"When Miqdad Jaffer, product lead at OpenAI, challenged the illusion of 'just add AI' earlier this summer, he drew a line between teams crushed by hype and those who turn artificial intelligence into a lasting advantage. "The most durable and defensible moat in AI is proprietary data," Jaffer wrote. "If your product generates unique structured data every time it is used, you pull further ahead in ways competitors cannot copy or buy.""
"Every major computing wave has rewritten the data layer. Service-oriented architecture standardized system interfaces. Business intelligence and data warehousing structured analytics. Big data platforms handled scale, and streaming moved data in real time. Each shift changed how developers modeled and connected information. AI is now pushing enterprises to rewrite the data layer again, this time around meaning, trust, and interoperability. So where to focus?"
Proprietary data provides enterprises with a durable competitive moat when captured and used by AI systems. Hyperscalers have scale and engineering resources; startups have clean slates; mature enterprises must overcome legacy architecture and technical debt to realize value. Historical shifts in computing have repeatedly rewritten the data layer, and AI demands a redesign focused on meaning, trust, and interoperability. The main obstacle is not data scarcity but disconnected systems with divergent schemas and frozen relational models. Enterprises must prioritize connecting existing systems, modernizing data pipelines, and encoding semantics and governance to enable AI-powered applications and agents.
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