Why AI Moats Still Matter and How They’ve Evolved

The article examines why competitive advantages—"AI moats"—remain crucial in artificial intelligence despite rapid technological shifts. It argues that moats built from proprietary data, specialized models, and integrated ecosystems continue to deliver durable value by improving performance, reducing costs, and creating high switching costs for customers. The piece outlines how these moats have changed: greater importance of fine‑tuned models, emphasis on domain‑specific datasets, reliance on compute and infrastructure partnerships, and growing regulatory and privacy constraints that raise the value of compliant, trusted platforms. It highlights key examples of moat strategies (proprietary user data, specialized applications, platform integrations, and partnerships with cloud providers) and explains tradeoffs — including rising capital and operational requirements, risks from open models, and potential erosion from improved open‑source capabilities. The article concludes that investors and companies should focus on sustainable signals of moat strength—unique datasets, deep product integrations, clear monetization, and regulatory compliance—while recognizing that moats are evolving and require active maintenance.