Podcasts about Nobel

durée : 00:05:54 - Affaires étrangères - par : Christine Ockrent - À trois ans de la fin de son mandat, et alors que les élections de mi-mandat approchent, Donald Trump, ce président hors normes qui ne conçoit aucun obstacle à son pouvoir, continue de voir dans l'obtention d'un prix Nobel un moyen de compléter sa puissance et sa gloire. - équipe : Luc-Jean Reynaud, Théa Corler Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

Empezamos con un refrán de pueblo —el cántaro vacío es el que más ruido hace— para recordar que los idiotas gritan mucho pero nunca fueron mayoría, y de ahí saltamos al cielo de Irán: un piloto de F-15 derribado (por segunda vez en semanas, ojo, la primera por fuego amigo) que asegura haber visto una "medusa" de drones interconectados flotando sobre él, un relato tan raro que ha abierto un debate sin cerrar dentro de la inteligencia estadounidense. Después, una Iotopía dedicada a tres nombres imprescindibles de lo insólito: Charles Fort, el hombre que fichó 25.000 hechos malditos; Charles Richet, premio Nobel entregado a la metapsíquica; y J. B. Rhine, el padre estadístico de la parapsicología. Viajamos también al año 365 para contar cómo el jefe de la oficina de secretos del Imperio Romano, Procopio, estuvo a punto de quedarse con media Roma con una túnica prestada y un trapito púrpura en la mano. Y dos casos ovni de primera división: Falcon Lake, el expediente canadiense de 1967 en el que Stefan Michalak acabó con el pecho quemado en cuadrícula y una investigación oficial incapaz de refutar su historia; y la carta privada de Barbara Graves, escrita en 1953 desde Durban y aparecida hace nada en una caja de un mercadillo inglés, que casualmente coincide en día y ciudad con el avistamiento de dos oficiales de la Fuerza Aérea sudafricana. Cerramos, como manda la casa, con música inclasificable: La Romántica Banda Local. Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Nobel laureate Professor Daniel Kahneman said we don't choose between experiences; we choose between the memory of an experience. If that's true — and the science says it is — then most organizations are investing in the wrong thing. In this episode, Colin and Ryan explore how memories are actually formed, the difference between short-term (working) and long-term memory, and why your brand is really just a memory structure in the minds of your customers. They dig into how heuristics and biases shape what we remember, why humans are relentless pattern-recognition machines, and what all of this means for the experiences you design. It's the first in a three-part series on the topic Colin calls his favorite of all. In this episode, you'll learn: Why memory — not the live experience — drives customer decisions, loyalty, and price tolerance The difference between short-term/working memory and long-term memory (and why most of your experience gets "binned" within seconds) How memories are stored as a connected network of "nodes," not isolated facts Why a brand, from the customer's point of view, is simply a memory structure, making brand management a form of memory management How customer expectations are built from memory, and what that means for "meeting and exceeding" them Where heuristics and biases come from — what's innate, what's learned, and what's situational The practical "so what" for designing experiences that customers actually remember Key quote: "We don't choose between experiences. We choose between the memory of an experience." — Professor Daniel Kahneman The hosts: Colin Shaw is a LinkedIn 'Top Voice' with a massive 286,000 followers and 87,000 subscribers to his 'Why Customers Buy' newsletter. Shaw is named one of the world's 'Top 150 Business Influencers' by LinkedIn. His company, Beyond Philosophy LLC, has been selected four times by the Financial Times as a top management consultancy. Shaw is co-host of the top 1.5% podcast 'The Intuitive Customer'—with over 600,000 downloads—and author of eight best-sellers on customer experience. Shaw is a sought-after keynote speaker. Follow Colin on LinkedIn. Ryan Hamilton is a Professor of Marketing at Emory University's Goizueta Business School and co-author of 'The Intuitive Customer' book. An award-winning teacher and researcher in consumer psychology, he has been named one of Poets & Quants' "World's Best 40 B-School Profs Under 40." His research focuses on how brands, prices, and choice architecture influence shopper decision-making, and his findings have been published in top academic journals and covered by major media outlets like The New York Times and CNN. His work highlights how psychology can help firms better understand and serve their customers. Ryan has a new book launch in June 2025 called "The Growth Dilemma: Managing Your Brand When Different Customers Want Different Things" Harvard Business Press. Follow Ryan on LinkedIn.   Resources & links: Kahneman, The Riddle of Experience vs. Memory (TED): https://www.ted.com/talks/daniel_kahneman_the_riddle_of_experience_vs_memory HBR, An Emotional Connection Matters More Than Customer Satisfaction: https://hbr.org/2016/08/an-emotional-connection-matters-more-than-customer-satisfaction Beyond Philosophy training & resources: https://beyondphilosophy.com  

A Krasznahorkai – Út a Nobel-díjig című könyvünk bemutatóján Bakó Sára, a Könyves Magazin újságírója Dávid Annát, a Magvető Kiadó igazgatóját és Szilák Flóra irodalomtörténészt kérdezte. A beszélgetés témái: Mióta van benne a levegőben, hogy Krasznahorkai László irodalmi Nobel-díjat kaphat? A Nobel-ünnepség Stockholmban Dávid Anna szemével. Mikor érezték, hogy világméretű életmű készül? Milyen irodalmi közegbe érkeztek a Krasznahorkai-művek a nyolcvanas években? Mit kell tudni Krasznahorkai keleti témájú regényeiről? Hogyan szerkesztik a Krasznahorkai-szövegeket a Magvetőben? Tudj meg mindent Krasznahorkairól! 2025 végén Nobel-díjjal jutalmazták Krasznahorkai Lászlót, 2026-ban a Könyves Magazin 200 oldalas kiadványban mutatja be az utat a legfontosabb irodalmi elismerésig. Soha nem látott fotók, levelek, információk, valamint írások angyalokról, reményről, humorról és természetesen a hosszúmondatról. Fél év kutatómunka fordítókkal, a német kiadóval, a magyar és a svéd szerkesztővel, hogy megfejtsük, hogyan lett Nobel-díjas Krasznahorkai László. Érdekel a megfejtésünk? Rendeld meg itt: https://konyvesmagazin.myshoprenter.hu/krasznahorkai

Ontem foi publicado um estudo sobre a economia portuguesa, com a participação de um Nobel da Economia, com algumas recomendações para os nossos governantes. Análise de Pedro Sousa Carvalho.See omnystudio.com/listener for privacy information.

Business Insider founder Henry Blodgett unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Business Insider founder Henry Blodget unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Business Insider founder Henry Blodget unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Business Insider founder Henry Blodgett unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Business Insider founder Henry Blodget unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Business Insider founder Henry Blodget unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Ősi óriás maradványaira bukkantak az apadó Duna medrében Kitiltották a kínai humanoidokat az USA-ból Bérelhető iPhone és Mac – elindult az Apple lízingprogramja Az út szélén fehéren világító fák azt üzenik, baj van A nagy Ram-csapda: hány Gb memóriára van valóban szükség manapság az okostelefonjában? Így verik át AI-jal és deepfake-kel a digitális befektetőket A mobilod is kaphat hőgutát - így védd meg tőle! Ariana Grande kiadatlan dalai a dark weben kötöttek ki – bárki megvehette őket Leállította a Nobel-díjat nyert AlphaFold-projektet a Google Deepmind Nyilvánosságra került egy csomó Claude-beszélgetés, mert nem védte megfelelően azokat az Anthropic Irányíthatatlanná vált és több online szolgáltatásra is lecsapott az OpenAI tesztelés alatt álló ügynöke Új eszközcsaládot fejleszt az OpenAI, hogy a gépelést felválthassák a beszélgetések A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ősi óriás maradványaira bukkantak az apadó Duna medrében Kitiltották a kínai humanoidokat az USA-ból Bérelhető iPhone és Mac – elindult az Apple lízingprogramja Az út szélén fehéren világító fák azt üzenik, baj van A nagy Ram-csapda: hány Gb memóriára van valóban szükség manapság az okostelefonjában? Így verik át AI-jal és deepfake-kel a digitális befektetőket A mobilod is kaphat hőgutát - így védd meg tőle! Ariana Grande kiadatlan dalai a dark weben kötöttek ki – bárki megvehette őket Leállította a Nobel-díjat nyert AlphaFold-projektet a Google Deepmind Nyilvánosságra került egy csomó Claude-beszélgetés, mert nem védte megfelelően azokat az Anthropic Irányíthatatlanná vált és több online szolgáltatásra is lecsapott az OpenAI tesztelés alatt álló ügynöke Új eszközcsaládot fejleszt az OpenAI, hogy a gépelést felválthassák a beszélgetések A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Las lecturas veraniegas son el centro de este episodio de El Placer de Viajar, centrado en la literatura pero siempre alrededor de los viajes. En este nuevo episodio veraniego de El Placer de Viajar, el programa de viajes de esRadio, los presentadores Carmelo Jordá y Kelu Robles reciben en el estudio a Laura Galdeano, redactora de cultura de Libertad Digital. Como apasionada de la lectura y de los viajes, Laura comparte una cuidada selección de recomendaciones literarias para disfrutar durante las vacaciones de agosto, tanto en la playa como en la montaña. La primera propuesta de la invitada es En la Patagonia, obra del célebre escritor Bruce Chatwin, un auténtico hito de la literatura de viajes. Este libro mezcla con maestría la antropología, la historia y la descripción de personajes muy singulares que el autor encuentra en su periplo por tierras sudamericanas. Laura detalla cómo Chatwin, obsesionado desde la infancia con un supuesto resto de brontosaurio que perteneció a su familia, decide dejar su trabajo y adentrarse en la región patagónica para buscar los vestigios de ese animal prehistórico, que finalmente resultó ser un milodón. A continuación, analizan Venecia, de Jan Morris, un libro que la redactora descubrió directamente en una pequeña librería veneciana. Jan Morris, que cubrió como reportera la mítica expedición británica al Everest, ofrece en este texto una visión de la ciudad italiana muy alejada de los típicos clichés románticos. A través de su prosa, describe con gran agudeza la vida cotidiana de sus habitantes, el encanto de la decadencia veneciana y dedica capítulos enteros a los gatos de Venecia y a las peculiaridades de sus célebres gondoleros. El recorrido continúa en el norte con Tierra de clanes, escrito por los conocidos actores Sam Heughan y Graham McTavish, protagonistas de la exitosa serie de televisión Outlander. El libro narra en formato de diario de viaje las andanzas de ambos amigos en una furgoneta a través de las tierras altas de Escocia. La obra destaca por el gran contraste humorístico entre sus autores —uno más quejumbroso y clásico, y el otro más joven y aventurero— mientras exploran los castillos, la historia escocesa y las destilerías de whisky de la zona. Para los amantes del país vecino, Laura sugiere Un año en la Provenza, del británico Peter Mayle. Este libro humorístico detalla las divertidas vivencias de un ejecutivo de publicidad que lo deja todo para instalarse en un caserón en el campo francés. Con un fino humor británico, Mayle narra el choque cultural con la población local, las reformas inacabables con obreros franceses y, sobre todo, el placer por la gastronomía provenzal y sus excelentes vinos, estructurando el relato mes a mes. Por último, en el apartado de no ficción, la invitada destaca España interminable, del neerlandés Cees Nooteboom. Este autor, eterno candidato al premio Nobel, muestra un profundo amor y respeto por la cultura y el arte españoles. Laura relata cómo el escritor plasmó su fascinación por las iglesias, la geografía y la espiritualidad de España desde su perspectiva de viajero del norte de Europa, ofreciendo también su conocida obra *Desvío a Santiago* como alternativa de lectura pausada. En la última parte del episodio, Carmelo Jordá no duda en sumar sus propias recomendaciones a la charla, sugiriendo el inmortal clásico de la generación beat En el camino, de Jack Kerouac, donde el viaje es el protagonista absoluto. Asimismo, propone la lectura de la célebre trilogía del escritor y aventurero Patrick Leigh Fermor, quien cruzó a pie Europa desde los Países Bajos hasta Constantinopla en el año 1933, dejando un testimonio extraordinario del continente antes de la catástrofe bélica. Y Kelu Robles aporta una joya del siglo XVIII, titulada Viaje de Londres a Génova a través de Inglaterra, Portugal, España y Francia, de Giuseppe Baretti. Editada en su día por el sello Reino de Redonda del escritor Javier Marías, esta obra epistolar ofrece una visión irónica, fresca y sumamente crítica sobre las costumbres españolas de la época, incluyendo divertidos comentarios sobre la ciudad de Madrid y la matanza en Extremadura. Como broche final para quienes prefieren la narrativa de ficción, Laura Galdeano aconseja sumergirse en la exitosa Trilogía del Baztán, de la escritora donostiarra Dolores Redondo. La invitada subraya cómo la autora convierte el húmedo valle navarro, la lluvia incesante y la bruma en un personaje activo de la trama policial. El viaje literario a través de localidades como Elizondo o Zugarramurdi, así como la inmersión en la rica mitología vasconavarra, son motivos más que suficientes para devorar esta saga durante la temporada estival. Escríbenos, explícanos qué te gusta más y si hay algo que no te gusta tanto de El Placer de Viajar, dinos de qué destinos quieres que hablemos y si quieres que tratemos algún tema y, por supuesto, pregúntanos lo que quieras en el correo del programa: elplacerdeviajar@libertaddigital.com.

Ukraine is shifting its long-range drone campaign to focus on critical Russian infrastructure, and Big Tech companies are facing an AI dilemma as they report quarterly earnings. Plus, Google DeepMind is leaving behind its Nobel-winning AlphaFold project for new ventures, and PwC published “thought leadership” reports containing AI-generated hallucinations.Mentioned in this podcast:Ukraine adapts strikes on Russian energy industry to hit critical componentsChip stocks tumble as AI sell-off deepensGoogle DeepMind dismantles Nobel-winning AlphaFold team in strategy shiftPwC published ‘thought leadership' reports marred by AI hallucinations Listen to Unhedged on Apple Podcasts, Pocket Casts or Spotify.Save 10% on tickets to the FT Weekend Festival with the code FTPodcast. Visit ft.com/festival to find out more.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.

00:00 Giriş 05:40 Trumpın Nobel Ödülü Alamaması18:32 Japonya'nın Yeni Başbakanı31:27 Çek Cumhuriyeti Seçimleri 45:09 Çin'in Nadir Toprak Elementlerine İhracat Yasağı55:27 Avrupa Birliği Çelik Vergisi1:14:13 Dünya Nükleer Enerji Dosyası1:54:41 Suudi Arabistanın Oyun Sektörü Yatırımları

Annie Jacobsen on BIOLOGICAL WAR: how a single lab leak could take us from outbreak to anarchy in six days — and why bio may be scarier than nuclear. WIN a signed copy of BIOLOGICAL WAR or NUCLEAR WAR, a meteorite & more — enter free: https://briankeating.com/annie (Join there for the free Biological War Scenario Simulation and Source Dossier + early access to my next Nobel-laureate interview.) Covid killed ~1% of the people it infected. Pneumonic plague, untreated, kills nearly 100% within 24 hours. In her new book Biological War: A Scenario, bestselling journalist Annie Jacobsen war-games what happens when an engineered pathogen escapes — drawing on interviews with presidential advisors, secretaries of defense, STRATCOM commanders, epidemiologists, and directors of Operation Warp Speed. We get into gain-of-function, the Soviet bioweapons program, mirror life, AI and bioweapons (including her conversation with Sam Altman), the Fermi Paradox and the Great Filter — and why the first 24 hours decide everything. ENTER THE GIVEAWAY + GET THE FREE SOURCE DOSSIER: https://briankeating.com/annie GET THE BOOK (Annie narrates the audiobook): https://www.amazon.es/Biological-War-non-fiction-thriller-bestselling/dp/1911742035 https://play.google.com/store/audiobooks/details?id=AQAAAEBqvjj-QM 0:00 Covid was 1%. Pneumonic plague is 100%. 1:31 Why this conversation matters (please subscribe) 2:05 Don't judge a book by its cover — Biological War 4:54 "You can only mitigate extermination" 5:50 Reporting a classified world without a clearance 9:00 Gain-of-function: cure or weapon? 11:22 BSL-4, Vektor & the Siberia lab leak 15:28 Katalin Karikó & the two sides of the lab bench 18:06 Paul Berg & when biology turned dangerous 20:42 Paranoia as doctrine: why the Soviets cheated 23:13 Tailored bioweapons & genetic privacy 27:16 Could it happen again? What Covid taught us 33:40 The WHO "not airborne" mistake & the collapse of trust 36:52 Both sides failed: the vaccine politics 39:48 The "euphoria" enhancement — dark-hearted evil 44:02 Why "airborne" changes everything 45:50 Climate vs. AI: the next scenario? 48:12 Biological twilight & "devolution" 52:52 Fritz Haber and the hubris of genius 54:36 AI + bio: the real threat 1:00:34 Mirror life: the extinction-level horror 1:04:44 Trinity Day, Iran & "the atomic weapon" 1:08:26 The Fermi Paradox & the Great Filter 1:09:34 A gift for Annie (a meteorite + a piece of the Moon) 1:16:36 Igor Domaradsky's secret notes 1:21:12 Daschle, anthrax & "inhuman things just to survive" 1:21:56 If you were President: shutting down the internet 1:27:18 Who dies first — first responders and mothers 1:33:02 Sam Altman, ChatGPT & the change OpenAI made 1:37:26 "You want me on that wall" 1:40:00 A hopeful ending ABOUT ANNIE JACOBSEN Pulitzer Prize finalist and author of Nuclear War: A Scenario, Operation Paperclip, Area 51, and Surprise, Kill, Vanish. GO DEEPER • Katalin Karikó — the mRNA breakthrough: https://open.spotify.com/episode/3oPZMPMlzsEFEyvVHA5dAE • Jay Bhattacharya — Follow Science, Not Scientists: https://www.youtube.com/watch?v=iTnJNXYFg9M • Freeman Dyson — Into the Impossible: https://www.youtube.com/watch?v=egpsFbbpkHQ A FEW SOURCES FROM THE BOOK (full dossier at https://briankeating.com/annie) • "Confronting risks of mirror life," Science (2024): https://doi.org/10.1126/science.ads9158 • Ken Alibek, Biohazard (the Soviet program from the man who ran it) • Biological Weapons Convention (1972): https://disarmament.unoda.org/en/our-work/weapons-mass-destruction/biological-weapons/biological-weapons-convention SUBSCRIBE so guests like Annie keep saying yes: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Newsletter + meteorite offer: https://BrianKeating.com/edu: https://briankeating.com/annie Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt Support Into the Impossible on Patreon, get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join What scares you more — nuclear war or biological war? Tell me in the comments. #AnnieJacobsen #BiologicalWar #Bioweapons #Pandemic #BrianKeating #IntoTheImpossible #NuclearWar #MirrorLife #AI #Biosecurity Learn more about your ad choices. Visit megaphone.fm/adchoices

Iniciativa do Acnur visa marcar os 75 anos da Convenção sobre Refugiados; nomes incluem artistas, atletas, empresários, líderes religiosos e vencedores do prêmio Nobel da Paz; objetivo é criar coalizão para defender aqueles fogem de conflitos, violência e perseguição.

Send us Fan MailVasant Dhar teaches data science at NYU's Stern School of Business and has spent more than 45 years at the frontier of artificial intelligence. He brought machine learning to Wall Street in the 1990s and founded SCT Capital Management, one of the first machine learning based hedge funds.He hosts the Brave New World podcast, downloaded more than a million times, where he has interviewed Nobel laureates, technologists, and global thinkers on the implications of AI. His work has appeared in The New York Times, The Wall Street Journal, Financial Times, Wired, and MIT Technology Review. His latest book, Thinking with Machines, traces AI from its origins to the present.For more than forty years, Vasant has built systems that sat right on the edge of human trust. People had to decide whether to rely on them or walk away.In this episode, he makes a stark claim: AI will not just change how we work; it will sort us into two groups. One uses it to extend their judgment. The other slowly hands that judgment over. The gap comes down to habits you are forming today, not some distant future.In this conversation, we discuss:Why trusting AI comes down to just two variables, and the simple test Vasant has applied since his Harvard Business Review piece a decade agoThe bifurcation Vasant believes AI is about to create, splitting humanity into two groups, and which side you do not want to be onWhat tennis great Roger Federer's win rate reveals about succeeding with algorithms, and the counterintuitive math behind every winning edge Why the edge returns to humans the moment everyone runs the same algorithms, and what only people can do in situations no system has seen The one word in the title Thinking with Machines that Vasant says matters most, and what it asks of how we work alongside AIThe areas of life where Vasant argues we may need to restrict AI entirely, and the legal framework we already have to govern itExplore the Conversation 00:00 Intro & AI Fun Fact: Trustworthy AI from Principles to Practice04:13 Meet Vasant Dhar: From the Internist System to Machine Learning on Wall Street07:10 The Origins of Thinking with Machines: The Biggest Surprise in 45 Years of AI09:33 Written for Everyone: AI's Accelerating Pace and the Call to Get Engaged11:23 When to Trust an Algorithm: The Green Zone and the Human Edge20:24 The Bifurcation of Humanity: Superhuman Amplification or Cognitive Decline24:52 The Four Eras of Machine Intelligence: From Specification to General Intelligence29:22 The Ethics of AI Agency: Where Machines Need Limits and Obligations34:14 Who Governs AI: Tort Law, Liability, and Emerging Legal Precedent37:44 AI in 2036: Multisensory Machines and the Integration of the Senses40:33 Teaching Machines to Smell: AI, Olfaction, and Disease Detection43:35 Where to Find Thinking with Machines and Connect with Vasant Dhar Resources:Subscribe to the AI & The Future of Work NewsletterConnect with Vasant on LinkedInAI fun fact articleOn the decision sprint process with Atif Rafiq, CEO & Bestselling Author

Bryan Johnson anunciou ter criado um clone bebé de si mesmo, mas na verdade reprogramou células usando um técnica que ganhou o Nobel da Medicina em 2012. A ideia é retardar o envelhecimentoSee omnystudio.com/listener for privacy information.

Economist Alvin Roth, winner of the economics Nobel in 2012 for his work on market design, joins Cardiff to discuss his new book, Moral Economics: From Prostitution to Organ Sales: What Controversial Transactions Reveal About How Markets Work.The book is about morally contested markets: transactions that participants would like to engage in but that others think they shouldn't be allowed to, even when it's hard to identify measurable harms to those objectors. It's also a great way to understand how markets interact with the law, politics, social norms, income inequality, human psychology, and so much more than just monetary exchange.In this chat, Alvin and Cardiff discuss:Medical aid in dying and the economics of “repugnant” marketsSex work, trafficking, and the unintended consequences of criminalizationPlural marriage and the challenge of designing new legal protectionsSports gambling and the rise of prediction marketsKidney exchanges, the transplantation shortage, and the opposition to cross-border exchangesThroughout the conversation, they confront the trade-offs involved in both allowing controversial markets and trying to ban them.Related links: Moral Economics: From Prostitution to Organ Sales: What Controversial Transactions Reveal About How Markets WorkAlvin Roth's website

Get Ryan Hawk's new book: The Price of Becoming: The Compounding Practices of High Performance There's a sentence most people never recover from. "He gives us a better chance to win than you do." That's what Ryan Hawk's college football coach told him, benching a highly recruited quarterback in favor of a freshman who would go on to win two Super Bowls and a Hall of Fame induction. Ryan calls it one of the luckiest things that ever happened to him — because it installed a question he asks himself before bed every night since: what did I do today to add value to someone else's life? In this conversation, Michael Trainer sits down with Ryan Hawk — host of the Learning Leader Show, author of The Price of Becoming — for a wide-ranging exploration of what actually compounds a life. Not just money. Not just skill. Relationships, proximity, and the rooms you choose to sit in. They get into the uncomfortable underside of Ryan's own thesis: if consistency compounds, what happens when you're consistently investing in the wrong people? Ryan doesn't dodge it — he talks about outgrowing friendships as a marker of growth, not disloyalty, and the discipline of choosing rooms that force you to level up rather than settle in. From there, the conversation moves through Ryan's answer to what love actually is — curiosity, full stop — the tactical, specific ways he and his wife make people feel seen (down to remembering someone's favorite bourbon), Jim Collins' definition of leadership as "the art of getting people to want to do what must be done," and the four-book journey of learning what it actually takes to launch work that spreads. This is a conversation about the architecture of a well-lived life — told through a man who has spent eleven years and 700 interviews studying exactly that, and is still, by his own account, trying to live it out one morning at a time. "We learn who we are in practice, not in theory." Michael Trainer has spent 30 years learning from Nobel laureates, neuroscientists, and wisdom keepers worldwide. He's the author of RESONANCE: The Art and Science of Human Connection (March 31, 2026), co-creator of Global Citizen and the Global Citizen Festival, and host of the RESONANCE podcast.Featured in Forbes, Inc, Good Morning America. Follow on YouTube

One of the biggest mysteries in cosmology seems to keep getting bigger. Astronomers have known since the 1930s that the universe is expanding, but in the 1990s, the discovery that this expansion is accelerating rather than slowing down came as a huge shock to the field. Something had to be driving that acceleration, and dark energy was proposed as the cause. What began as a seismic shock in cosmology eventually led to a Nobel prize. But this story has a sequel, and it comes with another major plot twist. The two main methods for estimating the universe’s present-day expansion rate are producing significantly different answers. As a result, how fast the universe is really expanding has become a matter of considerable debate — with no small amount of angst — and the discrepancy has become known as the Hubble tension. Perhaps most surprising of all is that one of the loudest voices raising concern is Adam Riess, the astrophysicist whose Nobel Prize-winning work helped ignite the acceleration debate in the first place. Riess joined co-host Steven Strogatz on The Joy of Why to explain how we got to this point, what the Hubble tension might be telling us, and what may happen next.

【欢迎订阅】 每天早上5:30，准时更新。 【阅读原文】 标题：Donald Trump's $2.2bn windfall invites comparisons with global strongmen正文：President Donald Trump flew to Medora, North Dakota, this week to dedicate the Theodore Roosevelt Presidential Library. It was notable for being his first trip aboard the new $400mn Boeing 747 gifted to him by the Emir of Qatar. Trump flew out on the same day it was disclosed he had earned more than $2.2bn since his return to the White House, a windfall without precedent in US presidential history that has raised troubling questions about conflicts of interest in his administration.知识点：dedicate v. /ˈdedɪkeɪt/to officially open a building or monument to honor someone 为……举行落成典礼，为（建筑物等）揭幕• The city plans to dedicate a new memorial to the victims of the disaster next month. 该市计划下月为灾难遇难者落成一所新的纪念馆。• A statue was dedicated to the Nobel laureate in her hometown. 在她的家乡，一座纪念这位诺贝尔奖得主的雕像举行了揭幕仪式。【节目介绍】 《早安英文-每日外刊精读》，带你精读最新外刊，了解国际最热事件：分析语法结构，拆解长难句，最接地气的翻译，还有重点词汇讲解。 所有选题均来自于《经济学人》《纽约时报》《华尔街日报》《华盛顿邮报》《大西洋月刊》《科学杂志》《国家地理》等国际一线外刊。 【适合谁听】 1、关注时事热点新闻，想要学习最新最潮流英文表达的英文学习者 2、任何想通过地道英文提高听、说、读、写能力的英文学习者 3、想快速掌握表达，有出国学习和旅游计划的英语爱好者 4、参加各类英语考试的应试者（如大学英语四六级、托福雅思、考研等） 【你将获得】 1、超过1000篇外刊精读课程，拓展丰富语言表达和文化背景 2、逐词、逐句精确讲解，系统掌握英语词汇、听力、阅读和语法 3、每期内附学习笔记，包含全文注释、长难句解析、疑难语法点等，帮助扫除阅读障碍。

Felix Rieseberg leads engineering for Claude Cowork and Claude Code Desktop at Anthropic. Before that he led desktop and web infrastructure at Notion and built software at Stripe, Slack, and Microsoft. In this episode of Summation, Felix and Auren discuss:The next AI step function: going from solving a problem to owning a responsibilityWhy AI will win many Nobel prizes before it wins a single PulitzerBringing AI into the physical world, from a $30 coffee-machine display to a Wi-Fi garage openerWhy "here's what your team can learn from the Navy SEALs" is bad management adviceYou can find Auren Hoffman on X at @auren and Felix Rieseberg on X at @felixrieseberg

On the heels of the announcement that Sigrid Undset's cause for canonization has been opened, Grettelyn Darkey and Joe Grabowski consider the Nobel laureate through the lens of G.K. Chesterton—the writer she admired, collected, and translated into Norwegian before visiting him at his home in Beaconsfield. Received into the Church just two years after G.K. Chesterton, Undset shared his conviction that sanctity belongs to the ordinary life of the laity, and her vivid portraits of medieval saints carry the same paradoxical spirit that made Chesterton's own work startle and delight. In This Episode: How Sigrid Undset's path into the Church paralleled G.K. Chesterton's—two converts of the same era drawn by the medieval and the Baroque Why G.K. Chesterton and Undset both located holiness in the vocation of the laity rather than the cloister The debt Undset owed to writers in G.K. Chesterton's orbit—Robert Hugh Benson's Lord of the World and the English Catholic revival Undset's translation of G.K. Chesterton's The Everlasting Man into Norwegian, and her reading of his distributism The shared Chestertonian paradox in Undset's fiction: making the strange familiar and the familiar strange Resources Mentioned: The Everlasting Man by G.K. Chesterton Gilbert Magazine Kristin Lavransdatter by Sigrid Undset Catherine of Siena by Sigrid Undset Lord of the World by Robert Hugh Benson Chapters: 00:00: Welcome and the news of Sigrid Undset's cause 02:12: Who Sigrid Undset was 06:20: What publishers wanted versus what readers loved 09:58: The Willa Cather parallel 11:51: Belief, doubt, and the interior life of the novel 16:41: Undset's road to conversion and Robert Hugh Benson 23:20: The radical cost of her conversion 24:04: Dorothy Day and saints who don't fit the mold 27:09: Collecting and translating G.K. Chesterton 30:38: When Undset met G.K. Chesterton 33:19: Holiness for the laity 38:15: Why Undset and G.K. Chesterton speak to today FOLLOW US Instagram Facebook X SUPPORT Consider making a donation Visit our Shop Produced by Saint Kolbe Studios

En este episodio te explico Save More Tomorrow, el programa de Richard Thaler (premio Nobel) que multiplicó por 4.5 el ahorro de millones de trabajadores en Estados Unidos y por qué México ya tiene los rieles para hacerlo pero le falta apretar el botón. ------ LA INFORMACIÓN DE ESTE PODCAST NO ES UNA RECOMENDACIÓN DE INVERSIÓN. Nada de lo contenido en este podcast constituye asesoría fiscal, contable, regulatoria, legal, de seguros o de inversiones, ni representa una oferta, solicitud o recomendación para comprar, vender o realizar cualquier operación con valores, esquemas de inversión colectiva, instrumentos financieros o servicios.

A Nobel laureate's surprising secret to genius wasn't just intellect, but a playful defiance of norms. This audiobook summary reveals his unconventional path.

There's (another) new open source king of AI.

Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we delve into a series of industry-shaping events, reflecting the ever-evolving landscape of drug development, regulatory challenges, and strategic maneuvers. In a significant move within the sector, Jasper Therapeutics has merged with Kira Pharmaceuticals, a strategic decision aimed at expanding its footprint in autoimmune therapeutics. This merger brings to Jasper a valuable asset in KP-104, a dual-inhibitor with the potential to address rare diseases—an area of considerable unmet need. Such consolidations highlight an industry trend where companies seek to bolster their portfolios with promising candidates that can tackle complex medical conditions. The merger exemplifies how strategic expansions are becoming increasingly integral to maintaining competitive edges in the biotech arena. Turning to clinical trials, Takeda's Zasocitinib has demonstrated promising results for patients suffering from moderate-to-severe plaque psoriasis. In Phase 3 trials, a significant 75% of patients achieved clearance of scalp psoriasis. Zasocitinib operates as a TYK2 inhibitor, targeting the IL-23 pathway—a crucial mechanism in autoimmune diseases like psoriasis. This breakthrough promises to enhance patient care by providing a more effective treatment option for those struggling with difficult-to-treat psoriasis. On the regulatory front, Novartis' Fabhalta (Iptacopan) has gained FDA approval for its role in slowing kidney function decline in patients with primary immunoglobulin A nephropathy. As a small molecule complement inhibitor, Iptacopan introduces a novel treatment class for this autoimmune kidney disorder. This approval underscores ongoing innovation within nephrology and offers renewed hope for improved patient outcomes. Recent regulatory updates also saw Novartis securing full FDA approval for Fabhalta—an affirmation of progress in addressing immunoglobulin A nephropathy through innovative therapeutic options. Business development remains a driving force in shaping industry landscapes. The acquisition of Mission Therapeutics' AKI candidate MTX652 by Dimerix is a prime example. This $5 million deal, with potential milestones up to $292 million, reflects high stakes and ambitions to advance treatments for acute kidney injuries—a field with significant unmet medical needs. The integration of artificial intelligence continues to revolutionize drug discovery processes. Aqemia's collaboration with Sanofi highlights this trend, showcasing AI's crucial role in expediting drug development and uncovering novel therapeutic targets. Their partnership potentially worth $140 million underscores AI's transformative potential within pharmaceutical research. Notably, Nobel laureate Jennifer Doudna's foray into AI-powered protein design signifies an exciting intersection between gene-editing technology and artificial intelligence. Her involvement signals potential revolutions in drug discovery through enhanced precision in protein engineering. However, regulatory challenges persistently loom over the industry. Novo Nordisk and Alvotech have faced FDA scrutiny concerning manufacturing deficiencies—an issue that accentuates the importance of stringent quality control and operational excellence in biologics manufacturing. The American Society of Health-System Pharmacists (ASHP) report on U.S. drug shortages during Q2 2026 reveals vulnerabilities within supply chains, notably impacting oncology drugs. These shortages emphasize the critical need for robust strategies to ensure consistent drug availability for essential therapies. Furthermore, geopolitical dynamics are influencing pharmaceutical supply chains. A U.S. Senate bill aimed at increasing transparency highlights concerns over China's dominance in drug ingredient supplies—an issue necessitating strategic adjustments by globally operating companies. Elsewhere within the sector, GSK made headlines by discontinuing the development of its chronic cough treatment camlipixant following mixed Phase 3 trial results—a setback illustrating the critical nature of trial outcomes in determining drug viability and market potential. In market trends, biotech IPOs have surged during the first half of 2026—a sign of robust investor interest fueled by innovations and favorable funding environments despite associated market volatility risks. Strategic adjustments continue across companies with mergers and acquisitions leading to workforce reductions—projected layoffs exceeding 14,000 within biopharma during H1 2026—as organizations streamline operations or pivot towards more promising research domains. Lastly, Merck's FDA approval for an oral PCSK9 inhibitor marks a significant achievement in cardiovascular care—representing another stride forward in therapeutic innovation. In summary, these developments encapsulate a landscape defined by scientific pursuits yielding mixed results amidst evolving regulatory interactions and strategic realignments—all contributing towards innovative healthcare solutions while navigating complex industry dynamics.Support the show

Récompenser l'innovation, c'est armer ceux qui la tueront.Philippe Aghion a passé sa carrière à modéliser cette contradiction qui lui a valu le prix Nobel d'économie 2025, partagé avec Joel Mokyr et Peter Howitt.Fils d'un couple juif arrivé en France en 1945, il grandit dans l'idéologie communiste de son père qu'il partage pendant toute sa jeunesse.Après un doctorat en économie à l'université de Harvard, il s'attaque à une question jamais vraiment résolue : d'où vient la croissance ?Le modèle dominant, de Robert Solow, l'expliquait par l'accumulation de capital et le progrès technique. Mais sans pouvoir définir l'origine du progrès technique, ce modèle restait incomplet aux yeux de tous.C'est ce trou qu'Aghion et Howitt tentent de combler à partir de la fin des années 80.Ils repartent du concept de destruction créatrice de Schumpeter pour prouver que la croissance vient de l'innovation cumulative. On innove pour faire des profits. Et chaque innovation rend la précédente obsolète.Mais ce modèle cache une contradiction.Pour pousser à l'innovation, il faut récompenser par des rentes. Sauf qu'une fois installés, les innovateurs d'hier utilisent ces rentes pour bloquer ceux de demain.Réguler une économie, c'est arbitrer ce conflit en permanence.Et c'est précisément à cette tâche que l'Europe a failli, selon lui.Il la décrit comme un géant réglementaire qui a tué sa propre politique industrielle et déplore la fuite de l'épargne des Européens vers les marchés américains.Dans cet échange, Philippe Aghion donne sa grille de lecture du monde :Son plan pour redresser les comptes de la France sans taxer davantage les richesPourquoi l'Europe a tant de mal à produire des licornes ? Et comment y remédier ?Le contrat d'évolution, son alternative au revenu universelComment l'IA accélère déjà la recherche, même au plus haut niveauPhilippe Aghion ne se décrit pas comme un optimiste, il considère que redresser la France et l'Europe est un « objectif de combat ». Et il livre dans cet épisode sa feuille de route pour y parvenir.Vous pouvez contacter Philippe sur LinkedIn.TIMELINE:00:00:00 - Les innovateurs d'hier bloquent ceux de demain00:10:59 - Vers un capitalisme social ?00:18:16 - Comment l'épargne européenne finance les licornes américaines00:25:33 - Son plan pour sauver les finances de la France00:31:16 - L'alternative de Philippe au revenu universel00:40:01 - L'Europe a réussi la paix mais raté la croissance00:45:51 - Le gaullisme 2.0 pour relever la France00:54:57 - "Ceux qui n'adoptent pas l'IA vont souffrir"Les anciens épisodes de GDIY mentionnés : #546 - Présidentielles 2027 - Édouard Philippe - Le courage politique et l'addiction française à la dépense publiqueNous avons parlé de :Inside Google's The GroveMichel DevoretFlexisécurité: le Danemark est-il un modèle?L'ENS Cachan devient l'ENS Paris-SaclayComment la Chine est devenue imbattable ?C'est quoi exactement la "taxe Zucman" ?Voici ce que devra faire en priorité le futur président de la République : les conseils de Philippe Aghion, Alexandra Roulet et Xavier JaravelLa Commission AttaliQuels sont les critères de Maastricht pour participer à l'UEM ?La mission French TechLes recommandations de lecture : Sapiens : Une brève histoire de l'humanité, de Yuval Noah HarariUn grand MERCI à nos sponsors : Squarespace : https://squarespace.com/doitQonto: https://qonto.com/r/2i7tk9 Brevo: brevo.com/doit eToro: https://bit.ly/3GTSh0k Payfit: payfit.com Club Med : clubmed.frCuure : https://cuure.com/product-onely (code DOIT)Vous pouvez retrouver la liste de tout le matériel utilisé pour enregistrer nos épisodes sur cette page.Vous souhaitez sponsoriser Génération Do It Yourself ou nous proposer un partenariat ?Contactez mon label Orso Media via ce formulaire.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

What should the government be doing to get us AI ready? How do we make sure people co-exist with AI and aren't just replaced by it? What role should trade unions have? What are 'tech towns' and how important are they in the AI race? Nobel prize winning economist Simon Johnson is back to tell us about his role as the chair of the government's AI Institute which will use workplace data shared by over thirty major corporations to track, in real-time, how AI adoption is shifting job availability, wage growth, and macroeconomic productivity across the UK. The Rest is Money is brought to you by Octopus Energy, Britain's smart energy pioneer. Email: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠the⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠restismoney@goalhanger.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ X: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@TheRestIsMoney⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@TheRestIsMoney⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@RestIsMoney⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Advertise with us: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Partnerships@goalhanger.com⁠⁠ For more Goalhanger Podcasts, head to ⁠www.goalhanger.com⁠ Video Editor: Dylan Bonham Producer: Isabelle Bougeard Exec Producers: Chris Sawyer and Tom Whiter Learn more about your ad choices. Visit podcastchoices.com/adchoices

Deux unes d'hebdomadaires français se répondent cette semaine. Sur chacune, le même visage, celui de Marine Le Pen. Et la même question : que se passerait-il si le Rassemblement national gouvernait réellement ? Les promesses du RN confrontées à la réalité L'Express met en scène Marine Le Pen assise sur un trône doré, entourée de chats, et titre : « Les 100 premiers jours du RN au pouvoir ». Avec ce sous-titre : « Enquête sur le programme qui plomberait la France ». Le dossier passe chaque promesse au tamis du réel. La mesure phare, le référendum sur l'immigration, se fracasse sur l'article 89 de la Constitution. Celui-ci encadre les révisions constitutionnelles et impose qu'avant tout référendum, le texte soit voté dans les mêmes termes par l'Assemblée nationale et le Sénat. Un verrou quasiment infranchissable pour le RN. L'hebdomadaire cite d'ailleurs un proche du dossier, désabusé : « Ce n'est pas la peine de faire croire aux Français qu'on peut consulter sur tout. » Quant à la promesse de Jordan Bardella de réduire de moitié la contribution française à l'Union européenne, ce serait « le fiasco assuré », tranche L'Express. Le parti se heurterait aussi à une difficulté très concrète : il ne parvient pas à recruter les hauts fonctionnaires dont il aurait besoin pour gouverner. Un proche de Marine Le Pen le reconnaît : « On a un vrai sujet sur les ressources humaines… On ne trouve pas de gens qui tiennent la route. » Le Pen-Bardella, un tandem moins solide qu'il n'y paraît Même préoccupation au Nouvel Obs, mais avec un autre angle. Sous le duo Le Pen-Bardella, ce titre : « Les failles cachées », accompagné d'une question : « Pourquoi leur ticket n'est pas si gagnant ». L'hebdomadaire décrit la fébrilité du tandem après la confirmation en appel de la condamnation de Marine Le Pen pour détournement de fonds publics. Jordan Bardella, écrit Le Nouvel Obs, « a vu s'envoler un bail virtuel de cinq ans à l'Élysée et doit se contenter d'un siège éjectable de Premier ministre ». Le vernis commence à craquer, notamment en raison des désaccords qui traversent le duo. Sur les retraites, Jordan Bardella affiche ainsi une posture favorable aux entreprises, tandis que Marine Le Pen défend un départ à 60 ou 62 ans. Un député confie à L'Express que des divergences de ce type, « il y en a des dizaines ». Le dossier du Nouvel Obs se referme sur cette formule d'un vieil ami de la famille : « Marine, quand elle est face à une difficulté, c'est là qu'elle devient vraiment une Le Pen ». Ce qui, note perfidement l'hebdomadaire, ne devrait pas rassurer l'extrême droite, car « les Le Pen ont toujours perdu ». Ces commerces vides qui servent à blanchir de l'argent Sur un tout autre sujet, Le Point publie une enquête sur le blanchiment d'argent. Elle commence par une balade dans Paris, rue de Bagnolet, devant un vendeur de kebabs désespérément vide à l'heure du déjeuner. Pourquoi cet établissement tient-il depuis des années ? Parce que, écrit Le Point, « ce commerce se moque éperdument de vendre des sandwichs » . Il s'agirait en réalité d'une officine de blanchiment. Son propriétaire disposerait d'une fortune en espèces d'origine inavouable, provenant par exemple du trafic de drogue, dont il lui faudrait justifier la provenance, quitte à inventer des clients. S'il déclare avoir servi 250 personnes à 50 euros dans le mois, le fisc n'a aucun moyen de le vérifier. Voilà pourquoi l'échoppe reste ouverte, même vide. La même mécanique serait à l'œuvre dans certains salons de massage, ongleries ou salons de coiffure. Des tickets de loterie gagnants peuvent même être rachetés afin de blanchir des flux financiers. Les autorités traquent ces pratiques, mais les délinquants continuent d'innover. La mort est-elle vraiment une mauvaise chose ? Une dernière question, presque philosophique : « Pourquoi mourons-nous ? » C'est le titre du livre du prix Nobel de chimie Venki Ramakrishnan, interrogé par L'Express. Et si la science permettait un jour de ne plus mourir ? Le scientifique raconte une conversation bien réelle, captée l'an dernier entre Vladimir Poutine et Xi Jinping, au cours de laquelle les deux dirigeants évoquaient leur intention de prolonger leur vie grâce à la recherche. Cette perspective l'inquiète. Jusqu'ici, explique-t-il, « on savait que les dictateurs finissent par mourir, laissant l'espoir que le régime suivant soit différent ». Mais « imaginez un monde où Trump, Poutine ou Xi vivraient pour toujours… Je ne suis pas sûr que ce serait un monde meilleur ». Pour Venki Ramakrishnan, la mort a aussi du bon : « Être mortel nous pousse à accomplir des choses. Mozart n'a vécu que 36 ans, mais il a eu le temps d'écrire 41 symphonies ». Ce dont l'humanité a besoin, conclut-il, ce n'est pas de permettre à quelques puissants de vivre pour toujours, mais d'offrir au plus grand nombre la possibilité de vivre longtemps et en bonne santé. Tout un programme.

durée : 01:12:56 - Les Nuits de France Culture - par : Albane Penaranda - Juillet 1966, l'émission "Il y a trente ans l'Espagne" revient sur la guerre civile espagnole. Écrivains, poètes et témoins font revivre par leurs écrits une guerre qui, de Madrid aux maquis français, fut le premier acte d'un long combat contre le fascisme. - équipe : Rafik Zénine, Hassane M'Béchour, INA - invités : José Bergamin Acteur, écrivain, poète, dramaturge, scénariste et intellectuel espagnol, François Mauriac Écrivain, académicien, prix Nobel de littérature (1885-1970) Vous aimez ce podcast ? Pour écouter tous les épisodes sans limite, rendez-vous sur Radio France

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president could still fire commissioners, as the Court now permits, but if those firings broke quorum, the agency would be unable to proceed until replacements were confirmed. The guardrail would

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.There was an issue with this only going to paid subscribers, so sending it again. Apologies to those who get it twice. I appreciate being paid so feel free to upgrade if you enjoy TWTW.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president

Apple accuse OpenAI d'avoir exploité ses secrets industriels • Des experts appellent à préparer l'économie au choc de l'IA • La CNIL encadre les pixels invisibles des newsletters • Netflix envisage de lancer des chaînes en continuAvec Bruno Guglielminetti (Mon Carnet)Apple contre OpenAI : bataille autour des secrets industrielsApple engage une offensive judiciaire autour du départ de Tang Tan, ancien responsable du design matériel devenu Chief Hardware Officer chez OpenAI. L'affaire pourrait peser sur les projets d'appareils d'OpenAI, développés avec les équipes de Jony Ive, alors que l'entreprise travaille à faire converger intelligence artificielle, objets physiques et interfaces vocales. OpenAI a officiellement intégré l'équipe de la startup io pour préparer une nouvelle génération de produits conçus avec Jony Ive.« We Must Act Now » : préparer le monde du travail à l'IAPlus de 200 économistes, chercheurs et dirigeants, dont seize prix Nobel et Yoshua Bengio, appellent les gouvernements à anticiper une transformation économique potentiellement plus profonde et plus rapide que la révolution industrielle. Le manifeste We Must Act Now insiste sur la formation, l'évolution des compétences et la nécessité de concevoir une IA qui complète le travail humain plutôt qu'elle ne l'efface.Newsletters : la CNIL s'attaque aux pixels invisiblesLes nombreux messages reçus par les abonnés français s'expliquent par les nouvelles recommandations de la CNIL sur les pixels de suivi intégrés aux courriels. Ces images invisibles d'un pixel permettent notamment de savoir si une newsletter a été ouverte ; les éditeurs doivent désormais mieux informer les destinataires et, selon les usages, recueillir leur consentement.Netflix réinvente la télévision linéaireNetflix envisagerait de lancer des chaînes thématiques diffusées en continu, afin de permettre aux abonnés de regarder un programme sans avoir à le choisir. Ce retour au flux télévisé traditionnel pourrait renforcer la bataille pour l'attention et les revenus publicitaires, au moment où Netflix doit également intégrer les chaînes et contenus de TF1 à son offre française.Les rendez-vous de l'étéDans Mon Carnet, Guillaume Roger, Ekumen, présente des outils d'intelligence artificielle destinés au monde agricole, tandis que Sacha Rubel, AWS, analyse les usages de l'IA en Europe, au Moyen-Orient et en Afrique. Sur Monde Numérique, la série « Tout comprendre » se poursuit avec un épisode consacré au Bluetooth, accompagnée de rediffusions d'entretiens avec Marion Carré, autrice du Paradoxe du tapis roulant, et le directeur du laboratoire Kyutai.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Joe Rogan ‘U.S. Elections are Stolen', Article by Ann Coulter on Voter Fraud, X Shorts- Our Constitution is a Toilet Rag, Covid Vax. Joe Rogan Finally SNAPS on JD Vance, Declares US Election ‘Are STOLEN!'

Long before onchain markets made mechanism design a daily engineering problem, Nobel Prize winner Paul Milgrom was asking how prices actually form — and how better auction rules could reshape actual markets. His work helped transform auction theory from an elegant branch of economics into a practical toolkit for allocating scarce resources, from wireless spectrum to digital ads to financial markets. In this episode of First Principles, Tim Roughgarden, Head of Research at a16z crypto, sits down with Milgrom alongside Scott Kominers — Harvard Business School professor and a16z crypto research partner — for a conversation about auctions, information, price discovery, and the design of complex markets. Together, they explore Milgrom's foundational work on auction theory, the famous Milgrom-Weber paper, the Grossman-Stiglitz paradox and the Glosten-Milgrom model of market microstructure, and why understanding how prices form matters for everything from prediction markets to decentralized finance. They also discuss Milgrom's work designing the FCC spectrum auctions — including the auctions that helped allocate wireless spectrum for technologies like mobile broadband and 5G — and the later FCC incentive auction, a massive market design challenge that combined economics, computer science, policy, and real-world implementation. Highlights 00:00 Intro: economics assumptions that are “just wrong” 02:19 Scott Kominers on the genius of Paul Milgrom 05:35 The price discovery problem economics forgot 07:48 The auction theory breakthrough of the 1980s 17:15 Why market microstructure matters for DeFi 24:17 When math teaches economics something new 29:22 Designing auctions people can actually use 32:40 How theory became spectrum auction design 36:22 The floppy disk that helped convince the FCC 41:05 What changed when auctions moved online 45:28 The auction that reorganized television 57:25 Why the best auctions feel simple 1:07:30 What economics and computer science can learn from each other 1:13:22 Futures markets for compute 1:15:12 Paul Milgrom's advice for builders About First Principles First Principles is a special limited series from a16z crypto about the scientific roots of modern computing — especially blockchains — told through rare conversations with the pioneers who helped shape the foundational ideas behind distributed systems, consensus protocols, economics, mechanism design, cryptography, zero knowledge, and more. People often tell the story of the Bitcoin whitepaper as if it appeared out of nowhere. But the ideas behind Bitcoin — and blockchains more broadly — come from decades of computer science, economics, mathematics, and cryptography. First Principles is a guide to that lineage, as told by the people who helped build it. Subscribe to follow along:https://www.youtube.com/playlist?list=PLjQ9HCQMu_8yIg60YAq67HDdvp7E_T5e8 Hear more from Tim Roughgarden: https://twitter.com/Tim_Roughgarden Scott Kominers: https://twitter.com/skominers Follow a16z crypto X: https://twitter.com/a16zcrypto LinkedIn: https://www.linkedin.com/showcase/a16zcrypto/posts/ YouTube: https://www.youtube.com/@a16zcrypto Substack: https://a16zcrypto.substack.com/subscribe/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What does a morally defensible, healthy, legal, commercial marketplace look like, and how can we design one for healthcare? Nobel laureate Alvin Roth, Professor of Economics at Stanford University and the George Gund Professor of Economics and Business Administration Emeritus at Harvard University, joins us to about his new book, Moral Economics: From Prostitution to Organ Sales, What Controversial Transactions Reveal About How Markets Work.

This week Jason Howell and Jeff Jarvis break down Apple's trade secret lawsuit against OpenAI, including text messages showing a former Apple engineer accessing confidential files after leaving for OpenAI. They also dig into GPT-5.6's triple-model launch, Fidji Simo stepping down from her number two role, and Demis Hassabis proposing a federal standards body for frontier AI modeled after financial regulators.Also in this episode: the White House unveils "Gold Eagle," a Treasury-led AI cyber threat clearinghouse. Nearly 200 economists and Nobel laureates warn that AI could cause unprecedented economic upheaval. Anthropic's new ad campaign unsettles viewers. Meta pulls its Instagram AI image generation tool three days after launch. Plus New York pauses data center permits, Grok Build gets caught uploading entire Git repos, Google Images gets a personalized redesign, and Anthropic launches Claude for Teachers. New episodes every Wednesday at aiinside.show. Note: Time codes subject to change depending on dynamic ad insertion by the distributor. CHAPTERS: 0:00 - Start 0:01:50 - Apple Sues OpenAI for Trade Secret Theft in Pivotal Case 0:06:44 - OpenAI Unaware of ‘Any Evidence' Showing Apple Lawsuit Has Merit 0:07:57 - OpenAI's First Device Will Be Movable, Screenless Speaker Built as AI Companion 0:14:35 - OpenAI releases GPT-5.6 and ChatGPT Work tool 0:16:40 - OpenAI unveils ChatGPT Work agent, GPT-5.6 models now available 0:27:09 - OpenAI's No. 2 Executive to Step Down in Latest Leadership Shake-Up 0:29:54 - A Framework for Frontier AI and the Dawning of a New Age 0:32:37 - White House details ‘Gold Eagle' clearinghouse for AI cyber threats 0:50:58 - Anthropic's newest ad is creeping people out 0:57:43 - Meta's new AI image maker draws fire over consent - Meta Suspends AI Image Feature After Days of Backlash 0:58:55 - New York becomes the first state to enact a data center moratorium 1:00:05 - Musk promises purge after Grok Build caught sending entire repos to the cloud 1:01:05 - Google Images gets a Pinterest-like redesign focused on discovery 1:02:32 - Anthropic is giving teachers free access to premium Claude features, details here Hosts: Jason Howell and Jeff Jarvis Download and subscribe to AI Inside in audio and video: https://aiinside.show/ Support the podcast on Patreon for special perks: https://www.patreon.com/aiinsideshow. You'll get ad-free episodes, members-only Discord, T-shirts and stickers you love, and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Learn more about your ad choices. Visit megaphone.fm/adchoices

Outline00:00 - Intro02:25 - Bachelier and the Théorie de la Spéculation03:05 - Stochastic processes, Brownian motion, and the heat equation09:45 - Poincaré's verdict, obscurity, and rediscovery13:50 - Robert C. Merton: from hot rods to MIT19:25 - Dynamic programming and Itô calculus24:35 - Merton's portfolio problem as stochastic optimal control31:10 - Options, dynamic hedging, and the Black–Scholes–Merton equation39:50 - LTCM: the dream team46:30 - August 1998: the crash49:00 - Fat tails and the ten-sigma defense51:40 - The ghosts of 2008 and echoes in the AI boom54:00 - Robustness embraced at last: Hansen and Sargent57:45 - OutroLinksBachelier's thesis, "Théorie de la Spéculation" (1900): https://www.numdam.org/item/10.24033/asens.476.pdfCourtault et al., "Louis Bachelier on the Centenary of Théorie de la Spéculation": https://doi.org/10.1111/1467-9965.00098Merton's Nobel autobiography: https://www.nobelprize.org/prizes/economic-sciences/1997/merton/biographical/Merton's MIT "Infinite History" interview: https://infinite.mit.edu/video/robert-c-merton-phd-%E2%80%9970/Mandelbrot, "The Variation of Certain Speculative Prices": https://doi.org/10.1086/294632Merton, "Optimum Consumption and Portfolio Rules in a Continuous-Time Model": https://doi.org/10.1016/0022-0531(71)90038-XMoehle & Boyd, "A Certainty Equivalent Merton Problem": https://doi.org/10.1109/LCSYS.2021.3111534Brigo & Mercurio, "Interest Rate Models: Theory and Practice": https://doi.org/10.1007/978-3-540-34604-3Armstrong, Brigo & Hanzon, "Optimal Projection Filters with Information Geometry": https://doi.org/10.1007/s41884-023-00108-xHu & Zhou, "Constrained Stochastic LQ Control with Random Coefficients, and Application to Portfolio Selection": https://doi.org/10.1137/S0363012904441969Black & Scholes, "The Pricing of Options and Corporate Liabilities": https://doi.org/10.1086/260062Merton, "Theory of Rational Option Pricing": https://doi.org/10.2307/3003143Merton, "Option Pricing When Underlying Stock Returns Are Discontinuous": https://doi.org/10.1016/0304-405X(76)90022-2Scholes' Nobel lecture: https://www.nobelprize.org/prizes/economic-sciences/1997/scholes/lecture/Merton's Nobel lecture: https://www.nobelprize.org/prizes/economic-sciences/1997/merton/lecture/Markowitz, "Portfolio Selection": https://doi.org/10.2307/2975974Michael Lewis, "Liar's Poker": https://en.wikipedia.org/wiki/Liar%27s_PokerEdwards, "Hedge Funds and the Collapse of Long-Term Capital Management": https://doi.org/10.1257/jep.13.2.189Lowenstein, "When Genius Failed": https://en.wikipedia.org/wiki/When_Genius_FailedTaleb, "Statistical Consequences of Fat Tails": https://arxiv.org/abs/2001.10488Taleb & West, "Working with Convex Responses: Antifragility from Finance to Oncology": https://doi.org/10.3390/e25020343Taleb, "The Black Swan": https://en.wikipedia.org/wiki/The_Black_Swan:_The_Impact_of_the_Highly_ImprobableTaleb, "Fooled by Randomness": https://en.wikipedia.org/wiki/Fooled_by_RandomnessMan Group, "The AI Bubble: Hidden Risks and Opportunities": https://www.man.com/insights/the-ai-bubbleSen. Warren's remarks at the Vanderbilt Policy Accelerator: https://www.banking.senate.gov/newsroom/minority/warren-remarks-at-vanderbilt-policy-accelerator-event-highlighting-economic-and-financial-risks-of-potential-ai-crashMeng & Chen, "Artificial Intelligence and Systemic Risk": https://arxiv.org/abs/2604.03272Doyle, "Guaranteed Margins for LQG Regulators": https://doi.org/10.1109/TAC.1978.1101791Safonov & Athans, "Gain and Phase Margin for Multiloop LQG Regulators": https://doi.org/10.1109/TAC.1977.1101470Hansen & Sargent, "Robust Control and Model Uncertainty": https://doi.org/10.1257/aer.91.2.60Hansen & Sargent, "Wanting Robustness in Macroeconomics": http://www.tomsargent.com/research/wanting.pdfSupport the showPodcast infoPodcast website: https://www.incontrolpodcast.com/Apple Podcasts: https://tinyurl.com/5n84j85jSpotify: https://tinyurl.com/4rwztj3cRSS: https://tinyurl.com/yc2fcv4yYoutube: https://tinyurl.com/bdbvhsj6Facebook: https://tinyurl.com/3z24yr43Twitter: https://twitter.com/IncontrolPInstagram: https://tinyurl.com/35cu4kr4Acknowledgments and sponsorsThis episode was supported by the National Centre of Competence in Research on «Dependable, ubiquitous automation» and the IFAC Activity fund. The podcast benefits from the help of an incredibly talented and passionate team. Special thanks to L. Seward, E. Cahard, F. Banis, F. Dörfler, J. Lygeros, ETH studio and mirrorlake . Music was composed by A New Element.

To attract someone honest, you don't need to be honest. You need to be a space that can hold honesty. That distinction — the match, not the mirror — is one of a dozen reframes in this conversation that will quietly rearrange how you think about love. Adam Roa is a poet, artist, and coach whose spoken word performance "You Are Who You've Been Looking For" became the most viral poetry performance in history — over 250 million views. But the man behind the poem spent years unable to express emotion at all, raised in a home where feelings weren't safe. His new book, Crazy Love, opens his actual journals from nearly two decades of searching for love, finding it, losing it, and putting himself back together. In this episode, Adam and Michael go deep into: — The mechanics of masculine and feminine energy — and the two karmic wounds ("I am not enough" / "I am too much") that every relationship will eventually force you to face — Why the Gottman Institute found that couples together fifty years are still fighting about the same things they fought about on their first dates — and why that's actually the key to lasting love — The "goddess time" problem: what to do when your partner's relationship to time (or anything else) triggers you — and how to build agreements instead of resentment — Why your triggers in dating are rarely about the thing itself — the body count, the lateness — and how to gather the data that actually matters — Creativity as life force: why your unique frequency is the most healing thing you can offer the world, and how to find the clues your soul has left you — The practice behind the virality: how showing up to create, over and over, without needing to know where it leads, is what makes you ready when the moment arrives Adam also performs two poems in full — including the one that reached a quarter of a billion people. "Vulnerability is the gateway to connection. If we are seeking connection — to each other, to ourselves, and to life itself — we have to be willing to be vulnerable through the authentic expression of our creative energy." — Adam Roa Adam's book Crazy Love is available now. Find Adam at adamroa.com and on Instagram @adam.roa. Michael Trainer has spent 30 years learning from Nobel laureates, neuroscientists, and wisdom keepers worldwide. He's the author of RESONANCE: The Art and Science of Human Connection (March 31, 2026), co-creator of Global Citizen and the Global Citizen Festival, and host of the RESONANCE podcast.Featured in Forbes, Inc, Good Morning America. Follow on YouTube

Scientists often talk about the importance of flora and fauna to the health of our planet, but Dr Toby Kiers, an evolutionary biologist and founder of the Society for the Protection of Underground Networks, wants us to consider another force: fungi. Her work charting the planet's vital underground systems has earned her numerous awards, including a MacArthur fellowship and a Tyler Prize for Environmental Achievement (sometimes called the ‘green' Nobel). She tells Ian Sample about her work mapping fungal networks on the remote Palmyra Atoll in the Pacific Ocean, and what the research reveals about fungi's often invisible role. Help support our independent journalism at theguardian.com/sciencepod

Long before crypto made coordination programmable, Nobel Prize winner Alvin Roth was designing markets where coordination could save lives.  In this episode of First Principles, Roth tells the story of how he helped build systems for some of the hardest matching problems in the world, from where doctors train and where students go to school to how kidney donors can reach the patients who need them.  He joins Tim Roughgarden, Head of Research at a16z crypto, and Scott Kominers — Harvard Business School professor, a16z crypto research partner, and one of Roth's former students — for a conversation about how market design moves from theory into the real world. They explore how economic theory becomes practical engineering, whether that's matching riders to Ubers, doctors to medical residencies, students to New York City high schools, or organ donors to people whose lives depend on it.  They also cover how these same problems show up in today's crypto networks. Roth explains why markets are not just natural forces, but engineered systems; why the details of timing, congestion, incentives, and trust can make or break a marketplace; and why some of the most important markets are the ones where simply exchanging money can't do the work.  This is a conversation about economics at its most practical and profound: how to design systems that coordinate people, solve real problems, and sometimes save lives.  00:00 Intro: Why market design matters 04:18 The economist as engineer  08:09 When theory meets the real world  07:02 Fixing the medical residency match  15:32 Why markets unravel  18:22 Redesigning NYC high school admissions  28:05 The hidden problem of congestion  34:47 How kidney exchange saves lives  45:26 How the internet changed market design  48:25 Airbnb, Uber and smarter marketplaces  51:28 Repugnant transactions and moral economics  53:32 When markets need social support  54:32 The unexpected effects of criminalizing surrogacy  01:04:58 Preference signals and the job market  01:18:53 A broken market: resettling refugees and other migrants  Hear more from:  Tim Roughgarden: https://twitter.com/Tim_Roughgarden  Scott Kominers: https://twitter.com/skominers  Follow a16z crypto:  X: https://twitter.com/a16zcrypto  LinkedIn: https://www.linkedin.com/showcase/a16zcrypto/posts/  YouTube: https://www.youtube.com/@a16zcrypto  Substack: https://a16zcrypto.substack.com/subscribe/ * ** As always, none of the following should be taken as investment, business, legal, or tax advice. Please see a16z.com/disclosures for more important information, including a link to a list of our investments. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Cet été, Émotions (au travail) prend des vacances. Mais pendant ce temps-là, découvrez chaque semaine un épisode du podcast Émotions qui résonne particulièrement avec notre vie professionnelle.Cet épisode a été initialement diffusé le 5 janvier 2026 sur le flux ÉmotionsLa nouvelle année commence, et on veut changer sa vie pour la rendre meilleure. Est-ce qu'il vaut mieux opter pour les bonnes vieilles résolutions, ou prendre de grandes décisions pour changer carrément de direction ? Trancher rapidement et nettement, un peu comme un pansement qu'il faut retirer d'un coup sans laisser trop de place à la peur ? Est-ce qu'il vaut mieux peser le pour et le contre ou faire enfin confiance à son intuition ?Pour répondre à ces questions, Marie Misset fait appel au psychologue du travail Adrien Chignard, qui s'est penché sur la question des changements de trajectoire et qui a coordonné l'ouvrage Burn Out. Des histoires vécues pour le prévenir, l'éviter, s'en sortir. Elle interroge également le psychiatre Frédéric Fanget, auteur du livre Oser. Thérapie de la confiance en soi, et l'économiste Olivier Sibony, professeur à HEC et à Oxford, notamment co-auteur de Noise. Pourquoi nous faisons des erreurs de jugements et comment les éviter avec le prix Nobel d'économie Daniel Kahneman. À travers les témoignages de Sarah, Lize et Vianney qui ont changé de vie du jour au lendemain, elle questionne les notions d'intuition et d'impulsion, la théorie du step by step, notre propre expertise sur nous-mêmes, notre rapport au risque et les biais cognitifs avec lesquels nous devons composer.Pour aller plus loin : L'article “The Art of Decision-Making” de Joshua Rothman paru dans le New Yorker L'article d'Audrey Parmentier sur les “repentis de la reconversion professionnelle” paru dans Le MondeEt si vous ne savez pas quoi écouter ensuite, on vous suggère l'épisode "Peut-on être sûr·e d'avoir pris la bonne décision ?"Si vous aussi vous voulez nous raconter votre histoire dans Émotions, écrivez-nous en remplissant ce formulaire ou à l'adresse hello@louiemedia.comÉmotions est un podcast de Louie Media. Marie Misset a tourné, écrit et monté cet épisode. La réalisation sonore est de Guillaume Girault. Le générique est réalisé par Clémence Reliat, à partir d'un extrait d'En Sommeil de Jaune. Elsa Berthault est en charge de la production.Publicités et Partenariats : creative@louiemedia.comPour avoir des news de Louie, des recos podcasts et culturelles, abonnez-vous à notre newsletter en cliquant ici. Vous souhaitez soutenir la création et la diffusion des projets de Louie Media ? Vous pouvez le faire via le Club Louie. Vous pouvez aussi vous abonner à Louie+ sur Apple Podcasts pour écouter les épisodes sans publicités et nos séries en avant-première. Chaque participation est précieuse. Nous vous proposons un soutien sans engagement, annulable à tout moment, soit en une seule fois, soit de manière régulière. Au nom de toute l'équipe de Louie : MERCI !Suivez Émotions sur Apple Podcasts, Spotify, Deezer.Suivez Louie Media sur Instagram, Facebook, et YouTube.Mots-clés : décisions - changements de vie - psychologie - émotions - choix de vie Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Joel Mokyr co-won the 2025 economics Nobel for exploring the question that traces back to the beginning of economics: how did sustained economic growth suddenly become normal? For nearly all of human history, cleverness didn't compound. What changed, according to Mokyr, was twofold: first, you need to know why something works, so that one advance can seed the next; second, you need a culture willing to tolerate the disruption. His new book contrasts Europe with China, showing how Europeans learned to cooperate with people they weren't related to, in guilds, monasteries, cities, and universities, while China organized itself around the extended clan. One path led to internal stability and peace; the other, more restless and outward-looking, was the one that decided the world could always be made better. Tyler and Joel discuss European corporations vs. Chinese clans, why the Catholic Church became obsessed with cousin-marriage, how persistent cultural trends really are, why Chinese cities became so populous relative to Europe, why it took so long for European living standards to surpass China's, why sinified invaders kept getting swallowed by the dynasties they conquered, how geography kept Europe fragmented and China unified, where India fits into the story, why the Romans never made spectacles, why British soldiers stood two inches taller than the French, what powered the sudden rise of 19th-century German science, how disruptive winning a Nobel is, and much more. Read a full transcript enhanced with helpful links, or watch the full video on the new dedicated Conversations with Tyler channel. Recorded February 20th, 2026. This episode was made possible through the support of the John Templeton Foundation. Other ways to connect Follow us on X and Instagram Follow Tyler on X Sign up for our newsletter Join our Discord Email us: cowenconvos@mercatus.gmu.edu Learn more about Conversations with Tyler and other Mercatus Center podcasts here. Timestamps: 00:00:00 - Intro 00:00:54 - Europe vs. China's Paths to Prosperity 00:10:22 - China's Growth 00:13:24 - Europe's Growth 00:18:56 - The Fall of Song China 00:21:56 - India 00:25:08 - Industrial Revolution 00:39:52 - 19th-Century German Science 00:43:37 - Being a Nobel Laureate 00:45:29 - Outro Photo Credit: Shane Collins

Scientist and physician Michael Nehls explains how Alzheimer's can be reversed. Michael Nehls, MD, PhD, is a physician and molecular geneticist. As a basic researcher, he has deciphered the genetic causes of various hereditary diseases at German and international research institutions. He has published two of his discoveries in collaboration with several Nobel laureates. His discovery of a key gene in the development of immunity was honored as a "Pillar of Immunology" by the prestigious American Association of Immunology. A science writer with a talent for making complex issues accessible to a wide audience, he has written several best-selling books that have been translated into many languages. As a private lecturer, he is a popular speaker at conferences and universities. Find him here: Substack: michaelnehls.substack.com Web: https://michael-nehls.com X: @NehlsMD Paid partnerships with: Angel Studios: Become a Premium Angel Guild member today at http://angel.com/TuckerCharity Mobile: A pro-life company serving pro-life customers and supporting pro-life causes for 30 years. Use promo code TUCKER to get a free phone with free activation, free shipping, and a free gift with every new line of service at https://charitymobile.com/Tucker VanMan: Use code TUCKER for 15% off your first order at http://vanman.shop/tucker Learn more about your ad choices. Visit megaphone.fm/adchoices

The Jack Carr Book Club June 2026 selection is THE LOST EMPIRE OF EMANUEL NOBEL by New York Times bestselling author Douglas Brunt. With the exception of the tsar, Emanuel Nobel was likely the wealthiest man in early 20th-century Russia, and one of the wealthiest in the world. Over three generations, he and his family grew the Russian petroleum industry into a behemoth that surpassed even John D. Rockefeller's Standard Oil. The Nobels imported best practices from America and improved on them, transforming every aspect of the industry. Though Emanuel's uncle Alfred would become world famous due to his creation of the Nobel Prize, the even more successful Nobels in Russia have been largely forgotten. The reason why is one of history's most gripping untold stories.Douglas Brunt is a New York Times bestselling author of THE MYSTERIOUS CASE OF RUDOLF DIESEL, host of the SiriusXM show Dedicated with Doug Brunt, and former cybersecurity executive. This conversation explores Doug's research, insights, and writing process behind THE LOST EMPIRE OF EMANUEL NOBEL.FOLLOW DOUGLAS BRUNTInstagram - @douglas_bruntX - @DougBruntFacebook - @dougbruntYouTube - @DedicatedwithDougWebsite - https://douglasbrunt-author.com/ FOLLOW JACK CARRInstagram - @JackCarrUSAX - @JackCarrUSAFacebook - @JackCarrYouTube - @JackCarrUSA Website - https://www.officialjackcarr.com/