Artificial intelligence is crossing a new frontier: world models. While large language models (LLMs) have dominated the spotlight, a new category of systems aims to go beyond words to understand and simulate the physics of the real world. But what are the actual capabilities and limits of these models? We asked the experts.
What world models are and how they differ from LLMs
World models do not merely process text; they strive to represent spatial, temporal, and causal dynamics of the physical environment. Unlike chatbots, these systems aspire to predict how a scene evolves, how objects interact, and what consequences actions have. This is a step toward AI that not only talks but acts in real contexts.
Promising applications: from robotics to urban planning
World models find use in robotics, where a robot can mentally simulate a path before moving, or in autonomous vehicle design. Companies like Google DeepMind and OpenAI are heavily investing in this technology. For instance, Waze has integrated Gemini AI for personalized routes, an example of how world models can improve real-time navigation (read the article on Waze and Gemini).
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Current limitations: imperfect simulations and high computational costs
Despite progress, world models remain approximations. Faithfully simulating every physical detail is impossible with current resources. As a researcher at Anthropic explains, "building a model that accurately predicts every interaction would require computing power comparable to that of the universe itself." Moreover, the lack of real data on extreme scenarios limits reliability. Model transparency is another hot topic, as highlighted by Anthropic's "hidden space of Claude", raising political questions about the use of these technologies.
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The future: toward AI that understands causality
Experts agree: the next step is to integrate world models with LLMs to create hybrid systems. This will allow reasoning about language and physics simultaneously. The road is still long, but the potential is enormous. For technical insights, see the Ars Technica article on the promises and limits of world models.
