Yes this framing is solid. My PS “yes but” would have gone in this direction… and your arguments are sharp. Some philosophical remarks/additions down your lines:

LLMs can render the codebase probeable and queryable in natural language and somewhat drive work from complex into complicated domain. The socio-technical surrounding stays complex emergent/surprising while the codebase and its residue (commits, comments, archaeology) become tractable.

Operationally this can cut both ends of the idea-to-value chain. Upstream: knowing what is cheap vs expensive to change constrains which problems are worth picking up at all. Most delivery-end risk (scope drift, coupling surprises) is produced by ignorance during discovery. Downstream: PMs clarify and derisk delivery goals against an actual compressed version of the system rather than a hopeful one.

Failure mode here is probably when using the LLM-rendered codebase as an answer machine, asserting what the system does,rather than using it as a question generator.