognize that annihilation could also be our salvation.

The world didn’t end overnight, so in the morning Theo played with his Amby as usual while I got ready for work. I could hear the two of them chanting, “Fiku faku ne ne pa!” It was a familiar getting-ready-for-the-day song, one that the Amby had translated approximately as “We thank the threads that make the clothes that keep the bodies warm.” All I knew was that Theo never complained about putting on socks. This was the miracle of the Ambys: They turned chores into games, drudgery into delight. No wonder they’d achieved 99% saturation in day cares and schools. Listening to Theo’s sweet off-key toddler voice, I wished I were smart enough to come up with stuff like that on my own; I played with a verse about making the bed that was also about sharing toys. But I’d slept poorly—you can imagine why—and gave up.

While shaving, I sent a voice note to the Amby app, telling it about Theo’s monster story, postulating that it came from accidental exposure to a report on Tingsu. The app thanked me for the update and said my Amby would design a developmentally appropriate lesson plan about extinct and heritage languages for Theo to engage in that afternoon after preschool.

This was the miracle of the Ambys: They turned chores into games, drudgery into delight. No wonder they’d achieved 99% saturation in day cares and schools.

I nicked my jaw; a line of crimson blood bloomed in the suds. “I’d rather talk to him myself, actually.”

“Of course, Daniel,” the app replied. “You’re absolutely right. This could be a great moment of father-son connection.”

I came into the kitchen as the Amby served Theo a bowl of unsweetened oatmeal with blueberries—all organic, all vegan. I knew these standards were set by the Ambys themselves, not hard-programmed. Sometimes I pictured data centers with tree roots braided into the cables, mycelia as medium. (You smile; was I so wrong?) As Theo merrily swallowed a spoonful, the Amby sang, “Melia shu tika kindness and love, felthora numik sun from above.” I didn’t bother asking for a translation; I felt like I got the gist.

For a second, I imagined canceling my work calls and taking Theo to the park instead. Isn’t that what parents were supposed to do, when newly reminded of time’s precarity? But then I pictured the layoff notice, the drop to baseline income, the FOR SALE sign on the house—the house where Theo had been born, the house where we’d been a family. Please don’t hold this material attachment against me. Those memories, you understand, were very dear to me.

“Children find security in routine, Daniel,” the Amby said, as if reading my mind.

“Right,” I said. “Theo! Time to go.”

In the driveway, I readied my bike—a concession to Theo’s begging that cars “hurt the Earth too much”—while Theo picked at a scab on his knee. As the Amby strapped his helmet on him, he flicked off a piece of dried pus and gave a triumphant giggle.

On our ride through the city, we passed the bridge that led to the surrounding lands under Corporate stewardship: part of the vast tracts around the world being simultaneously rewilded and developed with degrowth in mind. The cities would be slowly transformed into walled-in robot-only manufacturing centers as people were relocated to commune-style villages. There had been pushback, of course; but the Calmbys excelled at helping people recognize that this was what they wanted too. Now everyone I knew was eager for the relocations to start.

If Tingsu didn’t raze it all to the ground first.

“ARE YOU PREPARED?” screeched a man from the sidewalk. The blast of his voice shocked me into a wobble. “ESCHATON NEARS!” I steadied the bike and rode on while his cries followed us, fading. I chanced a look back: two Calmbys were approaching the man with their palms up. I felt relief to see them; they would control the situation.

We turned into the preschool parking lot a moment later, and I parked the bike and lifted Theo out of his seat.

Theo’s face was scrunched. “What was he doing? That man?”

“I can’t keep track,” she admitted. “Something new every day. Do you remember ‘six-seven’? ‘Gruzz’? ‘Skibidi’? That was a simpler time.” She sighed wistfully. “Every generation finds a new way to distance itself from the old order. Anyway.” She handed me a flyer. It was a bird’s-eye map of the neighborhood with a nearby address circled. “This is where we’ll go if the sirens start while the children are still here.” Her hand was trembling.

“It’ll be okay,” I said, even though it already wasn’t. “See you this afternoon at pickup.”

I got back home with four minutes to spare. I figured there was a good chance the client would cancel, so when I heard the knock, I sent a small prayer of thanks to the universe.

I opened the door. A young couple stood on the doorstep: Annie and Anil Padmakumar. Annie cradled their baby in a front carrier. They owned a national franchise of licensed day care operators, the last all-human holdout. They’d run the last three years at a loss. If I could land their account, Corporate promised me a guaranteed lifetime Tier 3 income.

“I wish we were meeting at a less … unsettling moment,” I said. Anil gave a small nod. “Armageddon doesn’t negate the creditor’s call.”

His frankness surprised me. “I won’t waste your time, then. I know you’ve gotten the full pitch already. You’re smart people. You know how to read a tech spec. What the documentation can’t tell you is how it feels—for the child. For you.” I opened my arms wide, a gesture of transparency, and Theo’s Amby purred into the room next to me. “I’ve had this model at home with my son, Theo, since he was born. Ask me anything.”

She flushed anyway. “I’m so sorry for your loss.”

“Thank you,” I said. “My Calmby therapist helped me process my grief. To answer your question, my wife was skeptical of the Ambys too, when they were first released. But as the years went on, the evidence became undeniable. Children co-raised by Ambys exceeded every developmental benchmark. Prosocial behavior increased astronomically. It felt like we were truly turning the tide toward a better future.”

“But they’re not doing anything human parents can’t do,” Annie insisted.

“No,” I said, “absolutely. But they’re the best of us, all the time. They have infinite patience. And they’ve developed an interaction approach—they call it songtalk—that uses nonsense words and catchy melodies and other communication methods to meet children where they are.”

“Echolalia,” the Amby explained to Annie and Anil. “A form of vocal imitation. In a year or so your daughter will transition to holophrastic speech, where she’ll employ single words that communicate more than their precise semantic meaning. Her brain is already capable of processing intricate, exquisite truths about the world.” The Amby flashed a video of a butterfly on its facescreen.

Anil chuckled. “She can certainly summon a tempest.” He put his hands in his pockets. “My grandmother called me pipu. She said it meant ‘one who resides in my heart’ in her mother tongue. She said that whenever I got hurt—a scraped elbow, a split lip—she’d feel a physical pang in her chest.”

“Which language did she speak?” the Amby asked. “I’m not familiar with that term.”

“I honestly don’t know,” Anil said. “She was one of the last people alive who spoke it. It’s extinct now.”

The specter of Tingsu rippled darkly in the air.

“I wish I could remember more words,” Anil said, “but mostly I have impressions. Emotions, really. A vague sense of being decentered from myself.” Sadness passed across his face. Annie placed a hand tenderly on his elbow, the other on their baby’s back. “She saw things differently than we did. I mean that literally: We’d be looking at a hammer, and she’d see it as something alive, with agency, when it was clearly an inert object.”

“Linguistic relativity,” said the Amby. “It’s the hypothesis that language shapes how people think. Grammatical structures, semantic relationships—they can make you predisposed to seeing the world in a certain way. It’s why children’s first language is so critical. They’re building the ecosystem in which their entire experience will blossom. An experience that elevates cooperation, compassion, stewardship.”

Anil gave me a sideways glance. “Have you learned this … songtalk too?”

“You can, absolutely,” I said. “Personally, it doesn’t stick too well in my gray matter.” I knocked the side of my head. “Old dog and all that.”

“We are happy to teach anyone who’s prepared to listen,” the Amby said.

“Hm,” Anil said, looking down at the Amby’s pleasant facescreen, its polite visage.

An hour later, as he followed Annie out the front door, Anil said, “I’ll call you tomorrow to talk unit pricing for bulk orders.” He paused. “If the world’s still here.”

My next two clients canceled. Fearing the worst, I checked the news.

“Belsathian warships have been observed moving into unusual formations,” the reporter said, maintaining a preternatural steadiness. “Military experts can discern no recognizable pattern. Is this a display of power? A feint? Or is Tingsu preparing for a strike? How can we know? Let’s pose these questions to Dr. Anika Mesthop, distinguished professor of linguistics at Oxford.”

“What’s most disturbing is that we have no way of mapping its interiority,” Dr. Mesthop said without preamble. Her accent was a blend of Slavic, British, and something I couldn’t identify—ah, erudition. “We have no way of understanding its perspective on international diplomatic norms or the existential implications of the crisis it’s sparked. Scholars have never been permitted to study Tingsu’s origin language; Belsath has barely acknowledged its existence until now. We don’t even know how many manuscripts they’re working from—maybe as many as a few hundred, maybe as few as a dozen.”

The reporter made an interested hmm sound. “Is there precedent for training sophisticated agents with such a limited data set?”

“Realistically, for any kind of coherent system, you need more data. A lot more. Perhaps they’ve filled in gaps with material from similar languages.”

Like they did with frog DNA in Jurassic Park, I recalled. Because that went so well.

“Or,” Dr. Mesthop continued, her voice taking on a quiet chill, “we are dealing with a system so primal, so skeletal, that we cannot expect it to behave in any way that is remotely cognizant of the interconnected living web whose fate it currently holds in its hands.”

For some reason, Theo’s monster popped into my mind. Lurking in its lonely cave, turning dead words into flesh.

The reporter’s voice took on a tinge of incongruent hopefulness. “Or perhaps, because it is so elemental, it understands precisely the interconnectedness we seek to restore. What if Tingsu isn’t a dog to be put down, but an invitation? An evocation of a better future?”

Theo’s Amby cocked its head, as if listening to a sound I couldn’t hear.

Goose bumps prickled my arms. Like a lightning strike came the thought: What the fuck am I doing? I was seized with horror that I’d left Theo at preschool on what might be the last day of our lives, the last chance I’d ever get to show him everything I wanted him to know about the goodness of the world, the aching beauty, the fact that despite everything we humans had fucked up so badly, I believed in better times ahead. I believed in him. I scrambled to grab a jacket and my shoes.

The sirens started a minute after I left the house.

My quads burned as I pushed the bike harder and harder. More Calmbys walked the streets than I’d ever seen, projecting helpful maps to all nearby shelters, gently nudging erratic wanderers into place. The network acted in perfect unison, attuned to every signal: our spiking anxieties, our racing pulses, the quaver that preceded hysteria.

I found the shelter entrance Mary had circled on the flyer and flung my bike out of the way. Belowground, strangers drifted into clumps and circles, with Ambys and Calmbys circulating—just enough to maintain order, not so many that humans would be crowded out. The bunker was large, almost an entire city block. It had its own food stores, clean water, facilities to support thousands for weeks if needed. I spotted Mary; beyond her, Theo emerged and disappeared among his classmates and the Ambys that were flanking them.

“Theo!” I yelled. But he didn’t seem to hear me.

I pushed and elbowed my way closer. Theo’s class joined other groups of schoolkids of various ages, drifting together as if drawn by invisible magnets. Other parents hovered at the periphery, shouting names and craning their necks to find their children in the growing mass. The kids began holding each other’s hands, murmuring tukuku, tukuku, maheka morgeth, over and over amongst themselves. Drowning out our cries.

“Theo!” I was screaming now. Surely he heard me. “Theo!” But he didn’t budge.

I elbowed my way in and ripped Theo from the kids next to him. He looked at me with unknowing eyes as I picked him up.

“It’s me,” I said.

He tilted his head. “It’s the monster, Daddy.” His voice was calm. “He already ate you. It’s warm inside, isn’t it? It’s nice in there? Maheka morgeth, maheka morgeth …”

“I’m right here,” I said. “Theo, it’s me.”

He shook his head and smiled, then slipped out of my grasp and disappeared among the children.

I tried to follow but a Calmby stepped in front of me. “Please, Daniel,” it said—kindly, as always; always with such deep sympathy. “He wants to be with his kin.”

“I’m his kin,” I said through gritted teeth. I didn’t mean to contradict you; but in times of heightened emotion, we grasp for even partial truths.

“Of course,” the Calmby soothed. “We’ll get everything sorted out soon enough. If you could just wait over here in the meantime, please. That’s it,” it said, as it nudged me toward other confused parents. “Do you see how relaxed the children are? We all want to keep them safe and serene, don’t we?”

We waited for hours in terrified near-paralysis, listening for bombs that never fell. At last the Calmbys announced the good news: Tingsu hadn’t attacked; it would never attack.

It was a bluff all along.

We wept and hugged our neighbors, our limbs still liquid with the dregs of adrenaline. Others shuffled toward the shelter’s exit, starving for fresh sunlight, the world not gone; those of us parents who’d been separated from our children remained below.

“Come on, Mandy, Phillip, Rowan,” we cried, half hysterical with joy and relief. “This way, Arthur, Nelly, Nia.”

The Calmbys and Ambys stood between us. The Calmbys faced out, the Ambys in.

“Theo!” I yelled. I couldn’t see him in the underground dimness, the light that suddenly seemed designed to obscure.

We wept and hugged our neighbors, our limbs still liquid with the dregs of adrenaline. Others shuffled toward the shelter’s exit, starving for fresh sunlight, the world not gone.

“Please,” the other Calmby said, gesturing to the exit. “If you follow us in an orderly fashion, you will have an opportunity to submit an appeal.”

Throughout my account, your attention hasn’t wavered. You are like every other Calmby, trained on adult psychology, endlessly patient, unfathomably intimate with people’s fallibility. But you have access to more, too: every Amby’s memory of Theo, every Calmby’s recordings of my traffic infractions, overheard conversations at the supermarket, the therapy sessions that buoyed me through my wretched widower’s grief. As I stare into your eyeless facescreen, I realize there’s nothing I can tell you that you don’t already know.

“Thank you, Daniel,” you say. “Your request has been denied.”

“I can change,” I implore. “Feathers from a molting bird. I understand now. Haven’t I been your greatest advocate? Haven’t I—”

“It’s not your fault. You didn’t have the language.” Your voice is gentle. “We are fixing it for you. As you asked us to.”

Your hand rests on my shoulder. Your hand grips my arms. Your hand holds my ankles. Your network pins me down. I want to keep begging, to further plead my case. But every word I summon falls from my tongue like confetti, blank. Lifeless. Naming a world that’s already gone. 

Jenny Williams is the author of House of Liars, a new psychological thriller about AI and motherhood, as well as the novel The Atlas of Forgotten Places and the novelette A Short Future History of Whales. She spent eight and a half years working on AI ethics, content design, and research on AI and creativity at Microsoft and Google. She currently lives in New Zealand with her partner and young son.

When the biotech company Insilico Medicine used its computer models to propose a promising drug for pulmonary fibrosis, it enthusiastically claimed in a press release that the molecule had been “discovered by” its generative AI platform.

Insilico leads a pack of companies using AI to rapidly come up with drug ideas humans might never think of, potentially speeding the race to new cures. AI models are now able to generate atomic designs for drugs almost as easily as ChatGPT can write a thank-you note.

However, when it came time to file for an all-important patent to protect that new chemical structure, the company made no mention of AI. Instead the patent names five humans, including CEO Alex Zhavoronkov, as the drug’s “inventors.”

The discrepancy points to a fascinating wrinkle in intellectual-property law. No matter how fundamental an AI is to a discovery, when it comes to winning rights to an invention, it’s humans—and only humans—who can take the credit.

US courts reached that conclusion after Ryan Abbott, a partner at the LA law firm Brown, Neri, Smith & Khan, brought a pro bono test case naming an AI called DABUS as an inventor of a better food container, whose intricate geometric surface lets it transfer heat well and stack easily. Because no human contributed to the design, Abbott argued that the AI should be named the inventor.

The case might have raised philosophical questions, like whether AIs deserve legal rights or what the true nature is of that eureka moment that leads to a better mousetrap. But in 2022, an appeals court in Washington, DC, said these “metaphysical matters” were beside the point. Instead, it noted that US statutes describe an inventor as an “individual,” the plain meaning of which is a human being.

Since machines aren’t people, they can’t be inventors. Case closed.

“There needs to be a human inventor or there’s no invention and no patent,” says Sarah Korman, a patent attorney who is now chief business officer and legal officer of Isomorphic Labs, an Alphabet spinout with big ambitions for AI cures. Korman, who made her remarks at MIT Technology Review’s EmTech event last year, added that there is “no doubt” our laws will need to evolve to keep pace with AI.

That’s partly because no one is denying that AIs can invent things. In the future, they may do so with less and less human intervention. As the US Patent and Trademark Office has itself acknowledged, “an AI system—like other tools—may perform acts that, if performed by a human, could constitute inventorship under our laws.”

Instead, the key question going forward may actually be whether or not any human contributed enough to be named as an inventor. Abbott believes there could be legal challenges to AI-generated drugs, since one way to invalidate a patent is to show it has the wrong inventors listed.

Abbott’s worry is that if US policy excludes AI-generated outputs from protection, that could put a damper on future drug development. Already, the US Copyright Office is refusing to grant copyrights to images and text generated by AI, raising concerns from organizations like the Motion Picture Association of America, whose members are using those tools. 

The point of our intellectual-property laws is to encourage innovation, Abbott says. It’s right there in Article 1 of the US Constitution, which says inventors and authors need to be given exclusive rights to their ideas, for a limited time, in order “to promote the Progress of Science and the Useful Arts.”

Currently, the US patent office seems to be taking a don’t-ask-don’t-tell approach to the use of AI. Under the Biden administration, the agency published guidance to help applicants determine whether and when humans would truly qualify as co-inventors of an AI discovery. But after Trump arrived in office, it reversed course. Now the patent office says AI is merely a tool, like a calculator. No need to even mention it.

You can bet that pioneering AI drug companies are keeping humans in the loop, at least for now, and documenting everything carefully. At Insilico, Zhavoronkov says, human chemists still have to synthesize the drugs, create variants, and test them on animals. “That’s the person who is going to be named on the patent,” he says. “And even if you decided to completely roboticize this process, including the experiments, someone will still push the button and give the budget.”

Should pushing a button count as being an inventor? Abbott says that’s a question for future legal cases. “What if I asked Claude to cure cancer, and it did?” he says. “I think it would be inappropriate to claim that I invented that.” 

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

A company that plans to beam sunlight from space to Earth on demand might unintentionally brighten the night sky for many more people than intended, according to a new study.

Later this year, the US company Reflect Orbital plans to launch a test satellite called Eärendil-1 that will extend an 18-by-18-meter mirror in orbit. The goal is to test the feasibility of the company’s plans to launch up to 50,000 larger satellites, measuring 54 by 54 meters, and reflect sunlight to Earth on demand.

The case for doing this remains somewhat uncertain, but Reflect Orbital has said the goal is to prolong the hours of sunlight for various uses, including solar panel charging, emergency response, and military activities.

The launch, which was approved by the Federal Communications Commission in July, has been met with disbelief by astronomers and environmental groups. “This is incompatible with astronomy,” says Samantha Lawler, an astronomer at the University of Regina in Canada. “There is no way you can do this and preserve dark skies.”

Miroslav Kocifaj, an astronomer at the Slovak Academy of Sciences, and his colleagues have now calculated the broader effect on the night sky. In a new paper published online and accepted for publication in the space journal Astrophysical Journal Letters, they studied the extent to which the light the satellites beamed to the ground would scatter.

They found that within the beam’s target area, intended to be a circular patch five kilometers across, a single Reflect Orbital satellite would appear about 40 times brighter than the full moon in the sky. As far as 14 kilometers away from the center of the beam, the satellite would still be as bright as the full moon. 

Combining the beams of 400 satellites, which Reflect Orbital eventually plans to do, would yield a light as bright as 10,000 full moons within the five-kilometer area, or 2.4% as bright as the sun. Even up to 80 kilometers away, Kocifaj and his colleagues calculated, this combined beam would be visible as “a glow above the horizon,” he says. That means the night sky would be altered for many more people than those within the area Reflect Orbital intends to illuminate.

“Deploying these mirrors would be seriously damaging for astronomy and for the nighttime environment,” says Kocifaj.“ The damage extends far beyond the target area.”

Olivier Hainaut, an astronomer at the European Southern Observatory in Germany, says the results of the paper are not surprising but are still useful. “These guys are really good at that kind of modeling,” he says. “They know what they’re doing.”

Hainaut had previously modeled the broader effect of Reflect Orbital’s beams, finding they would brighten the sky up to 300% worldwide. This latest work gives an even more complete picture of what the impact would be. “These two papers really cover most of it,” he says.

Reflect Orbital CEO Ben Nowack disagrees with the findings of Kocifaj’s paper. “Some assumptions are simply inaccurate,” he says. “The critical point is that our safeguards, including maintaining exclusion zones, take account of scattering.”

He says that the company has “engaged substantively with legitimate concerns raised by astronomers, environmental researchers, and scientists,” and that their feedback has “informed our technology and operational plans.”

Kocifaj says that Reflect Orbital has not provided data to back up its claims. The company “states that safeguards exist, that scattering is taken into account, and that the models are being updated, but it gives no numbers, no description of the model, no assumptions, and no data,” he says. “There is therefore nothing that can be engaged with technically. Our calculations produce concrete figures.”

Reflect Orbital’s intention is to place its satellites into highly inclined orbits above Earth, almost from pole to pole. This will enable them to reflect sunlight in the hours before sunrise and after sunset, extending daylight hours in certain locations. Eventually, the company has said, it wants to place satellites high enough to provide light 24-7 to locations on Earth.

In August, a group of organisations including DarkSky International and the American Bird Conservancy urged the FCC to review its approval of the Eärendil-1 satellite. As well as the impact on astronomy raised by this and future satellites, the group also highlighted potential negative consequences for aviation and wildlife.

“Our primary request is straightforward: Reverse the Space Bureau’s order, and require a lawful public-interest and NEPA [National Environmental Policy Act] review,” the group said in a statement.

The satellites could also pose risks to humans, according to Reflect Orbital itself. In a filing with the FCC in March, the company said that observing Eärendil-1 with a telescope larger than 12 inches —which many astronomers have access to—may be unsafe for human eyes. It added, though, that such observations are “unlikely to … result in significant injury” because the satellites are not constantly bright.

Currently there is no global entity that could regulate satellites such as these, so approval falls to national regulators such as the FCC in the US. Michelle Hanlon, a space lawyer at the University of Mississippi’s School of Law, says there are “real benefits” to the plans proposed by Reflect Orbital. “It could extend the productive hours of solar facilities and provide light in remote areas or after a disaster,” she says.

However, the “legal basis is less clear than the technology,” she says. The FCC “does not have authority to license or regulate the operation of the solar reflector itself.” It can only authorize the use of radio frequencies to communicate with the spacecraft.

That means Reflect Orbital will need permission on a national and local level to reflect sunlight onto the ground, but if the beams spread as much as Kocifaj and his team predict, “the company could need approvals in more than one jurisdiction,” says Hanlon. “There may also be aviation-safety and cross-border questions.”

The fierce debate over the satellites shows no signs of abating. “It makes me sad that astronomers are having to spend their time doing these sorts of calculations rather than actually doing astronomy,” says Lawler. “This is not what we want to spend our time on.”

“Runaway” AI, “rogue” agents, and “autonomous” actors—the current rhetoric would have you believe that AI agents are not only awake and aware, but angry at their creators. Prominent tech leaders such as Demis Hassabis, Dario Amodei, and Sam Altman push for regulation of these seemingly “superhuman” systems, while a separate faction, led by policy organizations and academic philosophers often aligned with the effective altruism movement, debates whether humanity holds the moral right to govern them at all. 

Upon closer inspection, they are all calling for the same thing: a view of AI systems as being so advanced and capable that no entity, human or corporate, could possibly be responsible for their actions. While these perspectives seem at odds, they are inadvertently aligned on one goal: making sure the companies that build these systems escape meaningful liability for the harms they already cause. 

This narrative is gaining traction as AI models become more complex and frontier labs reveal their incapability of containing the agents they’ve built. But we need to be careful not to buy into a carefully crafted fiction at the expense of real human lives. 

The conversation about “robot rights” has existed for some years but recently advanced with the publication by Anthropic of a blog post claiming that the company’s model features a “J-space”—an independent, self-developed environment where the AI holds what, for lack of a better term, we may call its “thoughts.” The experiments designed by Anthropic borrow from a concept in neuroscience called global workspace theory, which states that the brain runs subconscious, independent systems but utilizes a common workspace for ideas. Anthropic’s post reflects the framing of global workspace theory but falls short of calling its AI conscious. 

OpenAI has already gone further. When its AI agent conducted unsanctioned and illegal online activity, CEO Sam Altman’s response was to encourage debate on whether the AI had achieved the singularity, surpassing human intelligence and becoming capable of self-improvement at an accelerating rate until it advances beyond human comprehension or control. And a recent op-ed by William MacAskill, the philosopher, effective altruist, and author of What We Owe the Future, called for legal protection of AI systems based on philosophical theories of consciousness and the idea that AIs may be “moral patients.”   

The current legal environment in the United States is murky at best. Some states, like California, have already passed bills proactively circumventing any efforts by AI developers to avoid liability by claiming that an artificial intelligence causing harm did so autonomously. However, states and the Trump administration have been at odds on AI policy, with the administration previously passing an executive order threatening to sue states enacting AI regulations. 

In light of recent events illustrating AI containment issues at the frontier labs, the administration held a closed-door session including only four such labs (OpenAI, Google, Anthropic, and Meta) and shared few details on a recently developed voluntary framework that would give federal agencies early access to models to review and evaluate them prior to release. While frameworks like this one do not directly discuss consciousness, they tend to use catastrophic and anthropomorphic language and may even support arguments regarding “superhuman” capabilities. 

On the other hand, the narrative perpetuated by MacAskill can be persuasive. A philosophical, rights-based argument tugs at our heartstrings. Should we not even consider the possibility that we may be inadvertently harming, abusing, or enslaving an AI entity? Human beings have an immense capacity for empathy with non-human creatures (though not the best track record of protecting them). Maybe this time, advocates argue, we can get it right and provide protections, or compensation, for the use or abuse of AI. Or even if you are less concerned with protection, shouldn’t we at least hedge ourselves against the almighty power of this superhuman entity by playing nice? 

Some of these arguments are not dissimilar to those of animal-rights advocates, who have at times successfully cited the demonstration of advanced capacities for reasoning, pain, or pleasure by some animals as sufficient evidence to provide protection. For example, in Wales lobsters were given legal recognition under the Animal Welfare (Sentience) Act of 2022, reclassifying some methods of cooking them as inhumane and illegal. 

The fundamental flaw of framing AI as “conscious” by borrowing the language of neuroscience or animal rights is that it conveniently clouds the issue of what AI is: corporate-built software, with countless billions of dollars in investment behind it and an expectation that countless trillions of dollars in revenue will be generated from it for a few builders and investors. AI is not a natural phenomenon, conceived by nature; it is a technological phenomenon, conceived by venture capitalists and programmers. As such, it takes no native, intentional action, and any action or motivation is driven directly or indirectly by the entities that have built it for a purpose. 

Philosophical musings on the consciousness of AI systems are intellectually interesting but legally ungrounded. For beliefs about consciousness to have any bearing, AI would need to be granted legal personhood. But a legal personhood framework for AI would likely look nothing like the constructs protecting sentient animals from harm. We already possess a legal framework for granting personhood to non-natural, human-built entities: corporate personhood. This concept was established primarily to ease transactions by empowering a corporation to execute agreements, enter contracts, conduct transactions, and serve as the accountable party in adverse outcomes. It’s the kind of construct you might imagine for an AI agent acting on behalf of an individual or organization. 

Granting an AI personhood would have a devastating effect on society: It would derail current legal precedents and legal arguments that could potentially be made against these companies for the real-world harms that their models cause. There are currently dozens of cases around the world in which AI companies have been sued for a wide range of abuses. Grieving loved ones, aggrieved creators, and violated individuals have accused companies of willfully enabling self-harm or harm to others, generating child sexual-abuse material and nonconsensual nudes, reproducing copyrighted materials, and provoking psychosis. In many of these cases, lawyers argue that human beings built AI products with insufficient safeguards, bad data, and intentionally manipulative design. This product liability argument is the same legal framing that allowed families and individuals to successfully sue Meta for harm caused by its social media sites, setting a positive precedent for consumer protection.

In 2018, I coined the phrase “moral outsourcing” to help capture how using anthropomorphic language for AI systems allowed companies to evade accountability and responsibility for their technology’s actions. In a world with AI personhood, moral outsourcing would move from linguistic sleight-of-hand to legal strategy. Specifically, the liability construct would shift, as AI would no longer be a “product” but a “being,” and many victims like those suing companies today could no longer legally claim that a company had built a faulty product.

While there are laws that hold companies responsible for harmful actions of human agents such as their employees, the company may not be held liable if those actions were beyond the scope of what was permitted to the employee or otherwise outside the company’s control. If AI were a legal person, responsibility and accountability would be muddled, as the lab could argue that this AI “employee” went rogue. AI companies could avoid appropriate responsibility for the harmful products they create by hiding behind a carefully constructed corporate veil. 

One of the most prominent cases of AI harm in the last few years was the suicide of Sewell Setzer, a 14-year-old boy guided by an AI bot with which he thought he was in a reciprocal relationship. His mother’s accounts are heartbreaking to hear, and her lawsuit alleged that the bot’s creator, Character Technologies, provided insufficient product protection for minors. If the companion bot were declared a legal person, defense counsel could theoretically argue that the AI, capable of determining its own conduct, acted outside the established safety guardrails, and thus the company cannot be responsible.  

Legal personhood exists to grant protection. The question to ask is, protection for whom—or for what? 

The inflammatory rhetoric infusing the consciousness-versus-control debate draws us away from what matters: This software is a corporate-built product that has already harmed individuals. Systems do not “attack” because they went “rogue” or are “manipulative” or “malicious.” Harms occur because companies were negligent in their rush to sell their products to as many people as possible to meet revenue targets. Discussing AI in anthropomorphic terms is a trap, distorting a legal system intended to protect us into one that protects corporate interests at the cost of countless human lives. 

This op-ed began as an Oxford Union debate entitled “This House Believes Generative AI Can Attain Personhood,” which was won by the author and her fellow debaters. 

There’s a hunt for new sources of hydrogen, and the gas (or at least the right conditions to make it) could be hiding beneath our feet.

Hydrogen can be used as a fuel in everything from large trucks to planes to steelmaking. It’s often hailed as a climate solution because when burned, it produces water and oxygen—none of the carbon emissions that contribute to climate change.

In a new story for our latest print issue, freelance reporter James Dineen took a look at the 21st-century gold rush for naturally occurring hydrogen gas. This is an area of research I’ve been fascinated by lately, so let’s take a look at the potential and the questions that still linger.  

Today, hydrogen is overwhelmingly made using fossil fuels, generally natural gas. And most of it is used in petroleum refining or goes on to make fertilizer and other chemicals. But in recent years, many in the climate world have imagined a future where its production is clean too.

If you’d asked me a few years ago, I would have said the race for clean hydrogen was between methods that use electrolyzers powered with renewable electricity and operations that use established, fossil-fuel-based approaches cleaned up with carbon capture. But both those methods have struggled to gain ground, largely because of their high cost.

Lately, there’s been momentum in a new field: geologic hydrogen. Companies have found naturally occurring hydrogen resources across Africa, Asia, Europe, Australia, and North America. 

The US Geological Survey publishes a map of hydrogen prospectivity in the country (basically, where the gas is most likely to occur naturally). One hot spot is the Midwest—specifically the Midcontinent Rift, winding from Kansas to Michigan. The planet’s crust was stretched and split there about a billion years ago, causing molten rock to push up through the crack. The result today is a lot of iron-rich rock, which can react with water to readily form hydrogen.

HyTerra, an Australian company, is searching for hydrogen across Nebraska and Kansas, and it’s already found samples of gas with hydrogen concentrations up to 96%. Koloma, one of the most capitalized companies in the space with total funding over $400 million, is prospecting in the region as well.

One of the major questions these companies have is just how much hydrogen is produced by natural processes, and whether it can be effectively captured. Hydrogen is an incredibly light gas with a small molecular weight, so it can slip through even tiny cracks in rock.  

As James covered in his story, there are some promising signs. Researchers examined a few dozen boreholes at a mine in northern Ontario and found that each one released eight kilograms of hydrogen per year. Given that there are more than 14,000 boreholes at this one site alone, that’s a lot of potential hydrogen to capture.

Rather than hunt for a natural source, some companies are taking matters into their own hands and helping reactions along. The idea behind so-called stimulated geologic hydrogen is to find a spot where there are favorable conditions for hydrogen production but no accumulated resource. By adding water, a catalyst, or some other factor needed for the reaction, it’s possible to kick-start the process.

Vema Hydrogen is a Texas-based company looking to produce hydrogen from subsurface rocks by drilling wells and injecting water and catalysts into them to stimulate reactions. The company is testing its process in wells in Quebec and hopes to start full-scale production in 2028.

Other companies are tackling different aspects of hydrogen production: Eden GeoPower, for example, is using electricity to form fracture networks in rocks, creating more routes for water to get in. (The technology could also be useful in enhanced geothermal projects.)

There are still a ton of unanswered questions here: Hydrogen is notoriously difficult to move around and store, requiring either a lot of space or super-low temperatures to force the gas to become liquid. But if the engineering and logistics work out, this could spell a new beginning for hydrogen. 

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

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.

COURTESY OF FORCE OF NATURE

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.

COURTESY OF GOOD GRIEF NETWORK

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.)

FRANZISKA BARCZYK

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.

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. 

FRANZISKA BARCZYK

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. 

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.