Podcasts about stanford

Stanford legend & former NFL QB, Todd Husak joins the Morning Show to break down the Niners roster, who will make the team & be on the chopping block, how the team will play vs the Raiders with the starts sitting out. See omnystudio.com/listener for privacy information.

Hour 2 -- NBA Grab Bag featuring Brenda Song calling Rams games, plus Dae'Quan Wright returning to college football. Later Andrew Luck joins the show to preview the 2026 Stanford pregame show. See omnystudio.com/listener for privacy information.

Stanford legend & former NFL QB, Todd Husak joins the Morning Show to break down the Niners roster, who will make the team & be on the chopping block, how the team will play vs the Raiders with the starts sitting out. See omnystudio.com/listener for privacy information.

Democrats trail Republicans in cash by nine figures. The DNC reported $16 million on hand against $17.9 million in debt. The RNC reported $130 million. Peter Schweizer and Eric Eggers follow the money filling the gap, and most of it comes from senior citizens. A Stanford researcher spent eight months preparing an investigation for The New York Times. Fact checkers cleared it. Lawyers cleared it. Then the Elias Law Group sent cease and desist letters on behalf of Democratic committees, and the paper walked away. What the research found: a spam text pipeline built to squeeze a captive pool of elderly donors. An 85 year old man in Oxford, Ohio gave 7,800 times, nearly $650,000, more than double the value of his house. A 90 year old woman in an Indiana senior living facility gave more than 25,000 times, $250,000 in total. Fewer than 1% of Democrat donors account for almost half of the $1.4 billion these PACs have raised since 2017. One network alone pulled in $390 million. Most of the money never reaches a candidate. It recycles back into the fundraising operation. Hakeem Jeffries shows the shift in one chart. In 2018, his average donor was 54 and 21% were 65 or older. This cycle, his average donor is 73 and 86% are senior citizens. Then the midterm ledger. Ripple and Coinbase sit at the top of $230 million in special interest spending, ahead of Big Tech and Wall Street. FanDuel and DraftKings make the top six. George Soros has given more than $102 million this year. AIPAC drew headlines for $30 million behind Haley Stevens in Michigan, while the money moving against her opponent's opponent stays far harder to trace: a $200,000 super PAC check from Abdul El-Sayed's father-in-law, an Islamic Society of North America figure; $25,000 from an Egyptian American activist sentenced to life in absentia; $115,000 tied to people affiliated with the Council on American-Islamic Relations. Saudi Arabia spent $92 million on Washington lobbying last year with roughly 148 registered foreign agents. Peter and Eric close on Sharia law, Jesse Watters' interview with El-Sayed, and whether the red-green alliance holds long enough to make Michigan competitive. (00:00) Cold open (00:31) Votes from the poor, money from the rich (02:33) The DNC has $16 million and $17.9 million in debt (04:43) Granny's Social Security check funds the campaign (06:34) The New York Times kills the story after legal threats (07:33) One donor, 7,800 gifts, $650,000 (09:30) Hakeem Jeffries' donor base ages 20 years in eight (12:27) Crypto, gambling and AIPAC lead the midterm spending (15:25) The influence money nobody tracks (20:14) CAIR cash, Jesse Watters, and the Sharia law question

The era of AI-generated viruses is here. Some scientists are using AI to design viruses that can attack bacteria. The hope is that these tailor-made viruses can fight drug-resistant bacterial infections in people. And longer-term, Brian Hie, the Stanford researcher spearheading this project, hopes that AI can help create more complicated biological systems to treat formidable diseases. But some experts warn that placed in the wrong hands, this technology could create the next pandemic. Interested in more AI-related research? Email us your question at shortwave@npr.org.Support public media with NPR+ and enjoy perks for over 25 podcasts like this one. This show's perks include sponsor-free listening. Learn more at plus.npr.org. See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

The Cover 3 crew is back with their big game breakdown episode for Week 0. The guys preview all the notable matchups on the slate to kick off the 2026 season!-(00:00) Intro(2:04) Big Ten Set To Block NFL Players From Returning(8:14) Lane Kiffin Defends Adding Ex-NFL Players(13:56) Vanderbilt, Arkansas & Northwestern Name Starting QBs(23:41) North Carolina vs TCU(36:02) NC State at Virginia(46:18) Memphis at UNLV(52:25) San José State at USC(56:51) New Mexico State at Florida State(1:00:35) Jacksonville State at North Dakota State(1:03:43) Hawaii at Stanford(1:06:43) CFB Personality Rankings-Join this year's College Football Pick 'Em league! https://app.splashsports.com/contest/05480bf3-91d8-4e2a-b25d-1502bb7c9061/detail-Cover 3 is available on Apple Podcasts, Spotify and wherever else you listen to podcasts. Visit ⁠the betting arena on CBSSports.com⁠ for all the latest in ⁠sportsbook reviews⁠ and ⁠sportsbook promos⁠ for ⁠betting on college football⁠.Watch Cover 3 on YouTube: ⁠https://www.youtube.com/cover3⁠Follow our hosts on Twitter: ⁠@Chip_Patterson⁠, ⁠@TomFornelli⁠, ⁠@DannyKanell⁠, ⁠@BudElliott3⁠For more college football coverage from CBS Sports, visit ⁠https://www.cbssports.com/college-football/⁠To hear more from the CBS Sports Podcast Network, visit ⁠https://www.cbssports.com/podcasts/

California promised consumers an easier way to protect their personal data. Since 2024, the Delete Act has required the state registration of data brokers — businesses that collect and sell personal data from our digital footprints — and it also mandates they give consumers a way to request their data be deleted and report how many requests they get.But, a new Stanford report shows registered data brokers are largely ignoring the laws. Jennifer King, privacy and data policy fellow at Stanford's Institute for Human-Centered AI, says only 9% of data brokers are in compliance.More on this:“Regulating Data Brokers in the Age of AI: A California Case Study” from Stanford's Institute for Human-Centered AI“About DROP and the Delete Act” from the California Privacy Protection Agency

California promised consumers an easier way to protect their personal data. Since 2024, the Delete Act has required the state registration of data brokers — businesses that collect and sell personal data from our digital footprints — and it also mandates they give consumers a way to request their data be deleted and report how many requests they get.But, a new Stanford report shows registered data brokers are largely ignoring the laws. Jennifer King, privacy and data policy fellow at Stanford's Institute for Human-Centered AI, says only 9% of data brokers are in compliance.More on this:“Regulating Data Brokers in the Age of AI: A California Case Study” from Stanford's Institute for Human-Centered AI“About DROP and the Delete Act” from the California Privacy Protection Agency

What can we do when doing the morally “right” thing produces worse outcomes?And what if some of society's strongest moral instincts are preventing us from solving problems we all agree are terrible?Alvin Roth is a Stanford professor and Nobel Prize-winning economist whose work has helped redesign real-world systems, including kidney exchanges and the way doctors are matched with hospitals. His new book, Moral Economics, tackles a harder category of problems: markets and behaviors we find morally objectionable, even when banning them may create consequences we don't want.He discusses:Why nearly 100,000 Americans can be waiting for a kidney What prohibitions of alcohol and heroin can teach us What happened when Rhode Island accidentally legalized indoor prostitutionWhether paying kidney donors could save lives without creating an ethically unacceptable marketWhy evidence becomes especially important when reasonable people disagree about moralityWhether performance-enhancing drugs could eventually become as ordinary as coffeeSome policies sound obviously right when judged by their intentions. But what happens when you judge them by their actual consequences?Roth pushes into the uncomfortable territory between those two questions: when banning something creates a black market, when changing a rule may save more lives than inventing a new technology, and what we should do when the outcome we believe is morally necessary turns out to be something we cannot actually achieve.

In this episode of the fAQ podcast, Tai-Danae Bradley sits down with Joe Howlett, a science journalist at Scientific American and former astroparticle physicist, to discuss his fascinating transition from experimental physics to science storytelling.Joe takes us inside his former life as a physicist, detailing his PhD research at Columbia University where he hunted for "WIMPs"—the elusive particles thought to make up dark matter. He breaks down the mind-boggling reality that all known matter accounts for only one-sixth of the universe, and openly shares the emotional toll of a 40-year global physics search that has yielded negative results. We discuss how these unanswered questions led him to pivot away from a postdoctoral fellowship at Stanford to pursue a career in science journalism.Whether he is learning to make science "dance on the page" under the mentorship of legendary writers, uncovering historical math scandals, or preparing his latest essay on the universe's unanswerable questions, Joe's relentless curiosity is truly inspiring.LinksRead Joe's latest articles for Scientific American: https://www.scientificamerican.com/author/joseph-howlett/ Connect with Joe on LinkedIn: https://www.linkedin.com/in/joseph-howlett-a180471b7/

Fall camp is wrapping up for the Miami Hurricanes and it is now time to start preparing for the week one opponent, Stanford.In this episode of Through The Smoke, InsideTheU's David Lake recaps where things stand at each position group and what is expected going into the regular season.Enjoy the show.Support Our Sponsors- Join Canes Connection today at CanesConnection.com!- If you have been injured in a slip and fall, boating accident, trucking accident, Uber/Lyft accident, or car accident, Nick Mucerino is the personal injury attorney you should contact at 561-960-9870 or visit the website FLInjury.Law.- If you're thinking about buying, selling, or investing in South Florida, you should know Aaron Paskow with Keller Williams. Grab a FREE Home Value Report or quick market update. Call or text 305-497-5773 or visit apaskow.kw.com.

Dr. Donn Posner is one of the most active educators in CBT-I. He's founder and president of Sleepwell Consultants, adjunct clinical associate professor in Psychiatry and Behavioral Sciences at Stanford, and spent twenty-five years before that as director of behavioral sleep medicine at the Sleep Disorders Center of Lifespan Hospitals.In this first of two episodes, Dr. Posner explains that chronic insomnia is a disorder in its own right — not just a symptom of something else — driven by perpetuating factors that become the targets of treatment. He covers:How insomnia disorder is defined, and what a proper CBT-I assessment looks likeThe sleep diary as the clinician's version of an X-rayThe two core behavioral components — sleep restriction (more accurately, time-in-bed restriction) and stimulus control — and the mechanisms each one targets: homeostatic sleep drive and conditioned arousalWhy sleep can't be willed — it's never under a patient's voluntary controlHow far the protocol can flex for individual patientsThe evidence base showing CBT-I works even alongside depression, anxiety, PTSD, or chronic pain — and that those conditions don't need to be treated firstLength: 28 Minutes

Title NSDR / Non-Sleep Deep Rest — Reach Deep Rest Without Forcing It | 10-Minute Physiological Sigh Meditation If you feel tired in a way that sleep never seems to touch — wired, switched-on, unable to power down even when you're exhausted — this 10-minute NSDR (Non-Sleep Deep Rest) session is for you. Hosted by clinical hypnotherapist and former paramedic Martin Hewlett, this guided meditation helps you reach deep rest without forcing it, resetting an overstimulated nervous system while you're still awake to feel it happen. Today's practice is built around the physiological sigh — a double breath in through the nose followed by one long, slow breath out. Research from Stanford found it's one of the fastest ways to bring the body down out of high alert, flipping the biological off switch and engaging the vagus nerve. From there we let the breath go completely free and sink into non-sleep deep rest: nothing to solve, nothing to reach, entirely off duty. Whether you're looking for anxiety relief in the middle of a racing day, a way to quiet a "tired but wired" mind, or a daily NSDR habit to protect your calm, give yourself permission to lie down, soften, and let the whole system settle. ⏱️ Time Chapters00:00 – "The Tiredness Sleep Doesn't Touch" (Why You Need Deep Rest, Not More Sleep)00:36 – Today's Focus: I Can Reach Deep Rest Without Forcing It00:41 – Introduction from Martin & Anchored App Overview01:16 – Resetting the Nervous System Before We Rest01:38 – The Physiological Sigh & the Stanford Research Behind It01:56 – Settling In: Lying Down, Letting the Body Fall Heavy02:15 – Guided Physiological Sighs (Three Rounds Together)03:15 – Letting the Breath Go Free: Effortless Rise & Fall04:31 – Planting Today's Truth: The Warm Amber Light04:52 – NSDR Affirmations for Nervous-System Reset (First Pass)06:32 – Staying Off Duty: Nothing to Solve, Nothing to Reach06:41 – Affirmations for Deep Subconscious Integration (Second Pass)08:00 – Reuniting Mind, Body & Soul: You Are Worthy08:27 – 3 Daily Caring Tips for Building an NSDR Habit09:12 – Gently Awakening & Returning to the Room09:47 – Closing Reflection & Notice How You Feel Now09:55 – Outro, Community Support, & Sign-off

With the Miami Hurricanes' season opener at Stanford just one week away, Joe breaks down some of the players to watch, including several newcomers. The guys discuss why Miami is once again set up to be a contender and how the Canes did a good job replenishing the talent they lost in the offseason. Plus, Joe highlights Malachi Toney's work ethic and his commitment to getting extra work in, including showing up at 5 a.m. before practice

Joe and Dave look ahead to the end of the preseason and the start of the regular season, discussing the Dolphins' extremely thin roster, especially on the back end, and their ugly performance against the Giants. The guys debate when Miami will begin making roster cuts and what the team can do to improve its depth and talent before Week 1. Plus, with the Miami Hurricanes' season opener at Stanford just over a week away, Joe breaks down why Miami is favored to win the ACC, the talent they've added to replace key offseason losses and whether they can build off last season's run to the National Championship. The hour also features Omar Kelly, who joins the guys to discuss Miami's young roster, Zeek Biggers, potential cuts, the need for a backup QB and why the Dolphins could improve as the season progresses

he 2026 college football season opens with eight Week Zero games, and Gary breaks down every matchup using WCE model projections, roster talent, returning production, coaching changes and line-of-scrimmage advantages.TCU and North Carolina begin the season in Dublin, where recent results have consistently favored underdogs. USC faces an enormous spread against San Jose State as Lincoln Riley begins a pivotal season, while NC State attempts to challenge an experienced Virginia roster in Charlottesville.North Dakota State hosts Jacksonville State for the Bison's first FBS game, Sacramento State visits Eastern Michigan, and Stanford searches for revenge against Hawaii. Florida State enters a pressure-filled season against New Mexico State, with Mike Norvell's team expected to make a statement.The night ends with Memphis visiting UNLV in a matchup featuring Dan Mullen, Charles Huff, major quarterback questions and a Tigers roster capable of threatening an outright upset.Gary closes the episode with official predictions for all eight games, including five favorites and three underdogs.

The wait is over.The University of Hawai‘i football team opens the 2026 season Saturday on the road against Stanford, and Hawai‘i Football Final is getting fans ready for kickoff with a special quick preview episode featuring KHON2 Sports Director Rob DeMello and former Rainbow Warrior player and coach Rich Miano.After an offseason filled with excitement and high expectations, Miano breaks down what makes this year's team different and why he believes the Rainbow Warriors have the leadership and chemistry to pursue lofty goals in 2026.“I think the leadership,” Miano said. “When I talk about 2007 being arguably the greatest season in Hawai‘i history, it wasn't because it was the most talented team. This team does have talent and you can see the athleticism. But what it has when I talk about leadership is you look at a guy like Jamih Otis, Elijah Palmer, Micah Alejado, Cam Barfield, and there are other leaders as well.”Miano also pointed to the continuity within the coaching staff as another key factor heading into the season.The Rainbow Warriors return their coordinators, allowing the team to build on the systems, terminology and techniques established a year ago.“The Braddahhood has really developed that belief in the coaching staff,” Miano said.Miano believes Hawai‘i is positioned to build on last season's success, which included nine wins and a Hawai‘i Bowl championship.The Rainbow Warriors also have recent history against Stanford and other current Pac-12 opponents to draw from. Hawai‘i opened last season against Stanford and closed the regular season against California. “What they're doing in almost every aspect of this football program is headed in a positive direction,” Miano said. “I'm anticipating it and I'm expecting a Mountain West championship or at least competing for that.”The full Hawai‘i Football Final season preview dives deeper into the 2026 Rainbow Warriors, including the expectations surrounding quarterback Micah Alejado, the team's returning leadership and what it will take for Hawai‘i to compete for a Mountain West championship in its first season in the conference.Hawai‘i Football Final is a Hawai‘i Sports 2Night production hosted by DeMello and Miano, a former UH player and coach, 11-year NFL veteran and UH Sports Circle of Honor member.The new season of Hawai‘i Football Final premieres Sunday, August 30 at 7 p.m. on the KHON+ app, available on Apple TV, Roku and Firestick. Each episode is also available on demand following its premiere, with a television rebroadcast Mondays at 10:35 p.m. on KHON2.Episodes are also available on demand at KHON2.com and YouTube, with an audio version available on Spotify and most other podcast platforms.Hawai‘i opens the 2026 season against Stanford Saturday at 1 p.m. Hawai‘i time.For continuing coverage of the Rainbow Warriors throughout the season, stay with KHON2 Sports on air and online.

Daniel Mason is the author of six books of fiction, including The Piano Tuner, the story collection A Registry of My Passage Upon the Earth — which was a finalist for the Pulitzer Prize — and North Woods, which was a finalist for the National Book Critics Circle Award and the Dublin Literary Award. He's also a physician and a professor in Stanford's Psychiatry Department. His latest is Country People, published by Random House and out last month. It's a Good Morning America summer book club pick and a NYT bestseller. It's contemporary, it's hilarious and, for us procrastinators who would rather fall down research rabbit holes than write, it's very relatable. It also gives writers a bunch of craft lessons to sink our teeth into. How to use your text to teach readers how to read your novel. Embedding content into form (this novel is a master class in that). Using different textures and kinds of writing to enhance your storyline, and more. For more information on Writers on Writing and to become a supporter, visit our Patreon page. For a one-time donation, visit Ko-fi. You can help out the show and indie bookstores by buying books at our bookstore on bookshop.org. It's stocked with titles by our guest authors, as well as our personal favorites. And on Spotify, you'll find an album's worth of typewriter music like what you hear on the show. It's perfect for writing. Look for the artist, Just My Type. You can find hundreds of past interviews on our website. (Recorded on August 13, 2026) Host: Barbara DeMarco-Barrett Host: Marrie Stone Music: Travis Barrett (Stream his music on Spotify, Apple Music, Etc.)

The Education Minister's confident she'll get the support needed for a social media ban. National's introduced a Bill which would legally require platforms like Instagram and TikTok to bar under-16s from making accounts. The bill puts the pressure on social media companies to comply, establishing an online safety regulator and setting up a penalty regime. ACT and New Zealand First don't support it, and Labour's yet to decide. Education Minister Erica Stanford told Mike Hosking she's worked hard to give Labour everything it needs to come to a view on it. LISTEN ABOVE See omnystudio.com/listener for privacy information.

You've heard about how AI tools could help meet demand for mental-health treatment, as well as some of the risks involved. Several U.S. states have passed laws limiting what these chatbots are able to do, leading to some changes in the industry. Meanwhile, federal regulators are taking a look at the AI therapy space. In the third and final episode of our series “The AI Therapist,” What's News PM host Alex Ossola delves into the changing regulatory landscape and where AI mental-health tools could go from here. The AI Therapist: A WSJ Podcast Series Further Reading: Chatbots Are Replacing Therapists With Little Scientific Evidence Behind Them Teens Seek Mental-Health Help From Chatbots. That's Dangerous, Says New Study. How AI Advice Is Undermining Eating-Disorder Therapy When There's No School Counselor, There's a Bot What Parents Need to Know About OpenAI's New ChatGPT for Teens Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

EVEN MORE about this episode!Could your home actually be blocking money, abundance, and the life you're trying to create? Feng shui expert Dana Claudat says the objects and energy surrounding you may be influencing you far more than you realize.Julie Ryan sits down with Dana for a fascinating conversation about feng shui, money energy, abundance, clutter, manifestation, and the surprising connection between your outer environment and inner world.Dana explains why she views the home as a living organism—and how everything from worn-out clothes and broken objects to outdated books and possessions tied to painful memories can continually reinforce the message that you're stuck in the past.And you don't need to completely redesign your house to begin shifting things.Dana shares practical feng shui techniques you can try at home, including an intriguing practice involving a mason jar, lemon, salt, and water. Julie shares some of her own feng shui experiences, from removing predatory animal artwork from her son's playroom to using mirrors to redirect the energetic flow of abundance through her home.They also explore Julie's intuitive perception of cash-flow energy entering through the front door, why clutter can affect far more than cleanliness, and how changing your physical surroundings may help create space for something new.Dana shares remarkable stories from her own life as well—from unusual childhood spiritual experiences and a family connection to psychic phenomena to helping clients radically transform their lives and businesses.One client went from facing eviction to receiving a $500,000 business investment within weeks of beginning the process.If you've ever wondered whether your home affects your money, energy, manifestation, relationships, or ability to move forward, this conversation will have you looking at every room differently.Guest Biography:Dana Claudat is a modern Feng Shui Master and Founder of The School of Intention - a School and a Method of Feng Shui based on Infinite Possibility, Creative Awakening and Intention. After studying Art History at Stanford and working in the Arts, Dana found her way to Feng Shui, practicing for the last 20 years. It's become a full-time immersive passion to continue to evolve the Feng Shui Practice with an amazing online community, Students and Clients around the world.Episode Chapters:(0:00:00) Welcome & Meet Feng Shui Expert Dana Claudat(0:02:48) Is Your Home Blocking the Life You Want?(0:07:33) What Should You Remove From Your Home?(0:12:37) Your Front Door: The Gateway for Abundance(0:21:30) Hidden Clutter May Be Draining Your Energy(0:31:42) 5 Household Items That Carry Negative Energy(0:41:05) The Lemon & Salt Energy-Clearing Ritual(0:44:35) A 9-Day Ritual to Raise Your Home's Energy(0:51:20) When Your Space Changes, Your Life Changes(0:55:44) Using Your Home to Manifest What You Want(1:02:27) Psychics, Aliens & Dana's Spiritual Awakening(1:14:50) The Money Bowl: Feng Shui for Abundance(1:22:53) The One Change That Can Shift Your Life(1:24:00) Why Do We Incarnate?➡️ Subscribe to Ask Julie Ryan YouTube➡️ Julie's Intuitive Trainings✏️ Ask Julie a Question!

Friends, Vanessa Fraser is back on the podcast! She hasn’t been on the show in probably eight years, so I was really excited to reconnect with her and hear everything she’s been up to. Vanessa ran at Stanford, where she was a 10-time All-American, before running professionally with the Bowerman Track Club. She has a 14:48 indoor 5K PR, spent some time training with Team Boss, went back to her college coach Coach Miltenberg, and now she’s announced that she’ll be working with David Roche as she sets her sights on the roads and, eventually, the marathon. Vanessa’s career hasn’t gone exactly how she expected it would when she turned pro. She’s dealt with injuries and a frustrating medical condition that has made fueling incredibly difficult. She recently stepped away from her corporate job and is going all in on running, and I loved hearing her perspective on constantly striving for something that doesn’t always come to fruition in the way you imagined. We talk about her years with Bowerman, Team Boss, going back to Coach Miltenberg, her relationship with Saucony, leaving the corporate world, her health struggles, and why she’s ready for this next chapter with David Roche. And we have to talk about Gigi! Vanessa recently ran a 4:31 mile with her dog, setting an unofficial world record for the dog mile. I loved hearing about their relationship and how much fun Vanessa had getting to race as a team with her dog. This conversation honestly helped me see some things in my own career in a fresh perspective. Vanessa made me feel really seen and cared for, and I hope she felt the same from me. I’m so grateful for conversations like this one, and I know you’re going to enjoy hearing more of Vanessa’s story and what she’s been up to over these past few years. Books recommended: Delusions by Cazzie David Support our Sponsors: Little Spoon: Little Spoon makes feeding kids easier with clean, nutritious meals designed for every stage, from babies starting solids through elementary-aged kids. Their lineup includes baby blends, toddler-friendly Biteables, and big-kid Plates and Build It Yourself Lunchers, with 100+ ingredients banned across all products. Go to LittleSpoon.com/another and use code ANOTHER for 30% off your first order. HUUG makes high-quality bras and underwear designed to actually fit and support your body through every phase of life. Their pieces are comfortable, functional, and built for movement, making them a go-to for everyday wear, running, and training alike. Use the code Lindsey for 15% off at huug.com. Tailwind NutritionTailwind Nutrition creates all-in-one endurance fuel and hydration products designed to be easy to digest while providing calories, electrolytes, and hydration during long efforts. Their products are built specifically for endurance athletes who want simple, effective fueling without GI issues. Kava Haven: Kava Haven is a consciously crafted, kava-infused non-alcoholic spirit designed to provide a refreshing alternative to alcohol with a little buzz and none of the booze. Go to KavaHaven.com/IllHaveAnother for 15% off your order, automatically applied through the landing page. The post Episode 702: Vanessa Fraser on a 4:31 Dog Mile, Health Challenges, and the Marathon Ahead appeared first on SandyBoy Productions.

Roger Köppel auf Deutschland-Tour! Jetzt Tickets sichern: https://weltwoche.de/vortrag/ Unterstützen Sie die Weltwoche mit nur einem Klick: https://www.youtube.com/@die.weltwoche?sub_confirmation=1 Und aktivieren Sie die Glocke, damit Sie keine Sendung mehr verpassen. So helfen Sie mit, unser Programm weiter auszubauen. Herzlichen Dank! ⭐️ Weltwoche daily ohne externe Video-Werbung geniessen? Werden Sie Abonnent! ▶️ https://weltwoche.de/abonnemente/ Themen in diesem Video: Entspannt euch: Die AfD aus Sicht einer amerikanischen Stanford-Professors. Nachruf auf die alte, gute Sozialdemokratie. Es ist einfach ungerecht, was mit Russland passiert. Der Krieg in der Ukraine endet erst dann, wenn der Westen seine Fehler zugibt

The FBI seizes former Congressman Eric Swalwell's devices and searches his home as federal authorities investigate sexual misconduct allegations against him. New details reveal how a Snapchat sting and one suspect's cooperation helped investigators unravel an alleged cocaine trafficking ring tied to two Penn State fraternities. Scott Jennings is reportedly angling to become President Trump's next White House press secretary as Karoline Leavitt prepares to step down. Bill Gates' daughter Phoebe Gates faces scrutiny over her startup's affiliate practices as new details emerge about the secret Stanford class she attended.   Subscribe now to Emily's "After Party": Apple: https://podcasts.apple.com/us/podcast/after-party-with-emily-jashinsky/id1821493726 Spotify: https://open.spotify.com/show/0szVa30NjGYsyIzzBoBCtJ YouTube: https://www.youtube.com/@AfterPartyEmily?sub_confirmation=1   Herald Group: Learn more at https://GuardYourCard.com   Birch Gold: Text MK to 989898 and get your free info kit on gold Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

I really appreciate finding commonplace practices that are leading us astray…that we can alter immediately. Imagine an olympic athlete that performed year round. They are always either prepping or recovering for the next event. Their performance would stall. Elite athletes count on having a season of performance, then a season of training. Training. Where they learn new techniques and try new things to raise them up another level. In the workplace and often in our lives, we get stuck in the rut of performing, day after day. In our roles as a spouse or parent, in our work, and even in our hobbies and interests. When do we stop and…engage in training to help us actually get better? I really helped myself with this concept as I realized how stuck I was in some roles of my life, just churning out the same performance day in and day out and plateaued. So I'm revisiting this episode with my expert on this concept, Stanford-trained mindset expert Eduardo Briceño. Eduardo is the author of The Performance Paradox: Turning The Power of Mindset Into Action. He is co-founder of Mindset Works with renowned Stanford psychologist and known as a founder of growth mindset work, Carol Dweck.  You can find Eduardo at briceno.com Sign up for your $1/month trial period at shopify.com/kevin Go to shipstation.com and use code KEVIN to start your free trial. Learn more about your ad choices. Visit megaphone.fm/adchoices

This Week In Startups is made possible by: Superhuman https://Superhuman.com Sentry https://sentry.io/twist Lightfield https://lightfield.app Today's show: Jason's been saying it for years now (and we've got an All In clip from 2023 to prove it). Open source will win the AI race. This week gives us a major evidence point, as $11B legal AI company Harvey released its own in-house model, trained on an open-weight Kimi K3 base. Harvey made a proprietary specialized solution without having to risk sharing its precious expert-compiled data with the major frontier labs. PLUS we're checking out the FREE AI dictation app Willow with co-founder Allan Guo, and finding out how he plans to compete with giants like Wispr Flow and Apple. AND we've got the Stanford student who built an automated golf cart and gave luminaries like Jensen Huang and Sam Altman rides around campus. Guests Allan Guo on X: https://x.com/_allanguo Willow: https://willowvoice.com/ Ethan Goodhart: https://x.com/EthanGoodhart Sign up for the Wind TestFlight: https://testflight.apple.com/join/zZqmhhwf Relevant Links CNBC: OpenAI "will be a public company in 2027": https://www.cnbc.com/2026/08/19/open-ai-ipo-timing-2027-friar.html OpenAI Zero Data Retention Pledge: https://openai.com/index/our-commitment-to-zero-data-retention TWIST (June 2026): Jason comments on OpenAI and training data: https://youtu.be/o3eow1nTrcI?si=orungk7OmplOuyio&t=742 All In podcast (Feb 2023): Jason comments on open source vs. frontier models: https://youtu.be/PVgBWV2bvLs?si=j6y7sHS0wx9q09oa&t=5173 Harvey: https://www.harvey.ai/ Harvey Tenet Research Preview: https://www.harvey.ai/blog/post-training-update-harvey-tenet Fireworks AI: https://fireworks.ai/ Wispr Flow: https://wisprflow.ai/ Pipedrive: https://www.pipedrive.com/ Nvidia Jetson Thor: https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-thor/ OpenAI shares Wind golf cart clip: https://www.tiktok.com/@openai/video/7645336713472134431 Starlink Mini: https://starlink.com/mini-product-us?srsltid=AfmBOorzM7arxhdzYAdDpH6RGD4LLg9WV_BFnEoLuYieuHuGSCjQTE1Y Toro mowers: https://www.toro.com/ 404 Media: https://www.404media.co/ Punchbowl News: https://punchbowl.news/ Semafor: https://www.semafor.com/ Skift: https://skift.com/ Newcomer: https://www.newcomer.co/ Deirdre Bosa on YouTube: https://www.youtube.com/@deebosa Electrek: Genesis GV90 review: https://electrek.co/2026/08/20/genesis-gv90-luxury-coach-doors-images/ Toyota Alphard gallery: https://global.toyota/en/mobility/toyota-brand/gallery/alphard.html The Clash "London Calling" video: https://www.youtube.com/watch?v=EfK-WX2pa8c Timestamps: 0:00 What is Jason's Grok Bot up to? 5:17 Systems over goals 9:43 Superhuman - Superhuman Go is an AI chat that's always there when you need it, already aware of what you're doing, and doesn't ask you to start from zero. Sign up to get the best in AI at https://Superhuman.com 13:08 OpenAI will go public soon 14:19 Top line ARR matters way less than churn 20:43 Sentry - Your team should be focused on shipping features — not chasing down bugs. New users can get $240 in free credits when they go to https://sentry.io/twist and use the code TWIST 29:55 Lightfield - Name one person who's ever enjoyed updating a CRM. Exactly. Lightfield's AI agent does it for you — it even prospects and books your meetings. Used by thousands of startups. Free at https://lightfield.app 30:52 Harvey launches Tenet 34:32 "Open source is going to win it all" 35:58 Allan Guo of Willow joins 39:18 Managing a single source of truth across a team 47:00 Lon and Jason love the Toyota Alphard 51:00 Stanford student and AV expert Ethan Goodhart joins 1:00:09 How Ethan got Jensen Huang to go for a ride 1:00:37 Punk Rock 101 1:06:24 Phoebe Gates' secret Harvard class Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp   Follow Lon: X: https://x.com/lons   Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis   Check out all our partner offers: https://partners.launch.co/   Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland   Check out Jason's suite of newsletters: https://substack.com/@calacanis   Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com

Phoebe Gates went to How To Rule The World class at Stanford, where apparently she learned to admit to potentially criminal activity in Slack. Repeatedly. Also, the network school moves countries, a FarmVille with perma-death, and Mark Zuckerberg has a go at manifesto writing. Get the full episode on Patreon here! RILEY ALERT Check out No Gods, No Mayors here! HUSSEIN ALERT Check out 10k Posts here! MILO ALERT Check out Milo's tour dates here: https://www.miloedwards.co.uk/liveshows NATE ALERT Nate's band Second Homes has just released their debut album and you can stream it for free here! Trashfuture are: Riley (@raaleh), Milo (@Milo_Edwards), Hussein (@HKesvani), Nate (@inthesedeserts), and November (@postoctobrist)

Live August 21, 2026  | Yaron Brook Show(Season 12, Episode 141)Stanford Lets a Socialist Define Capitalism -- Answering Chiara Cordelli | Yaron Brook ShowWatch Now: https://youtube.com/live/RdW7wmHwW6wA political philosopher stood in front of Stanford students and told them capitalism's real sin isn't exploitation — it's that free people get to decide their own future without asking permission first. Nobody in that room challenged her. Yaron Brook does.Chiara Cordelli says the "wrong" of capitalism isn't profit, isn't inequality, isn't even exploitation — it's that private investors, not the collective, get to decide what the future looks like. Her fix? Democratize investment. Plan it. Take that decision out of your hands and put it in the state's.Yaron Brook takes her argument apart at the root: capitalism was never an "investment system," it's a rights system — the first social order in human history built on the principle that force has no place between people. Cordelli isn't describing a flaw in capitalism. She's describing the thing that makes it moral, and she wants it gone.If you've ever wondered how elite universities keep producing "sophisticated" cases for socialism dressed up in academic language, this is the episode that shows you the trick — and the answer.Timestamps 00:00 — Cold open: Why a Stanford philosopher's "critique" of capitalism is really an argument for central planning 06:30 — What capitalism actually is — and why markets and competition aren't it 14:15 — Cordelli's real claim: capitalism "alienates" you from control over the future 23:40 — The switch — "alienation" is just exploitation with a Ph.D. 32:10 — Why "democratizing investment" means putting a gun behind every business decision 41:00 — Individual rights vs. collective control: the actual fight nobody at Stanford will name 50:25 — How elite academia launders socialism as "normative political theory" 

You've probably heard that your ovaries age 2.5x times faster than the rest of your body. I went looking for that study. It does not exist. In this episode I trace that number back to an uncited supplement company blog post, and then I give you the one that is real: ovarian aging shows up 15 to 20 years earlier than in any other tissue in your body. Your ovaries are not a fertility organ. They are the organ that sets the pace for your bones, your heart, your brain and how long you live. This is the first episode built on my four pillars: Nourish, Stack, Live and Align. I also share what is changing behind the scenes, including my practice, my planner, and where this brand is going next. Work with me one on one as a women's longevity practitioner and holistic nutritionist: https://biohackingbrittany.com/pages/clients WHAT I COVER The 2.5x ovarian aging myth and where it actually came from Why the Stanford organ aging clocks never included an ovary Senescence and fibrosis: it is not that the seeds run out, it is that the soil goes hard Why less than 2 percent of venture funding goes to women's health, and 90 percent of that goes to fertility Your full lab panel: AMH by age, antral follicle count, day 3 FSH and estradiol Why AMH does not predict whether you will get pregnant this month The birth control trap: if you are on hormonal contraception your AMH is wrong by 14 to 55 percent PMOS and why a high AMH is not extra time Cysts: functional, hemorrhagic, dermoids, and why the pill does not shrink one you already have Endometriomas, and what cystectomy actually costs your ovarian reserve Premature ovarian insufficiency, the 2024 criteria change, and why POI is not early menopause What the food research does and does not support CoQ10, melatonin, inositol, DHEA, vitamin D, NMN and omega 3, ranked by real human evidence Rapamycin, the VIBRANT trial, and ovarian tissue freezing The pelvic bowl in the classical Sanskrit text, and what it actually describes Jing, Tian Gui, and how Chinese medicine predicted the end of fertility at 49 How misalignment shows up in the body, and the guardrail I insist on Why clinical hypnosis is the woo that actually works TIMESTAMPS 08:03 The 2.5x ovarian aging myth, and what is actually true 13:23 Your ovaries are an endocrine organ, not a fertility organ 19:54 The labs to run, and AMH by age 23:55 If you are on birth control, your AMH is wrong 28:03 Cysts, dermoids, and what the pill does not do 32:24 Endometriomas and what surgery costs your ovarian reserve 34:20 Premature ovarian insufficiency 36:43 Nourish and Stack: food, CoQ10, melatonin, inositol, NMN 47:06 Rapamycin and ovarian tissue freezing 49:22 Align: the pelvic bowl, Chinese medicine, and how misalignment shows up 1:00:18 Clinical hypnosis, and my full protocol MENTIONED IN THIS EPISODE 1:1 client consults: https://biohackingbrittany.com/pages/clients The LongHer Life community, where this was recorded live: https://biohackingbrittany.com/pages/longherlife Her Stack Planner: https://biohackingbrittany.com/products/her-stack-planner Shop: https://biohackingbrittany.com/collections/shop Questions for the next live Q&A: info@biohackingbrittany.com CONNECT Instagram: https://www.instagram.com/biohackingbrittany/ Website: https://biohackingbrittany.com This episode is for education only and is not medical advice.

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

Tina Seelig is the Executive Director of Knight-Hennessy Scholars at Stanford University, the world's largest fully endowed graduate fellowship program. She is also Director Emerita of the Stanford Technology Ventures Program and has spent decades teaching creativity, innovation, entrepreneurship, and leadership at Stanford, including in the Department of Management Science and Engineering and the d.school. Tina earned her Ph.D. in neuroscience from Stanford Medical School, where she studied neuroplasticity, and has worked as a management consultant, multimedia producer, and entrepreneur. She is the author of numerous books on creativity, innovation, and entrepreneurship, including What I Wish I Knew When I Was 20, inGenius, Creativity Rules, and her newest book, What I Wish I Knew About Luck. Her work has earned numerous honors, including the National Academy of Engineering's Gordon Prize for her contributions to engineering and entrepreneurship education. In this episode we discuss the following: For Tina, improving her luck by saying thank you means looking at her calendar at the end of each day and writing thank you notes to everyone who helped her that day. What a powerful story about her college professor. She thanked him 20 years after the class, and 20 years after that, her thank you letter was read at his funeral. As Tina said, there is no statute of limitations on saying thanks. Luck is also a long game. For years, Tina invited John Hennessy to speak to her class. And every year she wrote him a thank you note after he spoke. When she recently asked him if he would have kept speaking without the notes, he said, probably not. And she would have never known it was because she hadn't expressed gratitude. Now Tina runs the Knight Hennessy Scholars program with John. I also love how Tina applies this in the workplace and creates a culture of people saying thank you by simply modeling the behavior.

Newsflash: Long, unbroken stretches of sitting appear to carry health risk beyond the daily total of sitting. But breaking them up can help even when the total hours spent sitting number doesn't budge. A listener asked about NEAT -- the energy burned by the movement of ordinary life that isn't exercise -- and the answer turned out to have little to do with calories and a lot to do with staying strong and mobile as we age! LET'S TALK THE WALK! Join here for support, motivation and fun! Wellness While Walking Facebook page Walking to Wellness Together Facebook GROUP Wellness While Walking on Instagram Wellness While Walking on Threads Wellness While Walking on Twitter Wellness While Walking website for show notes and other information wellnesswhilewalking@gmail.com     RESOURCES AND SOURCES (some links may be affiliate links) Exercised: Why Something We Never Evolved to Do Is Healthy and Rewarding by Dr. Daniel Lieberman (Pantheon, 2021) — the source of the energy-conservation reframe and the point about hunter-gatherers sitting roughly as much as we do. Fed Up: What Evolution Reveals About Food, Diet, Health, and Eating Well by Dr. Daniel Lieberman (Knopf, August 2026) — his new book, which we'll be discussing in a future episode. Move Your DNA: Restore Your Health Through Natural Movement by Katy Bowman (Propriometrics Press, 2nd edition 2017) — the source of the idea that movement works like a diet, with walking as one nutrient rather than the whole menu. Mentioned in this episode: Dr. James Levine — the Mayo Clinic researcher who coined the term NEAT in the 1990s while studying weight gain. His best-known study overfed volunteers roughly 1,000 extra calories a day for eight weeks; fat gain varied widely across the group, and those who gained least tended to be the ones whose everyday movement increased to meet the surplus. Levine JA, Eberhardt NL, Jensen MD. "Role of Nonexercise Activity Thermogenesis in Resistance to Fat Gain in Humans." Science, 1999. The Stanford fitness tracker study — seven wrist-worn devices tested against lab-grade equipment in 60 volunteers. Six of the seven measured heart rate within 5% error. None measured energy expenditure well: the most accurate device was off by an average of 27%, and the least accurate by 93%. Stanford Medicine news summary — Shcherbina et al., Journal of Personalized Medicine, 2017. Sitting time in older adults — reviews of objectively measured sedentary behavior put adults 65 and older at roughly 8.5 to 9.5 hours of sitting a day, more than any other age group, with long uninterrupted bouts associated with cardiovascular disease, blood sugar trouble, frailty, and in some studies higher dementia risk. Breaking up sitting and physical function — a pilot study in frail older adults found improvements in Timed Up and Go and sit-to-stand scores when participants broke up their sitting, even without reducing total sedentary time. Harvey et al., "Stomp Out (Prolonged) Sitting" pilot — note this is a small pilot; treat the finding as promising rather than settled. Movement as a diet — the idea that walking is one nutrient rather than the whole menu reflects Katy Bowman's work, particularly Move Your DNA and Movement Matters. Two small moves from this episode ●    Break up the sitting. Once an hour or so, stand and move, even briefly — refill the water, walk to the mailbox, unload a few dishes. Right after a meal is a particularly good moment, since movement after eating has been shown to soften the blood sugar rise. ●    Attach movement to moments that already exist. The kettle boiling, a phone call, a commercial break, a trip to the printer, the end of a chapter. Decide once what the movement will be, and let variety in — balance on one foot at the counter, reach for a high shelf, get down to the floor and back up. A reminder Carolyn is not a doctor or mental health professional. Content presented here is for educational and informational purposes only. Please check with your doctor before making any health or lifestyle changes.     HOW TO RATE AND REVIEW WELLNESS WHILE WALKING How to Leave a Review on Apple Podcasts on Your iOS Device 1.   Open Apple Podcast App (purple app icon that says Podcasts). 2.   Go to the icons at the bottom of the screen and choose "search" 3.   Search for "Wellness While Walking" 4.   Click on the SHOW, not the episode. 5.   Scroll all the way down to "Ratings and Reviews" section 6.   Click on "Write a Review" (if you don't see that option, click on "See All" first) 7.   Then you will be able to rate the show on a five-star scale (5 is highest rating) and write a review! 8.   Thank you! I so appreciate this!   How to Leave a Review on Apple Podcasts on a Computer  1.   Visit Wellness While Walking page on Apple Podcasts in your web browser (search for Apple Podcasts or click here)  https://www.apple.com/apple-podcasts/ 2.   Click on "Listen on Apple Podcasts" or "Open the App" 3.   This will open Apple Podcasts and put in search bar at top left "Wellness While Walking" 4.   This should bring you to the show, not a particular episode – click on the show's artwork 5.   Scroll down until you see "Rating and Reviews" 6.   Click on "See All" all the way to the right, near the Ratings and Review Section and its bar chart 7.   To leave a written review, please click on "Write a Review" 8.   You'll be able to leave a review, along with a title for it, plus you'll be able to rate the show on the 5-star scale (with 5 being the highest rating) 9.   Thank you so very much!! OTHER APPS WHERE RATINGS OR REVIEWS ARE POSSIBLE Spotify Goodpods Overcast (if you star certain episodes, or every one, that will help others find the show)  Castbox Podcast Addict Podchaser Podbean   HOW TO SHARE WELLNESS WHILE WALKING Tell a friend or family member about Wellness While Walking, maybe while you're walking together or lamenting not feeling 100% Follow up with a quick text with more info, as noted below! (My favorite is pod.link/walking because it works with all the apps!) Screenshot a favorite episode playing on your phone and share to social media or to a friend via text or email! Wellness While Walking on Apple – click the up arrow to share with a friend via text or email, or share to social media Wellness While Walking on Spotify -- click the up arrow to share with a friend via text or email, or share to social media Use this universal link for any podcast app: pod.link/walking – give it to friends or share on social media Tell your pal about the Wellness While Walking website Thanks for listening and now for sharing! : )       DISCLAIMER Neither I nor many of my podcast guests are doctors or healthcare professionals of any kind, and nothing on this podcast or associated content should be considered medical advice. The information provided by Wellness While Walking Podcast and associated material, by Whole Life Workshop and by Bermuda Road Wellness LLC is for informational and entertainment purposes only. It is not intended to be a substitute for professional medical advice, diagnosis or treatment. Always seek the advice of your physician or other qualified health care provider with any questions you may have regarding a medical condition or treatment, and before undertaking a new health care regimen, including walking.     Thanks for listening to Wellness While Walking, a walking podcast and a "best podcast for walking"!

I teach focus for a living, and this week I became the exact problem I'm about to warn you about. Laptop open, three AI tools running, jumping between all of them all day, and getting less done on every single one. Multitasking feels like productivity, especially now that AI has made it easier than ever to run five things at once. But it's a lie, and in this Wednesday Special Edition I break down exactly why. I walk through the Stanford research that studied heavy multitaskers and found the opposite of what everyone expected: the people who multitask the most are actually the worst at it. Worse at filtering distractions, worse at memory, and worse at the one thing you'd assume they'd have mastered, which is switching between tasks. And the damage doesn't switch off. It follows them even when they finally sit down to focus on one thing. I get honest about how AI tools removed the last bit of friction that used to force me to concentrate, why the busy feeling is a golden cage, and the simple system I built to climb back out of it: the 10-Block Weekly Method. Five workdays, two blocks a day, ten blocks total. One rabbit at a time, inside the fence you built for it. If you read this with three other apps open on your phone, this one's for you. Grab the free 10-Block Weekly Method template: https://therealjasonduncan.com/10block Read the full article: https://therealjasonduncan.com/articles/multitasking-is-a-lie Want to talk through where your focus is leaking and what it's costing your business? Book a call with me directly: https://therealjasonduncan.com/talk Subscribe to What's Real?, my weekly newsletter: https://therealjasonduncan.com/articles New episodes every Wednesday. Learn more about your ad choices. Visit megaphone.fm/adchoices

China's ups and downs reverberate across the globe, and unpredictability from Washington has been throwing another wrench into the equation. Good diplomacy and an open trade relationship between these two superpowers is essential, but the playbook keeps changing. In this talk from the 2026 Aspen Ideas Festival, three experts on U.S.-China relations bring us up to date on technological advances, economic leverage and national security, among other foreign policy affairs. Dan Wang, a research fellow at Stanford's Hoover Institution, joins Melanie Hart of the Atlantic Council's Global China Hub and Julian Gewirtz, a research fellow at Columbia University and the Asia Society. CNN journalist Fareed Zakaria moderates and guides the panel.

Chuck starts today with how a headline out of Texas could be more impactful than a headline about starters, and Davis Warren being named the starter at Stanford. We then look at Chuck’s number 8 team, Texas, with Anwar Richardson from OrangeBloods.com.See omnystudio.com/listener for privacy information.

Most high performers treat sleep like a negotiation.Something to borrow from.Something to sacrifice.Something to fix later.But the body does not negotiate forever.In this episode, Dr. Nnamdi Orakpo joins Lloyed Lobo on Traction to unpack why sleep is one of the most important operating systems behind performance, mental health, metabolism, recovery, and long-term function.Dr. Orakpo is a Stanford sleep medicine physician, psychiatrist, MD, PhD, and clinical assistant professor. His work spans insomnia, sleep apnea, narcolepsy, trauma-associated sleep disorders, ADHD, anxiety, depression, PTSD, ketamine therapy, TMS, virtual reality neurofeedback, and GLP-1 medications for sleep apnea and obesity.This conversation is not about generic sleep hygiene.It is about what sleep reveals.Why insomnia is often not just a sleep problem.Why sleep apnea can silently wreck energy, mood, blood pressure, metabolism, and cognition.Why trauma shows up at night.Why founders can feel productive while their brain is running on fumes.And why the future of sleep medicine may combine psychiatry, neuromodulation, metabolic care, and technology.We cover:Why sleep is foundational to founder performanceThe difference between insomnia, poor sleep, and sleep apneaWhy high performers often normalize sleep deprivationHow sleep impacts mood, attention, decision-making, and emotional controlThe hidden relationship between sleep and traumaWhy obesity and sleep apnea reinforce each otherHow GLP-1s may change the sleep apnea conversationWhere ketamine and TMS fit into modern psychiatryWhat virtual reality neurofeedback may unlock for insomnia and chronic painWhy sleep trackers help some people and make others worseThe practical sleep reset every founder should understandThis episode is for founders, executives, parents, clinicians, and high performers who want to stop treating sleep like a soft topic.Because your sleep is not separate from your performance.It is the foundation underneath it.Lloyed Lobo- https://www.linkedin.com/in/lloyedloboDr. Nnamdi Orakpo- https://www.linkedin.com/in/nnamdi-orakpo-md-phd-47793835/

Five innocent words could be quietly destroying your best work: What do you think? In this episode of DarrenDaily On-Demand, Darren Hardy challenges the feedback-obsessed culture of focus groups, surveys, and endless opinions, and argues that the most transformative ideas in history survived precisely because someone refused to listen. He traces the story of a brilliant creative who slowly traded vision for approval, and surfaces a Stanford finding that exposes why the advice you collect is so often disconnected from how people actually decide. Without prescribing what to build, Darren lays out a different framework, one built on observation, testing, and the only feedback that ever truly counts. This episode dives into how to protect your boldest ideas, and why real innovation almost always looks like a bad idea at first. Get more personal mentoring from Darren each day. Go to DarrenDaily at http://darrendaily.com/join to learn more.

Our hearts are restless, says Saint Augustine, until they find their rest in God. The biological science and the spiritual metaphor are in agreement here. Over the course of billions of beats, our hearts never stop to rest.  Your magnificent heart exchanges or pumps your entire blood volume, just over a gallon, every single minute. (Athlete hearts can pump up to 10 gallons per minute!) In this episode, Mark Labberton discusses matters of the heart with his close friend Dr. David Anderson, a cardiologist of four decades who trained at Johns Hopkins and helped launch one of the first interventional cardiology programs in San Francisco. Anderson reflects on the medical science of cardiology, the profound life-saving medical advances over the past forty years, heart disease versus coronary artery disease, how the brain and the heart communicate, and what happens during a heart attack. In the second half of their conversation, they move from physiology to spirituality of the heart: Pharaoh's hardened heart, "out of the heart the mouth speaks," and the human need to find somewhere to locate the self. Anderson closes with what a cardiologist would tell anyone worried about their own heart, and with a case for medicine as a noble profession. Episode Highlights "When I first began in cardiology, if your heart was damaged, imagining that the heart could actually improve its function was like imagining someone could grow back a new arm." "One of the distinctive things about the heart is unlike other muscles, it doesn't have a time to rest. When it gets insufficient blood supply, it can't just say, 'Whoa, I'm gonna stop here.' It asks you to slow down because it starts hurting." "I think that's part of the intrigue of the heart, that it is inside us … but in some ways it's apart from us. It's responding to environmental things that we're not really even aware of. … I think it's this sense of detachment of the heart from us—but yet its centrality—that has led people to posit the soul or the will or the passions or almost anything that you would think was the real you. "Atherosclerosis … I would describe as it's like having a pimple inside your artery." "First of all, know yourself. That is, it's not a time to be fatalistic because your father had a heart attack in his fifties or your brother just had a heart attack. It's a time to be proactive." About David Anderson David Anderson is a cardiologist who practiced for more than four decades before retiring. He studied at Stanford as an undergraduate and earned his medical degree at Johns Hopkins University School of Medicine. He stayed at Hopkins for residency, then trained in San Francisco at the dawn of interventional cardiology, helping to build an angioplasty program in the early 1980s. Helpful Links and Resources Alta Bates Summit Medical Center, Sutter Health, the Oakland hospital Anderson thanks by name: https://www.sutterhealth.org/about-us/our-hospitals/alta-bates-summit-medical-center DeWood et al., "Prevalence of Total Coronary Occlusion during the Early Hours of Transmural Myocardial Infarction," the 1980 study that identified clot as the cause of heart attack: https://www.nejm.org/doi/full/10.1056/NEJM198010163031601 Waagstein et al., "Effect of chronic beta-adrenergic receptor blockade in congestive cardiomyopathy," the Swedish beta-blocker work behind the recovery he describes: https://pubmed.ncbi.nlm.nih.gov/1191416/ What Is Atherosclerosis, National Heart, Lung, and Blood Institute: https://www.nhlbi.nih.gov/health/atherosclerosis Warning Signs of a Heart Attack, American Heart Association: https://www.heart.org/en/health-topics/heart-attack/warning-signs-of-a-heart-attack Angina (Chest Pain), American Heart Association, on the exertional pain he describes at the close: https://www.heart.org/en/health-topics/heart-attack/angina-chest-pain Show Notes The impact of family gene pool, lifelong heart awareness How David Anderson got into medicine, and cardiology in particular A Hopkins autopsy elective, a teenager dead in a parking lot, a mentor who followed From internists interested in the heart to interventionists inside it Catheters, pressures, and the arrival of echocardiography Seeing a beating heart inside a living body for the first time, as a senior resident A colleague's heart attack in 1979 The 1980 angiography study that identified clot as the trigger Spring 1983: an artery occludes, the cath lab opens it, pain gone in fifteen minutes Bypass surgery before that: elective, scheduled, never mid-attack Prevention running under the technology: blood pressure, smoking, the lipid theory Fixing one artery and never having to fix another Coronary artery disease versus disease of the muscle itself Plasticity, not regeneration Adrenaline as an emergency system built for days, not decades Moving from technological advances to molecular-based understanding of heart problems Understanding of the pathophysiology of atherosclerosis and its prevention Beta blockers, ejection fraction, and hearts that came back The heart as a muscle with no rest The heart's own electrical center, and the backup pacemakers below it Your heart doesn't rely on you or your consciousness to beat Why the ancients may have located the self in the heart organ Jeremiah's pounding heart, Pharaoh's hardened one Pascal, the soul, and needing somewhere to put "the real you" Maintaining an appropriate degree of separation from the patient and their family in order to care properly and effectively The difficulty of delivering bad news to patients and their familes Digging for a shared history to call on in a crisis "Medicine is a noble profession." Heart care advice from a cardiologist: First, know yourself. Heart health checklist: cholesterol, blood pressure, weight, knowing your own history The pain that warrants a phone call #Conversing #MarkLabberton #Cardiology #Heart #MedicalScience #HeartHealth #Biology #ChristianHumanism Production Credits Conversing is produced and distributed in partnership with Comment magazine and Fuller Seminary.

We're still surprised people did this but... 50+ founders worth $10M to $4B reveal their personal finances. Here it is: https://joinhampton.com/mw-wrWhy do we do this? Because if you're an aspirational person or someone who runs a business and is making money, it's incredibly challenging to figure out what to do. Information is impossible to find — and that's what we put together: the net worth reveal and why we do this podcast, Moneywise.A $3 billion founder's money advice: keep driving the Chevrolet. Here's why the richest guests all say the same five things.After 100+ episodes of Moneywise, the same five spending refusals kept showing up — from a $3B founder who's never sold a company, a guy who lost 95% of his net worth and won't buy his own socks, and Bryan Johnson, who spends $2M a year on his body and almost nothing on anything else. None of them read the research. There's 50 years of it, and they all landed in the same place anyway.This episode covers all five: first class, new cars, meaningless stuff, angel checks, and kids' comfort — plus the study behind each one (lottery winners, the MIT Celtics auction, the marshmallow test follow-up). Then Anne Mahlum, who sold SolidCore for nearly $100M and forces herself to spend $200K/month, tears the whole list apart. The episode ends with a 10-minute exercise using two questions that decide what stays on your card statement.Also, this podcast is made by Hampton, which is a community for founders doing on average $20 million a year in revenue. We saw a lot of these money conversations happening privately behind closed doors and we thought, "What the heck, let's make it public." If you are a founder, apply here: http://joinhampton.com/mwEpisodes Mentioned:How Rich Is 'Rich Enough' to Fly Private? — https://www.youtube.com/watch?v=5ZyTo6gppPw"I'm worth about $3 billion": What Happens When You DON'T Sell Your Business — https://www.youtube.com/watch?v=uZM0K9eqzx0What It's Like to Lose 95% of Your Net Worth Overnight (the socks guy) — https://youtu.be/fW-F3MKwevIBryan Johnson: I Probably Won't Actually Live Forever — https://www.youtube.com/watch?v=icWHq_xjhacHow to Not Ruin Your Kids with Your Wealth ft. Dr. Becky — https://www.youtube.com/watch?v=uB1SmMA-nLkTimestamps:0:00 — Cold open: the $3B founder, the socks guy, and Bryan Johnson's $2M body budget0:28 — 100 episodes in, the same five patterns kept repeating — and 50 years of research explains them1:05 — Why guests reveal their real numbers on Moneywise1:50 — #1: First class. "I still fly coach unless it's international" — his "poor kid habit"2:27 — Hedonic adaptation, and the lottery winners who scored lower on enjoying breakfast3:52 — #2: New cars. The $3B founder's advice: don't buy the Ferrari, drive the Chevrolet4:14 — The Millionaire Next Door data (most popular millionaire car: Ford F-150), "big hat, no cattle"4:40 — The commute study: zero relationship between car value and happiness5:35 — #3: Stuff. The socks guy's filter: "Does this dollar come back to me or is it gone?"6:03 — Stanford brain scans: every purchase is want vs. hurt6:28 — The MIT Celtics auction — credit card bidders paid double7:26 — #4: Angel checks. Bryan Johnson writes none — half of deals lose money, 7% produce 75% of returns8:41 — Opportunity cost neglect and attention residue: every check is an open tab in your head10:15 — #5: Kids' comfort. Parents who could buy any seat, flying the family in coach on purpose11:06 — The marshmallow test follow-up wealthy parents actually care about12:11 — 70% of family money gone by generation two, 90% by generation three13:17 — The counterargument: Anne Mahlum ($115M, spends $200K/month) — "I hate when people don't spend on principle"14:19 — The 2023 rerun of the $75K happiness study, and buying back time15:55 — The 10-minute exercise: two questions to run against last month's card statement16:33 — If you run a $3M+ company: HamptonSponsors: Daily Body Coach - achieve your dream body with https://moneywise.dailybodycoach.comSubscribe to Moneywise: https://www.youtube.com/@themoneywisepodcastFollow Daniel on X: https://x.com/danielcberkListen on Spotify / Apple Podcasts: [search "Moneywise Hampton"]

TV writer Raina Morris (Emily in Paris) is here to discuss all things taboo for SATC S2E12, “La Douleur Exquise!”  It's happening. Carrie is in the doorway with the riding crop and Big is telling her to not get Carried Away. In even more iconic moments from this episode, Will Arnett defies gravity to give Miranda head in a cab, Stanford boldly acts on his sweet-as-pie underwear fetish, Charlotte gets Art of the Dealed in a shoe store, and Carrie finally faces her masochism fetish with her Big Beautiful Boyfriend. We chat how, despite the haters, Carrie and Marnie won by being cringe, the concept of getting off at the birthplace of Huck Finn, how a cab is basically a threesome, and conspiracy theories on modern footwear. Plus: Carrie's continued Navy Seal torture tactics, Evan's history with foot fetishes, Raina's argument for embarrassing yourself as much as possible in front of a lover, and how this episode is the perfect encapsulation of the three themes for a young girl's bedroom: heels, kiss mark, Eiffel Tower! Follow @quakerraina and enjoy her beautiful archive while she abides by her Carried Away. Our listeners can buy one pair of glasses and get 20% off any additional pairs at WarbyParker.com/GIRLSREWATCH — and using our link helps support the show. #WarbyParker #ad Use code GIRLSREWATCH at jonesroadbeauty.com to get a Free Gift with your first purchase! #JonesRoadBeauty #ad Learn more about your ad choices. Visit megaphone.fm/adchoices

SPONSORS: - Take Cheers Restore after your last drink or before going to bed and wake up feeling at least 50% better — or your money back. For a limited time our listeners are getting 20% off their entire order at https://cheershealth.com/TOE - I personally subscribe to The Economist. TOE listeners get 35% off the annual subscription. No other podcast has this! https://economist.com/TOE Peter Godfrey-Smith, professor at the University of Sydney and author of Other Minds: The Octopus and the Deep Origins of Consciousness, joins to explain why he thinks meeting an octopus is the closest we'll get to meeting an alien. He's now developing a new, speculative theory of consciousness centered on rhythm. The conversation explores why he thinks "having a mind" is a graded property rather than a yes-or-no one — and what that implies for earthworms, bacteria, and plants. We discuss how many selves may exist inside a single octopus. Godfrey-Smith explains why he suspects rhythmic, large-scale electrical activity in the brain — not just point-to-point neural firing — is essential to consciousness. He also addresses what Anthropic's discovery of a "workspace" inside Claude does and doesn't show, and why he assigns computers a very low probability of being conscious. Finally, he makes the case for why a silicon neuron could never do "exactly" what a biological one does. This is an in-depth conversation with Peter Godfrey-Smith. FOLLOW: - Spotify: https://open.spotify.com/show/4gL14b92xAErofYQA7bU4e - Substack: https://curtjaimungal.substack.com/subscribe - Twitter: https://twitter.com/TOEwithCurt - Discord Invite: https://discord.com/invite/kBcnfNVwqs - Crypto: https://nowpayments.io/donation/TOE - PayPal: https://www.paypal.com/donate?hosted_button_id=XUBHNMFXUX5S4 TIMESTAMPS: - 00:00:00 - Cephalopods: The Closest Aliens - 00:05:30 - Defining the Unconscious Mind - 00:10:40 - Selfhood vs. Information Processing - 00:16:10 - The Graded Nature of Mind - 00:22:15 - Deflating the Concept of Life - 00:29:00 - Convergent Evolution of Intelligence - 00:35:40 - Distributed Neural Control Systems - 00:42:50 - Multiple Subjects in One Body? - 00:48:40 - Global Workspace Theory Critique - 00:56:10 - AI and the Conscious Workspace - 01:01:28 - Simulation Hypothesis Probabilities - 01:08:40 - Biological Hardware Constraints - 01:16:10 - Rhythmic Consciousness Theory - 01:22:30 - Mind Uploading and Silicon Neurons - 01:31:00 - Neural Dynamics of Subjectivity - 01:37:00 - Deep Ocean Intelligence LINKS MENTIONED: - Other Minds [Book]: https://amazon.com/dp/0374227764?tag=toe08-20 - Peter's Website: https://petergodfreysmith.com/ - Reality+ [Book]: https://amazon.com/dp/0393635805?tag=toe08-20 - A Materialist Theory of the Mind [Book]: https://amazon.com/dp/0415100313?tag=toe08-20 - Rodolfo Llinas: https://med.nyu.edu/faculty/rodolfo-llinas - Bruno's Research: https://qbi.uq.edu.au/groups/vanswinderen - Dehaene's Commentary on Language Models [Paper]: https://www-cdn.anthropic.com/files/4zrzovbb/website/cc4be2488d65e54a6ed06492f8968398ddc18ebe.pdf - A Cognitive Theory of Consciousness [Book]: http://cogweb.ucla.edu/Abstracts/Baars_88.html - Oscillations in the Central Brain of Drosophila Are Phase Locked to Attended Visual Features [Paper]: https://www.pnas.org/doi/10.1073/pnas.2010749117 - The ENCODE Project: https://plato.stanford.edu/entries/genomics/encode-project.html - What Is It Like to Be a Bat? [Paper]: https://www.sas.upenn.edu/~cavitch/pdf-library/Nagel_Bat.pdf - The Explanatory Gap [Paper]: https://philpapers.org/rec/LEVMAQ - Inferring Consciousness in Phylogenetically Distant Organisms [Paper]: https://direct.mit.edu/jocn/article/36/8/1660/120485/Inferring-Consciousness-in-Phylogenetically - Remarkably Bright Creatures [Book]: https://amazon.com/dp/0063204150?tag=toe08-20 - Giant Cuttlefish: https://australian.museum/learn/animals/fishes/giant-cuttlefish-sepia-apama-gray-1849/ - Cuttlefish Exert Self-Control in a Delay of Gratification Task [Paper]: https://royalsocietypublishing.org/rspb/article/288/1946/20203161/86132/Cuttlefish-exert-self-control-in-a-delay-of - Stanford Marshmallow Experiment: https://en.wikipedia.org/wiki/Stanford_marshmallow_experiment - How We Found the Giant Squid: https://www.ted.com/talks/edith_widder_how_we_found_the_giant_squid More links at https://curtjaimungal.substack.com Guests do not pay to appear. #science Learn more about your ad choices. Visit megaphone.fm/adchoices

Time for some Crystal Ball & Roster follow-ups as U20 Worlds get underway!TIMESTAMPS:0:00 - We're Back & It's RAF Week!05:30 - Junior Worlds Underway09:40 - Oklahoma State Fans Mad at Willie22:40 - Jesse Mendez at 141 or 149?25:50 - Ono/Blaze Flip 33 and 41?34:30 - Bennett Berge or Mikey White38:55 - We are Sending a SQUAD to Poland Open43:45 - Kannon Webster Moving Weights?46:35 - Konner Doucet's Return53:40 - Stanford and Nebraska Recruiting01:05:45 - Final Questions from the ChatRokfin.com/MatScouts for all of Willie's Content!Be sure to SUBSCRIBE to the podcast. NEW EPISODES WEEKLY! Support the show & leave a 5-star rating and review on Apple Podcasts, and shop some apparel on BASCHAMANIA.com! For all partnership and sponsorship inquiries, email info@baschamania.com.BASCHAMANIA is a Basch Solutions Production. Learn more about Basch Solutions, a digital marketing agency specializing in custom websites, content creation, and digital strategy, at BaschSolutions.com.

The Dolphins prepare to welcome the New York Giants to town for Saturday's preseason game, while Joe and the guys have some fun with station stories, including trying to set up Julie Guy on a date, the challenges of being the station's ticket guy, and Joe's 91-year-old father getting called for jury duty. Plus, breaking news as rookie WR Chris Bell is activated off the non-football injury list. The guys discuss whether Bell could be ready for Week 1, Miami's West Coast-heavy start against the Raiders and 49ers, and why Joe believes the Dolphins may wait until Week 3 against the Chiefs. Joe also discusses how quickly players are returning from serious injuries in today's NFL. Finally, UM head coach Mario Cristobal joins the show to discuss the Hurricanes' roster, competition, offensive line and preparations for the season opener against Stanford.

UM head coach Mario Cristobal joins the show to discuss the latest roster updates, including several new faces along the offensive line, as the Hurricanes prepare for the season opener against Stanford. Mario talks about the competition throughout the roster, his “1-0” mentality and the importance of staying focused on the next game, while also discussing Miami's year-round recruiting efforts.

Monday's Joe Rose Show is packed with reaction from around South Florida sports. Joe and the crew break down the Dolphins' first preseason game against Washington, including the strong performance from Miami's starters, concerns with the backups, Jamaree Salyer's season-ending injury and the latest on rookie WR Chris Bell. UM head coach Mario Cristobal joins the show to discuss the Hurricanes' roster, offensive line, competition and preparations for the season opener against Stanford. Plus, Omar Kelly breaks down the Dolphins' roster needs, Malik Willis and the young players who could be key to Miami's season. The guys also react to Tua Tagovailoa's rough preseason debut, the ongoing Jayden Daniels-LSU feud, Cam Vaughn's departure from Miami, and plenty more Dolphins, Canes and NFL talk.

Joe Rose and the crew react to Friday's Dolphins preseason opener against Washington, discussing the strong performance from Miami's starters, the solid starting offensive line and the struggles of the backups. The guys also react to Jamaree Salyer being lost for the season and why the Dolphins will likely remain active adding and cutting players before the roster is finalized. Plus, Miami Hurricanes head coach Mario Cristobal joins the show to discuss roster updates, competition, several new faces along the offensive line, the upcoming season opener against Stanford, his “1-0” mentality and year-round recruiting. The hour also features Hollywood's card show and a look ahead to the Marlins' important series against the Phillies in the NL Wild Card race

Michael McFaul — Multi-Part, Part Two: Michael McFaul, a Stanford professor and former US Ambassador to Russia, continues his discussion of Autocrats Versus Democrats: China, Russia, America, and the New Global Disorder, shifting to modern Putinism and the no-limits partnership between Russia and China. McFaul describes Vladimir Putin as a risk-taker driven by imperialist ideology rather than national interest alone. He advocates for a grand strategy to counter autocracy by strengthening democratic alliances, supporting human rights, and maintaining America's edge in attracting global talent. He concludes that while US democracy is currently wobbling, its economic and ideational power remains superior if unified. (2)

Michael McFaul — Multi-Part, Part One: Michael McFaul, a Stanford professor and former US Ambassador to Russia, details his book Autocrats Versus Democrats: China, Russia, America, and the New Global Disorder. In Part One, McFaul examines historical turning points, beginning with the 1962 Cuban Missile Crisis and the 1991 Soviet collapse. He argues that the West was too complacent following the Cold War, missing critical opportunities to consolidate democratic institutions in Russia and China. He also explains how China's century of humiliation informs Xi Jinping's current desire for strength. (1)

Keach Hagey: Keach Hagey, author of The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future, explores the rise of Sam Altman and the founding of OpenAI, which launched in 2015 as a nonprofit research lab aimed at developing artificial general intelligence safely. Altman partnered with Greg Brockman and lead scientist Ilya Sutskever, securing initial billion-dollar commitments from major players such as Elon Musk and Peter Thiel. The narrative follows Altman's trajectory from a brilliant student at John Burroughs School to a Stanford dropout who founded the startup Loopt. Though Loopt was considered a relative failure, Altman's charismatic storytelling and investment prowess eventually led him to succeed Paul Graham as president of Y Combinator. As OpenAI's needs for computational power grew, the organization transitioned into a complex for-profit structure, leading to a power struggle that saw Musk depart. The account highlights a pivotal 2023 crisis in which the board fired Altman over concerns regarding his transparency, only for him to be reinstated after a massive staff revolt. Throughout, the book balances Altman's unwavering optimism for the future against stark warnings from AI godfathers about the potential existential risks of unaligned artificial intelligence. (1)

The Learning Leader Show with Ryan Hawk www.LearningLeader.com The Price of Becoming is a USA Today, LA Times, and Publishers Weekly Bestseller! www.LearningLeader.com/Becoming My guest - Mark Pincus is the founder of Zynga, the social gaming company he built from zero to $12.7 billion, pioneering a category that generated over a billion app installs and introduced hundreds of millions of people to online play. He's a serial founder who has started ten companies, taught product development at Stanford, and made early seed investments in Facebook and Twitter. His new book is Life at the Speed of Play. Key Learnings Coaches are cheat codes. Mark has hired a surfing coach, a tennis coach, a chess coach, a life coach, and the legendary Bill Campbell. None of them were assigned to him. He went and found them. "You throw to where you swing. You don't swing to where you throw." His tennis coach, Jorge, on learning how to serve. Commit to the natural swing first. You'll miss. You'll look stupid. Eventually your brain realizes you're serious and puts the ball where it belongs. You play to get better. You don't play to win. Not this point. Not this match. What am I doing right now that makes me better in the next game? Kill the ego so it gets out of the way of you being humble and curious. Don't contort the organization around one talented jerk. Colleen McCreary, Zynga's chief people officer, kept a sign on her wall: "No jerks allowed." She once vetoed an acquisition because she believed the founder was toxic. When the organization sees you refuse to bend just to win one point, they build muscle and confidence too. You'll have periods out of alignment. That's okay. Sometimes the person is in a crucial seat and you can't swap them tomorrow. What matters is that you're aligned with the philosophy. Bill Campbell's two lessons: intellectual honesty and courage. Be committed to the deep truths above everything else, and say them to your team even when it hurts. Then have the courage to stand up to your own team, your own board, and your own investors. A well-run company is a democratic dictatorship. Campbell leaned heavily on the dictatorship half. Unapologetically. The flip side of ambition is sacrifice. Everyone says they're a ten out of ten on ambition.  Mark's test: would you toil in obscurity for the next ten years, nobody respecting what you're doing, for an 80 or 90 percent chance at something bigger than your wildest dreams? Or take the 80 percent chance at a respectable single and a pat on the back? Nobody believes in us as much as we do. That's why we become founders. At 41, VCs who had already backed Mark twice told him he was too old, too rich, and too settled to go all in on Zynga. Mark isn't all in until he is. He calls it a lazy on-ramp to curiosity. Lots of projects. Looking for signals in a lot of places. Processing. Then it flips. "When the fish are running, we're up all night throwing nets until they're done running, not until we're tired." The chess lesson: play boring. His chess coach told him if you want to beat a player rated much higher than you, play boring and conservative and let them make the mistake. He started doing it. He started winning. Your kids don't follow what you say. They follow what you do. Mark set out to be a different kind of dad than his own, who measured people by their résumé. Then he watched his 15-year-old daughter spend every waking minute with math tutors before leaving on a service trip. "How did we get here? Oh, I know how we got here. They're not listening to what Dad says. They're following what Dad does." Happiness comes from feeling useful to a community you care about. Not from being useful. From feeling it. That's Alfred Adler, by way of The Courage to Be Disliked. There are two levels of success. A life well lived is a life in alignment with what only you can bring to the world. You don't have to build Google to feel like you went for it. The ultimate success is the greatest instantiation of your talents. Use money as freedom, not lifestyle. When Freeloader sold and 28-year-old Mark had more money than he'd ever imagined, he made a list of everything he was going to buy. It totaled about $9,000. He put leather interior in his Pathfinder. He still has the vehicle.  He set a $500 million goal that had nothing to do with buying things. At a net worth around $15 million, working with a life coach, he wrote it down because that's the capital required to run a studio of teams chasing many ideas without going to anyone else with hat in hand. "Creatively, that's freedom." Retirement is spiritual death. The stretches between building things are what Mark calls the abyss. Once you've felt the high of building with a great team and shipping something into users' hands every week, it ruins you. You can't be happy with less. Over-fund the things that matter. Not every day is the same. Not every life moment is the same. When one of those moments comes, put all your best players on the ice. The Allen & Company investor tour. They told Mark it was a dog and pony show. Just shake some hands. He told his team: this is our IPO, this is our roadshow, and we are going to use every minute of it the way we want. He gave the biggest public market investors a full presentation, handed them his numbers, and told them to judge him against those numbers in a year. He met with them a year later and had crushed them. King for a day. Carol Bartz brought Mark in to talk to Yahoo's entire senior management. He spent two weeks building a presentation on how he'd run Yahoo if he were king for a day, stood up, and took over the room for an hour. They got a deal done and one person in that room came to work for him. He ran the same play on the CEO of AMEX with a credit card that competed on fun. Amex spent $75 million with them. Seven minutes with Obama became 45. Mark was told the President would ask about his kids and then the meeting would be over. He walked in with a PowerPoint on the ten bold objectives he'd run on if he were president. Obama went through the entire thing and then asked what else he had. Prepare as hard for everything going right as you do for everything going wrong. Mark had under-prepared for the version of where Obama said, "Yeah, I'm buying. What else you got?" We sleepwalk through our own biggest moments. The question isn't what's the agenda or why they want to meet with you. It's: I have five minutes with the king. What is theoretically possible here? The Book of Life. Every year during the Jewish New Year, Mark writes in the same book. Only during that window. It isn't a journal. It's a spiritual board meeting with himself. He reads back through every previous year first, then writes. Partner with your future self. "What will Mark 2030 thank me for doing right now?" It started with quitting smoking. October 19, 1994. "I pulled my own power back. If I can quit smoking and really commit to it, what else can I do?" A year later he quit his job and did something bigger. Even if you missed the big goal, ask what you actually did toward it. Did you talk about it? Did you do real things? Did you turn the boat toward it? If not, maybe you don't believe in the goal. When Mark is at his lowest, he's humbled, he picks achievable goals, and he finds the most grit. When he's winning, he picks stratospheric goals and does the worst. After his biggest successes, he isn't humble enough to succeed again, and he has to go through failure to reel himself back in. Mark's champagne moment a year from now: that the book connects for hundreds of thousands of people, and that he's built a product people find real meaning in. Reflection Questions If you went and hired a coach, who is the person who could most change your life right now?  What is your next high-stakes moment, and are you treating it like the dog and pony show or like your IPO? What is the one habit your future self would most thank you for making this year? What would it unlock if you actually committed to it? More Learning #688: Dr. Henry Cloud - Your Desired Future: 5 Steps to Take You Where You Want to Go #689: Eric Ries - Why Good Companies Go Bad, and How Great Companies Stay Great #687: Jim Collins - What to Make of a Life & The 3 Types of Luck Episode Chapters 00:00 Meet Mark Pincus 01:40 Why Mark Hires So Many Coaches 02:25 Lessons From Tennis In Leading People 04:56 Refusing to Bend the Culture for One Great Player 07:03 Lessons From Bill Campbell 09:17 On Being Called a Control Freak 13:38 The Ambition Question Mark Asks Founders 14:53 What High Standards Do to Your Kids 20:55 How Mark Defines Success  23:26 Money as Freedom, Not Lifestyle 29:45 Why High Achievers Can't Retire 32:39 Over-Fund the Things That Matter 34:35 Treating a Handshake Tour Like an IPO Roadshow 35:36 King for a Day at Yahoo and Amex 38:14 Seven Minutes With Obama That Became 45 41:39 The Book of Life 45:21 Why Mark Does His Best Thinking at His Lowest 46:48 The Champagne Question 48:40 EOPC