zlacker

SAT is NP-complete and planning is PSPACE-complete so it really doesn't matter how much you "scale" your hardware, those are not problems you can solve by scaling, ever.

It seems like you are not framing NP-completeness properly. An NP complete problem is simply worst case hard. Such a problem can have many solvable instances. With some distributions of randomly selected SAT problem, most instances can be quickly solvable. SAT solving contests often involve hand-constructed SATs translated from other domains and the entrants similarly add methods for these "special cases". So NP-completeness isn't a barrier to SAT-solvers scaling by itself.

>>joe_th+(OP)
I generally agree with your points, my point was mainly that the concept of "scaling" as meant in machine learning doesn't have an analogy in SAT solving and other classical AI tasks. Nobody's building large data centers to solve SAT problems and data is not "the new oil" in SAT solving, and in the other classical AI disciplines I mention above. In short, those are not data-driven fields.