Consider a common failure pattern: a pull request arrives with clean naming, full coverage, and every check green. It is approved quickly. Months later, the module breaks and nobody can explain the design decisions because the intent lived in an AI conversation that was never preserved.

This is not only a hypothetical risk. In a randomized study of 52 developers learning a new library, the AI-assisted group did not finish significantly faster and scored 17% lower on a follow-up comprehension test. The strongest AI users asked conceptual questions and deliberately rebuilt understanding instead of delegating the entire task.

This session shows engineers and engineering leaders how AI-authored code fails differently: plausible duplication, hidden assumptions, unnecessary abstractions, brittle tests, and lost intent. Attendees will leave able to identify AI-specific review risks, apply a practical comprehension checklist, and make explainability an explicit quality signal before code is merged.

Audience: Software engineers, reviewers, and engineering leaders using AI coding tools.
Format: 45–60-minute evidence-driven talk with a pull-request review exercise.
Demo: Review an illustrative AI-authored module, expose five failure modes, and apply a comprehension checklist.
Research: Anthropic’s randomized developer study: https://www.anthropic.com/research/AI-assistance-coding-skills
Materials: Talk-specific checklist, slides, and recording are not yet published.
Vendor scope: Vendor-neutral.