Also I started in the pre-agents era and so I ended up with a pair-programming paradigm. Now everytime I conceptualize a new task in my head -- whether it is a few lines of data wrangling within a function, or generating an entire feature complete with integration tests -- I instinctively do a quick prompt-vs-manual coding evaluation and seamlessly jump to AI code generation if the prompt "feels" more promising in terms of total time and probability of correctness.
I think one of the skills is learning this kind of continuous evaluation and the judgement that goes with it.
Effective LLM usage these days is about a lot more than just the prompts.
it helps dramatically on finding bugs and issues. perhaps that's trivial to you, but it feels novel as we've only had effective agents in the last couple weeks.
Right now, you are entirely up to the random flawed information on the internet that you often can't repeat in trials, or your structured ideas on how to improve a thing.
That is difficult. It is difficult to take the information available right now, and come up with a reasonable way to improve the performance of LLMs through your ingenuity.
At some point it will be figured out, and every corporation will be following the same ideal setup, but at the moment it is a green field opportunity for the human brain to come up with novel and interesting ideas.
I believe that's difficult, and not just what google prefers. I guess we feel differently about it.