The author discusses challenges with large code diffs caused by AI use in code reviews and seeks advice on strategies to handle this while maintaining human comprehension.
<p>My coworkers are all fully AI pilled. As a consequence, every PR that comes my way for review is on average ~6k lines of diff.</p>
<p>It's well established that even PRs an order of magnitude smaller than this are too big to be effectively reviewed. I'm still clinging to the idea that us humans should understand how the systems we are building work, so I'm hesitant to use AI for reviews aside from more general question answering. Much of what it produces in the way of explanations are difficult to follow and overly verbose anyway.</p>
<p>I'm sure I'm not the only person here who has been dealing with this problem, so I'm curious if anyone here has found any strategies (organizational or personal) which have helped weather this maelstrom.</p>
# Surviving Code Reviews in the era of AI
Source: [https://lobste.rs/s/7tpc5q/surviving_code_reviews_era_ai](https://lobste.rs/s/7tpc5q/surviving_code_reviews_era_ai)
My coworkers are all fully AI pilled\. As a consequence, every PR that comes my way for review is on average ~6k lines of diff\.
It's well established that even PRs an order of magnitude smaller than this are too big to be effectively reviewed\. I'm still clinging to the idea that us humans should understand how the systems we are building work, so I'm hesitant to use AI for reviews aside from more general question answering\. Much of what it produces in the way of explanations are difficult to follow and overly verbose anyway\.
I'm sure I'm not the only person here who has been dealing with this problem, so I'm curious if anyone here has found any strategies \(organizational or personal\) which have helped weather this maelstrom\.
The article argues that human code reviewers should use AI to handle large diffs, and instead contribute their out-of-distribution knowledge and high-level context.
The author explains why they often reject AI-generated code even when it works, citing reasons like inability to explain the approach, overly large diffs, premature abstractions, and reduced system reasoning, and argues for mandatory human review.
The article discusses the challenges and best practices for writing high-quality code with AI assistance, emphasizing the need for rigorous code review and avoiding blind trust in AI-generated output.
An analysis of how AI coding agents have shifted the bottleneck from writing code to reviewing it, with data showing a 861% increase in code churn and a rise in defect rates, making code review the most leveraged skill in software engineering.
The article proposes using stacked branches (small, sequential pull requests) to make reviewing AI-generated code more manageable and effective, addressing the common problem of large, hard-to-review diffs.