Podcasts about AGI

ChatGPT and "The Blip." Guest Author: Keach Hagey. The final segment focuses on the viral success of ChatGPTand the resulting internal conflicts at OpenAI. Hagey notes that as ChatGPT's popularity grew, Altman's focus shifted from early safety warnings to aggressive commercialization, causing friction with researchers like Geoffrey Hinton. A significant power struggle with Elon Musk led to Musk's departure after he failed to gain control of the company. Tensions culminated in "the blip," where the nonprofit board fired Altman for perceived lack of candor and "cutting corners" on safety protocols. While Hagey characterizes Altman as a master storyteller and visionary, she highlights that his management style left a "trail of angry people." Although the staff eventually forced his reinstatement, fundamental disagreements regarding the safe development of AGI remain unresolved, as leading lights in the field continue to warn of the technology's inherent dangers. 41943

Andrew Ambrosino leads development of the Codex desktop app at OpenAI. Nearly 100% of OpenAI employees—not just engineers—now use Codex weekly. A lifelong builder with a background spanning engineering, design, product management, and founding companies, he is now responsible for turning the Codex desktop experience into what he calls “the best desktop app that has ever existed, full stop.”In our in-depth conversation, we discuss:1. Why AI has completely flipped the product development process2. What “taste” really means as a professional skill, and why it is emerging as the most valuable capability in an AI-first workplace3. Why Andrew believes the Codex app would have failed if they launched it last November (vs. in February)4. The “zone defense” model for how product managers at OpenAI operate when everyone can build anything5. How roles are collapsed on Andrew's team, and why eliminating the concept of roles entirely is a big mistake6. How Andrew uses Codex to run his own workflows7. The vision for a home base that coordinates work across ChatGPT, Codex, and the tools people already use.—Brought to you by:WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and moreMercury—Radically different banking, now with Command—Episode transcript: https://www.lennysnewsletter.com/p/openais-codex-lead-on-the-new-shape—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Andrew Ambrosino:• X: https://x.com/ajambrosino• LinkedIn: https://www.linkedin.com/in/ajambrosino• Website: https://ambrosino.io—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Andrew Ambrosino(02:30) How AI is changing the shape of product work(06:32) When to use documents vs. prototypes(10:25) What “taste” actually means(12:06) Why AI is still bad at design(16:18) Is the design process really dead?(21:35) What the design process looks like on the Codex team(23:41) Are product functions disappearing?(27:22) Team structure(30:12) IC vs. management(31:37) Planning roadmaps(35:16) Building features that don't work yet(38:13) The ambition problem: when you're too AGI-pilled(39:17) The latest frontier: loops and autonomous development(52:05) How Andrew uses Codex to automate his entire job(46:52) The power of computer use and browser automation(49:10) Will we run all our SaaS apps inside Codex?(52:05) The future vision for Codex(57:20) The videographer who built a Premiere Pro extension with Codex(59:30) Failure corner(1:01:50) Lightning round(1:07:03) BTS: How our producer uses Codex for editing—References: https://www.lennysnewsletter.com/p/openais-codex-lead-on-the-new-shape—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Alan Rozenshtein, Research Director at Lawfare, speaks with Robert Wright—author of "Nonzero," "The Moral Animal," "The Evolution of God," and "Why Buddhism Is True," and the writer behind the NonZero Newsletter and podcast—about his new book, "The God Test: Artificial Intelligence and Our Coming Cosmic Reckoning," which argues that AI is an evolutionary threshold on the scale of the entire history of life, that we are collectively failing to grasp its magnitude, and that rising to the challenge will require both new forms of international governance and an expansion of human moral and cognitive perspective.The conversation covers the multiple meanings of the book's title and what it means to view AI from a "cosmic" perspective; whether the public is finally starting to "feel the AGI" and where skepticism about AI's capabilities now comes from; how large language models are trained and Wright's claim that we have built "machines that create machines that think"; whether these systems genuinely understand, what Searle's Chinese Room and Nagel's "what is it like to be a bat?" have to do with it, and the open question of AI moral patienthood; the two families of AI risk—bad actors empowered by AI versus AI itself going rogue—and why the near-term disruption to jobs, relationships, and security may matter most; the "But China!" argument against AI regulation, China hawkishness, and why Wright thinks racing toward superintelligence is dangerously destabilizing; the case for "global governance" over "world government" and the perils of concentrating AI power at home; and why a book about AI and geopolitics closes with a call for mindfulness, cognitive empathy, and transcending the psychology of tribalism.To receive ad-free podcasts, become a Lawfare Material Supporter at www.patreon.com/lawfare. You can also support Lawfare by making a one-time donation at https://givebutter.com/lawfare-institute.Support this show http://supporter.acast.com/lawfare. Hosted on Acast. See acast.com/privacy for more information.

Apex Fintech Solutions provides the tools and services that enable hundreds of clients to launch, scale, and support digital investing for tens of millions of end investors. The company provides essential infrastructure and a comprehensive ecosystem of cloud-based products to enable and streamline trading, wealth management, cost basis, tax reporting, and, through its subsidiary Apex Clearing™, custody and clearing. LEARN MORE: https://apexfintechsolutions.com/?utm_source=Risk+Reversal&utm_medium=Podcast&utm_campaign=701PJ00000fnXhaYAE SUBSCRIBE to our newsletter: http://riskreversal.substack.com/ Dan Nathan and Guy Adami are joined by Gene Munster, Managing Partner at Deepwater Asset Management, for a wide-ranging conversation recorded as the market sold off into the close — and as the guys sign off from their current studio one last time. They open on Micron's blowout quarter and the 16 strategic five-year customer agreements that have it up 20%, debating whether the historic boom-and-bust cyclicality is finally being priced out of memory. From there, Gene makes his case that the AI trade is still in the "second inning," walking through AGI, the gap between hype and adoption, the threat cheap open-source models out of China pose to model pricing, and why he thinks Google has gotten the best return on its AI investment so far. The group also digs into Apple's pricing power as memory costs spike — and the 2019 upgrade-cycle scare that still haunts the bulls — before closing on the SpaceX IPO one week in, the Tesla–SpaceX roll-up bet, and the state of robotaxi and full self-driving. Articles Referenced Why aren't more companies adopting AI? (FT) Fatal Tesla Crash Into Texas Home Now Under Federal Safety Investigation (WSJ) —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.

What if happiness is not something that happens to you, but something you practise every single day?In this series, I select my favourite and most insightful moments from previous episodes of the podcast.Today, my guest Andrew Matthews, a globally celebrated author and international speaker whose books on happiness and resilience have sold over 8 million copies, shares three simple but powerful daily practices that happy people do consistently.Press play to discover what genuinely happy people do differently, and how you can start doing the same today.˚VALUABLE RESOURCES:Listen to the full conversation with Andrew Matthews in episode #472:https://personaldevelopmentmasterypodcast.com/472˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚

In this solo episode, Andy Storch makes the case for why AI fluency isn't optional — it's the new baseline for employability. While the macro debates around AI (government regulation, ethics, AGI timelines) are worth following, Andy argues that the wave is coming regardless. The real question is: will you be ready?Andy draws a powerful analogy between the early days of listing "proficient in Microsoft Office" on a résumé and what AI proficiency means for today's workforce — except the stakes are exponentially higher. He shares what he's hearing from talent development professionals, company leaders, and frontline employees, and explains why the biggest gap isn't technology — it's adoption.He also shares exciting news: Andy is launching an AI Kickstarter Workshop (available this fall) — a hands-on 90-minute session designed to get teams not just learning about AI, but actually using it and producing real output during the session.In This Episode, You'll Hear:Why the AI wave is happening whether we embrace it or not — and why that matters for L&DThe Microsoft Office analogy: how AI proficiency today mirrors a skill that once set candidates apart on résumésWhat Andy is hearing from leaders and talent development professionals about low AI adoption ratesThe "60% in the middle" — why most employees aren't anti-AI, they just don't know where to startWhy employees who avoid AI fluency risk being left behind in hiring — and what you can do about itHow organizations are paying for AI licenses that no one is using, and what to do insteadThe two types of speaking engagements Andy is now booking around AI enablement and career ownershipDetails on Andy's upcoming AI Kickstarter Workshop — a 90-minute session to get your team actually using AI toolsResources & Links Mentioned:Andy's website: andystorch.comEmail Andy directly: andy@andystorch.comAI tools mentioned: ChatGPT, Claude, GeminiWork With Andy:Ready to get your team AI-fluent? Andy is booking two types of engagements:AI Kickstarter Workshop — A 90-minute hands-on session to get your team using AI tools and producing real output. Available this fall.Career Ownership & AI Keynote — For teams that have already started AI training but need a session on owning their careers and taking advantage of the opportunities in front of them.Reach out at andy@andystorch.com or visit andystorch.com.About The Talent Development Hot Seat:Hosted by Andy Storch, this podcast connects talent development professionals with insights, ideas, and inspiration to grow their careers and their organizations. With over 600 episodes published since 2018, the Hot Seat is your resource for what's next in L&D.

Alan Rozenshtein, Research Director at Lawfare, spoke with Robert Wright—author of Nonzero, The Moral Animal, The Evolution of God, and Why Buddhism Is True, and the writer behind the NonZero Newsletter and podcast—about his new book, The God Test: Artificial Intelligence and Our Coming Cosmic Reckoning, which argues that AI is an evolutionary threshold on the scale of the entire history of life, that we are collectively failing to grasp its magnitude, and that rising to the challenge will require both new forms of international governance and an expansion of human moral and cognitive perspective. The conversation covered the multiple meanings of the book's title and what it means to view AI from a "cosmic" perspective; whether the public is finally starting to "feel the AGI" and where skepticism about AI's capabilities now comes from; how large language models are trained and Wright's claim that we have built "machines that create machines that think"; whether these systems genuinely understand, what Searle's Chinese Room and Nagel's "what is it like to be a bat?" have to do with it, and the open question of AI moral patienthood; the two families of AI risk—bad actors empowered by AI versus AI itself going rogue—and why the near-term disruption to jobs, relationships, and security may matter most; the "But China!" argument against AI regulation, China hawkishness, and why Wright thinks racing toward superintelligence is dangerously destabilizing; the case for "global governance" over "world government" and the perils of concentrating AI power at home; and why a book about AI and geopolitics closes with a call for mindfulness, cognitive empathy, and transcending the psychology of tribalism. Hosted on Acast. See acast.com/privacy for more information.

Sam Partee (CTO & co-founder of Arcade.dev) and Nate Barbettini (Founding Engineer at Arcade.dev) sit down at the MCP Dev Summit to unpack what nobody wants to admit about the Model Context Protocol: the security model is still full of sharp edges. From tool poisoning and prompt injection to why OAuth got bolted onto the spec, this is a builder 's-eye view of where MCP breaks — and how to ship agents safely anyway.What we get into:

Artificial Intelligence wird die Welt von Grund auf verändern und damit das am Ende auch gut geht, müssen wichtige Weichen gestellt werden, ist der AI-Experte Matthias Samwald von der Meduni Wien überzeugt. Ein Podcast von Pragmaticus.Das Thema:Wer denkt, Künstliche Intelligenz seien Chatbots wie ChatGPT oder Gemini, irrt. Die wahre Künstliche Intelligenz steht erst in den Startlöchern und wird das Leben, so wie wir es kennen, von Grund auf verändern.General Purpose AI, auch AGI („Artificial General Intelligence“), wird in allen Bereichen der Gesellschaft Transformationen bringen, davon ist Matthias Samwald, Professor für Artificial Intelligence an der MedUni Wien, überzeugt. Deshalb sei es dringend notwendig, wichtige Strukturen wie unser Wirtschaftssystem oder auch unsere Demokratie zu schützen, sagt er.Zugleich beklagt er ein mangelndes Problembewusstsein für die allumfassende Veränderungskraft dieser Technologie. Vor allem, was die rekursive AI betrifft, also jene AI-Tools, die AI selbst verbessern, erwartet er sich bahnbrechende Innovationen. Die großen ungeklärten Fragen in sämtlichen Bereichen der Wissenschaft könnten gelöst werden. Das Ende von Krebs, Lösungen für Clean Energy: All das könnte die KI auf den Weg bringen.Allein: Nur wer über die entsprechende Rechenleistung verfügt, wird in der Topliga der Problemlösungen durch KI mitspielen, ist Samwald überzeugt. Europa hat gerade, was die Verfügbarkeit von Energie etwa für Data Centers betrifft, massiven Nachholbedarf. Die große Gefahr sei, dass sich die Gesellschaft auf keine gemeinsame Linie in dieser Zukunftsfrage einigen könne und Fragmentierung zu Stillstand in Sachen Künstlicher Intelligenz führen werde.Denn an sich, so Samwald, liege ungeahntes Potenzial in Artificial Intelligence. Statt Dystopien wünscht er sich gemeinsame Utopien, in deren Zentrum das Wohl aller Menschen steht.Unser Gast in dieser Folge: Matthias Samwald ist Professor für Artificial Intelligence an der Medizinischen Universität Wien und leitet „Accelerate Europe“, eine Initiative, die darauf abzielt, Europa auf die KI-Revolution vorzubereiten, die Wissenschaft, Wirtschaft und Regierungsführung noch in diesem Jahrzehnt grundlegend verändern könnte. Er vertritt die Ansicht, dass Europa seine Traditionen der Rechenschaftspflicht, der Zusammenarbeit und des gemeinsamen Wohlstands in Wettbewerbsvorteile umwandeln kann, wenn es diese mit dem Ehrgeiz und dem Tempo verbindet, die die aktuelle Situation erfordert. Samwald ist Mitglied des EU Frontier AI Expert Forum, eines wissenschaftlichen Gremiums, das als fachliches Beratungsorgan des AI Act, der europäischen Gesetzgebung zur Künstlichen Intelligenz fungiert. Seine Mitglieder unterstützen die Europäische Kommission und nationale Behörden bei Fragen zu KI, etwa beim Erkennen systemischer Risiken der leistungsfähigsten KI-Modelle, sowie bei der Entwicklung von Methoden zu deren Evaluation. Die Berufung knüpft unmittelbar an Samwalds frühere Arbeit als Co-Chair des Kapitels „Safety & Security“ des EU Code of Practice for General-Purpose AI an, der 2025 fertiggestellt wurde und auf dem führende Entwickler wie OpenAI, Google und Anthropic mittlerweile aufbauen.   Dies ist ein Podcast von Der Pragmaticus. Sie finden uns auch auf Instagram, Facebook, LinkedIn und X (Twitter).

What would you do differently if a single moment forced you to confront how fear has been shaping your entire definition of success?In this episode, Chris Woods shares how a life-threatening health crisis, where he flatlined in hospital, became the turning point that exposed the hidden fear-driven patterns behind his high-achieving career at Google and beyond. If you've ever felt successful on the outside but anxious, driven, or disconnected on the inside, this conversation will help you make sense of what's really driving your decisions and how to start shifting it.By listening to this episode, you will discover how to:Recognise fear-based thinking patterns that quietly shape your career, choices, and sense of selfShift from fixing weaknesses to intentionally building your life around your natural strengthsRedefine success in a way that leads to genuine fulfillment rather than constant achievement pressurePress play now to learn how to step out of fear-driven success and start building a more aligned, strengths-led, and fulfilling way of living.˚KEY POINTS AND TIMESTAMPS:04:02 - Life before the health crisis and fear-driven achievement05:17 - Health crisis: tick bite, cardiac Lyme disease, and flatlining08:46 - Gratitude and realisation after a second chance12:35 - “Who would you be without your concerns?” exercise17:58 - Why focusing on strengths beats fixing weaknesses20:38 - Redefining success after leaving Google28:14 - Where to find Chris Woods and his coaching work˚MEMORABLE QUOTE:"I was living to build an obituary as opposed to living to build a life."˚VALUABLE RESOURCES:Chris' website: https://chriswoodscoach.com˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚

Argent, puces, États : dans les coulisses de la course à l'IA

Dialog, the pet Bilderberg style secret society of Peter Thiel, is now an open secret. Is that a good or bad thing for PT and friends? The countercultural capture of the online world has to be recognized.    Topics include: confusion, technological change, AI, AGI, possibility of Technological Singularity, economic bubble, practical applications and reasons for AI, Libertarianism is useless, enshitification of internet, Dialog secret society, Bilderberg, Peter Thiel, secret societies, Elon Musk as proof of concept for building Tech Oligarch cult of personality, elites of recent past, Palantir, US technocratic takeover, online content creators are the new propagandists, useful idiots and conscious agents, MAGA trojan horse worked, transhuman future, transhumanism, forcing masses into metaverse life, living in Roblox, luxury bunkers, true globalists, possibility that counter cultural social media accounts are actually helpful to the establishment, Conspiracy Culture thoroughly hijacked

Everyone loves the idea of a shortcut to disruption: convince 10% of the market, and the rest will follow. But what if one of business's favorite rules of thumb is completely wrong? In this episode of The Valley Current®, host Jack Russo examines emerging research suggesting that real business transformations may require closer to a 25% tipping point before change becomes unstoppable. Drawing on Malcolm Gladwell's The Tipping Point, committed-minority theory, and the Overton Window, Jack introduces a practical framework for separating genuine market shifts from overhyped narratives. He then puts today's biggest technological bets including AI, AGI, and humanoid robotics through a five-part stress test to determine whether they have truly crossed the chasm into mainstream adoption. Are these technologies reshaping the future of business, or are we mistaking momentum and headlines for inevitability? This episode offers leaders a smarter way to distinguish real tipping points from expensive illusions. Jack Russo Managing Partner Jrusso@computerlaw.com www.computerlaw.com https://www.linkedin.com/in/jackrusso "Every Entrepreneur Imagines a Better World"®️  

Beth breaks down Max Tegmark's 12 futures framework from Life 3.0 — from AI extinction to human utopia — and explains why world leaders need to start picking which scenario humanity actually wants. #AI #ArtificialIntelligence #MaxTegmark #Life30 #AGI #Superintelligence #FutureOfHumanity #Extinction #Philosophy #Metaphysics #QuantumBombs #HumanCondition #SamAltman #Anthropic #OpenAI #TechOligarchs #Consciousness #FreeWill #Utopia #Dystopia #DigitalSurveillance

today we provide a multifaceted analysis of the transition toward Artificial General Intelligence (AGI) and its subsequent evolution into superintelligence. Forecasting data from platforms like Metaculus and Manifold suggest a median arrival date for AGI around 2031, while researchers utilize biological anchors to estimate the computational power required to replicate human cognition. Google DeepMind and industry analysts explore the "intelligence explosion" that may follow, where self-improving systems rapidly surpass human capabilities across all domains. From a geopolitical perspective, RAND Corporation outlines various scenarios where the centralization or decentralization of this technology could either empower the United States, benefit its adversaries, or destabilize global security. The collection emphasizes that the coming decade will likely be defined by an intense industrial mobilization for computing infrastructure and a critical race for national security preeminence. Ultimately, the texts highlight the urgent need for interdisciplinary preparation to manage the profound economic, military, and existential shifts triggered by advanced AI.

Age of Transitions and Uncle The Podcast 6-19-2026 AoT#497Dialog, the pet Bilderberg style secret society of Peter Thiel, is now an open secret. Is that a good or bad thing for PT and friends? The countercultural capture of the online world has to be recognized. Topics include: confusion, technological change, AI, AGI, possibility of Technological Singularity, economic bubble, practical applications and reasons for AI, Libertarianism is useless, enshitification of internet, Dialog secret society, Bilderberg, Peter Thiel, secret societies, Elon Musk as proof of concept for building Tech Oligarch cult of personality, elites of recent past, Palantir, US technocratic takeover, online content creators are the new propagandists, useful idiots and conscious agents, MAGA trojan horse worked, transhuman future, transhumanism, forcing masses into metaverse life, living in Roblox, luxury bunkers, true globalists, possibility that counter cultural social media accounts are actually helpful to the establishment, Conspiracy Culture thoroughly hijackedUtp#403Uncle is back, and the Crack Room is lively. Topics include: World Cup ref shirt colors, TikTak, return to Human Computer, bright colorized nature photos for streams, clear hair shoebox hat, lively chat rooms, Baja Blast, Large Hadron Collider, public access TV, German pub Ochelli Radio Network fans, Scottish soccer fans in Boston, Mets and Tigers MLBFRANZ MAIN HUB:https://theageoftransitions.com/PATREONhttps://www.patreon.com/aaronfranzUNCLEhttps://unclethepodcast.com/ORhttps://theageoftransitions.com/category/uncle-the-podcast/FRANZ and UNCLE Merchhttps://theageoftransitions.com/category/support-the-podcasts/---BE THE EFFECTCash APP$TheOchelliEffectMrs.OLUNA ROSA CANDLEShttp://www.paypal.me/Kimberlysonn1Become a supporter of this podcast: https://www.spreaker.com/podcast/the-ochelli-effect--4331265/support.BE THE EFFECTListen/Chat on the Sitehttps://ochelli.com/listen-live/TuneInhttp://tun.in/sfxkxAPPLEhttps://music.apple.com/us/station/ochelli-com/ra.1461174708Ochelli Link Treehttps://linktr.ee/chuckochelliAnything is a blessing if you have the meansWithout YOUR support we go silent

In dieser Folge ordnen wir die Entwicklung im digitalen Ökosystem und im E-Commerce ein und sprechen zunächst über die Frage, wie sich Frontier-Modelle wie OpenAI und Anthropic von destillierten Modellen unterscheiden. Wir erklären, dass Destillation bedeutet, ein kleineres, günstigeres Modell mit dem Output eines großen Modells zu trainieren, und diskutieren, warum dies technisch schwer zu verhindern ist. Wir sprechen dann über die wirtschaftliche Seite dieser Modelle und darüber, ob die hohen Bewertungen von OpenAI und Anthropic gerechtfertigt sind. Aus unserer Sicht sind die Bewertungen sehr ambitioniert, weil die Kosten für neue Modelle stark steigen, viele Anwendungsfälle keine AGI erfordern und günstigere Modelle oft ausreichen. Dazu kommt, dass die Zahlungsbereitschaft vieler Nutzer und Unternehmen begrenzt ist. Ein weiterer Schwerpunkt ist die Frage, wer am Ende die Wertschöpfung kontrolliert. Wir diskutieren die These, dass große Plattformen wie Google, Apple oder Microsoft langfristig eher als Distributions- und App-Store-Ebene profitieren könnten, während die Modellanbieter unter Druck geraten. Dabei geht es auch um die geringe Zahl zahlender Nutzer und die Bedeutung einer besseren Nutzeroberfläche für KI-Anwendungen. Außerdem sprechen wir über einen kurzfristigen Zugangsstopp zu einem neuen Anthropic-Modell und was das über Abhängigkeiten von US-Technologie zeigt. Wir sehen darin vor allem ein Beispiel dafür, wie schnell Zugänge zu zentralen Tools eingeschränkt werden können, und warum europäische eigene Fähigkeiten wichtiger werden. Vorbereitungsdokument von Julian: https://www.kassenzone.de/wp-content/uploads/2026/06/Distillation.pdf Partner in der Folge: https://linktr.ee/kassenzone Community: https://kassenzone.de/discord Feedback zum Podcast? Mail an alex@kassenzone.de Disclaimer: https://www.kassenzone.de/disclaimer/ Kassenzone” wird vermarktet von Podstars by OMR. Du möchtest in “Kassenzone” werben? Dann https://podstars.de/kontakt/?utm_source=podcast&utm_campaign=shownotes_kassenzone Alexander Graf: https://www.linkedin.com/in/alexandergraf/ https://twitter.com/supergraf Youtube: https://www.youtube.com/c/KassenzoneDe/ Blog: https://www.kassenzone.de/ E-Commerce Buch 2019: https://amzn.eu/d/5Adc1ZH Plattformbuch 2024: https://amzn.eu/d/1tAk82E

Únete a la Lista de Espera de La Tribu Divisual: https://divisualproject.academy/crossover Youtube de Juanpe: https://www.youtube.com/@juanpe.divisual Instagram de Juanpe: https://www.instagram.com/juanpe.divisual/ ¿La IA nos hará más inteligentes… o más dependientes? La inteligencia artificial promete cambiarlo todo. Algunos aseguran que destruirá millones de empleos. Otros creen que nos llevará a una nueva era de productividad y prosperidad. En este episodio de Crossover hablamos con el emprendedor y experto en IA Juanpe Navarro para entender qué está ocurriendo realmente detrás del hype. Analizamos el estado actual de la inteligencia artificial, la llegada de la AGI, los agentes autónomos, el futuro del trabajo, la automatización de empresas, la guerra tecnológica entre Estados Unidos y China y el impacto que todo esto tendrá en nuestra sociedad. Pero también hablamos de algo más importante: el factor humano. ¿Estamos utilizando la IA para ser más eficientes o para dejar de pensar? ¿Puede una inteligencia artificial sustituir la creatividad, las emociones o las relaciones humanas? ¿Qué oportunidades reales existen para emprendedores, empresas y profesionales? Una conversación profunda sobre tecnología, productividad, futuro y sobre qué significa seguir siendo humanos en una era dominada por algoritmos. ⏱️ Índice temporal 00:00 Introducción: ¿la IA cambiará el mundo? 01:36 En qué punto estamos realmente con la inteligencia artificial 02:36 ¿Qué es la AGI y cuándo llegará? 04:14 ¿Puede la IA superar al ser humano? 06:23 ¿Existe un riesgo real para la humanidad? 07:02 ¿La IA destruirá empleos o creará más? 09:34 Comparación con la Revolución Industrial 11:43 La historia de Juanpe y su entrada en la IA 14:04 Cómo ganar dinero con la inteligencia artificial 15:54 Por qué NO recomienda usar IA para temas personales 19:23 Claude Code, N8N y las herramientas más importantes 24:36 Agentes autónomos: qué pueden hacer realmente 28:20 Cómo empezar a aprender IA desde cero 32:43 La guerra entre OpenAI, Google, Anthropic y China 38:26 ¿Está subvencionada la IA? ¿Subirán los precios? 43:36 El futuro de la creación de contenido con IA 50:11 El caso de Claude Mithos y los riesgos de la IA avanzada 56:29 ¿La IA hará una sociedad mejor o peor? 01:01:06 Conclusiones y despedida Temas que tratamos: * Qué es realmente la AGI y cuándo podría llegar * Los riesgos y oportunidades de la inteligencia artificial * Cómo afectará al empleo y a las empresas * Agentes autónomos y automatización de procesos * Herramientas como Claude Code, N8N o ChatGPT * La batalla entre OpenAI, Google, Anthropic y China * IA y creación de contenido audiovisual * El peligro de delegar demasiado pensamiento en la tecnología * Cómo prepararse profesionalmente para el futuro 📲 Síguenos en redes para no perderte el próximo episodio: • YouTube: https://www.youtube.com/channel/UCsKDcxNw7TaJwyjd2iH0QWg • Instagram: https://www.instagram.com/crossoverofc/ • TikTok: https://www.tiktok.com/@crossoverofc • X: https://x.com/crossoverofc 🔔 Suscríbete y activa la campanita para no perderte nada.

ID The Future listeners now get to enjoy two episodes each month from our sister podcast Mind Matters News, a production of the Discovery Institute's Walter Bradley Center for Natural and Artificial Intelligence. The Mind Matters News podcast brings you insight from computer scientists, engineers, inventors, neurosurgeons, and other experts who bring sanity to the conversation about natural and artificial intelligence, going beyond the hype to explore the undercurrents of these important ideas. And although the Mind Matters News podcast will not often explicitly discuss intelligent design, it regularly explores the nature of intelligence, the origin of information, and the things that make us uniquely human, all concepts that are central to the theory of intelligent design. Enjoy today's offering of Mind Matters News! In this installment of the Mind Matters News podcast, host Robert J. Marks welcomes economics professor and author Gary Smith to discuss the hype around artificial general intelligence (AGI) and AI's impact on the market. Smith is the Fletcher Jones Professor of Economics at Ponoma College and a frequent contributor to Mind Matters News. Smith argues that generative AI, embodied in services like ChatGPT and Google's Gemini, exhibits many characteristics of past market bubbles, including excessive hype, lack of profitability, and unrealistic expectations. Smith holds that generative AI models have limited practical economic value. They may be good at finding statistical patterns but struggle to distinguish meaningful, useful correlations from coincidental ones. Smith describes the fundamental challenge of teaching machines true understanding that goes beyond mere pattern recognition. A number of examples and stories are shared throughout. Source

One popular objection to AI concerns is to declare that LLMs can never be AGI. You need a "new paradigm". Therefore, AGI is so far in the future that it's not worth worrying about. A common counterargument is to claim that no, LLMs can become AGI. But even without that counterargument, I think the "therefore" fails on its own terms. The key question is: how much of a new paradigm do we need? The landmark discoveries on the road to modern LLMs are something like: 1950s: Neural networks 1967: Multi-layer perceptron 2010: Modern deep learning 2017: Transformer, LLM 2022: RLHF, chatbots 2024: Chain of thought / test-time compute We can think of this as an "evolutionary tree", where a given LLM (let's say Claude Opus 4.7) shares a recent "common ancestor" with all other chatbots, and only a very distant "common ancestor" with everything else descended from the multi-layer perceptron. If AGI needs a "new paradigm", what common ancestor can we expect AGI and LLMs to share? AGI will very likely use neural networks, because the human brain is a neural network and qualifies as an AGI. It will probably use deep learning, because although deep learning isn't exactly analogous to the brain, it seems like a pretty reasonable way to emulate the brain's learning algorithms onto computer hardware. Skeptics like Yann LeCun and Gary Marcus usually pinpoint LLMs/transformers as the step where we went wrong. LeCun thinks that the first AGIs may be within the deep learning paradigm (but not LLMs); Marcus thinks that they'll combine insights from deep learning with something else. How soon should we expect a new paradigm as revolutionary as LLMs/transformers? Since we got LLMs/transformers nine years ago, Lindy's Law suggests nine more years. How soon should we expect a new paradigm as revolutionary as deep learning? By the same logic, sixteen years from now. Lindy's Law has a heavy tail, which means we can't simply halve these to find our 25th percentile estimate. Our 25th percentile estimate for the next advance as exciting as LLMs should be three years from now; for deep learning, it's five years. So even if you think AGI will require a further paradigm shift as big as the invention of the LLM or as deep learning itself, you should have 25% chance it will be developed in the next 3 - 5 years. Which is about as long as the LLM-only crowd think things will take! This isn't an excuse for relegating the risk of AGI to some vague indefinite future. It could still be the late 2020s or early 2030s! (Might we expect that low-hanging-fruit effects make the next paradigm harder to find than the last one? In practice, fields get more researchers as time goes on, and that effect usually causes time-between-advances to be approximately constant. And in fact, the number of AI researchers has grown at an unprecedented pace for a scientific field, and growth will enter an even faster regime once AIs themselves can contribute. Overall these make me think things will go even faster than Lindy's Law predicts - but I think Lindy's Law is a useful upper bound.) (Would there still be a long time between the invention of the new paradigm and the point where it could be used to maximum effect? It took five years between the invention of the transformer and ChatGPT, the first commercially-successful transformer-based project. But most of that time was spent scaling up, and we've already scaled up. If we invent a new paradigm in 2030, then any frontier lab willing to bet on it can quickly provide it with levels of compute sufficient to train human-brain-sized models.) This is my attempt to talk to the new-paradigm-wanters in their own language, but I think there's also a subtler point that undermines this worldview. In the past, new paradigms have proven useful in allowing scaling to continue after an old paradigm passed the regime where it could efficiently convert scale to results. LLMs still seem to be able to convert scale to results; while this continues, new paradigms won't be necessary, and frontier labs won't risk pursuing them. If scaling ever hits a wall, there will be a few months of confusion as frontier labs look over various new-paradigm-proposals that they already have lying around, and throw them at the wall to see what breaks through. Then scaling will continue from wherever it left off. The best way to forecast future AI progress is to extrapolate from current LLM scaling. This should work if LLMs scale all the way to AGI. But it may also work even if they don't. First, because we might get the new paradigm so soon that it won't be a significant source of delay. And second, because the most likely place for a new paradigm to start is wherever LLMs stop working, going at the same rate. https://www.astralcodexten.com/p/new-paradigms-wont-save-you

Shelly Palmer has spent 45 years watching technology reshape every industry—from writing news themes for CBS to consulting with every major media company on AI strategy.On this year-end recap, he cuts through the noise with one devastating observation: 2025 was the year everyone talked about AI while almost nobody actually used it. Executives shook their heads knowingly in meetings, pontificated about capabilities the models don't yet have, and parroted nonsense they read from other people who knew nothing. But when you asked one innocent question, they crumbled.In the News: CES 2026 shapes up with Nvidia sponsoring two full days of AI training. Samsung is skipping the main floor for a massive offsite activation. Sony brings no electronics—only Honda's experimental vehicles. The TCL and Chinese companies' presence hinges on tariff policy. The innovation series breakfast that Shelly runs is becoming an official CES event after a decade of independence.The conversation spirals into deeper territory: $3 trillion in government money is stacked behind AI development. The U.S. explicitly states it must beat China to AGI—making this the Manhattan Project of our lifetime. Shelly walks through what he's seen in successful companies (leadership using the tech, paid "Tech Tuesdays" for AI experiments, cross-discipline teams with SecOps and legal at the table) versus the chaos of places with no process.He breaks down what's real—drone warfare, cybersecurity applications, robotics—versus what's hot air. And he makes a case that won't be killed by AI itself, but by militarized applications and the geopolitical arms race we're already in.5 Key Takeaways from Shelly:Leadership belief and hands-on use are non-negotiable. Companies winning with AI have senior leaders who actually use the technology. When the CEO walks into an LT meeting saying "I built this agent over the weekend," everyone else starts experimenting too.The recipe for AI success has three ingredients: leadership belief, paid time to experiment (Tech Tuesdays/Thursdays with real budgets), and cross-discipline teams (SecOps, legal, compliance, risk) paving the way. Chaos erupts without this structure.You cannot build a point of view on AI from reading blogs or watching YouTubers. Pick a personal project you care about, go hands-on with a model (Claude, Gemini, GPT), and complete it from beginning to end. Only lived experience grounds your understanding.AI parallelizes with web 1.0: In 1998, you had to hand-code HTML, build databases manually, write raw JavaScript. Today you can vibe code a site in 90 seconds. AI will eventually reach "spin me up an expert that does X" without asking questions—we're not there yet, but it's inevitable.It's both bubble and Manhattan Project. Some valuations are insane and will burst. But military applications, cyber warfare, drone control, robotics—those aren't going anywhere. The government won't back off. Both outcomes happen simultaneously.This episode is brought to you by Zappar, creators of Mattercraft—the leading visual development environment for building immersive 3D web experiences for mobile headsets and desktop.Mattercraft combines game engine power with web flexibility and features an AI assistant to help you design, code, and debug in real time in your browser. Build smarter at mattercraft.io. Hosted on Acast. See acast.com/privacy for more information.

Our guest this week, Alvin Wang Graylin spent 35 years in senior leadership roles across HTC, IBM, and other major tech companies. He ran HTC's VR division, came out of the famous HIT Lab, now teaches at MIT, holds a fellowship at Stanford, and just published a paper called "Beyond Rivalry" proposing a seven-point plan for deescalating US-China AI tensions and building a global safety net before the economy breaks.His thesis: America is the fastest in the AI race and the least prepared for what it's creating—a cliff where human labor theory of value collapses, capital concentration accelerates, and 40% of the population living month to month faces chaos.The conversation becomes a wide-ranging debate between Alvin, Charlie, and Rony about whether AGI will be benevolent by default (Alvin's position: research shows smarter AI seeks global coherence and becomes less controllable by individual humans, which may actually make it safer) or whether benevolence must be designed in from scratch.AI XR News You Should Know: Elon Musk merges SpaceX, xAI, and X into a single entity—Alvin dismantles the space data center concept with physics (vacuum cooling is a myth, micro-meteorite collisions would destroy hardware daily, and energy is only 10% of data center costs).Amazon invests $50 billion in OpenAI that round-trips back to AWS. Alphabet breaks revenue records at $400 billion but spooks investors by disclosing $90 billion in AI spending. ElevenLabs raises $500 million at $11 billion valuation. Rony's SynthBee hits unicorn status with $100 million raised at a multi-billion dollar valuation.Alvin warns the AI bubble dwarfs the dot-com era (298 companies raised $24 billion total during dot-com; OpenAI alone is raising that in a single private round) and predicts OpenAI may implode before going public.Key Moments Timestamps:[00:04:47] SpaceX/xAI/X merger: Rony calls it Elon's "return to Tony Stark form"[00:06:41] Alvin dismantles space data centers with physics: vacuum cooling myth, micro-meteorites, $7K/kg launch costs[00:10:04] Amazon's $50B investment in OpenAI as a round-trip to AWS; the scam economy[00:11:26] Alvin predicts OpenAI may implode before going public[00:14:23] Alvin on 35 years in AI: the technology is transformational but everyone's making a commodity product[00:17:04] The AI bubble dwarfs dot-com: $24B total vs. single private rounds today[00:19:04] Rony's contrarian: the $110 trillion global economy is what's being bet against[00:21:06] Labor theory of value collapses: what happens when humans exit the production cycle[00:23:00] America is fastest in the AI race and least prepared; 40% live month to month[00:24:00] Alvin's Stanford paper "Beyond Rivalry": a CERN for AI and global data pool[00:28:00] Davos reflections: the rest of the world is more rational than America[00:34:00] Chinese vs. American culture: reverence for teachers, respect for elders[00:42:00] Alvin's "Abundant" framework: valuing human dignity over production after AGI[00:44:22] The great debate: will AGI find benevolence naturally (Alvin) or must it be designed in (Rony)?[00:47:00] Rony on risk: AGI systems are unverifiable, untestable, and we cannot take the chanceListen to the full episode and subscribe to the AI XR Podcast for weekly conversations at the intersection of AI, XR, and the future of humanity.This episode is brought to you by Zappar, creators of Mattercraft—the leading visual development environment for building immersive 3D web experiences for mobile headsets and desktop. Build smarter at mattercraft.io. Hosted on Acast. See acast.com/privacy for more information.

Únete a la Lista de Espera de La Tribu Divisual: https://divisualproject.academy/crossover Youtube de Juanpe: https://www.youtube.com/@juanpe.divisual Instagram de Juanpe: https://www.instagram.com/juanpe.divisual/ ¿La IA nos hará más inteligentes… o más dependientes? La inteligencia artificial promete cambiarlo todo. Algunos aseguran que destruirá millones de empleos. Otros creen que nos llevará a una nueva era de productividad y prosperidad. En este episodio de Crossover hablamos con el emprendedor y experto en IA Juanpe Navarro para entender qué está ocurriendo realmente detrás del hype. Analizamos el estado actual de la inteligencia artificial, la llegada de la AGI, los agentes autónomos, el futuro del trabajo, la automatización de empresas, la guerra tecnológica entre Estados Unidos y China y el impacto que todo esto tendrá en nuestra sociedad. Pero también hablamos de algo más importante: el factor humano. ¿Estamos utilizando la IA para ser más eficientes o para dejar de pensar? ¿Puede una inteligencia artificial sustituir la creatividad, las emociones o las relaciones humanas? ¿Qué oportunidades reales existen para emprendedores, empresas y profesionales? Una conversación profunda sobre tecnología, productividad, futuro y sobre qué significa seguir siendo humanos en una era dominada por algoritmos. ⏱️ Índice temporal 00:00 Introducción: ¿la IA cambiará el mundo? 01:36 En qué punto estamos realmente con la inteligencia artificial 02:36 ¿Qué es la AGI y cuándo llegará? 04:14 ¿Puede la IA superar al ser humano? 06:23 ¿Existe un riesgo real para la humanidad? 07:02 ¿La IA destruirá empleos o creará más? 09:34 Comparación con la Revolución Industrial 11:43 La historia de Juanpe y su entrada en la IA 14:04 Cómo ganar dinero con la inteligencia artificial 15:54 Por qué NO recomienda usar IA para temas personales 19:23 Claude Code, N8N y las herramientas más importantes 24:36 Agentes autónomos: qué pueden hacer realmente 28:20 Cómo empezar a aprender IA desde cero 32:43 La guerra entre OpenAI, Google, Anthropic y China 38:26 ¿Está subvencionada la IA? ¿Subirán los precios? 43:36 El futuro de la creación de contenido con IA 50:11 El caso de Claude Mithos y los riesgos de la IA avanzada 56:29 ¿La IA hará una sociedad mejor o peor? 01:01:06 Conclusiones y despedida Temas que tratamos: * Qué es realmente la AGI y cuándo podría llegar * Los riesgos y oportunidades de la inteligencia artificial * Cómo afectará al empleo y a las empresas * Agentes autónomos y automatización de procesos * Herramientas como Claude Code, N8N o ChatGPT * La batalla entre OpenAI, Google, Anthropic y China * IA y creación de contenido audiovisual * El peligro de delegar demasiado pensamiento en la tecnología * Cómo prepararse profesionalmente para el futuro 📲 Síguenos en redes para no perderte el próximo episodio: • YouTube: https://www.youtube.com/channel/UCsKDcxNw7TaJwyjd2iH0QWg • Instagram: https://www.instagram.com/crossoverofc/ • TikTok: https://www.tiktok.com/@crossoverofc • X: https://x.com/crossoverofc 🔔 Suscríbete y activa la campanita para no perderte nada.

What if the love you received growing up was real, but expressed in a language that left you chasing perfection instead of feeling accepted?In this series, I select my favourite and most insightful moments from previous episodes of the podcast.Today, my guest Rohene Bouajram, international speaker and leadership coach, shares why self-love is not a nice addition to inner work, but it is its foundation.Press play to hear one of the most moving reflections on inner work, self-love and generational healing I have ever shared on this podcast.˚VALUABLE RESOURCES:Listen to the full conversation with Rohene Bouajram in episode #432:https://personaldevelopmentmasterypodcast.com/432˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚

Last 4 days before regular tickets sell out at AI Engineer World's Fair - this is the single biggest gathering of AI Engineers, Founders, Leaders, and Researchers in the world. Attendees get >$5000 worth of sponsor credits and talk tracks are looking FANTASTIC. Join us!The AI scaling debate always focuses on the question of “how do we get more GPUs?” but the better question may be: how do we make the most of ones we already have.The fact that a frontier lab like xAI could be running at sub-10% MFU (Model FLOPs Utilization) is just a hint at what the real problem may be.For context, older frontier-scale training runs were already much higher than 10%. GPT-3 was around 21% MFU. Gopher was around 32%. Megatron-Turing NLG was around 30%. PaLM reached around 46%. And our guest Anjney says best-in-class MFU today is closer to 60–70%.It's not necessarily that xAI is uniquely incompetent (it's clear they have talented folks) but rather the priorities may be flipped in the GPU arms race.While GPU access is a bottleneck, simply increasing CapEx won't automatically translate to better models as frontier AI is increasingly a systems problem: scheduling, utilization, networking, kernels, frameworks, data pipelines, parallelism, cluster reliability, and the thousand small decisions that determine whether your theoretical FLOPs become real training progress.From building Discord's developer platform and backing frontier AI companies like Anthropic, Mistral, Black Forest Labs, and Periodic Labs to now building AMP's independent compute grid, Anjney Midha has spent years close to the real bottlenecks of AI scaling. In this episode, Anjney joins swyx at Periodic Labs to unpack why the AI race is not just about buying more GPUs, why 95% utilization would have been considered an outage at Google, and why the next era of AI infrastructure has to be more aligned, more efficient, and more responsible.We go deep on AMP's vision for a compute grid that makes FLOPs flow like megawatts, the difference between full-stack AI labs and horizontal pooling, why AI data centers need community buy-in, and how compute markets could evolve into something closer to an independent system operator. Anjney also explains why DeepMind's unpublished research points to a market failure, why end-of-life prediction remains one of the most important AI applications he has thought about for fourteen years, and why “output maxing” may become a new discipline for frontier systems.We also discuss Anthropic's culture, why “luck favors the prepared mind” in coding models, how Claude cracked coding, why too much capital too early can make AI labs fragile, what Periodic Labs is trying to do with science and superconductors, why great researchers can become great CEOs, and why Silicon Valley is both deeply missionary and deeply mercenary.We discuss:* Why 95% utilization was considered an outage at Google* Why AI infrastructure waste compounds at frontier-lab scale* Why “move fast and break things” does not work for AI data centers* How data center backlash, power grids, and community incentives shape AI scaling* AMP's vision for making FLOPs flow like megawatts* Why compute needs an independent system operator* How interruptible demand and dynamic prioritization worked inside Google* Why DeepMind research hoarding creates negative externalities* AMP's 1.2GW base-load ambition and the need for 6GW of spike capacity* Why end-of-life prediction could become one of AI's most important healthcare applications* Frontier Systems, output maxing, and full-stack alignment* Why APIs and abstraction layers become lossy as organizations scale* Superconductors, standards, and the dream of lossless systems* SF Compute, open protocols, and the future of compute marketplaces* Why non-NVIDIA chips can still benefit from NVIDIA's reference architecture* Trust boundaries and why chip startups need visibility into future model architectures* Why VCs often underestimate researchers as CEOs* Scientists as star athletes of the mind* Why great CEOs need to be confrontational up and down the stack* Why leading the frontier matters more than “winning”* How Anthropic cracked coding* Why culture is fragile, not a permanent moat* Why hardship was a feature, not a bug, for Anthropic* Why Anthropic's P0 was coding from day one* Periodic Labs, physics as the constraint, and technical reality* Silicon Valley mercenaries, missionary teams, and what happens after a breakthroughAnjney Midha* LinkedIn: https://www.linkedin.com/in/anjney* X: https://x.com/AnjneyMidhaAMP PBC* Website: https://amppublic.com/* X: https://x.com/amppublicTimestamps00:00:00 Introduction00:00:09 Why AI Compute Is Being Wasted00:03:17 Responsible Infrastructure and Data Center Backlash00:06:07 AMP Grid: Making FLOPs Flow Like Megawatts00:12:41 Foundry, Frontier Labs, and Research Hoarding00:14:42 Gigawatt-Scale Compute and End-of-Life Prediction00:24:08 Frontier Systems, Output Maxing, and Alignment00:27:38 Compute Markets, SF Compute, and Non-NVIDIA Chips00:32:57 Trust Boundaries, Co-Design, and Researcher CEOs00:38:17 AI Coachella and First-Principles Thinking00:42:43 Leading vs Winning in Frontier AI00:45:54 How Anthropic Cracked Coding00:48:25 Culture, Hardship, and Anthropic's P000:54:03 Periodic Labs, Physics, and Silicon Valley Mercenaries00:56:26 Rishi Valley, Singapore, and Money as a Measure00:58:47 Closing ThoughtsTranscriptIntroduction: Anjney Midha, AMP, and Compute WasteSwyx [00:00:00]: We're in Periodic Labs with Anjney Midha, CEO, founder of AMP. Welcome.Compute Utilization: Node Allocation, MFU, and AlignmentAnjney [00:00:09]: Thanks for having me. At Google, there are two types of utilization usually, right? That you're measuring in these clusters. One is node allocation, and then the other's MFU. Node utilization is usually like what percentage of cards in the data center are just, used, and that, if it's not at, 95%-Swyx [00:00:29]: There is no excuseAnjney [00:00:29]: There's no excuse, right? I think 95% at Google, which is where my co-founder, Seb, came from, he built the Borg, PBorg/GQM scheduler at Google, and there I think 95% was considered an outage, so 96% node utilization is, should be standard. And most single-tenant clusters are not running at that. So that's one. And then MFU should be, I would say the best in class today is somewhere between 60 and 70%. I think this is a leadership question, right? Fundamentally it's an alignment question, which is are the people who are funding the cluster and then deploying the cluster actually aligned? And sometimes theoretically they are, but in practice the number of people in the chain, the supply chain between, the capital and all the way to whoever's managing the cluster and then whoever's measuring what the output is, are just so many, degrees of separation away that, the, The Have you ever heard the radian metaphor, which is at the beginning of an arc, if you have two arcs that are two lines that are just off by a few degrees, that-Swyx [00:01:33]: It spreads outAnjney [00:01:34]: It spreads out, right? Or at scale. And I think what's happening is a lot of cluster implementations and infrastructure, a lot of frontier labs and other teams, that's what's happening, is they're, they initialize the plan, which is kind of like North Star with a team that wants to do good, but then they're, required to scale so fast instead of iteratively that the wastage just compounds really fast at scale. And so I think we know the answer, which is just do iterative bring ups. If you spend time with people who've been in the semiconductor industry or the DSN industry for a long time, this is not new, and I don't think AI should be an excuse. Sure. Something What is new? Okay. We have a lot of new capabilities, but that doesn't mean just abandon common sense. Common sense should always be in fashion. ? AI scaling doesn't change the in fact, if anything, AI scaling should be putting a premium on the value of common sense and infrastructure because the margin of error now is so much lower and the costs of wastage are so much higher. And the cost of wastage, by the way, is not just economic. I'm, obviously I'm, I'm an investor, or I'm an investor by background. Over the last few years now we're running an AI infrastructure business called, AMP. And I think that it's okay to say this time is different on the capabilities front. We are genuinely getting capabilities at, of the, of a kind we haven't had before. That doesn't give you an excuse to say this time is different for everything, especially infrastructure. So look, I love the hacker mindset and the hustler mindset. Now, that's great for the startup mindset, but you remember this moment where Zuck went from saying, “Move fast, break things” to, move-Responsible Infrastructure and Data Center BacklashSwyx [00:03:10]: Fast and stable infrastructureAnjney [00:03:11]: Move fast with stable infrastructure. I think now we need to move fast with, responsible infrastructure. People are going to ask where the impact is. There was a really In our class yesterday, Scott Nolan, who's the founder of General Matter, came by at Stanford to speak about energy bottlenecks. And he had a phenomenal idea. He said, “if you look at the marginal unit economics of compute per hour,” he goes, “let's call it, $4 an hour. If you're having to bring up a new data center in a new community, why not just say we're going to charge 4.50 an hour, and that marginal impact or that marginal increase, we just literally take that and give it to the local community as cash?” I can tell you as a customer of that compute, I would love that. I'd be happy to pay an additional 50 cents per hour at scale.Swyx [00:03:57]: Wow. Yeah.Anjney [00:03:58]: Because if that means the public benefit is so clear to the communities that the data centers are coming up in, I'm going to feel like that compute is much more reliable. Up to 20% of all data centers this year in the US, my understanding is are at risk.Swyx [00:04:13]: Of community backlash?Anjney [00:04:14]: Correct. Of not getting the community support they need to get brought up.Swyx [00:04:19]: Wow. That's a huge number.Anjney [00:04:20]: Yeah. Now, we, I think we should dig into what that number is. I think it's a little bit of overstated. These things can get over-reported, but it-Swyx [00:04:27]: They don't just care about jobs. They care about all the other stuff around it, right? They care about power grid, they care about environments-Anjney [00:04:33]: Power grid, permitting, and so on. And imagine I think if you said there's a new AI deal. If we're bringing up a data center in your community, we're actually going to reduce the cost of your electricity bill. Okay, now we're talking. Right? The community's going, “Okay. Now this is a deal. I feel like a partner in this.” Right now that's not happening. There will be audits, there will be investigations, and when the, when the regulators come, I don't know when it's going to be, the folks who are moving fast and breaking things in the name of AI progress better be prepared. That's certainly not how we're procuring compute. Or we're, we're trying as much as we can to work with partners who have long-term track records. Many of whom, by the way, are not, AI providers. I think this whole idea of neoclouds being somehow this new category is a lot of marketing speak. There are really good, reliable, trusted data center providers in America who've been around 20 plus years. I love those folks. They know how to Sure. Are they sponsoring happy hours at NeurIPS? No. Are they legibly listed in Build? No. Are they hanging out in my, in, situational awareness parties? No. But they're adults. I trust them.Swyx [00:05:44]: They can run LAN. They can run power.Anjney [00:05:45]: They can run LAN, power, and shell. They have credit histories. We sit down, we have a conversations. Many of them live in Silicon Valley. They've, they've had to deal with the boom and bust cycles of the internet, and I love those folks. They are stable infrastructure partners and thinkers. And I think there's a lot of short-term thinking going on in the compute layer, and it's going to catch up to us. It's not going to be good.AMP Grid: Making FLOPs Flow Like MegawattsSwyx [00:06:07]: You talk about aligning incentives, and, I would think that aligning incentives means you have the full stack in one company, which is xAI and OpenAI, right? So you as a standalone infrastructure layer, why are you somehow more aligned to your portfolio companies than people who just own the whole thing?Anjney [00:06:28]: In systems design, right, there's, there's two regimes of, architecture, right? You have integration, and then you have pooling and utilization, right? So the Or rather, the way to increase utilization often is you can do systems integration where you collapse a lot of process into one node, or you can pull out a process from a node and share that amongst various That resource amongst several different nodes. And so we see the AMP grid, which is, the, what, the system we're building here, which is basically a compute grid. We're trying to do for compute what the electric grid-Swyx [00:07:02]: PowerAnjney [00:07:02]: Yeah, what the power grid did for electricity. It-- this is a pooling and utilization layer across clouds, And so we're actually the opposite of a full stack integration like approach.Swyx [00:07:12]: Super horizontal.Anjney [00:07:13]: Where it's much more horizontal and it's, it's multi-cloud, it's multi-silicon. The goal is to try to make FLOPs flow like megawatts, and that is very hard to do today for many reasons. There's stranded pools of compute all over the place and there's no fungibility. And so right now we do it at the level of scheduling, and we often do it at the economic layer. But as we start to announce what we're working on, it's extraordinary like how many folks are coming out of the woodworks and saying, “Hey, I'm actually working on a way to make compute fungible at this part of the stack and that part of the stack.” And as a grid, we'd like all of these folks to participate on the grid. There's, people often ask me, “Andra, are you a new cloud?” And I go, “No, actually neoclouds are suppliers.” sometimes they'll ask, “Are you a venture capital firm?” I go, “No, actually they are, they are demand like sort of off-takers of the grid.” We see ourselves as what's called an independent system operator. So if you study the history of the electric grid, once it became legible to a lot of factories and industrial sort of participants that, hey, actually it turns out pooling is a good idea. We should pool our generators instead of all having a generator running at half capacity in our backyard. There was a need for an independent entity who could coordinate all these parties. Transmission line, power generation, facilities, transmission lines, factories, and that neutral coordination mechanism is very critical. In order-- If you study like the history of grids, the most enduring ones were those that never owned their own assets. They were ones that had, or often started with long-term anchors who are uncorrelated sources of demand, a steel factory, a shoe mill or whatever in a particular town who weren't competitive, where the steel factory want to spike up at night, the shoe mill wanted to spike up during the day. So then you pool and you share, right? So each of you is guaranteed some base load, but then you kind of schedule your spikes to drive a peak utilization across the town. The gold standard, so to speak, historically, has been these utility companies like PJM Interconnect in the northeast of America, where they, over many years became this what's called an ISO, an independent system operator of the grid. So that's how we see ourselves. Economically, that's what we are. From a technical perspective, we started at the scheduling layer because Seb and Mihai, who, run engineering here, built that at-Swyx [00:09:28]: Did your schedulingAnjney [00:09:28]: They did that at Google. And, -Swyx [00:09:32]: And you have infra shops from Discord as well.Anjney [00:09:35]: I have some.Swyx [00:09:35]: I don't know, I don't know if Discord is like the primary identity, but what-whatever, I'm just kind of-Anjney [00:09:39]: No, D-Discord was-Swyx [00:09:40]: Choosing a well-known name.Anjney [00:09:42]: Well, I So I was running the developer platform there. The internal infrastructure I was not responsible for. That was actually a guy by the name of Mark Smith, who was extraordinary. And yes, Discord did pool So Discord is actually a counter example. I had the chance to learn a lot about fully, full stack infra there because-Swyx [00:09:56]: It's the same thing, yeahAnjney [00:09:57]: It's the, it's the other architecture which is, Discord built its own WebRTC vo-voice and video infra. So like Discord did not use-Swyx [00:10:08]: For the calls, yeah.Anjney [00:10:09]: Yeah, did not For communication, Discord did not use third party infra. It was all built in-house. And then the way you maximize utilization was you pool demand from the world's 200 million plus monthly active gamers, right? And so that's, that's how those stacks were constructed. Again, in systems design, the two concepts that keep coming up over and over again are abstraction and composition, right? And-Swyx [00:10:31]: Bundling and unbundlingAnjney [00:10:33]: Bundling and unbundling, abstraction, composition, like verticalization and-Swyx [00:10:36]: HorizontalAnjney [00:10:36]: Horizontalization. So in that sense, AMP is an independent system operator of the grid. We pool demand, we pool supply from a number of partners we trust At about 1.3 gigawatt scale over four years. And then we pool demand from some of the world's best, research labs and so on. We're sitting at one, periodic labs who need extraordinary long-term demand. And the idea is that, each of them is guaranteed base load on the grid, but they can spike up and down flexibly on, for compute, with much shorter timelines as needed. That was roughly the design of the program I came up with at a16z called Oxygen. The same-- That was the same design of the GQM, BorgX, Borg GQM implementation at Google that Mihai and Seb had built. Which was that how do you allow, teams inside of Google, on the internal infrastructure to be guaranteed capacity, for their base workloads? But when they need to spike up on research, how could they ensure that was sufficiently there? And of course, the big innovation that was not discovered, but kind of implemented in the space, this infra space maybe three, four years ago at Google was the idea of interruptible demand, right? Where you just queue up a bunch of jobs and through this like sort of credit system, there can be a bidding mechanism.Swyx [00:11:53]: Like priorities.Anjney [00:11:54]: It's a dynamic prioritization Basically. And jobs can get interrupted based on somebody else who's saying, “what? I have 10 tokens, 10 credits I want to spend on this job.” Another like team lead, research lead is “Genie 3 or whatever is only worth five, credits, and NanoBanana2 is worth 10 credits,” and so the NanoBanana job gets priority. That's a, that's a made up example.Swyx [00:12:15]: It's very real. Brain Marketplace was real. And, we've, we've covered this on the pod with David Luan, who was-Anjney [00:12:20]: Oh, great. OkaySwyx [00:12:20]: Was there. And the criticism is that, well, actually sometimes you need central command to go all in on a thing. And actually sometimes capitalism via credits doesn't work. Not, this is not a criticism of AMP. I'm just saying, this is a thing that has been tried, internally within Google, and it led to Google missing GPT.Foundry, Frontier Labs, and Research HoardingAnjney [00:12:41]: Like, we structured ourself essentially very similarly to Google. We are structured as a holdings company. So, Alphabet holdings is Alphabet holdings, and then they've got these subsidiaries called Google and-Swyx [00:12:51]: Other betsAnjney [00:12:52]: Other bets and so on. We've got, AMP holdings, and we've got our infrastructure business, and then we've got a capital business called Foundry that incubates new frontier AI labs or invests in them as venture capital, like Periodic. We put a few hundred million dollars into Anthropic from our fund earlier this year. So wherever we feel like teams are making progress, especially researchers and so on who've pushed the frontier inside of existing labs like DeepMind, I find, there comes a point where they feel misaligned with the dictatorship of Alphabet holdings. And at that point, sometimes the dictatorship doesn't want them anymore. And they're “Thank you. You've done your job here. You've kind of helped us through the zero to one phase, and for whatever reason, we're going to deprioritize your amazing, omni model or whatever it is, and instead we're going to prioritize coding.” And, I think that's a tragedy, but I get it. They're Sergey and team are running their own business there. But that doesn't mean we the rest of us should sit around waiting for that progress to get unlocked for the rest of the world and humanity. If you think about how much extraordinary research has happened inside of DeepMind over the last 10 years, I, Demis and Sergey and those guys did such a great job. But at the end of the day, so much of that has never seen the light of day?Swyx [00:14:00]: Or they're like papers only, but they never actually shipped it to production or-Anjney [00:14:03]: What's worse is the paper is actually not even being published anymore ‘cause there's a six-month embargo inside of DeepMind, right? We've heard about this where a paper comes out, and then I think there's a six-month embargo window where if anybody on the business team says, “This could be interesting” It's embargoed for life.Swyx [00:14:18]: Exactly. So the stuff that gets published is the stuff that's not good enough.Anjney [00:14:21]: There's an adverse selection problem, basically. Yeah. At this point-Swyx [00:14:25]: It's, it's a common complaint at NeurIPS, by the way, that's “Well, why would I look at the papers that are the trash of GDM?”Anjney [00:14:31]: Again, I think it's a tragedy. I get it. They're running their business, but the rest of the I think there's negative externalities of research being hoarded, and so that'there's a market failure. And somebody needs to unlock that research, and we can't do it on our own. We only have 1.2 gigawatts of compute. That's nothing. That's about $40 billion of cloud spend. We're going to need a lot-Gigawatt-Scale Compute and End-of-Life PredictionSwyx [00:14:51]: By the way, is that's a new number. I haven't, haven't come across that gigawatt number. That's huge.Anjney [00:14:56]: Yeah. And to be clear, we haven't secured all of it. That's how much demand we have started to secure. I think publicly we haven't actually confirmed how much we have for this year. In order-Swyx [00:15:04]: Where do you want to get to?Anjney [00:15:06]: I think the steady state would be that we have a base load pool Of 1.2 gigawatts at all times Of base load capacity. For spike capacity, right now my estimate is we need roughly six gigawatts over the next four years for all our teams to feel like they were able to keep moving the frontier, whatever they're working on, whether it's, like superconductor discovery over here. There's a new investment we're working on right now, which is in the end of life prediction space in healthcare. It's extraordinary how much you can, you can give this was actually my graduate school work. I went to grad school for bioinformatics at Stanford Med. And I know we-Swyx [00:15:40]: Econ, MCS, bio.Anjney [00:15:41]: So my-- I was this really weird cat where, I was never satisfied with my major options. So at one point I was an econ major, then I was a CS major, then I was a MCS major called mathematical computational science, and they decided they were going to end that major. So I took all that coursework, and I applied it to grad school, my graduate degree in bioinformatics, which was the master's program, and then I thought I was going to do a PhD. I never ended up doing it. I dropped out and went to work at Kleiner. But I was lucky enough to apprentice with this professor at, Stanford Med. His name is Nigam Shah, and he was working on end of life prediction. Stanford is one of the only research facilities in America that has a longitudinal patient data set that's larger at scale. I think it's at least 12 million patient lives. The only larger data set is at the VA, the Veterans Affairs, of America. And to do research, like do any deep learning and so on that data set, it was called the STRIDE data set at that time, you had to be a Stanford Med School affiliate, which is why I went and enrolled in the bioinformatics department. End of deep learning was early. Nigam Shah had the visibility-- the vision to see that, you could do end of life prediction to help palliative care. In America, the, over 30% of all Medicare, Medicaid spend, at least at that time, was spent on end of life care. And what's we grew up in Asia, so we all-- Yeah, at least I won't speak for you, but I have A very different relationship with death than I find folks who grew up in America do. In America, spiritually and culturally, especially in Western societies where Christianity, the Christian tradition sort of frames death as this terminal point, there's often a judgment day and so on. The way we view death is with a finality. In Indian culture, in Hindu culture, death is one-Swyx [00:17:35]: Also, he's Buddhist as well.Anjney [00:17:36]: You're Buddhist, yeah. So it's one, it's one step in a journey of many lives, right? And so, I grew up in this city called Chennai in the south of India, and when people die, you dance on the street. There's like a procession where your body is carried to be cremated and your family, like celebrates and there's drums and so on. It's this huge thing. And, It's because the idea is that you're going to be reincarnated. You've been liberated from the responsibilities of this life, and now you're onto your next. It's a new It's like going off to a new college or whatever, right? And so it was so alien to me when I got here as an undergrad- That the medical system works backwards from that assumption that we have to view death as this terminal thing and delay it, postpone it's a bad thing. And so at the time, clinical decision support in the United States was this very primitive field. Even to this day, physicians in the United States often will tell you when you have a terminal disease, this is your, we've diagnosed you, which is great. Our ability to diagnose you is extraordinary. You have somewhere between six months to six years to live. What do you do with that information? The error bars are so high that then you In times of uncertainty, we default to culture, and when the culture is let's-- this is a bad thing, I've got to prolong my life, then you start doing things like And just to, just sort of from a systems perspective, what's going on there is Physicians often feel like they need to provide such high error bars because there's always some uncertainty in end of life diagnosis, and if you provide the wrong Diagnosis or recommendation to your patient, you can be sued for medical malpractice. And then your license can be taken away. It can be catastrophic for your career. In contrast, if in countries where that's not the case, what you often observe is that patients, physicians are quite prescriptive with their recommendation. They say, “Hey, this is your condition. The literature says that you probably have this much time on Earth left. My expert opinion is that you are an outlier or whatever.” And they try to be more prescriptive, and that empowers a patient, right? ‘Cause then a patient can say, “I trust my doctor. They said on average, I have six months to live, but if I do these things, I may have a shot because of my particular predispositions or my genetic history or whatever.” And that empowers you to go about your life in a actually more scientific way than leaning on religion, culture, spirituality, and so on. In contrast, here, because of that medical malpractice sort of thing looming over your head, a physician never gives you a clear recommendation. So instead you say, “Okay, Doc, well, let's try it all.” And then you start a whole regime of drugs and therapies, and then you often spend weeks and weeks in the hospital, and that deteriorates your quality of life. And when that deteriorates your quality of life, you instead of spending your last few days doing the things you love with your family, you're spending it on a hospital bed. And that ends up being thirty percent of Medicare and Medicaid. So it's worse for the patients. The doctors feel terrible. The American taxpayer is paying a huge amount of money. And so this is why Nigam Shah, who was this professor at Stanford, said, “Anjney, if there's “ I kind of sat down with him. I was this young, I'd, I was twenty-one, and I was “I want to work on a big problem.” He's “The big problem is end of life care.” And so we tried to do deep learning to say, to-- So we started trying to run deep learning on these tried patient data sets to say, “Could you have an AI system make a recommendation that is orders of magnitude more precise about how much time you have left once you've been diagnosed with a terminal condition than a human?” And then if we can get that precision to be high enough, then you can empower the patient. And it turns out the tech works. Like it's-- Once you get the data set, like RL works. Honestly, even regression models work. You don't need to get that fancy. At the time, we were just trying, doing like very simple neural nets.Swyx [00:21:54]: Simple solutions, yeah.Anjney [00:21:54]: Today, what we can do with RL is extraordinary. The problem remains then and now is regulatory, because you actually can't shift the burden of the wrong clinical diagnoses from the physician to the AI system. And so at that time, I got quite disillusioned ten years ago for, twelve years ago where, ‘cause I felt I just didn't have the resources to influence regulation. Today, I'm very lucky. I'm in a different place. I've, I'm a lot older, and so I've been spending a lot of time on my next incubation, which is how can we unlock the, patient empowerment by training AI models to do end of life prediction much, with much more precision and ac-Swyx [00:22:37]: Oh, wow. You're still focused on this the whole time.Anjney [00:22:40]: The-- I haven't been able to get, this out of my mind a single day for the last fourteen years. This is the hill I want, I would like to die on. There's two, I would say. What? I actually, I'd prefer not to die.Swyx [00:22:51]: Yeah, exactly.Anjney [00:22:52]: But I think two bipartisan issues, I think two issues that should be bipartisan in America are how do we empower patients to make the right clinical decisions at the end of their life, such that we're reducing the taxpayer burden with science? It's just good old science, and AI can help here. And the second is, net positive data centers, ‘cause I think that's the biggest critical bottleneck on training and good enough AI models to help people at the end of their life. So there's sort of two sides of the, of the same scaling bottleneck curve, but those two, we formed AMP as a public benefit corporation. My wife and I, who you've met, you've met Viv. Her passion is education. Her family is a long line of educators and so on, and, of physicists. And so this class is my attempt to stop being the black sheep of the family and be a, an educator. But if I'm not educating, the thing I would be doing is working, on these two problems, whether on the political spectrum or as a researcher back at, in some lab. And my hope is if anyone's listening to this podcast, if they're passionate about either of those two topics, I'd love to hear from them. We'll, we'll we can share the contact in the show notes, but, we're looking for people to join both of those missions on the, on the political side as well as on the medical side, on the research side.Frontier Systems, Output Maxing, and AlignmentSwyx [00:24:08]: You said, this is a discipline that you want to form. You call it's called variously called Frontier System. It's variously called One Person Frontier Lab. What is the ideal name or shape of this? Like the, what is the mission?Anjney [00:24:24]: Of the class?Swyx [00:24:26]: Of the discipline that you're, exploring, right? I The class is called Frontier Systems. But like for me, maybe one phrase is you're, you're just anti-waste, right? Which is wasting GPUs, wasting in human and Medicare. But is there, is there a broader theme that I'm, that maybe you can encapsulate more succinctly?Anjney [00:24:45]: Yeah. The, from an engineering perspective, it's very simple. It's output maxing. It's the, it's the department of output maxing.Swyx [00:24:51]: Making the most of what we have.Anjney [00:24:52]: Exactly. I'm a huge believer in optimal outcomes. I think both in America and other countries, we are losing our appreciation for nuance, and this is the thing of And AI is the same case, right? Oh, the bitter lesson holds. Okay, fine. But that doesn't mean you just like throw 500 GB300, 500,000 GB300s at your suboptimal model scaling and you waste a bunch of compute. It also doesn't mean that, the most optimal is to have like 50 different architectures where there isn't enough standardization. One of the reasons Anthropic has had extraordinary sort of velocity is ‘cause they picked the transform architecture and said, “This is simple. Let's double down on it,” right? And now luckily there's enough investment going to the space that we can afford other architectures, but at the time, investment was just too fragmented into other architectures, so that arguably unlocked scaling. So I think there's a philosophy. I think we all owe it to ourselves to do output maxing with a new capability called AI on a global level. I think if I was starting a new department at Stanford, depending on how fuzzy or technical I wanted to be, I'd probably call it the Department of Alignment. Like-Swyx [00:25:59]: It's an overloaded termAnjney [00:26:01]: But it is, But alignment really Is a hard problem. And I think when you unlock it, full stack alignment is super hard in any organization and in any system. Like in a, in a venture capital firm, if you can have full stack alignment between your limited partners and your, the founders who are creating the value and ultimately the public that owns the IPO stock, that is a gift that keeps giving. And when you study the history of these systems, when they start off, they usually start out small scale where the feedback loop is actually so tight that there's alignment. And then the more you try to scale, the more division of labor happens, the more specialization happens, and at each step you add abstractions. And wherever there's an API interface, there's like loss. There's communication loss. And so I think a really cool thing would be for us to figure out is there a way for us to have our cake and eat it too as an engineering discipline? Is there a way to actually scale up and scale out Without losing any alignment, without lossy transmission?Swyx [00:27:01]: You mean standards?Anjney [00:27:02]: So standards is one way. The other way is you just have net new capabilities. So like what we're trying to do here is discover new superconductors. A room temperature superconductor would be a lossless transmission mechanism for energy. We would have flying cars. We are right within a few years of having a new room temperature superconductor. So I think those are the two. You either have to standardize On protocols or API specs that allow lossless communication, or you can come up with a whole new capability that unlocks so much abundance, the standardization doesn't matter ‘cause you just unlock net new capacity. This, the, so this is what I spend my days thinking about these days.Compute Markets, SF Compute, and Non-NVIDIA ChipsSwyx [00:27:38]: No, I think every infra person at, who wants scale and wants to output max does eventually end up thinking about this. We don't have time to go into it, but we have done an episode with SF Compute-Anjney [00:27:50]: Oh, coolSwyx [00:27:50]: That is trying to standardize The futures contract for compute. I don't, I don't know how that's going by the way, but like at some point this will be public.Anjney [00:27:57]: Oh, I think Evan is awesome and SF Compute is the kind of effort that I hope we can accelerate because what often happens is these exchanges are very hard to get, they, it's hard to bootstrap them, right? Because they often require-- There's many inefficiencies between parties. There's trust boundary inefficiencies in infrastructure because you don't trust, one part of the stack doesn't trust another part of the stack to give them visibility. There's capital markets inefficiencies, there's operational efficiencies. So if you can inject like a single shock to the system of a ton of compute demand or supply, then you can accelerate, these new flywheels. And so my hope is one day, or soon, if SF Compute needs extra like has excess capacity, they just hook it up to the grid and they get flooded with demand from us. And on the other side, if they have a ton of demand but they don't have supply, they just again hook up to the grid and it's a two-way protocol where they can just hook up to our capacity. And I don't think we're too far from that. Today our working implementation of it is mostly through a group of labs, universities, and a few sort of trusted parties who are, who all feel like they're in alignment to borrow an over sort of used word. But our hope is to just have it be an open protocol that anyone can hook up to on-Swyx [00:29:20]: Hook up for demand or hook up for supply? In primarily demand, it sounds like. Like you-Anjney [00:29:25]: No, bothSwyx [00:29:26]: You would want to offer demand.Anjney [00:29:27]: Both. Yeah. Unfortunately, what's happened in the last six weeks is, we thought we'd have a bunch of excess capacity by the end of this year. It's all gone.Swyx [00:29:37]: It's exploding.Anjney [00:29:38]: It, yeah. It's all gone. And so I have, my text messages are full of friends, we know many of these people, these are founders who've raised billions of dollars in San Francisco going, “Oh, any chance you have like 50 nodes in the next few weeks?”Swyx [00:29:51]: What is the scope for, non-Nvidia, right? You have Lisa Su coming and, Rainer Pope as well. And so There is a lot of demand for, more performance Alternative architectures and all that. At the same time, this hurts your standardization.Anjney [00:30:11]: I don't think so. So actually Rainer's a great example, right? Rainer is a CEO and founder of, MatX. I actually had him by for office hours in the class earlier today, and there was an insight he brought up that I hadn't considered before, which is when they decided to pick the standard For their data center, they picked the NVIDIA reference architecture. So the MatX chips Just plug in to any site that has an NVIDIA bring up planned. And, the-Swyx [00:30:42]: It's just software then. It's, it's not the-Anjney [00:30:44]: A-Swyx [00:30:44]: Hardware.Anjney [00:30:46]: Well, from an input and IO perspective It's the same footprint as an NVIDIA rack.Swyx [00:30:52]: That makes sense.Anjney [00:30:53]: Where they have done, innovated a bunch from what I can tell is on systems co-design. Which is where a lot of the gains are to be had. And so he picked He was “Anjney, we, there's just so much work to do when you're building a new chip company.”Swyx [00:31:08]: Can't fight every front.Anjney [00:31:08]: You just can't fight on every front. So my question to him was, “Well, you're working on this new chip. Their tape-out is next year. What, who are you going to partner with to host the chips?” And he said, “Whoever will host them. That's just not, that's not my focus.” And I said, “But how did you “ you decided back to our earlier systems design question, he decided that, he didn't want to be a full, fully integrated chip provider. The bottleneck they're focused on is the logic die, and they, he feels they can crank out a ton of performance gains through co-design there. But then that means you delegate, to our question earlier, it, you he's the data center provider is a different part of the stack, and so then he's dependent on that part of the ecosystem to host his chips to get the performance gains to the customer. So now you have another abstraction, and you might have loss. So I asked him, “How do you prevent loss?” And back to your point, he said, “I just picked the NVIDIA standard ‘cause I didn't want to Like I wanted to piggyback off of an existing protocol.” And that, what's great about NVIDIA is that reference architecture is known.Swyx [00:32:15]: Open.Anjney [00:32:15]: It's open. They've published it. So Jensen's actually enabled someone like Rainer to build a chip company like MatX, and I don't see them as competitive. The compute demand is so high. Like, I don't I think NVIDIA's not able to meet the demands of production, so we just need more chips. And I think it's very smart what MatX has done, which is say, “We're just going to we're not going to innovate on the data center design ‘cause actually, thank you, Jensen, you've done all the hard work. Where we can innovate is somewhere else.” And I think that's, that's very healthy. I think that's how we unblock new bottlenecks. And my view is these, the, chip teams like MatX, who have arrived at the insight that co-design is the way, The primary bottleneck for them is trust boundary. To do co-design well, you need visibility into the next model generation as soon as possible ‘cause it takes two years to tape out. So if by the time I bring my chip to market, your model architecture's changed, I'm host. Now, when he was inside Google, he was sitting next to the Gemini team. He was on Palm or whatever.Trust Boundaries, Co-Design, and Researcher CEOsSwyx [00:33:19]: His co-founder was the, was one, was one of the Palm guys, I think.Anjney [00:33:23]: Yes. Yes, exactly. So when you're inside the trust boundary of Google, then your systems co-design loop is super tight. When you leave as a founder, one of the biggest risks you take is now you're outside the trust boundary. And so what I love doing is helping chip teams who can help us unlock more capacity for the independent ecosystem access to trust. Because when I If I've been, involved with a lab from day one, and I was lucky enough to work with Anthropic, and then I'm on the board of Mistral and helped Black Forest Labs get started. I think at this point I'm on six or seven different teams.Swyx [00:33:57]: Only six? I feel like my mental number was going to be 13, but yeah, it's-Anjney [00:34:02]: No, I go deep with one at a time.Swyx [00:34:04]: You're founding CEO of Arena.Anjney [00:34:07]: Nah, that was an, that was an-Swyx [00:34:08]: Administrative CEOAnjney [00:34:09]: It was an administrative five-month gig where Whalen and Anastasios were graduating from their PhDs, and they didn't need a product team. So I helped recruit the head of engineering product and design. But Anastasios has always been the CEO of that company. I played a pinch-hitting I'm an intern. I was CEO intern For five months. -Swyx [00:34:33]: I interviewed him, and he's he's very well-spoken. I think he's a debate, former debate, champion. But also very quantitative and mathematical, which is-Anjney [00:34:41]: He-Swyx [00:34:41]: Such a unicorn.Anjney [00:34:43]: See, what's amazing about him? If you look at his output, he's an output maxer. By the time he was graduating from his PhD, which he only graduated last year, he had published more work with a citation count than, people twice his age. But at the same time, he'd already started a project called LLM Arena that was being used by millions of people As a side project. And time and time again, what I've realized is venture capitalists suck at seeing human beings as, dynamic agents where-Swyx [00:35:14]: They want to put you in a boxAnjney [00:35:15]: They want to put you in a box.Swyx [00:35:15]: This is your thing.Anjney [00:35:16]: So the first time I got introduced to Anastasios, somebody had told me “Oh, he's amazing, but he's a researcher.” I was “what? What do you mean he's a researcher?” That's what-Swyx [00:35:28]: Like he's not a CEO, not a founder.Anjney [00:35:29]: Not a CEO, exactly. I was “Are you crazy? Do you Have you met Dario?” Dario's a scientist. He's gone from zero to, what will soon be a trillion-dollar company in four years. Being a CEO, nominally speaking, is not that hard. Being a good CEO is hard. Being a great CEO actually requires a level of performance that scientists who have already published at the top of their field have accomplished. It is super hard to be a competitive scientist. To publish in academia over the last 20, 30 years, to make it to the top of your discipline at a place like Berkeley, you are a star athlete. Like, you are an athlete of the mind, and you perform at the highest levels. And to get there, whether you're, Anastasios or Whalen at Berkeley, or you are Robin, who-Swyx [00:36:23]: BFL, yeahAnjney [00:36:24]: With Black Forest, who created Stable Diffusion, or if you're, like Guillaume at Meta, who created Llama before he started Mistral. The amount of human leadership you have to demonstrate to get the resources, like get the trust of the organization, publish it, put it up. I would just fund researchers all day Right? If who have contributed already to the field. If they've, if they've put SOTA out there, they're, they're star athletes already. If they haven't done SOTA Look, they can still be good CEOs, but then I find the failure mode is that they just don't want to be CEOs, they primarily want to publish, and that's okay, too. One of the things we do with the AMP Grid is we donate excess compute. We have two nonprofits, like university labs. We carved out like a couple thousand H100s. But I do think there's extraordinary research being done on university campuses. My father-in-law's a physicist. He's a professor. Extraordinary work in physics, and we need that. But if you want to be a CEO, what you need to be willing To do is be super confrontational, outside of science. Like within the scientific community, some of the best researchers are very confrontational about their convictions, right? This architecture is right. To be a great CEO, you basically have to be willing to be confrontational up and down the stack.Swyx [00:37:41]: To your own team.Anjney [00:37:42]: To your own team-Swyx [00:37:43]: To customersAnjney [00:37:43]: Hiring, recruiting customers. Well, I would say, Yeah, pretty much to everyone Everybody. Of course-Swyx [00:37:50]: I see, I feel a little bit of that in my own work, but yeah, I can't imagine the stakes that Dario has had to go through. It's, it's pretty insane.Anjney [00:37:56]: No, I don't think the stakes are that different From how you're feeling it, right? Stakes are personal scaling vectors, right? The stakes that seem so low to you, like having this podcast where you can talk to somebody and just have a you're an extraordinary communicator, right? Like already in this conversation, you've pulled more out of me than most people, and I've been on 12 podcasts in the last two weeks.AI Coachella and First-Principles ThinkingSwyx [00:38:17]: I think I, we've just seen each other enough that there's some base trust.Anjney [00:38:20]: There's base trust.Swyx [00:38:20]: And I think, and I know that you, that I've done my homework and like I know that trust is a big deal for you, so.Anjney [00:38:27]: I think trust is about consistency, and you and I have seen each other In the community for years, right? Like, I remember the first time we met was at NeurIPS in New Orleans. I don't know if you remember that, luncheon.Swyx [00:38:38]: Oh my God.Anjney [00:38:39]: Reiko had set up this Reiko's amazing, and he set up this luncheon and-Swyx [00:38:43]: Yeah, I was “Who's this Discord guy?” I'm “Okay.” But-Anjney [00:38:45]: No, you weren't-Swyx [00:38:46]: You were just “You made some investments.”Anjney [00:38:47]: You were much less polite. You were “Who's this VC?” You're like-Swyx [00:38:51]: No, I Was I? Oh my God.Anjney [00:38:53]: It was-Swyx [00:38:53]: I'm so sorryAnjney [00:38:53]: It was visible on your face.Swyx [00:38:54]: I'm so sorry. But you weren't, you weren't The introduction was bad. I was I didn't know who you were.Anjney [00:39:00]: The, see, this is the thing about context, right? Like, but then I think I heard your accent. And I was “Are you-”Swyx [00:39:06]: Singapore, yeahAnjney [00:39:06]: “Are you Singaporean?” And you're “Yeah.” And I said, “I went to high school, JC, in Singapore.” And then the ice broke. But This is the there are in the scientific community, sometimes the stakes are very high for people who haven't had the emotional, what is called EQ Coaching and mentorship, right? Which is like to have scientific impact, you often need to be a extraordinary emotional, like emotionally in tune person with the folks you're trying to influence. And so what comes so naturally to you is actually a super high stakes thing to other people. And so I wouldn't assume that Dario's more stressed out than you. These things are you'd be surprised how similar and small sometimes the problems are to you That some of the world's biggest, leaders are facing. And that's what I've learned from this class. The guest speakers are Sam, Satya, Jensen.Swyx [00:40:01]: AI Coachella.Anjney [00:40:02]: Yeah. It's AI Coachella, right? So we got to get all the headliners, and they're I'm very lucky that some of these people have either mentored me over the years or I've done business with them. And when you, take the performative stuff out and any assumptions you may have about these people that you read in the press or on Twitter, We're all just humans. We're all trying to get along. And what's so special about this moment is AI is forcing, like scaling, the bitter lesson is forcing a lot of people to revise their assumptions for how the world works and go back to first principles or go and educate themselves. So the kind of people I was, I won't name who this person is, but I was at an event last week in Texas and, ran to somebody who said, “Anjney, I came across the class. What do you think about real time action prediction models?” And I was, don't know how happy it made me feel when they asked me that question. I know they've done the work. They've challenged themselves. I'm, they didn't ask me, “What do you think of world models?” They said, “What do you think of n-”Swyx [00:41:04]: Real time action predictionAnjney [00:41:05]: “action, real time action prediction models?” World models, don't get me wrong, are cool and everything, but you and I both know that is a layer of abstraction that is sometimes not usefully precise enough. Right? Ours-Swyx [00:41:16]: There's like four different kinds of world models.Anjney [00:41:17]: Yes, exactly.Swyx [00:41:18]: We've done the part with general intuition, by the way, which is very focused on, -Anjney [00:41:22]: Oh, cool. Yes. I love Pim. Pim is great. And this is what I love about people who've done that level of work. They realize they're not in competition with people who the rest of the world thinks they're in competition with.Swyx [00:41:34]: Because they're not in the category, they're in the specific thing they're trying to do.Anjney [00:41:37]: They're focused on their mission, and they have a systems understanding of the bottleneck they're trying to solve. And when somebody else says, “I'm working on real time, action prediction models too,” Pim goes, “Oh, I love that person. I want, I can learn from them.” But the minute they're “Oh, that person's a world model person,” it's “like which type of world model person?” But mostly they're just trying to figure out if it's a waste of their time, because we don't have enough time. So, Pim, for example, is super, loves this other company I work with we've talked about called Black Forest Labs. And he's mentioned to me multiple times that he's so, He thinks what Flux is doing is really cool. Andy Blattman came by and spoke in the class. And what I find over and over again is for people who do the work, who can be usefully precise enough about like what is actually going on in the world of frontier research, The sense of camaraderie is still well and alive, but it gets lost sometimes when you have to like abstract The technical complexities in, business terms And then the VCs are “How are you different from that world model?” I'm going to say Where do I even start to explain this stuff? And then the misalignment creeps in.Leading vs. Winning in Frontier AISwyx [00:42:43]: This is good. Yeah, I think, people listening get a sense of, what it is like to operate at a real level, like yourself, rather than at, the journalist level, where you have to sort of put everyone in, a rough category and create a narrative of competition, and who's winning today, who's behind.Anjney [00:42:58]: It-- this idea of winning is so Weird to me.Swyx [00:43:03]: You do want to win. You want you want competitiveness.Anjney [00:43:06]: No, I think you want to lead.Swyx [00:43:07]: You want SOTA.Anjney [00:43:07]: No, I think you want to lead. Yes, so you want to push the frontier. You want to push the SOTA. You want to do something that hasn't been done before. You want to capture value, but you don't want to capture so much value that, people think you're unaligned with your mission or trying to do what's best for the world. You want to capture enough value that you can keep innovating, right? And I think that people want to lead, they don't really This idea of winning and losing, again, I love Jensen. He's a, he's a leader. The mindset that he talked about on Dwarkesh's podcast, right? He's “I didn't wake up with a loser mindset.” I think that was awesome, right? Because he's, he's an engineer. Dwarkesh has done the work. So there's at least-- even though the, to me, it was very obvious they're talking about the same thing, they just passed each other. They just had to basically, Jensen has this, five-layer cake abstraction of how the industry works. And Dwarkesh had, I think from that podcast, had more of, a pre-training, mid-training, post-training systems loop concept.Swyx [00:44:04]: It's just a factor of who he talks to, right? Again, it's very clear.Anjney [00:44:06]: It's the systems It's the abstraction, the mental models, the It's the whole-- Dude, so much of the problem in the world is reasoning by analogy. And then the assumptions that are held invisibly.Swyx [00:44:19]: Yeah, I've, I've said, this is actually the best time in human history for first principles thinkers. Because everything you think will happen is actually now coming true.Anjney [00:44:28]: Correct. And the venture capital community is, notorious for this, where people look-- In times of uncertainty, they, cling to axioms that ended up being true from the previous era, and they kind of like proclaim them with confidence as if they're truths, but they're not. And it's very important to see the distinction between a heuristic and an axiom. An axiom can be proven-Swyx [00:44:55]: Like from internal consistency point of viewAnjney [00:44:56]: With internal consistency. A heuristic is a way you kind of a shortcut. And my God, the number of people I have had to put up with over the last few years who proclaim-- use heuristics As axioms to judge people, to judge which companies are going to succeed or the number of people who are “Oh, yeah, Anthropic, they're just training models right now,” but this one continue.Swyx [00:45:22]: Because that's a B2B SaaS?Anjney [00:45:23]: Yeah, the, like Which over the fullness of time, if you squint at it, maybe. But the way you arrive there is so important that you can-- you just, you can dismiss people. Here's what happened, right? What happened is Anthropic basically achieved takeoff in October of last year. That training run-Swyx [00:45:41]: Whatever, three seven?Anjney [00:45:42]: I forget the numbers now, but whatever that checkpoint was-Swyx [00:45:45]: We saw the cognition.Anjney [00:45:46]: Yeah. Right? You probably-- The, to those of us in the community, especially once post-training was done and it was released in December-Swyx [00:45:52]: Yeah. Can I sneak a sneaky question in there? I don't know if you have a perspective, maybe you don't, I just The number one question is how did Anthropic crack coding, right? Because Claude One, Claude Two, okay, like it was part of it, but it wasn't a big deal. And the leading hypothesis, it's a lucky dice roll that was then compounded, right? Like it was like Mildly better, but then they saw it and they were “Okay, let's really invest.”How Anthropic Cracked CodingAnjney [00:46:17]: I had this very annoying teacher. I went to this boarding school called Rishi Valley in India, which is like this, bird preserve. It's like three hundred and fifty acres of bird preserve in rural India, and there was no technology for seven years. There was this teacher, I won't name them, but they would have this-- I hated it every time he said this to me. He was “Luck fa-favors the prepared mind,” which is like a common saying, but the way he delivered it, always grated me, ‘cause he was always I was always one of those kids who got, a good grade without trying very hard. ‘Cause like high middle school is not that hard if you, if you're generally, paying attention and so on. And there was this one time where I-- But then I would get an eighty percent grade, and he would keep pushing me to say “The reason you didn't get the ninety-five plus percent is because you're not that lucky.” And I would say, “What do you mean?” ‘Cause I would think that I deserved that grade, and I would sometimes argue with him. And he'd say, “You didn't have a prepared mind. If you want to get lucky again “ There was basically one time where I got like ninety-five or ninety-six on this, on this subject, and I, now that I felt entitled. I was “Okay, I'm going to keep doing this,” and I didn't. And then he was “Luck favors a prepared mind. You got lucky last time, but you got to stay prepared.” And I didn't understand what he meant. Now, as I'm older, I'm okay, these adults actually knew a thing or two. Anthropic has been the most prepared company for four years. And so then when the right, context data comes in, the right developers start sending in, the right context diffs, Sure, you could say you got lucky, but if you ask me, they're pr-pretty damn prepared with paranoia for like four years. And you have to remember, it was so hard for them to get going early on that they had to do so much more with so much less that you just have to be prepared to be so efficient.Swyx [00:48:06]: Yes. There's numbers on their burn compared to OpenAI. I've, I've written about it, but they are so much more efficient in their, in their tech stack.Anjney [00:48:14]: It's not even It's not funny.Swyx [00:48:14]: Not even close.Anjney [00:48:15]: Yeah. But it's so clear, right? Like how to output max for the world. They have been prepared, and you could call that luck, but Luck favors the prepared mind.Culture, Hardship, and Anthropic's P0Swyx [00:48:25]: This is one of those things that I was going over some of your old lectures and, you were data, people think it's a moat and actually it's culture and actually it's team Actually. And I, it's-- there's different levels of moats, and this is the ultimate one that determines everything else. Which you can then compoundAnjney [00:48:43]: You're saying culture is the ultimate moat? Yeah. But the thing about culture is it's very fragile. So moats, I don't think they're-- there's very few moats I found that are actually moats. They're-- It's, it's a nice concept, but in reality, you have to replenish your culture. Ben Horowitz was, the speaker in CS153 on Tuesday, and I asked him this question about the culture bottleneck in teams because, there are several AI teams-Swyx [00:49:09]: His book, Hard Things About Hard ThingsAnjney [00:49:11]: Hard Thing About Hard Things. But more concretely, there are so many AI labs today that have all the cash they need, they have all the compute they need, and they're still not able to ship anything SOTA. And then you start seeing people leave and so on, and my diagnosis, it's, is it's the culture. And so I asked him, Ben, they're-- He's been one of the most aggressive investors in AI labs. He goes back to this thing which resonates in my mind a lot. It-- When I used to work at a16z, I would, book a conference room, and right outside the conference room, which is closest to the toilet ‘cause it was the fastest way for me to go use the bathroom between Zoom meetings-Swyx [00:49:45]: Oh my God, I'll put maxing my toilet optimization. Okay, never mind.Anjney [00:49:48]: It was not healthy in hindsight, but maybe this is TMI. But anyway, outside that conference on the wall was this quote that was printed that said, “Culture is not a set of beliefs, it's a set of actions.” And it's by Bushido, is this, Japanese philosopher. And if you stop taking the actions that demonstrate the mission alignment to what you've said to your team and to your-- the world matters to you, then your culture starts to fray. So it's not actually a moat, I would say. It's a very brittle, fragile thing that requires daily tending to like a garden. But if you figure out the system to keep that garden tended, which I think ultimately comes down to knowing yourself ‘cause you most naturally, if you're authentic and so on, you'll naturally make trade-offs that seem effortless to you, but that reinforce your culture. And then That becomes this very hard thing for other people to catch up to. And at Anthropic, from day one, there was this mission like-- missionary like zeal and belief that, hey, these capabilities will scale. These systems are stochastic, not deterministic. There will be error bars, and until we crack interpretability, there's risk. And at some point, people will go-- stop using Claude just for coding. They'll use it in some mission-critical context where there's-- it'll throw off a bug, and then people are going to come blame them, and they want to be on the right side of history where they said, “Yes, this is a powerful technology. We think it's going to change the world, And we want to be very measured and scientific about the fact that, ‘Hey, guys, these are stats models, statistical models.' That's how statistics works.” ultimately, when you're training neural nets, it is just a statistical system. And I think that Belief that safety is important and that it might seem toy-like in the early days, and sometimes, you could say, “Anjney, they totally over-exaggerated the risk,” like two years ago when they said, “Let's not launch Claude One,” or whatever. Well, okay, maybe in hindsight, but hindsight is twenty/twenty. And at the time, they didn't know how that model would be used, and to them it felt existential if somebody came and said, “You weren't responsible. It-- This wrote a bug.” The liability associated with that is massive. So how do you prevent against that? Well, day in, day out, you say safety. And when you start deviating from that, you have the team hold you accountable, you have the world hold you accountable, and I think that becomes a moat over time. At some point, that moat will get challenged and so on, and then it become fragile. I hope it endures because that's the beauty of having founders run the show, ‘cause they can make really hard trade-offs to do mission alignment. The hardest part is in the earliest days when you don't have a group of people who are going through difficulty, stress, crisis together, then your culture doesn't get defined sharply enough, and that's what I'm worried about right now, is there's so much money going to these labs. There's no hardship. There's no-Swyx [00:52:50]: To anyone who knowsAnjney [00:52:51]: There's no to anyone who knows. And that, in hindsight, was a feature, not a bug for Anthropic. The number of people who said no, the number of people who said, “Sorry, we're all doing investors in OpenAI,” that is competitive difference. It forces you to really understand, what is the hill you want to die on at the expense of everything else. What's the P zero? And there, P zero from day one was coding. The reason, the mechanism system there was if we crack coding, Then we will crack AGI. Our mission is AGI. We want to get there safely. If we focus on codin

Who is teaching the world's most powerful AI models to think?Turing is one of the largest data partners to OpenAI, Anthropic, Google, Meta, Microsoft, and Nvidia. At a $2.2 billion valuation it has become one of the most important infrastructure layers in the AGI race.Jonathan Siddharth started Turing in 2018 with a thesis that talent matching is a trillion-dollar problem. Turing reached unicorn status in 2021. Then, in 2022, as the foundation model race accelerated, OpenAI approached Turing to provide coding data for ChatGPT.Jonathan recognised that frontier AI labs faced an enormous bottleneck: high-quality training data and human intelligence at scale. Instead of remaining just a talent marketplace, he made a bet that most unicorn CEOs never make. He built a second business on top of the first and leaned back into his AI research roots.Jonathan has a clear view of what needs to happen before we get to super intelligence. The four keys to unlocking AGI: coding, reasoning, tool use, and multimodality. He believes we solve for those four, and AI can do almost anything a human can do in front of a computer. If you are excited about where the AGI race is heading this episode is for you00:00 - Trailer01:06 - What Turing does05:55 - Why OpenAI reached out to Turing8:28 - How GPT-3 became ChatGPT17:54 - How ImageNet breakthrough changed the world21:12 - The largest provider of coding data to AI labs24:34 - Four keys to super intelligence28:45 - Every human will run multiple companies in 10 years32:27 - Can agents have self-improvement loops?34:36 - The future of software engineering36:26 - Agents should create, humans should steer39:46 - Is the line between products and services companies blurring?40:42 - How an agent can handle hiring end-to-end43:36 - Every human can now write software45:22 - Will workflow SaaS disappear?47:46 - No fine-tuning vs fine-tuning camps51:49 - A case study in compute constraints57:06 - Why the world needs so much compute1:01:26 - Where Jonathan would invest today1:03:16 - Where cybersecurity is heading1:08:31 - How the world will look in 10 years-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

Fable — a short fantastical tale with a moral message. For weeks, Anthropic's Dario Amodei was telling anyone who'd listen that he'd invented an AI so powerful it could walk through walls, shoot sparrows from the sky, talk to horses, invent trees. A death star of tech. Whether this was part of the sales pitch or genuine alarm, the US Government has taken him at his word, and shut off access to it beyond America's shores. Suddenly, the biggest tech of the 21st century is at the heart of a new age of mercantilism. The world is waking up. But some of the world was already awake. TP Huang is one of the best China watchers out there. A programmer and tech specialist, he sees an even bigger battle coming. The divergence of chip supply chains, ever since the Biden Administration's October surprise in 2022, now stands ready to produce a space race over not just the chips, but the software itself. In that aspect, China is perpetually six months to a year behind. But is six months really decisive, when you can do everything at a fraction of the cost of your rivals? And when you're not just bowing down before a brain in a jar, but embedding AI in factories and robots? While the world focuses on AGI psychodrama in California and flat-footed despair in Brussels, this week, TP is talking to Andrew Collingwood about the flint-eyed Chinese strategy to build a truly insulated supply chain within the next ten years. You can read his full piece, published in two parts, on the Multipolarity Substack. https://multipolaritypod.substack.com/p/the-struggle-for-mastery-of-the-21st

L'intelligence générale, entre promesse de salut et risque de perte de contrôle

В этом выпуске: зачем грузить атомные часы на самолет и устанавливать mamba; в чем отличие FSST от FST; почему у Валеры сгорело от 3D-принтера Bambu Lab, а Саша оценил SDRPlay на троечку; массово переходим на DuckDuckGo; разбираем свежайший пейпер по AGI; а также обсуждаем темы слушателей. Шоуноты: [00:00:00] Чему мы научились за неделю Free AI… Читать далее →

Few thinkers have shaped our understanding of the future as profoundly as Ray Kurzweil. An American inventor, computer scientist, futurist, entrepreneur, and bestselling author, Kurzweil is widely regarded as one of the most influential technological forecasters of our time. For decades, he has accurately predicted many of the innovations that now define modern life, from mobile computing and artificial intelligence to digital assistants and large language models often years before they entered the mainstream. In this special conversation, Tony Robbins sits down with Ray Kurzweil in San Francisco to explore one of the most important questions facing humanity: What happens next? Together, they examine the accelerating pace of artificial intelligence, the path toward Artificial General Intelligence (AGI), the rise of autonomous agents, the future of work and education, breakthroughs in healthcare and longevity, and how these technologies may transform society over the coming decade. Kurzweil explains why his long-standing prediction of AGI by 2029 now appears increasingly conservative, why the next few years may bring more change than any period in human history, and how humanity may ultimately merge with the very technologies it creates. Whether you're excited, skeptical, inspired, or concerned about the future of AI, this conversation offers a rare opportunity to hear directly from one of the leading minds who helped foresee it. The future is arriving faster than most people realize. This episode won't just change how you think about what's coming next, it may help you prepare for it.

We are breaking down the entire short-list and ranking, review, and digging into the 2026 Nebula Novel nominees from worst to best. We dive deep into the writing styles, the structure, the highs, the frustratingly bad endings, and reveal exactly who took home the final trophy. Are these books actually masterclasses in modern sci-fi and fantasy, or did the hype train leave the tracks?  Here is our definitive 2026 Nebula breakdown:  7. Death of the Author by Nnedi Okorafor  You should read it if: You love deep-dives into African culture, Ibo and Yoruba roots, and tech concepts like futuristic exoskeleton legs. You shouldn't read it if: You require a persistent central conflict, cohesive subplots, or a "story-within-a-story" that actually goes somewhere. 6. Wearing the Lion by John Wiswell You should read it if: You want a cozy Hercules retelling where Hera calls Zeus a "dipshit" and Heracles tries to befriend mythological monsters instead of fighting them. You shouldn't read it if: You get annoyed by overly preachy or cloying endings, repetitive quest structures, or confusing second-person POV shifts. 5. Katabasis by R. F. Kuang  You should read it if: You are obsessed with dark academia themes, the dangers of academic flow states, and complex, highly allusional world-building. You shouldn't read it if: You need to deeply connect with your protagonists or get easily annoyed by writing that feels a little too self involved. 4. When We Were Real by Daryl Gregory You should read it if: You love quick, humorous POV switches, AGI, simulation theory, and brain emulation concepts. You shouldn't read it if: You are looking for a groundbreaking, deeply unique masterpiece—this one is cute, but a bit unspecial. 3. Sour Cherry by Natalia Theodoridou You should read it if: You like heavy foreshadowing, experimental voice-switching (shifting to 2nd person), and intense meta-narratives. You shouldn't read it if: You hate a massive buildup that doesn't actually come together or stick the landing at the end. 1. (TIED) The Incandescent by Emily Tesh You should read it if: You want adult-oriented cozy fantasy in a magic boarding school featuring a workaholic, middle-aged bisexual teacher and casual, biscuit-eating printer demons. You shouldn't read it if: A rushed, abrupt ending with a thin villain motivation is going to completely sour your overall enjoyment of a great setup. 1. (TIED & WINNER) The Buffalo Hunter Hunter by Stephen Graham Jones You should read it if: You want a beautifully written, highly literary Native American Blackfoot vampire revenge story set in the brutal landscape of the American West. You shouldn't read it if: You get bored by a monotonous middle section where the central premise loses steam and repeats itself. No spoilers anywhere in this episode. Join the Hugonauts book club on discord Or you can watch our episodes on YouTube if you prefer video All the books, plus timestamps: 00:00 Intro  00:46 Death of the Author by Nnedi Okorafor  02:26 Wearing the Lion by John Wiswell  05:29 Katabasis by R. F. Kuang  09:30 When We Were Real by Daryl Gregory  12:57 Sour Cherry by Natalia Theodoridou  16:30 The Incandescent by Emily Tesh  20:08 The Buffalo Hunter Hunter by Stephen Graham Jones

Voir, prédire, générer, agir : comprendre enfin ce qu'on met derrière le terme "IA"

Is so-called "self-deception" a real thing? How can one at once be aware that something is true and yet also be unaware of it? Is it even a coherent concept? Doesn't deception require at least two entities—the deceived and the deceiver? Can "self-deception" be explained as something much simpler, namely deception?Bruce summarizes his views on self-deception, moralizing, and dogmatism from a critical rationalist perspective, as well as it's relevance for AGI, "Super Intelligences", and human dogmatism.⁠⁠⁠⁠⁠⁠⁠⁠⁠Support us on Patreon⁠⁠⁠⁠⁠⁠⁠⁠

"If your entire technology is coming from a single country, and that country decides that every now and again they're going to shut off access to you, that's not a foundation you can build on." The US government just ordered Anthropic to ban access to its most advanced AI models, Fable 5 and Mythos 5. Seems like now is a good time to talk about sovereign AI. Cohere co-founder Nick Frosst joins to discuss how Canada's AI champion is built different than the other frontier LLM providers, how Star Trek informs the type of AI future the company is trying to create, and why he doesn't make a point of listening to Marc Andreessen about AGI. Did the Anthropic model ban prove Cohere is right about sovereign AI? Let's dig in. -- Amid global uncertainty, the path forward is clear: Canada's moment to build is now. Presented by Uber Canada, DMZ, and National Bank of Canada, BetaKit Most Ambitious is back, telling stories of nearly 100 Canadian innovators strengthening our nation's autonomy, security, and prosperity. Read BetaKit Most Ambitious now.

The U.S. government forced Anthropic to pull Fable 5 and Mythos 5 from general availability just days after they launched as the most capable models publicly available. Paul and Mike work through how it happened, Dario Amodei's policy essay, and the question every business leader should be sitting with: what do you build on when a model can be switched off? Then it's OpenAI's confidential IPO filing, Apple's long-awaited Siri AI, the real economics behind your AI subscription, and a DeepMind paper on the road from AGI to ASI. Show Notes: Access the show notes and show links here AI-Pulse Survey: Fill out this week's AI-Pulse Survey here. Timestamps: 00:00:00 — Intro 00:05:20 — Claude Fable 5 00:27:38 — OpenAI Files for IPO 00:36:02 — Apple's Siri AI Is Finally Here 00:44:02 — Is the Era of Affordable AI Over? 00:48:56 — Opendoor Ends Offshoring for AI-Native Workers 00:51:49 — From Prompts to Loops 00:57:48 — From AGI to ASI 01:03:07 — Europe 2031 01:06:53 — AI Use Case Spotlight 01:11:12 — AI Product and Funding Updates This episode is brought to you by AI Academy by SmarterX. AI Academy is your gateway to personalized AI learning for professionals and teams. Discover our new on-demand courses, live classes, certifications, and a smarter way to master AI. Learn more here. Visit our website Receive our weekly newsletter Join our community: Slack Community LinkedIn Twitter Instagram Facebook YouTube Looking for content and resources? Register for a free webinar Come to our next Marketing AI Conference Enroll in our AI Academy 

What if the biggest barrier holding you back during a career or identity transition isn't your skills, but how you're communicating who you are becoming?In this episode, communication trainer and TEDx organizer Ana Denis explores why moments of reinvention (whether in midlife, after moving countries, or changing careers) create deep communication challenges. She reveals how our shifting quietly shapes how confidently (or hesitantly) we express ourselves, and why many people feel like “frauds” when stepping into a new professional chapter.Discover how to communicate with clarity and confidence even when your professional identity is still evolvingUnderstand the hidden emotional and identity layers that affect everyday conversationsMove past imposter syndrome by reframing experience and taking practical action in your new directionListen to the full episode to learn how to communicate your evolving identity with authenticity and confidence during times of personal and professional change.˚KEY POINTS AND TIMESTAMPS:01:43 - On Reinvention, Communication, and Starting Over08:50 - Why Midlife Triggers the Search for Meaning11:09 - The 3 Hidden Layers of Every Conversation14:17 - Why Identity Shifts Make Communication Difficult18:05 - Confidence, Imposter Syndrome, and Feeling Like a Fraud23:49 - Practical Ways to Build Confidence in a New Direction28:57 - Cultural Conditioning and the Fear of Reinvention33:05 - The Fear of Judgment When Changing Identity35:09 - Why Supportive People Matter During Reinvention˚MEMORABLE QUOTE:"What I was struggling with was just giving myself permission to change instead of holding onto the past."˚VALUABLE RESOURCES:Ana's website: https://anadenis.com/˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚

She Tested 100% Accurate on Camera. Then We Asked About Epstein | Dallisa HockingBefore we touched a single conspiracy, I handed Dallisa a list of 10 questions about my own life that an AI generated at random. Her copper dowsing rods went 10 for 10. Once the rods proved themselves, I started asking the questions most people only whisper about, and the answers came faster than either of us was ready for.Dallisa Hocking is a fifth-generation psychic medium and channeler with over 11 years in the spiritual industry, who spent years predicting much of what we are living through now before Spirit told her to stop forecasting and start activating.This is not a comfort episode. It is an hour of watching a tool answer questions it should not be able to answer, and then going silent on the only two that might matter most.What we get into:The live calibration round where 10 randomly AI-generated questions about me all came back true, on cameraWhy she walked away from making predictions, and what Spirit told her to do insteadThe Joe Rogan question that crossed before I could finish asking itWhy the rods went to a dead standstill on only two questions: the simulation and AGI"They don't want us to crack that code" and her read on where AGI actually comes fromThe firmament, remote viewing, and why she feels we have never physically left EarthHer vision of mothers taking to the streets, and the draft she believes is comingThe July 4th window, a possible second pandemic, and "the great dismantling" already underwayWhether reality is a game, a prison, or both, and how to wake up inside it like TrumanThe one practice she calls non-negotiable for surviving what is comingCONNECT WITH DALLISA HOCKINGWebsite: https://spiritandspark.comInstagram: @dallisahocking → https://www.instagram.com/dallisahockingFacebook: https://www.facebook.com/dallisaYouTube: https://www.youtube.com/channel/UCOAtgAkuewh2CK6T2BscEXgLinkedIn: https://www.linkedin.com/in/dallisaSubstack: https://dallisahocking.substack.com2026 Summer Speaker Series (free): https://dallisa.thrivecart.com/2026-summer-speaker-series/Email: dallisa@spiritandspark.com

L'Intelligence Artificielle promet de changer notre monde... Une enquête indispensable pour y voir plus clair.

À retrouver à partir du 16 juin dans Sismique.L'intelligence artificielle est en train de devenir le projet industriel le plus massif de notre époque, et l'une des bascules les plus profondes de notre histoire récente. Et pourtant, le débat est piégé. D'un côté, ceux qui nous promettent un futur radieux. De l'autre, ceux qui annoncent l'effondrement. Entre les deux, la place est étrangement vide.C'est dans cette place vide que j'ai voulu poser cette série. Neuf épisodes pour regarder, lentement, ce qui se passe vraiment. Pas pour trancher à votre place. Pour vous donner de quoi penser.Au programme:La machine qui parle, comment cette technologie a basculé dans nos vies.Qu'appelle-t-on IA, ce que c'est, et ce que ce n'est pas.AGI, le rêve et la peur, cette super-intelligence qu'on nous promet.La course et ses bâtisseurs, l'argent, le récit, ceux qui tiennent la barre.La mégamachine, le corps physique de l'IA, ce qu'elle consomme, ce qu'elle rejette.L'humain sous assistance, ce que ça nous fait, à nous, individuellement.La société sous influence, ce que ça fait au collectif, à la vérité, au pouvoir.Qu'est-ce que l'intelligence, le pas de côté philosophique.Que peut-on encore choisir, ce qui reste possible.Une série pour les curieux, les inquiets, les enthousiastes lucides, et tous ceux qui sentent que cette histoire les concerne, sans toujours savoir par où la prendre.---Retrouvez tous les épisodes et les résumés sur www.sismique.frSismique est un podcast indépendant créé et animé par Julien Devaureix.

Alright, welcome to Part 2 of my conversation with Peter Diamandis—a man who lives and breathes exponential change, and isn't afraid to tackle the stuff everyone else would rather ignore. Now that you've braved everything breaking and the cracks in the old story, it's time to step fully into what happens next: regulation, UBI, riots and “derangement,” brain-computer interfaces, and some mind-blowing visions of what human purpose could look like when survival is no longer the point. Peter and I get into everything from whether AGI and superintelligence will help or harm us, the ethics of coding “morality” into AI, to whether humans are really just the boot disk for something greater coming next.This part goes deep on the forks ahead—will you opt out, numb out, become a creator, or actually merge your brain with the cloud? How are schools failing us (and what do our kids actually need to thrive)? We even get practical about what you can do right now to claim agency, think radically bigger, and make yourself anti-fragile—whether you want to start a company, change the world, or just live a life you can be proud of as the rules keep rewriting themselves in real-time. If you need just one episode to snap you out of fear and into action, this is it.Ketone IQ: Visit https://ketone.com/IMPACT for 30% OFF your subscription orderQuince: Free shipping and 365-day returns at https://quince.com/impactpodPlaud: Get 10% off with code IMPACT at https://plaud.ai/impactWhatnot:Download the Whatnot app today and get free shipping on your first order. AT&T Business: Switch to AT&T Business at business.att.comShopify: Sign up for your one-dollar-per-month trial period at https://shopify.com/impactTruemed: Check your eligibility and start saving at https://truemed.com/impactIncogni: Take your personal data back with Incogni! Use code IMPACT at the link below and get 60% off an annual plan: https://incogni.com/impactPique: 20% off at https://piquelife.com/impactWhat's up, everybody? It's Tom Bilyeu here:If you want my help...STARTING a business: join me here at ZERO TO FOUNDER: https://tombilyeu.com/zero-to-founder?utm_campaign=Podcast%20Offer&utm_source=podca[%E2%80%A6]d%20end%20of%20show&utm_content=podcast%20ad%20end%20of%20showSCALING a business: see if you qualify here.: https://tombilyeu.com/callGet my battle-tested strategies and insights delivered weekly to your inbox: sign up here.:https://tombilyeu.com/**********************************************************************If you're serious about leveling up your life, I urge you to check out my new podcast, Tom Bilyeu's Mindset Playbook —a goldmine of my most impactful episodes on mindset, business, and health. Trust me, your future self will thank you.**********************************************************************FOLLOW TOM:Instagram: https://www.instagram.com/tombilyeu/Tik Tok: https://www.tiktok.com/@tombilyeu?lang=enTwitter: https://twitter.com/tombilyeuYouTube: https://www.youtube.com/@TomBilyeuSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

What's up, everybody? It's Tom Bilyeu here:If you want my help...STARTING a business: join me here at ZERO TO FOUNDER: https://tombilyeu.com/zero-to-founder?utm_campaign=Podcast%20Offer&utm_source=podca[%E2%80%A6]d%20end%20of%20show&utm_content=podcast%20ad%20end%20of%20showSCALING a business: see if you qualify here.: https://tombilyeu.com/callGet my battle-tested strategies and insights delivered weekly to your inbox: sign up here.:https://tombilyeu.com/**********************************************************************If you're serious about leveling up your life, I urge you to check out my new podcast, Tom Bilyeu's Mindset Playbook —a goldmine of my most impactful episodes on mindset, business, and health. Trust me, your future self will thank you.**********************************************************************FOLLOW TOM:Instagram: https://www.instagram.com/tombilyeu/Tik Tok: https://www.tiktok.com/@tombilyeu?lang=enTwitter: https://twitter.com/tombilyeuYouTube: https://www.youtube.com/@TomBilyeuKetone IQ: Visit https://ketone.com/IMPACT for 30% OFF your subscription orderQuince: Free shipping and 365-day returns at https://quince.com/impactpodPlaud: Get 10% off with code IMPACT at https://plaud.ai/impactWhatnot:Download the Whatnot app today and get free shipping on your first order. AT&T Business: Switch to AT&T Business at business.att.comShopify: Sign up for your one-dollar-per-month trial period at https://shopify.com/impactTruemed: Check your eligibility and start saving at https://truemed.com/impactIncogni: Take your personal data back with Incogni! Use code IMPACT at the link below and get 60% off an annual plan: https://incogni.com/impactPique: 20% off at https://piquelife.com/impactIn this Friday edition of The Tom Bilyeu Show Live, Tom and Drew dig into a packed news day spanning geopolitics, markets, tech, and a long philosophical tangent on immortality. They open on Iran, breaking down the leaked 14-point "deal" circulating via Iranian state media — the $24 billion in frozen assets, the naval blockade, the Strait of Hormuz, and reconstruction demands — and why Tom is deeply skeptical that anything beyond a memorandum of understanding gets signed, plus what a bad deal could cost Trump heading into the midterms. From there, they pivot to a heated exchange over the SpaceX IPO and the Globe and Mail's "how to properly hate Elon Musk" headline, using it as a springboard into the psychology of resentment, the mechanics of transformational-tech bubbles, and a warning to retail investors about becoming "exit liquidity." The conversation moves through California's voting rules, ballot harvesting, and Trump's Save America Act and reconciliation push (with an extended back-and-forth on states' rights, the Constitution, and the Supreme Court), the UK's proposed device-level content-scanning law and the surveillance-state implications, a DOJ child-smuggling indictment tied to border policy, and the Epstein/Zorro Ranch mystery. They close on AI — unpacking Yann LeCun's argument against LLMs and AGI in favor of specialized world models — before spinning off into a wide-ranging debate about whether you'd actually want to live forever, the disposable-male hypothesis, and a contentious Alex Karp clip about GDP and gender.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

OpenAI, Anthropic, SpaceXand the AI IPO cycle face a structural problem: a cheap, capable open source exit is already drawing enterprise users away before either company goes public. ======================================================== Thank you to our sponsor! ⁠Fidelity⁠: Fidelity has been building in crypto and DeFi since 2014 — now they're hiring. Explore career opportunities at one of the most forward-thinking names in finance here: ⁠crypto.fidelitycareers.com⁠. ⁠Cape⁠: Your biggest crypto vulnerability isn't your wallet, it's your phone number. Cape is America's privacy-first mobile carrier that rotates your SIM identity daily and blocks SIM swaps before they happen. Get 33% off your first six months at cape.co/unchained (use code: UNCHAINED). ======================================================== A viral tweet by Tom Shaughnessy, founding partner of Delphi Ventures, identified the most basic way AI could blow up: a 40x subsidy gap between consumer AI subscriptions and enterprise API costs quietly pushing businesses toward open source inference providers at 1% of the price. Citadel Securities published a near-identical thesis shortly after. Shaughnessy joins Laura Shin to map the implications for the AI IPO wave, starting with SpaceX. Low floats and passive index demand should lift these stocks out of the gate, but public market disclosures will force OpenAI and Anthropic to reveal payback periods, margins, and subscriber numbers for the first time. He also argues OpenAI's reported price cuts target Anthropic's growth metrics before the IPO, not user demand. The episode also covers the China model wildcard, whether AI model restrictions amount to big brother fearmongering, and whether crypto's tools for capital formation could keep the AGI flywheel from stalling. Host: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Laura Shin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, Host / Unchained Guests: ⁠Tom Shaughnessy - Founding Partner of Delphi Ventures and Co-Founder of Delphi Digital Timestamps

Discover how a homegrown AI agent is outsmarting big-brand competitors, letting users tailor digital assistants with real memory and skills. The future isn't just smarter models, but everyday tech that learns exactly how you work. • Hermes AI agent's launch, mass adoption, and personalized capabilities • Open source vs. proprietary AI: model access, privacy, and funding hurdles • Apple's next-gen Siri and agentic platform ambitions unpacked • Noose Research model development, Nvidia partnerships, and training challenges • The risk of an "AI underclass" and ethics in model distribution • Anthropic's Fable release: strict guardrails, silent model downgrades, and open source tensions • Local models vs. cloud LLMs: cost, effectiveness, and practical tuning • Community-driven iterating: Hermes' rapid product evolution and user obsession • Vatican's AI encyclical: church perspectives on AI, morality, and the common good • AGI arrival debate: economic thresholds, capabilities, and human uniqueness • The reality of AI hallucinations, agent accuracy, and responsible usage • Legal fallout over AI-generated hallucinations in court filings • AI's growing role in Hollywood contracts and labor protections • Google's Gemini 3 live translation impresses but raises privacy flags • German courts label Google AI overviews as publisher speech, liability looms • AI detection tools like Pangram face scrutiny in real-world writing and education • Google Dream Beans app tests the limits of digital personal recommendations • Picks of the Week: Reddit AMA, Dream Beans, basketball and retro gaming, research critiques Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Jeffrey Quesnelle Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/machines Melissa.com/twit zscaler.com/security

Imagine you're living 15,000 years ago. Your people are hunter-gatherers and you sleep under the stars. If someone told you humans would one day build cities with millions of people, fly through the air, or carry all human knowledge in their pockets, you couldn't even begin to picture what they meant... Yet here we are.How did our lives change so far beyond recognition? The story is complex, but there's a rough pattern. A few times in history, some radical breakthrough in technology — like the development of the plough and the steam engine — has led to a wave of productivity, innovation, and social change that ultimately reshaped the world.Now we're on the cusp of a huge new breakthrough: artificial intelligence that can meet or exceed human capabilities across a wide range of tasks.This could bring another era of transformation. There could be an explosion of intelligence and innovation, and a whole new population of digital beings. And with this, civilisation could see changes at least as profound as those brought about by industrialisation or the rise of agriculture — but instead of taking hundreds or thousands of years to unfold, this time around the world could become unrecognisable over the span of decades or less.This transformation could bring enormous benefits, helping us solve currently intractable global problems. But it could also pose severe risks, some of which could be existential — meaning they could cause human extinction, or an equally permanent and severe disempowerment of humanity. There aren't nearly enough people trying to address these challenges, and we think that's a serious problem.This article is narrated by the author, Zershaaneh Qureshi. It explores how advanced AI could be so transformative, and why working on its risks may be your best opportunity to have a positive impact on the world. You can see the original article on the 80,000 Hours website: https://80000hours.org/problem-profiles/artificial-intelligence/ Chapters:Introduction (00:00:20)Section 1: AI could replace human labour in the most economically valuable fields (00:08:32)Section 2: Replacing human labour in the most economically valuable fields could trigger the next radical transformation of society (00:22:14)Section 3: This transformation could be extremely rapid and dramatic (00:28:02)Section 4: A rapid AI-driven transformation would raise a range of major challenges, including existential risks (00:36:40)Section 5: Work on these problems is tractable, but neglected (00:44:48)Objection 1: “You're overestimating how fast and how dramatically AI would transform the world.” (00:47:59)Objection 2: “It's hard to believe that AI could really pose existential risks.” (00:52:59)Objection 3: “Isn't all this talk of AI changing the world just a fad?” (00:59:22)Objection 4: “Isn't AI going to be just like every other technology?” (01:03:04)Objection 5: “Is it even possible to produce artificial general intelligence?” (01:06:16)Objection 6: “Even if AGI is achievable, what if we're really far away from building it?” (01:11:24)Objection 7: “Isn't the real danger from actual current AI and not some sort of futuristic AGI?” (01:14:05)Objection 8: “Technological progress is a good thing for humanity.” (01:18:10)Objection 9: “This all just sounds too sci-fi.” (01:19:50)Objection 10: “Can it really make sense to dedicate my career to solving an issue that's based on a speculative story about something that may or may not ever happen?” (01:22:15)Objection 11: “OK, AI might pose existential risks, but isn't ‘issue X' an even bigger problem?” (01:24:39)Learn more (01:27:51)Audio editing: Dominic ArmstrongProduction: Zershaaneh Qureshi, Elizabeth Cox, Katy Moore, and Lou Moran

Discover how a homegrown AI agent is outsmarting big-brand competitors, letting users tailor digital assistants with real memory and skills. The future isn't just smarter models, but everyday tech that learns exactly how you work. • Hermes AI agent's launch, mass adoption, and personalized capabilities • Open source vs. proprietary AI: model access, privacy, and funding hurdles • Apple's next-gen Siri and agentic platform ambitions unpacked • Nous Research model development, Nvidia partnerships, and training challenges • The risk of an "AI underclass" and ethics in model distribution • Anthropic's Fable release: strict guardrails, silent model downgrades, and open source tensions • Local models vs. cloud LLMs: cost, effectiveness, and practical tuning • Community-driven iterating: Hermes' rapid product evolution and user obsession • Vatican's AI encyclical: church perspectives on AI, morality, and the common good • AGI arrival debate: economic thresholds, capabilities, and human uniqueness • The reality of AI hallucinations, agent accuracy, and responsible usage • Legal fallout over AI-generated hallucinations in court filings • AI's growing role in Hollywood contracts and labor protections • Google's Gemini 3 live translation impresses but raises privacy flags • German courts label Google AI overviews as publisher speech, liability looms • AI detection tools like Pangram face scrutiny in real-world writing and education • Google Dream Beans app tests the limits of digital personal recommendations • Picks of the Week: Reddit AMA, Dream Beans, basketball and retro gaming, research critiques Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Jeffrey Quesnelle Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/machines Melissa.com/twit zscaler.com/security

Discover how a homegrown AI agent is outsmarting big-brand competitors, letting users tailor digital assistants with real memory and skills. The future isn't just smarter models, but everyday tech that learns exactly how you work. • Hermes AI agent's launch, mass adoption, and personalized capabilities • Open source vs. proprietary AI: model access, privacy, and funding hurdles • Apple's next-gen Siri and agentic platform ambitions unpacked • Noose Research model development, Nvidia partnerships, and training challenges • The risk of an "AI underclass" and ethics in model distribution • Anthropic's Fable release: strict guardrails, silent model downgrades, and open source tensions • Local models vs. cloud LLMs: cost, effectiveness, and practical tuning • Community-driven iterating: Hermes' rapid product evolution and user obsession • Vatican's AI encyclical: church perspectives on AI, morality, and the common good • AGI arrival debate: economic thresholds, capabilities, and human uniqueness • The reality of AI hallucinations, agent accuracy, and responsible usage • Legal fallout over AI-generated hallucinations in court filings • AI's growing role in Hollywood contracts and labor protections • Google's Gemini 3 live translation impresses but raises privacy flags • German courts label Google AI overviews as publisher speech, liability looms • AI detection tools like Pangram face scrutiny in real-world writing and education • Google Dream Beans app tests the limits of digital personal recommendations • Picks of the Week: Reddit AMA, Dream Beans, basketball and retro gaming, research critiques Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Jeffrey Quesnelle Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/machines Melissa.com/twit zscaler.com/security

What if most of your emotional suffering comes not from what's happening around you, but from the energy you're spending trying to control it?In this series, I select my favourite and most insightful moments from previous episodes of the podcast.Today, my guest, spiritual coach Reverend Rachel Harrison, shares a profound and practical teaching on emotional well-being: that we give our power away every time we attach our inner state to something outside ourselves, and exactly what to do the moment you catch yourself doing it.Press play to discover a simple but transformative mantra that brings your energy back to where it belongs: to you.˚VALUABLE RESOURCES:Listen to the full conversation with Rachel Harrison in episode #438:https://personaldevelopmentmasterypodcast.com/438˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚

Discover how a homegrown AI agent is outsmarting big-brand competitors, letting users tailor digital assistants with real memory and skills. The future isn't just smarter models, but everyday tech that learns exactly how you work. • Hermes AI agent's launch, mass adoption, and personalized capabilities • Open source vs. proprietary AI: model access, privacy, and funding hurdles • Apple's next-gen Siri and agentic platform ambitions unpacked • Noose Research model development, Nvidia partnerships, and training challenges • The risk of an "AI underclass" and ethics in model distribution • Anthropic's Fable release: strict guardrails, silent model downgrades, and open source tensions • Local models vs. cloud LLMs: cost, effectiveness, and practical tuning • Community-driven iterating: Hermes' rapid product evolution and user obsession • Vatican's AI encyclical: church perspectives on AI, morality, and the common good • AGI arrival debate: economic thresholds, capabilities, and human uniqueness • The reality of AI hallucinations, agent accuracy, and responsible usage • Legal fallout over AI-generated hallucinations in court filings • AI's growing role in Hollywood contracts and labor protections • Google's Gemini 3 live translation impresses but raises privacy flags • German courts label Google AI overviews as publisher speech, liability looms • AI detection tools like Pangram face scrutiny in real-world writing and education • Google Dream Beans app tests the limits of digital personal recommendations • Picks of the Week: Reddit AMA, Dream Beans, basketball and retro gaming, research critiques Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Jeffrey Quesnelle Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: helixsleep.com/machines Melissa.com/twit zscaler.com/security

Anthropic just released Claude Fable 5, the first public Mythos-class model and the start of the Claude 5 family. It is their most capable model ever but… kinda scary. This week on AI For Humans, the Mythos era goes public. Anthropic released Claude Fable 5, the first commercially available Mythos-class model and the first in the new Claude 5 line. It is the same underlying model as Mythos but shipped with conservative safeguards, questions about cybersecurity and biology get routed to Claude Opus 4.8 instead. We dig into what it can do, why Anthropic held it back, and what our future looks like as we get closer to AGI.  Then Apple goes AI again at WWDC: a profoundly revamped Siri AI, a dedicated Siri app, on-screen awareness, much better photo tools, and a foundation model setup that is local, multimodal, and partly powered by Google. Gavin is thrilled that the future has finally arrived, just not on the phone he bought last year. It is AI For Humans! THE MOST POWERFUL AI EVER RELEASED. WHAT COULD GO WRONG. SHOW LINKS Anthropic announces Claude Fable 5: https://www.anthropic.com/news/claude-fable-5-mythos-5 Dan Shipper's review of Fable 5: https://x.com/danshipper/status/2064393970856124501 Usable Fable 5 demo (Library of Babel): https://library-of-babel-iota.vercel.app/ Rumored Fable 5 preview: Minecraft build (XIVIX): https://x.com/XIVIX_134/status/2062972363084341341 Rumored Fable 5 preview (chetaslua): https://x.com/chetaslua/status/2063328265708896621 Rumored Fable 5 preview (testingcatalog): https://x.com/testingcatalog/status/2062915688134574173 Fable 5 voxel Power Rangers comparison: https://x.com/Lentils80/status/2064379168272642315 Noam Brown on the implications of scaling test-time compute: https://x.com/polynoamial/status/2064210146558136827 WWDC full presentation: https://www.youtube.com/live/hF8swzNR1-o Apple introduces Siri AI, a profoundly more capable and personal assistant: https://www.apple.com/newsroom/2026/06/apple-introduces-siri-ai-a-profoundly-more-capable-and-personal-assistant/ Apple says its new Google-infused AI is all about privacy: https://gizmodo.com/apple-says-its-new-google-infused-ai-is-all-about-privacy-2000768997 An actually useful Apple Intelligence use case: https://x.com/iupdate/status/2064078761856037112 Put a summary in your summary (notification summaries): https://x.com/i_zzzzzz/status/2064061955447406722 Gaussian splats coming to Apple Maps: https://x.com/bilawalsidhu/status/2064057313057439795  

There's a lot to unpack about the economic effects of artificial intelligence. It's clear that artificial intelligence is having a moment (to say the least) and that it has a profound impact on global GDP. But is it just a boom that will bust? Ed Zitron, author and host of the “Better Offline” podcast, is deeply worried about the long-term viability of the industry. He points out that AI lacks the basic traits that have been associated with previous software booms. This raises the question: is AI running more on unsustainable costs and vibes rather than long-term profit potential? According to Ed, the answer is clear.  Sign up for MS NOW Premium on Apple Podcasts to listen to this show and other MS podcasts without ads. You'll also get exclusive bonus content from this and other shows. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.