AI startups initially rely on speed as their primary moat, using rapid iteration to capture market share. As they scale, deeper advantages emerge—process power, proprietary data, and fine-tuned systems create barriers even for well-resourced players. Cornered resources like exclusive customer data or government contracts enhance defensibility, especially in regulated sectors. High switching costs arise when AI becomes embedded in enterprise workflows, while counter-positioning allows new entrants to disrupt incumbents. Network effects are reimagined through user-driven model improvement, and access to private data enables superior performance. Companies like Exa demonstrate how infrastructure built for AI agents can yield scalable advantages. Ultimately, startups need not build foundational models to win; context engineering, specialization, and timing allow them to capture value in narrow domains without displacing existing players, proving that defensibility in AI comes not just from technology, but from strategic execution and deep customer integration.