World models: the next big step
Feb 13, 2026
LLMs understand text. You write - they answer. But ask a language model what happens if you push a glass off a table - and it will give a statistically likely answer. Not because it understands physics, but because it has read a lot of texts about falling glasses.
This is a fundamental limitation. Language models work with tokens, not with reality.
World models are a different approach. This is AI that builds an internal model of the world and can simulate it. It doesn’t generate text about physics, it understands physics. It can predict what happens if you take action X in context Y.
Who’s working on this:
- AMI Labs - Yann LeCun left Meta and is raising €500M for a startup in Paris. His thesis: LLMs are a dead end for real AI.
- World Labs - Fei-Fei Li, the “godmother of AI” from Stanford. $230M from Andreessen Horowitz and Nvidia. Their product Marble generates interactive 3D worlds.
- DeepMind - Genie 3 creates interactive environments in which you can train AI agents.
- Decart - an Israeli startup, $21M from Sequoia. Their Oasis is a fully AI-generated Minecraft. No code, just a neural network.
- Odyssey - founders from the self-driving industry. They make “interactive video” - 3D worlds from text and images.
Practical applications are already visible. OpenAI’s Sora is learning to understand that objects don’t disappear when they leave the frame. Tesla FSD builds a model of its surroundings and predicts the behavior of other cars. Robots need to understand how objects interact in the physical world.
The path to AGI may lie through a combination: LLMs for communicating in natural language, world models for understanding the physical world. Layers, not one universal algorithm.
2026 is the year world models stop being a research topic and become products.
Originally posted in Russian on my telegram. This is a translation.