Yes, I should have been more careful with the wording. Some problems have polynomial numbers of possible solutions, so for those there is no discussion of this kind. The more important point is that for some problems with a more-than-polynomial solution space (since even exponential is not precise enough, the solution space can be more than polynomial, such as n! for traveling salesman) we still have polynomial solutions, whereas for others we do not know of such algorithms.

The question can actually be generalized further - is it the case that for any solvable problem it's possible to find a solution without verifying every possible solution, and more specifically, only a tiny subset of the solution space? This would apply even beyond P vs NP, the same question would arise for EXP vs NEXP (that is, problems where we can find a solution vs just verify a given solution in exponential time), and same for factorial or Ackerman or any other complexity we want to imagine.

Looking at the problem in this way should also make it easier to construct a more strictly practical version of P vs NP, though it actually gets much harder to pin down details. That is, we can construct complexity classes that better match "what is plausibly solvable with a real computer" vs "what is plausibly verifiable with a real computer" and ask a similar question: given any problem of the second kind, can there be any generic algorithm of the first kind that would solve it?

Maybe I'm reading a bit too much into it but knowing it is NP (i.e. can be verified in polynomial time by a Turing machine) means it can be solved in polynomial time by a nondeterministic turing machine and there's at worst 2^N branches in it. Sort of. Giving a naive expectation of 2^N runtime times some polynomial. Which is exponential.