A point that has become more obvious over the years is this: there are "soft" instances of NP-complete problems where you can find good approximations in reason

A point that has become more obvious over the years is this: there are "soft" instances of NP-complete problems where you can find good approximations in reasonable time, and there are "crunchy" instances of the same class of problem where you don't find a good approximation. Commercial ILP solvers (e.g. Gurobi, CPlex) profit from the fact that quite often it is possible to formulate NP-complete problems and find good solutions in acceptable time. Similarly, many of the "physical" ways of solving NP-complete problems work ok for easy instances and get unwieldly fast if confronted with difficult instances.