One signal

Aviator says its engineering team handled four times as many AI-generated pull requests by changing what it reviews: intent, acceptance criteria and evidence, rather than every line of code.

The New Stack reports that the team mined past reviews to turn repeated comments into automated checks, and used AI agents to generate proof for specific scenarios. Aviator says the approach cut code reverts by 40% and reduced median time to merge. It’s a useful counterpoint to the idea that more AI-written code simply demands more human eyeballs. The results are Aviator’s account, but the practical lesson is clear: review the reasons and risks behind a change, not just the size of its diff.

Discuss: If AI produces far more code, should teams spend less time reading diffs and more time testing whether the code meets its intent?

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