World Models Are AI’s Next Frontier

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World Models Are AI’s Next Frontier

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Celine Herweijer is Visiting Professor in Energy and Geopolitics at LSE and former Group Chief Sustainability Officer at HSBC.

Jul 15, 2026 10:00 AM UTC

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Celine Herweijer is Visiting Professor in Energy and Geopolitics at LSE and former Group Chief Sustainability Officer at HSBC.

Jul 15, 2026 10:00 AM UTC

Inside the labs building the next generation of AI, a phrase has been gaining weight: world models. A large language model like ChatGPT, Claude, or Gemini predicts what comes next in text. World models, in contrast, learn dynamics from observation, then simulate forward to test what happens next. They model the world itself, rather than just descriptions.

Yann LeCun, who left Meta in late 2025 to launch Advanced Machine Intelligence Labs, has built his research program around it. Demis Hassabis, who runs Google DeepMind, has made world models central to its push toward more general AI. Sam Altman has called OpenAI’s Sora a world simulator, a claim that is contested. Fei-Fei Li raised a billion dollars for her company World Labs to pursue what she calls “spatial intelligence.” Jensen Huang, meanwhile, is building the simulation platforms and compute behind the next wave of AI, as NVIDIA did for large language models.

The term is used loosely; not everything marketed as a “world model” qualifies in the strict architectural sense.

The bet, and the reason LeCun rejects video-generators like Sora, is that a model trained on how a system behaves rather than how it looks, an architecture he calls JEPA, will generalize better to the physical world. It is not a product category but an architecture that could take AI from fluent at language but with no real model of the physical world, to a grounded understanding of how that world behaves.

If they are right, this is more than another commercial AI cycle. It is the period in which the substrate of the next AI gets built, and what gets built now, by whom and on what data, will shape what AI can do for years. The potential for solving problems in climate, oceans, the biosphere, and the biology of disease is vast.

What AI has already accomplished for the Earth

I started my career as a climate scientist at NASA running ocean-atmosphere simulations on supercomputers. I later co-wrote, with the World Economic Forum and Microsoft’s chief environmental officer, two of the earliest reports on AI and the Earth system. A lot of what we predicted has happened.

AI now spots wildfires and methane leaks from orbit. Today’s weather forecasts are unrecognizably better. Google’s flood forecasting runs in over 150 countries. Neural weather models, from DeepMind’s GraphCast to systems now run by the public forecasting agencies themselves, have matched or beaten the best physics-based forecasts at a fraction of the compute cost, though they still trail on extremes and tail risk.

These are real gains. But the hardest problems have barely moved: what a hurricane will do at landfall, when the next drought breaks, how ocean circulation behaves as the ice melts.

Sub-seasonal weather forecasts—the window that drives water, energy, and agricultural planning—remain weak. The forests, soils, and vegetation that absorb roughly a third of our emissions carry the largest uncertainty in the entire carbon budget. Unlike fossil fuel emissions or atmospheric carbon dioxide, this land carbon sink cannot be measured directly at the global scale; it has to be inferred. And tropical convection, the storm systems that...