The AI-first startup playbook

Iterative Lean Startup principles are so well understood today that an minimum viable product (MVP) is a prerequisite for institutional venture funding, but few startups and investors have extended these principles to their data and AI strategy. They assume that validating their assumptions about data and AI can be done at a future time with people and skills they will recruit later. But the best AI startups we've seen figured out as early as possible whether they were collecting the right data, whether there was a market for the AI models they planned to build, and whether the data was being collected appropriately. So we believe firmly that you must try to validate your data and machine learning strategy before your model reaches the minimal algorithmic performance (MAP) required by early customers. Without that validation -- the data equivalent of iterative software beta testing -- you may find that the model you spend so much time and money building is less valuable than you hoped.