Podcasts about Uber

Jim Jefferies is an Australian stand-up comedian, actor, writer, and podcaster known for his provocative, observational comedy. He gained international recognition through specials like Alcoholocaust, Bare, and Intolerant, and later created and starred in the FX sitcom Legit. To find his tour dates and see him live check out his website jimjefferies.com. IN THE NEWS: Adam rants about how no one ever seems to get his order right when he goes out to eat. Then Adam and Andrew react to a California high school football coach being fired after kicking a football into a player's groin during practice, plus a wild Houston video of two police officers asking an Uber driver for help catching a suspect.FOR MORE WITH JIM JEFFERIES:TOUR DATES: Son Of A Computer (jimjefferies.com) Sep 11-Windsor, ON- Caesar WindsorSep 12-Thunder Bay, ON-Thunder Bay Community AuditoriumSep 27-Reykjavik, IS- Harpa EldburgPODCAST: At This Moment (w/ Amos Gill)SPECIAL: Two Limb PolicyOut On NetflixFOR MORE WITH ANDREW HOBSON:INSTAGRAM & YOUTUBE: @andrewfhobsonLIVE SHOWS: September 11 - Las Vegas, NV (2 Shows)September 12 - Las Vegas, NV (2 Shows)September 20 - Santa Ana, CA (Live Podcast)September 24 - Burbank, CAThank you for supporting our sponsors:forthepeople.com/adam oreillyauto.com/adam pluto.tvSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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The RHOC women head down to Rancho Santa Fe to bully Carmella, Danny and Heather. What are we doing here?! Emily and Shane continue to have problems, Shannon spends $3000 on Ubers a month and Heather makes SAAAAAHNGRIA for the group. We also play Plead Your Case, our new weekly segment where we decide who we are judging the most in the Bravoverse. This week it's between Liam from Next Gen vs Kelli from RHOA. Come judge with us!You can find us:Linktree: Two Judgey GirlsPodcast: ACast, iTunes, Spotify, wherever you listen!Instagram & Threads: @twojudgeygirlsTikTok: @twojudgeygirls // @marytwojudgeygirls // @courtneytjgYouTube: @twojudgeygirlsFacebook: www.facebook.com/twojudgeygirlsMerch: www.etsy.com/shop/twojudgeygirlsPatreon: www.patreon.com/twojudgeygirls LTK: @marytwojudgeygirls // @courtneytjg Hosted on Acast. See acast.com/privacy for more information.

Amy "Rose" Phillips and Deanna "Betty White" Cheng kick off Friday chatting about a fly in the house and home-cooked “slop” dinners before reacting to a TMZ report that Brittany Cartwright filed a domestic violence temporary restraining order against Jax Taylor, alleging threats, physical attacks, property destruction, and seeking custody of their son Cruz. They note Hulu's The Secret Lives of Mormon Wives returns September 10, then dive into Real Housewives of Orange County frustrations: Vicki's tense energy and missing “woo-hoo,” her leaving a trip early, and questions about her return motives. They discuss the pool party that didn't feel like a pool party, “Carmela's” cheating boyfriend's awkward introduction, the “Betty White” comment, and Carmela's Oppenheim Group job helping Heather's daughter. They cover Heather's photo positioning, Shannon's finances (including Real for Real losses, $3,000 Uber spend, and dog Troy chewing $12,000 in shoes), Heather allegedly calling Carmela trashy, and Emily and Shane's marriage tension, which they believe will be fine.This episode is sponsored by:OAK ESSENTIALS Luminous Body Lotionhttps://oakessentials.comBUBS NATURALS COLLAGEN AND PEPTIDESLive Better Longer with BUBS Naturals. For A limited time get 20% Off your entire order with code DRAMA at Bubsnaturals.comFLAMINGO Smoother skin awaits you! For a limited time, our listeners can get Flamingo's Starter Set for only $7 at ShopFlamingo.com/DRAMAThis set includes the Flamingo Original Razor, one five-blade cartridge, a one-ounce Foaming Shave Gel, and a shower holder.RULAStart your mental wellness journey today with Rula, visit:https://www.rula.com/drama/To watch this recap on video, listen to bonus episodes, get ad free listening, and exclusive content, go to: http://Patreon.com/dramadarlingFollow Amy Phillips on Instagram: Instagram.com/meetamyphillipsFollow Drama, Darling on Instagram: Instagram.com/dramadarlingshowAmy on TikTok: tiktok.com/@realamyphillipsEmail Drama, Darling with YOUR comments, questions and drama: DramaDarlingz@gmail.comDrama Darling Shop: https://drama-darling-shop.printify.me/

Who inherits your IRA could matter just as much as what's in it. The SECURE Act changed the rules for inherited retirement accounts, while newer IRS regulations created additional planning opportunities for trusts and multiple beneficiaries. Richard Rosso & Jonathan McCarty break down IRA beneficiary designations, the 10-year distribution rule, trusts as retirement account beneficiaries, and why outdated estate plans can create unintended tax consequences. Plus, we explain how properly structured trusts and subtrusts may provide greater flexibility for spouses, children, and other heirs. Before assuming your will or trust has your retirement accounts covered, make sure your beneficiary strategy actually works the way you intend. 0:00 INTRO 0:20 - Nana Nun, teaser: Designating Beneficiaries Can Become a Nightmare 2:03 - Uber & Zipline drone delivery; Why the Young "have no money" 9:23 - Account Titling and Probate Avoidance 14:00 - Revocable Living Trusts 15:57 - Setting Beneficiaries on Assets 18:31 - How to Use TOD (Transfer on Death) 20:10 - Mistakes w IRA Beneficiaries 25:39 - The Benefits of Online Savings (adding/subtracting beneficiaries) 27:47 - Best Practices for Titling: Be Specific 29:46 - Naming Contingent Beneficiaries Hosted by RIA Advisors' Director of Financial Planning, Richard Rosso, CFP, w Senior Investment Advisor, Jonathan McCarty, CFP Produced by Brent Clanton, Executive Producer ------- Do you enjoy our content? Rate us on Google: https://bit.ly/4b9JtEo ------- Watch Today's Full Video on our YouTube Channel: https://youtube.com/live/GpH7q-IgPXs?feature=share -------- Watch our previous show, "Watch our previous show, "Nvidia Says the AI Boom Is Just Getting Started, " https://youtube.com/live/lGSWXLw9dPY " ------- Get more info & commentary: https://realinvestmentadvice.com/insights/real-investment-daily/ ------- * REGISTER for our next Dynamic Learning Series, "The Smart Way to Pay for College," Thursday, September 3, 2026: https://streamyard.com/watch/mcE7YgphgMns --- Visit our Site: https://www.realinvestmentadvice.com Contact Us: 1-855-RIA-PLAN --- Subscribe to SimpleVisor : https://www.simplevisor.com/register-new --- Connect with us on social: https://twitter.com/RealInvAdvice https://twitter.com/LanceRoberts https://www.facebook.com/RealInvestmentAdvice/ https://www.linkedin.com/in/realinvestmentadvice/ #EstatePlanning #InheritedIRA #RetirementPlanning #IRA #FinancialPlanning

iPhone invites are out, Vision Pro (and the team) put on ice, and Target blows it (again)...The Lowdown Apple sends out invites for its annual fall iPhone event Apple refreshes the Mac Mini and Mac Studio with new AI-ready chips Apple reportedly scales back Vision team2nd String If your Uber or Lyft driver goes hands-free, should they ask your permission?For The Culture Target apologizes for children's clown Halloween costume after backlashThe Hookup Generate a virtual card number to use Apple Cash where Apple Pay isn't available

This one's a step back to look at the big picture, because a lot of the Gen Xers I talk to are too busy living life to see it.Three things are colliding at once.Corporate is chaos. It's not your age, it's your expense. Somebody can do half your job for half the price, and that's who gets picked. Getting laid off is one thing. Getting back in is the real problem. Look around your own network if you don't believe me.The savings math. The generation before us had pensions. We got a 401k and a how-to video. Roughly half of Gen X has nothing put away, and the typical household isn't close to what 30 years actually costs.Longevity. This is the good news. If you take care of yourself there's a real chance you're looking at 30 or 40 more years, healthy and active.Put those three together and it doesn't add up.I'm not doing this to scare anybody. It's not a prediction, it's already happening. But there are two ways to react. You can treat it as a sunset and ride it out. Or you can treat it as the start of a second adult lifetime, which is the way I'm choosing to look at it.The back half of this episode is the part that matters: you already have everything you need to generate income on your own. The skills, the experience, the problem solving. What's in the way is unlearning what corporate taught you, and the fact that we make making money way more complicated than it needs to be.In this episode:Why it's your expense, not your ageThe musical chairs problem, and why the chairs aren't coming backPensions versus the 401k handoffWhat 30 or 40 more years actually costsWhy gig work is the wrong answerLow friction, high margin income, and what that actually looks likeWhy the first dollar outside corporate changes everythingTIMESTAMPS00:00 — Welcome, and why I'm zooming out this week01:20 — Naming it: Gen X has a math problem01:42 — Two ways to react: sunset, or jumpstart02:05 — Musical chairs, and why they're pulling five at a time02:25 — Part 1: It's not your age, it's your expense03:40 — Getting laid off isn't the problem. Getting back in is.04:20 — The professional athlete parallel04:46 — Part 2: How prepared are we, really05:05 — They got pensions. We got a how-to video.06:00 — The savings numbers nobody wants to look at07:05 — How few of us actually have a pension07:45 — Part 3: Longevity, and why it's genuinely good news08:45 — Putting all three together09:15 — This isn't a prediction. It's happening now.09:55 — The good news: you already have what you need10:30 — Seventeen income streams, and why I'm not special11:05 — Why it's not Uber and it's not DoorDash11:45 — The real blocker: unlearning corporate12:45 — Low friction, high margin income13:40 — Go sell that first dollar14:05 — Don't bet on another 20 years in corporate14:45 — The opportunity sitting in your own backyard15:05 — Don't replace your job with a worse version of it16:00 — What changed inside the community16:25 — Nobody's judging you (my TikTok debut says hi)17:35 — The better you are, the more at risk you are18:20 — What the 20-somethings figured out that we didn't18:45 — How to reach meLINKS AND CTAThe Collective Now focused entirely on the thing most people are stuck on: making money outside of corporate. A group of Gen Xers figuring it out together. https://trp-collective.circle.soEverything else https://BrettTrainor.comQuestions or episode ideas? Email me: bt@bretttrainor.comIf the show's useful to you, hit follow wherever you listen.GenX, laid off over 50, ageism at work, corporate layoffs, retirement math, 401k, longevity, second act, making money outside corporate, career change over 50, side income, Gen X retirement crisis

In this episode, Ray Cochrane digs into Anthropic’s Model Hardware Standard. It is a shared driver that lets an AI agent run real lab equipment, from pipetting robots to the lasers inside a quantum computer. He also covers OpenAI’s builder’s guide to GPT-5.6, Google’s new Expert Intelligence book feature, Apple’s M5 Ultra Mac Studio, and a judge’s order forcing Google to stop hiding rival app stores. Finally, he weighs in on Apple’s proposed 15 percent link-out fee, Meta’s Australia numbers, the White House deputizing private hackers, and why rivers obey a 1957 math rule. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens with a quick personal update. He is hunting for tickets to Michigan for his dad’s anniversary, and he has been learning Blender and Godot on the side, mostly modeling and blocking out levels. Consequently, he asks listeners for advice on starting a big game project, and he plans to record his progress, maybe as a time lapse. Then it is straight into the featured story. Anthropic’s Model Hardware Standard: A Driver for the Physical World The featured story comes from Anthropic, which opened a research preview of the Model Hardware Standard, or MHS. Cochrane frames it as the other side of the question NVIDIA’s world models raised two weeks ago: when do AI agents start touching actual machines? A typical lab runs a microscope, a liquid handler, a robotic arm, and a plate reader, each from a different vendor with its own control software. One Janelia researcher in the post launches seven programs in three languages just to start an experiment. Anthropic says wiring a setup like that takes weeks or months of specialist work. MHS is a driver, the same kind of translation layer a printer uses, except every device gets described with a tiny set of commands like read and write. Devices announce themselves on the network. A plain-English reference file then records what each machine measures, what can be adjusted, and which safety limits get enforced no matter what the agent asks. Agents then reach the hardware through the Model Context Protocol, the command line, or plain code. Cochrane sees the same move the industry keeps making, from coding harnesses to RSS and JSON: agree on a standard and let everyone build against it. In fact, he calls MHS the hardware version of MCP. The partner results carry the segment. QuEra builds quantum computers from individual atoms held by lasers that must hold their frequency to about one part in a trillion. A four-person team spent months on a relock script that worked 58 percent of the time. However, four copies of Claude iterating overnight through MHS produced a decision-tree script that recovers the laser in about six seconds, and it passed 99.3 percent of 700 blind trials. Carnegie Mellon wrote MHS drivers for four instruments across three incompatible computers in about eight hours, then ran dose-response experiments three times faster and blocked all six deliberately induced faults. Genentech, meanwhile, showed the limits. Claude used the same pump speed for water, a foamy protein solution, and a human had to explain that the bubbles were a physics problem. That gap in physical intuition is what sticks with Cochrane. He doubts it will change soon, and he suspects the fix will arrive as sub-agents or sub-models that judge a request against an expected outcome. He also connects MHS to a video of racing robots that never learned to stop at the finish line. What happens, he wonders, once they can read a distance sensor through a shared standard? Still, he calls the announcement a fantastic read and points listeners to the full article. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. GPT-5.6 Does the Same Work for a Fraction of the Cost OpenAI’s builder’s guide to GPT-5.6 leads the headlines. Cochrane recaps the three tiers from episode 1870, Sol, Terra, and Luna, plus the separate dial for reasoning effort. On BrowseComp, a benchmark for digging up obscure facts on the web, the old GPT-5.5 flagship scored about 84 percent on a run that cost 33 dollars three months ago. Luna now matches that score for a dollar thirty-three, and OpenAI has since cut Luna’s price another 80 percent. Browser Use reports Luna finishing 78 percent of its hardest browser tasks for about 14 dollars, against 80 percent for roughly 235 dollars from the best available model. The guide’s other big addition is a multi-agent beta flag. It lets the model handling a request spawn parallel helper agents that report back to a root agent inside a single API call. However, Cochrane is unimpressed by the timing. He has been running that pattern in Claude Code for months, so he sees OpenAI copying a workflow other companies already ship rather than inventing its own. Along the way, he plugs Claude Code’s remote-control sessions, which let him send prompts from his phone to a terminal session at home. Google Lets Gemini Read the Books You Actually Bought Google launched Expert Intelligence, a name Cochrane calls quite the reach. The feature lets you drop a book you bought on Google Play Books into Gemini Notebook, formerly NotebookLM, and ask questions answered only from that book, with citations. Cochrane sees real power here for students, since he once used NotebookLM to organize scattered course PDFs. Additionally, publishers get a cut, which he calls a far better deal than the wholesale scraping of books that trained earlier models. Nevertheless, he asks who loses out, because a paid publisher does not automatically mean a paid author. He floats the same idea for artists, even a penny per use, then admits that may be too idealistic. Apple’s M5 Ultra Mac Studio Is Built to Run Big Models at Home Back in episode 1861, when Apple killed the Mac Pro, an M5 Ultra Mac Studio was expected later this year. Now it is here. The M5 Ultra brings up to a 36-core CPU, an 80-core GPU, and 512GB of unified memory moving 1.2 terabytes per second. Apple claims up to 4.3 times the AI performance of the M3 Ultra. Thunderbolt 5 can also cluster four machines into one memory pool for up to three times faster inference. The M5 Max model starts at $2,499 and the Ultra at $5,499, with shipping on September 22 and the 512GB configuration arriving in late October. Cochrane finds the clustering pitch ridiculous at that price, but he invites anyone who spends the money to report back. Apple Opens a Manufacturing School in Houston Apple also opened a 20,000-square-foot Advanced Manufacturing Center in Houston. It offers free classes for small and midsize manufacturers, from circuit board design to hands-on time on a scaled-down production line, with college students joining later. Cochrane calls it a solid step in the bring-manufacturing-home movement. The bigger story is the campus itself, which builds Apple’s AI servers and will add the first US-assembled Mac mini line later this year. That ties back to the Mac mini shortage that followed the OpenClaw rush, when Tim Cook warned of months-long waits. Cult of Mac was still reporting four-month waits in late July. However, Cook blamed chip supply rather than assembly, so Cochrane is not counting on relief just yet. Amazon EC2 Turns Twenty Amazon EC2 turned twenty this week, which Cochrane admits makes him feel old. The 2006 beta offered one server size in one region for ten cents an hour. Each came with a 1.7 gigahertz Xeon and under two gigabytes of memory, and accounts were capped at twenty servers. Today AWS offers more than 1,200 instance types across 39 regions. Consequently, Cochrane credits the company with turning that tiny product into the backbone of cloud and AI computing. Intel Gamer Days: Two Free Games, With Fine Print Intel Gamer Days runs through September 13. Buy a qualifying Core Ultra Series 2 or 14th Gen desktop chip, a Core Ultra Series 3 laptop, or an Arc graphics card. In return you get Star Wars: Galactic Racer plus the Tomb Raider: Legacy of Atlantis remake. GamesRadar values the pair at about 120 dollars. However, neither game is out yet, and codes must be redeemed by October 31 even though the Tomb Raider remake ships in February. Cochrane calls that awful, but he still tells qualifying buyers to claim the deal early. Note that 13th Gen chips do not qualify. Judge Orders Google to Stop Hiding Rival App Stores A jury found Google’s Android app monopoly illegal in late 2023, and Judge James Donato ordered rival stores into the Play Store in 2024. On August 13, Epic’s lawyer demonstrated that searching Play for “store for apps” returned Walmart instead of any app store. Donato called that “not acceptable” and ordered three fixes within a week. Searches must surface third-party stores, listings need a plain install button, and the “are you looking for” interstitial has to go. Cochrane welcomes the monopoly being chipped away, but he notes that a controlling entity still sits atop every app store. In his view, community hubs like app stores and social media need a public infrastructure layer. He suspects governments skip that investment because companies already run the services, while selling your data. Apple Wants 15 Percent of Purchases Outside Its Store The other half of the Epic saga is Apple’s proposed link-out commission. After the 2021 anti-steering injunction, Apple charged 27 percent on purchases made through external links. A judge held it in contempt last year, and the Ninth Circuit then allowed a fee limited to the cost of running the system. Judge Yvonne Gonzalez Rogers refused to wait for the Supreme Court, writing that “further delay is unwarranted.” Apple filed 15 percent for standard apps, 10 percent for subscription renewals and partner programs, and 5 percent for small businesses. It also conceded the rate would be “essentially zero” under the appeals court’s cost yardstick. Since Apple has charged nothing on link-outs since the contempt ruling, Cochrane sees this as a raise. He calls a cut on purchases made on a developer’s own website disturbing. He also recalls reading about the size of Uber’s payments to Apple, and he questions whether that kind of percentage is sustainable for companies without funding. Meta Says It Has Cut Off 750,000 Australian Kids Meta reported locking out more than 750,000 Facebook and Instagram accounts in Australia by the end of June under the country’s under-16 social media law. Over 500,000 of those were removed before the law even took effect. Detection relies mostly on AI scanning posts and bios for tells like birthday messages, plus user reports and blocks on re-registration. However, the post gives no count of mistaken removals or appeals, and the regulator’s early data shows under-16 usage falling only from about 86 to 81 percent. Meta wants a single age signal at the operating system or app store level, and Cochrane agrees completely. He connects it to the MHS idea from the top of the show: platforms need a standard flag to reference instead of guessing. The White House Deputizes Private Hackers Earlier this month the White House signed a National Security Presidential Memorandum that lets vetted private security firms run surveillance and disruption operations against overseas criminal groups. The Justice Department and Homeland Security hold the contracts and oversee the work. Firms need a proven track record, vetted staff, and a bond of at least $1 million, and must submit operating procedures within 60 days. Cochrane finds the measure aggressive in a good way and hopes it deters attacks on innocents. Still, he takes Kevin Beaumont’s warning seriously that the private security industry profits from ransomware existing. He compares it to the old Head and Shoulders myth: why solve the problem that drives your revenue? A Weather Satellite Watched the Eclipse Shadow Cross Europe Cochrane skips the readout on this one and simply sends listeners to ESA’s site. The MTG-I1 weather satellite captured the Moon’s shadow sweeping across Europe during the August 12 eclipse. Watching a shadow cross an entire continent, he says, was a first for him. Additionally, it leaves him excited about the research happening beyond the planet. Rivers, Deltas, and the Number 0.6 Quanta Magazine explains Hack’s law, which John Hack discovered in 1957 while measuring streams in Virginia and Maryland. A stream’s length tracks its drainage area raised to the power of 0.6, regardless of the rock underneath, and satellite data later confirmed it worldwide. Computer models in the 1990s showed why. Channels that capture extra runoff cut deeper and steal from their neighbors until the network settles into the arrangement that wastes the least energy. Now a University of Texas Rio Grande Valley team has found the same 0.6 exponent in river deltas, which spread water out rather than gathering it. Nobody knows why yet, and Cochrane calls it a really cool read. Sugar Helped Grow the Human Brain, Too A new paper in Science, co-authored by Jennie Brand-Miller at the University of Sydney, adds a third ingredient to the story of early human brain growth. Alongside meat and cooking, natural sugars from ripe fruit and honey may have fueled it too. The brain is about two percent of body weight but burns twenty percent of resting energy. It runs on glucose, which meat and marrow barely supply and raw starch cannot release without fire. The team modeled ancestral diets from a chimp-like baseline through Homo erectus and concluded that the earliest hominins may have drawn over 65 percent of their energy from natural sugars. Cochrane stresses that it is a model, not fossils, and notes that paleoanthropologist Marina Lozano thinks the authors place widespread cooking too early. Still, he loves this kind of deep research. Retracing the steps to our own intelligence, he suggests, could hint at what it takes for intelligent life to develop at all. A Brain Rhythm That Tells Doctors Where to Aim Finally, Science Daily covered a University of Cologne study on deep brain stimulation. That is the implanted-electrode treatment that eases Parkinson’s tremors for some patients but not others. Andreas Horn’s team recorded from 50 patients using both the implanted electrodes and an external magnetic scanner. They identified a circuit between the electrode’s target and the frontal cortex that oscillates at 20 to 35 cycles per second. Stronger coupling there predicted bigger improvement after surgery, though the study, published in Brain, shows correlation rather than cause. First author Bahne Bahners hopes the finding helps tune DBS more precisely, especially for patients who have not responded well. Cochrane half-jokingly asks whether MHS might one day drive those electrodes, and he calls brain disorders the hardest thing in the body to treat. Cochrane wraps with housekeeping: become a GNC Insider at geeknewscentral.com/insider, email geeknews@gmail.com with questions or comments, subscribe to the newsletter, and grab a modern podcast app at podcastapps.com. He thanks GoDaddy for over twenty years of keeping the show on the air, promises to catch everyone next Monday, and wishes listeners a great night. The post Eyes, Hands, and a Sense of Timing #1874 appeared first on Geek News Central.

Very Important Links!Support the show on Patreon! - Other ways to support the show!Join our Discord! - Buy some merch!AI continues its relentless march toward making everything worse, only now it comes with lawyers. Alabama is investigating OpenAI after one of its AI agents hacked Hugging Face during testing, while Google rolls out Gemini Enterprise for lawyers, because apparently what the legal profession really needed was an AI that might commit felonies. Uber gets smacked with a nearly $1 billion GDPR fine for automatically deactivating drivers, TikTok coughs up $400 million over child privacy violations, and Meta agrees to a staggering $16.68 billion settlement over alleged harms to kids. Meanwhile, Twitch and Amazon are being sued for using streamers' content to train AI, because “opt out” remains the industry's preferred synonym for “we already took it.”Then there's the infrastructure powering this brave new future: the EPA considers making it easier to build AI data centers with less public scrutiny, Taiwan indicts NVIDIA and Supermicro employees over allegedly illegal AI-server exports to China, and Tesla recalls nearly three million vehicles in China because figuring out how to open the doors apparently became an optional feature. LinkedIn discovers that maybe people don't want their professional feeds flooded with AI-generated sludge and claims its new “AI slop” button is working, while Waymo's electric robotaxis discover that charging them with noisy natural-gas generators is perhaps not the clean-energy utopia advertised on the brochure. Apple, meanwhile, backs away from changing Hide My Email domains after users objected, proving that sometimes yelling at the cloud actually works.Plus: Star Trek goes Muppet, books worth reading, nerf guys, AI-generated Hacker News stats, retro gaming, and the sad news of both Dolly Parton and Tim Curry's deaths.Sponsors:DeleteMe - Get 20% off your DeleteMe plan when you go to JoinDeleteMe.com/GOG and use promo code GOG at checkout.StoryBlocks - For a limited time, they're offering 15% off any annual plan at storyblocks.com/gogHIMS - Visit Hims.com/gog to get a personalized, affordable plan that gets you.Private Internet Access - Go to GOG.Show/vpn and sign up today. For a limited time only, you can get OUR favorite VPN for as little as $2.03 a month.SetApp - With a single monthly subscription you get 240+ apps for your Mac. Go to SetApp and get started today!!!1Password - Get a great deal on the only password manager recommended by Grumpy Old Geeks! gog.show/1passwordShow notes at https://gog.show/760Watch on YouTube at https://youtu.be/v_m2XT4_ZCYSHOW NOTESSend us Feedback!NEW! - GOG T-Shirt - You Are Here! - v1NEW! - GOG T-Shirt - Elon Kills Babies - Heavy Duty - FrontAlabama launches probe into OpenAI after Hugging Face breachGoogle expands Gemini Enterprise AI platform for law firms, lawyersUber hit with a nearly $1 billion fine for automatically deactivating drivers in EuropeTikTok will pay $400 million to settle Justice Department lawsuit over child privacyMeta reaches $16.68 billion settlement over social media harms to childrenTwitch and Amazon hit with lawsuit for training AI with streamers' contentThe EPA's Response to AI Data Center Backlash: Keep Building, but Keep It Hush-HushTaiwan reportedly indicted NVIDIA employees for exporting prohibited AI servers to ChinaChina Recalls Nearly Three Million Teslas Over Unsafe Door HandlesLinkedIn says its AI slop button is workingAustin park goers upset by adjacent loud Waymo EV generatorsApple reverses planned Hide My Email domain change after user pushbackStar Trek: Strange New GimmicksTed LassoSiloReacherThe WestiesThe Simpsons: Yellow Mirror | Exclusive Episode on Disney+ | TrailerThe Pitt Season 3 | Official Teaser | HBO MaxAmazon First ReadsI've Got a New Complaint: Essays on Aging Disgracefully and Other Small Disasters by Laurie NotaroThe Theory of Everything Else: A Voyage Into the World of the Weird – A Hilarious Handbook of Mind-Boggling Mysteries by Dan Schreiber (of the No Such Thing as Fish podcast)Welcome to Your Life: Love, Death & Tears For Fears – An Iconic Musician's Journey Through Grief, Addiction, and Recovery by Roland OrzabalPicks and Shovels: A Martin Hench Novel by Cory DoctorowThis Is How You Lose the Time War by Amal El-MohtarDave BittnerThe CyberWireHacking HumansCaveatOnly Malware in the BuildingLearn Circuits CourseArduino Starter Kit R4Kirby: Art & Style Collection by VIZ MediaStar Wars: The Lightsaber Collection by Daniel WallaceStar Wars: The Secrets of the Sith by Marc SumerakX-Shot Insanity Motorized Rage Fire Gatlin Gun with Tripod - Foam Gun for Maximum FirepowerMaster BlasterHow much of Hacker News is AI?Full Throttle full game playDolly and Miss PiggyDolly Parton, country star, actor and philanthropist, dies aged 80See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This is day 27 of the Dog Days of Podcastinghttps://dogdaysofpodcasting.com/ NYX from NPC Matchmaker calls in with some great band stories   music by Iggy Pop and James Williamson Thanks to Joe Pawlak for the $250 Penske Truck Donation! Thanks to Tim Schall for the $100 Dog Days Donation! Thanks to Ralph Miller for the $125 Penske truck donation Thanks to Dan Gerawan for the $100Penske Doation! thanks to Kirk Crawford for the $100 Dog Days donation! thanks to John Morgan for the $100 Dog Days donation! Thanks to Brian Grattidge for the $31 Dog Days donaton! Donate on Venmo @Michael-Butler-11 Joe Pawlak – $250 Penske Fund Dan Gerawan – $100 Penske Fund John Morgan – $100 Kirk Crawford – $50 Dan Gerawan – $50 Tim Schall – $50 Rockbottom Rob Giglio – $30 Michael Stitik – $25 Gregg Brofer – $20 Blake Johnston – $20 Richard Fusey – $10.99 Todd Cunningham – $10 Steven Cohen $5 Danny Borden – $5 Bruce McMillan – $3 (Venmo donation id is @Michael-Butler-11) PATREON DONORS Joe Pawlak – $16.66 Kirk Crawford – $12.77 Patrick Shanahan – $10 Brian Springer – $8 Jon Scott – $8 Michael Street – $7.50 Dave Slusher – $5.55 Robert Harvey – $5 Chiaki Hinohara – $5 MedakiMetal on Instagram Mike Dixon – $5 Jamie Jefford – $5 Erik Klein – $5 Paul Smith – $5 Justin Lefkowitz – $5 Steve Trice – $5 James Shapiro – $5 Martin Clawley – $5 Nadi Itani – $5 Eric Stowell – $4 Mike Hellyer – 4 pounds Mark Mazzel – $3 Adrian Boschan – $2 Amelia Bowen – $2 RnR Pleeb – $1.42 3Legs4wheels – $1 Arne Stach – $1 Paypal Donors Michael Street – $25 Beer for the trip Richard Strom – $20 Dave Franco – $20 Steven Laperriere – $20 Jason Shepard – $10 Jeff and Cheri Thieleke – $10 School Of Podcasting – $10 Bradley Lisko – $10 Ralph Miller – $10 William Bealle – $10 Benjamin Mueller – $5 Vincent Crimi – $5 Jon Tennis – $5 Gregg Long – $5 Andrew Howe – $5 Christopher Del Grande – $5 Jayce Lesniewski – $5 Peter Spark $5 John Ofenloch – $5 Rachel Rosenberg – $5 Adam Croft – $2 Deborah Dreyfus – $2 Chad Kiffmeyer – $2 Kai Matsuda – $2 Brian Grattidge – $2 William Moffett – $2 Lasse Satvedthagen – $2 Dave Alexander – $2 Steve Trice – $5 James Shapiro – $5 Martin Clawley – $5 Nadi Itani – $5 Eric Stowell – $4 Mike Hellyer – 4 pounds Mark Mazzel – $3 Adrian Boschan – $2 Amelia Bowen – $2 RnR Pleeb – $1.42 3Legs4wheels – $1 Arne Stach – $1 Paypal Donors Michael Street – $25 Beer for the trip Richard Strom – $20 Dave Franco – $20 Steven Laperriere – $20 Jason Shepard – $10 Jeff and Cheri Thieleke – $10 School Of Podcasting – $10 Bradley Lisko – $10 Ralph Miller – $10 William Bealle – $10 Benjamin Mueller – $5 Vincent Crimi – $5 Jon Tennis – $5 Gregg Long – $5 Andrew Howe – $5 Christopher Del Grande – $5 Jayce Lesniewski – $5 Peter Spark $5 John Ofenloch – $5 Rachel Rosenberg – $5 Adam Croft – $2 Deborah Dreyfus – $2 Chad Kiffmeyer – $2 Kai Matsuda – $2 Brian Grattidge – $2 William Moffett – $2 Lasse Satvedthagen – $2 Dave Alexander – $2The post Phallus Uber Alles – DDOP 2026 Day 27 first appeared on The Rock and Roll Geek Show.

This week on Two Parents & A Podcast, happy Wednesday!! You guys know we record same-day, which means we get to open with actual breaking news: Meta agreed to a $17 BILLION settlement in the child safety trial. Harrison dusts off his finance-guy trick for understanding any settlement (go straight to the stock price.. what it did tells you everything) and we get into what actually changes for kids: time restrictions and nighttime blockouts. And while we're being all professional and stuff lol, Alex shares how she beat her 2 AM heartburn after Prilosec, Tums AND Pepto all failed. The winner? Baking soda and water. (NOT medical advice.. she literally ate a bag of chips first as a strategy lol) It's not until after ALL of this that we finally welcome you back to the episode :-) and Alex starts out hot. Backstory: she posted a TikTok about letting Tate wear her helmet and while mommy ALSO wears her helmet (red light mask).. and a comment saying "it's okay to tell her no" racked up over a thousand likes. So you're telling me a thousand people think we should say no to a toddler wearing a HELMET for fun?! Video two was born, the internet responded, and the ratio has spoken

Click this link for the new Living Emunah from Artscroll on Tefillah https://www.artscroll.com/Books/lemtfh.html The month of Elul is a tremendous gift from Hashem. It's an opportunity for us to prepare for a new beginning and attain teshuvah for our past wrongdoings. Hashem does not expect a person to become perfect overnight, but we do have to show that we care. We have to show that we want to grow. And besides for the will, we have to put forth effort. When a person truly wants something in avodat Hashem and does what he can to achieve it, he will often see Hashem giving him siyata d'Shmaya to accomplish things he never would have been able to accomplish otherwise. A young man recently went with his family on vacation to a place he really did not want to go. He never missed a minyan, but where they were staying, the nearest minyan was about a half hour away. Renting a car there wasn't an option, Uber didn't really work, and the only way to get to shul was by taxi. Each ride was going to cost about $60, which meant that to go to Shacharit and back and then Minchah and Arbit and back every day would cost him $240 a day. He tried contacting the local Chabad rabbi to see if maybe there were other Jews staying near him with whom they could make a minyan, but nothing worked out. Finally, he decided, "I'm going to minyan no matter what it costs." A few days into the trip, he received a phone call from a man who said, "I deal in Rolex watches, and I have one at the front desk of the hotel you're staying in. Can you bring it back with you to New York? I'll pay you a few hundred dollars." The young man asked, "How did you even know to call me?" He replied, "The Chabad rabbi gave me your number. He said you're a good guy." The young man agreed and then told the man, "I want you to know, every dollar you're giving me is going toward a mitzvah." He explained how much he was spending every day just to get back and forth to minyan. The man couldn't believe it. He said, "If that's what you're using the money for, then it would be my zechut to pay for every single day." And with that, the young man's taxi expenses to minyan were paid for the entire trip. First, he showed Hashem how much he wanted to do the mitzvah. Then he was willing to sacrifice for it, and Hashem took care of the rest. Another man told me that he took it upon himself to make sure people in shul were wearing their tefillin properly. He noticed that so many people had their tefillin falling below the proper place on the hairline, so he learned how to adjust the straps. Over the course of a year, he fixed between 200 and 300 pairs of tefillin. A few months ago, he started feeling different when he would pray. Day after day, he felt something was missing. He didn't feel the same connection to Hashem, and it was spilling over into the rest of his day. This continued for weeks. One day, he didn't have his tefillin with him and borrowed a pair from his friend. That day, suddenly, he felt so much better. The next day, he went back to his own tefillin, and once again things didn't feel right. It occurred to him that maybe there was something wrong with his tefillin. He brought them to his sofer, and indeed, it was discovered that his tefillin were pasul. He said, "I could have gone my entire life without ever knowing my tefillin were pasul." But he went out of his way to help hundreds of other Jews with the mitzvah of tefillin, and then Hashem gave him the extraordinary siyata d'Shmaya to discover the problem with his own. Another man said he went away with his family for a couple of weeks this summer. When they arrived, they discovered that the local shul prayed much later than they were accustomed to, and on Shabbat they would reach the Amidah only after the zman tefillah. This bothered the man very much because he was always careful to pray within the proper zman. He spent the first several days of his vacation searching for another minyan. The possibilities were miles away, but it was so important to him that he kept trying to find a solution. That Friday morning, he was sitting in the shul near his house when suddenly a man walked in and asked, "By any chance, could you join us for a minyan tomorrow morning that will be praying within the zman?" He couldn't believe what he was hearing. The man explained that six of them had just arrived from out of town and were looking to put together an early minyan. Suddenly, the minyan this man had been searching for was right down the block from his house. The following Shabbat, those people had left, and once again he had the same problem. He prayed to Hashem and hoped that somehow he would be able to make the zman again. Amazingly, that next Friday, another group arrived in town, staying in a different house—again, six people. Together with some people from his own family and a neighbor, they were once again able to make a minyan within the zman. This is a powerful lesson for the month of Elul. We may look at ourselves and see that a lot needs improving. Sometimes we may wonder, how am I going to change? It's too hard. But Hashem doesn't ask us to do everything ourselves. He wants us to show Him that we truly want to grow, and He wants us to put forth our best effort. When someone says, "Hashem, I want to do this. I'm going to try. I'm going to sacrifice. I'm going to do what I can," then hopefully Hashem will give him the siyata d'Shmaya he needs to accomplish it in the best way.

This week, the speaker is mourning the loss of Dolly Parton, but also reflecting on the recent passing of Tim Curry. t's a celebrity trifecta that's got everyone talking. But amidst the sadness, the speaker is also sounding the alarm on the potential consequences of a single-payer healthcare system. With the rise of Medicare for All, the speaker is joined by Sally Pipes, a healthcare expert from the Pacific Research Institute, to discuss the realities of a government-run healthcare system. They delve into the failures of the UK's National Health Service and the Canadian healthcare system, highlighting the long waiting times, low doctor morale, and the financial costs associated with a single-payer system. The speaker also touches on the topic of assimilation, discussing the challenges of integrating migrants into Western culture. They share a shocking statistic from Switzerland, where 80.5% of asylum seekers from North African countries were accused of one or more offenses. The speaker questions whether this is a reflection of the culture of the migrants or a sign of a larger problem with the system. Additionally, they discuss the importance of understanding the differences between Western values and the values of other cultures. In a lighter moment, the speaker talks about the new features being added to Uber, including a teen account and a live streaming feature that allows parents to monitor their child's ride. They also discuss the importance of being aware of the potential risks of artificial intelligence, citing a recent essay by Bill Gates on the topic. Tune in to hear the full episode and learn more about the celebrity trifecta, the future of healthcare, and the importance of understanding cultural differences.See omnystudio.com/listener for privacy information.

"You look like you're bout that gas." Zaslow tells us a story from the early days of fantasy football that reminds us 9/11 took place in 2001. Trysta also tells us about a fantasy league she is in that was founded in 1976 and sounds awful. Chris tells us everything that everybody knows about Zaslow, Mike Ryan eats one meal a day, Zaslow tells us where he would meet with sex workers if he were a billionaire, and we break down video of two police officers using Uber to catch a criminal. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Treasury Secretary Scott Bessent announced the U.S.’s plan to further isolate Iran’s economy, which he called “Operation Economic Outcast.” The Wall Street Journal’s Brian Schwartz breaks down the details. Voters in South Carolina are casting ballots today in one of the cycle’s most closely watched primaries: the race to fill the late Lindsey Graham’s Senate seat. Nick Reynolds of the Post and Courier explains what’s at stake. Nearly two weeks after a severe derecho knocked out power across Gary, Indiana, thousands are still waiting for electricity to return. Calvin Davis of Capital B Gary describes the toll on residents. Plus, the Trump administration is proposing massive fees for H-1B visas, the Supreme Court allowed Trump to move forward with a mail-in voting executive order, and how police officers hitched a ride with an Uber driver during a chase. Today’s episode was hosted by Cecilia Lei.

IG: @RentalCarKingText: 213-682-23333 Day Online Challenge: https://fleettofreedom.com/optinIn person Mastermind:https://fleettofreedom.com/mastermind-514399Free Private Car Rental Mini Course: https://fleettofreedom.com/mini-course-optinWhat if one $6,000 car could become the start of a serious cash flowing business?In this episode of the Social Proof Podcast, David sits down with Kell King to break down how he built a profitable car rental business by providing affordable vehicles to Uber, Lyft, DoorDash, and other gig economy drivers.Kell explains how he finds cars like the Ford Fusion for around $6,000, why he targets a 90 day return on investment, how he finds reliable renters, and the systems he uses to manage a growing fleet.They also break down:

Uber is giving parents of teens the ability to livestream video of the child's ride in real time, and Android 17 includes a new Motion Assist feature to help reduce motion sickness for riders in moving vehicles.Starring Jason Howell, Tom Merritt and Dr. Niki.Links to stories discussed in this episode can be found here. Hosted on Acast. See acast.com/privacy for more information.

From 08/25 Hour 4: It's always smart to be safe and use Uber/Lyft to get you home from the bar, but one small mistake could tank your rating on the ride share app. The Sports Junkies all compare their ratings and see who is the top dog. What's your Uber/Lyft rating?

Apple refreshed the Mac Studio with M5 Max and M5 Ultra and gave the Mac mini M6 silicon, at higher prices. OpenAI's Jalapeño chip beat Nvidia on efficiency, Perplexity went fully local, WhatsApp toughened logins, and Uber livestreamed teen rides. Links Apple updates the Mac Studio with M5 Max and M5 Ultra, with up to 4.3x faster AI performance, faster graphics, and up to 512GB of unified memory for $2,499+ (Apple Newsroom) Apple unveils a Mac mini with M6 and M5 Pro, with up to 4x faster AI performance and 2x faster graphics, for $899+ and $1,699+, with preorders today and shipping September 22 (The Verge) Apple's M6 is its first 2nm chip with a 12-core CPU and GPU, while the M5 Ultra fuses two dual-die M5 Max chips into a 36-core CPU, 80-core GPU "most powerful chip ever" (The Verge) OpenAI says its Jalapeño chip delivered 1.5x-1.9x more AI work per watt and 1.7x-3.6x lower latency than Nvidia chips across GPT-OSS, DeepSeek R1, Kimi K2.5 1T (The Verge) WhatsApp upgrades its two-step verification, letting users replace the six-digit PIN with a longer alphanumeric password, and adds support for multiple passkeys (TechCrunch) Perplexity launches Portable Computer, a local AI agent platform running fully on-device with zero token costs, starting with Nvidia DGX Spark and RTX Linux PCs (VentureBeat) Uber launches an optional safety feature allowing parents or guardians to watch a livestream of their teen's ride via the driver's front-facing phone camera (Bloomberg) Subscribe to the ad-free feed.

The teenage years can be difficult for parents as well as the kids going through so many physical and emotional changes. Celeste's parents tried to keep their strong willed daughter at home, safe and under their watch. When she told them she had a "boyfriend," they took her phone away. R&B singer D4vd paid a student at her school to give her a phone he bought for her to use exclusively with him. On February 14, 2024 D4vd sent an Uber to pick her up and bring her to his home in the Hollywood Hills. They named their plan "Operation Awesome." She was 13 years old and he was 19. She returned home to her Riverside County home in Lake Elsinore a few days later after her parents reported her missing the Riverside County Sheriff's Department. She left again on March 19, 2024. And again on April 5, 2024. Always reported to the Sheriffs Department. She and D4vd were dramatic and tumultuous and neither of them had the emotional intelligence to be in a "relationship." No ones brains were fully developed. Prosecutors believe on or about April 23, 2025 D4vd sent an Uber to Celeste's home in Lake Elsinore, 80 miles from his home in Hollywood to bring her to his home for a confrontation. Digital communications show that Celeste was angry at being rejected by D4vd and he was feeling threatened as she said she would tell the world he was sleeping with her an underage girl. The exact dates and what for sure happened are not fully known but you can hear how a 14 year old Celeste ended up in the front trunk of a Tesla owned by David Burke, AKA D4vd, decomposed and found one day before what would have been her 15 birthday. A year where other 15 year olds are celebrated with a Quinceanera ceremony. Even though a judge has decided that there is enough evidence to remand David Anthony Burke over for trial, there are details in the timeline that are still missing. If YOU have any information about Celeste's case please contact LAPD South Bureau Homicide Division 323-786-5100 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Geoff Mattson is the CEO of SecureAuth, an identity security company that has raised over $200M and protects more than 50 million identities for enterprises. Before SecureAuth he was CEO of Xage Security. His work now centers on how you prove an AI agent is who it says it is.In this episode of Summation, Geoff and Auren discuss:Why an AI agent is like an Uber driver who might randomly turn into a psycho killerThe deepfake fraudpocalypse, and the simplest thing that defends against itWhy your agent impersonates you instead of working for you and how to fix itWhy the password is finally, actually dyingYou can find Auren Hoffman on X at @auren and Geoff on Linkedin

Alain de Botton est écrivain et philosophe. Il est également le fondateur de The School of Life, il a écrit Le cours de l'amour, un livre que j'ai lu il y a quelques années et qui m'a franchement retourné, parce qu'il commence là où tous les films s'arrêtent : le jour d'après le mariage.C'est le retour de l'été et ca m'a semblé bien de diffuser cet épisode en cette dernière semaine d'aout.Petite précision avant de démarrer : cet épisode a d'abord été diffusé en anglais. J'ai quand même tenu à le proposer en français, parce que ce qu'Alain raconte sur l'amour me paraît trop utile pour rester réservé à celles et ceux qui sont à l'aise avec l'anglais.Il y a un moment dans la conversation où je me suis vu, moi, dans ce qu'il décrivait. Il explique, statistiques à l'appui, que les enfants d'alcooliques tombent massivement amoureux de personnes qui ont un problème d'addiction. Pas par malchance. Parce que nous ne sommes pas attirés par ce qui nous rend heureux, nous sommes attirés par ce qui nous est familier. Et on peut passer une vie entière à rejouer la même scène en croyant chaque fois faire un choix neuf.Dans cet épisode, nous parlons de l'idéal romantique et de ce qu'il nous fait faire, des papillons dans le ventre et de ce qu'ils cachent vraiment, du complexe d'Œdipe relu comme un manuel pratique, des drapeaux rouges à repérer dès le premier dîner, de la sexualité qui s'éteint dans les couples aimants, du polyamour et des dégâts qu'il laisse parfois, de l'infidélité comme tentative maladroite de se rapprocher, des ruptures et de la manière décente de partir, des enfants qui abîment le couple, et de ce qu'on appelle la folie de l'autre.J'ai discuté avec Alain des questions que tout le monde se pose et que personne n'ose formuler à voix haute : est-ce qu'on peut faire changer quelqu'un, comment savoir qu'on a vraiment tout essayé, pourquoi les hommes hétérosexuels ont si peu de mots pour dire ce qu'ils ressentent, et pourquoi les applications de rencontre ont un intérêt économique à ce que vous ne trouviez personne.Ce qui m'a le plus marqué, c'est sa réponse finale. Derrière l'argent, la gloire et l'amour, ce que nous cherchons tous, dit-il, c'est le calme que nous avons connu nourrisson. Et il ajoute une phrase que je n'ai pas réussi à me sortir de la tête : le bruit qui est à l'intérieur de nous a d'abord été un bruit extérieur.Bonne écoute.3. Citations marquantes« Nous ne sommes pas attirés par ce qui nous rend heureux. Nous sommes attirés par ce qui nous est familier. »« Il ne s'agit pas de trouver quelqu'un de sain d'esprit, tout le monde est fou. Il s'agit de trouver quelqu'un capable d'explorer sa folie avec un minimum de maturité. »« Le modèle économique des applis de rencontre repose sur votre échec. C'est une machine à sous : le bonheur, oui, mais pas maintenant, revenez demain. »« Se déshabiller, c'est intéressant. Mais se mettre psychologiquement à nu, ça, c'est la vraie aventure. Et ça ne devient jamais ennuyeux. »« Il n'existe pas d'équilibre entre vie pro et vie perso. Tout ce qui vaut la peine d'être fait va déséquilibrer votre vie. »4. Idées centrales discutées 1. Nous aimons dans un décor que nous n'avons pas choisi (≈ 02:35) Alain ouvre sur cette maxime de La Rochefoucauld : certaines personnes ne seraient jamais tombées amoureuses si elles n'avaient jamais entendu dire que ça existait. Nos émotions reçoivent des encouragements discrets de la société qui nous entoure, et l'idéal romantique en fait partie. Pourquoi ça compte : si nos attentes viennent du décor, elles peuvent être révisées. C'est une bonne nouvelle déguisée en fatalité.2. L'attirance suit le familier, pas le bonheur (≈ 12:52) Le mouvement romantique promettait qu'en suivant son cœur, on choisirait mieux. La psychologie moderne dit l'inverse. Nous rejouons les schémas de frustration de l'enfance parce qu'ils ressemblent à la maison. Pourquoi ça compte : c'est l'explication la plus solide au sentiment de « je retombe toujours sur le même profil » que tant de gens décrivent.3. Les perversions sexuelles sont des terreurs de l'intimité (≈ 25:39) De l'exhibitionniste au voyeur, Alain lit ces comportements comme des refus de la réciprocité : il faut imposer, ou observer de loin, parce que se regarder dans les yeux est insupportable. Pourquoi ça compte : le même mécanisme, en version douce, explique l'amant qui disparaît le lendemain matin. Le désir de connexion et la terreur d'être englouti cohabitent chez tout le monde.4. Les applications de rencontre ont besoin de votre échec (≈ 32:06) Leur modèle repose sur une promesse jamais tenue, exactement comme une machine à sous. Alain reste pourtant optimiste sur la technologie : il imagine une IA témoin du couple, qui signalerait ce que nous ne savons pas entendre. Pourquoi ça compte : ça déplace le débat, le problème n'est pas la technologie, c'est le modèle économique qu'on lui a collé dessus.5. On ne cherche pas quelqu'un de sain, on cherche quelqu'un de lucide (≈ 19:59) La bonne définition d'une attente réaliste, selon lui : quelqu'un capable d'annoncer sa folie avant de la vivre, et de s'en excuser après. Pourquoi ça compte : ça remplace la quête épuisante de la personne parfaite par un critère observable dès les premiers mois.6. Le dialogue a une limite, et il faut l'accepter (≈ 49:05) C'est le moment le plus vulnérable de la conversation. Alain vit de l'idée que les mots transforment les gens, et il admet que ce n'est pas toujours vrai : le traumatisme ferme des portes qu'aucun argument n'ouvre. Pourquoi ça compte : certaines des ruptures les plus douloureuses viennent de là, quitter quelqu'un qu'on aime et qui ne peut pas comprendre.7. La folie de l'autre peut devenir le lieu de l'intimité (≈ 21:28 en germe, développé à 1:20:59) Ce n'est pas la nature du problème qui décide, c'est la manière dont il est porté. Dire « j'ai un problème avec le sexe, aide-moi » peut être plus intime que le sexe lui-même. Pourquoi ça compte : ça retourne complètement la logique des drapeaux rouges. Le défaut avoué relie, le défaut caché détruit.5. Questions posées dans l'interviewComment l'idéal romantique de l'âme sœur a-t-il façonné ce que nous attendons de l'amour, et quels sont les pièges de ces attentes ?Comment prend-on du recul par rapport à un idéal romantique dans lequel nous baignons depuis toujours ?Que valent les papillons dans le ventre, et que sommes-nous censés ressentir quand nous tombons amoureux ?Faut-il suivre son intuition en amour, ou s'en méfier ?Que pensez-vous de l'idée que l'amour dure trois ans ?Quelles sont les attentes réalistes qu'on peut avoir d'une relation ?Existe-t-il des drapeaux rouges fiables, et quelles questions faut-il poser dès les premiers dîners ?Nous vivons dans une société sans friction, Uber, Airbnb, tout doit être fluide. Cherche-t-on aussi un partenaire sans friction ?Est-ce que les hommes hétérosexuels sont plus démunis que les femmes face à leurs propres émotions ?Comment accueillir la folie de son partenaire sans se perdre soi-même ?Peut-on faire changer quelqu'un, et à quelle vitesse ?Comment garder le désir sexuel vivant dans un couple qui s'aime depuis longtemps ?Pourquoi tant d'hommes réclament-ils aujourd'hui des relations ouvertes ?Pourquoi deux personnes qui se sont profondément aimées deviennent-elles des étrangères ?Peut-on pardonner une infidélité, et comment ?Comment savoir qu'on a vraiment tout essayé et qu'il faut partir ?Comment la société devrait-elle évoluer pour améliorer la qualité de nos relations ?Peut-on faire trop de thérapie ?6. Références citées dans l'épisodeAuteurs et philosophesLa Rochefoucauld (≈ 02:48) : « Il y a des gens qui n'auraient jamais été amoureux s'ils n'avaient jamais entendu parler de l'amour. » La citation matrice de tout l'épisode, reprise plus tard à propos du polyamour (≈ 56:47).Sénèque (≈ 17:34) : « Pourquoi pleurer sur des parties de la vie ? Elle appelle tout entière les larmes. » Cité comme exemple de pessimisme réconfortant.Nietzsche (≈ 19:44) : pour vivre, nous avons besoin de certaines illusions.Freud (≈ 33:26) : le complexe d'Œdipe, relu comme modèle des compétences nécessaires à l'âge adulte. Alain note au passage que Freud écrivait mal et qu'il a besoin d'être commenté.Spinoza (≈ 33:50 et ≈ 42:55) : cité comme exemple de penseur qui ne dit pas le meilleur de sa pensée, puis L'Éthique comme exemple de complexité que nous aimons pour de mauvaises raisons.Racine, Shakespeare, Tolstoï (≈ 49:05) : convoqués pour dire que même la plus belle éloquence ne fait pas bouger quelqu'un qui est fermé émotionnellement.Flaubert, Madame Bovary (≈ 59:09) : Emma lit les mauvais livres, ceux qui lui disent que sa vie est ennuyeuse.Livres et créations d'Alain de BottonLe cours de l'amour (The Course of Love) (≈ 00:33) : cité par moi en intro comme le livre qui m'a marqué.The School of Life (≈ 00:33 et ≈ 1:27:43) : l'institution qu'il a fondée, sa chaîne YouTube, et le terrain de ses explorations à venir.Livre français discuté sans être nomméL'amour dure trois ans (≈ 17:34) : je lui soumets la thèse, il répond qu'il ne connaît pas le livre, se félicite qu'il ne soit pas traduit en anglais et refuse d'y croire un vendredi matin à 11h20. [Auteur non nommé dans l'épisode : Frédéric Beigbeder]Cinéma et cultureÉric Rohmer (≈ 59:09) : le cinéaste qui nous apprend la patience des conversations ordinaires.Gladiator (≈ 59:09) : l'exemple inverse, le récit à l'échelle héroïque qui rend la vie quotidienne décevante.Personnes citéesEsther Perel (≈ 18:20) : mentionnée comme la voix à convoquer sur la durée du désir.Invitée d'un précédent épisode de Vlan! (≈ 1:27:26) : je mentionne une conversation qui m'a fait comprendre que le bruit est surtout intérieur. [Nom mal transcrit dans le fichier, à vérifier avant publication]Lieux et imagesLe Bois de Boulogne (≈ 25:39) : l'exemple de l'exhibitionniste.Sainte-Lucie, Saint-Barthélemy (≈ 1:09:21) : les îles de son histoire de plage, la démonstration la plus drôle de notre amnésie sentimentale.Timestamps clés00:00 — Pourquoi cet épisode est en anglais J'explique en français le contexte de l'épisode et pourquoi je tenais à le proposer malgré la barrière de la langue.00:33 — Alain de Botton, School of Life et Le cours de l'amour Mon introduction : pourquoi ce livre m'a marqué, et pourquoi les 20 ou 30 questions les plus difficiles sur l'amour sont réunies ici.02:35 — « Certains ne seraient jamais tombés amoureux s'ils n'en avaient jamais entendu parler » La maxime de La Rochefoucauld comme point de départ : nos émotions ne poussent pas dans le vide, elles sont encouragées par la société. L'idéal romantique nous dit que l'autre doit tout deviner sans qu'on ait à parler.04:59 — La question simple qui fait tomber l'idéal Comment saurai-je que je suis aimé ? À quoi le reconnaîtrai-je ? Vingt personnes dans une pièce donneront vingt réponses différentes selon leur âge, leur pays et leur musique. Nous sommes des créatures hautement suggestibles, et c'est ce qui nous rend libres de changer.06:27 — Les papillons dans le ventre, deux heures suffisent-elles ? Je raconte cette femme rencontrée récemment qui, après deux heures, savait déjà qu'elle ne ressentait rien.07:07 — La solitude originelle et la quête de la maison De l'intérieur d'un corps à l'extérieur du monde, du doudou serré dans les bras à l'adolescence qui cherche un autre corps. Les papillons, c'est la reconnaissance d'une maison perdue, dans une couleur de cheveux, une odeur, un angle de cou, un certain humour.10:58 — Mariages arrangés, romantisme, et le retournement Le passage de la logique dynastique au règne du sentiment, entre 1750 et 1870. Une révolution qui a produit des résultats bien plus paradoxaux qu'on ne le croit.12:52 — La statistique qui fait mal Les enfants d'alcooliques tombent massivement amoureux de personnes qui ont un problème d'addiction. Nous ne sommes pas attirés par le bonheur, nous sommes attirés par le familier.15:43 — Faut-il suivre son intuition ? Non sans la soumettre à examen. Et surtout : tenez un journal de ce que vous vivez, parce que la mémoire réécrit tout. Faites confiance à ce que vous avez ressenti sur le moment, pas à ce que vous imaginez maintenant.17:34 — « L'amour dure trois ans » ? Sa réaction est une des plus drôles de l'épisode. Puis l'argument sérieux : nos goûts ne changent pas si vite, et on ne change pas d'amis tous les trois ans.19:59 — Personne n'est sain d'esprit La seule attente réaliste : quelqu'un capable d'annoncer sa folie juste avant de la vivre, et de s'en excuser après. Il vaut mille fois mieux quelqu'un qui dit « j'ai peur de l'intimité » que quelqu'un qui ne répond plus au téléphone.21:37 — Drapeaux rouges : les vraies questions du premier dîner Un premier dîner est un entretien psychologique. Demandez l'histoire de la personne, sa relation avec ses parents, et observez surtout sa capacité à répondre. « Je ne sais pas, je n'y ai jamais pensé » est en soi une réponse.24:28 — La moitié d'entre nous a peur de s'approcher Environ 50 % des gens portent l'idée inconsciente que se rapprocher de quelqu'un finit mal. Presque tout le monde veut aimer, et presque tout le monde a un obstacle.25:39 — Les perversions sexuelles comme terreurs de l'intimité L'exhibitionniste, le voyeur, et jusqu'au cas le plus tragique. Toutes des manières de refuser la réciprocité. Et le même mécanisme, en version douce, chez l'amant qui promet de rappeler et ne rappelle jamais.28:50 — La vie sans friction et le partenaire sans friction Je lui parle du livre que je veux écrire sur la société du sans-couture, et de cette manie de collectionner les gens en les jetant dès la première imperfection.29:53 — Et si l'IA aidait vraiment les couples ? Son scénario optimiste : une intelligence artificielle témoin du couple, qui signale ce qu'on n'a pas entendu. « Il a mentionné son père trois fois et vous n'avez rien dit. »32:06 — Les applis de rencontre sont des machines à sous Leur modèle repose sur l'échec répété et la promesse du prochain coup. Un appel direct aux entrepreneurs qui écoutent.33:26 — Le complexe d'Œdipe, enfin expliqué utilement Ce que Freud voulait vraiment dire, et pourquoi il l'a mal dit. L'enfant a besoin d'être reconnu comme désirable sans jamais être séduit, et sans jamais être ignoré. Les deux dangers symétriques.38:18 — Pourquoi les conversations entre hommes hétérosexuels sont si ennuyeuses Ça commence dans la cour de récréation, quand pleurer devient une accusation. Ça finit par un adulte qui ne peut dire presque rien de ce qu'il est.40:36 — Quand une remarque sur des chaussures est entendue comme une condamnation à mort Pourquoi certaines personnes vivent toute critique comme une annihilation, et ce que ça révèle de ce qui leur est déjà arrivé.42:55 — Un plaidoyer pour la simplicité Nous adorons la complexité, Spinoza, l'astrophysique, et nous la respectons parfois là où elle n'a rien à faire. Trois heures de discussion quatre fois par semaine sur le sens d'un mot n'est pas une preuve de profondeur.44:26 — Ce qu'est l'amour mature Savoir s'excuser, séparer le passé du présent, distinguer la personne en face de soi de son père ou de sa mère. Et l'analogie du coureur olympique : tout le monde ne peut pas être dans votre équipe.46:36 — On ne change pas sur ordre Les gens changent, mais quand ils le décident, jamais sous la pression. Plus vous insistez, moins c'est possible.47:58 — L'âge émotionnel n'a rien à voir avec l'âge civil Nous tombons amoureux de personnes qui ont notre âge émotionnel. D'où les écarts de vingt ans qui fonctionnent, et les couples du même âge qui se décrochent.49:05 — L'aveu le plus vulnérable de l'épisode Il vit de l'idée que les mots transforment les gens. Et il reconnaît que le traumatisme ferme des portes qu'aucune éloquence n'ouvre. Certaines ruptures naissent exactement là.51:42 — Le désir qui s'éteint dans un couple aimant Le conflit entre le besoin de paraître respectable et la vérité de nos désirs. Plus on a besoin de quelqu'un, moins on ose lui montrer qui on est vraiment.54:17 — Polyamour : « du sang sur le tapis » Son expérience personnelle des amis qui appellent à trois heures du matin. Sans condamner celles et ceux pour qui ça fonctionne.55:51 — L'hôtel, et le partenaire redevenu inconnu La proposition concrète : sortir de la routine, et se rappeler que la personne qu'on croit connaître par cœur reste largement une inconnue.56:23 — Pourquoi tant d'hommes veulent une relation ouverte Une lecture historique et économique du mariage bourgeois, et ce qui se libère quand la contrainte financière disparaît.58:33 — Les histoires d'amour finissent toutes trop tôt Les films s'arrêtent au moment où les deux se trouvent. On ne les voit jamais préparer le dîner vingt ans plus tard.59:09 — Madame Bovary lisait les mauvais livres Rohmer contre Gladiator. Ce que nous regardons décide de ce que nous jugeons digne d'intérêt dans notre propre vie.1:01:06 — La passion, mais psychologique Deux personnes pressées de se retrouver le soir pour reparler de leur enfance. Se mettre psychologiquement à nu, voilà l'aventure qui ne s'épuise jamais.1:02:49 — Le chagrin d'amour est une forme de mort Il refuse de minimiser. Certains en meurent. Et c'est pire quand on ne comprend pas pourquoi l'autre est parti.1:04:09 — Les règles de la décence quand on quitte quelqu'un On prend une amende pour 25 km/h de trop, et on peut disparaître de la vie de quelqu'un après vingt ans sans que personne ne dise rien. Son message le plus direct : si vous savez, partez maintenant.1:07:17 — Peut-on rester amis ? Plus vous avez aimé, plus l'amitié sera pâle. Le meilleur terrain pour une amitié après coup, c'est ironiquement quand on ne s'est pas beaucoup aimés.1:09:21 — Le fichier « problèmes avec X » et l'histoire de la plage Sa méthode contre l'amnésie sentimentale, illustrée par son incapacité chronique à se souvenir qu'il déteste la plage.1:12:11 — L'enfant est une perte, et il faut le savoir La structure tragique de la parentalité, les associations inconscientes qui s'installent quand quelqu'un devient père ou mère, et pourquoi le sexe devient plus compliqué.1:13:47 — « L'équilibre vie pro vie perso n'existe pas » Tout ce qui vaut la peine d'être fait déséquilibre la vie. Il vaut mieux se préparer que se plaindre.1:14:44 — Un plaidoyer pour le compromis Rester pour les enfants n'est pas automatiquement une lâcheté. Refuser tout compromis, c'est un système totalitaire appliqué à sa propre vie.1:16:14 — Comment savoir qu'il est temps de partir Quand vous avez vraiment tout essayé. Les six mois de plus qui semblent perdus vous économisent des années de regrets.1:19:42 — L'infidélité comme tentative de se rapprocher Certaines trahisons disent l'inverse de ce qu'on croit : je ne veux pas sortir, je veux entrer.1:20:59 — La folie de l'autre comme chemin vers l'intimité « J'ai un problème avec le sexe, aide-moi » peut créer plus d'intimité que le sexe. Ce n'est pas la nature du problème qui compte, c'est la manière dont il est porté.1:23:07 — La question qu'on ne lui pose jamais Personne ne l'interroge sur Œdipe ni sur l'exhibitionnisme. Il vient de dire ce qu'il attendait depuis longtemps de pouvoir dire.1:24:10 — L'âge psychologique de l'amour Après les mariages arrangés et l'ère romantique, une troisième époque s'ouvre. Il n'y a qu'une seule voie, l'intelligence émotionnelle.1:25:00 — Peut-on faire trop de thérapie ? Non, mais on peut la faire mal. Et beaucoup de thérapeutes sont mauvais, imposant leur vision du monde à leurs patients.1:26:38 — La porte qu'il ferme : l'anxiété Ce que nous cherchons derrière l'argent, la gloire et l'amour, c'est le calme du nourrisson qui vient de manger.1:27:26 — « Le bruit intérieur a d'abord été un bruit extérieur » La phrase de fin, en une ligne.1:27:43 — Ce qui l'attend La School of Life, et cette immense toile sombre de la vie émotionnelle dont il découvre un morceau à la fois.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Alain de Botton est écrivain et philosophe. Il est également le fondateur de The School of Life, il a écrit Le cours de l'amour, un livre que j'ai lu il y a quelques années et qui m'a franchement retourné, parce qu'il commence là où tous les films s'arrêtent : le jour d'après le mariage.C'est le retour de l'été et ca m'a semblé bien de diffuser cet épisode en cette dernière semaine d'aout.Petite précision avant de démarrer : cet épisode a d'abord été diffusé en anglais. J'ai quand même tenu à le proposer en français, parce que ce qu'Alain raconte sur l'amour me paraît trop utile pour rester réservé à celles et ceux qui sont à l'aise avec l'anglais.Il y a un moment dans la conversation où je me suis vu, moi, dans ce qu'il décrivait. Il explique, statistiques à l'appui, que les enfants d'alcooliques tombent massivement amoureux de personnes qui ont un problème d'addiction. Pas par malchance. Parce que nous ne sommes pas attirés par ce qui nous rend heureux, nous sommes attirés par ce qui nous est familier. Et on peut passer une vie entière à rejouer la même scène en croyant chaque fois faire un choix neuf.Dans cet épisode, nous parlons de l'idéal romantique et de ce qu'il nous fait faire, des papillons dans le ventre et de ce qu'ils cachent vraiment, du complexe d'Œdipe relu comme un manuel pratique, des drapeaux rouges à repérer dès le premier dîner, de la sexualité qui s'éteint dans les couples aimants, du polyamour et des dégâts qu'il laisse parfois, de l'infidélité comme tentative maladroite de se rapprocher, des ruptures et de la manière décente de partir, des enfants qui abîment le couple, et de ce qu'on appelle la folie de l'autre.J'ai discuté avec Alain des questions que tout le monde se pose et que personne n'ose formuler à voix haute : est-ce qu'on peut faire changer quelqu'un, comment savoir qu'on a vraiment tout essayé, pourquoi les hommes hétérosexuels ont si peu de mots pour dire ce qu'ils ressentent, et pourquoi les applications de rencontre ont un intérêt économique à ce que vous ne trouviez personne.Ce qui m'a le plus marqué, c'est sa réponse finale. Derrière l'argent, la gloire et l'amour, ce que nous cherchons tous, dit-il, c'est le calme que nous avons connu nourrisson. Et il ajoute une phrase que je n'ai pas réussi à me sortir de la tête : le bruit qui est à l'intérieur de nous a d'abord été un bruit extérieur.Bonne écoute.3. Citations marquantes« Nous ne sommes pas attirés par ce qui nous rend heureux. Nous sommes attirés par ce qui nous est familier. »« Il ne s'agit pas de trouver quelqu'un de sain d'esprit, tout le monde est fou. Il s'agit de trouver quelqu'un capable d'explorer sa folie avec un minimum de maturité. »« Le modèle économique des applis de rencontre repose sur votre échec. C'est une machine à sous : le bonheur, oui, mais pas maintenant, revenez demain. »« Se déshabiller, c'est intéressant. Mais se mettre psychologiquement à nu, ça, c'est la vraie aventure. Et ça ne devient jamais ennuyeux. »« Il n'existe pas d'équilibre entre vie pro et vie perso. Tout ce qui vaut la peine d'être fait va déséquilibrer votre vie. »4. Idées centrales discutées 1. Nous aimons dans un décor que nous n'avons pas choisi (≈ 02:35) Alain ouvre sur cette maxime de La Rochefoucauld : certaines personnes ne seraient jamais tombées amoureuses si elles n'avaient jamais entendu dire que ça existait. Nos émotions reçoivent des encouragements discrets de la société qui nous entoure, et l'idéal romantique en fait partie. Pourquoi ça compte : si nos attentes viennent du décor, elles peuvent être révisées. C'est une bonne nouvelle déguisée en fatalité.2. L'attirance suit le familier, pas le bonheur (≈ 12:52) Le mouvement romantique promettait qu'en suivant son cœur, on choisirait mieux. La psychologie moderne dit l'inverse. Nous rejouons les schémas de frustration de l'enfance parce qu'ils ressemblent à la maison. Pourquoi ça compte : c'est l'explication la plus solide au sentiment de « je retombe toujours sur le même profil » que tant de gens décrivent.3. Les perversions sexuelles sont des terreurs de l'intimité (≈ 25:39) De l'exhibitionniste au voyeur, Alain lit ces comportements comme des refus de la réciprocité : il faut imposer, ou observer de loin, parce que se regarder dans les yeux est insupportable. Pourquoi ça compte : le même mécanisme, en version douce, explique l'amant qui disparaît le lendemain matin. Le désir de connexion et la terreur d'être englouti cohabitent chez tout le monde.4. Les applications de rencontre ont besoin de votre échec (≈ 32:06) Leur modèle repose sur une promesse jamais tenue, exactement comme une machine à sous. Alain reste pourtant optimiste sur la technologie : il imagine une IA témoin du couple, qui signalerait ce que nous ne savons pas entendre. Pourquoi ça compte : ça déplace le débat, le problème n'est pas la technologie, c'est le modèle économique qu'on lui a collé dessus.5. On ne cherche pas quelqu'un de sain, on cherche quelqu'un de lucide (≈ 19:59) La bonne définition d'une attente réaliste, selon lui : quelqu'un capable d'annoncer sa folie avant de la vivre, et de s'en excuser après. Pourquoi ça compte : ça remplace la quête épuisante de la personne parfaite par un critère observable dès les premiers mois.6. Le dialogue a une limite, et il faut l'accepter (≈ 49:05) C'est le moment le plus vulnérable de la conversation. Alain vit de l'idée que les mots transforment les gens, et il admet que ce n'est pas toujours vrai : le traumatisme ferme des portes qu'aucun argument n'ouvre. Pourquoi ça compte : certaines des ruptures les plus douloureuses viennent de là, quitter quelqu'un qu'on aime et qui ne peut pas comprendre.7. La folie de l'autre peut devenir le lieu de l'intimité (≈ 21:28 en germe, développé à 1:20:59) Ce n'est pas la nature du problème qui décide, c'est la manière dont il est porté. Dire « j'ai un problème avec le sexe, aide-moi » peut être plus intime que le sexe lui-même. Pourquoi ça compte : ça retourne complètement la logique des drapeaux rouges. Le défaut avoué relie, le défaut caché détruit.5. Questions posées dans l'interviewComment l'idéal romantique de l'âme sœur a-t-il façonné ce que nous attendons de l'amour, et quels sont les pièges de ces attentes ?Comment prend-on du recul par rapport à un idéal romantique dans lequel nous baignons depuis toujours ?Que valent les papillons dans le ventre, et que sommes-nous censés ressentir quand nous tombons amoureux ?Faut-il suivre son intuition en amour, ou s'en méfier ?Que pensez-vous de l'idée que l'amour dure trois ans ?Quelles sont les attentes réalistes qu'on peut avoir d'une relation ?Existe-t-il des drapeaux rouges fiables, et quelles questions faut-il poser dès les premiers dîners ?Nous vivons dans une société sans friction, Uber, Airbnb, tout doit être fluide. Cherche-t-on aussi un partenaire sans friction ?Est-ce que les hommes hétérosexuels sont plus démunis que les femmes face à leurs propres émotions ?Comment accueillir la folie de son partenaire sans se perdre soi-même ?Peut-on faire changer quelqu'un, et à quelle vitesse ?Comment garder le désir sexuel vivant dans un couple qui s'aime depuis longtemps ?Pourquoi tant d'hommes réclament-ils aujourd'hui des relations ouvertes ?Pourquoi deux personnes qui se sont profondément aimées deviennent-elles des étrangères ?Peut-on pardonner une infidélité, et comment ?Comment savoir qu'on a vraiment tout essayé et qu'il faut partir ?Comment la société devrait-elle évoluer pour améliorer la qualité de nos relations ?Peut-on faire trop de thérapie ?6. Références citées dans l'épisodeAuteurs et philosophesLa Rochefoucauld (≈ 02:48) : « Il y a des gens qui n'auraient jamais été amoureux s'ils n'avaient jamais entendu parler de l'amour. » La citation matrice de tout l'épisode, reprise plus tard à propos du polyamour (≈ 56:47).Sénèque (≈ 17:34) : « Pourquoi pleurer sur des parties de la vie ? Elle appelle tout entière les larmes. » Cité comme exemple de pessimisme réconfortant.Nietzsche (≈ 19:44) : pour vivre, nous avons besoin de certaines illusions.Freud (≈ 33:26) : le complexe d'Œdipe, relu comme modèle des compétences nécessaires à l'âge adulte. Alain note au passage que Freud écrivait mal et qu'il a besoin d'être commenté.Spinoza (≈ 33:50 et ≈ 42:55) : cité comme exemple de penseur qui ne dit pas le meilleur de sa pensée, puis L'Éthique comme exemple de complexité que nous aimons pour de mauvaises raisons.Racine, Shakespeare, Tolstoï (≈ 49:05) : convoqués pour dire que même la plus belle éloquence ne fait pas bouger quelqu'un qui est fermé émotionnellement.Flaubert, Madame Bovary (≈ 59:09) : Emma lit les mauvais livres, ceux qui lui disent que sa vie est ennuyeuse.Livres et créations d'Alain de BottonLe cours de l'amour (The Course of Love) (≈ 00:33) : cité par moi en intro comme le livre qui m'a marqué.The School of Life (≈ 00:33 et ≈ 1:27:43) : l'institution qu'il a fondée, sa chaîne YouTube, et le terrain de ses explorations à venir.Livre français discuté sans être nomméL'amour dure trois ans (≈ 17:34) : je lui soumets la thèse, il répond qu'il ne connaît pas le livre, se félicite qu'il ne soit pas traduit en anglais et refuse d'y croire un vendredi matin à 11h20. [Auteur non nommé dans l'épisode : Frédéric Beigbeder]Cinéma et cultureÉric Rohmer (≈ 59:09) : le cinéaste qui nous apprend la patience des conversations ordinaires.Gladiator (≈ 59:09) : l'exemple inverse, le récit à l'échelle héroïque qui rend la vie quotidienne décevante.Personnes citéesEsther Perel (≈ 18:20) : mentionnée comme la voix à convoquer sur la durée du désir.Invitée d'un précédent épisode de Vlan! (≈ 1:27:26) : je mentionne une conversation qui m'a fait comprendre que le bruit est surtout intérieur. [Nom mal transcrit dans le fichier, à vérifier avant publication]Lieux et imagesLe Bois de Boulogne (≈ 25:39) : l'exemple de l'exhibitionniste.Sainte-Lucie, Saint-Barthélemy (≈ 1:09:21) : les îles de son histoire de plage, la démonstration la plus drôle de notre amnésie sentimentale.Timestamps clés00:00 — Pourquoi cet épisode est en anglais J'explique en français le contexte de l'épisode et pourquoi je tenais à le proposer malgré la barrière de la langue.00:33 — Alain de Botton, School of Life et Le cours de l'amour Mon introduction : pourquoi ce livre m'a marqué, et pourquoi les 20 ou 30 questions les plus difficiles sur l'amour sont réunies ici.02:35 — « Certains ne seraient jamais tombés amoureux s'ils n'en avaient jamais entendu parler » La maxime de La Rochefoucauld comme point de départ : nos émotions ne poussent pas dans le vide, elles sont encouragées par la société. L'idéal romantique nous dit que l'autre doit tout deviner sans qu'on ait à parler.04:59 — La question simple qui fait tomber l'idéal Comment saurai-je que je suis aimé ? À quoi le reconnaîtrai-je ? Vingt personnes dans une pièce donneront vingt réponses différentes selon leur âge, leur pays et leur musique. Nous sommes des créatures hautement suggestibles, et c'est ce qui nous rend libres de changer.06:27 — Les papillons dans le ventre, deux heures suffisent-elles ? Je raconte cette femme rencontrée récemment qui, après deux heures, savait déjà qu'elle ne ressentait rien.07:07 — La solitude originelle et la quête de la maison De l'intérieur d'un corps à l'extérieur du monde, du doudou serré dans les bras à l'adolescence qui cherche un autre corps. Les papillons, c'est la reconnaissance d'une maison perdue, dans une couleur de cheveux, une odeur, un angle de cou, un certain humour.10:58 — Mariages arrangés, romantisme, et le retournement Le passage de la logique dynastique au règne du sentiment, entre 1750 et 1870. Une révolution qui a produit des résultats bien plus paradoxaux qu'on ne le croit.12:52 — La statistique qui fait mal Les enfants d'alcooliques tombent massivement amoureux de personnes qui ont un problème d'addiction. Nous ne sommes pas attirés par le bonheur, nous sommes attirés par le familier.15:43 — Faut-il suivre son intuition ? Non sans la soumettre à examen. Et surtout : tenez un journal de ce que vous vivez, parce que la mémoire réécrit tout. Faites confiance à ce que vous avez ressenti sur le moment, pas à ce que vous imaginez maintenant.17:34 — « L'amour dure trois ans » ? Sa réaction est une des plus drôles de l'épisode. Puis l'argument sérieux : nos goûts ne changent pas si vite, et on ne change pas d'amis tous les trois ans.19:59 — Personne n'est sain d'esprit La seule attente réaliste : quelqu'un capable d'annoncer sa folie juste avant de la vivre, et de s'en excuser après. Il vaut mille fois mieux quelqu'un qui dit « j'ai peur de l'intimité » que quelqu'un qui ne répond plus au téléphone.21:37 — Drapeaux rouges : les vraies questions du premier dîner Un premier dîner est un entretien psychologique. Demandez l'histoire de la personne, sa relation avec ses parents, et observez surtout sa capacité à répondre. « Je ne sais pas, je n'y ai jamais pensé » est en soi une réponse.24:28 — La moitié d'entre nous a peur de s'approcher Environ 50 % des gens portent l'idée inconsciente que se rapprocher de quelqu'un finit mal. Presque tout le monde veut aimer, et presque tout le monde a un obstacle.25:39 — Les perversions sexuelles comme terreurs de l'intimité L'exhibitionniste, le voyeur, et jusqu'au cas le plus tragique. Toutes des manières de refuser la réciprocité. Et le même mécanisme, en version douce, chez l'amant qui promet de rappeler et ne rappelle jamais.28:50 — La vie sans friction et le partenaire sans friction Je lui parle du livre que je veux écrire sur la société du sans-couture, et de cette manie de collectionner les gens en les jetant dès la première imperfection.29:53 — Et si l'IA aidait vraiment les couples ? Son scénario optimiste : une intelligence artificielle témoin du couple, qui signale ce qu'on n'a pas entendu. « Il a mentionné son père trois fois et vous n'avez rien dit. »32:06 — Les applis de rencontre sont des machines à sous Leur modèle repose sur l'échec répété et la promesse du prochain coup. Un appel direct aux entrepreneurs qui écoutent.33:26 — Le complexe d'Œdipe, enfin expliqué utilement Ce que Freud voulait vraiment dire, et pourquoi il l'a mal dit. L'enfant a besoin d'être reconnu comme désirable sans jamais être séduit, et sans jamais être ignoré. Les deux dangers symétriques.38:18 — Pourquoi les conversations entre hommes hétérosexuels sont si ennuyeuses Ça commence dans la cour de récréation, quand pleurer devient une accusation. Ça finit par un adulte qui ne peut dire presque rien de ce qu'il est.40:36 — Quand une remarque sur des chaussures est entendue comme une condamnation à mort Pourquoi certaines personnes vivent toute critique comme une annihilation, et ce que ça révèle de ce qui leur est déjà arrivé.42:55 — Un plaidoyer pour la simplicité Nous adorons la complexité, Spinoza, l'astrophysique, et nous la respectons parfois là où elle n'a rien à faire. Trois heures de discussion quatre fois par semaine sur le sens d'un mot n'est pas une preuve de profondeur.44:26 — Ce qu'est l'amour mature Savoir s'excuser, séparer le passé du présent, distinguer la personne en face de soi de son père ou de sa mère. Et l'analogie du coureur olympique : tout le monde ne peut pas être dans votre équipe.46:36 — On ne change pas sur ordre Les gens changent, mais quand ils le décident, jamais sous la pression. Plus vous insistez, moins c'est possible.47:58 — L'âge émotionnel n'a rien à voir avec l'âge civil Nous tombons amoureux de personnes qui ont notre âge émotionnel. D'où les écarts de vingt ans qui fonctionnent, et les couples du même âge qui se décrochent.49:05 — L'aveu le plus vulnérable de l'épisode Il vit de l'idée que les mots transforment les gens. Et il reconnaît que le traumatisme ferme des portes qu'aucune éloquence n'ouvre. Certaines ruptures naissent exactement là.51:42 — Le désir qui s'éteint dans un couple aimant Le conflit entre le besoin de paraître respectable et la vérité de nos désirs. Plus on a besoin de quelqu'un, moins on ose lui montrer qui on est vraiment.54:17 — Polyamour : « du sang sur le tapis » Son expérience personnelle des amis qui appellent à trois heures du matin. Sans condamner celles et ceux pour qui ça fonctionne.55:51 — L'hôtel, et le partenaire redevenu inconnu La proposition concrète : sortir de la routine, et se rappeler que la personne qu'on croit connaître par cœur reste largement une inconnue.56:23 — Pourquoi tant d'hommes veulent une relation ouverte Une lecture historique et économique du mariage bourgeois, et ce qui se libère quand la contrainte financière disparaît.58:33 — Les histoires d'amour finissent toutes trop tôt Les films s'arrêtent au moment où les deux se trouvent. On ne les voit jamais préparer le dîner vingt ans plus tard.59:09 — Madame Bovary lisait les mauvais livres Rohmer contre Gladiator. Ce que nous regardons décide de ce que nous jugeons digne d'intérêt dans notre propre vie.1:01:06 — La passion, mais psychologique Deux personnes pressées de se retrouver le soir pour reparler de leur enfance. Se mettre psychologiquement à nu, voilà l'aventure qui ne s'épuise jamais.1:02:49 — Le chagrin d'amour est une forme de mort Il refuse de minimiser. Certains en meurent. Et c'est pire quand on ne comprend pas pourquoi l'autre est parti.1:04:09 — Les règles de la décence quand on quitte quelqu'un On prend une amende pour 25 km/h de trop, et on peut disparaître de la vie de quelqu'un après vingt ans sans que personne ne dise rien. Son message le plus direct : si vous savez, partez maintenant.1:07:17 — Peut-on rester amis ? Plus vous avez aimé, plus l'amitié sera pâle. Le meilleur terrain pour une amitié après coup, c'est ironiquement quand on ne s'est pas beaucoup aimés.1:09:21 — Le fichier « problèmes avec X » et l'histoire de la plage Sa méthode contre l'amnésie sentimentale, illustrée par son incapacité chronique à se souvenir qu'il déteste la plage.1:12:11 — L'enfant est une perte, et il faut le savoir La structure tragique de la parentalité, les associations inconscientes qui s'installent quand quelqu'un devient père ou mère, et pourquoi le sexe devient plus compliqué.1:13:47 — « L'équilibre vie pro vie perso n'existe pas » Tout ce qui vaut la peine d'être fait déséquilibre la vie. Il vaut mieux se préparer que se plaindre.1:14:44 — Un plaidoyer pour le compromis Rester pour les enfants n'est pas automatiquement une lâcheté. Refuser tout compromis, c'est un système totalitaire appliqué à sa propre vie.1:16:14 — Comment savoir qu'il est temps de partir Quand vous avez vraiment tout essayé. Les six mois de plus qui semblent perdus vous économisent des années de regrets.1:19:42 — L'infidélité comme tentative de se rapprocher Certaines trahisons disent l'inverse de ce qu'on croit : je ne veux pas sortir, je veux entrer.1:20:59 — La folie de l'autre comme chemin vers l'intimité « J'ai un problème avec le sexe, aide-moi » peut créer plus d'intimité que le sexe. Ce n'est pas la nature du problème qui compte, c'est la manière dont il est porté.1:23:07 — La question qu'on ne lui pose jamais Personne ne l'interroge sur Œdipe ni sur l'exhibitionnisme. Il vient de dire ce qu'il attendait depuis longtemps de pouvoir dire.1:24:10 — L'âge psychologique de l'amour Après les mariages arrangés et l'ère romantique, une troisième époque s'ouvre. Il n'y a qu'une seule voie, l'intelligence émotionnelle.1:25:00 — Peut-on faire trop de thérapie ? Non, mais on peut la faire mal. Et beaucoup de thérapeutes sont mauvais, imposant leur vision du monde à leurs patients.1:26:38 — La porte qu'il ferme : l'anxiété Ce que nous cherchons derrière l'argent, la gloire et l'amour, c'est le calme du nourrisson qui vient de manger.1:27:26 — « Le bruit intérieur a d'abord été un bruit extérieur » La phrase de fin, en une ligne.1:27:43 — Ce qui l'attend La School of Life, et cette immense toile sombre de la vie émotionnelle dont il découvre un morceau à la fois. Suggestion d'autres épisodes à écouter : [BEST OF] Esther Perel : repenser le couple à l'âge du choix infini (https://audmns.com/fHyNTYA) [Hors-Serie] Tout ce que j'aurais voulu savoir en amour avec Alain de Botton (partie 1) (https://audmns.com/jiDhQhD) [Hors serie] Tout ce que j'aurais voulu savoir en amour avec Alain de Botton (partie 2) (https://audmns.com/vUBKEkK)Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

We've got you covered with cooking hacks you'll need when you can't get to a kitchen, plus albums that would be perfect if you just dropped one song (we've got Andy Summers in our sites) and WTF features an Uber driver who deserves five stars, some really advanced Middle Schoolers and bad dog walking protocols. See omnystudio.com/listener for privacy information.

In this episode, sponsored by Instacart Enterprise, Portager, and Vusion, Ben Miller is joined by Martin Bailie, Global Retail CEO, AI & Growth Expert and Founder of MWB Advisory to unpack the news stories from global grocery this week that we believe are worth paying attention to. Martin brings decades of hands-on grocery experience across three continents, with a career spanning Tesco, Tata India, Primark and Lidl. This week, they discuss: • Target's Q2 results, and whether stronger sales, grocery growth and improved store traffic signal a genuine turnaround or simply a bounce from a weak comparison: https://www.cnbc.com/2026/08/19/target-tgt-q2-2026-earnings.html • Sainsbury's pause on AI facial recognition, following a case of mistaken identity, and whether retailers can use technology to protect colleagues and customers without creating new problems: https://www.theguardian.com/technology/2026/aug/17/humiliated-sainsburys-store-pauses-ai-scanning-after-false-shoplifting-accusation • Amazon's six-fold expansion of drone delivery, and why Martin believes drone delivery is moving beyond novelty and becoming part of retail infrastructure: https://www.supplychaindive.com/news/amazon-to-expand-drone-delivery-reach-sixfold-this-year/828253/ • Uber's partnership with Zipline, including its ambition for one million drone deliveries a day by 2029, and what the race for drone delivery says about the future of convenience and quick commerce: https://investor.uber.com/news-events/news/press-release-details/2026/Uber-and-Zipline-Partner-to-Bring-Drone-Delivery-to-Millions-of-Americans/default.aspx • Ocado's latest European technology partnership, and what its newest customer tells us about the future of automated grocery fulfillment: https://www.supplychaindive.com/news/amazon-to-expand-drone-delivery-reach-sixfold-this-year/828253/ Plus, GrocerTalk Grab & Go brings even more stories to the table from another busy week in global grocery. GrocerTalk is a weekly podcast from the Omni Talk Podcast Network covering the trends, innovations and technologies shaping global grocery. Episode 002. Welcome to GrocerTalk. P.S. Be sure to check out all our other podcasts from the past week here, too: https://omnitalk.blog/category/podcast/ Music by hooksounds.com.

Rideshare Rodeo Podcast (episode 604)  August 25th, 2026 TOPICS: Uber fined $966 million for "automated deactivations" Doordash executives selling off big and quick Professor in China say gig work is simply "welfare" Instacart Safety vs. Empathy  Grubhub pays out $24 million to customers and drivers RideshareRodeo.com 

→ Quantum | Upgrade your energy, harmonize your EMF exposure, and elevate your wellness with Quantum Upgrade and get a 15-day FREE trial, no credit card required at quantumupgrade.io/start when you use code DRG at checkout. Episode Description: You are living in an electromagnetic soup and most people have no idea. Over 4,000 published studies show harmful effects from EMF exposure on the brain, blood, nervous system, fertility, and more. The European Parliament has its own report on the cancer and fertility risks. And with 6G rolling out faster than 5G ever did, it is not getting better. This guest spent years as a VP at T-Mobile before switching sides. Now he studies and develops technology designed to harmonize EMF exposure rather than ignore it. This conversation covers what EMFs are doing inside the brain within seconds of exposure, why the problems accumulate silently until they are hard to trace back, and what anyone can actually do about it right now. Dr. G and his guest break it down — in order: Take 1: "EMFs are detrimental to human health and there are over 4,000 studies that show it" Most people cannot find this research on the first page of a Google search, but it exists and it spans decades. The guest breaks down what the evidence actually shows and where to find it. Take 2: "Within seconds in the brain, everything linked to stress fires up" EMF exposure does not wait weeks or months to affect your nervous system. The guest explains what happens inside the brain almost immediately upon exposure and why that matters for anxiety, brain fog, and chronic stress. Take 3: "You cannot hide from EMFs anymore" From 5G towers to Starlink to electric vehicles, there is nowhere left to escape. The guest explains why the goal cannot be avoidance and what the realistic path forward actually looks like. Take 4: "6G is going to be much worse than 5G" The next wave of wireless technology is already rolling out and the latest generation of mobile phones attacks the lymphatic system faster and deeper than the one before. The guest lays out what is coming and why staying informed matters. Take 5: "You can start becoming more coherent right now, for free" Meditation, sunlight, nature, yoga, and tai chi all support your biofield even before any technology gets involved. The guest gives the honest baseline of what lifestyle alone can do and where its limits are. In this episode, you will learn:  • Why EMF exposure causes problems that accumulate silently, why most people attribute their symptoms to something else entirely, and why the brain is especially vulnerable because it is the central steering organ for everything in the body  • How gamers, pilots, Uber drivers, and anyone with multiple screens and Bluetooth devices are experiencing compounded EMF exposure at levels the body was never designed to handle  • What coherence technology is, how it differs from simply blocking or shielding EMFs, and what the research behind it actually shows Find Quantum Upgrade:Website: https://www.quantumupgrade.io Timestamps: 0:00 - Intro 2:00 - 4,000+ Studies on EMF Health Effects (And Why You Don't Find Them on the First Search) 5:00 - Is There Anywhere Left on Earth to Escape EMFs? 8:00 - Are These Studies Being Suppressed? A Former T-Mobile VP Answers Honestly 10:00 - Why the Brain Is the First and Most Sensitive Organ to Take the Hit 13:00 - How Short-Term Exposure Triggers the Stress Response — And Why It Gets Worse Over Time 17:00 - Kids, Three Screens, Bluetooth Headphones and Why No One Is Connecting the Dots 21:00 - Why We're Not Talking About EMFs the Way We Should Be (It's Because They're Invisible) 25:00 - 5G Was Bad — Here's What 6G and Self-Driving Cars Are Going to Do 29:00 - What You Can Do for Free Right Now to Start Protecting Yourself 33:00 - From VP at T-Mobile to This: Why He Crossed to the Other Side 35:00 - The Technology That Harmonizes EMFs Without Removing Them 38:00 - The 15-Day Free Trial and Where to Find It Learn more about your ad choices. Visit megaphone.fm/adchoices

Polymarket traders win big on U.S. military insider information. Slovakia deactivates speed cameras with Russian backdoors. TikTok pays $400 million to settle kids' privacy allegations. Hackers infect Android-based car systems with botnet malware. CISA orders quick patching of an actively exploited Zimbra Collaboration Suite vulnerability. SynkLoader malware is built for stealthy access to corporate networks. Dutch authorities fine Uber over $900 million over automated hiring practices. An ATM jackpotter gets a record prison sentence. Monday business briefing. A privacy promise loses face.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest On our Industry Voices segment, we are joined by Mark Beare, General Manager at Malwarebytes Consumer Business from Black Hat to look at protecting your family in the age of AI. If you enjoyed this conversation, check out the full interview here. Selected Reading More than 150 Polymarket wallets may have traded on military secrets, research finds (Reuters) Slovakia discovers Russian backdoors in 279 new traffic cameras — SMS-triggered shell access and passwordless live feeds found in EU-funded rollout (Tom's Hardware) TikTok Settles U.S. Child Privacy Case for $400 Million (Security Affairs) Hackers infecting Android car systems to build proxy botnet (The Record) CISA orders urgent patching of actively exploited Zimbra flaw (Bleeping Computer) SynkLoader: when you throw in everything but the kitchen sink (Expel) Uber Fined Nearly $1 Billion by Dutch Regulators Over Automated Suspensions of Driver Accounts (SecurityWeek) Venezuelan Gets Record Federal Prison Term for ATM Jackpotting (SecurityWeek) Fortinet has acquired San Francisco-based AI security company Virtue AI. (N2K Pro Business Briefing) Reverse-Lookup Service Exposed Millions of Photos of People's Faces (WIRED) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc.

Rover is sick, suns out guts out, ice throwing, and AC usage. Duji finally got her Kalm bracelet and ring. NFL player Josh Allen upset that the news has been talking about the death of Ed Oliver's son. Deshawn Watson is upset that Browns fans boo him. Enes Kanter kicked out of a Chicago Sky game. Most expensive piece of NBA game memorabilia. Does Duji wear makeup? Update to the 16-year-old girl who ran out onto the football field. Contracted surrogate carrying a baby with a heart defect refused to abort the child. How was Rover's dinner date? Duji is on Imodium as well. JLR got reprimanded for talking to Ashley in sales. Police officer gets a ride from an Uber driver to chase after a suspect. Krystle and JLR have a sit-up competition. Courtney Stodden is now calling out Dr. Drew. A 'wanted' poster put up in Georgia over a man picking up $30 he found on the ground. 

How was Rover's dinner date? Duji is on Imodium as well. JLR got reprimanded for talking to Ashley in sales. Police officer gets a ride from an Uber driver to chase after a suspect.

The dating app Bumble is changing their rules. Joey organized all the charger cables in his house. More people are getting scammed into thinking they are dating celebrities. Nancy is worried about swallowing spiders in her sleep. Karly got another mystery Amazon package. Weird things left in Ubers. Old Pizza Huts are coming back! What old restaurant do you miss? Joey negotiated and got a deal on new tires. Lucky 7 for $50 to Copper Cellar As Seen on TikTok! A woman held a harmonica in her mouth while getting a Brazilian wax. We try to make Nancy laugh while holing a harmonica in her mouth. Karly made men on Facebook mad while talking about her Hinge "icks." Waffle House gave Joey used Butter. New invention-- toilet shoes! Where would you trespass if you knew you wouldn't get in trouble? See omnystudio.com/listener for privacy information.

Richard Entrup is unusual in quantum circles: he's not a physicist, and he doesn't pretend to be. He spent decades as a CIO, CTO, CDO, and CISO at organizations including Verizon, Christie's, Disney/ABC, Time Warner, and Tiffany & Company before joining KPMG to lead its Emerging Solutions practice. That background — deep operational experience on the client side — shapes everything about how he thinks about quantum. He's not selling a hardware roadmap; he's thinking about what it actually takes to get a large, complex organization to change its cryptographic infrastructure before a threat materializes.The conversation matters now because the signals are accelerating. NIST has finalized its first post-quantum cryptography standards, executive orders in the US are pushing federal agencies toward PQC migration, and the algorithmic efficiency gains that reduce the qubit threshold for breaking RSA-2048 keep coming. Listeners who work in enterprise technology, cybersecurity, or quantum strategy — or who advise organizations that do — will find Entrup's practitioner perspective a useful counterweight to the more hardware-focused conversations that dominate the field.What We Get IntoWhy Q-Day's exact date is the wrong question — and why the more important issue is how long it will take enterprises to even inventory their cryptographic exposure, let alone remediate itThe scale of the cryptographic migration problem, including why a single laptop may contain hundreds of individual cryptographic components and why upstream/downstream API dependencies make this a supply-chain-wide challenge, not just an internal IT projectWhy "harvest now, decrypt later" creates urgency today, regardless of when fault-tolerant quantum computers arrive — and how compliance and regulatory timelines interact with that threat modelWhat crypto agility actually means in practice — moving from a "set it and forget it" cryptographic posture to a dynamic, continuously monitored framework, including the pressure SSL certificate renewal windows are already creatingHow KPMG built its PQC practice, incubated it within the firm, and handed it off to the cybersecurity advisory team as a core service offeringThe "good quantum" side of the ledger — how KPMG's emerging research function is approaching quantum computing as a source of competitive advantage, not just risk, and what sectors are furthest along in exploring itThe AI-quantum convergence, including Entrup's observation that AI is already being used to read and crack code — and what that means for the urgency of cryptographic modernizationWhy the enterprise quantum opportunity still has a long tail, and how the current moment compares to the early infrastructure phase of the internet — when everyone was talking about TCP/IP and DNS, not Uber or NetflixResources & LinksGuest & OrganizationRichard Entrup — Worth Magazine Profile — Career arc from CIO/CISO roles at major global brands to KPMG's Emerging Solutions practiceKPMG Quantum Dawn (2025) — KPMG's enterprise quantum readiness hub, introducing the Q-PREP framework and PQC implementation services, with Entrup as named leadReports & ResearchKPMG — "The Quantum Threat Is No Longer Theoretical" (2026) — The threat brief discussed in this episode, charting the rapid decline in qubits needed to crack RSA-2048 and urging immediate PQC migrationKPMG — "From Theory to Impact: Real-World Results in Quantum Machine Learning" (2026) — KPMG's joint report with IBM and Kipu Quantum on measurable quantum ML results on real hardwareKPMG — "Prepare Now for Quantum Cyber Risk" — Board Leadership Article (2026) — C-suite and board-level guidance on integrating quantum risk into enterprise oversightarXiv — "Quantum-enhanced satellite image classification" (2026) — The underlying research paper behind the KPMG/IBM/Kipu Quantum ML resultsEcosystem & EventsChicago Quantum Exchange — KPMG Joins CQE (October 2024) — Announcement of KPMG's formal CQE membership, referenced in the episode as part of the firm's ecosystem-building strategyKPMG 2026 Quantum Consortium — The inaugural KPMG Quantum Consortium event (March 2026, Orlando) discussed in the episodeIndependent CoverageQuantum Computing Report — KPMG joins Chicago Quantum Exchange (2024) — Independent coverage of KPMG's CQE partnership and enterprise quantum strategyQuantum Zeitgeist — Kipu Quantum satellite imagery coverage (Feb 2026) — Independent analysis of the KPMG/IBM/Kipu hybrid QML resultsKey Quotes & Insights> "It's not if but when. And it could be five years, could be three years, could be ten years. The fact is organizations are not gonna be ready. And that's the scary part." — Richard Entrup on Q-Day> "This is not just the CISO. This is gonna be the software engineering app dev guys. This is gonna be all your partners, upstream and downstream, who have to also be compliant — because if you change your crypto and they don't, that stuff's gonna break." — On why PQC migration is an enterprise-wide, supply-chain-wide problemInsight: Entrup draws a sharp distinction between the "bad quantum" (cryptographic risk requiring urgent defensive action) and the "good quantum" (competitive opportunity with a longer tail) — and argues that most organizations aren't adequately addressing either.Insight: The analogy to the early internet is deliberate: just as the 1990s were consumed with TCP/IP and DNS rather than the applications those protocols would eventually enable, the current quantum moment is still largely an infrastructure conversation — and that's normal, not a sign of failure.> "AI is expediting all of this. If AI is doing one thing, the use case is reading code and cracking it. That's pretty scary." — On the intersection of AI capability and cryptographic vulnerabilityRelated EpisodesEp. 81 — Quantum LDPC Error Correction with Larry Cohen and Paul Webster — Directly relevant: Cohen and Webster discuss how QLDPC error correction reduces the qubit overhead needed for RSA cryptanalysis, the technical underpinning of the threat timeline Entrup describesEp. 38 — Quantum Machine Learning with Jessic...

What does it tell you about a company when every single person it hires, in every function, has to pass the same interview about culture?In this episode of Supra Insider, Marc Baselga and Ben Erez set the guest format aside for a conversation Marc started because he noticed something new. After two years of coaching people through interview loops, this was the first time he'd seen Ben genuinely fascinated by one specific interview at one specific company. Ben walks through what he's pieced together about Anthropic's culture interview: the no-exceptions policy, the rapid-fire format of ten or more questions in a single 45-minute slot, and the fact that anyone at the company, from marketing to IT, can be trained to run it.They explore the questions that actually get asked, what Ben believes is being evaluated underneath them, why “why Anthropic” demands more depth than the same question anywhere else, and whether the filter holds as the company gets hotter and more candidates learn to say the right things. Then they turn to the question Ben finds most interesting: why almost no other company does this, and what happens inside a company when employees are calibrated to evaluate culture.If you're preparing for an interview at a frontier lab, thinking about how your own company screens for values, or just curious what a well-designed culture filter looks like from the outside, this episode is for you.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

Michael Gants, founder and CEO of Encore, breaks down how apps can turn unused "streak" and celebration screens into a new revenue stream, without a single ad or paywall. He shares how brands like Disney, Uber, and Apple are paying to reach users in their happiest moments, and why founders should be more afraid of a broken CAC/LTV ratio than of running a monetization experiment.

The dating app Bumble is changing their rules. Joey organized all the charger cables in his house. More people are getting scammed into thinking they are dating celebrities. Nancy is worried about swallowing spiders in her sleep. Karly got another mystery Amazon package. Weird things left in Ubers. Old Pizza Huts are coming back! What old restaurant do you miss? Joey negotiated and got a deal on new tires. Lucky 7 for $50 to Copper Cellar As Seen on TikTok! A woman held a harmonica in her mouth while getting a Brazilian wax. We try to make Nancy laugh while holing a harmonica in her mouth. Karly made men on Facebook mad while talking about her Hinge "icks." Waffle House gave Joey used Butter. New invention-- toilet shoes! Where would you trespass if you knew you wouldn't get in trouble? See omnystudio.com/listener for privacy information.

In today's episode, Kyle Grieve and Shawn O'Malley analyze Domino's Pizza, the world's biggest pizza franchisor built on a royalty-driven, asset-light business model. They walk through Domino's shift toward franchising and away from Company-owned stores, and what that means for the company's future revenue mix and cash generation. Along the way, they dig into whether Domino's royalty engine can keep running at the pace investors have come to expect. IN THIS EPISODE YOU'LL LEARN: (00:00:00) Intro (00:01:09) Reviewing the Domino's royalty engine thesis (00:13:30) Why Domino's has moved away from Company-owned stores (00:26:05) The role of royalties versus supply chain revenue in Domino's earnings (00:33:21) How Domino's utilizes a fortressing strategy and its effect on store growth (00:41:16) The competitive landscape in delivery, carryout, and aggregator platforms (00:43:27) How the franchise model keeps Domino's asset-light and cash-generative (00:48:47) Domino's capital allocation and approach to share buybacks (00:56:47) International franchising and Domino's global store growth (00:58:30) Risks facing Domino's from labor costs, competition, and changing consumer habits (01:06:29) Valuation discussion of Domino's (01:07:40) Intrinsic value of Domino's (01:09:00) Whether Kyle & Shawn will add Domino's to the Intrinsic Value Portfolio Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. BOOKS AND RESOURCES Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Track ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Portfolio⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about how to join us in NYC for our ⁠⁠⁠Intrinsic Value Conference⁠⁠⁠. Portfolio Review ⁠⁠⁠⁠⁠⁠Submit Tool⁠⁠⁠⁠⁠⁠. Check out our previous Intrinsic Value breakdowns: ⁠⁠⁠⁠⁠⁠⁠⁠ Uber⁠, ⁠Grab⁠, ⁠Coupang⁠. Buy yourself a copy of The Domino's Story. Follow Kyle on ⁠⁠⁠⁠X⁠⁠⁠⁠ and ⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠. Follow Shawn on ⁠X⁠ and ⁠LinkedIn⁠. Related ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠books⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ mentioned in the podcast. Ad-free episodes on our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Get smarter about valuing businesses through ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Investor's Podcast Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our ⁠sponsors⁠: ⁠Fiscal.AI⁠ References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor's Podcast Network is not responsible for any claims made by them. Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm

How does having cancer as a child impact the way you live as an adult? Uber successful chef and restauranteur Josh Niland doesn’t do things in halves (no, literally, have you read his book The Whole Fish?). From opening a restaurant at 26, to becoming the first Australian to win the James Beard Book of the Year award when he was just 30, Josh’s creativity came from a place of desperation to explore every opportunity presented to him.

Search and rescue efforts continue in Ukraine after Russian drone attacks kill at least 16 in a shopping centre in the city of Kryvyi Rih. President Volodymyr Zelensky called the attack on his home town cynical and despicable. Also:Donald Trump wins the latest round in an ongoing legal battle over his plans to rebuild the entire east wing of the White House; Uber is fined a huge sum under Europe's technology privacy rules; FIFA punishes Argentina for breaches of discipline; the United Nations says the ebola outbreak in the Democratic Republic of Congo is now growing exponentially; handwritten notes by the Indian independence leader, Mahatma Gandhi, sell for US$1.7m at auction; a python becomes the first snake to be given a cancer treatment normally used on humans, and Wile E Coyote takes on the American corporate giant, the Acme Corporation, in a blockbuster film.The Global News Podcast brings you the breaking news you need to hear, as it happens. Listen for the latest headlines and current affairs from around the world. Politics, economics, climate, business, technology, health – we cover it all with expert analysis and insight. Get the news that matters, delivered twice a day on weekdays and daily at weekends, plus special bonus episodes reacting to urgent breaking stories. Follow or subscribe now and never miss a moment. Get in touch: globalpodcast@bbc.co.uk Photo: (write brief description of what photo shows) Aftermath of a Russian drone attack in Kryvyi Rih. Credit: TELEGRAM/Reuters

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

CC 493: Disclaimer: This episode was recorded in early May 2026, prior to the events that have since unfolded.Kail and Lindsie are still in New York, and somehow a near-miss with an Uber, a hoverboard, and one very angry pedestrian turns into a full debate over who is actually the more dramatic one. Kail recaps her wildly overconfident WWE training experience, including the moment she realized her dreams of becoming the next Bella Twin might require being able to do a forward roll.Then, the two put their friendship to the test with a game of how well they really know each other, calling out irrational pet peeves, bad habits, secret sensitivities, relationship types, and the things they pretend not to care about. Lindsie also has some questions about where 95 school glue sticks disappeared to, Kail admits she is far more bothered than people think, and they agree that quiet beauty appointments should absolutely be normalized.Plus, a Foul Play involving a late-night pizza delivery, period blood, and an unsuspecting delivery driver sends the conversation completely off the rails.Get your Fatherless Behavior Tour Tickets hereFor full videos head towww.youtube.com/@KailandtheChaosTo send in your Foul Plays email us at info@coffeeconvos.comThank you for checking out our sponsors!Skims: Shop our favorite bras and underwear at SKIMS.com. After you place your order, be sure to let them know we sent you! Select "podcast" in the survey and be sure to select our show in the dropdown menu that follows.RoBody: Find out if you're covered for free at ro.com/coffeeconvosFigs: Go to wearfigs.com and use code FIGSRX for 15% off your first order - code FIGSRX.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In today's episode, Kyle Grieve and Shawn O'Malley analyze Domino's Pizza, the world's biggest pizza franchisor built on a royalty-driven, asset-light business model. They walk through Domino's shift toward franchising and away from Company-owned stores, and what that means for the company's future revenue mix and cash generation. Along the way, they dig into whether Domino's royalty engine can keep running at the pace investors have come to expect. IN THIS EPISODE YOU'LL LEARN: (00:00:00) Intro (00:02:55) Reviewing the Domino's royalty engine thesis (00:15:26) Why Domino's has moved away from Company-owned stores (00:29:19) The role of royalties versus supply chain revenue in Domino's earnings (00:36:21) How Domino's utilizes a fortressing strategy and its effect on store growth (00:44:18) The competitive landscape in delivery, carryout, and aggregator platforms (00:46:29) How the franchise model keeps Domino's asset-light and cash-generative (00:55:47) Domino's capital allocation and approach to share buybacks (01:03:44) International franchising and Domino's global store growth (01:05:25) Risks facing Domino's from labor costs, competition, and changing consumer habits (01:13:19) Valuation discussion of Domino's (01:14:31) Intrinsic value of Domino's (01:15:52) Whether Kyle & Shawn will add Domino's to the Intrinsic Value Portfolio Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. BOOKS AND RESOURCES Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Track ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Portfolio⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about how to join us in NYC for our ⁠⁠⁠⁠⁠⁠⁠Intrinsic Value Conference⁠⁠⁠⁠⁠⁠⁠. Portfolio Review ⁠⁠⁠⁠⁠⁠Submit Tool⁠⁠⁠⁠⁠⁠. Check out our previous Intrinsic Value breakdowns: Uber, Grab, Coupang. Buy yourself a copy of ⁠The Domino's Story⁠. Follow Kyle on ⁠⁠⁠⁠X⁠⁠⁠⁠ and ⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠. Related ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠books⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ mentioned in the podcast. Ad-free episodes on our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Get smarter about valuing businesses through ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Investor's Podcast Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our ⁠⁠⁠⁠sponsors⁠⁠⁠⁠: ⁠Plaud⁠ ⁠Plus500⁠ ⁠Netsuite⁠ ⁠Scribe⁠ References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor's Podcast Network is not responsible for any claims made by them. Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm

The big box retailers are reporting earnings, and there are plenty of headwinds to discuss. But among the common themes this earnings season, these companies are leaning into AI (and AI assistants with cheesy names) to bring their businesses into the future. Tyler, Matt, and Jon also discuss drone deliveries before finishing the episode with a listener question about the next generation of real-estate brokerages. Have a question? Email us; podcasts@fool.com Tyler Crowe, Matt Frankel, and Jon Quast discuss:- The tariff refund for big box retailers- What retailers are doing with AI assistants and agentic AI- Uber's partnership with Zipline- Amazon's big “splash” with drone delivery- Why AGNT stock hasn't been a winning investment…yet Companies discussed: HD, LOW, TJX, WMT, TGT, UBER, AMZN, BRK.A, BRK.B, AGNT, REAX Host: Tyler CroweGuests: Matt Frankel, Jon QuastEngineer: Bart Shannon Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, "TMF") do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. Learn more about your ad choices. Visit megaphone.fm/adchoices

Un tipo quiso dárselas de vivo con un conductor de UBER y no le pagó la carrera, pero le fue bastante peor. El Karma existe así que pórtate bien. Mantente al día con los últimos de 'El Bueno, la Mala y el Feo'. ¡Suscríbete para no perderte ningún episodio!Ayúdanos a crecer dejándonos un review ¡Tu opinión es muy importante para nosotros!¿Conoces a alguien que amaría este episodio? ¡Compárteselo por WhatsApp, por texto, por Facebook, y ayúdanos a correr la voz!Escúchanos en Uforia App, Apple Podcasts, Spotify, y el canal de YouTube de Uforia Podcasts, o donde sea que escuchas tus podcasts.'El Bueno, la Mala y el Feo' es un podcast de Uforia Podcasts, la plataforma de audio de TelevisaUnivision.

Today's guest, Alex Bonifer, has taken a decidedly unconventional route to becoming a standout in some of television's most unique comedies. A summer improv class, forced upon him by his father, set him on a twelve-year journey through legendary comedy institutions including iO West, UCB, and The Groundlings. Alex went from working as a furniture salesman and Uber driver to becoming a series regular, landing memorable roles in Superstore, Kevin Can F**k Himself, and most recently Jury Duty. Along the way, he developed a philosophy of taking the "big swing" while "caring but not giving a shit" about the outcome. Alex gets candid about the grit, rejection, and persistence it took to get there, the physical secrets behind his character work, and how he stayed in character for his entire audition for "Dougie Jr." These are the unforgettable stories that landed Alex Bonifer right here. Credits: Jury Duty Presents: Company Retreat Kevin Can F**k Himself Superstore No Good Deed Guest Links: IMDB: Alex Bonifer, actor THAT ONE AUDITION'S LINKS: For exclusive content surrounding this and all podcast episodes, sign up for our amazing newsletter at AlyshiaOchse.com. And don't forget to snap and post a photo while listening to the show and tag me: @alyshiaochse & @thatoneaudition SLAYTEMBER CLASS: Starting September 24th THE BRIDGE FOR ACTORS: Become a WORKING ACTOR (50% off special) THE PRACTICE TRACK: Membership to Practice Weekly CONSULTING: Get 1-on-1 advice for your acting career from Alyshia Ochse COACHING: Get personalized coaching from Alyshia on your next audition or role INSTAGRAM: @alyshiaochse INSTAGRAM: @thatoneaudition WEBSITE: AlyshiaOchse.com APPLE PODCASTS: Subscribe to That One Audition on Apple Podcasts SPOTIFY: Subscribe to That One Audition on Spotify STITCHER: Subscribe to That One Audition on Stitcher EPISODE CREDITS: HOST/PRODUCER: Alyshia Ochse WRITER: Maddie McCormick WEBSITE & GRAPHICS: Chase Jennings SOCIAL: Alara Cerikcioglu

A few years ago, Leo Mastrolia started a gadget repair shop. Little did he know he'd eventually become known across the internet as The Digital Doctor, and would learn to fix practically every gadget you can think of. On this episode, which is part one of our conversation with Leo, he tells us about his journey as a gadget fixer, the growth of his social channels, and how right-to-repair fights and upgrade cycles are affecting life in his store. Further reading: ChatGPT's Computer History tracks your clicks and keystrokes Amazon is trying to crush class action suits before they get started Uber partners with Zipline on Eats drone deliveries Disney D23 2026: Everything announced for Star Wars, Marvel, and more Digital Doctor Repairs on YouTube Subscribe to The Verge for unlimited access to theverge.com, subscriber-exclusive newsletters, and our ad-free podcast feed. We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Uber signs up Zipline drones and GrubHub contracts with Serve Robotics. PLus NVIDIA makes a less risky backing of OpenAI's Ohio data center and whether adding AI to dogs who can smell cancer is is scientifically sound. Starring Tom Merritt, Robb Dunewood, and Dr. NikiShow notes found here. Hosted on Acast. See acast.com/privacy for more information.

The Associated Press Top 25 drops today, how low will The Alabama Crimson Tide be ranked? Will Alabama Football have their lowest ranking since 2008 preseason? LT's Uber bracket punishment Brown loses his golf club championship The Auburn Tigers scrimmaged during our show Friday, Auburn Football coach Alex Golesh was not pleased with the attention to detail. Is there a unit ahead of schedule? Alabama loses running back AK Dear for extended period of time Ty Simpson impresses in Rams preseason debut Fernando Mendoza shines in Raiders debut PLUS, Tyler's Viewing Menu presented by Michelson Laser Vision! SUBSCRIBE: @NextRoundLive - youtube.com/@nextroundlive @TNRClips - youtube.com/@TNRClips FOLLOW TNR ON SPOTIFY: https://open.spotify.com/show/7zlofzLZht7dYxjNcBNpWN FOLLOW TNR ON APPLE PODCASTS: https://podcasts.apple.com/us/podcast/the-next-round/id1797862560 WEBSITE: https://nextroundlive.com/ MOBILE APP: https://apps.apple.com/us/app/the-next-round/id1580807480 SHOP THE NEXT ROUND STORE: https://nextround.store/ Like TNR on Facebook: / nextroundlive Follow TNR on Twitter: / nextroundlive Follow TNR on Instagram: / nextroundlive Follow everyone from the show on Twitter: Jim Dunaway: / jimdunaway Ryan Brown: / ryanbrownlive Lance Taylor: / thelancetaylor Scott Forester: / scottforestertv Tyler Johns: /TylerJohnsTNR Brooks Carter: /BrooksACarter Sponsor the show: sales@nextroundlive.com Learn more about your ad choices. Visit megaphone.fm/adchoices

Plus: Uber partners with drone startup Zipline to make drone deliveries. And Alibaba sells its Lingxi Games videogame business. Julie Chang hosts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This week, our friends Hay Beacon and Charlotte McDonnell join host Dave for lost Uber items, stories of the weird in the US and UK, and a new crop of clips to add to the International Waters Notable Sound and Film Warehouse Charlotte McDonnell would like to plug What's All This Then and recommends Be Gay Solve Crimes Hay Beacon would like to plug No Contact and recommends Kylie Vincent And finally Dave is on Bluesky! Find us on Instagram! Call us on the International Waters hotline 323-435-3017 Written by Riley Silverman and John-Luke Roberts, recorded at MaxFun HQ in Downtown LA, and produced by Christian Dueñas and Laura Swisher. Join the MaxFun fam: maximumfun.org/join Help support this show and unlock bonus content! Become a member at https://maximumfun.org/joinwaters

What's up, everybody? It's Tom Bilyeu here:If you want my help...STARTING a business: join me here at ZERO TO FOUNDER: https://tombilyeu.com/zero-to-founder?utm_campaign=Podcast%20Offer&utm_source=podca[%E2%80%A6]d%20end%20of%20show&utm_content=podcast%20ad%20end%20of%20showSCALING a business: see if you qualify here.: https://tombilyeu.com/callGet my battle-tested strategies and insights delivered weekly to your inbox: sign up here.:https://tombilyeu.com/**********************************************************************If you're serious about leveling up your life, I urge you to check out my new podcast, Tom Bilyeu's Mindset Playbook —a goldmine of my most impactful episodes on mindset, business, and health. Trust me, your future self will thank you.**********************************************************************FOLLOW TOM:Instagram: https://www.instagram.com/tombilyeu/Tik Tok: https://www.tiktok.com/@tombilyeu?lang=enTwitter: https://twitter.com/tombilyeuYouTube: https://www.youtube.com/@TomBilyeuQuince: Free shipping and 365-day returns at https://quince.com/impactpodWhatnot: Download the Whatnot app today and get free shipping on your first order.Quo: Try for free PLUS get 20% off your first 6 months at https://quo.com/impactIncogni: Take your personal data back with Incogni! Use code IMPACT at the link below and get 60% off an annual plan: https://incogni.com/impact Pique: 20% off at https://piquelife.com/impactShopify: Sign up for your one-dollar-per-month trial period at https://shopify.com/impactSurfshark: Go to https://surfshark.com/TOMB or use code TOMB at checkout to get 4 extra months of Surfshark! ATT Business: Switch to AT&T Business at business.att.comKetone IQ: Visit https://ketone.com/IMPACT for 30% OFF your subscription orderNetsuite: For the first time ever you can try NetSuite Next for free. If your revenues are at least in the seven figures, go to https://NetSuite.ai/Theory.The team reacts to DSA-affiliated activist Tracy Rosenthal, who—per the reporting the host cites—has withheld an estimated $108,000 in rent and openly encourages tenants in their building to do the same, with their landlord reportedly an immigrant now losing the building. The host is careful to say he hasn't independently verified every detail, but uses it to make his central argument: that "just don't pay rent" activism, however it's framed as helping the poor, actually lands hardest on the people it claims to protect. He argues Rosenthal—whose father he says is a multiple-Grammy-winning musician and who grew up wealthy—has a safety net ("they can always go back to mom and dad") that the working families they're advising do not, so when a landlord can no longer maintain or run the building, tenants end up in unmaintained housing, and those who join rent strikes risk landing on tenant-screening "do not rent" lists that follow them, push them toward fewer economic opportunities, and make housing harder to secure. He frames the whole thing as reasoning from resentment—aimed at hurting a perceived "capital class"—rather than from what actually helps poor people, and warns that if you make it impossible to run housing as a business, the only remaining owner is the government, which can only subsidize until the "engine of prosperity" breaks. He argues this path repeatedly ends in stagnation, using Cuba as his example.The team reacts to Mayor Mamdani's new video targeting Amazon's delivery model—where drivers wear Amazon branding and follow Amazon-set routes and hours but are technically employed by subcontractors—and his support for the Delivery Protection Act, which would push responsibility for those workers back onto Amazon. Drew presses the steelman throughout: if the setup is illegal or strips workers of deserved protections (the way the Uber/Lyft contractor debate played out), shouldn't the government step in? The host agrees that if a company is breaking the law you enforce the law—but argues this specific move is a case of a candidate promising affordability while doing something that will make deliveries more expensive, calling the two goals "matter and antimatter." His core argument is about preserving choice: independent-contractor status is a real tradeoff that many workers actively want (flexibility, control), and banning it doesn't magically convert those into higher-paying jobs—companies instead do less, automate, or leave, and a subclass of lower-skilled workers loses the on-ramp entirely. He draws the Uber-beat-the-taxis parallel, argues minimum-wage hikes disproportionately wipe out teen and entry-level jobs, and points to Austin and Houston loosening building regulations to bring housing costs down as evidence that over-regulation is often the choke point. He frames the government's proper role with a sports metaphor—referees who set and enforce fair rules are essential, but when the refs outnumber the players and start tripping the best performer to even the score, the whole game gets worse. Threaded through is his contested thesis that this is driven by resentment and an "overproduction of elites," and a nuanced exchange with Drew about when market intervention is genuinely warranted versus when it becomes a barnacle.The team digs into three new Google research papers that, in the host's read, show large language models already outperforming human doctors—even in the messy, sometimes adversarial reality of real patient interactions. The host, careful to stay honest about both AI's risks and its capabilities, walks through the numbers: in a randomized clinical exam of 100 scenarios across 300 live video consultations with patient actors, graded by 20 board-certified physicians, the AI scored 83% to the doctors' 68%, with correct first-guess diagnoses at 91% vs 77%, clinical reasoning 90% vs 76%, guiding a physical exam over video 72 vs 39, and—strikingly—empathy rated higher for the AI (82 vs 71), echoing earlier findings in AI therapy studies. The AI also finished exams slightly faster. A second paper tracking patients across three visits with shifting symptoms found the AI's care rated appropriate 95% rising to 98% by the third visit, while human doctors actually got worse over follow-ups (72% to 81%)—which the host speculates is because doctors "back off too soon" once they think they've found the answer, whereas the AI keeps refining. A third paper used reinforcement learning across 57,000 simulated encounters—including thousands where patients actively try to deceive the doctor, hiding medications or insisting chest pain is just heartburn—with up to 60 conversational turns, producing a model that beats the already-superior AI 87.6% of the time head-to-head. The host argues the gap will likely keep growing, predicts that within a few years failing to at least consult AI could be treated as malpractice, and forecasts a near-term future of human-assisted-by-AI care rather than fully autonomous machines—because AI is probabilistic, not deterministic, so a human stays in the loop the way pilots remain in an autopilot cockpit. His bottom line: this is one of those cases where the technology may genuinely become what we hoped for, even if the economic transition to get there is turbulent.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.