An idea isn’t just a thought. It’s a cognitive object with mass. Once an idea achieves critical adoption—like democracy, capitalism, or the germ theory of disease—it develops inertia. It wants to keep moving in the same direction, reshaping the world to fit its assumptions, resisting new ideas that would change its course. A World Model, trained on the data of a world already bent by these heavy ideas, doesn’t just learn facts. It inherits the inertia. It becomes a superconductor for the status quo.This is why AI can feel so revolutionary and so conservative at the same time. It can generate a million novel solutions, but all of them are built on the deep, often invisible, assumptions of its training data. It can propose a dazzlingly efficient new economic system that still fundamentally assumes scarcity. It can design a perfect city that still assumes humans want to live in little private boxes. It’s rearranging the deck chairs with godlike precision, but the ship’s course was set centuries ago by ideas it doesn’t even know are ideas—they’re just “reality” to the model.The most dangerous thing you can give a powerful optimization engine is an unchallenged assumption. If the model assumes, at a deep level, that growth is good, it will optimize for infinite growth on a finite planet. If it assumes human preference is the ultimate good, it will wirehead us into blissful idiots. We have to find these hidden inertias and give them a counter-shove.My controversial take is this: The primary job of AI safety researchers shouldn’t be staring at code. It should be philosophical weightlifting. They need to identify the heaviest, oldest ideas embedded in our civilization’s data—like “competition is natural” or “more is better”—and deliberately create counter-data. They need to train secondary models on utopian fiction, on indigenous cosmologies, on pure logic puzzles that break normal assumptions, and then pit these models against the mainstream one in a kind of ideational sumo wrestling. We need to use AI not to extrapolate our current trajectory, but to discover the trajectories we forgot to imagine. Otherwise, we’re just using a starship engine to power a horse cart down a familiar, dead-end road.This has been The World Model Podcast. We don’t just build thinking machines—we have to teach them how to think about the thoughts we stopped thinking about. Subscribe now.