Comprehension Debt: The Ticking Time Bomb of LLM-Generated Code (codemanship.wordpress.com)

AI-generated code is creating a new kind of technical liability: “comprehension debt.” Much like legacy code written by someone else, code spit out at scale by LLMs requires time to understand before it can be safely changed. While some teams rigorously review, rework and test generated snippets—often erasing the initial time savings—others check minimally reviewed code into repos. That second approach multiplies risk: unexamined, lightly tested code will eventually need changes that are harder and slower to make because nobody understands the original intent or edge-case behavior. For the AI/ML community this matters because the promise of faster delivery from code-generating models can be illusory. Anecdotal experience suggests LLMs can successfully modify existing generated code perhaps ~70% of the time, but “doom loops” of repeated, failing model attempts are common. The result is slower bug fixes, brittle systems, and growing maintenance overhead that tools alone can’t always resolve. Practically, teams should account for increased review, testing and documentation costs, rethink CI/CD and ownership practices, and treat generated code as a first draft—not a finished artifact—to avoid piling up a rapidly expanding mountain of comprehension debt.