Why Reviewing AI-Generated Code Fails as a Justification

The blog post challenges the notion that reviewing AI-generated code is a defensible stance. It points out that code review processes were designed for human-written software. AI outputs often lack the contextual cues reviewers rely on. The author suggests that reliance on review alone ignores deeper quality issues. Automated testing and provenance tracking are presented as more effective safeguards. The piece warns that over‑emphasizing review can create a false sense of security. It calls for broader strategies to assess AI contributions in development pipelines. Readers are urged to reconsider review as the sole gatekeeper for AI code.