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This article argues that AI apps need a “minimum viable moat”—the smallest edge that survives a 3× model upgrade. It outlines five defensibility tactics: network effects, embedded workflows, proprietary/licensed data, user-driven data loops, and brand trust.
- If your AI app's edge is just "we're built on the current best model," that edge disappears in 3-6 months when the next model ships—so you need a "minimum viable moat" beyond raw model capability
- Five real defenses: network effects, embedded workflows, proprietary/licensed data, user-driven data loops (personalization that compounds), and brand trust
- ~80% of the world's data is private, making licensed or proprietary datasets a genuine barrier to replicate