Podcasts about Nobel Prize

I joined Peter McCormack to dig into one of the deepest unanswered questions in physics: what is time, and why does it only seem to move in one direction? My answer might surprise you. Time may not be a fundamental feature of reality at all, and understanding space, matter, and energy still doesn't mean we understand the universe itself. We cover: - What may have existed before the Big Bang - Why something can't truly come from nothing - How my work with the Simons Observatory could detect gravitational waves from the universe's earliest moments - What dark matter actually means - Why even a film as detail-obsessed as Interstellar still gets the physics wrong. The conversation then turns from cosmic time to human time: mortality, family, meaning, and attention. We get into UFO disclosure, the collapse of institutional trust, and whether AI can ever reproduce the embodied human insight that let Einstein transform physics. My take: AI remains a tool built to serve humanity, and it's time we stop apologizing for humanity's greatness. ———

A new week means new questions! Hope you have fun with these!Which US state has the only American diamond field open to the public?The Piano is the most popular musical instrument in the world. In which country was it invented?In the musical Grease, Danny Zuko, Kenickie, Sonny, Doody and Putzie are all members of what group?Saffron is s spice derived from which flower?In the NATO phonetic alphabet what word represents the letter G?Which war lasted from 1936-1939?Who recently made headlines saying they'll be returning to the U.K. by the end of the month?Erik Larson's book "The Splendid and the Vile" is a biography of what person during 1940?Popular in the 70s and 80s, what is the edible term for the device used for amateur non-commercial communication?Who was the first American woman to win the Nobel Peace Prize in 1931?In Norse mythology, Fenrir is destined to break free and devour Odin during Ragnarok; what type of creature is Fenrir?In I dream of Jeannie, besides the obvious, what part of Barbara Eden's body did censors demand her genie costume cover up?Cartoonist Gary Larson won 4 awards in 10 years from the National Cartoonist Society for what surrealist single-panel strip?In the film version of Little Shop of Horrors, Audrey II (the carnivorous plant) is voiced by Levi Stubbs, lead vocalist of which legendary Motown Group?The stars Elnath and Tianguan represents the horns in which constellation?On the periodic table, Element 111 is named after which German physicist, the first to win the Nobel Prize in Physics?MusicHot Swing, Fast Talkin, Bass Walker, Dances and Dames, Ambush by Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0/Don't forget to follow us on social media:Patreon – patreon.com/quizbang – Please consider supporting us on Patreon. Check out our fun extras for patrons and help us keep this podcast going. We appreciate any level of support!Website – quizbangpod.com Check out our website, it will have all the links for social media that you need and while you're there, why not go to the contact us page and submit a question!Facebook – @quizbangpodcast – we post episode links and silly lego pictures to go with our trivia questions. Enjoy the silly picture and give your best guess, we will respond to your answer the next day to give everyone a chance to guess.Instagram – Quiz Quiz Bang Bang (quizquizbangbang), we post silly lego pictures to go with our trivia questions. Enjoy the silly picture and give your best guess, we will respond to your answer the next day to give everyone a chance to guess.Twitter – @quizbangpod We want to start a fun community for our fellow trivia lovers. If you hear/think of a fun or challenging trivia question, post it to our twitter feed and we will repost it so everyone can take a stab it. Come for the trivia – stay for the trivia.Ko-Fi – ko-fi.com/quizbangpod – Keep that sweet caffeine running through our body with a Ko-Fi, power us through a late night of fact checking and editing!

The same mathematics that explains why a fridge magnet sticks to your refrigerator also explains why neural networks work when they have no right to. His group did the calculation. Nobody else had. Subscribe if you want science with evidence, not speculation. Goldenfeld is a Professor of Physics at UC San Diego, a Fellow of the Royal Society, and a Member of the National Academy of Sciences who spent 36 years at the University of Illinois applying condensed matter physics to problems everyone else had given up on: why the genetic code is optimal, why early life evolved impossibly fast, and why AI works despite being overparameterized beyond anything classical statistics can explain. The thread connecting all three is one idea: that what emerges from many things together is qualitatively different from the sum of its parts. That idea explains magnetism, AI, the origin of the genetic code, and why life may be inevitable wherever the laws of physics apply. He also argues that what is happening to science right now is not a disagreement about facts but a fracture in how people decide what is true, and that is a more dangerous problem. What you'll hear: -Why the same phase transition that explains magnetism also explains why AI works at all -Why Francis Crick concluded life must have come from outer space and what Goldenfeld found instead -What horizontal gene transfer has to do with how libraries work -Why Goldenfeld thinks Enceladus is a better bet for life than Europa -The purpose of life, stated as a thermodynamics problem -Why “different is more” is more useful than “more is different” “The impact you make is the ratio of what you do divided by what everybody else does. Minimize the denominator.” — Nigel Goldenfeld CHAPTERS 00:00 AI shouldn't work. It does. 00:56 What is a phase transition? 03:10 What the renormalization group does 08:54 Ising gave up. Wrong dimension. 13:32 Nigel almost met Ising. 40 minutes away. 26:38 AI is the best example of more is different 33:52 Bardeen won two Nobels. The transistor looked like chewing gum. 37:24 The three mysteries Crick couldn't solve 46:20 The genetic code can't evolve. And yet it did. 49:00 How early life evolved like a library 57:06 The purpose of life as a physics problem 01:00:04 Life is physics, not chemistry 01:05:56 Anti-science age. Not because of opinions. 01:13:00 20 seconds with your 20-year-old self 01:17:14 Different is more Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt Have a .edu email and live in the USA? You automatically win a meteorite: https://BrianKeating.com/edu Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Support Into the Impossible on Patreon, get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join Featured Guest: Nigel Goldenfeld website: https://guava.physics.ucsd.edu/~nigel/ Lectures on Phase Transitions and the Renormalization Group: https://www.amazon.com/dp/0201554097?lv=shuf&channelId=500&plpRedirect=mhFallbackNigel Goldenfeld on Twitter/X: https://x.com/NigelGoldenfeld My books: Losing the Nobel Prize (memoir): http://amzn.to/2sa5UpA Think Like a Nobel Prize Winner: https://a.co/d/03ezQFu Focus Like a Nobel Prize Winner: https://a.co/d/hi50U9U Galileo's Dialogue (first-ever audiobook): https://a.co/d/iZPi9Un Twitter/X: https://x.com/BrianKeating Substack: https://briankeating.substack.com Blog: https://briankeating.com/blog Audio-only: https://briankeating.com/podcast #intotheimpossible #briankeating #NigelGoldenfeld #physics #AI #originoflife #condensedmatterphysics #podcast Learn more about your ad choices. Visit megaphone.fm/adchoices

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.

It's Monday, August 24th, A.D. 2026. This is The Worldview in 5 Minutes heard on 140 radio stations and at www.TheWorldview.com.  I'm Adam McManus. (Adam@TheWorldview.com) By Adam McManus Urge Chinese ambassador to allow Chinese pastor to come to U.S. ChinaAid, an international group supporting persecuted Christians inside China, has launched a petition and advocacy campaign to bring Pastor John Cao back to his home in the U.S, reports International Christian Concern. Pastor John has been imprisoned in China for seven years for his missionary work on the China-Myanmar border, helping establish 16 schools that serve thousands of local children and families. While he was “released” in 2024, he is still blocked from returning to the United States, where his wife and sons -- all of whom are American citizens -- live. To make matters worse, Pastor John now has advanced prostate cancer, and his situation is even more critical. ChinaAid's campaign and petition, which I've signed, call for Pastor John's immediate release, particularly urging President Donald Trump to personally raise his situation in his meeting with Chinese President Xi Jinping on September 24th. Write a polite and urgent 4-sentence note to the Chinese Ambassador to America, Xie Feng, urging him to persuade Chinese President Jinping to release Pastor John Cao so he can reunite with his wife and children here in America. Send it to the Chinese Embassy, 3505 International Place NW, Washington, DC 20008. You can find that address in our transcript today at www.TheWorldview.com. Wolves in White Coats: How doctors profited from “gender medicine” Appearing on Epoch TV, Admiral Brian Christine, who serves as the Health and Human Services Assistant Secretary, discussed a disturbing new report entitled “Wolves in White Coats: How Doctors and Hospitals Pushed and Profited from the Fraud of so-called ‘Gender Medicine.'” He laid significant blame on the Biden administration and his predecessor, a man born Richard Levine in 1957, who pretended to be a woman named “Rachel.” CHRISTINE: “The gender ideology pushed on the American people, and these unfortunate children, by the Biden administration, the last administration, and by my predecessor ‘Rachel' Levine, he really pushed gender ideology on the country and even suggested removing age limitations on performing mutilating surgeries or castrating chemicals.” Admiral Christine defined what a sex-rejecting procedure was. CHRISTINE: “Sex rejecting procedures: What we mean is either castrating chemicals, cross-sex hormones, or even surgeries to change the external appearance of the body. These mutilating surgeries that don't do anything to change gender, but radically change the appearance of the body, and can certainly have complications along the way.” He revealed the alarming number of surgeries that were tragically performed on confused minors. CHRISTINE: “From 2019 up to 2023, $120 million spent on sex-rejecting procedures in this country. Over 5,500 surgeries, sex-rejecting surgeries on minors.” Not surprisingly, fraud abounded. CHRISTINE: “There was $11 million billed for ‘precocious' puberty in kids who were 13 to 17 years old. That's not precocious puberty. That's just puberty!” The financial incentive for the ethically-challenged doctors is obvious. CHRISTINE: “If you take a child who has gender dysphoria and you get them to agree to sex-rejecting procedures, you've created a patient for life. So, there can be a tremendous financial incentive for these practitioners and these clinics to have these patients who come back again and again and again.” Psalm 82:4 says, “Rescue the weak and the needy; deliver them from the hand of the wicked.” ‘Influencer' tries to justify her 26-week abortion A social media fitness and lifestyle influencer in Australia with 480,000 followers, named Danielle Mitchell, has publicly documented the details of her abortion at 26 weeks after discovering her baby had severe defects and might have died before birth, reports LifeSiteNews.com. MITCHELL: “We had our fetal medicine growth scan today. And they also had our results for the amnio[centesis], the chromosomal abnormalities, and some new findings on her brain, being that she won't survive.” She went viral for sharing a video on X in which she explains that she had scheduled an abortion. Oddly enough, she entitled it, “Our sweet baby girl goes to sleep with the angels today at 26 weeks.” Through tears, she said this. MITCHELL: “I feel like I'm at peace with my decision. I didn't know how late-term terminations work. “You lay on a bed, awake, and they have doctors and midwives in the room. Under ultrasound, [they put] a needle into the baby's heart, and inject it to stop it from beating. And then you slowly feel all the movements stop from the baby.  No more kicks. Get sent home for another 24 hours, back up to the hospital, and they induce you, and give birth to a sleeping baby.” Mitchell became extremely emotional, saying that she needed to “let out” the grief before she explained to her young daughter that her baby would not be here anymore. MITCHELL: “Baby sister is gonna go live in the clouds today with Nanny Sandy. Okay?” DAUGHTER: “I love you.” MITCHELL: “I love you too, baby. Cuddle?” DAUGHTER: (giggles) On X, Mitchell's video of herself explaining the abortion was shared without the context of the child's condition. She was lambasted there in a flurry of outrage, while on her Instagram page she was met with more sympathy by followers who perceived that she felt she had “no choice” and was sparing her baby suffering overall. Some social media users pointed out that the diagnoses and prognoses of doctors for unborn children are frequently false. LifeSiteNews noted, “A high likelihood that a baby will not survive birth is never a justification for the direct killing of innocent life.” In response, veteran pro-life activist Frank Pavone shared a prayer for women considering abortion, and for the protection of unborn children. He wrote, “Dear Lord, send Your heavenly angels to women thinking about abortion. Protect their unborn children, soften the hearts of their parents, and open their eyes to the truth that abortion takes an innocent human life. Amen.” Young brothers address science and the Bible And finally, Beau, age 11, and Gray Reusch, age 8, are young brothers who have gone viral on social media for creating Christian faith and apologetics content through their family's brand, Armor Up Kids. Their father is Will Reusch, the founder of Armor Up Academy. In one of their videos, they explore the Bible and science. GREY: “People say, ‘It's Bible versus science.'” BEAU: “But what if they've been saying the same thing the whole time?” GREY: “Watch this.” BEAU: “I'm Beau.” GREY: “And I'm Grey.” BEAU AND GREY: “Let's show you something crazy!” GREY: “The Bible says a grateful heart is good medicine. Proverbs 17:22.” BEAU: “Science says gratitude reduces depression and improves mental health.” GREY:  “Be transformed by the renewing of your mind. That's Romans 12:2.” BEAU: “Science calls that, here we go, neuroplasticity. It means your brain can literally change.” GREY: “Jesus said some things only come from prayer and fasting. That's Matthew 17:21.” BEAU: “Science says fasting helps your body heal and reset itself. It's called cellular autophagy. And it actually won the Nobel Prize.” GREY: “But a peaceful heart gives life, but envy rots the bones. That's Proverbs 14:30.” BEAU: “Science shows anger increases disease and stress in your body.” GREY: “Sing and make music for the Lord. That's Ephesians 5:19.” BEAU: “Science says music releases dopamine and reduces stress.” GREY: “So, the Bible isn't outdated.” BEAU: “It's ahead of its time.” GREY: “God designed your mind …” BEAU: “and science is just catching up.” GREY AND BEAU: “So, it's not Bible versus science. It's truth confirming truth! ” GREY: “Don't ignore what God already said.” BEAU: “You might be missing what actually works.” Psalm 111:2 says, "Great are the works of the Lord; they are pondered by all who delight in them." Close And that's The Worldview on this Monday, August 24th, in the year of our Lord 2026. Subscribe for free by Spotify, Amazon Music, or by iTunes or email to our unique Christian newscast at www.TheWorldview.com.  Plus, you can get the Generations app through Google Play or The App Store. I'm Adam McManus (Adam@TheWorldview.com). Seize the day for Jesus Christ.

Try TrueDark glasses: https://truedark.comTry Danger Coffee: https://dangercoffee.com/discount/davetubeTry Suppgrade Labs: https://shopsuppgradelabs.com/Try Longevity Gummies: https://www.timeline.com/partners/dave-aspreyMost people see the physical decline of aging as an inevitable process that can only be managed with medications, but this video cuts through that assumption by explaining that the smartest minds in science, including multiple Nobel Prize winners, have identified a single root cause of aging tied to a specific molecule called NAD+. You'll also learn:Why your body actively destroys NAD+ faster as you age, and why that's the real driver of fatigue and brain fogHow the gap between NAD+ production and destruction widens every year, quietly draining your cellular energyThe early signs your NAD+ levels are already too low, from slow-healing skin to workouts that take days to recover fromWhat actually flips your body's NAD+ production back on, and the exact order of diet, fasting, exercise, and supplementation that makes it workTry Danger Creatine: https://dangercoffee.com/products/danger-creatineTry TrueDark glasses: https://truedark.comTry Danger Coffee: https://dangercoffee.com/discount/davetubeTry Suppgrade Labs: https://shopsuppgradelabs.com/Thank you to our sponsors!ECHO Water | Go to http://echowater.com/daveand use code DAVE10 for 10% off your ECHO Flask.Viome | Check it out at viome.com and use code 10DAVE for 10% off. It's time to stop guessing and start knowing your body.iRestore | Reverse hair loss at www.irestore.com/DAVE and get exclusive savings on the iRestore Elite, use code DAVETimestamps:00:00 – The Aging Molecule01:03 – The War Within Your Cells03:28 – Builder vs. Demolition Crew04:34 – NAD+ and Brain Function06:00 – Root Cause Over Symptoms08:31 – Fixing the Imbalance10:10 – Fasting for NAD+11:30 – The REHIT Protocol12:37 – The Supplementation StepConnect with Dave Asprey!Website: https://daveasprey.comTikTok: https://www.tiktok.com/@daveaspreyofficialInstagram: https://www.instagram.com/dave.asprey/Facebook: https://www.facebook.com/Daveaspreyofficial/X: https://x.com/daveaspreyYouTube: https://www.youtube.com/c/daveaspreybprThe Human Upgrade Podcast: https://www.instagram.com/TheHumanUpgradePodcast/ https://m.facebook.com/Thehumanupgrade/Dave Asprey's BEYOND Conference: https://beyondconference.com/Dave Asprey's New Book - Heavily Meditated: https://daveasprey.com/heavily-meditated/Dave's favorite supplements: https://www.shopsuppgradelabs.com/discount/DAVE15Upgrade Labs: https://upgradelabs.com40 Years of Zen: https://40yearsofzen.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Today on the show, Trump cut short military drills between the US and South Korea this week, saying that they sent the wrong signal to the "unthreatening and respectful" North Korea. Fareed asks former NSA official and scholar Victor Cha if Trump is throwing over one longtime US ally to court a rogue state...again? Then, China spends a fraction of what the US does on data centers and lacks the most advanced AI chips. Why, then, does its AI industry appear to be catching up, and rivaling Silicon Valley? Fareed discusses with Evan Osnos, staff writer at the New Yorker. Later, Fareed speaks to climate scientist Katharine Hayhoe about this summer of record heat and raging wildfires in many parts of the world, and climate change's role in all of it. Finally, the V-Dem Institute's latest Democracy Report has downgraded the US from a liberal democracy to an electoral democracy. Why is American democracy in decline? Fareed asks Nobel Prize winning economist Daron Acemoglu. GUESTS: Victor Cha (@VictorDCha); Evan Osnos (@eosnos); Katharine Hayhoe (@KHayhoe); Daron Acemoglu (@DAcemogluMIT) Learn more about your ad choices. Visit podcastchoices.com/adchoices

If you've ever wondered whether there's a practical route to more consistent energy, deeper sleep, and improved well-being in midlife, you're not alone. So many women in their 40s, 50s, and 60s are searching for grounded ways to support their health, from hormones to brain fog to the daily realities of menopause symptoms. Today's episode explores one scientist's journey from research lab to real-world solutions, centered on a Nobel Prize-winning molecule and the hard-earned lessons behind it. Chris Burris, mechanical engineer, entrepreneur, and author of "Live Longer and Better," steps out from behind the lab bench to offer a candid, evidence-based look at the daily realities of longevity, sleep, headaches, and navigating the supplement industry. His experience spans decades of manufacturing, product safety, and the often-overlooked realities women face as hormone shifts and sleepless nights arrive. This conversation unpacks what's possible with science, what still demands more answers, and how the right information can lead to meaningful day-to-day change. By listening, you'll learn: • What a Nobel Prize-winning molecule actually is, how it works in the body, and why it matters for women in midlife • How sleep, energy, and hormone balance are connected and what the latest research reveals about practical steps forward • Why so many midlife women experience new headaches or fatigue, and how to approach these changes with clarity, not confusion • Real-life insight into the supplement industry, including quality concerns, what to look for, and how to make informed choices • How scientific discovery translates (or doesn't) to trustworthy options for women navigating menopause and perimenopause This episode delivers clarity, possibility, and validation for any woman feeling stuck between headlines, hormone shifts, and a changing body. You'll walk away with a deeper understanding grounded in science and lived experience about supporting your energy, sleep, and long-term health in ways that feel both reasonable and real. Resources: Episode website: https://www.natalietysdal.com/post/menopause-headaches-sleep https://myvitalc.com/nataliet https://www.natalietysdal.com https://www.instagram.com/ntysdal https://www.tiktok.com/@ntysdal https://www.facebook.com/NatalieTysdal

Send us Fan MailOnce upon a time, people blamed tuberculosis on vampires.Seriously.In this Public Health Is Weird episode, we head to 1892 Rhode Island, where the deaths of several members of one family led to an exhumation, a suspected vampire, and a truly horrifying attempt at a cure. The real culprit was tuberculosis. We explore the strange connection between TB and vampire folklore, the discovery and treatment of tuberculosis, a messy Nobel Prize controversy, and a bigger question: Why do humans reach for supernatural explanations when something frightening happens that we can't explain? Vampires, tuberculosis, human psychology, and somehow, Dr. Eeks' great grandfather and groundhog grease.Welcome to Public Health Is Weird. Be sure to check out the other Public Health is Weird episodes too! Work with Eeks? Perhaps you are a good match. Keep Causes or Cures Ad-Free with Listener SupportYou can contact Dr. Eeks at bloomingwellness.com.Follow Eeks on Instagram here.Follow on X. Or Facebook here.On Youtube.Or TikTok.SUBSCRIBE to the Eeks Weekly here! (the bits not posted on socia media)Sources used in the podcast are listed below and in my blog here. Food for the Dead, by Michael BellMysterious, medieval child vampire, The IndependentConsumptive Chic, by Carolyn DaySmithsonian Magazine, Kat Eschner, March, 2017CDC MMWR, 1982Streptomycin, American Chemical Society, 2014World's Top Infectious Killer, Science Alert, 2025Support the show

This Friday, I had to play back an all-time favorite. I was honored and grateful to bring you a conversation that I recorded in person with Gurudev Sri Sri Ravi Shankar at the Art of Living Center in Los Angeles. Gurudev Sri Sri Ravi Shankar is a leader of an international movement with more than 50 million followers who do breathwork every single morning. He has worked for 40 years as a global ambassador for peace at the highest levels. I did his breathwork exercises every morning without fail for five years while fixing my mind, body and nervous system, and I have a huge amount of respect for his work in the world. It was a joy to chat with him in person and his childlike curiosity, enthusiasm for teaching, and deep understanding of the world are echoed in every word of this episode. Gurudev is a true visionary, so I am grateful that he set aside time to talk with me so I could share his wisdom with you. Thank you to our sponsors! -Qualia | If you want to take the guesswork out of maintaining high NAD+ levels as you age, go to www.qualialife.com/daveNAD to get clinically proven Qualia NAD+ backed by a 100 day money back guarantee and code DAVENAD at checkout gets you an extra 15% off.-Essentia | Go to https://myessentia.com/dave and use code DAVE for $100 off The Dave Asprey Upgrade.-iRestore | Reverse hair loss at www.irestore.com/DAVE and get exclusive savings on the iRestore Elite, use code DAVEDave Asprey is a four-time New York Times bestselling author, founder of Bulletproof Coffee, and the father of biohacking. With over 1,000 interviews and 1 million monthly listeners, The Human Upgrade brings you the knowledge to take control of your biology, extend your longevity, and optimize every system in your body and mind. Each episode delivers cutting-edge insights inhealth, performance, neuroscience, supplements, nutrition, biohacking, emotional intelligence, and conscious living. New episodes are released every Tuesday, Thursday, Friday, and Sunday (BONUS). Dave asks the questions no one else will and gives you real tools to become stronger, smarter, and more resilient. Keywords: Sri Sri Ravi Shankar, Dave Asprey, Art of Living, Sudarshan Kriya, breathwork, pranayama, meditation, non-dual consciousness, gut feeling intuition, biohacking, quantum physics consciousness, Nobel Prize physics, curiosity and childlike wonder, skepticism vs cynicism, scientific temper, dogmatism, veterans PTSD breathwork, SKY campus happiness program, violence prevention, community building, dating apps romance, Elon Musk Mars, computer chip brain implant, duality and consciousness, equanimity, ego and inner voice, spirituality and science, yoga philosophy, guru interview, positive energy radiation, transcending fear Resources: • Get My 2026 Clean Nicotine Roadmap | Enroll for free at https://daveasprey.com/2026-clean-nicotine-roadmap/ • Dave Asprey's Latest News | Go to https://daveasprey.com/ to join Inside Track today. • Danger Coffee: https://dangercoffee.com/discount/dave15? • My Daily Supplements: SuppGrade Labs (15% Off) • Favorite Blue Light Blocking Glasses: TrueDark (15% Off) • Dave Asprey's BEYOND Conference: https://beyondconference.com • Dave Asprey's New Book – Heavily Meditated: https://daveasprey.com/heavily-meditated • Join My Substack (Live Access To Podcast Recordings): https://substack.daveasprey.com/ • Upgrade Labs: https://upgradelabs.com Timestamps: 00:00 - Trailer01:11 - Curiosity & Skepticism03:42 - Reality, Physics & Illusion06:34 - Non-Dual Consciousness08:37 - Origins of Breathwork13:36 - Trusting Your Gut Feeling14:28 - Violence, Peace & Veterans19:54 - Romance & Dating Apps23:44 - Science Behind Meditation27:27 - Fear of the UnknownSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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

"Do I need a trust or just a will?" might be the single most common estate planning question there is, and estate attorney Tim Semro says most people are asking it backwards. The real question isn't trust versus will, it's how do you avoid probate, and a trust is just one of several ways to get there. Tim returns to answer a full mailbag of real Stacker questions, covering everything from a $200,000 mistake buried in a lady bird deed to the exact reason so many families accidentally disqualify a parent from Medicaid.What You'll Walk Away WithWhy "trust versus will" is the wrong question, and the three-column framework that actually determines what you needWhat a lady bird deed is, when it makes sense, and the family conflict it can quietly set up down the roadThe tax detail buried in gifting property early that can cost your heirs tens of thousands of dollars they didn't expectWhy naming a power of attorney without having an honest conversation first is one of the most common and costly mistakes families makeThe five-year Medicaid look-back rule explained clearly, including what happens if you don't quite make it to five yearsHow debt actually works after someone dies, including a real statute of limitations window most people don't know existsA special needs trust structuring tip that can protect a family member's government benefits without giving up their inheritanceWhy This Matters NowEstate planning tends to get pushed to "someday" because it feels complicated, uncomfortable, or like it only matters once you're wealthy. But the actual decisions, who has power of attorney, how property transfers, what happens if a parent needs long-term care, apply to nearly every family, regardless of net worth. Getting the structure right isn't about predicting the future perfectly. It's about making sure the people you love aren't left guessing, fighting, or losing money to easily avoidable mistakes during an already difficult time.From the BasementA birthday trivia detour into the surprising origin of the Nobel Prize reveals it was born from a very specific kind of reputation crisis, proof that it's never too late to actively shape how you'll be remembered.Resources MentionedYour Money, Your Way by Tim Semro — Tim's book on estate planning, free to downloadSemro Henry Ltd. — Tim's estate planning law firmStacking Benjamins Field Kit — the all-in-one financial organization toolSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

How do we revive living standards and restore confidence in liberal democracy? Is the link between productivity and wages permanently broken? What is working-class liberalism? Why should Andy Burnham's first budget raise taxes on capital while cutting taxes on employment? And why are tech giants like Google and Amazon twice as wealthy as the British Empire at its peak? Robert investigates how to safeguard our way of life with Nobel Prize–winning economist Daron Acemoglu. Together, they discuss whether the prescriptions in Acemoglu's influential new book are practical enough to prevent liberal democracy from eroding further. The Rest is Money is brought to you by Octopus Energy, Britain's smart energy pioneer. This episode is brought to you by Accenture. https://Accenture.com/Spotify-UK Buy tickets for The Rest is Fest: https://www.southbankcentre.co.uk/whats-on/the-rest-is-money-live/ Email: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠the⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠restismoney@goalhanger.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ X: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@TheRestIsMoney⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@TheRestIsMoney⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@RestIsMoney⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Advertise with us: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Partnerships@goalhanger.com⁠ For more Goalhanger Podcasts, head to ⁠www.goalhanger.com⁠ Video Editor: Dylan Bonham Producer: Isabelle Bougeard Exec Producers: Bella Soames and Tom Whiter Learn more about your ad choices. Visit podcastchoices.com/adchoices

Aug 18, 2026; 6pm: New polling from Reuters shows President Trump's approval rating at 33 percent. MS NOW's Ari Melber reports and is joined by Nobel Prize-winning economist Paul Krugman. Plus, ABC is suing the FCC over threats to force its stations off the air. Melber breaks down the lawsuit with NPR's David Folkenflik. To listen to this show and other MS podcasts without ads, sign up for MS NOW Premium on Apple Podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The system that produces the world's most important discoveries runs on metrics that nobody agrees on, odds that would stop most people, and mentorship that sometimes looks like cruelty in blue ink. Subscribe if you want science with evidence, not speculation. Lipomi is Professor and Chair of Chemical and Sustainability Engineering at the University of Rochester and the author of Science Nonfiction: Behind the Scenes in University Research. His lab works on organic electronics, materials science, recyclable polymers, and the science of human touch. He trained under George Whitesides at Harvard, one of the most cited chemists who has never won the Nobel Prize. The conversation covers the H-index, what elite mentorship actually looks like when your advisor writes “This is illiterate” in blue ink on your outline, and what the future of academic research versus industry careers looks like in an era of AI, demographic headwinds, and graduate school debt that can't be discharged in bankruptcy. We also get into homochirality, OLED displays, and why Lipomi thinks the nanotechnology revolution already happened, we just didn't call it that. What you'll hear: -Whether the academic system is designed to produce scientists or to filter them out -What is Baumol's cost disease and why it explains rising tuition costs -Why the nanotechnology revolution already happened and nobody called it that -How nanotechnology is used in cancer drug delivery today versus what science fiction promised -What it actually takes to build a PhD career at the intersection of chemistry, mechanics, and neuroscience -Whether engineering students should be reading Plato — and what countries that skip it are getting right “Find a skill at the intersection of three or more interests that no one else is working on.” — Darren Lipomi CHAPTERS 00:00 He says don't go to college. He runs the department. 01:10 The H-index: imperfect, irreplaceable 02:04 1 in 20 PhDs gets the job 05:42 The meteorite and the origin of life 08:34 Did life's asymmetry come from space? 12:00 Who is an organic materials chemist at 3am? 13:52 The collaboration that started at a coffee shop 15:22 The nanobot revolution already happened 18:24 Nanotechnology inside your body right now 19:08 The $70 billion invention Kodak gave away 22:12 Kodak, Xerox, Bausch + Lomb: what Rochester built 26:06 Working under Whitesides at Harvard 28:54 What he wrote on the outline in blue ink 30:38 Who you work with matters more than what you work on 31:56 Department chair: hostage negotiator, not boss 34:36 Why the department changed its name after 110 years 36:00 The headwinds have never been stronger 38:44 Should engineers read Plato? 39:38 The debt you can't discharge in bankruptcy 40:06 Do you buy Baumol's cost disease? 43:14 Why he had to write the book 48:16 The Fisher-Price camcorder experiment 51:06 Teaching in one sentence. Research in one. 53:42 Losing Darren to Rochester: the Fernando Tatis analogy Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt Have a .edu email and live in the USA? You automatically win a meteorite: https://BrianKeating.com/edu Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Support Into the Impossible on Patreon, get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join Learn more about your ad choices. Visit megaphone.fm/adchoices

This week we talk about the liver, viral infections, and the NHS.We also discuss blood scandals, needle usage, and Nobel Prizes.Recommended Book: A World Appears by Michael PollanTranscriptThe term “hepatitis” refers to the inflammation of the liver, which can result from all kinds of things, including environmental toxins, the consumption of alcohol, or autoimmune diseases. It can also result from viral infections, and the most prominent liver-inflaming viruses are called viral hepatitis.There are five types of viral hepatitis, A, B, C, D, and E, and each of these viruses are distinct, not part of the same viral family, they're just similarly named because they impact the same organ.Hepatitis A and E are primarily spread through contaminated food and water, and generally resolve on their own, untreated, and cause relatively mild symptoms. Hepatitis B and C are spread through blood and other bodily fluids, and can linger in a host's body for decades before even showing symptoms. Hepatitis D is a parasite of Hepatitis B, and thus only infects people who carry Hepatitis B.Now again, these are all different conditions that just happen to inflame the liver, so impact and treatment also vary quite a lot. As I mentioned, A and E generally present with mild symptoms and tend to go away on their own, while B and C can stick around a long time. There's a vaccine for B, but no cure; you can treat it, but that treatment involves suppressing it, and keeping it suppressed, forever. Hep C, in contrast, is curable, and has been since 2014 using what are called direct-acting antiviral pills, but these pills, which are taken for 8 to 12 weeks, are expensive—ranging from $22-95k without insurance, though that price is often reduced substantially for those with insurance, down to as low as $5. This category of drug coverage is often rejected by insurance companies, though, in part because they're so expensive, that expense the result of little competition in this space; few companies make this type of drug, so those that do can charge more or less whatever they like.Some people with Hepatitis C clear it on their own; about 30% of people who contract it, in fact, clear it within a few months, medication-free. Which is good, because our understanding of this virus is relatively new. Up until 1989, Hep C didn't even have its own name: it was established as its own thing, not Hep A and not Hep B, back in the 1970s, and doctors knew that something that wasn't those two viruses, that was being spread by transfusions, was causing hepatitis symptoms, but they didn't know any real specifics, so they just called it “non-A, non-B hepatitis,” and that name stuck for more than a decade.In 1989 the virus was cloned using molecular techniques (as opposed to simply growing the virus, which wasn't proving fruitful in trying to isolate and identify the thing), and the folks who managed that cloning, and the person who later proved that the genome they cloned, alone, caused the disease, received a Nobel Prize in Medicine for their efforts in 2020.By 1991, antibody tests were available for Hep C, and many countries began screening donated blood for this virus, to ensure it wasn't working its way into their blood supply.And one instance of that screening process, or I suppose, an event that led up to mass screening, and the consequences that followed, are what I'd like to talk about today. The UK's efforts in trying to eliminate Hep C, and England's recently announced near-success in that pursuit.—Hepatitis C is an RNA virus with high genetic variability that makes developing a reliable vaccine difficult. And though somewhere between a quarter and a third of all cases clear on their own, those that don't clear on their own become chronic, lying in wait for twenty to thirty years, slowly accumulating fibrosis—thick scar tissue in the liver—which eventually results in cirrhosis, which means a liver that's so heavily scarred that the organ is no longer fully functional and the damage is permanent. From there, infected people often experience liver failure or hepatocellular (huh-pah-toe) carcinoma, liver cancer.So this virus is a sleeper, and unless it's caught by accident somewhere along the way, it slowly causes damage over time until the damage is too severe to reverse. About 80% of people who have it don't know they have it, and in some parts of the world medical injections are the most common transmitter, but in higher-income areas, it's usually transmitted by injectable drugs.Pre-2014 treatments for Hep C were pretty horrible, involving a combination antiviral therapy called pegylated interferon plus ribavirin that was injected weekly for six months to a year, and this was terribly tolerated by pretty much everyone, causing anemia, depression, and flu-like symptoms for the duration. It also only cured about 50% of people who received the full treatment, and a lot of people had to stop because it caused such ridiculous side effects.Another antiviral called Sofosbuvir (so-FAS-buh-vir), which kept Hep C from replicating in its host, hit the market in late-2013, and that led to a series of direct-acting antivirals that reduced the treatment period dramatically, allowing most people, 95%, to cure their Hep C entirely by taking generally well-tolerated pills for 8 to 12 weeks.These pills were staggeringly expensive from the get-go, with an entire treatment course initially costing about $84,000, or $1,000 a pill. This led to rationing, and saving these pills for the worst-impacted people who already had severe liver damage. There were also pretty stringent requirements attached to their distribution, including that people who received them could no longer drink alcohol, because it was considered a waste to give these crazy expensive, liver-saving drugs to people who would just go and hurt their liver more, anyway.In the UK, the demand for this treatment type was different than in most other countries, in large part because of something that happened back in the 1970s and 80s.The UK's publicly funded healthcare system, the NHS, was in the midst of a shortage of clotting factor, which are plasma proteins and ions that help blood clot and which are used for medical purposes. So they imported a bunch of plasma products from the US, and those products were sourced from the blood of paid donors—and that donor pool included prisoners and people who used injectable drugs. Just one Hep C contaminated blood donation could contaminate an entire batch of blood, and remember, they only started screening the blood supply for Hep C in 1991, and they didn't start treating their blood supply for Hep C until a little before that, 1985, so this was well before they had any idea what was in those blood products they were importing and administering.Consequently, between 1970 and the early 1990s, more than 30,000 NHS patients received transfusions or other blood product treatments contaminated with Hep B, Hep C, or HIV, and about a tenth of those people, around 3,000 patients, have since died of those conditions.The UK government leaned on denial and a refusal to look into the details of this for years, but in 2017 it announced an independent public inquiry into the matter, and in May of 2024, that inquiry concluded that this whole scandal was avoidable, that patients were knowingly exposed to “unacceptable risks,” and that there was a big cover up by government officials, doctors, and other people working with the NHS.As of mid-2026, only a little over 3,200 people of the more than 18,500 who registered claims, demanding compensation from the government because they were impacted by this scandal, have been paid out. The expected total expense for the UK government is on the order of 12.8 billion pounds, but a lot of people who are probably due a payout, and who are in poor and deteriorating health as a consequence of all this, don't yet have a sense of when they'll receive their payment.Back in 2016, before all that came to a head, the UK set itself an aggressive goal: to eliminate Hep C by the WHO's 2030 target, or before. It then ran a competitive tender for antivirals, inviting medical suppliers to submit competing bids, resulting in the largest single medicine procurement program in the NHS' history. The pharmaceutical companies that won their bids were also obliged, as part of the agreement, to help fund efforts to identify undiagnosed but infected patients, in addition to supplying antiviral pills, and this combination of investment and application led to the deployment of new tests and scanning machines, free postal test kits, the hiring of specialists, and services that focused on prisons and drug users.The impact of all this has been significant: a more than 61% decline in infections from 2015 to 2024, nearly half of all drug users with Hep C had cleared the virus in that time, and deaths from Hep C are down 36% over the past decade.The WHO treatment-coverage target—the percentage of people who are diagnosed getting treatment—was 80%, and England has hit 81.5%, which was recently announced to much fanfare. It hasn't yet hit the diagnosis target, however, which is to diagnose 90% of people who are estimated to have Hep C; they've hit 84.6%, which is still quite a lot of progress, even if they're not yet where they'd like to be. That's all based on models, of course, as are the assumed number of infections among people who use injectable drugs, which is also a spot where England is currently flagging; there's no centralized system in England to monitor needle and syringe provisions, and reinfection rates are around 8.8 per 100 person-years among people who had injected within three years of receiving treatment, and that rate is even higher for people who have ever been to prison, around 9.4 per 100.What that means in practice is that the English government overall has done a pretty astounding and effective job at negotiating their relationships with pharma companies and getting detection on track at that scale, but on more ground-level issues that are, interestingly, a lot cheaper to implement, but at times more politically complicated because of public sentiment about drug use and drug users, they're doing a lot less well—and important to note here is that these outcomes vary a bit across the four programs being run across the UK. Scotland and Wales are doing relatively better and worse in some regards compared to England, for instance.Also worth noting here that while England is broadly doing a great job with Hep C diagnosis and treatment, they aren't the first to achieve those WHO-set goals: Egypt reached Gold tier status according to the WHO's Hep C guidelines in October of 2023, at that point having diagnosed 87% of people who have the virus, and treating 93% of those who were diagnosed. They managed to cut incidence of the virus by 97% in just 8 years, leaning on a system of high-yield testing—they tested more than 60 million people during those 8 years—alongside a production scheme that included local manufacturing of antivirals, making them more available and affordable.All of which are generally good signs about where Hep C testing and treatment is going, at least in these regions. And it paints a optimistic picture for other countries that might want to replicate some of what's working within their own borders.Show Noteshttps://www.bbc.com/news/articles/c75gk620r22ohttps://en.wikipedia.org/wiki/Infected_blood_scandal_in_the_United_Kingdomhttps://en.wikipedia.org/wiki/Hepatitis_Chttps://en.wikipedia.org/wiki/Viral_hepatitishttps://en.wikipedia.org/wiki/Hepatitis_Bhttps://www.healthline.com/health/hepatitis-c/treatment-costshttps://www.who.int/news-room/fact-sheets/detail/hepatitis-chttps://publichealthscotland.scot/publications/surveillance-of-hepatitis-c-in-scotland/surveillance-of-hepatitis-c-in-scotland-progress-on-elimination-of-hepatitis-c-as-a-major-public-health-concern-2025-update/https://www.emro.who.int/media/news/egypt-becomes-the-first-country-to-achieve-who-validation-on-the-path-to-elimination-of-hepatitis-c.htmlhttps://www.gov.uk/government/publications/hepatitis-c-in-england-and-the-uk/hepatitis-c-in-england-2025https://www.england.nhs.uk/2026/08/100000-people-receive-treatment-to-cure-deadly-hep-c-virus-on-nhs-in-just-ten-years/https://www.hepctrust.org.uk/blog/2019/04/hepatitis-c-trust-welcomes-elimination-deal-hepatitis-c-and-calls-government-backed/https://commonslibrary.parliament.uk/research-briefings/cbp-10099/https://www.who.int/teams/global-hiv-hepatitis-and-stis-programmes/hepatitis/reports/global-hepatitis-report-2026 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit letsknowthings.substack.com/subscribe

This week on the Stay Tuned with Preet podcast, Nobel Prize-winning economist Daron Acemoglu joins Preet to discuss his latest book, What Happened to Liberal Democracy? Remaking a Politics of Shared Prosperity. Acemoglu explains why he thinks American politics is in  crisis and why a return to the foundational principles of liberalism—with shared prosperity at the forefront—is the best solution. They also discuss the Democratic socialist movement, the reasons for its rise, and whether “socialism” is even the right term to describe it. Then, Preet and Acemoglu turn to AI and its potential to become a pro-worker tool.  After the interview, Preet answers listener questions about FEMA emergency funding and whether the attorney general should be an elected position. In the bonus for Insiders, Acemoglu defines some of the key terms from his book and explains how concepts like liberalism can have different meanings. Join the Insider community for access to bonus content from Stay Tuned and weekly episodes of the Insider podcast hosted by Preet and Joyce Vance. Visit staytuned.substack.com to sign up. Thank you for supporting our work. Photo by Costas Baltas/Anadolu via Getty Images  Show notes and a transcript of the episode are available on our website.  Watch this episode on our Youtube channel. Shop Stay Tuned merch and featured books by our guests in our Amazon storefront. Have a question for Preet? Ask @PreetBharara on BlueSky, or Twitter with the hashtag #AskPreet. Email us at staytuned@cafe.com, or call 833-997-7338 to leave a voicemail. Stay Tuned with Preet is brought to you by CAFE and the Vox Media Podcast Network. Learn more about your ad choices. Visit podcastchoices.com/adchoices

For most of the last century liberal democracies offered a simple deal: the economy grows and, if you work hard, you get a fair shot at the rewards. According to 2024 Nobel Prize-winning economist Daron Acemoglu, that deal is broken. Acemoglu joins Bethany McLean and Luigi Zingales to discuss his new book, What Happened to Liberal Democracy?, and why the system that delivered unprecedented freedom and prosperity abandoned the working class in favor of the college-educated elite.  Acemoglu makes the case that things will only get worse as AI is being designed to replace human labor, driving wage stagnation and economic anxiety. But he thinks we can still turn things around, and he brought his solutions to our podcast. Connect with us:

Academic and critic Maria Delgado and writer and producer Jake Cunningham join Tom Sutcliffe to discuss the film Tony, a film about the early career of food writer Anthony Bourdain, who is played by Dominic Sessa. The film also stars Antonio Banderas, who becomes an unexpected mentor for the young chef.They also talk about Death Note: The Musical. Based on the best-selling Manga series of the same name, the Barbican show tells the story of a student who discovers a notebook which gives him the power to kill. The final item for review is Nobel Prize in Literature winner Mario Vargas Llosa's final novel, I Give You My Silence, where a music journalist is on a quest to tell the story of an unforgettable musician. The book is also a love letter to Peruvian culture. Sarah Ditum discusses Jason Arday's memoir, Great and Unfortunate Things. This programme was broadcast before the announcement of Jason Arday's death. Presenter: Tom Sutcliffe Producer: Claire Bartleet

It's time to be honest: Jonah Goldberg can't remember if he's ever had a Nobel Prize-winning guest on The Remnant. If that shocks you, calm down. It's called getting old. After today, however, Jonah can say with certainty that he has, as he is joined by Nobel laureate in economics Daron Acemoglu. Listen in as Jonah and Daron light up the Remnant bingo card like a Hanukkah bush, covering liberal democracy, community, subsidiarity, the welfare state, status, prosperity gaps, AI, automation, China, abundance, the working class, Zohran Mamdani, and the Democratic Party. Show Notes: —Why Nations Fail: The Origins of Power, Prosperity, and Poverty —What Happened to Liberal Democracy?: Remaking a Politics of Shared Prosperity —Daron Acemoglu in Financial Times: “Liberalism can win back the working class. Here's how” —Violence and Social Orders: A Conceptual Framework for Interpreting Recorded Human History —Jonah's book Suicide of the West The Remnant is a production of ⁠The Dispatch⁠, a digital media company covering politics, policy, and culture from a nonpartisan perspective. To access all of The Dispatch's offerings—including the Saturday Ruminant, audio versions of all our articles and newsletters, and Jonah's twice-weekly G-File—⁠click here⁠. Instructions on how to set up your members-only feed can be found here, and if you'd like to remove all ads from your podcast experience, consider becoming a premium Dispatch member ⁠by clicking here⁠.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Here's the Spotify description for this episode: In Episode 384 of The Real Jason Duncan Podcast, think about the last time a bill showed up that you couldn't pay. The anger. The fear. That knot in your stomach at 2 in the morning. Now imagine the money to cover it just landed in your account. Tell me you didn't just feel happier. Money can buy happiness. And whoever told you it can't was probably broke. In this solo Wednesday episode drawn from his What's Real newsletter, Jason dismantles one of the most repeated lies in the world — a coping mechanism dressed up as wisdom, passed down from person to person until everybody just repeats it without ever checking it. And then he hands you the research to defend it the next time someone hits you with the old line. In this episode, Jason covers: Why “money can't buy happiness” is a coping mechanism — and what it's actually covering up The 2010 Nobel Prize study that put a $75,000 ceiling on happiness — and why it became gospel for a decade What happened in 2021 when a researcher pinged 1.5 million people on their phones in real time and asked how they actually felt What Kahneman and Killingsworth found when they sat down together in 2023 — and what they landed on after starting out on opposite sides The one exception where money stops moving the needle — and why your gut already knew this The critical difference between “money can't fix everything” and “money can't buy happiness” — and why people use the true one to make the false one sound reasonable What Proverb 10:22 actually says about riches — and why the idea that money and misery are a package deal was never ancient wisdom What to do if more money would relieve the pressure you're under — and why there's no shame in just saying so Money can buy happiness. Go build a life you actually want and stop apologizing for wanting it.

What if your skin could just . . . remember how to act young again? That's essentially the pitch behind Biocogent's new RNActivate W, a next-generation active that doesn't add collagen or push it as peptides do. Instead, it goes upstream and silences a tiny piece of regulatory RNA (the so-called "wrinkle miRNA") that tells your skin cells to stop making collagen and elastin. Join Ella and Maggie for an ingredient deep dive into what microRNAs actually are, why the 2024 Nobel Prize in Medicine matters for your treatment room, what to look for on an INCI list when you see "s-RNA-5," and how to talk about this category with clients without overpromising. Spoiler: This isn't snake oil, but it isn't a retinol replacement either. ASCP Esty Talk with hosts Ella Cressman and Maggie Staszcuk   Produced by Associated Skin Care Professionals (ASCP) for licensed estheticians, ASCP Esty Talk is a weekly podcast, hosted by licensed estheticians, Ella Cressman, ASCP Skin Deep Magazine contributor, and Maggie Staszcuk, ASCP Program Director. We see your passion, innovation, and hard work and are here to support you by providing a platform for networking, advocacy, camaraderie, and education. We aim to inspire you to ask the right questions, find your motivation, and give you the courage to have the professional skin care career you desire.  About Ella Cressman:  Ella Cressman is a licensed esthetician, certified organic formulator, and business owner with more than 20 years of experience in corrective skin care. Known as an "ingredient junkie" and industry cheerleader, she empowers professionals to think beyond products and develop a deeper understanding of skin function and formulation. In addition to her practice, Cressman is the founder of the HHP Collective, a practitioner-led community focused on strengthening clinical reasoning and advancing professional growth within the esthetics industry.   Connect with Ella Cressman:  Website: www.hhpcollective.com  LinkedIn: linkedin.com/in/ella-cressman-62aa46a    About Maggie Staszcuk:  Maggie Staszcuk serves as the Program Director for ASCP and is the cohost of ASCP Esty Talk podcast. With over 18 years' experience in the esthetics industry, her diverse background includes roles in spa management, spa and med-spa services, and esthetics education. Since becoming a licensed esthetician in 2006, she carries a range of certifications in basic and advanced esthetics. Maggie is dedicated to equipping estheticians with the knowledge and resources they need to thrive in their careers.   Connect with Maggie Staszcuk:  P: 800.789.0411 EXT 1636  E: MStaszcuk@ascpskincare.com    About our Sponsors: Massage Envy is a national franchisor and does not independently own or operate any of the Massage Envy franchised locations nationwide. The Massage Envy franchise network, through its franchise locations, is the leading provider of massage services. Founded in 2002, Massage Envy now has approximately 1,100 franchise locations in 49 states that have together delivered more than 200 million massages and skin care services. Website: www.massageenvy.com/careers/career-areas/esthetician  Facebook: @MassageEnvyCareers LinkedIn: @MassageEnvy   GlossGenius Gaps in your schedule. Clients who don't rebook. Tight margins. High payment processing fees. Sound familiar? When you're running your own practice, you don't have time to figure out where you could be making more money. Especially when you're stitching together booking, payments, and a clunky EMR that only makes things harder. That's why we love GlossGenius — the business management platform that does the work for you. It fills your calendar, rebooks clients automatically, upsells high-margin services, and has the lowest flat-rate payment processing fees. Plus, all the HIPAA-compliant tools you need for charting, consents, and client records — without the admin chaos. GlossGenius grows your revenue and handles the busywork, so you can focus on your clients. Use code ESTY at GlossGenius.com for 50% off your first two months of their Gold or Platinum plan. GlossGenius. More Growth. Less Busywork. Visit https://glossgenius.com/ascp for more details.   About Associated Skin Care Professionals (ASCP):    Associated Skin Care Professionals (ASCP) is the nation's largest association for skin care professionals and your ONLY all-inclusive source for professional liability insurance, education, community, and career support. For estheticians at every stage of the journey, ASCP is your essential partner. Get in touch with us today if you have any questions or would like to join and become an ASCP member.  Connect with ASCP:  Website: www.ascpskincare.com  Email: getconnected@ascpskincare.com  Phone: 800-789-0411  Facebook: facebook.com/ASCPskincare  Instagram: @ascpskincare 

Forty years ago, a series of experiments demonstrated that the bizarre properties of the quantum world can be made concrete in a system big enough to be held in your hand. John Clarke, Michel Devoret and John Martinis – all at different points in their careers – collaborated to make this groundbreaking discovery. Join us as host Adam Smith speaks with the 2025 physics laureates about the importance of following your curiosity, the future of practical quantum computers and the special combination of personalities and skills that led to their successful collaboration.Nobel Prize Conversations with the 2025 physics laureates John Clarke, Michel Devoret and John Martinis. For a quick introduction to 2025's awarded discovery in physics, check out our Crash Course on quantum tunnelling or Göran Johansson's eloquent speech from the Nobel Prize award ceremony.Read complete profiles of John Clarke, Michel Devoret and John Martinis, and explore the 2025 Nobel Prize in Physics at our website, nobelprize.org.See the announcement of the Nobel Prize in Physics 2025 and the moments John Clarke, Michel Devoret and John Martinis were awarded their medals.This podcast was a production of Nobel Prize Outreach and Filt, and created in cooperation with Fundación Ramón Areces. Hosted on Acast. See acast.com/privacy for more information.

Can one molecule almost double our lifespan?Chris Burres is the founder and chief scientist at MyVitalC, the oldest and longest manufacturer of the Nobel Prize-winning ESS60 molecule. He came here to explain why almost nobody has heard of it.The story gets stranger the further in we go. Rice University scientists discovered this ball-shaped carbon molecule in 1985. It performs better than most existing materials in batteries, tires, and inks, and its antioxidant properties are far more powerful than vitamin C. Then a French toxicity study produced unexpected longevity results.Chris outlines the manufacturing process, the liver recovery data, the cancer cell study, the sleep testimonials, and the pet results that leave placebo out of the argument.If you've ever taken C60 and wondered whether you were drinking expensive pee, this one is for you.Visit myvitalc.com/lukestorey and use code STOREY for $30 off your first order.You'll learn:[0:00] Introduction[5:38] The rat study that found 90% longer lifespans[14:15] The graphite-vaporizing, oxygen-free reactor process behind manufacturing pure ESS60[21:18] Adding "not for human consumption" while biohackers kept calling about their 275-pound rats[34:14] The multi-billion-person sleep study we run every daylight savings time[48:47] Why five ampoules a flight became part of my air travel arsenal[55:29] Exploring whether C60 can detox your brain the way activated charcoal can't[1:27:50] The gossip behind the Nobel Prize and the grad students who discovered it but got nothing[1:37:50] Why manually stacking the youth peptide and ESS60 beats using an emulsifier[1:42:45] The Sesame Street test for spotting C60 products that contain zero C60[1:50:39] Three teachers who shaped ChrisResources Mentioned:Wizard Sciences: Use code LUKE for 15% off all products! (except subscriptions) | WebsiteSens.AI | ProductThe prolongation of the lifespan of rats by repeated oral administration of [60]fullerene by Baati et al. | ArticleAX3 Bio-Pure Astaxanthin | ProductPresence and Quantity of Botanical Ingredients With Purported Performance-Enhancing Properties in Sports Supplements by Cohen et al. | ArticleFullerene C60 Protects Against Intestinal Injury from Deoxynivalenol Toxicity by Improving Antioxidant Capacity by Liao et al. | ArticleC60 Fullerene Reduces the Development of Post-Traumatic Dysfunction in Rat Soleus Muscle by Prylutskyy et al. | ArticleREAD: Why We Sleep: Unlocking the Power of Sleep and Dream by Matt Walker | BookREAD: The Most Beautiful Molecule: The Discovery of the Buckyball by Hugh Aldersey-Williams | BookFull show notes at lukestorey.com/c60Related The Life Stylist Episodes:C-60: The Miracle Molecule for Biohacking Pets, Hair Loss, EMF, & Cancer W/ Ian Mitchell | PodcastHolon Brain Training: Transcend Trauma & Master the Flow State w/ Drs. Drew Pierson & Amy Albright | PodcastDave Asprey – Smarter Not Harder: Top Biohacks for Vitality, Longevity & Maximum Brain Power | PodcastFind more from Chris:MyVitalC | Website | Instagram | Facebook | X | TikTok | YouTubeRead: Live Longer and Better: Your Journey to Living Longer and Better Has Never Been More Achievable Than Today by Chris Burres and Jerome R. Corsi, PhD here.Read: The Longevity Molecule: The Secret to Doubling Lifespan to 152 Years (and Beyond) by Chris Burres here.Find more from Luke:Luke Storey | Instagram | Facebook | X | YouTube | LinkedInThe Life Stylist is Brought To You By:ACTIVATION PRODUCTS | Visit activationproducts.com/luke and use code LUKE for 15% off your order.LEELA QUANTUM | Go to lukestorey.com/leelaq and use code LUKE10 for 10% off your first order.BIOPTIMIZERS | Visit bioptimizers.com/luke and use code LUKE15 to save 15% off sitewide. Plus, get a free bottle of MassZymes while supplies last.ACTIVE SKIN REPAIR | Visit lukestorey.com/skinrepair and use code LUKE for 20% off your order.

Nobel Prize-winning MIT economist Daron Acemoglu joins Nick and Goldy to discuss his new book, What Happened to Liberal Democracy? Remaking a Politics of Shared Prosperity. They explore why so many people have lost faith in democracy, how inequality and weakened worker power have reshaped politics, and why democracy has to deliver in people's daily lives. They also dig into AI, automation, and whether new technology will concentrate even more wealth and power — or help build a more prosperous, democratic future. Daron Acemoglu is a Nobel Prize-winning economist and Institute Professor at MIT. He is one of the world's leading thinkers on political economy, institutions, inequality, technology, and the relationship between democracy and shared prosperity. His books include Why Nations Fail, The Narrow Corridor, and Power and Progress. His new book is What Happened to Liberal Democracy? Remaking a Politics of Shared Prosperity. Social Media: @dacemoglumit.bsky.social @DAcemogluMIT Further reading:  What Happened to Liberal Democracy? Remaking a Politics of Shared Prosperity New York Times - Nearly 200 Economists and Tech Leaders Warn of A.I. Threats NBER - Automation and Repression Check out THE BILLIONAIRE AGE on IDEAS  Website: http://pitchforkeconomics.com Facebook: Pitchfork Economics Podcast Bluesky: @pitchforkeconomics.bsky.social Instagram: @pitchforkeconomics Threads: pitchforkeconomics TikTok: @pitchfork_econ YouTube: @pitchforkeconomics LinkedIn: Pitchfork Economics Twitter: @PitchforkEcon, @NickHanauer Substack: The Pitch

John Martinis won the 2025 Nobel Prize in Physics for proving macroscopic quantum tunneling is real. Less than a year later he's telling why he left Google's quantum computing team to start over. Subscribe if you want the physics and the politics of building the impossible. Martinis is the co-founder of Colab and one of the physicists who proved ordinary quantum rules apply to macroscopic systems. He built his career on the same Josephson-junction hardware his Nobel is built on, then led Google's superconducting-qubit effort, the same team behind Google's 2019 quantum supremacy claim, before an internal reorg pushed him out. We go into Anthony Leggett's challenge to Schrödinger's cat, what decoherence in quantum mechanics really means, and whether quantum computing is the first technology ever born from pure theory rather than experiment. We also cover the internal Google reorg that pushed Martinis out and what he learned about building something new inside a large institution. Whether quantum mechanics applies to the macroscopic world and what it took to prove it Why Martinis thinks quantum computing may be the first technology born from pure theory, not experiment What negative authority means and why it matters for anyone building something new inside a large institution Whether the US can win the quantum computing race against China How Martinis thinks about quantum mechanics interpretations after spending a career inside the math “Always be on the lookout for the impossible, right?” John Martinis CHAPTERS 00:00  The Nobel call that almost wasn't 01:01  How his wife found out before he did 03:32  Anthony Leggett's challenge to Schrödinger's cat 05:53  Why a Josephson junction, not a quantum dot or trapped ion 07:45  Quantized oscillations: the “smoking gun” and Balmer's ghost 08:18  Measuring the system: the resonance experiment 11:21  Systematic effects: what separates a good scientist from a lucky one 13:42  The Ed Ohm story: the man who found the CMB and doubted it 15:01  Wigner's “unreasonably effective” math and the weirdest thing about QM 16:54  The transistor myth: chewing gum, coat hangers, and germanium 17:34  Microwave engineering meets quantum mechanics 19:26  Decoherence: the friction you can't live without 24:06  The heretical claim: does theory ever precede technology? 28:22  The “paper qubit” problem 29:34  What quantum computers are actually good for 32:27  Should quantum computing be regulated like AI? 33:35  US vs. China: the quantum computing race 35:44  Collapse, Copenhagen, or many worlds? Martinis's answer 37:43  Leaving Google: “essentially demoted” 40:48  Why Colab exists and what “negative authority” means 42:50  The real bottleneck: funding, not physics 43:31  Final advice: always be on the lookout for the impossible Qolab: https://qolab.ai/ Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt Have a .edu email and live in the USA? You automatically win a meteorite: https://BrianKeating.com/edu Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Support Into the Impossible on Patreon, get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join My books: Losing the Nobel Prize (memoir): http://amzn.to/2sa5UpA Think Like a Nobel Prize Winner: https://a.co/d/03ezQFu Focus Like a Nobel Prize Winner: https://a.co/d/hi50U9U Galileo's Dialogue (first-ever audiobook): https://a.co/d/iZPi9Un Twitter/X: https://x.com/BrianKeating Substack: https://briankeating.substack.com Blog: https://briankeating.com/blog Audio-only: https://briankeating.com/podcast Landing page: https://awake-mill-k25t.here.now #intotheimpossible #briankeating #JohnMartinis #NobelPrize #quantumcomputing #physics #podcast Learn more about your ad choices. Visit megaphone.fm/adchoices

"If you want to know what life's like when you're not the apex of intelligence, ask a chicken."That's the line that Geoffrey Hinton, the Nobel Prize-winning "Godfather of AI" dropped on Steven Bartlett's podcast. It stopped Cal cold. Along with a lot of other people. One of them was a digital anthropologist and best-selling author named Brian Solis, who's spent decades watching technology unfold. To Solis, Hinton's statement felt in the moment like we had reached The End. But when Cal caught up with Brian a year later, he found that the anthropologist had adapted and made a case for what's possible. It's all in the book he co-wrote with Dave Wright that's titled: Infinite. Cal's conversation with Brian did something he never expected. It made him think about himself, for the first time, as a leader. Stay tuned . . .

“If you really focus on the reader and creating something meaningful and impactful and excellent... it has longevity and shelf life and continued work to do out in the world well beyond you.” — Stacy EnnisIn this episode of the Sunlight Tax Podcast, I sit down with Stacy Ennis to explore what it really takes to write and publish a nonfiction book that grows your business, strengthens your brand, and creates lasting impact. We discuss the publishing process, book coaching, traditional publishing vs. self-publishing, and why integrity, strategy, and a clear message matter more than simply getting a book into the world.Stacy shares practical insights from years of helping authors craft books that not only sell, but also make a meaningful difference—offering valuable guidance for anyone considering writing a business book or becoming a published author.Also mentioned in today's episode:00:10 Introduction to Stacy Ennis and her background01:00 Stacy's journey from childhood reading to publishing expert02:36 Her experience in publishing and ghostwriting03:24 Working with authors with a bigger mission04:49 The ROI of writing a book and trust building05:56 Trusting Stacy's expertise after a trust-breaking experience07:43 The importance of mission and impact in writing09:04 The strategic value of a book for influence and marketing10:33 Shelf life and longevity of well-done work12:24 Reflections on the author journey and publishing routes14:36 Exploring traditional, self, and hybrid publishing16:36 The evolving landscape of publishing and author education18:24 Why hybrid and self-publishing are viable options20:28 The role of a book in business, trust, and influence24:18 The transformative process of organizing ideas27:37 The impact of writing on personal and business growth28:58 The importance of creative health and deep thought31:16 Rebuilding self-trust through the writing process32:44 The societal and professional importance of authorship33:01 Stacy's resources and podcastIf you enjoyed this episode, please rate, review and share it! Every review makes a difference by telling Apple or Spotify to show the Sunlight Tax podcast to new audiences.About Stacy Ennis:Stacy Ennis is a best-selling author, book coach, and speaker on a mission to help leaders clarify their ideas and harness their unique story to make an impact. Her background includes impacting more than 100 books in her 17-plus years in publishing; ghostwriting for a Nobel Prize winner in medicine; and leading as executive editor of Sam's Club's Healthy Living Made Simple, a publication that reached around 11 million readers. Stacy's work and writing have been featured in Yahoo!, Inc., Insider, Publisher's Weekly, Katie Couric, and the TEDx stage. Stacy is also the host of the podcast Beyond Better and holds a master's in writing and editing from the University of Cincinnati.Check Out Stacy Ennis' Work:* 10 Things You Should Know Before Writing a Nonfiction Book free resource* Author Influence Circle* Direct link to book a call with Stacy to chat about their book* Instagram @stacyennis* LinkedIn: Stacy Ennis* Study mentioned on the episodeEpisode Links:Check out my program, Money BootcampGet my Tax Help on SubstackGet your FREE visual guide to tax deductionsOrder my book: Taxes for Humans: Simplify Your Taxes and Change the World When You're Self-Employed Get full access to Taxes For Humans at sunlighttax.substack.com/subscribe

“Obituaries are about life, about the lives that people have lived. Death only figures in one sentence.” — Sam Roberts This is, remarkably, episode 3,000 of Keen On America — three thousand conversations since the show launched on TechCrunch in 2010. Fitting, then, that today's guest has written nearly 1,500 obituaries for the New York Times. Sam Roberts is a reporter on the Times' obituaries desk and has a new book about the art of the obit entitled Are They Dead Yet? We recorded this a week before airtime, so I began by asking who's going to die in the interim. He wasn't sure — but noted that the Times keeps 2,000 advance obituaries ready. There are, he added, never enough. Beware, though, the Rockefeller curse. Roberts wrote David Rockefeller's advance obituary, whereupon he learned that three other Times reporters had written Rockefeller advance obits — and all three were dead. (Rockefeller made it to 101.) Borrowing from Citizen Kane, Roberts hunts for the Rosebud moment, the epiphany that defines a life. He hunts for the soul of the subject, not the résumé. Hence his cheerful paradox that obituaries are about life, whereas death only figures in one sentence. As for famous last words, he's a skeptic — most were composed later by better writers. The real ones, he suspects, are “help,” or “get me a drink.” And the obituarist's own Rosebud moment? We have to go back to Brooklyn when he was a six-year-old boy. His father walked him to the corner to watch the Rosenberg funeral procession pass. “I want you to see history as it's happening,” he explained. Half a century later, Roberts got David Greenglass to admit he had lied about the testimony that sent his own sister, Ethel Rosenberg, to the electric chair. From the Times' Portraits of Grief after 9/11, Roberts learned the book's deepest lesson. There are no ordinary lives. Who would you miss more — your mayor or your mailman? Or maybe your obit writer (or even your podcaster)? Sam promised that if he died before airtime, he'd call. The phone hasn't rung. I'm still going too. On to episode 3,001. Five Takeaways •       2,000 Obits, Never Enough. The Times keeps some 2,000 advance obituaries on file, regularly updated — and people still surprise the desk (and presumably themselves) by dying unexpectedly. Print once had a deadline or two a day; now there's a deadline every minute. The occupational hazards are real: three Times reporters wrote advance obits of David Rockefeller and predeceased him — Roberts, the fourth, survived to see his run when Rockefeller died at 101. And the genre changes its readers: “no man who has seen his own obit is ever the same again.” Alfred Nobel, mistakenly obituarized as dynamite's merchant of death, endowed the Nobel Prizes in response — proof that a premature obituary can be the most consequential document a man ever reads.•       The Rosebud Method. Roberts' organizing device comes from Citizen Kane: the Rosebud moment, the epiphany — a teacher, a meeting, an accident — that changed the trajectory of a life and made it worth recording. The model, via The Economist's Ann Wroe, is the soul rather than the stenography: Wikipedia has the chronology; the obituary hunts the essence. Advance obits are never vetted by their subjects — facts checked, yes; the portrait, never — because an obituary is a news story, not a eulogy. And the form's great secret is optimism: obituaries are about life. Death gets one sentence. (Famous last words, meanwhile, are mostly posthumous fiction. The real ones: “help,” or “get me a drink.”)•       Giuliani, Trump, Epstein. The hardest working file on Roberts' desk: Rudy Giuliani — aggressive prosecutor, the mayor who proved New York governable, then a bitter Trump apologist indicted under the same statutes he once used on mobsters. How do you compress that into a lead paragraph? Andrew's suggestion — a narrative of somebody who had a soul and then sold it — requires Shakespeare; Roberts insists on newspaper objectivity, omitting nothing and editorializing never. The Trump obituary already exists (its subject, Roberts guesses, would sue). And context keeps moving after death: Bush the defeated loser became Bush the elder statesman; Epstein's reputation kept falling posthumously. Of the 150,000 people who die each day, three or four make the Times.•       The Rosenberg Rosebud. Roberts' own epiphany arrived at age six, on a Brooklyn street corner where his father took him to watch the Rosenberg funeral procession: “I want you to see history as it's happening.” His family otherwise met death with denial — six months after his father died, an aunt asked how Arthur was doing could only answer “so-so.” The Rosenberg thread ran through his whole career: decades later, for his book The Brother, David Greenglass admitted to Roberts that he had lied about the most incriminating testimony against his sister Ethel — evidence that, truthfully given, would likely have spared her the electric chair. When Roberts assigned graduate students to write his own advance obituary, none of them led with that. He would.•       No Ordinary Lives. From Portraits of Grief — the Times' profiles of virtually every victim of the World Trade Center attack — Roberts drew the book's central lesson: there are no ordinary lives. Who would you miss more, your mayor or your mailman? Carlyle asked whether history belongs to Hannibal or to the anonymous man who invented the spade. The desk has honored the principle in both directions: a front-page obituary for Hercule Poirot (the most famous Belgian, as Andrew noted), and a Jesus of Nazareth obituary written as the Times would have run it in AD 33 — ending, for lack of further confirmation, with the report that he was buried and his body disappeared. No beat generates more reader feedback. “As long as they're reading it, I'm happy.” About the Guest Sam Roberts is a fifty-year veteran of New York journalism, an obituaries reporter and former Urban Affairs correspondent at the New York Times, and the host of the Times' “Close Up,” which he inaugurated in 1992. His many books include The Brother, Grand Central, A History of New York in 101 Objects, and Only in New York. A history adviser to Federal Hall, he lives in New York with his wife and two sons. Are They Dead Yet? The Art of the Obit (Bloomsbury, August 11, 2026) draws on the nearly 1,500 obituaries he has written for the Times. References: •       Are They Dead Yet? The Art of the Obit by Sam Roberts (Bloomsbury, August 11, 2026). Carl Hiaasen: “Mordantly wonderful.” Roz Chast: “I almost died laughing.”•       The Brother by Sam Roberts — in which David Greenglass admitted lying about the testimony that sent his sister, Ethel Rosenberg, to the electric chair.•...

AUG. 7, 2026The rewards of vision and hard work."Lazy people want much but get little, but those who work hard will prosper." Pr 13:4 NLTIn the fall of 1894, Guglielmo retreated to his room on the third floor. All summer while on vacation, he read books and filled notebooks with squiggly diagrams. Now the time had come for him to work. Every day, he rose early. He worked all day and long into the night, to the point that his mother became alarmed. He had never been a robust person, but now he had become appallingly thin. His face was drawn, and his eyes were often glazed over with fatigue. Finally, the day came when he announced that his instruments were ready. He invited the family to his room and, pushing a button, he succeeded in ringing a bell on the first floor!While his mother was amazed, his father was not. He saw no use in being able to send a signal so short a distance. So Guglielmo labored on. Little by little, he made changes in his sending circuits so he could send a signal from one hill to the next and then beyond the hill. Eventually his invention was perfected-partly due to vision and inspiration, but mostly due to perseverance.Guglielmo Marconi was eventually hailed as the inventor of wireless telegraphy, radio's forerunner. He not only received a Nobel Prize in physics but also acquired a seat in the Italian senate. If you want what successful people have, you must be willing to do what successful people do-and 99 percent of their success is due to vision and hard work. "Lazy people want much but get little, but those who work hard will prosper."The rewards of vision and hard work Vision, inspiration and perseveranceShare This DevotionalSend us Fan MailSupport the showChanging Lives | Building Strong Family | Impacting Our Community For Jesus Christ!

Drew Forsyth is an award-winning portrait photographer and director based in the North West of England, working with commercial and advertising clients across the UK and internationally. His work centres on people who've given everything to be extraordinary at what they do — from dancers with English National Ballet and musicians of the BBC Philharmonic and the Hallé Orchestras, to Nobel Prize-winning scientists, politicians, and performers at the height of their careers. His photography has been featured by BBC News, The Guardian, The Times, and Rolling Stone, and recognised by the Royal Society of the Arts. He's an AOP Accredited Photographer and Fellow of the RSA, and speaks internationally at events like Photo North, Pas de Deux Photo Conference, and the Royal Photographic Society.In this episode, Drew and Nikki trace his path from a JCPenney-style family portrait studio to photographing world leaders, Nobel laureates, and major corporate campaigns — and he breaks down exactly how he prices that work today. They dig into setting boundaries on set (and how to say yes to more without losing your mind), why those early "conveyor belt" studio years became his real technical training, and why he believes his biggest competition isn't other photographers — it's being forgotten. Drew also walks through how he actually quotes corporate jobs (day rates, itemized breakdowns, and the cautionary tale of a client who blamed him for "cheap-looking" dresses), how personal passion projects like his "Breaking Ground" series and a helicopter shoot over Manhattan became his best marketing, and the "Helsinki Bus Station Theory" — his framework for why so many photographers give up on their style right before it becomes distinctive.Topics covered:Setting boundaries with clients without losing bookingsWhy "conveyor belt" studio work builds indispensable technical skillsThe accidental path from individual sessions to corporate/organizational clientsPricing corporate shoots: day rates, itemized quotes, and scope questionsWhy past-client relationships and personal introductions beat cold outreachMarketing through consistent presence, not constant hard-sellingPassion projects as a client-acquisition strategyThe Helsinki Bus Station Theory: staying the course creativelyIf you're building a photography business, want to grow your portrait photography income, or are curious about how to make money from photography online, this conversation is packed with actionable advice.

Marie Curie was one of the most remarkable scientists in history. She was the first woman to win a Nobel Prize, the first person to win two, and the only person ever to earn Nobel awards in separate fields of science. Today we explore the life and legacy of Marie Curie with the science writer Dava Sobel. Dava is the author of prominent and best-selling science history books, including Longitude, Galileo's Daughter, The Planets, A More Perfect Heaven, and The Glass Universe. Today's conversation is based on Dava's latest book, entitled, The Elements of Marie Curie, How the Glow of Radium Lit a Path for Women in Science.

They're shamed, banned, or driven underground but “repugnant markets” have a way of surviving. Organ sales, MAID, even blood sales fall into the category. Nobel Prize-winning economist Alvin Roth comes to Canadaland to discuss his latest book, Moral Economics: From Prostitution to Organ Sales, What Controversial Transactions Reveal about How Markets Work.Further Reading:The Nobel Prize - Alvin E. RothCan economics save lives? - Alvin E. RothRepugnance as a Constraint on Markets - Alvin E. RothA Conversation with Alvin Roth - The ConversationKidney Paired Donation Program - Canadian Blood ServicesHost: Gemma BoothroydCredits: Gemma Boothroyd (Reporter), Bruce Thorson (Senior Producer), Chad Galloway (Audio Editing & Production), Tristan Capacchione (Senior Production Supervisor), Jesse Brown (Editor and Publisher)Fact Checking by: Julian AbrahamAdditional Music by: Audio NetworkPhoto: Linda A. Cicero, Stanford UniversitySponsors: Shopify: Sign up for your one-dollar-per-month trial today at https://shopify.caoxio: Head over to https://canadaland.oxio.ca and use code CANADALAND for your first month free! Article: Article is offering our listeners $50 off your first purchase of $100 or more. To claim, visit https://article.com/canadaland and the discount will be automatically applied at checkout.Can't get enough Canadaland? Follow @Canadaland_Podcasts on Instagram for clips, announcements, explainers and more.If you value this podcast, support us! You'll get premium access to all our shows ad free, including early releases and bonus content. You'll also get our exclusive newsletter, discounts on merch at our store, tickets to our live and virtual events, and more than anything, you'll be a part of the solution to Canada's journalism crisis, you'll be keeping our work free and accessible to everybody. Hosted on Acast. See acast.com/privacy for more information.

Cosy fantasy, famous fathers, and spinning fact into fiction, with author Naomi Ishiguro. Our guest today is British author Naomi Ishiguro. She’s the author of the short story collection, Escape Routes, and the novel, Common Ground. If her name rings a bell, it could be because her dad is the Nobel Prize-winning author, Kazuo Ishiguro. The first book from her new fantasy series, The Rainshadow Series, is now out in the world. Book one, The Rainshadow Orphans, is a cosy fantasy read blending magical whimsy with real-world political stakes. In Bookmarked, Naomi recommends Nina Mingya Powles’ Tiny Moons: A Year of Eating in Shanghai and Small Bodies of Water. Enjoy! Follow Naomi Ishiguro here and Culture Club podcast here. See omnystudio.com/listener for privacy information.

For most of human history, seeing inside a living body meant surgery. Newt tells how Dr. Raymond Damadian and physicist Paul Lauterbur turned 1940s nuclear magnetic resonance research into the MRI scanner—a machine that images soft tissue in astonishing detail without radiation or a single incision. From Damadian's hand-built "Indomitable" scanner to today's 40 million annual U.S. procedures, this episode traces how basic physics research, conducted with no medical purpose in mind, became one of medicine's most powerful diagnostic tools—and examines the controversial Nobel Prize snub that still stings decades later.See omnystudio.com/listener for privacy information.

“Intelligence is 100 percent human. AI is a tool created by humans to distill, digest, and distribute intelligence.” — Keith Teare The working class died this week — at least in Palo Alto. Delivering the eulogy in our regular That Was The Week tech summary is my co-host Keith Teare. “Humans create intelligence,” (whatever that means) the Silicon Valley-based entrepreneur tells us. And so, in our AI age of supposedly abundant intelligence, he pronounces, human knowledge “should not be trapped inside experts, institutions, or companies.” Check your pockets, everyone. Silicon Valley has another freebie for you. With AI, the entrepreneur promises, intelligence is democratized. Everybody gets it. We will all have the intelligence of a Nobel laureate at our fingertips. Even Keith. And so he attacks Daron Acemoglu, the Nobel Prize-winning MIT economist who has called for a “pro-worker AI.” But, for Keith — a council-estate kid from Yorkshire whose lifetime ambition was to evacuate the working class — this is “complete bullshit.” Acemoglu's ideas, he says, are an example of the “fetishization of workers” when, in fact, we should be celebrating the end of the “working class.” What Acemoglu is calling for in his pro-worker AI manifesto is more government planning for today's transition to the AI epoch. But Keith disagrees. So I asked him three times what government should do while AI kills the working (and middle) class. “Allow it to happen,” he finally answers. “A good upheaval.” Good? The former “worker” will lack jobs, wages, healthcare, housing. Even food in an America now eliminating food stamps. No matter. Let them eat intelligence. Five Takeaways •       Humans Create Intelligence. Keith's editorial thesis distinguishes individual intelligence — where experts live, and always will — from the collective sum of everything all humans, living and dead, have ever contributed. That collective stock was once locked in encyclopedias, libraries, and universities; for the first time, AI can aggregate, distill, digest, and distribute it, at a price falling toward everyone. Knowledge, he writes, “should not be trapped inside experts, institutions, or companies” — but note the fine print: experts don't disappear in this democratization. If anything, they get elevated: the expert reading an AI's output about viruses understands it very differently than the rest of us.•       The End of the Age of Heroes? Noah Smith's much-shared essay argues that AI ends the era of the mathematical hero — and that's fine, since most people (truck drivers, financial advisers, executive assistants) never got to be heroes anyway. Keith's rebuttal turns on his central distinction: AI and intelligence are not the same word. There is no evidence, he argues, that AI creates new knowledge — it understands and distributes the existing stock. Innovation still takes individuals, and those individuals now start from a far higher floor, leveled up to everything already known. Heroes don't go away; they multiply. In the world of AI, he suspects, every single teacher becomes one.•       “What Even Is Pro-Worker AI?” The week's main event: Daron Acemoglu — via Yascha Mounk's Persuasion interview and an Atlantic essay, with What Happened to Liberal Democracy out next week — wants AI agencies, grant programs, and public competitions to build “pro-worker AI.” Keith's verdict: “complete bullshit.” The middle-class “fetishization of workers” is paternalistic; the wage is a temporary power relationship between employer and employee; and the end of the working class is precisely the progressive outcome — says the council-estate kid from Yorkshire whose aspiration was not to be working class. Pressed three times on what government should do amid the upheaval, Keith finally answered: “Allow it to happen… a good upheaval.” Though swap workers for people, he conceded, and he'd almost entirely agree — every teacher a hero, even in East Palo Alto.•       Bandwagons and Silences. Regular people are being arrested protesting data centers; Erin Brockovich — a Keen On guest some years back — is assembling class actions; Ezra Klein has begun folding anti-big-tech language into abundance. A politician-led bandwagon, Keith argues, regressive but keyed to genuine local concerns. The stranger fact is the silence on the other side: neither Altman nor Amodei nor Demis Hassabis is making the public case that AI benefits everybody — astonishing, Keith says, and the vacuum Acemoglu is trying to fill. Hassabis himself stepped aside at Google this week — a scientist returning to science as Sergey Brin becomes AI czar — while the Nobel-winning AlphaFold team has been quietly broken up. “Something strange is going on there.”•       A Drama in a Teacup. Is the AI economy real? Ed Zitron's stat — 70 percent of Amazon, Microsoft, and Google's AI revenue comes from OpenAI and Anthropic — is two-thirds right, says Keith, and no problem at all: beneath the concentration, the money comes from some two billion distributed users paying real subscriptions, and the revenues are sustainable. The Aschenbrenner postscript, via Porter Stansberry's post of the week: he bet the chip layer (Samsung, SK Hynix) when the value sat a layer up, got the timing wrong more than the thesis, sold to Citadel at a discount — and kept his Anthropic shares, remaining a multi-billionaire. As for the coming reality check: Anthropic and OpenAI will IPO only when public capital beats private, and SpaceX's wobble from $135 to $108 — through a 20 percent lockup release — counts as no catastrophe. Public markets, Keith reminds us, don't determine the success of the underlying business. About the Co-Host Keith Teare is the publisher of That Was The Week, the essential weekly tech newsletter, and founder and CEO of SignalRank Corporation. A serial entrepreneur — co-founder of, among others, EasyNet and RealNames — he was present at the creation of the UK internet and has spent four decades at the intersection of technology, capital, and ideas. He joins Keen On America every Sunday to make sense of the week in tech. His AI-assisted book in progress is titled Who Owns Intelligence. References: •       That Was The Week — Keith's newsletter, including this week's editorial, “Humans Create Intelligence.”•       Noah Smith — “The End of the Age of Heroes,” on what happens to human ambition when the machines do the math.•       Daron Acemoglu — the Yascha Mounk interview at Persuasion, the Atlantic essay on pro-worker AI, and What Happened to Liberal Democracy, out next week.•       The Financial Times — “Google's AI shakeup boosts Brin as DeepMind's Hassabis steps aside.”•       Ed Zitron — on the 70 percent of hyperscaler AI revenue that flows from OpenAI and Anthropic.•       Porter Stansberry — post of the week, on Leopold Aschenbrenner's losses, Citadel's discount, and the drama in a ...

In this episode of THE MENTORS RADIO, Host Dan Hesse talks with Bengt Holmström, the Emeritus Paul A. Samuelson Professor of Economics at MIT, to celebrate the 10-year anniversary of receiving the 2016 Nobel Prize in Economics for his contributions to contract theory. His research has advanced the understanding of incentives, organizations, corporate governance, and financial liquidity. Bengt is a member of the U.S. National Academy of Sciences, a Fellow of the American Academy of Arts and Sciences and the Econometric Society, and a foreign member of the Royal Swedish Academy of Sciences and the Finnish Academy of Science and Letters. He received his Ph.D. from Stanford and before MIT, he held faculty appointments at Yale and Northwestern. Dr. Holmstrom explains why factors that are hard to measure usually drive better behavior in incentive plans than those that are easy to measure.  He mentors his students that asking the right questions is far more important than answering questions. A self-described “AI optimist,” he considers the AI companion he has created, Charlie, clever and life-enriching.  LISTEN TO this radio broadcast live on iHeart Radio, or go to “THE MENTORS RADIO” podcast any time, anywhere, on any podcast platform – subscribe here and don't miss an episode! SHOW NOTES: BENGT HOLMSTRÖM: BIO: BIO: Bengt Holmström https://en.wikipedia.org/wiki/Bengt_Holmstr%C3%B6m BOOKS: Inside and Outside Liquidity, by Bengt Holmström and Jean Tirole — Two leading economists develop a theory explaining the demand for and supply of liquid assets. Why do financial institutions, industrial companies, and households hold low-yielding money balances, Treasury bills, and other liquid assets? When and to what extent can… WEBSITE: The Nobel Prize: Bengt Holmström MIT Economics: Bengt Holmström (includes access to presentations, etc)

In high school, Isabella Karle had to take a science class to fulfill a prerequisite. She chose chemistry at random. Karle would go on to become one of the most important chemists of her time, doing groundbreaking research in the field of X-ray crystallography. Her discoveries in the mid 1950s are a key reason many modern-day pharmaceuticals are available on pharmacy shelves. But Karle's collaborator–her husband, Jerome–would eventually receive the Nobel Prize in Chemistry for work Isabella also achieved.  Learn about your ad choices: dovetail.prx.org/ad-choices

James Tour is not your average university professor - not only is he a professor of three different fields (chemistry, materials science, and nanoengineering) at Rice University in Houston, Texas, but he's also started 17 companies, has many hundreds of patents, and has written over 850 scientific publications. However, those achievements are not what he's most excited about; rather, it's sharing his deeply-rooted faith and seeing thousands over the years coming to Jesus, including one of his Nobel Prize-winning colleagues. It's a stunning interview!▶️ Watch this episode on YouTube: https://youtu.be/rX2iMmmD8mwJesus and Science Foundation: jesusandscience.org Dr. Tour's website: jmtour.comYouTube | Facebook | Instagram | X.comDr. Tour's weekly email newsletter.He has read the book ‘The Gospel Focus of Charles Spurgeon by Steven J. Lawson over 50 times, so maybe it's worth reading likewise!---

Women's health has long been underserved by research, leaving many women without the information they need to understand their own bodies. On this week's episode of the WHOOP Podcast, WHOOP Global Head of Human Performance and Principal Scientist Dr. Kristen Holmes sits down with Dr. Elina Berglund Scherwitzl, Co-Founder & CEO of Natural Cycles° and Nobel Prize-winning particle physicist. The conversation explores how data is transforming women's reproductive health.After helping discover the Higgs boson, Dr. Berglund Scherwitzl turned her expertise in physics and data science toward her own health journey by creating the world's first and only FDA-cleared birth control app. Together, Dr. Holmes and Dr. Berglund Scherwitzl discuss how wearable technology and physiological data are giving women unprecedented insight into fertility, pregnancy planning, menstrual health, and perimenopause.The conversation explores the new WHOOP and Natural Cycles° integration, designed to make women's health feel less overwhelming and more intuitive by combining continuous wearable data with advanced insights on fertility, hormonal health, and perimenopause. Dr. Holmes and Dr. Berglund Scherwitzl discuss how body temperature and other biomarkers reveal hormonal changes, why perimenopause often begins earlier than many women realize, and how passive, personalized insights can reduce uncertainty, ease mental load, and empower women to make informed decisions about their health at every stage of life.(00:00) Cold Open (00:37) Meet The Scientist Behind Natural Cycles°(01:00) Why Dr. Berglund Scherwitzl Started Natural Cycles°(04:35) The Only FDA-Cleared Birth Control App(05:54) The WHOOP x Natural Cycles° Partnership(07:51) The Early Signs of Perimenopause (And How To Track Them)(13:08) What Really Happens To Your Hormones Over Time(17:34) Why Understanding Your Body Changes Everything(19:09) Your Sleep Could Reveal Early Signs of Perimenopause(22:03) The Small Habits That Make A Big Difference(24:14) The Most Common Menstrual Cycle Myths(26:09) What Your Wearable Can Tell You About Your Hormones(27:07) The Truth About Fertility Timing(29:16) Knowing Which Symptoms Matter Most(31:36) HRT: What Women Should Know(34:10) What You Can Expect From The WHOOP x Natural Cycles° Partnership(37:05) The Future of Women's HealthFollow Dr. Elina Berglund Scherwitzl:LinkedInWebsiteFollow Natural Cycles°:InstagramYouTubeWebsiteSupport the showFollow WHOOP:Sign up for WHOOP Advanced LabsTrial WHOOP for Freewww.whoop.comInstagramTikTokYouTubeXFacebookLinkedInFollow Will Ahmed:InstagramXLinkedInFollow Kristen Holmes:InstagramLinkedInFollow Emily Capodilupo:LinkedIn 

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Scott Dodelson spent ten years on the Dark Energy Survey testing the standard model of cosmology. It passed and missed by 2.5 sigma. The Director of Fermilab's Cosmic Physics Division on why the theory he helped build may be wrong, why nobody can find dark matter, and what it takes to change a scientific consensus. Subscribe if you want science with evidence, not speculation. Dodelson is Director of the Cosmic Physics Division at Fermilab, Professor of Astronomy and Astrophysics at the University of Chicago, and author of Modern Cosmology, the textbook a generation of cosmologists learned the field from. Lambda-CDM predicts how the early universe's tiny fluctuations grew into the structure we see today. The Dark Energy Survey was designed to check that prediction. The answer came back two and a half sigma off. That is both the most precise measurement ever made of how the universe grew, and a crack he cannot stop looking at. His question is not whether the model is close. It is whether close is enough to trust. We get into the Sigma-8 tension and what it actually takes for a scientific community to change its mind, the Dodelson-Widrow mechanism and his 1994 proposal that sterile neutrinos produced in the early universe could constitute dark matter, and the Neptune vs. Vulcan history of getting dark matter right and wrong. Asked which of those two situations we are in now, Dodelson's answer is: I have no idea. What you'll hear: Why the April 24, 1992 CMB discovery may have been the only science story ever to lead the New York Times front column The Dodelson-Widrow mechanism: how ordinary neutrinos in the early universe may have oscillated into the dark matter we see today Why Brian told Neil deGrasse Tyson to his face that we have already detected dark matter What it means when a theory can accommodate any result and whether inflation has that problem Why the particle physics community still does not trust cosmology's neutrino mass measurements What cosmology looks like in 2036 if Lambda-CDM breaks Killing the model is my dream. 0:00 None of it has been found in a lab. How many free passes do we get? 0:44 Dodelson helped build Lambda-CDM. His dream is to kill it. 2:10 10 years. One prediction. Two and a half sigma off. 5:42 "You're in charge" — what mentorship in science actually looks like 6:56 April 24, 1992: cosmology stopped being speculation 9:48 Geoff Burbidge went to his grave a steady-state believer 11:04 What a neutrino is and why it barely interacts with anything 12:36 Brian told Neil deGrasse Tyson we already detected dark matter 18:08 How ordinary neutrinos may have become dark matter 23:24 Lambda-CDM predicts Manhattan's density 13.7 billion years later 26:42 Two and a half sigma: technically a 1% chance the theory is right 28:04 Neptune was dark matter. Vulcan wasn't. Which one are we in now? 31:20 Dark matter, inflation, dark energy: none found in a lab 36:46 If both cracks are the same crack, Lambda-CDM is finished Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt Have a .edu email and live in the USA? You automatically win a meteorite: https://BrianKeating.com/edu Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Support Into the Impossible on Patreon, get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join Featured Guest: Scott Dodelson Substack: https://scottdodelson.substack.com/about Scott Dodelson on LinkedIn: https://www.linkedin.com/in/scott-dodelson-b6ba429/ Scott Dodelson on Twitter/X: https://x.com/ScottDodelson Modern Cosmology (book): https://www.sciencedirect.com/book/monograph/9780128159484/modern-cosmology Dark Energy Survey final results: https://www.darkenergysurvey.org/news-and-results/darchives/ Dodelson and Widrow 1994, Sterile neutrinos as dark matter: https://arxiv.org/abs/hep-ph/9303287 Previous ITI episode with Kyle Dawson on DESI: https://youtu.be/LPx4oiwGp2k?si=u_ZJWfJG5AhvBAEv  My books: Losing the Nobel Prize (memoir): http://amzn.to/2sa5UpA Think Like a Nobel Prize Winner: https://a.co/d/03ezQFu Focus Like a Nobel Prize Winner: https://a.co/d/hi50U9U Galileo's Dialogue (first-ever audiobook): https://a.co/d/iZPi9Un Twitter/X: https://x.com/BrianKeating Substack: https://briankeating.substack.com Blog: https://briankeating.com/blog Audio-only: https://briankeating.com/podcast #intotheimpossible #briankeating #cosmology #darkmatter #darkenergy #physics #LambdaCDM #podcast Learn more about your ad choices. Visit megaphone.fm/adchoices

Yascha Mounk and Daron Acemoglu examine how the digital age severed the link between economic growth and shared prosperity. Daron Acemoglu is an economist at MIT and a recipient of the 2024 Nobel Prize in Economics. His latest book is What Happened to Liberal Democracy? In this week's conversation, Yascha Mounk and Daron Acemoglu discuss why liberal democracy's post-war formula for shared prosperity broke down, how the rise of a college-educated professional class produced a cultural backlash against “social engineering,” and how to shape the future of artificial intelligence. We're delighted to feature this conversation as part of our series on Liberal Virtues and Values. This series, made possible with the generous support of the John Templeton Foundation, features content making the case that liberalism has its own distinctive set of virtues and values that are capable not only of responding to the dissatisfaction that drives authoritarianism, but also of restoring faith in liberalism as an ideology worth believing in—and defending—on its own terms. If you have not yet signed up for our podcast, please do so now by following this link on your phone. Email: leonora.barclay@persuasion.community Podcast production by Mickey Freeland and Leonora Barclay. Connect with us! Spotify | Apple X: @Yascha_Mounk & @JoinPersuasion YouTube: Yascha Mounk, Persuasion LinkedIn: Persuasion Community Learn more about your ad choices. Visit megaphone.fm/adchoices

In nature, enzymes are the catalysts that make much of biology work. They jumpstart chemical reactions that either wouldn't happen, or would happen super slowly. They break down food, build other molecules, extract energy, and more. What if we could harness evolution to engineer designer enzymes that do other specific jobs that benefit us?  Putting that idea into practice changed the game for chemistry, and earned Frances Arnold the Nobel Prize prize in 2018. She called it “directed evolution.” Today, thousands of labs use her methods to coax enzymes into doing things no one ever thought of. She joined Host Flora Lichtman in March 2026 to talk about where she sees this approach going in the future, and the personal evolution that brought her into science. Guest:  Dr. Frances Arnold is the Linus Pauling Professor of Chemical Engineering, Bioengineering and Biochemistry at the California Institute of Technology in Pasadena, California. Other episodes you may enjoy: Enzymes Are Taking On Our Plastic Problem Even Nobel Prize Winners Deal With Imposter Syndrome The transcript for this episode is available at sciencefriday.com. Subscribe to this podcast. Follow our show on Instagram, TikTok, Facebook, and Bluesky @scifri and sign up for our newsletters. Got a science question that's keeping you up at night? Call us: 877-472-4374 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Reality may be bigger, stranger, and far more crowded than our three-dimensional monkey brains can handle. This week on Hysteria 51, we count down ten horrifying theories about what could exist in dimensions we cannot see, from shadow matter and hidden forces to holographic universes, colliding branes, M-theory, and countless alternate versions of you making equally terrible decisions.Could gravity be leaking into another dimension? Is our universe just one thin membrane floating in a much larger cosmic void? Does every possible outcome create another reality where you remembered the password on the first try? We break down the real science behind extra dimensions, dark matter, the multiverse, many-worlds theory, and the terrifying possibility that reality has more rooms than advertised.So grab a flashlight, avoid making eye contact with the shadow universe occupying your couch, and prepare to discover why the cosmos is weird, physics is rude, and somewhere in another dimension, this episode is probably much better.SourcesTime and the Block UniverseStanford Encyclopedia of Philosophy – Timehttps://plato.stanford.edu/archives/spr2026/entries/time/Stanford Encyclopedia of Philosophy – Temporal Consciousnesshttps://plato.stanford.edu/archives/fall2025/entries/consciousness-temporal/Asher Peres – “Critique of the Wheeler-DeWitt Equation”https://arxiv.org/abs/gr-qc/9704061The Fifth ForceEöt-Wash Group – “New Test of the Gravitational Inverse-Square Law at the Submillimeter Range”https://arxiv.org/abs/2002.11761NA64 Collaboration – “Hunting Down the X17 Boson at the CERN SPS”https://arxiv.org/abs/2009.02756Kaluza-Klein Theory and Extra DimensionsCERN – Extra Dimensions, Gravitons, and Tiny Black Holeshttps://home.cern/science/physics/extra-dimensions-gravitons-and-tiny-black-holes/Theodor Kaluza – “On the Unity Problem of Physics”https://inspirehep.net/literature/14622Shadow Matter and Hidden SectorsEdward Kolb, David Seckel, and Michael Turner – “The Shadow World of Superstring Theories”https://www.nature.com/articles/314415a0Lawrence Krauss, Alan Guth, David Spergel, George Field, and William Press – “Inflation and Shadow Matter”https://www.nature.com/articles/319748a0Dark Energy and Dimensional LeakageNobel Prize – The 2011 Nobel Prize in Physicshttps://www.nobelprize.org/prizes/physics/2011/summary/Nobel Prize – The Accelerating Expansion of the Universehttps://www.nobelprize.org/prizes/physics/2011/popular-information/Gia Dvali, Gregory Gabadadze, and Massimo Porrati – “4D Gravity on a Brane in 5D Minkowski Space”https://arxiv.org/abs/hep-th/0005016The Holographic PrincipleLeonard Susskind – “The World as a Hologram”https://arxiv.org/abs/hep-th/9409089Juan Maldacena – “The Large N Limit of Superconformal Field Theories and Supergravity”https://arxiv.org/abs/hep-th/9711200Fermilab – The Holometerhttps://holometer.fnal.gov/index.htmlFermilab – Holometer Frequently Asked Questionshttps://holometer.fnal.gov/faq.htmlBrane Worlds and the BulkLisa Randall and Raman Sundrum – “A Large Mass Hierarchy from a Small Extra Dimension”https://arxiv.org/abs/hep-ph/9905221Lisa Randall and Raman Sundrum – “An Alternative to Compactification”https://arxiv.org/abs/hep-th/9906064The Ekpyrotic and Cyclic UniverseJustin Khoury, Burt Ovrut, Paul Steinhardt, and Neil Turok – “The Ekpyrotic Universe: Colliding Branes and the Origin of the Hot Big Bang”https://arxiv.org/abs/hep-th/0103239Paul Steinhardt and Neil Turok – “Cosmic Evolution in a Cyclic Universe”https://arxiv.org/abs/hep-th/0111098M-Theory and the String LandscapeEdward Witten – “String Theory Dynamics in Various Dimensions”https://arxiv.org/abs/hep-th/9503124Leonard Susskind – “The Anthropic Landscape of String Theory”https://arxiv.org/abs/hep-th/0302219T. Banks – “The Top (10^{500}) Reasons Not to Believe in the Landscape”https://arxiv.org/abs/1208.5715The Many-Worlds InterpretationHugh Everett III – “‘Relative State' Formulation of Quantum Mechanics”https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.29.454Stanford Encyclopedia of Philosophy – Many-Worlds Interpretation of Quantum Mechanicshttps://plato.stanford.edu/archives/sum2026/entries/qm-manyworlds/Email your Contest Entries to:ForthHandmedia@gmail.comEmail us your favorite WEIRD news stories:weird@hysteria51.comSupport the ShowGet exclusive content & perks as well as an ad and sponsor free experience at https://www.patreon.com/Hysteria51 from just $1ShopBe the Best Dressed at your Cult Meeting!https://www.teepublic.com/stores/hysteria51?ref_id=9022See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Two theoretical physicists helped design landmark surveys of 1,600+ scientists on the deepest unsolved questions in physics — and found almost no consensus at all. From string theory's shockingly low support to physicists admitting their models run on "belief," this episode exposes the faith hiding inside science. Adam Frank (University of Rochester astrophysicist and astrobiologist) and Niayesh Afshordi (Perimeter Institute / University of Waterloo cosmologist, co-author of the APS Physics Magazine "Big Mysteries" survey) join Brian to unpack what physicists actually believe versus what they can prove. We cover: - Why string theory pulled a shockingly small share of the vote against loop quantum gravity - What a Bayesian "prior" reveals about every scientist's hidden beliefs - Why one branch of physics quietly became unfalsifiable - How sociology and "tastemakers" can hijack scientific consensus - Why AI might become the field's unlikely savior. There is no sane statistical analysis that doesn't have a prior — that's your belief. Timestamps: 00:00 – Why scientists owe the public real answers 04:00 – Splitting time between research and outreach 07:55 – The survey that "rankled" Brian Keating 09:18 – Are physicists secretly just like Spock? 12:05 – Are we living in an anti-scientific age? 16:56 – Why most scientists refuse to go public 21:36 – Should "belief" ever appear in a survey? 23:12 – Is string theory really 21st-century physics? 26:20 – When sociology hijacks scientific consensus 28:39 – The hidden "prior" behind every experiment 33:01 – String theory's shockingly low vote count 39:03 – Four levels of belief, from data to faith 41:39 – Inside the 1,675-physicist mystery survey 46:12 – Kingmakers, tastemakers, and physics cliques 54:12 – Is advanced tech indistinguishable from magic? 59:23 – Could AI become physics' long-awaited savior? 1:01:47 – What's next for Adam and Niayesh ———

Erin Ryan and Sami Sage (Betches Media) dig into the Trump administration's move to terminate millions of dollars of federal grants for teen pregnancy prevention and what that says about the administration's vision of women. Then, they dive into Emily Wilson's scathing review of the Odyssey. Plus, Annie Andrews, Democratic Candidate for U.S. Senate in South Carolina, stops by to discuss her race and how the death of Lindsay Graham is shaking things up. And as always, we end with sanipetty.Emily Wilson: An Uncomplicated Man Government Overhauls Teen Pregnancy Program to Focus on Marriage and Starting Families (NYT 7/22 + NPR 7/7)A Nobel Prize winner decodes why people aren't having kids (NYT OPED 2025 + Claudia's Original Research & Ruben Gallego 7/23)

Huxley is an example of having a good attitude about publicly acknowledging errors and learning from them. He earned the nickname “Darwin’s bulldog” for his very vocal support of Darwin’s work, and he wanted to promote science and education, particularly to people that didn’t normally have access to those things. Research: Bernoulli, D., Jenkyns, H.C. “Thomas Henry Huxley, a stone tablet, coccoliths, and deep-sea sediments in the high Alps.” Int J Earth Sci (Geol Rundsch) 112, 1661–1669 (2023). https://doi.org/10.1007/s00531-023-02330-5 Bowen, Keith. “Thomas Henry Huxley.” Ebsco. 2024. https://www.ebsco.com/research-starters/history/thomas-henry-huxley Burkhardt, Richard W.. "Jean-Baptiste Lamarck". Encyclopedia Britannica, 14 Dec. 2025, https://www.britannica.com/biography/Jean-Baptiste-Lamarck Darwin, Charles. “On the Origin of Species.” JOHN MURRAY, ALBEMARLE STREET. London. 1859. https://www.gutenberg.org/files/1228/1228-h/1228-h.htm Desmond, Adrian J. "Thomas Henry Huxley". Encyclopedia Britannica, 25 Jun. 2026, https://www.britannica.com/biography/Thomas-Henry-Huxley Dolan, John R. “The famous and lesser-known illustrations of Thomas Huxley’s Bathybius.” History of Oceanography. International Commission of the History of Oceanography. https://oceansciencehistory.com/2020/07/13/the-famous-and-lesser-known-illustrations-of-thomas-huxleys-bathybius/ “Freshwater Bathybius.” Nature 4, 49–50 (1871). https://doi.org/10.1038/004049b0 “The Great Debate.” University of Oxford Museum of Natural History. https://oumnh.ox.ac.uk/great-debate “The History of Evolutionary Thought.” Understanding Evolution. Berkeley University. https://evolution.berkeley.edu/the-history-of-evolutionary-thought/1800s/early-concepts-of-evolution-jean-baptiste-lamarck/ Huxley, T.H. “Agnosticism.” K. Paul, Trench. London. 1889. https://archive.org/details/a622650700huxluoft/page/169/mode/1up Huxley, T.H. “Aphorisms and Reflections. From the Works of T. H. HUXLEY, Selected byHenrietta A. Huxley. Macmillan and Co. London. 1907. Accessed online: http://aleph0.clarku.edu/huxley/Book/Aphor.html Huxley, T.H. “Autobiography and Selected Essays.” Riverside College Classics. 1909. Accessed online: https://www.gutenberg.org/files/1315/1315-h/1315-h.htm#link2H_4_0007 Huxley, T.H. “Collected Essays, Volume V: Science and Christian Tradition.” https://www.gutenberg.org/files/15905/15905-h/15905-h.htm Huxley, T.H. “Evidence as to man's place in nature.” D. Appelton and Co. New York. 1863. https://archive.org/details/evidenceastomans00huxl/page/n1/mode/2up Huxley, T.H. “On Some Organisms Living at Great Depths in the North Atlantic Ocean.” Quarterly Journal of Microscopical Science. viii., new Series, 1868, pp. 203-212. http://aleph0.clarku.edu/huxley/SM3/bathy.html Huxley, T.H. “On the Relations of Man to the Lower Animals.” Project Gutenberg eBook. 2001. https://www.gutenberg.org/cache/epub/2932/pg2932-images.html Joshi RS. “The Inner Root Sheath and the Men Associated with it Eponymically.” Int J Trichology. 2011 Jan;3(1):57-62. https://pmc.ncbi.nlm.nih.gov/articles/PMC3129131/ Lamarck, Jean-Baptiste. “Zoological Philosophy.” Macmillan. London. 1914. https://archive.org/details/ZoologicalPhilosophy Lyons, Sherrie L. “Thomas Henry Huxley : the evolution of a scientist.” Prometheus Books. New York. 1999. McGraw, Donald J. “Bye-Bye Bathybius: The Rise and Fall of a Marine Myth.” Bios, vol. 45, no. 4, 1974, pp. 164–71. JSTOR, http://www.jstor.org/stable/4607257 “Nobel Prize in Physiology or Medicine 1963.” The Nobel Prize. https://www.nobelprize.org/prizes/medicine/1963/summary/ Pearce, JMS. “Thomas Henry Huxley.” Hektoen International. Sept. 18, 2020. https://hekint.org/2020/09/18/thomas-henry-huxley/ Schwartz, Joel S. “Darwin, Wallace, and Huxley, and ‘Vestiges of the Natural History of Creation.’” Journal of the History of Biology, vol. 23, no. 1, 1990, pp. 127–53. JSTOR, http://www.jstor.org/stable/4331120 “Thomas Henry Huxley.” Darwin Correspondence Project. University of Cambridge. https://www.darwinproject.ac.uk/thomas-henry-huxley “Thomas Henry Huxley (1825-1895).” Humanist Heritage. https://heritage.humanists.uk/thomas-henry-huxley/ Tunstad, Erik. “Darwin's Bulldog: Thomas Huxley 200 Years.” Prosa. July 10, 2025. https://prosa.no/artikler/aktuelt/darwins-bulldog-thomas-huxley-200-ar Wyhe, John van. “It ain't necessarily so ...” The Guardian. Feb 9, 2008. https://www.theguardian.com/science/2008/feb/09/darwin.myths See omnystudio.com/listener for privacy information.