Hey everyone, We’ve been curious to get some real numbers about the impact of AI coding agents on codebases, so we pulled the git history of 46 well-known open-source repos (16M+ lines of code, ~200k commits) and looked at what changed once they started using agents. The first thing that jumped out is that Claude Code is everywhere. It was behind 69% of the AI-tagged commits and showed up in 40 of the 46 repos. To be fair, part of that is because Claude Code adds a co-author line by default and most other tools don’t, so it’s the easiest one to spot. The stuff I found most interesting:
New code is more complex . After AI adoption, every 100 new lines carry 8.1 decision points (ifs, loops, switch cases) in the typical repo, up from 5.2 before. That’s about 50% more paths to test, and it went up in 27 of 30 repos.
Complexity started rising in 2025 . New code sat around 5 decision points per 100 lines from 2022 to 2024, then climbed to about 10 by October 2026. Repos with almost no AI-tagged commits got more complex too, just slower (1.9% a month vs 3.5% in repos after they adopted AI). So we can’t pin all of it on AI, but it rose faster where AI was in use.
Twice as many repos now get the worst complexity grade . We graded 39 repos A to D by how much complexity sits in functions with more than 10 decision points, the point where testing every path gets impractical. Using the same grade boundaries both years, 10 repos had a D in October 2024 and 20 had one in October 2026.
Each engineer ships more code . After AI adoption, each engineer ships 71% more production code a week, and the number of active contributors didn’t clearly change.
Less new code gets rewritten . The share of new code rewritten or deleted within 90 days fell from 18% to 12% (24 of 30 repos). And when a later commit touches that code again, it’s more often building on it (58% → 71% of those commits) and less often fixing it (15% → 11%). We used an LLM to classify each one.
Teams write more tests, but bug fixes haven’t gone down . Test commits per engineer more than doubled, yet bug-fix commits are still a quarter of all commits, same as before. Measured per 1,000 new lines of code, though, the amount of testing stayed about the same. One explanation is that the code now has 50% more branches per line, so the same amount of testing covers fewer of them. The data can’t prove that. For Claude Code, its commits are about 54% bigger than other commits in the same repos and get revisited 55% more. Per line it’s about as dense as everything else, it just does more in one go. Opus 5 commits are 28% less likely than Opus 4 to go over 500 lines. Could be the model getting better or all of us getting better at scoping tasks, hard to tell which. The report is here, and the raw data and scripts are public if you want to poke around: https://kerno.io/the-impact-of-ai-on-code-quality-2026/ https://github.com/kernoio/kerno-impact-ai submitted by /u/Fluffybaxter
Originally posted by u/Fluffybaxter on r/ClaudeCode