How to Kill the Code Review — Ankit Jain, Aviator
The traditional code review process is failing due to increasing code churn and reliance on AI agents, leading reviewers to only skim results. The speaker argues that code review must shift its focus from verifying line-by-line correctness (semantic accuracy) to preserving crucial non-technical aspects like knowledge sharing, mentorship, and architectural alignment. The proposed solution involves capturing the entire development session's decisions (the 'intent'), converting these into formal acceptance criteria, building an AI Slop Registry of recurring feedback, and generating a verifiable test plan that serves as the new review surface.
Key takeaways
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The Code Review Crisis
2:07
Code churn is high (861%), leading to increased bottlenecks at the review stage. Currently, over 30% of changes merge without any review, and even when AI reviews are used, human involvement often reduces to skimming rather than deep reading.
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Review Must Preserve Alignment
5:05
Code review's most critical function is not just catching bugs (semantic accuracy), but facilitating knowledge sharing, mentorship, and architectural alignment. This 'alignment' aspect must survive the shift to automated development.
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The New Review Surface: Intent vs. Diff
12:38
Instead of reviewing code diffs, the review surface should become a verification of 'intent' and 'evidence.' This is achieved by capturing user decisions from development sessions, converting them into acceptance criteria, and generating a test plan that runs against a live preview.