Large language models can explain what happens when a glass falls from a table. But explaining an event and actually understanding how the physical world behaves are not quite the same thing. That distinction is driving growing interest in a new approach to artificial intelligence: world models.
Today’s language models learn mainly by finding patterns in enormous amounts of text. World models aim to go further by learning how environments behave, allowing an AI system to predict the consequences of actions before taking them. In effect, the system can “imagine” possible futures and choose what to do next.
That capability could be especially important for robotics. A household or factory robot cannot safely learn everything through trial and error. Dropping objects, colliding with equipment or making thousands of physical mistakes would be costly and dangerous. With an internal model of its surroundings, a robot could simulate different actions first and select the safest or most useful option.
Researchers are already exploring several approaches. Google DeepMind’s Dreamer 4, for example, learns a predictive model of its environment and uses imagined trial and error to improve its behavior. Other researchers, including Yann LeCun and colleagues working on JEPA-style systems, argue that AI does not need to recreate every visual detail. Instead, it should learn abstract representations of the parts of reality that matter for planning and decision-making.
The possibilities extend beyond robots. World models could also help researchers explore new drugs, materials and chemical combinations by simulating potential outcomes before expensive laboratory testing.
But are world models the answer?. Human reasoning involves more than prediction: we use causal understanding, abstraction, social knowledge, self-awareness and an ability to recognize uncertainty. As the article notes, researchers remain divided over whether scaling world models can capture that richness.
World models may not replace language models. But they could give future AI something today’s chatbots still lack: a stronger sense of how actions change the world.
References
Cossins, D. (2026, August 8). The prediction machine. New Scientist, 271(3607), 30–34. https://doi.org/10.1016/S0262-4079(26)01235-2
