Great breakdown — the 4.5-day average cycle time from LinearB matches what we hear from teams constantly. The point about "review queue invisibility" really resonates. Most PRs don't die from bad code; they die from being invisible.
One pattern I'd add: the problem gets significantly worse as AI-generated PRs increase. When Copilot or Cursor is cranking out 3x the diffs, the review queue doesn't just fill up — it fills up with noise that's hard to distinguish from signal. Reviewers get fatigued and start rubber-stamping, which defeats the whole purpose.
We've been working on this exact problem at PRPulse — surfacing which PRs genuinely need human attention vs. which are safe to fast-track. The "rotating review champion" pattern you mention works a lot better when that person isn't wading through a mountain of AI-generated boilerplate first.
The "median PR cycle time" metric is 🎯. Simple, actionable, and actually moves the needle when teams track it weekly.
Great breakdown — the 4.5-day average cycle time from LinearB matches what we hear from teams constantly. The point about "review queue invisibility" really resonates. Most PRs don't die from bad code; they die from being invisible.
One pattern I'd add: the problem gets significantly worse as AI-generated PRs increase. When Copilot or Cursor is cranking out 3x the diffs, the review queue doesn't just fill up — it fills up with noise that's hard to distinguish from signal. Reviewers get fatigued and start rubber-stamping, which defeats the whole purpose.
We've been working on this exact problem at PRPulse — surfacing which PRs genuinely need human attention vs. which are safe to fast-track. The "rotating review champion" pattern you mention works a lot better when that person isn't wading through a mountain of AI-generated boilerplate first.
The "median PR cycle time" metric is 🎯. Simple, actionable, and actually moves the needle when teams track it weekly.