Every generation of apps hinges on a core advantage. Mobile grew fast because anyone could reach users through app stores. SaaS locked customers in with high switching costs. Now AI-driven products need a “Minimum Viable Moat”—the simplest edge that still holds up when model providers triple their capabilities. If you launch today on GPT-4 and Meta or OpenAI drops a next-gen model six months later, your lead vanishes unless you’ve built real defenses. You get defenses through things machines alone can’t replicate quickly. Network effects stay strong: each new user makes the service more valuable for everyone. Deeply embedded workflows—especially in specific industries—tie customers down. Owning exclusive data matters too; around 80% of data worldwide is private, so licensed or proprietary datasets become a barrier. Then there are user-driven data loops, like personalization engines that improve with each interaction. Finally, brand trust earned over countless positive experiences makes people stick around even if competitors catch up technically. If your AI app rests only on current model gaps, you’ll see that advantage evaporate in three to six months. Building a moat means weaving together those elements—user networks, embedded processes, unique data, feedback loops and a solid reputation—so that when Opus 5 or GPT-5 lands, your product still holds value beyond raw AI chops.