Aaron interviews Ivan Lee, founder and CEO of Datasaur, about what it takes to build private, secure AI for regulated enterprises. Lee traces the shift away from third-party model reliance to compliance pressure, IP protection, and cost, and frames the core decision as buy versus rent: enterprises historically rented frontier models by default, but Western and Chinese open-weight models have matured enough that companies like AT&T now run a growing share of workloads on owned infrastructure. He breaks the stack into three layers (infrastructure, model, and harness), argues models have become commoditized enough to be swapped like building materials, and calls the harness, the connective layer to internal data and tools, the least solved but highest-leverage piece. Lee describes agentic AI's shift from opt-in tools to opt-out, event-triggered workflows as the real 2025-2026 adoption unlock, while flagging FinOps trade-offs (agentic tasks can run 3 million tokens versus 2,000 for a chatbot query) and the custom benchmarking his team uses to earn CISO trust before production rollout.

SHOW: 1065

SHOW TRANSCRIPT: The Enterprise AI Show #1065

SHOW VIDEO: https://youtu.be/PkLSI2pRZs8

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GUEST BIO

Ivan Lee is the founder and CEO of Datasaur, which builds private, model-agnostic AI agents that deploy entirely inside a regulated enterprise's own infrastructure. He previously built AI products at Yahoo and Apple after Yahoo acquired his first company, Loki Studios, and holds a computer science degree from Stanford. Datasaur's clients include a leading GSIB, federal agencies, and Am Law 100 firms, and its backers include Initialized Capital and OpenAI president Greg Brockman.

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How Do Regulated Enterprises Deploy Private, Secure AI?

The Enterprise AI Show

How Do Regulated Enterprises Deploy Private, Secure AI?