Vertical Workflow Understanding Creates Defensible AI Products

When building AI products, success comes from understanding specific workflows and creating data flywheels that generate proprietary insights, rather than relying solely on technological breakthroughs.

The AI Product Differentiation Framework

Focus on data flywheels, not just model capabilities

• Proprietary data is a critical moat for AI companies
  • "Having the right data and the right data flywheels is so important"
  • "The models will get really good at whatever data you show it"
  • Start with unique data you have access to, then build mechanisms to generate more
• Create systems that continuously improve your data advantage
  • Example: Warp (coding assistant) collects data on which code suggestions users accept/reject
  • "If you are solving something actually valuable for businesses or people, and there's a lot of attention paid to it, a lot of work being done through it, you're going to have that edge"

Understand vertical-specific workflows deeply

• Product experience matters more as AI capabilities become commoditized
  • "More and more it's going to be less about the raw intelligence, it's going to be about the fine-tuning of what the model can do that really resonates with people"
  • "Coming forward, it's like really who has the best workflow and who has the best product"
• Solve for specific user needs rather than general capabilities
  • Example: Uber Reserve solved the specific anxiety of early morning airport rides
  • "Focus on what actually matters, which is this peace of mind and how many people really need it in that moment"

Craft delightful experiences that overcome distribution advantages

• Product craft can overcome incumbents' distribution advantages
  • "There is a level of product craft that will make it so that it's just worth it to switch or try something else"
  • "They've really figured out a way to make it so delightful that it's like 'yeah, I will install this piece of software'"
• Focus on "sanding down the edges" of user experience
  • Small details in product experience can create strong word-of-mouth
  • "When someone is going to come out and produce something that's so well crafted, I think people are going to pay attention"

Balance technical depth with product taste

• The most valuable AI product people combine technical understanding with product intuition
  • "The only person that I've worked with that has as much technical depth as she does have product taste... those two things are very rare to find together"
  • At AI companies, product teams need to work closely with research/model teams
  • "That tight collaboration" between product and AI research teams drives advances

Empathize deeply with user problems

• Follow the design thinking framework: empathize, define, ideate, prototype, test
  • "The first thing is empathizing... it's not just about theoretically understanding what the problems are, it's like really empathizing"
  • "The great products are when you really feel the pain and you really empathize with what people are experiencing"
• Dogfood your own product in real-world conditions
  • Example: Driving for Uber to understand the driver experience