I presume the down-to-earth "Deep Mathematics" and "Deep Differential Algorithms and Differential Datastructures" and "Deep Physics" sobering up.<p>There is gradual historical transfer of problems first residing in a vague "aristotelian logic" phase, then gradually ever more formal phase, first quantitative descriptive and eventually and only then the normative formalization (i.e. figures of merit etc..).<p>When physicists and engineers apply RMAD to known and understood (but computationally intensive) total potential/lagrangian/total-figure-of-merit... functions, people don't call it AI, not even necessarily machine learning.<p>When RMAD is used to optimize vague intuitionistic reasoning like LLMs we do call it AI.<p>Given the gradual transfer of problems from the vague domain to the explicit domain (formalize or fossilize), "after AI" comes the very same RMAD, but applied to find optima for formally specified and understood-in-the-sense-of-reductionism-but-not-in-emergent-sense systems. (i.e. a computer does not behave like a giant transistor).