I was already a very high performer before AI, leading teams, aligning product vision and technical capabilities, architecting systems and implementing at top-of-stack velocity. I have been involved in engineering around AI/ML since 2008, so I have pretty good understanding of the complexities/inconsistencies of model behavior. When I observed the ability of GPT3.5 to often generate working (if poorly written, in general) code, I knew this was a powerful tool that would eventually totally reshape development once it matured, but that I had to understand its capabilities and non-uniform expertise boundary to take advantage of its strengths without having to suffer its weaknesses. I basically threw myself fully into mastering the "art" of using LLMs, both in terms of prompting and knowing when/how to use them, and while I saw immediate gains, it wasn't until Gemini Pro 2.5 that I saw the capabilities in place for a fully agentic workflow. I've been actively polishing my agentic workflow since Gemini 2.5's release, and now I'm at the point where I write less than 10% of my own code. Overall my hand written code is still significantly "neater/tighter" than that produced by LLMs, but I'm ok with the LLM nailing the high level patterns I outline and being "good enough" (which I encourage via detailed system prompts and enforce via code review, though I often have AI rewrite its own code given my feedback rather than manually edit it).
I liken it to assembly devs who could crush the compiler in performance (not as much of a thing now in general, but it used to be), who still choose to write most of the system in c/c++ and only implement the really hot loops in assembly because that's just the most efficient way to work.