Experts from a16z, Box, and Microsoft debate the definition and future of AI agents. They explore the shift from monolithic AGI to specialized agent networks, the technical challenges of autonomous systems, and how this new platform will reshape enterprise software, workflows, and the very nature of work.
Professor Hannah Fry speaks with Jack Parker-Holder and Shlomi Fruchter about Genie 3, a general-purpose world model that generates diverse, interactive environments from prompts. The discussion covers its auto-regressive nature, which enables the creation of consistent, explorable worlds, its key differences from video models like Veo, and its foundational role in training AI agents and advancing toward AGI.
OpenAI's Chief Scientist Jakub Pachocki and researcher Szymon Sidor discuss the rapid progress towards AGI, focusing on the shift from traditional benchmarks to real-world capabilities like automating scientific discovery. They share insights into recent breakthroughs in mathematical and programmatic reasoning, highlighted by successes in competitions like the International Math Olympiad (IMO), and explore what's next for scaling and long-horizon problem-solving.
Julien Launay, CEO of Adaptive ML, discusses the evolution of Large Language Model (LLM) training, detailing the critical shift from pre-training to post-training with Reinforcement Learning (RL). He explains the nuances of RL feedback mechanisms (RLHF, RLEF, RLAIF), the role of synthetic data, and how his company provides the "RLOps" tooling to make these powerful techniques accessible to enterprises. The conversation also explores the future of AI, including scaling beyond data limitations and the path to a "spiky" AGI.
Greg Brockman discusses his journey from a math enthusiast to a programmer, his early days scaling Stripe, and the core philosophies that drive OpenAI. He covers the critical partnership between research and engineering, the future of coding with agentic systems, and the immense infrastructure and algorithmic challenges on the path to AGI.
Dwarkesh Patel and Noah Smith debate the definition of AGI, its economic implications, and timelines. They contrast an economic definition (automating white-collar work) with a cognitive one, exploring why current models lack economic value despite reasoning abilities due to a failure in 'continual learning'. The discussion covers the potential for explosive economic growth versus a collapse in consumer demand, the substitution vs. complementarity of human labor, and the geopolitical shift from population size to inference capacity as the basis of power.