The essay critiques various perspectives on world models, which are essential for developing virtual agents with artificial general intelligence. Drawing from sci-fi and psychology, it emphasizes that a world model should simulate all actionable possibilities of the real world for effective reasoning and action, and proposes a new hierarchical architecture for such models within a Physical, Agentic, and Nested (PAN) AGI framework.

The article discusses the limitations of large language models (LLMs) in relation to understanding and representing the world as true models. It argues that while LLMs can generate text that appears knowledgeable, they lack the genuine comprehension and internal modeling of reality that is necessary for deeper understanding. Furthermore, it contrasts LLMs with more robust cognitive frameworks that incorporate real-world knowledge and reasoning.

Elon Musk's xAI is entering the competition to develop world models, aiming to create advanced AI systems capable of understanding and designing physical environments. The startup has recently recruited specialists from Nvidia to enhance its efforts in leveraging videos and robotic data for training these next-generation AI models.