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.