The way I think about it is that understanding is formed in a top-down fashion now, instead of bottom-up. You can look at a feature developed by AI from the outside, and keep peeling the layers and examining them (or having AI explain them to you) until you learn how it works. It's like reading a textbook. It is different than writing the code yourself, or practicing the topic of the textbook yourself. But if you invest the time, it can be just as effective.

The issue of course is that if you do invest the time, then you're no longer saving time by using AI. You're just spending it reading and trying to understand something you didn't write. And that can be unpleasant in its own way.

My hot take is that for parts of a system that can be considered its core, forming a deep understanding is almost always important, and so is knowing how the different business domains integrate and where the connection points are. For many others, a high level understanding is sufficient. The difference is that now, with AI, you can make that choice. Before, you had to write everything yourself, and for any sufficiently complex and long-lived system it became impossible to hold all of it in your head.

I disagree that reading a textbook is just as effective as solving problems yourself (this is what i read your statement to mean). It seems fairly obvious from university experience that doing the homework yourself gets you better test grades in the end. You can of course have the AI write the homework but you can't then just watch the AI solve it lest it be drastically muted in effect. Trying things that fail, working hard towards a problem, banging your head against something impossible, these are all worthwhile endeavors. Of course reading the textbook is useful too, very useful! But the textbook does not teach you experience.