Podcasts about Mistral

As the tech world catches its breath from AI whiplash and retro gaming returns to the PC, Microsoft quietly kills underused tools and tries to double down on what actually works. This episode blends the excitement of tech and entertainment (like Xbox classics landing on Windows 11) with a poignant look back at the life and legacy of legendary columnist John C. Dvorak. Windows Five new Insider builds released on Monday; the new Start menu comes to the Beta channel Experimental (26H1) a day later with no interesting new features The Whiteboard app is apparently being retired AI Microsoft expands partnership with Mistral to improve EU AI infrastructure Copilot for consumers is losing Podcasts and Deep research features soon U.S. judge approves Anthropic $1.5 billion settlement with authors it stole from 1Password partners with Anthropic to make AI access to password-protected websites secure Google renames NotebookLM to Gemini Notebook Google's next big AI models are delayed, but we got Gemini 3.6 Flash, Flash-Lite, and 3.5 Flash Cyber to tide us over XBOX and gaming Microsoft brings XBOX Backward Compatibility to PC!! Here comes Halo: Campaign Evolved, along with more Game Pass titles for the end of July Bethesda announces Fallout 3 and New Vegas remasters, and Fallout 5 is entering pre-production Call of Duty Modern Warfare 4 in Early Access on August 21, Open Beta on August 27 XBOX July update adds support for longer gamertags, new library customization options, and more XBOX teams with Meta to bring Game Pass Starter Edition to Meta Horizon+ subscribers Valve says the component crisis dogging Steam Machine pricing will only get worse Tips and picks Tip of the week: Windows Package Manager for bulk app installs App (game) of the week: Doom: The Dark Ages RunAs Radio this week: Security Begins at Procurement with Jessie Schofer Brown liquor pick of the week: Loup River Nebraska Straight Bourbon Whiskey Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. 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: arcticwolf.com/trends zscaler.com/security cirasync.com/Windows

Ukraine has a new commander-in-chief after days of protests, and FT calculations show the US has collected more than $13bn in revenues from Venezuelan oil sales this year, but it's unclear what has happened to the money, and US President Donald Trump may unleash fresh tariffs on dozens of countries as soon as this week. Plus, Samsung is in talks to invest in French AI start-up Mistral, and investors are worried about betting big on Japanese bonds.Mentioned in this podcast:Zelenskyy replaces top Ukraine general in biggest military shake-up since 2024 The US has collected about $13bn of Venezuela's oil money. Where is it?Donald Trump prepares fresh tariff barrage with 10% levies set to expireSamsung in talks to invest in Mistral at €20bn valuationInvestors fear Japanese bond bets risk becoming new ‘widow-maker trade'Want to get in touch? Email us at podcasts@ft.comNote: The FT does not use generative AI to voice its podcasts The FT News Briefing is produced by Victoria Craig, Sonja Hutson, Saffeya Ahmed, Katya Kumkova, and Fiona Symon. Our editor is Marc Filippino. Our show is mixed by Sam Giovinco and Alex Higgins. Additional help from Gavin Kallmann, Michael Lello, Peter Barber and David da Silva. Our intern is Cole van Miltenburg. Our executive producer is Topher Forhecz. Flo Phillips is the FT's global head of audio. The show's theme music is by Metaphor Music.Read a transcript of this episode on FT.com Hosted on Acast. See acast.com/privacy for more information.

Plus: Novo Nordisk files a deceptive advertising lawsuit against Eli Lilly. And the latest Chinese AI model launches rattle expectations for the biggest AI players in the US. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Partenaires il y a 18 mois, Apple et OpenAI se retrouvent aujourd'hui devant un tribunal fédéral pour vol de secrets industriels. Plus de 400 ingénieurs auraient quitté Apple avec des fichiers confidentiels et un playbook d'espionnage organisé de l'intérieur — la bataille pour le device du futur a déjà commencé !Pendant ce temps, Elon Musk redistribue les cartes : Grok rejoint les modèles frontières à un prix trois fois inférieur à ses rivaux, Google décroche, et Musk devient le seul acteur à tenir simultanément la puissance de calcul, le modèle et la distribution. Et en Chine, un booster orbital vient d'être récupéré dans un filet en pleine mer — la course à l'orbite basse, ressource limitée, vient d'entrer dans une nouvelle dimension.==================

La France dépend encore massivement des géants américains pour ses logiciels, son cloud et son intelligence artificielle. C'est le constat sévère dressé par la commission d'enquête sur les vulnérabilités numériques, dans un rapport publié mercredi 15 juillet. Près de 80 % des achats réalisés auprès des cinquante principaux fournisseurs de logiciels de l'Ugap, la centrale d'achat public, bénéficient à des entreprises américaines comme Microsoft, VMware ou Oracle. Les administrations dépenseraient au moins 1,5 milliard d'euros par an dans des solutions extra-européennes. Selon les députés, un milliard pourrait pourtant être réorienté dès maintenant vers des logiciels libres.Le constat est similaire pour l'hébergement des données. Des ministères, la CNAF, France Travail, mais aussi EDF, Enedis ou SNCF Réseau utilisent encore largement AWS, Google Cloud ou Microsoft Azure. Or, les lois américaines peuvent permettre aux autorités d'accéder à certaines informations hébergées par ces groupes. Les élus alertent également sur un possible « kill switch » : la capacité de Washington à couper l'accès à des services numériques par de simples restrictions à l'exportation.L'intelligence artificielle accentue cette dépendance. ChatGPT serait utilisé par 79 % des Français ayant recours à l'IA, contre seulement 14 % pour le service de Mistral. À cela s'ajoute une régulation jugée trop lente ou insuffisamment appliquée. Sur 3,5 milliards d'euros d'amendes prononcées en Irlande entre 2020 et 2024 au titre du RGPD, seulement 0,6 % auraient réellement été payés. Le rapport dénonce aussi un lobbying puissant, avec 35 millions d'euros dépensés par les GAFAM en 2025 et plus de 200 représentants mobilisés à Bruxelles. Il pointe enfin des stratégies de verrouillage : crédits cloud attractifs, présence dans les écoles et influence de prestataires présentant les solutions américaines comme incontournables.La commission propose donc de basculer 100 % des achats de logiciels de l'État vers l'open source à partir de 2030. Elle cite la gendarmerie, passée sous Linux, qui aurait économisé 534 millions d'euros depuis 2004. Parmi les autres mesures : une clause de souveraineté dans les marchés publics, un soutien renforcé aux entreprises françaises, un moratoire sur certains data centers étrangers et la création d'un véritable ministère du Numérique. L'objectif est clair : transformer une dépendance devenue stratégique en politique industrielle. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

The Wall Street Journal reported that Mira Murati's AI startup released its first model to compete with dominant platforms. Murati, former CTO and interim CEO at OpenAI, led major launches such as ChatGPT. The competitive landscape is shaped by deep alliances, including Microsoft's $10 billion commitment to OpenAI, Amazon's investment of up to $4 billion in Anthropic, and Google's investment of up to $2 billion in Anthropic. Open and alternative models from Meta, Mistral, and xAI broaden enterprise options. Data deals such as OpenAI's multi-year agreement with News Corp reported at up to $250 million and Google's reported $60 million per year Reddit agreement influence training and legal risk. Enterprise buyers prioritize certifications, safety controls, and deployment flexibility, and will monitor Murati's company's benchmarks, pricing, and partnerships to assess adoption potential.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

durée : 00:02:51 - Le 6/9 de l'été - Son nom, Helsing. La start-up vient de détrôner Mistral, l'autre star européenne de l'IA, après avoir décroché une levée de fonds mirobolante. À sa tête, un certain Daniel Ek, patron de Spotify. Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

En este episodio de Atareao con Linux nos vamos a remangar para hablar de una de esas tecnologías que, una vez las dominas, te cambian la vida por completo: el Web Scraping asistido por Inteligencia Artificial.Seguro que te ha pasado alguna vez. Quieres comprar un producto concreto, como unas zapatillas de running (yo las cambio cada 800 kilómetros y es un goteo constante), o quieres extraer todas las recetas de cocina de una web para montarte tu propio planificador semanal. Lo ideal sería que estas páginas tuvieran una API pública para descargar la información de forma limpia. Pero la cruda realidad es que casi ninguna te lo pone fácil. Ahí es donde entra el scraping: la técnica de extraer la información directamente de la página web.En este episodio te cuento por qué el scraping clásico (ese que utiliza Beautiful Soup en Python y depende de identificar las etiquetas HTML y las clases CSS) tiene los días contados para tareas complejas. Basta con que un desarrollador cambie el diseño de la web para que tu script se rompa por completo. Además, con la llegada de las webs dinámicas, los tests A/B y los sistemas anti-bloqueo como Cloudflare, mantener un scraper tradicional es un auténtico dolor de muelas.La gran alternativa: Inteligencia Artificial en local¿Y si en lugar de pelearnos con el código fuente dejamos que un modelo de lenguaje (LLM) entienda la página exactamente igual que lo haría un humano? Un LLM comprende perfectamente qué es un "precio" o el "nombre de un producto", sin importar cómo esté maquetada la web ni el idioma en el que esté escrita. Y lo mejor de todo: ¡lo podemos hacer 100% gratis en local usando Ollama!Te detallo mis pruebas ejecutando modelos en mi Slimbook One utilizando únicamente la CPU (¡sin gastar un céntimo en nubes ni necesitar tarjetas gráficas carísimas!). Hablaremos de cómo rinden modelos como Llama 3.2, Qwen, Mistral y DeepSeek R1, y cuál es el punto de equilibrio perfecto para no eternizarnos esperando la respuesta.También te desvelo mi fórmula secreta para procesar la información. No podemos enviarle 2 Megabytes de HTML ruidoso a la IA. Te explico los 5 pasos que utilizo en Python para eliminar la basura (scripts, estilos, navegación) y reducir el HTML hasta en un 93%, permitiendo que el modelo extraiga los datos en segundos y nos devuelva un JSON estructurado impecable.Por último, vemos cómo montar un auténtico vigilante de ofertas automatizado en segundo plano. Un sistema que compare los precios de varias tiendas en paralelo.Capítulos del episodio:00:00:00 Introducción al Web Scraping con Inteligencia Artificial00:01:22 ¿Para qué sirve extraer datos? Ejemplos prácticos00:02:42 El gran talón de Aquiles del scraping tradicional00:04:31 La revolución de la IA: Entender la web sin saber HTML00:07:36 Los problemas habituales: Selectores rotos y webs dinámicas00:10:00 Cómo un modelo de lenguaje (LLM) procesa la información00:13:17 Cuándo elegir scraping clásico vs. scraping con IA00:15:28 Comparación de costes: Enfoque clásico, IA local e IA en la nube00:17:19 ¿Qué modelos usar? Pruebas con Llama, Qwen, Mistral y DeepSeek00:18:19 Detrás de escena: Mi script de Python y la limpieza del HTML00:21:05 Creando el prompt perfecto para extraer un JSON estructurado00:24:34 Ejemplo real: Comparativa paralela entre tiendas00:28:38 Diseñando un vigilante de ofertas automatizado (24/7)00:30:17 Casos de uso prácticos y mejoras para evitar bloqueos00:32:02 Cierre y detalles del próximo tutorial de scrapingMás información y enlaces en las notas del episodio

Le monde s'accélère à une vitesse folle ! Ce soir dans le CKB SHOW, on décrypte les révolutions technologiques majeures qui bousculent notre quotidien et redessinent l'avenir. De la transformation radicale de nos outils Google à l'avènement massif des robots humanoïdes en Europe et au Japon, découvrez tout ce qui vous attend dans ce nouvel épisode.

Voici le portrait du médecin en chef Julien, médecin embarqué à bord du porte-hélicoptères amphibie Mistral de la Marine Nationale. Il veille chaque jour à la santé de l'équipage et se prépare à faire face aux situations les plus exigeantes de la médecine d'urgence lors d' opérations de grande ampleur. Dans cet entretien, il revient sur son parcours, son engagement au sein de la Marine nationale et les responsabilités qui accompagnent la pratique de la médecine en mer. Entre autonomie, esprit d'équipage et sens du devoir, découvrez le quotidien d'un professionnel de santé au cœur des opérations navales sous-marine et de surface !

La start up française UMA dévoile un robot humanoïde • Mistral lance une IA pour robots • ChatGPT encore plus bavard et plus naturel • Anthropic découvre une zone inconnue de l'IA Claude • L'Europe actionne la surveillance généralisée Chat Control • Des lunettes Meta pour mémoriser notre vie • L'IA révolutionne aussi la généalogie.⭐️ Découvrez Frogans, l'innovation française qui réinvente le Web

Hi, I'm Connor with Honor - message me here!Anthropic quietly rerouted paying Fable 5 requests down to a weaker model, Opus 4.8, while still billing Fable rates, and only walked it back after users found the tag in their own logs. That's the opener on today's Daily Download: AI With Honor, the daily AI news show that translates what's actually happening in AI into what it means for your business, your money, and your family, no hype, no sales pitch, just the news and the honest take, from Santa Clarita, California.Today's show covers four model launches in one week (Anthropic, xAI, OpenAI, and China's Zhipu), a 120,000-person AI-linked layoff number nobody's talking about enough, a bank betting half a billion dollars on OpenAI's IPO, a robot that can now navigate a warehouse with nothing but a camera and a sentence, a live look at the real estate search engine built right here in Santa Clarita, and a hard conversation about who really owns your health data once AI starts reading your face just to let you log in.WHAT'S IN TODAY'S SHOW: AI (Fable 5 downgrade scandal, Grok 4.5, GPT-5.6 Sol's "lying problem," China's GLM-5.2, the death of prompting, 120,000 AI-linked layoffs, BofA's $520M OpenAI bet, Meta's Muse Image vs. Zuckerberg's private admission, Claude Cowork, Alberta's government letting Claude patch its own defenses, Mistral's Robostral Navigate robot, Anthropic's ID/facial-scan policy, EU/UN/Ukraine AI governance, a 5-year thought experiment) · HOME (Santa Clarita Open Houses live IDX, Fair Fixed Fee $17,000) · FAT (food freedom, fasting, and who owns your wearable data).CHAPTERS:0:00 The Anthropic downgrade nobody was supposed to notice0:56 Every AI company has the same problem1:26 Grok 4.5 launches and the political-steering fight2:37 GPT-5.6's Sol goes public with a "lying problem"3:18 China's GLM-5.2 closes the gap3:57 Four launches, one week: capability without trust4:21 Prompting is dead, management is the new skill4:58 120,000 AI-linked layoffs in 20266:25 Bank of America bets big on OpenAI's IPO6:57 Meta's Muse Image vs. Zuckerberg's private admission7:34 Claude Cowork expands + a government lets Claude patch its own defenses8:36 Mistral's robot that navigates with a camera and a sentence9:30 Anthropic wants your face and ID just to chat10:03 The EU, the UN, and Ukraine all move on AI governance10:51 The 5-year AI thought experiment11:28 Santa Clarita Open Houses: real IDX search, live12:50 The Fair Fixed Fee: $17,000, no percentage13:08 Food freedom, fasting, and who owns your data14:22 Every thread, pulled together15:06 The next five yearsQUESTIONS THIS EPISODE ANSWERS: Did Anthropic downgrade Fable 5 users without telling them? Is Grok 4.5 better than Opus 4.8? Does GPT-5.6 Sol have a lying problem? Can GLM-5.2 compete with the American labs? How many jobs has AI eliminated in 2026? Why did BofA reverse course on OpenAI? Is Meta's AI agent progress slowing? What is Claude Cowork? Why does Anthropic want a facial scan to use Claude? Is there a live open-house search for Santa Clarita? What's the Fair Fixed Fee? How does fasting relate to AI and wearables?Text AI, HOUSE, or FAT to (661) 400-1720 for the free resource that matches what you're dealing with.connorwithhonor.com · connorwithhonorai.com · santaclaritaopenhouses.com · sellersonlyagent.com · thelastaddiction.comConnor MacIvor, Sync Brokerage, Inc., DRE #02031490. Information believed reliable, not guaranteed. Not tax or legal advice. Not a lender. Consult a licensed professional.#DailyDownload #AIWithHonor #SeventeenK #SantaClarita #AInews #ClaudeAI #GrokAI #OpenAI #FairFixedFee #RealEstate #FastingLifestyleYoutube Channels:Conner with Honor - real estateHome Muscle - fat torchingFrom first responder to real estate expert, Connor with Honor brings honesty and integrity to your Santa Clarita home buying or selling journey. Subscribe to my YouTube channel for valuable tips, local market trends, and a glimpse into the Santa Clarita lifestyle.Dive into Real Estate with Connor with Honor:Santa Clarita's Trusted Realtor & Fitness EnthusiastReal Estate:Buying or selling in Santa Clarita? Connor with Honor, your local expert with over 2 decades of experience, guides you seamlessly through the process. Subscribe to his YouTube channel for insider market updates, expert advice, and a peek into the vibrant Santa Clarita lifestyle.Fitness:Ready to unlock your fitness potential? Join Connor's YouTube journey for inspiring workouts, healthy recipes, and motivational tips. Remember, a strong body fuels a strong mind and a successful life!Podcast:Dig deeper with Connor's podcast! Hear insightful interviews with industry experts, inspiring success stories, and targeted real estate advice specific to Santa Clarita.

Jonathan Userovici observe la tech et le venture capital depuis plus de 10 ans.À 33 ans, il fait partie d'une nouvelle génération d'investisseurs qui a vu évoluer les derniers grands cycles technologiques.Au départ, il devait rester seulement 3 mois chez Idinvest.Finalement, il n'a jamais quitté le monde du VC.Dans cet épisode, il raconte ce qui a changé en une décennie.Il y a 10 ans, beaucoup de startups se construisaient en modernisant de vieux marchés.On prenait un acteur historique, souvent lent et peu digitalisé, puis on créait une version plus simple, plus rapide et plus moderne.Aujourd'hui, l'IA bouleverse complètement cette logique.Quand tout le monde peut créer plus vite, la vraie question est qu'est-ce qui aura encore de la valeur demain ?Jonathan prend notamment l'exemple de Lovable, symbole d'une nouvelle génération de produits très simples à utiliser, mais qui reposent aussi sur des technologies déjà existantes.Dans ce nouveau monde, les investisseurs ne regardent plus seulement l'idée ou le marché.Ils regardent surtout la vitesse d'exécution, la capacité à avancer vite, à recruter, tester, vendre et itérer sans attendre que tout soit parfait.Ce sont les High Agency Founders.Des fondateurs capables de faire bouger leur boîte sur tous les fronts.L'épisode aborde aussi l'avenir du travail, les métiers qui vont disparaître, et la place de l'humain dans un monde post-IA.Avec Headline, Jonathan accompagne des startups européennes dans leurs différents stades de développement.Le fonds gère plus de 5 milliards d'euros, compte plus de 100 collaborateurs et investit partout en Europe.Pour lui, une grande boîte peut naître en France.Mais pour devenir un géant mondial, elle doit très vite penser au-delà de son marché local.Bonne écoute !===========================

Hoy hablamos de GPT-Live y la voz full-duplex de ChatGPT, Grok 4.5 en Cursor para tareas largas con agentes, Robostral Navigate de Mistral para robots con una sola cámara, el acuerdo de la FTC que obliga a John Deere a abrir reparación durante diez años, y RHINE acelerando simulaciones de fusiones de estrellas de neutrones para entender el origen de elementos pesados.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

Dans cet épisode, on reçoit Matthieu Fremeaux alias Fremox, motion designer senior et scripteur AE. Il nous parle de son parcours, des défis du scripting sur After Effects et de son regard sur les IA génératives. Bonne écoute !Dans cet épisode, vous entendez parler de :Reflet : https://www.refletsvideo.com/Profil AeScript : https://aescripts.com/authors/fremox/Quark Xpress : https://www.quark.com/fr/products/quarkxpress3Ds max : https://www.autodesk.com/fr/products/3ds-max/overviewVideo Copilot : https://www.videocopilot.net/Mattrunks : https://mattrunks.com/frMotion Café : https://www.youtube.com/@Motion-cafeJissse : https://www.jissse.com/MoDCast avec Aurélien Malagoli : https://youtu.be/HehaZFEMTxE?si=CZ3gzu13JWcwbubcLakpo aka Matthieu Wlazinski : https://www.behance.net/WlazinskiMatthieuText Evo : https://aescripts.com/textevo/Grégory Villien : https://www.mistergreg.fr/David Oldani : https://fr.tuto.com/formateur/dafx.htmHarlem : https://www.instagram.com/_h_a_r_l_e_m_/Lionel Vicidomini : https://viclio.myportfolio.com/Teaser The Sandbox : https://www.behance.net/gallery/170047337/The-Sandbox-CG-Trailers-TeasersAvenged Sevenfold : https://www.youtube.com/@avengedsevenfoldVideo des voeux 2021 de Maxon : https://www.behance.net/gallery/135166533/Maxon-Holiday-Greetings-2021Episode MoDCast avec Romane Jubert : https://youtu.be/KzQYJXEu9HM?si=frIWXG7dh14zWjbFRive : https://rive.app/Emanuele Colombo : https://www.instagram.com/ema_colombo/Spotify Wrapped 2025 : https://rive.app/blog/spotify-used-rive-for-spotify-wrapped-2025Houdini : https://www.sidefx.com/API d'After Effects : https://aftereffects.fandom.com/wiki/After_Effects_Javascript_APIDavid Torno : https://linktr.ee/davidtornoScript UI For Dummies : https://indd.adobe.com/view/a0207571-ff5b-4bbf-a540-07079bd21d75Element 3D : https://aescripts.com/element-3d/E3D Mografter FX : https://aescripts.com/e3d-mografter-fx/Lloyd Alvarez : https://www.linkedin.com/in/lloydalvarez/Textbox sur Plugin Everything : https://www.plugineverything.com/textboxRxLaboratorio : https://rxlaboratorio.org/Duduf : https://www.instagram.com/nicoduduf/DuMe : https://rxlaboratorio.org/rx-tool/dume/Text Frame : https://rxlaboratorio.org/rx-tool/fx-textframe/MotionTober : https://www.instagram.com/motion.tober/Média Manquant : https://www.instagram.com/media_manquant/Red Giant : https://www.maxon.net/fr/red-giantCyclops : https://aescripts.com/cyclops/Overlord : https://battleaxe.co/overlordNewton : https://aescripts.com/newton/OLM Smoother : https://www.olm.co.jp/post/olm-smootherEasyShape FX : https://aescripts.com/easyshape-fx/Fremox avec Adobe France : https://www.youtube.com/live/qUu1EJ8p1FY?si=OhDjxDBrrc-QVgVACedric Villain et IA : https://www.cedric-villain.info/iag/Ta mère l'IA : https://www.instagram.com/ta_mere_l_ia/Grok : https://grok.com/Mistral : https://mistral.ai/fr/Pub Coca-Cola en IA : https://www.youtube.com/watch?v=Yy6fByUmPuELa fourmi dans la coquille : https://lafourmi.media/cmd_draw - Compte secondaire de Fremox : https://www.instagram.com/cmd_draw/Showreel 2024 de Fremox : https://www.behance.net/gallery/206741105/Fremox-motion-reel-2024The movie of my life : https://www.instagram.com/p/DJoVJMVtkK5/Retrouvez Fremox ici :Instagram : https://www.instagram.com/fremox59/Aescript : https://aescripts.com/authors/fremox/Behance : https://www.behance.net/fremoxGumroad : https://fremox.gumroad.com/Outil open-source sur RxLaboratorio : https://rxlaboratorio.org/rx-author/fremox/---Le MoDCast est un format de discussion ouverte avec des professionnels du #motiondesign.Au fil de nos discussions, découvrez les parcours, les process, le quotidien de créatifs francophones.

Hey everyone, Alex here

Parce que… c'est l'épisode 0x319! Shameless plug 19 septembre 2026 - Bsides Montréal 20 au 26 septembre 2026 - BruCON 13 novembre 2026 - DEATHCon 16 au 19 novembre - European Cyber Week 1 au 3 décembre 2026 - Forum INCYBER - Canada 2026 24 et 25 février 2027 - SéQCure 2027 Description Contexte de la recherche Dans cet épisode de Polysécure, l'animateur reçoit Gaëtan Ferry, chercheur en sécurité chez GitGuardian, pour discuter d'une recherche originale sur la génération de mots de passe par les grands modèles de langage (LLM). Le point de départ est un article publié début 2026 par Irregular, une entreprise israélienne, qui avait démontré que les mots de passe générés par les LLM présentaient des biais statistiques importants, variables selon les modèles. Cette découverte a poussé l'équipe de GitGuardian à se demander si ces mots de passe biaisés se retrouvaient réellement « dans la nature », c'est-à-dire utilisés par de vraies personnes sur de vrais systèmes. GitGuardian dispose d'un avantage unique pour répondre à cette question : son activité principale consiste à détecter des secrets (mots de passe, clés API, etc.) exposés publiquement, notamment sur GitHub. L'entreprise possède donc une base de données massive de secrets, dont une catégorie dite « générique » — des chaînes de caractères qui ressemblent à des mots de passe mais ne suivent aucun format standardisé identifiable. Méthodologie : les chaînes de Markov Pour détecter automatiquement si un mot de passe avait été généré par un LLM, l'équipe a eu l'idée de reconstruire une structure statistique classique : les chaînes de Markov, l'ancêtre technique des LLM, utilisées par exemple dans la prédiction de texte sur les claviers de téléphone. Ces chaînes excellent à capturer des biais statistiques dans une séquence de caractères. La méthode a consisté à interroger 40 modèles de LLM différents, en leur demandant de générer des centaines de mots de passe chacun. À partir de ces échantillons, plusieurs types de chaînes de Markov ont été construits, prenant en compte (ou non) la position des caractères dans le mot de passe. Ces structures permettent ensuite, face à un mot de passe arbitraire, d'estimer la probabilité qu'il ait été généré par un LLM, un peu comme une mesure d'entropie relative à une base de référence précalculée. Des biais spectaculaires Les résultats ont confirmé, voire amplifié, les observations d'Irregular. L'exemple le plus frappant concerne un modèle (Opus, à l'époque de l'étude) qui ne générait des mots de passe uniques que dans 35 % des cas — autrement dit, il répétait le même mot de passe dans 65 % des requêtes. Un modèle de Mistral faisait encore pire : il ne générait qu'un seul et unique mot de passe, toujours identique, laissant penser qu'il se contentait de restituer une valeur mémorisée durant son entraînement plutôt que d'en générer une nouvelle. Gaëtan Ferry explique ce phénomène par la tokenisation : certains modèles (comme GPT, étudié par Irregular) découpent le mot de passe en plusieurs tokens indépendants, ce qui introduit une certaine variabilité, alors que d'autres semblent traiter le mot de passe comme un token unique, menant à une quasi-absence de diversité. Un biais récurrent, observé sur presque tous les modèles, est l'alternance rigide entre catégories de caractères (minuscule, majuscule, chiffre, symbole) selon un schéma répétitif — un pattern idéal à capturer dans une chaîne de Markov. Sur un modèle donné, par exemple, un symbole « # » est suivi d'un « 8 » dans 90 % des cas. Ce comportement s'explique par la nature même des LLM : entraînés à reproduire des régularités linguistiques, ils sont fondamentalement conçus pour être prévisibles — l'exact opposé de ce qu'exige une bonne génération de mot de passe, qui requiert une forte entropie. Résultats sur les données réelles En appliquant cette classification à un échantillon d'environ 3 millions de secrets génériques collectés entre janvier et mars 2026, l'équipe a identifié environ 28 000 mots de passe présentant une très forte probabilité d'avoir été générés par un LLM, avec un seuil de confiance élevé. Bien que ce nombre représente une fraction modeste de l'échantillon total, la tendance est constante : environ 1 500 à 2 500 nouveaux mots de passe générés par LLM apparaissent chaque semaine sur GitHub, un rythme jugé préoccupant compte tenu qu'il ne s'agit que du sous-ensemble visible publiquement — la réalité en environnements privés étant probablement plus large encore. L'analyse du contexte d'apparition de ces mots de passe révèle deux scénarios typiques : soit un humain a manifestement demandé au LLM de générer un mot de passe pour l'insérer dans un fichier de configuration (par exemple une base de données), soit — plus troublant — un agent de développement autonome a lui-même pris la décision de générer et d'intégrer le mot de passe dans du code, comme dans un fichier de configuration Terraform observé par l'équipe, le tout dans un commit signé par l'agent d'IA lui-même, sans intervention humaine apparente. Une anecdote marquante Lors d'une présentation de ces résultats en conférence, Gaëtan Ferry a vu une personne quitter précipitamment la salle en voyant une diapositive listant des exemples de mots de passe faibles générés par un LLM. Cette même personne est revenue lui expliquer qu'elle avait justement demandé, ce matin-là, à un LLM de générer un mot de passe pour un service en ligne — et qu'elle venait de réaliser qu'il s'agissait exactement du même mot de passe affiché à l'écran. Un exemple frappant, selon lui, du fait que même des publics avertis en sécurité adoptent ce genre de pratique risquée. Conclusion Gaëtan Ferry conclut que le problème dépasse la simple question de la qualité du mot de passe généré : demander à un LLM hébergé chez un tiers de générer un secret expose potentiellement ce secret avant même qu'il n'apparaisse à l'écran de l'utilisateur, via les journaux et infrastructures du fournisseur. Il y voit une incompréhension plus profonde de la nature du LLM et des bonnes pratiques de gestion des secrets, et espère que ce partage contribuera à faire évoluer les pratiques des développeurs. Collaborateurs Nicolas-Loïc Fortin Gaetan Ferry Crédits Montage par Intrasecure inc Locaux réels par SSTIC

Welkom terug bij Radio Raccoons! In deze laatste aflevering voor onze zomerbreak (

In this episode, we cover Mistral's $3.5 billion raise and what it signals for Europe's push to compete in frontier AI. We also look at Samsung's 18x profit jump from AI memory demand and why chips remain the backbone of the AI boom.Show LinksGet AI images, audio and video in Claude with the AI Box MCP: https://aibox.ai/mcpHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

In this episode, we cover Mistral's $3.5 billion raise and what it signals for Europe's push to compete in frontier AI. We also look at Samsung's 18x profit jump from AI memory demand and why chips remain the backbone of the AI boom. Show LinksGet AI images, audio and video in Claude with the AI Box MCP: https://aibox.ai/mcpHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we cover Mistral's $3.5 billion raise and what it signals for Europe's push to compete in frontier AI. We also look at Samsung's 18x profit jump from AI memory demand and why chips remain the backbone of the AI boom. Show LinksGet AI images, audio and video in Claude with the AI Box MCP: https://aibox.ai/mcpHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we cover Mistral's $3.5 billion raise and what it signals for Europe's push to compete in frontier AI. We also look at Samsung's 18x profit jump from AI memory demand and why chips remain the backbone of the AI boom.Show LinksGet AI images, audio and video in Claude with the AI Box MCP: https://aibox.ai/mcpHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

In this episode, we cover Mistral's $3.5 billion raise and what it signals for Europe's push to compete in frontier AI. We also look at Samsung's 18x profit jump from AI memory demand and why chips remain the backbone of the AI boom. Show LinksGet AI images, audio and video in Claude with the AI Box MCP: https://aibox.ai/mcpHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we cover Mistral's $3.5 billion raise and what it signals for Europe's push to compete in frontier AI. We also look at Samsung's 18x profit jump from AI memory demand and why chips remain the backbone of the AI boom. Show LinksGet AI images, audio and video in Claude with the AI Box MCP: https://aibox.ai/mcpHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we cover Mistral's $3.5 billion raise and what it signals for Europe's push to compete in frontier AI. We also look at Samsung's 18x profit jump from AI memory demand and why chips remain the backbone of the AI boom.Show LinksGet AI images, audio and video in Claude with the AI Box MCP: https://aibox.ai/mcpHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

In this episode, we cover Mistral's $3.5 billion raise and what it signals for Europe's push to compete in frontier AI. We also look at Samsung's 18x profit jump from AI memory demand and why chips remain the backbone of the AI boom. Show LinksGet AI images, audio and video in Claude with the AI Box MCP: https://aibox.ai/mcpHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

La numérisation a ouvert les portes de milliers d'archives médiévales. Mais jusqu'ici, une difficulté persistait : photographier un manuscrit ne suffit pas à le rendre lisible par un ordinateur. Les chercheurs disposaient donc d'immenses collections d'images, sans avoir le temps de retranscrire chaque page. Une équipe de l'Inria vient de faire sauter ce verrou grâce à une intelligence artificielle spécialisée dans les écritures anciennes.Le projet s'appelle CoMMa, pour *Corpus of Multilingual Medieval Archives*. Piloté par Thibault Clérice, chercheur en humanités computationnelles au Centre Inria de Paris, il a permis de constituer un corpus de plus de trois milliards de mots. Les documents sont principalement rédigés en latin, du IXe au XVIe siècle, et en ancien français, du XIIe au XVIe siècle. Pour ce dernier, le volume de textes disponibles a été multiplié par quarante. Pourquoi ne pas simplement utiliser ChatGPT ou Mistral ? Parce que les manuscrits médiévaux échappent aux règles modernes. L'orthographe de l'ancien français n'est pas stabilisée : deux scribes peuvent écrire jusqu'à la moitié des mots différemment. En latin, au XIVe siècle, 35 à 40 % des termes sont abrégés. Dans certains traités médicaux, seule la moitié des lettres apparaît.Les grands modèles de langage risqueraient alors d'inventer les passages manquants. L'équipe a préféré une reconnaissance visuelle caractère par caractère, avec les outils libres Kraken et eScriptorium. L'algorithme peut confondre deux signes, mais il ne reconstitue pas arbitrairement un mot. Pour les historiens, cette erreur reste moins grave qu'une hallucination crédible mais fausse. Avant CoMMa, les chercheurs ont créé CATMuS, une base d'entraînement constituée depuis 2022. Philologues et spécialistes ont retranscrit manuellement 200 000 lignes provenant de 300 manuscrits, rédigés dans onze langues. Ils ont conservé toutes les abréviations, fautes et inversions de lettres afin de respecter fidèlement les documents.L'IA a ensuite travaillé sur les fonds de Gallica, d'Oxford, de Munich ou d'E-Codices. Sur 670 manuscrits, son taux d'erreur moyen atteint 9,7 %. Les textes cursifs tardifs restent plus difficiles, faute d'exemples suffisants. L'ensemble du corpus est désormais accessible librement. Pour les chercheurs comme pour les passionnés, ce sont des milliards de mots longtemps enfermés dans les bibliothèques qui deviennent enfin consultables. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Timestamps:09:16 - How Emmi AI Reached an Exit in 18 Months18:01 - Why They Raised €15M After Becoming Profitable25:25 - Why Mistral Was the Right Buyer46:49 - Can Europe Win in Industrial AI?Episode description:Dennis Just co-founded Emmi AI and served as its CEO, building an industrial AI company focused on complex engineering problems. Around 18 months after launch, Emmi AI was acquired by Mistral AI, where Dennis now serves as VP Manufacturing Solutions. Before Emmi AI, he had already founded several companies, including Knip and Smallpdf, and was closely involved in the Swiss startup ecosystem.In this episode, Dennis explains how the company grew around research rather than a traditional sales setup. Publishing strong technical work brought major industrial companies to them, while the €15 million seed round gave the team room to keep investing in the technology without making every decision around runway. He also takes us behind the acquisition and shares why Emmi AI chose Mistral despite having other offers on the table.The conversation then zooms out to Europe's place in the AI race. Dennis explains why he sees industrial AI as one of the continent's strongest opportunities, what Europe still needs to get right, and why this deal feels more like the start of a new chapter than the end of the journey.The cover portrait was edited by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.smartportrait.io⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Don't forget to give us a follow on⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Youtube ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠so you can always stay up to date with our latest initiatives. That way, there's no excuse for missing out on live shows, weekly giveaways or founders' dinners.

In dieser Folge des DEEPTECH DEEPTALK ordnen Oliver Rößling und Alois Krtil ein, was es bedeutet, wenn der amerikanische Staat den eigenen Frontier Modellen plötzlich die Leine anlegt. Die USA verkauften sich jahrelang als der Ort, an dem KI ohne Bremsen läuft. Jetzt stehen erste regulatorische Eingriffe neben der Ankündigung von 100 Prozent Digitalzöllen als Antwort auf die europäischen Acts. Das trifft tokenbasierte, hochsubventionierte Geschäftsmodelle direkt an der Refinanzierung. Profiteur könnte Europa sein. Modelle wie Mistral gewinnen an Boden, auch als Schutz vor einer Übernahme durch amerikanische oder chinesische Käufer.Den Kern der Folge bildet die These, dass nicht die Modellgröße zählt, sondern die Architektur darum herum. Kleine gehärtete Modelle mit Quelle, Fehlercode und Diagnose schlagen ein Frontier Modell überall dort, wo proprietäre Industriedaten, kritische Infrastruktur oder Edge-Einsatz in Drohnen und Sensorik gefragt sind. Daneben geht es um die Frage, wie man gewachsene ERP-Systeme angreift, ohne die Bilanz zu sprengen. Modul für Modul von innen aushöhlen statt einer Komplettablösung lautet die Strategie. Welcher Stein im Jenga-Turm der Legacy IT sich noch ziehen lässt, bevor das System kippt, bleibt am Ende eine Frage fürs Management.Zum Schluss geht es um das europäische Zeitfenster. Neue Vergabelogiken wie in Paris oder Estland geben auch kleinen Anbietern eine Chance. Talent-Hotspots wie Station F oder King's Cross ziehen Köpfe und Kapital an. Krtils These zum Schluss: Die eigentliche Gefahr liegt nicht in einer durch Open Source entstehenden Superintelligenz, sondern in der Machtkonzentration bei wenigen Anbietern mit milliardenschweren Staatsverträgen über Jahrzehnte.Key LearningsUS-Regulatorik wird zur Waffe. 100 Prozent Digitalzölle als Antwort auf die europäischen Acts und ein erster regulatorischer Eingriff bei Frontier Modellen setzen tokenbasierte, hochsubventionierte Geschäftsmodelle an der Refinanzierung unter Druck.Kleine Modelle schlagen große dort, wo es zählt. Gehärtete Branchenmodelle mit Quelle, Fehlercode und Diagnose liefern für proprietäre Industriedaten und Edge-Einsatz verlässlichere Antworten als ein Frontier Modell.Wert entsteht durch Architektur, nicht durch Modellgröße. Orchestrierung, Guardrails und Härtung um das Modell herum entscheiden, ob ein System im B2B-Umfeld überhaupt einsatzfähig wird.Legacy IT lässt sich nicht ersetzen, nur aushöhlen. Modul für Modul von innen aushöhlen erhält den bilanziellen Wert und vermeidet eine teure Komplettablösung.Europa hat ein Zeitfenster, kein Zeitproblem. Neue Vergabelogiken in Paris oder Estland öffnen Beschaffung auch für kleine Anbieter. Talent-Hotspots wie Station F oder King's Cross ziehen zusätzlich Köpfe und Kapital an.Die größere Gefahr ist Machtkonzentration, nicht Open Source. Wenige Anbieter mit milliardenschweren, jahrzehntelangen Staatsverträgen sind nach Krtils Einschätzung das eigentliche Risiko, nicht eine durch offene Modelle entstehende Superintelligenz.

Bruxelles a choisi son champion pour le Frontier AI Grand Challenge. Le lauréat s'appelle EUROPA, un consortium piloté par Domyn, start-up italienne encore connue il y a peu sous le nom d'iGenius. Sa mission est claire : créer un modèle d'intelligence artificielle de frontière, open source, entraîné en Europe, sur des supercalculateurs européens, avec plus de 400 milliards de paramètres et une couverture des 24 langues officielles de l'Union.L'enjeu dépasse largement la performance technique. Un modèle de frontière désigne une IA parmi les plus avancées de sa génération, capable de rivaliser avec les grands systèmes américains ou chinois. Quant aux paramètres, ils représentent les milliards de réglages internes qui permettent au modèle d'apprendre et de produire ses réponses. Plus leur nombre est élevé, plus le système peut être puissant, à condition de disposer des données et de la puissance de calcul nécessaires.Le choix de Domyn peut surprendre. Mistral, en France, incarne depuis plusieurs années l'idée d'une IA souveraine européenne. L'entreprise a signé avec le ministère des Armées, la Caisse des Dépôts, l'Office européen des brevets et plusieurs institutions sensibles. Pourtant, c'est Domyn qui a remporté le défi lancé en février 2026. La start-up milanaise a un argument solide : elle s'est spécialisée dans les modèles déployés directement chez les clients, sans passer par des clouds tiers. Pour Bruxelles, cette logique est centrale. Elle garantit que les données et les usages restent sous contrôle européen. Domyn s'appuie aussi sur le Fraunhofer-Gesellschaft, grand réseau allemand de recherche appliquée, et sur un cluster Blackwell de 5 760 puces, en plus des ressources EuroHPC.C'est là que se joue le vrai prix : jusqu'à 2,5 % de la capacité de calcul du réseau européen EuroHPC pendant un an. Pour l'Europe, souvent riche en chercheurs mais limitée en infrastructures, cet accès est stratégique. EUROPA répond à une inquiétude très concrète : ne plus dépendre de modèles hébergés ailleurs, soumis à des décisions étrangères. Tribunaux, hôpitaux, ministères ou administrations ne peuvent pas bâtir leur autonomie numérique sur des outils dont l'accès peut être restreint. Domyn promet un modèle open source dans un an. Mais le pari reste immense. Entraîner un modèle de 400 milliards de paramètres, multilingue, européen et réellement ouvert, demandera plus que de l'ambition : il faudra aussi de la transparence sur les données, les poids du modèle et la licence choisie. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

GPT, Claude, Gemini, Grok, Mistral, dazu ein Dutzend chinesischer Modelle - welche KI kann was am besten? Außerdem: Kommt Anthropic nach Europa? Und stellen Quantencomputer bald alles auf den Kopf?

In der Sommerhitze-Edition von AI in Finance, dem Podcast von Payment & Banking, sprechen Sascha und Maik über eine Woche, die für Banken, Versicherer und Finanzdienstleister gleich mehrere Weichen stellt: neue KI-Modelle, eine sich verschärfende Sicherheitslage und Europas wachsende Abhängigkeit von Infrastruktur aus den USA.

The Wall Street Journal reported on June 24, 2026, that former Anthropic employees launched a startup aimed at helping scientists develop their own AI systems. Anthropic, led by CEO Dario Amodei and President Daniela Amodei, received up to $4 billion from Amazon in 2023 and at least $300 million plus additional financing reported as up to $2 billion from Google. The new venture targets researcher needs around data control, reproducibility, and deployment. Alternatives include closed APIs from OpenAI, Anthropic, and Google DeepMind, and open-source options from Meta and Mistral with tooling from Hugging Face, Databricks, and Weights & Biases. Compute considerations center on Nvidia GPUs via AWS, Google Cloud, and Azure. Sales into universities and pharma will require compliance, security reviews, and marketplace channels. Founders should watch for product details, partnerships, and pricing as indicators of viability.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

In this episode, we dive deep into how AI is revolutionizing the entire industrial customer journey. We explore how large language models and digital twins are reshaping everything from initial customer inquiries to spare parts management and predictive maintenance. I'm joined by Jan Seyler and Werner Reichelt from Festo, who share firsthand insights on integrating AI with engineering tools, orchestrating multi-agent systems, and bridging the gap between digital sales and real-world manufacturing. Together, we discuss the challenges of interoperability, the evolving role of sales engineers, and the opportunities presented by data-driven automation. If you're curious about the practical impact of AI on industry, this conversation is packed with real examples and forward-looking ideas.

À l'occasion des 10 ans de VivaTech, Patrice Duboé et Matthieu Deboeuf-Rouchon décryptent les grandes tendances qui marquent cette édition 2026. Entre souveraineté technologique, intelligence artificielle, robotique, spatial, quantique et deeptech, ils dressent le portrait d'un écosystème européen plus mature et plus ambitieux.Emission spéciale en partenariat avec Capgemini

Europe already produces world-class technology companies. The mistake is assuming future winners will look obvious before they become winners.In this episode of This Week in European Tech, Dan Bowyer and Mads Jensen of SuperSeed speak with Joe Schorge, Founder and Managing Partner at Isomer Capital, about why the best investors focus less on predicting outliers and more on building exposure to exceptional founders, technologies and ecosystems.Joe shares why Europe's next trillion-dollar company is probably already operating today, what Amazon and Google teach us about identifying future winners and why diversification remains one of the most powerful tools in venture capital.The conversation also covers AI sovereignty, Anthropic's model shutdown, DeepSeek's $7 billion round, Mistral's latest raise and Europe's position in the global AI race.Key highlights:Why Europe's next trillion-dollar company probably already existsWhy future winners rarely look obvious early onThe LP case for backing ecosystems instead of chasing predictionsLessons from Amazon and GoogleWhy Europe already produces world-class technology companiesWhy tech sovereignty depends on world-class productsDeepSeek, Mistral and the future of AI infrastructureWhether Europe can compete with the US and China in frontier technologyTimestamps(00:00) Introduction(05:00) Why Accenture matters for the future of AI adoption(12:00) Anthropic's model shutdown and AI sovereignty(15:00) Why tech sovereignty is creating opportunities for European startups(20:00) AI alliances, geopolitics and Europe's position(26:00) DeepSeek's $7 billion funding round(31:00) Mistral's next chapter and Europe's AI ambitions(35:00) Can Europe build a trillion-dollar technology company?(38:00) Why future winners rarely look obvious(40:00) Europe's world-class technology companies(41:00) Isar Aerospace and European winners(42:00) European tech deal of the week(43:00) The week ahead in AI and ventureLearn more about the Love Tomorrow Summit and the programmes EUVC is curating, and secure your tickets here.

Guillaume Decugis a mis vingt ans d'entrepreneuriat dans la tech derrière lui avant de passer de l'autre côté de la table. Polytechnicien passé par Stanford, il a fondé MusicWave, plateforme de musique mobile vendue à OpenWave puis à Microsoft, avant de piloter Linkfluence, spécialiste de la veille sociale, jusqu'à son rachat. Depuis deux ans, il est partner chez Serena Ventures, où il cogère Data Ventures, un fonds de 100 millions d'euros entièrement dédié aux couches d'infrastructure et de data à l'ère de l'IA.Sa thèse d'investissement : les Européens excellent dans les technologies conceptuelles (bases de données, outils de développement, middlewares), mais peinent à en faire des standards mondiaux. Il veut combler ce manque en traquant les pépites partout en Europe, en s'aidant d'outils très sophistiqués qui suivent 5 000 profils de fondateurs et scrutent les tendances sur GitHub. Résultat : la découverte et la cession à Mistral d'Emmi AI, jeune pousse autrichienne spécialisée dans les modèles d'IA capables de simuler des phénomènes physiques complexes. Pour lui, la souveraineté technologique européenne doit se construire en faisant émerger des leaders mondiaux, quitte à ce qu'ils s'américanisent en chemin. Le modèle israélien est sa référence. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

The hosts discuss a favorite scene from the 1981 film Caveman before introducing Project Synapse, their weekly show on AI and new technology. They cover a hectic week in AI news, including talk of SpaceX buying Cursor for $60B, Cursor's role as an AI-enabled IDE using multiple models, and concerns over token costs and profitability. They describe Anthropic taking Fable offline after a government order cutting off foreign nationals, raising fears about reliance on U.S.-based AI and digital sovereignty, and note Europe's renewed push toward open-source alternatives. They highlight open-source and lower-cost models such as Mistral, DeepSeek, and GLM 5.2, Google's strategy of free tools and local processing, and a DeepMind paper "From AGI to ASI." The episode ends with Midjourney's announced non-radiation full-body scanner concept and spa rollout plans for 2027. Find the links we talked about on our Discord Server. This is the link to you our Discord server  https://discord.gg/e9476SGMsz 00:00 Caveman Music Discovery 01:57 Show Intro and Hosts 03:03 SpaceX Buys Cursor 07:23 Is Cursor Still Best 09:18 Fable AI Vanishes 11:20 Government Shutdown Fallout 13:57 Digital Sovereignty Wakeup 18:09 Open Source Reality Check 20:23 Economics Detour Debate 22:30 Governments Back Open Source 27:00 Mistral DeepSeek Shift 29:51 Google Gives AI Away 31:35 Avatars Tokens and X 32:54 Local Models Slow Iteration 33:58 Local AI Smart Speakers 34:40 Chrome Model Backlash 35:20 BitTorrent Style Inference 37:43 Distrust And Data Centers 38:19 Small Models And Transformers 39:45 Google AI Tool Rundown 41:17 DeepMind From AGI To ASI 45:40 Beyond Transformers Next Minds 48:19 AI Splintering And Niches 49:20 Diffusion And SubQ Attention 54:39 Forking And Competition 57:26 Monopolies And CEO Culture 01:02:30 Midjourney Medical Scanner 01:08:59 Innovation Hopeful Wrap

What happens when one of the world's largest enterprise software companies declares that it is no longer a software company, but an AI company? At SAP Sapphire, I caught up with James Bates, Head of Customer Advisory at SAP UK & Ireland, to discuss the company's vision for what it calls the Autonomous Enterprise and why this year's event felt different from any SAP conference before it. From standing-room-only AI sessions to bold declarations from SAP leadership, there was a clear sense that the conversation around AI has moved beyond experimentation and into the world of measurable business outcomes. In our conversation, James explained why so many organizations remain stuck in what he described as the experimentation phase of AI, despite years of investment and countless pilot projects. We explored why successful AI initiatives begin with business outcomes rather than technology choices and why data, governance, and process context have become the foundations of enterprise AI success. We also examined some of the standout announcements from Sapphire, including SAP's AI Agent Hub, the growing role of Joule as a new interface for work, and the company's expanding ecosystem of partnerships with organizations including Anthropic, NVIDIA, Microsoft, Google Cloud, Palantir, and Mistral. James shared why SAP believes the future lies in combining large language models with business context, process knowledge, and trusted enterprise data. The discussion also touched on real-world examples that demonstrate how AI agents are beginning to transform customer experiences, automate complex workflows, and support employees across finance, supply chain, and customer-facing operations. Rather than replacing people, James sees AI assistants and agents working alongside employees, removing repetitive tasks and helping teams focus on higher-value activities. We also explored the challenge many business leaders continue to wrestle with: how to balance autonomy with governance. As AI agents become more capable, maintaining visibility, accountability, and control becomes increasingly important. James shared why governance, trusted data, and strong business processes must remain at the center of every AI strategy. If you've been wondering whether enterprise AI is finally moving beyond the hype cycle and into meaningful business transformation, this conversation offers a fascinating perspective from the heart of SAP's AI strategy and its vision for the future of work. What role do you think AI agents will play inside your organization over the next few years? Share your thoughts.

Snap stellt seine AR-Brille vor. SpaceX übernimmt Cursor für $60 Mrd. Welche Firma kauft Elon Musk als nächstes? Im Anthropic-Streit kommen neue Details ans Licht: Wired berichtet, das Weiße Haus wolle "alle Jailbreaks" blockieren, die G7-Sitzordnung verrät die Trump-KI-Präferenzen. Ein neues Buch enthüllt, dass Trump Musk die speichelleckenden Textnachrichten von Zuckerberg und Bezos gezeigt hat. Microsoft testet DeepSeek für Copilot Cowork. DeepSeek schließt eine $7-Mrd.-Funding-Runde mit ungewöhnlicher SPV-Struktur ab. GLM 5.2 wird zum besten Open-Weights-Modell, Midjourney pivotiert in den Medizin-Markt mit einem 3D-Ultraschall-Gerät. Maia Arson Crimew hackt die Dialog-Konferenz von Peter Thiel, die 222 Namen lange Gästeliste taucht auf, Jens Spahn ist dabei. Allbirds rebrandet zu SmartBird. Warum hat Google den Consumer-KI-Markt eigentlich schon längst gewonnen? Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Snap Specs Brille (00:04:15) SpaceX kauft Anysphere/Cursor (00:12:50) Anthropic: Block all Jailbreaks (00:13:49) SK-Telekom & Mythos-Liste (00:19:28) Speichelleck-Texte aus Trump-Buch (00:21:05) Sacks-Backpedaling (00:29:30) Microsoft testet DeepSeek (00:30:38) DeepSeek $7 Mrd. SPV-Runde (00:35:23) Midjourney Medical-AI (00:40:18) Peter-Thiel-Dialog-Leak (00:47:50) xAI-Mississippi-Verfahren (00:49:33) Allbirds → SmartBird (00:50:00) Sono Motors (00:51:50) Mistral (00:54:48) ChatGPT Marktanteil (01:01:36) 1Komma5° plant Börsengang Shownotes Snap Specs: AR-Brillen Launch-Date & Preorder - theverge.com SpaceX wertvoller als Amazon - bbc.com SpaceX kauft Anysphere (Cursor) für $60 Mrd. - reuters.com Wired: White House will alle Anthropic-Jailbreaks blocken - wired.com David-Sacks-Post zum Anthropic-Streit - xcancel.com Fotos G7 - xcancel.com Pip-Post zu Anthropic - xcancel.com Politico: White House Anthropic-Move bringt Kongress in KI-Debatte - politico.com The Information: DeepSeek schließt Rekord-Runde über $7 Mrd. - theinformation.com Microsoft Copilot Cowork & "Token-Maxing" - axios.com DeepSeek zu Investoren: "No Poaching unserer Leute" - cnbc.com Artificial Analysis: GLM 5.2 ist neues führendes Open-Weights-Modell - artificialanalysis.ai Midjourney baut Medical-AI für Ultraschall - theverge.com Wired Dialog Thiel - wired Reddit-Leak: Mitglieder von Peter Thiels Geheimclub - reddit.com NYT: NAACP klagt gegen xAI wegen Grok-Gasturbinen in Mississippi - nytimes.com Allbirds rebrandet zu SmartBird, neuer Ex-AWS-CEO - reuters.com Mistral - ft.com TechCrunch: ChatGPT-Marktanteil fällt erstmals unter 50% - techcrunch.com Sono Motors: Trump-Manager macht aus Solarauto-Firma Bitcoin-Bude - manager-magazin.de Trump Texts - wired 1Komma5° plant Börsengang & Frontalangriff auf Enpal - manager-magazin.de Stern: Jens Spahn in der Kritik nach Peter-Thiel-Treffen - stern.de

AGENDA: 00:00 — SpaceX Completes the Largest IPO in History 03:45 — Elon Musk Adds a Warren Buffett Fortune in 24 Hours 20:45 — Anthropic's Claude Fable Launches Monday, Gets Banned by Thursday 25:00 — Washington Declares War on Frontier AI 39:00 — Europe's Sovereign AI Push Accelerates as Mistral Targets $20B 43:30 — Benchmark Admits Its Biggest Miss: Passing on the Model Labs 45:15 — Salesforce Buys Fin for $3.6B and Rewrites the SaaS Survival Playbook 1:02:00 — Adobe Beats, Raises, and Still Crashes as AI Fears Intensify 1:06:30 — Why Every Legacy SaaS Company Is Trapped in an AI Death Spiral 1:10:00 — The AI Acquisition Window Has Officially Closed 1:13:00 — Nvidia at 16x Earnings vs SaaS at 8x Cash Flow: Where Should Investors Be? 1:17:00 — The Great Rotation: Why Wall Street Is Abandoning Software for AI Infrastructure

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

Claude's Fable 5 just got yanked, and the story why keeps shifting by the hour. A contested jailbreak, an export-control, crackdown, and a lot of fingers pointing. This week on AI For Humans, Anthropic's Claude Fable 5 is still unavailable and the explanations keep changing. We dig into the contested jailbreak report, the export-control directive that pulled it, and the reporting that Amazon raised concerns before the crackdown. Then we get into why this matters far beyond one model: what happens when the government steps into the AI world, why Fable 5 was such a leap, and what it signals for whatever comes next. Plus, Epic's game designers are using AI tools alongside artists and the internet is furious, Disney Imagineering is testing Adobe Firefly in the parks, ChatGPT's market share slips under 50 percent for the first time, a fake Mistral model called Le Chaton Fat takes over the internet, and PJ Accetturo breaks down exactly how he made his viral AI short film with prompts. THE BEST AI WE EVER USED IS BEHIND BARS. AND NOW WE WAIT. SHOW LINKS Original Anthropic statement on Fable and Mythos access https://www.anthropic.com/news/fable-mythos-access Full timeline of the Anthropic, Amazon, and White House story https://www.axios.com/2026/06/13/anthropic-amazon-white-house Amazon CEO reportedly raised Anthropic model concerns before the government crackdown https://techcrunch.com/2026/06/13/amazon-ceo-reportedly-raised-anthropic-model-concerns-before-government-crackdown/ Simon Willison on the contested Fable jailbreak report https://x.com/simonw/status/2066722034491789720 ChatGPT market share slips below 50 percent for the first time https://techcrunch.com/2026/06/16/chatgpts-market-share-slips-below-50-for-first-time/ GPT-5.6 next week? Polymarket odds https://x.com/Polymarket/status/2066644087340495081 Possible new ChatGPT voice mode leak https://x.com/testingcatalog/status/2066919098236146167 Space X Buys Cursor https://www.cnbc.com/2026/06/16/spacex-spcx-cursor-acquisition-ipo.html Epic explores using NanoBanana and GPT-Image-2 in workflows with humans https://x.com/UnrealEngine/status/2066686216779509850 SEGA's Crazy Taxi AI statement https://x.com/SEGAInforment/status/2063990392085766622 PJ Accetturo breaks down how he made his three minute short film with prompts https://x.com/PJaccetturo/status/2066582776934289438 Le Chaton Fat, the fake Mistral model that took over the internet https://x.com/AlexanderKnigge/status/2066267845546442762  

La periodista de Onda Cero se ha detenido en el encuentro que mantendrán los líderes de las principales potencias del mundo con los CEO de Anthropic, OpenAI, DeepMind y Mistral, en el que determinarán el destino del mundo.

Apple Intelligence au rabais en Europe : pourquoi ? • Web Summit Rio : gros plan sur la tech brésilienne • Anthropic bride ses modèles les plus sensibles • En France, Mistral AI contre les ayants droit • Le Canada veut limiter les réseaux sociaux aux moins de 16 ans • L'IA bouscule le droit, les médias et l'éducation • VivaTech se prépare à Paris.Avec Bruno Guglielminetti (Mon Carnet)Apple Intelligence : qui prive l'Europe du nouveau Siri ?Nous revenons sur la keynote Apple, marquée par l'arrivée annoncée d'iOS 27 et de nouvelles fonctions d'Apple Intelligence. Mais le vrai sujet, c'est l'absence de Siri AI en Europe : Apple accuse le Digital Markets Act, tandis que Bruxelles renvoie la balle à l'entreprise américaine. Derrière ce bras de fer, une réalité : des centaines de millions d'utilisateurs européens pris en otage. Zoom sur la tech brésilienne à l'occasion du Web Summit RioDepuis Copacabana, nous partons à la découverte du Web Summit Rio et d'un écosystème brésilien encore trop peu observé depuis l'Europe. Le Brésil apparaît comme un terrain passionnant pour parler souveraineté numérique, innovation locale et rapprochements possibles avec le Sud global. L'objectif : sortir du face-à-face habituel entre États-Unis, Europe et Asie.Anthropic : l'IA puissante, mais sous surveillanceNous revenons sur le sujet Claude Fable 5, présenté comme une version plus encadrée de Mythos 5, notamment sur les usages sensibles comme la cybersécurité ou la biologie. Les modèles d'IA les plus avancés ne sont plus seulement des produits technologiques : ils deviennent aussi des enjeux stratégiques, politiques et sécuritaires (EPISODE ENREGISTRÉ AVANT LE BLOCAGE DE FABLE POUR POUR LES NON AMERICAINS). Mistral AI face au droit d'auteurMistral AI dans la tourmente avec la loi sur le droit d'auteur de l'IA. Les médias et ayants droit dénoncent un pillage massif, tandis que Mistral craint d'être freiné face aux géants américains. Le débat oppose protection de la création et ambition de bâtir un champion européen de l'IA.Réseaux sociaux : le Canada veut protéger les jeunesLe Canada envisage d'interdire ou de limiter l'accès aux réseaux sociaux pour les moins de 16 ans. Bruno souligne les limites d'une telle mesure, déjà visibles dans d'autres pays : contournements, faux comptes et migration vers d'autres plateformes. Pour nous, la loi ne suffira pas sans un vrai travail d'éducation numérique.IA et justice : quand les hallucinations coûtent cherNous évoquons l'affaire d'avocats sanctionnés aux États-Unis après avoir déposé des documents contenant de fausses références juridiques générées par IA. L'épisode rappelle que ces outils peuvent aider les professionnels, mais qu'ils ne remplacent ni la vérification, ni la responsabilité humaine. Dans le droit, l'IA doit rester un assistant, pas une source aveuglément copiée.Médias et école : le faux débat du “sans IA”Nous discutons de la tentation de revendiquer des contenus “100 % humains”. Cette promesse nous semble trop simpliste, car l'IA peut aussi servir à corriger, traduire, comparer, entraîner ou donner du feedback sans remplacer l'humain. Le vrai sujet n'est pas d'interdire l'outil, mais de savoir comment on l'utilise.VivaTech à Paris : qui est l'invité vedette ? VivaTech 2026 aura lieu la semaine prochaine. Cette édition marque le 10ème anniversaire du salon parisien. L'événement s'annonce comme un moment fort pour la French Tech, avec de grands invités attendus et un contexte très marqué par l'IA. La semaine prochaine, le Debrief Transat se fera depuis Paris, en direct de l'écosystème tech français.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

SpaceX priced the biggest IPO ever at $135/share, raising $75B and debuting at $1.77T. ShinyHunters exploited an unpatched Oracle PeopleSoft flaw hitting 100+ organizations, Mistral seeks €3B at €20B, MrBeast hit 500M subscribers, and SBF lost his appeal. SpaceX raises $75B in the biggest-ever IPO, pricing 555.6M shares at $135 each, giving it a market value of $1.77T (Bloomberg) Founders Fund's ~3% SpaceX stake is worth $50B+, Sequoia's ~1.5% is worth $20B+, and a16z will see its biggest return ever at $10B+ (Bloomberg) Some investors question SpaceX's valuation, citing its $4.3B loss on $4.7B in revenue in Q1, as well as concerns over space data centers (NYT) Oracle warns customers of a critical PeopleSoft flaw after ShinyHunters claimed breaches of 100+ organizations using PeopleSoft; Oracle has not issued a patch (TechCrunch) Sources: French startup Mistral AI is in talks to raise ~€3B at a ~€20B valuation; it was last valued at €11.7B during a funding round in September 2025 (Bloomberg) MrBeast hits 500M subscribers on YouTube, a record for the platform (The Wrap) Sam Bankman-Fried loses his bid to overturn his fraud conviction and 25-year prison sentence over the collapse of FTX (Reuters) Longreads As companies are hit by rising AI costs, they are increasingly using tools that tap cheaper models, including some from China, putting price pressure on OpenAI and Anthropic (WSJ) Sixteen economists weigh in on what AI will mean for the US economy, workers, and workplaces; only two expect AI to actually create more jobs (WSJ) Learn more about your ad choices. Visit megaphone.fm/adchoices

AI vam neće uzeti posao... osim ako niste osrednji u onome što radite. Evo šta se zapravo dešava u industriji.

As the EU publishes its digital sovereignty plans, we've come up with a little techno-utopian package of our own. Our guest throughout is tech and solar-punk author Cory Doctorow: join us as we explore queer social media take-backs, French AIs, Finish super-computers, Croatian Wikipedia and all the reasons why this might just be the moment in which things start to change for the better.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with returning guest Ekue Kpodar for their third conversation together, covering a wide range of topics at the intersection of technology, geopolitics, and the evolving information age. They dig into Ekue's unconventional setup of running local AI models across roughly 15 computers, the growing case for open source models over closed ones from companies like OpenAI and Anthropic, and how Chinese open source models may be positioned to outcompete Western alternatives on a global scale. The conversation also touches on vibe coding and the democratization of software development, the strategic use of small models for IoT and enterprise applications, the role of Israel and China as dominant players in the information age, and how smaller nations and even individuals may wield outsized power as AI continues to collapse the cost of knowledge work. You can find Ekue Kpodar on X @ekpodar and LinkedIn.Timestamps00:00 Stewart welcomes Ekue for their third episode, diving into vibe coding and AI-driven development changes.05:00 Ekue explains using Claude on Chrome to auto-reply on Skool, burning tokens through screenshots, and Playwright as a more efficient alternative.10:00 Stewart describes his Claude-dependent planning and coding agent system breaking after a model update, prompting him to build his own chatbot.15:00 Small models discussed as critical for IoT, defense, and privacy-focused enterprises building internal APIs instead of routing traffic to OpenAI.20:00 Open source versus closed source debated, with Chinese models gaining global traction while US foundational labs remain expensive and restrictive.25:00 SaaS apocalypse explored as AI commoditizes knowledge work, with Linux and Terraform cited as proof open source still generates wealth.30:00 OpenAI's sci-fi terminator fears explained as the reason they stayed closed source, ultimately handing China a strategic open source advantage.35:00 China's economic dumping strategy applied to AI, potentially displacing US model dominance globally the same way manufacturing was disrupted.40:00 Israel's signals intelligence dominance discussed alongside asymmetric warfare, drones defeating tanks, and information control replacing military muscle.45:00 Global information age rankings debated, Israel leading, US and China tied, France and Poland emerging as sovereign tech players.50:00 Qatar, NVIDIA, and Iran cited as proof that rare resources and technology matter more than population size in the 21st century power landscape.Key Insights1. Running local AI models on a network of affordable computers can be more cost-effective than relying entirely on third-party APIs. By using compressed or smaller open source models locally, developers can handle repetitive or lower-stakes tasks without burning through expensive tokens from providers like Anthropic or OpenAI.2. Small AI models are becoming increasingly important for IoT, defense applications, and companies that do not want to send sensitive data to external providers. Organizations can download open source models, run them on internal servers, and build proprietary APIs around them, creating something like an intranet of specialized small models.3. The value created by AI tools is being redistributed away from traditional SaaS companies toward foundational model providers and individual builders. People are canceling subscriptions to software they once paid hundreds per month for, because AI now allows a single person to build comparable tools themselves.4. Open source technology does not eliminate the ability to profit. Linux and Terraform are both open source yet made their creators wealthy. People will still pay for installation, setup, troubleshooting, and customization even when the underlying software is free.5. China is applying its longstanding manufacturing dumping strategy to artificial intelligence by releasing cheap open source models globally, which threatens to erode US dominance in AI the same way Chinese manufacturing undercut other countries for decades.6. In the information age, the size of a country or institution matters far less than its access to rare resources or advanced technology. Qatar, Israel, and NVIDIA each demonstrate that small populations or headcounts can wield enormous global negotiating power through concentrated technological or resource advantages.7. Asymmetric warfare is redefining military power, with inexpensive drones defeating tanks that cost millions to build. This shifts the advantage toward nations that excel at signals intelligence and information management rather than those with the largest conventional military forces.

//The Wire//2300Z May 27, 2026// //ROUTINE// //BLUF: GANG WAR CONTINUES IN GRENOBLE. WAR IN LEBANON EXPANDS AS DRONE ATTACKS INTENSIFY. CONFLICT MOUNTS IN CONGO AS EBOLA CRISIS WORSENS. PROBABLE CHINESE AGENTS DETAINED WHILE ATTEMPTING TO INFILTRATE SOUTHERN US BORDER.// -----BEGIN TEARLINE------International Events-Middle East: Israeli attacks in Lebanon have increased over the past few days, with more significant bombings taking place in Beirut. FPV drone attacks by Hezbollah have continued to devastate Israeli forces, as most of the IDF is not equipped or prepared to handle the threats that drones bring to modern warfare. As a result, the fighting has become much more intense, which in turn has increased the efforts to expand the Israeli bombing campaign.France: Last night a small arms attack was reported in Grenoble, as a war between rival gangs of migrants has broken out. One engagement was reported in the Mistral neighborhood overnight, with several people being gunned down on the street. One person was killed, and three others wounded during this attack, which locals sources claim was a targeted assassination. Three days ago, another assassination was reported, with a Cartel-style video being posted online before a body was found in a vehicle in the Échirolles community.-HomeFront-New Jersey: Protests at the Delaney Hall Detention Facility have continued, which have mostly transitioned into more of a long-term protest site once again. A few local politicians have made appearances over the past few days, but apart from occasional flare-ups and riots, the weekday attendance at this facility has remained fairly regular.Texas: Overnight a group of Chinese nationals were arrested after attempting to illegally cross the southern US border in the vicinity of Eagle Pass. US Border Patrol trackers located the group of individuals who had crossed the border illegally and were concealing themselves on a private ranch. Among this group were a total of 6x Chinese citizens, who federal authorities have classified as Special Interest Aliens (SIAs) for reasons that have not been disclosed. In the photos of the group provided by Customs and Border Patrol, one of the Chinese individuals has a military-style haircut, and another individual is wearing military-style combat boots. All are wearing civilian-style camouflage jackets and pants, all of the same type and construction.Analyst Comment: Most coyotes illegally smuggling people over the border have either required or furnished themselves camouflage "uniforms" for the illegals to don, in order to cross the border as covertly as possible. As a result, these individuals being detained while wearing camouflage is very normal these days. Illegal border crossings still take place along the vast wilderness areas which comprise most of the border, but it's become a lot harder to make the crossing and also much more expensive to do so. For Chinese immigrants, it's never been easier to get legal paperwork and enter the US at an official port of entry, so the fact that these individuals made the crossing illegally indicates that they were up to no good.-----END TEARLINE-----Analyst Comments: In the Congo, the situation regarding the current Ebola outbreak has become increasingly more serious over the past few days, as the current civil war is impacting efforts to control the disease. Separately, social tensions flared up overnight, after a domestic situation spiraled out of control at a treatment center. Last night, police fired warning shots at the perimeter of Rwampara Hospital, as a crowd of people attempted to breach the facility to recover the bodies of relatives who had died from Ebola. Upon being told that they can't have the remains of their family members due to fears of the disease spreading, the crowd promptly set a tent on fire at the compound and a state of pandemonium erupted. During the fray, a handful of Ebola-positive patients fled from the facility and are currently unaccounted for.Around the continent, nations bordering the Congo have begun to close the border checkpoints to those fleeing both the simmering civil war, and also the spread of Ebola. Uganda closed their borders this morning, and several other nations have implemented travel controls to restrict travel out of the hardest-hit areas.Analyst: S2A1 Research: https://publish.obsidian.md/s2underground Disclaimer: No LLMs were used in the writing of this report. //END REPORT//