AI-powered coding tools are becoming increasingly common across development teams, but a new report suggests they still introduce significant challenges. According to an analysis by CodeRabbit, code generated by AI systems tends to produce more issues during review than code written solely by humans. The findings indicate that while AI assistants can speed up development, they may also increase the burden on reviewers and quality assurance processes.
The report found that AI-generated pull requests triggered 1.7 times more issues during pull-request analysis compared to human-authored code. On average, AI-written code resulted in 10.83 issues per pull request, while human-generated submissions averaged 6.45 issues. Pull requests where AI collaborated with humans also showed elevated problem counts, suggesting that partial automation does not fully mitigate these risks.
Beyond the raw numbers, CodeRabbit highlighted a concerning pattern in the distribution of issues. AI-generated pull requests showed a much heavier “long tail,” meaning they were more likely to produce unusually large and complex sets of review comments. These “busy” reviews often required deeper inspection, making AI-assisted submissions harder and more time-consuming to evaluate than traditional code changes.
The most common problems identified in AI-generated code were related to logic and correctness, but the trend extended across other critical areas as well. In categories such as maintainability, security, and performance, AI-assisted code consistently introduced more issues than human-only contributions. As a result, the report recommends that teams adopting AI coding tools establish stronger guardrails, review standards, and security checks to balance productivity gains with code quality and safety.