[sadid]

That’s true in a narrow functional sense, but it misses the role of a world model. Intelligence isn’t just about approximating input-output mappings, it’s about building structured, causal models that let an agent generalize, simulate, and plan. Universal approximation only says you could represent those mappings, not that you can efficiently construct them. Current LLMs seem intelligent because they encode vast amounts of knowledge already expanded by biological intelligence. The real question is whether an LLM, on its own, can achieve the same kind of efficient causal and world-model building rather than just learning existing mappings. It can interpolate new intermediate representations within its learned manifold, but it still relies on the knowledge base produced by biological intelligence. It’s more of an interpolator than an extrapolator: as an analogy.