AI-generated code reviewed by AI creates a circular validation problem: both systems share the same training patterns and assumptions, so code can pass all automated checks while still failing to meet actual business requirements. The core issue is structural — without a human-verified spec as an external reference point, automated reviews only confirm that code looks correct, not that it is correct. Research backs this up: PRs per author rose 20% with AI assistance but production incidents per PR increased 23.5%. The solution is targeted human involvement at high-leverage points — spec approval, acceptance criteria sign-off, and architectural decisions — while letting AI handle volume tasks like style, syntax, and coverage. A human-reviewed spec acts as an anchor that gives both automated and human reviews something concrete to verify against.
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