Explore the 2026 pivot from language to world models, featuring AMI Labs and Gemini 3. Learn how JEPA architectures and VLA robotics are bridging the causation gap to create machines that truly reason.

A world model that can’t reason is just a movie, and a reasoning model that can’t see is just a calculator. The future of AGI isn't just bigger models, but smarter architectures that prioritize causation over correlation.
Everything technical I need to know about world models and where they are today vs where they need to be tomorrow. Who is leading? Leading science? Areas of unknown? Expected timeline to bring to world. Implications it will have for the world. Give me the most up to date information as of March 2026


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Cree par des anciens de Columbia University a San Francisco

Nia: Jackson, I was watching a toddler play with blocks this morning, and it hit me—she already understands gravity and object permanence better than the most advanced AI on the planet. I mean, GPT-4 can write a physics paper, but it doesn't actually know that a glass of water will spill if you tip it.
Jackson: Exactly! You’ve hit on the exact reason why the AI world is shifting so fast right now. We’ve spent years building machines that can talk, but as of March 2026, the race is on to build machines that can actually *think* and *plan* by simulating physical reality.
Nia: It’s wild to think that even Yann LeCun left Meta to start AMI Labs just to chase this, seeking a five-billion-dollar valuation before even launching a product. He’s essentially betting that large language models are a dead end for true intelligence.
Jackson: It’s a massive pivot from next-token prediction to what he calls "world models." So, let’s dive into what these models actually are and why they’re being called the blueprint for the next decade of AI.