Why it matters: AI-generated code can run cleanly for weeks while quietly producing wrong answers, because the logs confirm that functions executed without ever recording what those functions decided.
The Details:
- When developers write code themselves, they carry a rough internal map of every branch and edge case, and that memory often catches bugs before any log file does. AI tools remove that map. You end up maintaining logic you reviewed but never built from scratch, so the usual fallback diagnostic simply is not there when something goes wrong.
- Most logging only announces activity: a process started, a batch finished, a function returned. None of that tells you what happened at the moments that actually matter, like a filter excluding one row too many or a join matching the wrong key. These silent-success bugs leave no trace unless the inputs to each decision point are captured at the instant the decision is made.
- Capable models tend to fold several logic steps into one tight expression because it reads well, which is great for readability and terrible for debugging later. Once a branch stops existing as its own named block, there is nothing discrete left to attach a log line to, so fixing it after the fact usually means rewriting code nobody fully understands yet.
- The fix is moving observability earlier, into the spec itself, before any prompt is written. Teams that do this turn decision points into named functions, log the values being branched on, and treat the ability to read a trustworthy trail from the logs as part of what "done" means, not a separate monitoring task bolted on afterward.
Bottom Line: Logs that only prove code ran are not evidence; logs that show what it decided are the only ones worth trusting.
