A recent industry report has sparked fresh debate about AI-generated code quality, highlighting that while artificial intelligence tools are accelerating software development, they may also be introducing more errors, security risks, and review overhead than human-written code. The analysis, released by CodeRabbit, suggests that teams relying heavily on AI-assisted coding need stronger review processes to prevent long-term risks.
Higher Error Rates in AI-Assisted Code
According to CodeRabbit’s findings, pull requests created with the help of AI tools contained noticeably more issues than those written entirely by human developers. On average, AI-assisted pull requests had 10.83 issues, compared to 6.45 issues in human-written code. This gap indicates that AI-generated code quality still lags behind traditional development in key areas.
The report further revealed that AI-generated pull requests showed 1.7 times more total issues. Even more concerning, the increase was not limited to minor mistakes. Critical issues were 1.4 times more frequent, while major issues appeared 1.7 times more often. These are the kinds of problems that can lead to system failures, data leaks, or production outages if they slip through reviews.
Security and Logic Problems Stand Out
One of the most alarming findings relates to security. The analysis showed that security-related issues were 57% more common in AI-assisted code. Logic and correctness errors rose by 75%, while maintainability problems increased by 64%. Performance-related issues were also 42% more frequent.
Common vulnerabilities identified in AI-generated code included improper password handling, insecure object references, cross-site scripting flaws, and unsafe deserialization practices. These are well-known security risks that experienced developers are trained to avoid, but AI models can unintentionally reproduce insecure patterns learned from public codebases.
This highlights a key concern about AI-generated code quality: AI does not truly understand context or intent. It predicts code based on patterns, which can lead to solutions that look correct but fail under real-world conditions.
The Growing Burden on Human Reviewers
While AI tools promise faster development, the report suggests that this speed comes with a hidden cost. More issues in pull requests mean longer review cycles and greater pressure on human reviewers. Developers are spending more time validating, correcting, and securing AI-produced code rather than writing new logic themselves.
As David Loker, AI Director at CodeRabbit, explained, AI coding tools dramatically increase output but also introduce predictable weaknesses. Organizations must actively mitigate these risks, or productivity gains could be offset by higher maintenance and security costs.
In practice, this means human developers are not being replaced. Instead, their role is shifting toward oversight, quality control, and risk management—areas where human judgment remains critical.
Not All Findings Are Negative
Despite the concerns, the report also points out areas where AI performs well. AI-generated code showed 1.76 times fewer spelling errors and 1.32 times fewer testability issues. This suggests that AI tools can help with code cleanliness, formatting, and boilerplate generation.
In other words, AI-generated code quality is not universally poor. It excels at repetitive or structured tasks but struggles with complex logic, security-sensitive workflows, and architectural decisions. Used correctly, AI can still be a valuable assistant rather than a replacement.
Understanding the Bigger Picture
The report also addresses rising vulnerability numbers across the industry. For example, Microsoft patched over 1,100 vulnerabilities in 2025, one of the highest totals on record. While this might seem alarming, it does not automatically mean code quality is declining.
With AI accelerating development, overall code output is growing rapidly. More code naturally leads to more discovered vulnerabilities, even if the percentage of flawed code remains stable. This context is important when evaluating the real impact of AI on software security.
At the same time, AI models continue to improve. Newer versions are being trained with better safeguards, stronger security awareness, and more refined outputs. Over time, these improvements may narrow the quality gap.
How Teams Can Use AI Responsibly
The key takeaway from the CodeRabbit analysis is not to abandon AI tools, but to use them wisely. Teams should treat AI as a productivity enhancer, not an autonomous developer. Strong code reviews, automated security scanning, and clear coding standards are essential when working with AI-assisted development.
By combining human expertise with AI efficiency, organizations can benefit from faster delivery without compromising safety. Ultimately, improving AI-generated code quality will depend on better tooling, smarter workflows, and a clear understanding of AI’s limitations.
As AI continues to reshape software development, the focus must remain on quality, security, and accountability—areas where human developers still play an irreplaceable role.
