zlacker

>>jinhku+(OP)
For me, AI is an enabler for things you can't do otherwise (or that would take many weeks of learning). But you still need to know how to do things properly in general, otherwise the results are bad.

E.g. I'm a software architect and developer for many years. So I know already how to build software but I'm not familiar with every language or framework. AI enabled me to write other kind of software I never learned or had time for. E.g. I recently re-implemented an android widget that has not been updated for a decade by it's original author. Or I fixed a bug in a linux scanner driver. None of these I could have done properly (within an acceptable time frame) without AI. But also none of there I could have done properly without my knowledge and experience, even with AI.

Same for daily tasks at work. AI makes me faster here, but also makes me doing more. Implement tests for all edge cases? Sure, always, I saved the time before. More code reviews. More documentation. Better quality in the same (always limited) time.

>>micw+wh
I've found this is exact opposite of what I'd dare do with AI, things you don't understand are things you can't verify. Consider you want a windowed pane for your cool project, so you ask an AI to draft a design. It looks cool and it works! Until you bring it outside where after 30 minutes it turns into explosive shrapnel, because the model didn't understand thermal expansion, nor did you.

Contrast this to something you do know but can't be arsed to make; you can keep re-rolling a design until you get something you know and can confirm works. Perfect, time saved.