AI code volume is pushing code review earlier, ahead of the pull request
AI now produces more code than humans can realistically review, with Meta's lines of code per human-landed diff reportedly up 106% in a year. One response argues review should move earlier, into pairing and design sessions, leaving the pull request to catch formatting and known security issues.
Why it matters
Pull requests have long been treated as the place where teams catch bugs, transfer knowledge, and align on architecture. As AI assistants generate a growing share of code, the volume arriving at review outpaces what humans can meaningfully read, so the traditional PR gate risks becoming a rubber stamp rather than a genuine check.
What changed
Citing figures raised in a panel discussion, the piece notes Meta's lines of code per human-landed diff reportedly rose 106% in a year, while DX's own data shows median pull request size up 64%. Rather than automating review away, the argument is to move the judgement earlier: explore alternative solutions before implementation, pair instead of reviewing finished work after the fact, run collective design sessions on a whiteboard before code is written, and encode architectural constraints as fitness functions that run automatically. Formatting, linting, known security issues, and anything deterministically testable should be automated rather than left to a human reviewer.
In an interview
A candidate could argue that as AI increases code volume, review value shifts from catching issues in a finished diff to earlier practices such as pairing, design sessions, and automated fitness functions, with the pull request becoming a smaller part of how a team maintains quality and shares knowledge.
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