The Calibration Room
What it was actually like to sit in twice-a-year stack-ranking calibration meetings with a forced X% Below Strong bucket, how a leave gap in someone's record got handled (or didn't), and why the Meta AI-ranking lawsuit is a predictable outcome.
I spent years as a manager sitting in calibration: every manager-and-up in the org in a room for four-plus hours, stack-ranking twenty-plus people per level into a forced distribution where 10% had to land in "Below Strong" and get PIP'd, no matter how the team actually performed. The part that stuck with me wasn't the curve. It was watching what happened when someone's numbers looked thin because they'd been out on medical or parental leave, and the spreadsheet had a column for output but not for "was on leave, exclude this window."
Key takeaways
- Forced distribution punishes the shape of the curve, not the work: twenty strong seniors still had to send two to a PIP twice a year, while a weaker performer elsewhere stayed safe
- The process gave nobody a clean way to handle a leave gap: you could vouch informally and spend political capital, but that was on you, not the process
- Surviving calibration with a leave gap came down to luck: who was in the room, whether your manager could tell a good story, whether a senior leader pushed back
- The Meta lawsuit is the human process with its one working part removed: the human version at least had a chance of someone noticing and speaking up
Who this is for
Anyone who's been on either side of a stack-ranking process, and anyone watching algorithmic performance management roll out and wondering what it replaces. Engineering-culture and management essay. Pairs with the Chaotic Commits episode on the Meta lawsuit.
The full piece is on Medium, and also on Substack.