I was super interested in genetic programming for a long time. It is similarly non-deterministically generated.
The utility lies in having the proper framework for a fitness function (how to choose if the generated code is healthy or needs iterations). I used whether it threw any interpretation-time errors, run-time errors, and whether it passed all of the unit tests as a fitness function.
That said, I think programming will largely evolve into the senior programmer defining a strategy and LLM agents or an intern/junior dev implementing the tactics.
> That said, I think programming will largely evolve into the senior programmer defining a strategy and LLM agents or an intern/junior dev implementing the tactics.
That's basically what goog wants alphaevolve to be. Basically have domain experts give out tasks that "search a space of ideas" and come up with either novel things, improved algorithms or limits / constraints on the problem space. They say that they imagine a world where you "give it some tasks", come back later, and check on what it has produced.
As long as you can have a definition of a broad idea and some quantifiable way to sort results, this might work.