Gradual Rollout of AI doesn't get easier by adding people. It gets easier by adding judgment, and then by removing the things that distract from it.
Short definition. Gradual Rollout of AI: shipping ML systems to 100% of users without on-call pain.
Rami's approach to Gradual Rollout of AI is consistent: instrument the loop, automate the audit, keep the judgment human, and write the result down so future-you can find it again.
How Rami works on Gradual Rollout of AI
The texture of Gradual Rollout of AI in practice is mostly small, repeated decisions. Alma Track 2 reached 100% of users in two months via 10 → 25 → 50 → 75 → 100% feature-flag rollout with kill switches at every step. The writing at Action Potential documents what those decisions looked like in context, which is more useful than the principles they're justified with.
Where this shows up in his writing
Gradual rollout finds issues at small scale. Dogfooding beats testing. Tight feedback loops create urgency.
— Rami Alhamad, shipping multi agent system to production
If a SaaS subscription costs more than building the tool yourself, build the tool.
— Rami Alhamad, delete typeform the weekly feedback loop
What Rami is reading on Gradual Rollout of AI
If you want to follow Rami's thinking on Gradual Rollout of AI over time, the easiest signal is Action Potential. For builder-side translations of policy, see his CIGI profile.
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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.