I think this is part of the reason why I've had a bit more success with AI Coding than some of my colleagues. My pre-llm workflow was to rapidly build a crappy version of something so that I could better understand it, then rework it (even throw away to the prototype) to build something I now know how I want to handle. I've found even as plenty of thought leaders talk about this general approach (rapid prototyping, continuous refactoring, etc ...) that many engineers are resistant and want to think through the approach and then build it "right." Or alternatively just whip something out and don't throw it away, but rather toil on fixes to the their crappy first pass.
With AI this loop is much easier. It is cheap to even build 3 parallel implementations of something and maybe another where you let the system add whatever capability it thinks would be interesting. You can compare and use that to build much stronger "theory of the program" with requirements, where the separation of concerns are, how to integrate with the larger system. Then having AI build that, with close review of the output (which takes much less time if you know roughly what should be being built) works really well.
With AI this loop is much easier. It is cheap to even build 3 parallel implementations of something and maybe another where you let the system add whatever capability it thinks would be interesting. You can compare and use that to build much stronger "theory of the program" with requirements, where the separation of concerns are, how to integrate with the larger system. Then having AI build that, with close review of the output (which takes much less time if you know roughly what should be being built) works really well.