Validation Strategy

Vertical SaaS built for AI-native workflows will not look like horizontal SaaS with an AI tab. It will be built around the assumption that humans review outputs rather than perform tasks — which requires fundamentally different UX, pricing models, and support structures than the products that preceded it.

The founders most likely to build products that last are not the most excited about AI. They are the most disciplined about identifying which parts of the customer’s problem AI solves genuinely better than the previous approach, and which parts it merely makes cheaper to approximate. The distinction determines what to build and what not to.

Most AI-native startups validate the technology before they validate the problem — the reverse of the order that matters. A technically impressive solution to a problem nobody urgently has is still a product without a market. The AI works. The question is whether the problem it solves is urgent enough to pay for.

Most founders building on foundation models worry about model failure. The real risk runs the other direction: the model improves, and the capability that justified your product is now a built-in feature. This post explains how to identify which parts of your differentiation are exposed to this risk and how to build around it.

The failure mode of an AI-native startup has changed. A founder with modern AI development tools can move from idea

The founders extracting the most value from AI coding tools are not using them to write more code faster. They are using them to explore more problem spaces per week before committing to any. Speed is a secondary benefit. Optionality is the strategy.

Founders who treat go-to-market as a post-build phase arrive in a market that has already organized around alternatives. Late entry is not a timing problem — it is a positioning problem that compounds with every month that passes. This post explains the mechanism and how to run go-to-market in parallel with product development.

Integrating an LLM into a product does not make it an AI product — it makes it a product with an AI feature. Customers adopt features differently than platforms, churn from them differently, and price them differently. Founders who miss this distinction build the wrong business model around the right technology.