AI can generate a working feature before you finish reading the prompt back. That speed hides a debt that only shows up months later, when nobody on the team can explain why the system works the way it does.
The Details:
The failure starts as a question swap. Developers ask "how do I implement this cache" instead of "why this caching strategy fits this context." The first question gets an answer in seconds. The second one forces tradeoffs into the open, and skipping it is what turns a fast feature into a fragile one six months out.
Watch for copy-paste velocity: code landing in the repo faster than anyone could explain it to a teammate. That is not proof the code is wrong. It is proof that ownership broke somewhere between generation and merge, and broken ownership is what turns a 2 AM production bug into a multi-hour hunt through unfamiliar logic.
Systems built this way tend to fragment into isolated components that each pass their own tests but never cohere into one architecture. Code review has to shift from checking correctness to checking whether a new piece actually belongs in the system, which means reviewers need to ask harder questions, not fewer.
Documentation written after the fact just describes the artifact; it cannot recover reasoning that already left the room. Writing down the rationale before shipping, even briefly, exposes the gaps in understanding while they are still cheap to fix, and gives future developers something more useful than a description of what the code does.
Bottom Line: The speed AI gives you is not free, it is a budget, and the teams that win are the ones who spend it on understanding, review, and documentation instead of banking it as raw output.
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