Episode 5 of The World Model Podcast takes on one of the biggest questions in modern AI: Do Large Language Models truly understand anything or are they just astonishing engines of correlation?We explore the deep divide between correlation and causation, and why LLMs despite their fluency operate more like brilliant mimics than grounded reasoners. They can describe physics with elegance, yet fail at predicting what happens when you remove a block from a tower. They generate text, not real simulations. They’ve read every book, but never dropped a ball.World models, by contrast, are built for causation. They learn the rules governing a system and simulate the consequences of actions within it. They don’t just talk about the world they interact with it, predict it, and reason about it.This episode highlights why the difference matters for real-world decision-making, and why the next transformative leap won’t come from bigger LLMs, but from fusing linguistic intelligence with causal reasoning. We imagine AI systems that combine an LLM’s encyclopaedic knowledge and planning ability with a world model’s grounded simulations tools capable of designing structures, evaluating physics, and explaining their reasoning along the way.Our central claim: the age of the pure LLM as the pinnacle of AI is already ending. The future belongs to hybrid architectures that bridge the causal chasm, uniting language and simulation in a single system capable of genuine understanding.We end with a preview of next week’s deep dive into the foundational 2018 paper that quietly set the stage for the entire world model revolution.If you want to grasp where AI is truly heading and why LLMs alone won’t get us there this episode is essential listening.