There’s a reasonable amount of data on what goes wrong when AI coding tools are adopted without measurement. Less has been written about what good looks like.
That’s partly because “good” is harder to define than “bad,” and partly because the industry is still early enough that there aren’t many established benchmarks. But the metrics exist, the data is available in git history, and the patterns of healthy AI-assisted codebases are starting to become visible.
The metrics that matter
A healthy AI-assisted codebase isn’t one where everything looks the same as it did before AI adoption. AI assistance changes the patterns of code production. The question is whether those changes are accumulating into quality or away from it.
Churn rate by origin. Healthy AI-assisted code should have a churn rate comparable to, or modestly higher than, human-authored code. GitClear’s research shows industry averages around 2× higher for AI-assisted commits. Teams with strong review processes and deliberate AI use are running lower. Teams without visibility into this metric are running somewhere on that distribution without knowing where.
Duplication rate. Duplicate code blocks should be growing at a rate the team can absorb and manage. A 4× increase is the industry average. Teams that are actively reviewing for duplication and building abstractions rather than adding implementations tend to run below it.
Net lines relative to gross. Net lines measure what accumulates in the codebase after churn is accounted for. In a healthy AI-assisted codebase, gross output is higher than it was before — more code is being written — and net output is also higher, meaning more of that code is surviving. A widening gap between gross and net suggests churn is accelerating.
Commit density and burst patterns. AI assistance tends to produce larger, denser commits. That’s not inherently a problem, but very large commits with very high line counts are harder to review effectively. Healthy patterns show AI assistance contributing to more frequent, moderately-sized commits rather than occasional very large ones.
The baseline comparison is the whole point
These metrics only become meaningful in comparison to a pre-AI baseline. Knowing that your churn rate is 1.6 tells you something. Knowing that it was 0.9 before AI adoption and is now 1.6 tells you something more specific.
The pre-AI baseline is what makes it possible to say whether AI assistance is improving or degrading the codebase, which developers are getting the most value from the tools, and whether the patterns are moving in the right direction over time.
Teams that established a baseline before rolling out AI assistance are in a fundamentally different position from teams that didn’t. They can answer questions that other teams are guessing at. Teams that didn’t establish one at the time can still reconstruct it from git history — it’s all there.
What the data looks like when things are going well
The pattern in teams where AI assistance is working well looks something like this: commit velocity has increased, churn rates are modestly higher than the pre-AI baseline but not dramatically so, duplication is being monitored and managed, and net lines are growing. Developers are using AI assistance on well-scoped tasks and reviewing the output carefully on more complex work.
This isn’t a description of perfect AI adoption. It’s a description of AI adoption that’s visible enough to manage. The numbers aren’t all ideal. They’re being watched. The team knows where it sits and can have a specific conversation about what to improve.
The conversation this enables
Most engineering managers can describe their team’s AI adoption in terms of which tools they’re using and roughly how often. Very few can describe it in terms of what it’s doing to code quality.
The managers who can are in a better position to talk to their team about where AI assistance is and isn’t working, to respond to leadership questions about AI ROI with something specific, and to make adjustments before quality problems compound into something harder to fix.
That’s what a healthy AI-assisted codebase looks like: not one where every metric is optimal, but one where every metric is known.
Scryable surfaces churn rates, duplication patterns, and net vs gross lines from your git history, with before/after comparisons to your pre-AI baseline. Get early access.