As AI coding tools become ubiquitous, the traditional line-by-line code review is becoming less effective. The author, who handles dozens of AI-generated changes daily, argues that the human reviewer's role should shift from scrutinizing every line to assessing the blast radius of each change. The key insight is that machines are better at verifying correctness through actual execution, while humans excel at understanding the broader impact of a change across projects. The author describes a workflow where AI code is automatically tested and validated, freeing reviewers to focus on questions like: What could this break? Which systems depend on this? How far does the ripple effect reach? This approach not only saves time but also catches issues that line-by-line review might miss. For teams adopting AI coding tools, this mindset shift is essential to maintain quality without becoming a bottleneck.
A pragmatic approach to reviewing AI-generated code: let machines verify correctness, humans assess impact scope.