Welcome to The World Model Podcast. We've spent considerable time discussing World Models as elegant mathematical constructs, trained on datasets and simulations. But there is a growing, powerful argument in cognitive science and AI that this view is fundamentally incomplete. That a true understanding of the world—a robust World Model—cannot be built through passive observation alone. It requires embodiment. It requires a physical presence that can act, that can feel the consequences of its actions, that can be shaped by the relentless, unforgiving feedback of reality. Today, we argue that to build a mind, you must first give it a body.The theory of embodied cognition posits that intelligence is not a disembodied algorithm running on a neural network. It is an emergent property of an entire organism interacting with its environment. Our concepts are not abstract symbols; they are grounded in sensory-motor experience. We understand 'heavy' not from a dictionary, but from the strain in our muscles. We understand 'fragile' from the sound and feel of something breaking.For an AI, this means a critical limitation. A purely software-based World Model, trained on YouTube videos and text, may learn to associate the word 'ball' with round shapes and the word 'bounce' with certain motions. But it has no intrinsic understanding of affordances. An affordance is what the environment offers an actor. A chair affords sitting. A handle affords grasping. These are not properties of the object alone; they are relationships between the object and a body with specific capabilities.A robot with a body learns these relationships through interaction. It learns how much force to apply to pick up a cup without crushing it. It learns the slippery affordance of ice by falling. This sensory-motor data is the foundational training set for a grounded World Model. It teaches the AI the laws of physics not as equations, but as lived experience.This is why the most impressive advances in robotic learning are increasingly leveraging simulation not just for training, but for building these embodied models. Companies like Boston Dynamics and research labs at Berkeley aren't just programming movements; they are training embodied AI in digital twins where they can practice millions of times. But crucially, the goal is to transfer that knowledge to a physical body. The body provides the ground truth, the ultimate test that prevents the model from drifting into useless fantasy.This principle suggests a major shift in AI research priorities. The path to general intelligence may not run through bigger language models, but through more capable and widely deployed robotics platforms. The AI that cleans your house, works in a warehouse, or explores Mars will, by necessity, develop a far richer, more robust World Model than any data-center-bound software.My controversial take is that the current obsession with large language models is a historical detour, a kind of 'disembodied intelligence' that will ultimately hit a wall of meaninglessness. True understanding is indexical—it is tied to a specific point of view, a specific set of sensors and actuators in a specific location. You cannot understand 'here' or 'now' or 'I' from a trillion tokens of text. You can only understand them by being an entity that has a 'here,' a 'now,' and an 'I.'Therefore, the companies and nations that will lead in AGI will be those that lead in robotics—not necessarily humanoid robots, but in the proliferation of cheap, versatile, embodied AI agents that can learn from the rich, multimodal stream of real-world interaction.This embodied intelligence we strive to create in machines already exists in its most advanced form within us. Our next episode connects the AI architecture we've discussed directly to the wetware inside our own skulls.This has been The World Model Podcast. We believe intelligence is built from the ground up. Subscribe now.