AI Code Review Is a Sycophant — Daily DevOps & .NET

AI code review tools like GitHub Copilot, Claude, and Cursor are useful for catching mechanical issues — null dereferences, anti-patterns, trivial security bugs, and style inconsistencies. However, they systematically miss higher-order problems: wrong abstractions, features that shouldn't exist, systemic codebase patterns, business logic correctness, and real-world performance issues. The core failure mode is structural sycophancy: AI reviewers are trained to approve the overall approach and suggest small fixes, making them incapable of saying 'close this PR and start over.' Over time, teams that rely heavily on AI review risk optimizing for what AI catches while neglecting design-level judgment. The recommended approach is to use AI review as a pre-filter for mechanical issues, while preserving human review for design decisions, domain correctness, and architectural judgment — treating AI approval as a weak signal, not an endorsement.