Genetic algorithms are a very easy to implement form of AI. The hard part is to create a fitness function which does what you actually want — In the wild, reality and competitors are your fitness function, and none needs to be created; conversely, in a simulation the fitness function is necessarily artificial, and can be made to reach any goal you want, from “compose a song” to “write the rules for an AI for this video game so I don’t have to” to “discover a new superconductor” to “design a clock” to “learn to walk”… etc., and all of those are real examples.

Separately, as an algorithm, evolution also has easily characterisable failure modes (local minima), and we do observe those failure modes in organic examples.

But, to emphasise, the fact artificial evolution requires us to write our own fitness functions is merely an implementation detail; these algorithms work because evolution works. They produce novel solutions without the authors of the programs creating the solutions.