3 ms·
Ask HN: What are the metrics for "AI-generated technical debt"?
Here’s one place where I think proponents and skeptics of agentic coding tools (Claude Code, Codex, etc.) tend to talk past each other:
Proponents say things like:
- “I shipped feature X in days instead of weeks.”
- “I could build this despite not knowing Rust / the framework / the codebase.”
- “This unblocked work that would never have been prioritized.”
Skeptics say things like:
- “This might work for solo projects, but it won’t scale to large codebases with many developers.”
- “You’re trading short-term velocity for long-term maintainability, security, and operability.”
- “You’re creating tons of technical debt that will surface later.”
I’m sympathetic to both sides. But the asymmetry is interesting: The pro side has quantifiable metrics (time-to-ship, features delivered, scope unlocked). The con side often relies on qualitative warnings (maintainability, architectural erosion, future cost).
In most organizations, leadership is structurally biased toward what can be measured: velocity, throughput, roadmap progress. “This codebase is a mess” or “This will be a problem in two years” is a much harder sell than “we shipped this in a week.”
My question: Are there concrete, quantitative ways to measure the quality and long-term cost side of agentic coding?. In other words: if agentic coding optimizes for speed, what are the best metrics that can represent the other side of the tradeoff, so this isn’t just a qualitative craftsmanship argument versus a quantitative velocity argument?
- cherry19870330 8mo agoOne 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.
- willj 8mo agoThanks! 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!