The taste of chocolate is also assuming information-theoretic models are correct and not a use-based, pragmatic theory of meaning.

I don't agree with information-theoretic models in this context but we come to the same conclusion.

Loss only makes sense if there was a fixed “original” but there is not. The information-theoretic model creates a solvable engineering problem. We just aren't solving the right problem then with LLMs.

I think it is more than that. The path forward with a use theory of meaning is even less clear.

The driving example is actually a great example of the use theory of meaning and not the information-theoretic.

The meaning of “driving” emerges from this lived activity, not from abstract definitions. You don't encode an abstract meaning of driving that is then transmitted on a noisy channel of language.

The meaning of driving emerges from the physical act of driving. If you only ever mount a camera on the headrest and operate the steering wheel and pedals remotely from a distance you still don't "understand" the meaning of "driving".

Whatever data stream you want to come up with, trying to extract the meaning of "driving" from that data stream makes no sense.

Trying to extract the "meaning" of driving from driving language game syntax with language models is just complete nonsense. There is no meaning to be found even if scaled in the limit.