One way to think about this is to look at second-order indicators rather than direct “debt” metrics.
For example:
- Change failure rate or rollback frequency after AI-assisted changes
- Time-to-fix regressions introduced by generated code
- Ratio of generated code that later gets rewritten or deleted
- Increase in review time or comment volume per PR over time
These don’t directly label something as “AI-generated debt,” but they capture the maintenance and coordination costs that tend to show up later.
It’s imperfect, but it frames the discussion in measurable signals rather than subjective warnings.
Thanks! That makes sense. I suppose this requires commit messages or PRs to indicate code was AI-generated vs. not, or to assume that commits after a certain time period were all from AI coding. It’d be an interesting analysis. Maybe there’s already a study out there.
Comments
One way to think about this is to look at second-order indicators rather than direct “debt” metrics.
For example: - Change failure rate or rollback frequency after AI-assisted changes - Time-to-fix regressions introduced by generated code - Ratio of generated code that later gets rewritten or deleted - Increase in review time or comment volume per PR over time
These don’t directly label something as “AI-generated debt,” but they capture the maintenance and coordination costs that tend to show up later.
It’s imperfect, but it frames the discussion in measurable signals rather than subjective warnings.
Thanks! That makes sense. I suppose this requires commit messages or PRs to indicate code was AI-generated vs. not, or to assume that commits after a certain time period were all from AI coding. It’d be an interesting analysis. Maybe there’s already a study out there.
In any case, thank you again!