AI-assisted PRs break main half as often as human ones

Julien Danjou's analysis of merge queues reveals intriguing insights from the 2026 State of Merge Queues report, where 153,000 merges across 160 engineering teams were studied over 90 days. Notably, AI-assisted pull requests (PRs) were found to break the main branch about half as often as those authored by humans, with a rate of 1.9% compared to 4.4%, challenging common perceptions about AI's reliability in code development. The study also highlights that the rate at which the main branch breaks scales significantly with team size, increasing 16-fold as teams grow, with larger teams facing more frequent integration challenges. Furthermore, private codebases were observed to break the main branch 4.5 times more often than open-source projects, likely due to the interdependent nature of private monorepos compared to the isolated contributions typical in open-source work. Despite the efficiency of batching PRs in reducing CI costs and maintaining safety, the practice remains underutilized, with 94% of private merges processed individually. The report suggests that engineering teams should focus on managing large human-authored changes in big teams rather than imposing stricter reviews on AI-assisted code, as the data does not support additional review friction for AI-generated contributions.

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