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.
There are some things here that folks making statements like yours often omit and it makes me very sus about your (over)confidence. Mostly these statements talk in a business short-term results oriented mode without mentioning any introspective gains (see empirically supported understanding) or long-term gains (do you feel confident now in making further changes _without_ the AI now that you have gained new knowledge?).
1. Are you 100% sure your code changes didn't introduce unexpected bugs?
1a. If they did, would you be able to tell if they where behaviour bugs (ie. no crashing or exceptions thrown) without the AI?
2. Did you understand why the bug was happening without the AI giving you an explanation?
2a. If you didn't, did you empirically test the AI's explanation before applying the code change?
3. Has fixing the bug improved your understanding of the driver behaviour beyond what the AI told you?
3a. Have you independently verified your gained understanding or did you assume that your new views on its behaviour are axiomatically true?
Ultimately, there are 2 things here: one is understanding the code change (why it is needed, why that particular change implementation is better relative to others, what future improvements could be made to that change implementation in the future) and skill (has this experience boosted your OWN ability in this particular area? in other words, could you make further changes WITHOUT using the AI?).
This reminds me of people that get high and believe they have discovered these amazing truths. Because they FEEL it not because they have actual evidence. When asked to write down these amazing truths while high, all you get in the notes are meaningless words. While these assistants are more amenable to get empirically tested, I don't believe most of the AI hypers (including you in that category) are actually approaching this with the rigour that it entails. It is likely why people often think that none of you (people writing software for a living) are experienced in or qualified to understand and apply scientific principles to build software.
Arguably, AI hypers should lead with data not with anecdotal evidence. For all the grandiose claims, the lack of empirical data obtained under controlled conditions on this particular matter is conspicuous by its absence.
> Are you 100% sure your code changes didn't introduce unexpected bugs?
Who is this ever? But I do code reviews and I usually generate a bunch of tests along with my PRs (if the project has at lease _some_ test infrastructure).
Same applies for the rest of the points. But that's only _my_ way to do these things. I can imagine that others do it a different way and that the points above are more problematic then.
Not to be pedantic but, do you _try_ to understand? Or do you _actually_ understand the changes? This suggests to me that there are instances where you don't understand the generated code on projects others than your own, which is literally my point and that of many others. And even if you did understand it, as I pointed out earlier, that's not enough. It is a low bar imo. I will continue to keep my mind open but yours isn't a case study supporting the use of these assistants but the opposite.
In science, when a new idea is brought forward, it gets grilled to no end. The greater the potential the harder the grilling. Software should be no different if the builders want to lay a claim on the name "engineer". It is sad to see a field who claims to apply scientific principles to the development of software not walking the walk.