Horizontal AI products get the press. Vertical AI products take the market. This is the operator's case for going deep into one industry instead of broad across many.
I run five production AI products across five different verticals, all built on the same technical spine. Wedding venues, hospitality CRM, personal life OS, cold-case investigation, and a wearable-driven AI companion. The ones that work and the ones that don't differ by exactly one thing: how deeply the prompt, the schema, and the failure modes encode the actual workflow.
The talk covers what "vertical" actually means in code. The four points where a horizontal AI product breaks contact with a real industry. Why most "AI for X" startups are horizontal products in costume. The specific moats that compound when you're vertical and don't exist when you're not. And the moment in each of my five products where I had to choose between staying generic and getting useful, and what happened when I picked one over the other.
For founders deciding what to build next, AI engineers picking which side of the ecosystem to bet on, and anyone who has shipped a feature for "everyone" and watched it land for no one.