AI Coding Metrics Have a Missing Middle
We can measure AI usage and software delivery. The hard part is understanding what happens between the two.
AI coding measurement has become surprisingly sophisticated in a very short time. We can measure active AI users. We can see which tools developers use. We can count prompts, accepted suggestions, tokens, AI-assisted commits, and increasingly even identify which parts of a codebase were created with AI assistance.
At the other end of the software delivery system, we already have mature engineering metrics:
- Pull request cycle time
- Review time
- Deployment frequency
- Change failure rate
- Rework
- Incidents
- Delivery lead time
Yet there is a large gap between these two worlds.
We know how much AI is being used.
We know how software is being delivered.
What we often don't know is what happened in between.
That missing middle is where I think most of the interesting AI productivity questions now live.
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