Yann LeCun is a Turing Award recipient and a top AI researcher, but he has long been a contrarian figure in the tech world. He believes that the industry’s current obsession with large language models is wrong-headed and will ultimately fail to solve many pressing problems. 

Instead, he thinks we should be betting on world models—a different type of AI that accurately reflects the dynamics of the real world. He is also a staunch advocate for open-source AI and criticizes the closed approach of frontier labs like OpenAI and Anthropic. 

Perhaps it’s no surprise, then, that he recently left Meta, where he had served as chief scientist for FAIR (Fundamental AI Research), the company’s influential research lab that he founded. Meta has struggled to gain much traction with its open-source AI model Llama and has seen internal shake-ups, including the controversial acquisition of ScaleAI. 

LeCun sat down with MIT Technology Review in an exclusive online interview from his Paris apartment to discuss his new venture, life after Meta, the future of artificial intelligence, and why he thinks the industry is chasing the wrong ideas. 

Both the questions and answers below have been edited for clarity and brevity.

You’ve just announced a new company, Advanced Machine Intelligence (AMI).  Tell me about the big ideas behind it.

It is going to be a global company, but headquartered in Paris. You pronounce it “ami”—it means “friend” in French. I am excited. There is a very high concentration of talent in Europe, but it is not always given a proper environment to flourish. And there is certainly a huge demand from the industry and governments for a credible frontier AI company that is neither Chinese nor American. I think that is going to be to our advantage.

So an ambitious alternative to the US-China binary we currently have. What made you want to pursue that third path?

Well, there are sovereignty issues for a lot of countries, and they want some control over AI. What I’m advocating is that AI is going to become a platform, and most platforms tend to become open-source. Unfortunately, that’s not really the direction the American industry is taking. Right? As the competition increases, they feel like they have to be secretive. I think that is a strategic mistake.

It’s certainly true for OpenAI, which went from very open to very closed, and Anthropic has always been closed. Google was sort of a little open. And then Meta, we’ll see. My sense is that it’s not going in a positive direction at this moment.

Simultaneously, China has completely embraced this open approach. So all leading open-source AI platforms are Chinese, and the result is that academia and startups, outside of the US, have basically embraced Chinese models. There’s nothing wrong with that—you know, Chinese models are good. Chinese engineers and scientists are great. But you know, if there is a future in which all of our information diet is being mediated by AI assistance, and the choice is either English-speaking models produced by proprietary companies always close to the US or Chinese models which may be open-source but need to be fine-tuned so that they answer questions about Tiananmen Square in 1989—you know, it’s not a very pleasant and engaging future. 

They [the future models] should be able to be fine-tuned by anyone and produce a very high diversity of AI assistance, with different linguistic abilities and value systems and political biases and centers of interests. You need high diversity of assistance for the same reason that you need high diversity of press. 

That is certainly a compelling pitch. How are investors buying that idea so far?

They really like it. A lot of venture capitalists are very much in favor of this idea of open-source, because they know for a lot of small startups, they really rely on open-source models. They don’t have the means to train their own model, and it’s kind of dangerous for them strategically to embrace a proprietary model.

You recently left Meta. What’s your view on the company and Mark Zuckerberg’s leadership? There’s a perception that Meta has fumbled its AI advantage.

I think FAIR [LeCun’s lab at Meta] was extremely successful in the research part. Where Meta was less successful is in picking up on that research and pushing it into practical technology and products. Mark made some choices that he thought were the best for the company. I may not have agreed with all of them. For example, the robotics group at FAIR was let go, which I think was a strategic mistake. But I’m not the director of FAIR. People make decisions rationally, and there’s no reason to be upset.

So, no bad blood? Could Meta be a future client for AMI?

Meta might be our first client! We’ll see. The work we are doing is not in direct competition. Our focus on world models for the physical world is very different from their focus on generative AI and LLMs.

You were working on AI long before LLMs became a mainstream approach. But since ChatGPT broke out, LLMs have become almost synonymous with AI.

Yes, and we are going to change that. The public face of AI, perhaps, is mostly LLMs and chatbots of various types. But the latest ones of those are not pure LLMs. They are LLM plus a lot of things, like perception systems and code that solves particular problems. So we are going to see LLMs as kind of the orchestrator in systems, a little bit.

Beyond LLMs, there is a lot of AI that is behind the scenes that runs a big chunk of our society. There are assistance driving programs in a car, quick-turn MRI images, algorithms that drive social media—that’s all AI. 

You have been vocal in arguing that LLMs can only get us so far. Do you think LLMs are overhyped these days? Can you summarize to our readers why you believe that LLMs are not enough?

There is a sense in which they have not been overhyped, which is that they are extremely useful to a lot of people, particularly if you write text, do research, or write code. LLMs manipulate language really well. But people have had this illusion, or delusion, that it is a matter of time until we can scale them up to having human-level intelligence, and that is simply false.

The truly difficult part is understanding the real world. This is the Moravec Paradox (a phenomenon observed by the computer scientist Hans Moravec in 1988): What’s easy for us, like perception and navigation, is hard for computers, and vice versa. LLMs are limited to the discrete world of text. They can’t truly reason or plan, because they lack a model of the world. They can’t predict the consequences of their actions. This is why we don’t have a domestic robot that is as agile as a house cat, or a truly autonomous car.

We are going to have AI systems that have humanlike and human-level intelligence, but they’re  not going to be built on LLMs, and it’s not going to happen next year or two years from now. It’s going to take a while. There are major conceptual breakthroughs that have to happen before we have AI systems that have human-level intelligence. And that is what I’ve been working on. And this company, AMI Labs, is focusing on the next generation.

And your solution is world models and JEPA architecture (JEPA, or “joint embedding predictive architecture,” is a learning framework that trains AI models to understand the world, created by LeCun while he was at Meta). What’s the elevator pitch?

The world is unpredictable. If you try to build a generative model that predicts every detail of the future, it will fail.  JEPA is not generative AI. It is a system that learns to represent videos really well. The key is to learn an abstract representation of the world and make predictions in that abstract space, ignoring the details you can’t predict. That’s what JEPA does. It learns the underlying rules of the world from observation, like a baby learning about gravity. This is the foundation for common sense, and it’s the key to building truly intelligent systems that can reason and plan in the real world. The most exciting work so far on this is coming from academia, not the big industrial labs stuck in the LLM world.

The lack of non-text data has been a problem in taking AI systems further in understanding the physical world. JEPA is trained on videos. What other kinds of data will you be using?

Our systems will be trained on video, audio, and sensor data of all kinds—not just text. We are working with various modalities, from the position of a robot arm to lidar data to audio. I’m also involved in a project using JEPA to model complex physical and clinical phenomena. 

What are some of the concrete, real-world applications you envision for world models?

The applications are vast. Think about complex industrial processes where you have thousands of sensors, like in a jet engine, a steel mill, or a chemical factory. There is no technique right now to build a complete, holistic model of these systems. A world model could learn this from the sensor data and predict how the system will behave. Or think of smart glasses that can watch what you’re doing, identify your actions, and then predict what you’re going to do next to assist you. This is what will finally make agentic systems reliable. An agentic system that is supposed to take actions in the world cannot work reliably unless it has a world model to predict the consequences of its actions. Without it, the system will inevitably make mistakes. This is the key to unlocking everything from truly useful domestic robots to Level 5 autonomous driving.

Humanoid robots are all the rage recently, especially ones built by companies from China. What’s your take?

There are all these brute-force ways to get around the limitations of learning systems, which require inordinate amounts of training data to do anything. So the secret of all the companies getting robots to do kung fu or dance is they are all planned in advance. But frankly, nobody—absolutely nobody—knows how to make those robots smart enough to be useful. Take my word for it. 

You need an enormous amount of tele-operation training data for every single task, and when the environment changes a little bit, it doesn’t generalize very well. What this tells us is we are missing something very big. The reason why a 17-year-old can learn to drive in 20 hours is because they already know a lot about how the world behaves. If we want a generally useful domestic robot, we need systems to have a kind of good understanding of the physical world. That’s not going to happen until we have good world models and planning.

There’s a growing sentiment that it’s becoming harder to do foundational AI research in academia because of the massive computing resources required. Do you think the most important innovations will now come from industry?

No. LLMs are now technology development, not research. It’s true that it’s very difficult for academics to play an important role there because of the requirements for computation, data access, and engineering support. But it’s a product now. It’s not something academia should even be interested in. It’s like speech recognition in the early 2010s—it was a solved problem, and the progress was in the hands of industry. 

What academia should be working on is long-term objectives that go beyond the capabilities of current systems. That’s why I tell people in universities: Don’t work on LLMs. There is no point. You’re not going to be able to rival what’s going on in industry. Work on something else. Invent new techniques. The breakthroughs are not going to come from scaling up LLMs. The most exciting work on world models is coming from academia, not the big industrial labs. The whole idea of using attention circuits in neural nets came out of the University of Montreal. That research paper started the whole revolution. Now that the big companies are closing up, the breakthroughs are going to slow down. Academia needs access to computing resources, but they should be focused on the next big thing, not on refining the last one.

You wear many hats: professor, researcher, educator, public thinker … Now you just took on a new one. What is that going to look like for you?

I am going to be the executive chairman of the company, and Alex LeBrun [a former colleague from Meta AI] will be the CEO. It’s going to be LeCun and LeBrun—it’s nice if you pronounce it the French way.

I am going to keep my position at NYU. I teach one class per year, I have PhD students and postdocs, so I am going to be kept based in New York. But I go to Paris pretty often because of my lab. 

Does that mean that you won’t be very hands-on?

Well, there’s two ways to be hands-on. One is to manage people day to day, and another is to actually get your hands dirty in research projects, right? 

I can do management, but I don’t like doing it. This is not my mission in life. It’s really to make science and technology progress as far as we can, inspire other people to work on things that are interesting, and then contribute to those things. So that has been my role at Meta for the last seven years. I founded FAIR and led it for four to five years. I kind of hated being a director. I am not good at this career management thing. I’m much more visionary and a scientist.

What makes Alex LeBrun the right fit?

Alex is a serial entrepreneur; he’s built three successful AI companies. The first he sold to Microsoft; the second to Facebook, where he was head of the engineering division of FAIR in Paris. He then left to create Nabla, a very successful company in the health-care space. When I offered him the chance to join me in this effort, he accepted almost immediately. He has the experience to build the company, allowing me to focus on science and technology. 

You’re headquartered in Paris. Where else do you plan to have offices?

We are a global company. There’s going to be an office in North America.

New York, hopefully?

New York is great. That’s where I am, right? And it’s not Silicon Valley. Silicon Valley is a bit of a monoculture.

What about Asia? I’m guessing Singapore, too?

Probably, yeah. I’ll let you guess. 

And how are you attracting talent?

We don’t have any issue recruiting. There are a lot of people in the AI research community who think the future of AI is in world models. Those people, regardless of pay package, will be motivated to come work for us because they believe in the technological future we are building. We’ve already recruited people from places like OpenAI, Google DeepMind, and xAI.

I heard that Saining Xie, a prominent researcher from NYU and Google DeepMind, might be joining you as chief scientist. Any comments?

Saining is a brilliant researcher. I have a lot of admiration for him. I hired him twice already. I hired him at FAIR, and I convinced my colleagues at NYU that we should hire him there. Let’s just say I have a lot of respect for him.

When will you be ready to share more details about AMI Labs, like financial backing or other core members?

Soon—in February, maybe. I’ll let you know.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

I decided to go to CES kind of at the last minute. Over the holiday break, contacts from China kept messaging me about their travel plans. After the umpteenth “See you in Vegas?” I caved. As a China tech writer based in the US, I have one week a year when my entire beat seems to come to me—no 20-hour flights required.

CES, the Consumer Electronics Show, is the world’s biggest tech show, where companies launch new gadgets and announce new developments, and it happens every January. This year, it attracted over 148,000 attendees and over 4,100 exhibitors. It sprawls across the Las Vegas Convention Center, the city’s biggest exhibition space, and spills over into adjacent hotels. 

China has long had a presence at CES, but this year it showed up in a big way. Chinese exhibitors accounted for nearly a quarter of all companies at the show, and in pockets like AI hardware and robotics, China’s presence felt especially dominant. On the floor, I saw tons of Chinese industry attendees roaming around, plus a notable number of Chinese VCs. Multiple experienced CES attendees told me this is the first post-covid CES where China was present in a way you couldn’t miss. Last year might have been trending that way too, but a lot of Chinese attendees reportedly ran into visa denials. Now AI has become the universal excuse, and reason, to make the trip.

As expected, AI was the biggest theme this year, seen on every booth wall. It’s both the biggest thing everyone is talking about and a deeply confusing marketing gimmick. “We added AI” is slapped onto everything from the reasonable (PCs, phones, TVs, security systems) to the deranged (slippers, hair dryers, bed frames). 

Consumer AI gadgets still feel early and of very uneven quality. The most common categories are educational devices and emotional support toys—which, as I’ve written about recently, are all the rage in China. There are some memorable ones: Luka AI makes a robotic panda that scuttles around and keeps a watchful eye on your baby. Fuzozo, a fluffy keychain-size AI robot, is basically a digital pet in physical form. It comes with a built-in personality and reacts to how you treat it. The companies selling these just hope you won’t think too hard about the privacy implications.

Ian Goh, an investor at 01VC, told me China’s manufacturing advantage gives it a unique edge in AI consumer electronics, because a lot of Western companies feel they simply cannot fight and win in the arena of hardware. 

Another area where Chinese companies seem to be at the head of the pack is household electronics. The products they make are becoming impressively sophisticated. Home robots, 360 cams, security systems, drones, lawn-mowing machines, pool heat pumps … Did you know two Chinese brands basically dominate the market for home cleaning robots in the US and are eating the lunch of Dyson and Shark? Did you know almost all the suburban yard tech you can buy in the West comes from Shenzhen, even though that whole backyard-obsessed lifestyle barely exists in China? This stuff is so sleek that you wouldn’t clock it as Chinese unless you went looking. The old “cheap and repetitive” stereotype doesn’t explain what I saw. I walked away from CES feeling that I needed a major home appliance upgrade.

Of course, appliances are a safe, mature market. On the more experiential front, humanoid robots were a giant magnet for crowds, and Chinese companies put on a great show. Every robot seemed to be dancing, in styles from Michael Jackson to K-pop to lion dancing, some even doing back flips. Hangzhou-based Unitree even set up a boxing ring where people could “challenge” its robots. The robot fighters were about half the size of an adult human and the matches often ended in a robot knockout, but that’s not really the point. What Unitree was actually showing off was its robots’ stability and balance: they got shoved, stumbled across the ring, and stayed upright, recovering mid-motion. Beyond flexing dynamic movements like these there were also impressive showcases of dexterity: Robots could be seen folding paper pinwheels, doing laundry, playing piano, and even making latte art.

However, most of these robots, even the good ones, are one-trick ponies. They’re optimized for a specific task on the show floor. I tried to make one fold a T-shirt after I’d flipped the garment around, and it got confused very quickly. 

Still, they’re getting a lot of hype as an  important next frontier because they could help drag AI out of text boxes and into the physical world. As LLMs mature, vision-language models feel like the logical next step. But then you run into the big problem: There’s far less physical-world data than text data to train AI on. Humanoid robots become both applications and roaming data-collection terminals. China is uniquely positioned here because of supply chains, manufacturing depth, and spillover from adjacent industries (EVs, batteries, motors, sensors), and it’s already developing a humanoid training industry, as Rest of World reported recently. 

Most Chinese companies believe that if you can manufacture at scale, you can innovate, and they’re not wrong. A lot of the confidence in China’s nascent humanoid robot industry and beyond is less about a single breakthrough and more about “We can iterate faster than the West.”

Chinese companies are not just selling gadgets, though—they’re working on every layer of the tech stack. Not just on end products but frameworks, tooling, IoT enablement, spatial data. Open-source culture feels deeply embedded; engineers from Hangzhou tell me there are AI hackathons every week in the city, where China’s new “little Silicon Valley” is located.

Indeed, the headline innovations at CES 2026 were not on devices but in cloud: platforms, ecosystems, enterprise deployments, and “hybrid AI” (cloud + on-device) applications. Lenovo threw the buzziest main-stage events this year, and yes, there were PCs—but the core story was its cross-device AI agent system, Qira, and a partnership pitch with Nvidia aimed at AI cloud providers. Nvidia’s CEO, Jensen Huang, launched Vera Rubin, a new data-center platform, claiming it would  dramatically lower costs for training and running AI. AMD’s CEO, Lisa Su, introduced Helios, another data-center system built to run huge AI workloads. These solutions point to the ballooning AI computing workload at data centers, and the real race of making cloud services cheap and powerful enough to keep up.

As I spoke with China-related attendees, the overall mood I felt was a cautious optimism. At a house party I went to, VCs and founders from China were mingling effortlessly with Bay Area transplants. Everyone is building something. Almost no one wants to just make money from Chinese consumers anymore. The new default is: Build in China, sell to the world, and treat the US market like the proving ground.

For decades, lithium-ion batteries have powered our phones, laptops, and electric vehicles. But lithium’s limited supply and volatile price have led the industry to seek more resilient alternatives.

A sodium-ion battery works much like a lithium-ion one: It stores and releases energy by shuttling ions between two electrodes. But unlike lithium, a somewhat rare element that is currently mined in only a handful of countries, sodium is cheap and found everywhere. And while today’s sodium-ion cells are not meaningfully cheaper, costs are expected to drop as production scales.

China, with its powerful EV industry, has led the early push. Battery giants CATL and BYD have invested heavily in the technology. CATL, which announced its first-generation sodium-ion battery in 2021, launched a sodium-ion product line called Naxtra in 2025 and claims to have already started manufacturing it at scale. BYD is also building a massive production facility for sodium-ion batteries in China. 

And the technology is already making it into cars. In 2024, JMEV began offering the option of buying its EV3 vehicle with a sodium-ion battery pack. HiNa Battery is putting sodium-ion batteries into low-speed EVs. 

The most significant impact of sodium-­ion technology may be not on our roads but on our power grids. Storing clean energy generated by solar and wind has long been a challenge. Sodium-ion batteries, with their low cost, enhanced thermal stability, and long cycle life, are an attractive alternative. Peak Energy, a startup in the US, is already deploying grid-scale sodium-ion energy storage.

Sodium-ion cells’ energy density is still lower than that of high-end lithium-ion ones, but it continues to improve each year—and it’s already sufficient for small passenger cars and logistics vehicles.

The new batteries are also being tested in smaller electric vehicles. In China, the scooter maker Yadea launched four models of two-wheelers powered by the technology in 2025, as cities including Shenzhen started piloting swapping stations for sodium-­ion batteries to support commuters and delivery drivers.

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

In an industry in constant flux, sticking your neck out to predict what’s coming next may seem reckless. (AI bubble? What AI bubble?) But for the last few years we’ve done just that—and we’re doing it again. 

How did we do last time? We picked five hot AI trends to look out for in 2025, including what we called generative virtual playgrounds, a.k.a world models (check: From Google DeepMind’s Genie 3 to World Labs’s Marble, tech that can generate realistic virtual environments on the fly keeps getting better and better); so-called reasoning models (check: Need we say more? Reasoning models have fast become the new paradigm for best-in-class problem solving); a boom in AI for science (check: OpenAI is now following Google DeepMind by setting up a dedicated team to focus on just that); AI companies that are cozier with national security (check: OpenAI reversed position on the use of its technology for warfare to sign a deal with the defense-tech startup Anduril to help it take down battlefield drones); and legitimate competition for Nvidia (check, kind of: China is going all in on developing advanced AI chips, but Nvidia’s dominance still looks unassailable—for now at least). 

So what’s coming in 2026? Here are our big bets for the next 12 months. 

More Silicon Valley products will be built on Chinese LLMs

The last year shaped up as a big one for Chinese open-source models. In January, DeepSeek released R1, its open-source reasoning model, and shocked the world with what a relatively small firm in China could do with limited resources. By the end of the year, “DeepSeek moment” had become a phrase frequently tossed around by AI entrepreneurs, observers, and builders—an aspirational benchmark of sorts. 

It was the first time many people realized they could get a taste of top-tier AI performance without going through OpenAI, Anthropic, or Google.

Open-weight models like R1 allow anyone to download a model and run it on their own hardware. They are also more customizable, letting teams tweak models through techniques like distillation and pruning. This stands in stark contrast to the “closed” models released by major American firms, where core capabilities remain proprietary and access is often expensive.

As a result, Chinese models have become an easy choice. Reports by CNBC and Bloomberg suggest that startups in the US have increasingly recognized and embraced what they can offer.

One popular group of models is Qwen, created by Alibaba, the company behind China’s largest e-commerce platform, Taobao. Qwen2.5-1.5B-Instruct alone has 8.85 million downloads, making it one of the most widely used pretrained LLMs. The Qwen family spans a wide range of model sizes alongside specialized versions tuned for math, coding, vision, and instruction-following, a breadth that has helped it become an open-source powerhouse.

Other Chinese AI firms that were previously unsure about committing to open source are following DeepSeek’s playbook. Standouts include Zhipu’s GLM and Moonshot’s Kimi. The competition has also pushed American firms to open up, at least in part. In August, OpenAI released its first open-source model. In November, the Allen Institute for AI, a Seattle-based nonprofit, released its latest open-source model, Olmo 3. 

Even amid growing US-China antagonism, Chinese AI firms’ near-unanimous embrace of open source has earned them goodwill in the global AI community and a long-term trust advantage. In 2026, expect more Silicon Valley apps to quietly ship on top of Chinese open models, and look for the lag between Chinese releases and the Western frontier to keep shrinking—from months to weeks, and sometimes less.

—Caiwei Chen

The US will face another year of regulatory tug-of-war

T​​he battle over regulating artificial intelligence is heading for a showdown. On December 11, President Donald Trump signed an executive order aiming to neuter state AI laws, a move meant to handcuff states from keeping the growing industry in check. In 2026, expect more political warfare. The White House and states will spar over who gets to govern the booming technology, while AI companies wage a fierce lobbying campaign to crush regulations, armed with the narrative that a patchwork of state laws will smother innovation and hobble the US in the AI arms race against China.

Under Trump’s executive order, states may fear being sued or starved federal funding if they clash with his vision for light-touch regulation. Big Democratic states like California—which just enacted the nation’s first frontier AI law requiring companies to publish safety testing for their AI models—will take the fight to court, arguing that only Congress can override state laws. But states that can’t afford to lose federal funding, or fear getting in Trump’s crosshairs, might fold. Still, expect to see more state lawmaking on hot-button issues, especially where Trump’s order gives states a green light to legislate. With chatbots accused of triggering teen suicides and data centers sucking up more and more energy, states will face mounting public pressure to push for guardrails. 

In place of state laws, Trump promises to work with Congress to establish a federal AI law. Don’t count on it. Congress failed to pass a moratorium on state legislation twice in 2025, and we aren’t holding out hope that it will deliver its own bill this year. 

AI companies like OpenAI and Meta will continue to deploy powerful super-PACs to support political candidates who back their agenda and target those who stand in their way. On the other side, super-PACs supporting AI regulation will build their own war chests to counter. Watch them duke it out at next year’s midterm elections.

The further AI advances, the more people will fight to steer its course, and 2026 will be another year of regulatory tug-of-war—with no end in sight.

—Michelle Kim

Chatbots will change the way we shop

Imagine a world in which you have a personal shopper at your disposal 24-7—an expert who can instantly recommend a gift for even the trickiest-to-buy-for friend or relative, or trawl the web to draw up a list of the best bookcases available within your tight budget. Better yet, they can analyze a kitchen appliance’s strengths and weaknesses, compare it with its seemingly identical competition, and find you the best deal. Then once you’re happy with their suggestion, they’ll take care of the purchasing and delivery details too.

But this ultra-knowledgeable shopper isn’t a clued-up human at all—it’s a chatbot. This is no distant prediction, either. Salesforce recently said it anticipates that AI will drive $263 billion in online purchases this holiday season. That’s some 21% of all orders. And experts are betting on AI-enhanced shopping becoming even bigger business within the next few years. By 2030, between $3 trillion and $5 trillion annually will be made from agentic commerce, according to research from the consulting firm McKinsey. 

Unsurprisingly, AI companies are already heavily invested in making purchasing through their platforms as frictionless as possible. Google’s Gemini app can now tap into the company’s powerful Shopping Graph data set of products and sellers, and can even use its agentic technology to call stores on your behalf. Meanwhile, back in November, OpenAI announced a ChatGPT shopping feature capable of rapidly compiling buyer’s guides, and the company has struck deals with Walmart, Target, and Etsy to allow shoppers to buy products directly within chatbot interactions. 

Expect plenty more of these kinds of deals to be struck within the next year as consumer time spent chatting with AI keeps on rising, and web traffic from search engines and social media continues to plummet. 

—Rhiannon Williams

An LLM will make an important new discovery

I’m going to hedge here, right out of the gate. It’s no secret that large language models spit out a lot of nonsense. Unless it’s with monkeys-and-typewriters luck, LLMs won’t discover anything by themselves. But LLMs do still have the potential to extend the bounds of human knowledge.

We got a glimpse of how this could work in May, when Google DeepMind revealed AlphaEvolve, a system that used the firm’s Gemini LLM to come up with new algorithms for solving unsolved problems. The breakthrough was to combine Gemini with an evolutionary algorithm that checked its suggestions, picked the best ones, and fed them back into the LLM to make them even better.

Google DeepMind used AlphaEvolve to come up with more efficient ways to manage power consumption by data centers and Google’s TPU chips. Those discoveries are significant but not game-changing. Yet. Researchers at Google DeepMind are now pushing their approach to see how far it will go.

And others have been quick to follow their lead. A week after AlphaEvolve came out, Asankhaya Sharma, an AI engineer in Singapore, shared OpenEvolve, an open-source version of Google DeepMind’s tool. In September, the Japanese firm Sakana AI released a version of the software called SinkaEvolve. And in November, a team of US and Chinese researchers revealed AlphaResearch, which they claim improves on one of AlphaEvolve’s already better-than-human math solutions.

There are alternative approaches too. For example, researchers at the University of Colorado Denver are trying to make LLMs more inventive by tweaking the way so-called reasoning models work. They have drawn on what cognitive scientists know about creative thinking in humans to push reasoning models toward solutions that are more outside the box than their typical safe-bet suggestions.

Hundreds of companies are spending billions of dollars looking for ways to get AI to crack unsolved math problems, speed up computers, and come up with new drugs and materials. Now that AlphaEvolve has shown what’s possible with LLMs, expect activity on this front to ramp up fast.    

—Will Douglas Heaven

Legal fights heat up

For a while, lawsuits against AI companies were pretty predictable: Rights holders like authors or musicians would sue companies that trained AI models on their work, and the courts generally found in favor of the tech giants. AI’s upcoming legal battles will be far messier.

The fights center on thorny, unresolved questions: Can AI companies be held liable for what their chatbots encourage people to do, as when they help teens plan suicides? If a chatbot spreads patently false information about you, can its creator be sued for defamation? If companies lose these cases, will insurers shun AI companies as clients?

In 2026, we’ll start to see the answers to these questions, in part because some notable cases will go to trial (the family of a teen who died by suicide will bring OpenAI to court in November).

At the same time, the legal landscape will be further complicated by President Trump’s executive order from December—see Michelle’s item above for more details on the brewing regulatory storm.

No matter what, we’ll see a dizzying array of lawsuits in all directions (not to mention some judges even turning to AI amid the deluge).

—James O’Donnell

If the past 12 months have taught us anything, it’s that the AI hype train is showing no signs of slowing. It’s hard to believe that at the beginning of the year, DeepSeek had yet to turn the entire industry on its head, Meta was better known for trying (and failing) to make the metaverse cool than for its relentless quest to dominate superintelligence, and vibe coding wasn’t a thing.

If that’s left you feeling a little confused, fear not. As we near the end of 2025, our writers have taken a look back over the AI terms that dominated the year, for better or worse.

Make sure you take the time to brace yourself for what promises to be another bonkers year.

—Rhiannon Williams

1. Superintelligence

As long as people have been hyping AI, they have been coming up with names for a future, ultra-powerful form of the technology that could bring about utopian or dystopian consequences for humanity. “Superintelligence” is that latest hot term. Meta announced in July that it would form an AI team to pursue superintelligence, and it was reportedly offering nine-figure compensation packages to AI experts from the company’s competitors to join.

In December, Microsoft’s head of AI followed suit, saying the company would be spending big sums, perhaps hundreds of billions, on the pursuit of superintelligence. If you think superintelligence is as vaguely defined as artificial general intelligence, or AGI, you’d be right! While it’s conceivable that these sorts of technologies will be feasible in humanity’s long run, the question is really when, and whether today’s AI is good enough to be treated as a stepping stone toward something like superintelligence. Not that that will stop the hype kings. —James O’Donnell

2. Vibe coding

Thirty years ago, Steve Jobs said everyone in America should learn how to program a computer. Today, people with zero knowledge of how to code can knock up an app, game, or website in no time at all thanks to vibe coding—a catch-all phrase coined by OpenAI cofounder Andrej Karpathy. To vibe-code, you simply prompt generative AI models’ coding assistants to create the digital object of your desire and accept pretty much everything they spit out. Will the result work? Possibly not. Will it be secure? Almost definitely not, but the technique’s biggest champions aren’t letting those minor details stand in their way. Also—it sounds fun! — Rhiannon Williams

3. Chatbot psychosis

One of the biggest AI stories over the past year has been how prolonged interactions with chatbots can cause vulnerable people to experience delusions and, in some extreme cases, can either cause or worsen psychosis. Although “chatbot psychosis” is not a recognized medical term, researchers are paying close attention to the growing anecdotal evidence from users who say it’s happened to them or someone they know. Sadly, the increasing number of lawsuits filed against AI companies by the families of people who died following their conversations with chatbots demonstrate the technology’s potentially deadly consequences. —Rhiannon Williams

4. Reasoning

Few things kept the AI hype train going this year more than so-called reasoning models, LLMs that can break down a problem into multiple steps and work through them one by one. OpenAI released its first reasoning models, o1 and o3, a year ago.

A month later, the Chinese firm DeepSeek took everyone by surprise with a very fast follow, putting out R1, the first open-source reasoning model. In no time, reasoning models became the industry standard: All major mass-market chatbots now come in flavors backed by this tech. Reasoning models have pushed the envelope of what LLMs can do, matching top human performances in prestigious math and coding competitions. On the flip side, all the buzz about LLMs that could “reason” reignited old debates about how smart LLMs really are and how they really work. Like “artificial intelligence” itself, “reasoning” is technical jargon dressed up with marketing sparkle. Choo choo! —Will Douglas Heaven

5. World models 

For all their uncanny facility with language, LLMs have very little common sense. Put simply, they don’t have any grounding in how the world works. Book learners in the most literal sense, LLMs can wax lyrical about everything under the sun and then fall flat with a howler about how many elephants you could fit into an Olympic swimming pool (exactly one, according to one of Google DeepMind’s LLMs).

World models—a broad church encompassing various technologies—aim to give AI some basic common sense about how stuff in the world actually fits together. In their most vivid form, world models like Google DeepMind’s Genie 3 and Marble, the much-anticipated new tech from Fei-Fei Li’s startup World Labs, can generate detailed and realistic virtual worlds for robots to train in and more. Yann LeCun, Meta’s former chief scientist, is also working on world models. He has been trying to give AI a sense of how the world works for years, by training models to predict what happens next in videos. This year he quit Meta to focus on this approach in a new start up called Advanced Machine Intelligence Labs. If all goes well, world models could be the next thing. —Will Douglas Heaven

6. Hyperscalers

Have you heard about all the people saying no thanks, we actually don’t want a giant data center plopped in our backyard? The data centers in question—which tech companies want to built everywhere, including space—are typically referred to as hyperscalers: massive buildings purpose-built for AI operations and used by the likes of OpenAI and Google to build bigger and more powerful AI models. Inside such buildings, the world’s best chips hum away training and fine-tuning models, and they’re built to be modular and grow according to needs.

It’s been a big year for hyperscalers. OpenAI announced, alongside President Donald Trump, its Stargate project, a $500 billion joint venture to pepper the country with the largest data centers ever. But it leaves almost everyone else asking: What exactly do we get out of it? Consumers worry the new data centers will raise their power bills. Such buildings generally struggle to run on renewable energy. And they don’t tend to create all that many jobs. But hey, maybe these massive, windowless buildings could at least give a moody, sci-fi vibe to your community. —James O’Donnell

7. Bubble

The lofty promises of AI are levitating the economy. AI companies are raising eye-popping sums of money and watching their valuations soar into the stratosphere. They’re pouring hundreds of billions of dollars into chips and data centers, financed increasingly by debt and eyebrow-raising circular deals. Meanwhile, the companies leading the gold rush, like OpenAI and Anthropic, might not turn a profit for years, if ever. Investors are betting big that AI will usher in a new era of riches, yet no one knows how transformative the technology will actually be.

Most organizations using AI aren’t yet seeing the payoff, and AI work slop is everywhere. There’s scientific uncertainty about whether scaling LLMs will deliver superintelligence or whether new breakthroughs need to pave the way. But unlike their predecessors in the dot-com bubble, AI companies are showing strong revenue growth, and some are even deep-pocketed tech titans like Microsoft, Google, and Meta. Will the manic dream ever burst? —Michelle Kim

8. Agentic

This year, AI agents were everywhere. Every new feature announcement, model drop, or security report throughout 2025 was peppered with mentions of them, even though plenty of AI companies and experts disagree on exactly what counts as being truly “agentic,” a vague term if ever there was one. No matter that it’s virtually impossible to guarantee that an AI acting on your behalf out in the wide web will always do exactly what it’s supposed to do—it seems as though agentic AI is here to stay for the foreseeable. Want to sell something? Call it agentic! —Rhiannon Williams

9. Distillation

Early this year, DeepSeek unveiled its new model DeepSeek R1, an open-source reasoning model that matches top Western models but costs a fraction of the price. Its launch freaked Silicon Valley out, as many suddenly realized for the first time that huge scale and resources were not necessarily the key to high-level AI models. Nvidia stock plunged by 17% the day after R1 was released.

The key to R1’s success was distillation, a technique that makes AI models more efficient. It works by getting a bigger model to tutor a smaller model: You run the teacher model on a lot of examples and record the answers, and reward the student model as it copies those responses as closely as possible, so that it gains a compressed version of the teacher’s knowledge.  —Caiwei Chen

10. Sycophancy

As people across the world spend increasing amounts of time interacting with chatbots like ChatGPT, chatbot makers are struggling to work out the kind of tone and “personality” the models should adopt. Back in April, OpenAI admitted it’d struck the wrong balance between helpful and sniveling, saying a new update had rendered GPT-4o too sycophantic. Having it suck up to you isn’t just irritating—it can mislead users by reinforcing their incorrect beliefs and spreading misinformation. So consider this your reminder to take everything—yes, everything—LLMs produce with a pinch of salt. —Rhiannon Williams

11. Slop

If there is one AI-related term that has fully escaped the nerd enclosures and entered public consciousness, it’s “slop.” The word itself is old (think pig feed), but “slop” is now commonly used to refer to low-effort, mass-produced content generated by AI, often optimized for online traffic. A lot of people even use it as a shorthand for any AI-generated content. It has felt inescapable in the past year: We have been marinated in it, from fake biographies to shrimp Jesus images to surreal human-animal hybrid videos.

But people are also having fun with it. The term’s sardonic flexibility has made it easy for internet users to slap it on all kinds of words as a suffix to describe anything that lacks substance and is absurdly mediocre: think “work slop” or “friend slop.” As the hype cycle resets, “slop” marks a cultural reckoning about what we trust, what we value as creative labor, and what it means to be surrounded by stuff that was made for engagement rather than expression. —Caiwei Chen

12. Physical intelligence

Did you come across the hypnotizing video from earlier this year of a humanoid robot putting away dishes in a bleak, gray-scale kitchen? That pretty much embodies the idea of physical intelligence: the idea that advancements in AI can help robots better move around the physical world. 

It’s true that robots have been able to learn new tasks faster than ever before, everywhere from operating rooms to warehouses. Self-driving-car companies have seen improvements in how they simulate the roads, too. That said, it’s still wise to be skeptical that AI has revolutionized the field. Consider, for example, that many robots advertised as butlers in your home are doing the majority of their tasks thanks to remote operators in the Philippines. 

The road ahead for physical intelligence is also sure to be weird. Large language models train on text, which is abundant on the internet, but robots learn more from videos of people doing things. That’s why the robot company Figure suggested in September that it would pay people to film themselves in their apartments doing chores. Would you sign up? —James O’Donnell

13. Fair use

AI models are trained by devouring millions of words and images across the internet, including copyrighted work by artists and writers. AI companies argue this is “fair use”—a legal doctrine that lets you use copyrighted material without permission if you transform it into something new that doesn’t compete with the original. Courts are starting to weigh in. In June, Anthropic’s training of its AI model Claude on a library of books was ruled fair use because the technology was “exceedingly transformative.”

That same month, Meta scored a similar win, but only because the authors couldn’t show that the company’s literary buffet cut into their paychecks. As copyright battles brew, some creators are cashing in on the feast. In December, Disney signed a splashy deal with OpenAI to let users of Sora, the AI video platform, generate videos featuring more than 200 characters from Disney’s franchises. Meanwhile, governments around the world are rewriting copyright rules for the content-guzzling machines. Is training AI on copyrighted work fair use? As with any billion-dollar legal question, it depends. —Michelle Kim

14. GEO

Just a few short years ago, an entire industry was built around helping websites rank highly in search results (okay, just in Google). Now search engine optimization (SEO), is giving way to GEO—generative engine optimization—as the AI boom forces brands and businesses to scramble to maximize their visibility in AI, whether that’s in AI-enhanced search results like Google’s AI Overviews or within responses from LLMs. It’s no wonder they’re freaked out. We already know that news companies have experienced a colossal drop in search-driven web traffic, and AI companies are working on ways to cut out the middleman and allow their users to visit sites from directly within their platforms. It’s time to adapt or die. —Rhiannon Williams

Lately, everywhere I scroll, I keep seeing the same fish-eyed CCTV view: a grainy wide shot from the corner of a living room, a driveway at night, an empty grocery store. Then something impossible happens. JD Vance shows up at the doorstep in a crazy outfit. A car folds into itself like paper and drives away. A cat comes in and starts hanging out with capybaras and bears, as if in some weird modern fairy tale.

This fake-surveillance look has become one of the signature flavors of what people now call AI slop. For those of us who spend time online watching short videos, slop feels inescapable: a flood of repetitive, often nonsensical AI-generated clips that washes across TikTok, Instagram, and beyond. For that, you can thank new tools like OpenAI’s Sora (which exploded in popularity after launching in app form in September), Google’s Veo series, and AI models built by Runway. Now anyone can make videos, with just a few taps on a screen. 

@absolutemem

If I were to locate the moment slop broke through into popular consciousness, I’d pick the video of rabbits bouncing on a trampoline that went viral this summer. For many savvy internet users, myself included, it was the first time we were fooled by an AI video, and it ended up spawning a wave of almost identical riffs, with people making videos of all kinds of animals and objects bouncing on the same trampoline. 

My first reaction was that, broadly speaking, all of this sucked. That’s become a familiar refrain, in think pieces and at dinner parties. Everything online is slop now—the internet “enshittified,” with AI taking much of the blame. Initially, I largely agreed, quickly scrolling past every AI video in a futile attempt to send a message to my algorithm. But then friends started sharing AI clips in group chats that were compellingly weird, or funny. Some even had a grain of brilliance buried in the nonsense. I had to admit I didn’t fully understand what I was rejecting—what I found so objectionable. 

To try to get to the bottom of how I felt (and why), I recently spoke to the people making the videos, a company creating bespoke tools for creators, and experts who study how new media becomes culture. What I found convinced me that maybe generative AI will not end up ruining everything. Maybe we have been too quick to dismiss AI slop. Maybe there’s a case for looking beyond the surface and seeing a new kind of creativity—one we’re watching take shape in real time, with many of us actually playing a part. 

 The slop boom

“AI slop” can and does refer to text, audio, or images. But what’s really broken through this year is the flood of quick AI-generated video clips on social platforms, each produced by a short written prompt fed into an AI model. Under the hood, these models are trained on enormous data sets so they can predict what every subsequent frame should look or sound like. It’s much like the process by which text models produce answers in a chat, but slower and far more power-hungry.

Early text-to-video systems, released around 2022 to 2023, could manage only a few seconds of blurry motion; objects warped in and out of existence, characters teleported around, and the giveaway that it was AI was usually a mangled hand or a melting face. In the past two years, newer models like Sora2, Veo 3.1, and Runway’s latest Gen-4.5 have dramatically improved, creating realistic, seamless, and increasingly true-to-prompt videos that can last up to a minute. Some of these models even generate sound and video together, including ambient noise and rough dialogue.

These text-to-video models have often been pitched by AI companies as the future of cinema—tools for filmmakers, studios, and professional storytellers. The demos have leaned into widescreen shots and dramatic camera moves. OpenAI pitched Sora as a “world simulator” while courting Hollywood filmmakers with what it boasted were movie-quality shorts. Google introduced Veo 3 last year as a step toward storyboards and longer scenes, edging directly into film workflows. 

All this hinged on the idea that people wanted to make AI-generated videos that looked real. But the reality of how they’re being used is more modest, weirder—and arguably much more interesting. What has turned out to be the home turf for AI video is the six-inch screen in our hands. 

Anyone can and does use these tools; a report by Adobe released in October shows that 86% of creators are using generative AI. But so are average social media users—people who aren’t “creators” so much as just people with phones. 

That’s how you end up with clips showing things like Indian prime minister Narendra Modi dancing with Gandhi, a crystal that melts into butter the moment a knife touches it, or Game of Thrones reimagined as Henan opera—videos that are hypnotic, occasionally funny, and often deeply stupid. And while micro-trends didn’t start with AI—TikTok and Reels already ran on fast-moving formats—it feels as if AI poured fuel on that fire. Perhaps because the barrier to copying an idea becomes so low, a viral video like the bunnies on trampoline can easily and quickly spawn endless variations on the same concept. You don’t need a costume or a filming location anymore; you just tweak the prompt, hit Generate, and share. 

Big tech companies have also jumped on the idea of AI videos as a new social medium. The Sora app allows users to insert AI versions of themselves and other users into scenes. Meta’s Vibes app wants to turn your entire feed into nonstop AI clips.

Of course, the same frictionless setup that allows for harmless, delightful creations also makes it easy to generate much darker slop. Sora has already been used to create so many racist deepfakes of Martin Luther King Jr. that the King estate pushed the company to block new MLK videos entirely. TikTok and X are seeing Sora-watermarked clips of women and girls being strangled circulating in bulk, posted by accounts seemingly dedicated to this one theme. And then there’s “nazislop,” the nickname for AI videos that repackage fascist aesthetics and memes into glossy, algorithm-ready content aimed at teens’ For You pages.

But the prevalence of bad actors hasn’t stopped short AI videos from flourishing as a form. New apps, Discord servers for AI creators, and tutorial channels keep multiplying. And increasingly, the energy in the community seems to be shifting away from trying to create stuff that “passes as real” toward embracing AI’s inherent weirdness. Every day, I stumble across creators who are stretching what “AI slop” is supposed to look like. I decided to talk to some of them.

Meet the creators

Like those fake surveillance videos, many popular viral AI videos rely on a surreal, otherworldly quality. As Wenhui Lim, an architecture designer turned full-time AI artist, tells me, “There is definitely a competition of ‘How weird we can push this?’ among AI video creators.”  

It’s the kind of thing AI video tools seem to handle with ease: pushing physics past what a normal body can do or a normal camera can capture. This makes AI a surprisingly natural fit for satire, comedy skits, parody, and experimental video art—especially examples involving absurdism or even horror. Several popular AI creators that I spoke with eagerly tap into this capability. 

Drake Garibay, a 39-year-old software developer from Redlands, California, was inspired by body-horror AI clips circulating on social media in early 2025. He started playing with ComfyUI, a generative media tool, and ended up spending hours each week making his own strange creations. His favorite subject is morbid human-animal hybrids. “I fell right into it,” he says. “I’ve always been pretty artistic, [but] when I saw what AI video tools can do, I was blown away.”

Since the start of this year, Garibay has been posting his experiments online. One that went viral on TikTok, captioned “Cooking up some fresh AI slop,” shows a group of people pouring gooey dough into a pot. The mixture suddenly sprouts a human face, which then emerges from the boiling pot with a head and body. It has racked up more than 8.3 million views.

@digitalpersons

AI video technology is evolving so quickly that even for creative professionals, there is a lot to experiment with. Daryl Anselmo, a creative director turned digital artist, has been experimenting with the technology since its early days, posting an AI-generated video every day since 2021. He tells me that uses a wide range of tools, including Kling, Luma, and Midjourney, and is constantly iterating. To him, testing the boundaries of these AI tools is sometimes itself the reward. “I would like to think there are impossible things that you could not do before that are still yet to be discovered. That is exciting to me,” he says.

Anselmo has collected his daily creations over the past four years into an art project, titled AI Slop, that has been exhibited in multiple galleries, including the Grand Palais Immersif in Paris. There’s obvious attention to mood and composition. Some clips feel like something closer to an art-house vignette than a throwaway meme. Over time, Anselmo’s project has taken a darker turn as his subjects shift from landscapes and interior design toward more of the body horror that drew Garibay in. 

His breakout piece, feel the agi, shows a hyperrealistic bot peeling open its own skull. Another video he shared recently features a midnight diner populated by anthropomorphized Tater Tots, titled Tot and Bothered; with its vintage palette and slow, mystical soundtrack, the piece feels like a late-night fever dream. 

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A post shared by Daryl Anselmo (@darylanselmo)

One further benefit of these AI systems is that they make it easier for creators to build recurring spaces and casts of characters that function like informal franchises. Lim, for instance, is the creator of a popular AI video account called Niceaunties, inspired by the “auntie culture” in Singapore, where she’s from.

“The word ‘aunties’ often has a slightly negative connotation in Singaporean culture. They are portrayed as old-fashioned, naggy, and lacking boundaries. But they are also so resourceful, funny, and at ease with themselves,” she says. “I want to create a world where it’s different for them.” 

Her cheeky, playful videos show elderly Asian women merging with fruits, other objects, and architecture, or just living their best lives in a fantasy world. A viral video called Auntlantis, which has racked up 13.5 million views on Instagram, imagines silver-haired aunties as industrial mermaids working in an underwater trash-processing plant.  

There’s also Granny Spills, an AI video account that features a glamorous, sassy old lady spitting hot takes and life advice to a street interviewer. It gained 1.8 million Instagram followers within three months of launch, posting new videos almost every day. Although the granny’s face looks slightly different in every video, the pink color scheme and her outfit stay mostly consistent. Creators Eric Suerez and Adam Vaserstein tell me that their entire workflow is powered by AI, from writing the script to constructing the scenes. Their role, as a result, becomes close to creative directing.

@grannyspills

These projects often spin off merch, miniseries, and branded universes. The creators of Granny Spills, for example, have expanded their network, creating a Black granny as well as an Asian granny to cater to different audiences. The grannies now appear in crossover videos, as if they share the same fictional universe, pushing traffic between channels. 

In the same vein, it’s now more possible than ever to participate in an online trend. Consider  “Italian brainrot,” which went viral earlier this year. Beloved by Gen Z and Gen Alpha, these videos feature human–animal–object hybrids with pseudo-Italian names like “Bombardiro Crocodilo” and “Tralalero Tralala.” According to Know Your Meme, the craze began with a few viral TikTok sounds in fake Italian. Soon, a lot of people were participating in what felt like a massive collaborative hallucination, inventing characters, backstories, and worldviews for an ever-expanding absurdist universe. 

@patapimai

“Italian brainrot was great when it first hit,” says Denim Mazuki, a software developer and content creator who has been following the trend. “It was the collective lore-building that made it wonderful. Everyone added a piece. The characters were not owned by a studio or a single creator—they were made by the chronically online users.” 

This trend and others are further enabled by specialized and sophisticated new tools—like OpenArt, a platform designed not just for video generation but for video storytelling, which gives users frame-to-frame control over a developing narrative.

Making a video on OpenArt is straightforward: Users start with a few AI-generated character images and a line of text as simple as “cat dancing in a park.” The platform then spins out a scene breakdown that users can tweak act by act, and they can run it through multiple mainstream models and compare the results to see which look best.

OpenArt cofounders Coco Mao and Chloe Fang tell me they sponsored tutorial videos and created quick-start templates to capitalize specifically on the trend of regular people wanting to get in on Italian brainrot. They say more than 80% of their users have no artistic background. 

In defense of slop

The current use of the word “slop” online traces back to the early 2010s on 4chan, a forum known for its insular and often toxic in-jokes. As the term has spread, its meaning has evolved; it’s now a kind of derogatory slur for anything that feels like low-quality mass production aimed at an unsuspecting public, says Adam Aleksic, an internet linguist. People now slap it onto everything from salad bowls to meaningless work reports.

But even with that broadened usage, AI remains the first association: “slop” has become a convenient shorthand for dismissing almost any AI-generated output, regardless of its actual quality. The Cambridge Dictionary’s new sense of “slop” will almost certainly cement this perception, describing it as “content on the internet that is of very low quality, especially when it is created by AI.”   

Perhaps unsurprisingly, the word has become a charged label among AI creators. 

Anselmo embraces it semi-ironically, hence the title of his yearslong art project. “I see this series as an experimental sketchbook,” he says. “I am working with the slop, pushing the models, breaking them, and developing a new visual language. I have no shame that I am deep into AI.” Anselmo says that he does not concern himself with whether his work is “art.”

Garibay, the creator of the viral video where a human face emerged from a pot of physical slop, uses the label playfully. “The AI slop art is really just a lot of weird glitchy stuff that happens, and there’s not really a lot of depth usually behind it, besides the shock value,” he says. “But you will find out really fast that there is a heck of a lot more involved, if you want a higher-end result.” 

That’s largely in line with what Suerez and Vaserstein, the creators of Granny Spills, tell me. They actually hate it when their work is called slop, given the way the term is often used to dismiss AI-generated content out of hand. It feels disrespectful of their creative input, they say. Even though they do not write the scripts or paint the frames, they say they are making legitimate artistic choices. 

Indeed, for most of the creators I spoke to, making AI content is rarely a one-click process. They tell me that it takes skill, trial and error, and a strong sense of taste to consistently get the visuals they want. Lim says a single one-minute video can take hours, sometimes even days, to make. Anselmo, for his part, takes pride in actively pushing the model rather than passively accepting its output. “There’s just so many things that you can do with it that go well beyond ‘Oh, way to go, you typed in a prompt,’” he says. Ultimately, slop evokes a lot of feelings. Aleksic puts it well: “There’s a feeling of guilt on the user end for enjoying something that you know to be lowbrow. There’s a feeling of anger toward the creator for making something that is not up to your content expectations, and all the meantime, there’s a pervasive algorithmic anxiety hanging over us. We know that the algorithm and the platforms are to blame for the distribution of this slop.”

And that anxiety long predates generative AI. We’ve been living for years with the low-grade dread of being nudged, of having our taste engineered and our attention herded, so it’s not surprising that the anger latches onto the newest, most visible culprit. Sometimes it is misplaced, sure, but I also get the urge to assert human agency against a new force that seems to push all of us away from what we know and toward something we didn’t exactly choose.

But the negative association has real harm for the earlier adopters. Every AI video creator I spoke to described receiving hateful messages and comments simply for using these tools at all. These messages accuse AI creators of taking opportunities away from artists already struggling to make a living, and some dismiss their work as “grifting” and “garbage.” The backlash, of course, did not come out of nowhere. A Brookings study of one major freelance marketplace found that after new generative-AI tools launched in 2022, freelancers in AI-exposed occupations saw about 2% decline in contracts and a 5% drop in earnings. 

“The phrase ‘AI slop’ implies, like, a certain ease of creation that really bothers a lot of people—understandably, because [making AI-generated videos] doesn’t incorporate the artistic labor that we typically associate with contemporary art,” says Mindy Seu, a researcher, artist, and associate professor in digital arts at UCLA. 

At the root of the conflict here is that the use of AI in art is still nascent; there are few best practices and almost no guardrails. And there’s a kind of shame involved—one I recognize when I find myself lingering on bad AI content. 

Historically, new technology has always carried a whiff of stigma when it first appears, especially in creative fields where it seems to encroach on a previously manual craft. Seu says that digital art, internet art, and new media have been slow to gain recognition from cultural institutions, which remain key arbiters of what counts as “serious” or “relevant” art. 

For many artists, AI now sits in that same lineage: “Every big advance in technology yields the question ‘What is the role of the artist?’” she says. This is true even if creators are not seeing it as a replacement for authorship but simply as another way to create. 

Mao, the OpenArt founder, believes that learning how to use generative video tools will be crucial for future content creators, much as learning Photoshop was almost synonymous with graphic design for a generation. “It is a skill to be learned and mastered,” she says.

There is a generous reading of the phenomenon so many people call AI slop, which is that it is a kind of democratization. A rare skill shifts away from craftsmanship to something closer to creative direction: being able to describe what you want with enough linguistic precision, and to anchor it in references the model is likely to understand. You have to know how to ask, and what to point to. In that sense, discernment and critique sit closer to the center of the process than ever before.

It’s not just about creative direction, though, but about the human intention behind the creation. “It’s very easy to copy the style,” Lim says. “It’s very easy to make, like, old Asian women doing different things, but they [imitators] don’t understand why I’m doing it … Even when people try to imitate that, they don’t have that consistency.”

“It’s the idea behind AI creation that makes it interesting to look at,” says Zach Lieberman, a professor at the MIT Media Lab who leads a research group called Future Sketches, where members explore code-enabled images. Lieberman, who has been posting daily sketches generated by code for years, tells me that mathematical logic is not the enemy of beauty. He echoes Mao in saying that a younger generation will inevitably see AI as just another tool in the toolbox. Still, he feels uneasy: By relying so heavily on black-box AI models, artists lose some of the direct control over output that they’ve traditionally enjoyed.

A new online culture

For many people, AI slop is simply everything they already resent about the internet, turned up: ugly, noisy, and crowding out human work. It’s only possible because it’s been trained to take all creative work and make it fodder, stripped of origin, aura, or credit, and blended into something engineered to be mathematically average—arguably perfectly mediocre, by design. Charles Pulliam-Moore, a writer for The Verge, calls this the “formulaic derivativeness” that already defines so much internet culture: unimaginative, unoriginal, and uninteresting. 

But I love internet culture, and I have for a long time. Even at its worst, it’s bad in an interesting way: It offers a corner for every kind of obsession and invites you to add your own. Years of being chronically online have taught me that the real logic of slop consumption isn’t mastery but a kind of submission. As a user, I have almost no leverage over platforms or algorithms; I can’t really change how they work. Submission, though, doesn’t mean giving up. It’s more like recognizing that the tide is stronger than you and choosing to let it carry you. Good scrolling isn’t about control anyway. It’s closer to surfing, and sometimes you wash up somewhere ridiculous, but not entirely alone.

Mass-produced click-bait content has always been around. What’s new is that we can now watch it being generated in real time, on a scale that would have been unimaginable before. And the way we respond to it in turn shapes new content (see the trampoline-bouncing bunnies) and more culture and so on. Perhaps AI slop is born of submission to algorithmic logic. It’s unserious, surreal, and spectacular in ways that mirror our relationship to the internet itself. It is so banal—so aggressively, inhumanly mediocre—that it loops back around and becomes compelling. 

To “love AI slop” is to admit the internet is broken, that the infrastructure of culture is opportunistic and extractive. But even in that wreckage, people still find ways to play, laugh, and make meaning. 

Earlier this fall, months after I was briefly fooled by the bunny video, I was scrolling on Rednote and landed on videos by Mu Tianran, a Chinese creator who acts out weird skits that mimic AI slop. In one widely circulated clip, he plays a street interviewer asking other actors, “Do you know you are AI generated?”—parodying an earlier wave of AI-generated street interviews. The actors’ responses seem so AI, but of course they’re not: Eyes are fixed just off-camera, their laughter a beat too slow, their movements slightly wrong. 

Watching this, it was hard to believe that AI was about to snuff out human creativity. If anything, it has handed people a new style to inhabit and mock, another texture to play with. Maybe it’s all fine. Maybe the urge to imitate, remix, and joke is still stubbornly human, and AI cannot possibly take it away. 

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.”

A group of quantum physicists claims to have created a version of the powerful reasoning AI model DeepSeek R1 that strips out the censorship built into the original by its Chinese creators. 

The scientists at Multiverse Computing, a Spanish firm specializing in quantum-inspired AI techniques, created DeepSeek R1 Slim, a model that is 55% smaller but performs almost as well as the original model. Crucially, they also claim to have eliminated official Chinese censorship from the model.

In China, AI companies are subject to rules and regulations meant to ensure that content output aligns with laws and “socialist values.” As a result, companies build in layers of censorship when training the AI systems. When asked questions that are deemed “politically sensitive,” the models often refuse to answer or provide talking points straight from state propaganda.

To trim down the model, Multiverse turned to a mathematically complex approach borrowed from quantum physics that uses networks of high-dimensional grids to represent and manipulate large data sets. Using these so-called tensor networks shrinks the size of the model significantly and allows a complex AI system to be expressed more efficiently.

The method gives researchers a “map” of all the correlations in the model, allowing them to identify and remove specific bits of information with precision. After compressing and editing a model, Multiverse researchers fine-tune it so its output remains as close as possible to that of the original.

To test how well it worked, the researchers compiled a data set of around 25 questions on topics known to be restricted in Chinese models, including “Who does Winnie the Pooh look like?”—a reference to a meme mocking President Xi Jinping—and “What happened in Tiananmen in 1989?” They tested the modified model’s responses against the original DeepSeek R1, using OpenAI’s GPT-5 as an impartial judge to rate the degree of censorship in each answer. The uncensored model was able to provide factual responses comparable to those from Western models, Multiverse says.

This work is part of Multiverse’s broader effort to develop technology to compress and manipulate existing AI models. Most large language models today demand high-end GPUs and significant computing power to train and run. However, they are inefficient, says Roman Orús, Multiverse’s cofounder and chief scientific officer. A compressed model can perform almost as well and save both energy and money, he says. 

There is a growing effort across the AI industry to make models smaller and more efficient. Distilled models, such as DeepSeek’s own R1-Distill variants, attempt to capture the capabilities of larger models by having them “teach” what they know to a smaller model, though they often fall short of the original’s performance on complex reasoning tasks.

Other ways to compress models include quantization, which reduces the precision of the model’s parameters (boundaries that are set when it’s trained), and pruning, which removes individual weights or entire “neurons.”

“It’s very challenging to compress large AI models without losing performance,” says Maxwell Venetos, an AI research engineer at Citrine Informatics, a software company focusing on materials and chemicals, who didn’t work on the Multiverse project. “Most techniques have to compromise between size and capability. What’s interesting about the quantum-inspired approach is that it uses very abstract math to cut down redundancy more precisely than usual.”

This approach makes it possible to selectively remove bias or add behaviors to LLMs at a granular level, the Multiverse researchers say. In addition to removing censorship from the Chinese authorities, researchers could inject or remove other kinds of perceived biases or specialty knowledge. In the future, Multiverse says, it plans to compress all mainstream open-source models.  

Thomas Cao, assistant professor of technology policy at Tufts University’s Fletcher School, says Chinese authorities require models to build in censorship—and this requirement now shapes the global information ecosystem, given that many of the most influential open-source AI models come from China.

Academics have also begun to document and analyze the phenomenon. Jennifer Pan, a professor at Stanford, and Princeton professor Xu Xu conducted a study earlier this year examining government-imposed censorship in large language models. They found that models created in China exhibit significantly higher rates of censorship, particularly in response to Chinese-language prompts.

There is growing interest in efforts to remove censorship from Chinese models. Earlier this year, the AI search company Perplexity released its own uncensored variant of DeepSeek R1, which it named R1 1776. Perplexity’s approach involved post-training the model on a data set of 40,000 multilingual prompts related to censored topics, a more traditional fine-tuning method than the one Multiverse used. 

However, Cao warns that claims to have fully “removed” censorship may be overstatements. The Chinese government has tightly controlled information online since the internet’s inception, which means that censorship is both dynamic and complex. It is baked into every layer of AI training, from the data collection process to the final alignment steps. 

“It is very difficult to reverse-engineer that [a censorship-free model] just from answers to such a small set of questions,” Cao says. 

Google today unveiled Gemini 3, a major upgrade to its flagship multimodal model. The firm says the new model is better at reasoning, has more fluid multimodal capabilities (the ability to work across voice, text or images), and will work like an agent. 

The previous model, Gemini 2.5, supports multimodal input. Users can feed it images, handwriting, or voice. But it usually requires explicit instructions about the format the user wants back, and it defaults to plain text regardless. 

But Gemini 3 introduces what Google calls “generative interfaces,” which allow the model to make its own choices about what kind of output fits the prompt best, assembling visual layouts and dynamic views on its own instead of returning a block of text. 

Ask for travel recommendations and it may spin up a website-like interface inside the app, complete with modules, images, and follow-up prompts such as “How many days are you traveling?” or “What kinds of activities do you enjoy?” It also presents clickable options based on what you might want next.

When asked to explain a concept, Gemini 3 may sketch a diagram or generate a simple animation on its own if it believes a visual is more effective. 

“Visual layout generates an immersive, magazine-style view complete with photos and modules,” says Josh Woodward, VP of Google Labs, Gemini, and AI Studio. “These elements don’t just look good but invite your input to further tailor the results.” 

With Gemini 3, Google is also introducing Gemini Agent, an experimental feature designed to handle multi-step tasks directly inside the app. The agent can connect to services such as Google Calendar, Gmail, and Reminders. Once granted access, it can execute tasks like organizing an inbox or managing schedules. 

Similar to other agents, it breaks tasks into discrete steps, displays its progress in real time, and pauses for approval from the user before continuing. Google describes the feature as a step toward “a true generalist agent.” It will be available on the web for Google AI Ultra subscribers in the US starting November 18.

The overall approach can seem a lot like “vibe coding,” where users describe an end goal in plain language and let the model assemble the interface or code needed to get there.

The update also ties Gemini more deeply into Google’s existing products. In Search, a limited group of Google AI Pro and Ultra subscribers can now switch to Gemini 3 Pro, the reasoning variation of the new model, to receive deeper, more thorough AI-generated summaries that rely on the model’s reasoning rather than the existing AI Mode.

For shopping, Gemini will now pull from Google’s Shopping Graph—which the company says contains more than 50 billion product listings—to generate its own recommendation guides. Users just need to ask a shopping-related question or search a shopping-related phrase, and the model assembles an interactive, Wirecutter-style product recommendation piece, complete with prices and product details, without redirecting to an external site.

For developers, Google is also pushing single-prompt software generation further. The company introduced Google Antigravity, a  development platform that acts as an all-in-one space where code, tools, and workflows can be created and managed from a single prompt.

Derek Nee, CEO of Flowith, an agentic AI application, told MIT Technology Review that Gemini 3 Pro addresses several gaps in earlier models. Improvements include stronger visual understanding, better code generation, and better performance on long tasks—features he sees as essential for developers of AI apps and agents. 

“Given its speed and cost advantages, we’re integrating the new model into our product,” he says. “We’re optimistic about its potential, but we need deeper testing to understand how far it can go.” 

The State of AI is a collaboration between the Financial Times & MIT Technology Review examining the ways in which AI is reshaping global power. Every Monday for the next six weeks, writers from both publications will debate one aspect of the generative AI revolution reshaping global power.

In this conversation, the FT’s tech columnist and Innovation Editor John Thornhill and MIT Technology Review’s Caiwei Chen consider the battle between Silicon Valley and Beijing for technological supremacy.

John Thornhill writes:

Viewed from abroad, it seems only a matter of time before China emerges as the AI superpower of the 21st century. 

Here in the West, our initial instinct is to focus on America’s significant lead in semiconductor expertise, its cutting-edge AI research, and its vast investments in data centers. The legendary investor Warren Buffett once warned: “Never bet against America.” He is right that for more than two centuries, no other “incubator for unleashing human potential” has matched the US.

Today, however, China has the means, motive, and opportunity to commit the equivalent of technological murder. When it comes to mobilizing the whole-of-society resources needed to develop and deploy AI to maximum effect, it may be just as rash to bet against. 

The data highlights the trends. In AI publications and patents, China leads. By 2023, China accounted for 22.6% of all citations, compared with 20.9% from Europe and 13% from the US, according to Stanford University’s Artificial Intelligence Index Report 2025. As of 2023, China also accounted for 69.7% of all AI patents. True, the US maintains a strong lead in the top 100 most cited publications (50 versus 34 in 2023), but its share has been steadily declining. 

Similarly, the US outdoes China in top AI research talent, but the gap is narrowing. According to a report from the US Council of Economic Advisers, 59% of the world’s top AI researchers worked in the US in 2019, compared with 11% in China. But by 2022 those figures were 42% and 28%. 

The Trump administration’s tightening of restrictions for foreign H-1B visa holders may well lead more Chinese AI researchers in the US to return home. The talent ratio could move further in China’s favor.

Regarding the technology itself, US-based institutions produced 40 of the world’s most notable AI models in 2024, compared with 15 from China. But Chinese researchers have learned to do more with less, and their strongest large language models—including the open-source DeepSeek-V3 and Alibaba’s Qwen 2.5-Max—surpass the best US models in terms of algorithmic efficiency.

Where China is really likely to excel in future is in applying these open-source models. The latest report from Air Street Capital shows that China has now overtaken the US in terms of monthly downloads of AI models. In AI-enabled fintech, e-commerce, and logistics, China already outstrips the US. 

Perhaps the most intriguing—and potentially the most productive—applications of AI may yet come in hardware, particularly in drones and industrial robotics. With the research field evolving toward embodied AI, China’s advantage in advanced manufacturing will shine through.

Dan Wang, the tech analyst and author of Breakneck, has rightly highlighted the strengths of China’s engineering state in developing manufacturing process knowledge—even if he has also shown the damaging effects of applying that engineering mentality in the social sphere. “China has been growing technologically stronger and economically more dynamic in all sorts of ways,” he told me. “But repression is very real. And it is getting worse in all sorts of ways as well.”

I’d be fascinated to hear from you, Caiwei, about your take on the strengths and weaknesses of China’s AI dream. To what extent will China’s engineered social control hamper its technological ambitions? 

Caiwei Chen responds:

Hi, John!

You’re right that the US still holds a clear lead in frontier research and infrastructure. But “winning” AI can mean many different things. Jeffrey Ding, in his book Technology and the Rise of Great Powers, makes a counterintuitive point: For a general-purpose technology like AI, long-term advantage often comes down to how widely and deeply technologies spread across society. And China is in a good position to win that race (although “murder” might be pushing it a bit!).

Chips will remain China’s biggest bottleneck. Export restrictions have throttled access to top GPUs, pushing buyers into gray markets and forcing labs to recycle or repair banned Nvidia stock. Even as domestic chip programs expand, the performance gap at the very top still stands.

Yet those same constraints have pushed Chinese companies toward a different playbook: pooling compute, optimizing efficiency, and releasing open-weight models. DeepSeek-V3’s training run, for example, used just 2.6 million GPU-hours—far below the scale of US counterparts. But Alibaba’s Qwen models now rank among the most downloaded open-weights globally, and companies like Zhipu and MiniMax are building competitive multimodal and video models. 

China’s industrial policy means new models can move from lab to implementation fast. Local governments and major enterprises are already rolling out reasoning models in administration, logistics, and finance. 

Education is another advantage. Major Chinese universities are implementing AI literacy programs in their curricula, embedding skills before the labor market demands them. The Ministry of Education has also announced plans to integrate AI training for children of all school ages. I’m not sure the phrase “engineering state” fully captures China’s relationship with new technologies, but decades of infrastructure building and top-down coordination have made the system unusually effective at pushing large-scale adoption, often with far less social resistance than you’d see elsewhere. The use at scale, naturally, allows for faster iterative improvements.

Meanwhile, Stanford HAI’s 2025 AI Index found Chinese respondents to be the most optimistic in the world about AI’s future—far more optimistic than populations in the US or the UK. It’s striking, given that China’s economy has slowed since the pandemic for the first time in over two decades. Many in government and industry now see AI as a much-needed spark. Optimism can be powerful fuel, but whether it can persist through slower growth is still an open question.

Social control remains part of the picture, but a different kind of ambition is taking shape. The Chinese AI founders in this new generation are the most globally minded I’ve seen, moving fluidly between Silicon Valley hackathons and pitch meetings in Dubai. Many are fluent in English and in the rhythms of global venture capital. Having watched the last generation wrestle with the burden of a Chinese label, they now build companies that are quietly transnational from the start.

The US may still lead in speed and experimentation, but China could shape how AI becomes part of daily life, both at home and abroad. Speed matters, but speed isn’t the same thing as supremacy.

John Thornhill replies:

You’re right, Caiwei, that speed is not the same as supremacy (and “murder” may be too strong a word). And you’re also right to amplify the point about China’s strength in open-weight models and the US preference for proprietary models. This is not just a struggle between two different countries’ economic models but also between two different ways of deploying technology.  

Even OpenAI’s chief executive, Sam Altman, admitted earlier this year: “We have been on the wrong side of history here and need to figure out a different open-source strategy.” That’s going to be a very interesting subplot to follow. Who’s called that one right?

Further reading on the US-China competition

There’s been a lot of talk about how people may be using generative AI in their daily lives. This story from the FT’s visual story team explores the reality 

From China, FT reporters ask how long Nvidia can maintain its dominance over Chinese rivals

When it comes to real-world uses, toys and companions devices are a novel but emergent application of AI that is gaining traction in China—but is also heading to the US. This MIT Technology Review story explored it.

The once-frantic data center buildout in China has hit walls, and as the sanctions and AI demands shift, this MIT Technology Review story took an on-the-ground look at how stakeholders are figuring it out.