AI tools in software development promise faster builds, lower costs, and near-instant output. But when it comes to code quality, speed alone doesn’t tell the whole story. New research from CodeRabbit suggests that AI-generated code can introduce more risk than many teams expect—raising concerns about reliability, security, and long-term maintainability.
What the Data Reveals About AI’s Limitations
CodeRabbit analyzed 470 open-source GitHub pull requests, comparing AI-assisted submissions to those written entirely by humans. The findings were eye-opening. AI-influenced code contained roughly 1.7 times more issues, including logic errors, maintainability challenges, and performance problems.
While AI can generate code quickly, it often lacks situational context, architectural awareness, and defensive thinking. On average, AI-assisted pull requests logged 10.83 issues, compared to 6.45 issues in human-written code.
This difference fuels the ongoing debate around human versus artificial intelligence code quality. More critically, high-severity issues appeared far more often in AI-generated code, leading to longer reviews and a greater chance of defects making it into production.
Security Risks in AI-Generated Code
One of the most concerning findings involves security vulnerabilities. According to the report, these appeared 1.5 to 2 times more frequently in AI-generated code. Common issues included insecure object references, weak password handling, and exposure to cross-site scripting (XSS) attacks.
AI tools often draw from public code repositories, which may include outdated or insecure patterns. Without an understanding of threat modeling or evolving security standards, AI can unintentionally replicate known weaknesses.
Human developers bring something AI can’t: experience. They understand how data moves through systems, where attackers are likely to strike, and why certain shortcuts create future risk. That insight helps stop small issues from becoming major liabilities.
The Hidden Cost: Technical Debt
Another challenge is the gradual buildup of technical debt from automated programming tools. AI-generated code may function on day one, but it’s often harder to maintain, scale, or hand off to another developer down the road.
This reinforces the importance of human oversight. Skilled developers question assumptions, identify edge cases, and align code with real business requirements. Used correctly, AI accelerates repetitive work and assists with drafts or testing—but it shouldn’t operate unchecked.
Using AI Without Sacrificing Quality
There’s no doubt AI is reshaping the software development lifecycle. It increases output and shortens timelines—but it doesn’t eliminate the need for experienced developers.
Organizations getting the best results are combining AI’s speed with human review and accountability. That means enforcing code review standards, tracking defect rates, and prioritizing secure, maintainable code over sheer velocity.
The CodeRabbit data makes one thing clear: removing human judgment from the process introduces unnecessary risk. The most sustainable approach blends smart tools with skilled people.
