AI Made Your Team Faster. Why Are Reviews Slower?

Your teammate builds a feature with AI in 20 minutes. Then you spend the afternoon working out what it does. Are you becoming the bottleneck? I think that question hides two different problems: the amount of code coming at reviewers, and the understanding that hasn't happened before the code reaches them. They need different fixes. In this video, I use one example (a spreadsheet import that quietly brings back an old phone number) to show what understanding a code change actually involves, and end with two things to try on your next few pull requests. Chapters 0:00 Am I the bottleneck? 0:50 Two sources of review burden 2:27 What understanding a code change involves 4:04 When the author understands the code 6:18 Making the volume manageable 7:52 Using AI to build understanding 9:17 What to try on your next PR Sources Asdaque et al., "Novice Developers Produce Larger Review Overhead for Project Maintainers while Vibe Coding" (arXiv, 2026): https://arxiv.org/abs/2602.23905 LLVM AI Tool Use Policy: https://llvm.org/docs/AIToolPolicy.html Google Engineering Practices, Small CLs: https://google.github.io/eng-practice... Anthropic, "How AI assistance impacts the formation of coding skills" (2026): https://www.anthropic.com/research/AI... If you're an author: before your next pull request, ask yourself whether you could help someone reason through what the code will do. If you're a reviewer: what's actually taking your time? Tell me in the comments. #CodeReview #AICoding #SoftwareEngineering