The Sequence Opinion #662: From Words to Worlds: Some Observations About World Models
Some ideas about of the definitive pillars of AGI
LLMs regularly dazzle us with words but can they operate in real world environments. Today, we would like to deep dive into one of the most fascinating frontier of AI: spatial intelligence and world models.
While LLMs have redefined how machines parse and generate text, they offer only a sliver of what we might call general intelligence. The missing dimension? Spatial understanding—the ability to perceive, model, and act in physical environments. Unlike LLMs that operate in purely symbolic spaces, spatially-grounded AI systems build internal models of their environments, enabling them to navigate, manipulate, and reason about the world around them. This shift from language to space marks a foundational evolution in AI: one where models are not just intelligent about the world, but also in it.
This essay explores the emergence of spatially-grounded world models, with a focus on key architectural innovations, embodied environments, and the underlying distinctions between spatial reasoning and language-based cognition.