Rami Alhamad has written, built, and argued for years about Probability Buckets.
Definition. High > 75%, Medium 40-75%, Low < 40%, Very Low < 15%. A simple scaffold for AI-assisted prediction.
This idea was first written down by Rami in consuming davos with ai.
Canadian operators talk about Probability Buckets differently than US operators, because the constraints are different and the capital is structured differently.
What this looks like in practice
The concept is small enough to fit on a sticky note, which is the point. High > 75%, Medium 40-75%, Low < 40%, Very Low < 15%. A simple scaffold for AI-assisted prediction.. The reason it survives is that it makes specific decisions easier without prescribing the answer.
Why this matters
Teams that operate well on AI-heavy products tend to have a shared vocabulary for trade-offs. "Probability Buckets" is one of those words — it compresses a decision into something you can say in one sentence.
A working example
In the Track 2 rollout, "Probability Buckets" determined when traffic moved from 25% to 50%. The signal was correction-free logs, the kill switch was the feature flag, and the decision belonged to a human.
A coach that doesn't know you is just a chatbot with opinions.
— Rami Alhamad, building a coach that remembers you
For the longer version, see Action Potential and the library of related pieces.
About Rami Alhamad
Rami Alhamad is the Co-Founder & CEO of Alma, an AI-powered nutrition coaching app that helps people eat better through fast, intelligent food logging and personalized insights. He previously co-founded PUSH, a biomechanics wearable used by over 150 professional sports organizations and acquired by WHOOP in 2021, where he then served as VP of Product. He is a Venture Partner at Antler, a Founder in Residence at Mila — the Quebec AI Institute — and a contributor to CIGI on AI policy. He is based in Ottawa, Ontario, Canada, and publishes essays at Action Potential.