What even is the AI bubble?

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

In July, a widely cited MIT study claimed that 95% of organizations that invested in generative AI were getting “zero return.” Tech stocks briefly plunged. While the study itself was more nuanced than the headlines, for many it still felt like the first hard data point confirming what skeptics had muttered for months: Hype around AI might be outpacing reality.

Then, in August, OpenAI CEO Sam Altman said what everyone in Silicon Valley had been whispering. “Are we in a phase where investors as a whole are overexcited about AI?” he said during a press dinner I attended. “My opinion is yes.” 


This story is part of MIT Technology Review’s Hype Correction package, a series that resets expectations about what AI is, what it makes possible, and where we go next.


He compared the current moment to the dot-com bubble. “When bubbles happen, smart people get overexcited about a kernel of truth,” he explained. “Tech was really important. The internet was a really big deal. People got overexcited.” 

With those comments, it was off to the races. The next day’s stock market dip was attributed to the sentiment he shared. The question “Are we in an AI bubble?” became inescapable.

Who thinks it is a bubble? 

The short answer: Lots of people. But not everyone agrees on who or what is overinflated. Tech leaders are using this moment of fear to take shots at their rivals and position themselves as clear winners on the other side. How they describe the bubble depends on where their company sits.

When I asked Meta CEO Mark Zuckerberg about the AI bubble in September, he ran through the historical analogies of past bubbles—railroads, fiber for the internet, the dot-com boom—and noted that in each case, “the infrastructure gets built out, people take on too much debt, and then you hit some blip … and then a lot of the companies end up going out of business.”

But Zuckerberg’s prescription wasn’t for Meta to pump the brakes. It was to keep spending: “If we end up misspending a couple of hundred billion dollars, I think that that is going to be very unfortunate, obviously. But I’d say the risk is higher on the other side.”

Bret Taylor, the chairman of OpenAI and CEO of the AI startup Sierra, uses a mental model from the late ’90s to help navigate this AI bubble. “I think the closest analogue to this AI wave is the dot-com boom or bubble, depending on your level of pessimism,” he recently told me. Back then, he explained, everyone knew e-commerce was going to be big, but there was a massive difference between Buy.com and Amazon. Taylor and others have been trying to position themselves as today’s Amazon.

Still others are arguing that the pain will be widespread. Google CEO Sundar Pichai told the BBC this month that there’s “some irrationality” in the current boom. Asked whether Google would be immune to a bubble bursting, he warned, “I think no company is going to be immune, including us.”

What’s inflating the bubble?

Companies are raising enormous sums of money and seeing unprecedented valuations. Much of that money, in turn, is going toward the buildout of massive data centers—on which both private companies like OpenAI and Elon Musk’s xAI and public ones such as Meta and Google are spending heavily. OpenAI has pledged that it will spend $500 billion to build AI data centers, more than 15 times what was spent on the Manhattan Project.

This eye-popping spending on AI data centers isn’t entirely detached from reality. The leaders of the top AI companies all stress that they’re bottlenecked by their limited access to computing power. You hear it constantly when you talk to them. Startups can’t get the GPU allocations they need. Hyperscalers are rationing compute, saving it for their best customers.

If today’s AI market is as brutally supply-constrained as tech leaders claim, perhaps aggressive infrastructure buildouts are warranted. But some of the numbers are too large to comprehend. Sam Altman has told employees that OpenAI’s moonshot goal is to build 250 gigawatts of computing capacity by 2033, roughly equaling India’s total national electricity demand. Such a plan would cost more than $12 trillion by today’s standards.

“I do think there’s real execution risk,” OpenAI president and cofounder Greg Brockman recently told me about the company’s aggressive infrastructure goals. “Everything we say about the future, we see that it’s a possibility. It is not a certainty, but I don’t think the uncertainty comes from scientific questions. It’s a lot of hard work.”

Who is exposed, and who is to blame?

It depends on who you ask. During the August press dinner, where he made his market-moving comments, Altman was blunt about where he sees the excess. He said it’s “insane” that some AI startups with “three people and an idea” are receiving funding at such high valuations. “That’s not rational behavior,” he said. “Someone’s gonna get burned there, I think.” As Safe Superintelligence cofounder (and former OpenAI chief scientist and cofounder) Ilya Sutskever put it on a recent podcast: Silicon Valley has “more companies than ideas.”

Demis Hassabis, the CEO of Google DeepMind, offered a similar diagnosis when I spoke with him in November. “It feels like there’s obviously a bubble in the private market,” he said. “You look at seed rounds with just nothing being tens of billions of dollars. That seems a little unsustainable.”

Anthropic CEO Dario Amodei also struck at his competition during the New York Times DealBook Summit in early December. He said he feels confident about the technology itself but worries about how others are behaving on the business side: “On the economic side, I have my concerns where, even if the technology fulfills all its promises, I think there are players in the ecosystem who, if they just make a timing error, they just get it off by a little bit, bad things could happen.”

He stopped short of naming Sam Altman and OpenAI, but the implication was clear. “There are some players who are YOLOing,” he said. “Let’s say you’re a person who just kind of constitutionally wants to YOLO things or just likes big numbers. Then you may turn the dial too far.”

Amodei also flagged “circular deals,” or the increasingly common arrangements where chip suppliers like Nvidia invest in AI companies that then turn around and spend those funds on their chips. Anthropic has done some of these, he said, though “not at the same scale as some other players.” (OpenAI is at the center of a number of such deals, as are Nvidia, CoreWeave, and a roster of other players.) 

The danger, he explained, comes when the numbers get too big: “If you start stacking these where they get to huge amounts of money, and you’re saying, ’By 2027 or 2028 I need to make $200 billion a year,’ then yeah, you can overextend yourself.”

Zuckerberg shared a similar message at an internal employee Q&A session after Meta’s last earnings call. He noted that unprofitable startups like OpenAI and Anthropic risk bankruptcy if they misjudge the timing of their investments, but Meta has the advantage of strong cash flow, he reassured staff.

How could a bubble burst?

My conversations with tech executives and investors suggest that the bubble will be most likely to pop if overfunded startups can’t turn a profit or grow into their lofty valuations. This bubble could last longer than than past ones, given that private markets aren’t traded on public markets and therefore move more slowly, but the ripple effects will still be profound when the end comes. 

If companies making grand commitments to data center buildouts no longer have the revenue growth to support them, the headline deals that have propped up the stock market come into question. Anthropic’s Amodei illustrated the problem during his DealBook Summit appearance, where he said the multi-year data center commitments he has to make combine with the company’s rapid, unpredictable revenue growth rate to create a “cone of uncertainty” about how much to spend.

The two most prominent private players in AI, OpenAI and Anthropic, have yet to turn a profit. A recent Deutsche Bank chart put the situation in stark historical context. Amazon burned through $3 billion before becoming profitable. Tesla, around $4 billion. Uber, $30 billion. OpenAI is projected to burn through $140 billion by 2029, while Anthropic is expected to burn $20 billion by 2027.

Consultants at Bain estimate that the wave of AI infrastructure spending will require $2 trillion in annual AI revenue by 2030 just to justify the investment. That’s more than the combined 2024 revenue of Amazon, Apple, Alphabet, Microsoft, Meta, and Nvidia. When I talk to leaders of these large tech companies, they all agree that their sprawling businesses can absorb an expensive miscalculation about the returns from their AI infrastructure buildouts. It’s all the other companies that are either highly leveraged with debt or just unprofitable—even OpenAI and Anthropic—that they worry about. 

Still, given the level of spending on AI, it still needs a viable business model beyond subscriptions, which won’t be able to  drive profits from billions of people’s eyeballs like the ad-driven businesses that have defined the last 20 years of the internet. Even the largest tech companies know they need to ship the world-changing agents they keep hyping: AI that can fully replace coworkers and complete tasks in the real world.

For now, investors are mostly buying into the hype of the powerful AI systems that these data center buildouts will supposedly unlock in the future. At some point the biggest spenders, like OpenAI, will need to show investors that the money spent on the infrastructure buildout was worth it.

There’s also still a lot of uncertainty about the technical direction that AI is heading in. LLMs are expected to remain critical to more advanced AI systems, but industry leaders can’t seem to agree on which additional breakthroughs are needed to achieve artificial general intelligence, or AGI. Some are betting on new kinds of AI that can understand the physical world, while others are focused on training AI to learn in a general way, like a human. In other words, what if all this unprecedented spending turns out to have been backing the wrong horse?

The question now

What makes this moment surreal is the honesty. The same people pouring billions into AI will openly tell you it might all come crashing down. 

Taylor framed it as two truths existing at once. “I think it is both true that AI will transform the economy,” he told me, “and I think we’re also in a bubble, and a lot of people will lose a lot of money. I think both are absolutely true at the same time.”

He compared it to the internet. Webvan failed, but Instacart succeeded years later with essentially the same idea. If you were an Amazon shareholder from its IPO to now, you’re looking pretty good. If you were a Webvan shareholder, you probably feel differently. 

“When the dust settles and you see who the winners are, society benefits from those inventions,” Amazon founder Jeff Bezos said in October. “This is real. The benefit to society from AI is going to be gigantic.”

Goldman Sachs says the AI boom now looks the way tech stocks did in 1997, several years before the dot-com bubble actually burst. The bank flagged five warning signs seen in the late 1990s that investors should watch now: peak investment spending, falling corporate profits, rising corporate debt, Fed rate cuts, and widening credit spreads. We’re probably not at 1999 levels yet. But the imbalances are building fast. Michael Burry, who famously called the 2008 housing bubble collapse (as seen in the film The Big Short), recently compared the AI boom to the 1990s dot-com bubble too.

Maybe AI will save us from our own irrational exuberance. But for now, we’re living in an in-between moment when everyone knows what’s coming but keeps blowing more air into the balloon anyway. As Altman put it that night at dinner: “Someone is going to lose a phenomenal amount of money. We don’t know who.”

Alex Heath is the author of Sources, a newsletter about the AI race, and the cohost of ACCESS, a podcast about the tech industry’s inside conversations. Previously, he was deputy editor at The Verge.

What we still don’t know about weight-loss drugs

<div data-chronoton-summary="

  • Mixed research results Despite promising applications, recent studies delivered disappointments: GLP-1 drugs failed to slow Alzheimer’s progression in a major trial.
  • Pregnancy concerns People who stop taking GLP-1s before pregnancy may experience excessive weight gain and potentially higher risks of complications. Conflicting studies have created confusion about pre-pregnancy use, while postpartum usage is increasing without understanding potential impacts.
  • Long-term questions When people stop taking GLP-1s, most regain significant weight and see worsening heart health. Scientists still don’t know if indefinite use is necessary or safe, nor understand long-term effects on children or healthy-weight people using them for weight loss.

” data-chronoton-post-id=”1128511″ data-chronoton-expand-collapse=”1″ data-chronoton-analytics-enabled=”1″>

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

Weight-loss drugs have been back in the news this week. First, we heard that Eli Lilly, the company behind the drugs Mounjaro and Zepbound, became the first healthcare company in the world to achieve a trillion-dollar valuation.

Those two drugs, which are prescribed for diabetes and obesity respectively, are generating billions of dollars in revenue for the company. Other GLP-1 agonist drugs—a class that includes Mounjaro and Zepbound, which have the same active ingredient—have also been approved to reduce the risk of heart attack and stroke in overweight people. Many hope these apparent wonder drugs will also treat neurological disorders and potentially substance use disorders, too.

But this week we also learned that, disappointingly, GLP-1 drugs don’t seem to help people with Alzheimer’s disease. And that people who stop taking the drugs when they become pregnant can experience potentially dangerous levels of weight gain during their pregnancies. On top of that, some researchers worry that people are using the drugs postpartum to lose pregnancy weight without understanding potential risks.

All of this news should serve as a reminder that there’s a lot we still don’t know about these drugs. This week, let’s look at the enduring questions surrounding GLP-1 agonist drugs.

First a quick recap. Glucagon-like peptide-1 is a hormone made in the gut that helps regulate blood sugar levels. But we’ve learned that it also appears to have effects across the body. Receptors that GLP-1 can bind to have been found in multiple organs and throughout the brain, says Daniel Drucker, an endocrinologist at the University of Toronto who has been studying the hormone for decades.

GLP-1 agonist drugs essentially mimic the hormone’s action. Quite a few have been developed, including semaglutide, tirzepatide, liraglutide, and exenatide, which have brand names like Ozempic, Saxenda and Wegovy. Some of them are recommended for some people with diabetes.

But because these drugs also seem to suppress appetite, they have become hugely popular weight loss aids. And studies have found that many people who take them for diabetes or weight loss experience surprising side effects; that their mental health improves, for example, or that they feel less inclined to smoke or consume alcohol. Research has also found that the drugs seem to increase the growth of brain cells in lab animals.

So far, so promising. But there are a few outstanding gray areas.

Are they good for our brains?

Novo Nordisk, a competitor of Eli Lilly, manufactures GLP-1 drugs Wegovy and Saxenda. The company recently trialed an oral semaglutide in people with Alzheimer’s disease who had mild cognitive impairment or mild dementia. The placebo-controlled trial included 3808 volunteers.

Unfortunately, the company found that the drug did not appear to delay the progression of Alzheimer’s disease in the volunteers who took it.

The news came as a huge disappointment to the research community. “It was kind of crushing,” says Drucker. That’s despite the fact that, deep down, he wasn’t expecting a “clear win.” Alzheimer’s disease has proven notoriously difficult to treat, and by the time people get a diagnosis, a lot of damage has already taken place.

But he is one of many that isn’t giving up hope entirely. After all, research suggests that GLP-1 reduces inflammation in the brain and improves the health of neurons, and that it appears to improve the way brain regions communicate with each other. This all implies that GLP-1 drugs should benefit the brain, says Drucker. There’s still a chance that the drugs might help stave off Alzheimer’s in those who are still cognitively healthy.

Are they safe before, during or after pregnancy?

Other research published this week raises questions about the effects of GLP-1s taken around the time of pregnancy. At the moment, people are advised to plan to stop taking the medicines two months before they become pregnant. That’s partly because some animal studies suggest the drugs can harm the development of a fetus, but mainly because scientists haven’t studied the impact on pregnancy in humans.

Among the broader population, research suggests that many people who take GLP-1s for weight loss regain much of their lost weight once they stop taking those drugs. So perhaps it’s not surprising that a study published in JAMA earlier this week saw a similar effect in pregnant people.

The study found that people who had been taking those drugs gained around 3.3kg more than others who had not. And those who had been taking the drugs also appeared to have a slightly higher risk of gestational diabetes, blood pressure disorders and even preterm birth.

It sounds pretty worrying. But a different study published in August had the opposite finding—it noted a reduction in the risk of those outcomes among women who had taken the drugs before becoming pregnant.

If you’re wondering how to make sense of all this, you’re not the only one. No one really knows how these drugs should be used before pregnancy—or during it for that matter.

Another study out this week found that people (in Denmark) are increasingly taking GLP-1s postpartum to lose weight gained during pregnancy. Drucker tells me that, anecdotally, he gets asked about this potential use a lot.

But there’s a lot going on in a postpartum body. It’s a time of huge physical and hormonal change that can include bonding, breastfeeding and even a rewiring of the brain. We have no idea if, or how, GLP-1s might affect any of those.

Howand whencan people safely stop using them?

Yet another study out this week—you can tell GLP-1s are one of the hottest topics in medicine right now—looked at what happens when people stop taking tirzepatide (marketed as Zepbound) for their obesity.

The trial participants all took the drug for 36 weeks, at which point half continued with the drug, and half were switched to a placebo for another 52 weeks. During that first 36 weeks, the weight and heart health of the participants improved.

But by the end of the study, most of those that had switched to a placebo had regained more than 25% of the weight they had originally lost. One in four had regained more than 75% of that weight, and 9% ended up at a higher weight than when they’d started the study. Their heart health also worsened.

Does that mean that people need to take these drugs forever? Scientists don’t have the answer to that one, either. Or if taking the drugs indefinitely is safe. The answer might depend on the individual, their age or health status, or what they are using the drug for.

There are other gray areas. GLP-1s look promising for substance use disorders, but we don’t yet know how effective they might be. We don’t know the long-term effects these drugs have on children who take them. And we don’t know the long-term consequences these drugs might have for healthy-weight people who take them for weight loss.

Earlier this year, Drucker accepted a Breakthrough Prize in Life Sciences at a glitzy event in California. “All of these Hollywood celebrities were coming up to me and saying ‘thank you so much,’” he says. “A lot of these people don’t need to be on these medicines.”

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

What is the chance your plane will be hit by space debris?

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

In mid-October, a mysterious object cracked the windshield of a packed Boeing 737 cruising at 36,000 feet above Utah, forcing the pilots into an emergency landing. The internet was suddenly buzzing with the prospect that the plane had been hit by a piece of space debris. We still don’t know exactly what hit the plane—likely a remnant of a weather balloon—but it turns out the speculation online wasn’t that far-fetched.

That’s because while the risk of flights being hit by space junk is still small, it is, in fact, growing. 

About three pieces of old space equipment—used rockets and defunct satellites—fall into Earth’s atmosphere every day, according to estimates by the European Space Agency. By the mid-2030s, there may be dozens. The increase is linked to the growth in the number of satellites in orbit. Currently, around 12,900 active satellites circle the planet. In a decade, there may be 100,000 of them, according to analyst estimates.

To minimize the risk of orbital collisions, operators guide old satellites to burn up in Earth’s atmosphere. But the physics of that reentry process are not well understood, and we don’t know how much material burns up and how much reaches the ground.

“The number of such landfall events is increasing,” says Richard Ocaya, a professor of physics at the University of Free State in South Africa and a coauthor of a recent paper on space debris risk. “We expect it may be increasing exponentially in the next few years.”

So far, space debris hasn’t injured anybody—in the air or on the ground. But multiple close calls have been reported in recent years. In March last year, an 0.7-kilogram chunk of metal pierced the roof of a house in Florida. The object was later confirmed to be a remnant of a battery pallet tossed out from the International Space Station. When the strike occurred, the homeowner’s 19-year-old son was resting in a next-door room.

And in February this year, a 1.5-meter-long fragment of SpaceX’s Falcon 9 rocket crashed down near a warehouse outside Poland’s fifth-largest city, Poznan. Another piece was found in a nearby forest. A month later, a 2.5-kilogram piece of a Starlink satellite dropped on a farm in the Canadian province of Saskatchewan. Other incidents have been reported in Australia and Africa. And many more may be going completely unnoticed. 

“If you were to find a bunch of burnt electronics in a forest somewhere, your first thought is not that it came from a spaceship,” says James Beck, the director of the UK-based space engineering research firm Belstead Research. He warns that we don’t fully understand the risk of space debris strikes and that it might be much higher than satellite operators want us to believe. 

For example, SpaceX, the owner of the currently largest mega-constellation, Starlink, claims that its satellites are “designed for demise” and completely burn up when they spiral from orbit and fall through the atmosphere.

But Beck, who has performed multiple wind tunnel tests using satellite mock-ups to mimic atmospheric forces, says the results of such experiments raise doubts. Some satellite components are made of durable materials such as titanium and special alloy composites that don’t melt even at the extremely high temperatures that arise during a hypersonic atmospheric descent. 

“We have done some work for some small-satellite manufacturers and basically, their major problem is that the tanks get down,” Beck says. “For larger satellites, around 800 kilos, we would expect maybe two or three objects to land.” 

It can be challenging to quantify how much of a danger space debris poses. The International Civil Aviation Organization (ICAO) told MIT Technology Review that “the rapid growth in satellite deployments presents a novel challenge” for aviation safety, one that “cannot be quantified with the same precision as more established hazards.” 

But the Federal Aviation Administration has calculated some preliminary numbers on the risk to flights: In a 2023 analysis, the agency estimated that by 2035, the risk that one plane per year will experience a disastrous space debris strike will be around 7 in 10,000. Such a collision would either destroy the aircraft immediately or lead to a rapid loss of air pressure, threatening the lives of all on board.

The casualty risk to humans on the ground will be much higher. Aaron Boley, an associate professor in astronomy and a space debris researcher at the University of British Columbia, Canada, says that if megaconstellation satellites “don’t demise entirely,” the risk of a single human death or injury caused by a space debris strike on the ground could reach around 10% per year by 2035. That would mean a better than even chance that someone on Earth would be hit by space junk about every decade. In its report, the FAA put the chances even higher with similar assumptions, estimating that “one person on the planet would be expected to be injured or killed every two years.”

Experts are starting to think about how they might incorporate space debris into their air safety processes. The German space situational awareness company Okapi Orbits, for example, in cooperation with the German Aerospace Center and the European Organization for the Safety of Air Navigation (Eurocontrol), is exploring ways to adapt air traffic control systems so that pilots and air traffic controllers can receive timely and accurate alerts about space debris threats.

But predicting the path of space debris is challenging too. In recent years, advances in AI have helped improve predictions of space objects’ trajectories in the vacuum of space, potentially reducing the risk of orbital collisions. But so far, these algorithms can’t properly account for the effects of the gradually thickening atmosphere that space junk encounters during reentry. Radar and telescope observations can help, but the exact location of the impact becomes clear with only very short notice.

“Even with high-fidelity models, there’s so many variables at play that having a very accurate reentry location is difficult,” says Njord Eggen, a data analyst at Okapi Orbits. Space debris goes around the planet every hour and a half when in low Earth orbit, he notes, “so even if you have uncertainties on the order of 10 minutes, that’s going to have drastic consequences when it comes to the location where it could impact.”

For aviation companies, the problem is not just a potential strike, as catastrophic as that would be. To avoid accidents, authorities are likely to temporarily close the airspace in at-risk regions, which creates delays and costs money. Boley and his colleagues published a paper earlier this year estimating that busy aerospace regions such as northern Europe or the northeastern United States already have about a 26% yearly chance of experiencing at least one disruption due to the reentry of a major space debris item. By the time all planned constellations are fully deployed, aerospace closures due to space debris hazards may become nearly as common as those due to bad weather.

Because current reentry predictions are unreliable, many of these closures may end up being unnecessary.

For example, when a 21-metric-ton Chinese Long March mega-rocket was falling to Earth in 2022, predictions suggested its debris could scatter across Spain and parts of France. In the end, the rocket crashed into the Pacific Ocean. But the 30-minute closure of south European airspace delayed and diverted hundreds of flights. 

In the meantime, international regulators are urging satellite operators and launch providers to deorbit large satellites and rocket bodies in a controlled way, when possible, by carefully guiding them into remote parts of the ocean using residual fuel. 

The European Space Agency estimates that only about half the rocket bodies reentering the atmosphere do so in a controlled way. 

Moreover, around 2,300 old and no-longer-controllable rocket bodies still linger in orbit, slowly spiraling toward Earth with no mechanisms for operators to safely guide them into the ocean.

“There’s enough material up there that even if we change our practices, we will still have all those rocket bodies eventually reenter,” Boley says. “Although the probability of space debris hitting an aircraft is small, the probability that the debris will spread and fall over busy airspace is not small. That’s actually quite likely.”

How do our bodies remember?

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

“Like riding a bike” is shorthand for the remarkable way that our bodies remember how to move. Most of the time when we talk about muscle memory, we’re not talking about the muscles themselves but about the memory of a coordinated movement pattern that lives in the motor neurons, which control our muscles. 

Yet in recent years, scientists have discovered that our muscles themselves have a memory for movement and exercise.

When we move a muscle, the movement may appear to begin and end, but all these little changes are actually continuing to happen inside our muscle cells. And the more we move, as with riding a bike or other kinds of exercise, the more those cells begin to make a memory of that exercise.

When we move a muscle, the movement may appear to begin and end, but all these little changes are actually continuing to happen inside our muscle cells.

We all know from experience that a muscle gets bigger and stronger with repeated work. As the pioneering muscle scientist Adam Sharples—a professor at the Norwegian School of Sport Sciences in Oslo and a former professional rugby player in the UK—explained to me, skeletal muscle cells are unique in the human body: They’re long and skinny, like fibers, and have multiple nuclei. The fibers grow larger not by dividing but by recruiting muscle satellite cells—stem cells specific to muscle that are dormant until activated in response to stress or injury—to contribute their own nuclei and support muscle growth and regeneration. Those nuclei often stick around for a while in the muscle fibers, even after periods of inactivity, and there is evidence that they may help accelerate the return to growth once you start training again. 

Sharples’s research focuses on what’s called epigenetic muscle memory.Epigenetic” refers to changes in gene expression that are caused by behavior and environment—the genes themselves don’t change, but the way they work does. In general, exercise switches on genes that help make muscles grow more easily. When you lift weights, for example, small molecules called methyl groups detach from the outside of certain genes, making them more likely to turn on and produce proteins that affect muscle growth (also known as hypertrophy). Those changes persist; if you start lifting weights again, you’ll add muscle mass more quickly than before.

In 2018, Sharples’s muscle lab was the first to show that human skeletal muscle has an epigenetic memory of muscle growth after exercise: Muscle cells are primed to respond more rapidly to exercise in the future, even after a monthslong (and maybe even yearslong) pause. In other words: Your muscles remember how to do it.

Subsequent studies from Sharples and others have replicated similar findings in mice and older humans, offering further supporting evidence of epigenetic muscle memory across species and into later life. Even aging muscles have the capacity to remember when you work out.

At the same time, Sharples points to intriguing new evidence that muscles also remember periods of atrophy—and that young and old muscles remember this differently. While young human muscle seems to have what he calls a “positive” memory of wasting—“in that it recovers well after a first period of atrophy and doesn’t experience greater loss in a repeated atrophy period,” he explains—aged muscle in rats seems to have a more pronounced “negative” memory of atrophy, in which it appears “more susceptible to greater loss and a more exaggerated molecular response when muscle wasting is repeated.” Basically, young muscle tends to bounce back from periods of muscle loss—“ignoring” it, in a sense—while older muscle is more sensitive to it and might be more susceptible to further loss in the future. 

Illness can also lead to this kind of “negative” muscle memory; in a study of breast cancer survivors more than a decade after diagnosis and treatment, participants showed an epigenetic muscle profile of people much older than their chronological age. But get this: After five months of aerobic exercise training, participants were able to reset the epigenetic profile of their muscle back toward that of muscle seen in an age-matched control group of healthy women.  

What this shows is that “positive” muscle memories can help counteract “negative” ones. The takeaway? Your muscles have their own kind of intelligence. The more you use them, the more they can harness it to become a lasting beneficial resource for your body in the future. 

Bonnie Tsui is the author of On Muscle: The Stuff That Moves Us and Why It Matters (Algonquin Books, 2025).

Trump is pushing leucovorin as a new treatment for autism. What is it?

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

At a press conference on Monday, President Trump announced that his administration was taking action to address “the meteoric rise in autism.” He suggested that childhood vaccines and acetaminophen, the active ingredient in Tylenol, are to blame for the increasing prevalence and advised pregnant women against taking the medicine. “Don’t take Tylenol,” he said. “Fight like hell not to take it.” 

The president’s  assertions left many scientists and health officials perplexed and dismayed. The notion that childhood vaccines cause autism has been thoroughly debunked

“There have been many, many studies across many, many children that have led science to rule out vaccines as a significant causal factor in autism,” says James McPartland, a child psychologist and director of the Yale Center for Brain and Mind Health in New Haven, Connecticut.

And although some studies suggest a link between Tylenol and autism, the most rigorous have failed to find a connection. 

The administration also announced that the Food and Drug Administration would work to make a medication called leucovorin available as a treatment for children with autism. Some small studies do suggest the drug has promise, but “those are some of the most preliminary treatment studies that we have,” says Matthew Lerner, a psychologist at Drexel University’s A.J. Drexel Autism Institute in Philadelphia. “This is not one I would say that the research suggests is ready for fast-tracking.” 

The press conference “alarms us researchers who committed our entire careers to better understanding autism,” said the Coalition for Autism Researchers, a group of more than 250 scientists, in a statement.

“The data cited do not support the claim that Tylenol causes autism and leucovorin is a cure, and only stoke fear and falsely suggest hope when there is no simple answer.”

There’s a lot to unpack here. Let’s begin. 

Has there been a “meteoric rise” in autism?

Not in the way the president meant. Sure, the prevalence of autism has grown, from about 1 in 500 children in 1995 to 1 in 31 today. But that’s due, in large part, to diagnostic changes. The latest iteration of the Diagnostic and Statistical Manual of Mental Illnesses, published in 2013, grouped five previously separate diagnoses into a single diagnosis of autism spectrum disorder (ASD).

That meant that more people met the criteria for an autism diagnosis. Lerner points out that there is also far more awareness of the condition today than there was several decades ago. “There’s autism representation in the media,” he says. “There are plenty of famous people in the news and finance and in business and in Hollywood who are publicly, openly autistic.”

Is Tylenol a contributor to autism? 

Some studies have found an association between the use of acetaminophen in pregnancy and autism in children. In these studies, researchers asked women about past acetaminophen use during pregnancy and then assessed whether children of the women who took the medicine were more likely to develop autism than children of women who didn’t take it. 

These kinds of epidemiological studies are tricky to interpret because they’re prone to bias. For example, women who take acetaminophen during pregnancy may do so because they have an infection, a fever, or an autoimmune disease.

“Many of these underlying reasons could themselves be causes of autism,” says Ian Douglas, an epidemiologist at the London School of Hygiene and Tropical Medicine. It’s also possible women with a higher genetic predisposition for autism have other medical conditions that make them more likely to take acetaminophen. 

Two studies attempted to account for these potential biases by looking at siblings whose mothers had used acetaminophen during only one of the pregnancies. The largest is a 2024 study that looked at nearly 2.5 million children born between 1915 and 2019 in Sweden. The researchers initially found a slightly increased risk of autism and ADHD in children of the women who took acetaminophen, but when they conducted a sibling analysis, the association disappeared.  

Rather, scientists have long known that autism is largely genetic. Twin studies suggest that 60% to 90% of autism risk can be attributed to your genes. However, environmental factors appear to play a role too. That “doesn’t necessarily mean toxins in the environment,” Lerner says. In fact, one of the strongest environmental predictors of autism is paternal age. Autism rates seem to be higher when a child’s father is older than 40.

So should someone who is pregnant  avoid Tylenol just to be safe?

No. Acetaminophen is the only over-the-counter pain reliever that is deemed safe to take during pregnancy, and women should take it if they need it. The American College of Obstetricians and Gynecologists (ACOG) supports the use of acetaminophen in pregnancy “when taken as needed, in moderation, and after consultation with a doctor.” 

“There’s no downside in not taking it,” Trump said at the press conference. But high fevers during pregnancy can be dangerous. “The conditions people use acetaminophen to treat during pregnancy are far more dangerous than any theoretical risks and can create severe morbidity and mortality for the pregnant person and the fetus,” ACOG president Steven Fleischman said in a statement.

What about this new treatment for autism? Does it work? 

The medication is called leucovorin. It’s also known as folinic acid; like folic acid, it’s a form of folate, a B vitamin found in leafy greens and legumes. The drug has been used for years to counteract the side effects of some cancer medications and as a treatment for anemia. 

Researchers have known for decades that folate plays a key role in the fetal development of the brain and spine. Women who don’t get enough folate during pregnancy have a greater risk of having babies with neural tube defects like spina bifida. Because of this, many foods are fortified with folic acid, and the CDC recommends that women take folic acid supplements during pregnancy. “If you are pregnant and you’re taking maternal prenatal vitamins, there’s a good chance it has folate already,” Lerner says.

“The idea that a significant proportion of autistic people have autism because of folate-related difficulties is not a well established or widely accepted premise,” says McPartland.

However, in the early 2000s, researchers in Germany identified a small group of children who developed neurodevelopmental symptoms because of a folate deficiency. “These kids are born pretty normal at birth,” says Edward Quadros, a biologist at SUNY Downstate Health Sciences University in Brooklyn, New York. But after a year or two, “they start developing a neurologic presentation very similar to autism,” he says. When the researchers gave these children folinic acid, some of their symptoms improved, especially in children younger than six. 

Because the children had low levels of folate in the fluid that surrounds the spine and brain but normal folate levels in the blood, the researchers posited that the problem was the transport of folate from the blood to that fluid. Research by Quadros and other scientists suggested that the deficiency was the result of an autoimmune response. Children develop antibodies against the receptors that help transport folate, and those antibodies block folate from crossing the blood-brain barrier. High doses of folinic acid, however, activate a second transporter that allows folate in, Quadros says. 

There are also plenty of individual anecdotes suggesting that leucovorin works. But the medicine has only been tested as a treatment for autism in four small trials that used different doses and measured different outcomes. The evidence that it can improve symptoms of autism is “weak,” according to the Coalition of Autism Scientists. “A much higher standard of science would be needed to determine if leucovorin is an effective and safe treatment for autism,” the researchers said in a statement.  

How to measure the returns on R&D spending

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

Given the draconian cuts to US federal funding for science, including the administration’s proposal to reduce the 2026 budgets of the National Institutes of Health by 40% and the National Science Foundation by 57%, it’s worth asking some hard-nosed money questions: How much should we be spending on R&D? How much value do we get out of such investments, anyway? To answer that, it’s important to look at both successful returns and investments that went nowhere.

Sure, it’s easy to argue for the importance of spending on science by pointing out that many of today’s most useful technologies had their origins in government-funded R&D. The internet, CRISPR, GPS—the list goes on and on. All true. But this argument ignores all the technologies that received millions in government funding and haven’t gone anywhere—at least not yet. We still don’t have DNA computers or molecular electronics. Never mind the favorite examples cited by contrarian politicians of seemingly silly or frivolous science projects (think shrimp on treadmills).

While cherry-picking success stories help illustrate the glories of innovation and the role of science in creating technologies that have changed our lives, it provides little guidance for how much we should spend in the future—and where the money should go.

A far more useful approach to quantifying the value of R&D is to look at its return on investment (ROI). A favorite metric for stock pickers and PowerPoint-wielding venture capitalists, ROI weighs benefits versus costs. If applied broadly to the nation’s R&D funding, the same kind of thinking could help account for both the big wins and all the money spent on research that never got out of the lab.

The problem is that it’s notoriously difficult to calculate returns for science funding—the payoffs can take years to appear and often take a circuitous route, so the eventual rewards are distant from the original funding. (Who could have predicted Uber as an outcome of GPS? For that matter, who could have predicted that the invention of ultra-precise atomic clocks in the late 1940s and 1950s would eventually make GPS possible?) And forget trying to track the costs of countless failures or apparent dead ends.

But in several recent papers, economists have approached the problem in clever new ways, and though they ask slightly different questions, their conclusions share a bottom line: R&D is, in fact, one of the better long-term investments that the government can make.

This story is part of MIT Technology Review’s “America Undone” series, examining how the foundations of US success in science and innovation are currently under threat. You can read the rest here.

That might not seem very surprising. We’ve long thought that innovation and scientific advances are key to our prosperity. But the new studies provide much-needed details, supplying systematic and rigorous evidence for the impact that R&D funding, including public investment in basic science, has on overall economic growth.

And the magnitude of the benefits is surprising.

Bang for your buck

In “A Calculation of the Social Returns to Innovation,” Benjamin Jones, an economist at Northwestern University, and Lawrence Summers, a Harvard economist and former US Treasury secretary, calculate the effects of the nation’s total R&D spending on gross domestic product and our overall standard of living. They’re taking on the big picture, and it’s ambitious because there are so many variables. But they are able to come up with a convincing range of estimates for the returns, all of them impressive.

On the conservative end of their estimates, says Jones, investing $1 in R&D yields about $5 in returns—defined in this case as additional GDP per person (basically, how much richer we become). Change some of the assumptions—for example, by attempting to account for the value of better medicines and improved health care, which aren’t fully captured in GDP—and you get even larger payoffs.

While the $5 return is at the low end of their estimates, it’s still “a remarkably good investment,” Jones says. “There aren’t many where you put in $1 and get $5 back.”

That’s the return for the nation’s overall R&D funding. But what do we get for government-funded R&D in particular? Andrew Fieldhouse, an economist at Texas A&M, and Karel Mertens at the Federal Reserve Bank of Dallas looked specifically at how changes in public R&D spending affect the total factor productivity (TFP) of businesses. A favorite metric of economists, TFP is driven by new technologies and innovative business know-how—not by adding more workers or machines—and is the main driver of the nation’s prosperity over the long term.

The economists tracked changes in R&D spending at five major US science funding agencies over many decades to see how the shifts eventually affected private-sector productivity. They found that the government was getting a huge bang for its nondefense R&D buck.

The benefits begin kicking in after around five to 10 years and often have a long-lasting impact on the economy. Nondefense public R&D funding has been responsible for 20% to 25% of all private-sector productivity growth in the country since World War II, according to the economists. It’s an astonishing number, given that the government invests relatively little in nondefense R&D. For example, its spending on infrastructure, another contributor to productivity growth, has been far greater over those years.

The large impact of public R&D investments also provides insight into one of America’s most troubling economic mysteries: the slowdown in productivity growth that began in the 1970s, which has roiled the country’s politics as many people face stunted living standards and limited financial prospects. Their research, says Fieldhouse, suggests that as much as a quarter of that slowdown was caused by a decline in public R&D funding that happened roughly over the same time.

After reaching a high of 1.86% of GDP in 1964, federal R&D spending began dropping. Starting in the early 1970s, TFP growth also began to decline, from above 2% a year in the late 1960s to somewhere around 1% since the 1970s (with the exception of a rise during the late 1990s), roughly tracking the spending declines with a lag of a few years.

If in fact the productivity slowdown was at least partially caused by a drop in public R&D spending, it’s evidence that we would be far richer today if we had kept up a higher level of science investment. And it also flags the dangers of today’s proposed cuts. “Based on our research,” says Fieldhouse, “I think it’s unambiguously clear that if you actually slash the budget of the NIH by 40%, if you slash the NSF budget by 50%, there’s going to be a deceleration in US productivity growth over the next seven to 10 years that will be measurable.”

Out of whack

Though the Trump administration’s proposed 2026 budget would slash science budgets to an unusual degree, public funding of R&D has actually been in slow decline for decades. Federal funding of science is at its lowest rate in the last 70 years, accounting for only around 0.6% of GDP.

Even as public funding has dropped, business R&D investments have steadily risen. Today businesses spend far more than the government; in 2023, companies invested about $700 billion in R&D while the US government spent $172 billion, according to data from the NSF’s statistical agency. You might think, Good—let companies do research. It’s more efficient. It’s more focused. Keep the government out of it.

But there is a big problem with that argument. Publicly funded research, it turns out, tends to lead to relatively more productivity growth over time because it skews more toward fundamental science than the applied work typically done by companies.

In a new working paper called “Public R&D Spillovers and Productivity Growth,” Arnaud Dyèvre, an assistant professor at of economics at HEC Paris, documents the broad and often large impacts of so-called knowledge spillovers—the benefits that flow to others from work done by the original research group. Dyèvre found that the spillovers of public-funded R&D have three times more impact on productivity growth across businesses and industries than those from private R&D funding.

The findings are preliminary, and Dyèvre is still updating the research—much of which he did as a postdoc at MIT—but he says it does suggest that the US “is underinvesting in fundamental R&D,” which is heavily funded by the government. “I wouldn’t be able to tell you exactly which percentage of R&D in the US needs to be funded by the government or what percent needs to be funded by the private sector. We need both,” he says. But, he adds, “the empirical evidence” suggests that “we’re out of balance.”

The big question

Getting the balance of funding for fundamental science and applied research right is just one of the big questions that remain around R&D funding. In mid-July, Open Philanthropy and the Alfred P. Sloan Foundation, both nonprofit organizations, jointly announced that they planned to fund a five-year “pop-up journal” that would attempt to answer many of the questions still swirling around how to define and optimize the ROI of research funding.

“There is a lot of evidence consistent with a really high return to R&D, which suggests we should do more of it,” says Matt Clancy, a senior program officer at Open Philanthropy. “But when you ask me how much more, I don’t have a good answer. And when you ask me what types of R&D should get more funding, we don’t have a good answer.”

Pondering such questions should keep innovation economists busy for the next several years. But there is another mystifying piece of the puzzle, says Northwestern’s Jones. If the returns on R&D investments are so high—the kind that most venture capitalists or investors would gladly take—why isn’t the government spending more?

“I think it’s unambiguously clear that if you actually slash the budget of the NIH by 40%, if you slash the NSF budget by 50%, there’s going to be a deceleration in US productivity growth over the next seven to 10 years that will be measurable.”

Jones, who served as a senior economic advisor in the Obama administration, says discussions over R&D budgets in Washington are often “a war of anecdotes.” Science advocates cite the great breakthroughs that resulted from earlier government funding, while budget hawks point to seemingly ludicrous projects or spectacular failures. Both have plenty of ammunition. “People go back and forth,” says Jones, “and it doesn’t really lead to anywhere.”

The policy gridlock is rooted in in the very nature of fundamental research. Today’s science will lead to great advances. And there will be countless failures; a lot of money will be wasted on fruitless experiments. The problem, of course, is that when you’re deciding to fund new projects, it’s impossible to predict which the outcome will be, even in the case of odd, seemingly silly science. Guessing just what research will or will not lead to the next great breakthrough is a fool’s errand.

Take the cuts in the administration’s proposed fiscal 2026 budget for the NSF, a leading funder of basic science. The administration’s summary begins with the assertion that its NSF budget “is prioritizing investments that complement private-sector R&D and offer strong potential to drive economic growth and strengthen U.S. technological leadership.” So far, so good. It cites the government’s commitment to AI and quantum information science. But dig deeper and you will see the contradictions in the numbers.

Not only is NSF’s overall budget cut by 57%, but funding for physical sciences like chemistry and materials research—fields critical to advancing AI and quantum computers—has also been blown apart. Funding for the NSF’s mathematical and physical sciences program was reduced by 67%. The directorate for computer and information science and engineering fared little better; its research funding was cut by 66%.

There is a great deal of hope among many in the science community that Congress, when it passes the actual 2026 budget, will at least partially reverse these cuts. We’ll see. But even if it does, why attack R&D funding in the first place? It’s impossible to answer that without plunging into the messy depths of today’s chaotic politics. And it is equally hard to know whether the recent evidence gathered by academic economists on the strong returns to R&D investments will matter when it comes to partisan policymaking.

But at least those defending the value of public funding now have a far more productive way to make their argument, rather than simply touting past breakthroughs. Even for fiscal hawks and those pronouncing concerns about budget deficits, the recent work provides a compelling and simple conclusion: More public funding for basic science is a sound investment that makes us more prosperous.

How do AI models generate videos?

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

It’s been a big year for video generation. In the last nine months OpenAI made Sora public, Google DeepMind launched Veo 3, the video startup Runway launched Gen-4. All can produce video clips that are (almost) impossible to distinguish from actual filmed footage or CGI animation. This year also saw Netflix debut an AI visual effect in its show The Eternaut, the first time video generation has been used to make mass-market TV.

Sure, the clips you see in demo reels are cherry-picked to showcase a company’s models at the top of their game. But with the technology in the hands of more users than ever before—Sora and Veo 3 are available in the ChatGPT and Gemini apps for paying subscribers—even the most casual filmmaker can now knock out something remarkable. 

The downside is that creators are competing with AI slop, and social media feeds are filling up with faked news footage. Video generation also uses up a huge amount of energy, many times more than text or image generation. 

With AI-generated videos everywhere, let’s take a moment to talk about the tech that makes them work.

How do you generate a video?

Let’s assume you’re a casual user. There are now a range of high-end tools that allow pro video makers to insert video generation models into their workflows. But most people will use this technology in an app or via a website. You know the drill: “Hey, Gemini, make me a video of a unicorn eating spaghetti. Now make its horn take off like a rocket.” What you get back will be hit or miss, and you’ll typically need to ask the model to take another pass or 10 before you get more or less what you wanted. 

So what’s going on under the hood? Why is it hit or miss—and why does it take so much energy? The latest wave of video generation models are what’s known as latent diffusion transformers. Yes, that’s quite a mouthful. Let’s unpack each part in turn, starting with diffusion. 

What’s a diffusion model?

Imagine taking an image and adding a random spattering of pixels to it. Take that pixel-spattered image and spatter it again and then again. Do that enough times and you will have turned the initial image into a random mess of pixels, like static on an old TV set. 

A diffusion model is a neural network trained to reverse that process, turning random static into images. During training, it gets shown millions of images in various stages of pixelation. It learns how those images change each time new pixels are thrown at them and, thus, how to undo those changes. 

The upshot is that when you ask a diffusion model to generate an image, it will start off with a random mess of pixels and step by step turn that mess into an image that is more or less similar to images in its training set. 

But you don’t want any image—you want the image you specified, typically with a text prompt. And so the diffusion model is paired with a second model—such as a large language model (LLM) trained to match images with text descriptions—that guides each step of the cleanup process, pushing the diffusion model toward images that the large language model considers a good match to the prompt. 

An aside: This LLM isn’t pulling the links between text and images out of thin air. Most text-to-image and text-to-video models today are trained on large data sets that contain billions of pairings of text and images or text and video scraped from the internet (a practice many creators are very unhappy about). This means that what you get from such models is a distillation of the world as it’s represented online, distorted by prejudice (and pornography).

It’s easiest to imagine diffusion models working with images. But the technique can be used with many kinds of data, including audio and video. To generate movie clips, a diffusion model must clean up sequences of images—the consecutive frames of a video—instead of just one image. 

What’s a latent diffusion model? 

All this takes a huge amount of compute (read: energy). That’s why most diffusion models used for video generation use a technique called latent diffusion. Instead of processing raw data—the millions of pixels in each video frame—the model works in what’s known as a latent space, in which the video frames (and text prompt) are compressed into a mathematical code that captures just the essential features of the data and throws out the rest. 

A similar thing happens whenever you stream a video over the internet: A video is sent from a server to your screen in a compressed format to make it get to you faster, and when it arrives, your computer or TV will convert it back into a watchable video. 

And so the final step is to decompress what the latent diffusion process has come up with. Once the compressed frames of random static have been turned into the compressed frames of a video that the LLM guide considers a good match for the user’s prompt, the compressed video gets converted into something you can watch.  

With latent diffusion, the diffusion process works more or less the way it would for an image. The difference is that the pixelated video frames are now mathematical encodings of those frames rather than the frames themselves. This makes latent diffusion far more efficient than a typical diffusion model. (Even so, video generation still uses more energy than image or text generation. There’s just an eye-popping amount of computation involved.) 

What’s a latent diffusion transformer?

Still with me? There’s one more piece to the puzzle—and that’s how to make sure the diffusion process produces a sequence of frames that are consistent, maintaining objects and lighting and so on from one frame to the next. OpenAI did this with Sora by combining its diffusion model with another kind of model called a transformer. This has now become standard in generative video. 

Transformers are great at processing long sequences of data, like words. That has made them the special sauce inside large language models such as OpenAI’s GPT-5 and Google DeepMind’s Gemini, which can generate long sequences of words that make sense, maintaining consistency across many dozens of sentences. 

But videos are not made of words. Instead, videos get cut into chunks that can be treated as if they were. The approach that OpenAI came up with was to dice videos up across both space and time. “It’s like if you were to have a stack of all the video frames and you cut little cubes from it,” says Tim Brooks, a lead researcher on Sora.

A selection of videos generated with Veo 3 and Midjourney. The clips have been enhanced in postproduction with Topaz, an AI video-editing tool. Credit: VaigueMan

Using transformers alongside diffusion models brings several advantages. Because they are designed to process sequences of data, transformers also help the diffusion model maintain consistency across frames as it generates them. This makes it possible to produce videos in which objects don’t pop in and out of existence, for example. 

And because the videos are diced up, their size and orientation do not matter. This means that the latest wave of video generation models can be trained on a wide range of example videos, from short vertical clips shot with a phone to wide-screen cinematic films. The greater variety of training data has made video generation far better than it was just two years ago. It also means that video generation models can now be asked to produce videos in a variety of formats. 

What about the audio? 

A big advance with Veo 3 is that it generates video with audio, from lip-synched dialogue to sound effects to background noise. That’s a first for video generation models. As Google DeepMind CEO Demis Hassabis put it at this year’s Google I/O: “We’re emerging from the silent era of video generation.” 

The challenge was to find a way to line up video and audio data so that the diffusion process would work on both at the same time. Google DeepMind’s breakthrough was a new way to compress audio and video into a single piece of data inside the diffusion model. When Veo 3 generates a video, its diffusion model produces audio and video together in a lockstep process, ensuring that the sound and images are synched.  

You said that diffusion models can generate different kinds of data. Is this how LLMs work too? 

No—or at least not yet. Diffusion models are most often used to generate images, video, and audio. Large language models—which generate text (including computer code)—are built using transformers. But the lines are blurring. We’ve seen how transformers are now being combined with diffusion models to generate videos. And this summer Google DeepMind revealed that it was building an experimental large language model that used a diffusion model instead of a transformer to generate text. 

Here’s where things start to get confusing: Though video generation (which uses diffusion models) consumes a lot of energy, diffusion models themselves are in fact more efficient than transformers. Thus, by using a diffusion model instead of a transformer to generate text, Google DeepMind’s new LLM could be a lot more efficient than existing LLMs. Expect to see more from diffusion models in the near future!

What is Signal? The messaging app, explained.

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

With the recent news that the Atlantic’s editor in chief was accidentally added to a group Signal chat for American leaders planning a bombing in Yemen, many people are wondering: What is Signal? Is it secure? If government officials aren’t supposed to use it for military planning, does that mean I shouldn’t use it either?

The answer is: Yes, you should use Signal, but government officials having top-secret conversations shouldn’t use Signal.

Read on to find out why.

What is Signal?

Signal is an app you can install on your iPhone or Android phone, or on your computer. It lets you send secure texts, images, and phone or video chats with other people or groups of people, just like iMessage, Google Messages, WhatsApp, and other chat apps.

Installing Signal is a two-minute process—again, it’s designed to work just like other popular texting apps.

Why is it a problem for government officials to use Signal?

Signal is very secure—as we’ll see below, it’s the best option out there for having private conversations with your friends on your cell phone.

But you shouldn’t use it if you have a legal obligation to preserve your messages, such as while doing government business, because Signal prioritizes privacy over ability to preserve data. It’s designed to securely delete data when you’re done with it, not to keep it. This makes it uniquely unsuited for following public record laws.

You also shouldn’t use it if your phone might be a target of sophisticated hackers, because Signal can only do its job if the phone it is running on is secure. If your phone has been hacked, then the hacker can read your messages regardless of what software you are running.

This is why you shouldn’t use Signal to discuss classified material or military plans. For military communication your civilian phone is always considered hacked by adversaries, so you should instead use communication equipment that is safer—equipment that is physically guarded and designed to do only one job, making it harder to hack.

What about everyone else?

Signal is designed from bottom to top as a very private space for conversation. Cryptographers are very sure that as long as your phone is otherwise secure, no one can read your messages.

Why should you want that? Because private spaces for conversation are very important. In the US, the First Amendment recognizes, in the right to freedom of assembly, that we all need private conversations among our own selected groups in order to function.

And you don’t need the First Amendment to tell you that. You know, just like everyone else, that you can have important conversations in your living room, bedroom, church coffee hour, or meeting hall that you could never have on a public stage. Signal gives us the digital equivalent of that—it’s a space where we can talk, among groups of our choice, about the private things that matter to us, free of corporate or government surveillance. Our mental health and social functioning require that.

So if you’re not legally required to record your conversations, and not planning secret military operations, go ahead and use Signal—you deserve the privacy.

How do we know Signal is secure?

People often give up on finding digital privacy and end up censoring themselves out of caution. So are there really private ways to talk on our phones, or should we just assume that everything is being read anyway?

The good news is: For most of us who aren’t individually targeted by hackers, we really can still have private conversations.

Signal is designed to ensure that if you know your phone and the phones of other people in your group haven’t been hacked (more on that later), you don’t have to trust anything else. It uses many techniques from the cryptography community to make that possible.

Most important and well-known is “end-to-end encryption,” which means that messages can be read only on the devices involved in the conversation and not by servers passing the messages back and forth.

But Signal uses other techniques to keep your messages private and safe as well. For example, it goes to great lengths to make it hard for the Signal server itself to know who else you are talking to (a feature known as “sealed sender”), or for an attacker who records traffic between phones to later decrypt the traffic by seizing one of the phones (“perfect forward secrecy”).

These are only a few of many security properties built into the protocol, which is well enough designed and vetted for other messaging apps, such as WhatsApp and Google Messages, to use the same one.

Signal is also designed so we don’t have to trust the people who make it. The source code for the app is available online and, because of its popularity as a security tool, is frequently audited by experts.

And even though its security does not rely on our trust in the publisher, it does come from a respected source: the Signal Technology Foundation, a nonprofit whose mission is to “protect free expression and enable secure global communication through open-source privacy technology.” The app itself, and the foundation, grew out of a community of prominent privacy advocates. The foundation was started by Moxie Marlinspike, a cryptographer and longtime advocate of secure private communication, and Brian Acton, a cofounder of WhatsApp.

Why do people use Signal over other text apps? Are other ones secure?

Many apps offer end-to-end encryption, and it’s not a bad idea to use them for a measure of privacy. But Signal is a gold standard for private communication because it is secure by default: Unless you add someone you didn’t mean to, it’s very hard for a chat to accidentally become less secure than you intended.

That’s not necessarily the case for other apps. For example, iMessage conversations are sometimes end-to-end encrypted, but only if your chat has “blue bubbles,” and they aren’t encrypted in iCloud backups by default. Google Messages are sometimes end-to-end encrypted, but only if the chat shows a lock icon. WhatsApp is end-to-end encrypted but logs your activity, including “how you interact with others using our Services.”

Signal is careful not to record who you are talking with, to offer ways to reliably delete messages, and to keep messages secure even in online phone backups. This focus demonstrates the benefits of an app coming from a nonprofit focused on privacy rather than a company that sees security as a “nice to have” feature alongside other goals.

(Conversely, and as a warning, using Signal makes it rather easier to accidentally lose messages! Again, it is not a good choice if you are legally required to record your communication.)

Applications like WhatsApp, iMessage, and Google Messages do offer end-to-end encryption and can offer much better security than nothing. The worst option of all is regular SMS text messages (“green bubbles” on iOS)—those are sent unencrypted and are likely collected by mass government surveillance.

Wait, how do I know that my phone is secure?

Signal is an excellent choice for privacy if you know that the phones of everyone you’re talking with are secure. But how do you know that? It’s easy to give up on a feeling of privacy if you never feel good about trusting your phone anyway.

One good place to start for most of us is simply to make sure your phone is up to date. Governments often do have ways of hacking phones, but hacking up-to-date phones is expensive and risky and reserved for high-value targets. For most people, simply having your software up to date will remove you from a category that hackers target.

If you’re a potential target of sophisticated hacking, then don’t stop there. You’ll need extra security measures, and guides from the Freedom of the Press Foundation and the Electronic Frontier Foundation are a good place to start.

But you don’t have to be a high-value target to value privacy. The rest of us can do our part to re-create that private living room, bedroom, church, or meeting hall simply by using an up-to-date phone with an app that respects our privacy.

Jack Cushman is a fellow of the Berkman Klein Center for Internet and Society and directs the Library Innovation Lab at Harvard Law School Library. He is an appellate lawyer, computer programmer, and former board member of the ACLU of Massachusetts.

How the Rubin Observatory will help us understand dark matter and dark energy

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

We can put a good figure on how much we know about the universe: 5%. That’s how much of what’s floating about in the cosmos is ordinary matter—planets and stars and galaxies and the dust and gas between them. The other 95% is dark matter and dark energy, two mysterious entities aptly named for our inability to shed light on their true nature. 

Cosmologists have cast dark matter as the hidden glue binding galaxies together. Dark energy plays an opposite role, ripping the fabric of space apart. Neither emits, absorbs, or reflects light, rendering them effectively invisible. So rather than directly observing either of them, astronomers must carefully trace the imprint they leave behind. 

Previous work has begun pulling apart these dueling forces, but dark matter and dark energy remain shrouded in a blanket of questions—critically, what exactly are they?

Enter the Vera C. Rubin Observatory, one of our 10 breakthrough technologies for 2025. Boasting the largest digital camera ever created, Rubin is expected to study the cosmos in the highest resolution yet once it begins observations later this year. And with a better window on the cosmic battle between dark matter and dark energy, Rubin might narrow down existing theories on what they are made of. Here’s a look at how.

Untangling dark matter’s web

In the 1930s, the Swiss astronomer Fritz Zwicky proposed the existence of an unseen force named dunkle Materie—in English, dark matter—after studying a group of galaxies called the Coma Cluster. Zwicky found that the galaxies were traveling too quickly to be contained by their joint gravity and decided there must be a missing, unobservable mass holding the cluster together.

Zwicky’s theory was initially met with much skepticism. But in the 1970s an American astronomer, Vera Rubin, obtained evidence that significantly strengthened the idea. Rubin studied the rotation rates of 60 individual galaxies and found that if a galaxy had only the mass we’re able to observe, that wouldn’t be enough to contain its structure; its spinning motion would send it ripping apart and sailing into space. 

Rubin’s results helped sell the idea of dark matter to the scientific community, since an unseen force seemed to be the only explanation for these spiraling galaxies’ breakneck spin speeds. “It wasn’t necessarily a smoking-gun discovery,” says Marc Kamionkowski, a theoretical physicist at Johns Hopkins University. “But she saw a need for dark matter. And other people began seeing it too.”

Evidence for dark matter only grew stronger in the ensuing decades. But sorting out what might be behind its effects proved tricky. Various subatomic particles were proposed. Some scientists posited that the phenomena supposedly generated by dark matter could also be explained by modifications to our theory of gravity. But so far the hunt, which has employed telescopes, particle colliders, and underground detectors, has failed to identify the culprit. 

The Rubin observatory’s main tool for investigating dark matter will be gravitational lensing, an observational technique that’s been used since the late ’70s. As light from distant galaxies travels to Earth, intervening dark matter distorts its image—like a cosmic magnifying glass. By measuring how the light is bent, astronomers can reverse-engineer a map of dark matter’s distribution. 

Other observatories, like the Hubble Space Telescope and the James Webb Space Telescope, have already begun stitching together this map from their images of galaxies. But Rubin plans to do so with exceptional precision and scale, analyzing the shapes of billions of galaxies rather than the hundreds of millions that current telescopes observe, according to Andrés Alejandro Plazas Malagón, Rubin operations scientist at SLAC National Laboratory. “We’re going to have the widest galaxy survey so far,” Plazas Malagón says.

Capturing the cosmos in such high definition requires Rubin’s 3.2-billion-pixel Large Synoptic Survey Telescope (LSST). The LSST boasts the largest focal plane ever built for astronomy, granting it access to large patches of the sky. 

The telescope is also designed to reorient its gaze every 34 seconds, meaning astronomers will be able to scan the entire sky every three nights. The LSST will revisit each galaxy about 800 times throughout its tenure, says Steven Ritz, a Rubin project scientist at the University of California, Santa Cruz. The repeat exposures will let Rubin team members more precisely measure how the galaxies are distorted, refining their map of dark matter’s web. “We’re going to see these galaxies deeply and frequently,” Ritz says. “That’s the power of Rubin: the sheer grasp of being able to see the universe in detail and on repeat.”

The ultimate goal is to overlay this map on different models of dark matter and examine the results. The leading idea, the cold dark matter model, suggests that dark matter moves slowly compared to the speed of light and interacts with ordinary matter only through gravity. Other models suggest different behavior. Each comes with its own picture of how dark matter should clump in halos surrounding galaxies. By plotting its chart of dark matter against what those models predict, Rubin might exclude some theories and favor others. 

A cosmic tug of war

If dark matter lies on one side of a magnet, pulling matter together, then you’ll flip it over to find dark energy, pushing it apart. “You can think of it as a cosmic tug of war,” Plazas Malagón says.

Dark energy was discovered in the late 1990s, when astronomers found that the universe was not only expanding, but doing so at an accelerating rate, with galaxies moving away from one another at higher and higher speeds. 

“The expectation was that the relative velocity between any two galaxies should have been decreasing,” Kamionkowski says. “This cosmological expansion requires something that acts like antigravity.” Astronomers quickly decided there must be another unseen factor inflating the fabric of space and pegged it as dark matter’s cosmic foil. 

So far, dark energy has been observed primarily through Type Ia supernovas, a special breed of explosion that occurs when a white dwarf star accumulates too much mass. Because these supernovas all tend to have the same peak in luminosity, astronomers can gauge how far away they are by measuring how bright they appear from Earth. Paired with a measure of how fast they are moving, this data clues astronomers in on the universe’s expansion rate. 

Rubin will continue studying dark energy with high-resolution glimpses of Type Ia supernovas. But it also plans to retell dark energy’s cosmic history through gravitational lensing. Because light doesn’t travel instantaneously, when we peer into distant galaxies, we’re really looking at relics from millions to billions of years ago—however long it takes for their light to make the lengthy trek to Earth. Astronomers can effectively use Rubin as a makeshift time machine to see how dark energy has carved out the shape of the universe. 

“These are the types of questions that we want to ask: Is dark energy a constant? If not, is it evolving with time? How is it changing the distribution of dark matter in the universe?” Plazas Malagón says.

If dark energy was weaker in the past, astronomers expect to see galaxies grouped even more densely into galaxy clusters. “It’s like urban sprawl—these huge conglomerates of matter,” Ritz says. Meanwhile, if dark energy was stronger, it would have pushed galaxies away from one another, creating a more “rural” landscape. 

Researchers will be able to use Rubin’s maps of dark matter and the 3D distribution of galaxies to plot out how the structure of the universe changed over time, unveiling the role of dark energy and, they hope, helping scientists evaluate the different theories to account for its behavior. 

Of course, Rubin has a lengthier list of goals to check off. Some top items entail tracing the structure of the Milky Way, cataloguing cosmic explosions, and observing asteroids and comets. But since the observatory was first conceptualized in the early ’90s, its core goal has been to explore this hidden branch of the universe. After all, before a 2019 act of Congress dedicated the observatory to Vera Rubin, it was simply called the Dark Matter Telescope. 

Rubin isn’t alone in the hunt, though. In 2023, the European Space Agency launched the Euclid telescope into space to study how dark matter and dark energy have shaped the structure of the cosmos. And NASA’s Nancy Grace Roman Space Telescope, which is scheduled to launch in 2027, has similar plans to measure the universe’s expansion rate and chart large-scale distributions of dark matter. Both also aim to tackle that looming question: What makes up this invisible empire?

Rubin will test its systems throughout most of 2025 and plans to begin the LSST survey late this year or in early 2026. Twelve to 14 months later, the team expects to reveal its first data set. Then we might finally begin to know exactly how Rubin will light up the dark universe. 

What China’s critical mineral ban means for the US

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

This week, China banned exports of several critical minerals to the US, marking the latest move in an escalating series of tit-for-tat trade restrictions between the world’s two largest economies.

In explicitly cutting off, rather than merely restricting, materials of strategic importance to the semiconductor, defense, and electric vehicle sectors, China has clearly crossed a new line in the long-simmering trade war. 

At the same time, it selected minerals that won’t cripple any industries—which leaves China plenty of ammunition to inflict greater economic pain in response to any further trade restrictions that the incoming Trump administration may impose. 

The president-elect recently pledged to impose an additional 10% tariff on all Chinese goods, and he floated tariff rates as high as 60% to 100% during his campaign. But China, which dominates the supply chains for numerous critical minerals essential to high-tech sectors, seems to be telegraphing that it’s prepared to hit back hard.

“It’s a sign of what China is capable of,” says Gracelin Baskaran, director of the Critical Minerals Security Program at the Center for Strategic and International Studies, a bipartisan research nonprofit in Washington, DC. “Shots have been fired.”

What drove the decision?

China’s announcement directly followed the Biden administration’s decision to further restrict exports of chips and other technologies that could help China develop advanced semiconductors used in cutting-edge weapon systems, artificial intelligence, and other applications.

Throughout his presidency, Biden has enacted a series of increasingly aggressive export controls aimed at curbing China’s military strength, technological development, and growing economic power. But the latest clampdown crossed a “clear line in the sand for China,” by threatening its ability to protect national security or shift toward production of more advanced technologies, says Cory Combs, associate director at Trivium China, a research firm.

“It is very much indicative of where Beijing feels its interests lie,” he says.

What exactly did China ban?

In response to the US’s new chip export restrictions, China immediately banned exports of gallium, germanium, antimony, and so called “superhard materials” used heavily in manufacturing, arguing that they have both military and civilian applications, according to the New York Times. China had already placed limits on the sale of most of these goods to the US.

The nation said it may also further restrict sales of graphite, which makes up most of the material in the lithium-ion battery anodes used in electric vehicles, grid storage plants, and consumer electronics. 

What will the bans do?

Experts say, for the most part, the bans won’t have major economic impacts. This is in part because China already restricted exports of these minerals months ago, and also because they are mostly used for niche categories within the semiconductor industry. US imports of these materials from China have already fallen as US companies figured out new sources or substitutes for the materials. 

But a recent US Geological Survey study found that outright bans on gallium and germanium by China could cut US gross domestic product by $3.4 billion. In addition, these are materials that US politicians will certainly take note of, because they “touch on many forms of security: economic, energy, and defense,” Baskaran says. 

Antimony, for example, is used in “armor-piercing ammunition, night-vision goggles, infrared sensors, bullets, and precision optics,” Baskaran and a colleague noted in a recent essay.

Companies rely on gallium to produce a variety of military and electronics components, including satellite systems, power converters, LEDs, and the high-powered chips used in electric vehicles. Germanium is used in fiber optics, infrared optics, and solar cells

Before it restricted the flow of these materials, China accounted for more than half of US imports of gallium and germanium, according to the US Geological Survey. Together, China and Russia control 50% of the worldwide reserves of antimony.

How does it affect climate tech?

Any tightened restrictions on graphite could have a pronounced economic impact on US battery and EV makers, in part because there are so few other sources for it. China controls about 80% of graphite output from mines and processes around 70% of the material, according to the International Energy Agency

“It would be very significant for batteries,” says Seaver Wang, co-director of the climate and energy team at the Breakthrough Institute, where his research is focused on minerals and manufacturing supply chains. “By weight, you need way more graphite per terawatt hour than nickel, cobalt, or lithium. And the US has essentially no operating production.”

Anything that pushes up the costs of EVs threatens to slow the shift away from gas-guzzlers in the US, as their lofty price tags remain one of the biggest hurdles for many consumers.

How does this impact China’s economy? 

There are real economic risks in China’s decision to cut off the sale of materials it dominates, as it creates incentives for US companies to seek out new sources around the world, switch to substitute materials, and work to develop more domestic supplies where geology allows.

“The challenge China faces is that most of its techniques to increase pain by disrupting supply chains would also impact China, which itself is connected to these supply chains,” says Chris Miller, a professor at Tufts University and author of Chip War: The Fight for the World’s Most Critical Technology.

Notably, the latest announcement could compel US companies to develop their own sources of gallium and germanium, which can be extracted as by-products of zinc and aluminum mining. There are a number of zinc mines in Alaska and Tennessee, and limited extraction of bauxite, which produces aluminum, in Arkansas, Alabama, and Georgia.

Gallium can also be recycled from numerous electronics, providing another potential domestic path for US companies, Combs notes.

The US has already taken steps to counter China’s dominance over the raw ingredients of essential industries, including by issuing a $150 million loan to an Australian company, Syrah Resources, to accelerate the development of graphite mining in Mozambique.

In addition, the mining company Perpetua Resources has proposed reopening a gold mine near Yellow Pine, Idaho, in part to extract antimony trisulfide for use in military applications. The US Department of Defense has provided tens of millions of dollars to help the company conduct environmental studies, though it will still take years for the mine to come online, noted Baskaran and her colleague. 

Wang says that China’s ban might prove “shortsighted,” as any success in diversifying these global supply chains will weaken the nation’s grip in the areas it now dominates. 

What happens next?

The US is also likely to pay very high economic costs in an escalating trade war with China. 

Should the nation decide to enact even stricter trade restrictions, Combs says China could opt to inflict greater economic pain on the US through a variety of means. These could include further restricting or fully banning graphite, as well other crucial battery materials like lithium; cutting off supplies of tungsten, which is used heavily in the aerospace, military, and nuclear power sectors; and halting the sale of copper, which is used in power transmission lines, solar panels, wind turbines, EVs, and many other products. 

China may also decide to take further steps to prevent US firms from selling their goods into the massive market of Chinese consumers and industries, Miller adds. Or it might respond to stricter export restrictions by turning to the US’s economic rivals for advanced technologies.

In the end, it’s not clear either nation wins in a protracted and increasingly combative trade war. But it’s also not apparent that mutually assured economic damage will prove to be an effective deterrent. Indeed, China may well feel the need to impose stricter measures in the coming months or years, as there are few signs that President-elect Trump intends to tone down his hawkish stance toward China.

“It’s hard to see a Trump 2.0 de-escalating with China,” Baskaran says. “We’re on a one-way trajectory toward continued escalation; the question is the pace and the form. It’s not really an ‘if” question.”