Google I/O Afterparty: The Future of Human-AI Collaboration, From Veo to Mariner

Fresh off impressive releases at Google’s I/O event, three Google Labs leaders explain how they’re reimagining creative tools and productivity workflows. Thomas Iljic details how video generation is merging filmmaking with gaming through generative AI cameras and world-building interfaces in Whisk and Veo. Jaclyn Konzelmann demonstrates how Project Mariner evolved from a disruptive browser takeover to an intelligent background assistant that remembers context across multiple tasks. Simon Tokumine reveals NotebookLM’s expansion beyond viral audio overviews into a comprehensive platform for transforming information into personalized formats. The conversation explores the shift from prompting to showing and telling, the economics of AI-powered e-commerce, and why being “too early” has become Google Labs’ biggest challenge and advantage. Hosted by Sonya Huang, Sequoia Capital 00:00 Introduction 02:12 Google's AI models and public perception 04:18 Google's history in image and video generation 06:45 Where Whisk and Flow fit 10:30 How close are we to having the ideal tool for the craft? 13:05 Where do the movie and game worlds start to merge? 16:25 Introduction to Project Mariner 17:15 How Mariner works 22:34 Mariner user behaviors 27:07 Temporary tattoos and URL memory 27:53 Project Mariner's future 29:26 Agent capabilities and use cases 31:09 E-commerce and agent interaction 35:03 Notebook LM evolution 48:26 Predictions and future of AI Mentioned in this episode:  Whisk: Image and video generation app for consumers Flow: AI-powered filmmaking with new Veo 3 model Project Mariner: research prototype exploring the future of human-agent interaction, starting with browsers NotebookLM: tool for understanding and engaging with complex information including Audio Overviews and now a mobile app Shop with AI Mode: Shopping app with a virtual try-on tool based on your own photos Stitch: New prompt-based interface to design UI for mobile and web applications. ControlNet paper: Outlined an architecture for adding conditional language to direct the outputs of image generation with diffusion models