Vertical AI Startups Search for Moats Beyond the Model

Every vertical AI pitch now faces the same first question: what survives when the model underneath is available to everyone? The credible answers have converged on three assets, proprietary data generated by the product's own use, workflow depth that makes switching operationally painful, and regulatory position in industries where compliance is the product.

The data answer is the most scrutinized. Investors distinguish between data a company merely stores and data whose feedback loop measurably improves the product, and diligence now includes tracing that loop rather than accepting its assertion.

The pattern rewards founders from the industries they serve, whose distribution and credibility compress the sales cycles that generic teams grind through. The consensus forming across the market is old wine relabeled: in applications, the model is an ingredient, and the moat is everything wrapped around it.