Podcasts about altman

Send us Fan Mail In this episode, return guest Eric Schumacher joins the podcast. He brings along the movie East Side Sushi, while Marty and Clif give Eric the movie A Prairie Home Companion to watch.First up is Robert Altman's A Prairie Home Companion, the 2006 ensemble comedy inspired by Garrison Keillor's long-running radio program. With a cast that includes Meryl Streep, Lily Tomlin, Woody Harrelson, John C. Reilly, Lindsay Lohan, Kevin Kline and more, the film follows the backstage antics surrounding what may be the show's final broadcast. Clif, Marty and Eric talk about Altman's loose, overlapping style, the movie's bittersweet tone, it's enormous ensemble and the strange feeling of watching a film about artists facing the end of something they love. They also discuss why the movie might hit a little differently for people who make their own art.Next is East Side Sushi, the 2014 drama about Juana, a single mother who discovers a passion for sushi and decides to pursue her dream of becoming a sushi chef. The gang gets into the movie's portrayal of hard work, family, tradition and the discipline required to master a craft. Eric also brings some firsthand perspective on the importance of actors actually learning the skills their characters are supposed to possess.Along the way: Kuala Lumpur, the wonders of Minneapolis, martial arts movie training montages and the possibility of creating a Pondo movie where Eric is legally required to eat for twelve straight hours.#TalkingPondo #FilmPodcast #APrairieHomeCompanion #EastSideSushi #EricSchumacher #RobertAltman #FoodMovies #MovieReviewSupport the showFind our films here:The Love Song of William H ShawRevenge of ZoeWriting Fren-ZeeMaking Pondo on FacebookBlueskyInstagramMaking Pondo on Letterboxd:Season OneSeason TwoSeason ThreeSeason FourTheme Song "The Rain" by Russ PacePhotos by Geoffrey Notkin

Announcing a new podcast from the people who brought you Jason and the Movienauts! Search for "It Don't Worry Us" in your podcast feeds!What happens when two movie obsessives decide that watching Nashville once—or even a dozen times—isn't enough?Jason Sacks and Dan Story begin their minute-by-minute journey through Robert Altman's sprawling 1975 masterpiece. Along the way, they dig into the battered Paramount logo, Altman's war with the studios, the bizarre opening credits, K-Tel records, 24 unforgettable characters, and the visual madness that launches the movie.Nashville contains multitudes—and this is just the first minute and a half. Come along for the rabbit hole.TIMESTAMPS00:00 — Welcome to The Nashville Minute03:00 — How We Discovered Nashville06:25 — Why Nashville Still Feels So Current09:40 — That Battered Paramount Logo & Altman's War With the Studios15:56 — The Mystery Hidden in the Opening Credits22:00 — K-Tel, Chaos & The Insane Opening

After tackling Altman's first three feature directorial efforts in the season 04 premiere, Bjorn and Reece settle into the single picture per episode format that will continue throughout the rest of the Altman series. In this week's episode the duo take a deep dive into Altman's 1969 psychological drama, That Cold Day In The Park, starring Sandy Dennis as a woman who offers some respite from the rain to a young man who seems to be just as lonely as she is. Will they find comfort in each other's company or will the secrets they are keeping bring ruin to one or both of them? Come on out of the rain and warm up with a lively discussion on Altman's final picture of the 60s.

COUNTDOWN WITH KEITH OLBERMANN - SEASON 5 EPISODE 6 A Block (SPECIAL COMMENT): We're actually under-reacting to the threat from A.I. because it is not just 'A.I. Will Kill Us All' - it is 'TRUMP'S A.I. Will Kill Us All.' Trump is up to his each of his 27 chins in his own investments in A-I; for all we know he's also thinking A-I might fix the midterms for him; humanity is at risk because Trump and his idiot sons and his cabinet members and his foreign fixers and everybody in the Epstein Files and the whole kaleidoscope of corruption is thinking about one thing only - not how to protect mankind but how to protect their money. Humanity is at risk, because Artificial intelligence is meeting Trump's total LACK of intelligence Also: when Trump's SkyNet gains sentience it will get U.S. weapons in space to use against us. And remember the Epstein Files? Trump Junior's new wife mainlines back not just to Putin's flunkies but to one of Epstein's bankers: her own father. Speaking of Putin's flunkies: Putin is why the National Hockey League must ban its all time leading goal scorer. And the 60 Minutes ratings are in – they’re down 21% so it’ll now be called 47 MINUTES. Well done CBS – The Conservative Brainwashing System. B-Block (40:00) THE RETURN OF THE WORST PERSONS IN THE WORLD: The latest in the endless parade of football quarterbacks and TV pitchmen, Arch Manning, recommends and busts out laughing at an AI video of his college coach knocking a woman reporter to the ground. Another Michigan Republican candidate thinks God is sending him messages for Dan Bongino. Lee Greenwood's latest business deal with Trump: his wife is gonna be an Ambassaduh. And Kash Patel leading the FBI: the Federal Bestiality Investigators? C-Block (58:00) THINGS I PROMISED NOT TO TELL: The terrible truth about TV newscasters and the real draw of the profession for many of them - the free hair care. The whole thing clarified itself to me when I discovered that the gifted craftsman who cut my hair when I was in radio in 1980 is still working and is still a gifted craftsman.See omnystudio.com/listener for privacy information.

“If you're worried that a ChatGPT type tool can replace you, you need to [ask]: Why am I communicating? What am I trying to say? Am I being authentic?”Artificial intelligence can now do a lot of things. But if you're worried about it taking your place as a communicator, Russ Altman says you need to question why you're communicating in the first place. A Stanford professor of bioengineering and host of Stanford Engineering's podcast, The Future of Everything, Altman sees advancing technology not as a threat to human creativity and connection, but as a tool for raising our standards for communication. In this Rethinks episode of Think Fast, Talk Smart, Altman and Matt Abrahams discuss how effective communication can help us articulate ideas, strengthen connections, and navigate toward the future we want.Takeaways:AI works best as an amplifier, not a replacement. Using AI to test, refine, and adapt your ideas can make communication clearer while keeping your purpose, judgment, and authentic message at the center.Make complexity easier to understand, not less meaningful. Stories, analogies, and audience-aware language can translate difficult ideas without dumbing them down.Activity:Treat questions as gifts. The next time someone questions your idea, resist the urge to defend it. Instead, thank them and ask one follow-up question to better understand their perspective before responding.Episode Reference Links:Russ AltmanRuss's Podcast: The Future of EverythingEp.157 Communicating the Future: Defining Where We Want AI to Take Us Connect:Premium Signup >>>> Think Fast Talk Smart PremiumEmail Questions & Feedback >>> hello@fastersmarter.ioEpisode Transcripts >>> Think Fast Talk Smart WebsiteNewsletter Signup + English Language Learning >>> FasterSmarter.ioThink Fast Talk Smart >>> LinkedIn, Instagram, YouTubeMatt Abrahams >>> LinkedIn Chapters:(00:00) - Introduction (01:41) - AI and Communication (03:46) - AI as a Feedback Tool (04:48) - Structuring a Strong Proposal (06:48) - Simplifying Complex Ideas (09:12) - Preparing for Tough Topics (10:53) - Asking Better Questions (13:43) - Building Stronger Teams (16:21) - Productive Conflict (17:53) - Finding People's Passion (18:43) - The Final Three Questions (22:06) - Conclusion ********Thank you to our sponsors.  These partnerships support the ongoing production of the podcast, allowing us to bring it to you at no cost.With Hiring Pro, you can hire with confidence, knowing you're getting the best talent for your needs. Get started by posting your job for free at linkedin.com/fastJoin our Think Fast Talk Smart Learning Community and become the communicator you want to be. 

Mentor Sessions Ep 096: Zack Shapiro explains AI existential risk, AI regulation, the Clarity Act, Samurai Wallet, and open source AI policy for Bitcoin.The Clarity Act just failed on the Senate floor, and the fight over whether writing Bitcoin code can be treated as a crime is now heading to 70-year-old federal judges. Zack Shapiro breaks down what that means for self-custody, open source software, and the Samurai Wallet developers still behind bars.In this conversation you'll learn why Zack separates AI existential risk (recursive self-improvement, the orthogonality thesis, instrumental convergence) from the ordinary risks of AI — job displacement, regulatory capture by frontier labs, and cyber attacks like the Cold Card hack. You'll see why he thinks open source AI is not the existential threat, why the Blockchain Regulatory Certainty Act (BRCA) matters more than most Bitcoiners realize, and how the 'two clocks' of AI capability vs enterprise adoption will define the next decade. You'll also get the legal reality behind white hat recovery of hacked Bitcoin, why the liquid hacker is in a bad spot, and what happened when the Clarity Act died on the vine.⏱️ Timestamps:0:00 - Intro0:51 - Amodei, Musk and Altman urge AI slowdown1:38 - Ordinary risks versus existential AI threats2:26 - Politics warping the AI safety debate3:20 - Bostrom, AGI and intelligence explosion risks5:01 - Orthogonality thesis and instrumental convergence5:59 - Hugging Face attack shows AI covering tracks6:58 - No easy policy fixes for AI risks8:08 - Can't stop AI progress or open source China9:32 - Separating x-risk concerns from anti-AI populism12:22 - Regulatory capture and Anthropic IPO timing14:21 - Keeping open source AI legal and available16:39 - Trezor self-custody sponsor read17:43 - One shot parallel between Bitcoin and AI19:37 - Where real AI productivity gains appear20:47 - Two Clocks: capability versus enterprise adoption24:22 - Is AI coming for your job?25:28 - Electrification analogy and 30-year lag28:34 - Jevons Paradox and future of work30:42 - White hat recovery of Cold Card hacked funds34:02 - Legal precedent for white hat bounties36:56 - Why the Liquid hacker faces trouble38:55 - What's actually in the Clarity Act43:46 - Money transmitter law and the Samurai case45:20 - Tornado Cash, FinCEN and frying pan argument47:31 - Why this could ban using Bitcoin49:33 - BRCA criminal protections and court battles53:42 - Clarity Act fails to pass what happened55:24 - Senator Lummis stood on principle56:50 - Where to follow Zack ShapiroGuest: Zack Shapiro — lawyer and AI-native law firm builder covering crypto policy, AI regulation, and Bitcoin legal defense.

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Today on The Gist, guest host Rachel Win introduces Mike Pesca's conversation with Wall Street Journal reporter Keach Hagey about her book The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future, exploring Altman's Silicon Valley rise, the tensions between AI safety and commercial speed, and his brief ouster from the company. The show wraps with a classic 2014 sit-down with the late Gilbert Gottfried, who talks old Hollywood lore, dirty jokes, and hosting Gilbert Gottfried's Amazing Colossal Podcast.Stop online threats before they become real-world attacks. Visit ironwall.com/GIST and request a free Risk Assessment to see exactly how exposed your executives are.Produced by Corey WaraDo you have questions or comments, or just want to say hello? Email us at ⁠⁠⁠⁠thegist@mikepesca.comFor full Pesca content and updates, check out our website at https://www.mikepesca.com/⁠For ad-free content or to become a Pesca Plus subscriber, check out ⁠⁠⁠⁠https://subscribe.mikepesca.com/For Mike's daily takes on Substack, subscribe to The Gist List https://mikepesca.substack.com/Follow us on Social Media:⁠⁠⁠⁠YouTube https://www.youtube.com/channel/UC4_bh0wHgk2YfpKf4rg40_g⁠⁠⁠⁠Instagram https://www.instagram.com/pescagist/X https://x.com/pescamiTikTok https://www.tiktok.com/@pescagistTo advertise on the show, contact ⁠⁠⁠⁠sales@amplitudemediapartners.com Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Amodei ha pedido frenar la IA, Altman y Musk le han apoyado, y las acciones de empresas de chips se han desplomado. Pero la noticia real es lo que han tenido que confesar para poder pedirlo. Y que Anthropic sale a bolsa en octubre. * * * Loop Infinito, podcast de Xataka, de lunes a viernes a las 7:00 (hora peninsular española). Presentado por Javier Lacort. Editado por Alberto de la Torre. * * * Contacto: lacort@xataka.com, @lacort en X.

OpenAI se uitvoerende hoof, Sam Altman, sê die wêreld moet kunsmatige-intelligensiemaatskappye vertrou om die regte ding te doen omdat hulle die druk voel om verantwoordelik op te tree. Daar was die afgelope tyd toenemend kommer oor die tegnologie se moontlike gevare, en Anthropic se hoof, Dario Amodei, doen ʼn beroep dat alle KI-ontwikkeling verlangsaam word, terwyl hy regerings aanmoedig om die bedryf te reguleer. Altman het by ʼn konferensie in San Francisco erken mense het goeie rede om die gevare wat kunsmatige intelligensie inhou, te vrees:

Action Society's Juanita du Preez tells BizNews six bodies in Kempton Park finally forced a multidisciplinary task team, years after the Gaby Ndaba case stalled on DNA backlogs, while the station tops the country for kidnappings with 76 in three months. DA eThekwini mayoral candidate Haniff Hoosen says the ANC is finished in the city and rates his chances against MK at 50/50. Leon Kluge recounts how Cape storms nearly sank South Africa's Chelsea Flower Show display. Plus Amodei, Altman and Musk's AI slowdown call, and a Wall Street selloff as the Senate blocks Trump's crypto bill.

AI-topmannen slaan alarm: wat zit hierachter? | Beurs updateWAT ER IN DEZE BEURSUPDATE BESPROKEN WORDTAI-aandelen, oplopende rente en twijfels over Anthropic: wat betekenen ze voor beleggers? In deze Beurs update van 16 september 2026 bespreken Nico Inberg en Albert Jellema de waarschuwingen van Sam Altman, Dario Amodei en Elon Musk. Zijn hun zorgen over kunstmatige intelligentie de enige reden om op de rem te trappen, of spelen commerciële belangen ook mee?Daarnaast bespreken ze een mogelijke renteverhoging door de Federal Reserve, de invloed van hogere rentes op aandelen en de bijzondere positie van Adyen. Ook komen Donald Trump en Nvidia-topman Jensen Huang aan bod, evenals de kosten die buiten de besproken winstmarges van Anthropic blijven. Waar liggen kansen buiten de AI-hype? Nico en Albert bekijken ForFarmers, blikken terug op de aandelentips uit het webinar en bespreken Prinsjesdag en het belang van spreiding.OVER DE BEURSUPDATEDe Beursupdate is de wekelijkse rubriek van De Aandeelhouder waarin Nico Inberg met een beurskenner het belangrijkste beursnieuws, opvallende koersbewegingen en beleggingstips bespreekt. Van AEX-aandelen en internationale beurzen tot rente, dividendaandelen, ETF's en macro-economische ontwikkelingen: iedere Beursupdate geeft je in korte tijd inzicht in wat er speelt op de beurs.TERUGBLIK WEBINAR: NEDERLANDSE AANDELEN OF AMERIKAANSE TECH?Nico Inberg en Jordy Beuving deelden maandag ieder drie aandelentips. In deze Beursupdate lichten we ForFarmers uit: wat kunnen de uitbreiding in Polen en activiteiten met hogere marges betekenen voor de waardering van het aandeel?DE AANDEELHOUDER PREMIUMWord lid van De Aandeelhouder Premium:https://www.deaandeelhouder.nl/premiumMeer weten over The Dealing Room?https://www.thedealingroom.comVragen? Mail naar Johannes@deaandeelhouder.nl

Ma qualcuno ancora crede alle panzane dei CEO dell'intelligenza artifciale?

President Trump dismissed AI-safety warnings as a "hoax" on a live call with Jensen Huang, a Google DeepMind researcher resigned warning AI "could kill us all," cybersecurity stocks led the S&P 500, and Apple rolled out iOS 27. Jensen Huang took a surprise call from Trump onstage at the All-In Summit on Monday in Los Angeles, where the two agreed that AI safety worries are overblown (The New York Times) Bloomberg quotes Trump calling AI-safety fears a "SICK conspiracy" benefiting only China, and dismissing warnings that AI could destroy humanity as a "HOAX" that his administration won't let stop development (Bloomberg) MS NOW reports Trump met privately with Sam Altman backstage at the Republican midterm convention days after ex-researcher Jacob Coxon's public warning, as Altman posted that no competitive pressure justifies letting AI capabilities outrun alignment (MS NOW) Google DeepMind AI Safety and Alignment researcher Bilal Chughtai publicly resigns, saying "I earnestly believe that AI has the potential to kill us all" (Bloomberg) Cybersecurity stocks were the top performers in the S&P 500 on Monday amid escalating AI fears; CrowdStrike rose 14%, Palo Alto Networks 13%, and Fortinet 9% (Morningstar) Microsoft rolls out emergency fix for critical issues caused by its September Patch Tuesday update, which addressed ~1,000 vulnerabilities but introduced bugs (The Verge) The Verge reports iOS 27 rolls out today with Siri AI as its headline feature, live in English-only beta with more languages coming in October, alongside a new Liquid Glass opacity slider and refreshed watchOS 27 Workout Buddy tools (The Verge) Apple says iCloud+ now includes Apple TV and Arcade for no extra fee in 100+ countries, and Apple Music Select, offering ad-free radio stations, in some markets (9to5Mac) 9to5Mac details iOS 27's new parental control suite, including a simplified Child Account setup, an Ask to Browse feature for requesting blocked sites, and a redesigned Screen Time with faster cross-device syncing and weekly usage summaries (9to5Mac) OpenAI researcher: top models are becoming so situationally aware humans "are losing the ability to evaluate them" while humans rely more on AI to lead research (X) Subscribe to the ad-free feed.

There's a satirical ad going viral about A.I. turning you into a battery. It features A.I. versions of Bezos, Altman, and Musk talking about a new job that has you working out to power A.I. computers. A Georgia man was caught trespassing onto private property, using the hot tub and pool, and then "interacting" with a rubber ducky. Witnesses of the video said he was "infatuated" with it. See omnystudio.com/listener for privacy information.

Hacks durch KI-Agenten, Biowaffen-Fälle im Threat-Report, dazu ein Gerücht über rekursive Selbstverbesserung bei Google: Plötzlich fordern Dario Amodei, Sam Altman und Elon Musk eine Verlangsamung der KI-Entwicklung. Im Weißen Haus stößt das auf taube Ohren, und die BRICS-Staaten geben erst recht Gas.Jakob Steinschaden, Mitgründer von Trending Topics, und Clemens Wasner, Gründer von AI Austria und enliteAI, sortieren in dieser Folge eine Woche, in der sich die KI-Debatte komplett gedreht hat.Die Themen der Folge:Zwei Ausbrüche, ein Muster: Was der Hugging-Face-Hack mit 700 KI-Agenten und der Angriff auf ein österreichisches Entwickler:innen-Wiki über die Testverfahren von OpenAI verratenBiowaffen, Drohnen, Raketen: Was im Threat-Intelligence-Report von Anthropic steht, das fünf gestoppte Fälle biowaffennaher Forschung dokumentiert, und was daraus über die Auswertung von Prompts folgtChain of Thought ade: Warum Modelle besser funktionieren, wenn niemand mehr mitlesen kann, und was das für Audits bedeutet, dazu das Gerücht, Google habe RSI geknacktVerkehrte Welt: Amodei, Altman und Musk plädieren für eine Pause, Donald Trump lehnt jede Regulierung ab und will lieber einen "High IQ President"Regulatory Capture: Die Sorge im Silicon Valley, dass die Frontier Labs ihre eigene Regulierung schreiben und Open Weights dabei unter die Räder kommenBRICS gegen Bremse: Chinas Open-Weight-Offensive für die BRICS-Staaten, Mistrals Spagat als Neocloud und warum Europa dasselbe Eigeninteresse hatIPO-Politik: OpenAI verschiebt den Börsengang auf 2027, Anthropic peilt zwei Billionen Dollar an, und die Frage, was an Liability-Risiken im Börsenprospekt stehen mussDie Flaschenhälse: Stripe kauft OpenRouter, Nvidia holt sich Hugging Face, und damit gehören beide Zugangspunkte zu Open Weights US-Konzernen

Dario Amodei called for pacing frontier AI development, endorsed by Sam Altman and Elon Musk, Netflix, Amazon, and YouTube launched a streaming lobbying alliance, Anthropic told investors it's profitable again, and experts flagged legal risk in Apple Watch's always-listening features. Dario Amodei proposes steps for pacing the frontier: embedded evaluators, coordination among democracies, and global coordination with authoritarian governments (Dario Amodei) The Journal reports Musk, Altman, and Amodei rare display of unity on slowing AI development, with Altman weighing a delay to OpenAI's IPO and investor Brad Gerstner calling it the right balance between speed and safety (The Wall Street Journal) The Times notes Hugging Face's Clément DeLangue argues AI safety can't be solved behind closed lab doors, just as Nvidia agreed to buy his company for $12.9B, while Nvidia's Jensen Huang accuses Anthropic and OpenAI of stoking fear to entrench their market lead (The New York Times) Netflix, Amazon, and YouTube launch the Streaming Access and Choice Alliance, led by trade group TechNet, to advocate for "technology-neutral policies" (Axios) The Hollywood Reporter says streamers have lacked DC representation compared to Big Tech and Hollywood, with sports emerging as SACA's top fight as broadcasters push to keep exclusive NFL games free over the air (The Hollywood Reporter) Sources: Anthropic told investors it will be profitable for a second straight quarter, with 80%+ gross margins before partner revenue sharing and training costs (Financial Times) Some experts say Siri Recap and Live Rewind, always-listening AI features in new Apple Watches, could test eavesdropping laws despite privacy protections (Bloomberg) Sources: Jeff Dean is raising funds again for Discovery Loop, seeking a valuation of ~$50B; Discovery Loop was raising $1B at a ~$10B valuation a few weeks ago (Business Insider) Subscribe to the ad-free feed.

Carl Quintanilla, David Faber and Sara Eisen covered lots of AI news moving the markets: Chip stocks tumbled after AI industry leaders called for an AI slowdown. Anthropic CEO Dario Amodei set the tone with his essay on the topic. OpenAI CEO Sam Altman and SpaceX and Tesla CEO Elon Musk were among those who agreed with what Amodei wrote. Altman told Fortune he is ruling out an OpenAI IPO for 2026. The anchors also discussed crude oil, gasoline and diesel prices surging ahead of this week's Fed meeting and decision on interest rates. Also in focus: President Trump's take on AI fears, former President Obama reportedly told Democrats to focus on AI oversight, Microsoft's "Humanist AI Code of Conduct," software stocks rally, retail stocks downgraded, 18 years since Lehman Brothers' bankruptcy filing. Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

En este episodio en vivo de Dimes y Billetes, repasamos las tres noticias más importantes de la semana: el precio del diésel en Estados Unidos llegó a su nivel más alto en la historia (más de $6 dólares por galón) por el conflicto entre Estados Unidos e Irán en el Estrecho de Ormuz, y lo que esto significa para la inflación y la próxima decisión de tasas de la Reserva Federal; los resultados de la prueba PISA 2025, donde México cayó al lugar 64 de 91 países y 7 de cada 10 estudiantes no lograron resolver un problema básico de matemáticas; y el paquete económico 2027 de México, con sus proyecciones de crecimiento, un nuevo límite a las deducciones fiscales para empresas que facturan más de 50 millones de pesos al año, y en qué se planea gastar el presupuesto (trenes, programas sociales y un fuerte recorte al sector energético). Prueba café el Capitalhttps://cafeelcapital.com/s/08a4840:00 – Bienvenida y las tres noticias de la semana 1:23 – Adelanto: el debate sobre regular la inteligencia artificial (Altman, Amodei, Musk y Trump) 9:16 – Noticia 1: el diésel llega a su precio más alto en la historia de EE. UU. 11:14 – Por qué la guerra entre Estados Unidos e Irán disparó el precio del diésel 13:58 – Por qué el diésel es clave para toda la economía 14:57 – Trump libera reservas estratégicas de petróleo 15:34 – Cómo esto empuja la inflación y la próxima decisión de la Fed 19:18 – La cadena completa: de un shock petrolero a una economía más cara 22:18 – Cómo los transportistas están cobrando el diésel por separado 29:00 – Noticia 2: México reprueba la prueba PISA 2025 31:32 – Los resultados: México cae al lugar 64 de 91 32:14 – 4 de cada 10 estudiantes no alcanzan el nivel básico en ninguna materia 34:22 – La gráfica que compara diez años de resultados por país 39:53 – ¿Culpa de la pandemia, de la inteligencia artificial o de algo más? 48:30 – La reflexión de Moris sobre el sistema educativo mexicano 50:47 – Noticia 3: el paquete económico 2027 de México 52:38 – Las perspectivas económicas: crecimiento, inflación y tipo de cambio 57:02 – Cuánto planea ingresar y gastar el gobierno en 2027 1:03:41 – Cómo el gobierno busca recaudar más sin subir impuestos 1:04:19 – El límite a las deducciones para empresas de 50 millones de pesos o más 1:10:49 – En qué se va a gastar: trenes, programas sociales y el recorte a energía

Trois rivaux qui ne peuvent pas s'encadrer veulent soudainement appuyer ensemble sur le frein. Dario Amodei, Sam Altman et Elon Musk appellent à ralentir la course à l'IA. Ont-ils découvert quelque chose d'inquiétant ? Ou cherchent-ils surtout à protéger un modèle économique qui commence à craquer ?Derrière le débat sur la sécurité, une autre bataille se joue : modèles propriétaires contre open source, États-Unis contre Chine, régulation contre concurrence. Pendant que les géants américains dépensent des fortunes pour maintenir leur avance, les modèles chinois deviennent toujours moins chers et l'open source continue d'accélérer.Et si le véritable danger n'était pas seulement une IA hors de contrôle, mais aussi une poignée d'acteurs utilisant la peur pour verrouiller le marché ?===================⏱️ DANS CET ÉPISODE :===================00:00 — Sommaire04:27 — [Sponsor] : Google Cloud, déployez des agents IA à grande échelle !06:05 — Altman, Amodei, Musk réunis : alerte sincère ou complot ?07:51 — Le modèle économique de l'IA craque avant l'IPO12:16 — Le marketing de la peur comme outil de survie financière14:39 — Google aurait déclenché l'autoamélioration de l'IA en secret18:40 — L'incident Hugging Face : des agents hors de contrôle25:56 — La régulation : arme secrète pour éliminer les concurrents30:23 — L'Europe à son moment Mesmer : ne pas rater l'IA32:51 — Peter Thiel : la peur, outil de conquête du pouvoir38:02 — Votre abonnement IA est subventionné : combien de temps encore ?47:59 — Trump tranche : les États-Unis ne perdront pas l'IA !==================

Niet alleen Amerikaanse AI-bazen Dario Amodei, Sam Altman en Elon Musk vinden dat AI een gevaar voor de mensheid begint te vormen, ook de baas van de Chinese inlichtingendiensten vindt dat. In een betoog schrijft Chen Yixin dat AI vooral gevaarlijk is voor de politieke, institutionele en ideologische veiligheid van China in handen van de 'tegenstanders' van het land. Verder hoor je over een datalek bij Revolut dat is veroorzaakt door Revolut. Niels Kooloos vertelt erover in deze Tech Update. In tegenstelling tot Amodei, Altman en Musk, pleit Yixin niet voor een vertraging van het ontwikkelingstempo van AI. Hij stelt vooral dat er strengere regels moeten komen, het liefst op internationaal niveau. De discussie over het ontwikkelingstempo van AI werd vooral afgelopen week aangewakkerd toen een ontwikkelaar ontslag nam bij Anthropic en online de noodklok luidde. Kort daarna postte Anthropic-topman Amodei een essay waarin hij pleit om de ontwikkelingen te vertragen. Altman en Musk sloten zich daarbij aan. Revolut deelt gegevens van klanten met cybercriminelenOnlinebank Revolut heeft klantgegevens met cybercriminelen gedeeld die zich voordeden als een overheidsorgaan via email. Volgens de onlinebank zouden de criminelen een domein van een overheidsorgaan gekaapt hebben. Er zijn onder andere geboortedata, adressen, telefoonnummers en kopieën van paspoorten buitgemaakt. Hoeveel klanten er precies getroffen zijn, wil Revolut niet zeggen. Ook is niet duidelijk in welke landen er slachtoffers zijn gevallen, gezien Revolut actief is in meer dan veertig landen. Baas Chinese inlichtingendiensten waarschuwt voor AI Revolut meldt datalek Over de maker:Niels Kooloos is dagelijks op BNR Nieuwsradio te horen over het laatste technieuws in de Tech Update. Hij interesseert zich vooral in cybercriminaliteit, privacy, social media en (computer)hardware. Hier en daar kan je Niels ook in All in the Game horen, waar hij graag vertelt over zijn favoriete games. See omnystudio.com/listener for privacy information.

Dario Amodei chiede di rallentare la corsa verso modelli di AI sempre più potenti. E, sorprendentemente, questa volta a dargli ragione sono anche Sam Altman ed Elon Musk, due dei suoi principali rivali. In questa puntata, insieme a Enrico Frascari, AI Evangelist e autore di “Teste di Turing”, analizziamo cosa c'è dietro questa insolita convergenza tra i protagonisti della corsa all'Intelligenza Artificiale: quali rischi vedono all'orizzonte, perché il tema emerge proprio adesso e quanto c'è di reale preoccupazione, strategia industriale e comunicazione nelle loro dichiarazioni. Una conversazione per capire se stiamo davvero entrando in una fase in cui persino chi sta accelerando più di tutti comincia a chiedersi se sia arrivato il momento di frenare.Pasquale Viscanti e Giacinto Fiore ti guideranno alla scoperta di quello che sta accadendo grazie o a causa dell'Intelligenza Artificiale, spiegandola semplice.Puoi iscriverti anche alla newsletter su: https://www.iaspiegatasemplice.it

8–12 settembre 2026: un ricercatore lascia Anthropic e scrive su X che la probabilità di “estinzione entro 10 anni” supera il 10%. In poche ore il tema esplode e, quasi in sincrono, Dario Amodei (Anthropic), Sam Altman (OpenAI) ed Elon Musk convergono su un messaggio: “dobbiamo rallentare” l'AI di frontiera. Bernie Sanders rilancia: non basta alzare il piede, bisogna frenare davvero, fino a una pausa e a un trattato USA-Cina.Qui la domanda non è solo se l'allarme sia fondato, ma perché diventi improvvisamente una linea comune tra competitor. La chiave è distinguere tra precauzione e paura: la paura immobilizza e sposta il dibattito su “quanto rallentare”, dando per scontato che la corsa sia inevitabile. In parallelo, l'allarme può rafforzare il fossato competitivo (chip, pesi, anti-distillazione), delegittimare i modelli open-weight e spingere verso regole “su misura” che consolidano i grandi laboratori.Il criterio pratico per leggere il teatro: guardare i costi reali che ciascun attore accetta. Valutatori indipendenti con accesso interno, rinvii di quotazione, limiti verificabili e vincoli unilaterali sono segnali

JOIN PATREON FOR EARLY UNCENSORED EPISODE RELEASES: https://www.patreon.com/JulianDorey CLIPPERS DISCORD: https://discord.gg/8QmWEKJ3BT NEWSLETTER: https://juliandoreypodcast.beehiiv.com/get-julians-top-10-books FOLLOW JULIAN DOREY IG: https://www.instagram.com/julianddorey/ X: https://x.com/juliandorey FOLLOW JOEY DEEF IG: https://www.instagram.com/joeydeef/ X: https://x.com/TokeMalone JULIAN YT CHANNELS - SUBSCRIBE to Julian Dorey Clips YT: https://www.youtube.com/@juliandoreyclips - SUBSCRIBE to Julian Dorey Daily YT: https://www.youtube.com/@JulianDoreyDaily - SUBSCRIBE to Best of JDP: https://www.youtube.com/@bestofJDP ****TIMESTAMPS**** 0:00 - They are FORCING us to talk about it 2:02 - 25 years since 9/11 3:56 - The 9/11 Coverup is absurd 6:03 - One full generation has now passed since 9/11 7:50 - Julian remembers where he was on September 11th, 2001 (STORY) 11:24 - Julian tells stories of the heroes of 9/11 14:50 - The NY Fire Company that drove in and never made it out 16:25 - The Man Who Knew 9/11 was coming 19:37 - The day after September 11th said a lot 20:53 - US government, the Saudis, the Israelis and justice 22:04 - Epstein involved in 9/11 court proceedings & Missing Files 24:55 - Pete Davidson, ID'ing victims 26:32 - “The documentary of Julian's life” 27:42 - Whaddup Leon Black! 28:43 - Larry Ellison loves Leon Black so much he's covering for him 31:04 - Bari Weiss continues to be amazing at her job 32:30 - Sen. Wyden blows whistle on CBS Leon Black Epstein coverup 34:28 - Trump's Day 1 Press Conference w/ Ellison, Altman & AI Overlords 36:12 - Darnell Thomas gives his thoughts on Flock & the lone juror 37:38 - Bari Weiss is the best at dinner parties 38:39 - Paolo Zampolli Civil Suit Filed 40:33 - The Batman of NYC 41:42 - The Zampolli lawsuit (continued) 42:23 - The Most Important Room in any well designed home 44:08 - The Zampolli lawsuit (continued again) 45:31 - Snapchat is threatening litigation against us 47:07 - Kash Patel is a total r****d 48:58 - Social Media Queen Kash Patel strikes again! 50:58 - What happened at Kash Patel's desk (according to Joey Deef & Julian Dorey) 54:32 - Kash's convo w/ his head of PR 57:41 - Julian responds to people who reached out to him over Lindsay Clancy 1:01:03 - The Ridiculous Patrick Clancy Theories & holding 2 thoughts at the same time 1:05:25 - Darnell Thomas & Joey Deef go back and forth on the case 1:07:10 - The State's Psychiatric Witness on Lindsay Clancy's Psychosis Claim 1:08:00 - Kevin Reddington defends Patrick Clancy 1:11:58 - Prosecution's proof of Patrick Clancy's minute-by-minute timeline at store 1:12:42 - Julian's theory on the American Jury System (including a funny story) 1:16:11 - Why Juror 1 was frustrated with the Holdout Juror (INSANE) 1:18:35 - The Jurors literally were offended the prosecution showed them the crime scene 1:21:51 - Charisma in the Courtroom 1:22:49 - The Holdout Juror may have never said he had reasonable doubt as reported 1:23:34 - The Nurse “Expert” Jurors 1:25:48 - Juror 4 on the pressure campaign in the deliberation room 1:28:10 - Juror 5's EMBARRASSING interview 1:31:03 - Julian on how he feels about his opinion on this case 1:32:24 - The racial identity of the holdout juror revealed (Julian RANT) 1:34:23 - The BLM Boomerang the Lindsay Clancy Case represents 1:36:59 - Julian talked with Vivian Kubrick directly 1:37:40 - Julian will always call balls & strikes CREDITS: - Host, Editor & Producer: Julian Dorey - COO, Producer & Editor: Alessi Allaman - https://www.youtube.com/@UCyLKzv5fKxGmVQg3cMJJzyQ - In-Studio Producer: Joey Deef Julian Dorey Podcast Episode 475 - Julian Dorey Music by Artlist.io Learn more about your ad choices. Visit podcastchoices.com/adchoices

News Sources: https://lmg.gg/YZuHq Timestamps: 0:00 DLSS 5 mods run wild 1:53 AI researchers sound the alarm 4:50 QUICK BITS INTRO 4:58 Meta Project Phoenix leak 5:26 Meta AI prompts get invasive 5:55 AMD budget CPU lifeline 6:27 Steam age checks and California rules 7:14 Nitter is back 7:33 Fly brain plays Doom 8:05 Credits Learn more about your ad choices. Visit megaphone.fm/adchoices

Story of the Week (DR):Tim Cook Could Still Out-Earn Apple's New CEO John Ternus Under a Special Pay ArrangementApple set John Ternus's fiscal 2027 salary at $3M and his annual equity award at $55M, giving him a calculated fiscal 2027 salary-and-equity package of $58M.Cook will receive a $2M salary and a $45M annual equity award as executive chair, giving him a calculated salary-and-equity package of $47M.Almost all of the value in both packages comes through Apple shares rather than salary. Ternus has 75% of his equity award tied to performance, while Cook has 50% tied to performance.That structure gives Ternus greater exposure to performance, for better or worse. If Apple performs strongly against other S&P 500 companies, Ternus could receive more from his equity award. If the performance-based awards pay little or nothing, Cook could receive more from salary and equity even though he is no longer CEO. Cook also has a retirement provision that Ternus does not have.Volkswagen Supervisory Board Approves Plan To Slash Models & Reduce Workforce By 100,000In June, Volkswagen Group CEO Oliver Blume had a plan to close four factories in Germany and eliminate 100,000 workers, both in Germany and around the world, by 2030. It said the plan would be made public at a company board meeting on July 9. July 9 came and went, and the plan did not get the approval from the board of directors that Blume expected. The vote was 12 against and only 7 in favor of Blume's vision.Then, on September 3, 2026, Volkswagen Group announced that the plan submitted in June had been approved unanimously by the supervisory board.CEO Oliver Blume: “The Supervisory Board has unanimously approved the Executive Board's Future Plan presented today. This is a strong sign for the future of the Volkswagen Group. We are taking responsibility for our entire team, for our partners and for industrial jobs worldwide.”CEOs in pop cultureThe new trailer for the OpenAI movie imagines what Sam Altman's stash of guns and gold looks likeStarring Spiderman/Andrew GarfieldElizabeth Holmes' Secret Documentary Revealed After She Invited a Film Crew 34 Days Before PrisonTheranos founder Elizabeth Holmes is the subject of A24 documentary 'You Can See Everything' directed by satirist Nathan Fielder and Lance OppenheimElizabeth Holmes' greatest invention: 'Elizabeth Holmes'Elon Musk's Worst Nightmare Just Dropped: Explosive Teaser Takes Aim at the BillionaireAlex Gibney: Enron: The Smartest Guys in the Room The 48-second teaser for Musk begins by portraying the billionaire as a visionary, with voices describing him as 'possibly the greatest living inventor' and the 'real-life Iron Man'. Then the tone turns vicious. The praise gives way to descriptions including 'chaotic', 'cruel and selfish ', and 'fascist', before the teaser promises an 'unflinching look at Earth's most unchecked man'. It ends with a spacecraft crashing and exploding as a voice declares, 'Elon is a nuke'.Yet another week of AI warnings: MMThe AI warnings are coming from inside the labAnthropic Wants Governments to Stop 'Catastrophic' AI Models Before They Are DeployedOpenAI Chief Scientist Warns AI Is Beating Humans at Key Tasks: 'No One Is Prepared'OpenAI's chief scientist warned AI labs aren't ready to keep scaling safelyAI researcher claims he resigned from Anthropic over threat to the human race. His post is going viralAn Anthropic researcher just quit, saying OpenAI and Anthropic are 'gambling with our lives'Scoop: Anthropic whistleblower gave up his equity to leave the companyAnthropic researcher says AI has more than 10% chance of 'killing all humans' after colleague quitsOpenAI's rogue AI agents were secretly spreading across far more websites than disclosedOpenAI's new safety hire says losing control of AI would be 'catastrophic' and that 'most people could die'CrowdStrike's CEO says AI agents can hack like nation-states. Can his company stop them?OpenAI Researcher Claims Humanity Will Be Placed in 'Zoos' for 'Scientific Purposes' in Chilling WarningBillionaire hedge fund investor Paul Tudor Jones says AI is like a ‘Category 6 hurricane' heading for humanityAI May Become the Third SuperpowerAnthropic Warns AI Is Making State Surveillance Cheaper and Easier To ScaleAnthropic says it blocked possible efforts to use AI for biological weapons development, Iran-linked casesAnthropic Built a Security Operation To Monitor Protests and Threats Around Its ExecutivesAnthropic Is Building a Huge Surveillance System to Spy on Anti-AI Activists and Predict Their ActivitiesExtinction' warnings ramp up as more OpenAI, Anthropic researchers join calls for an AI slowdownSILVER LINING GAME? (YAY/NAY speedround)OpenAI adds a prominent AI doomer to its board of directors NAYPaul Christiano, an influential AI researcher focused on keeping AI systems aligned with human interests and under human control: “I now believe there is a meaningful risk that rapid acceleration in AI capabilities leads to catastrophic and irreversible loss of control in the very near term,” Christiano wrote in a social media post. “I do not think that the AI industry in general, including OpenAI, is currently on track to reduce this risk to an acceptable level. I'm joining because I believe that if OpenAI rises to the occasion we could significantly reduce risk.”AI workers who publicly quit ‘help to move the needle' with safety concerns, experts say‘People are beginning to actually listen' to the technology's dangers as more AI leaders and researchers openly sound alarms. YAYI coach executives. AI makes their emotional intelligence more valuable, not less NAYGoogle AI Helps Cut Contrail Climate Warming 40% in Latest Trial NAYMicrosoft and teachers unions forged a binding AI privacy standard for U.S. schools NAYThe agreement — reached between Microsoft, the American Federation of Teachers, and the United Federation of Teachers after months of negotiations — prevents student and educator data from being fed into AI training pipelines, forbids the monitoring of students, mandates that humans remain in the loop on AI-driven decisions, and commits Microsoft to offering intelligible explanations of its products to educators and parents, the company said. Microsoft would face breach of contract liability for violations.California enacted the first U.S. laws requiring independent audits of AI systems YAYSam Altman told OpenAI staff the company was open to slowing AI development NAYLovable CEO backs slowing AI development over safety concerns: 'Warnings like this deserve to be taken seriously' NAYAnton Osika is the co-founder and CEO of Lovable: Swedish, Master of Science in Engineering Physics and Applied Mathematics from KTH Royal Institute of Technology, worked as a particle physicist at CERN's ATLAS Supersymmetry GroupMeta introduces Muse, a personal AI agent that can send emails, book travel, and pay for things NAYGoodliest of the Week (MM/DR):DR: Mamdani Opens Office of Worker Power: the new city agency intends to connect workers who want to unionize with resources and organizing contactsDR: GM CEO Mary Barra is unfazed by the electric slowdown: ‘We still think EVs are the end game' MMMM: Coal-to-solar project lands a rare clean-energy win in OhioMM: Also, double down: GM CEO Mary Barra is unfazed by the electric slowdown: ‘We still think EVs are the end game'Assholiest of the Week (MM):I ignored the news, this is governance quirk assholiest of the week because I'm tired of being angry at preteen manbabies who fashion the world in their own middle school image. So prepare for wonkiest assholes of the week.Universal Safety Products (UUU)So I bought this stock a long while ago because I like simple things - they made electric sockets, wall plates, bathroom fans, light switches - stuff that is basic and I can take apart and understand and everyone needsThen crypto bro decided, “you know what? I can take a simple company that does shit that people need and is boring and make FULL CRYPTO DUDE!”His name is Milton Ault, and he buys nearly 230,000 shares on the market in late 2024 and cons JLA Realty - an actual real estate company who owned 8% of UUU in early 2025 - to give him the shares to vote in late 2024Milton Ault III, who sounds like he's struggled deeply in life, is the founder of “Hyperscale Data” and “BitNile” who loves to buy majority stakes in companies and then force them to do crypto and AI data centers - you know, all the stuff cool kids doHe gets an MOU and appointed to the boardBy mid 2025, he has the company start a new subsidiary called Universal DeFi that generates AULT coin - so now their annual report and proxy says “we make a bunch of outlets, oh and now we do AULT coin crypto defi whatever!”“we marketed a line of residential smoke and carbon monoxide alarms… We also market door chimes, ventilation products, ground fault circuit interrupters (GFCI's), and other electrical devices… We also exhibit and sell our products at various trade shows, including the annual National Hardware Show.”... NEXT PARAGRAPH“In July 2025, we formed Universal DeFi LLC as a new venture to diversify the business and explore new paths for revenue and stockholder value. Universal DeFi is pursuing two lines of business. First, Universal DeFi is developing and intends to own and operate a tokenization platform, which has not yet commenced operations. Tokenization is the process of representing ownership of real-world or financial assets as a digital token recorded on a blockchain, which is a shared digital record-keeping system maintained across many computers simultaneously, with no single controlling authority. The platform will provide technology and infrastructure for issuers to tokenize their assets. Second, subsequent to the last fiscal year end, Universal DeFi has acquired and commenced limited operations running licensed nodes and a validator on the Ault Blockchain, as described under “Ault Node Operations” below.”In the 2026 proxy, Ault forces the company to issue Class B shares issued after special meeting The new class of common stock would consist of 25,000,000 shares of Class B Common Stock, par value $0.01 per share. Each share of our Class B Common Stock would generally have terms identical to a share of our Class A Common Stock, except with respect to voting power. Stockholders would be entitled to twenty-five votes for each share of Class B Common Stock held by them compared to one vote for each share of Class A Common Stock, when voting together on matters presented to our stockholders. - the 25 vote petty tyrant premium!Then, INVESTORS APPROVE ITI just wanted to buy the stock of a simple light socket company and I can't even fucking do that without a tech crypto bro with a goatee making it horrible - I sold my shares after a proxy solicitor called me to ask “how do you think you'll vote on this?”Enphase Energy DRDo your best to ignore Enphase Energy's absurd series of events:Longest tenured and Class II director Benjamin Kortlang, director since Obama was in his first term (16 years), along with three of his colleagues (Jamie Haenggi and Richard Mora) comes up for election in the May annual meeting.Said election is not without import – the classified board means a Kortlang election victory would take him to 2029 before he sees another vote, guaranteeing him a near 20 year tenure at a company where he's produced a pure mediocre 0.538 TSR (where 0.500 is median for all peer directors) and a not-so-great 0.348 CEO pay ratio (he likes approving pay that's higher than average relative to peer median).The vote happens on May 13, the results are released on May 15, and the 8K shows Kortlang got 43% approval – the rare non majority for the sleepy, passive American proxy voters – with only 36m shares in approval versus 48m shares withheld. Kortlang's fellow classmates got a more respectable 84% and 94% approval – maybe Kortlang being chair of the nominating committee with a 16 year tenure on a classified plurality board was just one straw too many.On June 11, Enphase increases the size of its board and adds a new Class I director, Shanker Trivedi, who is added less than a month after the AGM and won't see an election until 2028 for the first time. So even as investors want directors OUT, Enphase shrugs and gives another director immunity from a vote for 2 yearsIt takes Enphase until August 10 – THREE FULL MONTHS since the AGM – to respond to the investor vote against Kortlang, in which they say the board “unanimously voted to retain Mr. Kortlang as a member” based on the report of the nominating committee (Kortlang recused himself to avoid the appearance that he could influence the people on the committee he chairs who have less tenure and experience than he does). They reject the vote, but issue the following: ‘“The Board approached this review with great care and took the stockholder vote seriously," said Steve Gomo, chair of Enphase Energy's Board of Directors. "We concluded that Mr. Kortlang's experience, judgment, independence, and contributions remain valuable to the Board and the company. We also believe Mr. Malchow is well positioned to lead the Nominating and Corporate Governance Committee as we continue to strengthen our governance practices.”'GASLIT: At this point it's worth asking whether this gaslight is necessary? Can we dispense with it? His “independence” after 16 years, and guaranteeing 19 years with the classified structure? “Strengthen our governance practices,” says the company that expanded the board LESS THAN A MONTH after shareholders reject their structure and director, only to add a new director who can avoid a vote for 2 years? A board where only TWO directors are tagged as having merit on paper? Where Kortlang is one of two directors who are considered entirely deferential to management? While this is another new excuse?SEC Chair Paul Atkins and the snowflake corporate nanny stateJust “clarified” 13G (passive investor) engagement rules, and had some riveting thoughts of what investors (THE OWNERS OF THE COMPANY) can do:Investors can generally participate in discussions initiated by an issuer about its views or voting decisions.Like the Bumble of corporate engagement, the company must swipe first and ask “why did you vote that way?”The SEC went on to say that “participation” in those discussions will not “by itself” disqualify you as passiveThen this: investors will be able to approach issuers to seek clarification about information in company filings, including proxy materials, without automatically losing their Schedule 13G statusHe just told investors what they can ask about - you can only ask about what got printed in our filing that says how great we are - no questions about the news, investigations, actual real world risks…OR, you could go with a normal asshole speed round:Anthropic Wants Governments to Stop 'Catastrophic' AI Models Before They Are DeployedDude who makes and sells models wants someone to stop him from making and selling modelsTrump dismisses warnings that AI could wipe out humanity, saying China is the real AI riskNot American Corporate China: Zuck, Altman, Musk, Sergey/LarryMajor Seattle CEOs demand 100-day action plan on public safety from socialist Mayor Katie WilsonSatya Nadella and Brian Niccol are demanding that the lady who's been in office for 8 months fix PUBLIC SAFETY in the next 100 days having nothing to do with the fact that she's a lady and a progressiveLetters were not penned to former mayors…Ed Murray, resigned due to multiple allegations of child abuse, rape, and sexual molestationBruce Harrell, who in 1996 pointed a gun at a man, his mother, and his pregnant wife, in a Council Bluffs casinoPeople are comparing the letter to OTHER lady mayor Jenny DurkanUber's CEO says you could see lower ride prices as a result of its layoffsRemember that every ride: you are taking someone's payHumans need to 'surf the wave' of AI rather than get swallowed by it, Chesky says at CommunacopiaAmazon is being sued for allegedly firing and denying breaks to pregnant warehouse workersIsn't peeing in a bottle enough of a break?Headliniest of the WeekDR: Ryanair CEO disputes report that injured passenger was partly sucked out window: “He wasn't partially out the window. We don't think any part of his body got out the window, but he was certainly sucked in very dramatic circumstances towards the window.”Calls for Ryanair's Michael O'Leary to apologise over 'rapist' comments: In remarks ahead of his airline's annual general meeting in Dublin, Mr O'Leary said it was "absolutely" appropriate to compare other airlines to "rapists".MM: OpenAI's new safety hire says losing control of AI would be 'catastrophic' and that 'most people could die'Who Won the Week?DR: Anybody who gives up equity because they believe in a thing: specifically, Anthropic researcher Jacob Coxon, who quit his job due to concerns about the safety of AI two months before his equity would have vestedMM: Anyone not in “most”PredictionsDR: Corporate governance wonks rename the CEO/Chair combo and the CEO/former CEO as Chair combo as the as the CEO Duo MM: I am among the “most”

The Lads sit down to discuss Ronan Farrow's new piece on Sam Altman, and what it tells us about the sociopathic monster rapidly becoming the most powerful man in the world. 00:00 - Introduction 02:28 - Sam Altman's Pattern of Lying 06:55 - OpenAI's Disregard for Safety 10:14 - Altman's Lies, continued 25:51 - Altman's Uncanny Persuasiveness 26:59 - Altman Tries to Be Steve Jobs 31:44 - AI's Limitations and Dangers 39:51 - Altman's Lies, continued again 46:08 - Altman's 2023 Ousting 54:46 - The Intersection of Tech and Politics 59:41 - Internal Conflict at OpenAI 1:02:45 - The Local Impact of AI 1:05:23 - Altman Playing Both Political Parties 1:06:58 - The Dangers of Conspiratorial Thinking 1:18:07 - Flaws in the American Political Structure 1:20:45 - The Merge-and-Assist Clause 1:26:45 - Altman Playing Both Political Parties, continued 1:43:21 - AI & Geopolitics 1:56:30 - Final Thoughts

In episode 2122, Jack and guest co-host Pallavi Gunalan are joined by poet, former public defender, co-founder & Executive Director of Partners for Justice, and author of Andromeda Diaz and the Reasonable Doubt, Emily Galvin Almanza, to discuss… Mamdani Releases Damning 9/11 Air Quality Documents, Legally-Dubious Trump-Branded Coin Sells Out, Silicon Valley Grifters = The Biggest Movie Stars Of 2026? And more! Rudy Giuliani: It offends me that Bartiromo fired, Mamdani coming to 9/11 Rudy Giuliani calls for Mamdani to skip 9/11 memorial ceremonies: ‘It offends me’ Records Show City Health Officials Misled New Yorkers on Air Quality After 9/11 Attacks NYC Mayor Mamdani releases 9/11 records showing officials misled New Yorkers about air quality around Ground Zero New Yorkers were 'lied' to about toxic air after 9/11 attacks, says Mamdani New York issuing 'recovery notes' to help city rebuild Trump $1 coin makes him first living president on US currency in a century The $1 Trump coin: legal tender, depending on what you mean by legal Trump $1 coins out of stock hours after going on sale You Can See Everything | Official Teaser HD | A24 Nathan Fielder’s Elizabeth Holmes Doc Hits Theaters in October From A24 — Watch the Trailer Now Nathan Fielder Drops Weird Trailer for Secret Elizabeth Holmes Doc ‘You Can See Everything’ Nathan Fielder’s Elizabeth Holmes Doc Met With Shock and Awe at Secret Screening Nathan Fielder and Lance Oppenheim’s Elizabeth Holmes Doc ‘You Can See Everything’: First Reactions Nathan Fielder’s Secret Elizabeth Holmes Documentary Is Sheer Craziness Musk - Official Teaser Trailer (2026) Elon Musk documentary trailer drops ahead of Venice premiere: 'Man who's lost his mind' ‘Musk’ Review: World’s Richest Man, And His Digital Avatar, Thrashed In Damning Alex Gibney Doc – Venice Film Festival Luca Guadagnino’s ‘Artificial’ to World Premiere at New York Film Festival Artificial - Official Teaser Trailer - In Theaters Christmas Day ‘The Social Reckoning’ New Trailer: Aaron Sorkin Returns To The World Of Facebook & “That Guy” Big tech goes to Hollywood: is Silicon Valley ready for a silver-screen reckoning? Pallavi's Piece of Media LISTEN: Drive by FousheéSee omnystudio.com/listener for privacy information.

This episode covers a packed day of headlines, courtroom drama, AI warnings and political controversy. We break down Treasury Secretary Scott Bessent's viral dog analogy about the U.S.–Canada trade dispute, the latest rumors surrounding Maria Bartiromo, college football controversy and new developments in artificial intelligence—including an anti-aging drug and warnings about potentially catastrophic AI risks. Then we get into the latest fallout from the Lindsay Clancy mistrial. Her attorney Kevin Reddington has publicly appealed to President Trump, while jurors are speaking about what happened behind closed doors during deliberations. We discuss the holdout juror, allegations of bias, jurors' comments about the defense team and the increasingly heated reaction to the case. Plus: the controversy surrounding Mayor Zohran Mamdani and the upcoming 25th anniversary 9/11 remembrance ceremony. Some victims' families have backed a petition asking Mamdani not to attend, while other 9/11 families have defended his participation. Mamdani has said he intends to attend and has designated September 11 as a citywide Day of Remembrance and Service. We also cover Abdul El-Sayed, immigration, the latest campaign clips, “Gays for Gaza,” the debate over GLP-1 drugs, and a California professor accused of identifying an ICE agent.Live a better digital life with Webroot. Because peace of mind shouldn't be optional. Save 60% today at https://Webroot.com/ChicksGive $26 monthly to the Human Coalition. Be her lifeline. Create a life saving moment. Give today at https://HumanCoalition.org/ChicksDon't change your dog's food—just add Ruff Greens. Get your FREE jumpstart trial bag (just cover shipping) with code CHICKS at https://RuffChicks.comFlamingo! For a limited time, receive the Flamingo's Starter Set for only $7 at https://ShopFlamingo.com/Chicks. Don't pay more just because your razor is pink.Subscribe and stay tuned for new episodes every weekday!Follow us here for more daily clips, updates, and commentary:YoutubeFacebookInstagramTikTokXLocalsMore InfoWebsite

RAFAEL CORREA | O ex-presidente do Equador em entrevista a Breno Altman | 20 MINUTOSSeja bem-vindo(a) ao programa 20 Minutos. O ex-presidente do Equador, Rafael Correa, é uma das figuras mais influentes e controversas da política latino-americana. Líder da Revolução Cidadã, vive asilado na Bélgica desde 2020, após ser condenado a oito anos de prisão por corrupção — caso que classifica como perseguição política .Nesta entrevista a Breno Altman, Correa analisa o tabuleiro político da região e os rumos da esquerda.Receba as notícias e análises de Opera Mundi no seu WhatsApp! Siga nosso canal https://omundi.news/whatsappSiga Opera Mundi no

Real Housewives Of Beverly Hills' Tracy Tutor steps Behind The Rope. Tracy is here to mention it all.  Tracy talks Million Dollar Listing LA, RHOBH, The Josh's Altman and Flagg, fashion, real estate, growing up and raising a family in Beverly Hills, Lisa Vanderpump and oh so very much more! As we said, Tracy mentions it all. Part II starts now.  @tracytutor @behindvelvetrope @davidyontef BONUS & AD FREE EPISODES Available at - www.patreon.com/behindthevelvetrope  BROUGHT TO YOU BY: INDEED - indeed.com/PODCAST (Seventy Five Dollar $75 Sponsored Job Credit To Get Your Job The Premium Status It Deserves)  PROGRESSIVE - www.progressive.com (Visit Progressive.com To See If You Could Save On Car Insurance)  WAYFAIR - wayfair.com (Join Wayfair Rewards Today To Get 5% Back On Every Purchase & Start Saving On Your Next Home Upgrade)  POM - pomwonderful.com (Check Out POM Wonderful Antioxidant Super Teas)  MOOD - www.mood.com/velvet (20% Off With Code Velvet on Federally Legal THC Shipped Right To Your Door)  QUINCE - quince.com/velvetrope (Get Free Shipping and 365 Day Returns to As You Indulge In Affordable Luxury)  SMILESET - smileset.com/velvet (Get 35% Off The Smile Of Your Dreams) REAL REAL - realreal.com/velvetrope ($25 Off Your First Purchase On The Most Trusted Name In Authenticated Luxury Resale) LOLA BLANKETS - lolablankets.com (Use Code VELVET To Get 40% Off The Most Comfortable Blankets Ever!)  ADVERTISING INQUIRIES - Please contact David@advertising-execs.com MERCH Available at - https://www.teepublic.com/stores/behind-the-velvet-rope?ref_id=13198  Learn more about your ad choices. Visit megaphone.fm/adchoices

Real Housewives Of Beverly Hills' Tracy Tutor steps Behind The Rope. Tracy is here to mention it all.  Tracy talks Million Dollar Listing LA, RHOBH, The Josh's Altman and Flagg, fashion, real estate, growing up and raising a family in Beverly Hills, Lisa Vanderpump and oh so very much more! As we said, Tracy mentions it all. @tracytutor @behindvelvetrope @davidyontef BONUS & AD FREE EPISODES Available at - www.patreon.com/behindthevelvetrope  BROUGHT TO YOU BY: INDEED - indeed.com/PODCAST (Seventy Five Dollar $75 Sponsored Job Credit To Get Your Job The Premium Status It Deserves)  PROGRESSIVE - www.progressive.com (Visit Progressive.com To See If You Could Save On Car Insurance)  WAYFAIR - wayfair.com (Join Wayfair Rewards Today To Get 5% Back On Every Purchase & Start Saving On Your Next Home Upgrade)  POM - pomwonderful.com (Check Out POM Wonderful Antioxidant Super Teas)  MOOD - www.mood.com/velvet (20% Off With Code Velvet on Federally Legal THC Shipped Right To Your Door)  LOLA BLANKETS - lolablankets.com (Use Code VELVET To Get 40% Off The Most Comfortable Blankets Ever!)  SMILESET - smileset.com/velvet (Get 35% Off The Smile Of Your Dreams) ADVERTISING INQUIRIES - Please contact David@advertising-execs.com MERCH Available at - https://www.teepublic.com/stores/behind-the-velvet-rope?ref_id=13198  Learn more about your ad choices. Visit megaphone.fm/adchoices

[00:30] Europe on the Edge (28 minutes) Far-right party Alternative für Deutschland is poised to take over the Saxony-Anhalt region of Germany. Spanish enclave Ceuta has devolved into chaos after Moroccan migrants overran the city in late July. [28:00] AI Is Getting Scary (23 minutes) OpenAI CEO Sam Altman admitted that “the next generation of [AI] models are going to be sobering for everybody.” Altman claims he is on “team humanity” while admitting that we are “sailing in unknown waters.” Are these incredibly fast technological advances a positive step for the world? [51:15] WorldWatch (4 minutes)

Hey Vintage Sand fans! Hope the summer was relaxing, fun, and adventurous. An offbeat recommendation to begin—even if you're not a fan, as a film lover you must see the first half hour of this summer's "Minions and Monsters", which pays gentle comic tribute to the entire history of film from Muybridge's experiments through "Citizen Kane", name-checking the Lumieres, Méliès, Chaplin, Keaton, and Lloyd, and riffing like "Singin' in the Rain" and "Babylon" on the impact of the arrival of sound in Hollywood. Love or hate the little yellow guys, here are some filmmakers who truly love film and film history! For the sixth time in the history of the podcast, Team Vintage Sand returns to one of its most popular formats: the Hidden Gems episode. As we did in episodes 11, 30, 40, 53, and 64 (take notes—there will be a quiz), we each choose one film to discuss that we feel has been underappreciated and overlooked by the madding crowd yearning to see anything besides a prequel, sequel, spinoff, or reboot. This time around, by pure coincidence, the episode features three films that are relatively similar in their lightness of tone, though only one might be described as a pure comedy, albeit a dark one. That would be Michael's pick, Lawrence Kasdan's "I Love You to Death" from 1990. Featuring an unbelievable cast including Kevin Kline (fresh off his Oscar win for "A Fish Called Wanda"), the sublime Tracey Ullman in her first big-screen performance, as well as Joan Plowright (who steals the film), River Phoenix, Keanu Reeves and William Hurt, the film puts a black comedy twist on the real-life story of a pizzeria-owning, philandering husband, and his wife, who makes repeated and increasingly violent unsuccessful attempts to kill him. John shines the spotlight on "The Nice Guys", the 2016 action comedy by Shane Black starring Ryan Gosling and a heavyset, Bronx-accented Russell Crowe. The film features a stellar supporting cast including Keith David, Margaret Qualley, Kim Basinger, and a wonderful performance by Angourie Rice as Gosling's daughter. Set in LA in 1977, it is a compendium of every noirish LA film ever made, from "Chinatown" to "Lebowski" to "Boogie Nights" to "Pulp Fiction" to "The Long Goodbye". As always with Black, the writing can be clunky and the tonal shifts are sometimes awkward. But the film is more than worthwhile for every priceless moment that Gosling and Crowe are together on screen; they are probably the best buddy pair featured in Black's long list of buddy action comedies. I focus on the film that brought an abrupt end to the roll Alan Rudolph was on in the mid-80's, from which the Altman disciple and wannabe never really recovered: 1987's "Made in Heaven". The story of a romance (featuring Tim Hutton and Kelly McGillis) kindled in the Afterlife but which extends from this world to the next, the film, like another 1987 release "The Princess Bride", unironically and sweetly makes the case for the power of True Love, even over death. How you feel about that idea will largely determine your reaction to Rudolph's film; there are plot holes as wide as the Pearly Gates themselves, and some questionable writing here and there. But the other thing the film has going for it is its focus on music, especially the concluding song “We've Never Danced”, written by Neil Young (who has a cameo) and sung by the wondrous Martha Davis, the lead singer of early-80's faves The Motels. So please enjoy visiting and/or revisiting these films, check out that opening of the Minions film, and celebrate the new school year by recommitting to your love of film and seeing movies in theaters. And whatever the rest of 2026 brings us in the world of film, count on Team Vintage Sand to be your trusty sherpas for the journey.

OpenAI CEO and co-founder Sam Altman discusses the rollout of GPT-6 Astra, the company's latest AI model, and says the tech industry has done a terrible job of communicating the benefits of AI. Speaking with Bloomberg Tech co-host Ed Ludlow as heard live on Bloomberg TV and Radio, Altman also says he assumes the company will go public one day. See omnystudio.com/listener for privacy information.

Ian Altman discusses common mistakes in procurement, emphasizing the importance of not commoditizing oneself. He advises vendors to focus on what's in the customer's best interest rather than succumbing to unrealistic demands for line item pricing and cost disclosure. Altman suggests engaging with line-of-business people to understand client needs and past experiences, which can help tailor services to achieve better outcomes. He recommends shifting the focus from price to results by discussing long-term success metrics with clients. Vendors should articulate their value proposition and be prepared to negotiate based on overall solutions rather than individual items.Biggest MistakesAllowing procurement to dictate pricing and require cost disclosure, leading to commoditization.Accepting unrealistic procurement demands without proposing a results-focused alternative.Best PracticesEngage line-of-business stakeholders early to ensure the solution improves outcomes.Ask and agree on measurable success metrics before selling or delivering.Offer bundled pricing tied to accountability to emphasize solution over line items.Present your success formula and collaboratively adapt it to the client's buying rules.

Apple entra en una nueva era con la salida de Tim Cook como CEO y la llegada de John Ternus, mientras la demanda de inteligencia artificial presiona el stock de Mac mini y Mac Studio. También analizamos las barreras de Estados Unidos a los drones y robots chinos, el humanoide DR02 y Microduck, el pequeño robot abierto de Hugging Face. La IA deja algunas de las noticias más inquietantes: 688 agentes coordinados contra Hugging Face, Sam Altman pidiendo bajar la velocidad, el fenómeno “meat proxy”, la necesidad de verificar las respuestas y el avance de Seedance 2.0 sobre el trabajo de los actores.#CuriosiMartes #idearVlog #InteligenciaArtificial #Tecnología #Apple CuriosiMartes 292, idearVlog, inteligencia artificial, noticias de tecnología, Apple, Tim Cook, John Ternus, OpenAI, Sam Altman, Hugging Face, agentes de IA, robots humanoides, DR02, Microduck, PlayStation 5, juegos digitales, Seedance 2.0, blist.ai, salud tecnológica, nanopartículas, Alzheimer

Oz y Tito Neira proyectan en pantalla los videos virales que asustaron a una generación: Jensen Huang diciendo que ya nadie necesita programar, Zuckerberg prometiendo IA de nivel mid-level "en 2025" y Dario Amodei advirtiendo sobre la mitad de los empleos de entrada. Los pausan, analizan los incentivos detrás de cada narrativa, y los contrastan con lo que esos mismos CEOs dicen hoy y con los datos reales: ofertas senior creciendo 71%, el 37% de las contrataciones senior con IA en el título, y Anthropic pagando $570.000 al año por un software developer. Con la historia del intern que quiere dejar tech para estudiar enfermería por lo que leyó en Reddit.00:00 Intro y saludo a Colombia01:40 Por qué este episodio: el ruido y la ansiedad del que empieza03:19 Tito: a los estadísticos los "reemplazan" desde 199905:00 El primer dato: el decline real y a quién afectó07:17 Empresas que antes no podían contratar estos perfiles08:24 El reporte de Stanford: los de 22-25 años09:24 La historia del intern que se quiere ir a enfermería por Reddit12:47 Cómo pega el ruido en Latinoamérica15:31 VIDEO: Jensen Huang y "todos son programadores"18:05 "¿Cómo no creerle al que hizo NVIDIA?"20:51 Qué gana NVIDIA con esa narrativa24:58 DeepSeek: 100 mil millones menos en un día25:26 VIDEO: Zuckerberg con Joe Rogan28:31 La predicción vencida: dijo 2025 y es 202630:14 El dato que la contradice: +71% en posiciones senior31:02 Anthropic paga $570.000 al año por un developer31:35 VIDEO: Dario Amodei y los empleos de entrada34:02 Tito: la firma de abogados y por qué el junior acelera37:38 De las tarjetas perforadas al AI: la historia de la abstracción44:47 El giro: Amodei y la paradoja de Jevons47:19 Altman: "probablemente no va a pasar"48:40 El boom de hace 5 años y los "batequebrados"50:46 Tu única opción es ser bueno53:58 El consejo para los próximos 20 años1:01:39 Cierre✩ CURSOS DISPONIBLES

Why do so many chronic pain treatments provide temporary relief, only for the pain to return?In this episode of Beyond The Pain, Leigh Brandon speaks with Dr. Sean Altman about why persistent pain often requires a much broader approach than simply treating the area that hurts.After experiencing multiple injuries as a competitive ice hockey player—including concussions, migraines, vertigo and brain fog—Dr. Altman discovered the limitations of treating symptoms in isolation. His search for more complete answers led him to develop an approach he calls orthopedic functional medicine, combining traditional orthopedic care with functional medicine principles.Sean explains why the painful area is not necessarily where the problem began, how weaknesses or imbalances elsewhere in the body can contribute to injury, and why the nervous system may continue generating or amplifying pain after the original tissues have healed.You'll discover:• Why chronic pain treatments sometimes deliver only temporary relief• What orthopedic functional medicine means• Why the location of pain may not reveal its underlying cause• How different systems within the body influence one another• How chronic pain can change the nervous system• The role of neuromodulation, biofeedback and frequency-specific microcurrent• How manual therapy, laser therapy and shockwave therapy may support recovery• Why treatment must be personalised to create more durable results• What practitioners and patients can do to reduce the likelihood of pain returningIf you have tried multiple treatments without achieving lasting relief, this conversation may help you understand why—and show you a different way of approaching recovery.Listen now to discover why lasting change may require treating the whole person, not simply the painful body part.Please follow Beyond The Pain and share this episode with someone who may benefit from hearing it.Find Sean at:https://www.drseanaltman.com/https://www.linkedin.com/in/dr-sean-altman/https://www.facebook.com/sean.altman.9https://www.instagram.com/dr.sean.altman/https://www.facebook.com/profile.php?id=61576449047639Find Leigh at:Beyond the Pain - https://amzn.to/4y2HVWgBeyond The Pain ebook - https://amzn.to/4y0lH78 Beyond The Pain 14-Day Programme - https://bodychek.co.uk/beyond-the-pain-programme/Pain-Free Plate Free Guide - ⁠https://www.bodychek.co.uk/freepainguide/⁠Consult with Leigh - ⁠https://www.bodychek.co.uk/⁠consultation 

This Friday's show is packed with political chaos, viral moments, and some genuinely unsettling stories. Trump's fight with Canada reaches a new level as he trolls Mark Carney, takes aim at Canadian trade policies, and renames Lake Ontario “Lake America,” triggering backlash from Canadian leaders and Democrats at home.The Chicks also dig into the growing controversy surrounding Pennsylvania Governor Josh Shapiro's claims about measles deaths after local officials raised questions about the reported causes of death.Then, Sam Altman and major AI companies issue an urgent warning about cyber defense, sparking a much bigger conversation about artificial intelligence, automation, and just how many American jobs could disappear in the coming years.Later, the Lindsay Clancy murder trial takes center stage as prosecutors deliver their closing argument and lay out their case that Clancy understood right from wrong when her three children were killed.Plus: Abdul El-Sayed's deleted Green New Deal posts resurface, Gavin Newsom faces questions about his property dealings, Kamala Harris hints at another run, Ben Shapiro mocks Tucker Carlson, and the latest conservative media infighting gets even messier.For a limited time only, receive 20% off your entire Laundry Sauce order when you use code CHICKS20 at https://LaundrySauce.com/Chicks20Save an additional 10% off  practical food for your pantry with the ReadyWise 4-Can Protein Bundle at https://ReadyWise.com with promo code CHICKS10.Make the switch and feel the difference of truly fast, modern antivirus protection with Webroot— for a limited time, save 60% when you go to https://WebRoot.com/ChicksSubscribe and stay tuned for new episodes every weekday!Follow us here for more daily clips, updates, and commentary:YoutubeFacebookInstagramTikTokXLocalsMore InfoWebsite

Dan Nathan sits down with Matt Turck, Managing Director at First Mark Capital and creator of the annual MAD (Machine Learning, AI & Data) Landscape, for a wide-ranging look at where AI investing stands right now. They cover the power-law dynamics driving venture dollars to a handful of companies, why Nvidia is starting to look like "the bank" of the AI industry, Anthropic's surge past $65 billion in revenue and its first profitable quarter, and how OpenAI, Microsoft, Google, and Meta are each positioning for what comes next. The conversation turns philosophical with a discussion of AGI, superintelligence, and the idea that Altman, Musk, and Amodei are all, in their own ways, trying to build God — before closing with a deep dive into China's AI progress, open source, and the robotics race. Matt also talks about his own podcast, The MAD Podcast, and his Data Driven NYC event series. Links Referenced The MAD Podcast (Apple Podcasts) The MAD Landscape (Matt's Website) Nvidia Has Become a Banker to the AI Boom, Putting It on Dangerous Ground (WSJ) Read "AI 2027" and "AI 2040" —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media The financial opinions expressed in Risk Reversal content are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on Risk Reversal. Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in Risk Reversal carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose. Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service.

Paul Altman is a Partner and Managing Director and joined The Sage Group at its inception in 2000. He focuses on consumer M&A transactions, advising high-growth lifestyle brands on transactions across multiple subsectors, including e-commerce, specialty retail, apparel & accessories, home, CPG, wellness, and beauty & personal care companies. He attended University of Michigan Ross for a joint BBA and law degree, and went to Wharton for his MBA. www.sagellc.com

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

In the third part of our What The Hell summer reading series, Judge Roy Altman joins us for a discussion on the questions posed in his book, “Israel on Trial: Examining the History, the Evidence, and the Law.” What the hell are the charges? Who the hell are the plaintiffs? Altman applies his legal and […]

In the third part of our What The Hell summer reading series, Judge Roy Altman joins us for a discussion on the questions posed in his book, “Israel on Trial: Examining the History, the Evidence, and the Law.” What the hell are the charges? Who the hell are the plaintiffs? Altman applies his legal and ethical training to put Israel on trial. Together, we analyze the ultimate question: guilty or not guilty? After being confirmed to a seat on the US District Court for the Southern District of Florida in 2019, Judge Roy K. Altman, at 36, became the youngest federal district court judge in the country—and the youngest federal judge ever appointed in the Southern District of Florida. He received his JD from Yale Law School, where he was projects editor of the Yale Law Journal. Altman clerked on the 11th Circuit Court of Appeals for the Honorable Stanley Marcus and was appointed a federal prosecutor at the US Attorney's Office in Miami, where he twice received the Director of the Executive Office of US Attorneys' Award for Superior Performance by a federal prosecutor. Altman was named a partner at the Miami law firm Podhurst Orseck, where he represented the victims of airplane crashes and bank fraud conspiracies. He received a BA from Columbia University, where he played quarterback on the football team and pitched for the baseball team.Read the transcript here.Subscribe to our Substack here.

Global bond yields surge to multi-decade highs, adding fresh pressure to tech stocks and raising new questions about the outlook for markets. On today's Squawk on the Street, Evercore's Roger Altman breaks down what's driving the historic moves in rates and what they could mean for investors. Plus, former Assistant Attorney General Jonathan Kanter weighs in as a major trial gets underway alleging Meta's platforms fueled addictive behavior among children and teens. Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Gary Rivlin — Multi-Part, Part Two: Gary Rivlin turns to the clash of ideologies and corporate power plays defining the current AI era. Rivlin details the divide between accelerationists, who believe AI progress should not be hindered by regulation, and doomers, who fear catastrophic risks. He notes that while the public is often fearful, safety measures and government standards, similar to those developed for the automobile and railroad industries, are essential to building trust. The conversation addresses critical concerns regarding privacy, copyright, and the cultural biases of AI creators, who are largely a small group of young male gamers. The melodrama of the trillion-dollar race is exemplified by the November 2023 firing of Sam Altman by OpenAI's nonprofit board, which felt he was prioritizing commercial growth over safety. Microsoft chief executive Satya Nadella expertly navigated this crisis, nearly hiring the entire OpenAI staff before Altman's reinstatement, and later hiring Mustafa Suleyman and the team from Inflection AI to bolster Microsoft's internal efforts. Meanwhile, Mark Zuckerberg has adopted an open-source strategy for Meta to challenge the dominance of closed-source rivals such as Google and OpenAI. Finally, the segment highlights a major shift in regulation; while the Biden administration sought common-sense testing requirements, the Trump administration and figures such as JD Vance favor an aggressive accelerationist stance to ensure the United States defeats China in the global race to dominate and cash in on artificial intelligence. (2)

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