I am saying we rarely see giant exponents (on polynomial-time algorithms) in practice, because most algorithms that we intuitively think of as 'hard' turn out to be in NP or PSPACE or other complexity classes that we have decent reason to suspect have a lower-bound exponential running time.
P: O(n^k), where large k makes it 'slow' but still polynomial
NP: probably O(e^kn), k>1, where k close to 1 makes it 'fast', but still exponential
The objection is that k can be big for some polynomial algorithms but small (near 1) for other exponential ones, so we can have 'slow' polynomial algorithms and 'fast' exponential ones.
But in practice, k is usually small for polynomial algorithms and not close to 1 for exponential ones so simply knowing whether an algorithm is exponential or polynomial tells you something.
The 'hard' problems you gave me all (probably) fall into the exponential runtime class, whereas the easier ones (like finding any solution to a Rubiks) fall into the polynomial runtime. So your intuition of 'hard' vs. 'easy' matches complexity theory's evaluation, even though theoretically there could be really slow 'easy' problems and really fast 'hard' ones.
