The genetic algorithm comparison is actually pretty apt. Generate variations, evaluate fitness, keep the survivors. The main difference is that LLMs have a much richer prior about what "good" looks like, so the search space is dramatically smaller than random mutation.

But it raises an interesting question about where the fitness function comes from. In traditional GAs you define it explicitly. With LLM-generated code, the fitness function is often just "does it pass the tests" - which means the quality of your tests becomes the actual bottleneck, not the quality of the code generation.

I wonder if that shifts the core skill of programming from "write correct code" to "write correct specifications." And if so, is that actually a new problem, or is it the same problem formal methods people have been working on for decades, just wearing a different hat?

If you extend this line of thinking a lot, given we traditionally author the software, everything kind of boils down to a genetic algorithm.