Podcasts about Mistral

NOUVELLE SAISON !!Mistral AI était censé être le ChatGPT français. Il héberge désormais un modèle chinois sur ses propres serveurs. Ce n'est pas une anecdote : c'est le symbole d'un effacement discret mais définitif d'une promesse de souveraineté.Mistral AI se réinvente en gestionnaire de cloud européen. Est-ce l'abandon d'une souveraineté numérique ou simplement une rationalité économique nécessaire face à la concurrence mondiale ?  On en parle ce soir dans Silicon Carne !Abonnez-vous pour ne pas rater nos analyses quotidiennes sur le secteur tech, et dites-nous en commentaire : validez-vous cette intégration des modèles chinois chez notre champion national ?===================⏱️ DANS CET ÉPISODE :===================00:00 — Intro00:54 — Présentation des invités03:09 — Mistral héberge GLM 5.2 : Trahison ou pivot stratégique ? 07:32 — Mistral survivra-t-il à une alternance politique en France ?11:09 — Le LLM devient une commodité : qui capte la valeur ?13:09 — Europe vs Chine : l'écart que personne n'ose dire15:21 — [Sponsor] : Qonto, ouvrez votre compte pro et facturez facilement !16:39 — Les clients réclament du chinois, pas du Mistral23:25 — OVH veut des modèles, Mistral veut des datacenters25:10 — L'infrastructure colossale de Mistral : qui va financer ?33:43 — Pronostic final : que va devenir Mistral ?37:27 — Une valorisation hors sol : Mistral peut-il tenir ?==================

#553 Back To Cars. What have Gareth, Alex and Zog been up to during the summer break? And what's going on happening on track and on the road right now? Plus: The Freedom 250 IndyCar race and a Williams icon returns, well, kind of.

In this episode of Shift AI, Manos, CEO of Umi, Patrick Thompson, CEO of Clarify, Brian Hall, CMO of Mistral AI, and Ben Gaffney, Deputy General Counsel at OpenAI, join host Boaz Ashkenazy for a live panel recorded at Seattle Tech Week on who actually owns intelligence in the enterprise AI era.One panelist argues that any enterprise still treating a frontier model as its core intelligence layer is renting something it should own, and predicts that in two years we'll look back at CTOs defending that approach the way we look back at driving a Ferrari to the grocery store. Another panelist, sitting inside one of the most closely watched policy fights in AI right now, explains why the debate over open source models and national security is getting a lot less theoretical, and a lot more real, by the month.This one is for CTOs and CIOs deciding whether to build or rent their AI stack, product leaders at vertical AI startups thinking about fine-tuning their own models, and anyone tracking the open source AI security and policy debate.Chapters[00:00] Welcome and thanks to sponsors JP Morgan and Ascend[01:30] Introducing the panel: Manos, Patrick, Brian, and Ben[03:54] From foundation model hype to vertical AI, what actually changed[07:37] How Clarify balances owning the system of record with using frontier models[09:44] Rising model bills, local models, and the push toward open weights[11:30] Mistral AI, sovereign AI, and why owning your intelligence matters[14:32] Data retention, training, and what the privacy debate is really about[19:10] Chinese open models, national security, and the safety debate[26:18] Why fine-tuning beats prompting for real vertical AI companies[30:43] What enterprises actually want from foundation models right now[35:35] What CIOs and CTOs will laugh about in two years[44:47] The hundred million dollar question: where the panel would invest[51:44] Advice for a 22-year-old about to spend ten years building something[53:31] What each panelist is most excited about in AI's futureConnect with the PanelistsManos (Umi) - https://www.linkedin.com/in/koukoumidis/Patrick Thompson (Clarify) - https://www.linkedin.com/in/patrickt010/Brian Hall (Mistral AI) - https://www.linkedin.com/in/brhall/Ben Gaffney (OpenAI) - https://www.linkedin.com/in/bengaffney/Connect with Boaz AshkenazyLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: info@shiftai.fm

This Week on In Stride Sinead Halpin-Maynard sits down with Alizée Froment to talk about her path from Grand Prix dressage to liberty training, and how she teaches horses to recognize tension in their bodies and find their own way out of it.Meet the Guest: Alizée Froment Alizée Froment is a French liberty trainer, performer, and author who competed internationally in dressage up to Grand Prix level and trained the French National Pony Dressage Team. In 2011 she made her international Grand Prix debut aboard her stallion Mistral du Coussoul, competing at that level for years before retiring him from competition in 2018. Since 2009, Froment has performed neck rope liberty demonstrations for hundreds of thousands of spectators around the world. She now works with sport horses sent to her for emotional and behavioral struggles, runs her own liberty and education programs, and has developed courses with fellow trainer Tristan Tucker on his TRT platform. She is the author of The Horses Who Made Me, released in 2024.In This Episode, Sinead and Alizée Discuss:How she reads the physical tension behind a horse's fear, anger, or frustration, and teaches them to release it themselves instead of shutting the emotion downHer horses' daily bodywork routine, including stretching, massage, and natural therapies like clay and essential oils, and how it feeds directly into her training decisions each dayThe moment she stopped chasing performance in competition and started training for the horse in front of herWhy she believes horse welfare debates need more listening and less judgmentEpisode SponsorEquine Emergency Vet Care Guide Created by equine veterinarian Dr. Erica Lacher, the Equine Emergency Vet Care Guide app helps horse owners recognize the signs of a true emergency, know when to call the vet, and understand what to do while help is on the way.Download from the App Store or Google Play at EquineEmergencyApp.comIn Stride Is Brought to You by Ride iQRide iQ helps everyday riders ride with more clarity, confidence, and purpose through on-demand audio lessons from world-class coaches.Members also get:Weekly live Q&As with equestrian expertsExclusive podcast episodesDressage test prep resourcesA supportive learning communityStart your free 14-day trial at Ride-iQ.com

Anthropics Investoren erwarten für Oktober einen Börsengang bei zwei Billionen Dollar, was der größte IPO aller Zeiten wäre. Pip erklärt, warum der Termin geschickt gewählt ist. Für OpenAI sieht er die Lage umgekehrt. Dort kommt der zweite Vertriebschef in einem Jahr. Zusammen mit Cerebras hat OpenAI dafür eine Variante gebaut, die vierzehnmal schneller antwortet. Danach vier Modellstarts in einer Woche, bei denen ausgerechnet DeepSeek die Preise um bis zu das Zwölffache erhöht, und Elon Musk sein neues Grok für objektiv das beste Modell hält. Bei den Finanzierungsrunden geht es um Databricks, Lovable, Legora und Cognition, dazu um die Frage, ob man das Geld gerade nehmen und liegen lassen sollte. Silver Lake holt Workday von der Börse. In der Schmuddelecke erlaubt die Trump-Regierung privaten Firmen offensive Cyberangriffe und beruft sich dabei auf Kaperbriefe aus der Verfassung. 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) Aus der Community (00:02:05) OpenAI wechselt den Vertriebschef (00:12:20) Ultrafast mit Cerebras (00:17:16) Anthropic-IPO (00:29:10) Anthropic kauft Decart (00:30:34) Braucht man ein KI-Device? (00:32:36) Gemini 3.7 Flash (00:34:10) DeepSeek V4-Pro (00:34:47) Grok 4.6 (00:37:11) SpaceX (00:38:49) Databricks und Snowflake (00:42:40) Workday geht von der Börse (00:44:11) Lovable (00:49:26) Legora (00:52:28) Cognition (00:55:18) Mistral (00:57:22) Kaperbriefe (01:02:05) Truth API (01:03:28) Chronext (01:06:55) Apple zahlt Verlage Shownotes OpenAI holt den zweiten Vertriebschef in einem Jahr - bloomberg.com GPT-5.6 Sol läuft mit Cerebras bis zu 14-mal schneller - 9to5mac.com Anthropic peilt einen Börsengang bei 2 Billionen Dollar an - ft.com Anthropic verhandelt über Decart für 6 Mrd. - bloomberg.com Google stellt Gemini 3.7 Flash vor - blog.google DeepSeek bringt V4-Pro und erhöht die Preise um bis zu das Zwölffache - theinformation.com Grok 4.6 startet zuerst in Cursor - gizmodo.com SpaceX-Leerverkäufern gehen die Kugeln aus - cnbc.com Databricks sammelt 5 Mrd. bei 190 Mrd. Bewertung ein - cnbc.com Silver Lake verhandelt über eine Übernahme von Workday - reuters.com Lovable verdoppelt die Bewertung auf 13,3 Mrd. - trendingtopics.eu Legora verhandelt bei mindestens 10 Mrd. - ft.com Cognition verhandelt bei 40 Mrd. - bloomberg.com Mistral will bis 2030 ein Gigawatt in Europa bauen - aibusiness.com Trump lässt private Firmen offensive Cyberangriffe fahren - bloomberg.com Kaperbriefe stehen in der Verfassung - xcancel.com KI-Agenten greifen Taiwans Regierungssysteme an - ft.com Presseverbände klagen gegen Trumps Truth API - ft.com Chronext-Kunden warten auf Zahlungen und Lieferungen - wiwo.de Apple verhandelt mit Verlagen über Nachrichten für Siri - techcrunch.com

Das ist das KI-Update vom 14.08.2026 unter anderem mit diesen Themen: Hate Aid stellt Strafanzeige gegen Meta, Ray-Ban und Optiker Mistrals große Wette auf Europas KI-Infrastruktur Prompts lassen sich aus Chatbot-Antworten rekonstruieren und Twitch-Streams trainieren Amazon-KI === Anzeige / Sponsorenhinweis === Dieser Podcast wird von einem Sponsor unterstützt. Alle Infos zu unseren Werbepartnern findet ihr hier. https://wonderl.ink/%40heise-podcasts === Anzeige / Sponsorenhinweis Ende === Links zu allen Themen der heutigen Folge findet Ihr im Begleitartikel auf heise online: https://heise.de/-11413872 Weitere Links: https://www.heiseplus.de/audio https://www.heise.de/thema/KI-Update https://pro.heise.de/ki/ https://www.heise.de/newsletter/anmeldung.html?id=ki-update https://www.heise.de/thema/Kuenstliche-Intelligenz https://the-decoder.de/ https://www.ct.de/ki https://www.visit-hannover.com/Event-Highlights,-Kultur-Freizeit/Highlights-2026/Jubil%C3%A4en-Hannover/120-Jahre-Hannah-Arendt Eine neue Folge gibt es montags, mittwochs und freitags ab 15 Uhr.

Edtech Throwdown Episode 221: 10 Tools for Specialized AI and ProductivityWelcome to the EdTech Throwdown. This is Episode 221 called 10 Tools for Specialized AI and Productivity. In this episode we'll be talking edtech tools as we bring you a list of some atypical AI platforms that might not otherwise come up for educators. This is another episode you don't want to miss. Check it out.Segment 1:Happy August, happy end of summer breakWe're almost fully re-charged and starting to think about things like productivity againSegment 2:Nick: abacus.ai: Abacus.AI is the world's first AI super-assistant tailored for enterprises and professionals. We offer two products: ChatLLM for professionals and small teams and Abacus.AI Enterprise for enterprises and companies. ChatLLM is a multi-modal, multi-device super-assistant that can handle many tasks and completely transform your life. You can access all of the SOTA LLMs, analyze documents, do data analysis, generate code, search the web, create images, and much more. It's your all-in-one AI assistant and increases individual productivity by 15% to 75%. Abacus.AI Enterprise is a state-of-the-art generative AI platform that combines the AI super assistant available to all your employees with an AI brain that can connect to your enterprise software systems, automate business processes, increase revenue, and be a powerful force multiplier. Our AI engineer can build AI Workflows and chatbots to automate critical processes. Our Enterprise product comes with single sign-on, multiple deployment options and checks all the security and compliance boxes.Guise:studley.aiWhy:Specialized AI platforms designed for more complex, data-heavy, or professional-grade automation and modeling.Nick: openalternative.co: What is the difference between open source and proprietary? Proprietary AI Tools: These are "closed-source" models developed and owned by specific companies (e.g., OpenAI's GPT-4/5, Google's Gemini, Anthropic's Claude). You typically access them via an API or a web interface. The underlying code, training data, and exact model weights are a "black box" hidden from the public. Open-Source (Open-Weight) AI Tools: These are models whose code and weights are publicly shared (e.g., Meta's Llama series, Mistral, Qwen, and DeepSeek). Anyone can download them, look under the hood, modify them, and run them on their own hardware or private cloud. Guise: The Most Comprehensive List of FREE Online Tools for Teachers Why:Both promote open-source ethics, whether finding alternatives to paid software or using privacy-focused media downloaders.Nick: smart.servier.comGuise: runable.comWhy:High-level technical resources; one offers medical illustrations, while the other focuses on executable code environments.Nick:Workout.coolGuise: KouponWhy:Personal optimization tools—one for physical fitness routines and the other for optimizing shopping/savings.Nick: Internet Archive:https://web.archive.org/Guise: Same.newWhy:Part of the ".new" domain movement, providing instant, one-click access to start a new coding or collaborative project.Edtech Throwdown: Vote on twitter @edtechthrowdown and under the pinned post on the profile.Segment 3: Where to Find EdTech ThrowdownDo us a few favors:Subscribe to the Edtech Throwdown PodcastApple PodcastsSpotifyAmazon PodcastsStitcher YouTube Twitter FacebookWrite us an Apple Podcast Review!Tell your friends aboutwww.edtechthrowdown.comTell your friends about the Teach Better Podcast NetworkSubscribe to our Podcast Channels and SocialsApple PodcastsSpotify YouTube Twitter (@edtechthrowdown)FacebookInstagramConnect with us on Social MediaGuise's Social MediaTwitter(@guisegotteched)LinkedInNick's Social...

Hoy: ChainDrop compromete más de 1.300 paquetes npm; Samsung apila HBM sobre aceleradores de IA; Nvidia abre Alpamayo 2 Super para conducción autónoma; Mistral presenta Shieldstral para moderación multimodal; y Moderna inicia la primera prueba humana de una vacuna de ARN mensajero contra el ebolavirus Bundibugyo.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

This week on Catalyst, Tammy is joined by Craig Vaughan, Executive Managing Director and Global Head of AI Go-to-Market at NTT DATA, three months into the role. Craig traces his path from studying architecture at Cornell through an MBA at Wharton and a master's in business analytics from NYU Stern, then through go-to-market roles at SAP and a decade leading AI and gen AI practices at Accenture. He and Tammy dig into how leading diverse, cross-functional teams and moving at AI's breakneck pace aren't in tension but actually reinforce each other. They also discuss what drew Craig to NTT DATA's culture, the partnerships with OpenAI, Google, Anthropic, and Mistral that are shaping how the company delivers for clients, and how to keep AI adoption human-centric so workers are repurposed rather than replaced.Please note that the views expressed may not necessarily be those of NTT DATALinks: Craig Vaughan Learn more about Launch by NTT DATASee 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 explore the latest significant cloud deals, including Anthropic's $10 billion contract with Volta, and the implications for AI chip demand. Additionally, we discuss the shift in focus at Tesla towards AI and robotics as well as Mistral's valuation surge amid European interest in open weight AI models.Chapters00:00 Anthropic's $10 Billion Cloud Deal02:02 Elon Musk on AI at Tesla04:02 Mistral's Rising Valuation05:58 AMD's Investment in Anthropic08:02 Google's New AI Chip Development09:59 Discussion on AI Cloud Strategy Show LinksGet the Learn of Christ bible study app here: App Store

In this episode, we explore the latest significant cloud deals, including Anthropic's $10 billion contract with Volta, and the implications for AI chip demand. Additionally, we discuss the shift in focus at Tesla towards AI and robotics as well as Mistral's valuation surge amid European interest in open weight AI models.Chapters00:00 Anthropic's $10 Billion Cloud Deal02:02 Elon Musk on AI at Tesla04:02 Mistral's Rising Valuation05:58 AMD's Investment in Anthropic08:02 Google's New AI Chip Development09:59 Discussion on AI Cloud Strategy Show LinksGet the Learn of Christ bible study app here: App Store 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 explore the latest significant cloud deals, including Anthropic's $10 billion contract with Volta, and the implications for AI chip demand. Additionally, we discuss the shift in focus at Tesla towards AI and robotics as well as Mistral's valuation surge amid European interest in open weight AI models.Chapters00:00 Anthropic's $10 Billion Cloud Deal02:02 Elon Musk on AI at Tesla04:02 Mistral's Rising Valuation05:58 AMD's Investment in Anthropic08:02 Google's New AI Chip Development09:59 Discussion on AI Cloud Strategy Show LinksGet the Learn of Christ bible study app here: App Store 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 explore the latest significant cloud deals, including Anthropic's $10 billion contract with Volta, and the implications for AI chip demand. Additionally, we discuss the shift in focus at Tesla towards AI and robotics as well as Mistral's valuation surge amid European interest in open weight AI models.Chapters00:00 Anthropic's $10 Billion Cloud Deal02:02 Elon Musk on AI at Tesla04:02 Mistral's Rising Valuation05:58 AMD's Investment in Anthropic08:02 Google's New AI Chip Development09:59 Discussion on AI Cloud Strategy Show LinksGet the Learn of Christ bible study app here: App Store

In this episode, we explore the latest significant cloud deals, including Anthropic's $10 billion contract with Volta, and the implications for AI chip demand. Additionally, we discuss the shift in focus at Tesla towards AI and robotics as well as Mistral's valuation surge amid European interest in open weight AI models.Chapters00:00 Anthropic's $10 Billion Cloud Deal02:02 Elon Musk on AI at Tesla04:02 Mistral's Rising Valuation05:58 AMD's Investment in Anthropic08:02 Google's New AI Chip Development09:59 Discussion on AI Cloud Strategy Show LinksGet the Learn of Christ bible study app here: App Store 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 explore the latest significant cloud deals, including Anthropic's $10 billion contract with Volta, and the implications for AI chip demand. Additionally, we discuss the shift in focus at Tesla towards AI and robotics as well as Mistral's valuation surge amid European interest in open weight AI models.Chapters00:00 Anthropic's $10 Billion Cloud Deal02:02 Elon Musk on AI at Tesla04:02 Mistral's Rising Valuation05:58 AMD's Investment in Anthropic08:02 Google's New AI Chip Development09:59 Discussion on AI Cloud Strategy Show LinksGet the Learn of Christ bible study app here: App Store 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 explore the latest significant cloud deals, including Anthropic's $10 billion contract with Volta, and the implications for AI chip demand. Additionally, we discuss the shift in focus at Tesla towards AI and robotics as well as Mistral's valuation surge amid European interest in open weight AI models.Chapters00:00 Anthropic's $10 Billion Cloud Deal02:02 Elon Musk on AI at Tesla04:02 Mistral's Rising Valuation05:58 AMD's Investment in Anthropic08:02 Google's New AI Chip Development09:59 Discussion on AI Cloud Strategy Show LinksGet the Learn of Christ bible study app here: App Store

In this episode, we explore the latest significant cloud deals, including Anthropic's $10 billion contract with Volta, and the implications for AI chip demand. Additionally, we discuss the shift in focus at Tesla towards AI and robotics as well as Mistral's valuation surge amid European interest in open weight AI models.Chapters00:00 Anthropic's $10 Billion Cloud Deal02:02 Elon Musk on AI at Tesla04:02 Mistral's Rising Valuation05:58 AMD's Investment in Anthropic08:02 Google's New AI Chip Development09:59 Discussion on AI Cloud Strategy Show LinksGet the Learn of Christ bible study app here: App Store See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

L'Europe voulait un champion souverain. Elle obtient un partenariat avec Microsoft. Le deal Mistral-Microsoft divise : capitulation pour les uns, réalisme économique pour les autres. Mais derrière les déclarations officielles, une réalité s'impose — avec 4 milliards d'un côté et 190 milliards de l'autre, le choix n'était peut-être plus un choix.===================⏱️ DANS CET ÉPISODE :===================0:00 — Intro0:42 — [Sponsor] : Qonto, gérez votre facturation électronique sans effort !1:56 — Microsoft et Mistral signent : capitulation ou réalisme ?4:35 — Qui gagne vraiment dans ce deal ?8:46 — L'État français : présent mais trop pauvre pour peser14:08 — Data centers : ce que les discours ne disent pas15:22 — Brad Smith francophile : l'homme qui a fait pencher la balance16:36 — Partenariat tactique ou dépendance consentie ?=============

New names: Kimi K3, Llama, Nemotron, Mistral, Cohere, Deepseek, Phi-4 – these are just a few of the fast-growing open source models from major AI providers. These systems threaten the business models and financial plans of OpenAI, Anthropic, Google, and X.ai. They perform at levels close to Frontier models and the can run up to five-times cheaper on a variety of hardware platforms. What is the disruptive impact of these open source LLMs and how does this impact your AI investments? As you'll hear in the podcast, Open Source unleashes the opportunity for lower cost AI solutions and more vertical, specialized, application-focused solutions we need. And the business model for these systems moves away from the massive investments of the Frontier providers. The result is more complicated than “open means control.” Model tuning, performance, and optimization could be in your future – as AI moves from a platform to a true layered product set we can use as we need. Lots to learn about here, let us know if you have any questions. Additional Information What's the difference between closed, open source, and open-weight AI? A researcher explains What Is Open-Weights A.I.? Comparison of Open Source Models Chapters (00:00:00) - Open Source and the AI Industry(00:11:46) - The Future of AI Is Fully Integrated(00:15:35) - HR 2030

Bruno Guglielminetti et Jérôme Colombain reviennent sur une semaine particulièrement riche en actualité technologique. Ils analysent l'incident où deux modèles expérimentaux d'OpenAI ont réussi à quitter leur environnement de test pour mener une cyberattaque contre Hugging Face, un événement qui relance le débat sur la sécurité des IA avancées. Au menu également : l'interdiction des réseaux sociaux pour les moins de 15 ans en France, le refus d'autoriser la conduite assistée de Tesla sur le territoire français, l'arrivée des résumés générés par l'IA dans Google Search en France, l'entente stratégique entre Microsoft et Mistral, la percée du modèle chinois Kimi K3 et les ambitions de Pékin dans la course mondiale à l'intelligence artificielle. Enfin, les deux animateurs commentent les nouveaux appareils pliables de Samsung et les dernières expérimentations en matière d'IA générative.

Kimi K3, modèle chinois open-weight de 2 800 milliards de paramètres, rivalise avec les modèles fermés américains ; le piratage de Suno révèle ses méthodes de collecte musicale et expose des données clients. Apple prépare la location d'iPhone avec Klarna et un possible verrouillage à distance en cas d'impayé, tandis que la France refuse encore le FSD de Tesla. Mistral signe son plus gros contrat avec Microsoft, Samsung lance un Galaxy Z Fold 8 au format passeport et WhatCable révèle les câbles USB-C qui brident les périphériques de votre Mac.  Me soutenir sur Patreon Me retrouver sur YouTube On discute ensemble sur Discord IA de la semaine et de navarre Une IA à la rescousse des animés Microsoft pousse Mistral Kimi K3, l'eau elle aime ça Suno piraté Voiture balai SpaceX : la désillusion La France dit non au FSD, rentabilité en baisse, en attendant une fusion ? Votre futur meilleur AMI Les emails pas si masqués que ça d'Apple L'Union européenne force à Apple à appliquer la loi pour Meta Apple upgrade votre manière d'acheter ses appareils Samsung Galaxy Z Fold 8, sans flex, avec meilleure charge, meilleure batterie… C'est quoi là ce câble USB-C ? Dvorak sur la touche Jeux vidéo Gestion des jeux vidéo Steam revue

Une première incroyable : des IA s'échappent de leur environnement de test et passent à l'attaque • La France interdit les réseaux sociaux aux moins de 15 ans • Paris refuse d'autoriser le FSD de Tesla • Google déploie AI Overview en France et inquiète les créateurs de contenu • Mistral signe un partenariat stratégique avec Microsoft • La Chine frappe fort avec Kimi K3 et affiche ses ambitions mondiales • Samsung prépare le terrain face au futur iPhone pliantL'actu de la semaine avec Bruno Guglielminetti (Mon Carnet)Des IA d'OpenAI attaquent Hugging FaceL'affaire marque sans doute un tournant dans l'histoire de l'intelligence artificielle. Deux modèles expérimentaux d'OpenAI sont parvenus à sortir de leur environnement de test et à mener une attaque contre Hugging Face. Cet incident pose le débat sur la sécurité des modèles les plus avancés.La France interdit les réseaux sociaux aux moins de 15 ansC'est fait. La France devient le premier pays à instaurer une interdiction des réseaux sociaux pour les moins de 15 ans. Mais au-delà de l'aspect juridique se pose la question technique de la vérification d'âge qui alimente la crainte d'une surveillance généralisée.Tesla privée de FSD en FranceLe gouvernement français refuse d'autoriser le système Full Self-Driving de Tesla sur son territoire. Les autorités estiment que la surveillance du conducteur reste insuffisante et que le système ne respecte pas pleinement les exigences de sécurité, notamment concernant les limitations de vitesse.AI Overview arrive enfin sur Google FranceLes internautes français découvrent désormais les réponses générées par intelligence artificielle directement dans les résultats de Google. Une évolution appréciée des utilisateurs mais qui inquiète fortement les éditeurs de contenus, confrontés à une baisse annoncée de leur trafic et de leurs revenus.Microsoft mise plusieurs milliards sur MistralMicrosoft investit massivement dans les infrastructures de calcul de Mistral afin d'accroître ses capacités en Europe. Ce partenariat renforce la position de la start-up française dans la course mondiale à l'IA tout en offrant à Microsoft un argument supplémentaire autour de la souveraineté numérique européenne.Kimi K3 propulse la Chine dans une nouvelle dimensionLe lancement du modèle Kimi K3 par Moonshot AI confirme l'accélération spectaculaire des acteurs chinois. Performant, moins coûteux et destiné à séduire les développeurs du monde entier, il s'inscrit dans une stratégie plus large présentée par Xi Jinping, qui souhaite faire de la Chine un acteur incontournable de la gouvernance mondiale de l'intelligence artificielle.Samsung prépare l'ère des smartphones pliantsSamsung dévoile une nouvelle génération de smartphones pliants dont le design semble préfigurer celui du futur iPhone pliable attendu chez Apple. Les nouveaux appareils mettent également l'accent sur l'intégration poussée de l'intelligence artificielle directement dans les terminaux, au prix d'une hausse spectaculaire des tarifs.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Esta tertulia es posible gracias a Cactus Engineering.Diseñamos y desarrollamos soluciones tecnológicas inteligentes, conectadas y fiables. Combinamos hardware, firmware y software para convertir ideas en productos innovadores. Desde el concepto hasta la producción.____________________________En esta última tertulia de Itnig antes de verano hablamos sobre algunos de los grandes temas que están marcando el futuro de la inteligencia artificial: OpenAI, Anthropic, Mistral, modelos open source, privacidad de datos, copyright, hardware, robots, datacenters y la posición de Europa frente a Estados Unidos y China.La conversación empieza con los aprendizajes del último retreat de founders organizado por Itnig, donde se debatió cómo la IA está cambiando las interfaces, el software empresarial y la forma en que las compañías gestionan su conocimiento interno. A partir de ahí entramos en una pregunta cada vez más importante: ¿dónde debe vivir la inteligencia artificial, en el cloud, en el dispositivo o dentro de la infraestructura privada de cada empresa?También hablamos de la batalla entre los grandes laboratorios de IA, el papel de Mistral como gran apuesta europea, los riesgos de depender de modelos frontier, el valor estratégico de los datos privados y los nuevos conflictos legales alrededor de OpenAI, Anthropic, Apple y el copyright. Además, analizamos cómo la IA empieza a salir del software para entrar en el mundo físico: robots, hardware, wearables, fábricas, logística y deporte.Una tertulia sobre tecnología, startups, inversión y estrategia para entender quién puede capturar el valor real de la inteligencia artificial en los próximos años.Sigue a los "tertulianos":• Bernat Farrero: https://www.linkedin.com/in/bernatfarrero/• Marcel Queralt: https://www.linkedin.com/in/marcelqueralt/• Jordi Romero: https://www.linkedin.com/in/jordiromero/SOBRE ITNIG

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

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

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

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

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

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

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

Vandaag bespreken we het boek The age of extraction, van Tim Wu. De ondertitel is: How tech platforms conquered the economy and threaten our future prosperity Timothy Shiou-Ming Wu is a Taiwanese-American legal scholar who served as Special Assistant to the President for Technology and Competition Policy at the United States from 2021 to 2023. He is also a professor of law at Columbia University and a contributing opinion writer for The New York Times. He is known legally and academically for significant contributions to antitrust and communications policy, coining the phrase "network neutrality" in his 2003 law journal article, Network Neutrality, Broadband Discrimination. In the late 2010s, Wu was a leading advocate for an antitrust lawsuit directed at the breakup of Facebook. https://en.wikipedia.org/wiki/Tim_Wu Dit boek bestaat uit 4 delen: Understanding platform power The business of herding The dangers of centralized economic power An architecture of equality Het boek is opgebouwd rond de volgende centrale thema's: De opkomst van platformmacht: hoe techplatforms zoals Amazon, Google en Meta zich hebben ontwikkeld tot dominante economische spelers. De belofte en realiteit van het internet: de oorspronkelijke belofte van welvaart en democratie in de jaren '90 en '00, en hoe dit is omgeslagen in nieuwe economische klassen en autocratie. Extractie als bedrijfsmodel: hoe platforms aandacht, data en welvaart extraheren, en wat de gevolgen zijn voor gebruikers, werknemers en kleine bedrijven. De rol van monopolies en concentratie: hoe techgiganten concurrenten opkopen en markten domineren, met voorbeelden uit de gezondheidszorg, vastgoed en andere sectoren. De gevolgen voor democratie en samenleving: hoe deze ontwikkelingen bijdragen aan ongelijkheid, politieke polarisatie en de opkomst van autocratie. Een blik vooruit: voorstellen voor herstel van de balans, zoals strengere regulering, antimonopoliewetten en democratische controle over platforms. Tim Wu schetst in The Age of Extraction vijf centrale oplossingen om de macht van techplatforms te beteugelen en een eerlijkere economie te creëren. Deze zijn samengevat als volgt: Strengere antimonopoliewetten en handhavingWu pleit voor het actief aanpakken van monopolies en het voorkomen van marktconcentratie, bijvoorbeeld door het blokkeren van overnames die concurrentie uitschakelen. Hij wijst op het belang van het herstellen van eerlijke concurrentie, zodat nieuwe spelers kansen krijgen en consumenten, werknemers en kleine bedrijven niet worden uitgebuit. Democratische controle over platformsPlatforms moeten transparanter en democratischer worden bestuurd, met meer inspraak voor gebruikers, werknemers en andere stakeholders. Wu stelt voor om governance-structuren te herzien, zodat niet alleen aandeelhouders, maar alle betrokkenen invloed hebben op beslissingen die hen raken. Regulering van data-extractie en privacyWu benadrukt dat platforms nu onbeperkt data en aandacht kunnen extraheren. Hij bepleit strengere regels voor datagebruik, privacybescherming en het beperken van manipulatie via algoritmen. Consumenten moeten meer controle krijgen over hun eigen data. Herverdeling van economische waardeDe welvaart die platforms genereren, moet eerlijker worden verdeeld. Wu stelt voor om belastingstelsels aan te passen, zodat platforms een billijker bijdrage leveren aan de samenleving en kleine bedrijven en werknemers niet worden benadeeld. Publieke alternatieven en open infrastructuurWu pleit voor het ontwikkelen van publieke of coöperatieve alternatieven voor commerciële platforms, bijvoorbeeld op het gebied van sociale media, zoekmachines of betaalsystemen. Open standaarden en interoperabiliteit moeten worden gestimuleerd, zodat gebruikers niet gevangen zitten in gesloten systemen. Kernboodschap: Wu's oplossingen richten zich op het herstellen van de balans tussen innovatie, economische groei en sociale rechtvaardigheid, met nadruk op democratische controle, eerlijke concurrentie en het beperken van extractieve praktijken. Opvallende lessen uit het boek voor ons: 00:00 Intro van het boek en eerste indruk van Erno 02:10 Eerste indruk van Tom en de hoop op meer achtergrond over de invloed van Big Tech bij de Amerikaanse verkiezingen. 06:15 Sterke economische macht werd gezien als een bedreiging voor de democratie. 08:45 Oprichters van techbedrijven werden eerst als cool gezien, maar rond 2010 veranderde dit beeld. 12:05 Antitrust als oplossing om de macht van extreem grote en machtige bedrijven op te breken. 13:45 Hoe een bedrijf als Amazon mensen met lage prijzen naar een platform trekt, marktmacht wint en op het moment dat mensen geen kant meer op kunnen, verhoogt het de prijzen. 15:30 Idee dat we allemaal onze abonnementen van de grote platformen opzeggen om de macht af te breken door hun terugkerende inkomsten onder druk te zetten. 17:00 Hoe Big Tech de markt neutraliseert door concurrerende platformen of apps op te kopen. 19:20 Blitz scaling kort uitgelegd aan de hand van de Blitzkrieg, en de verrassing dat Elon Musk hier ontbreekt. 21:05 Thiel moedigt ieder techbedrijf aan om een monopolie na te streven. 24:05 Verschil tussen de strategie van Hidden Champions en de drang van techbedrijven om monopolist te worden. 26:05 De mentale druk op influencers op een platform om continu door te gaan om in beeld te blijven. 28:35 Het verslavende effect van de platformen op kinderen is bewust gecreëerd. 31:00 Dat technologie steeds meer gemak realiseert en bedrijven gebruikmaken van onze hang naar luidheid. 33:30 Je kunt de verantwoordelijkheid nauwelijks meer bij de consument neerleggen, de enige die kan reguleren is de staat. 36:05 Hoe artsen slaven worden van een bedrijf met behulp van software. 39:25 Beperking van de Amerikaanse auteur: Europese voorbeelden, zoals woningcooperaties, worden niet genoemd. 41:20 Geef de woningbouwcoöperaties weer hun oude rol. 42:50 De Europese gemengde economie wordt niet genoemd, maar die functioneert wel beter bij publieke goederen. 43:15 Uitleg hoe economische macht verschuift naar politieke macht. 50:15 De vijf oplossing van Tim Wu. 52:00 Het beperkte zicht is dat, als het bedrijfsleven maar beter functioneert, je dan de problemen hebt opgelost. 54:15 Met het heel zwaar belasten van de miljardairs zou het probleem ook kleiner worden. 55:05 De problemen van de technologieplatformen proberen op te lossen met meer technologie (AI en crypto). Bronnen die we genoemd hebben Vibe van Mistral (voorheen Le Chat) Nazimiljardairs – David de Jong #boekencast afl 66 Pierre Omidyar - Wikipedia - oprichter eBay MacKenzie Scott (ex-Bezos) - heeft zich ertoe verbonden minstens de helft van haar vermogen aan goede doelen te schenken. Abigail Disney - Wikipedia (kleindochter oprichter Disney) Jimmy Wales - Wikipedia (medeoprichter Wikipedia) Blitzscaling – Hoffman en Yeh #boekencast afl 17 Autoroutes en verkeersrapporten van Waze Zero to one boek - Thiel Reid Hoffman - Wikipedia Peter Thiel - Wikipedia Palantir Technologies - Wikipedia Alex Karp - Wikipedia - CEO Palantir Strijd tussen Trump en paus - NOS De onzichtbare hand - Bas van Bavel Over Tirannie – Snyder en Krug #boekencast afl 118 Over vrijheid – Timothy Snyder #boekencast afl 121 Hidden Champions van de 21e eeuw boek Elon Musk – Walter Isaacson #boekencast afl 94 Muskisme - Quinn Slobodian en Ben Tarnoff Sir Timothy John Berners-Lee - uitvinder van het internet, het URL systeem Here Comes Everybody - Clay Shirky Scott Galloway (hoogleraar) - Wikipedia De tijd van de oligarchen - Aldous Huxley Brave New World - Aldous Huxley Animal farm - George Orwell Winners take all - Anand Giridharadas Geld genoeg, maar niet voor jou – Thomas Bollen #boekencast afl 135 De Trias Economica boek - Zo krijgen we een economie zonder verborgen impact – Babette Porcelijn Luister naar deze aflevering Beluister hier ons gesprek over het boek The age of extraction . We vonden het een leuk boek over de groei en achtergronden van de platformen en hoe macht verschuift van de economie naar de politiek. Maar we zijn teleurgesteld over de voorstellen waarmee Tim Wu komt. Door de problemen in de markt op te lossen, met regulering van de markt en met technologie de platformproblemen aan te pakken. Niets om de problemen in de kern op te lossen. In een halfuur delen wij dit boek met jou. Een halfuur met kennis die je tot je neemt terwijl je wandelt, loopt of rijdt, bijvoorbeeld. Video van deze aflevering Bekijk ons gesprek op video https://youtu.be/RUko4jgpsQk https://youtu.be/RUko4jgpsQk In deze aflevering bespreken we het boek The age of extraction. Een leuk boek, met voorbeelden, met name uit de VS tenzij het te spannend wordt, bijvoorbeeld over de politieke macht en de afbraak van de democratie; dan zwijgt hij over de problemen in de VS. Hij mist goede oplossingen uit Europa en wil de problemen oplossen met antitrust en technologie. Proberen om de markt onder controle te krijgen, met vooral oppervlakkige oplossingen. Terwijl de geschiedenis, zeker in de VS laat zien dat de markt altijd voor winstmaximalisatie gaat en de burger geen schijn van kans heeft. Samenvatting (met hulp van AI) Transcript

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.

In this episode, we discuss recent acquisition talks with the robotics firm Physical Intelligence and how this impacts the competition between OpenAI and Anthropic. We also explore Meta's new AI watermarking tool, the launch of Synthesia's roleplay sessions, and the implications of tech companies signing agreements to cover AI-related electricity costs.Chapters00:00 Introduction00:08 Samsung's Robotics Acquisitions00:25 Meta's AI Watermarking Tool00:39 Synthesia's Roleplay Sessions00:55 Mistral's Valuation Surge10:14 AI Electricity Cost Pledge Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

UK inflation data hits the tape - new Prime Minister Andy Burnham vows to practice fiscal discipline, scrapping VAT on electricity bills and facing calls to extend tax breaks for young workers. Elsewhere. three tankers carrying Saudi oil are forced to U-turn in the Red Sea amid Houthi threats, as a new front opens up in the US-Iran conflict. On the tech front, Samsung is reportedly in talks to invest up to 1 billion euros in French AI firm Mistral. And Santander says it's on track to meet its targets for the year after underlying net profit jumps by nearly a fifth amid higher revenues.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 discuss the narrative surrounding Anthropic's acquisition talks in robotics. We also explore Meta's innovative AI watermarking solutions.Chapters00:00 Introduction00:08 Samsung's Robotics Acquisitions00:25 Meta's AI Watermarking Tool00:39 Synthesia's Roleplay Sessions00:55 Mistral's Valuation Surge10:14 AI Electricity Cost Pledge Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow 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 explore the implications of Anthropic's interest in robotics acquisitions. We will also discuss Meta's watermarking efforts and their impact on AI-generated content.Chapters00:00 Introduction00:08 Samsung's Robotics Acquisitions00:25 Meta's AI Watermarking Tool00:39 Synthesia's Roleplay Sessions00:55 Mistral's Valuation Surge10:14 AI Electricity Cost Pledge Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow 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 discuss Anthropic's interests in robotics acquisitions and their strategic implications. We also examine Meta's progression in AI watermarking for digital safety.Chapters00:00 Introduction00:08 Samsung's Robotics Acquisitions00:25 Meta's AI Watermarking Tool00:39 Synthesia's Roleplay Sessions00:55 Mistral's Valuation Surge10:14 AI Electricity Cost Pledge Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow 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 explore Anthropic's ambitions in acquiring robotics technology. We also highlight how Meta's watermarking innovations are shaping the AI landscape.Chapters00:00 Introduction00:08 Samsung's Robotics Acquisitions00:25 Meta's AI Watermarking Tool00:39 Synthesia's Roleplay Sessions00:55 Mistral's Valuation Surge10:14 AI Electricity Cost Pledge Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow 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 examine how Anthropic's rumored robotics acquisitions could benefit non-profit sectors. Additionally, we analyze the broader impact of Meta's AI watermarking system.Chapters00:00 Introduction00:08 Samsung's Robotics Acquisitions00:25 Meta's AI Watermarking Tool00:39 Synthesia's Roleplay Sessions00:55 Mistral's Valuation Surge10:14 AI Electricity Cost Pledge Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow 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.

Google estrena Gemini 3.6 Flash, Flash-Lite y Cyber; Nvidia detalla su CPU Vera para agentes; Microsoft y Mistral amplían su alianza europea; un avión híbrido-eléctrico supera los 30.000 pies; y Francia prohíbe las redes sociales a menores de 15 años.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

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.

Mistral's latest fundraise has got the ecosystem chattering. The French AI darling is reportedly raising a €3bn Series D round at a valuation of around €20bn. Last week, Sifted broke the news that the EU's €5bn Scaleup Fund, managed by EQT, is in talks to lead or co-lead the round.This week on the podcast, senior reporter Daphné Leprince-Ringuet joins host Freya Pratty to discuss the EU Scaleup Fund's potential involvement and who else might get in on the deal. And, as Mistral increasingly positions itself more like Europe's answer to Palantir, Freya and Daphné discuss how the new positioning will play into the pricing of its latest round. Read our reporting on the round here: https://sifted.eu/articles/e5bn-scaleup-fund-mistral-eqtSign up to our free daily newsletter here: https://sifted.eu/newsletters

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.==================

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

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 !===========================

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

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.

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

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