AI can build an MVP (Minimum Viable Product) in hours. That's not the hard part. The hard part is figuring out what the MVP should actually be. Before I ask AI to build anything, I want to understand: → Who is using it? → What are they trying to accomplish? → What information and entities exist? → What states can they be in? → What happens when something goes wrong? → Who can see, edit or approve what? → What actually belongs in the MVP? Only then do I start thinking about the interface. I've found that the clearer the product architecture, the more useful AI becomes. AI makes execution cheaper. That makes product thinking more important, not less. It's one of the biggest lessons I've learned while building products with AI. Problem → Users → Entities → Workflows → Architecture → UX → Build AI can accelerate almost every step. But it still needs someone to understand what should be built in the first place.