Yann LeCun thinks the entire AI industry is building on a dead end. And he just convinced Jeff Bezos, Nvidia, Samsung, Temasek, Mark Cuban, Eric Schmidt, and Tim Berners-Lee to give him a billion dollars to prove it.
AMI Labs, short for Advanced Machine Intelligence, closed a $1.03 billion seed round on March 10 at a $3.5 billion pre-money valuation. It is the largest seed round in European history. The Paris-based company was founded less than four months ago, after LeCun left Meta in November 2025 following twelve years as the company’s chief AI scientist.
That is a staggering amount of capital for a company with no product, no revenue, and a thesis that boils down to: everything OpenAI, Anthropic, and Google are doing is a sophisticated parlor trick.
The Thesis
LeCun’s argument, which he has been making publicly for years, is that autoregressive language models cannot achieve real intelligence. These systems predict the next word in a sequence. That is all they do. Scale them up, train them on more data, add reinforcement learning from human feedback, and they still just predict the next word. LeCun has said existing AI systems don’t understand the world as well as a housecat and called the autoregressive approach “kind of a hack.”
His alternative is something called JEPA: Joint Embedding Predictive Architecture. Instead of predicting raw outputs token by token, JEPA learns abstract representations of reality and predicts how those representations evolve. Think of it as the difference between predicting what someone will say next versus understanding why they would say it. JEPA was first introduced as a paper during LeCun’s time at Meta, and early results showed strong performance on computer vision benchmarks while using significantly less compute than pixel-level prediction models.
AMI wants to build “world models” on top of this architecture. Systems that understand physics, maintain persistent memory, and can plan complex action sequences. Not chatbots. Not text generators. AI that can predict the consequences of actions in the physical world, then choose the best path forward.
The Team and the Money
LeCun is executive chairman, not CEO. Day-to-day operations belong to Alex LeBrun, a former Meta colleague and co-founder of the digital health startup Nabla. The founding team, profiled by TechCrunch in January, is stacked with Meta FAIR alumni. Saining Xie, the chief science officer, came from both Google DeepMind and Meta. Pascale Fung, the chief research and innovation officer, was a senior director of AI research at Meta-FAIR and a chair professor at Hong Kong University of Science and Technology. Michael Rabbat, VP of World Models, was a research director at Meta-FAIR and associate professor at McGill. Laurent Solly, the COO and only non-technical founder, spent nearly 13 years as a Meta VP for Europe.
The investor list reads like a who’s who of people who got rich off the current paradigm and are now hedging against it. Bezos Expeditions co-led the round alongside Cathay Innovation, Greycroft, Hiro Capital, and HV Capital. Toyota Ventures and Samsung came in as strategic backers. Nvidia invested too, which is worth noting since Nvidia sells GPUs to every autoregressive AI company on the planet.
AMI plans to operate from four locations: Paris (headquarters), New York (where LeCun teaches at NYU), Montreal, and Singapore.
What This Actually Means
The target applications are telling. AMI is going after industrial automation, robotics, healthcare, and wearables. Domains where getting the answer wrong kills people or destroys equipment. These are exactly the places where autoregressive models fail most dangerously, because they hallucinate with confidence and have no grounding in physical reality.
Nabla, LeBrun’s health tech company, is already AMI’s first announced partner. Healthcare is the guinea pig.
But be clear about what this round actually is: a spectacular bet on LeCun’s reputation. JEPA has shown promising results on vision benchmarks. It has not demonstrated anything close to the generalized capability that would justify a $4.5 billion post-money valuation. No one outside of academic papers has shipped a JEPA-based product that works at scale. LeCun is selling a direction, not a destination.
“Dead end” is doing a lot of heavy lifting in LeCun’s pitch, and it deserves pushback. Whether autoregressive AI is a dead end depends entirely on who is using it and for what.
If you are a Fortune 500 enterprise betting billions on AI infrastructure that needs to understand physics, maintain persistent memory, and plan complex actions in the physical world, then yes, current LLMs have real limitations. They hallucinate. They lack grounding. They cannot reason about consequences in the way that robotics and industrial automation demand. LeCun has a point, and the investor list reflects that companies like Toyota and Samsung, with real manufacturing operations, see the gap.
But if you are a consumer, a small business owner, or a startup trying to build faster, autoregressive models are not a dead end. They are the most powerful productivity tool most people have ever had access to. They pass bar exams, write production code, draft marketing copy, and conduct preliminary medical assessments. For these users, the current paradigm is a net positive, and it keeps getting better. The “hack” works astonishingly well for a hack.
The real question is not whether JEPA replaces transformers. It is whether organizations using either architecture are aligned on how they deploy it. Any AI approach, autoregressive or world-model, becomes a liability when teams are on different paths, when there are no SOPs governing its use, and when the people building with it are not communicating with the people accountable for outcomes. The architecture matters less than the alignment around it.
LeCun is a Turing Award winner who invented convolutional neural networks. When he says the current path has a ceiling, the smartest money in the room takes the bet. But a billion dollars on JEPA does not make autoregressive AI useless for the hundreds of millions of people getting genuine value from it right now. It means the technology is forking, not dying. Different tools for different problems.
The expensive question is whether AMI can ship a product before the autoregressive paradigm either hits its ceiling or adapts past it. OpenAI is not sitting still. Neither is Anthropic. Neither is DeepMind. And the last several years of AI research are littered with alternative architectures that were supposed to replace transformers and didn’t.