Long-form interviews and conversations with tech, AI, and finance leaders (30min+)
Crypto Kid delivers a bearish short-term outlook for Bitcoin and Ethereum, citing breakdowns from key technical patterns and historically low volume, while highlighting Hyperliquid's surprising institutional ETF traction and system resilience as the only standout altcoin in the current downturn.
TV writer Lauren Bans discovered escape rooms as an unlikely remedy for heartbreak after her husband's infidelity ended their marriage while she was seven months pregnant, finding that the intense cognitive focus required by immersive puzzles succeeded where therapy, Reiki, and traditional distractions failed.
Professor Michael I. Jordan critiques the hype around AGI and prediction-based LLMs, arguing that modern AI lacks economic and social thinking; he advocates for 'inference' systems grounded in game theory and market dynamics that respect human agency and create collective value.
Professor Michael I. Jordan critiques AGI as harmful PR that demoralizes young engineers, arguing that true intelligence requires economic and social systems thinking—treating billions of humans as agents in value-creating ecosystems—rather than isolated prediction engines built without intellectual foundations.
Pfizer CEO Albert Bourla discusses leveraging COVID-19 vaccine success to build organizational resilience, executing $80+ billion in strategic acquisitions targeting antibody-drug conjugates and obesity treatments, and restructuring R&D to double clinical success rates, while predicting AI will fundamentally reshape medicine within five years.
Journalist Megan Greenwald examines how private equity's incentive structure allows firms to profit through financial engineering and debt loading even when portfolio companies fail, creating devastating consequences for workers, communities, and industries ranging from retail to healthcare.
This lecture establishes that training data—not architecture—is the most critical and secretive component of modern language models, examining the technical impossibility of crawling the 'entire internet,' the three-stage data pipeline from raw web to specialized post-training, and the tightening legal constraints of copyright law and terms of service that increasingly restrict what can be legally used for training.
This lecture explores how to evaluate language models, examining different definitions of "good" from benchmark scores to economic usage, diving deep into perplexity as a foundational metric and its limitations, and tracing the evolution of exam-based benchmarks like MMLU from novel challenges to saturated metrics.
This lecture explores practical implementations of scaling laws for language models, focusing on the MiniCPM paper's techniques for stabilizing training across scales, including Maximal Update Parameterization (MUP) for consistent learning rates and Warm-up Stable Decay (WSD) schedules for efficient training continuation.
This lecture covers practical training of text-to-image diffusion models, detailing the evolution from UNet to Diffusion Transformer architectures, the three-stage training pipeline (pre-training, post-training, and tuning), and critical optimizations including flow matching loss functions, logit-normal time step sampling, and resolution-aware noise scheduling.
Stripe Sessions 2026 marked the company's most ambitious product launch day in history, centered on building economic infrastructure for the AI era. The keynote revealed a parabolic spike in new business formation since January 2026 and introduced tools including the Machine Payment Protocol, Link wallet for agents, and Stripe Projects to enable autonomous agent-to-agent commerce.
Sam Altman discusses the recent 'parabolic' inflection in AI capabilities, particularly for coding with GPT 5.5 and Codex, while outlining OpenAI's evolution into a massive-scale 'intelligence utility' provider focused on automating general computer work through agents like OpenClaw.