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.  Read the full op-ed on how agents could accelerate scientific 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. Register here to join the session at 19:00 GMT / 2:00 pm ET / 11:00 am PT. The must-reads 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 polluterThe 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 concernsTests 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 cyberattacksA 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 ChinaThe 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 MetaIrregular’s tests allowed the models to access the public internet. (CNBC) 8 NASA is flying into wildfire storms to understand themThe mission will study their effects on fires and the atmosphere. (Economist $) 9 A chatbot-free childhood may become a status symbolAI 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. Take a look inside his starter kit for civilization. —Tiffany Ng We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + 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.

EXECUTIVE SUMMARY 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. The costs are huge. OpenAI is set to spend $50 billion on computing this year, according to the company’s president, Greg Brockman. And the International Energy Agency predicts that the total amount of electricity consumed by data centers will double by 2030. 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.
Read the full story on how the CIC framework has shaped government action. —Eileen Guo, Gisela Perez de Acha, and Martin Sona
Join us next week for a subscriber-only Roundtables discussion about how the “censorship-industrial complex” idea started, where it’s going, and what it means for the future of American democracy and the internet. Subscribers can register for free. The must-reads 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 timeScientists 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 yetA 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 sandboxKimi 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 airportThe 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 MythosIt 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 carbonThe savings outweighed EV manufacturing footprints. (New Scientist $) 7 Trump could save millions from crypto rules meant to limit conflictsAn 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 partsThe “doughnut-shaped” device could cost more than $300. (Verge) 9 Scientists created a virtual alien lifeform to search for extraterrestrialsThe model could help identify signs of life on distant planets. (404 Media) 10 Poo transplants are helping people eat peanuts againThe 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.
Pálmadóttir believes the lava from a single eruption could yield enough building material to lay the foundations of an entire city. Find out how she aims to turn the idea into reality. —Elissaveta M. Brandon

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.
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} @media (min-width: 60rem) { .flourish-embed { width: 60vw; transform: translateX(-50%); left: 50%; position: relative; } } “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 Post reported, 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 widely regarded 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 in April 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 exchanged tweets and threads about censorship and other conspiracy theories for years. Only a few weeks after Benz’s Rogan appearance, Musk was using the “censorship-industrial complex” language directly—and USAID was on life support. 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 many accounts, 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.”
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} 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.