I'm not sure I agree the term applies. Comprehension debt, as I understand it, is just the dependency trap mentioned in that arxiv paper you linked. It means that the AI might have written something coherent or not, but you as a human evaluator have little means to judge it. Because you've relied on it too much and the scope of the code has exceeded the feasibility of reading it manually.

When I talk about an incoherent mess, I'm talking about something different. I mean that as the codebase grows and matures, subtle details and assumptions naturally shift. But the AI isn't always cleaning up the code that expressed those prior assumptions. These issues compound to the point that the AI itself gets very confused. This is especially dangerous for teams of developers touching the same codebase.

I can't share too much detail here, but some personal experience I ran into recently: we had feature ABC in our platform. Eventually another developer came in, disagreed with the implementation, and combined some aspects of it into a new feature XYZ. Both were AI generated. What _should_ have happened is that feature ABC was deleted from the code or refactored into XYZ. But it wasn't, so now the codebase has two nearly identical modules ABC and XYZ. If you ask Claude to edit the feature, you've got a 50/50 shot on which one it chooses to target, even though feature ABC is now dead, unreachable code.

You might say that resolving the above issue is easy, but these inconsistencies become quite numerous and unsustainable in a codebase if you lean on AI too much, or aren't careful. This is why I say that having a super clear vision up front is important, because it reduces this kind of directional churn.