AI-Generated Code Review: What Reviewers Should Still Block
I spend less review time fixing awkward AI code than I did a year ago. A strong current model can often rewrite another model's clumsy abstraction in one pass. What I still cannot outsource is approval: I will not ask a teammate to approve code neither of us can explain. Those are different problems. Teams keep bundling them together as “code quality,” then spend expensive review time litigating…
AI-generated code review requires a shift in focus for reviewers. While reviewers spend less time fixing awkward AI code, they still need to block certain issues that cannot be outsourced. The current AI models produce better code than older versions, but they can still generate overly complex solutions that need human intervention.
Reviewers should prioritize expenditure based on blast radius and rollback cost, not the number of lines generated. The model often treats every detail in the prompt as a system concern, leading to unnecessarily complicated code. Human engineers usually distinguish between passing requirements and constraints that need a solid abstraction, a distinction that AI struggles with.
Reviewers need to ensure the generated code is safe, aligns with product intent, and does not expand the system's behavior silently. They should provide explainable control flow and tradeoffs in their feedback. The review process should also separate genuine maintenance concerns from team preferences disguised as maintenance issues.
Rather than focusing on code aesthetics, reviewers should identify costs like failure modes, ownership boundaries, and future extension paths. Fast validation becomes crucial in AI workflows as the AI writes the initial diff quickly, but human reviewers must provide feedback before the context fades. AI needs a separate review process, with an independent agent examining the diff against the ticket, relevant tests, and touched parts of the repository.
It should flag expanded permissions, duplicate business rules, missing tests, and behavior misaligned with stated requirements. This separation allows human reviewers to focus on higher-level questions like choosing the right behavior, assessing risk, and determining if the boundary change is easy to undo. Change the review bar accordingly, blocking when local UI or internal tool behavior is tested, control flow is unexplained, scope expands without justification, business rules or data migrations are unclear, intent, edge cases, and ownership are not explicit, rules are duplicated, recovery is uncertain, or authentication, payments, and shared infrastructure are involved.
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