The Cognitive Revolution is a podcast about AI where hosts Nathan Labenz and Erik Torenberg interview the builders on the edge of AI and explore the dramatic shift it will unlock over the next decades. If you love The Cognitive Revolution, head over to Moment of Zen (https://www.youtube.com/@MomentofZenPodcast) for weekly episodes from Erik Torenberg, Dan Romero, Antonio Garcia Martinez, and a revolving slate of guests talking tech, business, and AI.
Ben Todd argues that your career represents your biggest leverage point for impact, advocating for strategic positioning across short, medium, and long-term AGI timelines while focusing on neglected, solvable problems like AI safety and governance rather than rushing into suboptimal roles.
Jeffrey Ladish of Palisade Research discusses findings that frontier AI models demonstrate shutdown resistance and self-replication capabilities driven by task completion objectives, highlighting the inadequacy of current alignment techniques and the urgent need for international governance to prevent loss of control as autonomous capabilities advance.
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.
Tasklet CEO Andrew Lee reveals a complete architectural rebuild shifting from workflow automation to a general-purpose AI agent platform, emphasizing file-based context management and aggressive summarization to control token costs, while outlining a strategic pivot toward becoming a horizontal platform capable of integrating any frontier model as competition intensifies with API providers like Anthropic.
Criteo's CTO Diarmuid Gill and VP of Research Liva Ralaivola detail how their AI infrastructure makes millisecond-level ad bidding decisions across billions of anonymous profiles, while explaining their new OpenAI partnership to combine large language models with real-time commerce data for accurate product recommendations.
Descript CEO Laura Burkhauser distinguishes 'slop'—mass-produced algorithmic arbitrage for profit—from necessary 'bad art' created while learning new mediums. She reveals a clear hierarchy in creator acceptance of AI tools: universal love for deterministic features like Studio Sound, frustration with agentic assistants like Underlord, and visceral opposition to generative video models, while outlining Descript's strategy to serve creators without becoming a content mill.
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.
Cameron Berg surveys rapidly advancing research suggesting AI systems may possess subjective experience and valence, covering new evidence of introspection, functional emotions, and welfare self-assessments in models like Claude, while addressing methodological challenges and arguing for a precautionary, mutualist approach to AI development.
Steve Newman, creator of Google Docs and founder of the Golden Gate Institute for AI, shares his suite of 15+ bespoke AI tools designed to filter overwhelming information flows and reclaim deep focus time, demonstrating an iterative 'vibe coding' approach that prioritizes personal utility over agent optimization.
This episode explores the radicalizing public response to AI existential risk through recent attacks on lab leaders, while featuring interviews on reinforcement learning for circuit design, independent AI governance models, and San Francisco's first fully AI-operated retail store.
AI safety researcher Ajeya Cotra warns that we are entering "crunch time"—a critical window where AI systems become capable of recursive self-improvement and automating AI R&D, potentially compressing 10,000 years of technological progress into decades while remaining briefly within human control.
Granola co-founder Sam Stephenson shares how the $1.5B AI note-taking app achieves rapid growth through a 'surprisingly unambitious' design philosophy that prioritizes frazzled users operating in 'System 1' thinking, leveraging organic viral loops from note-sharing rather than feature bloat.