World models could become the next major AI paradigm by helping systems represent environments, predict outcomes, simulate possibilities, plan, and act. The convergence of Yann LeCun, Demis Hassabis, and Fei-Fei Li suggests growing momentum around models built for decisions, not just generation.

What Comes Next for AI? Our Bet Is World Models

Why Yann LeCun, Demis Hassabis, and Fei-Fei Li are converging on AI systems that represent, predict, simulate, plan, and act.

**I’ve been thinking. **Turing Post has always had one goal: to connect the dots. And for the last three years, I think we have done it quite successfully.

But this summer, when we intentionally slowed down to step back and see the whole picture, I realized that we had gone too far into the weeds and spread our attention too thin. Most of us did. And it’s understandable. AI moves so fast that every week brings ten new things that seem too significant to ignore.

But where is it moving?

So I’ve been thinking, reading, listening, and connecting the dots again. And my bet is on world models.

If you shrug at this point, fair. Even people working on world models cannot agree on one definition. Depending on whom you ask, a world model can be a latent predictor, a simulator, a model-based reinforcement learning system, a spatial generator, or the internal representation an agent uses to understand its environment.

That disagreement is exactly why I think we should concentrate on this topic and build a map of machine intelligence while it’s developing.

One of the strongest signals for me is that @ylecun (Yann LeCun), @demishassabis (Demis Hassabis), and @drfeifei (Fei-Fei Li) have all moved into world models.

They are coming at it from completely different directions, and that is exactly what makes me more curious. If three people who shaped modern AI in such different ways are now circling the same problem, I want to know what they see there.

Image created via ChatGPT

Image created via ChatGPT

My simplified definition of a world model as a concept is that it creates some picture of the environment it is operating in, some ability to anticipate what may happen next, and some way to choose what to do.

Don’t we all want this?

And if you think about it: that is also where most of the money being spent on AI is trying to lead. Companies are not paying billions because they need more text. They want better decisions.

Which experiment should we run? Which code change will break production? Which route should a robot take? Which inventory decision creates a shortage three weeks from now? What should an agent do after its first plan fails?

Autoregressive models, diffusion, statistics, and generation will remain part of this. But generating a likely continuation is not the same as maintaining the state of an environment, testing possible futures, and choosing an action. The next stage of AI will probably combine these methods rather than replace one with another.

**How will it be useful for me, you might ask me. **Fair again. And I think that for a software developer, a world model could mean an agent that understands a codebase as a changing system, predicts the effects of an edit, and tests a plan before touching production. For an AI engineer, it could mean training and evaluating agents inside environments where actions have consequences. For a business leader, it could mean moving from summarizing what happened to testing what may happen under different decisions.

And it can be anything.

So, coming out of this summer, and considering the Almanac idea I introduced last week, we are making a few changes to Turing Post.

We will use world models as our editorial backbone. This does not mean covering only systems marketed as “world models.” We will follow how machines represent, predict, simulate, plan, and act across physical, digital, and scientific environments.

We will still cover OpenAI, NVIDIA, agents, robotics, science, infrastructure, and architectures, but not simply because they released something. We will cover them when they change this larger story.

This is a hypothesis we are going to test, not a declaration that world models have already won. The term may become too broad. Some of its promises may collapse. We will follow that too.

But for the first time in a while, I feel we have a question precise enough to guide us and large enough to grow with:

How are machines building their picture of the world, what happens when they begin to act on it, and what do they need to ignore in order to succeed?

**Share your thoughts. **I always value them

📹 And here is a quick overview of the differences and similarities in Yann LeCun’s, Fei-Fei Li’s, and Demis Hassabis’s approaches. Watch it → https://www.youtube.com/watch?v=luZkSc_A-0I