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The Playbook for Turning Small AI Experiments Into Scalable Products

How tiny AI wins grow into enduring companies

In partnership with

👋Hey friends,

A few weeks ago, a founder gave me a demo of his new AI startup.
Beautiful dashboard. Clean interface. Dozens of integrations.

When he finished, I asked one question:

“What’s the one workflow your users can’t live without?”

He paused. Then smiled a little too long.
That silence told me everything.

It reminded me how easy it is to believe that success in AI comes from building more — more features, more models, more hype — when in reality, it comes from making one small thing work so reliably that people build their habits around it.

Every lasting AI product — from Jasper to Rewind to Notion — started with a single workflow that worked just a little better than expected. Then they doubled down, learned fast, and scaled slowly.

That’s what separates products that grow from demos that fade.

So today’s issue is all about that: the discipline of starting small, learning fast, and building deep.

In today’s edition, we’ll explore:

  • Why small, focused experiments beat big ambitions.
    How narrowing your wedge accelerates learning and adoption.

  • How to turn one clever AI idea into a repeatable workflow.
    The process of transforming a working prompt into a system that runs (and improves) on its own.

  • Why scaling learning matters more than scaling code.
    The simple framework top AI teams use to compound insight over time.

  • Common Pitfalls When Scaling AI Products.
    The four mistakes that quietly kill momentum — and how to avoid them before they derail your loop.

  • The Macro Shift — From Tools to Ecosystems.
    Why the future belongs to adaptive systems that learn faster than competitors can copy them.

Let’s dive in.

— Naseema Perveen

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