The rapid adoption of AI coding assistants has brought undeniable productivity gains, but a new report provides crucial data on the quality trade-offs. An analysis of real-world pull requests reveals that AI-generated code introduces a measurably higher volume of defects across critical categories, prompting a need for adjusted development and review practices.
AI-generated pull requests contain ~1.7x more issues on average than human-written code.
Critical and major defects are up to 1.7x higher in AI-authored changes.
Security vulnerabilities rise 1.5–2x, with notable increases in improper password handling and insecure object references.
Logic and correctness issues increase by 75%, including business logic errors and unsafe control flow.
Code readability problems surge more than 3x, and performance inefficiencies appear nearly 8x more often.
The report recommends mitigations like project-context prompts, stricter CI enforcement, and AI-aware PR checklists.
While developer productivity tools powered by AI have become ubiquitous, hard data on their output quality has been scarce. CodeRabbit's "State of AI vs Human Code Generation" report, analyzing 470 open-source GitHub pull requests, provides a clear, quantitative assessment. The findings indicate that the acceleration in code production comes with a significant and consistent increase in defects across all major software quality dimensions.
This data helps explain high-profile postmortems from 2025 that implicated AI-assisted changes. The study moves beyond anecdote, revealing specific, predictable failure modes. The elevated defect rates are not random but cluster in areas where AI lacks the contextual understanding and rigorous reasoning of an experienced developer.
The report categorizes the heightened risks introduced by AI-generated code, offering teams clear areas for focused review and mitigation. The most pronounced increases are in maintainability and performance, with code readability problems—such as naming and formatting inconsistencies—jumping over threefold. Performance inefficiencies, like excessive I/O operations, were nearly eight times more common.
Perhaps more critically, the rise in logic defects (75%) and security vulnerabilities (1.5-2x) poses direct risks to application correctness and safety. AI-generated code showed a particular propensity for business logic errors, misconfigurations, and insecure handling of credentials and object references. “These findings reinforce what many engineering teams have sensed throughout 2025,” said David Loker, Director of AI, CodeRabbit. “AI coding tools dramatically increase output, but they also introduce predictable, measurable weaknesses that organizations must actively mitigate.”
The report concludes not by discouraging AI use but by providing a roadmap for its safer adoption. The key is to compensate for the known weaknesses of AI assistants through enhanced process and tooling. Recommendations include enriching AI prompts with project-specific context and business rules to reduce logical errors.
On the technical side, enforcing style and security through policy-as-code—stricter CI pipelines with mandatory linters, formatters, and security scanners—can automatically catch entire categories of AI-introduced issues. Finally, human review processes must evolve with AI-aware PR checklists that explicitly prompt reviewers to verify error handling, concurrency correctness, configuration validation, and secure credential management.
The era of AI-assisted development requires a corresponding evolution in quality assurance. By understanding the specific vulnerabilities AI introduces and implementing targeted guardrails, engineering organizations can harness the productivity benefits while proactively managing the associated risks to code quality, security, and performance.
CodeRabbit is the category-defining platform for AI code reviews, built for modern engineering teams navigating the rise of AI-generated development. By delivering context-aware reviews that pull in dozens of points of context, CodeRabbit provides the most comprehensive reviews coupled with customization features to tailor your review to your codebase and reduce the noise. CodeRabbit helps organizations catch bugs, strengthen security, and ship reliable code at speed. Trusted by thousands of companies and open-source projects worldwide, CodeRabbit is backed by Scale Venture Partners, NVentures: NVIDIA's venture capital arm, CRV, Harmony Partners, Flex Capital, Engineering Capital and Pelion Venture Partners.
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