Oct 13, 2026

David Silver and Lasse Espeholt of Ineffable Intelligence: A Superlearner for the Age of Experience

David Silver, co-founder of Ineffable Intelligence, outlines his bold vision for building a "superlearner"—an AI that discovers science, mathematics, and language autonomously, free from human data or explicit guidance. Drawing parallels to AlphaGo Zero's success, Silver argues that human-language reasoning is a ceiling, not a foundation, for advanced AI. He details their approach using complex simulated "crucibles" and simple rewards, aiming to develop the "science of superintelligence" in simulation for safety and accelerated progress. With a powerful GPU cluster and a small, elite team, Silver forecasts strong evidence within a year and superintelligence within three.

Sep 24, 2026

World Models Need Causality, Not Pretty Pixels — Christopher Manning, Moonlake AI

Christopher Manning, Stanford professor and now at Moonlake AI, traces AI history from Dartmouth in 1956 and Shakey the robot through the rise of large language models. He then argues for embodied intelligence built on action-conditioned simulation, and shows how Moonlake turns a photo into a manipulable, code-based world that can close the sim-to-real gap.

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.

Jul 29, 2026

SimulationMaxxing: How Nubank ships agents 20× faster with simulations — Shreya Rajpal, Snowglobe

Nubank, serving 135 million customers, uses AI agents for support. The talk reveals how simulated data for evaluations (evals) has enabled them to ship AI agents 20x faster. By addressing the bottleneck of multi-turn, stateful eval data, Snowglobe's grounded simulations create realistic customer interactions, allowing rapid testing, derisking, and significant improvements in customer satisfaction and self-service rates, even for open-source model experimentation.

Jul 28, 2026

The Next Frontier of AI Is Spatial Intelligence | Fei-Fei Li on a16z

Fei-Fei Li and Yunzhu Li discuss World Labs' acquisition of SceniX, focusing on building "spatial intelligence" and "large world models" to enable robots to understand and interact with the physical world. They elaborate on SceniX's "real-to-sim-to-real" pipeline, emphasizing how simulation, coupled with generative models like Marble, addresses the data bottleneck in robotics by providing consistent, scalable, and efficient training and evaluation environments. The conversation covers the role of counterfactual reasoning, the development of robotics foundation models, and the strategic focus on semi-structured environments for pragmatic, reliable robot deployment.

Jul 25, 2026

From Agent Traces to Agent Simulations — Rustem Feyzkhanov, Snorkel AI

Rustem Feyzkhanov discusses the critical need for companies to build private, production-aligned benchmarks for AI agents. He explains how to turn agent traces into repeatable simulations, why public benchmarks are insufficient, and how a CI pipeline for agents, integrating observability and experimentation, can ensure reliable evaluation, continuous improvement, and effective release management, moving beyond simple pass rates to measure cost, latency, and policy adherence.