The increasing volume of AI-generated code challenges traditional code review practices. While AI accelerates code production, human reviewers struggle to keep pace with the expanded review workload. Current code review processes aim to ensure quality, security, and knowledge sharing, but these functions are being overloaded. The author argues that many benefits attributed to code review should be addressed earlier in the development cycle. For instance, exploring alternative solutions is better done before implementation. Knowledge transfer is more effective through pair programming or direct mentorship rather than post-completion review. Collective ownership and architectural understanding can be fostered through collaborative design sessions and mob programming. Deterministic checks like formatting and linting should be automated. Shifting feedback loops earlier using practices like pair programming and trunk-based development is crucial. Human judgment remains valuable for significant architectural changes or complex issues. Relying on AI to mimic human review preserves the ceremony without addressing its underlying purpose. The real problem might be using code review as a catch-all for various development needs. As AI increases code output, a more deliberate approach to maintaining human understanding of systems is necessary. The focus should shift from reviewing code faster to engaging in these important conversations earlier in development.