That's pretty much what the author is arguing here:
> Due to the tremendous amount of work that has been done on the Traveling Salesman Problem, the TSP is one such problem. If you have an instance of the TSP of practical interest (i.e. it makes a difference to your life if you solve it or not, and it is really a TSP, not the result of some weird set of set of reductions from some other problem), then I bet you the Concorde program of Bill Cook and others will get you a provably optimal solution in a reasonable amount of time.
The difference between problems that are hard in the average case, and those that are only hard in the worst case, is really important in cryptography. The knapsack cryptosystem is a good example of this: https://en.wikipedia.org/wiki/Merkle%E2%80%93Hellman_knapsac...
NP-complete problems admit no polynomial-time solution for every possible answer, and even if they run in more-than-polynomial time, there's no constraint on what those constants look like. Suppose the best algorithm takes 2^n / 2^100000 seconds. That's definitely more than polynomial, but also pretty fast for real-world problem sizes.
It turns out that even SAT solvers are also pretty good in practice on real-world-sized problems, to the point where Fedora's and SUSE's dependency solvers (in dnf and zypper, respectively) convert your package dependencies to an instance of SAT and throw it at a generic SAT solver, because that's better-optimized.
I believe the point the posted article is making is that reasonable progress has been made against such problems of sizes that seem 'large' in terms of normal human experience, through brute-force computation and clever heuristic tricks. That doesn't mean they don't scale in the expected way or that a true shortcut has been found.