Vertical AI Startups: Solving Industry-specific Problems by Combining AI and Subject Matter Expertise — Bradford Cross

a four-factor definition of what I consider to be a vertical AI startup:...
full-stack fully-integrated solution to the end customer problem from the interface ... down the stack to the functionality, models, and data;...
deep subject matter expertise... trust, respect, and relationships within the industry.... combine the subject matter and technical expertise to model the domain richly and drive innovation;...
Defensible AI businesses are built on proprietary data... in two phases... bootstrap stage, you are building a unique set of training data by aggregating publicly available data and enriching it in some challenging way... Once you have bootstrapped, you are building a ‘data flywheel’ into your products... capturing totally unique data over time from how your product is used... designed precisely to serve the needs of your models, which are designed to serve the needs ... of the customer;...
1. Don’t get ripped off: Solve the business problem directly for the end customer and put yourself in a position of leverage... Avoid being disintermediated from the end customer ;...
2. Tasks get commoditized... Look at low level tasks as building blocks that you compose into higher level solutions;...
4. Enterprise exits come in cohorts:... be part of a wave of investment focused on a particular cohort of startups going after a niche... analyze verticals where both the customer segments and the venture capital community are keen to see solutions......
look for big addressable markets (TAM) with healthy margins.;...
Are there already a lot of other smart people working on this who are probably already the winning cohort? .;...
timing was the dominant factor driving success... be really honest with yourself about whether the consumers/business you are targeting are really ready for what you have to offer them.