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AMI Labs Raises $1.03B to Build AI Beyond Language Models

AMI Labs, Yann LeCun's post-Meta startup, raised $1.03B at a $3.5B valuation to build world models based on reasoning rather than next-token prediction.

#AI funding#world models#Yann LeCun#AMI Labs#AI research#reasoning AI#Meta#AI startups
Visual illustration for AMI Labs Raises $1.03B to Build AI Beyond Language Models

Yann LeCun spent over a decade at Meta arguing that large language models were the wrong path to intelligence. On Tuesday, he put $1.03 billion behind that conviction.

Advanced Machine Intelligence Labs (AMI Labs), the startup LeCun co-founded after leaving Meta at the end of 2025, announced it has closed a seed round at a $3.5 billion pre-money valuation, making it Europe’s largest seed raise on record. The round was co-led by Cathay Innovation, Greycroft, Hiro Capital, HV Capital, and Bezos Expeditions, with additional backing from Nvidia and Temasek.

The Turing Prize winner and his team are building what they call world models: AI systems capable of reasoning, planning, and understanding physical reality. These are capabilities LeCun has long argued that today’s transformer-based LLMs structurally cannot achieve.

What World Models Actually Are

The theoretical foundation of AMI Labs is the Joint Embedding Predictive Architecture (JEPA), a framework LeCun first proposed in 2022 while still at Meta. Unlike language models that predict the next token, JEPA-based systems learn abstract representations of how the world works: how objects move, how cause and effect operate, how future states can be anticipated without hallucinating plausible-sounding text.

AMI Labs CEO Alexandre LeBrun, former head of Nabla (the AI-native digital health company), put the timeline plainly to TechCrunch: “AMI Labs is a very ambitious project, because it starts with fundamental research. It’s not your typical applied AI startup that can release a product in three months.” He acknowledged it could take years for world models to reach commercial scale. He then predicted the term will be everywhere within six months regardless. “My prediction is that ‘world models’ will be the next buzzword. In six months, every company will call itself a world model to raise funding.”

The field is already attracting serious capital. Fei-Fei Li’s World Labs secured a $1 billion raise in February 2026. SpAItial closed a $13 million seed last year. AMI now commands the largest check of the cohort.

LeCun’s Thesis Against LLMs

LeCun has been vocal for years that statistical next-token prediction is an architectural dead end for general intelligence. At AMI, that argument becomes a product strategy.

In an interview with Reuters, LeCun framed the company’s core bet: “Current AI approaches based on predicting the next word or pixel will not produce broadly capable intelligent agents by themselves.” AMI aims to build systems capable of operating in complex real-world environments. Not summarizing documents or answering questions, but navigating the physical and logical structure of the world.

Near-term target customers reflect this industrial focus: manufacturers, automakers, aerospace companies, biomedical firms, and pharmaceutical groups. First disclosed partners include Toyota and Samsung. The healthcare angle comes through Nabla, where LeBrun remains chairman and which will be AMI’s earliest deployment partner.

Consumer applications are further out but explicitly part of the roadmap. LeCun cited domestic robots as the clearest long-term use case. “You need a domestic robot to have some level of common sense to really understand the physical world.” He noted active conversations with Meta about integrating AMI technology into Ray-Ban Meta smart glasses.

The Strategic Context

The timing of this raise is notable. Meta, the company LeCun built his reputation inside, has accelerated hard in exactly the direction he left behind. Since LeCun’s departure, Meta reorganized its AI division under former Scale AI CEO Alexandr Wang as Meta Superintelligence Labs, doubling down on frontier LLM development. The implicit bet of AMI Labs is that the LLM arms race will hit a ceiling, and that whoever solves world models first will be positioned at the layer above it.

That thesis plays out against a backdrop where AI companies have increasingly been rewarded for scale at extraordinary valuations and where the reasoning capabilities of frontier models remain a central competitive battleground. AMI is placing a long bet: that reasoning built on physical understanding, not language pattern-matching, is where the next capability jump comes from.

Whether $1.03 billion is enough to find out is a different question. LeBrun’s own timeline suggests years before commercial validation. But with Bezos, Nvidia, and Temasek aligned, AMI Labs is not a research project waiting for funding. It is funded research waiting to be proven right.

For AI researchers and enterprise buyers, AMI Labs represents a live test of a long-standing academic argument. LeCun has published and presented on the limits of statistical prediction for over a decade. The $1.03 billion raise converts that argument into an engineering program with a deadline, a customer list, and investors who will eventually need a return.

Sources