This podcast episode explores two X moonshot projects aimed at revolutionizing computer chips. Project Positron focused on creating specialized chips for real-time AI inference, acting as 'brains for robots'. Project Bodger took a meta-approach, using AI and inverse design to automate the chip design process itself, aiming to overcome the limitations of Moore's Law.
Demis Hassabis, CEO of Google DeepMind, outlines the remaining challenges on the path to AGI, including memory, continual learning, and true reasoning. He discusses how learnings from AlphaGo are shaping agent development, the strategic importance of powerful small models like Gemma, and his vision for AI as the ultimate tool for scientific discovery, offering a framework for identifying breakthrough opportunities and advice for founders building in the age of AI.
A developer-focused, demo-heavy session on rapid AI prototyping using the Google DeepMind stack. It covers how to leverage the full capabilities of AI Studio, from video analysis and code execution with Gemini 3.1 Flash, to building full-stack applications with databases, and exploring the frontiers of generative media with Genie 3, Veo 3.1 Lite, and Lyria 3.
Maxime Labonne from Liquid AI shares a playbook for post-training frontier small models (under 1GB) for on-device deployment. The talk breaks down the LFM2.5 recipe, which includes on-policy preference alignment and agentic reinforcement learning, and addresses unique challenges at the 1B scale, such as capability interference and 'doom loops', offering concrete solutions to build efficient models for tasks like data extraction and tool use.
Linda Haviv discusses the evolving AI landscape, arguing that systems thinking is becoming more critical than coding. She highlights how non-linear career paths and domain-specific expertise provide a competitive edge, and explores how the democratization of technology is fueling a new wave of entrepreneurship and content creation for tech professionals.
AI and security experts debate the complex relationship between open source and AI, weighing the foundational role of open source in innovation against the significant security challenges of both proprietary and open models, and exploring the difference between 'secure' and 'securable' systems.