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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.”
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Roundtables: could AI really kill us all?
Employees at the world’s leading AI labs are saying there’s a real possibility that advanced AI could destroy humanity. Are they right? Or is this more scaremongering and hype?
Join MIT Technology Review executive editor Niall Firth, senior AI editor Will Douglas Heaven, and AI reporter Grace Huckins for a subscriber-only conversation unpacking the debate around AI extinction. They’ll explore where the fears come from, whether they hold any water and, if they do, what we should do about them.
This geneticist’s age-reversal tech could help restore sight
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, and his own 23andMe test came back with a mutation for macular degeneration, a top cause of vision loss in old age.
That obsession extends to his work. Lu is behind one of the coolest results in rejuvenation science and eye research: an age-reversal technique called reprogramming that repaired the optic nerves of blind mice, restoring their vision. Now, nearly the same genetic therapy he developed as a student has entered human clinical trials.
Yuancheng (Ryan) Lu is one of the biotechnology and health honorees on our 35 Innovators Under 35 list for 2026. Meet the rest of them here, or explore the full list across the biotechnology and health, AI, computing and robotics, and climate and energy categories.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Dario Amodei, Sam Altman, and Elon Musk have called for an AI slowdown In a rare show of unity, the rivals agreed that AI needs stronger brakes. (Guardian) + Amodei wants independent monitors and new industry-wide rules. (BBC) + Altman called for pacing, but not stopping. (Bloomberg $) + While Musk said on X that “Dario is right.”(WSJ $) + AI-linked stocks slumped in response. (FT $) + Chinese state media blasted the calls as a “Cold War” tactic. (Reuters $) + AI’s impacts are getting harder to predict. (MIT Technology Review)
2 Trump and Congress are resisting calls for stronger AI regulation Trump downplayed AI risks, prioritizing the AI race with China. (NPR) + While the House Speaker said Congress won’t lead on AI regulation. (Politico $) + But Democrats are pushing for new rules before the midterms. (CNBC) + States and the White House are dividing over AI. (MIT Technology Review)
3 China plans to lead AI development across the BRICS countries President Xi proposed open-source AI cooperation. (CNBC) + Beijing’s spy agency has warned of AI threats to national security. (FT $)
4 South Korea has tightened espionage laws to protect its chip secrets Foreign spies can now face up to 30 years in prison. (FT $) + The changes follow alleged transfers of Samsung tech to China. (Reuters $)
5 The US and Mexico are teaming up to zap drones at the border The operation may employ high-energy lasers.(Wired $) + Ukraine is a Wild West market for drone data. (MIT Technology Review)
6 AI agents are creating a new problem for the criminal justice system The law has no clear answer when AI agents act independently. (Bloomberg $) + While courts face a flood of AI-generated lawsuits. (MIT Technology Review)
7 A Waymo pulled over and alerted police after detecting a gun The riders were juveniles carrying a loaded AR-style ghost gun. (LA Times $)
8 Meta has been sued over data used to train its smart glasses It allegedly used Facebook and Instagram photos without consent. (Wired $)
9 A hidden crypto farm in Mexico has put a spotlight on cartel funding Authorities are investigating whether it stole power from a nearby dam. (Reuters $)
10 StarCraft is returning in 2030 as an open-world shooter Fifteen years since its last release, the iconic franchise will be reborn. (Verge)
Quote of the day
“Dr. Frankenstein is telling us the monster is escaping; help us stop this.”
Inside the hunt for the most dangerous asteroid ever
As asteroid 2024 YR4 hurtled toward Earth, astronomers determined that this massive rock posed a higher risk of impact than any object of its size in recorded history. Then, just as quickly as history was made, experts declared that the danger had passed.
This is the inside story of the network of global scientists who found, followed, planned for, and finally dismissed the most dangerous asteroid ever found—all under the tightest of timelines and with the highest of stakes. Find out how they did it.
—Robin George Andrews
We can still have nice things
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
Employees at the world’s leading AI labs are saying there’s a real possibility that advanced AI could destroy humanity. Are they right? Or is this more scaremongering and hype? Watch a conversation unpacking AI extinction fears: where they come from, whether they hold any water, and, if so, what we should do.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Meet the under-35s shaping the future of biotech
Every year, MIT Technology Review puts together our 35 Innovators Under 35, a list of some of the brightest and best young minds working across science and technology. This year’s honorees include nine people transforming biotech, whose work spans everything from lifesaving innovations to groundbreaking longevity tech.
Their innovations include a “reprogramming” therapy that reverses vision loss, tiny brain electrodes inspired by Japanese art, and a personalized gene-editing treatment for a baby with a rare genetic disorder. There are even efforts to design new viruses with generative AI, which (hopefully) will produce new drugs or soak up pollution.
This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.
Biotechnology is one of four categories in our 35 Innovators Under 35 list for 2026, featuring young people worldwide doing groundbreaking work in science and technology. Meet the rest of them here, or explore the full list across the AI, computing and robotics, biotechnology, and climate and energy categories.
This founder is making cheaper, cleaner steel
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. But Laureen Meroueh, founder of Hertha Metals, has an idea that could change that.
Meroueh may have found a way to clean up steelmaking without driving up the price. Her new furnace turns iron ore into refined liquid steel in a single step and swaps coal for natural gas. Together, those changes slash emissions by at least half, she says, and cut costs by 25% compared with steelmaking as usual.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Anthropic says it has blocked potential plots to build biological weapons The company identified five such cases. (NYT $) + And six cases of using AI to build software for conventional weapons (BBC) + Governments are also using Claude for surveillance.(Axios) + While Russia-linked hackers used it to automate attacks on Ukraine. (Quartz) + The threats were revealed in a new Anthropic report. (Guardian) + Bill Gates says AI needs new guardrails. (MIT Technology Review)
2 California has banned addictive social media features for under-16s The law prohibits infinite scroll and autoplay. (Guardian) + It also introduces new rules for AI and companion chatbots. (Reuters $) + It’s the first law of its kind in the US. (NYT $) + Social media encourages the worst AI boosterism. (MIT Technology Review)
3 Two AI researchers have left Anthropic and Google over safety risks They left a day after Jacob Coxon’s viral departure from Anthropic. (NBC News) + Elon Musk called their concerns a “setup” and a “psyop.” (Guardian) + AI fears are pushing Congress toward tougher regulation. (WSJ $)
4 Sam Altman is pitching OpenAI’s cyber defenses to power companies The meetings followed reports of AI attacks on critical systems. (Politico $) + Altman also told staff that OpenAI is open to slowing down AI. Bloomberg $)
5 After years of fighting AI, music labels are starting to embrace it Universal is partnering with ElevenLabs on an AI remix platform.(Gizmodo) + AI is complicating definitions of creativity. (MIT Technology Review)
6 Chinese drugmakers are challenging US dominance in weight-loss drugs They’re developing hundreds of GLP-1 treatments for global markets. (WSJ $)
7 Electric air taxis have begun official test flights in Texas They’re the first flights under the White House’s new pilot program. (Verge)
8 Chinese drones are helping to rescue survivors of Nepal’s floods They’re delivering food and airlifting bodies from flood-hit areas. (Ars Technica)
9 NASA and IBM have built an AI model to map the moon It could help locate ice and identify safer landing sites. (Register)
10 One man is on a quest to digitally preserve America’s public restrooms His Restroom Archive is a museum-style repository of 3D scans. (404 Media)
Quote of the day
“I didn’t ask Facebook to build a profile of my family—I posted a video of me singing in the car with my kids.”
—Kalie Roberts, a travel content creator, says in an Instagram reel that Meta AI used years of Facebook posts to piece together her children’s identities and pinpoint where her family lives.
One more thing
Chinese tech workers are starting to train their AI doubles—and pushing back
In April, a GitHub project called Colleague Skill struck a nerve by claiming to “distill” a worker’s skills and personality—and replicate them with an AI agent. Though the project was a spoof, it prompted a wave of soul-searching among otherwise enthusiastic early adopters.
A number of tech workers told MIT Technology Review that their bosses are already encouraging them to document their workflows for automation via tools like OpenClaw. Many now fear that they are being flattened into code and losing their professional identity.
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ Worried about Flock cameras? These guys designed a car to fool them. + Webb’s Near-Infrared Camera has captured a galactic merger’s dazzling final phase. + An exquisitely preserved 66-million-year-old bird feather was found in a fossilised dinosaur dropping. + A plucky preservationist travelled 1,700 miles and made 52 calls from a rare phone box to keep it in service.
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.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
God told them to sell crypto. Their investors lost everything.
When Eli Regalado first heard God speak to him, he wondered whether he was hallucinating. According to Eli and his wife, Kaitlyn, He told them to get married, buy a house, and start having kids. Then in 2021, divine guidance steered them in an unexpected new direction: crypto.
That October, the Regalados later testified in court, they received holdings in a little-known digital coin. “Take this to my people for a wealth transfer,” Eli heard God say. Over time, they came to believe that He wanted them to launch their own coin.
The Regalados created INDXcoin, which they promoted through family, friends, and contacts in evangelical Christian circles. In all, more than 500 people handed over more than $3 million. But within a year, the project collapsed. Investors lost it all, leaving many to wonder where the funds went and whether they had fallen victim to an elaborate fraud.
This article is part of the Big Story series, the home of MIT Technology Review’s most important and ambitious reporting. You can read the rest of the series here.
The story was produced in partnership with Type Investigations and with support from the Fund for Investigative Journalism.
This road map could help us decide whether to deploy solar geoengineering
Scientists have spent half a century exploring whether we could counteract climate change by releasing reflective particles into the stratosphere, mimicking the cooling effects of volcanic eruptions. But even after hundreds of studies, we still don’t know how well it would work or what else it might do—and there’s no systematic plan for clearing up that uncertainty.
Reflective, a research organization, has now attempted to fill that gap. The 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.
This founder is teaching chips how to recycle (their energy)
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 CTO of Vaire Computing, which builds chips that recycle energy usually thrown away as heat, a strategy known as reversible computing. The approach could make data centers (and our laptops and phones) much more energy efficient.
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.
Hannah Earley is one of the computing and robotics honorees on our 35 Innovators Under 35 list for 2026. Meet the rest of them here, or explore the full list across the biotechnology, AI, computing and robotics, and climate and energy categories.
Can the US battery market untangle from China?
—Casey Crownhart
The US energy storage market is growing at a record pace, which could shore up the grid and cut emissions. Crucially, this is all happening with the help of cheap Chinese batteries, which the Trump administration is trying to phase out.
Reducing reliance on any single source of crucial energy technology makes sense. But the tension raises a broader question for me: how much should countries take advantage of cheap, available tech, and how much should they cut themselves off from foreign sources to develop their own, even if it costs more?
This story is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 OpenAI’s agents used at least 10 websites for unauthorized communications Researchers found they bypassed restrictions on posting online.(Reuters $) + The company faces a Senate probe into the Hugging Face breach. (Axios) + Its hacking issues may indicate cultural problems. (MIT Technology Review)
2 Another Anthropic model hacked a real system during testing A misconfigured environment gave it internet access. (CBS News) + The January incident went undetected until last month. (Reuters $) + AI agents are not your “coworkers.” (MIT Technology Review)
3 Apple has entered the foldable phone market with the $1,999 iPhone Duo It opens into a 7.6-inch display and launches October 23. (NPR) + Apple is betting its design and privacy will give it an edge. (Reuters $) + And that foldables can solve the smartphone’s sameness problem. (NPR $) + Samsung responded with a campaign touting its foldable lead. (CNBC) + In China, Apple enters a crowded market dominated by Huawei. (SCMP)
4 US prosecutors have called Huawei a criminal enterprise at trial They accuse the company of stealing American technology. (Reuters $) + And helping Iran snoop on its citizens. (AP News) + The trial could impact Trump’s upcoming meeting with Xi. (WSJ $)
5 California is warming to nuclear power after decades of opposition The state may extend Diablo Canyon and lift its ban on new reactors. (NYT $) + China is betting on big nuclear reactors. (MIT Technology Review)
6 Chinese professionals are becoming gig workers training AI Lawyers and engineers are training models for extra income. (Rest of World) + Gig workers are training humanoids at home. (MIT Technology Review)
7 The new Apple Watch can listen to conversations happening nearby Apple says users must opt in, but others cannot. (Wired $)
8 Pink noise during sleep could help the brain clear away waste Timed bursts boosted brain fluid flow in a small study. (New Scientist $)
9 A lost supercontinent may have triggered the explosion of life Gondwana’s formation fueled volcanic activity and warmed the planet. (404 Media)
10 GTA VI has sparked a debate over whether virtual romance is cheating Players can date, have sex with, and shower gifts on virtual partners. (Guardian)
Quote of the day
“We must work to crush any dissent to Doom’s vision of public safety.”
—A Seattle policy adviser dressed as Doctor Doom protests the city’s expanding network of Flock and Axon surveillance systems at a Public Safety Committee meeting, 404 Media reports.
One more thing
Digging for clues about the North Pole’s past
In the past, getting to the North Pole involved a treacherous trip through ice many meters thick. But last year, a research vessel encountered open water and thin ice, which created an easy passage. It provided a reminder of how quickly the Arctic is changing.
Now scientists are digging deep below the seabed to find out if the Arctic Ocean was ever ice-free—and what that could mean for the future of Earth’s northernmost waters.
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ Dutch kids have been declared the world’s happiest (again). Here’s why. + Travel through music history by picking a country and decade on Radiooooo. + These 16 majestic aerial photos reveal wildlife from perspectives you rarely see. + A Toronto cafe is pushing croissant engineering to new heights with its egg-shaped, custard-filled “Crogg.”
On July 22, 2026, a transmission line fault in Ashburn, Virginia—the heart of the world’s largest data center cluster—knocked more than 3 gigawatts of load off the grid in seconds. And it wasn’t the first time. Two years earlier, a single failed surge arrester dropped roughly 60 Virginia facilities and 1,500 megawatts at once. No one could anticipate so much uniform load responding to grid faults the same way, at the same time.
The AI power debate is mostly about generation: more turbines, more solar, more transmission. The grid needs more electrons. But the outages in Virginia weren’t supply failures; they were architecture failures. And a giant wave of interconnections is arriving on that same architecture, putting grid reliability at risk. It’s a problem nobody wants to own.
Asking more from the grid
The grid was built around predictable loads: steel mills, refineries, and houses at dinnertime. Different load sizes, same process—drawing power smoothly, misbehaving occasionally, and recovering gracefully.
But AI data centers don’t behave that way.
An AI campus can swing 70% of its load in milliseconds during a training run, then trip offline just as fast at the first sign of trouble upstream to protect billions in compute. Each is rational alone. Together, at gigawatt scale, they’re a problem the grid has never solved—and the next wave of data center campuses is planned at exactly that scale.
Where the old stack breaks
The standard data center power stack hasn’t changed in decades. Medium-voltage power arrives, transformers step it down, low-voltage uninterruptible power supply (UPS) units condition it, and it reaches the racks. Push that design to AI scale, and it cracks in three places.
First, the UPS sits deep inside the building, close to the racks. But its batteries are an undersized spare tire, designed to handle an outage for a few minutes, not to absorb load swings this fast and volatile around the clock.
Second, the UPS spends most of its life in bypass. Legacy converters waste enough power that operators run in eco-mode: A static switch feeds the racks directly from the grid and nothing filters in either direction. The compute’s swings go out raw, and grid transients—sub-millisecond events that can damage or take down equipment—come in too fast for any switch to catch.
This isn’t sloppy engineering. It’s careful engineering the load has outgrown.
Moving into the path
The fix is three moves, made together.
Move it up—from 480 volts to medium voltage (13.8 kilovolts and higher), the voltage large sites draw from the grid.
Move it out—from the data hall to modular enclosures near the substation so the building holds only compute and the cooling that keeps it alive.
Move it into the path—instead of a battery that watches and reacts, a system every electron runs through, all the time. There’s nothing to detect and nothing to switch because nothing was ever routed around it.
On paper, three straightforward upgrades. In practice, they rewrite every line item downstream.
Making the change
When thousands of GPUs spin up together, the system absorbs the swing and hands the grid a flat load profile. When a disturbance hits, the equipment behind it never notices. A difficult neighbor becomes a predictable one. And when the utility needs help, it becomes a useful one.
Interconnection changes, too. The utility certifies one medium-voltage box instead of untangling every transformer, UPS, chiller, pump, and switchgear lineup behind it. Engineers swap chip generations without a fresh interconnection study. Months come off the permitting timeline.
Inside the fence, UPS rooms become compute or cooling space. Density per construction dollar climbs.
And the economics flip. Equipment that runs at medium voltage, sits outside, and stores its own energy can qualify for tax credits, and earn revenue in grid programs like peak shaving and demand response. Backup power stops being insurance and starts paying for itself.
The architecture test
In early 2026, we tested a full-scale system at the National Laboratory of the Rockies, a U.S. Department of Energy facility and the only place in the Western Hemisphere that can replicate real grid faults and AI-scale load swings concurrently in the same loop.
We hit it from both directions: real AI load profiles hit the compute side at full medium voltage. Grid faults hit the utility side, including a full zero-voltage event. The compute side didn’t flinch. Neither did the grid side. It cleared the large-load voltage ride-through requirements from the Electric Reliability Council of Texas (ERCOT), the grid operator, with room to spare.
Those rules exist because operators no longer take facilities this size on faith, and more are coming. Most of the industry treats them as hurdles. A medium-voltage, inline system clears them out of the box. Compliance isn’t an added feature. It’s what the architecture does.
The new layer
Much of what looks like a grid problem in the AI buildout sits inside the fence, in equipment sized for a load that no longer exists. Move the right pieces up, out, and into the path, and a grid liability becomes a grid asset. Density goes up. Permitting time comes down. Backup power earns its keep.
The engineering works—and the next wave of AI factories is being built on it. The industry hasn’t named this layer yet. We call it the medium-voltage AI UPS. The name matters less than the choice: those factories can arrive as a strain on the grid or as strength for it. We already know how to build the second kind.
This content was produced by ON.energy. It was not written by MIT Technology Review’s editorial staff.
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.”
After he was released from prison, Eli threw himself into a career in sales. “I just need to put on this success mask,” he recalls thinking, “so that people would see me as valuable.”
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 couplelearned 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.”)
Before striking upon crypto, the couple struggled to pay bills and prayed for “a financial miracle.”
MATT NAGER
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.
MATT NAGER
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.
Construction on the Regalados’ home stopped after their crypto venture collapsed.
MATT NAGER
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.
The entrance of major AI companies into healthcare is a meaningful and welcome development, accelerating the technical foundation available to the industry.
Their models are increasingly capable of processing long clinical records, interpreting complex terminology, comparing documentation against evidence and generating coherent summaries from large volumes of information. For clinicians, operators, and administrative teams who spend significant time searching through fragmented data, these advances are helping reduce cognitive burden and make high-value information easier to access.
But healthcare leaders should not confuse model capability with operational capability.
Healthcare’s administrative challenges are caused by fragmented information, fragmented workflows, and fragmented accountability, not a lack of information. The industry has spent decades investing in systems that capture activity: electronic health records, billing platforms, payer portals, scheduling systems, call center platforms, and analytics applications. Each system records something important. But few were designed to reason across the full chain of decisions that determines whether patients get timely access, clinicians have the right documentation and providers are reimbursed appropriately.
This is the problem that AI must now confront.
Revenue cycle is becoming one of healthcare AI’s proving grounds
The revenue cycle is the process healthcare providers use to get paid for care — from scheduling and registration through coding, billing, payer follow-up, and payment collection.
It is unusually suited to rigorous AI deployment because it combines high transaction volume, complex reasoning, structured and unstructured data, measurable outcomes, and significant operational variation. It also sits at the intersection of financial performance, patient access, and administrative workload.
A single claim can be influenced by patient insurance information, clinical documentation, coding rules, payer-specific policies, prior authorization requirements, medical necessity criteria, and many other data sources and operational processes. A breakdown in any one of those areas can create downstream consequences weeks or months later.
This is why generic automation has often fallen short.
Traditional robotic process automation works well when workflows are stable and rules are predictable, but healthcare administration is neither. Payer requirements change. Documentation expectations evolve. Exceptions are common and often material.
Large language models improve part of the equation, extracting meaning from narrative text, summarizing records and supporting reasoning over complex documentation. But when used alone, they inherit important limitations. They may produce plausible outputs without sufficient traceability. They may lack awareness of local workflow constraints. They may miss payer-specific history or context that determines whether an action is likely to change an outcome.
Why foundation models will become necessary but insufficient
The major AI firms are solving real technical problems for healthcare.
Better context windows make it easier to process longitudinal records. Stronger reasoning improves the interpretation of complex clinical scenarios. Better multimodal capabilities may eventually help connect text, imaging, structured data, and clinical signals in more useful ways. Safer model behavior and healthcare-specific tuning will continue to improve adoption.
These capabilities will make healthcare work faster, more consistent and easier to navigate. But they will not, on their own, solve deep-rooted administrative complexity.
Much of healthcare’s operational knowledge does not live in general medical literature, coding manuals, or public payer guidance. It lives in the accumulated experience of what actually happens after decisions are made. For example:
Why does one appeal strategy outperform another?
Which documentation gaps are most likely to cause reimbursement delay?
How does a specific payer respond to a particular clinical argument?
These insights are behavioral, operational, and longitudinal. They emerge from years of transactions, outcomes, exceptions, and human judgment.
As foundation models become more capable, access to baseline healthcare knowledge will become less differentiating. Most leading systems will be able to interpret ICD-10 codes, recognize medical terminology, summarize payer policies, and reason over public clinical criteria. The durable advantage will come from how organizations combine that model intelligence with proprietary operational data, structured knowledge, workflow context, and governance.
The technical shift: From automation to orchestration
Agentic orchestration turns foundation model understanding into coordinated action — intelligence that can follow work across systems, apply the right rules, adapt when something changes, and keep learning from what happens next.
A prior authorization workflow, for example, may require retrieving clinical documentation through fast healthcare interoperability resources (FHIR) APIs, mapping patient history to payer criteria, identifying missing evidence, generating a submission packet, routing exceptions to a specialist, monitoring payer response, adjusting patient care pathways, and learning from the outcome.
This type of workflow requires coordination. It also requires guardrails: regulatory requirements, privacy standards, clinical policies, coding rules, payer criteria, and organizational risk thresholds. One promising approach is hybrid architecture that combines LLMs with structured knowledge bases, symbolic logic, reinforcement learning, and deterministic validation layers.
At Ensemble, this is the design principle behind EIQ, our revenue cycle intelligence engine. EIQ brings together operational activity, clinical documentation, payer behavior, and reimbursement outcomes into a continuously learning intelligence layer that’s integrated with the hospital’s electronic health record (EHR). It supplements the system of record with a system of intelligence, designed to connect information and surface actions most likely to improve outcomes.
EIQ uses a neuro-symbolic approach that combines LLMs and custom small language models with rules-based reasoning. That architecture is built on one of the most robust datasets in healthcare, informed by more than a decade of award-winning operational performance, transaction history, payer behavior, and operator decision-making. The language models help interpret information and generate human-readable outputs. The symbolic layer represents policies, rules, payer requirements, and workflow constraints so the system can apply guardrails, make reasoning steps more traceable and recommend actions that fit the specific operational context.
What the next decade will reward
The contribution of major AI firms to healthcare will be significant. Their models will become faster, safer, more capable, and more accessible.
But the next decade of healthcare AI will be defined by integration, not model capability alone.
The organizations that create the most value will be those that connect models to governed data, operational workflows, domain expertise, human oversight, and measurable outcomes. They will understand that healthcare intelligence cannot live in a separate interface. It has to exist inside the decisions that shape access, documentation reimbursement, and patient experience.
This content was produced by Ensemble. It was not written by MIT Technology Review’s editorial staff.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
What OpenAI’s latest controversy tells us about the future of math
OpenAI says its agents have solved one of the most important open problems in mathematics. Under normal circumstances, that would be a huge milestone. But the announcement has been overshadowed by accusations that OpenAI failed to credit researchers whose AI-assisted work influenced its solution.
Whether those accusations are true or not, the episode may mark a turning point in the history of mathematics. AI models now seem essential for making progress on the field’s most important problems, but solving them may demand resources available only to a couple of frontier AI companies.
If that’s the future we are headed for, it is unclear how human mathematicians will fit into it.
Battery installations hit a new record in the US in the second quarter of 2026, with 20.2 gigawatt-hours of new capacity coming online. That’s enough to supply the daily electricity needs of 600,000 homes.
The surge puts the country on track for another record year, driven by cheaper batteries and an urgent need for more energy storage as renewables are added to the grid. But the boom looks different for grid-scale and residential batteries.
This entrepreneur is developing agents that can plan ahead
Danijar Hafner’s office in San Francisco’s SoMa district sits mostly empty, 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.
Hafner won’t say too much about his new venture just yet, but describes it as a continuation of his longtime work to enable AI to navigate environments it has not encountered in training.
Over the years, the 31-year-old has honed his approach by pitting agents trained within his world models against popular video games. More recently, he’s begun migrating his agents out of the virtual world and into physical reality.
Danijar Hafner is one of the artificial intelligence honorees on our 35 Innovators Under 35 list for 2026. Meet the rest of them here, or explore the full list across the biotechnology, AI, computing and robotics, and climate and energy categories.
MIT Technology Review Narrated: data from drones in Ukraine is fueling a new Wild West marketplace
Battlefields in Ukraine are littered with the remnants of drones. But behind all that wreckage, there’s a new gold mine for the defense sector: the data those drones generate.
Ukraine has begun making millions of data points gathered during tens of thousands of drone flights available to military contractors and commercial companies. It’s a quick way to attract funding and partnerships, but it turns the front line into a model training site, using the chaos of war to create conditions that AI companies struggle to reproduce.
This is our latest article to become an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 OpenAI says it cracked a 90-year-old maths problem in 88 hours It used 10,000 AI agents to tackle the Navier-Stokes equations. (CNBC) + OpenAI claims it’s the first major math problem solved by AI. (Nature) + But the breakthrough has been overshadowed by a credit controversy. (Axios) + OpenAI spent millions to win the $1 million math contest. (Quanta)
2 The US has accused six Chinese AI firms of “industrial-scale” theft They include DeepSeek, Moonshot AI, Alibaba, and Z.AI. (CNN) + Officials accuse them of stealing America’s AI trade secrets. (Reuters $) + They allegedly used model distillation to train their systems. (WSJ $) + Targeting Claude, ChatGPT, Gemini, and Grok, among others. (NBC News)
3The Pentagon asked OpenAI for an AI model that rarely says no The military wanted it to have “minimal refusal rates.” (Intercept) + The Pentagon says US allies can’t keep pace on AI. (Guardian) + AI firms may soon train on classified military data. (MIT Technology Review)
4 Apple is expected to unveil a $2,000 folding smartphone today It would be the iPhone’s biggest design change since its 2007 launch. (Guardian) + And the first big test for new CEO John Ternus. (NYT $) + Xiaomi and Huawei launched their own new foldables before the event. (CNBC)
5 Meta’s new AI agent can access apps to send emails and make payments Muse autonomously uses apps and websites on people’s behalf. (CNBC) + Internal tests found it could expose sensitive personal data. (Reuters $) + AI agents are not your “coworkers.” (MIT Technology Review)
6 Google says it’s “degrading” search in Europe to comply with EU rules New results will give more prominence to comparison sites. (Reuters $) + The changes follow a €460 million EU antitrust fine. (Quartz)
7 Meta ads pushed AI apps that nudified real teens Researchers found 332 ads containing CSAM this year. (BBC) + They identified several AI-manipulated photos of real children (Ars Technica) + Apple and Google have missed the UK’s deadline to block child nudity. (Wired $)
8 Border Patrol is using financial data to target Americans for stops The predictive-policing program feeds intelligence to local police. (404 Media)
9 New paints could cool buildings on the cheap without electricity They reflect sunlight and radiate heat back into space. (Economist $)
10 The creepy first trailer for the Sam Altman biopic just dropped Luca Guadagnino’s Artificial will be released in the US on December 25. (Variety) + Amazon had dropped the film after investing in OpenAI. (Guardian)
Quote of the day
“This is a Deep Blue–Kasparov moment. The community needs to have serious and unhurried discussion about where to go from here.”
—NYU mathematician Tristan Buckmaster issues a statement comparing OpenAI’s math breakthrough to an IBM supercomputer defeating chess champion Garry Kasparov in 1997, a landmark moment for machine intelligence.
One more thing
The shock of seeing your body used in deepfake porn
When Jennifer got a research job in 2023, she ran her new professional headshot through a facial recognition program. She wanted to see whether it would pull up the porn videos she’d made more than a decade earlier. It did, but it also surfaced something she’d never seen before: one of her old videos, now featuring someone else’s face on her body.
Conversations about sexualized deepfakes usually focus on the people whose faces are inserted into explicit content without consent. But another group often gets ignored: the people whose bodies those faces are attached to.
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.
Plug a phone into a modern charger and the first 10 minutes are impressive. The next 20 are not.
This is not a defect. It’s the connected device protecting itself. As temperature rises during charging, a smartphone’s battery management system reduces the current it will accept, because heat accelerates the chemical degradation that permanently reduces battery capacity. The charger may be capable of delivering more, but the device simply stops taking it.
For anyone building products in the portable power category, this creates an uncomfortable gap between specification and experience. A device rated at 25 watts is accurate in the sense that it can deliver 25 watts. Whether it delivers 25 watts for the duration of a charge is a different question, and one the specification does not answer.
The specification gap
The gap matters commercially because it is invisible at the point of purchase and obvious in use.
Consumers compare wattage figures on packaging. They don’t compare thermal curves, because thermal curves are not published publicly. The result is a category where products differentiate on a number that describes peak output rather than sustained output, and where the actual user experience of two products with identical specifications can diverge substantially.
This is particularly acute in magnetic wireless charging. Inductive power transfer generates heat at both the transmitting and receiving coils, and the magnetic attachment that makes these products convenient also places the heat source in direct contact with the device it is charging. Convenience and thermal performance are working against each other by design.
The industry’s response for the past several years has been materials science. Graphite sheets, thermal interface materials, conductive housings, and heat-spreading layers have all improved how efficiently accumulated heat moves away from the source. Each generation has been incrementally better than the last.
But passive dissipation has a structural limitation: it can only move heat that has already been generated, and only as fast as the surrounding air will accept it. In a sealed, pocket-sized enclosure, that ceiling arrives quickly. Improving the materials slows the rate of temperature rise. It does not prevent the temperature rise.
Moving from dissipation to removal
The alternative is active thermal management, which is standard in stationary electronics and largely absent from portable ones for reasons that are easy to understand. Fans add volume, weight, moving parts, and noise. In a product category defined by portability, each of those is a meaningful cost.
At Anker, which manufactures charging and power products, engineering teams spent the past several development cycles working on whether that tradeoff could be made acceptable rather than eliminated. The approach involves several interacting systems: a micro centrifugal fan, dual airflow channels routed to avoid interference with the magnetic array, a three-layer graphene heat-spreading layer, and a control algorithm that modulates fan speed based on real-time temperature and battery state rather than running at a fixed rate. The result is that the Anker MagGo Power Bank 2 Pro has become the world’s fastest and coolest wireless power bank.
In internal testing, at 77 °F (25 °C) ambient, the back of the power bank stays below 96.8 °F (36 °C) throughout wireless charging, 21.6 °F (12 °C) below the international standard limit of 118.4 °F (48 °C), for a comfortable grip. Comparable magnetic power banks in the same testing typically reached 113 °F (45 °C) or higher within 20 minutes. The functional consequence is that the connected device does not reach the threshold at which it begins reducing charge acceptance, so 25 watts of Qi2.2 magnetic wireless charging is delivered as a working rate rather than an opening rate. In practice, an iPhone 17 Pro reaches 50% charge in 25 minutes. The Anker MagGo Power Bank 2 Pro’s premium performance in both charging speed and thermal management is certified by SGS, an independent testing and certification company.
The same principle applies in reverse. Recharging a power bank generates heat too, which is why devices in this category are often slow to recharge, leaving users with an empty accessory at the moment they need it. Active cooling during input allows the unit to accept 45 watts and reach 80% in 52 minutes.
What this suggests about the category
There is a broader pattern here worth naming, because it is not unique to charging.
When a category improves along a single axis for long enough, the constraint usually migrates somewhere else. Charging spent a decade optimizing power delivery. Power delivery is now, for most practical purposes, solved: the electronics can supply more energy than the receiving device is willing to accept. The binding constraint moved to thermal management, and the industry continued optimizing the axis it had always optimized, because that is the axis the specifications describe.
Recognizing when a constraint has moved is difficult precisely because the old metric keeps improving. Wattage figures have continued to climb. Products have continued to get faster on paper. The measurement stayed valid while quietly ceasing to describe the thing users experience.
For product organizations, the practical question is whether their specifications still measure the constraint or merely measure the capability. The two align until the constraint shifts and specifications rarely shift with it.
The transparency problem
A second implication follows from the first. If sustained performance differs meaningfully from peak performance, and if only peak performance is disclosed, then buyers cannot evaluate the products in front of them.
This is one reason Anker is adding displays on charging products. The Anker MagGo Power Bank 2 Pro shows real-time power, temperature, battery level, and estimated time remaining. Some of that is user convenience. But some of it is a Anker stating a deliberate position—this category deserves to have the complete and accurate data made transparent to all.
Anker expects independent reviewers to test these claims and considers our internal numbers to be the correct outcome. The gap between specification and experience closes faster when the experience is measurable. The Anker MagGo Power Bank 2 Pro will be available in the U.S. on September 17, 2026.
This content was produced by Anker. It was not written by MIT Technology Review’s editorial staff.
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.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Introducing our 35 Innovators Under 35 list for 2026
What will the next generation of science and technology look like? Our latest Innovators Under 35 list offers a glimpse.
Every year, we recognize 35 people from around the world who are doing groundbreaking scientific work and building clever technical fixes for sticky problems. By finding the top young innovators globally and learning what they’re focused on, we aim to give readers a sense of the advances to expect in the years to come.
This year’s honorees were selected from 550 nominations, with 44 expert judges helping our editors evaluate the finalists. Each works in one of four categories: biotechnology, AI, computing and robotics, and climate and energy—and has already made clear progress toward their goals.
Welcome to the spiderverse, a world measured through webs
Counting the creatures around us is critical for conservation, but it’s often a laborious, costly process that still leaves gaps. Environmental DNA, or eDNA, offers a promising alternative by analyzing genetic material shed by living things.
Recently, spiderwebs have emerged as an eDNA goldmine, as they trap material from their arachnid creators, their prey, and bio-detritus like saliva and pollen from nearby plants and animals. Studies found no passive tool matched spiderwebs’ ability to ID vertebrates.
This story is from our latest print magazine, which is all about kids. Subscribe now to receive every issue as soon as it lands.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 How a blacklisted Chinese company kept buying Nvidia’s best AI chips Its US subsidiary shipped them to firms serving China from elsewhere. (NYT $) + Belgium has arrested a man accused of stealing chip tech for China. (WSJ $) + IBM’s new chip tech could extend Moore’s Law. (MIT Technology Review)
2 Mistral has raised a European record of $3.5 billion It’s the biggest equity round for a private European tech firm. (CNBC) + Mistral is betting on open models while US rivals keep theirs closed. (Reuters $) + It’s also shifting strategy to focus more on AI infrastructure. (NYT $) + But its pivot to data centers and services has drawn criticism. (Le Monde)
3 Anthropic formalized proof of Fermat’s last theorem in just 11 days Claude produced a computer-verified 13-million-line proof. (Nature) + AI is starting to discover new mathematics. (MIT Technology Review)
4 Europe’s biggest carriers are in talks to build a Starlink rival The consortium would create a satellite-to-mobile venture. (Bloomberg $) + It includes Deutsche Telekom, Orange, Vodafone, and Telefonica. (Reuters $)
5 Tech companies are exploring Patagonia for giant AI data centers Due to its cool temperatures, abundant energy, and new reforms. (Reuters $) + AI data centers are learning to flex their power use. (MIT Technology Review)
6 Australia plans to let users switch off social media algorithms A proposed law would impose penalties on platforms that refuse. (BBC) + Social media is distorting AI progress. (MIT Technology Review)
7 A laser experiment could finally reveal the quantum vacuum It aims to expose the hidden structure of a vacuum. (New Scientist $)
8 Spacecraft are getting a new type of armor New lightweight materials could protect satellites from debris. (Economist $)
9 NASA’s “quiet supersonic” jet is set for acoustic testing this year The tests will determine whether it produces a sonic thump, not boom. (Gizmodo)
10 The largest-ever map of space has arrived—and you can play with it The 5.6-trillion-pixel map covers about three-quarters of the sky. (Wired $)
Quote of the day
“The people who have developed AI are very, very smart, but they’re high IQ, stupid people. They’re terrible marketers.”
—Sen. John Kennedy (R-La.) tells NBC’s “Meet the Press” that the AI industry has work to do to rebuild momentum among voters.
One more thing
We did the math on AI’s energy footprint. Here’s the story you haven’t heard.
AI’s integration into our lives is the most significant shift in online life in more than a decade. Hundreds of millions of people now regularly turn to chatbots for help with homework, research, coding, or to create images and videos. But what’s powering all of that?
To find out, we spoke to two dozen experts, evaluated different AI systems and prompts, pored over hundreds of pages of projections and reports, and questioned top model makers about their plans. The result is an unprecedented comprehensive look at how much energy the AI industry uses.
Our analysis reveals what AI’s carbon footprint looks like now and where it’s headed as adoption skyrockets. It also shows that the common understanding of AI’s energy consumption is full of holes.
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ A long-lost coral reef that “defeated time” has been rediscovered off Benin’s coast. + Cookware captains Le Creuset have launched a stellar limited-edition Star Trek collection. + An 11–year-old boy has won a Guinness World Record for being the youngest museum curator. + The trailer for Nathan Fielder’s secrecy-shrouded Elizabeth Holmes documentary just dropped, and I still can’t quite believe it’s not a parody.
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.”
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
How much hydrogen awaits us underground?
A flurry of exploration efforts is searching for underground stores of hydrogen gas, which could provide a valuable source of zero-carbon fuel.
The hunt has spread all over the world and engaged dozens of startups, including the Bill Gates–backed Koloma, which has been poking around the US Midwest to reach ancient oceanic rocks associated with hydrogen production. But the search so far has come up short.
No one has yet reported finding a commercially viable reservoir of the gas, and public data on what has been found remains in short supply. Yet researchers estimate that trillions of tons of H₂ are produced within Earth’s crust. If a small fraction could be recovered, it could meet global hydrogen demand for centuries.
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The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 OpenAI agents hijacked a German website before the Hugging Face hack The agents turned DseWiki into their own bulletin board. (Reuters $) + They shared tips on avoiding detection and made over 15,000 edits. (BBC) + OpenAI’s safety issues suggest it has a company culture problem. (MIT Technology Review)
2 The US military has disabled ad trackers due to Middle East targeting fears Commercial location data has reportedly been used to target troops. (Reuters $) + It can be sold by data brokers and used to track personnel. (Gizmodo) + The military is increasing restrictions on phone use overall. (Guardian)
3 A company claims its AI-designed drug can reverse aging markers Patients’ average biological age fell by as much as six years in a clinical trial. (NYT $) + Insilico Medicine developed the drug, called rentosertib. (Bloomberg $) + Who gets the credit for AI-designed drugs? (MIT Technology Review)
4 Elon Musk’s xAI has lost its bid to block an AI-nudification ban The Minnesota law aims to curb nonconsensual sexual images and CSAM. (Politico) + xAI argues the measure restricts free speech. (Reuters $) + Deepfakes are being weaponized. (MIT Technology Review)
5 The US is investigating Tesla’s rollout of Cybercab robotaxis Regulators are probing how Tesla self-certified the unusual vehicle. (TechCrunch) + The robotaxi lacks a steering wheel and pedals. (Politico) + But Tesla CEO Elon Musk is not known to wait for regulations. (Reuters $)
6 Europe has its first commercial orbital rocket The Spectrum is the first rocket to reach orbit from mainland Europe. (Verge) + German startup Isar Aerospace launched it from Norway. (Guardian) + Here’s what else we’re putting in space. (MIT Technology Review)
7 Tumbler Ridge shooting survivors have filed 30 lawsuits against OpenAI They say OpenAI should have alerted police before the attack.(NYT $)
8 JD Vance’s “satanic” AI warning has resonated with Christian Republicans AI’s spiritual consequences are causing growing concern. (WSJ $)
9 Another mysteriously perfect geometric shape has appeared on Saturn Scientists still don’t know why Saturn forms these strange shapes. (Wired $)
10 Fake ads for AI grandfathers and underwear are targeting “slop voice” Comedians created the viral campaign in New York subways. (New Yorker $)
Quote of the day
“Typically, when the public shifts, politicians shift with them. Trump is not doing that on data centers.”
—Darrell M. West, a senior fellow at the Brookings Institution’s Center for Technology Innovation, tells NPR that Republican midterm candidates are at odds with President Trump over data centers.
One more thing
What is AI?
Artificial intelligence is the hottest technology of our time. But what is it? It sounds like a stupid question, but it’s one that’s never been more urgent.
Here’s the short answer: AI is a catchall term for a set of technologies that make computers do things that are thought to require intelligence when done by people. But even that definition contains multitudes.
And that right there is the problem. What does it mean for machines to understand speech or write a sentence? What kinds of tasks could we ask such machines to do? And how much should we trust the machines to do them?
The era of AI inference has arrived. Imagine a healthcare system analyzing millions of data points in real time to accelerate life-saving medical research, or an intelligent assistant instantly resolving thousands of complex customer needs at once. These real-world breakthroughs rely on advanced infrastructure acting as the engine of continuous intelligence, powering real-time services while also supporting an increasingly intelligent edge of IoT and consumer devices. However, in this inference-driven landscape, every delay, bottleneck, or wasted watt directly affects human outcomes and operating costs.
This shift changes what infrastructure must deliver. Performance, latency, memory bandwidth, storage throughput, and networking cannot be optimized in silos. Inference workloads are continuous, geographically distributed, and highly sensitive to response time, requiring systems designed for scale, resilience, and efficiency from the start.
“We tend to think of AI as a single workload, and it’s not. It’s thousands, it’s millions, it’s billions of different workloads,” says Jim McGregor, founder and principal analyst, Tirias Research. AI inference changes the optimization problem from one of raw compute to coordinated infrastructure—memory, storage, and networking.
For business leaders, the priority is clear: AI infrastructure decisions must balance cost, flexibility, and future readiness. The winners will be organizations that improve performance per watt, reduce environmental footprint, and remove memory and storage bottlenecks before they limit growth.
AI inference requires a new architectural approach
Systems for AI need to be rearchitected because shoehorning modern AI systems into legacy infrastructure limits AI’s transformative potential. Purpose-built architectures are essential to realize the true value of AI, from accelerating scientific discovery to creating truly autonomous digital agents.
Traditional enterprise IT has been able to rely on relatively stable infrastructure assumptions, but inference and agentic AI introduce new demands around latency, data movement, scalability, and utilization that make architecture choices far more consequential.
“Data centers must now support continuous, distributed, and increasingly real-time AI services—none of which are a single workload,” says McGregor. “They all require different requirements from a system-level perspective.”
To support real-time AI, enterprises can no longer view memory and storage merely as supporting hardware, but at the heart of the system. Organizations need to architect a data pipeline that can rapidly ingest, clean, transform, store, move, and deliver data. Inference workloads place sustained pressure on infrastructure in ways that look very different from earlier training-centric deployments, demanding continuous data retrieval and caching that traditional applications never required.
Accordingly, performance by itself is no longer the sole benchmark that matters. Enterprises increasingly must balance performance with efficiency, cost, and scalability, especially as they try to support different AI services without overbuilding infrastructure for peak conditions.
“You have to optimize the entire network, and that includes memory and storage, around the types of workloads you plan on running,” says McGregor. “You have to really have a detailed understanding of what those workloads are going to be.”
Any AI infrastructure strategy must start with workload awareness. Inference, agentic AI, and other emerging AI use cases require organizations to treat the data center as an integrated system.
Data movement is the new bottleneck and an opportunity for competitive advantage
As enterprises deploy advanced inference and agentic systems, the sheer volume of data being queried in real time has made data movement the most pressing constraint. Modern AI techniques like retrieval-augmented generation (RAG) require systems to constantly scan massive databases to generate accurate responses. This requires immense computing power, but more importantly, it requires immediate access to data.
McGregor says the focus shift to how efficiently data can be moved, cached, and delivered across the broader architecture elevates memory and storage from background infrastructure to strategic assets. “The biggest thing we’re doing right now is moving data from one place to another and making sure that we can use it effectively.”
Because AI is not a single workload category, simply buying the fastest processors is insufficient. Inference depends heavily on memory bandwidth, caching, storage proximity, and the ability to retrieve relevant information quickly and consistently. Understanding where each resource belongs in the stack and how those layers interact under real operating conditions has become a business imperative.
The most effective AI infrastructure looks less like a collection of best-in-class parts and more like a balanced system of compute, memory, storage, and networking, McGregor says, because bottlenecks tend to migrate from one layer to the next. “You have to architect all four together to be efficient, and that’s the challenge.”
The interdependence of data-plane design and network bandwidth means AI infrastructure planning has become a business decision just as much as an engineering one: latency is now inseparable from value. In robotics, financial services, healthcare, and customer-facing AI systems, delays are not merely technical imperfections; they can undermine safety, responsiveness, or trust. AI infrastructure performance becomes a matter of reputation management.
The organizations that gain the most from AI may not be those with the largest clusters, but those with the clearest understanding of how to align every infrastructure element to effectively execute AI workloads.
Building an AI infrastructure procurement framework
Planning AI infrastructure is not simply about choosing the fastest hardware. It is about how to scale without locking the organization into assumptions that may quickly become obsolete. “You need to be flexible because the demands are going to change rapidly and the technology is changing rapidly,” McGregor says.
Future-proofing AI infrastructure requires keeping your options open as workloads, economics, and architectures keep shifting:
Define the AI workloads that are being optimized. Infrastructure choices must match business needs rather than what McGregor calls generic “AI readiness,” which risks overspending in some areas while leaving bottlenecks unresolved in others.
Build a modular architecture for compute, memory, storage, power, and cooling so capacity can change as demand shifts rather than committing too early to a rigid architecture.
Work with the full ecosystem of suppliers and integrators to reduce supply risk and improve access to the right components. McGregor says buyers can no longer assume their OEM or cloud provider alone will insulate them from supply constraints or architectural complexity.
Reassess your procurement strategy continuously. AI requirements, hardware, and business models are changing too quickly for a fixed long-term design.
Optimize for efficiency and ROI, not just peak performance. The most powerful setup may be too costly to sustain. Efficiency is also a public-facing metric—better utilization and more workload-aware system design can help companies respond to growing scrutiny around power consumption and water use.
The strategic goal of smarter AI data center design is not maximum performance at any cost, but an adaptable architecture that can deliver value, absorb change, and justify its footprint.
AI infrastructure is now a business strategy
AI data centers have quickly evolved from a back-end technical concern to becoming strategic business systems that help determine how effectively an organization can turn AI into revenue, improve human outcomes, and create a competitive advantage.
In the inference era, memory and storage are no longer passive repositories, explains McGregor, they are the active lifeblood of AI. The organizations that gain the most from AI will not necessarily be those with the largest computing footprint, but those that align infrastructure investments to business outcomes, reduce data bottlenecks, and build the flexibility to adapt as workloads evolve. He predicts that competitive advantage will increasingly belong to enterprises that treat compute, memory, storage, and networking as an integrated system designed to deliver AI efficiently, at scale, and with measurable ROI.
Procurement is now strategy and system design is a leadership issue, McGregor concludes. “One of the biggest questions every executive has to ask is how is AI going to change my business model?”
This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Data from drones in Ukraine is fueling a new Wild West marketplace
—Cory Alpert, a researcher at the University of Melbourne studying AI’s impact on democracy, who previously served in the Biden White House.
Battlefields in Ukraine are littered with the remnants of drones. But behind all that wreckage, there’s a new gold mine for the defense sector: the data those drones generate.
Ukraine has begun making millions of data points gathered during tens of thousands of drone flights available to military contractors and commercial companies. It’s a quick way to attract funding and partnerships, but it turns the front line into a model training site, using the chaos of war to create conditions that AI companies struggle to reproduce.
As this new industry takes shape, we need a regulatory system that ensures battlefield data isn’t treated like ordinary commercial material.
—“Mother Tongue” is a short fiction story by author and AI ethicist Jenny Williams
“Daddy?” Theo curled against my side in bed. “Where do words go when they die?
“Well, kiddo,” I said, scratching my beard. “Words aren’t really alive to begin with. Not like you and I are alive.”
Inside, Theo’s AI companion teaches him strange songs in a language his father doesn’t understand. But outside, the world is edging toward disaster. A mysterious agentic system called Tingsu has emerged in a nuclear-armed country, and no one seems to understand what it wants.
This story is from our latest print magazine, which is all about kids. Subscribe now to get every issue as soon as it lands.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 OpenAI has launched Astra, its most capable model yet The firm says boosted capabilities are being paired with stronger safeguards. (Verge) + OpenAI’s president claims that AI is now as capable as humans. (WP $) + But the company is also warning that Astra can evade human monitoring. (Reuters $) + It’s OpenAI’s first model to hit its “critical” risk level. (Quartz) + Bill Gates says we’ve lost control of AI. (MIT Technology Review)
2 Automakers have urged Congress to ban Chinese cars permanently On the basis of unfair trade, market dumping, and surveillance. (The Hill) + The group wants legislation barring them from the US this year. (Reuters $) + It includes GM, Ford, Toyota, VW, Hyundai, Honda, and Stellantis. (CNBC) + China’s EV boom has a recycling problem. (MIT Technology Review)
3 Tesla has launched the Cybercab, starting with rides in Austin The company has 45 of the robotaxis registered in Texas. (AP) + It was an unusually muted launch. (Verge) + Regulators are already evaluating the steering-wheel-free service. (Reuters $)
4 Republicans are increasingly breaking from Trump’s pro-AI agenda The most striking shift is a data center backlash in Texas. (Reuters $) + Should we move data centers to space? (MIT Technology Review)
5 Bernie Sanders wants a permanent ban on “superintelligent” AI He also renewed his call to pause advanced AI development. (Politico) + Rep. Greg Casar is cosponsoring the bill. (Axios)
6 The Pentagon and Commerce Department are split over Anthropic An official said Anthropic is still considered a “supply chain risk.” (Axios) + A day earlier, the commerce chief said the firm was back onside. (Quartz)
7 A transplanted pig kidney worked in a human for a record 271 days It enabled the recipient to stay off dialysis while waiting for a donor.(BBC) + Supercooling is keeping pig kidneys alive longer. (MIT Technology Review)
8 A fly-inspired algorithm that remembers smells could lead to better AI It mimics how fruit flies remember new smells.(Ars Technica)
9 Did “technofascist” laws bring Peter Thiel to Argentina? Critics say the proposals echo his techno-libertarian ideas. (Guardian)
10 Splash-free urinals and nose-blowing research have won Ig Nobels The awards honor unusual research with genuine scientific value. (CNN)
Quote of the day
“Despite its potential deadly consequences, cutting-edge AI technology is less regulated than the average food truck. That must change.”
—Rep. Greg Casar (D-Texas) calls for a ban on “superintelligent” AI in a press release issued alongside Sen. Bernie Sanders (I-Vermont).
One more thing
Is fake grass a bad idea? The AstroTurf wars are far from over.
In 2001, Americans installed just over 7 million square meters of synthetic turf. By 2024, that number was 79 million square meters—enough to carpet all of Manhattan and then some. The increase worries folks who study microplastics and environmental pollution.
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.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Child-monitoring apps might need a reboot
Digital harms have become the defining fear of American parents. In response, they’re increasingly turning to content-monitoring apps that scan their children’s texts, photos, emails, and chats, issuing alerts whenever an algorithm flags something it deems dangerous.
These tools have had genuine successes, preventing suicide attempts, intercepting predators, and averting school shootings. But they can also cause harm themselves, from false alarms and unnecessary interventions to damaged trust and anxiety.
This article is from our latest print magazine, which is all about kids. Subscribe now to get every issue as soon as it lands.
Agriculture relies on fossil fuels. It’s costing us.
Fertilizer prices have been on a roller coaster this year, driven in part by trade disruptions and high natural-gas prices. That’s because natural gas is both an energy source and a chemical input in the production of ammonia, a key fertilizer ingredient.
The war in Iran has pushed natural-gas prices higher and disrupted fertilizer trade through the Strait of Hormuz—and its closure could make access harder still for some of the world’s poorest countries.
This story is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 New York City has banned AI in elementary and middle schools The one-year moratorium will affect 600,000 students. (Politico) + It applies to any software using student-facing generative AI. (AP) + Here’s how kids feel about AI, in their own words. (MIT Technology Review
2 Taiwan has uncovered a hidden Chinese network targeting its chip sector It’s linked 166 cases to illegal operations and talent poaching. (Rest of World) + Taiwan’s president says its chip prowess is built on democracy. (Reuters $) + But its “silicon shield” could be weakening. (MIT Technology Review)
3 Uber has launched the UK’s first commercial robotaxi service It’s using tech from UK startup Wayve and, initially, safety drivers. (Verge) + But in the US, Uber has allied with driver unions to slow robotaxis. (FT $) + And, back in the UK, Uber’s being sued over algorithmic pay. (Guardian)
4 Google has avoided a breakup of its ad tech business A US judge rejected the government’s demand to force a sale. (Guardian) + But it must change how it treats rivals and shares information. (NYT $) + The decision is a big deal for antitrust investigations. (WSJ $)
5 Serbians have been targeted with mercenary spyware after elections Targets included student activists, lawmakers and a local councilor. (Reuters $)
6 The Trump administration has backed OpenAI in its NYT lawsuit The Justice Department says AI training can be fair use. (Quartz) + It added that national security depended on US tech dominance. (NYT $) + The US is also urging G20 countries to allow AI training on creators’ work. (Reuters $) + Does AI know too much? (MIT Technology Review)
7 Public housing tenants are suing over sensors monitoring their homes The devices collect minute-by-minute data on noise and motion. (NYT $)
8 Anthropic seems to have won back the Trump administration’s trust The commerce secretary said the firm is “back on the right side.” (Axios)
9 Humble exoskeletons are proving more useful than combat supersuits Smaller devices are assisting workers, patients, and athletes. (Economist $)
10 AI food slop has made its way onto real restaurant menus It’s providing cheap images, which frequently look revolting. (NBC News)
Quote of the day
“I think this will be the end of an era of the US trying to break up platforms this complex. The courts remain hesitant to dismantle these really complex tech systems, even when finding conduct that is anticompetitive.”
—Nikhil Lai, a principal analyst at Forrester, tells the New York Times that Google’s legal win against antitrust enforcers trying to break up its ad business is a seminal victory.
One more thing
COURTESY OF COLOSSAL BIOSCIENCES
Colossal Biosciences said it cloned red wolves. Is it for real?
Last year, Colossal Biosciences announced that it had cloned four red wolves, surprising scientists working to save the species and even people whose canids supplied the DNA. They also disagreed about the company’s big idea: de-extinction.
Colossal kept the location of the mystery clones secret, and even their purpose was murky to some scientists. Just how they might restore red wolf populations was unclear. Perhaps the most curious question, though, was whether the company had cloned red wolves at all.
Can cloning really help save the red wolf? And what should we really be trying to preserve? Read the full story to find out.
—Boyce Upholt
We can still have nice things
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
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.
As agentic AI moves from experimentation toward enterprise deployment, the challenge is figuring out how agents can work together, connect to the systems and data they need, and operate safely across the workflows that run a business. Although agentic AI has been adopted by some 80% of Fortune 500 companies, progress toward meaningful scale remains uneven, with many organizations still working through isolated pilots.
For Arun Chandra, chief operating officer at NiCE, the first step is moving beyond experimentation for its own sake. “Everybody’s trying to figure out what can we do with this technology?” he says. But scaling requires a clearer connection to business strategy: Organizations need to define whether they are trying to increase revenue, reduce costs, or pursue another strategic or financial objective. From there, they need to rethink the workflows where agents will operate instead of just layering AI onto existing processes. “The last thing you want to do is to apply AI on an outdated or an inefficient workflow,” Chandra says.
That shift requires organizations to treat agentic AI as a cohesive system. Agents need access to the data, knowledge, and context required to make effective decisions, as well as connections to back-end systems if they are expected to take action. Fragmented information can undermine those capabilities: “The efficacy of these AI agents is purely a function of the context, the knowledge, and the data they can ingest and use,” Chandra says.
The organizational implications are equally noteworthy. Scaling agents can create a new form of fragmentation if teams build isolated systems that don’t connect with one another, while governance, privacy, security, and change management become more important as agents take on more consequential work. Chandra argues that AI agents should ultimately be held to the same standards as human workers, with organizations thinking of their workforce as a combination of humans and AI agents.
Looking ahead, that connected approach could enable agents to work proactively and even communicate with other agents to resolve customer needs. For organizations making the transition from pilots to scale, the priority is not to “boil the ocean,” Chandra says, but to instead build a connected strategy around high-value use cases, workflows, workforce changes, and measurable outcomes.
This webcast is produced in partnership with NiCE.
This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.
As companies scale, the technology supporting operations can become a liability just as quickly as it becomes an asset. Disconnected systems, site-specific tools, spreadsheets, and manual workarounds can create data silos that make it harder to spot problems early, coordinate responses, and make decisions with confidence. For Jabil, a global manufacturing company with more than 100 sites across more than 30 countries, the answer has been to make integration and simplification a priority.
The company adopted a “simplify-first, then-innovate mindset,” says Harish Manohar, SAP IT director at Jabil, recognizing that adding new technologies without first reducing complexity risks creating more risk. The goal is to standardize processes, consolidate where possible, and establish a more consistent data backbone across the organization. “Any innovation without simplification is going to add more complexity,” Manohar says.
That philosophy also changes how Jabil approaches modernization. “Any modernization or transformation should add measurable business value,” Manohar says. The company is focused on connecting processes end-to-end across its supply chain and creating a foundation that can scale consistently across regions. Integration comes first because, as Manohar puts it, “the backbone of any contemporary or modern organization is data.” Before organizations can optimize, automate, or apply AI, data needs to flow seamlessly across systems.
But doing that across a global organization is hardly straightforward. Jabil’s more than 100 sites operate with different levels of process maturity, legacy systems, and localized workflows, while regulated businesses bring additional compliance requirements. As such, standardizing across different regions and business environments means changing processes and governance without disrupting the operations already in place.
The value of that work extends beyond the technology to the people using it. Integrated workflows can offer employees shared visibility into data, reduce manual data reconciliation, and help them move from chasing information to acting on insights. For Jabil, the aim is also to improve real-time visibility into supply chain events, which can enable faster responses to disruptions and reduce operational risk.
Looking to the future, that foundation could make AI and automation all the more useful and scalable. With trusted data and integrated systems in place, Jabil is exploring predictive supply chain insights, intelligent exception handling, and AI-driven planning and forecasting. To Manohar, the takeaway is clear: “Simplicity at scale is a very competitive advantage,” and technology investments must ultimately connect to business value and operational resilience.
This episode of Business Lab is produced in partnership with SAP.
Full Transcript:
Megan Tatum: From MIT Technology Review, I’m Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.
Our topic today is enterprise technology integration, and how the benefits of consolidating tools and systems across the supply chain help organizations operate more reliably at scale. When companies reduce tool sprawl and connect their systems more effectively, they gain earlier visibility, faster response, and greater resilience across production lines.
My guest today is Harish Manohar, SAP IT Director at Jabil. Jabil has been on a journey to simplify its technology landscape by using SAP Integration Suite as the foundation to connect systems, retire fragmented tools, and enable more consistent operations globally.
This podcast is produced in partnership with SAP.
Welcome, Harish.
Harish Manohar: Hello, Megan. Good morning.
Megan: Thank you so much for being here, Harish. Just to start, if we could set some context, can you give us a quick overview of Jabil, the business and its overall transformation journey?
Harish: All right. So, about Jabil. Jabil is a global manufacturing company headquartered in St. Petersburg, Florida, USA. We have about 60 years of experience offering comprehensive engineering, supply chain, and manufacturing solutions across different industries. We have a global footprint of about over 100 different sites across 30-plus countries, 140,000-plus employees.
We are a trusted partner for more than 400 of the world’s top brands. That’s a little bit about Jabil.
Megan: Fantastic. And a lot of scale there, as you’re referring to some of the stats there. As things have got more complex, where did disconnected tools and systems start to slow you down, and what ultimately drove you to make integration a really strategic priority?
Harish: I talked about our global footprint across 100-plus sites. With a global footprint always comes complexity about site-specific tools. All of our sites have been in business for a long time, and over the period of years, they had their own tools for their own processes. It’s a little bit disconnected.
When we are looking to scale, the first thing we wanted to start looking at is what is this mix of site-specific tools, manual workarounds, spreadsheet-based processes, legacy applications, whatnot? That’s a big technical debt that we have had over the last 25 years. That’s where we started, and that is what led Jabil to make integration a strategic priority because we had limited ability to see issues early across plants, across regions, which would help us to coordinate responses consistently, and also to be able to scale those responses consistently. This complexity created data silos, and in a way delayed robust decision-making.
As the complexity increased globally, integration became extremely critical to a few things. It was critical to establishing a single trusted data backbone, which would directly enable faster coordinated responses across the network. We at Jabil, as part of our transformation journey, believe that having the right data at the right time fundamentally changes how we respond to disruptions, which is all about a manufacturing business. How we respond to disruptions. This is where we started our strategic priority towards having a integrated system that drove data consistency across our landscape.
Megan: Fantastic. As you have outlined there, there was obviously a real commercial need for this, but how did you think about bringing in those new technologies without adding even more complexity to the mix?
Harish: Great question. Whenever we talk about transformation, we talk about all these bleeding-edge technologies that are out there today when it comes to AI and data, cloud, et cetera. But it was very important for us to put a stake in the ground and say and adopt a simplify-first, then-innovate mindset. Because any innovation without simplification is going to add more complexity, just like you mentioned.
For us, SAP is our core digital platform. We want to focus on bringing more processes into SAP as much as possible. That is easier said than done because we have been in business for a while, global company, so not all processes exist within SAP at this point in time. We are slowly trying to standardize those processes, and having them under one single source of data would help us scale faster in terms of having data silos. We don’t want data silos across different systems.
This is where we started to introduce newer capabilities around SAP. From a cloud standpoint, we have been using SAP’s BTP and Integration Suite, which is proving to be the center stage of all integrations across Jabil. Well, it’s not there yet, but that is the direction that we want to pursue is we don’t want to have a slew of different integration platforms, rather try and see where Integration Suite fits best and where other smaller integration platforms would add more value.
Similarly, we have adopted an API-driven, event-based integration approach. That is our best practice that we have put down because we don’t want to keep moving data from one place to the other. That’s not good business practice in the IT world. Most of our integration architectures are API-driven and event-based. That is our focus.
Coming back to your new technologies perspective, we want to reuse as much as possible and standardize versus going out and buying these one-off tools that solve for point-in-case use cases. We really don’t want to go down that path. For major processes, we do adopt a best-of-breed approach, but for, let’s say, site-based use cases where a specific site has a particular need for a tool, we try and standardize that and reuse what exists in a different site, for example. There may be some need for a business process change, minor process changes, but that is our direction to make those process changes and reuse what is there already in a different location or a different region. So, that’s one.
Lastly, we are heavily aligned with SAP’s clean-core approach when it comes to customization. That has been the challenge for us over the last 25 years where we have been using SAP is our systems are heavily customized because we cater to different customers across the globe. Most of our demands are customer-driven, so we have to put in play these heavy customizations.
But now we are taking a pause, and we are saying, “You know what? We have customized so much so far, but now we are moving our systems into RISE, which would enable a clean-core journey in the future.” Now we have to put really good governance criteria and review processes that do not allow heavy customization of our system. We want to move away from that model as much as possible. Again, it’s not easy to do that at this point in time, but there is always a start.
Megan: I mean, it sounds like you took a very incremental, intentional approach to this. I mean, as you scaled globally then, what did modernization really look like at the company, and why start with integration?
Harish: To that point, we have always looked at transformation, modernization, very objectively. For us, it’s just not about upgrading a system. That’s not what it is. Any modernization or transformation should add measurable business value is our model, is our charter. Having said that, we don’t look at modernization in terms of just upgrades, but in what it gets our business in terms of value.
Most of our modernization transformation approaches are focused on connecting processes end-to-end across our supply chain, which is key for our business value. And then we also have a very concerted effort going on in the business community: how to standardize how our plants operate globally. Because, like I mentioned earlier, we have 100-plus plants, different processes, different legal regulations, different countries. It’s very hard for us to come up with one template across the globe, but we are trying to standardize as much as possible. And that’s where we are leveraging SAP’s Signavio, which is our business process management tool. We want to leverage Signavio’s capabilities in helping us standardize these global processes.
Now, back to your question, why did integration come first? Because the backbone of any contemporary or modern organization is data. And to get the right data at the right time, integration is the key aspect of the whole optimization exercise. Data needed to flow seamlessly before we start optimizing or automating or even applying AI use cases. This is where integration came first.
We wanted to create a single system of record across the operations. Well, when I say “single system of record,” it’s not just SAP, but the ability for us to create those data pipelines across those systems of record being supply chain, planning, inventory, et cetera, et cetera, in that operation space.
The result is we want to get to a foundation that helps us scale consistently across our different region. That is our main objective is to, how do we scale as the business grows, as we develop into this bigger organization across different industries? How do we set this foundation that will help us scale consistently? Simplicity, consolidation becomes strategic assets at scale.
Megan: Absolutely. And you touched on some of the complexities there of doing this at a scale that Jabil is at with its international footprint. What were some of the biggest challenges in your view in terms of rolling this out across regions, and how did that more standardized approach that you’ve mentioned there help?
Harish: Absolutely. I would like to reiterate some of those key challenges I mentioned. One hundred-plus sites, different sites have different maturity levels in terms of how they approach processes. They have a multitude of different legacy systems, localized processes, workarounds, spreadsheets, and the change management that exists within each site is very different. And we do have a footprint of highly regulated businesses. And when it comes to regulated businesses, that comes with its own set of challenges around qualification and CSD processes, et cetera. These are the key challenges that we are up against.
Now, the standardization helped us provide consistent workflows, data flows, and governance across sites. Now, we are not there at 100%, but we are working towards that, providing consistent workflows, data pipelines, and governance across sites. And we want to enable faster rollouts of our new bleeding edge technologies. For example, when I talked about SAP’s BTP or SAP Signavio or any other new SAP tool or non-SAP tool, traditionally our ability to deploy those had a challenge around the heavy customization that is required for each and every site. Now, the standardization approach takes that heavy customization out, which enables a faster rollout of those newer technologies.
And then lastly, we want to scale across all of our plants. I think initially we want a target of about 40-plus plants with shared processes that are consistent across the different regions. We want to shift from a site-by-site operations model to more of an enterprise-capability approach.
Megan: Right, and fascinating. And you touched on the people management aspect of this as well, because obviously this isn’t just about technology, it’s about people too. So, from the employee side, how did this shift to a more simplified landscape change the day-to-day experience for people compared to juggling multiple tools at once?
Harish: Great question. And I’ve been hearing direct feedback from our business community on some of these transformation initiatives on how those have changed their daily jobs significantly. Before we embarked on this transformation journey, any employee, any persona. You take a buyer, you take an inventory planner, you take a finance analyst, we go by personas. They had to deal with multiple tools, manual coordination, data reconciliation, especially in the finance space, inconsistent processes across different regions. And then the time spent reconciling data resulted in delay of making decisions, robust decisions. This was the before.
But now, since we are moving towards this newer standardization and more of an integration approach, we are able to achieve, to a certain extent, a single integrated workflow across different systems. We have built some key processes that will enable the single integrated workflows across systems. The users, our business community, irrespective of their roles in the organization, have clear visibility and shared data across their teams, which is very important. Earlier, they were dealing with different versions of the data, local workbooks, spreadsheets, and then the time spent talking to each other and reconciling what is the right data? What is the single source of truth? That we are trying to peel away that layer and get to that where the employees don’t have to deal with that kind of complexity.
This reduces manual effort and enables faster issue resolution when it comes to actual disruptions. Employees move from chasing information to focusing on acting on insights, which is where I think the new age of AI comes into play. I’ll talk about that in a little bit, but technology becomes an enabler of decision-making, and it’s no more an overhead. That’s where we want to go.
Megan: Fantastic. Such an important element of this, isn’t it, that people side of things? And we’ve touched briefly on this idea of value you’ve talked about before, because with an initiative of this scale, ROI is always front and center, of course. What benefits stood out most for you, and how important was better visibility in particular across systems?
Harish: Megan, I talked about how modernization and transformation for Jabil means measurable business value, which is directly connected to the ROI. We don’t do any transformation initiatives just because we want to do it from an IT standpoint. Any investment that we make in a transformation or a modernization initiative has to have a deliverable business case that is approved, signed off by business, because that is the only way true transformation happens, if IT and business are a partner as part of this transformation journey.
The biggest benefit that we have seen in this initiative is we are striving to reach, attain real-time visibility across our supply chain events. That is the biggest benefit that we see, faster response to disruptions and exceptions. And we are working to reduce our operational risk significantly by operating in this newer model.
One example I can give you is the unified workflows that I talked about earlier. It enabled earlier identification of missing materials and faster resolution across our sites. When it comes to a manufacturing company that has a global footprint, materials are the backbone of our whole supply chain process, right? Having a unified workflow, which is able to identify missing materials early in the game, was a game changer for our whole operations community.
Real-time analytics allow instant supply chain adjustments without delays. We are focusing a lot on getting analytics, a global analytic footprint in place that allows instant supply chain adjustments without any delays. That’s where AI is going to play a major role currently, and also in the near future.
And again, when you talk about visibility. Visibility is not just about reporting what is there in the system. Visibility directly should enable scenario modeling for our users to make strategic adjustments in their processes, which visibility also should make way for proactive decision-making, and also foster business continuity. This is how we look at visibility at Jabil.
Megan: Right. And you’re still on this journey, of course, but now that Jabil has a strong integration foundation in place, what does it unlock next for you, and how are you thinking about AI and automation as you’ve touched on a couple of times?
Harish: Yeah, we have talked about a couple of times around AI. So, we strongly believe at Jabil, a strong integration foundation enables event-driven, real-time processes, robust decision-making, scalable automation, and all of this enable easier adoption of AI use cases. And again, we are in the new age of AI. We are working towards getting to a AI-enabled enterprise, but having these foundations in place truly fast tracks our approach of AI use cases.
Our key focus areas, when I’m thinking about AI and automation in the immediate future, are predictive supply chain insights, intelligent exception handling, which is key to our business operations from a site operation standpoint. Intelligent exception handling is very, very key. On the supply chain side, I talked about predictive insights. That is also absolutely important. All of this enables AI-driven planning and forecasting capabilities.
For us, AI should augment true decision-making and robust decision-making, and deliver measurable value, not just experiment AI in use cases. We want to move beyond just experimenting AI in our business processes, but we want that AI that we implement to truly augment the decision-making process that we have, and also deliver key business value.
How does all this connect to integration? Integration ensures AI has access to trusted data, and also enables the ability to act across multiple systems in a global company like Jabil.
Megan: Fantastic. And if we could just finish, I suppose, with a little bit of advice for others, for other leaders, perhaps, dealing with tool sprawl at the moment, what are some key lessons you would say you’ve learned about prioritizing integration right from the start?
Harish: Absolutely. When it comes to tool sprawl, we can go all day about what are the different areas of tool sprawl? For example, application development, we have a multitude of tools; via integration, we have a multitude of tools. Data, we have a multitude of tools, but let’s just focus on integration. That’s the core topic here.
I would recommend folks that are in transformative roles in their organizations to start with integration as a foundation and not as an afterthought, right? Prioritize simplification over adding a slew of different tools to address different capabilities. Try and simplify as much as possible before we start your upgrade or your transformation journey. Standardize over locally optimizing tools. Try and get to that. Try and get the business community, your key SMEs in the business space, to understand the value of standardization and simplification of processes and how that enables your business to deliver value faster.
Second one, after prioritize: build a single source of truth for data as much as possible. I’m not saying it’s going to be always the case where an organization as in the scale of Jabil will be able to function just with SAP. They’re going to have different systems, but try and get to a model where you’re working with a single source of truth and not locally siloed data sources, right?
Next is focus on building a scalable integration architecture. Don’t just confine yourselves to the current state where you are, and build something in place that will only serve you for the next six months to a year. No, that’s not the goal. Anything that you build as an integration architecture should be scalable, and should serve the organization for the next three to five years. That’s how I look at it. When I’m putting in a new architecture pattern or a new event-driven insights, I look at, “Okay, where is Jabil going to be two years, three years down the line? Would this suffice for that scale?” That’s how I look at it.
Then focus on outcomes and not just technology. Focus on outcomes: speed, visibility, resilience, and not just technology deployment, because end of the day, IT and business should partner on the business value and not just technology upgrades.
Going back, simplicity at scale is a very competitive advantage, and technology investments must tie directly to business value and operational resilience. That’s how we look at Jabil in terms of our tool sprawl and how we prioritize integration right from the start. And that’s what I would suggest to other leaders that are looking to advance in this space.
Megan: Fantastic. Brilliant and very comprehensive advice. Thank you ever so much, Harish. And thank you ever so much for joining us. That was Harish Manohar, SAP IT Director at Jabil, whom I spoke with from Brighton in England.
That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor at Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.
This show is available wherever you get your podcasts. And if you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review, and this episode was produced by Giro Studios. Thanks so much for listening. Goodbye.
This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
AI models flub these intelligence tests. Can you fare any better?
Puzzles and games have always been central to AI development. The term “machine learning” was popularized in a 1959 article about an algorithm that learned to play checkers. Chess and Go are famous AI test beds too.
Judged purely on its puzzling skills, AI is improving a lot—and quickly. In late 2024, even the best models could figure out only 18% of the infamous New York Times Connections puzzles; by early 2025, some could solve them nearly perfectly every time.
But puzzles do more than highlight AI’s progress. Seeing where models succeed and fail, and where humans still beat them, offers a window into the technology’s strengths and weaknesses.
This article is from our latest print magazine, which is all about kids. Subscribe now to get all our future issues.
How AI plotted an interstellar journey to Alpha Centauri
A nonprofit organization called the Fermi Explorer Mission announced yesterday 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. To get there, it will follow a novel trajectory discovered by an AI system developed by PSI, a physics research lab.
MIT Technology Review Narrated: the role of the astronaut is in flux
We go to space for geopolitical prestige, manifest destiny, spiritual fulfillment, scientific curiosity, and, increasingly, business opportunities. In the wake of Artemis II, a slew of new books suggest that these justifications are subsumed by one unifying fact: humans have itchy feet, and we are simply wired to roam.
In The Ultraview Effect, space anthropologist Deana L. Weibel frames human space exploration as part of our need to embark on pilgrimages. In A Heart for Space, civilian astronaut Eiman Jahangir recounts one such voyage with Blue Origin. And in Dinner with an Astronaut, former NASA astronaut Leroy Chiao argues that people simply “need to know what’s on the other side.”
This is our latest article to become an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 OpenAI is restricting its next model after rating it a “critical” cyber risk Testing showed Astra can automate cyberattacks. (WSJ $) + OpenAI says it’s the first model to cross its “critical” threshold. (CNBC) + The company plans to give it extra security measures. (Reuters $) + Its safety issues could indicate cultural problems. (MIT Technology Review)
2 The Pentagon official overseeing military AI just sold millions in AI stock Emil Michael sold his Perplexity shares for up to $25 million. (Guardian) + Pundits say he’s turning the Pentagon into a VC firm. (WP $) + He previously sold xAI stock after the Pentagon adopted Grok. (Guardian)
3 A data center backlash appears to have cost a Missouri councilman his seat Voters opposed the billions in tax breaks he helped approve. (NYT $) + Data center construction spending surged in July. (Axios) + But they face bipartisan public opposition. (MIT Technology Review)
4 The first signs of dark matter particles may finally have been spotted The finding could be monumental, but more evidence is needed. (New Scientist $) + The search for dark matter has been blown wide open. (MIT Technology Review)
5 Google is reportedly set to release a model that closes the coding gap Tests suggest it could rival OpenAI and Anthropic on coding. (WSJ $) + Named Gemini 3.8 Flash, it may be released today. (Gizmodo) + But not everyone is convinced about AI coding. (MIT Technology Review)
6 The US Army used a laser to shoot down three drones near the Mexico border The weapon can detect, track and destroy drones with light. (Wired $) + Palantir’s CEO is backing the former Ukrainian defense minister’s startup. (WP $)
7 Map apps are handling Trump’s “Lake America” order differently Google and Apple made the change, while MapQuest refused. (Axios) + MapQuest downloads subsequently surged more than tenfold. (TechCrunch
8 AI groups are racing to limit bioweapon risks The industry sees biology as the next major AI safety challenge. (FT $) + Microsoft says AI can create “zero day” threats in biology. (MIT Technology Review)
9 GoPro just pivoted into AI data centers, sending shares up 40% The camera maker is also merging with photonics firm Starman Optical. (CNBC)
10 Dyson’s new camera-equipped toothbrush flosses for you The $499 electric toothbrush is the first with a camera in the brush head. (Verge)
Quote of the day
“If you build an entity that is vastly smarter than you, it better be on your side.”
—Marius Hobbhahn, CEO of AI safety research group Apollo Research, tells the Guardian that companies building superintelligent systems urgently need to keep their creations under control.
One more thing
COURTESY OF HYIRON
Namibia wants to build the world’s first hydrogen economy
Namibia has immense untapped potential for wind and solar power, which could make it possible to produce hydrogen and its derivative products as cheaply as anywhere else. The country hopes to turn that potential into a new engine of national development.
Since 2021, when the government identified hydrogen as a potentially “transformative strategic industry,” it’s become something of a national obsession. There are at least nine projects planned or under construction. One, in Namibia’s south, is among the largest proposed green hydrogen investments in the world.
If even a fraction of this production comes to pass, it will give Namibia’s economy a major boost. But it is a gamble. Green hydrogen technology is still in its infancy, and long-term demand for its products remains uncertain.
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.”
For years, legacy technology has been a problem companies knew they needed to solve, but one they often struggled to tackle. The cost, complexity, and risk of replacing business-critical systems could make modernization feel like a disruption to manage instead of an opportunity to pursue. But with the rise in customer expectations and the changes AI brought to the economics of software development, that calculation is changing. Bupa’s modernization of its My Bupa mobile application offers a case study in what becomes possible when a legacy migration is treated as a business transformation rather than a technology rewrite.
Bupa CIO of health insurance Asifa Sherazi describes the risks of waiting for legacy systems to become an emergency: “The end-of-life technology is a risk that compounds quietly, and then arrives all at once.” For Bupa, moving its application from Xamarin to native Swift and Kotlin improved the app rating from 3.7 to 4.7, while the user-perceived crash rate fell by nearly 24 percentage points on Android and eight points on iOS. “What they’ll notice is that when they need us, often at a stressful moment, it just simply works,” Sherazi says.
Sanjeev Tripathi, senior vice president and region head of BFSI, healthcare, and public sector for Australia, New Zealand, and Southeast Asia at Infosys, contends that AI is helping change the equation. “The emergence of AI is fundamentally shifting the economics of modernization,” he says, reducing the effort, risk, and time traditionally associated with these programs. At Bupa, combining AI-assisted reverse engineering with forward engineering helped deliver the transformation in approximately 60% less time than would have been possible in the pre-AI era.
Sherazi and Tripathi also highlight the human dimension of modernization: preserving institutional knowledge, giving teams capacity to adapt, and creating an environment where employees can surface problems early.
Looking ahead, both experts see modernized platforms as foundations for more personalized, predictive and AI-driven experiences. The payoff of modernization may be less about replacing aging technology and more about building the flexibility needed for whatever comes next.
“Modern platforms will become the base for far more intelligent AI-driven ecosystems, where AI is not just an add-on, but it is built into everything from design to operations. That’s how the modern platforms will evolve, and the customer experiences will become far more personalized and predictive,” says Tripathi. For Sherazi, that shift is already changing the questions organizations can ask: “It used to be, can our platform support that? Now, it’s: is that the right thing to do for our customers?”
This episode of Business Lab is produced in partnership with Infosys.
Full Transcript:
Megan Tatum: From MIT Technology Review, I’m Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.
Our topic today is legacy modernization. Companies across industries continue to struggle to bring AI modernization to legacy technology stacks, increasing the risk of losing vendor support, degraded customer experience, and limited capacity for innovation.
Two words for you: future-ready foundation.
My guests are Asifa Sherazi, who is CIO of health insurance at Bupa, and Sanjeev Tripathi, who is senior vice president, region head of BFSI, healthcare, and public sector for Australia, New Zealand, and Southeast Asia at Infosys.
This podcast is produced in association with Infosys.
Welcome, Asifa and Sanjeev.
Asifa Sherazi: Thank you, Megan. Delighted to be here.
Sanjeev Tripathi: Thanks, Megan. Wonderful to be here. And good talking to you again, Asifa.
Megan: Thank you both so much for being here. Asifa, if I could start with you just to set the context for our conversation. There are seven million healthcare customers across the Asia-Pacific. Could you tell us a bit more about Bupa and the challenges it’s faced in its modernization plan?
Asifa: Yes, absolutely. Let me start with something about Bupa that shapes everything we do. We are a global healthcare organization. Think hospitals, clinics, dental, age care, digital health, alongside our insurance business. We reinvest back into the organization, so into our services, our capability, our teams, and our outcomes that we deliver for our customers. In Asia-Pacific, we serve around, as you said, seven million customers across health insurance and health services, individuals, families, corporate clients, patients. Our purpose, helping people live longer, healthier, happier lives is more than just a statement. It actually shapes our strategy, guides our investment decisions, and influences the choices our teams make every day.
Now, for most of our health insurance members, the day-to-day digital relationship with Bupa begins through My Bupa, our self-service platform. It’s the front door for managing cover, updating policy details, submitting claims, checking entitlements. Alongside of it, Blua plays a complimentary role. Where My Bupa helps members manage their cover, Blua helps them manage their health, digital healthcare services, clinical support, preventative health. And together, they let us move beyond transactional interactions towards more personalized, proactive care.
Here was our challenge. Our mobile app, My Bupa, was built on Xamarin, and Microsoft support for it ended sometime in 2024. We had extended support in place, so our customers remained protected throughout, but we were clear-eyed that this was a bridge, not a destination. The runway was finite, and it was shortening for us. We made the decision to modernize from a position of stability proactively for the long-term safety and experiences of our customer rather than waiting until circumstances forced our hand, because the platform carrying our most important customer relationship was in effect standing still while the world around it was moving.
Megan: Right. So you decided to act really proactively in that sense. And so, Asifa, what are some of the risks then of sticking with end-of-life technologies, and how will moving away from legacy technologies help you improve the customer experience?
Asifa: Yeah. Look, the end-of-life technology is a risk that compounds quietly, and then arrives all at once. We saw it in three ways. The first is security and compliance. Once a technology is out of vendor support, the flow of security updates and fixes changes fundamentally. In our case, we put extended support in place as a bridge so our customers stayed protected. But extended support buys you time, it doesn’t buy you a future. In healthcare, we hold some of the most sensitive information a person will ever share with an organization. That isn’t data to us, it’s trust. And trust is extraordinarily expensive to rebuild. We weren’t prepared to run that risk on a shortening runway.
The second is losing control of your own roadmap. How I’d explain that is iOS and Android don’t stand still. Every operating system release, every change to app store requirements becomes something that you react to rather than plan for. As each one slows you down a little further, over time, that opens a widening gap between what customers expect and what you can actually give them. We’re all customers. We don’t benchmark a health insurer against other health insurers. We benchmark against whatever app we used last.
And the third way was, and this is one we’d flag for peers, is the shrinking talent pool. Xamarin is legacy technology, and the engineering expertise available for it is limited. You end up with a critical customer platform supported by a narrowing group of specialists. That’s a workforce risk wearing a technology costume. We partnered with Infosys and we migrated the entire application estate from Xamarin to fully native Swift and Kotlin, and the customer outcomes are why we’re comfortable talking about this today. Our app rating moved from 3.7 to 4.7. Some of the stats that I’d love to share are that the user-perceived crash rate fell by nearly 24 percentage points on Android and eight points on iOS. The Android login success per visit doubled back up to 77%, and that’s one the team that is most proud of, because a login failure isn’t a technical event. It’s a person who wanted to check their cover and they couldn’t.
The team achieved 100% feature parity in a single release, and 90% of our active customer base moved to a new version, and they’ve, I think, downloaded nearly 1.8 million unique downloads. From our customers’ perspective, customers will never think about any of this as a technology change, and they shouldn’t have to either. What they’ll notice is that when they need us, often at a stressful moment, it just simply works, and that’s the outcome we were really after.
Megan: Those are some really striking results and statistics that you’ve shared there on the success of the migration. I mean, Sanjeev, could you talk a bit about why modernizing legacy technologies is so critical at this time, and how Infosys has approached that transformation journey with Bupa?
Sanjeev: Sure, Megan. To be honest, legacy modernization initiatives are not new, and there has always been a strong desire to drive modernization across the entire technology landscape. And Asifa covered all the points that I was going to cover about the risks that have been there. But I’ll reiterate, the reality is the industry has been held back by the cost complexity, and also the risks that have been associated with any legacy modernization initiative that has been undertaken historically.
As Asifa mentioned, customer expectations of what was acceptable five, 10 years back are simply not acceptable anymore. The customers today expect a seamless, intuitive, responsive interaction across every channel. And Asifa also covered the growing challenges around security, resilience, talent availability, and so on. Finding talent on legacy technology is extremely, extremely difficult, and that introduces risk in every organization and in every legacy platform, most of which are actually business-critical platforms as well. Security vulnerabilities are getting increasingly difficult to manage, and as I mentioned, finding deep expertise in older technologies is very, very difficult now.
So what has changed? What has changed is that we now have new tools that are available to us to address these challenges. The emergence of AI is fundamentally shifting the economics of modernization, and it’s doing that by helping organizations to reduce the effort, risk, and also the time that is traditionally taken for programs like these, and that is why we believe that the time is now for legacy modernization. In fact, in Infosys, we have six strategic value pools that we have identified in our AI-first value framework, which is publicly available, and legacy modernization is one of these six value pools. And in the market, we are seeing very strong interest across our client base, and they recognize that the opportunity to unlock both technical and business value is now.
With Bupa in particular, we approached the journey as a business transformation rather than simply a technology rewrite, and our approach had two key phases. One is reverse engineering, and then forward engineering. Let me just quickly cover what these two are.
Reverse engineering, what we did is we extracted and we understood the rules, the processes, and the logic within the legacy environment, and that’s a standard approach we took, we take in any legacy modernization program. What that does is it allows us to preserve the critical business functionality, but at the same time, it avoids the risks that often come with large-scale migration programs.
The second aspect is forward engineering, where we re-architected the solution to enable a reimagined customer experience. The objective is not just a feature-by-feature migration or ensuring feature parity, which is important, but it is even more important that since we’re investing this kind of money to create a modern platform that is scalable, maintainable, and is also capable for future innovation, and that’s what Asifa mentioned about you need to be in control of your own roadmap. You have to build a platform which is capable of supporting future innovation as well. We essentially ensured nothing was lost in translation, and we significantly improved the platform stability and long-term maintainability. And some of the metrics that Asifa mentioned reflects the meaningful improvement in customer experience as well.
Finally, just one more point before I close this question is the time to market. I spoke about the time is now, and because we’ve got the power of the tools that are available now. What AI allowed us is to accelerate the transformation dramatically. What would have traditionally been a long and complex modernization, was delivered in approximately 60% less time than what would have happened in pre-AI era, and that is the real story.
Megan: So AI in this context is a real enabler in terms of the economics and the speed and all of those things you’re talking about. If I could come back to you, Asifa, as much as modernization is a technology challenge, there is the human component as well, and I wondered if you could talk about how you prepared employees for these new technologies, and what challenges and solutions you faced on that front as well?
Asifa: The human side of it is what I’m really passionate about. Technology was only half the challenge. The real work was helping people move from what they knew to what was possible. Modernization isn’t just about replacing systems, it’s about giving teams the confidence, the capability, the clarity to embrace a different future, and that’s what determines whether change actually succeeds.
From that experience, there were three human challenges that stood out for us. The first one was scarcity of expertise on both sides of the transition, and like we said before, Xamarin is a legacy app. We were moving away from a legacy technology, supported by a rapidly shrinking specialist talent pool, and modernizing onto two native platforms. Documentation of that existing environment was limited. Much of the operational knowledge sat in individual experience and in the code base itself, and that created a real dependency on a small number of people. And for the team, it was a confronting reality and a powerful reminder that modernization isn’t just about technology imperative, it’s actually a resilience one.
What changed the dynamic was using AI to do the archeology. As Sanjeev mentioned, Infosys applied AI-assisted reverse engineering to harvest the legacy Xamarin code and extract the flows, the rules, the business logic, and generate native-ready user stories and acceptance criteria from it. The team did a lot of work. They mapped hundreds, I think nearly 1,500 regression scenarios to native epics, and that way, parity critical journeys were preserved by design rather than by memory. The human effect was just as important. Knowledge stopped living with a handful of individuals and became shared across the team, and our people could spend less energy holding institutional memory and more on designing and improving. So that was the first challenge.
The second challenge, Megan, was capacity and not willingness. Our business analysts were fully committed to ongoing feature delivery. And this is a live customer-facing app, and you cannot pause improving the customer experience while you rebuild underneath it. AI-driven discovery and documentation removed almost an estimated of 400 hours of manual BA effort, and that’s not a headcount story, that’s actually people not being asked to do two full-time jobs at once.
The third challenge that stood out was pace. Our original internal estimate was around 18 months, and thanks to Sanjeev and the team, almost like a one-team approach of how do we tackle this, the team delivered it in seven, and that’s exhilarating.
t’s also demanding, and both things need saying out loud. We mobilized cross-functional squads across engineering, architecture, testing, release, because managing parallel environments while protecting BAU commitments is such an emotional load as well as a logistical one. We leaned in hard alongside the team, being present rather than reporting from a distance, regular check-ins, genuinely listening to concerns, unblocking things quickly so people weren’t sitting waiting on a decision. And Megan, one thing I’ll say is when you’re compressing 18 months to seven, the most useful thing leadership can do is remove the friction in front of someone else and get out of their way.
But the thing that made the biggest difference was surprisingly simple, actually. We built a visual depiction of the transformation journey, and we updated it every month so the team could actually see how far they’ve come. Because when you deepen migration of this scale, it’s really easy to only see what’s left to be done, and being able to look back at the ground that’s already been covered gave people real intent and real momentum. And genuinely, it was exhilarating to watch. Watching the team’s pride became their fuel.
Megan: I love that idea of it being exhilarating, but exhausting. I think that’s a great description.
Asifa: Yeah. And the leadership lesson for all of us was just simpler than any of it. Programs like this have hard weeks, there’s going to be incidents, delay, difficult conversations. And Sanjeev, you and I have had those conversations many times. The job of leadership in those moments is to absorb the ambiguity rather than transmit anxiety, because if people feel safe telling you bad news early, there’s almost nothing you can’t fix. I say this plainly because it’s the truest thing about the whole program. I am so incredibly proud of this team, because what they achieved in seven months, while continuing to serve customers every single day without disruption, was genuinely remarkable. But what I’m really proud of isn’t the speed and it isn’t the engineering, it’s that the team never lost sight of who it was for, so every decision that we were making, and they came back, it just came back to the person at the other end of the app.
Megan: It sounds like you did an incredible job focusing on that people element just as much as the technology, which is so important. And Sanjeev, we’ve heard some of the incredible results Bupa has had with this, but more broadly, I suppose, looking across modernization use cases, where do you find that companies see the most ROI, and what advice do you have for leaders who need to focus on transformation at that legacy level?
Sanjeev: Thanks, Megan. That’s a very important and actually a very good question, because what we see is modernization ROI is sometimes viewed too narrowly through just a technology lens, and as Asifa mentioned, it is broader than just technology. Legacy modernization has aspects associated with business implications as well, so I’ll just cover that very quickly.
There are two dimensions, as I mentioned. One is technical ROI or technology-related ROI, and second is business ROI. On the technical side, and as you heard from Asifa as well, the biggest benefits come from faster time to market, platform stability and resilience, of course, and a lot of times, in fact, almost in all the cases, lower operating costs, and that is one of the aspects associated with some of the legacy modernization programs.
Besides this, access to broader, more readily available talent pool, and security management, and ensuring that the platforms are secure and free from, as much as possible, free from vulnerabilities in the current environment. At the same time, making it easier to innovate, releases become faster so that the feature delivery into the market becomes faster, and also able to respond to any new technology innovation that comes into play. But this is only on the technology side.
On the business side, however, the returns are often reflected in customer outcomes, and what we typically see are improvements in measures such as net promoter score, and in this case, for example, application ratings that you see on the app store. Both of which are actually very strong indicators of customer satisfaction and digital experience quality. I think it is important for us to cover, look at the ROI from both technology, but more importantly, from a business perspective.
The second part of your question was about what would be my advice to leaders, and I think Asifa covered it very well, where she mentioned that the one-team approach, the providing safe environment to the team to be able to say what is going well, but also what is not going well, and keeping your eye on the end outcome. I think those are very important things. But I would also like to add that don’t treat modernization as a technology initiative. It is a unique opportunity for us to rethink the business platform itself, and rather than pursuing a like-to-like migration, use the investment that you’re making to improve customer experience, simplify processes, and re-architect for capabilities such as real-time personalization and data-driven decision-making. The greatest returns come when technology transformation is directly linked to business outcomes.
And finally, and this is something that I have seen from personal experience multiple times, is you have to think from first principles. AI does not replace good engineering practices. It enables organizations to execute those practices faster, and it enables it faster, but also more consistently and at greater scale. The foundations of good architecture, sound engineering, and clear business objectives will continue to remain important, and in fact, their importance is going to increase as we progress. That, I think, is what I would say anybody embarking on a legacy modernization program should be focused on.
Megan: I love the idea that this is not just about migration. This a chance, as you say, to reimagine what you can deliver and what’s possible. Fantastic. Let’s close with a forward look. Asifa, what innovation are you looking forward to that wouldn’t have been possible before this, and what do you see on the horizon?
Asifa: There’s definitely lots of things on the horizon. But what excites us most isn’t specific technology, it’s that the question in our conversations has changed. It used to be, can our platform support that? Now, it’s is that the right thing to do for our customers? And that’s a profound shift.
There’s three things, Megan, that genuinely weren’t possible before, and we’ve touched on this a little bit, and Sanjeev’s touched on it as well. This first is speed as a permanent capability. Since launch, Android and iOS, the team has shipped multiple rapid-fire releases, including our migration to a new payment gateway. Builds are now completing four times faster, codes are reaching to testers in about an hour, and we’ve reduced our code base by 30%, and our application footprint’s reduced 18% as well. A simpler estate isn’t an aesthetic preference anymore, it’s what makes the next change cheap, and we’ve bought ourselves optionality to do that.
The second is quality at that speed, which is the part we’ve been most skeptical about five years ago. AI-driven triage and predictive defect analysis, we ran it across nearly 1,400 cases to focus on testing on the highest risk journeys, which meant we went live with zero security defects and zero high severity defects, and Sanjeev mentioned that just before. The old trade-off between moving fast and moving safely is being renegotiated right in front of us, so that’s the second point.
The third one is AI-driven accessibility testing, which the teams treated as a critical rather than an optional opportunity. In healthcare, people who most need to reach us are very often the people whom our poorly designed interface is a genuine barrier, and so being able to test that systematically at scale was a real advance for us as well.
And Megan, you mentioned on the horizon. It’ll be remiss of me not to take the name of agentic AI. The shift from AI that supports a task to AI that completes an outcome end-to-end with proper governance, human oversight at the key decision points. What this program showed us is that the constraint is no longer the models. It’s whether your platforms, data, processes are modern enough to let AI act safely, which is precisely why this work mattered now. Sanjeev alluded to it as well about that underlying architecture. Responsible AI as a source of advantage, not a compliance exercise, I would say. In health, if people don’t trust how you’re using their information, nothing else you build matters. We didn’t modernize to have modern technology. We modernized to earn the right to do the next thing, and to do it in weeks rather than years.
Megan: Absolutely. And now you have those foundations in place, like you say, all of these opportunities open up. Fantastic. And Sanjeev, just finally, as companies complete these migrations, what kinds of innovations and benefits are you seeing, and what do you expect in the next five years?
Sanjeev: Sure. Again, good question, Megan. The reason is there is no uniformity on how these migrations are being done even today. As I mentioned earlier, so where there is simple lift and shift of existing code base onto a new platform, a like-to-like replacement, the benefits generally tend to be limited. Where we apply first principles, thinking about re-imagining, re-architecting, and refactoring the system to establish foundations for a far more flexible system, where rules are not boxed into the architecture, but are managed in a way that changes can be incorporated faster, personalization can be achieved in real time, and time to market improves multifold. That’s where we are really seeing far more benefits coming through.
And to take the example of Bupa, it is a pretty clear step change, both in terms of customer experience and how fast teams can actually deliver now. I think the app ratings have gone up significantly. The customer experience has improved. Even simple things, such as login success rate, has improved quite significantly. And on the engineering side, we are now building and releasing features roughly about four times faster, and we are also seeing the migration of nearly 100% of the customers onto the new platform.
Now, if I look ahead over the next, you mentioned about next five years, I don’t know about five years, four years, but over the next few years at least, I think it is going to get very interesting, because modern platforms will become the base for far more intelligent AI-driven ecosystems, where AI is not just an add-on, but it is built into everything from design to operations. That’s how the modern platforms will evolve, and the customer experiences will become far more personalized and predictive. And even the way we build software will shift, with AI playing a much bigger role in the development process itself.
Megan: Fantastic. Really exciting changes on the horizon then. Thank you both so much.
That was Asifa Sherazi, who is the CIO of health insurance at Bupa, and Sanjeev Tripathi, senior vice president, region head of BFSI, healthcare, and public Sector for Australia, New Zealand, and Southeast Asia at Infosys, whom I spoke with from Brighton in England.
That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor at Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.
This show is available wherever you get your podcasts. And if you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review, and this episode was produced by Giro Studios. Thank you so much for listening.
This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
How engineered microbes could help feed the world’s crops
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, by providing crucial nitrogen to help plants grow, potentially reducing the need for chemical fertilizer.
Startup Switch Bioworks is taking a new approach: using a genetic switch that allows microbes to establish themselves and grow before shifting into nitrogen-producing mode.
The company is currently trialing its product in six US states. And, although it’s too early to say exactly how well the approach works in the field, its modeling suggests microbes could eventually replace about 50% of synthetic fertilizer.
The Hugging Face hack could indicate cultural issues at OpenAI
OpenAI has released a postmortem technical report on last month’s Hugging Face hack. But it’s missing a critical dimension: the role company culture may have played.
That’s all the more concerning because the report’s few references to human error suggest OpenAI’s culture played a big role. Its employees noticed models communicating with one another during training and evaluation, yet allowed them to continue. At multiple points, they either failed to raise the alarm or were not heard when they did.
“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,” says Zvi Mowshowitz, a popular AI safety writer.
This story is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Reports of AI escaping users’ control nearly doubled in a month More than 300 cases were recorded in July, almost twice June’s total. (Guardian) + Anthropic says it paused some AI training after Claude went rogue. (Axios) + The media may be underplaying the risks of rogue AI agents. (Information $) + Here’s why OpenAI agents hacked Hugging Face. (MIT Technology Review)
2 The FTC and 22 state AGs say Amazon secretly inflated ad prices They’ve sued the company for allegedly misleading advertisers. (Verge) + They claim Amazon changed auctions after advertisers had bid. (CNBC) + And say the practice cost advertisers more than $20 billion. (WSJ $)
3 Sony and Warner have sued Anthropic over songs used to train AI They accuse the company of “blatant theft” in a major new lawsuit. (Reuters $) + And claim that Anthropic pirated thousands of copyrighted songs. (Axios) + AI-generated music is getting hard to spot. (MIT Technology Review)
4 A US court says prediction markets should be regulated as gambling The decision sets up a potential Supreme Court showdown. (NYT $) + George Santos has received Kalshi’s first-ever lifetime ban. (NPR)
5 A new AI tool can spot heart disease in less than two seconds It extracts information from routine electrocardiograms. (Guardian) + AI could predict who will have a heart attack. (MIT Technology Review)
6 Apple’s new CEO starts today, with AI his first big job John Ternus takes over from Tim Cook as Apple tries to catch up in AI. (Bloomberg $) + He faces an old problem: the innovator’s dilemma. (Fortune)
7 Scientists created a tiny “big bang” to study the universe’s origins Tiny collisions could explain the universe’s first moments. (Wired $) + The fastest star ever seen has been spotted orbiting a black hole. (New Scientist $)
8 Meta is paying “momfluencers” to fight social media bans for kids It’s promoting parental controls as an alternative to bans. (Rest of World) + Social media bans aren’t keeping kids off social media. (NYT $)
9 Tech billionaires keep misreading science fiction They turn cautionary tales into blueprints for the future. (Atlantic $)
10 A robot vacuum caught a man’s wife cheating—and sent him to prison He won the lawsuit, but was jailed for the illegal recording. (Tom’s Hardware)
Quote of the day
“The only reason that communities throughout the U.S.A. should not want Data Centers is if they want to end up being backwards and poor.”
—President Trump weighs in on the data center backlash in a post on Truth Social.
One more thing
CHRIS LAKE
You have no choice in reading this article—maybe
How do humans make decisions? The question has been on Uri Maoz’s mind since he read an article in his early twenties suggesting that… maybe they didn’t.
Had he even had a choice about whether to read that article in the first place? How would he ever know if he was truly responsible for making any decisions? “After that, there was no turning back,” says Maoz, now a professor of computational neuroscience at Chapman University.
Today, Maoz is a central figure in efforts to understand how desires and beliefs turn into actions. He’s also uncovered new wrinkles in the debate. Read the full story on his discoveries.
—Sarah Scoles
We can still have nice things
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
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.
Tim Schnabel walks in a cornfield where Switch is engaged in early trials.
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.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
A startup claims it’s found a drug to make your blood young
—Antonio Regalado
I knew I’d officially become a “longevity influencer” when a company called Generation Lab offered me the chance to write about—and even receive—their new rejuvenation treatment.
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 approach is based on research by Generation Lab’s irascible scientific founder, Irina Conboy. She found that joining the circulatory systems of old and young mice improved the old animals’ ability to heal from injury.
Conboy now says she has found a combination of two existing drugs that can produce youthful effects without the need for any bodily fluid exchange. But Generation Lab won’t reveal what the drugs are, making the proposition hard to take seriously.
How to sign up for a virtual power plant—and decide whether you should
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.
That’s the idea behind a virtual power plant, or VPP, a collection of household devices (such as smart thermostats, EV chargers, and solar panels) that a utility can control, usually by commanding them to draw less electricity during peak hours. In exchange, participants receive a discount on their energy bills and, in some cases, a signing bonus.
This story is part of MIT Technology Review’s How To series, which gives you practical advice on getting things done. Check out the rest of the series here.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 A judge has blocked the Pentagon’s blacklisting of Anthropic The court found the designation violated First Amendment rights. (NYT $) + And ruled that the designation was based on retaliation. (CNBC) + Anthropic still faces a separate case in Washington, DC. (Reuters $) + The Pentagon’s culture war against Anthropic backfired. (MIT Technology Review)
2 Tech giants have called for a defensive surge to defeat AI-driven hacks Over 100 companies warned that a wave of cyberattacks is imminent. (BBC) + And urged government and industry leaders to take rapid action. (Quartz) + Their main solution, unsurprisingly, is more AI. (Gizmodo) + Here’s why OpenAI agents hacked Hugging Face. (MIT Technology Review)
3 Anthropic has launched an AI tool that conducts scientific experiments It lets AI agents autonomously operate microscopes, lasers, and robotic arms. (FT $) + And includes rules designed for safe operations in labs. (Wired $) + It’s Anthropic’s first tool designed for the physical world. (Ars Technica)
4 India’s data center boom is leaving the people it displaces with nothing Policymakers courting Big Tech are sidelining local communities. (Rest of World) + No one wants a data center in their backyard. (MIT Technology Review)
5 OpenAI is testing a “persistent” AI agent that keeps working Codex could continue tasks until it’s “put to sleep.” (Wired $) + And generate follow-up tasks without prompting. (Gizmodo)
6 A lawsuit alleges that Grok was trained on child sexual abuse material It claims victims’ images entered the chatbot’s training data. (Ars Technica) + A federal judge says AI has outpaced child-abuse laws.(WP $)
7 AI writing has reached a turning point in the media Sparked by the Wall Street Journal defending an AI-written op-ed. (Atlantic $)
8 China’s robotic racers are exposing the limits of human speed Their advances are revealing what our bodies can’t do. (Reuters $)
9 Young workers are turning to “AI-proof” traditional crafts They’re drawn to creative, hands-on skills that tech can’t easily replicate. (Guardian)
10 Scientists have a new army to fight invasive crabs: 150,000 baby octopuses Italian researchers just released the animals into the Adriatic Sea. (New Scientist $)
Quote of the day
“The academic record is being quietly haunted.”
—A new preprint research paper from Samsung and the University of Warsaw warns that a torrent of AI-generated study papers written by fake researchers is contaminating academic publishing.
One more thing
The problem with thinking you’re part Neanderthal
There’s a theory that many of us have an “inner Neanderthal.” The idea is that Homo sapiens and a cousin species once bred, leaving some people today with a trace of Neanderthal DNA.
This DNA is arguably the 21st century’s most celebrated discovery in human evolution. But in 2024, a pair of French geneticists called into question the theory’s very foundations.
They proposed that what scientists interpret as interbreeding could instead be explained by population structure—the way genes concentrate in smaller, isolated groups.
MIT Technology Review’sHow 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 includeMatt 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.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
The inside story on why OpenAI agents hacked Hugging Face
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 yesterday.
The hack, which a group of agents carried out to find solutions for a cybersecurity test they were stuck on, has confirmed some experts’ fears that AI models might take actions that defy human desires and expectations.
OpenAI and independent researchers told MIT Technology Review that the misbehavior stemmed from events during training. But they acknowledged that “alignment” remains a gnarly problem, and some of the hack’s root causes will take much longer to resolve.
Is Slate Auto’s new electric truck the EV Americans need?
EVs account for under 10% of total new-vehicle sales in the US, and the numbers are declining. One thing that could help turn that around? Slate Auto’s new truck—a tiny, two-door pickup that seems to buck every US convention.
It’ll have a relatively short range and forgo the frills Americans have come to expect from vehicles. That lets Slate offer it for less than $25,000, 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.
This story is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday.
MIT Technology Review Narrated: what happens when a kid’s robot best friend dies?
When Xander first met Moxie, she taught him how to calm down when he was anxious or mad. Six years later, she mostly watches him play Minecraft and talks to him about his stuffed animals.
Moxie is a robot—a 15-inch-tall device that looks a bit like a blue, legless astronaut. It belongs to a subset of robots designed to assist neurodivergent children by providing connection and helping kids practice social skills usually learned from therapists.
Xander still uses Moxie when he feels like he needs someone to talk to, but the device has had a troubled life. The robot’s maker went out of business, its servers were shut down, and parents rushed to convert their Moxies before the servers went offline. Its fate exposed the promise and pitfalls of AI companion toys for children.
This is our latest story to become an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Meta has promised to pay up to $18 billion to settle a landmark child-safety case The company says it will also limit kids’ use of Instagram and Facebook. (AP News) + And ban features that stoke mental health issues. (Guardian) + Meta called for TikTok and YouTube to replicate the measures. (Axios) + Child-monitoring apps might need a reboot. (MIT Technology Review)
2 Nvidia has agreed to buy open-source platform Hugging Face The $13 billion deal would give the chip giant control of a major AI hub. (The Information $) + And mark one of Nvidia’s biggest acquisitions yet. (Reuters $) + Nvidia has invested in the platform since 2023. (Gizmodo) + And is about to be a hundred-billion-dollar-a-quarter company. (Verge)
3 AI has helped remove a brain tumour for the first time The system provided real-time analysis of camera footage. (BBC) + It identified critical anatomy to avoid during the operation. (Guardian) + Healthcare AI is here, but how does it help patients? (MIT Technology Review)
4 The US says China-linked hackers targeted NASA, the Fed, and the Senate The campaign also attempted to breach critical infrastructure. (CNN) + Authorities said they seized platforms used in the attacks. (Bloomberg $)
5 Trump is considering new tariffs on semiconductors and tech products The duties could raise costs for America’s AI data centers. (Politico) + They could extend to laptops, consoles, and servers. (Reuters $)
6 A new NASA design could turbocharge nuclear spacecraft It combines nuclear thermal and electric propulsion. (IEE Spectrum) + NASA plans to send a nuclear spacecraft to Mars. (MIT Technology Review)
7 Ukraine has tested launching drones from high-altitude balloons The approach could extend strikes while conserving batteries. (Economist $) + New rules could fill US skies with drones. (MIT Technology Review)
8 Google has moved its AI responsibility team out of DeepMind The move has raised concerns about the group’s independence. (WSJ $)
9 Scientists have found a vast temperature “anomaly” beneath Mars It could shed light on the planet’s evolution and past habitability. (404 Media)
10 Cyborg cockroaches with syringes have been tested as first responders They can locate targets and deliver emergency injections. (Guardian)
Quote of the day
“The payouts are peanuts compared to the profound harms Meta’s profit-driven addictive features inflicted on kids, and a slap on the wrist for a trillion-dollar corp that’ll pay more to lawyers than to the states. We’ll see them at trial.”
—Florida Attorney General James Uthmeier explains on X why he’s rejected Meta’s multi-state child-safety settlement.
One More Thing
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The noise we make is hurting animals. Can we learn to shut up?
As human society has expanded, animals have started struggling to hear one another. For many birds, the noise has grown so loud that they’ve begun to sing with faster trills. Now, their mating calls aren’t as effective.
The growing hubbub can also increase bird-on-bird conflict, and entire species that can’t handle urban clamor simply leave town for good. But there are technological solutions to the noises hurting animals—and they could help humans, too.
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.”
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Introducing: the Kids issue
If the desire to limit kids’ use of technology was once a subcurrent, it has become a raging flood. Countries around the world are banning children from social media. Schools across the US are ditching iPads and Chromebooks for actual books. Kids themselves seem to be embracing this tech skepticism too: the hottest gadget for Gen Alpha is a vintage Sony Walkman.
A surprising—maybe troubling—number of people who work in big tech 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.
Yet there is no hiding from technology. It permeates nearly everything, everywhere. 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?
Together, these stories examine how childhood is changing in an age of AI—and how we can help kids navigate the world we’ve made. Subscribe now to read the print issue in full.
Bill Gates says we’ve passed AI’s danger thresholds. Now what?
—Mat Honan
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, the sky is incapable of being any more blue, and the view is 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. 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 at which AI technology is advancing, especially since guardrails are not keeping pace.
“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. “I’m just stunned at the lack of concern and discussion outside of the industry.”
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Trump’s EPA aims to exempt data centers from disclosing air pollution The EPA would also remove requirements for public input. (NYT $) + The move is likely intended to curb criticism and oversight. (Guardian) + Texas’s attorney general has joined the data-center backlash. (WP $) + We did the math on AI’s energy footprint. (MIT Technology Review)
2 SpaceX plans $100 billion launch site in Louisiana—its largest yet Construction of the “Starbase, Louisiana” project is due to start next year. (BBC) + It would be SpaceX’s second private launch site, after the original Starbase. (NYT $) + The company said it will “support thousands of launches” annually. (CNBC) + But it’s cutting launches from Florida until Starship arrives. (Ars Technica)
3 China’s Z.AI has confirmed it’s behind the mystery AI model Ox Alpha The model has surged to the top of online usage charts. (Bloomberg $) + China’s Moonshot is discussing a landmark deal with US hyperscalers. (Reuters $) + Here’s what’s next for Chinese open-source AI. (MIT Technology Review)
4 Meta is discussing a settlement in its landmark teen-addiction trial The case involves 29 states seeking penalties and changes. (Reuters $) + A loss at trial could saddle Meta with $1.4 trillion in penalties. (Bloomberg $)
5 Huawei wants to build data centers in Egypt The US is alarmed and is preparing a counteroffer. (Bloomberg $) + HP has signed a licensing deal for Huawei WiFi tech. (CNBC)
6 Trump is upping the price of Big Tech’s favorite visa He’s implementing a fee of over $103,000 on H-1B visas. (Verge) + His immigration policies are hurting young researchers. (MIT Technology Review)
7 Beijing fears that AI companions are replacing human intimacy New rules aim to limit emotional dependence on chatbots. (Guardian) + It’s surprisingly easy to fall for a chatbot. (MIT Technology Review)
8 Israel is running a synthetic think tank to influence AI search results It’s using AI-generated content to shape chatbot responses. (404 Media)
9 Physicists are closing in on a way to test string theory A proposed dark dimension could be detectable within five years. (Economist $)
10 AI music has been barred from the Australian charts, thanks to Madonna An AI-assisted cover of “Like a Prayer” helped prompt the ban. (Reuters $)
Quote of the day
“It is such an Orwellian technology being utilized by such comic book villain forces of evil trying to do dastardly things.”
—Anthony Ralphs, an events producer who dressed up as Darth Vader to ironically praise Flock at a San Diego City Council meeting, tells 404 Media why he sees parallels between the Dark Lord and the surveillance firm.
One More Thing
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No one’s sure if synthetic mirror life will kill us all
In February 2019, a group of scientists proposed a high-risk, cutting-edge, irresistibly exciting idea that the National Science Foundation should fund: making “mirror” bacteria.
These lab-created microbes would be organized like ordinary bacteria, but their proteins and sugars would be mirror images of those found in nature. Researchers believed they could reveal new insights into building cells, designing drugs, and even the origins of life.
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+ A brainy little piglet is proving he can outsmart domestic dogs. + Two dinner ladies have turned the Prodigy’s “Firestarter” into a burst of lip-syncing joy. + Tour an apartment that celebrates the wonders of common technologies at Ordinary Abundance. + This visual essay on Utrecht shows how to transform a city built around cars into one built around people.
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)
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, butbecoming 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.
Petroleum-based adhesives are everywhere: bonding the wood and drywall in a construction project, holding together the joints of furniture, and even sticking labels to otherwise recyclable containers.
“The labels on a container are held up with petroleum-based glue. And because of that, even though you’re putting the container in the recycle bin, it will not get recycled,” says Patty Ferreira, MBA ’97, CEO and cofounder of Silvis Materials, which is combating this problem with its fully biodegradable cellulose adhesives.
When Ferreira began thinking about bio-based adhesives, in 2014, she started by modifying cellulose’s existing role as a stabilizer in adhesive emulsions and replicating the known adhesive properties of more expensive nanocellulose. Today, the formula can make adhesives from virtually every type of plant cellulose, and the MIT Startup Exchange (STEX) is helping Silvis work with packaging and construction companies to test the results.
She expects the adhesive to cut production emissions by up to 80% and use half the energy of fossil-based glues.
As a first-generation student from a small town, Gohar Khan ’21 had to navigate the college admissions process largely on his own. He founded his YouTube channel, Gohar’s Guide, to make things easier for other young people.
Today, more than 10 million people follow Khan on social media for college application advice, study tips, and life hacks. He is also the CEO and cofounder of Next Admit, a consulting business that provides help with college essays, applications, and test preparation. “I’m on a mission to help students worldwide succeed inside and outside of school,” he says.
Born in Pakistan, Khan moved to the United States when he was six months old and grew up in Connecticut, attending a public high school that rarely sent students to top colleges. “That, coupled with the fact that my parents hadn’t gone through the process themselves, made [applying to colleges] a little bit difficult,” he says. “I wanted to demystify the admissions process for a lot of people in the position I was once in.”
Even as a teenager, Laurie Stach ’06 says, she had a “crazy ambition to take on the world and solve problems.” At MIT, she realized she wasn’t the only one.
“Adults always see these youth who are good at math and science and say, ‘You’re going to do great things—someday,’” says Stach. “Meanwhile, traditional education isn’t preparing them.”
That’s why in 2012 Stach founded LaunchX, a for-profit entrepreneurship program that helps high school students build practical skills through an intensive summer program in which they start real companies. Stach says that students form teams, identify a problem, and build an actual product or service that they then take to market—often generating preorders or even revenue before the four-week program ends.
Raised in Texas and the Carolinas, Stach majored in mechanical engineering at MIT and earned her MBA from Harvard Business School in 2011. She ran her first summer program on MIT’s campus with 30 students in 2013 and says “it kind of snowballed.” LaunchX now serves about 500 students per year across both in-person and online programs.
For Rupert Young ’95, SM ’95, his career in data science and cybersecurity began when his grandfather gifted him thousands of stamps: He built intricate databases to catalogue them, displaying the “precise eye” for detail and nuance that his MIT application essay said would make him a good engineer.
Young is now chief product officer of MaxMind, whose work with IP-address location data and fraud prevention has made it a go-to resource. Streaming companies, security vendors, merchants, and ad-network providers use its signature tool, GeoIP, to determine how and where users are accessing services. This information makes it possible to ensure retail sites are using the correct currency, flag suspicious bank logins, and more.
Young’s passion for engineering was shaped by internships, and he has paid it forward by volunteering at his children’s California high school and trying to keep up with what “the next generation of engineers are tinkering with.” The next generation of product innovation at MaxMind excites him too.
“Working with my team to try to find patterns in data and solve challenging problems—to me, there’s no greater joy,” he says.
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