Code review is a taste problem

As AI rapidly increases the volume of generated code, code review is evolving from line-by-line bug detection into a broader decision-making process centered on collaboration, organizational alignment, knowledge sharing, verification, and product judgment. The author argues that planning and review may increasingly merge, with reviewers assessing concise statements of intent, constraints, and acceptance criteria rather than only large code diffs, allowing senior engineers to focus on whether a change solves the right problem. The appropriate degree of direct code inspection should vary according to a system’s risk and sensitivity, from internal tools to medical-device software. Many recurring review comments could also be classified as deterministic or testable and converted into automated rules, creating an institutional “AI slop register” that uses AI to enforce accumulated engineering standards. Although the term “code review” may become less accurate, the underlying practice will remain essential as a forum for evaluating changes through code, intent, and shared organizational memory.

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