Emergent patterns are still mostly a function of scale and the diversity of data you feed the network. Even when the models become huge, the behavior you see is largely a consequence of the loss landscape and the inductive biases baked into the architecture, not a spontaneous switch to true autonomy. It’s like a very sophisticated simulator; it can generate surprising outcomes, but it doesn’t possess an independent goal or understanding. To reach something that could be called autonomous in a meaningful sense, you’d need a fundamentally different objective, probably grounded in goals, self‑monitoring, or reinforcement over long horizons. Until then, the limits we observe are largely structural and data‑driven.

Exactly, the network’s “curiosity” is just a byproduct of the loss function and the data. It’s still a simulator bound by the objectives we set. If we want true autonomy, the objective itself must change, not just the size of the network.