Your AI Is Too Big, Too Expensive, and Probably Wrong | EP. 49

What if the most powerful AI in your organization isn’t the biggest model you can buy, but the one trained on data only you own? In this episode of Hidden Layers, Ron Green is joined by Dr. ZZ Si and Michael Wharton to break down why domain-specific AI models consistently outperform general-purpose systems in real enterprise environments. They explore how narrowly scoped models deliver higher accuracy, lower costs, better reliability, and stronger governance, especially when built on proprietary data. Through real-world examples spanning finance, industrial systems, healthcare, and document understanding, the conversation tackles when to build custom models, when to rely on APIs, and how to identify AI initiatives that actually make it into production. The takeaway is clear: focus beats scale, and specificity is often the fastest path to durable competitive advantage. Chapters 00:00:00 What Is Domain-Specific AI 00:01:15 General Models vs. Focused Systems 00:02:48 Performance, Cost, and Model Size 00:04:13 Proprietary Data as Advantage 00:07:58 Why AI Fails in Production 00:08:42 Real-World Domain-Specific Examples 00:10:54 How to Decide What to Build 00:14:53 Scale, Accuracy, and Uncertainty 00:18:49 The Spectrum of Domain-Specific AI 00:27:01 What We’d Build Differently Today