Comment by freehorse
> Best counter claim: Not all code has the same risk. Some code is low risk, so the risk of error does not detract from the speed gained. For example, for proof of concepts or hobby code.
Counter counter claim for these use cases: when I do proof of concept, I actually want to increase my understanding of said concept at the same time, learn challenges involved, and in general get a better idea how feasible things are. An AI can be useful for asking questions, asking for reviews, alternative solutions, inspiration etc (it may have something interesting to add or not) but if we are still in the territory "this matters" I would rather not substitute the actual learning experience and deeper understanding with having an AI generate code faster. Similar for hobby projects, do I need that thing to just work or I actually care to learn how it is done? If the learning/understanding is not important in a context, I would say then using AI to generate the code is a great time-saver. Otherwise, I may still use AI but not in the same way.
Fair. I rescind those examples and revise my counter: When you gain much more from speed than you lose with errors, AI makes sense.
Revised example: Software where the goal is design experimentation; like with trying out variations of UX ideas.