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Read more Skild AI is developing a hardware-agnostic foundation model for robots that can be customized for various uses.
Read more Six months ago, Waymo disclosed it was providing 250,000 robotaxi rides. But it’s grown significantly since, a leaked letter says.
Read more This news comes as a bill was introduced in Congress last week to prevent these exact kinds of chip exports.
Read more The tech giant says the idea behind the new feed is to display recommendations so users can discover and virtually try on items. Nearly everything in the feed is shoppable, with direct links to merchants.
Read more Welcome back to The State of AI, a new collaboration between the Financial Times and MIT Technology Review. Every Monday, writers from both publications debate one aspect of the generative AI revolution reshaping global power. You can read the rest of the series here.
In this final edition, MIT Technology Review’s senior AI editor Will Douglas Heaven talks with Tim Bradshaw, FT global tech correspondent, about where AI will go next, and what our world will look like in the next five years.
(As part of this series, join MIT Technology Review’s editor in chief, Mat Honan, and editor at large, David Rotman, for an exclusive conversation with Financial Times columnist Richard Waters on how AI is reshaping the global economy. Live on Tuesday, December 9 at 1:00 p.m. ET. This is a subscriber-only event and you can sign up here.)
Will Douglas Heaven writes:
Every time I’m asked what’s coming next, I get a Luke Haines song stuck in my head: “Please don’t ask me about the future / I am not a fortune teller.” But here goes. What will things be like in 2030? My answer: same but different.
There are huge gulfs of opinion when it comes to predicting the near-future impacts of generative AI. In one camp we have the AI Futures Project, a small donation-funded research outfit led by former OpenAI researcher Daniel Kokotajlo. The nonprofit made a big splash back in April with AI 2027, a speculative account of what the world will look like two years from now.
The story follows the runaway advances of an AI firm called OpenBrain (any similarities are coincidental, etc.) all the way to a choose-your-own-adventure-style boom or doom ending. Kokotajlo and his coauthors make no bones about their expectation that in the next decade the impact of AI will exceed that of the Industrial Revolution—a 150-year period of economic and social upheaval so great that we still live in the world it wrought.
At the other end of the scale we have team Normal Technology: Arvind Narayanan and Sayash Kapoor, a pair of Princeton University researchers and coauthors of the book AI Snake Oil, who push back not only on most of AI 2027’s predictions but, more important, on its foundational worldview. That’s not how technology works, they argue.
Advances at the cutting edge may come thick and fast, but change across the wider economy, and society as a whole, moves at human speed. Widespread adoption of new technologies can be slow; acceptance slower. AI will be no different.
What should we make of these extremes? ChatGPT came out three years ago last month, but it’s still not clear just how good the latest versions of this tech are at replacing lawyers or software developers or (gulp) journalists. And new updates no longer bring the step changes in capability that they once did.
And yet this radical technology is so new it would be foolish to write it off so soon. Just think: Nobody even knows exactly how this technology works—let alone what it’s really for.
As the rate of advance in the core technology slows down, applications of that tech will become the main differentiator between AI firms. (Witness the new browser wars and the chatbot pick-and-mix already on the market.) At the same time, high-end models are becoming cheaper to run and more accessible. Expect this to be where most of the action is: New ways to use existing models will keep them fresh and distract people waiting in line for what comes next.
Meanwhile, progress continues beyond LLMs. (Don’t forget—there was AI before ChatGPT, and there will be AI after it too.) Technologies such as reinforcement learning—the powerhouse behind AlphaGo, DeepMind’s board-game-playing AI that beat a Go grand master in 2016—is set to make a comeback. There’s also a lot of buzz around world models, a type of generative AI with a stronger grip on how the physical world fits together than LLMs display.
Ultimately, I agree with team Normal Technology that rapid technological advances do not translate to economic or societal ones straight away. There’s just too much messy human stuff in the middle.
But Tim, over to you. I’m curious to hear what your tea leaves are saying.
FT/MIT TECHNOLOGY REVIEW | ADOBE STOCK
Tim Bradshaw responds“
Will, I am more confident than you that the world will look quite different in 2030. In five years’ time, I expect the AI revolution to have proceeded apace. But who gets to benefit from those gains will create a world of AI haves and have-nots.
It seems inevitable that the AI bubble will burst sometime before the end of the decade. Whether a venture capital funding shakeout comes in six months or two years (I feel the current frenzy still has some way to run), swathes of AI app developers will disappear overnight. Some will see their work absorbed by the models upon which they depend. Others will learn the hard way that you can’t sell services that cost $1 for 50 cents without a firehose of VC funding.
How many of the foundation model companies survive is harder to call, but it already seems clear that OpenAI’s chain of interdependencies within Silicon Valley make it too big to fail. Still, a funding reckoning will force it to ratchet up pricing for its services.
When OpenAI was created in 2015, it pledged to “advance digital intelligence in the way that is most likely to benefit humanity as a whole.” That seems increasingly untenable. Sooner or later, the investors who bought in at a $500 billion price tag will push for returns. Those data centers won’t pay for themselves. By that point, many companies and individuals will have come to depend on ChatGPT or other AI services for their everyday workflows. Those able to pay will reap the productivity benefits, scooping up the excess computing power as others are priced out of the market.
Being able to layer several AI services on top of each other will provide a compounding effect. One example I heard on a recent trip to San Francisco: Ironing out the kinks in vibe coding is simply a matter of taking several passes at the same problem and then running a few more AI agents to look for bugs and security issues. That sounds incredibly GPU-intensive, implying that making AI really deliver on the current productivity promise will require customers to pay far more than most do today.
The same holds true in physical AI. I fully expect robotaxis to be commonplace in every major city by the end of the decade, and I even expect to see humanoid robots in many homes. But while Waymo’s Uber-like prices in San Francisco and the kinds of low-cost robots produced by China’s Unitree give the impression today that these will soon be affordable for all, the compute cost involved in making them useful and ubiquitous seems destined to turn them into luxuries for the well-off, at least in the near term.
The rest of us, meanwhile, will be left with an internet full of slop and unable to afford AI tools that actually work.
Perhaps some breakthrough in computational efficiency will avert this fate. But the current AI boom means Silicon Valley’s AI companies lack the incentives to make leaner models or experiment with radically different kinds of chips. That only raises the likelihood that the next wave of AI innovation will come from outside the US, be that China, India, or somewhere even farther afield.
Silicon Valley’s AI boom will surely end before 2030, but the race for global influence over the technology’s development—and the political arguments about how its benefits are distributed—seem set to continue well into the next decade.
Will replies:
I am with you that the cost of this technology is going to lead to a world of haves and have-nots. Even today, $200+ a month buys power users of ChatGPT or Gemini a very different experience from that of people on the free tier. That capability gap is certain to increase as model makers seek to recoup costs.
We’re going to see massive global disparities too. In the Global North, adoption has been off the charts. A recent report from Microsoft’s AI Economy Institute notes that AI is the fastest-spreading technology in human history: “In less than three years, more than 1.2 billion people have used AI tools, a rate of adoption faster than the internet, the personal computer, or even the smartphone.” And yet AI is useless without ready access to electricity and the internet; swathes of the world still have neither.
I still remain skeptical that we will see anything like the revolution that many insiders promise (and investors pray for) by 2030. When Microsoft talks about adoption here, it’s counting casual users rather than measuring long-term technological diffusion, which takes time. Meanwhile, casual users get bored and move on.
How about this: If I live with a domestic robot in five years’ time, you can send your laundry to my house in a robotaxi any day of the week.
JK! As if I could afford one.
Further reading
What is AI? It sounds like a stupid question, but it’s one that’s never been more urgent. In this deep dive, Will unpacks decades of spin and speculation to get to the heart of our collective technodream.
AGI—the idea that machines will be as smart as humans—has hijacked an entire industry (and possibly the US economy). For MIT Technology Review’s recent New Conspiracy Age package, Will takes a provocative look at how AGI is like a conspiracy.
The FT examined the economics of self-driving cars this summer, asking who will foot the multi-billion-dollar bill to buy enough robotaxis to serve a big city like London or New York.
A plausible counter-argument to Tim’s thesis on AI inequalities is that freely available open-source (or more accurately, “open weight”) models will keep pulling down prices. The US may want frontier models to be built on US chips but it is already losing the global south to Chinese software.
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.
4 technologies that didn’t make our 2026 breakthroughs list
If you’re a longtime reader, you probably know that our newsroom selects 10 breakthroughs every year that we think will define the future. This group exercise is mostly fun and always engrossing, with plenty of lively discussion along the way, but at times it can also be quite difficult.
The 2026 list will come out on January 12—so stay tuned. In the meantime, we wanted to share some of the technologies from this year’s reject pile, as a window into our decision-making process. These four technologies won’t be on our 2026 list of breakthroughs, but all were closely considered, and we think they’re worth knowing about. Read the full story to learn what they are.
MIT Technology Review Narrated: The quest to find out how our bodies react to extreme temperatures
Scientists hope to prevent deaths from climate change, but heat and cold are more complicated than we thought. Researchers around the world are revising rules about when extremes veer from uncomfortable to deadly. Their findings change how we should think about the limits of hot and cold—and how to survive in a new world.
This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 A CDC panel voted to recommend delaying the hepatitis B vaccine for babies
Overturning a 30-year policy that has contributed to a huge decline in the virus. (STAT)
+ Why childhood vaccines are a public health success story. (MIT Technology Review)
2 Critical climate risks are growing across the Arab region
Drought is the most immediate problem countries are having to grapple with. (Ars Technica)
+ Why Tehran is running out of water. (Wired $)
3 Netflix is buying Warner Bros for $83 billion
If approved, it’ll be one of the most significant mergers in Hollywood history. (NBC)
+ Trump says the deal “could be a problem” due to Netflix’s already huge market share. (BBC)
4 The EU is fining X $140 million
For failing to comply with its new Digital Services Act. (NPR)
+ Elon Musk is now calling for the entire EU to be abolished. (CNBC)
+ X also hit back by deleting the European Commission’s account. (Engadget)
5 AI slop is ruining Reddit
Moderators are getting tired of fighting the rising tide of nonsense. (Wired $)
+ How AI and Wikipedia have sent vulnerable languages into a doom spiral. (MIT Technology Review)
6 Scientists have deeply mixed feelings about AI tools
They can boost researchers’ productivity, but some worry about the consequences of relying on them. (Nature $)
+ ‘AI slop’ is undermining trust in papers presented at computer science gatherings. (The Guardian)
+ Meet the researcher hosting a scientific conference by and for AI. (MIT Technology Review)
7 Australia is about to ban under 16s from social media
It’s due to come into effect in two days—but teens are already trying to maneuver around it. (New Scientist $)
8 AI is enshittifying the way we write 
And most people haven’t even noticed. (NYT $)
+ AI can make you more creative—but it has limits. (MIT Technology Review)
9 Tech founders are taking etiquette lessons
The goal is to make them better at pretending to be normal. (WP $)
10 Are we getting stupider?
It might feel that way sometimes, but there’s little solid evidence to support it. (New Yorker $)
Quote of the day
“It’s hard to be Jensen day to day. It’s almost nightmarish. He’s constantly paranoid about competition. He’s constantly paranoid about people taking Nvidia down.”
—Stephen Witt, author of ‘The Thinking Machine’, a book about Nvidia’s rise, tells the Financial Times what it’s like to be its founder and chief executive, Jensen Huang.
One more thing
COURTESY OF OCEANBIRD
How wind tech could help decarbonize cargo shipping
Inhabitants of the Marshall Islands—a chain of coral atolls in the center of the Pacific Ocean—rely on sea transportation for almost everything. For millennia they sailed largely in canoes, but much of their seafaring movement today involves big, bulky, diesel-fueled cargo ships that are heavy polluters.
They’re not alone. Cargo shipping is responsible for about 3% of the world’s annual greenhouse-gas emissions, and that figure is currently on track to rise to 10% by 2050.
The islands have been disproportionately experiencing the consequences of human-made climate change: warming waters, more frequent extreme weather, and rising sea levels. Now its residents are exploring a surprisingly traditional method of decarbonizing its fleets. Read the full story.
—Sofia Quaglia
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.)
+ Small daily habits can help build a life you enjoy.
+ Using an air fryer to make an epic grilled cheese sandwich? OK, I’m listening…
+ I’m sorry but AI does NOT get to ruin em dashes for the rest of us.
+ Daniel Clarke’s art is full of life and color. Check it out!
If you’re a longtime reader, you probably know that our newsroom selects 10 breakthroughs every year that we think will define the future. This group exercise is mostly fun and always engrossing, but at times it can also be quite difficult.
We collectively pitch dozens of ideas, and the editors meticulously review and debate the merits of each. We agonize over which ones might make the broadest impact, whether one is too similar to something we’ve featured in the past, and how confident we are that a recent advance will actually translate into long-term success. There is plenty of lively discussion along the way.
The 2026 list will come out on January 12—so stay tuned. In the meantime, I wanted to share some of the technologies from this year’s reject pile, as a window into our decision-making process.
These four technologies won’t be on our 2026 list of breakthroughs, but all were closely considered, and we think they’re worth knowing about.
Male contraceptives
There are several new treatments in the pipeline for men who are sexually active and wish to prevent pregnancy—potentially providing them with an alternative to condoms or vasectomies.
Two of those treatments are now being tested in clinical trials by a company called Contraline. One is a gel that men would rub on their shoulder or upper arm once a day to suppress sperm production, and the other is a device designed to block sperm during ejaculation. (Kevin Eisenfrats, Contraline’s CEO, was recently named to our Innovators Under 35 list). A once-a-day pill is also in early-stage trials with the firm YourChoice Therapeutics.
Though it’s exciting to see this progress, it will still take several years for any of these treatments to make their way through clinical trials—assuming all goes well.
World models
World models have become the hot new thing in AI in recent months. Though they’re difficult to define, these models are generally trained on videos or spatial data and aim to produce 3D virtual worlds from simple prompts. They reflect fundamental principles, like gravity, that govern our actual world. The results could be used in game design or to make robots more capable by helping them understand their physical surroundings.
Despite some disagreements on exactly what constitutes a world model, the idea is certainly gaining momentum. Renowned AI researchers including Yann LeCun and Fei-Fei Li have launched companies to develop them, and Li’s startup World Labs released its first version last month. And Google made a huge splash with the release of its Genie 3 world model earlier this year.
Though these models are shaping up to be an exciting new frontier for AI in the year ahead, it seemed premature to deem them a breakthrough. But definitely watch this space.
Proof of personhood
Thanks to AI, it’s getting harder to know who and what is real online. It’s now possible to make hyperrealistic digital avatars of yourself or someone you know based on very little training data, using equipment many people have at home. And AI agents are being set loose across the internet to take action on people’s behalf.
All of this is creating more interest in what are known as personhood credentials, which could offer a way to verify that you are, in fact, a real human when you do something important online.
For example, we’ve reported on efforts by OpenAI, Microsoft, Harvard, and MIT to create a digital token that would serve this purpose. To get it, you’d first go to a government office or other organization and show identification. Then it’d be installed on your device and whenever you wanted to, say, log into your bank account, cryptographic protocols would verify that the token was authentic—confirming that you are the person you claim to be.
Whether or not this particular approach catches on, many of us in the newsroom agree that the future internet will need something along these lines. Right now, though, many competing identity verification projects are in various stages of development. One is World ID by Sam Altman’s startup Tools for Humanity, which uses a twist on biometrics.
If these efforts reach critical mass—or if one emerges as the clear winner, perhaps by becoming a universal standard or being integrated into a major platform—we’ll know it’s time to revisit the idea.
The world’s oldest baby
In July, senior reporter Jessica Hamzelou broke the news of a record-setting baby. The infant developed from an embryo that had been sitting in storage for more than 30 years, earning him the bizarre honorific of “oldest baby.”
This odd new record was made possible in part by advances in IVF, including safer methods of thawing frozen embryos. But perhaps the greater enabler has been the rise of “embryo adoption” agencies that pair donors with hopeful parents. People who work with these agencies are sometimes more willing to make use of decades-old embryos.
This practice could help find a home for some of the millions of leftover embryos that remain frozen in storage banks today. But since this recent achievement was brought about by changing norms as much as by any sudden technological improvements, this record didn’t quite meet our definition of a breakthrough—though it’s impressive nonetheless.
Another way to ensure more credit goes to original creators.