A pattern keeps repeating in AI engineering teams: someone reads about an evolved kernel beating hand-tuned baselines, gets excited, and proposes "let's evolve our X." A few months later, the experiment quietly dies. Selection pressure produced noise. Generations didn't improve. The team concludes that evolutionary methods are overhyped. The conclusion is wrong. The hypothesis was wrong. Evolutionary search is not a universal optimizer. It is a specific tool that requires specific conditions in the problem space. When those conditions hold, evolution outperforms hand-tuning, grid search, and even gradient methods (when gradients aren't available). When they don't hold, evolution is strictly worse than random sampling — you pay the cost of population maintenance for none of the benefit of selection.…