In partnership with
Happy Monday, AI builders.
Something interesting is happening in the AI market.
Two AI products can look almost identical to a customer. They answer the same questions, automate similar workflows, and even use the same foundation models. And yet one becomes a highly profitable business while the other struggles to make money every time a customer clicks "Generate."
The difference is rarely obvious from the product itself.
It sits underneath the interface, inside the economics of the business.
Traditional SaaS became more profitable as it scaled because serving one more customer cost very little. AI changes that equation. Every prompt consumes compute. Every reasoning task carries an inference cost. Every retrieval step, context window, integration, and human review adds another layer to the cost of delivering value.
Which is why building a successful AI product is no longer just a product challenge.
It is an economics challenge.
The companies that build lasting AI businesses will not necessarily have the smartest models or the most impressive demos. They will understand how to balance customer value with infrastructure costs, design workflows that improve margins, retain customers for longer, and grow revenue without allowing costs to grow at the same pace.
That shift is becoming one of the most important competitive advantages in AI.
In today's edition, we'll explore:
Why AI economics are fundamentally different from traditional SaaS
Where AI products actually make and lose money
The Hidden Economics Framework
Practical ways to improve AI unit economics
A worksheet to identify hidden costs and opportunities
— Naseema Perveen
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