Retrain the Model
You don't argue with a stale model. You retrain it.
LinkedIn locked Hao out for posting three times in ten minutes — its spam filter is a model trained on the old world's data, where no human writes that fast. But that morning the three posts were real: ideas argued out loud with AI as a sparring partner, work that used to take hours done in minutes. Orgs run on models like that filter too — approval chains, review cycles, meeting cadence, planning timelines, all trained on how fast a human used to work. You don't argue with a stale model; you retrain it.
- Every rule in your org is a model trained on the old world's data — approval chains, review cycles, planning timelines all encode how fast a human used to work.
- AI changed the data. You don't argue with a stale model — you retrain it.
Transcript
LinkedIn locked me out for a bit a few months ago. My crime: posting three times in about ten minutes. The system looked at that and decided: “No human writes that fast. Must be a bot.”
Here is what actually happened that morning. I had three ideas in my head. I talked them through with AI, by voice — thinking out loud, arguing back, rewriting as I went. The ideas were mine. The writing was mine. AI was a sparring partner.
By the time I sat down, all three were ready to post. Work that used to take hours took minutes.
The way I see it, LinkedIn's spam filter is a model — and every model is trained on the old world's data. In the old world, three real posts — three real posts in ten minutes — means a bot. So the model looked at me and said: “Broken.” But the model is what's broken down.
And the models like this are everywhere. Your org runs on them, too. Approval chains, review cycles, meeting cadence, planning timelines — all trained on how fast a human used to work.
So here is my working theory: the rules were trained on old data. AI just changed the data. You don't argue with a stale model — you retrain it.
So which of your models is still running on old data?