Long-form interviews and conversations with tech, AI, and finance leaders (30min+)
Stanford instructor Apur frames generative AI as a supercycle with inverted economics where semiconductor and infrastructure costs dominate revenues while application-layer value remains elusive, questioning whether this structure represents a temporary capex cycle or a new permanent equilibrium.
Jake Cooper, founder of Railway, explains how the 'agent-native cloud' hit 3 million users and 100,000 weekly signups by betting that manual coding is obsolete, detailing their journey from a $500K/month free tier loss to bare metal infrastructure ownership.
Wedbush tech analyst Dan Ives argues the AI revolution represents a multi-year bull market where the US currently leads China, but warns the industry faces critical headwinds from self-created PR crises around job displacement and regulatory fragmentation that threaten data center buildouts and autonomous vehicle deployment.
This seminar presents a neuro-symbolic approach to robot learning that combines neural visual representations with physics-based constraint optimization to enable one-shot skill acquisition, achieving over 90% success rates on novel objects compared to 0% for standard policy learning methods.
UC Berkeley's Icon Lab presents game-theoretic frameworks enabling robots to safely interact with humans and other agents by modeling joint prediction as potential games, reducing computational costs by 20x while solving the challenge of multiple social equilibria in real-time navigation.
Language models exhibit a fundamental 'reversal curse' where they fail to generalize relational knowledge (like reversing 'A is B's parent') when learned through parameter updates, yet perform perfectly when the same information is provided in-context, revealing distinct computational modes for parametric versus contextual learning.
YC's Garry Tan and Diana Hu explain how AI coding agents are creating '1000x engineers' and enabling tiny teams to generate tens of millions in revenue within months rather than years. They detail the shift from AI copilots to autonomous software factories requiring rigorous testing frameworks and strategic prompting skills to achieve production-grade output at unprecedented scale.
This seminar explores how personalized foundation models trained on wearable data can predict intervenable health events (like cravings or blood pressure spikes) to enable just-in-time digital interventions, but emphasizes that HCI is critical to solve adherence challenges, inconsistent labeling, and misaligned evaluation metrics.
Former law enforcement officer David Paulides discusses his investigation into thousands of mysterious disappearances in U.S. national parks, highlighting cases with inexplicable circumstances and alleging systematic obstruction by the National Park Service, which refuses to release decades-old case files and demands exorbitant fees for basic data.
Grayscale's Krista Lynch explains the mechanics behind institutional Bitcoin ETF adoption, including how whales convert holdings into ETF shares and why tokenization volume has surged 245% despite crypto price declines as the industry pivots toward infrastructure building.
Jordan Peterson explains how voluntary responsibility, truthful articulation, and confronting the past differentiate healthy discipline from resentment, while arguing that our true beliefs are revealed through actions rather than professed ideology.
Google DeepMind's Logan Kilpatrick and Tulsee Doshi detail the launch of Gemini 3.5 Flash, Omni video generation, and Spark agent features, emphasizing a strategic pivot toward cost-adjusted performance and standardized agent infrastructure ('anti-gravity') across Google's product ecosystem rather than competing solely on absolute model capability.