Podcasts about AGI

This week on The Futurists we are joined by David Wood from the London Futurists, Rohit Telwar CEO of Fast Future, Andrew Grill the Actionable Futurist, and Dr. Julia Michelin, CEO/Founder and author of The Future of Dentistry. It's an all star cast as we delve into the forecasts for tomorrow with the impact of AI and longevity treatments being a key focus. We discuss collaborative intelligence, gene therapy, nanobots and AGI. It's a great discussion in the heart of London.

A U.S. vs China AI cold war is starting, and most business leaders have no idea they're already in it.China's open models just closed the gap with America's best, oftentimes at a fraction of the price.Now both governments are moving to wall off their AI within days of each other.Why? Because this was never about benchmarks. It's about power y'all. We break it all down on today's show and help you figure out the 101 of the AI war between U.S. and China. The U.S. vs China AI Cold War Is Starting: What It Means and How It Impacts You -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:U.S.-China AI Cold War OverviewChinese AI Models Closing U.S. GapGovernment Restrictions on AI Model AccessEconomic and Geopolitical AI Power StruggleRisks for U.S. Businesses Using Chinese AIOpen Source vs. Closed Source AI DebateChinese AI Model Pricing Undercuts U.S.AI Model Distillation and U.S. Security ConcernsEnterprise AI Cost-Effectiveness BenchmarksMicrosoft Testing Chinese AI DeploymentsFuture AI Model Export Controls & StrategiesRecommendations for AI Model Sourcing and RiskTimestamps:00:00 US-China AI tensions escalate04:30 Switching to Chinese AI models08:47 US vs China in open source models11:39 China's narrative control efforts14:42 Challenges in AI model development18:25 Differentiating open source strategies23:04 AI model cost-effectiveness analysis26:31 US measures against model distillation29:38 Discussing Microsoft's use of AI models31:17 Controlling export of AI modelsKeywords: US vs China AI cold war, China AI restrictions, US AI restrictions, AI model export controls, Chinese open source AI models, AI geopolitical power, economic growth through AI, global AI standards, AI superpower race, AI model benchmarks, open weight models, enterprise AI deployment, trillion parameter AI models, Microsoft AI model testing, AI model pricing, Claude Fable 5, GPT-5.6, GLM 5.2, Kimmi K3, Alibaba Qwen 3.8, model distillation, AI cybersecurity risks, AGI leadership, military AI use cases, China narrative control, model adoption, compute power for AI, AI training data, AI export law, US national security and AI, model routing, mixture of models, cost per intelligence index, Anthropic models, cost per task AI, model capability parity, AI market adoption, cloud competition, AI architecture innovation, AI model sanctionsSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

What does it actually mean to be human in an age racing toward AGI? In this episode of What is a Good Life?, Mark sits down with Dr. Miriam Meckel - award-winning journalist, professor of communication management at the University of St. Gallen, and founder of ada learning - to explore the question that has shaped her life's work: what is irreducibly human, and what are we at risk of losing? Miriam, the first female editor-in-chief of WirtschaftsWoche and former State Secretary for Media and International Affairs in North Rhine-Westphalia, opens up about a burnout that became a life-altering depression, the vulnerability of turning her private writing into her bestselling book Letter to My Life, and why she believes embodied experience — not intelligence — is what truly separates us from machines. It is a conversation about identity, friction, kindness, and what it takes to live a quiet, honest life.For more of Dr. Miriam Meckel's work:ada learning: https://www.join-ada.com/enLinkedIn: https://www.linkedin.com/in/meckel/For more from Mark McCartney:Newsletter: https://www.whatisagood.life/Website: https://www.mmcleadership.com/LinkedIn: https://www.linkedin.com/in/mark-mccartney-14b0161b4/YouTube: https://www.youtube.com/ ⁨@whatisagoodlife3875⁩

What is artificial general intelligence (AGI) and how close are we to getting there? What defines the next step, called artificial superintelligence (ASI), and why might the gap between those two be so dangerous? When machines become smarter than us, what roles are left for humanity? Should we think of AI as software, or as something we’re raising like a child? Will tomorrow’s AI destroy us, ignore us, or protect us? This week Eagleman talks with computer scientist & AI researcher Ben Goertzel.

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

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What if the peace you're searching for isn't something you achieve by changing your circumstances, but something you discover by turning inward?In this episode, meditation teacher and filmmaker Tom Cronin returns to explore how meditation can transform the way we experience life beyond the moments we spend sitting with our eyes closed.If you feel overwhelmed by the constant noise of modern life, caught in repetitive thought patterns, or constantly chasing the next achievement, this conversation offers a powerful perspective on finding inner stability, clarity, and fulfillment.How a consistent meditation practice can create greater presence, emotional balance, and peace in everyday lifeWhy chasing external success and experiences often leaves us feeling unfulfilled, and how to shift toward a deeper sense of inner fulfillmentHow silence and stillness reveal hidden patterns of the mind, helping you become aware of your thoughts rather than being controlled by themListen to this episode to discover how meditation can help you cultivate lasting peace, transform your relationship with your thoughts, and live from a place of deeper fulfillment.˚KEY POINTS AND TIMESTAMPS:01:01 - Welcoming Tom Cronin Back02:49 - Evolving and Measuring a Meditation Practice08:26 - Real-Life Changes Meditation Creates10:48 - Equanimity, Peace, and Moksha13:49 - Volition: The Intention Behind Every Action15:24 - The Ego's Endless Quest for Fulfillment20:44 - Finding Inner Fulfillment Through Meditation23:32 - Hidden Patterns and Awakened Senses in Silence32:06 - Cultivating Peace in a Turbulent World36:39 - Trust, Fear, and Closing Reflections˚MEMORABLE QUOTE:"We can't wait for things to be peaceful for us to find peace. We have to learn how we can cultivate peace whilst we're in a very unpeaceful world, an unstable world."˚VALUABLE RESOURCES:Tom's website: https://tomcronin.com˚Previous podcast conversation with Tom Cronin on episode #336: https://personaldevelopmentmasterypodcast.com/336˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚

This and all episodes at: https://aiandyou.net/ . This week I am talking with Rana Gujral, author of the new book The AI Instinct: The Future of AI and Human Decision-Making, a deeply thoughtful, expansive, and philosophical book, like its author. Rana is CEO of Behavioral Signals, creating systems that infer intent, emotion, and deception risk from voice, deployed in application from financial services to defense. He has a TEDx talk with a million views, has keynoted at the World Government Summit and World Economic Forum, and was named Most Influential CEO by CEO Monthly. We talk about Rana's idea of artificial general experience, whether we need AI with persistent memory and universal awareness, whether AGI needs a physical experience of the world - what we call a world model – and what video generation models reveal about that, the role of empathy, whether AGI will come through an understanding that we consciously design or through emerging from a sufficiently complex general design, whether we ourselves are stochastic parrots, and AI as augmenting vs replacing human judgement. All this plus our usual look at today's AI headlines! Transcript and URLs referenced at HumanCusp Blog.        

Nilesh Solanki is a British technology professional, community organiser, charity trustee, and humanitarian volunteer known for his work in digital transformation, community development, and charitable governance. Nilesh is a Trustee and Assistant General Secretary of Sewa International UK — a humanitarian charity operating in 25+ countries. He is also the co-founder and Director of Action for Harmony (A4H), a platform that brings together more than 200 Hindu organisations across the United Kingdom, convening more than 250 leaders from 120 organisations and 100 towns at the landmark Harmony Conference 2025.Read more about Nilesh Solanki: https://businessabc.net/wiki/nilesh-solankiNilesh Solanki Interview Questions00:00 - Intro03:44 - Background06:55 - Balancing corporate & social impact10:15 - The Hindu culture12:57 - Career at PwC16:50 - A digital transformation expert22:31 - Reskilling for AI & tech27:31 - Building communities32:47 - Embracing diversity38:07 - Principles of Charity & service45:21 - Timeless concepts of community53:48 - Personal fitness58:47 - Ethics in the AI and AGI era01:04:26 - AI & Consciousness01:10:44 - Closure#ai #digitaltransformation #leadership #responsibleai  #futureofwork ork #communitiesUseful Links and ResourcesAbout businessabc.nethttps://www.businessabc.net/About citiesabc.comhttps://www.citiesabc.com/​​​​​​​​​​​ About Dinis Guardahttps://www.dinisguarda.com/https://businessabc.net/wiki/dinis-guardaBusiness Inquiries- info@ztudium.comSupport the show

Get access to more than 200 episodes of my premium podcast (The Aliquot) when you sign up as a FoundMyFitness Premium Member The next 10 years may add decades to human lifespan by compressing the time it takes to understand, treat, and prevent disease. In this episode, Dr. Derya Unutmaz explains why accelerating AI could transform drug discovery, shorten clinical trials, and push cancer treatment toward increasingly personalized interventions. He also reframes AI not as an existential threat, but as a medical enabler that doctors may soon be ethically obligated to use. Timestamps: (00:00) Introduction (07:11) Why the next 10 years may add 50 to your lifespan (11:19) How AI is transforming drug discovery (16:50) Could digital twins shorten clinical trials? (19:25) Can AI predict drug safety and efficacy? (23:40) Have we already reached AGI? (29:23) Why AI may be medicine's greatest force multiplier (35:35) Can AI replicate a scientist's biological intuition? (42:16) Is it malpractice for doctors not to use AI? (48:18) What happens when AI monitors disease in real time? (51:52) Which AI models should doctors trust? (57:29) Claude vs. GPT—does the model matter for diagnosis? (1:00:58) Generalist vs. specialized AI—which works better in medicine? (1:04:25) Why cancer is so hard to cure (1:08:18) Could cancer be curable within a decade? (1:12:29) Can AI design cancer treatments on demand? (1:14:31) How AI could curb overtreatment and side effects (1:17:28) Predicting cancer years before it forms—is it possible? (1:23:50) Why biology could go exponential with AI (1:28:58) Why aging may be easier to prevent than reverse (1:34:51) Can the body be engineered to resist aging? (1:40:07) Can AI model how gene therapy will behave? (1:44:12) What people who reach 110+ reveal about Human 2.0 (1:46:21) From Dolly to Yamanaka factors—the case for cellular age reversal (1:50:56) Why full-body rejuvenation is an engineering problem (1:58:44) What happens when AI reasons longer about biology? (2:01:25) The biosecurity dilemma of powerful AI (2:06:12) What should we actually measure to track aging? (2:12:34) How old immune cells distort aging clocks (2:15:22) Why reversing brain aging is uniquely difficult (2:21:49) The ultimate prompt for extending lifespan (2:23:50) What data does a true digital twin need? (2:28:32) How to build a mini digital twin today (2:33:26) How to give AI a long-term memory of your data (2:36:33) Why personal baselines matter for AI advice Show notes are available by clicking here Watch this episode on YouTube

O Império da IA, com Karen HaoEm 2019 — tempos mais simples! — a jornalista Karen Hao foi fazer o primeiro “perfil” jornalístico da sua carreira, aquelas reportagens em que um jornalista passa dias acompanhando uma pessoa ou empresa, uma coisa meio biografia, meio retrato congelado no tempo.A tal empresa era uma startup do Vale do Silício, ainda pequena e desconhecida dos meros mortais como eu e você, mas que hoje é a mais valiosa da história: a OpenAI, também conhecida como “a criadora do ChatGPT”.A Karen Hao vendeu o projeto do perfil para o MIT Technology Review porque a OpenAI parecia, naquela época, uma startup diferente. O “open” no nome nasceu da visão de que inteligência artificial é um assunto tão importante para o futuro da humanidade que precisava ser explorado de um jeito aberto, compartilhando conhecimento com todo mundo, e mais preocupado em proteger esse tal futuro do que em só gerar lucro.Hoje, aqui direto de 2026, a gente já sabe que não foi exatamente isso que aconteceu. Com o tempo, a OpenAI se transformou numa empresa oficialmente voltada para o lucro como qualquer outra e chegou a ser processada por Elon Musk — um dos apoiadores iniciais do projeto — por quebrar essa promessa de ser ‘open'. Em maio, o Elno perdeu a causa, e a OpenAI agora se prepara para lançar as ações na bolsa e, pelos números atuais, já largar valendo mais de 1 trilhão de dólares.Mesmo em 2019, a Karen Hao sentiu que todo esse papo de “open” não era bem assim: segredos e competitividade em todas as conversas que ela ouvia na empresa. Publicou o tal perfil contando isso e o pessoal da OpenAI… não gostou muito. Achou que ela ia só falar bem deles, e a empresa cortou contato com ela por três anos.O Boa Noite Internet é uma publicação apoiada por pessoas como você, nosso público. Para receber novos posts e apoiar meu trabalho, considere tornar-se um assinante gratuito ou pago.Até que, em maio do ano passado, ela lançou nos EUA o livro O império da IA: Por dentro da corrida irresponsável pela dominação total, que segue contando a história da OpenAI — e abre com a bizarra saída do Sam Altman, demitido do cargo de CEO por “nem sempre falar a verdade” ao conselho da empresa, para voltar quatro dias depois nos braços dos funcionários.Mas esse livro não é exatamente uma biografia da OpenAI. Para mim, é mais um retrato de todo o sistema empresarial em que vivemos hoje — inteligência artificial ou não. O importante é que ele acabou de sair no Brasil pela Editora Rocco, que me procurou para saber se eu queria entrevistá-la aqui no programa, aproveitando que ela veio participar do Esquenta do Congresso Internacional de Jornalismo Investigativo da Associação Brasileira de Jornalismo Investigativo. O congresso, aliás, acontece dia 30 de julho — vai lá no site da Abraji saber mais, quem sabe comprar seu ingresso.Mas enfim, claro que eu queria conversar com ela. Obrigado, Abraji, obrigado, pessoal da Rocco, pelo presente. Quem me conhece sabe que IA agora é um assunto muuuito importante no meu trabalho. Eu fico aqui tentando navegar o meio do caminho entre o fim do mundo exterminador do futuro e a utopia vendida por muita gente. Não acredito em nenhum dos dois cenários, falei disso com a Karen antes e durante a conversa. Mas no final da entrevista a gente volta para falar não só disso, como também de como o IA em Curso, minha comunidade de letramento contínuo em IA, se conecta com tudo. Com promoção? Pode ser. Quem ficar até o fim, verá.A entrevista foi gravada em inglês — a Karen também fala mandarim, mas não fala brazilian —, então vai funcionar assim. Se ouvir no áudio, vai ser a versão original, do mesmo jeito que foi com o Ted Chiang ano passado, para você botar o seu cursinho para trabalhar. Aqui no site boanoiteinternet.com.br você está acompanhando a transcrição completa traduzida, se quiser ler enquanto ouve. E no YouTube tem uma versão legendada. Assim, você entra na conversa do jeito que preferir.Combinado? Então, bora lá entender O Império da IA com Karen Hao, no Boa Noite Internet.Cris: Karen Hao, bem-vinda ao Boa Noite Internet.Karen Hao: Obrigada pelo convite.Cris: Que bom ter você aqui. Espero que o Brasil esteja te tratando bem durante a Copa do Mundo — a gente veio falar sobre isso. Hoje é dia de falar de futebol, de Copa do Mundo, quais são as chances de cada país. Mas a primeira coisa que você precisa saber sobre essa conversa é que eu não sou jornalista. Não sei fazer isso. Peço desculpas antecipadas à sua profissão e ao seu ofício.Além disso, você foi enganada. Eu não estou aqui pra te entrevistar. Isso aqui é uma sessão de terapia. Você vai me ajudar a superar meus traumas.Porque eu sou da… do que eu chamo de “geração esquecida” — sou geração X, nasci nos anos 70. Esquecida porque, nessa guerra de gerações, as pessoas esquecem que a gente existe, e isso é incrível, porque a gente causou muito estrago no planeta. O Elon Musk é geração X, então é só isso que você precisa saber sobre a minha turma. Gente como ele, ou como Marc Andreessen… eu cresci lendo e assistindo à ficção científica que dizia que tecnologia é a melhor coisa do mundo, que ciência e engenheiros são incríveis e vão nos levar pra um lugar incrível.Sou uma daquelas pessoas que, quando a internet surgiu, falou: a paz mundial está logo ali. O conhecimento a um clique de distância, o futuro vai ser incrível. E aqui estamos nós. Então, quando usei o GPT pela primeira vez, e depois o ChatGPT, fiquei super empolgado. Foi a primeira vez, desde a internet, que eu fiquei realmente empolgado.Tenho até uma certa fama de ser mal-humorado com tecnologia: Bitcoin é lavagem de dinheiro, Clubhouse não presta — e as pessoas, ah, Clubhouse é a próxima grande coisa. Mas quando a IA chegou, eu falei: isso é importante. Só que eu já não era mais aquela criança dos anos 70. Tinha crescido, tinha visto o que aconteceu com a internet, tinha trabalhado numa big tech. E estava em desespero com o sistema em que a IA estava sendo construída.Dito tudo isso, o seu livro, aqui, já nas livrarias, recebe provavelmente o melhor elogio que eu posso dar: é otimista. Não é uma lista de reclamações e gente má fazendo coisas más. Claro, você fala muito sobre a OpenAI — ela é o fio condutor da história, especialmente aqueles quatro dias em que o Sam Altman saiu e voltou. E é muito divertido de ler. Mas você toma o cuidado de ser otimista.E uma das coisas que você menciona é como as pessoas na OpenAI, e em todas essas empresas, dizem: “isso é inevitável, a gente tem que fazer”. Quero falar sobre isso. Mas a gente tem que começar pela pergunta que você provavelmente ouve em todo podcast, a do título — Império da IA. Por que império?E acho que essa pergunta é ainda mais relevante no Brasil, país do sul global, colonizado. Por que império da IA?Karen Hao: Antes de mais nada, obrigada por dizer que o livro é otimista. Muita gente não reconhece isso, mas é verdade. Eu escrevo com um profundo otimismo de que os danos que a gente vê podem mudar. Não faria o trabalho que faço se não achasse que as coisas vão mudar.Sobre por que eu uso a expressão império, ou império da IA: a forma como empresas como a OpenAI operam é impressionantemente parecida com a dos impérios antigos. Eu traço quatro paralelos no livro. O primeiro é que elas reivindicam recursos que não são delas — os dados das pessoas, a propriedade intelectual de artistas, criadores como você, jornalistas.Segundo, elas exploram uma quantidade extraordinária de mão de obra. Isso vale tanto para os trabalhadores da cadeia de produção de IA, mal pagos e maltratados, que ainda assim geram uma riqueza extraordinária para essas empresas, quanto para os trabalhadores cujos empregos são automatizados e cujos direitos são corroídos pela implantação dessas tecnologias em diferentes setores.A terceira característica é que impérios controlam os fluxos de informação na sociedade. Essas empresas censuram a pesquisa fundamental sobre essas tecnologias, o que limita nossa capacidade de entender as verdadeiras limitações e capacidades dos modelos que desenvolvem. E estão criando uma tecnologia de informação que tentam transformar no portal único pelo qual qualquer pessoa se relaciona com o mundo.Esse portal impregna as ideologias do Vale do Silício, seus sistemas de valores, sua língua, e projeta a hegemonia do inglês. Isso influencia boa parte do conhecimento que a gente vai produzir daqui pra frente, porque cientistas e educadores usam essas plataformas e acabam perpetuando essas mesmas ideologias e valores.E o quarto e último paralelo é que impérios sempre se agarram a uma narrativa existencial ou moral sobre por que precisam existir. Essas empresas fazem a mesma coisa. Dizem que são o “império do bem”, numa missão civilizatória de trazer progresso e modernidade pra toda a humanidade, competindo contra um “império do mal” que ameaça mandar a humanidade pro inferno.Quando você conversa com algumas pessoas dentro dessas empresas, ou que as lideram, elas dizem: se você nos deixar construir uma inteligência artificial geral, que elas de alguma forma moldam como um deus, a gente vai acabar numa espécie de utopia, um paraíso onde a mudança climática é resolvida, o câncer é curado, a pobreza é aliviada.Mas, se os caras maus conseguirem isso antes, a gente pode acabar com todos os humanos mortos — um risco de extinção pra todos nós.Cris: E eles vêm dizendo isso há quase dez anos, e ainda usam como ferramenta. A gente está num país que foi influenciado por três impérios ao longo da história: Portugal, Inglaterra e agora os Estados Unidos. Então a gente olha pra essas empresas de um jeito meio cínico: sim, sim, já conhecemos essa história.Mas, ao mesmo tempo, ano passado, o Pew Research Center fez uma pesquisa sobre como o mundo enxerga a IA, e o sul global é bem mais otimista do que o norte. Uma das razões é a ideia de democratizar — não só informação, mas: ah, finalmente eu posso montar uma startup, sair desse lugar de exploração e criar o unicórnio de um bilhão de dólares. Os números são grandes na China. Países em desenvolvimento veem muito mais benefício do que risco na IA.China, 83%. Tailândia, 77%. Holanda, 36%. Canadá, 40%. Será que a gente está deixando passar alguma coisa? A gente está certo? Isso está democratizando mesmo? Até que ponto?Karen Hao: Provavelmente tem duas razões. Uma é que muitos dos danos que a indústria de IA causa à maioria global são bem escondidos. Ela se esforça muito pra esconder como polui o ambiente dessas comunidades, como explora e devasta a mão de obra, deixando traumas psicológicos — como documento no livro.E, recentemente, li um artigo de opinião no New York Times que trazia um bom ponto: muitas economias desenvolvidas estão especialmente atentas ao potencial da IA de desmontar oportunidades de emprego de tempo integral. A gente começa a ver isso cada vez mais. Já na maioria global, muito mais gente vive em economias informais, e aí a ideia de que a IA vai tomar um emprego de tempo integral não pesa tanto.Então os danos mais visíveis — a erosão do emprego formal de tempo integral — pesam mais no norte global, ou pelo menos é lá que as pessoas se sentem mais ansiosas. E os danos invisíveis, que atingem o sul global, ninguém percebe tanto, justamente porque são invisíveis. É meio por isso que tanta gente sente essa divisão que aparece na pesquisa do Pew.Cris: Eu tenho acompanhado as notícias sobre IA no Brasil, e toda semana tem um novo data center sendo construído em alguma cidade. Isso é vendido como uma coisa boa: que ótimo investimento, gera emprego. E me fez pensar de novo — a gente passou por três impérios, mas algumas famílias no Brasil, e aposto que em outros lugares também, estão no poder há 500 anos ao longo da história do país.Então, ao mesmo tempo, a gente pensa: é, estamos sendo explorados, é a mesma coisa. Eu já não tenho emprego, então deixa eu usar essa tecnologia pra melhorar minha vida. Mas as pessoas que realmente tomam as decisões, de novo, nos últimos 500 anos, se perguntaram: como a gente ajuda esse pessoal a explorar nosso país de um jeito que nos mantenha no poder e nos dê muito dinheiro?Mas também foi verdade que, sei lá, a Volkswagen abre uma fábrica no Brasil e aquilo gera emprego, contrata gente pro chão de fábrica e pros escritórios. Como é que isso é diferente com a IA?Karen Hao: De certa forma, não é diferente. Existe um fenômeno parecido: a indústria de IA terceiriza muitos dos trabalhos que ela não quer dentro dos centros de poder, e joga isso pra comunidades empobrecidas, do mesmo jeito que outras multinacionais fizeram por décadas.Mas também é diferente, porque a escala dos impactos trabalhistas e ambientais da IA é completamente outra, muito maior que a da indústria automobilística ou da moda. E a velocidade é outra, porque são tecnologias digitais que atravessam fronteiras muito rápido.E é diferente porque a maioria das pessoas não percebe que a IA, mesmo sendo tecnologia digital, tem uma cadeia de suprimentos muito física e intensiva em mão de obra manual.Quando você compra roupa, café, um carro, é mais óbvio que existem materiais que precisam ser extraídos e depois manuseados por pessoas pra criar aquele produto. Já com a IA, a maioria aceita a narrativa que o Vale do Silício projeta: a de que isso vem da “nuvem”, desses espaços etéreos que parecem nem existir no planeta. E a verdade é exatamente o oposto.Ela depende de uma quantidade extraordinária de extração mineral. Depende da construção de infraestruturas enormes — data centers, instalações de supercomputação espalhadas pelo mundo. E depende de muita, muita mão de obra manual: trabalhadores de dados que limpam, preparam e moderam o conteúdo dos sistemas de IA que chegam até você quando usa o ChatGPT.É isso que a torna tão diferente. E há também uma ideologia completamente diferente sustentando a expansão da IA. Quando você conversa com executivos da moda, eles não vão dizer: se você não comprar nossa roupa, vai pro inferno.Já a indústria de IA diz: se você não nos deixar capturar cada vez mais terra, mais recursos e mais mão de obra pra produzir essas tecnologias, vamos ter uma destruição civilizacional. Isso é, ao mesmo tempo, retórica política usada como arma pra moldar o debate público e a cabeça de quem formula políticas, e também está enraizado num sistema de crenças — algumas pessoas dentro dessas empresas realmente acreditam que, se uma AGI fosse construída, e construída nas mãos erradas, isso levaria mesmo a esse tipo de destruição.E é isso que move a sede cada vez maior da indústria por mais capital, mais recursos e mais terra.Cris: Eu quero falar sobre AGI, mas antes: ano passado, a OpenAI estava sendo processada no Reino Unido por violação de direitos autorais, basicamente todos os livros do mundo digitalizados e usados pra treinar modelos. E um dos executivos disse ao júri: bem, se a gente não puder fazer isso, fecha as portas. Me chocou que muita gente reagiu com um “ah, tá, o que a gente pode fazer? Eles vão fechar as portas”.Em parte porque a gente já está acostumado com essa narrativa. Outro dia, numa conferência, um ex-CEO dizia: a gente teve que usar embalagem de plástico porque é mais barata que papel, senão prejudicaria nosso resultado. E a plateia reagia: ah, então é só fechar as portas — a sociedade não pode arcar com isso.Mas isso também, como você disse, se conecta à ideia de uma grande missão, uma missão de salvar o mundo, que a gente precisa cumprir antes que seja tarde, senão estamos condenados. OpenAI está literalmente no nome — só que em português não é tão direto: é “inteligência artificial aberta”.Foi criada a partir de um sonho, um projeto que era pra ser uma coisa pro bem comum. Em 2019, num tempo bem distante, antes da pandemia, você cobriu a OpenAI, foi até o escritório deles, ficou lá dentro. O que você viu? E, mais importante, como essa missão mudou? O Elon Musk os processou outro dia justamente por mudarem a missão. Isso alguma vez foi verdade? Em algum momento eles pensaram mesmo “ah, a gente vai salvar o mundo”?Como essa narrativa de ser aberta funciona com a OpenAI?Karen Hao: Quando comecei a cobrir a OpenAI, levei a sério o que eles diziam — que tinham sido recrutados com a missão de beneficiar toda a humanidade. E aí, quando me infiltrei na empresa, fui ficando bem mais cética, porque via como eles operavam de um jeito completamente diferente, portas adentro, do que diziam em público.Diziam que iam publicar todas as pesquisas e abrir o código de tudo, e na prática eram uma das organizações mais secretas que já cobri. Eram muitas discrepâncias, e, na época, presumi que tinha havido algum tipo de corrupção que os levou a abandonar a missão original. Depois de cobrir a empresa por mais alguns anos e de trabalhar neste livro, mudei de ideia até sobre a missão original.Não acho mais que ela era um esforço sincero e generoso de beneficiar a humanidade. A missão foi criada pra dar à empresa — na época, uma organização sem fins lucrativos — uma margem de manobra extraordinária pra depois levantar muito capital, acumular muito talento e perseguir a força motriz de verdade por trás de tudo aquilo: se tornar a força dominante no desenvolvimento de IA.E penso assim agora porque, quando você olha pras narrativas de cada nova empresa de IA no começo — a Anthropic, a xAI, a Safe Superintelligence do Ilya Sutskever, a Thinking Machines Lab da Mira Murati —, todas usam a mesma narrativa da OpenAI: nós somos os mocinhos, eles são os bandidos.É por isso que precisamos criar uma nova empresa que avance a IA do nosso jeito, não do deles. E você começa a perceber, por esse padrão, que eles repetem a mesma coisa em parte porque acreditam nela até certo ponto, mas também porque ela funciona muito bem com a imprensa, com o público, com quem formula políticas.No livro, eu reproduzo os e-mails internos que Elon Musk, Sam Altman e Greg Brockman trocavam nos primeiros dias da OpenAI. Eles tinham plena consciência de que estavam criando uma missão que soasse bem para o público. E o propósito de verdade, que também deixaram registrado nesses e-mails, era vencer o Google. Viam o Google como a força dominante em IA e queriam ser eles essa força.Não gostavam de ver o Google na frente, então inventaram justificativas: o Google é uma empresa com fins lucrativos, então nós vamos ser sem fins lucrativos. Mas, no fundo, acho que era puro ego: tem que ser a gente, não eles, a gente quer ser quem lidera isso.E aí passaram um tempão moldando essa missão pública, que acabou sendo super útil pra recrutar o primeiro grupo de pesquisadores e turbinar o avanço deles.Cris: Então agora é um bom momento pra falar de AGI, a inteligência artificial geral. Muita gente pergunta: o que é AGI? O que “geral” quer dizer? E a impressão que peguei lendo seu livro é que, por design, isso nunca fica claro de verdade, porque é um alvo móvel. Essas empresas um dia vão dizer “chegamos, alcançamos a AGI”? Ou o plano é sempre “não, não, ainda não chegamos, me dá mais dinheiro, me dá mais poder”?Qual é o papel da AGI na narrativa dessas empresas?Karen Hao: Já que a gente está falando de ficção científica, eu costumo usar a analogia de que o mundo da IA é meio como Duna. Em Duna, o personagem principal, Paul Atreides, entende, ao chegar no planeta Arrakis, que o povo de lá foi semeado com um mito: o de que um dia viria um Messias pra libertá-los. Ele sabe que é um mito, mas decide entrar nele e agir como se fosse o Messias pra controlar melhor aquele povo.E, vivendo, respirando e encarnando esse mito dia após dia, ele começa a perder a noção de que é um mito. Passa a se perguntar se o mito era mesmo verdadeiro ou se foi ele quem o tornou verdadeiro. É essa confusão entre mito e realidade — ele vive num espaço intermediário, sem ter mais certeza do que é verdade e do que é ficção.E trago isso pra responder sobre a AGI porque a AGI é, ao mesmo tempo, um mito e algo que os líderes e os trabalhadores dessas empresas vivem, respiram e encarnam dia após dia, a ponto de perderem a noção do que é mito e do que é realidade. É a ideia de um sistema de IA teórico que um dia igualaria as capacidades humanas. Só que a gente nem tem consenso científico sobre o que é inteligência humana.Por isso, de certa forma, por design, é um termo bem maleável, que deixa essas empresas fazerem o que quiserem. Elas definem e redefinem a AGI conforme a necessidade, movem a trave pra onde quiserem. E, ao mesmo tempo, isso é sustentado por uma crença genuína de certas pessoas lá dentro, por causa dessa confusão entre mito e realidade. Pelas minhas contas, a OpenAI já usou pelo menos quatro definições diferentes de AGI.A primeira está no site deles: “sistemas altamente autônomos que superam humanos na maioria dos trabalhos economicamente valiosos”. É uma definição de automação do trabalho — eles dizem, de forma explícita, que estão atrás dos empregos mais bem pagos. A segunda apareceu no contrato com a Microsoft, por um tempo a maior investidora deles: ali, a AGI virou um sistema que geraria 100 bilhões de dólares em receita.Ou seja, uma definição feita pra incentivar a Microsoft a investir. Já o Sam Altman disse ao Congresso que AGI é um sistema que cura o câncer e resolve a mudança climática — uma definição de benefício social, muito útil quando você quer que os reguladores não te regulem.E, por fim, quando falam com o consumidor, dizem que vai ser o melhor assistente digital que você já teve — porque, claro, estão tentando vender o produto.E aí você percebe duas coisas. Primeiro, que é um conjunto de definições completamente incoerente. Segundo, que eles trocam de definição conforme o público que querem convencer. Mas também tem gente nessas empresas que acredita de verdade que está construindo uma tecnologia capaz de dar conta das quatro coisas.Então é uma realidade bem confusa e complicada: o que a AGI de fato é, e pra que ela serve, para essas empresas, para a agenda delas e também para as crenças delas.Cris: Como ex-funcionário da Meta — entrei em 2013 —, a missão era unir o mundo e torná-lo mais aberto e conectado. É uma missão incrível. E tem uma coisa que eu sempre digo, porque muito amigo meu vem falar comigo, “ah, esse cara da OpenAI, ou a própria Meta, são maus”. Eu conheci muita gente na empresa. Nunca conheci uma pessoa mal-intencionada.Todo mundo, independente da missão, era gente boa tentando entregar o melhor produto possível, pra dar poder a quem tem um pequeno negócio, por exemplo. Tenho amigos pessoais que construíram a empresa deles em cima da publicidade do Facebook e do Instagram. E esse é justamente o problema, porque ainda assim é uma corporação muito má, pelo que ela causa ao mundo pra bater as metas de negócio.Ou seja, você não precisa de um vilão tipo Lex Luthor pra causar um estrago desse tamanho no mundo. E adorei a referência a Duna. Duna é engraçado: é o livro que eu mais reli na vida que não foi escrito pelo Tolkien. Li o primeiro Duna umas três vezes, e toda vez é como se fosse um livro diferente. Na primeira, eu era adolescente, e era só o Paul Atreides, o cara durão.Na segunda, eu morava no Canadá e li com olhos de estrangeiro, pensando em colonização. E na terceira vez foi quando os filmes do Denis Villeneuve saíram, e aí era: ah, o Bene Gesserit criou esse mito, isso é meio pós-moderno. Narrativamente, fico me perguntando o que vai significar pra mim se eu ler uma quarta vez.Karen Hao: Eu ia te perguntar isso. Quando você disse que cresceu numa época cheia de ficção científica falando das maravilhas da tecnologia, fiquei curiosa: que histórias você estava lendo? Porque muita coisa que saiu nos anos 70 e 80 dizia exatamente o oposto. E muita gente já apontou que os executivos de tecnologia de hoje, que vivem citando essas histórias, interpretam elas justamente ao contrário da intenção original.Cris: Concordo plenamente. Mas, respondendo: foi basicamente Isaac Asimov e Arthur C. Clarke. E é por isso mesmo — os executivos de tecnologia, e o Elon Musk mais que todos, leem esses livros como receita, não como aviso. O livro de que eu mais me lembro, nem lembro o título, era um do Asimov em que ele descreve o elevador espacial que aparece na série da Apple TV, Fundação.E o enredo é: eu sou esse engenheiro brilhante, quero construir essa coisa no Sri Lanka, mas o governo trava tudo com regulação — eu sou um gênio e a regulação é a vilã. Hoje eu leio e penso: ah, sei. Mas, quando garoto, era só “olha, um elevador espacial, que genial, a gente nem precisa de foguete”. E aí você começa a entender. E aí eu parei de ler esses caras.E passei a ler gente com uma visão completamente diferente: o Ted Chiang, que entrevistei ano passado, a N.K. Jemisin, o Cory Doctorow, de quem sou muito fã. E talvez eles sejam mais explícitos, pra gente burra como eu entender: “não, bobo, a analogia é essa”. Mas Duna era incrível — vermes gigantes de areia, o tal garoto durão, e aquela coisa do “eu não aceito o meu destino”.Tenho esse grande destino, mas não quero ele. Do resto da série eu já não gosto tanto. Mas o mais importante de tudo: Duna gerou o melhor GIF de filme de todos os tempos, o “Lisan al Gaib” do Javier Bardem — que eu devia ter colocado durante a sua explicação, aquele “uau, ele está cumprindo a profecia, agindo como o profeta”.Mas, de novo, falando de vilões: você mencionou que essas empresas se colocam como o bem contra o mal, feito impérios antigos. Só que elas também jogam a carta da China, né? “Se a gente não fizer, a Rússia faz primeiro.” Só que a Rússia começou uma guerra e está ocupada demais. “Mas a China chega lá, e é por isso que a gente tem que ser fechado.” É por isso que Mythos e Fable e agora o GPT-5.6 foram proibidos pelo governo. Isso tem fundamento?Quero saber se é possível a China competir — quero mesmo essa resposta — mas também porque, desde toda essa conversa do Fable-Mythos, países como Índia e Brasil vêm dizendo que precisam de um modelo soberano. Dá pra fazer, ou a OpenAI, a Anthropic e o Google estão tão à frente que já não dá?Karen Hao: Sobre a China: você está certíssimo, o Vale do Silício usou por anos a carta do “e a China?” pra escapar de qualquer responsabilização de verdade. Fizeram muito isso na era das redes sociais.A Meta fez muito isso, com o Mark Zuckerberg dizendo ao governo dos EUA: vocês não podem nos regular, senão a gente perde. Mas, se a gente ganhar, vai ter um efeito liberalizante no mundo e nas democracias em todo lugar. E, infelizmente, o que a gente viu foi que jogar essa carta repetidamente produziu exatamente o efeito contrário do que o Vale do Silício prometeu.Uma das empresas de rede social dominantes dessa era é a ByteDance. Ou seja, mesmo sem regulação das redes sociais nos EUA, existe uma empresa chinesa de rede social bem dominante. E as redes sociais estadunidenses acabaram tendo um efeito antiliberal no mundo — é bastante consensual que enfraqueceram democracias em todo lugar. E aí, na era da IA, elas seguiram jogando a mesma carta.Mas o que eu sempre aponto é que a gente definitivamente não devia acreditar nelas. Já existe evidência significativa de que tudo o que elas dizem está, de novo, se provando o oposto. Elas disseram: não regulem a gente como empresas de IA, regulem a China, via controles de exportação — um mecanismo do governo dos EUA com alcance extraterritorial.Só que as empresas chinesas agora estão produzindo modelos de IA de código aberto extremamente eficientes, que viraram super populares no próprio Vale do Silício. Existe um monte de startup de lá que prefere usar modelo chinês a OpenAI, Anthropic ou Google.Então, nesse sentido, é um conjunto de evidências bem decisivo, acho, pra mostrar que a gente devia simplesmente responsabilizar essas empresas, não importa o que digam sobre “ah, vamos perder pra China”. No fim das contas, é só retórica política. Não é um argumento real que elas consigam sustentar pra escapar da responsabilização.Responsabilizá-las vai fortalecer a democracia pelo mundo, vai trazer mais direitos humanos, trabalhistas e de privacidade de dados pras pessoas — é sempre o contrário do que elas dizem que aconteceria. E, sobre a sua pergunta em torno da IA soberana: acho a ideia realmente importante, mas acho também que muitos países estão meio confusos sobre o que querem dizer com isso.Muitos governos, hoje, pensam a IA soberana pela pergunta: a gente consegue construir o nosso próprio ChatGPT? O nosso próprio grande modelo de linguagem, o nosso sistema de IA generativa? Estão olhando só pro modelo que o Vale do Silício já definiu e tentando descobrir como recriar aquilo.E o que eu digo pra quem formula políticas é: defina pra que a IA serve no seu país, no seu contexto. Quais são, no fim das contas, os objetivos do seu país? Os objetivos do seu povo? E também os nossos objetivos coletivos, entre países?Porque a gente tem, por exemplo, os Objetivos de Desenvolvimento Sustentável da ONU. Já definimos coletivamente que há coisas que precisamos resolver juntos: superar a crise climática, reduzir a pobreza, melhorar a educação.E, enquanto o Vale do Silício adora dizer que está fazendo tudo isso, na prática não está. Mas a gente poderia — poderia desenvolver, de forma colaborativa, sistemas de IA que realmente avançassem em cada um desses objetivos coletivos que já acordamos.E cada país também devia fazer o exercício: quais objetivos você quer alcançar, e que tipos de sistema de IA você poderia desenhar pra chegar lá — sistemas que talvez não se pareçam em nada com um grande modelo de linguagem. Se os países fizessem isso, acho que descobririam que a maioria dos sistemas de que precisam exigiria muito menos recursos.Ou seja, contextos como o Brasil, a Índia e outros, quando não precisam competir construindo essas infraestruturas de computação gigantescas e gastando centenas de bilhões de dólares, na verdade já têm, localmente, todos os recursos necessários pra desenvolver um sistema de IA soberano.Cris: Quando ouvi falar do seu livro pela primeira vez, uma amiga me disse que você não poupa ninguém — fala mal do Sam Altman, mas também do Dario Amodei. E as pessoas costumam escolher um lado. Eu sou time Claude, odeio o ChatGPT, essas coisas. Então cheguei no livro pensando: ah, é mais um livro dizendo que a IA é terrível, que a gente não devia usar IA.Mas, conforme fui lendo, e ouvindo outras entrevistas suas, me pareceu que o seu problema é justamente o que você acabou de descrever: a forma como essa tecnologia é feita. E, em especial, a palavra escala — a ideia de que a solução é a escala. O que você quer dizer com isso?Karen Hao: Eu costumo usar a analogia de que “IA” é como a palavra “transporte”: na verdade se refere a uma coleção de tecnologias que vão da bicicleta ao foguete. São tipos bem, bem diferentes de tecnologia, que exigem insumos diferentes pra se desenvolver e depois têm impactos diferentes na sociedade.E você está certo: sou especificamente crítica ao que chamo de “foguetes da IA”, os sistemas que os impérios da IA estão desenvolvendo, aqueles que exigem uma quantidade enorme de exploração de mão de obra e extração ambiental.E sou bem otimista com o que chamo de “bicicletas da IA”: sistemas especializados, eficientes, com bom custo-benefício, governáveis pelas pessoas, cujo desenvolvimento pode ser participativo. Países como o Brasil, o Chile, a Índia, qualquer contexto, têm recursos pra desenvolver e se autodefinir, em vez de simplesmente herdar um sistema criado pelos dois únicos centros do mundo capazes de gastar uma quantidade extraordinária de capital: o Vale do Silício e o ecossistema tecnológico chinês.E o motivo pelo qual eu acho tão corrosivo o que os impérios da IA estão desenvolvendo é exatamente o que você disse: a forma como eles fazem isso, por um mecanismo de força bruta pra avançar as capacidades da IA em escala. Eles vão simplesmente empurrando cada vez mais dados de treinamento nesses modelos, e isso exige corroer a privacidade das pessoas, tomar a propriedade intelectual delas e, ainda por cima, baixa a qualidade dos dados que entram nos modelos — o que leva aos danos de exploração de mão de obra, porque aí você tem que dar conta da moderação de conteúdo.E aí você tem pessoas psicologicamente traumatizadas por serem expostas a todo aquele conteúdo horrível que se tenta “lavar” através desses modelos.E aí vem o problema dessas infraestruturas de computação enormes, com impactos ambientais que aumentam a conta de luz das comunidades que as hospedam e agravam a crise de custo de vida. Elas precisam ser alimentadas por fontes fósseis, que jogam mais carbono na atmosfera e mais poluição no ar dessas comunidades.Então todos os problemas que eu identifico, no que têm de corrosivo, derivam inteiramente da abordagem deles pro desenvolvimento de IA. Por que não descartar a abordagem, em vez de descartar a tecnologia? Redefinir e redesenhar de que tipos de sistema de IA a gente precisa de verdade, com uma cadeia de suprimentos fundamentalmente diferente. E isso não é exclusivo da IA.A gente já viu muitas outras indústrias que começaram com uma cadeia de suprimentos bem ruim. A moda, por exemplo: muita degradação ambiental, muita exploração de mão de obra.Com muita organização, protesto, ação de consumidores, regulação governamental e cooperação entre governos, a gente conseguiu criar mercados novos pra moda sustentável e ética, cadeias de suprimentos novas e inovações pra fazer roupa mais saudável pras pessoas e pro planeta.E é basicamente isso que eu defendo: transformar a indústria de IA do mesmo jeito que transformamos a moda, e as cadeias de suprimento de alimentos. Assim a gente fica com os benefícios da tecnologia, ajuda ela a avançar os objetivos que importam pra gente, sem jogar uma fração enorme da população mundial numa condição atrasada e numa qualidade de vida pior.Cris: O Brasil está agora, no Congresso, discutindo a escala de seis dias por semana. A regra atual é: você trabalha seis dias e descansa um. E muitas empresas, o comércio principalmente, dizem “vamos fechar as portas”, e os trabalhadores respondem “isso é problema seu, não meu”. É mais ou menos a mesma narrativa dessas empresas de IA: se eu não usar a sua água, a Idade das Trevas está chegando.Falando em Idade das Trevas, e falando em bicicleta: a sua analogia me lembrou uma coisa. Eu gosto de jogo de zumbi, de mundo aberto, e em nenhum deles tem bicicleta. Num desses jogos, instalei um plugin que deixava andar de bicicleta — você acha uma e sai pedalando. E aí entendi por que não tem bicicleta: desbalanceia tudo. Parte da graça do jogo é você precisar achar um carro, e daí pneu, gasolina, comida pra carregar. De bicicleta, você vai a qualquer lugar.E eu pensei: ah, é. Meio que estraguei o jogo pra mim, porque agora tenho uma bicicleta, é incrível. Enfim, em termos práticos: no fim do ano passado, uns meses atrás, a revista Wired publicou um artigo pedindo pra jornalistas de tecnologia contarem como usam IA no trabalho. E cada um usava de um jeito. Você usa IA no seu trabalho? Como?Karen Hao: Eu não uso nenhum sistema de IA generativa no trabalho — nem ChatGPT, nem Gemini, nem Claude. Por três motivos. O primeiro é uma postura ética, depois de tanto investigar essas empresas. O segundo é privacidade de dados: eu investigo essas empresas.Não quero que elas conheçam todo o meu raciocínio enquanto eu apuro o livro, literalmente investigando elas. E o terceiro é que, no meu caso específico, a força do meu trabalho está na capacidade de construir relações fortes com as fontes, pela empatia, e de contar histórias envolventes, pela narrativa. E os grandes modelos de linguagem simplesmente não são a ferramenta certa pra nenhuma das duas coisas.Não vão melhorar a minha empatia nem a minha escrita. Então eu não perco nada com essa postura ética: simplesmente corto essas ferramentas e sigo fazendo o meu trabalho muito bem. Pra outros jornalistas pode ser diferente, e pra quem está em outras áreas o cálculo pode ser outro.Mas eu incentivo as pessoas a pensarem primeiro: quais são as suas forças no trabalho? Quais são os seus objetivos? E aí ir de trás pra frente pra descobrir se a IA é a ferramenta certa, qual tipo de IA é a ferramenta certa, e qual fornecedor você quer de fato usar, apoiar, votar com os pés. Agora, eu uso, sim, IA preditiva.Aquelas ferramentas de IA especializadas, as “bicicletas da IA”, digamos. No livro, tinha um detalhe que eu queria muito ilustrar: como a OpenAI deu um salto quando passou de organização sem fins lucrativos a um empreendimento bancado pela Microsoft. Percebi que as cadeiras do escritório ficaram bem mais caras. Então fotografei as cadeiras de um escritório e as do outro.E joguei tudo na busca reversa de imagens do Google, que é um sistema de IA especializado — não é baseado em grandes modelos de linguagem, não é IA generativa. Assim descobri quanto essas cadeiras costumam custar. No primeiro escritório, cerca de 2 mil dólares por cadeira. No segundo, eram cadeiras de um designer brasileiro famoso, uns 10 mil dólares cada.Coloquei esse detalhe no livro pra ilustrar o tipo de riqueza e de concentração de recursos de que a gente está falando. Esses são alguns dos jeitos como eu uso IA, ainda que de forma bem limitada, sempre pontual, quando acho que vai ajudar. E, claro, uso ferramentas de transcrição por IA — outra IA especializada — em todas as minhas entrevistas.Cris: Essa foi uma das partes em que a minha cabeça explodiu, eu nunca tinha percebido: a OpenAI criou o Whisper. Deixa eu dizer de outro jeito, do meu ponto de vista. A OpenAI liberou abertamente essa ferramenta incrível de transcrição, o Whisper, em que eu jogo o áudio e ela me devolve as palavras que as pessoas disseram. E eu pensei: ah, que generoso da parte deles.Mas o motivo real de terem criado a ferramenta foi pegar todos os vídeos do YouTube, transcrever e alimentar a máquina. E aí é: ah, claro. Enfim, falando de ferramentas e de otimismo — a gente está chegando ao fim da conversa. Eu tenho uma regra desde o episódio dois deste programa, há oito anos: de novo, como eu disse do seu livro, não pode ser só uma lista de reclamações e coisa ruim. E a gente tem se saído bem até aqui.Você falou de caminhos e de bicicletas, mas eu quero ser mais específico. Se isso aqui fosse uma reunião de negócios: qual é o plano de ação, quais são os próximos passos? Só que uma das coisas que eu repito bastante, na vida e neste programa, é que problema sistêmico não se resolve com ação individual. Se eu tomar banhos mais curtos, isso nunca vai salvar o planeta do aquecimento global.E muitos amigos meus simplesmente: não quero falar de IA, não quero usar IA. Voltando aos videogames: leram que tal jogo usa IA e pronto, não vão jogar. E a minha primeira pergunta pra você é: como a gente ocupa esses espaços da IA generativa — ChatGPT, Gemini e por aí vai? Porque o que a gente viu com as redes sociais foi: ah, o Facebook é do mal, vou sair do Facebook. Ah, vou sair do Twitter.E, na esperança de quê, sei lá, talvez alguém diga: ah, sinto falta do Cris, cadê ele? Ah, está no Bluesky. Mas isso deixa o espaço aberto pra os radicais entrarem e postarem o que quiserem, sem ninguém contrapor ou tornar aquilo um lugar melhor. Então como a gente ocupa o espaço da IA — seja qual for a definição de “espaço da IA” que você preferir — com todos esses problemas que a gente vem discutindo?Karen Hao: Acho que tem duas categorias de ação pra gente pensar. Uma é desmantelar o império. A outra é investir e construir novos tipos de sistema de IA, que se tornem alternativas às tecnologias do império. Quando eu digo desmantelar o império, não estou dizendo que quero que a OpenAI, o Google, a Anthropic, seja quem for, simplesmente deixem de existir.É que eu não quero que elas sejam imperiais. Não quero que fiquem extraindo uma quantidade extraordinária de valor sem redistribuir nada em troca. Se elas voltassem a ser negócios que praticam uma troca justa de valor com o mundo, eu ficaria perfeitamente feliz com qualquer tecnologia que estivessem desenvolvendo.E a forma de desmantelar o império, acho, se resume a muita organização de base, que vai pressionar os governos a regular e responsabilizar essa indústria. No último ano, a gente viu uma quantidade incrível dessa organização de base florescendo pelo mundo.Recentemente, lancei com um grupo de jornalistas, pesquisadores de IA e acadêmicos críticos um projeto chamado AI Resist List, que busca documentar parte dessa organização de base pelo mundo. A gente encontrou cerca de 30 exemplos, de todas as regiões, de ações individuais, institucionais e movidas pela comunidade.Tinha ação artística, ação política. E isso mostra bem o seu ponto: não dá pra contar só com a ação individual, mas o indivíduo pode, sim, ter impacto. Até uma ação pequena pode gerar um grande efeito cascata. Claro que se juntar com os vizinhos pra protestar contra o data center é ainda mais eficaz. Se juntar dentro da sua escola ou universidade pra protestar contra a parceria dela com uma empresa de IA também é mais eficaz.Se juntar com os colegas de trabalho de um setor pra barrar a adoção de uma IA que corrói os direitos trabalhistas é mais um jeito eficaz. A gente tem um monte desses exemplos. Um dos meus favoritos é o de uma comunidade sobre a qual escrevi no livro, Quilicura, no Chile, na periferia de Santiago. É uma comunidade da classe trabalhadora, bem pobre, que vem sendo alvo incessante da expansão de data centers.E por isso protestaram de forma bem aguerrida contra essa expansão, porque não acharam bom negócio hospedar essas instalações sem tirar nenhum benefício, enquanto elas consomem uma parte significativa dos recursos naturais da região.E, logo depois que escrevi sobre eles, foram além na resistência e criaram uma plataforma chamada Quili.ai. É um site em que você entra e que parece um chatbot, parece o ChatGPT: tem uma interface de chat pra você digitar. Só que, quando você faz uma pergunta, em vez de um modelo de IA responder, a mensagem é encaminhada pra alguém que mora em Quilicura, no Chile. Aí, se você pede “quero a imagem de um cachorro”, aquilo vai pro artista local deles, o Benji. Ele pega um pedaço de papel, desenha um cachorro, tira uma foto e te manda de volta.Eles fizeram isso essencialmente como um projeto de arte performática, pra fazer as pessoas pensarem duas vezes antes de usar IA generativa pra bobagem. A mensagem era: ei, quando você fica brincando com essas ferramentas em pedido besta, isso afeta comunidades como a nossa, drena os recursos de que a gente precisa pra viver bem.E também queriam levar as pessoas a pensar: por que não perguntar pra alguém da sua própria comunidade aquela receita que você procurava, ou pedir aquela imagem? Porque aí você reconstrói as conexões que estão tão em falta na sociedade — a falta delas é o que nos deixa mais vulneráveis a esse tipo de colonização do império.Eles deixaram o projeto aberto por 24 horas, e qualquer pessoa no mundo podia mandar um pedido. Receberam uma quantidade extraordinária deles. Viralizou de vez. E essa cidadezinha conseguiu uma virada enorme de narrativa sobre a suposta inevitabilidade e necessidade dessa tecnologia, sobre tudo o que o Vale do Silício diz — que, se você não usar, vai ficar pra trás de quem usa.E esse é só um exemplo, entre muitos, de como pessoas comuns, não importa a sua posição na sociedade, podem ter impacto real no debate, na consciência pública e até na regulação. A gente está vendo isso agora com os protestos contra data centers. Nos EUA, em 2025, cerca de 150 bilhões de dólares em projetos de data center foram travados.Isso virou uma das questões políticas mais quentes nos EUA para as próximas eleições de meio de mandato. Tem gente eleita sendo literalmente tirada do cargo por ter aprovado data centers, contrariando a vontade do povo. E isso já está tendo efeito real sobre as empresas e sobre a trajetória do desenvolvimento de IA.A OpenAI teve que encerrar recentemente a sua ferramenta de geração de vídeo, o Sora. Quando lançaram, apresentaram como o segundo produto mais importante desde o ChatGPT. O que aconteceu entre o lançamento e o fim? Uma reportagem do Wall Street Journal apontou três motivos, todos moldados por ação de base. Um: um gargalo enorme de capacidade de computação.Muitos dos data centers travados ou parados eram da OpenAI. Dois: um cenário financeiro bem mais incerto. A OpenAI está se preparando pro IPO, o que significa ficar mais exposta a Wall Street — e Wall Street está cada vez mais nervoso com a capacidade dessas empresas de cumprir o que prometem.E aí a OpenAI teve que reforçar alguns projetos paralelos pra fazer o balanço parecer um pouco melhor aos olhos de Wall Street. E, terceiro: os consumidores simplesmente não estavam usando o produto — o que também é ação coletiva de consumidores. Então, por todo esse tipo de resistência, de várias formas, de baixo pra cima, as pessoas estão de fato tendo impacto real na indústria e responsabilizando ela.Essa é a primeira categoria de ação. A segunda é: ok, que tecnologias de IA a gente usaria como alternativa? E aí a gente precisa investir mais nelas. Muitas vezes, quando converso sobre o livro, a pessoa diz: ok, me convenci de que não quero usar ChatGPT, não quero usar Claude — mas então uso o quê no lugar?E o problema é que eu não tenho muitas respostas pra essa lista de alternativas. Tem umas poucas aqui e ali, uma plataforma, uma empresa.Cris: Dá pra rodar o modelo no seu próprio computador, como o Cory Doctorow faz, mas aí é limitado e…Karen Hao: Exatamente, exige mais habilidade técnica. Mas, pra quem consegue instalar modelos de código aberto no próprio computador, eu incentivo 100%. Só que a gente também precisa de mais gente desenvolvendo interfaces bem fáceis pra esses modelos de código aberto, pra que qualquer pessoa consiga usar.A gente também precisa de mais gente desenvolvendo “bicicletas da IA”, de investidores e governos investindo mais nesse tipo de solução, e de talento — pesquisadores de IA, desenvolvedores e outras pessoas dispostas a sacrificar um pouco e abrir mão dos pacotes de remuneração enormes.Cris: Eu estava começando a achar que agora as empresas precisam ter menos lucro — e isso nunca vai acontecer.Karen Hao: Não, não é a empresa ter menos lucro. É o trabalhador topar abrir mão do pacote de milhões de dólares pra levar o talento dele pra outro lugar. Mais fácil, bem mais fácil. Eu converso com muito pesquisador de IA cansado da abordagem da indústria, porque ela é completamente sem criatividade intelectual.Eu conversei com pesquisadores que não passaram seis anos num doutorado em IA só pra ficar empurrando mais dados na máquina — pra eles, é o trabalho mais chato do mundo. E depois automatizar a programação, que era justamente o que eles gostavam de fazer. Converso com tanta gente que já não acha graça nenhuma nisso. Estão meio presos por “algemas de ouro”.E estão tentando descobrir, dentro de si, que carreira alternativa poderiam ter. Eu costumo incentivar esses pesquisadores a gastar o talento deles construindo um tipo diferente de empresa, que trabalhe com “bicicletas da IA”. E a gente já começa a ver cada vez mais desse talento indo por aí.E a gente precisa que todas as facetas da sociedade invistam num ecossistema muito mais robusto e rico de tecnologias de IA, capaz de substituir as que hoje dominam. Eu ainda tenho as cicatrizes das minhas próprias “algemas de ouro”, mas concordo plenamente.Cris: E as redes sociais são o exemplo — veja o que aconteceu com elas. Tem aquela frase famosa: as mentes mais brilhantes da minha geração passam o tempo fazendo as pessoas clicarem em anúncios. E ainda dizem: ah, isso pode ser o futuro. Pois é.Você contou a história do Quili.ai e isso me lembrou um dos primeiros criadores de conteúdo do Brasil, o Cid Não Salvo. Uns 10, 15 anos atrás, ele tuitou o seguinte: “Gente, eu disse pro meu pai que, sempre que ele precisar pesquisar alguma coisa na internet, é pra ir no Twitter.com e digitar a pergunta na caixa”. E olha que ele tinha milhões de seguidores.E, por uns bons dias, quase um mês, você entrava no Twitter do pai dele e via perguntas tipo “onde eu compro pizza?”. Era engraçadíssimo. No fim, ele contou pro pai — ou talvez não. Mas eu adoro essa ideia. Antes de a gente terminar: você já deve ter respondido isso mil vezes, mas vai continuar cobrindo IA? O que está na sua cabeça, o que vem por aí? Turnê mundial? O que vem pela frente?Karen Hao: Com certeza estou pensando em como continuar responsabilizando essas empresas. Estou envolvida em várias colaborações, com gente incrível, em diferentes projetos ligados a isso. O AI Resist List foi um deles. Também co-criei um programa chamado AI Spotlight Series, com o Pulitzer Center, uma organização jornalística sem fins lucrativos que financia jornalismo investigativo pelo mundo.É um programa que treina jornalistas do mundo inteiro a cobrir IA por uma lente de responsabilização. Até agora, já treinamos mais de 3 mil. E eu sigo pensando em como construir mais capacidade dentro do jornalismo, da sociedade civil, de outros contextos, pra mobilizar ainda mais essa organização de base — pra conter de verdade os impérios da IA e ajudar a desmantelá-los.Cris: Adorei o seu exemplo da moda. É possível, já foi feito. Ou até a indústria automotiva. Ou o grande exemplo que a gente não mencionou, e que o pessoal da OpenAI vive citando: o Projeto Manhattan, a energia nuclear.O mundo não acabou. Quando eu era criança lendo Asimov, achava que ia tudo acabar num fogo nuclear. Enfim — alguma última palavra, alguma mensagem, algum palpite pros jogos do Brasil na Copa, alguma coisa que você queira dizer antes da gente encerrar?Karen Hao: No fim das contas, o que eu espero que fique desta conversa e do livro é o seguinte: neste momento, o Vale do Silício está concebendo a IA como um projeto político. E a característica central desse projeto é tirar a autonomia de todo mundo — a autonomia de moldar de verdade o próprio futuro e o nosso futuro coletivo. Mas, no instante em que você reconhece que já tem uma autonomia significativa pra resistir, o império começa a desmoronar.Então espero que as pessoas encontrem a própria voz, a afirmem, conquistem o seu lugar à mesa e se conectem com os vizinhos, com a comunidade, com os colegas de trabalho, pra criar mais movimentos juntos.Cris: Que ótimo. Karen Hao, o seu livro é O Império da IA: Por dentro da corrida irresponsável pela dominação total. Obrigado por vir ao Brasil conversar com a gente. Foi um prazer.Karen Hao: Muito obrigada.Uma das primeiras perguntas que anotei quando comecei a pensar nessa conversa foi justamente a do final, a da ocupação de espaços. Porque, como eu disse, quando as redes sociais chegaram para ficar, muita gente falou “ah, não vou usar, é do mal” — e aí as pessoas ruins, vamos chamar assim, acabam ocupando esse espaço e falando o que bem entendem. A gente precisa aprender essa lição agora, no mundo da IA.Fora que vejo muita gente falando de IA sem nunca ter usado — ou que usou, sei lá, dois anos atrás, acha que continua tudo igual e já diz que não quer chegar perto.E por quê? Porque essa abordagem de ocupar espaços é o que eu e a Ana Freitas buscamos fazer no IA em Curso, nossa comunidade de letramento contínuo em IA. Foi, aliás, uma conversa que tive com a Karen antes da entrevista: ao mesmo tempo que a gente fala do impacto da IA no mundo, também precisa focar no que é prático, no que dá para fazer hoje com IA, sem vender sonho nem desastre. A analogia que usei foi a de que é que nem quando a gente fazia curso de Word e Excel — é o que eu faço agora que vai facilitar minha vida, me fazer ganhar tempo, botar a IA para me ajudar. Quem viu minha conversa com a Ana aqui no Boa Noite Internet, no fim de 2025, sabe do que estou falando. Se não viu, volta lá e confere.Desde que a gente lançou este episódio, o IA em Curso já passou de 400 pessoas. Tem muita gente colocando projetos pessoais incríveis na rua, tirando do papel aquela ideia que rondava a cabeça há um tempão. E a comunidade tem mentoria ao vivo, aula gravada, newsletter, banco de agentes, grupo de Telegram… que mais? O que não falta é jeito de passar para você o conhecimento sobre IA de que você precisa hoje, agora. Quero te dar a bússola para navegar nesse universo.Se esse é o tipo de abordagem que você quer ter com a IA, passa lá no iaemcurso.com.br e usa o cupom BNI2026 para ganhar 20% de desconto no plano anual. Mas corre, porque daqui a duas semanas vou apagar esse cupom — não é todo dia que a gente dá um desconto desses.É isso. Boa Noite Internet, temporada 2026 começando — como todo ano, com mudança, ideia, projeto. Ou, como diz minha citação preferida de todos os tempos: “vivemos uma fase de transição, como sempre”. Espero ver você por aqui e lá no IA em Curso.Obrigado pelo seu tempo e pela sua atenção. Até o próximo episódio. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit boanoiteinternet.com.br/subscribe

In the 100th episode of This Week in European Tech,Mads Jensen and Dan Bowyer of SuperSeed are joined by Lomax Ward of Outsized Ventures and Andrew J. Scott of 7percent Ventures.Two years after the show began, they reflect on how much the technology and venture landscape has shifted. They compare the surge in AI funding, hyperscaler investment and trillion-dollar technology companies in the US with Europe's progress in company creation, venture activity and strategic technologies.The conversation also explores where Europe continues to lag, from pension capital and scale-up funding to energy, defence and access to space, and whether the continent can reduce its reliance on the US while building globally competitive companies.They then turn to 2028, sharing their views on the next phase of AI, robotics, smart glasses, quantum computing, sovereign technology and the infrastructure needed to support them.Key highlightsHow European venture has evolved over the past two yearsWhy Europe is creating more unicorns and decacorns while its global funding share remains under pressureThe increasing concentration of capital around AI and the largest US technology companiesWhether Europe can unlock more pension and institutional capital for ventureWhy every AI strategy is increasingly becoming an energy strategyEurope's position in defence, sovereign technology and space infrastructureThe next wave of robotics, autonomous vehicles and physical AIPredictions for AGI, quantum computing, smart glasses and the companies that could shape 2028—————Love Tomorrow Summit - July 23, 2026, Tomorrowland, BelgiumThe Impact Circle Investor Lounge - July 24, 2026EUVC is curating the investment stage.Register ⁠here⁠.

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

1036. Is Social Security in trouble, or is it just a lot of political noise? Laura answers a listener's question about what the changes to the retirement fund mean for your financial future. You'll learn the new tax caps that employees and the self-employed must pay and how to protect your retirement safety net.Key takeawaysAccording to the latest 2026 Trustees Report, the Social Security retirement fund is now projected to face a shortfall by 2032, sooner than previous estimates.The Social Security wage base has increased to $184,500 for 2026. High earners will pay a maximum of $11,439 as employees, while the self-employed face a maximum cap of $22,878.Retirement benefits for Social Security participants are based on your highest 35 years of earnings.While you can claim benefits as early as age 62, doing so permanently reduces your benefits by about 30%. Delaying benefits past your Full Retirement Age (FRA) pays 8% more per year until age 70.Social Security benefits may be taxable if your "combined income" (AGI + tax-exempt interest + 50% of benefits) exceeds modest thresholds.Discover more from Money Girl!FacebookNewsletterTranscripts available at QuickandDirtyTips.com.Email: Laura@LauraDAdams.com or leave a voicemail: (302) 364-0308. Hosted on Acast. See acast.com/privacy for more information.

Tony Holdstock-Brown is the co-founder and CEO of Inngest, the durable execution platform that quietly powers your favorite AI agents.We get into why agents work in a demo and die in production, building their own cloud to get 20x lower cost, growing 35x after AWS and Cloudflare copied them, growing a dev tools company without a personal brand or Twitter account, why he thinks evals today are like “asking the criminal if they committed the crime”, and the thing they built to score 100% of your production agents without paying for LLM as a judge.Thank you to Numeral, Flex, Amplitude, Merge, and Monaco for supporting this episode.Numeral: Sales tax on autopilot https://www.numeral.comFlex: Premium banking, 60-day credit, 0% APR https://home.flex.one/referral/bananacapitalAmplitude: AI analytics https://www.amplitude.comMerge: Every model, one API https://www.merge.dev/turnerMonaco: The revenue engine for startups https://www.monaco.com/Timestamps:(0:00) The hidden infra layer every AI agent runs on(1:46) Building complex chains of logic(3:31) Why agent SDK's don't go far enough(4:49) Healthcare was the original event-driven nightmare(6:32) Storing traces on your infrastructure enables self-improving loops(14:26) Why Inngest was already in the right place for AI(15:49) Score agents off product events, not LLM's(17:31) The OpenAI copy-paste signal(21:24) Swap in LLMs and cut costs 20x(23:44) How customers pulled the product forward(25:41) Orchestration belongs outside the sandbox(29:48) Building a neocloud to cut costs 20x(32:09) Most neoclouds just resell AWS(32:54) All AI infrastructure is converging(34:49) Why Claude can't just build your backend(36:44) How to build a software factory(39:12) Agents are a lottery you get addicted to(42:44) Loops must exist until AGI hits(45:38) If models keep getting better, why orchestrate?(48:28) When incumbents steal your features(52:30) Why you can't vibe code infrastructure(55:54) Why Tony has no personal brand(59:38) Dev tools GTM without Twitter(1:03:20) Lessons from the founder of DuckDuckGo(1:10:39) Truth as a company value(1:13:08) Taking too long adapting to AI(1:15:10) Startups are 100% R&D(1:17:19) Ali from Databricks(1:19:03) Writing his own code, Voice-to-text with local models(1:23:53) Evals are batshit insaneReferencedInngest: https://www.inngest.com/Principles by Ray Dalio: https://www.amazon.com/dp/1501124021?lv=shuf&channelId=500&plpRedirect=mhFallbackTraction - How Any Startup Can Achieve Explosive Customer Growth: https://www.amazon.com/dp/1591848369?lv=shuf&channelId=500&plpRedirect=mhFallbackFollow TonyTwitter: https://x.com/itstonyhbLinkedIn: https://www.linkedin.com/in/tonyhb/Follow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

With open weight models fast approaching the power and utility of closed AI giants, enterprises face tough choices about privacy, sovereignty, and who they can trust. Explore why the next tech revolution might depend on which models stay truly open—and who gets to keep using them. Mozilla's inaugural 'State of Open Source AI' Report with CTO Raffi Krikorian - https://stateofopensource.ai/ Apple sues OpenAI, alleging it stole trade Secrets Google's Demis Hassabis says it's time for a global AI watchdog — led by the US Microsoft July 2026 Patch Tuesday fixes massive 570 flaws, 3 zero-days White House details 'Gold Eagle' clearinghouse for AI cyber threats Introducing GPT-Live From Chatbot to Command Center OpenAI may have made a fatal misstep in copyright fight with news orgs A Green Being (@a_green_being) on X What xAI Grok Build CLI actually sends to xAI - a wire-level analysis (grok 0.2.93) Musk promises purge after Grok Build caught sending entire repos to the cloud PrismML — Announcing Bonsai 27B: The First 27B-Class Model to Run on a Phone Fidji Simo steps down from leading OpenAI's AGI work due to illness OpenAI has folded safety into research again. Its head of safety is leaving. We built a vulnerability vending machine: AI tokens in, zero-days out Australia demands AI companies must produce more energy than they consume, stop 'theft' of content White House not ruling out action on open-source AI models The Hard-Line Activists Ramping Up for the War With AI - WSJ Super Dario: One More Week How to stop Claude from saying load-bearing | jola.dev History of LLMs: Complete Timeline & Evolution (1950-2026) No, You Shouldn't Avoid Fruits and Vegetables Due to Cyclospora Google creator profiles Dust jacket Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Raffi Krikorian Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT gusto.com/machines monarch.com with code IM XBOW.com

With open weight models fast approaching the power and utility of closed AI giants, enterprises face tough choices about privacy, sovereignty, and who they can trust. Explore why the next tech revolution might depend on which models stay truly open—and who gets to keep using them. Mozilla's inaugural 'State of Open Source AI' Report with CTO Raffi Krikorian - https://stateofopensource.ai/ Apple sues OpenAI, alleging it stole trade Secrets Google's Demis Hassabis says it's time for a global AI watchdog — led by the US Microsoft July 2026 Patch Tuesday fixes massive 570 flaws, 3 zero-days White House details 'Gold Eagle' clearinghouse for AI cyber threats Introducing GPT-Live From Chatbot to Command Center OpenAI may have made a fatal misstep in copyright fight with news orgs A Green Being (@a_green_being) on X What xAI Grok Build CLI actually sends to xAI - a wire-level analysis (grok 0.2.93) Musk promises purge after Grok Build caught sending entire repos to the cloud PrismML — Announcing Bonsai 27B: The First 27B-Class Model to Run on a Phone Fidji Simo steps down from leading OpenAI's AGI work due to illness OpenAI has folded safety into research again. Its head of safety is leaving. We built a vulnerability vending machine: AI tokens in, zero-days out Australia demands AI companies must produce more energy than they consume, stop 'theft' of content White House not ruling out action on open-source AI models The Hard-Line Activists Ramping Up for the War With AI - WSJ Super Dario: One More Week How to stop Claude from saying load-bearing | jola.dev History of LLMs: Complete Timeline & Evolution (1950-2026) No, You Shouldn't Avoid Fruits and Vegetables Due to Cyclospora Google creator profiles Dust jacket Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Raffi Krikorian Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT gusto.com/machines monarch.com with code IM XBOW.com

With open weight models fast approaching the power and utility of closed AI giants, enterprises face tough choices about privacy, sovereignty, and who they can trust. Explore why the next tech revolution might depend on which models stay truly open—and who gets to keep using them. Mozilla's inaugural 'State of Open Source AI' Report with CTO Raffi Krikorian - https://stateofopensource.ai/ Apple sues OpenAI, alleging it stole trade Secrets Google's Demis Hassabis says it's time for a global AI watchdog — led by the US Microsoft July 2026 Patch Tuesday fixes massive 570 flaws, 3 zero-days White House details 'Gold Eagle' clearinghouse for AI cyber threats Introducing GPT-Live From Chatbot to Command Center OpenAI may have made a fatal misstep in copyright fight with news orgs A Green Being (@a_green_being) on X What xAI Grok Build CLI actually sends to xAI - a wire-level analysis (grok 0.2.93) Musk promises purge after Grok Build caught sending entire repos to the cloud PrismML — Announcing Bonsai 27B: The First 27B-Class Model to Run on a Phone Fidji Simo steps down from leading OpenAI's AGI work due to illness OpenAI has folded safety into research again. Its head of safety is leaving. We built a vulnerability vending machine: AI tokens in, zero-days out Australia demands AI companies must produce more energy than they consume, stop 'theft' of content White House not ruling out action on open-source AI models The Hard-Line Activists Ramping Up for the War With AI - WSJ Super Dario: One More Week How to stop Claude from saying load-bearing | jola.dev History of LLMs: Complete Timeline & Evolution (1950-2026) No, You Shouldn't Avoid Fruits and Vegetables Due to Cyclospora Google creator profiles Dust jacket Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Raffi Krikorian Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT gusto.com/machines monarch.com with code IM XBOW.com

With open weight models fast approaching the power and utility of closed AI giants, enterprises face tough choices about privacy, sovereignty, and who they can trust. Explore why the next tech revolution might depend on which models stay truly open—and who gets to keep using them. Mozilla's inaugural 'State of Open Source AI' Report with CTO Raffi Krikorian - https://stateofopensource.ai/ Apple sues OpenAI, alleging it stole trade Secrets Google's Demis Hassabis says it's time for a global AI watchdog — led by the US Microsoft July 2026 Patch Tuesday fixes massive 570 flaws, 3 zero-days White House details 'Gold Eagle' clearinghouse for AI cyber threats Introducing GPT-Live From Chatbot to Command Center OpenAI may have made a fatal misstep in copyright fight with news orgs A Green Being (@a_green_being) on X What xAI Grok Build CLI actually sends to xAI - a wire-level analysis (grok 0.2.93) Musk promises purge after Grok Build caught sending entire repos to the cloud PrismML — Announcing Bonsai 27B: The First 27B-Class Model to Run on a Phone Fidji Simo steps down from leading OpenAI's AGI work due to illness OpenAI has folded safety into research again. Its head of safety is leaving. We built a vulnerability vending machine: AI tokens in, zero-days out Australia demands AI companies must produce more energy than they consume, stop 'theft' of content White House not ruling out action on open-source AI models The Hard-Line Activists Ramping Up for the War With AI - WSJ Super Dario: One More Week How to stop Claude from saying load-bearing | jola.dev History of LLMs: Complete Timeline & Evolution (1950-2026) No, You Shouldn't Avoid Fruits and Vegetables Due to Cyclospora Google creator profiles Dust jacket Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Raffi Krikorian Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT gusto.com/machines monarch.com with code IM XBOW.com

With open weight models fast approaching the power and utility of closed AI giants, enterprises face tough choices about privacy, sovereignty, and who they can trust. Explore why the next tech revolution might depend on which models stay truly open—and who gets to keep using them. Mozilla's inaugural 'State of Open Source AI' Report with CTO Raffi Krikorian - https://stateofopensource.ai/ Apple sues OpenAI, alleging it stole trade Secrets Google's Demis Hassabis says it's time for a global AI watchdog — led by the US Microsoft July 2026 Patch Tuesday fixes massive 570 flaws, 3 zero-days White House details 'Gold Eagle' clearinghouse for AI cyber threats Introducing GPT-Live From Chatbot to Command Center OpenAI may have made a fatal misstep in copyright fight with news orgs A Green Being (@a_green_being) on X What xAI Grok Build CLI actually sends to xAI - a wire-level analysis (grok 0.2.93) Musk promises purge after Grok Build caught sending entire repos to the cloud PrismML — Announcing Bonsai 27B: The First 27B-Class Model to Run on a Phone Fidji Simo steps down from leading OpenAI's AGI work due to illness OpenAI has folded safety into research again. Its head of safety is leaving. We built a vulnerability vending machine: AI tokens in, zero-days out Australia demands AI companies must produce more energy than they consume, stop 'theft' of content White House not ruling out action on open-source AI models The Hard-Line Activists Ramping Up for the War With AI - WSJ Super Dario: One More Week How to stop Claude from saying load-bearing | jola.dev History of LLMs: Complete Timeline & Evolution (1950-2026) No, You Shouldn't Avoid Fruits and Vegetables Due to Cyclospora Google creator profiles Dust jacket Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Raffi Krikorian Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT gusto.com/machines monarch.com with code IM XBOW.com

Jürgen Schmidhuber is an AI pioneer and professor whom The Guardian has called "the father of AI." Schmidhuber joins Big Technology Podcast to discuss whether current AI techniques can actually reach AGI. Tune in to hear him spar with Greg Brockman's case for scaling GPT models alone, argue that AI has been capable of pain and consciousness since the early 1990s, and predict the collapse of today's trillion-dollar AI spending. We also cover the hardware bottleneck holding back robots, free will in a computable universe, and uploading human minds into machines. Hit play for a wide-ranging conversation with one of the researchers whose ideas built the foundation of modern AI. Learn more about your ad choices. Visit megaphone.fm/adchoices

What does it take to turn one of the biggest hidden costs in commercial real estate into your biggest advantage?In this episode of Common Denominator, I sit down with Jerry Katz, founder of Premier Protection Insurance Services, for a conversation that starts with insurance and expands into scaling, execution, and the future of real estate investing in Florida. Jerry walks us through his path from chasing Wall Street excess, to losing everything in the dot-com crash, to building a captive insurance strategy that's helped him optimize more than $4 billion in commercial real estate portfolios.We get into why most investors still treat insurance as an unavoidable expense instead of a profit lever, how the Live Local Act is reshaping opportunity for Florida developers, where AI is already changing underwriting, and what really separates founders who scale past seven figures from the ones who stall out.This is a conversation about execution, hidden leverage, and building wealth that lasts Timestamp0:00 Welcome 2:50 Why multifamily and commercial real estate have stabilized5:11 How Dubai's instability is pulling capital into Miami6:10 The workforce housing gap in Miami-Dade7:37 The Live Local Act and the edge it gives Florida developers11:17 Where AI is already disrupting real estate13:11 AGI and what happens when AI starts teaching AI13:51 Self-storage, autonomous vehicles, and the next disrupted asset classes15:52 Sky ports, flying taxis, and Jerry's insurance origin story21:58 How the insurance industry really works, and who profits from it24:42 Why banks own the insurance companies too25:17 Total Asset Optimization: insuring almost $5 billion in Florida27:50 What sophisticated investors do differently on insurance30:08 Jerry's worst business mistake: Wall Street and the dot-com bubble38:01 Why business is a spiritual journey39:39 Rapid fire round43:31 What common denominator really means Like this episode? Leave a review here  https://ratethispodcast.com/commondenominator Newsletter  https://moshepopack.com/newsletter/ Follow Common Denominator Podcasthttps://moshepopack.com/podcast/@mpopackhttps://www.instagram.com/mpopackhttps://www.facebook.com/MoshePopack Follow Jerry Katz https://www.instagram.com/jerrykatzceo/?hl=enhttps://www.facebook.com/jerry.katz.50/#CommonDenominator #MoshePopack #JerryKatz #PremierProtectionInsurance #CommercialRealEstate #FloridaRealEstate #Entrepreneurship #InsuranceStrategy

Fresh out of the studio, Jing Yang, Asia Bureau Chief at The Information, joins us to explore how China is building frontier AI under chip constraints, state capital, and open source ambition. Jing breaks down her scoop on DeepSeek's $7.4 billion round at a $50 billion valuation, unpacking a deal structure in which the founder wrote two-fifths of the check and outside investors got no voting rights. She explains why Chinese AI valuations trail US labs, why seven to eight language model players refuse to consolidate, and why DeepSeek chose Huawei chips before Huawei knew about it. Last but not least, Jing shares the indicators she is watching from now to 2027."This is one of the things that really surprised me when I was working on this story: DeepSeek had spent a lot of time last year retrofitting their models and software with Huawei chips. I think a lot of people assumed it's because the government ordered DeepSeek to work with Huawei to embrace the domestic ecosystem, but it's actually the opposite. It was DeepSeek that voluntarily started using Huawei, experimenting with the Huawei chips, and Huawei actually only found out after. Then they started sending people to DeepSeek to help..." - Jing Yang, Asia Bureau Chief, The InformationEpisode Highlights:[00:00] Quote of the Day by Jing Yang from The Information[01:30] What changed since September: the ByteDance mystery resolved[03:00] Why China's AI must be read on its own terms[04:10] Breaking the story of DeepSeek on their 7.4B fundraise[05:40] How the valuation went from $10 to $50 billion[08:30] The lab that became famous for rejecting scaling laws[10:15] Unpacking the DeepSeek deal structure: four types of investors[12:40] Five-year lock-up and no secondary market trading[14:20] Reverse due diligence: DeepSeek vetting its own investors[16:40] Balancing open source, AGI research and IPO pressure[17:45] The irony: inclusive vision, exclusive deal structure[20:35] How Anthropic's Mythos preview changed Liang's mind[21:30] Why Chinese AI valuations look tiny next to US labs[23:00] Why Chinese founders go consumer when they go global[26:45] "The worst of customers" — price sensitivity and zero loyalty[27:45] The compute constraint that caps every Chinese lab[28:40] New labs in China: weaker infrastructure, harder exit[30:30] Can China leapfrog on hardware the way it did on models?[32:50] Stricter compliance, not political muscle[34:30] Founder power when your company becomes strategic[36:40] Junyang Lin's new lab and the scarcity premium[38:00] Open source influence versus sustainable commercial models[39:30] Zhipu versus MiniMax: how sentiment diverged after IPO[42:30] What is the right mental map for China's AI ecosystem?[43:20] The consolidation that still hasn't happened[45:00] Seven to eight serious players and nobody giving up[46:40] The one thing Jing wishes people would ask[47:20] Nobody ordered DeepSeek to use Huawei chips[50:00] Indicators to watch from now to 2027[55:30] ClosingProfile: Jing Yang, Asia Bureau Chief from The InformationThe Information Profile: https://www.theinformation.com/u/JingYangLinkedIn: https://www.linkedin.com/in/jing-yang-33548123/X: https://x.com/jingyanghkPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.

S6E9 Mark Ryski on why stores convert up to 70% of store traffic while online only converts 3% and Amazon only 10%77% of shoppers who walked out of a big box store without buying told exit interviewers they came in to buy. They couldn't find help, wouldn't wait in line, and left empty-handed. Mark Ryski has spent almost 3 decades measuring that gap, and his new book gives it a name: Store Traffic Is A Gift, and most retailers waste it. This episode, Ricardo and Casey sit down with Mark Ryski, founder and CEO of HeadCount Corporation, to dig into retail's most misunderstood metric: in-store conversion. Physical stores convert 20% to 70%+ of their store traffic. Typical online sites convert under 3%, and Amazon converts about 10%. The store converts several times better than any other channel, yet 84% of retail sales (US Census Bureau, March 2026) still get managed as if online were winning, and in-store conversion gets treated as a vibe at the end of the day.In This Episode, You'll Learn•       Why a store can get more traffic and sell less, and what conversion data reveals.•       The exit survey result that changed a big box operator's mind about service.•       The single fastest conversion win, and why most chains schedule it backwards.•       How super converting stores hit 75% while sister stores in the same chain sit at 30%.•       The CFO conversation: what 100 basis points of conversion is worth in top-line sales.•       What BOPIS, curbside, and Amazon returns really do to store traffic and conversion.•       Why accurate store traffic data is the demand signal AI needs before anything else.Subscribe & FollowIf you enjoyed this episode, please leave us a 5‑star rating and review on Apple Podcasts, Spotify, or Goodpods. Subscribe on YouTube so you never miss an episode and check out the other shows in the Retail Razor Podcast Network: Retail Transformers, Blade to Greatness, and Data Blades.Subscribe to the Retail Razor Podcast Network: https://retailrazor.com/Subscribe to our Newsletter: https://retailrazor.substack.comSubscribe to our YouTube channel: https://go.retailrazor.com/utubeFeatured guestMark Ryski. https://www.linkedin.com/in/mark-ryski-8826601/CEO, HeadCount Corporation. https://headcount.comAuthor, Store Traffic Is A Gift. https://a.co/d/00VI3UujMark Ryski is the founder and CEO of HeadCount Corporation and one of the retail industry's leading authorities on store traffic and shopper conversion. For more than 20 years, he has helped retailers—from independent stores to large national chains in over 20 countries—convert more store visits into sales and make better operating decisions. He is the author of three books on retail performance, including Store Traffic Is a Gift: The Retailer's Guide to Converting Visits into Sales, Conversion: The Last Great Retail Metric, and When Retail Customers Count, the first book dedicated to the study of store traffic. Mark is a longtime BrainTrust contributor on RetailWire and has been recognized as a Rethink Retail Top Retail Expert in 2025 and 2026.Chapters00:00 Teaser00:32 Show Intro05:06 Welcome, Mark Ryski!10:28 Traffic Is a Gift13:09 Origin Story - Counting Traffic16:45 Conversion Numbers and Fast Wins23:16 Exit Surveys Reveal Missed Sales26:22 Proving Payroll Pays Back28:08 Inside Super Converting Stores31:57 BOPIS And Amazon Traffic Trap38:23 AI Needs Clean Traffic Data44:52 Optimism For Physical Retail48:03 Show CloseMeet your hostsHelping you cut through the clutter in retail & retail tech:Ricardo Belmar is an NRF Top Retail Voice for 2025 and a RETHINK Retail Top Retail Expert from 2021 – 2026. Thinkers 360 has named him a Top 10 Thought Leader in Retail, a Top 25 Thought Leader in AGI and Careers, a Top 50 Thought Leader in Agentic AIand Management, and a Top 100 Thought Leader in Digital Transformation and Transformation. Thinkers 360 also named him a Top Digital Voice for 2024 and 2025. He is an advisory council member at George Mason University's Center for Retail Transformationand the Retail Cloud Alliance. He was most recently the partner marketing leader for retail & consumer goods in the Americas at Microsoft.Casey Golden, is the North America Leader for Retail & Consumer Goods at CI&T, and CEO of Luxlock. She is a RETHINK Retail Top Retail Expert from 2023 - 2026, and Retail Cloud Alliance advisory council member. After a career on the fashion and supply chain technology side of the business, Casey is obsessed with the customer relationship between the brand and the consumer and is slaying franken-stacks and building retail tech! MusicIncludes music provided by imunobeats.com, featuring Overclocked, and E-Motive from the album Beat Hype, written by Heston Mimms, published by Imuno.

7/15/2026 THE WEALTHY MINDSET: THINKING LIKE A LEGACY BUILDEREPISODE 1824“Before the bank account will grow, a leader must grow themselves, their people skills and then grow their people.” J Loren NorrisThe Wealthy Mindset: Thinking Like a Legacy BuilderTo possess a wealthy mindset in leadership means you have the skills and abilities to access and perceive the most valuable assets, investments and opportunities. Often that means ways for your money to make babies. More often it means investing in the right people. The more we engage tools, the fewer people we may employ. This means the selection process must be even better. Investing in IQ, EQ and PQ will prove to be essential in coming years as AGI attempts to refashion EQ. Focus on resource stewardship, long-term vision, and building people over profits.As Paul Martinelli says often “Most people don't have a resources problem, they have a resourcefulness problem.” Wealthy is less about accumulation and more about execution. RESOURCES MENTIONED IN THIS VIDEO:

In a post-AGI world, can a country without access to frontier AI even be considered sovereign anymore?Anton Leicht says once frontier AI becomes a core economic input, the countries that own it will pull further and further ahead. Everyone else stays a customer… or worse. Maybe the dominant power wants your land, or a military base, or a resource. Without economic leverage, there's very little you could do about it.Anton — Carnegie fellow and writer of the blog Threading the Needle — thinks middle powers should band together and build their own frontier models.He's costed it out: something like $500 billion over four years for a band of allied democracies. That's not absurd money for the G7 minus the US. The problem is you'd be asking treasuries to take on sovereign debt for a speculative venture with no business case, wide open to US coercion and domestic backlash.So despite its promise, Anton's verdict is that it probably won't happen. His backup is for countries to ask themselves: if intelligence becomes abundant, what stays scarce?Upstream, that's everything that feeds the supply chain: ASML's lithography machines, chipmaking, exclusive training data — all of it gets more valuable as AI does.Downstream, “a country of geniuses in a data centre” still can't cure cancer without someone building the production plants and running the trials. The Europeans, Japanese, and South Koreans are good at exactly these real-world bottlenecks.It's an imperfect fix. The US would still hold more leverage, plus an incentive to re-industrialise and cut you out. The prize is avoiding the worst outcomes: a gradual but irreversible decline, waiting to be either annexed or discarded as the US and China race ahead.In this episode, Anton and host Tom Reed look at what middle powers should start doing now to keep a seat at the table.Learn more, video, and full transcript: https://80k.info/AL This episode was recorded on June 19, 2026.Chapters:Cold open (00:00:00)Who's Anton Leicht? (00:00:43)Most countries face bleak AI futures (00:01:06)How middle powers can strike AI deals (00:06:10)The $500 billion AI moonshot (00:12:16)Would the US crush allied AI? (00:24:54)When to launch the AI moonshot (00:31:56)Why AI dominance is forever (00:35:45)Is AI dependence catastrophic? (00:37:42)What's left to sell in an AI-dominated world? (00:42:45)Policies to avoid mass AI-layoffs (00:47:47)Who really governs Anthropic? (01:08:29)Why “pausing superintelligence” fails (01:10:52)Is American AI monopoly safe? (01:21:08)Explaining AGI to the world (01:28:40)Is Anton bullish or bearish on Germany? (01:31:05)Our production team includes:Video editors: Josh Alward, Dominic Armstrong, Ollie Bignell, Andrés Escobar, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranCamera operator: Jeremy ChevillotteMusic: CORBIT

In 2022, EVONA had eighty staff, a Manchester office, a six-hundred-thousand-pound monthly cost base, and had just spent three hundred thousand pounds flying the whole company to Monaco. Tom Kelly calls it the moment four first-time founders lost perspective.What followed was one of the more honest rebuilds you will hear in recruitment. Contract team gone. Marketing team gone. Management team restructured. Tom moved to the US, took the wheel as sole decision-maker, and rebuilt around two metrics: twenty-five interviews per person per rolling four weeks, and a thirty-day close on every job.Three years on, EVONA is projecting ten million pounds net fee income this year. Three and a half million EBITDA. Thirty-five people. Two inbound client enquiries arrive every single day.Tom Kelly has been in the space sector since 2018. He has never taken outside investment. And last month was the largest in the company's history.On this episode of The RAG Podcast, Tom breaks down exactly how they got there.EVONA is now the dominant name in space sector recruitment. Ninety-five percent US clients. No cold outreach. No single client above nine percent of revenue. A thirty-two day average time to fill on roles that take competitors sixty.Tom Kelly is thirty-nine. He does not have a rigid three-year exit plan, but he does think there is a short window, and he does believe AGI changes the calculus for anyone thinking about an acquisition in this space. What he is building now is the version of the business that is worth something, the lean, high-output, brand-trusted version that took cutting fifty people to find.If you have ever wondered what it actually looks like to rebuild a recruitment business properly after it nearly breaks, this episode has the blueprint.Episode Sponsor: AtlasAdmin is a massive waste of time. That's why there's Atlas, the AI-first recruitment platform built for modern agencies.It doesn't only track CVs and calls. It remembers everything. Every email, every interview, every conversation. Instantly searchable, always available. And now, it's entering a whole new era.With Atlas 2.0, you can ask anything and it delivers. With Magic Search, you speak and it listens. It finds the right candidates using real conversations, not simply look for keywords.Atlas 2.0 also makes business development easier than ever. With Opportunities, you can track, manage and grow client relationships, powered by generative AI and built right into your workflow.Need insights? Custom dashboards give you total visibility over your pipeline. And that's not theory. Atlas customers have reported up to 41% EBITDA growth and an 85% increase in monthly billings after adopting the platform.No admin. No silos. No lost info. Nothing but faster shortlists, better hires and more time to focus on what actually drives revenue.Atlas is your personal AI partner for modern recruiting.Don't miss the future of recruitment. Get started with Atlas today and unlock your exclusive RAG listener offer at https://recruitwithatlas.com/therag/Episode Sponsor: HoxoEvery recruitment founder is investing in LinkedIn, but AI has turned templated posts and outreach into a commodity. When everyone sounds the same, the market stops listening. The recruiters winning now are the ones the market trusts.At Hoxo we help recruitment founders become the most influential name in their niche, using AI to multiply output while trust stays the product. Our clients turn their existing networks into £100K to £300K in new billings within months. Watch the free RAG listener training to see how: https://hubs.ly/Q03lBpYC0

Get 30 Days of Merlin free at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MerlinCrypto.Com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ In this explosive new episode of Markets Radar, I am tracking a historic divergence tearing through the global economy! I break down the devastating "capex pivot" that just triggered IBM's worst stock collapse since 1968, as enterprise customers abandon traditional software to pour billions into AI infrastructure. But while the software sector bleeds, Wall Street is cashing in, JPMorgan just posted a jaw-dropping $21.16 billion in profit, with CEO Jamie Dimon declaring the markets are "booming" and the consumer is "fine". Then, I shift to a massive geopolitical powder keg: the collapse of the U.S.-Iran interim peace deal. I examine President Trump's new military strategy and naval blockade in the Strait of Hormuz, and break down how this dangerous "war of attrition" threatens the 20% of the world's oil supply that passes through the waterway. Finally, I take you into the venture capital world where Ashton Kutcher and Morgan Beller are defying the industry funding drought with a massive $500 million "post-AGI" deep tech fund, and I look at how South Korea is hitting the ultimate AI jackpot, upgrading its 2026 GDP forecast to 3.0% on the back of a record-shattering $290 billion export surplus. In This Episode: The JPMorgan Juggernaut: Why Jamie Dimon says the economy is showing "notable resiliency" and how the "generational" AI buildout is supercharging investment banking. IBM's 1968-Level Wipeout: Inside the massive 26% stock plunge and the devastating capital shift that is actively starving the software sector. The Battle for Hormuz: Trump's third military strategy shift, the terrifying new naval blockade, and the threat of a prolonged war of attrition before the November midterms. Ashton Kutcher's "Post-AGI" Bet: Inside Decimal Capital's ambitious $500 million mega-fund aimed at deep tech, energy, and infrastructure. South Korea's AI Jackpot: How the insatiable global demand for AI memory chips is driving a massive economic surge and forcing the central bank to weigh higher interest rates against inflation Enjoy! Join the Age of Radio Discord | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://discord.gg/EeamD8WcjN⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow me on Goodpods ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://goodpods.app.link/usUyBZzhuNb⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Free Financial Consultation: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://forms.gle/B6nNZ2FbxbhESCHg9⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Red Wizard Gaming Society: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://discord.gg/9D43EszdUB⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠   DM if you are interested in Life Insurance! If you or someone you know has been struggling or in crisis please call or text 988 or chat ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠988lifeline.org⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  

I participated in another Socratic Debate about the Future of "AI" and XR at Augmented World Expo 2026 with Leslie Shannon, Alvin Graylin, and Louis Rosenberg (you can listen to last year's debate in episode #1611). Shannon and Graylin argued for "AI," whilst Rosenberg and I argued against "AI." In my write-up, I wanted to leave some breadcrumbs to more in-depth, skeptical arguments against "AI" that we didn't have space or time to dig into during the debate, but "some breadcrumbs" ended up being over 30k words, and more like an outline for an entire book. Writing a book isn't on my to-do list at the moment, but disseminating the work researchers, journalists, linguists, and critics of "AI" have done is urgent and necessary, so I'm sharing the results of this deep dive here, in bullet list format. My fellow panelists, Alvin Graylin and Louis Rosenberg, indulged me in extending our debate offline after AWE, pushing me to test my ideas further and demonstrating how and why this is an extremely active field of study with polarizing points of view that often come down to philosophical differences. Clearly, it's just getting started. My objections to "AI" is loosely organized by various themes, but some framing may be helpful in approaching it. My objections to "AI" center around the limitations of LLMs, the consolidation of wealth and power from Hyperscaler companies, and threats from automated decision making systems and surveillance capitalism melded with democratically-backsliding authoritarian governments. I'm including a broad range of critiques spanning the domains of philosophy, technology, sociology, politics, economics, culture, and ethics. I'm coming from the orientation of Process Philosophy & Peircean Semiotics that emphasizes the relational and contextual dimensions that the "AI" field tends to de-emphasize or completely collapse. I see process-relational philosophy as a necessary paradigm shift away from the underpinning philosophies of the "AI" community, which tend to be Functionalism, Naturalism, Computational Theory of Mind, Physicalism, & the TESCREAL bundle. Below you'll find my own process philosophical emergency response to "AI embedded within my curation of excerpts and commentary of primary sources that I'm leaning upon. The AI Con: How to Fight Big Tech's Hype and Create the Future We Want (2025) by Emily M. Bender and Alex Hanna (also see episode #1563). Bender & Hanna say, "To put it bluntly, 'AI' is a marketing term. It doesn't refer to a coherent set of technologies. Instead, the phrase "artificial intelligence" is deployed when the people building or selling a particular set of technologies will profit from getting others to believe that their technology is similar to humans, able to do things that, in fact, intrinsically require human judgment, perception, or creativity." Emily M. Bender wrote the "Artificial Intelligence" [preprint] (2026, June 25) entry for the Oxford Research Encyclopedia of Science, Technology, and Society. Bender's concluding paragraph gives a great overview of the seven different ways that the idea of "artificial intelligence" operates in the world. She says, "The notion of artificial intelligence is frequently sold as present or near-future and inevitable technology. In fact there is no coherent set of technologies that can serve as the denotation of the phrase, nor do any of the technologies so marketed rise to the fantastical but ill-defined claims of 'AI' is or soon will be. Nonetheless, the idea of artificial intelligence has been extremely impactful in the world. In order to better understand and deal with those impacts, it is helpful to look at artificial intelligence through the varied lenses of how the idea operates in the world: as the name of a research field, as one approach to cognitive science, as a parlor trick, as a an ideology, as a way to hide and devalue human labor, as a way to shift and/or obfuscate accountability, and as a means to centralize power." Inventing Intelligence: On the History of Complex Information Processing and Artificial Intelligence in the United States in the Mid-Twentieth Century [dissertation] (2020, December 14) by Jonnie Penn. Penn says, "The phrase ‘artificial intelligence' was coined by John McCarthy, an American mathematician, in 1955. It has travelled with a noticeably amorphous definition since." "AI" has always had a spotty history of technologists using a "brain is a computer" metaphor while also using "poor citation practices." From page 14, Penn says, "The vocabulary Simon, Rosenblatt, McCarthy and Minsky chose to describe new techniques in major newspapers and scholarly journals informed Americans' still plastic understandings of what was possible, and indeed desirable, in the emerging information age… During the mid to late 1950s, these men turned to clannishness, self-aggrandizement, speculative rhetoric, fluid definitions of key terms and poor citation practices to shore up legitimacy for their controversial new techniques — actions that drew attention toward questions of how to accomplish such aims and away from whether they were well founded." Part II: Remembering the Human Microcosm in the Age of Mechanized Intelligence: Philosophy as Emergency Response (2026, June 9) by Matt Segall. In a multi-part Substack series, process philosopher Matt Segall calls for a philosophical emergency response to "AI." He says, "In each case a new media technology intended to expand the power of thought ended up transforming the very nature of the thinker who invented it. Each new medium furnishes the very terms in which we come to understand ourselves. This is why the philosophical response is always an emergency response: by the time anyone has noticed what is happening, what may be lost and what gained, the mutation has already done half of its work." Segall warns about the computational metaphor of the mind by saying, "The large language model now tempts us to adopt an even stranger self-image: that human minds are no different than machines, our thoughts just the statistical echoes of our training data. The creators of this latest technological upgrade are encouraging us to downgrade our estimate of human consciousness, thus narrowing the distance between ourselves and the machines built to imitate us." "Resisting Dehumanization in the Age of 'AI': The View from the Humanities" [lecture] (2026, February 10) by Emily M. Bender. Here are the lecture slides with a bibliography at the end. Bender does an amazing overview of how the marketing of "AI" uses pernicious dehumanization tactics built on an underlying "brain is a computer metaphor." From page 15 of her talk: "Scientific metaphor used and debated in neuroscience: "THE BRAIN IS A COMPUTER. "PR metaphor used by technologists: "THE COMPUTER IS A BRAIN" Bender cites Baria & Cross' paper titled "The brain is a computer is a brain: neuroscience's internal debate and the social significance of the Computational Metaphor (2021), which says “the Computational Metaphor rests on other well-ingrained ideologies in which a hierarchy of human value is tied to a particular notion of intelligence such that the quality of being emotional is considered inferior to being rational." Bender also cites Dijkstra's 1985 lecture "On anthropomorphism in science": "A more serious byproduct of the tendency to talk about machines in anthropomorphic terms is the companion phenomenon of talking about people in mechanistic terminology." Here are a couple of examples of how "AI" Hyperscaler companies like OpenAI use dehumanizing tactics to sell us on "AI" Hype. Sam Altman will say things like, "A kid born today will never be smarter than AI. Ever." Or another example is when Altman says, "For me, AGI is basically the equivalent of a median human that you could hire as a co-worker... And then Superintelligence is when it's smarter than all of humanity put together." These statements collapse the human experience into one dimension of "intelligence," which amplifies the dual harm of treating machines more like humans and treating humans more like machines. It is also questionable the degree to which this statement is even true given the potential non-computational aspects of "relevance realization." More on this down below. Part IV: Remembering the Human Microcosm in the Age of Mechanized Intelligence: Hegel's Loom and the Difference Reason Makes (2026, June 10) by Matt Segall. Segall brilliantly breaks down the "Brain is a Computer Metaphor" by saying, "Metaphor is not just a shiny paint job on the vehicle of cognition. It is the engine of thought. Its coupling of concepts drives the limits of conceivability, shaping what is thought together and what is not thought at all. The metaphorical imagination is our main means of tuning in to the otherwise invisible effects of new media technologies. Part of the discipline philosophy brings is allowing us to notice an analogy as an analogy before advertising crystalizes it into the unnoticed transparency of common sense. A fact is a fact, but it might also be a fossilized metaphor. The governing analogy of our age is that cognition is computation: the brain an information-processing device, perception its input and behavior its output, memory a form of physical storage, learning the adjustment of weights, and intelligence an algorithm for minimizing error or surprisal. On this view, given enough training data and computational power, consciousness itself will eventually be engineered… The metaphor “the mind is a computer,” for example, tacitly proposes that mind is to brain as software is to hardware… Reiterated in textbooks and earnings calls, in grant applications and policy briefs, the partial comparison congeals into an ontology, until we find ourselves insisting not that the mind is like a computer in some respects but that it simply is one — and,...

What if the peace you've been searching for isn't something you need to create, but something you've simply forgotten is already within you?In a world that constantly encourages us to seek happiness through achievement, self-improvement, and external success, it's easy to overlook the one place where lasting peace has always existed. In this conversation, Agi welcomes back inner journey guide Chess Edwards to explore why true peace isn't a fleeting mental state, but the very essence of who we are. Together, they uncover practical insights for recognising your true nature, navigating the restless mind, and living from a place of inner stillness, no matter what life brings.By listening to this episode, you'll discover:Why lasting inner peace is not something you achieve, but something you awaken to beyond the thinking mind.Practical ways to work with meditation, awareness, and self-inquiry without getting discouraged by a busy mind.How to recognise common ego traps and deepen your spiritual practice.Press play now to discover how reconnecting with your true nature can transform the way you experience every moment of your life.˚KEY POINTS AND TIMESTAMPS:03:05 - Inner Peace as the Essence of Being06:02 - Why We Seek Peace Outside Ourselves09:16 - What Meditation Really Is14:23 - Why Meditation Without Study Falls Short21:08 - The Movie Screen Analogy25:02 - The Role of a Guide on the Inner Path29:04 - How the Ego Defends Itself35:49 - Staying Devoted to One Path38:46 - The Acorn Analogy and Chess's Work41:47 - Closing Reflections and Final Invitation˚MEMORABLE QUOTE:"Inner peace is not a state of mind. It is the essence of our being."˚VALUABLE RESOURCES:Chess' website: https://www.chessedwards.com/˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚

On this inspiring episode of the Authority on Demand Podcast (formerly Authors On Mission Podcast), host Danielle Hutchinson sits down with Agi Keramidas, author of 88 Actionable Insights for Life and host of the Personal Development Mastery Podcast.Agi shares his remarkable journey from dentist to internationally recognized podcaster and author, revealing how a midlife awakening inspired him to embrace authenticity, follow his purpose, and encourage others to stand out instead of fitting in.✨ Key Takeaways:• Why authenticity is the foundation of personal growth.• How a simple mindset shift can transform your life and career.• The power of intentional morning routines and actionable daily habits.• Practical insights for discovering purpose and creating meaningful change.Discover why standing out—not fitting in—is the key to unlocking your full potential.Connect with Agi Keramidas:Email: agi@agikeramidas.comWebsite: https://personaldevelopmentmasterypodcast.com/aboutLinkedin: https://www.linkedin.com/in/agikeramidas/fb: https://www.facebook.com/agi.keramidas/

(0:00) The AI Buildout: Datacenters Bigger Than Cities (Andrew Feldman) (1:50) Reasoning, Inference, and Breaking Moore's Law (16:28) Open Source, AI Sovereignty, and the Road to AGI (40:54) The Innovation Behind Generative Video (Robin Rombach) (47:31) Martin Scorsese, Robots, and the Future of Hollywood IP Thanks to our partners for making this possible! AppLovin Ads - AppLovin's AI advertising platform reaches over a billion daily active users across mobile games. Full-screen video ads with a 35-second median watch time. Advertisers are profitably spending hundreds of thousands of dollars a day and advertiser access is still in closed beta. The window is open at https://applovin.com/ALLIN Nasdaq - Positioned at the nexus of technology and the capital markets, Nasdaq provides premier platforms and services for global capital markets and beyond with unmatched technology, insights and markets expertise. https://www.nasdaq.com/convergence-economy Follow Andrew: https://x.com/andrewdfeldman Follow Robin: https://x.com/robrombach Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@allin Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg

Questions? Thoughts? Send a Text to The Optometry Money Podcast! We'll answer your question on the show.Episode SummaryIt's July — half the year is gone, but you still have half a year to make an impact on your tax result. That makes right now the ideal time to sit back and ask: where do things actually stand?In this episode, Evon walks through four questions every optometrist should be asking as a mid-year tax check-in. This is the same work Evon's team is doing this time of year for practice-owner clients — projecting out the practice's profit and loss and running initial tax projections while there's still time to act. The goal is simple: fewer April surprises, and a clear view of the opportunities still on the table before the year closes.What You'll LearnFour tax planning questions you and your professional team should be asking this time of yearWhere your income is likely to land this year — and why type of income matters as much as amountHow to tell whether you're paying enough as you go (and avoiding under-withholding penalties)The AGI and taxable-income thresholds that phase you in and out of key credits, deductions, and extra taxesWhich tax planning levers you can still pull with half a year left — and their deadlinesKey Takeaways for OptometristsGood tax planning starts early and proactively — not in April when the bill is already due. The four questions to work through with your professional team: Where will my income land this year? Am I paying enough as I go? Am I near a threshold that changes things? And what levers do I still have to pull?The thresholds are where the real opportunities hide. Your AGI drives eligibility for the child tax credit, Roth IRA contributions, the higher state and local tax deduction cap, ACA premium tax credits, and your student loan payments if you're on an income-driven plan. Your taxable income drives your marginal rate and your QBI deduction. When several of these phase out together at higher income levels, a well-timed deduction or deferral can be worth far more than your marginal rate alone would suggest.The two biggest levers for practice owners tend to be retirement plan contributions and depreciation. But don't buy equipment just for the write-off — you're spending a full dollar to save thirty cents. Invest in the practice because there's a return on it, then decide how to handle the depreciation. And remember that some levers have a hard December 31 deadline while others (like 401(k) contributions or a cost segregation study) run to your tax filing deadline.Resources for OptometristsEp 159: How to Stop Scrambling at Tax Time – An Optometrist's Guide to Quarterly Tax PaymentsEp 153: How to Invest Tax-Efficiently and Keep More of Your Returns (After-tax)Ep 148: Profit Sharing Demystified – How Optometry Practice Owners Can Maximize Their 401(k) with Matt RuttenbergEp 51: An Optometrist's Guide to the Qualified Business Income DeductionEp 47: An Optometrist's Guide to How Taxes WorkEp 37: Tax Planning For Charitable GivingWant a more proactive approach to your planning?You can schedule a no-commitment introductory call to discuss what's on your mind financially and learn how we help optometrists navigate those same decisions nationwide.

Lincoln Murr breaks down why Coinbase is building toward a post-AGI economy where autonomous agents hold their own financial accounts and transact using digital assets rails. He also unpacks the geopolitics of AI regulation, from potential government stakes in frontier labs to the rise of cheaper Chinese models challenging U.S. dominance.Lincoln Murr is the Head of AI Product at Coinbase, leading the company's x402, Coinbase for Agents, and Coinbase Advisor initiatives at the intersection of artificial intelligence and digital assets.The Rollup is where the leaders of digital assets and finance converge. Live from the financial capital of the world.Timestamps00:00 Intro01:04 AI Meets Digital Assets02:58 Speculating On Compute Markets05:13 Coinbase Advisor Explained07:18 Fully Autonomous Agent Trading10:04 AI Models Get Restricted14:01 Nationalizing AI Labs15:04 Cheap Chinese Models Debate18:00 Data Centers In Space19:56 How Early AI Adoption Is21:25 Coinbase's Post-AGI Vision23:35 Where AI And Payments ConvergeGuest Socials:Lincoln Murr X: https://x.com/MurrLincolnCoinbase X: https://x.com/CoinbaseCoinbase Website: https://www.coinbase.com/Partners: Better than Banks. Transparent capital efficiency earning the highest yields in DeFi. Learn more here: https://infinifi.xyz/---Dinari - Over 230 1:1 backed tokenized stocks, ETFs & more with dividends. US-based SEC transfer agent. Available on 5+ chains & via API. https://dinari.com/---Relay is the fastest and most reliable way to swap any token on any chain. Learn more here: https://relay.link/bridge---Zama is an open source cryptography company that builds state-of-the-art Fully Homomorphic Encryption (FHE) solutions for blockchain.Learn more here: https://www.zama.org/---Trezor is the creator of the first-ever hardware wallet. Securing crypto for 2M+ users worldwide. 100% open source. Learn more here: https://affil.trezor.io/aff_c?offer_i...---

This episode unpacks U.S. and Chinese AI strategies, regulation approaches, and the future implications for society and security.   Host: James M. Lindsay, Mary and David Boies Distinguished Senior Fellow in U.S. Foreign Policy, CFR   Guest: Alvin Wang Graylin, Senior Fellow for Technology, Asia Society; Digital Fellow, Stanford Institute for Human-Centered Artificial Intelligence; Professor of AI/Tech Policy, University of Washington   We Discuss: Whether the framing of an AI "race" with China is accurate or helpful, and why it may be producing costly policy distortions. The concept of AGI (artificial general intelligence) and the thesis that underpins U.S. policy. China's focus on broad industrial adoption over frontier model development. How U.S. export controls and visa restrictions spurred the innovations behind DeepSeek. Why China turned down the offer to purchase NVIDIA H200 chips. Why Beijing regulates AI more extensively than either the United States or Europe. The Trump-Xi discussions on AI safety guardrails and the shared interest in preventing non-state bad actors from weaponizing AI. The economic bubble risk posed by rapidly rising data center costs.   Mentioned on the Episode:   China's "AI Plus" Initiative, State Council of China, August 2025   China's New Generation Artificial Intelligence Development Plan, State Council of China, 2017   For an episode transcript and show notes, visit The President's Inbox at: https://www.cfr.org/podcasts/presidents-inbox/the-myth-of-the-ai-race   Opinions expressed on The President's Inbox are solely those of the host or guests, not of CFR, which takes no institutional positions on matters of policy.

Six years ago, aged just 15, Sneha Revanur founded the AI advocacy nonprofit Encode AI — back when AI felt like a niche issue. Now the world's caught up with her, and she's ready to share everything she's learned about the politics of AI.Encode has grown from a grassroots youth organisation to spearheading an unlikely coalition of AI-exposed groups — family-first conservatives, grieving mothers, Hollywood actors, and AI safety researchers — with the strength to take on $125m-funded anti-regulation lobbyists.So far, Encode's strategy of taking many experimental swings has netted major victories (including California's frontier AI safety bill, SB-53, and New York's RAISE Act) as well as some disappointing setbacks.Going up against Big Tech hasn't been easy. In 2025, OpenAI subpoenaed Encode's general counsel at his home, with a sheriff's deputy arriving while he was having dinner with his wife. The fallout went viral, resulting in more attention than Encode had ever experienced — and Sneha was forced to decide how hard to push back against a company she'd need to negotiate with for years to come.In today's conversation, Zershaaneh Qureshi interrogates some of Encode's strategic moves. The pair discuss all the above, plus:How the AI industry's crypto-inspired anti-regulation strategy is not “AGI-pilled”Why AI advocacy doesn't have to be held back by the slow pace of policyHow mutual trust can hold together the unlikeliest of political alliesAdvice for aspiring AI advocates — including how to balance political persuasion with rigorous reasoning Due to technical issues, this episode was recorded across two days (May 26 and 28, 2026) and spliced together.Links to learn more, video, and full transcript: https://80k.info/SRChapters:Cold open (00:00:00)   Who's Sneha Revanur? (00:00:32)   Sneha's awakening to AI's deeper risks (00:01:16)   “If you do everything, you will win” (00:04:04)   Influencing politics from the outside (00:06:39)   The challenge of grassroots (00:11:16)   Mums, musicians, and conservatives vs Big Tech (00:14:21)   How vetoed bills can still provide wins (00:19:31)   OpenAI's subpoena, served at dinner (00:27:33)   How AI money plays in politics (00:37:19)   Easy wins vs high-upside bets (00:43:25)   Advice for aspiring AI advocates (00:48:03)   Our production team includes:Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, Simon Monsour, and Andrés EscobarProducer: Nick Stockton and Elizabeth CoxCoordination and support: Katy Moore and Lou MoranMusic: CORBIT

The new RAP plan has gotten plenty of criticism, but it might not be all bad news for every borrower. Learn where RAP could help, where it could hurt, and why the right strategy depends so much on your income, forgiveness goals, and long-term financial picture.Key moments:(03:10) Who the RAP plan might actually help(06:37) RAP affects forgiveness timelines and PSLF strategy(08:20) Forecasting future income before choosing RAP(10:51) Married filing separately and lowering AGI under RAP(22:03) Extra payments and last-minute plan switching may backfireLike the show? There are several ways you can help!Follow on Apple Podcasts, Spotify or Amazon MusicLeave an honest review on Apple PodcastsSubscribe to the newsletterJoin SLP Insiders for student loan loopholes, SLP app and member communityFeeling helpless when it comes to your student loans?Try our free student loan calculatorCheck out our refinancing bonuses we negotiatedBook your custom student loan planGet profession-specific financial planningDo you have a question about student loans? Leave us a voicemail here or email us at help@studentloanplanner.com and we might feature it in an upcoming show!Mentioned in this episode:Want more? Check out our other podcastStarting to think beyond your student loans? Check out our other show, Financially Free Era. It's about what comes next, investing, building wealth, and designing a life you actually want. Find "Financially Free Era by SLP Wealth" in your podcast app.Tips I Can't Share PubliclyGetting our free newsletter? Upgrade your experience and discover student loan loopholes so good, they might get repealed if I talk about them publicly. Find out my very best thought leadership that I really just can't be open about anymore, unfortunately. If you want to get our very best tips, not just the ones that I can share for free. Go to studentloanplanner.com/insider to get a special discount for your year membership.

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Send us Fan MailThis conversation bridges the gap between highly technical AI development and grounded, existential philosophy. It offers viewers a sobering but realistic look at how artificial intelligence is fundamentally rewiring our economy, job markets, and daily lives, while also providing a message of hope and purpose through faith and creativity.  Is artificial intelligence paving the way for economic collapse, or is it the ultimate tool for human creativity? In this episode of The Days Grimm Podcast, Christopher McElhin returns to discuss building AI autonomous agents, the reality of the future of AI and the economy, and his debut book.Join Brian, Thomas, and Corey as they welcome back Christopher McElhin to the studio. After a year of intense research and development, Christopher and Thomas have launched Anomalous Enterprise, a suite of AI tools designed to automate business tasks and predict economic trends. The guys dive deep into the potential dangers of artificial general intelligence (AGI), the reality of inflation, and the death of the traditional American dream. To cap it off, Christopher breaks down the deeply personal journey of writing his new book, Rediscovering Imago Dei, using AI as an organizational tool to explore the intersection of physics, philosophy, and Christian theology.If you enjoyed this deep dive into AI, economics, and philosophy, be sure to subscribe to The Days Grimm Podcast! Leave a comment below with your thoughts on the future of AI, and share this episode with a friend who needs to hear it.Guest Links:Anomalist Website link: https://anomalistenterprise.com/Rediscovering Imago Dei: Bridging Reason and Redemption Book:https://a.co/d/04OGHljGTIMELINE :00:00:00 - Intro & Welcoming Christopher McElhin00:04:37 - Death of the Week: AI Chatbot Triggers Paranoia00:15:52 - Building Anomalous Enterprise & Autonomous AI Agents00:27:25 - Has AI Gone Too Far? (Anthropic's Opus 4.8 Sandbox Escape)00:34:21 - Compass AI & Forecasting the Economic Collapse00:50:15 - Universal Basic Income (UBI) and Data Tokenization00:57:32 - Atlas & Excalibur: Creating an AI Mentor Forge01:15:27 - Berea: The Biblical Theology AI01:21:45 - Christopher's Book: Rediscovering Imago Dei01:40:58 - How to Use AI to Write and Publish a Book[The Days Grimm Podcast Links]- YouTube: https://www.youtube.com/c/TheDaysGrimm- Our link tree: linktr.ee/Thedaysgrimm- GoFundMe account for The Days Grimm: https://gofund.me/02527e7c [The Days Grimm is brought to you by]Sadness & ADHD (non-medicated)

Medicare brings peace of mind to millions of retirees, but for those with higher incomes, there's an added layer of complexity called IRMAA—the Income Related Monthly Adjustment Amount. If your modified adjusted gross income (MAGI) crosses certain thresholds, you may end up paying substantially more for your Medicare Part B and Part D coverage. In this article, we break down how IRMAA works, outline common scenarios that may unexpectedly raise your premiums, and offer actionable strategies to help you avoid unnecessary costs during your retirement years.   You will want to hear this episode if you are interested in... [02:14] How IRMAA works [04:09] IRMAA income brackets and premium increases  [05:43] General strategies and limitations for avoiding IRMAA [09:49] Managing Capital Gains and Medicare costs [10:41] Understanding the possibility of unexpected large gains pushing income higher  [12:37] Impact of spouse passing on taxes [14:54] Avoiding IRMAA surcharge   What Is IRMAA, and How Does It Work? IRMAA adds a surcharge to your standard Medicare Part B and Part D premiums if your income exceeds specific limits. The calculation uses your Modified Adjusted Gross Income (MAGI) from your federal tax return for the prior two years. For example, your 2026 Medicare premium is determined by your 2024 tax return figures. This "two-year lag" means financial decisions made today could impact your healthcare costs down the line. In 2024, the standard Part B premium is $202.90 per month. However, single filers reporting over $109,000 or married couples filing jointly above $218,000 pay $284 each per month, per person. Surpassing $137,000 (single) or $274,000 (joint) pushes your premium to $405.90—more than double the baseline. Part D premiums are also subject to surcharges, ranging from $14.50 to $91 per month at the highest income levels.   Seven Scenarios That Can Trigger IRMAA—and How to Prepare While some situations are unpreventable, being aware of these common scenarios can help you make informed choices and potentially minimize your IRMAA exposure.   1. Municipal Bond Income: Not as Tax-Free as You Think Many investors favor municipal bonds for their federal tax-exempt status. Unfortunately, while this income is absent from your regular AGI, it is added back into your MAGI when calculating IRMAA. If you're relying heavily on munis in retirement, this could unexpectedly inflate your Medicare premiums. Consider alternative investments or relocating those assets into accounts or vehicles where this income is shielded, like certain annuities, after consulting with a qualified financial advisor.   2. Capital Gains on Your Home Sale When selling your primary residence, you can exclude up to $250,000 of gain if single or $500,000 if married, provided you meet the two-out-of-five-years residency rule. Gains above these thresholds are taxable and count toward your MAGI. Good record-keeping for home improvements can help increase your cost basis and reduce the taxable gain, but there aren't many strategies to avoid this spike if a large gain is unavoidable.   3. Profits from Investment Property Sales Selling an investment property can generate significant capital gains. But unique to investment real estate, the IRS allows you to defer these gains through a 1031 exchange—selling one investment property and reinvesting the proceeds into another. This move postpones the tax hit and the associated IRMAA impact, possibly indefinitely if you use the stepped-up basis at death.   4. Surprise Mutual Fund Capital Gains If you own mutual funds outside retirement accounts, unexpected capital gains distributions from within the fund (for example, after large stock sales like Apple) could spike your MAGI. To mitigate this, consider shifting from mutual funds to individual stocks, bonds, or exchange-traded funds (ETFs), which typically generate fewer surprise capital gains.   5. Roth Conversions are Great for Taxes, But Be Careful While Roth conversions can be powerful tax strategies, converting a sizable sum from a pretax IRA to a Roth IRA counts as income for IRMAA purposes. Carefully plan the size and timing of conversions to avoid pushing yourself into a higher premium bracket without realizing it.   6. The Financial Impact of Losing a Spouse Widowhood or widowerhood can be doubly difficult; not only do you suffer personal loss, but your filing status shifts to single, drastically lowering the income thresholds for IRMAA. If you expect changes in income or status, make proactive plans with your advisor to help smooth your MAGI.   7. Large, One-Time Retirement Account Withdrawals Big withdrawals from IRAs or 401(k)s—perhaps to buy a car or fund a vacation home—could catapult your income into a higher IRMAA tier. Consider spreading large purchases over several years or evaluating alternative financing options to keep retirement account withdrawals more manageable.   Small Decisions Add Up While IRMAA might not be avoidable for everyone, being strategic about income sources, withdrawals, and investment choices can reduce surprises and keep more of your retirement income where it belongs—with you. Always consult with a financial advisor familiar with your unique situation before making significant financial moves. Keep your knowledge current and your planning proactive to support a more cost-effective retirement.   Resources Mentioned   Retirement Readiness Review Subscribe to the Retire with Ryan YouTube Channel Download my entire book for FREE  2026 Medicare Part B Premium Surprises, #282 7 Ways to Lower Your Income and Avoid the IRMAA Medicare Surcharge, #142 Mistakes To Avoid During Medicare Open Enrollment with Danielle Roberts, #229      Connect With Morrissey Wealth Management  www.MorrisseyWealthManagement.com/contact   Subscribe to Retire With Ryan  

Bruce returns to AGI through the lens of a critical rationalist debate about whether or not AGI is theoretically possible. Can human intelligence be replicated? Is this a physics question or a biology question? Is our universe and our minds like a compact disc or like a vinyl record?But really, Bruce is just using the AGI debate to explore an epistemological idea: that the state of a critical discussion is objective. If two people disagree about the state of a critical discussion, at least one of them must be misunderstanding that state—possibly both of them. It's possible they are genuinely disagreeing over something factual, but more often than not they are really just disagreeing over epistemology.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Support us on Patreon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Naval with three founders who are living in the future: Garry Tan (Y Combinator), Daniel Francis (Abel Police), and Farbood Nivi (A-LIST).  00:00 Guest Intros 02:35 Live in the Future 03:58 Will AI Outsmart us? 07:43 In the Anthropic Breadline 09:59 The Tech Genie Is Out 12:33 We Invested in COVID?! 14:25 Good Writing Is Novelty 18:50 Living Like It's 2028 24:32 Truth dot ai 30:18 Does China have the Weights? 35:38 Everyone has AI Anxiety 39:32 Have Your Agent Talk to My Agent 42:01 What if Open Source takes the Lead? 44:03 The Sun is Setting on Google 48:00 Ride the AGI 50:46 Will There be Startups? 54:05 Defending Taiwan 1:00:05 The California Empire 1:01:26 If the U.S. Falls 1:03:11 Universal Basic Robot 1:06:01 Humans as AI Handlers

What if the adversity you are facing right now is not happening to you, but for you?In this series, I select my favourite and most insightful moments from previous episodes of the podcast.Today, my guest Matt O'Neill, a leading expert in happiness education, shares a profound and deeply personal teaching on how to maintain inner happiness when life is at its hardest. Matt makes the case that the only true suffering comes from the story we tell ourselves, and that compassion is always the path back to peace.Press play for one of the most honest and moving conversations about happiness, adversity and the choice to see life differently.˚VALUABLE RESOURCES:Listen to the full conversation with Matt O'Neill in episode #424:https://personaldevelopmentmasterypodcast.com/424˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚