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 for a conversation with senior AI editor Will Douglas Heaven and AI reporter Grace Huckins 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.
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+ 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.
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+ 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.
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