Competing in a Generative World with Justin Norman

Justin Norman, author of Product Management for AI and co-founder of Vera, a startup focused on security for generative AI, talks with Ben Lorica about how product management has changed since Generative AI came on the scene. He discusses the issues retrieval-augmented generation (RAG) raises for product management; how reliability has become part of a product’s value; how companies that have lagged in their adoption of AI can use generative AI as a way to catch up; and the ability of open source AI in helping smaller companies compete with more established companies.Points of Interest0:00: You wrote Product Management for AI back in 2020 and 2021. How have things changed for product managers since then?3:04: Do companies that lead with operations and infrastructure for traditional AI maintain an advantage with Generative AI? Or does Generative AI allow companies that are just starting to catch up?5:09: Can new companies use open source to compete with established companies? Can open source help capture value as well as larger proprietary models?6:08: What do product managers struggle with when implementing RAG? What's the relationship between fine-tuning and RAG?10:58: RAG gives you value out of the box, but the key to success is how the data is organized.13:57: Are VCs underinvesting in certain parts of the pipeline? There is lots of investment in AI, but not as much investment in startups working on necessary technologies like ETL and data engineering.16:31: Why is reliability important for generative AI? How is generative AI different from other applications that we’re familiar with, and what implications does this have for product management?21:03: Are enterprises realizing that efficiency is important for succeeding with generative AI?23:44: We’re familiar with dashboards for monitoring and managing traditional software products. What would you imagine a dashboard for generative AI models to be? What do you need to be monitoring?28:49: Very few developers working in machine learning have also done frontend development or worked on user experience (UX). However, understanding user interaction can help you to improve your model.30:44: You're working with the father of digital forensics, Hany Farid. Should we be worried about DeepFakes?