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Ask HN: What are the metrics for "AI-generated technical debt"?

1•willj•1h ago
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?

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