Instabase founder and CEO Anant Bhardwaj joins a16z Infra partner Guido Appenzeller to discuss the revolutionary impact of LLMs on analyzing unstructured data and documents (like letting banks verify identity and approve loans via WhatsApp) and shares his vision for how AI agents could take things even further (by automating actions based on those documents). In more detail, they discuss: - Why legacy robotic process automation (RPA) struggles with unstructured inputs. - How Instabase developed layout-aware models to extract insights from PDFs and complex documents. - Why predictability, not perfection, is the key metric for generative AI in the enterprise. - The growing role of AI agents at compile time (not runtime). - A vision for decentralized, federated AI systems that scale automation across complex workflows. Follow everyone on X: - Anant Bhardwaj - https://x.com/anantpb - Guido Appenzeller - https://x.com/appenz Check out everything a16z is doing with artificial intelligence, including articles, projects, and more podcasts, here: https://a16z.com/ai/ Timestamps: 00:00 Introduction 00:43 What is Unstructured Data and How Can The UD Problem Be Solved? 07:26 Use Cases: How Enterprises Can Use Unstructured Data 13:07 The Shift In How Enterprise and Consumers View AI Capabilities 15:03 Documents: The Role of Humans vs AI in Unstructured Data 16:06 The Most Interesting Use Case, Lending Over WhatsApp 19:13 Main Barriers For Enterprise AI Adoption 21:25 AI Agents + Enterprise Workflow 25:23 Future Of AI As Decentralized, Federated, Execution 28:05 Technical Advances Of AI Impacted User Experience 32:35 Three Reasons Why Enterprise Adoption Of AI Is Necessary