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My First Million

Academic and business leaders discuss navigating unprecedented policy uncertainty, noting that while financial markets remain calm, text-based uncertainty indicators have hit historic highs since 2025, requiring leaders to focus on structural trends rather than temporary government incentives and engage only on social issues core to their business purpose.

All-In Podcast

OpenAI's missed user and revenue targets have sparked IPO concerns and internal leadership tension, but recent product improvements with ChatGPT 5.5 may give them an edge over Anthropic's struggling Opus 4.7 in the critical coding market. The entire sector faces severe power and compute constraints that favor hyperscalers while driving the need for algorithmic innovations like model pruning to meet exploding demand.

The Diary of a CEO

Panelists debate whether Erika Kirk should remain CEO of Turning Point USA following her husband Charlie Kirk's assassination, as she faces conspiracy theories and intense scrutiny while defending herself at the White House Correspondents Dinner.

AI Engineer

AI companies are growing three times faster than traditional SaaS but face unique pricing challenges due to unpredictable compute costs and razor-thin margins, requiring a shift from static subscription models to flexible hybrid pricing that prioritizes rapid iteration and customer-perceived value over technical metrics.

Jordan Peterson

Peterson contrasts Freudian repression with Piagetian coordination to argue that psychological health emerges from organizing motivations into functional abstractions rather than fighting social constraints. He contends that controlling your mind requires navigating society as a complex dance rather than escaping a prison, while defining mental health as movement toward an ideal rather than mere normality.

Cognitive Revolution

Kyle Corbitt explains that unlike supervised fine-tuning (SFT), which destructively overwrites model weights and causes catastrophic forgetting, reinforcement learning (RL) optimizes performance by minimally adjusting logits within the model's existing reasoning pathways—delivering higher performance ceilings and lower inference costs for specific tasks, though frontier models may still dominate creative domains.

AI Engineer

This workshop demonstrates how to bridge the gap between AI prototypes and production systems using Brain Trust's observability platform, featuring Trainline's experience deploying multi-agent AI applications serving 27 million users.

New York Times Podcasts

New York Times book critics Gilbert Cruz, Tina Jordan, and Dwight Garner reflect on two decades of literary evolution, tracing how memoir scandals, dystopian YA blockbusters, Scandinavian crime waves, and the autofiction revolution reshaped reading habits and publishing trends.

Stripe

AI leaders Nat Friedman and Daniel Gross join Stripe's Collison brothers to discuss how we're in the 'slow' beginning of the singularity, where human bottlenecks still constrain model improvement but will soon give way to AI self-improvement, creating profound economic uncertainty and a new golden age of personal AI agents that fundamentally alter human-technology relationships.

How I Built This (NPR)

NPR security correspondents analyze the third assassination attempt on President Trump at the White House Correspondents' Dinner, examining suspect Cole Allen's background and motives while debating whether the Secret Service response constituted a security success or systemic failure.

Stanford Online

Anjney Midha frames the current AI landscape as 'the great transition,' where industrial-scale model training meets a complete restructuring of the eight-layer infrastructure stack, while arguing that relationships and obsessions remain the ultimate asymmetric advantages for founders against entrenched incumbents.

Stanford Online

This lecture introduces scaling laws as predictive power-law relationships that enable practitioners to optimize language model training on small budgets and confidently extrapolate performance to million-dollar large-scale runs, while tracing these empirical patterns back to classical machine learning theory and sample complexity research from the 1990s.