When an AI tool writes two hundred lines that pass every test on the first try, something other than code gets produced. A decision gets made about tradeoffs, alternatives, and assumptions, and that decision lives only inside a chat window that closes the moment the PR merges. Nothing carries it into a comment or a log. Three weeks later an edge case appears, the original developer is on another project, and no one can explain why the system behaves the way it does. The code survived. The reasoning did not.
The Details:
- This gap does not show up in a linter or a code review checklist because it has no artifact. Technical debt lives in a backlog ticket you can point to. This debt lives in the absence of something in people's heads, so it stays invisible until an outage forces the question.
- A useful test: ask two engineers to sketch the system's architecture from memory. If they draw different pictures, or if the architecture only exists inside the files, the team is already carrying unbilled debt. Another signal is when review questions shift from "why this approach" to "what does this even do" about code the team shipped itself last month.
- The fix is cheap by design. A short markdown file recording what options were considered and why one was chosen takes ninety seconds and pays off the first time someone new joins or the author forgets. Asking AI tools for three ranked options instead of one finished answer keeps the actual decision, and the understanding attached to it, in human hands.
- Rotating which engineers work with AI on which subsystem prevents comprehension from pooling in one person who eventually leaves and takes it with them.
Bottom Line: Code generated by AI gets cheaper every quarter. Understanding it does not, and teams that skip that cost now will pay it later with interest, at the worst possible hour.
