AI Trust Level measures developer confidence in the correctness, quality, and reliability of code produced by AI coding assistants. It is typically captured through surveys and reflects the subjective comfort level engineers have when working with AI-generated suggestions. Low trust leads to excessive manual verification that negates productivity gains, while uncalibrated high trust can allow defects to slip through review.
Developer confidence in AI accuracy. Trust is declining globally (40% to 29%) as complexity grows.
Trust declining from 40% to 29%. At scale, inconsistent trust levels create review bottlenecks — some devs trust AI output, others don't, causing friction.
Trust directly drives acceptance. Only 3.8% ship AI code confidently.
Low trust = more verification overhead. Reviewers spend more time scrutinizing AI code.
Low trust increases mental overhead from constant verification and second-guessing.