Right, and all of that is what I consider to be the "code" effort:

Deep research in the codebase, deciding on the flavor of code to write that matches the project, deciding how you'll model the feature with types, how to architect it so that it's testable, writing the tests, foreseeing cases beyond the obvious path, etc.

What changed is that it can be automated. Or, just grant a world where AI is perfect at implementation.

Now our time/energy/attention is freed up to concentrate the work around planning what to build. And the interesting part is that it becomes the input into the AI implementor.

This is a good thing since we tended to skip the planning stage since it's hard in its own way. Or we start building something and then try to synthesize a high level direction from it, yet now since refactoring is so expensive, we're committed to a solution.