It really does feel like leaders in large organizations are lost on the valuable metrics when it comes to code in general. The mantra seems to often be, make number go higher - regardless of if it's valuable data or not.
well, it's not per-engineer. instead, the managers get team-wide metrics so the idea is that they know what the quality of their teams code is, and they can recommend trainings / meetings on how to improve on X part of their AI workflow.
My team is hyper productive with Cursor, using different models for different tasks. My senior engineers approve PRs, but it's not humanly possible and is demoralising to review the amount and rate of code being churned. Sure there are a lot of automated tests. We struggle with a measure for code quality that scales. We mitigate by having small releases and running A/B instances but I still lose sleep over pace vs quality.