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Hey friends, happy Monday.
Over the past year, I’ve noticed a pattern in conversations with founders building with AI.
It usually starts the same way.
There’s excitement. A breakthrough moment. A demo that feels like magic.
The model performs.
The outputs are sharp.
The prototype works beautifully in a controlled setting.
And for a while, momentum builds.
But then something quieter happens.
Six months later, revenue hasn’t meaningfully moved. Adoption is uneven. The team is still “experimenting.” The excitement fades into ambiguity.
Not because AI failed.
But because the workflow never carried enough economic weight to justify automation in the first place.
This is the uncomfortable truth most people don’t talk about.
Most AI ideas are technically feasible.
Very few are economically meaningful.
We’ve entered a world where building is easier than ever. Models are powerful. APIs are accessible. Infrastructure is abundant.
But abundance creates a new bottleneck.
Judgment.
Specifically, the judgment to decide which workflows deserve automation and which ones are distractions dressed up as innovation.
In my experience, the difference usually comes down to one question:
Is this workflow worth at least $10K?
Not in theory.
Not in future upside.
Not in slide-deck potential.
In actual economic friction.
If you improved this workflow by 30 percent, would revenue move? Would costs meaningfully decline? Would risk decrease in a measurable way?
If the answer is vague, that’s a signal.
If the answer is clear, you might have something.
This week’s edition is about building that instinct.
We’ll unpack:
The Economic Friction Test and how to apply it honestly
The 4 Filters of a $10K Workflow
A practical scoring model you can run in under an hour
Real examples of workflows that cross the threshold
A fast validation playbook to avoid wasting six months
And why most AI ideas fail quietly, even when the tech works
The goal is simple.
To stop building impressive AI features.
And start building economically meaningful ones.
Let’s start with the core principle.
— Naseema Perveen
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