Sep 25, 2026

Runway’s Bet Beyond Video: World Models, Robotics, and the Neural OS — Anastasis Germanidis

Anastasis Germanidis, co-founder of Runway, discusses the company's journey from early generative creative tools to frontier video models, emphasizing their pivot towards general-purpose world simulation. He details the evolution of Gen-1, Gen-2, and Gen-3 (scaling 10x after Sora's release), highlighting the importance of controllability and the inevitability of real-time generation. The conversation delves into cutting-edge applications like Interface World Models (a fully neural operating system) and World Models for Robotics, where third-person video data and robust simulation are key to training intelligent agents.

Sep 04, 2026

Why World Models Could Change Robotics, 3D, and Creativity

World Labs co-founders discuss Atlas, their frontier world model centered on "new view prediction." This paradigm unifies 3D generation and reconstruction, enabling precise spatial understanding from sparse inputs. The conversation covers its multimodal architecture, the shift from dense to sparse reconstruction, and its profound implications for creative industries, architecture, and particularly robotics, where it addresses critical data scarcity challenges for simulation and policy learning.

Aug 21, 2026

Simulating Humanity: from Generative Agents to 8 Billion Digital Twins — Joon Sung Park, Simile AI

Joon Sung Park, co-founder and CEO of Simile, traces his path from painting to the Smallville generative agents paper and explains how Simile builds behavioral foundation models and digital twins. The conversation covers why frontier models fail to reproduce irrational human behavior, the role of interviews, observational data, and randomized controlled trials, the 85% accuracy result, scaling laws for simulation, Schelling's agent-based models, the economics of simulating large populations, and the long-term vision of simulating 8 billion people to tackle problems like climate change and democratic instability.

Aug 20, 2026

Going In Deep On Data | YC Paper Club

Three experts discuss the evolving landscape of data in AI, covering its critical role in model performance, challenges in sourcing and evaluating expert data, the development of advanced data generation techniques for diffusion models, and the complexities of multilingual pre-training.

Aug 20, 2026

Why Most AI Agents Fail Horribly

Maarten Grootendorst discusses the foundational understanding developers need for modern AI tools, emphasizing core LLM concepts like tokens, embeddings, and attention. He provides a pragmatic view on AI agents, distinguishing hype from practical applications like coding assistants, and explores the role of memory, guardrails, and the growing importance of open-weight models for control and efficiency in AI infrastructure.

Aug 10, 2026

Why Deep Networks Don’t Need to Memorize Everything — Matthieu Wyart

Matthieu Wyart, a statistical physicist, argues that deep networks discover abstractions by recovering hidden data hierarchies, allowing them to escape the curse of dimensionality. He explains how this mechanism, combined with predicting latent representations instead of raw tokens, can significantly improve sample efficiency. The discussion also covers the physics of rough loss landscapes, machine creativity, diffusion models, and a theoretical framework for neural scaling laws.