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Hey friends, Happy Monday!
Something unusual is happening in AI product building right now.
Teams are moving faster than ever, shipping prototypes in days, layering agents into workflows, and launching tools that look surprisingly polished from the outside. On paper, this should lead to better products, because iteration speed has always been a core advantage in startups. Faster cycles usually mean faster learning.
And yet, a large share of these products quietly fail after the demo.
They get attention. They get early users. They even get praise. But they rarely become part of real work, which is the only thing that matters in the long run.
The reason is subtle but important. AI has changed what is easy and what is hard.
Building something functional is no longer the bottleneck. Judgment is.
When building becomes cheap, the cost of building the wrong thing increases. Teams can now invest weeks or months into products that look impressive but are built on weak workflows, which is why many AI products feel like they are solving interesting problems instead of important ones.
This is the AI product trap.
The next generation of strong AI companies will not come from teams that simply build faster. They will come from teams that validate more precisely, design around real workflows, and understand where intelligence actually fits inside a system of work.
What we’ll explore
Why AI has changed the economics of product validation
Why many AI products fail after the demo
The shift from product-first to workflow-first thinking
A practical framework to evaluate AI product opportunities
The AI system stack every serious product needs
A 7-day workflow validation playbook
How revenue and GTM are changing in AI products
Common failure patterns and how to avoid them
A worksheet you can apply immediately
— Naseema Perveen
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IN PARTNERSHIP WITH VIKTOR
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