🎙️ Podcasts

Impact Theory

The video argues that the US-Iran conflict is fundamentally an economic war disguised as nuclear non-proliferation, driven by Trump's urgent need to secure trillions in Gulf sovereign wealth to fund American AI infrastructure and ensure political survival, while Iran strategically targets that digital infrastructure to disrupt capital flows and force regional defense spending.

Stripe

Bret Taylor explores how AI agents are shifting from polished but forgetful tools to messy, context-rich systems that leverage markdown memory and code repository structures, predicting software engineering will evolve from writing code to crafting 'harnesses' of documentation while enterprises move beyond APIs toward agent-accessible infrastructure.

The David Lin Report

Former Lehman VP Lawrence McDonald warns that the private credit market is experiencing a 2008-style liquidity crisis as funds break quarterly redemption promises on illiquid assets, while stagflationary pressures and AI-driven job losses create a policy trap for the Federal Reserve.

All-In Podcast

Harvard professor Graham Allison warns that the US-Iran conflict is characterized by extreme strategic uncertainty and driven largely by Netanyahu's long-standing obsession with destroying the Iranian regime, risking a chaotic regime change quagmire despite extraordinary American military success.

Stanford Online

Percy Liang reflects on AI's transformation from academic curiosity to global infrastructure, debunking sci-fi misconceptions about capabilities while arguing that academia's role in long-term research and critical evaluation remains essential as the job market shifts away from traditional entry-level software engineering.

Stanford Online

This lecture examines AI's economic impact through the lens of supply chains and organizational strategy, demonstrating why understanding compute monopolies, labor market shifts, and corporate decision-making is as critical as tracking algorithmic capabilities.

Stanford Online

This lecture argues that AI developers bear unique ethical responsibility for societal outcomes, framing AI as a dual-use technology that requires active steering toward beneficial applications while preventing misuse and accidental harms through rigorous auditing and an ecosystem-aware approach.

Stanford Online

This lecture introduces modern language models as industrial-scale systems requiring millions of dollars and trillions of tokens to train, explaining their fundamental operation as auto-regressive next-token predictors that encode language structure through massive statistical modeling.

Stanford Online

This lecture introduces First Order Logic as a powerful extension of propositional logic that uses objects, predicates, functions, and quantifiers to compactly represent complex relationships and generalizations without enumerating every possible instance.

Stanford Online

This lecture introduces logic as a formal language for knowledge representation and reasoning, contrasting it with probabilistic methods and natural language. It establishes the foundational framework of syntax, semantics, and inference rules, then dives into propositional logic's mechanics including formulas, models, and interpretation functions.

Stanford Online

This lecture explains how to learn Bayesian network parameters from fully observed data through simple counting and normalization, while reviewing probabilistic inference methods and d-separation rules for determining conditional independence.

Stanford Online

This lecture explains how Bayesian networks compactly represent joint probability distributions through local conditional probabilities, then contrasts inefficient rejection sampling with Gibbs sampling—an MCMC method that iteratively modifies existing samples to satisfy evidence, enabling efficient approximate inference even with rare events.