[throwaway219450]

I had a discussion with folks at work about what information is worth retaining in the face of AI doing everything for us. A lot of what we have in our heads to qualify as "domain experts" is pretty esoteric. How to invoke command line tools, gotchas because library A uses one convention over library B, AWS vs GCP; so much is specific to a tool rather than a method. There are also a lot of entrenched tools that are effectively unfixable due to the risk of breaking changes, so you have to shrug and accept + learn that's how it works.

Catch-22 is it's still important to know the fundamentals so you know what to ask for, but if you don't know the esoterica, the model is eventually going to make an assumption and screw things up. And the models don't have much taste either in prose, or in coding/comment style.

[galaxyLogic]

Yes I think the problem is not that AI "has all the answers". It is that we don't have "all the questions". If we don't understand the whole of what AI produces for us, we can't ask it to make parts of it different. We are not in control any more of what exactly is produced, and we don't want to give AI all the control, because then it would not be doing what we want it to do.

Therefore it is critical that whatever AI produces is understandable to us humans. That is why we must demand that AI tools and agents produce "well designed" well-structured software. That's the bottlenexk to progress I think. Even AI can't deal with exponential complexity explosion.