It turns into a specification problem.

This, IMHO, is the critical point and why a lot of “deep” development work doesn’t benefit much from the current generation of AI tools.

Last week, I was dealing with some temporal data. I often find working in this area a little frustrating because you spend so much time dealing with the inherent traps and edge cases, so using an AI code generator is superficially attractive. However, the vast majority of my time wasn’t spent writing code, it was getting my head around what the various representations of certain time-based events in this system actually mean and what should happen when they interact. I probably wrote about 100 test cases next, each covering a distinct real world scenario, and working out how to parameterise them so the coverage was exhaustive for certain tricky interactions also required a bit of thought. Finally, I wrote the implementation of this algorithm that had a lot of essential complexity, which means code with lots of conditionals that needs to be crystal clear about why things are being done in a certain order and decisions made a certain way, so anyone reading it later has a fighting chance of understanding it. Which of those three stages would current AI tools really have helped with?

I find AI code generators can be quite helpful for low-level boilerplate stuff, where the required behaviour is obvious and the details tend to be a specific database schema or remote API spec. No doubt some applications consist almost entirely of this kind of code, and I can easily believe that people working on those find AI coding tools much more effective than I typically do. But as 'manoDev says in the parent comment, deeper work is often a specification problem. The valuable part is often figuring out the what and the why rather than the how, and so far that isn’t something AI has been very good at.