The models that we are paying to generate tokens are already not really just LLMs, as anyone studying language models ten years ago (or someone who describes them as "next token predictors") would understand them. Doing a bunch of reinforcement learning so that a model performs better at ssh'ing into my server and debugging my app is already realllly stretching the definition of "language pattern".

I think when we do get AI that can perform as well as a human at functionally all tasks, they will be multi-paradigm systems; some components will not resemble anything in any commercial system today, but one component will be recognizably LLM-like, and act as an essential communication layer.