Podcasts about Sora

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

Critterz was one of the first feature films built primarily with AI, but OpenAI abruptly shut down Sora, the very tool its director was relying on. Nik Kleverov, Chief Creative Officer of Native Foreign, joins Rapid Response from the Cannes Lions festival to make the case for why AI isn't killing Hollywood, it's about to reinvent it. He breaks down why 2026 is year zero for mass adoption of AI in the film industry, and why human talent is more in demand than ever when great storytelling is the goal. Kleverov also confronts the darker side of the technology: deepfakes, scams, and the blurring line between real and generated, and offers a surprising possible solution.Visit the Rapid Response website here: https://www.rapidresponseshow.com/See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Episode #432 of BGMania: A Video Game Music Podcast. Today on the show, Bryan and Bedroth present the annual Randomizer episode, this year with a very special twist. Bryan's best friend and beloved companion of the last 12+ years unfortunately suddenly passed away on July 2nd. Randomizer IV is completely dedicated to his life and memory. Run free and rest easy, Avenger. Email the show at bgmaniapodcast@gmail.com with requests for upcoming episodes, questions, feedback, comments, concerns, or any other thoughts you'd like to share! Special thanks to our Executive Producers: Jexak, Xancu, Jeff & Mike. EPISODE PLAYLIST AND CREDITS Invasion! from Space Engineers [Karel Antonín, 2019] The Final Battle! from Shining Force III [Motoi Sakuraba, 1998] Rhynehard's Supper from Unruly Heroes [Caisheng Bo & Julien Koechlin, 2019] New Sunlight from Albert Odyssey 2: Jashin no Taidou [Naoki Kodaka, 1994] Raid Before Dawn from MARVEL Future Revolution: The Convergence [Netmarble Monster Sound Team, 2021] Rose from Goddess of Victory: NIKKE [Cosmograph, Zekk & Feryquitous, 2022] The Hunt is Coming from The Witcher 3: Wild Hunt [Marcin Przybyłowicz, 2015] Divine Punishment -Hanzo Hattori- from Samurai Shodown [Jun Hoshina, 2019] Missing Space -Stage 8- from Night Slave [Izuho Yoshitani, 1996] Nijiiro no Mahou -Rainbow-Colored Magic- from Sora no Tsukurikata: Under the Same Sky, Over the Rainbow [Felion Sounds feat. MiLO, 2016] Across The Lightning -Opening- from RAIDEN III x MIKADO MANIAX [Fantom Iris, 2023] The Caste System from The Pink Panther's Passport to Peril [Jared Faber feat. C.E. Smith, Tayla & Brian "Pikasso" Gaidry, 1996] Sweet Cider Girls from Radirgy2 [Daisuke Nagata feat. Yuka Fujita, Runacchi☆hoshi, Reika Wakasugi & Kaede Horikawa, 2024] Joule's Wish from Azure Striker Gunvolt [Ryo Kawakami, 2014] LINKS Patreon: https://patreon.com/bgmania Website: https://bgmania.podbean.com/ Discord: https://discord.gg/cC73Heu Facebook: BGManiaPodcast X: BGManiaPodcast Instagram: BGManiaPodcast TikTok: BGManiaPodcast YouTube: BGManiaPodcast Twitch: BGManiaPodcast PODCAST NETWORK Very Good Music: A VGM Podcast Listening Religiously

Welcome to RIMScast. Your host is Justin Smulison, Business Content Manager at RIMS, the Risk and Insurance Management Society.   In this episode, Justin interviews Jeff McKissack about participating and speaking at RIMS events, and his upcoming RIMS Texas Regional Conference session on August 11th in San Antonio, on the increasing overlap between reputation management and risk management. Jeff shares the critical impact of reputation risk and the growing threat of AI-generated content. He explains how even executives have caused reputational damage to their organizations, and he reveals a practical step that risk managers can take immediately to reduce the likelihood of reputation-related incidents and subsequent litigation. Justin and Jeff discuss the RIMS Texas Regional Conference, and how you can connect with Jeff after his presentation.   Listen for ways to implement reputation management strategies in your organization.   Key Takeaways: [:01] About RIMS and RIMScast. [:16] About this episode of RIMScast. We will be joined by Jeff McKissack of Defense by Design to talk all about reputation risk management. But first… [:39] RIMS-CRMP Workshop. We are delighted to announce that on August 27th and 28th, RIMS President Manny Padilla will be leading the two-day in-person workshop at St. John's University at 101 Astor Place in New York City. A link to the registration is in this episode's show notes. [:59] RIMS-CRMP Virtual Workshops. The next RIMS-CRMP Exam Prep with PARIMA will be held virtually on July 21st and 22nd. Registration links are in this episode's notes. [1:11] We have a summertime webinar. On July 16th, Zurich will present "Too Hot to Ignore: Heat-Related Injuries and Workers' Compensation." Register at RIMS.org/webinars and via the link in this episode's show notes. [1:25] Also on the webinars page, you will see a two-part series hosted by the RIMS Membership Department. The "Classroom to Career" webinar series highlights how RIMS equips students with the knowledge, skills, and connections needed to thrive in risk management careers. [1:41] Participants will gain insights into industry trends, career pathways, and practical tools that help them confidently step into the evolving world of risk management after graduation. These sessions will be hosted on September 1st and 9th. [1:55] These sessions are member exclusives and are complimentary for RIMS members, of course. So, if you are interested in becoming a member, this would be the time. Visit RIMS.org/membership. [2:04] You can enroll now in the Virtual RIMS CRO Certificate Program in Advanced Enterprise Risk Management hosted by the famous James Lam. Beginning July 15th, workshops will be held bi-weekly from 11:00 a.m. to 3:00 p.m. ET. The registration link is in the show notes. [2:28] The RIMS ERM Conference 2026 will be held on November 19th and 20th in Columbus, Ohio. Registration will open in July. Be on the lookout for the call for nominations for the RIMS ERM Global Award of Distinction. Visit RIMS.org/ERM2026 in July for that announcement. [2:49] RIMS is back on YouTube. Our handle is @RIMSOfficialChannel. We've got plenty of videos there, including RIMScast, RIMScast Canada video podcasts, and other informative and entertaining content from RIMS. Subscribe to the channel today! [3:08] On with the Show! Our guest today is the Founder of Defense by Design, Jeff McKissack, a noted authority in the fields of threat assessment and the prevention of violent crime, with over 35 years of experience. [3:22] Jeff provides continuing education seminars for those in the educational, medical, legal, financial, real estate, human resources, and risk management sectors. He is one of the favorite speakers in the RIMS Texas Chapters. [3:36] You can find him at the RIMS Texas Regional Conference 2026 on August 11th at 1:45, when he will present "How Reputation Management is Impacting Risk Management." [3:46] As you will hear in this interview, his session at the RIMS Texas Regional will dovetail with the Risks of Physical Harm and Violence. He's here to provide a preview of his session. [3:55] We talk about the Risk Management impact of social media, AI, and deepfake technologies on operations and profits, and how employee activity online and off the clock can ripple through staffing, operations, morale, productivity, profits, and brand image.  [4:10] This episode is a year in the making. Let's get to it! [4:12] Interview! Jeff McKissack, Welcome to RIMScast! [4:29] Jeff has a strong history with RIMS and the RIMS Texas Chapters. He has spoken at DFW RIMS on several occasions. Jeff Strege brought Jeff down to speak at Houston RIMS. He spoke at a couple of conferences co-hosted by South Texas RIMS. [4:59] Last year, Jeff was at the Inaugural Texas Regional RIMS Conference in San Antonio. He's there again this year. [5:15] Jeff loves RIMS, in general, because he doesn't have to explain himself. When he speaks to HR or risk management, his two favorite positions, even above the C-Suite, risk managers and HR directors totally get what he does and why.  [5:34] Jeff says the common response he typically gets is they've never met anybody who does what he does or speaks the language he speaks. He's heard it for 39 years. He loves speaking to people in risk and insurance companies because he speaks the language of liability. [5:54] Jeff does a lot of work with attorneys, as well, on both sides. He tells folks he's trilingual because of all these different industries. He's multifaceted. [6:06] Jeff talks about years of working with risk professionals and feeling their pain points. He reverse-engineers many of those scenarios. He asks, Where are the points where this could have been prevented, or at the bare minimum, mitigated? [6:43] A president of an association of independent electrical contractors called him for help. He asked, You have people working hours, in isolated locations, with expensive equipment on their trucks? What could go wrong? Without much of a push, he's able to jump in and help. [7:20] Jeff says there are only three types of businesses: businesses that have internal employees, businesses that have external employees, and businesses that have both. The risks for those three categories are very different from each other. [7:40] Jeff explains some of the differences in risks between having internal employees operating equipment and external employees in trucks on the road with equipment. There are different security concerns, risk concerns, and liability concerns. Address all the concerns. [8:16] Jeff recently saw a police video of a dump truck plowing through an intersection, with its brakes out, into a ravine. Fortunately, no one was killed. Why did the brakes give out? Who was responsible for checking them? They dodged a major bullet by nobody being injured or killed. [9:11] Jeff says he has three wheelhouses: physical risk, data risk, and reputational risk. He doesn't do active shooter training. He deals with factors that can lead to an active shooter before the shooting happens. He deals in prevention, not in reaction. [9:42] Jeff doesn't deal in cybersecurity but in data. He doesn't talk about people who hack your computers; he talks about people who hack your people. That bleeds over into reputation management. [9:57] Reputation management, with the internet and social media, has taken on a different dynamic. [10:07] Either your employees go online and self-incriminate themselves by putting something out there to the world that reflects upon your company, or they do something off the clock that can still have an impact on your company and its reputation. [10:22] Jeff mentions two courtrooms that we want to avoid: an actual courtroom, and the court of popular opinion. [11:01] Jeff says some things before social media still ended up being headline stories in the traditional media that reflected negatively upon employers. Social media has massively amplified that dynamic. [11:31] Jeff's talk is about how reputation management is impacting risk management. It will be held on August 11th at 1:45 p.m. during the RIMS Texas Regional Conference 2026. It examines how reputation management and risk management increasingly overlap. [11:53] Jeff says risk management and HR management often think that whatever happens off the clock is not on them — until it is. There was an Assistant District Attorney some months ago who walked out of a bar "two out of three sheets to the wind." [12:17] The cops wanted her to get an Uber and go home. She made a huge scene about it, captured on the bodycams. She ended up being arrested. "You don't know who I am. You can't do this to me!" The next day, not only was she fired, she lost her license. [12:33] There are YouTube channels devoted to police bodycams. People and companies suffer the reputational consequences of those videos. Once they're on YouTube, they go viral. [12:54] A lot of channels have hundreds of thousands to millions of views, reflecting on a company's public image, even though it was one of their employees off the clock. [13:40] Jeff says the biggest thing is education and training. We think people know what we know and the way we think. No, they don't. Jeff compares it to schools teaching social ills. As risk managers, you need to teach employees responsibility if you want to change their behavior. [14:40] Jeff says, Do not expect what you do not inspect. [14:50] A Quick Break! There are so many other wonderful RIMS events coming up in 2026. The Annual Florida RIMS Educational Conference will be held from July 28th through August 1st at the lovely Ritz-Carlton in Naples, Florida. A link to the event is in this episode's show notes. [15:09] Register now for the Second Annual RIMS Texas Regional Conference, which will be held from August 10th through the 12th at the Grand Hyatt on the San Antonio River Walk. Visit RIMS.org/Events for registration information. [15:26] The hotel cutoff date is July 10th. Reservations may still be made after the cutoff date subject to availability; however, the negotiated group rate is no longer guaranteed past July 10th, so reserve now. [15:40] The 11th Annual Chicagoland Risk Forum will return to the Old Post Office on Thursday, September 24th, 2026. Visit ChicagolandRiskForum.org for more information. [15:51] The RIMS Western Regional Conference will be held from October 4th through the 7th in Seattle, Washington. The agenda is live, and registration is open. Visit RIMSWesternRegional.com and the link in this episode's show notes for more information. [16:08] Save the dates October 18th through the 21st. We will be in Quebec City to celebrate the 50th Live RIMS Canada Conference. Booth sales are open, and sponsorship opportunities are still available. Advance registration is open now. [16:24] Visit RIMSCanadaConference.ca for more information. Also, remember to check out RIMS.org/Canada for our spinoff show, RIMScast Canada, hosted by National Conference Committee Chair, Aaron Lukoni. [16:39] The RIMS ERM Conference 2026 will be held on November 19th and 20th in Columbus, Ohio. Registration opens in July. [16:49] Be on the lookout for an announcement about submissions for the RIMS Global ERM Award of Distinction. Visit RIMS.org/ERM2026.  [17:00] Let's Return to Our Interview with Jeff McKissack! [17:07] Jeff says some of these reputational concerns have been C-Suite executives. Jeff just taught about a mayor arrested in Kentucky for shoplifting a $70 pair of shorts at a department store. Jeff guesses the mayor felt he was owed those shorts. [17:32] Last week, Jeff was talking about a city manager in Texas who felt it was a good idea, when she took her employees to a conference, to take them to a local strip club that night. After a few drinks, she got up on stage and started dancing around the pole. [17:46] Somebody recorded that and put it on social media, and the city manager got fired. City managers aren't easy to come by. In risk management, we have to understand that when these types of things occur, they impact multiple areas, both immediately and simultaneously. [18:05] These incidents affect staffing, operations, morale, productivity, profits, and public image. Every one of those six areas has associated dollar figures. This is why reputation management now must be taken into consideration under risk management. [18:50] Jeff says if I get an employee compromised in a controversial, scandalous, or criminal situation, whether I capture them on camera, or if I create the environment for said compromise to occur, and I set them up to be on camera, do I have them in the palm of my hand? [19:25] Reputational concerns, if they're not addressed and talked about before, and an employee gets caught in something that happens or was made to happen, it can be used as blackmail to have them do things internally because they have access that outside parties want. [20:08] About a year ago, Jeff says a case made national news of a high school principal who was facing the wrath of his community because of a racial rant he was supposedly caught saying as the principal of a school with minority students. [20:27] It came out that the athletic director had taken a digital sample of the principal's voice, maybe from a voicemail, and created an entire verbal tirade from scratch. That was proven through forensics. [20:47] Meanwhile, the principal had almost lost his job and career and was facing physical threats to his family from the public over something that had never happened. That showed Jeff we were entering a new atmosphere. [21:03] Jeff says, Sora 2, one of the newer AI video platforms,  has already been called out for causing problems for police because people are creating videos of real people committing not-so-real crimes. With AI, it looks real. [21:19] Jeff describes a hypothetical situation where an AI video would be used to charge an employee of a crime, leading to an arrest and a record. Think of the liabilities you and your company would face. None of those are cheap. [22:02] Justin asks about verifying the legitimacy of a video. Jeff says there is software out there that does a pretty good job of figuring out if this video is AI-generated. [22:32] What camera captured the action? Was it an internal, company-owned camera, or something someone supposedly captured on a phone camera? One of those is more susceptible to manipulation. [22:53] Now, you have to be a little bit skeptical. Never has there been a more important time in our history to live by the adage, "Innocent until proven guilty." Jeff says a healthy dose of skepticism is called awareness. [23:50] Everybody has vulnerabilities. The first step in preventing a lot of these things from occurring is recognizing your own vulnerabilities, stereotypes, and false premises. Jeff says stereotyping is judging based on appearances; profiling is judging based on behaviors. [24:26] Jeff says you almost have to go into this blind, deaf, and dumb to do a true, analytical, unbiased evaluation. [24:34] Sponsorships! You can sponsor a RIMScast episode for this, our weekly show, or a dedicated episode. RIMScast is proud of its longstanding relationships with AXA XL, Global Risk Consultants, Alliant, Zurich, and more. [24:57] Links to many of these episodes are in the show notes. RIMScast sponsorships can be bundled with whitepapers and webinar sponsorships. [25:04] RIMScast has a global audience of risk and insurance professionals, legal professionals, students, business leaders, C-Suite executives, and more. Let's collaborate and help you reach them! [25:18] Reach out to Ted Donovan at  TDonovan@RIMS.org or Sales@RIMS.org. Let's find the best opportunity for you and your organization. [25:30] Let's Conclude Our Interview with Jeff McKissack! [25:54] You can't do anything about the various AI generation platforms out there. People, for the most part, are using them for all the right reasons. The thing you have to be concerned about is if it's used to accuse someone of something. [26:10] Jeff describes speaking to the Society of Government Meeting Professionals. They are meeting planners for all the alphabet agencies, the Department of War, FAA, CDC, and more. Jeff has spoken at their annual conference for years. [26:26] Jeff told them, you're bringing all these people here for an annual conference at one place, at one time, under one roof. Who do you think might want to compromise someone there, so that by the time they return home, they've been puppeteered? [26:44] Jeff is getting these meeting planners to start thinking about security in a different way, and educating the people. [26:50] "Hey, we love having you here, but when you go out to bars and restaurants, here are some things you need to think of, because some people here may know who you are and what you represent by way of access." [26:38] Jeff says, if something happens, and they get lured back to a hotel room, for various things, and they get slipped a Mickey and pass out, and a couple of weeks later they get a video by text or email, they don't even know if it's a deep fake or not because they were blacked out. [27:27] These are things we all have to understand how easy it is. Jeff constantly admonishes employees, when he speaks at that level, to remember it's not about them. They may not have wealth, but they absolutely represent access to things that people want. [27:48] You have to understand that you may have a target on your back you don't realize you have. When people approach you, ask yourself, does everything that happens in Vegas stay in Vegas? Or does it stay on social media and YouTube? YouTube remembers. [28:50] Jeff shares something from his presentation. The first step is not the stopping point. The first step is "Do you have a social media agreement in writing that your employees have signed?" If not, you, HR, and your General Counsel need to get together and word-smith it, ASAP. [29:18] Jeff says he talked to a VP of a company with over 8,000 employees. When the VP was hired as a VP, he was paid to sign two documents. He was paid $50 to sign the NDA, so he wouldn't take the sales list if he left. He was paid $100 to sign their social media agreement. [29:39] The company was more concerned about him doing something that impacted its public image than about taking the client list. First step is to wordsmith that agreement and how you incorporate it into a Code of Conduct as far as the off-the-clock expectations. [30:08] Jeff shares a case from New York City where a young sitting judge had an OnlyFans page. He was fired for it and tried to fight it. He lost because the verbiage in the City Manual for his position said: "extra-judicial activities … do not detract from the dignity of judicial office." [31:02] Jeff spoke at a risk management conference. He showed three lawsuits that were settled in the last two weeks: $125K, $225K, $485K for employees at state and government universities, entities, and institutions over the Charlie Kirk assassination. They had been fired.  [31:20] They came back and sued for wrongful termination and got settlements because there was nothing written that dictated employee behaviors online. [31:45] You've got to have the paper trail or digital trail. If it's not in the Employee Handbook, add it as an addendum they sign, and put it in their file. [31:57] Next year, when new hires come on board, have it as part of the Employee Handbook, whether it's the social media agreement and/or your expectations/code of conduct. At the bare minimum, have those things in order. That's your starting point. [32:40] Jeff says when you hire someone, treat the Handbook like the Apple Agreement. Going into detail about the policy is best addressed in education and training. [32:59] Jeff talks not only to executives but also to employees. The biggest thing he is trying to get HR, Risk Management, and the C-Suite to understand is that if you're going to alter the behaviors, you've got to teach them what to do otherwise. [33:30] You're not going to get your employees to go cold-turkey on social media, but you can teach them to apply critical thinking skills so that they police themselves and you don't have to. [33:45] You have to do it in a way that tells them what's in it for them. People expect them to come home with a paycheck. Tell them all their plans can be interrupted if they do something foolish now. [34:39] Sometimes, Jeff does not have time in the session to take questions from the audience. He always tells folks he'll be around in the hallways because they may have questions they don't want to ask in front of everybody else there. Or, reach out to him on LinkedIn, or call him. [35:22] In this arena, a lot of people don't want to put out their concerns. Justin says Jeff is very approachable. He wears a hat, and it has become a branding point for him. Justin asked him for a headshot with the hat, to make him more recognizable, because people know him in Texas. [36:58] Justin asks about other cases involving social media outside of work. Jeff refers to the city manager who was let go because of a pole dance that showed up on YouTube, and the shoplifting mayor, also on YouTube. The judge had a side gig that was questionable behavior. [37:35] Jeff says some teachers have been fired over video where they had their kids in the background in the classroom while they were twerking, or dancing with kids. One teacher had his students give him a haircut in the classroom. [37:51] Jeff recalls the wording cited from the City Handbook about the dignity of the office. That covers a lot. Things like that are subjective, but most people know it when they see it. [38:15] Justin asks if we're in a society where you have to live your life assuming that everything is going to be broadcast? Jeff wouldn't say broadcast, but at least captured on camera. [38:25] When Jeff is doing employee training, he goes through cameras that are on the market. You're only thinking of the cameras you see. You are not thinking of cameras that could be out there, recording you. The ATM captures you and looks across the street. [38:52] When Jeff parks in maybe a questionable area, he always tries to find a Tesla. On average, there are seven or eight cameras around that Tesla. He'll take a picture of the license plate of the Tesla next to him, so if anything happens, he can give the picture to the cops. [39:38] Jeff says he relays to people, executives and employees alike, strategies, critical thinking. How do you use technology to your favor instead of having it used against you? [39:51] Jeff McKissack, thank you so much for joining us here on RIMScast. Jeff says he is looking forward to seeing you all in August! [39:58] Special thanks again to Jeff McKissack of Defense by Design. Be sure to attend his session at the RIMS Texas Regional Conference in San Antonio at the Grand Hyatt on the San Antonio River Walk on August 11th. [40:13] Jeff McKissack will deliver the presentation, "How Reputation Management is Impacting Risk Management," at 1:45 p.m. Be sure to follow up with him in the hallways afterwards and let him know that you heard him here on RIMScast. [40:27] Plug Time! Become a RIMS member and get access to the tools, thought leadership, and network you need to succeed. Visit RIMS.org/membership or email membershipdept@RIMS.org for more information. [40:44] Risk Knowledge is the RIMS searchable content library that provides relevant information for today's risk professionals. Materials include RIMS executive reports, survey findings, contributed articles, industry research, benchmarking data, and more. [41:00] For the best reporting on the profession of risk management, read Risk Management Magazine at RMMagazine.com. It is written and published by the best minds in risk management. [41:14] Justin Smulison is the Business Content Manager at RIMS. Please remember to subscribe to RIMScast on your favorite podcasting app. You can email us at Content@RIMS.org. [41:26] Practice good risk management, stay safe, and thank you again for your continued support!   Links: RIMS Texas Regional Conference 2026 | Aug. 10‒12 in San Antonio | Register Now! RIMS Risk Management Magazine | Contribute | Q2 2026 Issue Now Available RIMScast on YouTube! 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RIMS, the Foundation for Risk Management The Strategic and Enterprise Risk Center RIMS Diversity Equity Inclusion Council RIMS-CRMP Stories RIMScast Canada — Episodes Now Live RISK PAC | RIMS Advocacy Defense by Design Upcoming RIMS-CRMP Virtual Workshops: RIMS-CRMP Exam Prep with PARIMA — Virtual — July 21‒22, 2026 RIMS-CRMP Exam Prep Workshop — Live In NY — Aug 27‒28! Full RIMS-CRMP Prep Course Schedule See the full calendar of RIMS Virtual Workshops   Upcoming RIMS Webinars: RIMS.org/Webinars "Too Hot To Ignore: Heat-Related Injuries and Workers' Compensation" | July 16 | Presented by Zurich "RIMS Student Series: Classroom to Career Part 1" | Sept 1 "RIMS Student Series: Classroom to Career Part 2" | Sept 9   Related RIMScast Episodes: "Strategy and Change with Ward Ching and Aaron Olson" "Mid-Year Risk Roundup 2026 with Morgan O'Rourke and Hilary Tuttle" "Live From Texas 2025!" "Leadership Lessons with Major General (Ret.) Robert F. 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RIMS Virtual Workshops On-Demand Webinars RIMS-Certified Risk Management Professional (RIMS-CRMP) RISK PAC | RIMS Advocacy RIMS Strategic & Enterprise Risk Center RIMS-CRMP Stories — Featuring RIMS President Manny Padilla!   RIMS Events, Education, and Services: RIMS Risk Maturity Model®   Sponsor RIMScast: Contact sales@rims.org or pd@rims.org for more information.   Want to Learn More? Keep up with the podcast on RIMS.org, and listen on Spotify and Apple Podcasts.   Have a question or suggestion? Email: Content@rims.org.   Join the Conversation! Follow @RIMSorg on Facebook, Twitter, and LinkedIn.   About our guest: Jeff McKissack, President, Defense By Design   Production and engineering provided by Podfly.  

Fluent Fiction - Japanese: A Summer of Friendship and Festive Healing in Kyoto Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ja/episode/2026-07-06-22-34-02-ja Story Transcript:Ja: 夏の夕暮れ、京都大学の寮は活気に満ちていました。En: On a summer evening, the dormitory of Kyoto University was brimming with energy.Ja: 学生たちは七夕祭りの準備に忙しく、楽しげな声が響いていました。En: The students were busy preparing for the Tanabata festival, and cheerful voices echoed throughout.Ja: その中で、ソラは寮の部屋に一人、静かに座っていました。En: Amidst it all, Sora sat quietly in his dorm room alone.Ja: ソラの腕には包帯が巻かれていて、彼の顔には少しの悲しみが漂っていました。En: Sora's arm was wrapped in a bandage, and a slight sadness lingered on his face.Ja: ソラは努力家で、剣道の大会で優勝することを目標にしていました。En: Sora was a hard worker, aiming to win a kendo tournament.Ja: しかし、彼の腕が折れてしまい練習ができません。En: However, with his broken arm, he couldn't practice.Ja: それでも諦めず、ソラはどうにかして回復する方法を考えていました。En: Still, not wanting to give up, Sora was thinking of ways to recover.Ja: ソラの側には、親友のユキがいました。En: Sora's best friend, Yuki, was beside him.Ja: ユキはいつも明るく、ソラを励ましてくれます。En: Yuki was always cheerful and encouraged Sora.Ja: 「ソラ、大丈夫だよ。きっと治るよ。En: "It's okay, Sora. You'll heal for sure.Ja: 今日は祭りに行こう。気分転換になるよ」とユキは笑顔で言いました。En: Let's go to the festival today. It'll be a nice change of pace," Yuki said with a smile.Ja: その瞬間、ソラのもう一人の親しい存在、ハルトが部屋に入ってきました。En: At that moment, another close friend of Sora, Haru, entered the room.Ja: ハルトはソラの剣道のライバルで、実力も高く知られています。En: Haru was Sora's kendo rival, known for his high skill level.Ja: 「ソラ、元気か？En: "Sora, how's it going?Ja: 俺たち、今日は楽しく過ごそうぜ」とハルトも優しく声をかけました。En: Let's have fun today," Haru gently encouraged.Ja: ソラは迷いました。En: Sora was hesitant.Ja: 祭りに行くのか、練習を続けるのか。En: Should he go to the festival or continue practicing?Ja: ただ、心のどこかで分かっていました。彼は今、楽しい時間を求めていると。En: However, somewhere in his heart, he knew he desired a good time.Ja: 黙って腕に目をやると、「...行こう、祭りに」と小さく微笑みました。En: Silently glancing at his arm, he softly smiled and said,Ja: 行こう、祭りに。En: Let's go to the festival.Ja: 外は賑やかさで溢れ、大学の庭には色とりどりの短冊が風に揺れています。En: Outside was overflowing with vibrancy, the university garden adorned with colorful tanzaku swaying in the wind.Ja: ソラ、ユキ、そしてハルトは短冊にそれぞれ願いを書きました。En: Sora, Yuki, and Haru each wrote their wishes on tanzaku.Ja: ソラは「みんなと一緒に楽しく過ごしたい」と心から思いを込めました。En: Sora wholeheartedly wished to have fun together with everyone.Ja: 祭りの夜、ソラは長い間味わっていなかった、心からの喜びを感じました。En: During the festival night, Sora experienced a joy he hadn't felt in a long time.Ja: 友だちと共に過ごす時間の価値を改めて実感し、En: He once again realized the value of spending time with friends,Ja: 彼の心は軽くなりました。En: and his heart felt lighter.Ja: 優勝のことだけが重要ではなかったのです。En: Winning wasn't the only important thing.Ja: いつしか、ソラは自分の価値を別の視点から見上げていました。En: Before long, Sora began seeing his worth from another perspective.Ja: 勝つことだけではなく、En: It wasn't just about winning,Ja: 大切な人たちと共にいること。En: but about being with those who mattered to him.Ja: そのことが彼に新しい平穏をもたらしてくれたのです。En: This brought him a newfound peace.Ja: 穏やかな夜風が吹く中で、ソラは友だちとの時間を心から楽しんでいました。En: In the gentle night breeze, Sora truly enjoyed his time with his friends.Ja: そして、彼はどんなときでも支えてくれる二人の存在に感謝し、En: He appreciated the presence of the two who always supported him,Ja: 静かに空を見上げました。En: and he quietly looked up at the sky.Ja: 星が一際輝いて、ソラたちを祝福しているようでした。En: The stars seemed to shine more brilliantly, as if blessing Sora and his friends. Vocabulary Words:dormitory: 寮brimming: 満ちているenergy: 活気echoed: 響いていたslight: 少しのlinger: 漂うtournament: 大会broken: 折れてしまったrecover: 回復hesitant: 迷うvibrancy: 賑やかさadorned: 飾られてbrilliantly: 一際輝いてblessing: 祝福perspective: 視点peace: 平穏encouraged: 励ましてslightly: 小さくglancing: 目をやるwholeheartedly: 心からrealized: 実感したappreciated: 感謝したpresence: 存在gentle: 穏やかなcheerful: 明るいchange of pace: 気分転換silently: 黙ってdesired: 求めているworth: 価値support: 支えてくれる

Hi, I'm Connor with Honor - message me here! Washington just got offered a 5 percent stake in OpenAI worth $42.6 billion. Anthropic said no and countered with a plan to pay YOU a dividend instead. On today's episode: what that split actually means for regular people, the AI model that was illegal to use in the US for 18 days and just got un-banned, Zuckerberg admitting AI agents "hasn't accelerated" at Meta, OpenAI killing Sora and the billion dollar Disney deal, Claude Sonnet 5 going free-default at $2/$10 per million tokens, and why Palantir's CEO says the labs are chasing power over usefulness.Then we come back down to Santa Clarita: live 7-day MLS numbers pulled straight off the market this morning, 69 homes closed and 54 fell out in the same week, and what that means if you're thinking about selling under the Fair Fixed Fee program, Connor's $17,000 all-in fixed fee standard.We close with an honest conversation about surviving a Fourth of July barbecue without turning one plate into a lost month, and the full Santa Clarita Fourth of July events guide, before this valley throws itself a 250th birthday party.AI for everyone. Not just the wealthy.Watch the video version: https://youtu.be/dksBsZjwHfsReal estate, the Fair Fixed Fee program: sellersonlyagent.com | AI business systems: honorelevate.com | Voice AI for small business: hireaivoice.com | Food freedom and fasting: thelastaddiction.comConnor T. MacIvor · CalDRE #01238257 · Sync Brokerage, Inc. · DRE #02031490#DailyDownload #AIWithHonor #SeventeenK #SantaClarita #FairFixedFeeYoutube Channels:Conner with Honor - real estateHome Muscle - fat torchingFrom first responder to real estate expert, Connor with Honor brings honesty and integrity to your Santa Clarita home buying or selling journey. Subscribe to my YouTube channel for valuable tips, local market trends, and a glimpse into the Santa Clarita lifestyle.Dive into Real Estate with Connor with Honor:Santa Clarita's Trusted Realtor & Fitness EnthusiastReal Estate:Buying or selling in Santa Clarita? Connor with Honor, your local expert with over 2 decades of experience, guides you seamlessly through the process. Subscribe to his YouTube channel for insider market updates, expert advice, and a peek into the vibrant Santa Clarita lifestyle.Fitness:Ready to unlock your fitness potential? Join Connor's YouTube journey for inspiring workouts, healthy recipes, and motivational tips. Remember, a strong body fuels a strong mind and a successful life!Podcast:Dig deeper with Connor's podcast! Hear insightful interviews with industry experts, inspiring success stories, and targeted real estate advice specific to Santa Clarita.

Petri Kajander pohtii mitä tekoäly oikeasti on ja kutsuu sitä temuälyksi. Keskustelu kulkee paikallisista kielimalleista ja agenttisesta koodauksesta tilien yllättäviin porttikieltoihin ja tietoturvan sudenkuoppiin.Kaksikko käsittelee tekoälykuplan kestävyyttä, inferenssin hinnoittelua sekä sitä miksi malli arvaa hyvin mutta ei ymmärrä tekemäänsä. Mukana myös App Store -julkaisun realiteetit, sosiaalisen median slop ja ajatus ihmisen augumentoinnista tekoälytyöntekijöillä. Lopuksi puhutaan siitä miten avoin internet kaventuu ja miksi generalistin laaja näkemys nousee taas arvoonsa.00:00 No niin, Petri Kajander vieraana Temuälystä02:10 Paikallismallit Macin raudalla ja muisti03:21 Mac Mini, OpenClaw ja paikallisagentit04:57 Älkää lähettäkö Samille mitään luottamuksellista05:56 Sora, TikTok ja sadan päivän haaste07:42 Työkalut eivät tee luovaa mestaria09:37 CS50, Python ja ensimmäiset äpit10:56 Nootti-domainin tarina12:46 Nuotit, Läppää ja Apple-vero15:35 Teleporteri-vertaus ja inferenssikoneet16:54 Miksi tekoäly ei ymmärrä tekemäänsä17:25 Tunnettujen tilien kaappaukset20:15 Läppää bännättiin Threadsissä yhden sanan vuoksi21:25 Google poisti tilin mangapiirrosten vuoksi22:32 EU-asetus, Ranska ja salauksen sääntely24:20 Henkilötunnusvuodot ja käänteinen todistustaakka26:45 Tekoäly oikeissa käsissä ja turvaverkot28:25 Suhteellisuudentaju ja Gell-Mann-amnesia30:40 Kolmikerroksinen aivo ja lähteenä X32:19 Vuoden 2022 rauhallisempi feedi ja slop34:32 Skillsit kouluttavat tekoälytyöntekijöitä36:36 Ihmiskreippaus ja soittelun paluu39:27 Antropikin rajat ja siirtymä openiin41:43 Kiire pörssiin ja valuaatiot43:18 Datakeskukset, kupla ja tekoälytyön arvo45:23 Julkinen internet on jo syöty46:47 Reasoning-mallit, AGI ja esimerkit49:05 Myrkytetty koulutusdata ja fossiilikerros51:38 Androidisaatio ja ihmisen augumentointiNeuvottelija Sisäpiirissä Kotisivut SEO ja GEO

Hey there, and a very happy Wednesday! This is your Disney News for Wednesday, July 1st, 2026. I hope you're ready to sprinkle a little Disney magic into your day! - Walt Disney World preps for "Dreamlights Enchantment" nighttime show at Magic Kingdom featuring stunning projections and fireworks—set to debut this summer. - Tokyo Disneyland's "Natsu Matsuri" summer festival blends Disney charm with Japanese tradition, featuring themed food stalls, games, and a colorful parade with characters in yukatas. - Disneyland's Haunted Mansion in California receives enchanting renovations, adding new effects and ghostly surprises while maintaining its classic charm. - Disney+ announces a new animated series based on the Kingdom Hearts video games, promising heart-filled adventures with Sora and friends across Disney and Pixar worlds. Thanks for tuning in, and I hope you have a magical day. Remember to check in tomorrow for more Disney updates. See you tomorrow!

Fluent Fiction - Japanese: Balancing Dreams: A Tanabata Night of Family, Festival & Faith Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ja/episode/2026-07-01-07-38-19-ja Story Transcript:Ja: 空に輝く星が、夏の夜を優しく照らしていました。En: The stars shining in the sky gently illuminated the summer night.Ja: この日は七夕です。En: This day was Tanabata.Ja: 家族のみんなは家の中を色とりどりの短冊や笹の葉で飾りつけました。En: Everyone in the family decorated the inside of the house with colorful strips of paper and bamboo leaves.Ja: そこは、大きな家族の家で、まるでお祭りのような雰囲気が漂っています。En: It was the home of a large family, and the atmosphere was like that of a festival.Ja: そらは、家の広いリビングに立っていました。En: Sora was standing in the spacious living room of the house.Ja: 彼は若い大人ですが、家族と自分の夢の間に引き裂かれていました。En: He was a young adult, but he was torn between his family and his own dreams.Ja: そらは家族のことを大切に思っていますが、心の奥では自由を求めています。En: Sora cherished his family, but deep down, he longed for freedom.Ja: 今日は特に忙しいです。En: Today was particularly busy.Ja: 妹のあいこが急に熱を出してしまい、そらは心配でたまりません。En: His younger sister, Aiko, suddenly developed a fever, and Sora was very worried.Ja: あいこは元気だったのに、急に顔が真っ赤になってしまいました。En: Aiko had been fine, but suddenly her face turned bright red.Ja: そらは急いであいこの部屋に行きました。En: Sora hurried to Aiko's room.Ja: 「あいこ、大丈夫？」と優しく声をかけました。En: "Are you okay, Aiko?" he asked gently.Ja: 「ちょっとしんどい」とあいこは小声で答えます。En: "I feel a little unwell," Aiko replied in a small voice.Ja: その時、そらたちのいとこのかずきが別の都市からやって来ました。En: At that moment, their cousin Kazuki arrived from another city.Ja: 「お土産だよ！」と元気に言って、七夕の話をたくさんしてくれました。En: "I brought souvenirs!" he said energetically, sharing many stories about Tanabata.Ja: かずきはこの日のために、たくさんの飾りを持ってきていました。En: Kazuki had brought many decorations for this day.Ja: そらとかずきは、あいこを少しでも元気づけようと、部屋を飾り付け始めました。En: Sora and Kazuki began to decorate the room, hoping to cheer up Aiko even a little.Ja: 医者を呼ばないといけないかもしれないと、そらは不安を感じ始めます。En: Sora began to feel anxious, wondering if they might need to call a doctor.Ja: しかし、そらは決心しました。あいこを一人にしない。En: However, he made a decision—he would not leave Aiko alone.Ja: そして、家で小さなお祝いをすることにしました。En: They decided to have a small celebration at home.Ja: 夕方、あいこの熱が上がってしまいました。En: By evening, Aiko's fever had risen.Ja: そらは近くの医者に電話をしました。En: Sora called a nearby doctor.Ja: 「すぐに来ます」と医者は言いました。En: "I'll be there right away," the doctor said.Ja: そらとかずきは、医者が来るまでの間、できるだけのことをしました。En: Sora and Kazuki did all they could until the doctor arrived.Ja: 飾りを一緒に作ったり、涼しい風が入るよう窓を開けました。En: They made decorations together and opened the windows to let in the cool breeze.Ja: そらは焦る気持ちを抑えて、あいこの顔を見つめました。En: Sora suppressed his anxious feelings and looked at Aiko's face.Ja: やがて、医者が到着しました。En: Eventually, the doctor arrived.Ja: 診察の結果、特に大きな問題はないとのこと。En: The examination revealed no major problems.Ja: 医者は「休めばすぐによくなりますよ」とそらに言いました。En: The doctor told Sora, "She'll get better with some rest."Ja: そらは少し安心しました。En: Sora felt slightly relieved.Ja: 夜になり、そらは家の中で特別な小さな七夕祭りを用意しました。En: As night fell, Sora prepared a special small Tanabata festival in the house.Ja: かずきの協力で、部屋はまるで夢のような空間になりました。En: With Kazuki's help, the room was transformed into a dreamlike space.Ja: 飾りつけた短冊は、みんなの願いをそっと映しています。En: The decorated strips of paper gently reflected everyone's wishes.Ja: あいこはベッドの中から、笑顔でその光景を見つめました。En: From her bed, Aiko watched the scene with a smile.Ja: 「ありがとう、お兄ちゃん」と言って、あいこの目がきらきらと輝きます。En: "Thank you, big brother," she said, and her eyes sparkled brightly.Ja: そらは、その瞬間に気づきました。家族と過ごす特別な時間が、こんなにも心温まるものであることに。En: In that moment, Sora realized how heartwarming it was to spend special time with family.Ja: 彼にとって、大切なのは目の前にある日常の中で幸せを見つけることでした。En: For him, the important thing was finding happiness in the everyday life in front of him.Ja: 心の中で、そらは新しい道を見つけたのです。En: In his heart, Sora found a new path.Ja: 彼は家族と共にいる喜びを再確認し、責任と自由のバランスを見つけたのでした。En: He reaffirmed the joy of being with his family and found a balance between responsibility and freedom. Vocabulary Words:illuminated: 照らしてdecorated: 飾りつけましたatmosphere: 雰囲気spacious: 広いtorn: 引き裂かれてcherished: 大切にfreedom: 自由worried: 心配suddenly: 急にfever: 熱souvenirs: お土産energetically: 元気にanxious: 不安decision: 決心celebration: お祝いdoctor: 医者breeze: 風suppressed: 抑えてexamination: 診察revealed: 明らかになりましたrelieved: 安心transformed: 変わったsparkled: 輝きheartwarming: 心温まるreaffirmed: 再確認balance: バランスresponsibility: 責任gently: 優しくwishes: 願いdreamlike: 夢のような

Fluent Fiction - Korean: Tea, Tradition, and a Creative Bond in Bukchon Village Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ko/episode/2026-06-30-07-38-20-ko Story Transcript:Ko: 여름의 어느 날, 서울의 북촌한옥마을에는 활기가 넘쳐흘렀습니다.En: One summer day, Bukchon Hanok Village in Seoul was brimming with energy.Ko: 좁은 골목길과 전통 한옥들이 어우러진 이곳에는 마치 시간 여행을 온 듯한 느낌이 들었습니다.En: This area, where narrow alleys and traditional hanoks harmonize, felt like stepping back in time.Ko: 한옥의 처마 밑으로 불어오는 바람은 여름의 더위를 식히며, 사람들은 여유롭게 거닐고 있었습니다.En: The breeze blowing under the eaves of the hanoks cooled the summer heat, and people leisurely strolled around.Ko: 소라는 서울에 막 이사 온 역사 애호가였습니다.En: Sora was a history enthusiast who had just moved to Seoul.Ko: 그녀는 문화에 대한 깊은 열정을 가지고 있었지만, 새로운 사람들과 어울리기에는 조금 어려워했습니다.En: She had a deep passion for culture but found it a bit difficult to mingle with new people.Ko: 그래서 이번 전통 다도 수업은 그녀에게 큰 도전이었습니다.En: Therefore, this traditional tea ceremony class was a big challenge for her.Ko: 기대와 걱정이 섞인 마음으로 수업이 열리는 한옥에 들어섰습니다.En: With a mix of anticipation and anxiety, she entered the hanok where the class was held.Ko: 그곳에는 지호가 있었습니다.En: There was Jiho.Ko: 그는 지역 예술가로, 자신의 예술에 영감을 주기 위해 전통 문화를 배우고자 이곳을 찾았습니다.En: He was a local artist who came to learn about traditional culture to inspire his art.Ko: 사람들과 쉽게 어울리는 성격 덕분에 주변 사람들과 금방 친해졌습니다.En: With his easygoing personality, he quickly became friendly with those around him.Ko: 다도 수업이 진행되면서, 소라의 조심스러운 손길이 누군가에게 포착되었습니다.En: As the tea ceremony class progressed, Sora's careful gestures caught someone's attention.Ko: 지호는 그녀에게 다가가 조심스럽게 말을 건넸습니다. ""안녕하세요, 이 차 맛이 참 좋죠?"En: Jiho approached her and cautiously spoke, ""Hello, this tea tastes great, doesn't it?"Ko: 소라는 깜짝 놀랐지만, 그의 밝은 미소에 용기를 내어 대답했습니다. ""네, 이곳의 분위기도 참 멋진 것 같아요."En: Although surprised, Sora gathered courage from his bright smile and answered, ""Yes, the atmosphere here is also quite wonderful."Ko: 두 사람은 대화를 나누며 서로의 관심사를 알아갔습니다.En: The two engaged in conversation, learning about each other's interests.Ko: 소라는 한국 역사에 대한 깊은 관심을 이야기했고, 지호는 자신의 예술에 대해 털어놓았습니다.En: Sora talked about her deep interest in Korean history, and Jiho shared about his art.Ko: 그들은 서로의 이야기에서 각자의 열정과 목표를 발견했습니다.En: They discovered their passion and goals through each other's stories.Ko: 다도 수업이 끝나갈 즈음, 두 사람은 같은 목표를 공유하고 있다는 것을 깨달았습니다.En: As the tea ceremony class was coming to an end, they realized they shared the same goal.Ko: "Uri 함께 문화 예술 프로젝트를 해보는 건 어떨까요?" 지호가 제안하자, 소라는 잠시 망설였다가 곧 수긍했습니다.En: "Uri 함께 문화 예술 프로젝트를 해보는 건 어떨까요?" Jiho suggested, and after a moment of hesitation, Sora agreed.Ko: "좋아요, 저도 그게 정말 재미있을 것 같아요."En: "Sounds good, I think it would be really fun."Ko: 소라와 지호는 그날 밤 북촌한옥마을의 벤치에 나란히 앉아 있었습니다.En: That night, Sora and Jiho sat side by side on a bench in Bukchon Hanok Village.Ko: 바람은 부드럽게 불고, 도시의 불빛이 은은히 빛났습니다.En: The breeze blew softly, and the city's lights glowed gently.Ko: 두 사람은 서로에게서 영감을 얻고, 새로운 가능성을 느꼈습니다.En: They gained inspiration from each other and sensed new possibilities.Ko: 소라는 새로운 인간 관계를 맺는 것이 두렵지 않다는 것을 깨달았고, 지호는 자신의 예술이 전통과 연결될 수 있는 자신감을 얻었습니다.En: Sora realized she was no longer afraid of forming new relationships, and Jiho found confidence that his art could connect with tradition.Ko: 그들은 서로를 바라보며 미소 지었습니다.En: They smiled at each other.Ko: 두 사람의 우정은 이제 막 시작되었고, 앞으로 펼쳐질 이야기는 그들만이 만들어 갈 수 있었습니다.En: Their friendship had just begun, and the stories to unfold were theirs to create. Vocabulary Words:brimming: 넘쳐흐르다enthusiast: 애호가mingle: 어울리다anticipation: 기대anxiety: 걱정easygoing: 사람들과 쉽게 어울리다gestures: 손길cautiously: 조심스럽게atmosphere: 분위기engaged: 대화를 나누다passion: 열정goals: 목표hesitation: 망설임inspiration: 영감breeze: 바람glowed: 은은히 빛나다possibilities: 가능성confidence: 자신감connect: 연결되다art: 예술tradition: 전통journey: 여행leisurely: 여유롭게projects: 프로젝트afraid: 두렵다bench: 벤치strolled: 거닐다discovered: 발견하다realized: 깨닫다stories: 이야기

Join our mastermind community: https://www.skool.com/apparel-success-mastermindTry the best Ai design platform: https://www.design.com/rob88AI video generators are getting insanely realistic, and in this video I show how clothing brand owners can use tools like Kling AI, Seedance, Google Veo, Sora, Runway, Adobe Firefly, Poyo.ai, Higgsfield AI, ChatGPT, ElevenLabs, CapCut, and Premiere Pro to create realistic ads, TikTok videos, Instagram Reels, product videos, and social media content without spending thousands on photoshoots, models, or videographers.I tested the biggest AI video tools to see which ones worked best for clothing brands using real product reference images. I break down why Google Veo, Sora, and Runway struggled, why Kling AI created some of the most realistic results, and why Seedance 2.0 might be one of the best AI video generators for accurate clothing brand content, fabric, logos, product details, and lifestyle scenes.If you run a streetwear brand, gymwear brand, hoodie brand, outdoor brand, or apparel business, this video shows how AI can help you create better ads, Meta ads, TikTok ads, Instagram content, UGC-style videos, and product marketing faster than ever.

Fluent Fiction - Japanese: Finding Courage in Arashiyama: Sora's Summer Epiphany Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ja/episode/2026-06-28-07-38-19-ja Story Transcript:Ja: 京都の夏。En: Summer in Kyoto.Ja: 嵐山の竹林は静かで美しい。En: The bamboo grove in Arashiyama is quiet and beautiful.Ja: 空は雲ひとつなく、竹の葉がそよ風に揺れている。En: The sky is cloudless, and the bamboo leaves sway in the gentle breeze.Ja: 今日は七夕。En: Today is Tanabata.Ja: 空は心の中で大きな決断を抱えて歩いていた。En: Sora walked, carrying a big decision in his heart.Ja: 空は、手術をするかどうか悩んでいた。En: Sora was troubled over whether to undergo surgery.Ja: 手術は彼の人生を変える可能性がある。En: The surgery could potentially change his life.Ja: 友達の春人が隣で明るく話している。En: His friend Haruto was cheerfully talking beside him.Ja: 「今日は楽しもうよ！」と春人は空に言った。En: "Let's have fun today!" Haruto said to Sora.Ja: 彼の声は楽しげで、空の心を軽くしようとしていた。En: His voice was cheerful, trying to lighten Sora's heart.Ja: しかし、空の心は重たい。En: However, Sora's heart was heavy.Ja: 竹林を歩きながら、彼はふと目を閉じる。En: As he walked through the bamboo grove, he closed his eyes for a moment.Ja: そこに現れたのは、知恵深い明子だった。En: There appeared Akiko, wise and insightful.Ja: 明子は年老いたけれど、その目はなおのこと輝いていた。En: Akiko was old, but her eyes still shone brightly.Ja: 「私も同じ道を歩いたわ」と、彼女は静かに言った。En: "I walked the same path," she quietly said.Ja: 「怖かったわ。でも、人生は勇気と今この瞬間を生きることなのよ」と、明子は続けた。En: "I was scared. But life is about courage and living in the moment," Akiko continued.Ja: 彼女が経験した決断は同じようなもので、彼女の言葉は空の胸に響いた。En: The decision she had experienced was similar, and her words resonated in Sora's heart.Ja: 日が暮れ、竹林はランタンの光で照らされた。En: As night fell, the bamboo grove was illuminated by lanterns.Ja: 空は決心した。En: Sora made a decision.Ja: 彼は短冊を取り出し、願いを書いた。「勇気が欲しい」と。En: He took out a tanzaku and wrote his wish: “I want courage.”Ja: それから、彼は春人と一緒に祭りに戻った。En: After that, he returned to the festival with Haruto.Ja: 音楽が響き、人々の笑い声が空を包む。En: Music echoed and the laughter of people enveloped Sora.Ja: 空は心の中で平和を感じた。En: In his heart, Sora felt peace.Ja: 手術の恐れにとらわれるのではなく、今この時を感じることができた。En: He could feel the present moment rather than being held captive by the fear of surgery.Ja: 嵐山の竹が穏やかに揺れている。En: The bamboo in Arashiyama swayed gently.Ja: 空は今まで感じたことのない静けさに包まれていた。En: Sora felt enveloped in a tranquility he had never felt before.Ja: そして、彼は笑った。En: And then, he smiled.Ja: 「これから、一歩一歩、今を大切にして生きていこう」と心に誓った。En: "From now on, step by step, I'll live cherishing the present," he vowed to himself.Ja: 物語の終わりに、空は何かを理解した。En: At the end of the story, Sora understood something.Ja: 人生は今この瞬間の集まりで、未来を恐れるのではなく、今日を生きることが大事なのだと。En: Life is a collection of present moments, and it is important to live today instead of fearing the future.Ja: 彼は満ち足りた気持ちで竹林を後にした。En: He left the bamboo grove with a satisfied feeling. Vocabulary Words:grove: 竹林sway: 揺れるbreeze: そよ風decision: 決断troubled: 悩んでいたsurgery: 手術undergo: 経験するilluminated: 照らされたlantern: ランタンgentle: 穏やかcloudless: 雲ひとつなくcheerfully: 明るくinsightful: 知恵深いresonate: 響くcourage: 勇気tanzaku: 短冊wish: 願いechoed: 響きenveloped: 包むtranquility: 静けさcherishing: 大切にmoment: 瞬間captivated: とらわれるvowed: 誓うsatisfied: 満ち足りたcollection: 集まりfear: 恐れfuture: 未来present: 今path: 道

MILEI TEM TUDO PARA SER REELEITO – MAS LULA TAMBÉMNeste episódio do Stock Pickers, Paolo Di Sora, da RPS Capital, tem uma conversa franca sobre o cenário político e macroeconômico da Argentina e do Brasil, os efeitos das decisões de Javier Milei e Lula, e o que isso significa para investidores. A conversa também passa por juros, Bolsa, risco fiscal, popularidade, valuation e oportunidades de investimento.Se você acompanha mercado financeiro, política, macroeconomia, ações, Argentina, Brasil, Milei, Lula, Bolsa e oportunidades de investimento, este episódio é para você.

Food can be the loudest voice in your head, even when you are not hungry. We sit down with best-selling author Sora Vernikoff, who healed her own compulsive overeating and built a no-diet weight loss program around one core skill: learning how to eat and stop. If you are tired of binge eating, yo-yo dieting, and that constant “what's next to eat” loop, this conversation offers a different path that focuses on behavior change rather than deprivation.We dig into Sora's framework for why overeating and overthinking often feel identical: both are driven by repeating thoughts you do not feel able to release. She breaks down the role of the subconscious mind, why willpower alone keeps failing, and how calming food thoughts can free up the strength to handle the uncomfortable feelings you have been trying to escape. If you have ever felt stuck in a mental replay, whether it's donuts in the kitchen or a fight you cannot stop rehashing, you will recognize yourself here.Sora also teaches a practical technique you can test immediately: the Green Technique. You ask “How much is enough?” and “How much is too much?” before you eat, then you set aside a clear “marker” amount you do not touch. That one move changes the moment from automatic eating to intentional portion control, while still allowing the foods you love. We also cover why diets so often backfire, how rigid rules can trigger binges, and where to find Sora's tools at nodieting.net and OverthinkersCoach.com.If this helps, subscribe, share the episode with a friend who feels stuck, and leave a review so more people can find real-world support for overeating, overthinking, and sustainable weight loss. Support the show Thank you to our sponsor Complete Coverage Football - http://www.completecoveragefootball.com

How Ankit Nayal scaled organic TikTok to 50 million views with an AI content factory, and why half of them were wasted until conversion came first.Most founders who burn through their paid ad budget pivot to organic with one or two accounts and hope something works.Ankit Nayal pivoted to organic and went to 150 to 200 TikToks a day.He runs this for his app Flamme. He has crossed 50 million views. He told me more than half of those views were wasted, because conversion was not in his framework yet. The episode is about what he built once that became obvious.The path there started in a cave. After losing his ad budget in 2025, Ankit scrolled TikTok for four hours a day for two months. He compared it to having McDonald's every meal. Out of that came the VSC framework. Viral: an under-5,000-follower account with a 100K-view post that is still picking up trend score. Scalable: a format that replicates cleanly across accounts. Convertible: a video that actually pulls downloads. Memes pulled 0.1% conversion. A girl reacting to a hook pulled 0.5%. A 100K-view reaction beat a 2M-view meme on bang for buck.The system around the framework is more cumbersome than most posts about it admit. He started by filming himself and concluded that a brown man with an Indian accent was not the best fit for the American market. He moved to Russian creators sourced through Kwork.ru at one dollar a minute and twenty-five cents per ten-to-fifteen-second reaction. ChatGPT translation overhead killed that workflow. He moved to Sora 2, then to Seedance. Every clip gets broken into five-second blocks because the model starts hallucinating past five seconds. A CapCut filter layer with ten effects scrubs the plastic skin off AI faces. Phones get lined up on physical farms because the TikTok API gets content flagged.The funnel sequence he ends on is the part that stuck with me. Organic first, then UGC, then paid. Most founders run it backwards.Video Chapters: 00:00 Introduction03:00 Losing the paid ad budget on a dating app06:00 Four hours of TikTok a day for two months11:00 The VSC framework14:00 Why memes converted nothing18:00 Russian creators on Kwork20:00 Moving to Sora 2 and Seedance22:00 The CapCut plastic-skin filter23:00 The five-second hallucination limit26:00 Why lip sync breaks scale31:00 The phone farm38:00 Which products should not run organic TikTok39:00 Organic, then UGC, then paidTopics covered:- Organic TikTok at scale for consumer apps- The VSC framework: viral, scalable, convertible- AI UGC production with Sora, Seedance, and CapCut- Creator sourcing on Kwork and the limits of real UGC- Phone farms and TikTok content flagging- Why B2B founders should not run organic TikTokLearn more:https://mobileuseracquisitionshow.com/episode/[slug]/ - Episode page https://www.linkedin.com/in/annayal/ - Connect with Ankit on LinkedIn https://www.annayal.com/ - Ankit's website https://intelligentartifice.kit.com - Newsletter

In marshes across the country, birds awaken on a summer morning. Tall dense grasses and reeds often make marsh birds hard to see, but their voices carry easily across the lush, green landscape. You can hear birds like the Redhead, the Sora, the American Bittern, the Ruddy Duck, this Yellow-headed Blackbird, and many more. More info and transcript at BirdNote.org. Want more BirdNote? Subscribe to our weekly newsletter. Sign up for BirdNote+ to get ad-free listening and other perks.  BirdNote is a nonprofit. Your tax-deductible gift makes these shows possible.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Fluent Fiction - Japanese: Haruto's Honest Vote: Art, Mistakes, and Integrity Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ja/episode/2026-06-20-07-38-20-ja Story Transcript:Ja: ハルトは元気な若者です。En: Haruto is an energetic young man.Ja: 彼はいつも楽しそうに絵を描いています。En: He always seems to enjoy drawing pictures.Ja: 美術を使って町を良くしたいと思っています。En: He wants to use art to improve the town.Ja: その日は、町の小さな投票所で夏の暑さが彼を包んでいました。En: On this day, the summer heat enveloped him at the town's small polling station.Ja: 「今日は大事な日だ。」ハルトは思いました。En: "Today is an important day," Haruto thought.Ja: 新しい地域美術プロジェクトを支持するために、投票をしたいと決めていました。En: He had decided to vote in support of a new local art project.Ja: 投票所は地域センターの一角にありました。En: The polling station was located in a corner of the community center.Ja: 部屋はシンプルですが、明るく温かい雰囲気でした。En: The room was simple, but it had a bright and warm atmosphere.Ja: そこで彼は友人のユキとソラと出会いました。En: There, he met his friends Yuki and Sora.Ja: 「ハルト、今日は投票の日だね。」ユキが微笑みました。En: "Haruto, today is voting day, isn't it?" Yuki smiled.Ja: 「うん、僕はアートプロジェクトのために投票するよ。」彼は嬉しそうに答えました。En: "Yeah, I'm going to vote for the art project," he replied happily.Ja: 彼は投票用紙を受け取り、個室に入りました。En: He received a ballot and entered a booth.Ja: ついつい鉛筆を手にすると、いつもの癖で絵を描いてしまいました。En: Almost instinctively, he picked up a pencil and, out of habit, started drawing.Ja: 気がつくと、投票用紙にはかわいい動物の絵がいっぱいでした。En: When he realized what he was doing, the ballot was filled with cute animal drawings.Ja: 「あ、やばい！」彼は驚きました。En: "Oh no!" he was surprised.Ja: 絵を描いてしまい、有効な投票ができませんでした。En: He had drawn pictures on it and couldn't cast a valid vote.Ja: 窮地に陥ったハルトは考えました。En: Caught in a dilemma, Haruto thought about what to do.Ja: 恥ずかしい思いをするのは嫌だけど、新しい用紙をお願いしないと投票ができません。En: He didn't want to feel embarrassed, but he couldn't vote without requesting a new ballot.Ja: 彼はどうするか悩みましたが、誠実に行動することを選びました。En: He was troubled but chose to act honestly.Ja: 彼はボランティアの方に近寄り、静かに事情を説明しました。En: He approached a volunteer and quietly explained the situation.Ja: 「すみません、用紙に絵を描いちゃいました。新しい用紙をいただけますか？」En: "Excuse me, I ended up drawing on the ballot. Could I have a new one, please?"Ja: ボランティアの女性は優しく微笑みました。En: The volunteer lady smiled kindly.Ja: 「大丈夫ですよ。正直に話してくれてありがとう。こちらが新しい用紙です。」En: "It's okay. Thank you for being honest. Here's a new ballot."Ja: 新しい用紙を受け取ったハルトは、今度こそきちんと投票しました。En: With a new ballot in hand, Haruto voted correctly this time.Ja: 彼の心は軽くなり、自己主張する自信がついてきました。En: His heart felt lighter, and he gained confidence in expressing himself.Ja: 投票所を出た彼は、ユキとソラに向かって微笑みました。En: As he left the polling station, he smiled at Yuki and Sora.Ja: 「うまくいったよ。間違えても大丈夫、正直でいることが大切だね。」En: "It went well. Even if you make a mistake, being honest is what matters."Ja: その日、ハルトは一つ大切なことを学びました。誠実さが評価されること、自分の考えを素直に表現することの大切さ。En: That day, Haruto learned something important: the value of honesty and the importance of expressing his thoughts honestly.Ja: そして彼は、これからも町のために頑張ることを決意しました。En: And he resolved to continue working hard for the sake of the town. Vocabulary Words:energetic: 元気なdrawing: 絵を描くenveloped: 包まれたpolling station: 投票所ballot: 投票用紙booth: 個室instinctively: ついついhabit: 癖dilemma: 窮地embarrassed: 恥ずかしいtroubled: 悩んだvolunteer: ボランティアhonestly: 誠実にexpressing: 表現するconfidence: 自信resolved: 決意したimportance: 大切さatmosphere: 雰囲気instinctively: ついついinvalid: 無効なhonest: 正直なsake: ためにvolunteer: ボランティアkindly: 優しくexpress: 表現するyoung man: 若者improve: 良くするvote: 投票するexplanation: 説明community center: 地域センター

In this special live episode recorded at SynthBee headquarters in South Florida, hosts Charlie Fink, Ted Schilowitz, and Rony Abovitz bring listeners inside a special gathering of neuroscientists, philosophers, and technologists debating the future of AI. Moving beyond hype, the conversation focuses on "Collaborative Intelligence" vs. Artificial General Intelligence (AGI), exploring whether we are building tools that amplify humanity or autonomous systems that will eventually replace it.Instead of traditional interviews, the hosts invite workshop speakers to the hot seat for rapid-fire insights on the deepest questions in tech: Can we measure an AI's true intentions? Is consciousness a physics problem? And how do we ensure these systems remain compatible with human flourishing?News HighlightsDisney invests $1B in OpenAI & licenses IP: The hosts debate whether this is a masterstroke to engage fans with user-generated Sora content or a "Yahoo powered by Google" mistake that hands the keys to the kingdom to a rival.Valve launches new PCVR hardware: A quick look at the attempt to revive the high-end PC VR market.Meta adds real-time vision to Ray-Bans: The next step in multimodal AI wearables.Guest HighlightsDr. Uri Maoz (Neuroscientist, Chapman/Caltech): Discusses the "black box" problem of neural networks, comparing the opacity of AI to the human brain, and how neuroscience tools might help us detect deception in AI systems.Dr. Walter Sinnott-Armstrong (Ethics Professor, Duke): Argues that ethical AI regulation shouldn't be a monolith; different cultures need "sovereignty of ethics" to allow diverse moral frameworks to coexist rather than one centralized Silicon Valley standard.Dr. Julio Frenk (Chancellor, UCLA): Frames the AI race as a battle between "Computational Democracy" (distributed, transparent power) and "Computational Autocracy" (centralized control), warning that universities must preserve critical thinking or risk losing the ability to govern AI at all.Reed Maxwell & Laura Condon (Hydrologists, Princeton/Arizona): Reveal how AI is modeling the planet's water crisis, predicting "black swan" climate events, and why funding for this critical earth-science work is mysteriously disappearing.Danny M (12-Year-Old Prodigy): Steals the show with a stunningly articulate take on AI consciousness, "trapped man" experiments, and how fractal geometry might map neural weights—proving the next generation is more ready for this future than we are.Dr. Aaron Schurger (Psychology, Chapman): Explores the neuroscience of spontaneous action and free will, debating whether "telepathic" connections and quantum effects in the brain could be the missing link for true human-AI compatibility.Jared Ficklin (Chief Product Officer, SynthBee): The former Frog Design fellow argues we must shift the conversation from AI "capability" to "compatibility," using the intuitive connection humans have with dogs or horses as the benchmark for successful AI interfaces.Thanks to our sponsor Zappar!Subscribe for weekly insider perspectives from veterans who aren't afraid to challenge Big Tech.New episodes every Tuesday. Watch full episodes on YouTube. Hosted on Acast. See acast.com/privacy for more information.

Lucas Rizzotto is one of the most distinctive artists working at the intersection of technology and human experience. He built Where Thoughts Go, a VR piece that proved genuine connection was possible inside a headset when everyone said it wasn't. He followed it with Pillow, a mixed reality app designed around the bedroom. He then spent months letting an AI algorithm run his life — wearing Mantra smart glasses, building a surveillance and memory system on himself, and documenting it as an ongoing series on Instagram and TikTok. Now he's making a live cinematic experience called Escape the Internet, which he calls Broadway crossed with a video game crossed with standup comedy. It premiered as a ghost debut at SXSW this year.Mike Boland, analyst and founder of AR Insider, sits in for Rony Abovitz in this episode. The conversation opens on the Rec Room shutdown — $250 million raised, a $3.5 billion valuation, and now a wind-down. The panel connects the collapse to a pattern: VR has always been an exotic pursuit sold as a mainstream one, and the unit economics of concurrent immersive social spaces are nearly impossible. The discussion moves to OpenAI shutting down Sora, the AI video generation race between Google VO3 and Kling, the rise of AI slop in social feeds, and Lucas confirming he quit LinkedIn because it's unreadable.AI XR News: Rec Room is shutting down after raising $250M at a $3.5B peak valuation. Snapchat is acquiring its remaining assets. OpenAI closed down Sora, overwhelmed by competition from Google VO3 and Kling. AI-only social feeds from Meta and Grok are not gaining traction — users are tuning them out.Key Moments:[05:37] – Ted's thesis: VR is an exotic pursuit that was never going to be mainstream, and Rec Room would have been healthier if it accepted that early[07:33] – Lucas: Ready Player One was the worst thing to happen to XR — it gave executives a fictional roadmap to fund[18:38] – Ted asks whether Apple can do for mixed reality what it did for the smartphone — and the panel is skeptical[27:42] – Mike on physics as the hard ceiling: Moore's Law doesn't apply to waveguides and optics the way it applies to chips[29:02] – Lucas explains why he dropped display glasses for his wearable AI experiment — they increase engineering complexity by 50x[32:17] – Lucas's AI-controlled life series: a complex algorithm watches him, mines personal data, and tells him what to do to find happiness — including an unplanned trip to Lithuania[34:12] – Ted asks if the experiment is a net positive or negative. Lucas: neutral if you're in control, net negative if Meta or OpenAI are running the system[37:52] – Lucas on convenience as a death by a thousand cuts: he optimized his life in Berlin to have everything within three minutes and became miserable[41:00] – Charlie on Where Thoughts Go: assigned it to students every semester; it only works if you surrender to it[47:15] – Escape the Internet: hundreds of people in a movie theater, all on their phones, playing a shared cinematic narrative. Lucas calls it a modern version of church[53:40] – The standup model applied to software: Lucas tested Escape the Internet at SXSW and cut 50% of the material that didn't get a reactionThis conversation sits at the intersection that the AI XR Podcast lives for: technology as creative material, not just commercial tool. Lucas's view that we've been building things people use all the time when we should be building things that blow their minds for two hours and then get out of the way is one of the sharper critiques of the attention economy you'll hear this year.This episode is brought to you by Zappar and Mattercraft — the leading visual development environment for building immersive 3D web experiences on mobile, headsets, and desktop. Mattercraft now includes an AI assistant that helps you design, code, and debug in real time, right in your browser. Start building at mattercraft.io.Subscribe to the AI XR Podcast so you never miss a conversation. Hosted on Acast. See acast.com/privacy for more information.

Shelley Palmer,media technologist, advisor, and author with over 700,000 daily newsletter subscribers, returns to the show. He's one of the sharpest thinkers writing about AI today, and this conversation covers the full arc: from social media liability to the trust collapse coming for all of us, and into the real productivity gains and surveillance trade-offs of living inside an AI-first workflow.The episode opens with the Google and Meta lawsuit verdict and quickly moves past the legal question. Shelley's position is precise: you can't legislate parenting, but you can legislate transparency, and the tech industry has failed on that front entirely. The $6 million judgment against Meta and Google is a rounding error — not a deterrent. What matters is what platforms actually engineered: engagement above all else, backed by neuroscience, probabilistic math, and dopamine feedback loops optimized for shareholders, not users.AI XR News You Should Know: OpenAI is ending Sora and pivoting hard to Codex and enterprise. Ben Affleck secured $900 million from Netflix for a custom AI filmmaking tool. Epic Games cut 1,000 jobs as Fortnite loses audience. NVIDIA's Jensen Huang introduced Nemo Claw and Open Shell at GTC — a corporatized framework for personal AI agents.Key Moments[00:01:15] – Charlie opens noting the show missed one episode in nearly 300 — his daughter's wedding[00:01:55] – OpenAI kills Sora; the Critters director goes dark before the episode[00:04:45] – Google and Meta lose their social media addiction lawsuit; Meta also loses in New Mexico[00:08:07] – Shelley on what can actually be legislated: not parenting, but transparency[00:11:42] – Shelley on Zuckerberg: he genuinely believed connection would be net positive; ask him today[00:13:31] – "Planetarily net negative. No matter what good it does, it does more harm."[00:18:16] – Rony on dopamine engineering: neuroscientists studying pixel size, color, sound to refine addiction[00:19:40] – Shelley reframes it: engagement maximization for shareholders, no more insidious than that[00:23:19] – The physiological change argument: humans evolved to default to trust; AI-generated everything breaks that[00:31:50] – Rony's counterpoint: trust will reset local; the software ecosystem will follow[00:36:53] – Shelley: "Our business increased last year. Everyone on my staff is doing 400 times the work."[00:44:42] – AI-first means automating every workflow you can honestly automate — and knowing what isn't ready[00:45:06] – Jensen's Nemo Claw and Open Shell: the safer path to personal AI agents, and what it actually costs[00:49:42] – The surveillance trade-off: an effective AI agent requires more personal data exposure than anything before it[00:51:24] – Apple's Secure Enclave play: why Tim Cook may win the AI trust war in the endThe productivity gains are real, but so is the privacy exposure, and the systems that earn trust — at every level — are the ones that will survive.This episode is brought to you by Zappar, the company behind Mattercraft — the leading visual development environment for building immersive 3D web experiences across mobile, headsets, and desktop. Mattercraft now features an AI assistant that helps you design, code, and debug in real time, right in your browser.Start building at mattercraft.io. Subscribe to the AI XR Podcast wherever you listen.Watch the full episode for the full breakdown. Available where podcasts are. Full videos available on YouTube. https://youtu.be/S_AECjELYyo Hosted on Acast. See acast.com/privacy for more information.

YEAR 6 IS FINALLY HERE!  GO CHECK OUT OUR YOUTUBE TO SEE OUR BRAND-NEW INTRO!  You can find the animator using the link below! https://www.fiverr.com/syedahumna56/do-professional-pixel-art-animation-of-your-choice?utm_medium=shared&utm_source=copy_link&utm_campaign=gig&utm_term=AyNLxkP   *Intro includes minor edits not provided by the original animator. All animated assets were provided by the animator listed above, with some text assets added in post by Keeping Up With The Nerds.   Check out our affiliated links! Opus clips Partner link: https://www.opus.pro/?via=Nerd   Check out our Website: Keepingupwiththenerds.com   The summer heat is bringing the ultimate gaming heat!

Most duck hunters have flushed a Sora rail from the cattails at some point, but few know much about these secretive marsh birds. In this episode, wildlife biologist Eamon Harrity joins the show to discuss the fascinating world of rails. We cover Sora migration, nesting habits, habitat needs, population trends, and the unique adaptations that allow these birds to thrive in dense wetland environments. Eamon also shares stories from his research on Ridgway's Rails and discusses some of the biggest unanswered questions surrounding rail behavior and conservation. If you've ever heard the distinctive call of a Sora echoing across a marsh and wondered what you were hearing, this episode is for you. Topics discussed:• What exactly is a rail?• Sora migration and wintering grounds• Nesting and breeding behavior• Why rails are so difficult to study• How rails find isolated wetlands during migration• Rail hunting history and regulations• Wetland management and conservation• The future of rail research Follow the North American Waterfowler Podcast for new episodes every week. Contact Elliott: freelanceduckhunting@gmail.com Support the Show: Patreon.com/freelanceduckhunting Partners of the Show Flight Day Ammunition www.flightday.com Code NAW10 Shotty Gear www.shottygear.com Code: FDH10 Weatherby www.weatherby.com Mammoth Guardian Dog Kennels www.mammothpet.com Search Mammoth Guardian Dog Create on Amazon Learn more about your ad choices. Visit megaphone.fm/adchoices

Chris reminds us that we don't Sora 'bout Bruno. Kelley buys a privacy screen for the litterbox. Robert tells us about the game From those guys who made the one Software. RIP our wallets this September. Question of the Week Do you want them to make a 3D-2D remake of the first six Final Fantasy games? Check out the show notes here! The post RPG Cast – Episode 816: “Summer Games Infestation” appeared first on RPGamer.

Fluent Fiction - Japanese: Awakening in Kyoto: A Journey Through Art and Tradition Find the full episode transcript, vocabulary words, and more:fluentfiction.com/ja/episode/2026-06-13-22-34-01-ja Story Transcript:Ja: 春の終わり、京都の美しい美術館で、そらとカイトは家族の再会の後、絵画展を訪れていた。En: At the end of spring, in a beautiful museum in Kyoto, Sora and Kaito visited an art exhibition after a family reunion.Ja: そらは久しぶりに京都に来た大学生で、美術に興味があるが、家族の期待に少し疲れていた。En: Sora was a university student who had not been to Kyoto in a while and was interested in art, but she felt a bit weighed down by her family's expectations.Ja: 一方、カイトは美術館のキュレーターであり、アートと家庭の伝統の間で揺れていた。En: On the other hand, Kaito was a curator at the museum, torn between art and familial traditions.Ja: 美術館は静かで落ち着いた空間だった。En: The museum was a quiet and serene space.Ja: 大きな窓から自然光が入り込み、芸術作品を優しく照らす。En: Natural light streamed in through large windows, gently illuminating the art pieces.Ja: 訪れる人々は、浮世絵から現代のインスタレーションまで、多様な作品を見て回る。En: Visitors wandered around, viewing a diverse range of works, from ukiyo-e to modern installations.Ja: そらはカイトに言った。「家族のこと、もっと理解したい。でも、何だか今まで受けてきた期待が重く感じるの。」En: Sora said to Kaito, "I want to understand my family more. But somehow, the expectations I've had until now feel heavy."Ja: カイトは微笑んで答えた。「そら、自分のペースで楽しんでいいよ。作品を見て、自分の感じたことを大事にしてね。」En: Kaito smiled and replied, "Sora, it's okay to enjoy at your own pace. Look at the works and cherish what you feel."Ja: そらは作品の前に立ち、心を開いた。En: Standing in front of a piece, Sora opened her heart.Ja: ある絵に目が留まった。En: Her eyes rested on one particular painting.Ja: それは静かな田園の風景で、彼女の心に響いた。En: It was a quiet rural landscape that resonated with her heart.Ja: 思わず足を止め、カイトに尋ねた。「この絵は、どんな意味があるの？」En: Unable to help herself, she stopped and asked Kaito, "What does this painting mean?"Ja: カイトは少し考え、話し始めた。「この絵は、自然と人の共生を描いている。En: Kaito thought for a moment and began to speak, "This painting depicts the coexistence of nature and people.Ja: 昔の人々は、小さな自然から多くを学んで生きていたんだ。」En: People in the past lived learning a lot from small aspects of nature."Ja: そらは驚いたように言った。「この絵、私の心が自由になる感じがするよ。En: Surprised, Sora said, "This painting makes me feel like my heart is becoming free.Ja: でも、どうしたら家族と自分の自由を両立できるのかな？」En: But how can I balance my family and my personal freedom?"Ja: カイトは優しく言った。「伝統を受け入れることは大切だけど、それを超えて自分自身を大事にすることも大切なんだ。」En: Kaito said gently, "Accepting tradition is important, but it's also important to cherish yourself beyond that."Ja: 二人は長い間その絵の前で話し、そらは次第に自分の道を見つける勇気を得た。En: The two talked in front of the painting for a long time, and Sora gradually gained the courage to find her own path.Ja: 彼女はカイトの言葉に力をもらい、家族の伝統を受け入れつつ、自分らしさを失わないことの大切さを学んだ。En: Inspired by Kaito's words, she learned the importance of embracing her family's traditions without losing her own identity.Ja: 美術館を出ると、そらは風に揺れる桜を見上げた。En: As they left the museum, Sora looked up at the cherry blossoms swaying in the wind.Ja: 「ありがとう、カイト。おかげで新しい視点を得たよ。」En: "Thank you, Kaito. Thanks to you, I've gained a new perspective."Ja: カイトも微笑んで、「こちらこそ、そらの考える力に感動したよ。」と返した。En: Kaito also smiled and responded, "Thank you, too. I'm impressed by your capacity for thought, Sora."Ja: こうして、二人は互いに新しい理解と絆を持ち、心温まる一日を終えた。En: Thus, the two ended their heartwarming day with a new understanding and bond.Ja: 春の空は穏やかに、彼らの未来を照らしていた。En: The gentle spring sky shone upon their future. Vocabulary Words:reunion: 再会serene: 落ち着いたilluminating: 照らすdiverse: 多様なweighed down: 疲れていたexpectations: 期待rural: 田園resonated: 響いたcoexistence: 共生surprised: 驚いたbalance: 両立accepting: 受け入れるtradition: 伝統embracing: 受け入れつつidentity: 自分らしさcapacity: 力perspective: 視点curator: キュレーターgentle: 優しくpersonal freedom: 自分の自由cherish: 大事にするopened her heart: 心を開いたcoexistence: 共生heartwarming: 心温まるbond: 絆swaying: 揺れるimpressed: 感動したfamilial traditions: 家庭の伝統art installations: インスタレーションnatural light: 自然光

Linktree: ⁠⁠https://linktr.ee/Analytic⁠⁠Join The Normandy For Ad-Free NME, Additional Bonus Audio And Visual Content For All Things Nme+! Join Here:⁠⁠ ⁠⁠https://ow.ly/msoH50WCu0K⁠⁠ In this segment of Notorious Mass Effect, Analytic Dreamz reacts to the official Kingdom Hearts IV Teaser Trailer released in June 2026.Analytic Dreamz delivers a detailed breakdown of the long-awaited teaser, covering Sora's new design and abilities, the mysterious new Keyblade, stunning updated graphics, and the first major story hints for the next chapter in the Kingdom Hearts saga. The segment explores potential new Disney worlds, returning characters, and the evolving lore following Kingdom Hearts III.From the emotional tone and cinematic visuals to gameplay implications and Square Enix's direction for the series, Analytic Dreamz analyzes every key moment and what it means for fans.Join Analytic Dreamz for a full trailer reaction, frame-by-frame breakdown, theories, and predictions for Kingdom Hearts IV as the franchise enters its next era.This segment is essential listening for Kingdom Hearts fans excited about the future of Sora and the fight for light.Privacy & Opt-Out: https://redcircle.com/privacy

Anime Was (Not) A Mistake is the champions! It's another summer and you know what that means, chilling with friends and taking in a well-deserved vacation...or does it? Dan and Jonathan are suddenly flung into a digital world in an unprecedented event we like to call Prodigious Summer: Volume I. Join us for the summer of digivolution as we examine EVERY episode of Digimon Adventure 01 and 02. (Skipping around but discussing them all) As the newly dubbed "Digi Destined" Tai, Matt, Izzy, Sora, Joe, Mimi and T.K. befriend partner Digimon and seek to destroy the forces of evil through friendship we will be there with them every step of the way.  With new bonds created with their Digimon friends watch as Digivices, tags, and crests work together to ascend to higher levels. We go from fighting a digidevil on File Island, taking down an Elvis impersonator ape, and finally return to the real world to confront a vampiric threat! Relive the excitement of a well-timed Pepper Breath. Mourn the loss of some close allies... Wizardmon, here's looking at you. And most importantly know that the power to digivolve lives inside your heart! Jonathan definitely misses his shopping and social life, but Dan seems to be fitting right in... wait...where did he get those goggles from?  Rate, Review, Subscribe, and Listen to Us on Podbean/iTunes/Stitcher/Spotify Follow us on Instagram:@animewasnotamistakepodcast Or on Facebook:@animewasnotamistakepod Music Provided: “Digimon Are The Champions” – Shuki Levy and Paul Gordon – Saban Entertainment – Digimon: Adventure - Digimon: Adventure OST - 1999 “Previously on Digimon” Takanori Arisawa  – Saban Entertainment – Digimon: Adventure - Digimon: Adventure OST - 1999 “Digimon's Heroic Theme” – Project Trinity Covers - Digimon: Adventure - 2012

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June is here so guess what? It's officially Hot AI Summer.

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

Find out more about Sora here. Find out more about the Ride Home Fund here. Learn more about your ad choices. Visit megaphone.fm/adchoices

Most of the AI timeline debate happens in software. Benchmark scores, model releases, the shape of the capability curve. Jon Billow watches a different number for a living: lead times.Billow is on the leadership team at BNS, a firm that manufactures and installs electrical and communication infrastructure. The same critical power equipment his teams put into data centers also goes onto Navy and Coast Guard ships, more than 150 of them. He emailed John Sherman because he thinks the people forecasting AI's arrival are missing what he sees on the construction side every week. The buildout can only move as fast as its slowest part, and right now almost every part is backed up for years.That email is what got him on the show. Here is the heart of what he laid out.The constraint nobody prices inTo bring a large data center online, Billow says, a long list of things has to land at the same time: permitting, grid interconnect, critical power, cooling, and the compute itself. Miss one and the whole project waits. And nearly every item on that list carries a backlog measured in many months, sometimes years.The pinch point he keeps returning to is critical power equipment. According to Billow, the orders all funnel back to roughly five manufacturers, Eaton, ABB, Schneider, GE Vernova among them, and all of them are slammed. He notes that even the US government is having a hard time getting its allocation for ship programs, because it is standing in the same line as every hyperscaler. On top of that, more municipalities are now requiring data centers to bring their own behind-the-meter power generation, which adds another category of equipment backlog and a skill most operators have never needed before. Hooking up to the grid is one thing. Building gas turbines and finding electricians who can parallel generators is another, and the skilled trades are already stretched thin.A factor of five to sevenSherman pushed him to put a number on the gap. If a company says a project lands in a year, how far off is that really?Billow's read: the US has roughly 50 gigawatts of total data center capacity today, with about a quarter of it allocated to AI. Around five gigawatts are under active construction and another seven to twelve sit in backlog. Set that against the order-of-magnitude jumps the labs are talking about and his estimate is blunt. “If I was to be a betting man I would say it's in the order of five to seven years.” Whatever timeline you have been handed, in other words, multiply it.The tells from inside the labsHe pointed to two recent signals that the infrastructure is already the limiting factor. OpenAI walking back a large commitment tied to its Sora video product, which Billow reads as a company looking at finite compute and deciding where to spend it. And Anthropic delaying a model, which he attributes partly to security concerns and partly to the reality of constrained compute capacity. The software keeps leapfrogging. The ground underneath it does not move at the same speed.Why this could be good newsBillow does not frame any of this as a reason to relax. He frames it as time. If the physical buildout runs years behind the hype, that is runway to get governance and alignment right rather than scrambling after the fact. He drew the parallel Sherman's audience knows well, comparing the moment to how the world slowly built doctrine around nuclear risk, and argued the work now is to use the delay deliberately.His closing image stuck with us. He said he wants to tell his grandkids that we were building the car while it was going down the road at 55 miles an hour, but we had the presence of mind to put in seat belts because we knew who was in the back seat.Where they did not agreeThe conversation did not paper over the tension. Sherman described his time in Holly Ridge, Louisiana, a town of about 2,000 mostly elderly people living next to a data center he compared to the size of Manhattan, with construction dust in the air and water residents will not drink. He found it overwhelmingly sad. Billow sees the same structures differently, as a testament to human ingenuity that can be sited and built responsibly if we choose to. Both things sat in the room at once, and the episode is better for letting them.Going deeperWe pulled the headline argument into this piece. The full breakdown for paid subscribers goes into the parts that get more technical and more political:* Compute governance as the most feasible near-term guardrail, including chip tracking and why the industry pushes back hard* The anonymous-compute problem and why “confidential computing” worries safety researchers* China's narrow-AI approach and what it implies about the data center race* Recursive self-improvement, Jevons paradox, and whether you even need new data centers to reach the danger zone* The regulatory carve-out tech enjoys, and the NDA story coming out of LouisianaIf you want that version, upgrade your subscription and it lands in your inbox. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com/subscribe

In 2018, researchers at MIT unveiled an artificial intelligence so disturbing it earned a name straight out of a psychological thriller: Norman (as in Bates). Unlike typical AIs, Norman was exposed to some of the darkest corners of the internet, causing it to see horror in the mundane. Though designed as an experiment, Norman became a cautionary tale about how artificial minds can mirror humanity's most disturbing tendencies. For a full list of sources, please visit: sosupernaturalpodcast.com/dark-web-norman-the-psychopathic-ai   Did you know you can listen to So Supernatural ad-free? Join the Crime Junkie Fan Club! Visit https://crimejunkiepodcast.com/fanclub/ to view the current membership options and policies. So Supernatural is an Audiochuck and Crime House production. Find us on social! Instagram: @sosupernaturalpod Twitter: @_sosupernatural Facebook: /sosupernaturalpod Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of Reading With Your Kids, Jed welcomes Jimmy Vee, author, magician, ventriloquist, marketer, and proud "weirdo," to celebrate his new series beginning with There Are No Dinos In This Book. Jimmy shares how his background in magic, ventriloquism, and marketing copywriting fuses into a unique creative voice for kids—funny, interactive, and packed with personality. He explains how the classic children's magic idea of "look no see"—where kids see something the magician "doesn't"—became the structural engine of his book. On the page, the narrator insists there are no dinosaurs, while kids spot visual clues and "argue" with the narrator, recreating the energy of a live magic show in a read‑aloud experience. Jed notes that it's the kind of book you can't read flat; it demands performance, voices, and engagement. Jimmy walks through the challenge of capturing live-show energy in static text, drawing on his experience writing mass‑media ads and picturing himself on stage as he drafts. He talks about tailoring humor across ages, the joy of "selfish jokes" that mostly please the performer, and the wild differences between intimate school shows and massive, anything‑goes crowds in places like Puerto Rico and El Salvador. They also dive into titles and covers as marketing hooks, unpacking how Jimmy built memorable names like PD Perfect Pants and Professor Nincompoop, using alliteration, rhythm, and a clear hook to stand out in a tiny thumbnail. In the final segment, Jed briefly visits with returning guest Helena Ku Rhee to spotlight her new picture book Sora's Seashells, a gentle, name-centered story about identity, kindness, and family love.

The Keyblade War begins with a flurry of opening attacks from the Organization. When all seems lost, Sora recalls the cursed plot device at his disposal: Time travel!You can find us on social media under khbhpodcast or use your Gummiphone to email us at khbhpodcast@gmail.com

The plot strikes back when Sora, Donald, and Goofy receive a distressing call from Chip and Dale.

Selkies rise from the sea in Celtic legend. Wild. Powerful. Unstoppable. This week on the Irish & Celtic Music Podcast, we celebrate the women of Celtic music who carry that same energy. From the north shore to the black water, Show 757 is an hour of music that will pull you under in the best possible way. It's the Irish & Celtic Music Podcast #757  -  -  Subscribe now at CelticMusicPodcast.com! One Street Over, Gillian Boucher & Bob McNeill, Fialla, Eloise & Co., The Leftovers, The Bow Tides, Low Lily, Tara's Folk, Sue Spencer, Eimear Arkins, Sora, Louise Bichan, Kim Carnie, THE DIVINERS GET CELTIC MUSIC NEWS IN YOUR INBOX The Celtic Music Magazine is a quick and easy way to plug yourself into more great Celtic culture. Enjoy seven weekly news items with what's happening with Celtic music and culture online. Subscribe now and get 34 Celtic MP3s for Free. VOTE IN THE CELTIC TOP 20 FOR 2026 This is our way of finding the best songs and artists each year. You can vote for as many songs and tunes that inspire you in each episode. Your vote helps me create this year's Best Celtic music episode. You have just three weeks to vote this year. Vote Now! You can follow our playlist on YouTube to listen to those top voted tracks as they are added every 2 - 3 weeks. THIS WEEK IN CELTIC MUSIC 0:08 - Boxing Robin "Ned Coleman's #2/The Orphan" from Land of the Noon - Day Moon Gypsy Youngraven - vocals, guitar, and bodhrán 3:28 - WELCOME 5:25 - Gillian Boucher & Bob McNeill "Mountain Road #2" from Race for the Sun Gillian Boucher: fiddle, piano 11:22 - Fialla "Maid in Her Father's Garden" from Home & Away Katie: Vocals, Guitar, Bodhrán, Irish Stepdancing 14:44 - Eloise & Co. "Hanter Dro 1953 Kraozon/Hanter Dro 1930 Gregam/Meetinghouse Hanter Dro" from avec Elodie Becky Tracy (fiddle, octave fiddle) Rachel Bell (accordion) Rachel Aucoin (piano) 18:35 - The Leftovers "Down By the Glenside" from Heart of Buffalo Elizabeth Shea: vocals 21:34 - FEEDBACK 24:17 - The Bow Tides "Trip to Gaelicia" from Sailing On Ellery Klein: fiddle Jessie Burns: fiddle Katie Grennan: fiddle, champion Irish dancer 28:29 - Low Lily "Where We Belong" from Angels in the Wreckage LIZ SIMMONS: Guitar & Vocals NATALIE PADILLA: Fiddle, Banjo & Vocals 32:06 - Tara's Folk "How many Roads" from remember how we fall Julien Casanova -  fiddle Catherine de Vençay -  cello 36:19 - Sue Spencer "Free in the Harbour" from North Shore Sue Spencer: Guitar, Vocals 40:22 - THANKS 42:51 - Eimear Arkins "The St. Louis Waltz (Waltz)" from Here & There Eimear Arikins: Fiddle, Vocals 47:10 - Sora "Selkie" from Ghostlines Sora -  Voice, Piano, Violin 50:09 - Louise Bichan "Auch" from The Lost Summer Louise Bichan: fiddle 53:58 - Kim Carnie "Eolas Gradhaich" from A' Chailleach Kim Carnie: vocals 57:20 - CLOSING 58:40 - THE DIVINERS "Daychovo Horo" from earshot (EP) ILSE DE ZIAH: Cello; Fiddle; Vocals 1:01:23 - CREDITS Support for this program comes from Hank Woodward. Support for this program comes from Dr. Annie Lorkowski of Centennial Animal Hospital in Corona, California. Support for this program comes from John Sharkey White, II. Support for this program comes from International speaker, Joseph Dumond, teaching the ancient roots of the Gaelic people. Learn more about their origins at Sightedmoon.com Support for this program comes from Cascadia Cross Border Law Group, Creating Transparent Borders for more than twenty five years, serving Alaska and the world. Find out more at   www.CascadiaLawAlaska.com The Irish & Celtic Music Podcast was produced by Marc Gunn, The Celtfather and our Patrons on Patreon. The show was edited by Mitchell Petersen with Graphics by Miranda Nelson Designs. Visit our website to follow the show. You'll find links to all of the artists played in this episode. Todd Wiley is the editor of the Celtic Music Magazine. Subscribe to get 34 Celtic MP3s for Free. Plus, you'll get 7 weekly news items about what's happening with Celtic music and culture online. Best of all, you will connect with your Celtic heritage. Please tell one friend about this podcast. Word of mouth is the absolute best way to support any creative endeavor. Clean energy is the single most powerful tool we have to fight climate change. Solar, wind, hydro  -  every kilowatt of clean power displaces the fossil fuels warming our planet. The big picture matters. So do the small choices you make every day. This week's tip comes from the 5 Rs of Sustainability: Refuse. Before you buy something new, ask yourself if you actually need it. Every item you don't buy is one that never had to be made, shipped, or eventually thrown away. Refusing is the most underrated act of sustainability there is. Start there. Your wallet and the planet will both thank you. Promote Celtic culture through music at http://celticmusicpodcast.com/. WELCOME THE IRISH & CELTIC MUSIC PODCAST * Helping you celebrate Celtic culture through music. I am Marc Gunn. I'm a Celtic musician and also host of Pub Songs & Stories. Every song has a story, every episode is a toast to Celtic and folk songwriters. Discover the stories behind the songs from the heart of the Celtic pub scene. This podcast is for fans of all kinds of Celtic music. We are here to build a diverse Celtic community and help the incredible artists who so generously share their music with you. If you hear music you love, please email the artists to let them know you heard them on the Irish & Celtic Music Podcast. These musicians are not part of some corporation. They are small indie groups that rely on people just like you to support their music so they can keep creating it. Please show your generosity. Buy a CD, Album Pin, Shirt, Digital Download, or join their community on Patreon. You can find a link to all of the artists in the shownotes, along with show times, when you visit our website at celticmusicpodcast.com. ALBUM PINS ARE CHANGING THE WAY WE HEAR CELTIC MUSIC Looking for a fresh way to support the music you love? Meet the Album Pin. Album Pins are lapel pins themed to a specific album — and each one comes with a digital download. Wear your music. All of my latest pins are wood - burned and locally produced, which means a smaller footprint and a one - of - a - kind feel you won't find anywhere else. Pick yours up at magerecords.com THANK YOU PATRONS OF THE PODCAST! Every episode of the Irish & Celtic Music Podcast exists because of you. Your support makes this possible, week after week, year after year. That is not a small thing. Your generosity covers real costs: audio engineering, graphic design, the Celtic Music Magazine, show promotion, and buying music directly from the independent Celtic artists we feature. You are the reason this music reaches new ears every single week. Not a patron yet? Here is what you are missing. Patrons get early access to episodes, music - only editions, free MP3 downloads, exclusive stories and artist interviews, and a vote in the Celtic Top 20. Join us today and help keep Celtic music alive, independent, and growing. Every single patron matters. Slainte! A special thanks to our Celtic Legends: Fuzzy, Dave and Rosie Donnelly, Rick Boyce, Bruce, Daniel Ide, Brian McReynolds, Marti Meyers, Alan Schindler, Margreta Silverstone, Emma Bartholomew, Dan mcDade, Jeff A, Gerald F Boyle, Miranda Nelson, Nancie Barnett, Gary R Hook, Lynda MacNeil, Kelly Garrod, Mike Schock, Shawn Cali HERE IS YOUR THREE STEP PLAN TO SUPPORT THE PODCAST Go to our Patreon page. Decide how much you want to pledge every month, $4, $12, $25. Keep listening to the Irish & Celtic Music Podcast to celebrate Celtic culture through music. You can become a generous Patron of the Podcast on Patreon at SongHenge.com. TRAVEL WITH CELTIC INVASION VACATIONS Every year, I take a small group of Celtic music fans on the relaxing adventure of a lifetime. We don't see everything. Instead, we stay in one area. We get to know the region through its culture, history, and legends. You can join us with an auditory and visual adventure through podcasts and videos. Learn more about the invasion at http://celticinvasion.com/ #celticmusic #irishmusic #celticmusicpodcast I WANT YOUR FEEDBACK What are you doing today while listening to the podcast? Send me a photo. If you're in a Celtic band, send me an audio recording of you performing live. Just audio. I'll use it in a podcast episode later this year. Email me at follow@bestcelticmusic. Asa Swain commented on Patreon: "I like hearing you talk, but thanks for releasing a "music only" version for everyone. I appreciate it." woodland folk replied to question, "how does the podcast make your life better?": I listen to ur podcast on my phone. on my closed fiddle case, mingled with birdsong, gentle hissing wood,the sun comes up early over the Mendips.the wind is still fresh... A battle of wills, my playlist rarely is enough....two tunes I play in the city right now I heard on one of my favourite episodes to date(man of the house),"the silver spear"& the blue idol.... I listened to this episode in a wood near the coast. maby five yrs ago, a deep cashcrop, scented pine. the needles leave a sponge rug moss covers old stumps & oaks, older by far than the rest of the wood that grow in crearings. deer whistle & bark in the night "home is were the heart is" & have on occasion gone back to listen again... The music u play suits the wood my friend..."  

Rogério Montanare, Thiago Siqueira e Central Pandora (Matheus e Sora) conversam sobre um gênero que lança todos os anos grandes filmes: ficção-científica. Dessa vez decidimos conversar sobre os melhores filmes lançados dos anos 2000 pra cá! Listinha? LISTONA! Vamos bater papo sobre os 30 melhores filmes de sci-fi do século 21!!! Quem é o rei do gênero: Christopher Nolan ou Denis Villeneuve? Quem escreve melhor que Alex Garland?Falamos sobre "Ela" (2013), "Distrito 9" (2009), "Planeta dos Macacos: O Confronto" (2014), "Wall-E" (2008), "Interestelar" (2014), "Brilho Eterno de uma Mente Sem Lembranças" (2004), "Mad Max: Estrada da Fúria" (2015), "Expresso do Amanhã" (2013), "Um Lugar Silencioso" (2018), "Filhos da Esperança" (2006) e muito mais!!|| ASSINE O SALA VIP DO RAPADURACAST- Escute um podcast EXCLUSIVO do RapaduraCast toda semana! http://patreon.com/rapaduracast

OpenAI woke up this week and chose violence.

Get your tickets to our L.A. live show here! After the smash success of ChatGPT, OpenAI positioned its video generation model Sora as AI's next consumer-friendly frontier. Disney signed on to the vision, promising a huge investment and allowing the studio's characters to appear in Sora videos. Then OpenAI abruptly shut Sora down. WSJ's Berber Jin takes us inside the pivot and explores what it means for the AI industry. Jessica Mendoza hosts. Further Listening: - OpenAI's 'Code Red' Problem - Is the AI Boom… a Bubble? - Artificial: The OpenAI Story Sign up for WSJ's free What's News newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices

We love a ranking here on The Vergecast, and it's time for the hardest one yet: David and Nilay compare notes on the 50 best products Apple has ever made, and see how their answers stack up to the many, many voters on The Verge this week. Before that, though, it's time for a bit of AI news — surprise, it's enterprise software! — and the comeback of the Hype Desk. After all that, and after the rankings, we do a round of Brendan Carr is a Dummy, talk about the fediverse, and repurpose our old iMacs. Vote for The Vergecast in the Webby Awards! A vote for The Vergecast is a vote that Brendan Carr is a dummy, that buttons are good, and that party speakers rule the world. Voting is open until April 16.  ⁠https://vote.webbyawards.com/PublicVoting#/2026/podcasts/shows/technology⁠ Further reading: ⁠OpenAI's big numbers: $122 billion funding round, 900 million weekly ChatGPT users. ⁠ ⁠Why OpenAI killed Sora ⁠ ⁠I think Google is taking a couple digs at OpenAI about Sora. ⁠ ⁠Apple's third-party Siri Extensions could lead to an AI App Store. ⁠ ⁠Microsoft's new ‘superintelligence' game plan is all about business⁠ ⁠OpenAI acquires TBPN | OpenAI⁠ ⁠Apple turns 50: celebrating five decades of the tech giant ⁠ ⁠Everything is iPhone now ⁠ ⁠Steve Jobs and the greatest run of products in tech history ⁠ ⁠How the invention of QuickTime changed computers forever ⁠ ⁠The triumphs and failures of Apple without Steve Jobs ⁠ ⁠The Apple product that really changed the industry: the MacBook Air ⁠ ⁠Apple at 50: a visual history ⁠ ⁠The origin story of Apple's long-running relationship with Foxconn ⁠ ⁠Apple's long, bitter App Store antitrust war ⁠ ⁠Snazzy Labs' iMac - Studio Display Mod Guide⁠ ⁠Flipboard Surf launches social websites combining Bluesky, Mastodon, RSS, and more⁠ ⁠These Raspberry Pi price hikes are no joke ⁠ ⁠Today is the final day to save up to $150 on a PS5 before the price goes up ⁠ ⁠Sony temporarily suspends memory card sales due to shortages ⁠ ⁠The White House has an app now, and Trump wants you to report people to ICE on it ⁠ ⁠What's inside the White House app? ⁠ Subscribe to The Verge for unlimited access to theverge.com, subscriber-exclusive newsletters, and our ad-free podcast feed.We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. Learn more about your ad choices. Visit podcastchoices.com/adchoices

You better lean in before a 25 year old beats you to the punch! This week we're talking about developments in Sheryl Sandberg's business, Diner Goths, Sophie Rain, and more! 13 min: Sheryl Sandberg 23 min: Alpine Divorce 30 min: Diner Goths  40 min: RIP Sora AI  49 min: Who is Sophie Rain  65 min: Caps Off ___________________________________ Keep up with all the latest: https://www.goodnoticings.com/ Read our many musings on Substack: https://goodnoticings.substack.com/ Join the Patreon for new, exclusive episodes every Friday! https://www.patreon.com/c/goodnoticings Follow us on: TikTok- @goodnoticingspod Instagram- @goodnoticingspod Theme song by: Bri Connelly  ___________________________________ SORA:  https://www.theverge.com/ai-artificial-intelligence/902368/openai-sora-dead-ai-video-generation-competition https://techcrunch.com/2026/03/29/soras-shutdown-could-be-a-reality-check-moment-for-ai-video/ Alpine Divorce:  https://www.theguardian.com/lifeandstyle/ng-interactive/2026/mar/17/alpine-divorce-abandoned-hiking-trail Diner Goths:  https://www.thenewatlantis.com/publications/american-diner-gothic Sophie Rain:  https://www.gq.com/story/sophie-rain-profile Learn more about your ad choices. Visit podcastchoices.com/adchoices

Luke finally heard from the court about the erroneous parking ticket he received. He's somewhat satisfied with the results. He and Andrew also discuss the end of the A.I. app called Sora, which could let anyone make convincing deep fakes depicting real people. And, speaking of robots, Luke is ready for them to fully take over at least one aspect of Major League Baseball.

OpenAI is shutting down its video generator Sora less than six months after it launched, and just three months since it signed a deal with Disney. Is this an A.I. company fine tuning its offerings, or the long-awaited popping of the A.I. bubble?Guest: Jason Koebler, cofounder of 404 Media.Want more What Next? Subscribe to Slate Plus to access ad-free listening to the whole What Next family and across all your favorite Slate podcasts. Subscribe today on Apple Podcasts by clicking “Try Free” at the top of our show page. Sign up now at slate.com/whatnextplus to get access wherever you listen.Podcast production by Elena Schwartz, Paige Osburn, Anna Phillips, Madeline Ducharme, and Rob Gunther. Hosted on Acast. See acast.com/privacy for more information.

P.M. Edition for Mar. 30. The Labor Department proposed a new rule that would make it easier to invest in private markets through 401(k)s. It comes as investors pull money from some private-credit funds. WSJ retirement reporter Anne Tergesen explains the risks. Plus, last year OpenAI hyped up its new AI video product, Sora. So why did it abruptly pull the plug last week? WSJ tech reporter Berber Jin tells us. And the CEO of Air Canada is stepping down after he offered condolences for the LaGuardia Airport crash in English and not in French. Alex Ossola hosts. Sign up for the WSJ's free What's News newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices

This week: Just minutes before Trump posted about talks with Iran, oil markets saw a flurry of activity. Conspiracy theories followed. Felix Salmon, Elizabeth Spiers, and Emily Peck dissect the suspicious timing of those trades and the possibility of insider trading within the Trump administration. Then, the hosts react to the surprising ruling on Meta and social media addiction. And: OpenAI's sudden decision to shut down its consumer-facing video generation platform, Sora. In the Slate Plus episode: The treasury market rom-comWant to hear that discussion and hear more Slate Money? Join Slate Plus to unlock weekly bonus episodes. Plus, you'll access ad-free listening across all your favorite Slate podcasts. You can subscribe directly from the Slate Money show page on Apple Podcasts and Spotify. Or, visit slate.com/moneyplus to get access wherever you listen. Podcast production by Jessamine Molli. Hosted on Acast. See acast.com/privacy for more information.

It's Indicators of the Week (now on YouTube!). It's our weekly look at some of the most fascinating economic numbers from the news. On today's episode: The US ain't doing too hot in attracting European tech workers; OpenAI takes its video generator Sora behind the barn; and a rapper, pound cake, and the police. Related episodes: OpenAI's deals are looking a little frothy We're about to lose a lot of foreign STEM workers For sponsor-free episodes of The Indicator from Planet Money, subscribe to Planet Money+ via Apple Podcasts or at plus.npr.org. Fact-checking by Julia Ritchey and Vito Emanuel. Music by Drop Electric. Find us: TikTok, Instagram, Facebook, Newsletter.  To manage podcast ad preferences, review the links below:See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy

Meta fined $375M for child safety failures. Musk lost 3 lawsuits in a week. Sam Altman compared to a Nazi. Netflix raised prices again. The Pentagon can't quit Claude. Reddit wants your face scan. Star Trek's streaming era is over. But the thin black line holds!

Kara and Scott unpack the Trump administration stacking an AI council with Big Tech names, the market-moving chaos around shifting Iran statements, and surprising Democratic wins in Florida — including in Trump's own backyard. Then, the TSA mess continues, Meta and YouTube are found liable in landmark social media addiction cases, and OpenAI calls it quits on Sora, just as Scott predicted. Watch this episode on the ⁠⁠Pivot YouTube channel⁠⁠.Follow us on Instagram and Threads at ⁠⁠@pivotpodcastofficial⁠⁠.Follow us on Bluesky at ⁠⁠@pivotpod.bsky.social⁠⁠Follow us on TikTok at ⁠⁠@pivotpodcast⁠⁠.Send us your questions by calling us at 855-51-PIVOT, or email pivot@voxmedia.com Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this edition of You Trend Do That On TV, Jack and Miles discuss the numerous special elections, the "MAGA activist" who pushed 2020 election fraud claims getting busted for election fraud, Open AI shuttering Sora, an update on Trump & Bibi's war with Iran and much more!See omnystudio.com/listener for privacy information.