You're basically describing the no-free lunch theorem, but in practice we can define fairly robust generic models for certain things, like "physics". That's why

You're basically describing the no-free lunch theorem, but in practice we can define fairly robust generic models for certain things, like "physics". That's why optical mice work pretty well. You can start to add unjustified assumptions and that'll make the world model weaker, yes. But starting with pretty basic assumptions like "you can segment an object from an series of images because each solid object will move on its own trajectory", or even more basic like "objects have edges" and then a few dozen samples per second, and suddenly you have a fairly robust way to detect things. Same for predicting where something will go. If you can estimate an objects current velocity, acceleration, and jerk with reasonable precision, you don't really need a highly predictive heuristic for the world model. For decision making you need more robust heuristics, like "the car to my left has right of way at the stop sign", but you don't need that level of heuristic to identify that there is a car and that it isn't part of the pavement and that it is currently sitting still.