AI-generated code now accounts for 15-25% of lines shipped by the average engineering team — and according to Qodo's 2025 State of AI Code Quality report, it introduces 1.7x more issues than human-written code. That gap does not close on its own. The teams controlling it are the ones measuring the right signals.
1. Code Churn Rate
Code churn measures how often recently written code is rewritten or deleted within a short window — typically 21 days. It is one of the clearest early signals that generated code is not production-grade. Between 2021 and 2024-2025, industry code churn rates rose from a 3.3% baseline to 5.7-7.1%, a trend that closely tracks the adoption curve of AI coding assistants.
Tracking churn by code origin reveals whether engineers are confidently extending AI output or quietly undoing it. Most version control platforms expose this metric natively — the work is tagging files by AI origin at commit time. If churn on AI-touched files runs more than 2x above your non-AI baseline, the agent is shipping speculative code that humans are silently correcting. That delta is ungoverned risk.
2. Defect Density Per AI-Generated PR
Defect density — bugs, security findings, or style violations per thousand lines — needs to be broken out by code origin. The same Qodo report shows maintainability and code quality errors run 1.64x higher in AI-generated code versus human-written. AI-assisted coding is also linked to a 4x increase in code cloning, compounding defect density by spreading errors across multiple files simultaneously.
Without segmenting by origin, this signal disappears into your overall defect rate. Instrument your CI pipeline to tag AI-origin PRs, then compute defect density separately for each cohort.
3. Security Finding Rate
Research published in the Proceedings of the West Virginia Academy of Science evaluated AI models across Common Weakness Enumeration (CWE) vulnerability classes — including SQL injection, buffer overflow, and path traversal — and found consistent gaps in model handling of these patterns. The practical implication: scan AI-generated PRs for CWE-top-25 categories at the PR gate, not at deployment. Teams integrating static analysis into CI should configure AI-origin filters to surface these findings separately from human-code results. Track findings per 100 AI PRs. Any upward trend versus your human-code baseline is a governance gap.
4. AI Code Acceptance Rate
Not all AI suggestions that reach a PR were reviewed carefully. Acceptance rate — the share of AI-suggested lines that merged without modification — tells you how much unreviewed AI output is flowing into production. Industry-wide, around 30% of AI-generated code is accepted by developers. Teams running acceptance rates above 60-70% without corresponding quality controls have a thin human review layer between AI output and production. This metric is a proxy for how much your engineers are trusting the agent versus verifying it.
5. Review Cycle Time for AI-Generated PRs
Volume creates pressure to merge faster. 82% of developers used AI coding tools weekly in Q1 2025, with 59% running three or more tools in parallel, according to Jellyfish's 2025 AI Metrics in Review. Higher output volume means more PRs per day and stronger implicit pressure to review quickly.
Track whether review cycle time for AI-heavy commits is compressing below the threshold where security review is genuinely possible. For non-trivial changes, a sub-four-hour window is a warning sign — not a win. Teams that moved from 0% to full AI adoption reduced median PR cycle times by 24%. That is not an argument against productivity — it is an argument for automating low-signal checks so reviewers can focus on high-risk changes.
Metrics at a Glance
What to Do Now
Tag PRs by AI origin in your CI system. No tagging means no segmentation, and no segmentation means no metrics.
Baseline all five metrics today, before any further expansion of AI tool coverage in your team.
Set alert thresholds: churn >50% above baseline, defect density >30% above baseline, any increase in security findings per AI PR, acceptance rate >65%, review cycle time <4 hours on non-trivial changes.
Route AI-generated PRs that breach thresholds to a mandatory human reviewer before merge — targeted gate, not a blanket default.
Review these metrics weekly. Sprint-end cadence is too slow to catch accumulating risk in an AI-assisted codebase.
Automated risk scoring at the PR gate makes this operational rather than manual. Re-entry.ai scores every pull request against these signals in real time, flags high-risk AI-generated commits before they merge, and gives engineering leads the visibility needed to govern AI-assisted codebases without slowing delivery.