[solarwindy]

Granted.

Although, setting aside the question of sentience, there’s a more serious point I’d make about the dissimilarity between the always-on nature of human cognition, versus the episodic activation of an LLM in next-token prediction—namely, I suspect these current model architectures lack a fundamental element of what makes us generally intelligent, that we are constantly building mental models of how the world works, which we refine and probe through our actions (and indeed, we integrate the outcomes of those actions into our models as we sleep).

Whether a toddler discovering kinematics through throwing their toys around, or adolescents grasping social dynamics through testing and breaking of boundaries, this learning loop is fundamental to how we even have concepts that we can signify with language in the first place.

LLMs operate in the domain of signifiers that we humans have created, with no experiential or operational ground truth in what was signified, and a corresponding lack of grounding in the world models behind those concepts.

Nowhere is this more evident than in the inability of coding agents to adhere to a coherent model of computation in what they produce; never mind a model of the complex human-computer interactions in the resulting software systems.