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 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.
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.
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.
Why haven’t these gaps closed? Not for lack of computing power, now trillions of times more powerful. Not for lack of data, now planetary in scale. Not for lack of physics, well established for the parts we understand. The bottleneck is representation: finding a way to model systems we can’t describe exactly because the physics is only partly understood and the measurements are sparse. Simply scaling up today’s language models doesn’t solve that.
The hardest problems in science sit in the gap: too poorly understood to write down in equations, too sparsely observed to learn from data alone.
Some of this is already visible. AlphaFold, NVIDIA’s Earth-2, and GraphCast are in operational use across biology and weather forecasting. They work where physics is partly understood, and observations are rich. What none yet does is learn the dynamics of the open systems whose uncertainty has barely narrowed: sea level, the carbon cycle, the coupled behavior of a warming planet.
There is also a hard limit. A world model cannot forecast a regime the Earth has never entered, like the world after an Atlantic circulation collapse, because there is no data from the far side to learn from. No method can. But the relevant question is how close we are to it, which these systems can begin to help answer.
The opportunity
Enterprise applications have customers, contracts, and quarterly results. In 2026, the investment following world models is overwhelmingly driven by these metrics. But where the output is a public good, such as a narrower sea-level range, a better carbon-cycle model, or earlier warning of how the next drug-resistant pathogen will spread, the commercial model breaks down.
The same architectures can work for the systems we live inside, given different data and different choices. The question is whether the labs commit to these problems as ambitiously as to enterprise, and whether the institutions holding the world’s most valuable scientific data make it available.
Beyond the enterprise
But getting better at the easy problems is not the same as reaching the hard ones. Whether world models reach the open systems depends on which data gets stitched together, which questions the leading labs take on, and whose problems get attention.
The technical work will continue regardless. The question is no longer whether we can build world models. It is what we choose to model—and whether the hardest problems shape these systems from the start, or inherit them.
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