tautology problem — AI confirming itself.

Short summary

AI-generated code faces a fundamental verification gap: when the same model writes code, tests, and reviews—all from the same source of reasoning—a misunderstood requirement cascades through all three layers uncaught. Anthropic's April 2026 postmortem revealed that even their full verification stack (human review, automated review, unit tests, e2e tests, automated verification, dogfooding) failed to catch regressions before shipping. The author proposes DQA, a specification-driven trust layer that independently validates commits against original requirements.

• •AI-generated code, tests, and reviews share the same reasoning source, allowing systematic errors to propagate unchecked.
• •Anthropic's own verification stack failed to catch regressions before shipping—a real-world case study.
• •Author proposes DQA: a spec-driven trust layer that independently validates commits against original requirements.

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