alt Hacker NewsThe 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?
Taking the metaphor further, the traditional way of programming was to manually encode the logic, and the new way is to program the environment and context to let the correct program emerge through the constraints. The stricter and more precise the constraints, the closer the result is to what you want.
So then, as you say, being able to specify exactly what you want becomes the central skill of programming - I mean, describe the behavior not in terms of the final code, which is an implementation detail, but how it interacts with a given environment. That was always the case since in higher-level languages, including C, what we write is not the final code, which is technically the compiled result.
A difference I notice is that, now, even junior devs are expected to be the "mentor" to language models - teaching and guiding them to generate well-written code with plenty of tests, asserts, and other guardrails. In another comment someone said, breaking down a large program into smaller modules is useful - which is common sense, but we now have to guide an LLM to know and apply best practices, design patterns, useful tricks to improve code organization or performance, etc.
That means, it would be valuable to codify best practices, as documentation in Markdown as well as described in code, as specs and tests. Programming is becoming meta-programming. We're shifting emphasis from assembling genetic code manually to preparing the environment for such code to evolve.