The AI Productivity Bill Comes Due in Production

The easiest place for an AI rollout to look successful is the velocity dashboard. Pull request count is up. Cycle time improves. More code gets merged. The tool has a tidy story to tell.Production usually tells the longer version. The review queue gets heavier. The same two senior engineers become the validation layer for a larger volume of plausible patches. Support sees more small changes with surprising edge cases. The team ships more often, but on-call starts to feel more expensive. None of this proves the AI rollout failed. It proves the dashboard stopped too early.That is the standard I would use for this debate: AI can make code cheaper. It cannot make a weak idea useful, and it cannot make production work free.The AI productivity conversation is still too comfortable measuring the part of software work that AI makes easiest to see. Lines of code, pull requests, story points, and deployment frequency are all close to production of work. They are not the same as value. A team…