Yeah. You have a function, so basically a long array of numbers, and you want to find the maximum. If the data in the array has some structure, like it's sampled from a sine wave or something, you can use some strategies to find the maximum. Like gradient descent, or binary search. Something.

But if the array is filled with random numbers, looking at other arrays elements give absolutely no hint on what might be in an array element you haven't yet looked at. So there doesn't exist any more efficient strategies to find the maximum number, than linear or random search.

And the space of all possible functions mostly consists of discontinuous functions that are, for all purposes, just samples of random noise.

This is all NFL theorems say. I really don't understand how they got be such a big deal.