Certainly natural language prose can articulate human-level concepts and priorities that code cannot. The fact that LLMs can interpret and apply these things is novel and useful. I agree with the author there is a lot of juice that can be squeezed out of this.
But on the other hand, whenever I start writing code (with or without LLM assistance), there is always a huge amount of learning and iteration that happens. No matter how thoroughly I thought through everything, or how perfect the idea seemed in my head, it always evolved once the implementation was concrete. And furthermore, once you have code running and people depend on it, the code is the reality, and any other documentation or vision statements have a tendency to go stale. Obviously AI makes it easier to keep documentation up-to-date, but the disconnect between the idea of the system and what the system actually does remains.
For this reason I don't see any huge value in deliberately moving to a higher layer. All the same modalities can still be applied with AI: comments, commit messages, PR messages, literate programming, READMEs, AGENT.mds, tech specs, tech 1-pagers, vision statements, etc. I write a doc when it's useful to communicate to one or more parties, otherwise I try to keep everything as minimal and possible, often that means just live in the code.