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.

China figured out how to sell EVs. Now it has to bury their batteries.

In the past decade, China has seen an EV boom, thanks in part to government support. Buying an electric car has gone from a novel decision to a routine one; by late 2025, nearly 60% of new cars sold were electric or plug-in hybrids.

But as the batteries in China’s first wave of EVs reach the end of their useful life, early owners are starting to retire their cars, and the country is now under pressure to figure out what to do with those aging components.

The issue is putting strain on China’s still-developing battery recycling industry and has given rise to a gray market that often cuts corners on safety and environmental standards. National regulators and commercial players are also stepping in, but so far these efforts have struggled to keep pace with the flood of batteries coming off the road. Read the full story.

—Caiwei Chen

The AI doomers feel undeterred

It’s a weird time to be an AI doomer.This small but influential community believes, in the simplest terms, that AI could get so good it could be bad—very, very bad—for humanity.

The doomer crowd has had some notable success over the past several years: including helping shape AI policy coming from the Biden administration. But a number of developments over the past six months have put them on the back foot. Talk of an AI bubble has overwhelmed the discourse as tech companies continue to invest in multiple Manhattan Projects’ worth of data centers without any certainty that future demand will match what they’re building.

So where does this leave the doomers? We decided to ask some of the movement’s biggest names to see if the recent setbacks and general vibe shift had altered their views. See what they had to say in our story.

—Garrison Lovely

This story is part of our new Hype Correction package, a collection of stories designed to help you reset your expectations about what AI makes possible—and what it doesn’t. Check out the rest of the package.

Take our quiz on the year in health and biotechnology

In just a couple of weeks, we’ll be bidding farewell to 2025. And what a year it has been! Artificial intelligence is being incorporated into more aspects of our lives, weight-loss drugs have expanded in scope, and there have been some real “omg” biotech stories from the fields of gene therapy, IVF, neurotech, and more.

Jessica Hamzelou, our senior biotech reporter, is inviting you to put your own memory to the test. So how closely have you been paying attention this year?

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.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 TikTok has signed a deal to sell its US unit 
Its new owner will be a joint venture controlled by American investors including Oracle. (Axios)
+ But the platform is adamant that its Chinese owner will retain its core US business. (FT $)
+ The deal is slated to close on January 22 next year. (Bloomberg $)
+ It means TikTok will sidestep a US ban—at least for now. (The Guardian)

2 A tip on Reddit helped to end the hunt for the Brown University shooter
The suspect, who has been found dead, is also suspected of killing an MIT professor. (NYT $)
+ The shooter’s motivation is still unclear, police say. (WP $)

3 Tech leaders are among those captured in newly-released Epstein photos
Bill Gates and Google’s Sergey Brin are both in the pictures. (FT $)
+ They’ve been pulled from a tranche of more than 95,000. (Wired $)

4 A Starlink satellite appears to have exploded
And it’s now falling back to earth. (The Verge)
+ On the ground in Ukraine’s largest Starlink repair shop. (MIT Technology Review)

5 YouTube has shut down two major channels that share fake movie trailers
Screen Culture and KH Studio uploaded AI-generated mock trailers with over a billion views. (Deadline)
+ Google is treading a thin line between embracing and shunning generative AI. (Ars Technica)

6 Trump is cracking down on investment in Chinese tech firms
Lawmakers are increasingly worried that US money is bolstering the country’s surveillance state. (WSJ $)
+ Meanwhile, China is working on boosting its chip output. (FT $)

7 ICE has paid an AI agent company to track down targets
It claims to be able to rapidly trace a target’s online network. (404 Media)

8 America wants to return to the Moon by 2028
And to build some nuclear reactors while it’s up there. (Ars Technica)
+ Southeast Asia seeks its place in space. (MIT Technology Review)

9 Actors in the UK are refusing to be scanned for AI
They’re reportedly routinely pressured to consent to creating digital likenesses of themselves. (The Guardian)
+ How Meta and AI companies recruited striking actors to train AI. (MIT Technology Review)

10 Indian tutors are explaining how to use AI over WhatsApp
Lessons are cheap and personalized—but the teachers aren’t always credible. (Rest of World)
+ How Indian health-care workers use WhatsApp to save pregnant women. (MIT Technology Review)

Quote of the day

“Trump wants to hand over even more control of what you watch to his billionaire buddies. Americans deserve to know if the president struck another backdoor deal for this billionaire takeover of TikTok.”

—Democratic senator Elizabeth Warren queries the terms of the deal that TikTok has made to allow it to continue operating in the US in a post on Bluesky.

One more thing

Synthesia’s AI clones are more expressive than ever. Soon they’ll be able to talk back.

—Rhiannon Williams

Earlier this summer, I visited the AI company Synthesia to create a hyperrealistic AI-generated avatar of me. The company’s avatars are a decent barometer of just how dizzying progress has been in AI over the past few years, so I was curious just how accurately its latest AI model, introduced last month, could replicate me.

I found my avatar as unnerving as it is technically impressive. It’s slick enough to pass as a high-definition recording of a chirpy corporate speech, and if you didn’t know me, you’d probably think that’s exactly what it was.

My avatar shows how it’s becoming ever-harder to distinguish the artificial from the real. And before long, these avatars will even be able to talk back to us. But how much better can they get? And what might interacting with AI clones do to us? Read the full story.

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 or skeet ’em at me.)

+ You can keep your beef tallow—here are the food trends that need to remain firmly in 2025.
+ The Library of Congress has some lovely images of winter that are completely free to use.
+ If you’ve got a last minute Christmas work party tonight, don’t make these Secret Santa mistakes.
+ Did you realize Billie Eilish’s smash hit Birds of a Feather has the same chord progression as Wham’s Last Christmas? They sound surprisingly good mashed together.

In August 2025, Wang Lei decided it was finally time to say goodbye to his electric vehicle.

Wang, who is 39, had bought the car in 2016, when EVs still felt experimental in Beijing. It was a compact Chinese brand. The subsidies were good, and the salesman talked about “supporting domestic innovation.” At the time, only a few people around him were driving on batteries. He liked being early.

But now, the car’s range had started to shrink as the battery’s health declined. He could have replaced the battery, but the warranty had expired; the cost and trouble no longer felt worth it. He also wanted an upgrade, so selling became the obvious choice.

His vague plans turned into action after he started seeing ads on Douyin from local battery recyclers. He asked around at a few recycling places, and the highest offer came from a smaller shop on the outskirts of town. He added the contact on WeChat, and the next day someone drove over to pick up his car. He got paid 8,000 yuan. With the additional automobile scrappage subsidy offered by the Chinese government, Wang ultimately pocketed about 28,000 yuan.

Wang is part of a much larger trend. In the past decade, China has seen an EV boom, thanks in part to government support. Buying an electric car has gone from a novel decision to a routine one; by late 2025, nearly 60% of new cars sold were electric or plug-in hybrids.

But as the batteries in China’s first wave of EVs reach the end of their useful life, early owners are starting to retire their cars, and the country is now under pressure to figure out what to do with those aging components.

The issue is putting strain on China’s still-developing battery recycling industry and has given rise to a gray market that often cuts corners on safety and environmental standards. National regulators and commercial players are also stepping in, building out formal recycling networks and take-back programs, but so far these efforts have struggled to keep pace with the flood of batteries coming off the road.

Like the batteries in our phones and laptops, those in EVs today are mostly lithium-ion packs. Their capacity drops a little every year, making the car slower to charge, shorter in range, and more prone to safety issues. Three professionals who work in EV retail and battery recycling told MIT Technology Review that a battery is often considered to be ready to retire from a car after its capacity has degraded to under 80%. The research institution EVtank estimates that the year’s total volume of retired EV batteries in China will come in at 820,000 tons, with annual totals climbing toward 1 million tons by 2030. 

In China, this growing pile of aging batteries is starting to test a recycling ecosystem that is still far from fully built out but is rapidly growing. By the end of November 2025, China had close to 180,000 enterprises involved in battery recycling, and more than 30,000 of them had been registered since January 2025. Over 60% of the firms were founded within the past three years. This does not even include the unregulated gray market of small workshops.

Typically, one of two things happens when an EV’s battery is retired. One is called cascade utilization, in which usable battery packs are tested and repurposed for slower applications like energy storage or low-speed vehicles. The other is full recycling: Cells are dismantled and processed to recover metals such as lithium, nickel, cobalt, and manganese, which are then reused to manufacture new batteries. Both these processes, if done properly, take significant upfront investment that is often not available to small players. 

But smaller, illicit battery recycling centers can offer higher prices to consumers because they ignore costs that formal recyclers can’t avoid, like environmental protection, fire safety, wastewater treatment, compliance, and taxes, according to the three battery recycling professionals MIT Technology Review spoke to.

“They [workers] crack them open, rearrange the cells into new packs, and repackage them to sell,” says Gary Lin, a battery recycling worker who worked in several unlicensed shops from 2022 to 2024. Sometimes, the refurbished batteries are even sold as “new” to buyers, he says. When the batteries are too old or damaged, workers simply crush them and sell them by weight to rare-metal extractors. “It’s all done in a very brute-force way. The wastewater used to soak the batteries is often just dumped straight into the sewer,” he says. 

This poorly managed battery waste can release toxic substances, contaminate water and soil, and create risks of fire and explosion. That is why the Chinese government has been trying to steer batteries into certified facilities. Since 2018, China’s Ministry of Industry and Information Technology has issued five “white lists” of approved power-battery recyclers, now totaling 156 companies. Despite this, formal recycling rates remain low compared with the rapidly growing volume of waste batteries.

China is not only the world’s largest EV market; it has also become the main global manufacturing hub for EVs and the batteries that power them. In 2024, the country accounted for more than 70% of global electric-car production and more than half of global EV sales, and firms like CATL and BYD together control close to half of global EV battery output, according to a report by the International Energy Agency. These companies are stepping in to offer solutions to customers wishing to offload their old batteries. Through their dealers and 4S stores, many carmakers now offer take-back schemes or opportunities to trade in old batteries for discount when owners scrap a vehicle or buy a new one. 

BYD runs its own recycling operations that process thousands of end-of-life packs a year and has launched dedicated programs with specialist recyclers to recover materials from its batteries. Geely has built a “circular manufacturing” system that combines disassembly of scrapped vehicles, cascade use of power batteries, and high recovery rates for metals and other materials.

CATL, China’s biggest EV maker, has created one of the industry’s most developed recycling systems through its subsidiary Brunp, with more than 240 collection depots, an annual disposal capacity of about 270,000 tons of waste batteries, and metal recovery rates above 99% for nickel, cobalt, and manganese. 

“No one is better equipped to handle these batteries than the companies that make them,” says Alex Li, a battery engineer based in Shanghai. That’s because they already understand the chemistry, the supply chain, and the uses the recovered materials can be put to next. Carmakers and battery makers “need to create a closed loop eventually,” he says.

But not every consumer can receive that support from the maker of their EV, because many of those manufacturers have ceased to exist. In the past five years, over 400 smaller EV brands and startups have gone bankrupt as the price war made it hard to stay afloat, leaving only 100 active brands today. 

Analysts expect many more used batteries to hit the market in the coming years, as the first big wave of EVs bought under generous subsidies reach retirement age. Li says, “China is going to need to move much faster toward a comprehensive end-of-life system for EV batteries—one that can trace, reuse and recycle them at scale, instead of leaving so many to disappear into the gray market.”

In just a couple of weeks, we’ll be bidding farewell to 2025. And what a year it has been! Artificial intelligence is being incorporated into more aspects of our lives, weight-loss drugs have expanded in scope, and there have been some real “omg” biotech stories from the fields of gene therapy, IVF, neurotech, and more.   

As always, the team at MIT Technology Review has been putting together our 2026 list of breakthrough technologies. That will be published in the new year (watch this space). In the meantime, my colleague Antonio Regalado has compiled his traditional list of the year’s worst technologies.

I’m inviting you to put your own memory to the test. Just how closely have you been paying attention to the Checkup emails that have been landing in your inbox this year?!

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

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

The 8 worst technology flops of 2025

Welcome to our annual list of the worst, least successful, and simply dumbest technologies of the year.

We like to think there’s a lesson in every technological misadventure. But when technology becomes dependent on power, sometimes the takeaway is simpler: it would have been better to stay away.

Regrets—2025 had a few. Here are some of the more notable ones.

—Antonio Regalado

A brief history of Sam Altman’s hype

Each time you’ve heard a borderline outlandish idea of what AI will be capable of, it often turns out that Sam Altman was, if not the first to articulate it, at least the most persuasive and influential voice behind it.

For more than a decade he has been known in Silicon Valley as a world-class fundraiser and persuader. Throughout, Altman’s words have set the agenda. What he says about AI is rarely provable when he says it, but it persuades us of one thing: This road we’re on with AI can go somewhere either great or terrifying, and OpenAI will need epic sums to steer it toward the right destination. In this sense, he is the ultimate hype man.

To understand how his voice has shaped our understanding of what AI can do, we read almost everything he’s ever said about the technology. His own words trace how we arrived here. Read the full story.

—James O’Donnell

This story is part of our new Hype Correction package, a collection of stories designed to help you reset your expectations about what AI makes possible—and what it doesn’t. Check out the rest of the package here.

Can AI really help us discover new materials?

One of my favorite stories in the Hype Correction package comes from my colleague David Rotman, who took a hard look at AI for materials research. AI could transform the process of discovering new materials—innovation that could be especially useful in the world of climate tech, which needs new batteries, semiconductors, magnets, and more.

But the field still needs to prove it can make materials that are actually novel and useful. Can AI really supercharge materials research? And what would that look like? Read the full story.

—Casey Crownhart

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 China built a chip-making machine to rival the West’s supremacy 
Suggesting China is far closer to achieving semiconductor independence than we previously believed. (Reuters)
+ China’s chip boom is creating a new class of AI-era billionaires. (Insider $)

2 NASA finally has a new boss
It’s billionaire astronaut Jared Isaacman, a close ally of Elon Musk. (Insider $)
+ But will Isaacman lead the US back to the Moon before China? (BBC)
+ Trump previously pulled his nomination, before reselecting Isaacman last month. (The Verge)

3 The parents of a teenage sextortion victim are suing Meta
Murray Dowey took his own life after being tricked into sending intimate pictures to an overseas criminal gang. (The Guardian)
+ It’s believed that the gang is based in West Africa. (BBC)

4 US and Chinese satellites are jostling in orbit
In fact, these clashes are so common that officials have given it a name—”dogfighting.” (WP $)
+ How to fight a war in space (and get away with it) (MIT Technology Review)

5 It’s not just AI that’s trapped in a bubble right now
Labubus, anyone? (Bloomberg $)
+ What even is the AI bubble? (MIT Technology Review)

6 Elon Musk’s Texan school isn’t operating as a school
Instead, it’s a “licensed child care program” with just a handful of enrolled kids. (NYT $)

7 US Border Patrol is building a network of small drones
In a bid to expand its covert surveillance powers. (Wired $)
+ This giant microwave may change the future of war. (MIT Technology Review)

8 This spoon makes low-salt foods taste better
By driving the food’s sodium ions straight to the diner’s tongue. (IEEE Spectrum)

9 AI cannot be trusted to run an office vending machine
Though the lucky Wall Street Journal staffer who walked away with a free PlayStation may beg to differ. (WSJ $)

10 Physicists have 3D-printed a Cheistmas tree from ice
No refrigeration kit required. (Ars Technica) 

Quote of the day

“It will be mentioned less and less in the same way that Microsoft Office isn’t mentioned in job postings anymore.”

—Marc Cenedella, founder and CEO of careers platform Ladders, tells Insider why employers will increasingly expect new hires to be fully au fait with AI.

One more thing

Is this the electric grid of the future?

Lincoln Electric System, a publicly owned utility in Nebraska, is used to weathering severe blizzards. But what will happen soon—not only at Lincoln Electric but for all electric utilities—is a challenge of a different order.

Utilities must keep the lights on in the face of more extreme and more frequent storms and fires, growing risks of cyberattacks and physical disruptions, and a wildly uncertain policy and regulatory landscape. They must keep prices low amid inflationary costs. And they must adapt to an epochal change in how the grid works, as the industry attempts to transition from power generated with fossil fuels to power generated from renewable sources like solar and wind.

The electric grid is bracing for a near future characterized by disruption. And, in many ways, Lincoln Electric is an ideal lens through which to examine what’s coming. Read the full story.

—Andrew Blum

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 or skeet ’em at me.)

+ A fragrance company is trying to recapture the scent of extinct flowers, wow. 
+ Seattle’s Sauna Festival sounds right up my street.
+ Switzerland has built what’s essentially a theme park dedicated to Saint Bernards. 
+ I fear I’ll never get over this tale of director supremo James Cameron giving a drowning rat CPR to save its life

Welcome to our annual list of the worst, least successful, and simply dumbest technologies of the year.

This year, politics was a recurring theme. Donald Trump swept back into office and used his executive pen to reshape the fortunes of entire sectors, from renewables to cryptocurrency. The wrecking-ball act began even before his inauguration, when the president-elect marketed his own memecoin, $TRUMP, in a shameless act of merchandising that, of course, we honor on this year’s worst tech list.

We like to think there’s a lesson in every technological misadventure. But when technology becomes dependent on power, sometimes the takeaway is simpler: it would have been better to stay away.

That was a conclusion Elon Musk drew from his sojourn as instigator of DOGE, the insurgent cost-cutting initiative that took a chainsaw to federal agencies. The public protested. Teslas were set alight, and drivers of his hyped Cybertruck discovered that instead of a thumbs-up, they were getting the middle finger.

On reflection, Musk said he wouldn’t do it again. “Instead of doing DOGE, I would have, basically … worked on my companies,” he told an interviewer this month. “And they wouldn’t have been burning the cars.”

Regrets—2025 had a few. Here are some of the more notable ones.

NEO, the home robot

Imagine a metal butler that fills your dishwasher and opens the door. It’s a dream straight out of science fiction. And it’s going to remain there—at least for a while.

That was the hilarious, and deflating, takeaway from the first reviews of NEO, a 66-pound humanoid robot whose maker claims it will “handle any of your chores reliably” when it ships next year.

But as a reporter for the Wall Street Journal learned, NEO took two minutes to fold a sweater and couldn’t crack a walnut. Not only that, but the robot was teleoperated the entire time by a person wearing a VR visor.

Still interested? Neo is available on preorder for $20,000 from startup 1X.

More: I Tried the Robot That’s Coming to Live With You. It’s Still Part Human (WSJ), The World’s Stupidest Robot Maid (The Daily Show) Why the humanoid workforce is running late (MIT Technology Review), NEO The Home Robot | Order Today (1X Corp.)

Sycophantic AI

It’s been said that San Francisco is the kind of place where no one will tell you if you have a bad idea. And its biggest product in a decade—ChatGPT—often behaves exactly that way.

This year, OpenAI released an especially sycophantic update that told users their mundane queries were brilliantly incisive. This electronic yes-man routine isn’t an accident; it’s a product strategy. Plenty of people like the flattery.

But it’s disingenuous and dangerous, too. Chatbots have shown a willingness to indulge users’ delusions and worst impulses, up to and including suicide.

In April, OpenAI acknowledged the issue when the company dialed back a model update whose ultra-agreeable personality, it said, had the side effect of “validating doubts, fueling anger, urging impulsive actions, or reinforcing negative emotions.”

Don’t you dare agree the problem is solved. This month, when I fed ChatGPT one of my dumbest ideas, its response began: “I love this concept.”

More: What OpenAI Did When ChatGPT Users Lost Touch With Reality (New York Times), Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence (arXiv), Expanding on what we missed with sycophancy (OpenAI)

The company that cried “dire wolf”

When you tell a lie, tell it big. Make it frolic and give it pointy ears. And make it white. Very white.

That’s what the Texas biotech concern Colossal Biosciences did when it unveiled three snow-white animals that it claimed were actual dire wolves, which went extinct more than 10 millennia ago.

To be sure, these genetically modified gray wolves were impressive feats of engineering. They’d been made white via a genetic mutation and even had some bits and bobs of DNA copied over from old dire wolf bones. But they “are not dire wolves,” according to canine specialists at the International Union for Conservation of Nature.

Colossal’s promotional blitz could hurt actual endangered species. Presenting de-extinction as “a ready-to-use conservation solution,” said the IUCN, “risks diverting attention from the more urgent need of ensuring functioning and healthy ecosystems.”

In a statement, Colossal said that sentiment analysis of online activity shows 98% agreement with its furry claims. “They’re dire wolves, end of story,” it says.  

More: Game of Clones: Colossal’s new wolves are cute, but are they dire? (MIT Technology Review), Conservation perspectives on gene editing in wild canids (IUCN),  A statement from Colossal’s Chief Science Officer, Dr. Beth Shapiro (Reddit)

mRNA political purge

Save the world, and this is the thanks you get?

During the covid-19 pandemic, the US bet big on mRNA vaccines—and the new technology delivered in record time. 

But now that America’s top health agencies are led by the antivax wackadoodle Robert F. Kennedy Jr., “mRNA” has become a political slur.

In August, Kennedy abruptly canceled hundreds of millions in contracts for next-generation vaccines. And shot maker Moderna—once America’s champion—has seen its stock slide by more than 90% since its Covid peak.

The purge targeting a key molecule of life (our bodies are full of mRNA) isn’t just bizarre. It could slow down other mRNA-based medicine, like cancer treatments and gene editing for rare diseases.

In August, a trade group fought back, saying: “Kennedy’s unscientific and misguided vilification of mRNA technology and cancellation of grants is the epitome of cutting off your nose to spite your face.”

More: HHS Winds Down mRNA Vaccine Development (US Department of Health and Human Services),  Cancelling mRNA studies is the highest irresponsibility (Nature), How Moderna, the company that helped save the world, unraveled (Stat News)

​​Greenlandic Wikipedia

Wikipedia has editions in 340 languages. But as of this year, there’s one less: Wikipedia in Greenlandic is no more.

Only around 60,000 people speak the Inuit language. And very few of them, it seems, ever cared much about the online encyclopedia. As a result, many of the entries were machine translations riddled with errors and nonsense.

Perhaps a website no one visits shouldn’t be a problem. But its existence created the risk of a linguistic “doom spiral” for the endangered language. That could happen if new AIs were trained on the corrupt Wikipedia articles.  

In September, administrators voted to close Greenlandic Wikipedia, citing possible “harm to the Greenlandic language.”

Read more:  Can AI Help Revitalize Indigenous Languages? (Smithsonian), How AI and Wikipedia have sent vulnerable languages into a doom spiral (MIT Technology Review), Closure of Greenlandic Wikipedia (Wikimedia)

Tesla Cybertruck

There’s a reason we’re late to the hate-fest around Elon Musk’s Cybertruck. That’s because 12 months ago, the polemical polygon was the #1 selling electric pickup in the US.

So maybe it would end up a hit.

Nope. Tesla is likely to sell only around 20,000 trucks this year, about half last year’s total. And a big part of the problem is that the entire EV pickup category is struggling. Just this month, Ford decided to scrap its own EV truck, the F-150 Lightning. 

With unsold inventory building, Musk has started selling Cybertrucks as fleet vehicles to his other enterprises, like SpaceX.

More: Elon’s Edsel: Tesla Cybertruck Is The Auto Industry’s Biggest Flop In Decades (Forbes), Why Tesla Cybertrucks Aren’t Selling (CNBC), Ford scraps fully-electric F-150 Lightning as mounting losses and falling demand hits EV plans (AP)

Presidential shitcoin

Donald Trump launched a digital currency called $TRUMP just days before his 2025 inauguration, accompanied by a logo showing his fist-pumping “Fight, fight, fight” pose.

This was a memecoin, or shitcoin, not real money. Memecoins are more like merchandise—collectibles designed to be bought and sold, usually for a loss. Indeed, they’ve been likened to a consensual scam in which a coin’s issuer can make a bundle while buyers take losses.

The White House says there’s nothing amiss. “The American public believe[s] it’s absurd for anyone to insinuate that this president is profiting off of the presidency,” said spokeswoman Karoline Leavitt in May.

More: Donald and Melania Trump’s Terrible, Tacky, Seemingly Legal Memecoin Adventure (Bloomberg), A crypto mogul who invested millions into Trump coins is getting a reprieve (CNN), How the Trump companies made $1 bn from crypto (Financial Times), Staff Statement on Meme Coins (SEC)

“Carbon-neutral” Apple Watch

In 2023, Apple announced its “first-ever carbon-neutral product,” a watch with “zero” net emissions. It would get there using recycled materials and renewable energy, and by preserving forests or planting vast stretches of eucalyptus trees.

Critics say it’s greenwashing. This year, lawyers filed suit in California against Apple for deceptive advertising, and in Germany, a court ruled that the company can’t advertise products as carbon neutral because the “supposed storage of CO₂ in commercial eucalyptus plantations” isn’t a sure thing.

Apple’s marketing team relented. Packaging for its newest watches doesn’t say “carbon neutral.” But Apple believes the legal nitpicking is counterproductive, arguing that it can only “discourage the kind of credible corporate climate action the world needs.”

More: Inside the controversial tree farms powering Apple’s carbon neutral goal (MIT Technology Review), Apple Watch not a ‘CO2-neutral product,’ German court finds (Reuters), Apple 2030: Our ambition to become carbon neutral (Apple)

Judging from headlines and social media posts in recent years, one might reasonably assume that AI is going to fix the power grid, cure the world’s diseases, and finish my holiday shopping for me. But maybe there’s just a whole lot of hype floating around out there.

This week, we published a new package called Hype Correction. The collection of stories takes a look at how the world is starting to reckon with the reality of what AI can do, and what’s just fluff.

One of my favorite stories in that package comes from my colleague David Rotman, who took a hard look at AI for materials research. AI could transform the process of discovering new materials—innovation that could be especially useful in the world of climate tech, which needs new batteries, semiconductors, magnets, and more. 

But the field still needs to prove it can make materials that are actually novel and useful. Can AI really supercharge materials research? What could that look like?

For researchers hoping to find new ways to power the world (or cure disease or achieve any number of other big, important goals), a new material could change everything.

The problem is, inventing materials is difficult and slow. Just look at plastic—the first totally synthetic plastic was invented in 1907, but it took until roughly the 1950s for companies to produce the wide range we’re familiar with today. (And of course, though it is incredibly useful, plastic also causes no shortage of complications for society.)

In recent decades, materials science has fallen a bit flat—David has been covering this field for nearly 40 years, and as he puts it, there have been just a few major commercial breakthroughs in that time. (Lithium-ion batteries are one.)

Could AI change everything? The prospect is a tantalizing one, and companies are racing to test it out.

Lila Sciences, based in Cambridge, Massachusetts, is working on using AI models to uncover new materials. The company can not only train an AI model on all the latest scientific literature, but also plug it into an automated lab, so it can learn from experimental data. The goal is to speed up the iterative process of inventing and testing new materials and look at research in ways that humans might miss.

At an MIT Technology Review event earlier this year, I got to listen to David interview Rafael Gómez-Bombarelli, one of Lila’s cofounders. As he described what the company is working on, Gómez-Bombarelli acknowledged that AI materials discovery hasn’t yet seen a big breakthrough moment. Yet.

Gómez-Bombarelli described how models Lila has trained are providing insights that are “as deep [as] or deeper than our domain scientists would have.” In the future, AI could “think” in ways that depart from how human scientists approach a problem, he added: “There will be a need to translate scientific reasoning by AI to the way we think about the world.”

It’s exciting to see this sort of optimism in materials research, but there’s still a long and winding road before we can satisfyingly say that AI has transformed the field. One major difficulty is that it’s one thing to take suggestions from a model about new experimental methods or new potential structures. It’s quite another to actually make a material and show that it’s novel and useful.

You might remember that a couple of years ago, Google’s DeepMind announced it had used AI to predict the structures of “millions of new materials” and had made hundreds of them in the lab.

But as David notes in his story, after that announcement, some materials scientists pointed out that some of the supposedly novel materials were basically slightly different versions of known ones. Others couldn’t even physically exist in normal conditions (the simulations were done at ultra-low temperatures, where atoms don’t move around much).

It’s possible that AI could give materials discovery a much-needed jolt and usher in a new age that brings superconductors and batteries and magnets we’ve never seen before. But for now, I’m calling hype. 

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

Omar Yaghi was a quiet child, diligent, unlikely to roughhouse with his nine siblings. So when he was old enough, his parents tasked him with one of the family’s most vital chores: fetching water. Like most homes in his Palestinian neighborhood in Amman, Jordan, the Yaghis’ had no electricity or running water. At least once every two weeks, the city switched on local taps for a few hours so residents could fill their tanks. Young Omar helped top up the family supply. Decades later, he says he can’t remember once showing up late. The fear of leaving his parents, seven brothers, and two sisters parched kept him punctual.

Yaghi proved so dependable that his father put him in charge of monitoring how much the cattle destined for the family butcher shop ate and drank. The best-­quality cuts came from well-fed, hydrated animals—a challenge given that they were raised in arid desert.

Specially designed materials called metal-organic frameworks can pull water from the air like a sponge—and then give it back.

But at 10 years old, Yaghi learned of a different occupation. Hoping to avoid a rambunctious crowd at recess, he found the library doors in his school unbolted and sneaked in. Thumbing through a chemistry textbook, he saw an image he didn’t understand: little balls connected by sticks in fascinating shapes. Molecules. The building blocks of everything.

“I didn’t know what they were, but it captivated my attention,” Yaghi says. “I kept trying to figure out what they might be.”

That’s how he discovered chemistry—or maybe how chemistry discovered him. After coming to the United States and, eventually, a postdoctoral program at Harvard University, Yaghi devoted his career to finding ways to make entirely new and fascinating shapes for those little sticks and balls. In October 2025, he was one of three scientists who won a Nobel Prize in chemistry for identifying metal-­organic frameworks, or MOFs—metal ions tethered to organic molecules that form repeating structural landscapes. Today that work is the basis for a new project that sounds like science fiction, or a miracle: conjuring water out of thin air.

When he first started working with MOFs, Yaghi thought they might be able to absorb climate-damaging carbon dioxide—or maybe hold hydrogen molecules, solving the thorny problem of storing that climate-friendly but hard-to-contain fuel. But then, in 2014, Yaghi’s team of researchers at UC Berkeley had an epiphany. The tiny pores in MOFs could be designed so the material would pull water molecules from the air around them, like a sponge—and then, with just a little heat, give back that water as if squeezed dry. Just one gram of a water-absorbing MOF has an internal surface area of roughly 7,000 square meters.

Yaghi wasn’t the first to try to pull potable water from the atmosphere. But his method could do it at lower levels of humidity than rivals—potentially shaking up a tiny, nascent industry that could be critical to humanity in the thirsty decades to come. Now the company he founded, called Atoco, is racing to demonstrate a pair of machines that Yaghi believes could produce clean, fresh, drinkable water virtually anywhere on Earth, without even hooking up to an energy supply.

That’s the goal Yaghi has been working toward for more than a decade now, with the rigid determination that he learned while doing chores in his father’s butcher shop.

“It was in that shop where I learned how to perfect things, how to have a work ethic,” he says. “I learned that a job is not done until it is well done. Don’t start a job unless you can finish it.”

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Most of Earth is covered in water, but just 3% of it is fresh, with no salt—the kind of water all terrestrial living things need. Today, desalination plants that take the salt out of seawater provide the bulk of potable water in technologically advanced desert nations like Israel and the United Arab Emirates, but at a high cost. Desalination facilities either heat water to distill out the drinkable stuff or filter it with membranes the salt doesn’t pass through; both methods require a lot of energy and leave behind concentrated brine. Typically desal pumps send that brine back into the ocean, with devastating ecological effects.

I was talking to Atoco executives about carbon dioxide capture earlier this year when they mentioned the possibility of harvesting water from the atmosphere. Of course my mind immediately jumped to Star Wars, and Luke Skywalker working on his family’s moisture farm, using “vaporators” to pull water from the atmosphere of the arid planet Tatooine. (Other sci-fi fans’ minds might go to Dune, and the water-gathering technology of the Fremen.) Could this possibly be real?

It turns out people have been doing it for millennia. Archaeological evidence of water harvesting from fog dates back as far as 5000 BCE. The ancient Greeks harvested dew, and 500 years ago so did the Inca, using mesh nets and buckets under trees.

Today, harvesting water from the air is a business already worth billions of dollars, say industry analysts—and it’s on track to be worth billions more in the next five years. In part that’s because typical sources of fresh water are in crisis. Less snowfall in mountains during hotter winters means less meltwater in the spring, which means less water downstream. Droughts regularly break records. Rising seas seep into underground aquifers, already drained by farming and sprawling cities. Aging septic tanks leach bacteria into water, and cancer-causing “forever chemicals” are creating what the US Government Accountability Office last year said “may be the biggest water problem since lead.” That doesn’t even get to the emerging catastrophe from microplastics.

So lots of places are turning to atmospheric water harvesting. Watergen, an Israel-based company working on the tech, initially planned on deploying in the arid, poorer parts of the world. Instead, buyers in Europe and the United States have approached the company as a way to ensure a clean supply of water. And one of Watergen’s biggest markets is the wealthy United Arab Emirates. “When you say ‘water crisis,’ it’s not just the lack of water—it’s access to good-quality water,” says Anna Chernyavsky, Watergen’s vice president of marketing.

In other words, the technology “has evolved from lab prototypes to robust, field-deployable systems,” says Guihua Yu, a mechanical engineer at the University of Texas at Austin. “There is still room to improve productivity and energy efficiency in the whole-system level, but so much progress has been steady and encouraging.”

---

MOFs are just the latest approach to the idea. The first generation of commercial tech depended on compressors and refrigerant chemicals—large-scale versions of the machine that keeps food cold and fresh in your kitchen. Both use electricity and a clot of pipes and exchangers to make cold by phase-shifting a chemical from gas to liquid and back; refrigerators try to limit condensation, and water generators basically try to enhance it.

That’s how Watergen’s tech works: using a compressor and a heat exchanger to wring water from air at humidity levels as low as 20%—Death Valley in the spring. “We’re talking about deserts,” Chernyavsky says. “Below 20%, you get nosebleeds.”

That still might not be good enough. “Refrigeration works pretty well when you are above a certain relative humidity,” says Sameer Rao, a mechanical engineer at the University of Utah who researches atmospheric water harvesting. “As the environment dries out, you go to lower relative humidities, and it becomes harder and harder. In some cases, it’s impossible for refrigeration-based systems to really work.”

So a second wave of technology has found a market. Companies like Source Global use desiccants—substances that absorb moisture from the air, like the silica packets found in vitamin bottles—to pull in moisture and then release it when heated. In theory, the benefit of desiccant-­based tech is that it could absorb water at lower humidity levels, and it uses less energy on the front end since it isn’t running a condenser system. Source Global claims its off-grid, solar-powered system is deployed in dozens of countries.

But both technologies still require a lot of energy, either to run the heat exchangers or to generate sufficient heat to release water from the desiccants. MOFs, Yaghi hopes, do not. Now Atoco is trying to prove it. Instead of using heat exchangers to bring the air temperature to dew point or desiccants to attract water from the atmosphere, a system can rely on specially designed MOFs to attract water molecules. Atoco’s prototype version uses an MOF that looks like baby powder, stuck to a surface like glass. The pores in the MOF naturally draw in water molecules but remain open, making it theoretically easy to discharge the water with no more heat than what comes from direct sunlight. Atoco’s industrial-scale design uses electricity to speed up the process, but the company is working on a second design that can operate completely off grid, without any energy input.

Yaghi’s Atoco isn’t the only contender seeking to use MOFs for water harvesting. A competitor, AirJoule, has introduced MOF-based atmospheric water generators in Texas and the UAE and is working with researchers at Arizona State University, planning to deploy more units in the coming months. The company started out trying to build more efficient air-­conditioning for electric buses operating on hot, humid city streets. But then founder Matt Jore heard about US government efforts to harvest water from air—and pivoted. The startup’s stock price has been a bit of a roller-­coaster, but Jore says the sheer size of the market should keep him in business. Take Maricopa County, encompassing Phoenix and its environs—it uses 1.2 billion gallons of water from its shrinking aquifer every day, and another 874 million gallons from surface sources like rivers.

“So, a couple of billion gallons a day, right?” Jore tells me. “You know how much influx is in the atmosphere every day? Twenty-five billion gallons.”

My eyebrows go up. “Globally?”

“Just the greater Phoenix area gets influx of about 25 billion gallons of water in the air,” he says. “If you can tap into it, that’s your source. And it’s not going away. It’s all around the world. We view the atmosphere as the world’s free pipeline.”

Besides AirJoule’s head start on Atoco, the companies also differ on where they get their MOFs. AirJoule’s system relies on an off-the-shelf version the company buys from the chemical giant BASF; Atoco aims to use Yaghi’s skill with designing the novel material to create bespoke MOFs for different applications and locations.

“Given the fact that we have the inventor of the whole class of materials, and we leverage the stuff that comes out of his lab at Berkeley—everything else equal, we have a good starting point to engineer maybe the best materials in the world,” says Magnus Bach, Atoco’s VP of business development.

Yaghi envisions a two-pronged product line. Industrial-scale water generators that run on electricity would be capable of producing thousands of liters per day on one end, while units that run on passive systems could operate in remote locations without power, just harnessing energy from the sun and ambient temperatures. In theory, these units could someday replace desalination and even entire municipal water supplies. The next round of field tests is scheduled for early 2026, in the Mojave Desert—one of the hottest, driest places on Earth.

“That’s my dream,” Yaghi says. “To give people water independence, so they’re not reliant on another party for their lives.”

Both Yaghi and Watergen’s Chernyavsky say they’re looking at more decentralized versions that could operate outside municipal utility systems. Home appliances, similar to rooftop solar panels and batteries, could allow households to generate their own water off grid.

That could be tricky, though, without economies of scale to bring down prices. “You have to produce, you have to cool, you have to filter—all in one place,” Chernyavsky says. “So to make it small is very, very challenging.”

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Difficult as that may be, Yaghi’s childhood gave him a particular appreciation for the freedom to go off grid, to liberate the basic necessity of water from the whims of systems that dictate when and how people can access it.

“That’s really my dream,” he says. “To give people independence, water independence, so that they’re not reliant on another party for their livelihood or lives.”

Toward the end of one of our conversations, I asked Yaghi what he would tell the younger version of himself if he could. “Jordan is one of the worst countries in terms of the impact of water stress,” he said. “I would say, ‘Continue to be diligent and observant. It doesn’t really matter what you’re pursuing, as long as you’re passionate.’”

I pressed him for something more specific: “What do you think he’d say when you described this technology to him?”

Yaghi smiled: “I think young Omar would think you’re putting him on, that this is all fictitious and you’re trying to take something from him.” This reality, in other words, would be beyond young Omar’s wildest dreams.

Alexander C. Kaufman is a reporter who has covered energy, climate change, pollution, business, and geopolitics for more than a decade.

Rolling out enterprise-grade AI means climbing two steep cliffs at once. First, understanding and implementing the tech itself. And second, creating the cultural conditions where employees can maximize its value. While the technical hurdles are significant, the human element can be even more consequential; fear and ambiguity can stall momentum of even the most promising initiatives.

Psychological safety—feeling free to express opinions and take calculated risks without worrying about career repercussions1—is essential for successful AI adoption. In psychologically safe workspaces, employees are empowered to challenge assumptions and raise concerns about new tools without fear of reprisal. This is nothing short of a necessity when introducing a nascent and profoundly powerful technology that still lacks established best practices.

“Psychological safety is mandatory in this new era of AI,” says Rafee Tarafdar, executive vice president and chief technology officer at Infosys. “The tech itself is evolving so fast—companies have to experiment, and some things will fail. There needs to be a safety net.”

To gauge how psychological safety influences success with enterprise-level AI, MIT Technology Review Insights conducted a survey of 500 business leaders. The findings reveal high self-reported levels of psychological safety, but also suggest that fear still has a foothold. Anecdotally, industry experts highlight a reason for the disconnect between rhetoric and reality: while organizations may promote a safe to experiment message publicly, deeper cultural undercurrents can counteract that intent.

Building psychological safety requires a coordinated, systems-level approach, and human resources (HR) alone cannot deliver such transformation. Instead, enterprises must deeply embed psychological safety into their collaboration processes.

Key findings for this report include:

• Companies with experiment-friendly cultures have greater success with AI projects. The majority of executives surveyed (83%) believe a company culture that prioritizes psychological safety measurably improves the success of AI initiatives. Four in five leaders agree that organizations fostering such safety are more successful at adopting AI, and 84% have observed connections between psychological safety and tangible AI outcomes.
• Psychological barriers are proving to be greater obstacles to enterprise AI adoption than technological challenges. Encouragingly, nearly three-quarters (73%) of respondents indicated they feel safe to provide honest feedback and express opinions freely in their workplace. Still, a significant share (22%) admit they’ve hesitated to lead an AI project because they might be blamed if it misfires.
• Achieving psychological safety is a moving target for many organizations. Fewer than half of leaders (39%) rate their organization’s current level of psychological safety as “very high.” Another 48%report a “moderate” degree of it. This may mean that some enterprises are pursuing AI adoption on cultural foundations that are not yet fully stable.

Download the report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

The great AI hype correction of 2025

Some disillusionment was inevitable. When OpenAI released a free web app called ChatGPT in late 2022, it changed the course of an entire industry—and several world economies. Millions of people started talking to their computers, and their computers started talking back. We were enchanted, and we expected more.

Well, 2025 has been a year of reckoning. For a start, the heads of the top AI companies made promises they couldn’t keep. At the same time, updates to the core technology are no longer the step changes they once were.

To be clear, the last few years have been filled with genuine “Wow” moments. But this remarkable technology is only a few years old, and in many ways it is still experimental. Its successes come with big caveats. Read the full story to learn more about why we may need to readjust our expectations.

—Will Douglas Heaven

This story is part of our new Hype Correction package, a collection of stories designed to help you reset your expectations about what AI makes possible—and what it doesn’t. Check out the rest of the package here, and you can read more about why it’s time to reset our expectations for AI in the latest edition of the Algorithm, our weekly AI newsletter. Sign up here to make sure you receive future editions straight to your inbox.

Quantum navigation could solve the military’s GPS jamming problem

Since the 2022 invasion of Ukraine, thousands of flights have been affected by a far-reaching Russian campaign of using radio transmissions that jammed its GPS system.

The growing inconvenience to air traffic and risk of a real disaster have highlighted the vulnerability of GPS and focused attention on more secure ways for planes to navigate the gauntlet of jamming and spoofing, the term for tricking a GPS receiver into thinking it’s somewhere else.

One approach that’s emerging from labs is quantum navigation: exploiting the quantum nature of light and atoms to build ultra-sensitive sensors that can allow vehicles to navigate independently, without depending on satellites. Read the full story.

—Amos Zeeberg

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 The Trump administration has launched its US Tech Force program
In a bid to lure engineers away from Big Tech roles and straight into modernizing the government. (The Verge)
+ So, essentially replacing the IT workers that DOGE got rid of, then. (The Register)

2 Lawmakers are investigating how AI data centers affect electricity costs
They want to get to the bottom of whether it’s being passed onto consumers. (NYT $)
+ Calculating AI’s water usage is far from straightforward, too. (Wired $)
+ AI is changing the grid. Could it help more than it harms? (MIT Technology Review)

3 Ford isn’t making a large all-electric truck after all
After the US government’s support for EVs plummeted. (Wired $)
+ Instead, the F-150 Lightning pickup will be reborn as a plug-in hybrid. (The Information $)
+ Why Americans may be finally ready to embrace smaller cars. (Fast Company $)
+ The US could really use an affordable electric truck. (MIT Technology Review)

4 PayPal wants to become a bank in the US
The Trump administration is very friendly to non-traditional financial companies, after all. (FT $)
+ It’s been a good year for the crypto industry when it comes to banking. (Economist $)

5 A tech trade deal between the US and UK has been put on ice
America isn’t happy with the lack of progress Britain has made, apparently. (NYT $)
+ It’s a major setback in relations between the pair. (The Guardian)

6 Why does no one want to make the cure for dengue?
A new antiviral pill appears to prevent infection—but its development has been abandoned. (Vox)

7 The majority of the world’s glaciers are forecast to disappear by 2100
At a rate of around 3,000 per year. (New Scientist $)
+ Inside a new quest to save the “doomsday glacier”. (MIT Technology Review)

8 Hollywood is split over AI
While some filmmakers love it, actors are horrified by its inexorable rise. (Bloomberg $)

9 Corporate America is obsessed with hiring storytellers
It’s essentially a rehashed media relations manager role overhauled for the AI age. (WSJ $)

10 The concept of hacking existed before the internet
Just ask this bunch of teenage geeks. (IEEE Spectrum)

Quote of the day

“So the federal government deleted 18F, which was doing great work modernizing the government, and then replaced it with a clone? What is the point of all this?”

—Eugene Vinitsky, an assistant professor at New York University, takes aim at the US government’s decision to launch a new team to overhaul its approach to technology in a post on Bluesky.

One more thing

How DeepSeek became a fortune teller for China’s youth

As DeepSeek has emerged as a homegrown challenger to OpenAI, young people across the country have started using AI to revive fortune-telling practices that have deep roots in Chinese culture.

Across Chinese social media, users are sharing AI-generated readings, experimenting with fortune-telling prompt engineering, and revisiting ancient spiritual texts—all with the help of DeepSeek.

The surge in AI fortune-telling comes during a time of pervasive anxiety and pessimism in Chinese society. And as spiritual practices remain hidden underground thanks to the country’s regime, computers and phone screens are helping younger people to gain a sense of control over their lives. Read the full story.

—Caiwen Chen

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 or skeet ’em at me.)

+ Chess has been online as far back as the 1800s (no, really!)
+ Jane Austen was born 250 years ago today. How well do you know her writing? ($)
+ Rob Reiner, your work will live on forever.
+ I enjoyed this comprehensive guide to absolutely everything you could ever want to know about New England’s extensive seafood offerings.

Can I ask you a question: How do you feel about AI right now? Are you still excited? When you hear that OpenAI or Google just dropped a new model, do you still get that buzz? Or has the shine come off it, maybe just a teeny bit? Come on, you can be honest with me.

Truly, I feel kind of stupid even asking the question, like a spoiled brat who has too many toys at Christmas. AI is mind-blowing. It’s one of the most important technologies to have emerged in decades (despite all its many many drawbacks and flaws and, well, issues).

At the same time I can’t help feeling a little bit: Is that it?

If you feel the same way, there’s good reason for it: The hype we have been sold for the past few years has been overwhelming. We were told that AI would solve climate change. That it would reach human-level intelligence. That it would mean we no longer had to work!

Instead we got AI slop, chatbot psychosis, and tools that urgently prompt you to write better email newsletters. Maybe we got what we deserved. Or maybe we need to reevaluate what AI is for.

That’s the reality at the heart of a new series of stories, published today, called Hype Correction. We accept that AI is still the hottest ticket in town, but it’s time to re-set our expectations.

As my colleague Will Douglas Heaven puts it in the package’s intro essay, “You can’t help but wonder: When the wow factor is gone, what’s left? How will we view this technology a year or five from now? Will we think it was worth the colossal costs, both financial and environmental?” 

Elsewhere in the package, James O’Donnell looks at Sam Altman, the ultimate AI hype man, through the medium of his own words. And Alex Heath explains the AI bubble, laying out for us what it all means and what we should look out for.

Michelle Kim analyzes one of the biggest claims in the AI hype cycle: that AI would completely eliminate the need for certain classes of jobs. If ChatGPT can pass the bar, surely that means it will replace lawyers? Well, not yet, and maybe not ever. 

Similarly, Edd Gent tackles the big question around AI coding. Is it as good as it sounds? Turns out the jury is still out. And elsewhere David Rotman looks at the real-world work that needs to be done before AI materials discovery has its breakthrough ChatGPT moment.

Meanwhile, Garrison Lovely spends time with some of the biggest names in the AI safety world and asks: Are the doomers still okay? I mean, now that people are feeling a bit less scared about their impending demise at the hands of superintelligent AI? And Margaret Mitchell reminds us that hype around generative AI can blind us to the AI breakthroughs we should really celebrate.

Let’s remember: AI was here before ChatGPT and it will be here after. This hype cycle has been wild, and we don’t know what its lasting impact will be. But AI isn’t going anywhere. We shouldn’t be so surprised that those dreams we were sold haven’t come true—yet.

The more likely story is that the real winners, the killer apps, are still to come. And a lot of money is being bet on that prospect. So yes: The hype could never sustain itself over the short term. Where we’re at now is maybe the start of a post-hype phase. In an ideal world, this hype correction will reset expectations. 

Let’s all catch our breath, shall we?

This story first appeared in The Algorithm, our weekly free newsletter all about AI. Sign up to read past editions here.

In late September, a Spanish military plane carrying the country’s defense minister to a base in Lithuania was reportedly the subject of a kind of attack—not by a rocket or anti-aircraft rounds, but by radio transmissions that jammed its GPS system. 

The flight landed safely, but it was one of thousands that have been affected by a far-reaching Russian campaign of GPS interference since the 2022 invasion of Ukraine. The growing inconvenience to air traffic and risk of a real disaster have highlighted the vulnerability of GPS and focused attention on more secure ways for planes to navigate the gauntlet of jamming and spoofing, the term for tricking a GPS receiver into thinking it’s somewhere else. 

US military contractors are rolling out new GPS satellites that use stronger, cleverer signals, and engineers are working on providing better navigation information based on other sources, like cellular transmissions and visual data. 

But another approach that’s emerging from labs is quantum navigation: exploiting the quantum nature of light and atoms to build ultra-sensitive sensors that can allow vehicles to navigate independently, without depending on satellites. As GPS interference becomes more of a problem, research on quantum navigation is leaping ahead, with many researchers and companies now rushing to test new devices and techniques. In recent months, the US’s Defense Advanced Research Projects Agency (DARPA) and its Defense Innovation Unit have announced new grants to test the technology on military vehicles and prepare for operational deployment. 

Tracking changes

Perhaps the most obvious way to navigate is to know where you started and then track where you go by recording the speed, direction, and duration of travel. But while this approach, known in the field as inertial navigation, is conceptually simple, it’s difficult to do well; tiny uncertainties in any of those measurements compound over time and lead to big errors later on. Douglas Paul, the principal investigator of the UK’s Hub for Quantum Enabled Precision, Navigation & Timing (QEPNT), says that existing specialized inertial-navigation devices might be off by 20 kilometers after 100 hours of travel. Meanwhile, the cheap sensors commonly used in smartphones produce more than twice that level of uncertainty after just one hour. 

“If you’re guiding a missile that flies for one minute, that might be good enough,” he says. “If you’re in an airliner, that’s definitely not good enough.” 

A more accurate version of inertial navigation instead uses sensors that rely on the quantum behavior of subatomic particles to more accurately measure acceleration, direction, and time.

Several companies, like the US-based Infleqtion, are developing quantum gyroscopes, which track a vehicle’s bearing, and quantum accelerometers, which can reveal how far it’s traveled. Infleqtion’s sensors are based on a technique called atom interferometry: A beam of rubidium atoms is zapped with precise laser pulses, which split the atoms into two separate paths. Later, other laser pulses recombine the atoms, and they’re measured with a detector. If the vehicle has turned or accelerated while the atoms are in motion, the two paths will be slightly out of phase in a way the detector can interpret. 

Last year the company trialed these inertial sensors on a customized plane flying at a British military testing site. In October of this year, Infleqtion ran its first real-world test of a new generation of inertial sensors that use a steady stream of atoms instead of pulses, allowing for continuous navigation and avoiding long dead times.

Infleqtion also has an atomic clock, called Tiqker, that can help determine how far a vehicle has traveled. It is a kind of optical clock that uses infrared lasers tuned to a specific frequency to excite electrons in rubidium, which then release photons at a consistent, known rate. The device “will lose one second every 2 million years or so,” says Max Perez, who oversees the project, and it fits in a standard electronics equipment rack. It has passed tests on flights in the UK, on US Army ground vehicles in New Mexico, and, in late October, on a drone submarine. 

“Tiqker operated happily through these conditions, which is unheard-of for previous generations of optical clocks,” says Perez. Eventually the company hopes to make the unit smaller and more rugged by switching to lasers generated by microchips. 

Magnetic fields

Vehicles deprived of satellite-based navigation are not entirely on their own; they can get useful clues from magnetic and gravitational fields that surround the planet. These fields vary slightly depending on the location, and the variations, or anomalies, are recorded in various maps. By precisely measuring the local magnetic or gravitational field and comparing those values with anomaly maps, quantum navigation systems can track the location of a vehicle. 

Allison Kealy, a navigation researcher at Swinburne University in Australia, is working on the hardware needed for this approach. Her team uses a material called nitrogen-vacancy diamond. In NV diamonds, one carbon atom in the lattice is replaced with a nitrogen atom, and one neighboring carbon atom is removed entirely. The quantum state of the electrons at the NV defect is very sensitive to magnetic fields. Carefully stimulating the electrons and watching the light they emit offers a way to precisely measure the strength of the field at the diamond’s location, making it possible to infer where it’s situated on the globe. 

Kealy says these quantum magnetometers have a few big advantages over traditional ones, including the fact that they measure the direction of the Earth’s magnetic field in addition to its strength. That additional information could make it easier to determine location. 

The technology is far from commercial deployment, but Kealy and several colleagues successfully tested their magnetometer in a set of flights in Australia late last year, and they plan to run more trials this year and next. “This is where it gets exciting, as we transition from theoretical models and controlled experiments to on-the-ground, operational systems,” she says. “This is a major step forward.” 

Delicate systems

Other teams, like Q-CTRL, an Australian quantum technology company, are focusing on using software to build robust systems from noisy quantum sensors. Quantum navigation involves taking those delicate sensors, honed in the placid conditions of a laboratory, and putting them in vehicles that make sharp turns, bounce with turbulence, and bob with waves, all of which interferes with the sensors’ functioning. Even the vehicles themselves present problems for magnetometers, especially “the fact that the airplane is made of metal, with all this wiring,” says Michael Biercuk, the CEO of Q-CTRL. “Usually there’s 100 to 1,000 times more noise than signal.” 

After Q-CTRL engineers ran trials of their magnetic navigation system in a specially outfitted Cessna last year, they used machine learning to go through the data and try to sift out the signal from all the noise. Eventually they found they could track the plane’s location up to 94 times as accurately as a strategic-grade conventional inertial navigation system could, according to Biercuk. They announced their findings in a non-peer-reviewed paper last spring. 

In August Q-CTRL received two contracts from DARPA to develop its “software-ruggedized” mag-nav product, named Ironstone Opal, for defense applications. The company is also testing the technology with commercial partners, including the defense contractors Northrop Grumman and Lockheed Martin and Airbus, an aerospace manufacturer. 

“Northrop Grumman is working with Q-CTRL to develop a magnetic navigation system that can withstand the physical demands of the real world,” says Michael S. Larsen, a quantum systems architect at the company. “Technology like magnetic navigation and other quantum sensors will unlock capabilities to provide guidance even in GPS-denied or -degraded environments.”

Now Q-CTRL is working on putting Ironstone Opal into a smaller, more rugged container appropriate for deployment; “Ironstone Opal’s first deployment was, and looked like, a science experiment,” says Biercuk. He anticipates delivering the first commercial units next year. 

Sensor fusion

Even as quantum navigation emerges as a legitimate alternative to satellite-based navigation, the satellites themselves are improving. Modern GPS III satellites include new civilian signals called L1C and L5, which should be more accurate and harder to jam and spoof than current signals. Both are scheduled to be fully operational later this decade. 

US and allied military users are intended to have access to far hardier GPS tools, including M-code, a new form of GPS signal that is rolling out now, and Regional Military Protection, a focused GPS beam that will be restricted to small geographic areas. The latter will start to become available when the GPS IIIF generation of satellites is in orbit, with the first scheduled to go up in 2027. A Lockheed Martin spokesperson says new GPS satellites with M-code are eight times as powerful as previous ones, while the GPS IIIF model will be 60 times as strong.

Other plans involve using navigation satellites in low Earth orbit—the zone inhabited by SpaceX’s internet-providing Starlink constellation—rather than the medium Earth orbit used by GPS. Since objects in LEO are closer to Earth, their signals are stronger, which makes them harder to jam and spoof. LEO satellites also transit the sky more quickly, which makes them harder still to spoof and helps GPS receivers get a lock on their position faster. “This really helps for signal convergence,” says Lotfi Massarweh, a satellite navigation researcher at Delft University of Technology, in the Netherlands. “They can get a good position in just a few minutes. So that is a huge leap.”

Ultimately, says Massarweh, navigation will depend not only on satellites, quantum sensors, or any other single technology, but on the combination of all of them. “You need to think always in terms of sensor fusion,” he says. 

The navigation resources that a vehicle draws on will change according to its environment—whether it’s an airliner, a submarine, or an autonomous car in an urban canyon. But quantum navigation will be one important resource. He says, “If quantum technology really delivers what we see in the literature—if it’s stable over one week rather than tens of minutes—at that point it is a complete game changer.”

This story was updated to better reflect the current status of Ironstone Opal.

When the ABB FIA Formula E World Championship launched its first race through Beijing’s Olympic Park in 2014, the idea of all-electric motorsport still bordered on experimental. Batteries couldn’t yet last a full race, and drivers had to switch cars mid-competition. Just over a decade later, Formula E has evolved into a global entertainment brand broadcast in 150 countries, driving both technological innovation and cultural change in sport.  

“Gen4, that’s to come next year,” says Dan Cherowbrier, Formula E’s chief technology and information officer. “You will see a really quite impressive car that starts us to question whether EV is there. It’s actually faster—it’s actually more than traditional [internal combustion engines] ICE.” 

That acceleration isn’t just happening on the track. Formula E’s digital transformation, powered by its partnership with Infosys, is redefining what it means to be a fan. “It’s a movement to make motor sport accessible and exciting for the new generation,” says principal technologist at Infosys, Rohit Agnihotri. 

From real-time leaderboards and predictive tools to personalized storylines that adapt to what individual fans care most about—whether it’s a driver rivalry or battery performance—Formula E and Infosys are using AI-powered platforms to create fan experiences as dynamic as the races themselves. “Technology is not just about meeting expectations; it’s elevating the entire fan experience and making the sport more inclusive,” says Agnihotri.  

AI is also transforming how the organization itself operates. “Historically, we would be going around the company, banging on everyone’s doors and dragging them towards technology, making them use systems, making them move things to the cloud,” Cherowbrier notes. “What AI has done is it’s turned that around on its head, and we now have people turning up, banging on our door because they want to use this tool, they want to use that tool.” 

As audiences diversify and expectations evolve, Formula E is also a case study in sustainable innovation. Machine learning tools now help determine the most carbon-optimal way to ship batteries across continents, while remote broadcast production has sharply reduced travel emissions and democratized the company’s workforce. These advances show how digital intelligence can expand reach without deepening carbon footprints. 

For Cherowbrier, this convergence of sport, sustainability, and technology is just the beginning. With its data-driven approach to performance, experience, and impact, Formula E is offering a glimpse into how entertainment, innovation, and environmental responsibility can move forward in tandem. 

“Our goal is clear,” says Agnihotri. “Help Formula E be the most digital and sustainable motor sport in the world. The future is electric, and with AI, it’s more engaging than ever.” 

This episode of Business Lab is produced in partnership with Infosys. 

Full Transcript:  

Megan Tatum: From MIT Technology Review, I’m Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab, and into the marketplace.  

The ABB FIA Formula E World Championship, the world’s first all-electric racing series, made its debut in the grounds of the Olympic Park in Beijing in 2014. A little more than 10 years later, it’s a global entertainment brand with 10 teams, 20 drivers, and broadcasts in 150 countries. Technology is central to how Formula E is navigating that scale and to how it’s delivering more powerful personalized experiences.  

Two words for you: elevated fandom.  

My guests today are Rohit Agnihotri, principal technologist at Infosys, and Dan Cherowbrier, CTIO of Formula E.  

This episode is produced in partnership with Infosys.  

Welcome, Rohit and Dan. 

Dan Cherowbrier: Hi. Thanks for having us. 

Megan: Dan, as I mentioned there, the first season of the ABB FIA Formula E World Championship launched in 2014. Can you talk us through how the first all-electric motor sport has evolved in the last decade? How has it changed in terms of its scale, the markets it operates in, and also, its audiences, of course? 

Dan: When Formula E launched back in 2014, there were hardly any domestic EVs on the road. And probably if you’re from London, the ones you remember are the hybrid Priuses; that was what we knew of really. And at the time, they were unable to get a battery big enough for a car to do a full race. So the first generation of car, the first couple of seasons, the driver had to do a pit stop midway through the race, get out of one car, and get in another car, and then carry on, which sounds almost farcical now, but it’s what you had to do then to drive innovation, is to do that in order to go to the next stage. 

Then in Gen2, that came up four years later, they had a battery big enough to start full races and start to actually make it a really good sport. Gen3, they’re going for some real speeds and making it happen. Gen4, that’s to come next year, you’ll see acceleration in line with Formula One. I’ve been fortunate enough to see some of the testing. You will see a really quite impressive car that starts us to question whether EV is there. It’s actually faster, it’s actually more than traditional ICE. 

That’s the tech of the car. But then, if you also look at the sport and how people have come to it and the fans and the demographic of the fans, a lot has changed in the last 11 years. We were out to enter season 12. In the last 11 years, we’ve had a complete democratization of how people access content and what people want from content. And as a new generation of fan coming through. This new generation of fan is younger. They’re more gender diverse. We have much closer to 50-50 representation in our fan base. And they want things personalized, and they’re very demanding about how they want it and the experience they expect. No longer are you just able to give them one race and everybody watches the same thing. We need to make things for them. You see that sort of change that’s come through in the last 11 years. 

Megan: It’s a huge amount of change in just over a decade, isn’t it? To navigate. And I wonder, Rohit, what was the strategic plan for Infosys when associating with Formula E? What did Infosys see in partnering with such a young sport? 

Rohit: Yeah. That’s a great question, Megan. When we looked at Formula E, we didn’t just see a racing championship. We saw the future. A sport, that’s electric, sustainable, and digital first. That’s exactly where Infosys wants to be, at the intersection of technology, innovation, and purpose. Our plan has three big goals. First, grow the fan base. Formula E wants to reach 500 million fans by 2030. That is not just a number. It’s a movement to make motor sport accessible and exciting for the new generation. To make that happen, we are building an AI-powered platform that gives personalized content to the fans, so that every fan feels connected and valued. Imagine a fan in Tokyo getting race insights tailored for their favorite driver, while another in London gets a sustainability story that matters to him. That’s the level of personalization we are aiming for. 

Second, bringing technology innovation. We have already launched the Stats Centre, which turns race data into interactive stories. And soon, Race Centre will take this to the next level with real time leaderboards to the race or tracks, overtakes, attack mode timelines, and even AI generated live commentary. Fans will not just watch, they will interact, predict podium finishes, and share their views globally. And third, supports sustainability. Formula E is already net-zero, but now their goal is to cut carbon by 45% by 2030. We’ll be enabling that through AI-driven sustainability, data management, tracking every watt of energy, every logistics decision. and modeling scenarios to make racing even greener. Partnering with a young sport gives us a chance to shape its digital future and show how technology can make racing exciting and responsible. For us, Formula E is not just a sport, it’s a statement about where the world is headed. 

Megan: Fantastic. 500 million fans, that’s a huge number, isn’t it? And with more scale often comes a kind of greater expectation. Dan, I know you touched on this a little in your first question, but what is it that your fans now really want from their interactions? Can you talk a bit more about what experiences they’re looking for? And also, how complex that really is to deliver that as well? 

Dan: I think a really telling thing about the modern day fan is I probably can’t tell you what they want from their experiences, because it’s individual and it’s unique for each of them. 

Megan: Of course. 

Dan: And it’s changing and it’s changing so fast. What somebody wants this month is going to be different from what they want in a couple of months’ time. And we’re having to learn to adapt to that. My CTO title, we often put focus on the technology in the middle of it. That’s what the T is. Actually, if you think about it, it’s continual transformation officer. You are constantly trying to change what you deliver and how you deliver it. Because if fans come through, they find new experiences, they find that in other sports. Sometimes not in sports, they find it outside, and then they’re coming in, and they expect that from you. So how can we make them more part of the sport, more personalized experience, get to know the athletes and the personalities and the characters within it? We’re a very technology centric sport. A lot of motor sport is, but really, people want to see people, right? And even when it’s technology, they want to see people interacting with technology, and it’s how do you get that out to show people. 

Megan: Yeah, it’s no mean feat. Rohit, you’ve worked with brands on delivering these sort of fan experiences across different sports. Is motor sports perhaps more complicated than others, given that fans watch racing for different reasons than just a win? They could be focused on team dynamics, a particular driver, the way the engine is built, and so on and so forth. How does motor sports compare and how important is it therefore, that Formula E has embraced technology to manage expectations? 

Rohit: Yeah, that’s an interesting point. Motor sports are definitely more complex than other sports. Fans don’t just care about who wins, they care about how some follow team strategies, others love driver rivalries, and many are fascinated by the car technology. Formula E adds another layer, sustainability and electric innovation. This makes personalization really important. Fans want more than results. They want stories and insights. Formula E understood this early and embraced technology. 

Think about the data behind a single race, lap times, energy usage, battery performance, attack mode activation, pit strategies, it’s a lot of data. If you just show the raw numbers, it’s overwhelming. But with Infosys Topaz, we turn that into simple and engaging stories. Fans can see how a driver fought back from 10th place to finish on the podium, or how a team managed energy better to gain an edge. And for new fans, we are adding explainer videos and interactive tools in the Race Center, so that they can learn about their sport easily. This is important because Formula E is still young, and many fans are discovering it for the first time. Technology is not just about meeting expectations; it’s elevating the entire fan experience and making the sport more inclusive. 

Megan: There’s an awful lot going on there. What are some of the other ways that Formula E has already put generative AI and other emerging technologies to use? Dan, when we’ve spoken about the demand for more personalized experiences, for example. 

Dan: I see the implementation of AI for us in three areas. We have AI within the sport. That’s in our DNA of the sport. Now, each team is using that, but how can we use that as a championship as well? How do we make it a competitive landscape? Now, we have AI that is in the fan-facing product. That’s what we’re working heavily on Infosys with, but we also have it in our broadcast product. As an example, you might have heard of a super slow-mo camera. A super slow-mo camera is basically, by taking three cameras and having them in exactly the same place so that you get three times the frame rate, and then you can do a slow-motion shot from that. And they used to be really expensive. Quite bulky cameras to put in. We are now using AI to take a traditional camera and interpolate between two frames to make it into a super slow image, and you wouldn’t really know the difference. Now, the joy of that, it means every camera can now be a super slow-mo camera. 

Megan: Wow. 

Dan: In other ways, we use it a little bit in our graphics products, and we iterate and we use it for things like showing driver audio. When the driver is speaking to his engineer or her engineer in the garage, we show that text now on screen. We do that using AI. We use AI to pick out the difference between the driver and another driver and the team engineer or the team principal and show that in a really good way. 

And we wouldn’t be able to do that. We’re not big enough to have a team of 24 people on stenographers typing. We have to use AI to be able to do that. That’s what’s really helped us grow. And then the last one is, how we use it in our business. Because ultimately, as we’ve got the fans, we’ve got the sport, but we also are running a business and we have to pick up these racetracks and move them around the world, and we have all these staff who have to get places. We have insurance who has to do all that kind of stuff, and we use it heavily in that area, particularly when it comes to what has a carbon impact for us. 

So things like our freight and our travel. And we are using the AI tools to tell us, a battery for instance, should we fly it? Should we send it by sea freight? Should we send it by row freight? Or should we just have lots of them? And that sort of depends. Now, a battery, if it was heavy, you’d think you probably wouldn’t fly it. But actually, because of the materials in it, because of the source materials that make it, we’re better off flying it. We’ve used AI to work through all those different machinations of things that would be too difficult to do at speed for a person. 

Megan: Well, sounds like there’s some fascinating things going on. I mean, of course, for a global brand, there is also the challenge of working in different markets. You mentioned moving everything around the world there. Each market with its own legal frameworks around data privacy, AI. How has technology also helped you navigate all of that, Dan? 

Dan: The other really interesting thing about AI is… I’ve worked in technology leadership roles for some time now. And historically, we would be going around the company, banging on everyone’s doors and dragging them towards technology, making them use systems, making them move things to the cloud and things like that. What AI has done is it’s turned that around on its head, and we now have people turning up, banging on our door because they want to use this tool, they want to use that tool. And we’re trying to accommodate all of that and it’s a great pleasure to see people that are so keen. AI is driving the tech adoption in general, which really helps the business. 

Megan: Dan, as the world’s first all-electric motor sport series, sustainability is obviously a real cornerstone of what Formula E is looking to do. Can you share with us how technology is helping you to achieve some of your ambitions when it comes to sustainability? 

Dan: We’ve been the only sport with a certified net-zero pathway, and we have to stay that part. It’s a really core fundamental part of our DNA. I sit on our management team here. There is a sustainability VP that sits there as well, who checks and challenges everything we do. She looks at the data centers we use, why we use them, why we’ve made the decisions we’ve made, to make sure that we’re making them all for the right reasons and the right ways. We specifically embed technology in a couple of ways. One is, we mentioned a little bit earlier, on our freight. Formula E’s freight for the whole championship is probably akin to one Formula One team, but it’s still by far, our biggest contributor to our impact. So we look about how we can make sure that we’ve refined that to get the minimum amount of air freight and sea freight, and use local wherever we can. That’s also part of our pledge about investing in the communities that we race in. 

The second then is about our staff travel. And we’ve done a really big piece of work over the last four to five years, partly accelerated through the covid-19 era actually, of doing remote working and remote TV production. Used to be traditionally, you would fly a hundred plus people out to racetracks, and then they would make the television all on site in trucks, and then they would be satellite distributed out of the venue. Now, what we do is we put in some internet connections, dual and diverse internet connections, and we stream every single camera back. 

Megan: Right. 

Dan: That means on site, we only need camera operators. Some of them actually, are remotely operated anyway, but we need camera operators, and then some engineering teams to just keep everything running. And then back in our home base, which is in London, in the UK, we have our remote production center where we layer on direction, graphics, audio, replay, team radio, all of those bits that break the color and make the program and add to that significant body of people. We do that all remotely now. Really interesting actually, a bit. So that’s the carbon sustainability story, but there is a further ESG piece that comes out of it and we haven’t really accommodated when we went into it, is the diversity in our workforce by doing that. We were discovering that we had quite a young, equally diverse workforce until around the age of 30. And then once that happened, then we were finding we were losing women, and that’s really because they didn’t want to travel. 

Megan: Right. 

Dan: And that’s the age of people starting to have children, and things were starting to change. And then we had some men that were traveling instead, and they weren’t seeing their children and it was sort of dividing it unnecessarily. But by going remote, by having so much of our people able to remotely… Or even if they do have to travel, they’re not traveling every single week. They’re now doing that one in three. They’re able to maintain the careers and the jobs they want to do, whilst having a family lifestyle. And it also just makes a better product by having people in that environment. 

Megan: That’s such an interesting perspective, isn’t it? It’s a way of environmental sustainability intersects with social sustainability. And Rohit, and your work are so interesting. And Rohit, can you share any of the ways that Infosys has worked with Formula E, in terms of the role of technology as we say, in furthering those ambitions around sustainability? 

Rohit: Yeah. Infosys understands that sustainability is at the heart of Formula E, and it’s a big part of why this partnership matters. Formula E is already net-zero certified, but now, they have an ambitious goal to cut carbon emissions by 45%. Infosys is helping in two ways. First, we have built AI-powered sustainability data tools that make carbon reporting accurate and traceable. Every watt of energy, every logistic decision, every material use can be tracked. Second, we use predictive analytics to model scenarios, like how changing race logistics or battery technology impact emissions so Formula E can make smarter, greener decisions. For us, it’s about turning sustainability from a report into an action plan, and making Formula E a global leader in green motor sport. 

Megan: And in April 2025, Formula E working with Infosys launched its Stats Centre, which provides fans with interactive access to the performances of their drivers and teams, key milestones and narratives. I know you touched on this before, but I wonder if you could tell us a bit more about the design of that platform, Rohit, and how it fits into Formula E’s wider plans to personalize that fan experience? 

Rohit: Sure. The Stats Centre was a big step forward. Before this, fans had access to basic statistics on the website and the mobile app, but nothing told the full story and we wanted to change that. Built on Infosys Topaz, the Stats Centre uses AI to turn race data into interactive stories. Fans can explore key stat cards that adapt to race timelines, and even chat with an AI companion to get instant answers. It’s like having a person race analyst at your fingertips. And we are going further. Next year, we’ll launch Race Centre. It’ll have live data boards, 2D track maps showing every driver’s position, overtakes and more attack timelines, and AI-generated commentary. Fans can predict podium finishes, vote for the driver of the race, and share their views on social media. Plus, we are adding video explainers for new fans, covering rules, strategies, and car technology. Our goal is simple: make every moment exciting and easy to understand. Whether you are a hardcore fan or someone watching Formula E for the first time, you’ll feel connected and informed. 

Megan: Fantastic. Sounds brilliant. And as you’ve explained, Dan, leveraging data and AI can come with these huge benefits when it comes to the depth of fan experience that you can deliver, but it can also expose you to some challenges. How are you navigating those at Formula E? 

Dan: The AI generation has presented two significant challenges to us. One is that traditional SEO, traditional search engine optimization, goes out the window. Right? You are now looking at how do we design and build our systems and how do we populate them with the right content and the right data, so that the engines are picking it up correctly and displaying it? The way that the foundational models are built and the speed and the cadence of which they’re updated, means quite often… We’re a very fast-changing organization. We’re a fast-changing product. Often, the models don’t keep up. And that’s because they are a point in time when they were trained. And that’s something that the big organizations, the big tech organizations will fix with time. But for now, what we have to do is we have to learn about how we can present our fan-facing, web-facing products to show that correctly. That’s all about having really accurate first-party content, effectively earned media. That’s the piece we need to do. 

Then the second sort of challenge is sadly, whilst these tools are available to all of us, and we are using them effectively, so are another part of the technology landscape, and that is the cybersecurity basically they come with. If you look at the speed of the cadence and severity of hacks that are happening now, it’s just growing and growing and growing, and that’s because they have access to these tools too. And we’re having to really up our game and professionalize. And that’s really hard for an innovative organization. You don’t want to shut everything down. You don’t want to protect everything too much because you want people to be able to try new things. Right? If I block everything to only things that the IT team had heard of, we’d never get anything new in, and it’s about getting that balance right. 

Megan: Right. 

Dan: Rohit, you probably have similar experiences? 

Megan: How has Infosys worked with Formula E to help it navigate some of that, Rohit? 

Rohit: Yeah. Infosys has helped Formula E tackle some of the challenges in three key ways, simplify complex race data into engaging fan experience through platforms like Stats Centre, building a secure and scalable cloud data backbone for the real-time insights, and enabling sustainability goals with AI-driven carbon tracking and predictive analytics. This solution makes the sport interactive, more digital, and more responsible. 

Megan: Fantastic. I wondered if we could close with a bit of a future forward look. Can you share with us any innovations on the horizon at Formula E that you are really excited about, Dan? 

Dan: We have mentioned the Race Centre is going to launch in the next couple of months, but the really exciting thing for me is we’ve got an amazing season ahead of us. It’s the last season of our Gen3 car, with 10 really exciting teams on the grid. We are going at speed with our tech innovation roadmap and what our fans want. And we’re building up towards our Gen4 car, which will come out for season 13 in a year’s time. That will get launched in 2026, and I think it will be a game changer in how people perceive electric motor sport and electric cars in general. 

Megan: It sounds like there’s all sorts of exciting things going on. And Rohit too, what’s coming up via this partnership that you are really looking forward to sharing with everyone? 

Rohit: Two things stand out for me. First is the AI-powered fan data platform that I’ve already spoken about. Second is the launch of Race Centre. It’s going to change how fans experience live racing. And beyond final engagement, we are helping Formula E lead in sustainability with AI tools that model carbon impact and optimize logistics. This means every race can be smarter and greener. Our goal is clear: help Formula E be the most digital and sustainable motor sport in the world. The future is electric, and with AI, it’s more engaging than ever. 

Megan: Fantastic. Thank you so much, both. That was Rohit Agnihotri, principal technologist at Infosys, and Dan Cherowbrier, CITO of Formula E, whom I spoke with from Brighton, England.  

That’s it for this episode of Business Lab. I’m your host, Megan Tatum. I’m a contributing editor and host for Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print, on the web and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.  

This show is available wherever you get your podcasts. And if you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review and this episode was produced by Giro Studios. Thanks for listening. 

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Introducing: the AI Hype Correction package

AI is going to reproduce human intelligence. AI will eliminate disease. AI is the single biggest, most important invention in human history. You’ve likely heard it all—but probably none of these things are true.

AI is changing our world, but we don’t yet know the real winners, or how this will all shake out.

After a few years of out-of-control hype, people are now starting to re-calibrate what AI is, what it can do, and how we should think about its ultimate impact.

Here, at the end of 2025, we’re starting the post-hype phase. This new package of stories, called Hype Correction, is a way to reset expectations—a critical look at where we are, what AI makes possible, and where we go next.

Here’s a sneak peek at what you can expect:

+ An introduction to four ways of thinking about the great AI hype correction of 2025.

+  While it’s safe to say we’re definitely in an AI bubble right now, what’s less clear is what it really looks like—and what comes after it pops. Read the full story.

+ Why OpenAI’s Sam Altman can be traced back to so many of the more outlandish proclamations about AI doing the rounds these days. Read the full story.

+ It’s a weird time to be an AI doomer. But they’re not giving up.

+ AI coding is now everywhere—but despite the billions of dollars being poured into improving AI models’ coding abilities, not everyone is convinced. Read the full story.

+ If we really want to start finding new kinds of materials faster, AI materials discovery needs to make it out of the lab and move into the real world. Read the full story.

+ Why reports of AI’s potential to replace trained human lawyers are greatly exaggerated.

+ Dr. Margaret Mitchell, chief ethics scientist at AI startup Hugging Face, explains why the generative AI hype train is distracting us from what AI actually is and what it can—and crucially, cannot—do. Read the full story.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 iRobot has filed for bankruptcy
The Roomba maker is considering handing over control to its main Chinese supplier. (Bloomberg $)
+ A proposed Amazon acquisition fell through close to two years ago. (FT $)
+ How the company lost its way. (TechCrunch)
+ A Roomba recorded a woman on the toilet. How did screenshots end up on Facebook? (MIT Technology Review)

2 Meta’s 2025 has been a total rollercoaster ride
From its controversial AI team to Mark Zuckerberg’s newfound appreciation for masculine energy. (Insider $)

3 The Trump administration is giving the crypto industry a much easier ride
It’s dismissed crypto lawsuits involving many firms with financial ties to Trump. (NYT $)
+ Celebrities are feeling emboldened to flog crypto once again. (The Guardian)
+ A bitcoin investor wants to set up a crypto libertarian community in the Caribbean. (FT $)

4 There’s a new weight-loss drug in town
And people are already taking it, even though it’s unapproved. (Wired $)
+ What we still don’t know about weight-loss drugs. (MIT Technology Review)

5 Chinese billionaires are having dozens of US-born surrogate babies
An entire industry has sprung up to support them. (WSJ $)
+ A controversial Chinese CRISPR scientist is still hopeful about embryo gene editing. (MIT Technology Review)

6 Trump’s “big beautiful bill” funding hinges on states integrating AI into healthcare
Experts fear it’ll be used as a cost-cutting measure, even if it doesn’t work. (The Guardian)
+ Artificial intelligence is infiltrating health care. We shouldn’t let it make all the decisions. (MIT Technology Review)

7 Extreme rainfall is wreaking havoc in the desert
Oman and the UAE are unaccustomed to increasingly common torrential downpours. (WP $)

8 Data centers are being built in countries that are too hot for them
Which makes it a lot harder to cool them sufficiently. (Rest of World)

9 Why AI image generators are getting deliberately worse
Their makers are pursuing realism—not that overly polished, Uncanny Valley look. (The Verge)
+ Inside the AI attention economy wars. (NY Mag $)

10 How a tiny Swedish city became a major video game hub
Skövde has formed an unlikely community of cutting-edge developers. (The Guardian)
+ Google DeepMind is using Gemini to train agents inside one of Skövde’s biggest franchises. (MIT Technology Review)

Quote of the day

“They don’t care about the games. They don’t care about the art. They just want their money.”

—Anna C Webster, chair of the freelancing committee of the United Videogame Workers union, tells the Guardian why their members are protesting the prestigious 2025 Game Awards in the wake of major layoffs.

One more thing

Recapturing early internet whimsy with HTML

Websites weren’t always slick digital experiences.

There was a time when surfing the web involved opening tabs that played music against your will and sifting through walls of text on a colored background. In the 2000s, before Squarespace and social media, websites were manifestations of individuality—built from scratch using HTML, by users who had some knowledge of code.

Scattered across the web are communities of programmers working to revive this seemingly outdated approach. And the movement is anything but a superficial appeal to retro aesthetics—it’s about celebrating the human touch in digital experiences. Read the full story.

—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 or skeet ’em at me.)

+  Here’s how a bit of math can help you wrap your presents much more neatly this year.
+ It seems that humans mastered making fire way, way earlier than we realized.
+ The Arab-owned cafes opening up across the US sound warm and welcoming.
+ How to give a gift the recipient will still be using and loving for decades to come.

Depending who you ask, AI-powered coding is either giving software developers an unprecedented productivity boost or churning out masses of poorly designed code that saps their attention and sets software projects up for serious long term-maintenance problems.

The problem is right now, it’s not easy to know which is true.

As tech giants pour billions into large language models (LLMs), coding has been touted as the technology’s killer app. Both Microsoft CEO Satya Nadella and Google CEO Sundar Pichai have claimed that around a quarter of their companies’ code is now AI-generated. And in March, Anthropic’s CEO, Dario Amodei, predicted that within six months 90% of all code would be written by AI. It’s an appealing and obvious use case. Code is a form of language, we need lots of it, and it’s expensive to produce manually. It’s also easy to tell if it works—run a program and it’s immediately evident whether it’s functional.

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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.

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Executives enamored with the potential to break through human bottlenecks are pushing engineers to lean into an AI-powered future. But after speaking to more than 30 developers, technology executives, analysts, and researchers, MIT Technology Review found that the picture is not as straightforward as it might seem.  

For some developers on the front lines, initial enthusiasm is waning as they bump up against the technology’s limitations. And as a growing body of research suggests that the claimed productivity gains may be illusory, some are questioning whether the emperor is wearing any clothes.

The pace of progress is complicating the picture, though. A steady drumbeat of new model releases mean these tools’ capabilities and quirks are constantly evolving. And their utility often depends on the tasks they are applied to and the organizational structures built around them. All of this leaves developers navigating confusing gaps between expectation and reality. 

Is it the best of times or the worst of times (to channel Dickens) for AI coding? Maybe both.

A fast-moving field

It’s hard to avoid AI coding tools these days. There are a dizzying array of products available, both from model developers like Anthropic, OpenAI, and Google and from companies like Cursor and Windsurf, which wrap these models in polished code-editing software. And according to Stack Overflow’s 2025 Developer Survey, they’re being adopted rapidly, with 65% of developers now using them at least weekly.

AI coding tools first emerged around 2016 but were supercharged with the arrival of LLMs. Early versions functioned as little more than autocomplete for programmers, suggesting what to type next. Today they can analyze entire code bases, edit across files, fix bugs, and even generate documentation explaining how the code works. All this is guided through natural-language prompts via a chat interface.

“Agents”—autonomous LLM-powered coding tools that can take a high-level plan and build entire programs independently—represent the latest frontier in AI coding. This leap was enabled by the latest reasoning models, which can tackle complex problems step by step and, crucially, access external tools to complete tasks. “This is how the model is able to code, as opposed to just talk about coding,” says Boris Cherny, head of Claude Code, Anthropic’s coding agent.

These agents have made impressive progress on software engineering benchmarks—standardized tests that measure model performance. When OpenAI introduced the SWE-bench Verified benchmark in August 2024, offering a way to evaluate agents’ success at fixing real bugs in open-source repositories, the top model solved just 33% of issues. A year later, leading models consistently score above 70%. 

In February, Andrej Karpathy, a founding member of OpenAI and former director of AI at Tesla, coined the term “vibe coding”—meaning an approach where people describe software in natural language and let AI write, refine, and debug the code. Social media abounds with developers who have bought into this vision, claiming massive productivity boosts.

But while some developers and companies report such productivity gains, the hard evidence is more mixed. Early studies from GitHub, Google, and Microsoft—all vendors of AI tools—found developers completing tasks 20% to 55% faster. But a September report from the consultancy Bain & Company described real-world savings as “unremarkable.”

Data from the developer analytics firm GitClear shows that most engineers are producing roughly 10% more durable code—code that isn’t deleted or rewritten within weeks—since 2022, likely thanks to AI. But that gain has come with sharp declines in several measures of code quality. Stack Overflow’s survey also found trust and positive sentiment toward AI tools falling significantly for the first time. And most provocatively, a July study by the nonprofit research organization Model Evaluation & Threat Research (METR) showed that while experienced developers believed AI made them 20% faster, objective tests showed they were actually 19% slower.

Growing disillusionment

For Mike Judge, principal developer at the software consultancy Substantial, the METR study struck a nerve. He was an enthusiastic early adopter of AI tools, but over time he grew frustrated with their limitations and the modest boost they brought to his productivity. “I was complaining to people because I was like, ‘It’s helping me but I can’t figure out how to make it really help me a lot,’” he says. “I kept feeling like the AI was really dumb, but maybe I could trick it into being smart if I found the right magic incantation.”

When asked by a friend, Judge had estimated the tools were providing a roughly 25% speedup. So when he saw similar estimates attributed to developers in the METR study he decided to test his own. For six weeks, he guessed how long a task would take, flipped a coin to decide whether to use AI or code manually, and timed himself. To his surprise, AI slowed him down by an median of 21%—mirroring the METR results.

This got Judge crunching the numbers. If these tools were really speeding developers up, he reasoned, you should see a massive boom in new apps, website registrations, video games, and projects on GitHub. He spent hours and several hundred dollars analyzing all the publicly available data and found flat lines everywhere.

“Shouldn’t this be going up and to the right?” says Judge. “Where’s the hockey stick on any of these graphs? I thought everybody was so extraordinarily productive.” The obvious conclusion, he says, is that AI tools provide little productivity boost for most developers. 

Developers interviewed by MIT Technology Review generally agree on where AI tools excel: producing “boilerplate code” (reusable chunks of code repeated in multiple places with little modification), writing tests, fixing bugs, and explaining unfamiliar code to new developers. Several noted that AI helps overcome the “blank page problem” by offering an imperfect first stab to get a developer’s creative juices flowing. It can also let nontechnical colleagues quickly prototype software features, easing the load on already overworked engineers.

These tasks can be tedious, and developers are typically  glad to hand them off. But they represent only a small part of an experienced engineer’s workload. For the more complex problems where engineers really earn their bread, many developers told MIT Technology Review, the tools face significant hurdles.

Perhaps the biggest problem is that LLMs can hold only a limited amount of information in their “context window”—essentially their working memory. This means they struggle to parse large code bases and are prone to forgetting what they’re doing on longer tasks. “It gets really nearsighted—it’ll only look at the thing that’s right in front of it,” says Judge. “And if you tell it to do a dozen things, it’ll do 11 of them and just forget that last one.”

LLMs’ myopia can lead to headaches for human coders. While an LLM-generated response to a problem may work in isolation, software is made up of hundreds of interconnected modules. If these aren’t built with consideration for other parts of the software, it can quickly lead to a tangled, inconsistent code base that’s hard for humans to parse and, more important, to maintain.

Developers have traditionally addressed this by following conventions—loosely defined coding guidelines that differ widely between projects and teams. “AI has this overwhelming tendency to not understand what the existing conventions are within a repository,” says Bill Harding, the CEO of GitClear. “And so it is very likely to come up with its own slightly different version of how to solve a problem.”

The models also just get things wrong. Like all LLMs, coding models are prone to “hallucinating”—it’s an issue built into how they work. But because the code they output looks so polished, errors can be difficult to detect, says James Liu, director of software engineering at the advertising technology company Mediaocean. Put all these flaws together, and using these tools can feel a lot like pulling a lever on a one-armed bandit. “Some projects you get a 20x improvement in terms of speed or efficiency,” says Liu. “On other things, it just falls flat on its face, and you spend all this time trying to coax it into granting you the wish that you wanted and it’s just not going to.”

Judge suspects this is why engineers often overestimate productivity gains. “You remember the jackpots. You don’t remember sitting there plugging tokens into the slot machine for two hours,” he says.

And it can be particularly pernicious if the developer is unfamiliar with the task. Judge remembers getting AI to help set up a Microsoft cloud service called Azure Functions, which he’d never used before. He thought it would take about two hours, but nine hours later he threw in the towel. “It kept leading me down these rabbit holes and I didn’t know enough about the topic to be able to tell it ‘Hey, this is nonsensical,’” he says.

The debt begins to mount up

Developers constantly make trade-offs between speed of development and the maintainability of their code—creating what’s known as “technical debt,” says Geoffrey G. Parker, professor of engineering innovation at Dartmouth College. Each shortcut adds complexity and makes the code base harder to manage, accruing “interest” that must eventually be repaid by restructuring the code. As this debt piles up, adding new features and maintaining the software becomes slower and more difficult.

Accumulating technical debt is inevitable in most projects, but AI tools make it much easier for time-pressured engineers to cut corners, says GitClear’s Harding. And GitClear’s data suggests this is happening at scale. Since 2022, the company has seen a significant rise in the amount of copy-pasted code—an indicator that developers are reusing more code snippets, most likely based on AI suggestions—and an even bigger decline in the amount of code moved from one place to another, which happens when developers clean up their code base.

And as models improve, the code they produce is becoming increasingly verbose and complex, says Tariq Shaukat, CEO of Sonar, which makes tools for checking code quality. This is driving down the number of obvious bugs and security vulnerabilities, he says, but at the cost of increasing the number of “code smells”—harder-to-pinpoint flaws that lead to maintenance problems and technical debt. 

Recent research by Sonar found that these make up more than 90% of the issues found in code generated by leading AI models. “Issues that are easy to spot are disappearing, and what’s left are much more complex issues that take a while to find,” says Shaukat. “That’s what worries us about this space at the moment. You’re almost being lulled into a false sense of security.”

If AI tools make it increasingly difficult to maintain code, that could have significant security implications, says Jessica Ji, a security researcher at Georgetown University. “The harder it is to update things and fix things, the more likely a code base or any given chunk of code is to become insecure over time,” says Ji.

There are also more specific security concerns, she says. Researchers have discovered a worrying class of hallucinations where models reference nonexistent software packages in their code. Attackers can exploit this by creating packages with those names that harbor vulnerabilities, which the model or developer may then unwittingly incorporate into software. 

LLMs are also vulnerable to “data-poisoning attacks,” where hackers seed the publicly available data sets models train on with data that alters the model’s behavior in undesirable ways, such as generating insecure code when triggered by specific phrases. In October, research by Anthropic found that as few as 250 malicious documents can introduce this kind of back door into an LLM regardless of its size.

The converted

Despite these issues, though, there’s probably no turning back. “Odds are that writing every line of code on a keyboard by hand—those days are quickly slipping behind us,” says Kyle Daigle, chief operating officer at the Microsoft-owned code-hosting platform GitHub, which produces a popular AI-powered tool called Copilot (not to be confused with the Microsoft product of the same name).

The Stack Overflow report found that despite growing distrust in the technology, usage has increased rapidly and consistently over the past three years. Erin Yepis, a senior analyst at Stack Overflow, says this suggests that engineers are taking advantage of the tools with a clear-eyed view of the risks. The report also found that frequent users tend to be more enthusiastic and more than half of developers are not using the latest coding agents, perhaps explaining why many remain underwhelmed by the technology.

Those latest tools can be a revelation. Trevor Dilley, CTO at the software development agency Twenty20 Ideas, says he had found some value in AI editors’ autocomplete functions, but when he tried anything more complex it would “fail catastrophically.” Then in March, while on vacation with his family, he set the newly released Claude Code to work on one of his hobby projects. It completed a four-hour task in two minutes, and the code was better than what he would have written.

“I was like, Whoa,” he says. “That, for me, was the moment, really. There’s no going back from here.” Dilley has since cofounded a startup called DevSwarm, which is creating software that can marshal multiple agents to work in parallel on a piece of software.

The challenge, says Armin Ronacher, a prominent open-source developer, is that the learning curve for these tools is shallow but long. Until March he’d remained unimpressed by AI tools, but after leaving his job at the software company Sentry in April to launch a startup, he started experimenting with agents. “I basically spent a lot of months doing nothing but this,” he says. “Now, 90% of the code that I write is AI-generated.”

Getting to that point involved extensive trial and error, to figure out which problems tend to trip the tools up and which they can handle efficiently. Today’s models can tackle most coding tasks with the right guardrails, says Ronacher, but these can be very task and project specific.

To get the most out of these tools, developers must surrender control over individual lines of code and focus on the overall software architecture, says Nico Westerdale, chief technology officer at the veterinary staffing company IndeVets. He recently built a data science platform 100,000 lines of code long almost exclusively by prompting models rather than writing the code himself.

Westerdale’s process starts with an extended conversation with the model to develop a detailed plan for what to build and how. He then guides it through each step. It rarely gets things right on the first try and needs constant wrangling, but if you force it to stick to well-defined design patterns, the models can produce high-quality, easily maintainable code, says Westerdale. He reviews every line, and the code is as good as anything he’s ever produced, he says: “I’ve just found it absolutely revolutionary,. It’s also frustrating, difficult, a different way of thinking, and we’re only just getting used to it.”

But while individual developers are learning how to use these tools effectively, getting consistent results across a large engineering team is significantly harder. AI tools amplify both the good and bad aspects of your engineering culture, says Ryan J. Salva, senior director of product management at Google. With strong processes, clear coding patterns, and well-defined best practices, these tools can shine. 

But if your development process is disorganized, they’ll only magnify the problems. It’s also essential to codify that institutional knowledge so the models can draw on it effectively. “A lot of work needs to be done to help build up context and get the tribal knowledge out of our heads,” he says.

The cryptocurrency exchange Coinbase has been vocal about its adoption of AI tools. CEO Brian Armstrong made headlines in August when he revealed that the company had fired staff unwilling to adopt AI tools. But Coinbase’s head of platform, Rob Witoff, tells MIT Technology Review that while they’ve seen massive productivity gains in some areas, the impact has been patchy. For simpler tasks like restructuring the code base and writing tests, AI-powered workflows have achieved speedups of up to 90%. But gains are more modest for other tasks, and the disruption caused by overhauling existing processes often counteracts the increased coding speed, says Witoff.

One factor is that AI tools let junior developers produce far more code. As in almost all engineering teams, this code has to be reviewed by others, normally more senior developers, to catch bugs and ensure it meets quality standards. But the sheer volume of code now being churned out is quickly saturating the ability of midlevel staff to review changes. “This is the cycle we’re going through almost every month, where we automate a new thing lower down in the stack, which brings more pressure higher up in the stack,” he says. “Then we’re looking at applying automation to that higher-up piece.”

Developers also spend only 20% to 40% of their time coding, says Jue Wang, a partner at Bain, so even a significant speedup there often translates to more modest overall gains. Developers spend the rest of their time analyzing software problems and dealing with customer feedback, product strategy, and administrative tasks. To get significant efficiency boosts, companies may need to apply generative AI to all these other processes too, says Jue, and that is still in the works.

Rapid evolution

Programming with agents is a dramatic departure from previous working practices, though, so it’s not surprising companies are facing some teething issues. These are also very new products that are changing by the day. “Every couple months the model improves, and there’s a big step change in the model’s coding capabilities and you have to get recalibrated,” says Anthropic’s Cherny.

For example, in June Anthropic introduced a built-in planning mode to Claude; it has since been replicated by other providers. In October, the company also enabled Claude to ask users questions when it needs more context or faces multiple possible solutions, which Cherny says helps it avoid the tendency to simply assume which path is the best way forward.

Most significant, Anthropic has added features that make Claude better at managing its own context. When it nears the limits of its working memory, it summarizes key details and uses them to start a new context window, effectively giving it an “infinite” one, says Cherny. Claude can also invoke sub-agents to work on smaller tasks, so it no longer has to hold all aspects of the project in its own head. The company claims that its latest model, Claude 4.5 Sonnet, can now code autonomously for more than 30 hours without major performance degradation.

Novel approaches to software development could also sidestep coding agents’ other flaws. MIT professor Max Tegmark has introduced something he calls “vericoding,” which could allow agents to produce entirely bug-free code from a natural-language description. It builds on an approach known as “formal verification,” where developers create a mathematical model of their software that can prove incontrovertibly that it functions correctly. This approach is used in high-stakes areas like flight-control systems and cryptographic libraries, but it remains costly and time-consuming, limiting its broader use.

Rapid improvements in LLMs’ mathematical capabilities have opened up the tantalizing possibility of models that produce not only software but the mathematical proof that it’s bug free, says Tegmark. “You just give the specification, and the AI comes back with provably correct code,” he says. “You don’t have to touch the code. You don’t even have to ever look at the code.”

When tested on about 2,000 vericoding problems in Dafny—a language designed for formal verification—the best LLMs solved over 60%, according to non-peer-reviewed research by Tegmark’s group. This was achieved with off-the-shelf LLMs, and Tegmark expects that training specifically for vericoding could improve scores rapidly.

And counterintuitively, the speed at which AI generates code could actually ease maintainability concerns. Alex Worden, principal engineer at the business software giant Intuit, notes that maintenance is often difficult because engineers reuse components across projects, creating a tangle of dependencies where one change triggers cascading effects across the code base. Reusing code used to save developers time, but in a world where AI can produce hundreds of lines of code in seconds, that imperative has gone, says Worden.

Instead, he advocates for “disposable code,” where each component is generated independently by AI without regard for whether it follows design patterns or conventions. They are then connected via APIs—sets of rules that let components request information or services from each other. Each component’s inner workings are not dependent on other parts of the code base, making it possible to rip them out and replace them without wider impact, says Worden. 

“The industry is still concerned about humans maintaining AI-generated code,” he says. “I question how long humans will look at or care about code.”

A narrowing talent pipeline

For the foreseeable future, though, humans will still need to understand and maintain the code that underpins their projects. And one of the most pernicious side effects of AI tools may be a shrinking pool of people capable of doing so. 

Early evidence suggests that fears around the job-destroying effects of AI may be justified. A recent Stanford University study found that employment among software developers aged 22 to 25 fell nearly 20% between 2022 and 2025, coinciding with the rise of AI-powered coding tools.

Experienced developers could face difficulties too. Luciano Nooijen, an engineer at the video-game infrastructure developer Companion Group, used AI tools heavily in his day job, where they were provided for free. But when he began a side project without access to those tools, he found himself struggling with tasks that previously came naturally. “I was feeling so stupid because things that used to be instinct became manual, sometimes even cumbersome,” says Nooijen.

Just as athletes still perform basic drills, he thinks the only way to maintain an instinct for coding is to regularly practice the grunt work. That’s why he’s largely abandoned AI tools, though he admits that deeper motivations are also at play. 

Part of the reason Nooijen and other developers MIT Technology Review spoke to are pushing back against AI tools is a sense that they are hollowing out the parts of their jobs that they love. “I got into software engineering because I like working with computers. I like making machines do things that I want,” Nooijen says. “It’s just not fun sitting there with my work being done for me.”

Each time you’ve heard a borderline outlandish idea of what AI will be capable of, it often turns out that Sam Altman was, if not the first to articulate it, at least the most persuasive and influential voice behind it. 

For more than a decade he has been known in Silicon Valley as a world-class fundraiser and persuader. OpenAI’s early releases around 2020 set the stage for a mania around large language models, and the launch of ChatGPT in November 2022 granted Altman a world stage on which to present his new thesis: that these models mirror human intelligence and could swing the doors open to a healthier and wealthier techno-utopia.

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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.

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Throughout, Altman’s words have set the agenda. He has framed a prospective superintelligent AI as either humanistic or catastrophic, depending on what effect he was hoping to create, what he was raising money for, or which tech giant seemed like his most formidable competitor at the moment. 

Examining Altman’s statements over the years reveals just how much his outlook has powered today’s AI boom. Even among Silicon Valley’s many hypesters, he’s been especially willing to speak about open questions—whether large language models contain the ingredients of human thought, whether language can also produce intelligence—as if they were already answered. 

In conclusion … 

Altman didn’t dupe the world. OpenAI has ushered in a genuine tech revolution, with increasingly impressive language models that have attracted millions of users. Even skeptics would concede that LLMs’ conversational ability is astonishing.

But Altman’s hype has always hinged less on today’s capabilities than on a philosophical tomorrow—an outlook that quite handily doubles as a case for more capital and friendlier regulation. Long before large language models existed, he was imagining an AI powerful enough to require wealth redistribution, just as he imagined humanity colonizing other planets. Again and again, promises of a destination—abundance, superintelligence, a healthier and wealthier world—have come first, and the evidence second. 

Even if LLMs eventually hit a wall, there’s little reason to think his faith in a techno-utopian future will falter. The vision was never really about the particulars of the current model anyway. 

It’s a weird time to be an AI doomer.

This small but influential community of researchers, scientists, and policy experts believes, in the simplest terms, that AI could get so good it could be bad—very, very bad—for humanity. Though many of these people would be more likely to describe themselves as advocates for AI safety than as literal doomsayers, they warn that AI poses an existential risk to humanity. They argue that absent more regulation, the industry could hurtle toward systems it can’t control. They commonly expect such systems to follow the creation of artificial general intelligence (AGI), a slippery concept generally understood as technology that can do whatever humans can do, and better. 

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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.

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Though this is far from a universally shared perspective in the AI field, the doomer crowd has had some notable success over the past several years: helping shape AI policy coming from the Biden administration, organizing prominent calls for international “red lines” to prevent AI risks, and getting a bigger (and more influential) megaphone as some of its adherents win science’s most prestigious awards.

But a number of developments over the past six months have put them on the back foot. Talk of an AI bubble has overwhelmed the discourse as tech companies continue to invest in multiple Manhattan Projects’ worth of data centers without any certainty that future demand will match what they’re building. 

And then there was the August release of OpenAI’s latest foundation model, GPT-5, which proved something of a letdown. Maybe that was inevitable, since it was the most hyped AI release of all time; OpenAI CEO Sam Altman had boasted that GPT-5 felt “like a PhD-level expert” in every topic and told the podcaster Theo Von that the model was so good, it had made him feel “useless relative to the AI.” 

Many expected GPT-5 to be a big step toward AGI, but whatever progress the model may have made was overshadowed by a string of technical bugs and the company’s mystifying, quickly reversed decision to shut off access to every old OpenAI model without warning. And while the new model achieved state-of-the-art benchmark scores, many people felt, perhaps unfairly, that in day-to-day use GPT-5 was a step backward. 

All this would seem to threaten some of the very foundations of the doomers’ case. In turn, a competing camp of AI accelerationists, who fear AI is actually not moving fast enough and that the industry is constantly at risk of being smothered by overregulation, is seeing a fresh chance to change how we approach AI safety (or, maybe more accurately, how we don’t). 

This is particularly true of the industry types who’ve decamped to Washington: “The Doomer narratives were wrong,” declared David Sacks, the longtime venture capitalist turned Trump administration AI czar. “This notion of imminent AGI has been a distraction and harmful and now effectively proven wrong,” echoed the White House’s senior policy advisor for AI and tech investor Sriram Krishnan. (Sacks and Krishnan did not reply to requests for comment.) 

(There is, of course, another camp in the AI safety debate: the group of researchers and advocates commonly associated with the label “AI ethics.” Though they also favor regulation, they tend to think the speed of AI progress has been overstated and have often written off AGI as a sci-fi story or a scam that distracts us from the technology’s immediate threats. But any potential doomer demise wouldn’t exactly give them the same opening the accelerationists are seeing.)

So where does this leave the doomers? As part of our Hype Correction package, we decided to ask some of the movement’s biggest names to see if the recent setbacks and general vibe shift had altered their views. Are they angry that policymakers no longer seem to heed their threats? Are they quietly adjusting their timelines for the apocalypse? 

Recent interviews with 20 people who study or advocate AI safety and governance—including Nobel Prize winner Geoffrey Hinton, Turing Prize winner Yoshua Bengio, and high-profile experts like former OpenAI board member Helen Toner—reveal that rather than feeling chastened or lost in the wilderness, they’re still deeply committed to their cause, believing that AGI remains not just possible but incredibly dangerous.

At the same time, they seem to be grappling with a near contradiction. While they’re somewhat relieved that recent developments suggest AGI is further out than they previously thought (“Thank God we have more time,” says AI researcher Jeffrey Ladish), they also feel frustrated that some people in power are pushing policy against their cause (Daniel Kokotajlo, lead author of a cautionary forecast called “AI 2027,” says “AI policy seems to be getting worse” and calls the Sacks and Krishnan tweets “deranged and/or dishonest.”)

Broadly speaking, these experts see the talk of an AI bubble as no more than a speed bump, and disappointment in GPT-5 as more distracting than illuminating. They still generally favor more robust regulation and worry that progress on policy—the implementation of the EU AI Act; the passage of the first major American AI safety bill, California’s SB 53; and new interest in AGI risk from some members of Congress—has become vulnerable as Washington overreacts to what doomers see as short-term failures to live up to the hype. 

Some were also eager to correct what they see as the most persistent misconceptions about the doomer world. Though their critics routinely mock them for predicting that AGI is right around the corner, they claim that’s never been an essential part of their case: It “isn’t about imminence,” says Berkeley professor Stuart Russell, the author of Human Compatible: Artificial Intelligence and the Problem of Control. Most people I spoke with say their timelines to dangerous systems have actually lengthened slightly in the last year—an important change given how quickly the policy and technical landscapes can shift. 

“If someone said there’s a four-mile-diameter asteroid that’s going to hit the Earth in 2067, we wouldn’t say, ‘Remind me in 2066 and we’ll think about it.’”

Many of them, in fact, emphasize the importance of changing timelines. And even if they are just a tad longer now, Toner tells me that one big-picture story of the ChatGPT era is the dramatic compression of these estimates across the AI world. For a long while, she says, AGI was expected in many decades. Now, for the most part, the predicted arrival is sometime in the next few years to 20 years. So even if we have a little bit more time, she (and many of her peers) continue to see AI safety as incredibly, vitally urgent. She tells me that if AGI were possible anytime in even the next 30 years, “It’s a huge fucking deal. We should have a lot of people working on this.”

So despite the precarious moment doomers find themselves in, their bottom line remains that no matter when AGI is coming (and, again, they say it’s very likely coming), the world is far from ready. 

Maybe you agree. Or maybe you may think this future is far from guaranteed. Or that it’s the stuff of science fiction. You may even think AGI is a great big conspiracy theory. You’re not alone, of course—this topic is polarizing. But whatever you think about the doomer mindset, there’s no getting around the fact that certain people in this world have a lot of influence. So here are some of the most prominent people in the space, reflecting on this moment in their own words. 

Interviews have been edited and condensed for length and clarity. 

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The Nobel laureate who’s not sure what’s coming

Geoffrey Hinton, winner of the Turing Award and the Nobel Prize in physics for pioneering deep learning

The biggest change in the last few years is that there are people who are hard to dismiss who are saying this stuff is dangerous. Like, [former Google CEO] Eric Schmidt, for example, really recognized this stuff could be really dangerous. He and I were in China recently talking to someone on the Politburo, the party secretary of Shanghai, to make sure he really understood—and he did. I think in China, the leadership understands AI and its dangers much better because many of them are engineers.

I’ve been focused on the longer-term threat: When AIs get more intelligent than us, can we really expect that humans will remain in control or even relevant? But I don’t think anything is inevitable. There’s huge uncertainty on everything. We’ve never been here before. Anybody who’s confident they know what’s going to happen seems silly to me. I think this is very unlikely but maybe it’ll turn out that all the people saying AI is way overhyped are correct. Maybe it’ll turn out that we can’t get much further than the current chatbots—we hit a wall due to limited data. I don’t believe that. I think that’s unlikely, but it’s possible. 

I also don’t believe people like Eliezer Yudkowsky, who say if anybody builds it, we’re all going to die. We don’t know that. 

But if you go on the balance of the evidence, I think it’s fair to say that most experts who know a lot about AI believe it’s very probable that we’ll have superintelligence within the next 20 years. [Google DeepMind CEO] Demis Hassabis says maybe 10 years. Even [prominent AI skeptic] Gary Marcus would probably say, “Well, if you guys make a hybrid system with good old-fashioned symbolic logic … maybe that’ll be superintelligent.” [Editor’s note: In September, Marcus predicted AGI would arrive between 2033 and 2040.]

And I don’t think anybody believes progress will stall at AGI. I think more or less everybody believes a few years after AGI, we’ll have superintelligence, because the AGI will be better than us at building AI.

So while I think it’s clear that the winds are getting more difficult, simultaneously, people are putting in many more resources [into developing advanced AI]. I think progress will continue just because there’s many more resources going in.

The deep learning pioneer who wishes he’d seen the risks sooner

Yoshua Bengio, winner of the Turing Award, chair of the International AI Safety Report, and founder of LawZero

Some people thought that GPT-5 meant we had hit a wall, but that isn’t quite what you see in the scientific data and trends.

There have been people overselling the idea that AGI is tomorrow morning, which commercially could make sense. But if you look at the various benchmarks, GPT-5 is just where you would expect the models at that point in time to be. By the way, it’s not just GPT-5, it’s Claude and Google models, too. In some areas where AI systems weren’t very good, like Humanity’s Last Exam or FrontierMath, they’re getting much better scores now than they were at the beginning of the year.

At the same time, the overall landscape for AI governance and safety is not good. There’s a strong force pushing against regulation. It’s like climate change. We can put our head in the sand and hope it’s going to be fine, but it doesn’t really deal with the issue.

The biggest disconnect with policymakers is a misunderstanding of the scale of change that is likely to happen if the trend of AI progress continues. A lot of people in business and governments simply think of AI as just another technology that’s going to be economically very powerful. They don’t understand how much it might change the world if trends continue, and we approach human-level AI. 

Like many people, I had been blinding myself to the potential risks to some extent. I should have seen it coming much earlier. But it’s human. You’re excited about your work and you want to see the good side of it. That makes us a little bit biased in not really paying attention to the bad things that could happen.

Even a small chance—like 1% or 0.1%—of creating an accident where billions of people die is not acceptable. 

The AI veteran who believes AI is progressing—but not fast enough to prevent the bubble from bursting

Stuart Russell, distinguished professor of computer science, University of California, Berkeley, and author of Human Compatible

I hope the idea that talking about existential risk makes you a “doomer” or is “science fiction” comes to be seen as fringe, given that most leading AI researchers and most leading AI CEOs take it seriously. 

There have been claims that AI could never pass a Turing test, or you could never have a system that uses natural language fluently, or one that could parallel-park a car. All these claims just end up getting disproved by progress.

People are spending trillions of dollars to make superhuman AI happen. I think they need some new ideas, but there’s a significant chance they will come up with them, because many significant new ideas have happened in the last few years. 

My fairly consistent estimate for the last 12 months has been that there’s a 75% chance that those breakthroughs are not going to happen in time to rescue the industry from the bursting of the bubble. Because the investments are consistent with a prediction that we’re going to have much better AI that will deliver much more value to real customers. But if those predictions don’t come true, then there’ll be a lot of blood on the floor in the stock markets.

However, the safety case isn’t about imminence. It’s about the fact that we still don’t have a solution to the control problem. If someone said there’s a four-mile-diameter asteroid that’s going to hit the Earth in 2067, we wouldn’t say, “Remind me in 2066 and we’ll think about it.” We don’t know how long it takes to develop the technology needed to control superintelligent AI.

Looking at precedents, the acceptable level of risk for a nuclear plant melting down is about one in a million per year. Extinction is much worse than that. So maybe set the acceptable risk at one in a billion. But the companies are saying it’s something like one in five. They don’t know how to make it acceptable. And that’s a problem.

The professor trying to set the narrative straight on AI safety

David Krueger, assistant professor in machine learning at the University of Montreal and Yoshua Bengio’s Mila Institute, and founder of Evitable

I think people definitely overcorrected in their response to GPT-5. But there was hype. My recollection was that there were multiple statements from CEOs at various levels of explicitness who basically said that by the end of 2025, we’re going to have an automated drop-in replacement remote worker. But it seems like it’s been underwhelming, with agents just not really being there yet.

I’ve been surprised how much these narratives predicting AGI in 2027 capture the public attention. When 2027 comes around, if things still look pretty normal, I think people are going to feel like the whole worldview has been falsified. And it’s really annoying how often when I’m talking to people about AI safety, they assume that I think we have really short timelines to dangerous systems, or that I think LLMs or deep learning are going to give us AGI. They ascribe all these extra assumptions to me that aren’t necessary to make the case. 

I’d expect we need decades for the international coordination problem. So even if dangerous AI is decades off, it’s already urgent. That point seems really lost on a lot of people. There’s this idea of “Let’s wait until we have a really dangerous system and then start governing it.” Man, that is way too late.

I still think people in the safety community tend to work behind the scenes, with people in power, not really with civil society. It gives ammunition to people who say it’s all just a scam or insider lobbying. That’s not to say that there’s no truth to these narratives, but the underlying risk is still real. We need more public awareness and a broad base of support to have an effective response.

If you actually believe there’s a 10% chance of doom in the next 10 years—which I think a reasonable person should, if they take a close look—then the first thing you think is: “Why are we doing this? This is crazy.” That’s just a very reasonable response once you buy the premise.

The governance expert worried about AI safety’s credibility

Helen Toner, acting executive director of Georgetown University’s Center for Security and Emerging Technology and former OpenAI board member

When I got into the space, AI safety was more of a set of philosophical ideas. Today, it’s a thriving set of subfields of machine learning, filling in the gulf between some of the more “out there” concerns about AI scheming, deception, or power-seeking and real concrete systems we can test and play with. 

“I worry that some aggressive AGI timeline estimates from some AI safety people are setting them up for a boy-who-cried-wolf moment.”

AI governance is improving slowly. If we have lots of time to adapt and governance can keep improving slowly, I feel not bad. If we don’t have much time, then we’re probably moving too slow.

I think GPT-5 is generally seen as a disappointment in DC. There’s a pretty polarized conversation around: Are we going to have AGI and superintelligence in the next few years? Or is AI actually just totally all hype and useless and a bubble? The pendulum had maybe swung too far toward “We’re going to have super-capable systems very, very soon.” And so now it’s swinging back toward “It’s all hype.”

I worry that some aggressive AGI timeline estimates from some AI safety people are setting them up for a boy-who-cried-wolf moment. When the predictions about AGI coming in 2027 don’t come true, people will say, “Look at all these people who made fools of themselves. You should never listen to them again.” That’s not the intellectually honest response, if maybe they later changed their mind, or their take was that they only thought it was 20 percent likely and they thought that was still worth paying attention to. I think that shouldn’t be disqualifying for people to listen to you later, but I do worry it will be a big credibility hit. And that’s applying to people who are very concerned about AI safety and never said anything about very short timelines.

The AI security researcher who now believes AGI is further out—and is grateful

Jeffrey Ladish, executive director at Palisade Research

In the last year, two big things updated my AGI timelines. 

First, the lack of high-quality data turned out to be a bigger problem than I expected. 

Second, the first “reasoning” model, OpenAI’s o1 in September 2024, showed reinforcement learning scaling was more effective than I thought it would be. And then months later, you see the o1 to o3 scale-up and you see pretty crazy impressive performance in math and coding and science—domains where it’s easier to sort of verify the results. But while we’re seeing continued progress, it could have been much faster.

All of this bumps up my median estimate to the start of fully automated AI research and development from three years to maybe five or six years. But those are kind of made up numbers. It’s hard. I want to caveat all this with, like, “Man, it’s just really hard to do forecasting here.”

Thank God we have more time. We have a possibly very brief window of opportunity to really try to understand these systems before they are capable and strategic enough to pose a real threat to our ability to control them.

But it’s scary to see people think that we’re not making progress anymore when that’s clearly not true. I just know it’s not true because I use the models. One of the downsides of the way AI is progressing is that how fast it’s moving is becoming less legible to normal people. 

Now, this is not true in some domains—like, look at Sora 2. It is so obvious to anyone who looks at it that Sora 2 is vastly better than what came before. But if you ask GPT-4 and GPT-5 why the sky is blue, they’ll give you basically the same answer. It is the correct answer. It’s already saturated the ability to tell you why the sky is blue. So the people who I expect to most understand AI progress right now are the people who are actually building with AIs or using AIs on very difficult scientific problems.

The AGI forecaster who saw the critics coming

Daniel Kokotajlo, executive director of the AI Futures Project; an OpenAI whistleblower; and lead author of “AI 2027,” a vivid scenario where—starting in 2027—AIs progress from “superhuman coders” to “wildly superintelligent” systems in the span of months

AI policy seems to be getting worse, like the “Pro-AI” super PAC [launched earlier this year by executives from OpenAI and Andreessen Horowitz to lobby for a deregulatory agenda], and the deranged and/or dishonest tweets from Sriram Krishnan and David Sacks. AI safety research is progressing at the usual pace, which is excitingly rapid compared to most fields, but slow compared to how fast it needs to be.

We said on the first page of “AI 2027” that our timelines were somewhat longer than 2027. So even when we launched AI 2027, we expected there to be a bunch of critics in 2028 triumphantly saying we’ve been discredited, like the tweets from Sacks and Krishnan. But we thought, and continue to think, that the intelligence explosion will probably happen sometime in the next five to 10 years, and that when it does, people will remember our scenario and realize it was closer to the truth than anything else available in 2025. 

Predicting the future is hard, but it’s valuable to try; people should aim to communicate their uncertainty about the future in a way that is specific and falsifiable. This is what we’ve done and very few others have done. Our critics mostly haven’t made predictions of their own and often exaggerate and mischaracterize our views. They say our timelines are shorter than they are or ever were, or they say we are more confident than we are or were.

I feel pretty good about having longer timelines to AGI. It feels like I just got a better prognosis from my doctor. The situation is still basically the same, though.

This story has been updated to clarify some of Kokotajlo’s views on AI policy.

Garrison Lovely is a freelance journalist and the author of Obsolete, an online publication and forthcoming book on the discourse, economics, and geopolitics of the race to build machine superintelligence (out spring 2026). His writing on AI has appeared in the New York Times, Nature, Bloomberg, Time, the Guardian, The Verge, and elsewhere.

Some disillusionment was inevitable. When OpenAI released a free web app called ChatGPT in late 2022, it changed the course of an entire industry—and several world economies. Millions of people started talking to their computers, and their computers started talking back. We were enchanted, and we expected more.

We got it. Technology companies scrambled to stay ahead, putting out rival products that outdid one another with each new release: voice, images, video. With nonstop one-upmanship, AI companies have presented each new product drop as a major breakthrough, reinforcing a widespread faith that this technology would just keep getting better. Boosters told us that progress was exponential. They posted charts plotting how far we’d come since last year’s models: Look how the line goes up! Generative AI could do anything, it seemed.

Well, 2025 has been a year of reckoning. 

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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.

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For a start, the heads of the top AI companies made promises they couldn’t keep. They told us that generative AI would replace the white-collar workforce, bring about an age of abundance, make scientific discoveries, and help find new cures for disease. FOMO across the world’s economies, at least in the Global North, made CEOs tear up their playbooks and try to get in on the action.

That’s when the shine started to come off. Though the technology may have been billed as a universal multitool that could revamp outdated business processes and cut costs, a number of studies published this year suggest that firms are failing to make the AI pixie dust work its magic. Surveys and trackers from a range of sources, including the US Census Bureau and Stanford University, have found that business uptake of AI tools is stalling. And when the tools do get tried out, many projects stay stuck in the pilot stage. Without broad buy-in across the economy it is not clear how the big AI companies will ever recoup the incredible amounts they’ve already spent in this race. 

At the same time, updates to the core technology are no longer the step changes they once were.

The highest-profile example of this was the botched launch of GPT-5 in August. Here was OpenAI, the firm that had ignited (and to a large extent sustained) the current boom, set to release a brand-new generation of its technology. OpenAI had been hyping GPT-5 for months: “PhD-level expert in anything,” CEO Sam Altman crowed. On another occasion Altman posted, without comment, an image of the Death Star from Star Wars, which OpenAI stans took to be a symbol of ultimate power: Coming soon! Expectations were huge.

And yet, when it landed, GPT-5 seemed to be—more of the same? What followed was the biggest vibe shift since ChatGPT first appeared three years ago. “The era of boundary-breaking advancements is over,” Yannic Kilcher, an AI researcher and popular YouTuber, announced in a video posted two days after GPT-5 came out: “AGI is not coming. It seems very much that we’re in the Samsung Galaxy era of LLMs.”

A lot of people (me included) have made the analogy with phones. For a decade or so, smartphones were the most exciting consumer tech in the world. Today, new products drop from Apple or Samsung with little fanfare. While superfans pore over small upgrades, to most people this year’s iPhone now looks and feels a lot like last year’s iPhone. Is that where we are with generative AI? And is it a problem? Sure, smartphones have become the new normal. But they changed the way the world works, too.

To be clear, the last few years have been filled with genuine “Wow” moments, from the stunning leaps in the quality of video generation models to the problem-solving chops of so-called reasoning models to the world-class competition wins of the latest coding and math models. But this remarkable technology is only a few years old, and in many ways it is still experimental. Its successes come with big caveats.

Perhaps we need to readjust our expectations.

The big reset

Let’s be careful here: The pendulum from hype to anti-hype can swing too far. It would be rash to dismiss this technology just because it has been oversold. The knee-jerk response when AI fails to live up to its hype is to say that progress has hit a wall. But that misunderstands how research and innovation in tech work. Progress has always moved in fits and starts. There are ways over, around, and under walls.

Take a step back from the GPT-5 launch. It came hot on the heels of a series of remarkable models that OpenAI had shipped in the previous months, including o1 and o3 (first-of-their-kind reasoning models that introduced the industry to a whole new paradigm) and Sora 2, which raised the bar for video generation once again. That doesn’t sound like hitting a wall to me.

AI is really good! Look at Nano Banana Pro, the new image generation model from Google DeepMind that can turn a book chapter into an infographic, and much more. It’s just there—for free—on your phone.

And yet you can’t help but wonder: When the wow factor is gone, what’s left? How will we view this technology a year or five from now? Will we think it was worth the colossal costs, both financial and environmental? 

With that in mind, here are four ways to think about the state of AI at the end of 2025: The start of a much-needed hype correction.

01: LLMs are not everything

In some ways, it is the hype around large language models, not AI as a whole, that needs correcting. It has become obvious that LLMs are not the doorway to artificial general intelligence, or AGI, a hypothetical technology that some insist will one day be able to do any (cognitive) task a human can.

Even an AGI evangelist like Ilya Sutskever, chief scientist and cofounder at the AI startup Safe Superintelligence and former chief scientist and cofounder at OpenAI, now highlights the limitations of LLMs, a technology he had a huge hand in creating. LLMs are very good at learning how to do a lot of specific tasks, but they do not seem to learn the principles behind those tasks, Sutskever said in an interview with Dwarkesh Patel in November.

It’s the difference between learning how to solve a thousand different algebra problems and learning how to solve any algebra problem. “The thing which I think is the most fundamental is that these models somehow just generalize dramatically worse than people,” Sutskever said.

It’s easy to imagine that LLMs can do anything because their use of language is so compelling. It is astonishing how well this technology can mimic the way people write and speak. And we are hardwired to see intelligence in things that behave in certain ways—whether it’s there or not. In other words, we have built machines with humanlike behavior and cannot resist seeing a humanlike mind behind them.

That’s understandable. LLMs have been part of mainstream life for only a few years. But in that time, marketers have preyed on our shaky sense of what the technology can really do, pumping up expectations and turbocharging the hype. As we live with this technology and come to understand it better, those expectations should fall back down to earth.  

02: AI is not a quick fix to all your problems

In July, researchers at MIT published a study that became a tentpole talking point in the disillusionment camp. The headline result was that a whopping 95% of businesses that had tried using AI had found zero value in it.  

The general thrust of that claim was echoed by other research, too. In November, a study by researchers at Upwork, a company that runs an online marketplace for freelancers, found that agents powered by top LLMs from OpenAI, Google DeepMind, and Anthropic failed to complete many straightforward workplace tasks by themselves.

This is miles off Altman’s prediction: “We believe that, in 2025, we may see the first AI agents ‘join the workforce’ and materially change the output of companies,” he wrote on his personal blog in January.

But what gets missed in that MIT study is that the researchers’ measure of success was pretty narrow. That 95% failure rate accounts for companies that had tried to implement bespoke AI systems but had not yet scaled them beyond the pilot stage after six months. It shouldn’t be too surprising that a lot of experiments with experimental technology don’t pan out straight away.

That number also does not include the use of LLMs by employees outside of official pilots. The MIT researchers found that around 90% of the companies they surveyed had a kind of AI shadow economy where workers were using personal chatbot accounts. But the value of that shadow economy was not measured.  

When the Upwork study looked at how well agents completed tasks together with people who knew what they were doing, success rates shot up. The takeaway seems to be that a lot of people are figuring out for themselves how AI might help them with their jobs.

That fits with something the AI researcher and influencer (and coiner of the term “vibe coding”) Andrej Karpathy has noted: Chatbots are better than the average human at a lot of different things (think of giving legal advice, fixing bugs, doing high school math), but they are not better than an expert human. Karpathy suggests this may be why chatbots have proved popular with individual consumers, helping non-experts with everyday questions and tasks, but they have not upended the economy, which would require outperforming skilled employees at their jobs.

That may change. For now, don’t be surprised that AI has not (yet) had the impact on jobs that boosters said it would. AI is not a quick fix, and it cannot replace humans. But there’s a lot to play for. The ways in which AI could be integrated into everyday workflows and business pipelines are still being tried out.   

03: Are we in a bubble? (If so, what kind of bubble?)

If AI is a bubble, is it like the subprime mortgage bubble of 2008 or the internet bubble of 2000? Because there’s a big difference.

The subprime bubble wiped out a big part of the economy, because when it burst it left nothing behind except debt and overvalued real estate. The dot-com bubble wiped out a lot of companies, which sent ripples across the world, but it left behind the infant internet—an international network of cables and a handful of startups, like Google and Amazon, that became the tech giants of today.  

Then again, maybe we’re in a bubble unlike either of those. After all, there’s no real business model for LLMs right now. We don’t yet know what the killer app will be, or if there will even be one. 

And many economists are concerned about the unprecedented amounts of money being sunk into the infrastructure required to build capacity and serve the projected demand. But what if that demand doesn’t materialize? Add to that the weird circularity of many of those deals—with Nvidia paying OpenAI to pay Nvidia, and so on—and it’s no surprise everybody’s got a different take on what’s coming. 

Some investors remain sanguine. In an interview with the Technology Business Programming Network podcast in November, Glenn Hutchins, cofounder of Silver Lake Partners, a major international private equity firm, gave a few reasons not to worry. “Every one of these data centers—almost all of them—has a solvent counterparty that is contracted to take all the output they’re built to suit,” he said. In other words, it’s not a case of “Build it and they’ll come”—the customers are already locked in. 

And, he pointed out, one of the biggest of those solvent counterparties is Microsoft. “Microsoft has the world’s best credit rating,” Hutchins said. “If you sign a deal with Microsoft to take the output from your data center, Satya is good for it.”

Many CEOs will be looking back at the dot-com bubble and trying to learn its lessons. Here’s one way to see it: The companies that went bust back then didn’t have the money to last the distance. Those that survived the crash thrived.

With that lesson in mind, AI companies today are trying to pay their way through what may or may not be a bubble. Stay in the race; don’t get left behind. Even so, it’s a desperate gamble.

But there’s another lesson too. Companies that might look like sideshows can turn into unicorns fast. Take Synthesia, which makes avatar generation tools for businesses. Nathan Benaich, cofounder of the VC firm Air Street Capital, admits that when he first heard about the company a few years ago, back when fear of deepfakes was rife, he wasn’t sure what its tech was for and thought there was no market for it.

“We didn’t know who would pay for lip-synching and voice cloning,” he says. “Turns out there’s a lot of people who wanted to pay for it.” Synthesia now has around 55,000 corporate customers and brings in around $150 million a year. In October, the company was valued at $4 billion.

04: ChatGPT was not the beginning, and it won’t be the end

ChatGPT was the culmination of a decade’s worth of progress in deep learning, the technology that underpins all of modern AI. The seeds of deep learning itself were planted in the 1980s. The field as a whole goes back at least to the 1950s. If progress is measured against that backdrop, generative AI has barely got going.

Meanwhile, research is at a fever pitch. There are more high-quality submissions to the world’s major AI conferences than ever before. This year, organizers of some of those conferences resorted to turning down papers that reviewers had already approved, just to manage numbers. (At the same time, preprint servers like arXiv have been flooded with AI-generated research slop.)

“It’s back to the age of research again,” Sutskever said in that Dwarkesh interview, talking about the current bottleneck with LLMs. That’s not a setback; that’s the start of something new.

“There’s always a lot of hype beasts,” says Benaich. But he thinks there’s an upside to that: Hype attracts the money and talent needed to make real progress. “You know, it was only like two or three years ago that the people who built these models were basically research nerds that just happened on something that kind of worked,” he says. “Now everybody who’s good at anything in technology is working on this.”

Where do we go from here?

The relentless hype hasn’t come just from companies drumming up business for their vastly expensive new technologies. There’s a large cohort of people—inside and outside the industry—who want to believe in the promise of machines that can read, write, and think. It’s a wild decades-old dream. 

But the hype was never sustainable—and that’s a good thing. We now have a chance to reset expectations and see this technology for what it really is—assess its true capabilities, understand its flaws, and take the time to learn how to apply it in valuable (and beneficial) ways. “We’re still trying to figure out how to invoke certain behaviors from this insanely high-dimensional black box of information and skills,” says Benaich.

This hype correction was long overdue. But know that AI isn’t going anywhere. We don’t even fully understand what we’ve built so far, let alone what’s coming next.

On April 28, 2022, at a highly anticipated concert in Spokane, Washington, the musician Paul McCartney astonished his audience with a groundbreaking application of AI: He began to perform with a lifelike depiction of his long-deceased musical partner, John Lennon. 

Using recent advances in audio and video processing, engineers had taken the pair’s final performance (London, 1969), separated Lennon’s voice and image from the original mix and restored them with lifelike clarity.

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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.

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For years, researchers like me had taught machines to “see” and “hear” in order to make such a moment possible. As McCartney and Lennon appeared to reunite across time and space, the arena fell silent; many in the crowd began to cry. As an AI scientist and lifelong Beatles fan, I felt profound gratitude that we could experience this truly life-changing moment. 

Later that year, the world was captivated by another major breakthrough: AI conversation. For the first time in history, systems capable of generating new, contextually relevant comments in real time, on virtually any subject, were widely accessible owing to the release of ChatGPT. Billions of people were suddenly able to interact with AI. This ignited the public’s imagination about what AI could be, bringing an explosion of creative ideas, hopes, and fears.

Having done my PhD on AI language generation (long considered niche), I was thrilled we had come this far. But the awe I felt was rivaled by my growing rage at the flood of media takes and self-appointed experts insisting that generative AI could do things it simply can’t, and warning that anyone who didn’t adopt it would be left behind.

This kind of hype has contributed to a frenzy of misunderstandings about what AI actually is and what it can and cannot do. Crucially, generative AI is a seductive distraction from the type of AI that is most likely to make your life better, or even save it: Predictive AI. In contrast to AI designed for generative tasks, predictive AI involves tasks with a finite, known set of answers; the system just has to process information to say which answer is right. A basic example is plant recognition: Point your phone camera at a plant and learn that it’s a Western sword fern. Generative tasks, in contrast, have no finite set of correct answers: The system must blend snippets of information it’s been trained on to create, for example, a novel picture of a fern. 

The generative AI technology involved in chatbots, face-swaps, and synthetic video makes for stunning demos, driving clicks and sales as viewers run wild with ideas that superhuman AI will be capable of bringing us abundance or extinction. Yet predictive AI has quietly been improving weather prediction and food safety, enabling higher-quality music production, helping to organize photos, and accurately predicting the fastest driving routes. We incorporate predictive AI into our everyday lives without evening thinking about it, a testament to its indispensable utility.

To get a sense of the immense progress on predictive AI and its future potential, we can look at the trajectory of the past 20 years. In 2005, we couldn’t get AI to tell the difference between a person and a pencil. By 2013, AI still couldn’t reliably detect a bird in a photo, and the difference between a pedestrian and a Coke bottle was massively confounding (this is how I learned that bottles do kind of look like people, if people had no heads). The thought of deploying these systems in the real world was the stuff of science fiction. 

Yet over the past 10 years, predictive AI has not only nailed bird detection down to the specific species; it has rapidly improved life-critical medical services like identifying problematic lesions and heart arrhythmia. Because of this technology, seismologists can predict earthquakes and meteorologists can predict flooding more reliably than ever before. Accuracy has skyrocketed for consumer-facing tech that detects and classifies everything from what song you’re thinking of when you hum a tune to which objects to avoid while you’re driving—making self-driving cars a reality. 

In the very near future, we should be able to accurately detect tumors and forecast hurricanes long before they can hurt anyone, realizing the lifelong hopes of people all over the world. That might not be as flashy as generating your own Studio Ghibli–ish film, but it’s definitely hype-worthy. 

Predictive AI systems have also been shown to be incredibly useful when they leverage certain generative techniques within a constrained set of options. Systems of this type are diverse, spanning everything from outfit visualization to cross-language translation. Soon, predictive-generative hybrid systems will make it possible to clone your own voice speaking another language in real time, an extraordinary aid for travel (with serious impersonation risks). There’s considerable room for growth here, but generative AI delivers real value when anchored by strong predictive methods.

To understand the difference between these two broad classes of AI, imagine yourself as an AI system tasked with showing someone what a cat looks like. You could adopt a generative approach, cutting and pasting small fragments from various cat images (potentially from sources that object) to construct a seemingly perfect depiction. The ability of modern generative AI to produce such a flawless collage is what makes it so astonishing.

Alternatively, you could take the predictive approach: Simply locate and point to an existing picture of a cat. That method is much less glamorous but more energy-efficient and more likely to be accurate, and it properly acknowledges the original source. Generative AI is designed to create things that look real; predictive AI identifies what is real. A misunderstanding that generative systems are retrieving things when they are actually creating them has led to grave consequences when text is involved, requiring the withdrawal of legal rulings and the retraction of scientific articles.

Driving this confusion is a tendency for people to hype AI without making it clear what kind of AI they’re talking about (I reckon many don’t know). It’s very easy to equate “AI” with generative AI, or even just language-generating AI, and assume that all other capabilities fall out from there. That fallacy makes a ton of sense: The term literally references “intelligence,” and our human understanding of what “intelligence” might be is often mediated by the use of language. (Spoiler: No one actually knows what intelligence is.) But the phrase “artificial intelligence” was intentionally designed in the 1950s to inspire awe and allude to something humanlike. Today, it just refers to a set of disparate technologies for processing digital data. Some of my friends find it helpful to call it “mathy maths” instead.

The bias toward treating generative AI as the most powerful and real form of AI is troubling given that it consumes considerably more energy than predictive AI systems. It also means using existing human work in AI products against the original creators’ wishes and replacing human jobs with AI systems whose capabilities their work made possible in the first place—without compensation. AI can be amazingly powerful, but that doesn’t mean creators should be ripped off. 

Watching this unfold as an AI developer within the tech industry, I’ve drawn important lessons for next steps. The widespread appeal of AI is clearly linked to the intuitive nature of conversation-based interactions. But this method of engagement currently overuses generative methods where predictive ones would suffice, resulting in an awkward situation that’s confusing for users while imposing heavy costs in energy consumption, exploitation, and job displacement. 

We have witnessed just a glimpse of AI’s full potential: The current excitement around AI reflects what it could be, not what it is. Generation-based approaches strain resources while still falling short on representation, accuracy, and the wishes of people whose work is folded into the system. 

If we can shift the spotlight from the hype around generative technologies to the predictive advances already transforming daily life, we can build AI that is genuinely useful, equitable, and sustainable. The systems that help doctors catch diseases earlier, help scientists forecast disasters sooner, and help everyday people navigate their lives more safely are the ones poised to deliver the greatest impact. 

The future of beneficial AI will not be defined by the flashiest demos but by the quiet, rigorous progress that makes technology trustworthy. And if we build on that foundation—pairing predictive strength with more mature data practices and intuitive natural-language interfaces—AI can finally start living up to the promise that many people perceive today.

Dr. Margaret Mitchell is a computer science researcher and chief ethics scientist at AI startup Hugging Face. She has worked in the technology industry for 15 years, and has published over 100 papers on natural language generation, assistive technology, computer vision, and AI ethics. Her work has received numerous awards and has been implemented by multiple technology companies.

When the generative AI boom took off in 2022, Rudi Miller and her law school classmates were suddenly gripped with anxiety. “Before graduating, there was discussion about what the job market would look like for us if AI became adopted,” she recalls. 

So when it came time to choose a speciality, Miller—now a junior associate at the law firm Orrick—decided to become a litigator, the kind of lawyer who represents clients in court. She hoped the courtroom would be the last human stage. “Judges haven’t allowed ChatGPT-enabled robots to argue in court yet,” she says.

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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.

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She had reason to be worried. The artificial-intelligence job apocalypse seemed to be coming for lawyers. In March 2023, researchers reported that GPT-4 had smashed the Uniform Bar Exam. That same month, an industry report predicted that 44% of legal work could be automated. The legal tech industry entered a boom as law firms began adopting generative AI to mine mountains of documents and draft contracts, work ordinarily done by junior associates. Last month, the law firm Clifford Chance axed 10% of its staff in London, citing increased use of AI as a reason.

But for all the hype, LLMs are still far from thinking like lawyers—let alone replacing them. The models continue to hallucinate case citations, struggle to navigate gray areas of the law and reason about novel questions, and stumble when they attempt to synthesize information scattered across statutes, regulations, and court cases. And there are deeper institutional reasons to think the models could struggle to supplant legal jobs. While AI is reshaping the grunt work of the profession, the end of lawyers may not be arriving anytime soon.

The big experiment

The legal industry has long been defined by long hours and grueling workloads, so the promise of superhuman efficiency is appealing. Law firms are experimenting with general-purpose tools like ChatGPT and Microsoft Copilot and specialized legal tools like Harvey and Thomson Reuters’ CoCounsel, with some building their own in-house tools on top of frontier models. They’re rolling out AI boot camps and letting associates bill hundreds of hours to AI experimentation. As of 2024, 47.8% of attorneys at law firms employing 500 or more lawyers used AI, according to the American Bar Association. 

But lawyers say that LLMs are a long way from reasoning well enough to replace them. Lucas Hale, a junior associate at McDermott Will & Schulte, has been embracing AI for many routine chores. He uses Relativity to sift through long documents and Microsoft Copilot for drafting legal citations. But when he turns to ChatGPT with a complex legal question, he finds the chatbot spewing hallucinations, rambling off topic, or drawing a blank.

“In the case where we have a very narrow question or a question of first impression for the court,” he says, referring to a novel legal question that a court has never decided before, “that’s the kind of thinking that the tool can’t do.”

Much of Lucas’s work involves creatively applying the law to new fact patterns. “Right now, I don’t think very much of the work that litigators do, at least not the work that I do, can be outsourced to an AI utility,” he says.

Allison Douglis, a senior associate at Jenner & Block, uses an LLM to kick off her legal research. But the tools only take her so far. “When it comes to actually fleshing out and developing an argument as a litigator, I don’t think they’re there,” she says. She has watched the models hallucinate case citations and fumble through ambiguous areas of the law.

“Right now, I would much rather work with a junior associate than an AI tool,” she says. “Unless they get extraordinarily good very quickly, I can’t imagine that changing in the near future.”

Beyond the bar

The legal industry has seemed ripe for an AI takeover ever since ChatGPT’s triumph on the bar exam. But passing a standardized test isn’t the same as practicing law. The exam tests whether people can memorize legal rules and apply them to hypothetical situations—not whether they can exercise strategic judgment in complicated realities or craft arguments in uncharted legal territory. And models can be trained to ace benchmarks without genuinely improving their reasoning.

But new benchmarks are aiming to better measure the models’ ability to do legal work in the real world. The Professional Reasoning Benchmark, published by ScaleAI in November, evaluated leading LLMs on legal and financial tasks designed by professionals in the field. The study found that the models have critical gaps in their reliability for professional adoption, with the best-performing model scoring only 37% on the most difficult legal problems, meaning it met just over a third of possible points on the evaluation criteria. The models frequently made inaccurate legal judgments, and if they did reach correct conclusions, they did so through incomplete or opaque reasoning processes. 

“The tools actually are not there to basically substitute [for] your lawyer,” says Afra Feyza Akyurek, the lead author of the paper. “Even though a lot of people think that LLMs have a good grasp of the law, it’s still lagging behind.” 

The paper builds on other benchmarks measuring the models’ performance on economically valuable work. The AI Productivity Index, published by the data firm Mercor in September and updated in December, found that the models have “substantial limitations” in performing legal work. The best-performing model scored 77.9% on legal tasks, meaning it satisfied roughly four out of five evaluation criteria. A model with such a score might generate substantial economic value in some industries, but in fields where errors are costly, it may not be useful at all, the early version of the study noted.  

Professional benchmarks are a big step forward in evaluating the LLMs’ real-world capabilities, but they may still not capture what lawyers actually do. “These questions, although more challenging than those in past benchmarks, still don’t fully reflect the kinds of subjective, extremely challenging questions lawyers tackle in real life,” says Jon Choi, a law professor at the University of Washington School of Law, who coauthored a study on legal benchmarks in 2023. 

Unlike math or coding, in which LLMs have made significant progress, legal reasoning may be challenging for the models to learn. The law deals with messy real-world problems, riddled with ambiguity and subjectivity, that often have no right answer, says Choi. Making matters worse, a lot of legal work isn’t recorded in ways that can be used to train the models, he says. When it is, documents can span hundreds of pages, scattered across statutes, regulations, and court cases that exist in a complex hierarchy.  

But a more fundamental limitation might be that LLMs are simply not trained to think like lawyers. “The reasoning models still don’t fully reason about problems like we humans do,” says Julian Nyarko, a law professor at Stanford Law School. The models may lack a mental model of the world—the ability to simulate a scenario and predict what will happen—and that capability could be at the heart of complex legal reasoning, he says. It’s possible that the current paradigm of LLMs trained on next-word prediction gets us only so far.  

The jobs remain

Despite early signs that AI is beginning to affect entry-level workers, labor statistics have yet to show that lawyers are being displaced. 93.4% of law school graduates in 2024 were employed within 10 months of graduation—the highest rate on record—according to the National Association for Law Placement. The number of graduates working in law firms rose by 13% from 2023 to 2024. 

For now, law firms are slow to shrink their ranks. “We’re not reducing headcounts at this point,” said Amy Ross, the chief of attorney talent at the law firm Ropes & Gray. 

Even looking ahead, the effects could be incremental. “I will expect some impact on the legal profession’s labor market, but not major,” says Mert Demirer, an economist at MIT. “AI is going to be very useful in terms of information discovery and summary,” he says, but for complex legal tasks, “the law’s low risk tolerance, plus the current capabilities of AI, are going to make that case less automatable at this point.” Capabilities may evolve over time, but that’s a big unknown.

It’s not just that the models themselves are not ready to replace junior lawyers. Institutional barriers may also shape how AI is deployed. Higher productivity reduces billable hours, challenging the dominant business model of law firms. Liability looms large for lawyers, and clients may still want a human on the hook. Regulations could also constrain how lawyers use the technology.

Still, as AI takes on some associate work, law firms may need to reinvent their training system. “When junior work dries up, you have to have a more formal way of teaching than hoping that an apprenticeship works,” says Ethan Mollick, a management professor at the Wharton School of the University of Pennsylvania.

Zach Couger, a junior associate at McDermott Will & Schulte, leans on ChatGPT to comb through piles of contracts he once slogged through by hand. He can’t imagine going back to doing the job himself, but he wonders what he’s missing. 

“I’m worried that I’m not getting the same reps that senior attorneys got,” he says, referring to the repetitive training that has long defined the early experiences of lawyers. “On the other hand, it is very nice to have a semi–knowledge expert to just ask questions to that’s not a partner who’s also very busy.” 

Even though an AI job apocalypse looks distant, the uncertainty sticks with him. Lately, Couger finds himself staying up late, wondering if he could be part of the last class of associates at big law firms: “I may be the last plane out.”

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.” 

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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.

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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.