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.