It's predicated on the assumption that a random discovery from a zero-comprehension state is more likely to get you to a goal than an evolution from a state that has at least some correctness.

More generally, it disingenuously disregards the fact that the definition of the problem brings with it an enormous set of preconceptions. Reductio ad absurdum, you should just start training a model on completely random data in search of some unexpected but useful outcome.

Obviously we don't do this; by setting a goal and a context we have already applied constraints, and so this really just devolves into a quantitative argument about the set of initial conditions.

(This is the entire point of the Minsky / Sussman koan.)