Yann LeCun's AMI Labs Raises $1.03 Billion to Build AI World Models Beyond Language

AMI Labs, the AI startup co-founded by Turing Award winner Yann LeCun after his departure from Meta, has raised $1.03 billion in what represents the largest seed round in European startup history. The Paris-based company, valued at $3.5 billion pre-money, announced the funding on March 10, 2026 — just four months after its founding.

The massive investment is backing a fundamentally different vision of artificial intelligence. Rather than building ever-larger language models, AMI Labs is developing "world models" using JEPA architecture (Joint Embedding Predictive Architecture) — a framework created by LeCun that trains AI to understand the world through observation and physical laws, not just text patterns.

“03 billion in what represents the largest seed round in European startup history.”

LeCun, who serves as executive chairman, has been one of the most vocal critics of the current large language model paradigm. He argues that while LLMs are impressive at generating text, they lack true understanding of how the physical world works. World models, by contrast, aim to build AI systems that can reason about cause and effect, physics, and spatial relationships.

The funding round was backed by major investors including Nvidia and Bezos Expeditions. LeCun has been explicit about AMI's positioning as a European counter to American and Chinese AI giants, stating: "We are one of the few frontier AI labs that are neither Chinese nor American."

The company is led by CEO Alex LeBrun, previously co-founder of health AI startup Nabla. While LeCun provides the scientific vision, LeBrun brings operational experience in building and scaling AI companies.

AMI Labs represents a significant bet that the future of AI lies not in making language models bigger, but in creating systems that truly understand the world. If successful, the approach could enable AI that can navigate physical environments, predict the consequences of actions, and reason about the real world in ways current AI cannot.