Normally, if you want to achieve some goal, there is a whole pile of tasks you need to be able to complete to achieve it. If you don't have the ability complete any one of those tasks, you will be unable to complete the goal, even if you're easily able to accomplish all the other tasks involved.
AI raises your capability floor. It isn't very effective at letting you accomplish things that are meaningfully outside your capability/comprehension, but if there are straightforward knowledge/process blockers that don't involve deeper intuition it smooths those right out.
You're probably envisioning a more responsible use if it (floor raising, "meaningfully inside your comprehension"), that is actually not what I'm referring to at all ( "assumes everything that tool might be used for is now a problem or domain they are an expert in"). A meta tech can be used in many ways and yours is close to what I believe the right method is. But I'm asserting that the danger is massive over reliance and over confidence in the "transferability".
Nothing has changed. Few projects start with you knowing all the answers. In the same way AI can help you learn, you can learn from books, colleagues, and trial and error for tasks you do not know.
Before AI, if I had the knowledge/skill to do something on the large scale, but there were a bunch of minute/mundane details I had to figure out before solving the hard problems, I'd just lose steam from the boredom of it and go do something else. Now I delegate that stuff to AI. It isn't that I couldn't have learned how to do it, it's that I wouldn't have because it wouldn't be rewarding enough.
Its the same with any "general" tech. I've seen it since genetic algorithms were all the rage. Everyone reaches for the most general tool, then assumes everything that tool might be used for is now a problem or domain they are an expert in, with zero context into that domain. AI is this times 100x, plus one layer more meta, as you can optimize over approaches with zero context.