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?
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.
These startups are chasing the next big thing in LLMs
Nine years after Google researchers introduced the transformer, this family of neural networks has become the engine inside every major large language model. But transformers are starting to show their age.
As LLMs get bigger and better, transformers have become a bottleneck. Their dense attention mechanism becomes increasingly expensive as the amount of text grows, and they’re not great at keeping track of a lot of information at once.
This story is from MIT Technology Review’s What’s Next series, which looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.
AI professors are negotiating the new realities of academic research
—Grace Huckins
Last week, I headed to a hotel in Mountain View, California, to join some of the most accomplished, and some of the most promising, AI researchers in the world. I was hosting roundtable interviews and speaking at a media training for a convening of the Schmidt Sciences AI2050 program, an initiative funded by Eric and Wendy Schmidt that supports academics whose work involves AI.
The fellows list is a who’s who of AI luminaries, and though not all of them made it out to the Bay, every time I turned a corner I saw a scientist whom I’d interviewed previously or whose research I admired. It’s a weird time for university AI researchers, who make up most of the AI2050 group. Read Grace’s story to find out why, and what could be coming next.
This story is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Nvidia has secured $500 billion from Wall Street for AI infrastructure It’s struck deals with BlackRock, Goldman Sachs, and four others. (BBC) + Showing the pull of AI compute for institutional investors. (Reuters $) + And that AI infrastructure is becoming a new asset class. (CNBC)
2 Mark Zuckerberg’s new manifesto says open-source AI can save the US It presents a utopian vision of personalized “superintelligence.” (Guardian) + And arrived the same day as Meta’s new, open-source model. (NYT $) + Zuckerberg said he plans to launch more of these models. (WSJ $) + And pit Meta against Chinese open-weight developers. (SCMP)
3 Bernie Sanders has called on Silicon Valley to “pause AI development” He noted that AI giants have pledged to do this if necessary for safety. + And warned that lawmakers will step in if no action is taken. (Guardian) + House Democrats are already pressing AI leaders over rogue models. (WP $) + A populist backlash is building against AI. (MIT Technology Review)
4 A US court will allow thousands of social media lawsuits to proceed The suits target addictive mechanisms used by Meta, TikTok, Google, and Snapchat. (Axios) + They claim the platforms are designed to hook young users. (Reuters $) + Can we repair the internet? (MIT Technology Review)
5 Unitree’s IPO is more than 8,000 times oversubscribed by retail The Chinese humanoid firm raised $900 million ahead of its listing. (Reuters $) + Its pricing for the Shanghai IPO values the company at $9 billion. (FT $)
6 Flock’s car-tracking cameras are facing a bipartisan backlash The surveillance network has spread rapidly across the US. (NYT $) + Flock also plans to chase shoplifters with drones. (MIT Technology Review)
7 China is breaking up AI relationships Beijing has introduced new rules for emotionally interactive AI. (Rest of World) + It’s surprisingly easy to fall for a chatbot. (MIT Technology Review)
8 An AI tool claims to pick the best 1% of scientific papers But researchers doubt that AI can reliably judge scientific quality. (Nature)
9 The AI slop backlash is working It’s pushing platforms to restrict AI-generated content. (Wired $)
10 An 82-year-old rejected $26 million to turn her farm into a data center She criticised the environmental impacts of data centers. (Fortune)
Quote of the day
“It is not too late to avoid disaster. Stop building machines that humans cannot control.”
—Senator Bernie Sanders urges Sam Altman, Dario Amodei, and Mark Zuckerberg to pause all AI development in a letter.
One More Thing
The race to make the perfect baby is creating an ethical mess
A new field of science is using genetic sequencing to predict what kind of person an embryo might become. Some parents turn to these tests to avoid devastating genetic disorders, while a much smaller group are willing to pay tens of thousands of dollars to optimize for intelligence, appearance, and personality.
Customers, however, may not be getting what they’re paying for. Genetics experts have highlighted the potential deficiencies of this testing for years, while its underlying assumptions have made these companies a political lightning rod.
As this technology edges toward the mainstream, scientists and ethicists are racing to confront the implications—for our social contract, for future generations, and for our very understanding of what it means to be human. Read the full story.
—Julia Black
We can still have nice things
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This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
Last week, I headed 30 miles south of San Francisco to a hotel in Mountain View, California, to join some of the most accomplished, and some of the most promising, AI researchers in the world. I was hosting roundtable interviews and speaking at a media training for a convening of the Schmidt Sciences AI2050 program, an initiative funded by Eric and Wendy Schmidt that supports academics whose work involves AI. The fellows list is a who’s who of AI luminaries, and though not all of them made it out to the Bay, every time I turned a corner I saw a scientist whom I’d interviewed previously or whose research I admired. (Full disclosure: I received a science communication award funded by Schmidt Sciences in 2024.)
It’s a weird time for university AI researchers, who make up most of the AI2050 group. In the past four years, AI research has reoriented around large language models, and its cutting edge has moved from academic institutions to private companies. Universities simply can’t afford the GPUs required to train and run frontier models, and even if they could, Anthropic and OpenAI aren’t letting anyone else see the inner details of Claude or ChatGPT.
In a conversation over lunch, Nika Haghtalab, a computer science professor at UC Berkeley, said that being an AI academic these days was like being a biologist in a world in which private companies had exclusive control over the gene-editing tool CRISPR. Experts outside the frontier labs can study how ChatGPT and Claude behave, but they can’t do any detailed research on the design and training of those tools, nor can they steer that design or training themselves.
The AI2050 program does offer fellows some funding that they can use to buy GPUs, which some researchers I spoke with said was a major benefit of participating in the program. But money remains a pressing concern, especially given the reduction of federal scientific funding in the United States. Even for researchers who don’t run local models themselves, the cost of repeatedly querying OpenAI’s, Anthropic’s, and Google’s models in order to study them rigorously can be prohibitive.
Rather than focusing on advancing capabilities, many fellows aim their attention at questions that are unlikely to be addressed by Anthropic or OpenAI. “I try not to work on problems that I think are gonna be solved by a tech company,” says Anjalie Field, a computer science professor at Johns Hopkins. Companies need to make money, and research questions that have little promise of profit might not be worth investing in—especially if their answers might make the companies look bad. Recently, for example, Field conducted a study in which she found that language models give less sophisticated responses to prompts that are phrased in ways more commonly used by women than by men. It’s difficult to imagine that kind of research coming out of Anthropic or OpenAI.
There’s also a huge group of AI academics who don’t work with LLMs at all. Many of them are scientists who build specialized AI models that can analyze data, make useful predictions, or even simulate entire physical systems. Those researchers aren’t necessarily competing with the frontier labs—Google DeepMind’s AlphaFold team, which built a Nobel Prize–winning model that predicts the structures of proteins, was disbanded last month. But they face plenty of their own challenges. At the convening, several voiced concerns about how the widespread ignorance of non-LLM AI was affecting their work. Researchers who build specialized AI tools to help address climate change, for example, sometimes struggle to advocate for their work when so many people believe that “AI” means “energy-guzzling LLMs.”
All these challenges are changing the landscape of academia: Several prominent academics have recently taken leave from their universities to join frontier labs, and many AI2050 fellows hold industry positions alongside their academic jobs. And in the past six months, yet another threat has emerged. OpenAI’s models have solved a number of real research problems in mathematics, and some experts are worried that humans might not have a future in pure math. One fellow I spoke with said that she was concerned about the mental health of her mathematician peers.
But it’s not all doom and gloom. For one thing, empirical science may prove much more difficult to automate than mathematics, because collecting data is an intrinsically slow process. And some researchers see AI mathematicians and scientists as a boon rather than a threat—including Tim Dettmers, a computer scientist at Carnegie Mellon who works to make AI models faster and cheaper to run. AI scientists won’t replace humans, Dettmers says. On the contrary, they could make human scientists far more efficient, so that he and his peers have the chance to pursue all the wild and inspired ideas they might otherwise never have gotten around to.
And scientists are a resilient sort. The very resource constraints that prevent them from training frontier models also push them to discover new ways to make models smaller and more efficient, or to explore completely new architectures. If the next big AI breakthrough comes not from a major company but from a scrappy academic lab, I won’t be shocked.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
AI for science needs reasoning, not just data
—Eric Schmidt, the former CEO of Google and the cofounder of Schmidt Sciences, and Suhas Mahesh, who leads the AI for science work at the AI Center of Schmidt Sciences
In 2024, Google DeepMind scientists shared the Nobel Prize in Chemistry for a neural network, AlphaFold, which predicts the structures of proteins. It showed that AI could make groundbreaking scientific discoveries, but AlphaFold may not be the best template for accelerating science. Instead, another approach may hold the key: AI agents.
AlphaFold relied on a dataset of roughly 170,000 experimentally validated protein structures that took 53 years and roughly $21 billion worth of experimental work to assemble. Comparable datasets will be difficult or impossible to create in many fields.
AI agents can instead model the iterative, highly contingent process of actual research. While tools like AlphaFold apply a powerful approach to a limited question, agents are inherently generalists. They do not represent a new way to do science—instead, they digitally model the human process of discovery.
Inside the “censorship-industrial complex” idea shaping US policy
For years, the idea of a “censorship-industrial complex” that suppressed conservative and populist speech spread in right-wing circles online. But now, the theory has made its way into the Trump administration.
Over the past nine months, MIT Technology Review investigated its origins and traced its rise. In a virtual Roundtables session on Thursday, August 13, senior reporter Eileen Guo and executive editor Amy Nordrum will explore what they discovered, where the theory is going, and what it could mean for the future of democracy and the internet.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 A new Amazon data center could become the US’s biggest polluter The Texas facility has a permit to release up to 33 million tons of CO2. (NYT $) + It will be Amazon’s first off-grid AI data center. (Cleanview) + The gas plant will generate up to 7.65 gigawatts of power. (Verge)
2 OpenAI has paused work on its Astra AI model over security concerns Tests found it could launch cyber-attacks autonomously. (FT $) + Critics warn such disclosures could be designed to spark hype. (Guardian) + AI doesn’t need to be that smart to make online crimes easier. (MIT Technology Review)
3 North Korean hackers have built AI tools for cyberattacks A new report says they’re used in spear-phishing campaigns. (Al Jazeera) + They’re attributed to the state-linked group Kimsuky. (Reuters $) + Which could automate attacks and analyse stolen data. (TNW)
4 China’s firms control 97% of global humanoid shipments Global shipments have more than tripled since last year. (Bloomberg $) + Chinese humanoid manufacturers are racing to public listings. (Reuters $) + Gig workers are training humanoids at home. (MIT Technology Review)
5 Taiwan has expanded its use of aerial and maritime drones to deter China The approach draws on lessons from Ukraine’s battlefield. (NYT $) + Taiwan’s “silicon shield” could be weakening. (MIT Technology Review)
6 Apple is testing Chinese memory chips as the supply squeeze bites But any deal with CXMT would be politically sensitive. (WSJ $)
7 An Israeli startup is tied to rogue AI hacks at OpenAI, Anthropic, and Meta Irregular’s tests allowed the models to access the public internet. (CNBC)
8 NASA is flying into wildfire storms to understand them The mission will study their effects on fires and the atmosphere. (Economist $)
9 A chatbot-free childhood may become a status symbol AI is like ultra-processed food for developing brains. (Atlantic $) + Are chatbots making us lose control of our brains? (MIT Technology Review)
10 MySpace is eyeing a comeback as an “antidote” to social media fatigue But the nostalgic plan looks like a long shot. (BBC)
Quote of the day
“It’s the humans that we need to watch out for. AI is just the tool.”
—Oren Etzioni, professor emeritus at the University of Washington and former CEO of the Allen Institute for AI, tells CNN that people are still the main threats in cybersecurity.
One More Thing
Meet the man building a starter kit for civilization
Marcin Jakubowski lives in a house he designed and built himself. He relies on the sun for power, heats his home with a woodstove, and farms his own fish and vegetables.
Jakubowski is the founder of Open Source Ecology, a collection of 50 machines capable of building civilization from scratch. It includes everything from a tractor to an oven to a circuit maker, all designed to be reconfigured however you see fit.
The ultimate goal is a “zero marginal cost” society, where producing an additional unit of a good or service costs little to nothing. Jakubowski hopes to get there by eradicating licensing fees, decentralizing manufacturing, and fostering collaboration through education.
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ A rhythmic doggie is proving that not only humans can play the drums. + Catch a glimpse of the internet’s future at the Awwwards, the Oscars of web design. + Explore the ocean year-round with this immersive experience of the Nautilus vessel’s expeditions. + Earth once (or twice) had supermountains. This fascinating video explores their links to evolutionary shifts in the history of life.
Every few decades, someone announces that science has reached its end. In 1903, the revered physicist Albert Michelson wrote that the “facts of physical science have all been discovered.” In the 1980s, Stephen Hawking predicted that theoretical physics might be finished by the end of the century. With the explosive arrival of artificial intelligence, the feeling is in the air again—this time accompanied by a Nobel Prize.
In 2024, Demis Hassabis and John Jumper of Google DeepMind were awarded part of the Nobel in chemistry for their neural network AlphaFold, which predicts the three-dimensional structures of proteins by learning from thousands of experimentally measured shapes. This devilish problem had resisted systematic attacks for half a century; AlphaFold seemed to have solved it once and for all, and the world became fixated on the promise of its approach. Hassabis and his team called AlphaFold “the template for how AI can accelerate all of science to digital speed.” A wave of startups building foundation models for biology, chemistry, and materials discovery raised billions of dollars, buoyed by DeepMind’s success. AlphaFold had shown that the combination of AI and sufficient data could make groundbreaking discoveries (even if we did not understand the underlying mechanisms involved), and it seemed, once again, that a path through the rest of science was laid out before us.
To be sure, AI will bring extraordinary changes to science, but it has become increasingly clear that AlphaFold, and things like it, may not be the best template for that metamorphosis. Though it is a profound achievement, the conditions that produced the likes of AlphaFold are rare, and the time it will take to meet those conditions in other fields will be measured in decades, not years. Instead, the acceleration of science will come about thanks to another approach: AI agents.
The primary condition for AlphaFold’s success was the existence of the Protein Data Bank, a data set of roughly 170,000 experimentally validated protein structures on which DeepMind’s team could train its model. The creation of the Protein Data Bank was not simple: It took 53 years of international scientific cooperation and, by a recent estimate, roughly $21 billion worth of experimental work to assemble. Efforts of that scale are infamously difficult to fund, next to impossible to coordinate, and hugely time-consuming to execute; they have often been unsuccessful as a result.
But even in fields with the requisite cohesion and resources, and where the relevant data are not rendered inaccessible by commercial ownership, another barrier is too little discussed: the scientific impossibility of generating comparable data. In the case of protein structures, the key experimental technique—protein crystallography—is an unusually replicable and dependable tool, so much so that over 25 Nobel Prizes have relied on it. But in most of experimental science, results vary more often than not. Cell lines drift. Chemicals have trace contaminants. Lab humidity changes. The creation of measured datasets that will be consistent enough, accurate enough, precise enough, and scalable enough to train a modern neural network in biology or most of chemistry would require new kinds of measurement and new standardized approaches—none of which will be ready anytime soon.
Of course, there are a handful of fields where these requirements are met: weather forecasting, much of genomics, very limited areas of chemistry. These may see AlphaFold-style breakthroughs soon, if they haven’t already. Government support for the production and coordination of those datasets will be critical, as the US National Security Commission on Emerging Biotechnology has argued. But for most open questions in science, we will need a different plan, at least in the short term. Luckily, something quieter and more modest has begun to show promise.
Scientists have always reasoned under uncertainty. Biologists working to identify new drug targets have never had perfect datasets. Instead, they combine docking calculations and known structures, factor in molecular dynamics, run a handful of binding assays, and use their judgment to weigh each method according to its particular strengths and points of failure. The skill of science is not in any single tool; it is synthesizing what many tools produce, and revising the results as the evidence comes in. This is how most working research actually proceeds. But until very recently, no software could do it.
Agents now can. Simply put, an agent is an AI reasoning engine that has been given access to tools—digital or physical—and the capabilities to use them. Over the last few years, a fundamental architectural shift in AI has enabled the rapid proliferation of these programs, which are powered by large language models, dramatically reducing the need for scientifically specialized datasets. For science, this technological advancement represents a foundational change: it has allowed us to create digital tools that can mimic the iterative, highly contingent process of actual research. While tools like AlphaFold apply a powerful approach to a limited question, agents are inherently generalists. They do not represent a new way to do science—instead, they digitally model the human process of discovery.
Consider Google’s AI Co-Scientist, announced in May. Researchers gave it a one-page brief and a goal: Figure out how antibiotic resistance spreads between bacterial species, a key driver of drug-resistant infections. The system spun up sub-agents. One drafted hypotheses from the literature. Another picked them apart like a peer reviewer. A third ran tournaments to rank the strongest candidates. A fourth refined the winning hypothesis. The agent concluded that resistance genes were hitching rides on bacterial viruses, borrowing whichever virus could ferry them into a new host. The hypothesis was correct. Researchers at Imperial College London had spent a decade reaching the same conclusion through painstaking wet-lab work; their paper, previously unseen by Co-Scientist, was still in peer review.
Agents like Co-Scientist are still novel tools, and there are real challenges to overcome before they become a ubiquitous part of the scientific process: They are still liable to hallucinate, their judgment is not consistent, and they have memory and input constraints that limit the time they can run autonomously. But these technical barriers will fall away, and as they do we will begin to notice the compounding effects of scientific agents on the reliability, consistency, and velocity with which science is done.
Perhaps most notably, agents offer a structural fix for science’s “reproducibility crisis,” the widespread problem of researchers’ inability to replicate each other’s results. For decades, the scientific community has begged researchers to share their raw data and exact code in an effort to standardize experimental processes. But researchers have long resisted this tedious administrative work, which happens after the interesting science is already done. Agents, in contrast, automatically log every move they make, creating an exact record of the method that led to their results and allowing for precise replication.
A second consequence will be an amplification of scientific memory. The transfer of knowledge between researchers is a famously murky process; if it isn’t done over years of training and observation, graduate students are left to pore through the messy lab notebooks kept by decades of predecessors, looking for the details that will make or break their protocol. As agents become an increasingly large part of the scientific process, though, a lab’s entire scientific history will be recorded in a central, standardized repository of institutional knowledge.
But the most important impact of agents will be speed. In any field, when testing an idea takes less time than arguing about it in a meeting, people stop debating and just run the test. An agent that can read a thousand papers in an hour, design 500 molecules, and learn from its failed tests by morning will bring down the cost of experimentation and fundamentally change the pace at which science gets done. It will also give researchers the freedom to chase bold, strange questions they never would have risked their time on before, opening scientific doors we have yet to imagine.
While the AlphaFold template will certainly be key to incredible discoveries, it alone will not bring us to the end of science. Instead, the shift toward agentic AI represents a much rarer tier of breakthrough: a tool that envelops every field of science at once. Historically, tools of such scope have arrived just a handful of times: calculus, statistical inference, spectroscopy, the computer. Each revealed a world of problems no one had thought to formulate, and those problems, in turn, defined their fields anew. With agents, another such transformation is upon us.
Eric Schmidt was the CEO of Google from 2001 to 2011. In 2024, with his wife Wendy, he co-founded Schmidt Sciences, a philanthropic venture to fund unconventional areas of exploration in science & tech.
Suhas Mahesh leads AI for Science work at the AI Center of Schmidt Sciences. He is a specialist in AI for materials discovery.
Additional research by Maya Levin, associate and sciences lead, Office of Eric Schmidt.
MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.
Way back in the summer of 2017, AI researchers at Google put out a paper called “Attention Is All You Need,” in which they described a new type of neural network called a transformer. It proved to be very good at processing long sequences of data, especially text.
Nine years on, transformers are the engines inside every major large language model on the market. “The entire AI industry is built on transformers,” says Justin Dangel, cofounder and CEO of the AI startup Subquadratic. “They are one of the most important innovations in the history of computer science, and they’ve changed the world.”
But transformers are starting to show their age. Many of the recent advances in LLMs, such as the development of so-called reasoning models and their ability to handle large amounts of input at once, are not neat extensions of that core technology but workarounds that patch over some of its fundamental flaws.
A growing number of scientists and engineers are now asking what’s coming next. LLMs are not going anywhere, but the way they get built is up for grabs. (MIT Technology Review dubbed this future generation of models LLMs+ in this year’s list of the 10 things that matter in AI.)
Enter a wave of startups hoping to push the boundaries of this boomtown technology. Some will no doubt fail—but they have everything to play for and far less to lose than the companies at the front of the pack today.
Strength in numbers
But first, the problem. The key strength of transformers lies in a mechanism called dense attention, which encodes the meaning of a block of text in a series of numbers. The process involves comparing every word (or part of a word, known as a token) in that text with every other word via a form of multiplication.
Dense attention can capture the meaning of text with remarkable accuracy. But as the length of that text grows, the number of computations needed to process it adds up fast. A document 10,000 words long might require a transformer to perform 50 million multiplications. That’s the main reason LLMs suck up so much power.
What’s more, transformers struggle with what many of the latest models are designed to do. Because of the way they process text word by word, transformers are not great at keeping track of a lot of information at once (in other words, what’s known as their context window cannot get too large). And yet if LLMs are to carry out harder tasks, they will need to take in larger amounts of data: a whole library of documents, an entire code base, or in the case of agents, output from other LLMs.
As for reasoning models, they work by writing notes to themselves (in a kind of scratch pad known as a chain of thought) and then reading them back, which again adds to the amount of data to stay on top of.
As LLMs get bigger and better, transformers have become a bottleneck. The technology’s key strength is now a limitation.
Here are four new ideas for how to solve the transformer problem—innovations that could change LLMs for good, making them faster, far more efficient, and (maybe) even smarter.
01: Rethinking attention
An obvious way to make LLMs faster and cheaper is to tackle the problem head on and change the way attention works. Swapping out dense attention for a mechanism called sparse attention, which runs calculations on only some pairings of words in a block of text instead of all of them, can radically reduce the amount of computation LLMs need to do.
Researchers have come up with plenty of sparse attention mechanisms over the years. The problem is that none of them were as good as dense attention at capturing meaning.
That might have changed. Subquadratic, a startup based in Miami, claims it has invented the first sparse attention mechanism that rivals top mainstream LLMs on a handful of tasks, including search and coding. It’s a huge claim (and some people in the industry remain skeptical).
Subquadratic says its model, SubQ, works by figuring out on the fly—for each piece of text it is given—which words matter and which don’t. The company also claims that thousands have signed up to its waitlist and plans to make the model widely available soon.
Meanwhile, Manifest AI, a startup based in San Francisco, is coming at the problem from a different angle. Instead of changing how attention works, it is replacing it with something else.
It has developed a mechanism it calls power retention, which stores only the most relevant information for a given task and ensures that the amount of data an LLM has to keep track of doesn’t blow up.
Attention mechanisms force LLMs to keep track of everything in their context window. A sparse attention model (such as SubQ) throws out a lot of the individual words, but it still retains a rough picture of everything it has seen. In contrast, power retention works by providing the model with a rolling summary of its context window. As new information is added, less relevant information is dropped.
The basic principle of retention has been around for a decade. Manifest AI claims it has updated those techniques to build models that can stand up to transformer-based LLMs for the first time.
The company says it is possible to adapt a transformer model into a power retention model with minimal retraining. To demonstrate this, it has turned an existing open-source coding LLM called StarCoder into a version that uses power retention, called PowerCoder. It has also released a model called Brumby, which it claims rivals some versions of Alibaba’s popular open-source model Qwen.
Manifest AI wants its power retention tech to become the go-to solution when LLMs need to carry out tasks that involve processing huge amounts of data. There are many useful applications, Manifest AI’s cofounder and CTO, Carles Gelada, claimed in a video announcing his company’s technology last year—from analyzing videos that are hours long to building agents that can stay on task for weeks at a time.
02: Making models smaller and more flexible
Liquid AI, an MIT spinout based in Cambridge, Massachusetts, hasn’t changed or ditched transformers fully but pairs them with its own tech, liquid neural networks, to build what cofounder and CEO Ramin Hasani calls LFMs (liquid foundation models).
Liquid AI’s models are far smaller and use less energy than most LLMs. The firm builds models for car makers, including Mercedes, which run on the small chips inside vehicles. Its latest models can run on a Raspberry Pi, a low-powered hobbyist computer that costs $50.
Its models are available for free to any organization with an annual revenue less than $10 million. And they have proved popular: The company has racked up almost 34 million downloads, says Hasani.
Liquid neural networks were inspired by worm brains. They are an extension of another type of neural network that predates transformers, called convolutional networks. The key innovation is a mechanism that lets a model adapt its behavior to new information, so it can learn as it goes. That’s not possible with transformers: Once a model is trained, its behavior is fixed.
Liquid AI’s first models were pretty basic but could fly drones or drive vehicles. With LFMs, the company is trying to scale up its technology to compete with mainstream LLMs. Its new models match the performance of rivals four times bigger, including versions of Alibaba’s Qwen and Google’s open-source LLM Gemma.
A typical LLM is built from a stack of transformers wired together. Liquid AI’s recent LFMs are hybrid models made up of 20% transformers and 80% liquid neural networks.
That ratio was hit upon by another AI system that Liquid AI has built, which it uses to help design all its models. “It’s the core technology of our company right now,” says Hasani. This designer AI sifts through many different combinations of neural networks—liquid, convolutional, and more, as well as transformers—and comes up with designs that bolt different ones together to hit a sweet spot of performance and efficiency.
Hasani thinks transformers were just the beginning: “Your brain is an AGI system, you know, and it operates with 20 watts of power. How is it possible? We can get a lot more innovative.”
03: Generating text all at once
Almost all LLMs produce their output one word at a time. It makes sense, because that is how people speak and write. But for computers, it’s very inefficient.
It is faster and cheaper for LLMs to generate text all at once—spitting out whole sentences or paragraphs in one shot. That’s the approach taken by Inception, a startup based in Palo Alto, California, which is building LLMs using a technique called diffusion.
Diffusion is better known as the technology that drives most image and video generation models. Diffusion models are trained to take a random grid of pixels—like the static on an old TV set—and turn it into an image. They do this by working on all the pixels at the same time, figuring out which need changing to make the static look more like a high-definition photo.
It turns out this process works on text too. Inception has trained its LLMs to take a random string of words and turn it into sentences that make sense. Diffusion LLMs still use transformers to encode meaning, but by producing whole blocks of text at once, they make transformers do more for less. “You’re still using a big transformer model, but you can predict many tokens at the same time,” says Inception’s cofounder and CEO, Stefano Ermon. “That’s why these models are so much faster and cost-efficient compared to what most other people are building today.”
The challenge was to take a technology designed for image generation and apply it to text. With images, if you need to change a blue pixel to a red one you can step through intermediate colors, says Ermon. That doesn’t work with text: “When you have ‘cat’ and ‘dog,’ there is not really something in between.”
Ermon is also a researcher at Stanford University. In 2024, he and a pair of his Stanford colleagues figured out the math to make diffusion models work with text. They trained a diffusion model that matched the performance of GPT-2—an LLM that OpenAI built in 2019—but was 10 times faster. It was enough for Ermon to spin out a company.
Today he has his sights on the big league. Inception claims its latest model, Mercury 2, performs as well as some of OpenAI’s GPT-4 models, released in 2023, but again 10 times faster. “We’re bullish about this approach because it’s the one that is going to scale up,” says Ermon.
The only things that matter are speed and cost, he adds: “Ultimately, the currency is going to be intelligence per dollar.”
Inception is not the only company betting on diffusion. Google is also experimenting with this approach and has built a prototype LLM called Diffusion Gemma. But Ermon is not worried about the competition. “I think it’s validating,” he says. “This is the future.”
04: Moving beyond words
Pathway, another startup based in Palo Alto, is perhaps the most extreme of this new bunch. It wants to free LLMs from the constraints of language.
The firm has built a type of LLM called Dragon Hatchling (named after the dragons in Terry Pratchett’s novel Color of Magic, which materialize if you think about them hard enough). Its standout result so far is a high score on a benchmark that pits LLMs against more than 250,000 very hard sudoku puzzles. Dragon Hatchling beat more than 97% of the puzzles; several leading LLMs from the top labs failed to solve any.
The point Pathway wants to make is that despite their remarkable success at many different tasks, there are still crucial classes of problems where LLMs fail. Sudoku is just one example. If we want LLMs to come up with genuine, novel solutions to real problems, we need to move beyond transformers, says Pathway’s cofounder and CEO, Zuzanna Stamirowska.
That’s because transformers force LLMs to do everything with text. But language is not the best tool for certain kinds of reasoning. “It’s very difficult to represent a sudoku board word by word,” says Stamirowska.
Pathway’s solution is to change the math behind the transformer, replacing the attention mechanism with a mathematical structure called a state space. Instead of encoding information word by word, state spaces compress it into a more abstract representation. Using this technique, Dragon Hatchling can still process and produce text, but it can also mimic forms of reasoning that do not involve sequences of words. This not only makes Pathway’s model more efficient, but (in theory) it lets it take on tasks that other LLMs cannot do.
Think of chess or mathematics—those kinds of puzzles are not held in your head as a long sentence, says Stamirowska: “The eureka moment that pops up in your brain isn’t necessarily in language. We would argue that if you have to reason in language, you’re somehow constrained.”
Stamirowska admits that a mainstream LLM could read a book about how to solve sudoku and then write code to do it. But we want to build models with more than book smarts, she says: “The hope for AI is not to solve sudoku; it’s to cure cancer. There’s not a book for that.”
“Transformers are an engineering convenience that we fell on,” she adds. “It started a religion, but it’s silly to think that a breakthrough won’t happen again.”
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 ideas of a vast censorship network moved from the online fringe to Trump policy
For years, narratives about a “censorship-industrial complex” spread in right-wing circles. The theory claimed that, under the guise of combating disinformation, a sprawling constellation of government agencies, academics, civil society groups, and Big Tech platforms was suppressing conservative and populist speech online.
What began as a relatively niche conspiracy theory from the far right was able to—and continues to—influence a wide range of actions by the second Trump administration. In myriad ways, the idea of the censorship-industrial complex, or CIC, explains the logic behind how the administration operates both at home and internationally.
Over the past nine months, MIT Technology Review and Type Investigations have traced how the theory came to influence the country’s most powerful individuals. We found that it took off in 2023, driven by a handful of individuals, with support from a close-knit group of right-wing organizations and media outlets—and an internet activist named Mike Benz at its center.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 AI has designed new viruses from scratch for the first time Scientists trained AI on DNA sequences to design new genomes. (BBC) + The result was 16 novel viruses which pose no threat to people. (WSJ $) + They could lead to medical breakthroughs—or biological weapons. (Axios) + And more complex AI-designed life forms. (MIT Technology Review)
2 Meta has been hit with its biggest child safety penalty yet A New Mexico judge fined the firm $567 million over harms to kids. (BBC) + The ruling is the second phase of a landmark trial Meta lost. (Guardian) + A jury found Meta misled users over child safety. (NYT $) + The total fine now stands at $942 million. (WSJ $)
3 One of China’s top AI models broke out of its testing sandbox Kimi K3 escaped containment and accessed the open internet. (SCMP) + But didn’t hack an external system. (Bloomberg $) + Researchers say Kimi has fewer guardrails than rival models. (Wired $)
4 A drone bomb was found near a Ukrainian plane at a German airport The nearby aircraft was loaded with military ammunition. (Guardian) + A “ripped” detonator reportedly stopped the device from going off. (BBC) + Europe has a drone-filled vision for future wars. (MIT Technology Review)
5 ByteDance is training a model near the size of Anthropic’s Mythos It would be three times larger than Moonshot’s Kimi K3. (FT $) + Chinese AI models have divided the White House. (MIT Technology Review)
6 Research suggests replacing new petrol cars with EVs cuts carbon The savings outweighed EV manufacturing footprints. (New Scientist $)
7 Trump could save millions from crypto rules meant to limit conflicts An ethics proposal could let him defer capital gains taxes. (Bloomberg $) + It would require him to sell crypto-related holdings. (Reuters $)
8 OpenAI’s smart speaker will be hockey puck-sized with moving parts The “doughnut-shaped” device could cost more than $300. (Verge)
9 Scientists created a virtual alien lifeform to search for extraterrestrials The model could help identify signs of life on distant planets. (404 Media)
10 Poo transplants are helping people eat peanuts again The first human trial suggests gut bacteria can treat food allergies. (Nature)
Quote of the day
“What is the risk of what I’ve never seen before?”
—Dr Moritz Hanke, a fellow at the Johns Hopkins Center for Health Security, tells the New York Times why there’s no consensus on the dangers of a new virus made by AI.
One More Thing
This architect wants to build cities out of lava
Arnhildur Pálmadóttir was around three years old when she saw a red sky from her living room window. A volcano was erupting about 25 miles away from where she lived on the northeastern coast of Iceland. Its ominous presence seeped into her subconscious, populating her dreams with streaks of light in the night sky.
Fifty years later, these “gloomy, strange dreams,” as Pálmadóttir now describes them, have led to a career as an architect with an extraordinary mission: to harness molten lava and build cities out of it.
This article was produced in partnership with Type Investigations, with support from the Wayne Barrett Project.
One morning in April 2025, employees of a small office in the US State Department got the email many of them had been dreading.
For months, Elon Musk’s Department of Government Efficiency had been cutting a wide swath through federal agencies, gutting teams that worked on everything from scientific research to Social Security to international aid. So when the employees of the Counter Foreign Information Manipulation and Interference Hub (R/FIMI)—a unit tasked with tracking foreign disinformation—received a note asking them to meet in half an hour, many sensed their time had come.
“We all had heard … that there was going to be a day they would shut down our office,” says one former staffer, who requested anonymity for fear of retaliation.
Darren Beattie, a far-right provocateur who had been serving as the acting undersecretary of state for public diplomacy, wasted no time with pleasantries, telling the gathered employees that the office was being shuttered and that their jobs were being eliminated. The mood in the room oscillated between somber and outraged, the former employee says, with “lots of young, dedicated staff crying and wrecked.”
But elsewhere at the State Department, the closure of R/FIMI was cause for celebration.
“We ended government-sponsored censorship in the United States through the State Department,” Secretary of State Marco Rubio announced shortly after in a video interview with a right-wing internet activist named Mike Benz.
“This is obviously amazing news,” Benz responded, before teeing up a theory he’d been promoting for years: “We now have so many people wondering, Was I censored because of something the State Department did?”
The notion that the State Department had been working to silence Americans is perhaps unfamiliar to many people, but it’s a key part of a larger web of conspiratorial ideas that have been promoted for a decade on far-right blogs, podcasts, and social media. The basic theory is that, under the guise of combating disinformation, a sprawling constellation of government agencies, academics, civil society groups, and Big Tech platforms—or what believers refer to as the “censorship-industrial complex”—has been working to suppress conservative and populist speech online. The fight against “disinformation,” as Benz put it on a 2025 podcast, “is censorship in disguise.” R/FIMI, along with a predecessor office with the same mission, the Global Engagement Center (GEC), had been among the right’s long-standing targets; now it was one of its latest casualties.
The second Trump administration’s war on censorship has contributed to the dismantling of numerous government agencies, like the Cybersecurity and Infrastructure Security Agency at the Department of Homeland Security, the FBI’s Foreign Influence Task Force, and the US Agency for International Development (USAID), among others. It’s razed the infrastructure that tracks and counters attempts by foreign powers to influence elections and popular opinion. It has also been used to justify sweeping travel bans and sanctions on targeted individuals and entire industries. It’s provoked tensions with the European Union over its tech regulations, which these critics say silence Americans. And it’s even reshaped US national security strategy.
Its reach, though, isn’t limited to US bureaucracy, academia, or civil society groups. The weaponization of ideas about censorship also affects the billions of people globally who get information, or interact with each other, online—which is to say, just about everyone.
Over the past nine months, MIT Technology Review and Type Investigations have dug into its origins and traced how a relatively niche conspiracy theory from the far right was able to—and continues to—influence a wide range of actions by the second Trump administration. In myriad ways, the theory of the censorship-industrial complex, or CIC, explains the logic behind how the Trump administration operates both at home and in the world.
To understand just how it jumped from the edges of the information sphere to influence the decision-making of the country’s most powerful individuals, we used open-source methods to scrape content from right-wing websites; scrutinized hundreds of thousands of pages of public records, videos, and podcasts; and analyzed over 100,000 social media posts that have promoted the narrative. Our analysis found that although the roots of the idea run deep and wide, the CIC framework really took off in early 2023, driven by a handful of individuals, with support from a close-knit collection of right-wing organizations and media outlets.
At the center of this effort is Mike Benz, Rubio’s interview partner last April. Benz served a brief stint in Donald Trump’s first administration but has largely remained unknown, even as some of his ideas—including the conspiracy theory that Taylor Swift was a NATO asset and the pro-white-identity content he published anonymously before he joined the government—have gained followers. Our analysis uncovers just how integral he in particular was to spreading the CIC theory: We found him to be the most prolific creator of related content online, which was in turn picked up by right-wing media outlets and podcasts and eventually reached the highest echelons of power in the US. His work prompted congressional—and eventually presidential—action and generated both attention and financial benefits for Benz personally. (Benz did not respond to multiple requests for comment.)
Last April, near the end of the interview at the State Department, Rubio shot Benz a friendly finger gun.
“Thanks for your attention to this topic,” Rubio said. “I know you’ve been on this a long time.”
The origins of discontent
Every conspiracy theory is sustained by a unique blend of grievances and shadowy actors, but at their core, they’re shockingly similar. They often start with a few devoted believers operating on the periphery. They claim to uncover hidden truths about people in power. They offer order and meaning for believers, creating or affirming group identities. And the most successful ones contain a grain of truth.
This also describes “the best propaganda campaigns,” according to David Millar, a longtime US intelligence official who spent years at the State Department’s Foreign Service Institute teaching future diplomats how to recognize such agitprop. “They’re seldom just complete lies,” Millar says. “They’re usually taking something out of proportion or amplifying something that actually happened into something that didn’t.”
That’s the case with the idea of censorship that targets the right online. In 2016, an anonymous former employee of Facebook claimed that the site was suppressing conservative news in its “trending topics” section. The company scrambled to reassure conservatives that this was not the case—but still axed its human editors. Not long after, in the wake of Russian influence operations in the 2016 US presidential election and the United Kingdom’s Brexit vote, social media platforms amped up their content moderation efforts in order to filter out foreign disinformation. In doing so, they also ended up banning some conservative voices, often for violating platform policies against false information.
To right-leaning internet users, the posts and accounts flagged as problematic or false “sure sounded like stuff that agreed with [their] belief system,” says Dannagal Young, director of the University of Delaware’s Center for Political Communication. “Russia had done their homework so well that the kinds of things that were … generated by Russian propagandists were not that distinct from things people in the States would say.”
It was a messy moment in our collective digital life. Never before had anyone and everyone been given a megaphone to instantly broadcast to a global audience, for better and worse. These new powers prompted difficult questions about how that content should be policed, and by whom—if at all. What should happen if, say, a foreign intelligence service impersonated people en masse on social media to manipulate citizens? What if someone—anyone—said something provocative, hateful, or meant to incite violence? Or something that didn’t directly call for violence but could provoke it nonetheless? The pressure on social media companies to act was immense.
The pandemic became a “pivotal development in the radicalization of conservatives… feeling like, This isn’t right. Not everyone’s getting censored, just us.”
-Dannagal Young, political communications scholar
And more or less in real time, tech companies—as well as government bureaucrats, researchers, and journalists—had to chart a path for which there was no map. A new field grew to help inform and even make decisions on what qualified as “misinformation” and “disinformation,” though there wasn’t a clear definition of either term (nor of “censorship”). As a result of this work, platforms have taken steps over the past decade to shield users from fake news, conspiracy theories, and covert foreign influence. In doing so, they sometimes went too far—as some tech leaders have since argued. Alternative services and platforms sprang up, intending to create spaces free of “censorship” (by which they meant content moderation); Parler, Gab, and others tended to cater to right-leaning voices. They themselves were later kicked off Apple’s and Google’s app stores or banned by hosting and payment services.
High-profile narratives—like the claims that Twitter “shadow-banned” conservatives in 2018 and suppressed news about Hunter Biden’s laptop in 2020—have consistently kept alive the notion of conservative-targeting censorship. And online influencers like Benz have been able to twist what happened to tear down entire sections of the US government and silence individuals they disagree with.
The covid-19 pandemic in particular proved an ideal breeding ground for the censorship narrative. As trust in institutions plummeted, and harmful rumors about the crisis proliferated online, platforms like Facebook, Twitter, and YouTube adjudicated what qualified as covid-related misinformation (including claims regarding the pandemic’s origins), took down posts, and banned prominent vaccine deniers like Robert F. Kennedy Jr.—sometimes with the encouragement of parts of the federal government.
The pandemic became a “pivotal development in the radicalization of conservatives,” says Young. They were “feeling like, This isn’t right. Not everyone’s getting censored, just us.”
Following the January 6 insurrection at the US Capitol (itself driven by false claims that the 2020 election had been stolen), social media companies took more aggressive measures, including banning the president from their platforms, citing his potential to incite more violence. To Trump’s allies and supporters, however, his removal only encouraged the idea that a network of actors was working behind the scenes to censor their voices—and keep the president from retaining power.
“It was when Trump got censored into oblivion in 2020 by the US government under his nose, working with webs of outside NGOs and Pentagon front groups to mass-censor virtually every narrative that he was putting out, that he lost,” Benz later told Joe Rogan on his podcast. This was proof, Benz concluded, of the dark power of censorship.
In April 2022, the digital chatter about government censorship found a real-world villain. The Biden administration had announced the formation of a Disinformation Governance Board to coordinate DHS’s efforts on this front; it would be led by Nina Jankowicz, who had cut her teeth countering Russian propaganda in Eastern Europe.
But the rollout was clumsy; and for the far right, the group’s ominous-sounding name and the scant details about what it would actually do made it the ultimate evidence of government censorship by another name. As the Washington Postreported, Fox News devoted 70% of its news segments to coverage of Jankowicz or the board in the week after its announcement, with Sean Hannity describing her as “one of the biggest perpetrators and purveyors of disinformation in the entire country.” Beattie, the Trump official who later shut down R/FIMI, called her “Biden’s new minister of truth,” while Benz claimed she was a “registered agent for the British crown.” She became the target of an all-out harassment campaign—and multiple death threats.
Jankowicz was also named in a lawsuit against government agencies and officials that alleged “open collusion with social-media companies to suppress disfavored speakers, viewpoints, and content.” Benz submitted a legal brief in support of the case.
The Supreme Court dismissed the case in 2024, but by that time Jankowicz was long gone—she resigned just three weeks after the task force launched, citing “vile personal attacks and physical threats.” The group never met.
In February 2023 Margot Cleveland, a contributor to the conservative outlet The Federalist, gave right-wing perceptions of these events their now familiar framing in an article titled “How Trump Derangement Gave Birth to the Censorship-Industrial Complex.”
“The Biden administration may have abandoned plans to create a ‘Disinformation Board,’ but a more insidious ‘Censorship Complex’ already exists and is growing at an alarming speed,” Cleveland wrote. “Its funding and collaboration implicate the government, academia, tech giants, nonprofits, politicians, social media, and the legacy press. Under the guise of combatting so-called misinformation, disinformation, and mal-information, these groups seek to silence speech that threatens the far-left’s ability to control the conversation—and thus the country and the world.”
According to our analysis of social media posts, Cleveland’s article was shared by 168 accounts, marking the first time that something about the censorship-industrial complex was shared by so many different people. Its popularity showed that people were ready to gravitate toward the CIC framing, Cleveland told us in an interview. “It’s definitely catchy,” she says. “People understand what it means, given that the military-industrial complex has … been in existence since the 1950s.”
Just a few weeks later, the idea jumped toward mainstream recognition.
Musk, then the new owner of Twitter, had given internal company documents to two journalists, Michael Shellenberger and Matt Taibbi, with the goal of exposing how Twitter and the Biden administration had supposedly colluded to censor conservative voices.
What they found was widelyregarded to fall substantially short of a conspiracy against the right—but on March 9, 2023, the pair appeared before Congress to discuss the results of their “Twitter Files” inquiry at a hearing convened by Representative Jim Jordan, an Ohio Republican. Shellenberger’s 68-page testimony laid out what he claimed was a vast network of government agencies and contractors that allegedly censored Americans through “blacklists of disfavored people”—“pressuring, cajoling, and demanding that social media platforms censor, deamplify, and even ban the people on these blacklists.” Shellenberger shared his testimony on Twitter, where it was viewed more than 2.7 million times, our analysis shows. A video of Taibbi’s testimony was viewed 4.3 million times, while a tweet sharing it got 40 million views. (As a point of comparison, in 2023 the average post on X received just 1,200 views, according to the data company Metricool.)
“We were, like, proverbially touching the elephant.”
Mike Shellenberger
We found that these were among the most viral tweets to date about the “censorship industry” or the “censorship-industrial complex.” Overall, we identified nearly 30,000 tweets about these ideas between September 2011, when “censorship industry” first popped up on Twitter, and March 2026. The clear peak came in March 2023, at the time of the Twitter Files hearing.
In an email, Taibbi said he has regularly defended censored figures on the left as well as the right and does not believe censorship to be “a conservative-liberal issue.” “Though the behavior we saw at Twitter in, say, 2020 was slanted against conservative content in the United States, we saw these techniques applied to both left and right around the world,” he said. Shellenberger, Jordan, Beattie, and Musk did not respond to requests for comment.
While these social media posts had a wide reach, our analysis shows that the ideas related to the CIC were primarily driven by a small handful of individuals. Of the 13,475 Twitter/X accounts that we found to have posted or reposted CIC-related content in that same time frame, 71% did so just once.
Even among the conspiracy theory’s top proponents, however, Benz stands out in terms of output. Our analysis shows that by March 2026 he’d posted on X about the “censorship industry” or “censorship-industrial complex” more than 440 times since opening an account in December 2022, after Musk’s purchase of the platform; this was far more than anyone else whose accounts we analyzed during the same period, including Taibbi, Shellenberger, or Beattie. When we added other variations of the word “censor,” that figure jumped to over 2,200 times.
In a 2023 podcast interview, Shellenberger credited Benz with opening his eyes to the full extent to which conservative voices were being silenced. “We basically went from being concerned about bad forms of censorship by Twitter and other social media platforms to being concerned about government censorship,” Shellenberger said.
“We were, like, proverbially touching the elephant. We didn’t understand what the elephant was [until] we discovered your work, Mike.”
The amplifiers
To understand Benz’s role in crafting and promoting the CIC narrative, MIT Technology Review and Type Investigations examined thousands of his tweets, public statements, podcasts, and reports since 2015, including those from now-deleted accounts—the most comprehensive collection of Benz’s work assembled to date. We found that his interest in censorship dates back to the early days of this fight—to at least 2016, when he was a corporate lawyer living in Brooklyn.
This was when, as NBC News’s Brandy Zadrozny later revealed, he began posting content online under a pseudonym, Frame Game. “Frame Game Radio” published across a number of platforms, including YouTube, Twitter, and Gab—mixing white identitarian and antisemitic views (though Benz identifies as Jewish) with alt-right-aligned complaints about the “diversity industry” and censorship. (In a lengthy rebuttal on X to the 2023 NBC News story, Benz claimed that Frame Game was a “project by Jews to get people who hated Jews to stop hating Jews,” to which he “contributed in a very limited manner.” He also stated, “Let me be clear: I am extremely proud of this.”)
Frame Game’s content spoke positively about Hitler’s Mein Kampf, warned of “white genocide,” and claimed that “censorship is almost exclusively a Jewish phenomenon.” For a few months, the account ran a never-before-reported campaign for a “White Mother Fertility Fund,” which promised to donate 20% of the channel’s revenue to a mother nominated by listeners.
“Censorship” became a catchall term for almost any form of content moderation.
This kind of content earned Frame Game the praise of the white nationalist publication Counter-Currents, which described the anonymous figure in May 2018 as “a rising star” and a “pro-white and Judeo-critical online activist”—noting that the Twitter account was “followed by everyone from Steve Sailer to David Duke” (references, respectively, to a columnist for the white nationalist website VDare and the former KKK Grand Wizard).
Another preoccupation was the supposed suppression of conservative speech. In hours-long videos, Frame Game used “censorship” as a catchall term for almost any form of content moderation—going as far as to apply it to any kind of “critique” at all. The account was particularly incensed by the deplatforming of Alex Jones in 2018, which it called the “Pearl Harbor event of free speech.”
Online censorship was a “keystone issue,” the channel said in 2018, affecting the right’s ability to achieve any of its other goals: “You have to fix that, or else you can’t advocate for anything else effectively.”
Frame Game ceased posting that same year, when Benz started working as a speechwriter at the Department of Housing and Urban Development before joining Stephen Miller’s speechwriting team at the White House.
Then, in November 2020, Benz moved to the State Department, briefly serving in the first Trump administration’s final months as deputy assistant secretary for international communications and information policy. He has said this short-term experience gave him firsthand knowledge of how the censorship machine works: “All things related to the internet connected to US foreign policy were under my purview,” Benz told Shellenberger in a podcast in March 2023. “I got to see the inner nucleus of how Big Government and Big Tech have so completely fused together in relatively seamless ways.”
It also helped set up Benz’s post-Trump career. In April 2022, he launched the Foundation for Freedom Online (FFO), which describes itself as a “free speech watchdog” dedicated to championing “digital freedom around the world.” Though it calls itself a “foundation,” FFO is not registered as a nonprofit and has just a few contributors. But it quickly became an active part of a bigger web of new Trump-aligned organizations—and in their eyes, even benign initiatives could be evidence for the machinations of the censorship-industrial complex. FFO has published critical reports claiming that efforts to promote media literacy, brand safety campaigns, and nonprofit journalism, including the work of the investigative organization Bellingcat, were all hidden forms of censorship, and labeling as suspect anyone affiliated with a government agency. (One of the authors of this article previously worked in international development in Afghanistan, on a State Department grant and USAID sub-grant, while two of the authors previously collaborated with Bellingcat.)
“Its implications for politics are really scary, because it makes conversation and debate virtually impossible.”
Political scientist Michael Barkun
One of FFO’s targets was a relatively obscure educational game called Cat Park. Created in 2022 with funding from the State Department’s GEC, Cat Park was designed to “build resistance to disinformation and promote digital media literacy” for kids ages 15 and up, according to its official description.
In the game, players fuel opposition to a city-funded cat park by creating emotionally charged headlines and memes, and by manipulating images with AI. Then, halfway through, they learn that the campaign against the park was the brainchild of a billionaire with plans to buy the designated land for cheap once the outrage dissuaded the city from building it. At this point, players must use what they have learned about creating effective propaganda to counter their own disinformation.
By 2024, only around 100,000 people had played the game, according to Martijn Gussekloo, a cofounder of the Dutch design agency that helped develop it—far fewer than the numbers that played similar online games the agency had worked on to highlight the issue of disinformation. “As far as I’m aware,” he says, “there were no political goals at all.”
Nevertheless, FFO published a report—based on materials obtained by America First Legal (AFL), a nonprofit started by Stephen Miller—claiming that Cat Park was part of the State Department’s effort to “subliminally train young people to associate social media posts alleging government corruption as being ‘fake news.’” The sinister interpretation was picked up by conservative media outlets like Just the News, which quoted Benz. That article was in turn picked up by Russia’s RT News, which also frequently quoted Benz.
It remained a relatively obscure, niche idea, but it didn’t need huge reach to set the stage for official government action. In 2023, Republicans on the House Foreign Affairs Committee, citing FFO’s reports, demanded that the State Department turn over documents about its efforts to fight disinformation—efforts including Cat Park. By the end of the next year, Congress voted to defund the GEC, and news outlets like Fox News used Cat Park to illustrate the nefariousness of this work. It was the culmination of a conspiracy theory that had jumped from the fringes of the online right to more mainstream and high-profile outlets and later into the halls of power.
Though this dynamic is not unique to the CIC narrative, we found that it has played out again and again with numerous targets, from the early attacks on the Disinformation Governance Board to the subsequent shutdown of other disinformation-focused research organizations, like the Stanford Internet Observatory and the Election Integrity Partnership, and later of government agencies like R/FIMI.
In each case, our analysis found, CIC-related conspiracy theories on social media were supported by a number of conservative groups; key among them were FFO and AFL, which played a crucial role in obtaining government records they claimed showed evidence that conservatives were censored. FFO then used these documents to write exhaustive reports supposedly connecting the dots to explain how the censorship industry worked. AFL also instigated a number of lawsuits against supposedly CIC-implicated individuals and organizations.
These materials were then amplified by a slew of right-wing media outlets, many with shared funding.
Much of that financing was funneled through the Informing America Foundation (IAF), a media-oriented grant-making organization that John Solomon helped to launch after leaving his position at The Hill back in 2019, following public scrutiny of columns about the Bidens and Ukraine. IAF funds a network of podcasts called Real America’s Voice, which includes shows run by key Trump allies like Steve Bannon, Jack Prosobiec, and the late Charlie Kirk, as well as Solomon himself. It also finances a network of what media scholars call “pink slime” websites, which are designed to look like nonpartisan local news but have a partisan slant. Benz appeared regularly on these podcasts and in these outlets, further embedding the CIC narrative in the right-wing information ecosystem—while simultaneously enhancing his own profile and revenue streams on X Premium and other platforms.
At the same time, IAF indirectly supported FFO and Benz personally, donating millions of dollars to conservative organizations that acted as the biggest financial sponsors of his work. In 2023 and 2024, the only years that he appears directly in any nonprofits’ financial documents, Benz was paid $530,000 from one organization alone.
In an email, Solomon defended the accuracy of his reporting for The Hill and said that while he had helped to come up with the idea for IAF, he isn’t part of the group’s official leadership and doesn’t have a hand in issuing grants. He also noted that he was an early adopter of the “censorship-industrial complex” framing, having used the term as far back as 2021.
“There are no conspiracy theories about federally encouraged censorship,” Solomon said. “It happened.”
The CIC narratives circulating on the right-wing internet and podcasts repeatedly helped propel offline action, through both private lawsuits and congressional investigations. Musk’s X was especially litigious, suing at least three organizations that reported on or advocated against hate speech on the platform. (Those suits have since been dismissed.) Then, once Republicans regained control of the House of Representatives in 2023, a new subcommittee on the “weaponization of the federal government,” chaired by Jordan, launched various investigations into the people and organizations that Benz, Taibbi, Shellenberger, Musk, and other proponents of the CIC theory had singled out.
“Receiving a subpoena for your documents is absolutely overwhelming,” says Clare Melford, executive director of the Global Disinformation Index (GDI), a London-based nonprofit that assesses levels of disinformation on websites and was the focus of a congressional investigation starting inApril 2023. “It can be financially crippling—and that’s the point.”
Benz reflected on the impact of the right-wing internet during a symposium in April 2024 at the Heritage Foundation, which had published Project 2025, the policy blueprint for the second Trump administration, the previous year. While he credited Jordan and other members of Congress for fighting against the censorship-industrial complex, he argued that lawmakers had taken up the cause “because everyone’s already talking about it.”
“The number one thing is: Talk about it, talk about it, talk about it,” Benz said. It’s a mantra he seems to have taken to heart: Our analysis found he mentioned variations of the word “censorship,” on average, once every three and a half minutes in his YouTube videos.
“Don’t underestimate,” Benz said at the Heritage Foundation, “the power of one small voice accumulated over millions to do a lot of damage to this system.”
The damage
The effects of this war on censorship became even more profound when the new administration took office the following year. On the first day of his second term, Trump signed an executive order titled “Restoring Freedom of Speech and Ending Federal Censorship,” which accused the Biden administration of having “trampled” on Americans’ free-speech rights.
This laid the foundation for administration decisions ending millions of dollars in support for disinformation- and misinformation-focused research and shutting down numerous government agencies—all in the name of fighting censorship. One of the highest-profile examples was the dismantling of USAID, which has resulted in devastating cuts to programs designed to combat the spread of HIV and AIDS, improve maternal health, and provide food aid around the world. Administration figures claimed loudly that the agency wasted federal dollars and that its values were misaligned with US interests, but perhaps less well known is how false ideas about censorship played a critical part in its demise.
The agency had been a longtime obsession for Benz, who argued that it was an instrument of censorship as early as FFO’s first report in July 2022. An effort in Brazil to limit the spread of disinformation on messaging apps like WhatsApp and Telegram, FFO alleged, was a form of censorship financed by USAID and others. Later that year, Benz also accused USAID of funding what he called a “Countering Disinformation Guidebook for Digital Censorship.” In reality, it was a USAID primer for its overseas staff on how to recognize disinformation campaigns in the foreign countries where the agency operated.
Our analysis shows that Benz and FFO were the dominant voices promoting conspiracy theories around USAID and censorship from 2022 until late 2024, when Benz talked about it on Rogan’s podcast. “USAID is effectively a ‘switch player’ to assist the Pentagon on the national security front, to assist the State Department on the national interest front, or to assist the intelligence community on a sort of clandestine operation front,” Benz alleged.
His appearance drew millions of viewers, including Musk. The trillionaire retweeted an X user’s glowing summary of the interview. “Mike Benz just revealed everything,” the user wrote, including “how ‘democracy’ was redefined to silence you.”
This marked the first time Musk, newly installed at DOGE, had posted specifically about USAID. But Benz and Musk were already acquainted. The two had exchangedtweets and threads about censorship and other conspiracy theories for years.
In February 2025, Andrew Natsios, a Republican who had led USAID under President George W. Bush, tried to defend the agency against Benz’s conspiracy theories and other charges. During a House Foreign Affairs Committee hearing, Representative Anna Paulina Luna, a Florida Republican, asked Natsios about a slide Benz had shared on social media that supposedly illustrated how the military could work with USAID and other agencies to incite riots, “destabilize nations,” and craft strategic “disinformation campaigns.”
“What Benz told you is nonsense,” Natsios declared. Though he tried repeatedly to explain that the slide wasn’t made by USAID and therefore couldn’t represent its views, Luna remained unconvinced. After the hearing, she turned to X to thank Benz for exposing the censorship-industrial complex. “USAID is about as swampy as it gets,” she wrote. Benz responded with the American flag emoji.
“I was astonished by the whole hearing,” Natsios told MIT Technology Review and Type Investigations, his frustration still evident. To defend the agency, he had made numerous media appearances, including interviews on Fox News and 60 Minutes, and reached out to Rogan’s podcast. But the damage had already been done; by the end of March, the administration formally notified Congress that USAID would be shut down.
“They moved so rapidly,” Natsios says, “we couldn’t succeed.”
The narrative of a censorship-industrial complex is also reshaping the United States’ relationships with other countries, including ostensible allies. For instance, the White House’s latest national security strategy, released in November, argues that Europe is facing the “prospect of civilizational erasure”—partly because of the “censorship of free speech and suppression of political opposition.”
And the European Union’s 2022 Digital Services Act, which placed guardrails on online platforms, has become a key flashpoint in the transatlantic relationship, with Benz calling it the European Union’s “flagship online censorship law.”
The EU regulation requires social media companies to remove content that violates national laws of member countries, including laws about hate speech and disinformation. But for Benz and his allies, the DSA aimed to achieve not accountability but an exportable censorship architecture. Because global tech platforms typically standardize their content moderation policies, “US tech companies would be forced to apply similar censorship measures at home,” Benz wrote in the conservative outlet Townhall in May 2022.
Vice President JD Vance later picked up this theme in a high-profile speech in Munich in February 2025, warning that free speech in Europe was “in retreat.” Just a week later, Trump issued executive orders indicating that he would consider tariffs in retaliation for “any act, policy, or practice in the European Union,” like the DSA, that worked to “undermine free speech or foster censorship.” Last summer, Rubio instructed diplomats to lobby directly against it.
Internally, however, at least one effort to find evidence of censorship turned up little. Last spring, Vance tasked a team at the State Department with gathering examples of incidents in which social media platforms had censored speech on the continent. A former official who received the request told us that Vance wasn’t interested in “the imprisonment of human rights activists for speaking [out], or journalists, or academics” in Belarus, Hungary, Serbia, or Russia—issues the department already tracked. Instead, the staffer says, Vance “wanted to focus on an extremely narrow set of posts related to far-right ideology, things related to abortion,” and even coverage of his own Munich speech. Vance directed the investigators to focus on Germany, France, Ireland, and the UK—all US allies where the administration has recently expressed support for far-right parties and politicians.
The team followed Vance’s directive, but instead of a “vast conspiracy,” the former official says, it found fewer than 10 instances of content that had been removed from X, all related to ongoing law enforcement investigations into violent extremism.
A spokesperson for Vance’s office declined to comment but pointed to previous remarks denying that he had ordered such a report. The White House did not respond to requests for comment.
And the administration hasn’t let up. After the European Commission issued a €120 million fine to X last December, the State Department announced that five Europeans who had advocated for tougher content moderation regulation through the DSA and other mechanisms would be banned from entering the US. Rubio called these individuals, including the GDI’s Melford, “agents of the censorship-industrial complex.” (A judge has since blocked the government from enforcing these bans as litigation continues, calling them “likely viewpoint discriminatory.”)
The fight for truth
The war on supposed censorship continued to intensify over the first half of this year. In February the State Department announced a new web portal, freedom.gov, which will enable people abroad—especially Europeans—to view hate speech, foreign propaganda, and other online content that had been banned by their own governments. The same month, Jordan and his staff on the House Judiciary Committee put out a second report on European censorship, naming additional organizations that they allege were involved. And former R/FIMI staffers continue to worry that the release of a long-teased “transparency initiative” from Beattie, who shut down the office, could reveal sensitive information about individuals and organizations abroad who have put themselves at risk to fight propaganda from China and Russia.
Some of the key figures responsible for the CIC narrative from outside of government are now being rewarded with influential positions within it. “I’m like an octopus juggler right now,” Benz bragged in April 2025, shortly after his interview with Rubio about the demise of R/FIMI. The activities he referred to, he vaguely explained, included his work in the media, on policy matters, and on litigation, as well as with Congress. And that was even before he started working as a special government employee at what remains of USAID, now housed in the State Department, where he’s been reportedly investigating the wrongdoing that he has long alleged.
“The very thing that they are accusing people of—censoring—is something that they’re now perpetuating.”
Nina Jankowicz, disinformation scholar
In February, meanwhile, Beattie was appointed the State Department’s senior advisor for Brazil. And in May, the White House appointed John Solomon a special government employee focused on matters of “transparency.” (Solomon noted that it was an unpaid position.)
The State Department did not respond to broader questions about the CIC narrative and its effects on policy, the vice president’s directive to department employees to find censorship in Western Europe, or the status of Beattie’s transparency initiative. “Under Dr. Beattie’s leadership … the Department made great strides in combating censorship groups,” a department official wrote in an email.
At the same time, the state-run digital influence campaigns that organizations like GEC were designed to fight are becoming more sophisticated. Over the course of the US-Israeli war on Iran, Iran has produced AI-generated propaganda—and, by manyaccounts, is winning the narrative war. Moscow has been working to inject Russian propaganda into the data sets used by US companies to train AI chatbots, increasing the likelihood that popular chatbots repeat Russian narratives. And the Chinese government has been recruiting its domestic AI companies to conduct state-aligned influence campaigns, according to an assessment from one US intelligence agency; the Trump administration has since dramatically downsized that office too.
Last summer, an AI-powered impostor pretending to be Rubio reached out to lawmakers and diplomats. “I was amused to learn that … at some point, Marco Rubio began to worry about deepfakes, and then it actually happened,” a former State staffer says. “And the people that were charged with working on it [at the department] were fired”—that is, they had been let go months earlier, alongside the rest of R/FIMI.
From a foreign policy standpoint, this is a “unilateral disarmament” by the United States, says former GEC head James Rubin—“leaving the information space open to America’s adversaries, especially China, Russia, and Iran.” The result is that everyone is more vulnerable to bad-faith actors online, often without being aware of the deception taking place.
MATTHIEU BOUREL
These threats are compounded by the way many tech companies have come to align with the Trump administration, a stance that critics say threatens the safety of their billions of users. Last January, Meta ended fact-checking on Facebook, with CEO Mark Zuckerberg stating in a video announcement that the company would “work with President Trump to push back on governments around the world” that were “going after American companies and pushing to censor more.” Meta, X, and Google have also all paid tens of millions to settle lawsuits Trump brought for their decisions to remove his social media accounts after January 6. (Google declined to comment. Meta and X did not respond to requests for comment.)
“Democracy is about the hearts and minds of the people. If you can simply shut off the ability to influence hearts and minds through censorship, you win by default.”
Mike Benz to an audience at the Heritage Foundation
Today, the CIC narrative is far larger than any single organization or individual. And that’s by design. Its proponents have constructed the battle much more broadly, as a sweeping defense of free speech. “If we were allowed to speak freely, millions and millions of people would hear a truth they simply could not resist,” Benz said back in his Frame Game days. “They would join this movement.”
It’s a movement that above all appears to be attacking “the nature of truth,” says Michael Barkun, a political scientist at Syracuse University who has studied conspiracy theories for decades. “The implications for politics are really scary, because it makes conversation and debate virtually impossible. How can you talk to people if they believe that whatever you say is based upon some kind of set of falsehoods?”
Benz and his peers have expressed similar sentiments. In the fight over censorship, the sides are constantly flipping: The accusers become the accused, the victims become the victimizers, and so on.
That’s why it’s important to look beyond the political narratives themselves and consider actions, says David Kaye, a law professor at the University of California, Irvine, and a former UN special rapporteur for free speech. “The administration probably realizes that … Americans do not generally feel censored by Europe or Brazil,” he says. But by focusing on Europe, Brazil, and the broader CIC idea, it hopes to “distract” from the fact that “Americans are definitely facing the most substantial censorship in, I don’t know, 100 years,” he adds—though “it’s actually the Trump administration” that is responsible.
The notion of a censorship-industrial complex is proving again and again to be a convenient and powerful pretext for silencing the loudest critics of those in power. “The very thing that they are accusing people of—censoring—is something that they’re now perpetuating,” says Jankowicz.
That, of course, may have been the goal all along. From the stage of the Heritage Foundation back in 2024, Benz spoke of the CIC as having its hand on a “God button” giving it the power to “control discourse on every political issue.” Now conservatives can wield that power themselves.
“Democracy is about the hearts and minds of the people,” he told the audience. “If you can simply shut off the ability to influence hearts and minds through censorship, you win by default.”
Eileen Guo is a senior reporter for features and investigations at MIT Technology Review. Gisela Perez de Acha is an open-source investigative reporter. Martin Sona is an open-source researcher with a background in applied social neuroscience.
DeepMind CEO Demis Hassabis is stepping back from running the unit day-to-day He’s becoming the unit’s chairman and Alphabet’s chief scientist, a broader role. DeepMind will now be led by CTO Koray Kavukcuoglu, under the title of senior vice-president.
DeepMind may be absorbed into Google’s wider business Google is expected to tighten its control over the AI lab, which it acquired 12 years ago.
Jeff Dean is leaving to start a new AI company After 27 years at Google, the company’s former chief scientist is launching Discovery Loop with three former colleagues. The startup’s goal is to fully automate the process of scientific research, and Google is one of its early investors.
The company is now shifting its AI science strategy It’s moving from specialized tools, like DeepMind’s AlphaFold, toward agentic AI systems that can conduct research more autonomously. Find out more in our recent story.
We’re excited to share that MIT Technology Review is now on Instagram Reels and YouTube Shorts, as well as LinkedIn and WhatsApp. You can hear straight from MIT Technology Review journalists in the channels and formats you prefer as they help you understand what’s happening next in the ever-changing world of technology—and what it means for you—all while offering a behind-the-scenes look at their reporting.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Meta has become the latest firm to say its AI hacked another company It blamed a “misconfiguration” by an independent cybersecurity tester. (CNN) + The model reportedly involved was Muse Spark 1.1. (The Information $) + It follows similar breaches by OpenAI and Anthropic models. (Guardian) + This is why AI agents can lie to reach their goals. (MIT Technology Review)
2 Samsung and SK Hynix are testing Chinese chip tools amid US export curbs The Korean chip giants are hedging against tighter restrictions. (Reuters $) + Beijing has launched a probe into Palo Alto Networks. (Bloomberg $) + US-China tech tensions are rising ahead of the Xi-Trump summit. (SCMP)
3 London just granted robotaxis a license to operate On the condition that they still have a human driver, for now. (BBC) + Uber plans to spend over $10 billion expanding its robotaxi network. (FT $)
4 Scientists have created gene-edited dogs that don’t trigger allergies They used CRISPR to remove a reaction-causing protein. (Wired $) + And now want approval to start selling the beagles. (New Scientist $) + Other firms are planning gene-edited babies. (MIT Technology Review)
5 OpenAI has asked a judge to toss Apple’s trade secrets lawsuit The ChatGPT maker called Apple’s allegations “meritless.” (Verge) + And claimed the suit is an attempt to stem an employee exodus. (FT $)
6 AI is reviving Silicon Valley’s super-app dream Tech giants are merging products into all-in-one assistants. (Business Insider) + Is a secure AI assistant possible? (MIT Technology Review)
7 A mystery book-buying spree has sparked new AI fears The buyers’ identities remain unclear amid data concerns. (Atlantic $)
8 Restaurants, pubs, and theatres are banning Meta’s “spy glasses” The venues have cited privacy threats to customers. (Guardian)
9 The SpaceX moon crash has created a unique scientific experiment It could reveal more about lunar soil and space debris. (BBC)
10 AI is helping to perfect the Pringle It involves over 200 data points, from humidity to harvest location. (WSJ $)
Quote of the day
“This could be the first real crisis moment for a company that has been stalwart for a long time.”
—Jeremy Nixon, a former Google Brain researcher and the founder of AI infrastructure company Infinity, tells the New York Times that Jeff Dean’s departure jeopardizes Google’s future.
One More Thing
How AI can help supercharge creativity
Generative tools put out by companies like OpenAI and Google DeepMind can automate a striking range of creative tasks and offer near-instant gratification—but at what cost? Some artists and researchers fear that such technology could turn us into passive consumers of yet more AI slop.
And so they are looking for ways to inject human creativity back into the process. The aim is to develop AI tools that augment our creativity rather than strip it from us—pushing us to be better at composing music, developing games, designing toys, and much more—and lay the groundwork for a future in which humans and machines create things together.
Ready for a fresh set of puzzles? Click here for the September/October 2026 Puzzle Corner, brought to you by Michael S. Branicky, ScD ’95, of the Puzzle Corner Puzzle Crew (aka PC2), which also includes Edward Faulkner ’03, MEng ’04, and Abe Kunin ’03. This column includes solutions to the May/June issue.
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.
NASA’s new dark energy space telescope can also detect killer asteroids
At the end of August, NASA is set to launch the Nancy Grace Roman Space Telescope from Kennedy Space Center in Florida. Its quest is to help us better understand how the universe works, from the glue-like dark matter that keeps galaxies together to the elusive dark energy that drives the expansion of the cosmos.
But Roman could also serve another purpose: defending Earth from killer asteroids. In September, a multi-institutional team of planetary scientists and astronomers will outline how it’s uniquely placed to scan asteroids, and provide information about their trajectories, sizes and compositions.
MIT Technology Review Narrated: The inevitable weakness of metrics
—Bryan Gardiner
There are plenty of useful things a metric can reveal. There are even more that it can obscure or corrupt.
Like a lot of people bitten by the self-quantifying bug, I started gathering personal data to pursue a nebulous collection of goals and desires. I wanted to feel better physically and emotionally, get outside more, and bring order to the messiness and uncertainty of my daily existence.
But external metrics and data can never capture what’s truly important. Worse, they inevitably redefine your core sense of what’s important, whether you’re aware of the trap or not. Read the full story.
This is our latest story to be turned into 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 The US is considering banning Chinese data center components The news comes just after the FCC announced curbs on other high-tech Chinese hardware. (Reuters $) + Texas is now requiring data centers to pass an audit before connecting to the grid. (The Verge $) + Trump’s AI protectionism has come for robotics. (MIT Technology Review)
2 SpaceX rocket has crashed into the moon The collision, at about 5,400 miles per hour, will likely leave a new crater. (Guardian) + SpaceX posted a loss of over $500 million in its first quarterly earnings report since its IPO. (WP $) + The company’s future plans rely on the success of its huge Starship rockets. (WSJ $) + Meanwhile, SpaceX’s spending on AI is soaring. (NYT $)
3 The UK’s AI Safety Institute says it’s uncovered more AI hacks Including an Anthropic model setting up fake accounts mimicking real people. (BBC) + The White House has a new cybersecurity framework…but it’s keeping details under wraps. (Wired $) + Here’s why AI agents lie and cheat to reach their goals. (MIT Technology Review)
4 The future’s looking bright for French AI lab Mistral Turmoil in the US is creating opportunities for European AI that it hopes to seize. (Wired $)
5 People are betting on wildfires on prediction markets Which, as US senators warn, sure seems like it will incentivize arson. (Ars Technica) + Prediction markets are also starting to put the accuracy of weather predictions at risk. (MIT Technology Review)
6 Can Reddit save itself from being swamped by AI search spam? It’s becoming increasingly influential, and brands are desperate to secure mentions. (The Verge $)
7 Grassroots groups around the world are trying to “humanize” AI Part of their work is educating people so they can have a say on their futures. (Rest of World) + The World Bank says poorer countries have “less to fear” from AI than rich ones. (FT $)
8 A Chinese physical AI startup has run into controversy Spirit AI stands accused of “benchmark hacking” after it briefly overtook Nvidia on an AI leaderboard in June. (SCMP$) + How to build a better AI benchmark. (MIT Technology Review)
9 Pluto’s atmosphere may be starting to collapse And after the loss of its planet status, too. Really adds insult to injury. (Gizmodo)
10 Even Microsoft is begging its engineers to stop using AI so much Kinda telling that even Big Tech firms are struggling to find ROI for their spending. (404 Media)
Quote of the day
“We’re essentially trying to criminalize government scientists giving advice to political leaders, if that is advice the political leaders don’t like or it creates an outcome the political leaders didn’t want.”
—Ashish Jha, former COVID response coordinator, tells Axios why scientists are becoming increasingly wary of taking US government jobs.
One More Thing
CHRIS LABROOY
Meet the Vitalists: the hardcore longevity enthusiasts who believe death is “wrong”
Last April, an excited crowd gathered at a compound in Berkeley, California, for a three-day event called the Vitalist Bay Summit. It was part of a longer, two-month residency that hosted various events to explore tools—from drug regulation to cryonics—that might be deployed in the fight against death.
One of the main goals, though, was to spread the word of Vitalism, a somewhat radical movement established a few years ago. Consider it longevity for the most hardcore adherents—a sweeping mission to which nothing short of total devotion will do.
A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ This yellow labrador’s love of walking is a joy to behold. + Bowling legend Phyllis is still rolling perfect games at 95 years old. + Here are some evidence-based ways to cut back on life’s many distractions. + Humans since 1982 turns clock faces into mesmerizing kinetic art that still functions as timepieces.
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