GitClear Report 2025: The Truth About AI-Written Code Quality

A massive GitClear analysis reveals what really happens when developers rely on AI coding tools — cleaner commits, faster delivery, and hidden technical debt.

When AI coding assistants first entered the developer ecosystem, the promise was intoxicating: more productivity, fewer bugs, faster releases. But as teams scaled their use of AI-generated code, one big question emerged — is the quality of this new code actually good? The analytics company GitClear decided to find out. In its 2025 “AI Code Quality” report, the company analyzed over 10 million commits from organizations using tools like GitHub Copilot, Cursor, and Bolt. The data spans three years — before and after the introduction of generative coding systems. The findings challenge both sides of the debate. The Paradox of Efficiency On the surface, productivity skyrocketed. Teams using AI tools produced 55% more commits per month on average. But GitClear’s analysis showed that those commits were also smaller, narrower, and less interconnected than before. The AI-generated code tended to appear in isolated patches — quick fixes, micro-features, or UI elements — while deeper architectural refactoring nearly disappeared. In GitClear’s words: “Developers are shipping more, but touching less.” This means that while teams appear more active, the underlying complexity of their codebases grows unchecked. AI tools excel at local improvements, but rarely perform global reasoning across large systems — leaving hidden dependencies and performance issues to accumulate. The Hidden Cost: Silent Technical Debt The report introduces a new metric called Cumulative Refactor Deficit (CRD) — a measure of how often projects postpone deep cleanups in favor of surface-level edits. AI-heavy repositories showed a 34% higher CRD than traditional codebases. In other words, developers using AI assistants tend to accept more “temporary fixes” that become permanent over time. The cause isn’t laziness; it’s a subtle shift in how people think when AI is involved. When an assistant produces functional code instantly, there’s less emotional incentive to revisit and polish it later. Code Quality by the Numbers GitClear used static analysis, test coverage tracking, and semantic diff metrics to evaluate quality. Key insights included: Bug frequency: 19% lower short-term, but 12% higher over 6 months (suggesting delayed consequences). Code readability: 26% improvement in style and naming consistency, driven by LLM “standardization.” Maintainability index: dropped by 17%, due to fragmented structures and shallow hierarchies. Team review participation: fell by nearly 30%, as developers trusted AI output “out of the box.” Perhaps most alarming was a growing review gap: senior developers stopped reviewing small PRs, assuming they were safe. Over time, these micro-commits began to erode cohesion and introduce subtle logical errors. Developer Behavior Shift One of the most human findings was behavioral. The report tracked how developers interacted with AI-assisted commits. At first, most treated AI-generated code cautiously — reading, editing, and re-testing before merging. But within weeks, review depth declined. Developers began to trust the AI’s style consistency as a signal of reliability, even when the logic was flawed. GitClear calls this phenomenon “the illusion of correctness.” The visual neatness and confident tone of AI output created a false sense of safety. The Vibe Coding Connection In the emerging world of Vibe Coding — where developers converse with AI instead of typing code — these findings are even more significant. Vibe Coding tools, by design, encourage higher-level reasoning and conversational direction rather than direct control. The GitClear report suggests that as developers shift further from the keyboard, the psychological distance from the codebase grows — echoing the “material disengagement” identified in academic studies. But there’s also good news. Teams that combined AI tools with structured oversight frameworks (like Kiro’s orchestration logs or Lovable’s reasoning reports) showed nearly zero loss in long-term quality. The problem, it seems, isn’t the AI — it’s the lack of context and supervision. What Comes Next GitClear ends its report with a balanced view. AI coding is neither a disaster nor a miracle — it’s a force multiplier that must be handled with architectural discipline. The future belongs to teams that integrate AI audit layers: systems that analyze not just what code is generated, but why it was written that way. As one lead engineer quoted in the study put it: “AI gave us speed. Now we need brakes.” In the context of Vibe Coding, this means that the next generation of tools must combine conversation with accountability — logging reasoning steps, tracking dependencies, and ensuring every automated improvement fits the broader vision of the software. The GitClear report closes with a warning that could define the next decade of AI development: “Unchecked acceleration isn’t progress. It’s entropy disguised as productivity.”

Published on October 9, 2025