Yes, and each person has a different perception of what is "good enough". Perfectionists don't like AI code.

My main reason is: Why should I try twice or more, when I can do it once and expand my knowledge? It's not like I have to produce something now.

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sgc 4 days ago | parent [-]

If it takes 10x the time to do something, did you learn 10x as much? I don't mind repetition, I learned that way for many years and it still works for me. I recently made a short program using ai assist in a domain I was unfamiliar with. I iterated probably 4x. Iterations were based on learning about the domain both from the ai results that worked and researching the parts that either seemed extraneous or wrong. It was fast, and I learned a lot. I would have learned maybe 2x more doing it all from scratch, but I would have taken at least 10x the time and effort to reach the result, because there was no good place to immerse myself. To me, that is still useful learning and I can do it 5x before I have spent the same amount of time.

It comes back to other people's comments about acceptance of the tooling. I don't mind the somewhat messy learning methodology - I can still wind up at a good results quickly, and learn. I don't mind that I have to sort of beat the AI into submission. It reminds me a bit of part lecture, part lab work. I enjoy working out where it failed and why.

	▲	skydhash 3 days ago \| parent [-]
		The fact is that most people skip learning about what works (learning is not cheap mentally). I’ve seen teammates just trying stuff (for days) until something kinda works instead of spending 30 mns doing research. The fact is that LLMs are good for producing something that looks correct, and waste the reviewer time. It’s harder to review something than writing it from scratch. Learning is also exponential, the more you do it, the faster it is, because you may already have the foundations for that particular bit.