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David Sinclair, AO, PhD, is a longevity scientist, Professor of Genetics at Harvard Medical School, author of the book "Lifespan: Why We Age—and Why We Don't Have To," and host of the "Lifespan" podcast.www.youtube.com/c/davidsinclairpodcast  www.simonandschuster.com/books/Lifespan/David-Sinclair/9781501191978  https://lifespan.com  https://davidasinclair.com Perplexity: Download the app or ask Perplexity anything at https://pplx.ai/rogan. Visit https://superpower.com/rogan for $20 off your membership. This video is sponsored by BetterHelp. Visit https://BetterHelp.com/JRE Learn more about your ad choices. Visit podcastchoices.com/adchoices

A groundbreaking new study claims to have found a way for a fully paralyzed person to communicate entirely via thought. But as we learned in an episode earlier this year, the scientists behind it have a checkered past. This episode was produced by Miles Bryan, edited by Matt Collette, engineered by Efim Shapiro, fact-checked by Tori Dominguez and Laura Bullard, and hosted by Sean Rameswaram. Transcript at vox.com/todayexplained Support Today, Explained by making a financial contribution to Vox! bit.ly/givepodcasts Learn more about your ad choices. Visit podcastchoices.com/adchoices

The Andrej Karpathy episode.

During this interview, Andrej explains why reinforcement learning is terrible (but everything else is much worse), why AGI will just blend into the previous ~2.5 centuries of 2% GDP growth, why self driving took so long to crack, and what he sees as the future of education.

It was a pleasure chatting with him.

Watch on YouTube; read the transcript.

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Timestamps

(00:00:00) – AGI is still a decade away

(00:29:45) – LLM cognitive deficits

(00:40:05) – RL is terrible

(00:49:38) – How do humans learn?

(01:06:25) – AGI will blend into 2% GDP growth

(01:17:36) – ASI

(01:32:50) – Evolution of intelligence & culture

(01:42:55) - Why self driving took so long

(01:56:20) - Future of education

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dwarkesh.com

If you buy Salesforce to avoid fixing a broken sales process, you fail. If you buy AI to avoid fixing a broken business, you fail faster.

Mark Cuban thinks AI could transform healthcare, but it won’t fix opaque prices and broken incentives on its own. His starting point for employers: run every benefits contract through AI and ask where you’re getting ripped off. Mark joins a16z General Partner Julie Yoo and Erik Torenberg to discuss what he’s learned building Cost Plus Drugs, why trust is its core product, and how greater transparency could put pressure on healthcare costs. They also explore where AI can help, what personalized medicine could look like, how the role of doctors may change, and why Mark believes founders should use AI to learn, not as an excuse to stop thinking.

What should school look like if AI can help students learn dramatically faster? Alpha School founder Joe Liemandt argues the answer isn’t to fill the extra time with more academics, but to redesign school around the skills traditional classrooms struggle to teach: agency, teamwork, entrepreneurship, communication, and doing hard things. Joe joins Erik Torenberg and Horowitz and Andreessen Academy co-founder Gagan Biyani to discuss Alpha’s model, where students spend two hours a day on core academics and the rest developing real-world skills through projects. They explore what that approach can teach higher education, why motivation may matter more than curriculum, and how HAA is applying similar principles to college-age students by putting building, relationships, and real-world experience at the center of the learning experience. They also discuss what parents need to believe before choosing a new educational model, why Joe expects AI to drive an explosion in entrepreneurship, and why the most important question for students may not be which field is safest from AI, but what they care enough about to build.

Steven Kotler is a bestselling author and the executive director of the Flow Research Collective, where he studies the science of peak performance. Most of us have felt it. Hours disappear, everything else fades away, and the work seems to do itself. But what’s actually happening in those moments, and can you make them happen on purpose? Kotler has spent decades studying flow, and he makes the case that it’s far more trainable than most people think. We talk about what brings it on, how hard a task should be before it pulls you in, and why the hobby you gave up as an adult might matter more than you realize. We also get into what frustration is really telling you when you’re learning, how play compares with practice, how to focus on work you’d rather not be doing, and how to think clearly when there’s more information than ever and fewer people checking it. ----- Chapters: (0:00) Intro (0:10) The Most Addictive State on Earth (0:38) Signs You're in Flow (Most People Miss These) (1:51) The Real Secret to Peak Performance (3:03) The Hidden Biology Behind Flow and Focus (4:09) How to Get Into a Flow State (5:31) Why You Need One "Flow Activity" (7:18) The Hidden Thing Blocking Your Flow (7:45) The Burnout Mistake High Performers Make (8:50) Unexpected Flow Triggers (12:02) Why Willpower Won't Stop Distractions (15:34) Why ADHD Can Be a Flow Advantage (23:20) The #1 Way to Increase Performance (25:48) The Motivation Myth Holding You Back (27:26) Why Spite Is a Dangerous Motivator (30:44) The Best Mental State for Learning (33:03) Why Frustration Helps You Learn (35:41) The Daily Habit That Boosts Flow Most (38:53) The Thinking Mistake Almost Everyone Makes (50:25) The Moment Your Brain Learns Best (57:31) The Surprising Way to Relieve Autoimmune Symptoms ----- Follow Steven Kotler: X: https://x.com/steven_kotlerhttps://x.com/steven_kotler ----- Newsletter: The Brain Food newsletter delivers actionable insights and thoughtful ideas every Sunday. It takes 5 minutes to read, and it’s completely free. Learn more and sign up at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠fs.blog/newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ------ Follow Shane Parrish: X: ⁠⁠⁠⁠⁠⁠⁠https://x.com/shaneparrish⁠⁠ Insta: ⁠⁠https://www.instagram.com/farnamstreet/⁠⁠ LinkedIn: ⁠⁠https://www.linkedin.com/in/shane-parrish-050a2183/⁠⁠ ----- Thank you to the sponsors for this episode: +Applovin: Launch your first AppLovin campaign and grow your business: ⁠https://applovin.com/shane⁠ +HeyGen is a message-first AI video platform that helps people and AI agents turn ideas into professional video in minutes. Try for free at ⁠https://www.heygen.com/tkp⁠ +Matic: the robot vacuum that does it all—grab yourself a year of free bags here. ⁠http://maticrobots.com/shaneparrish⁠ +LMNT: My go-to zero sugar electrolytes — get a free LMNT Sample Pack here: ⁠https://DrinkLMNT.com/TKP⁠ ----- + Members get the longer, extended version of this conversation, with additional content not included in the public release. Learn more: ⁠https://fs.blog/membership/⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

AI agents are approaching a point where they can use computers faster than expert humans, turning the user interface itself into a universal way for AI to interact with software. OpenAI’s Ari Weinstein and James Sun argue that computer use is now entering its scaling moment, as better models, faster execution, and stronger safety controls make agents practical for more consumer and enterprise work. Ari and James join a16z’s Seema Amble and Fabrizio Serafini to explain how computer-using agents are already navigating legacy ERPs, reconciling financial data, handling procurement, and operating software without APIs or custom integrations. They also discuss why the harness around the model can matter as much as model intelligence, how enterprises can set deterministic guardrails around agent behavior, and why computer use could simultaneously extend the life of legacy software and accelerate the arrival of new AI-native applications. Looking ahead, they make the case for a more proactive and fluid relationship with computers, where agents work in the background, collaborate with users in real time, and increasingly adapt to us rather than requiring us to learn how to use them.