Podcasts about mit

When most people think of negotiation, they picture big, high-stakes moments: haggling over the price of a house, negotiating a starting salary, or hammering out the terms of a business deal. But negotiation is also something we do every day. It's working out who's taking on which chores with your spouse, convincing a coworker to help on a project, or even deciding where to go for dinner with friends. The better you understand negotiation — which is really about influencing other people's decisions — the better you'll be at navigating conflicts and opportunities both big and small.My guest has spent decades studying the art and science of wielding that influence. John Richardson teaches negotiation at MIT's Sloan School of Management and is the co-author of Never Settle. Today on the show, John shares what he learned from negotiation legends like Roger Fisher, co-author of Getting to Yes, and FBI hostage negotiator Chris Voss. He unpacks the skills of master negotiators, including how to build reciprocity, uncover what people actually want, and keep your emotions from hijacking your judgment. We also get into the part of negotiation that makes people the most uncomfortable: the back-and-forth over hard numbers like price and salary and how to handle those conversations with greater confidence and better results.Resources Related to the PodcastAoM Podcast #234: Haggling and Deal Making Advice From a FBI Hostage Negotiator (With Chris Voss)AoM Podcast #559: How to Handle Difficult Conversations (With Sheila Heen)Influence: The Essential Guide to the Psychology of Influence and Persuasion in Everyday Life by Robert B. CialdiniAoM Article: How Labeling Your Emotions Can Help You Take ControlGetting to Yes: Negotiating Agreement Without Giving In by Roger FisherStart with NO…The Negotiating Tools that the Pros Don't Want You to Know by Jim CampConnect With John RichardsonNever Settle websiteJohn's faculty page0:00 Everyday Negotiation & Meet John Richardson2:18 John's Path Into Negotiation9:50 Why People Fear Negotiation12:34 Roger Fisher's Philosophy (Getting to Yes)17:46 Chris Voss & Hostage Negotiation Tactics22:54 Give Them a Sandwich (Reciprocity)30:06 Using Someone's Name33:59 Managing Your Emotions39:00 Figuring Out What Everyone Wants46:29 Distributive Negotiation & Anchoring54:52 Alternatives, Saying No & ClosingSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Die Weltmeisterschaft ist vorüber, Spanien hat triumphiert und nach Knicks-Trubel und 250-Jahre-Jubiläum kommen wir in New York ein wenig zur Ruhe. Wir ziehen deshalb ein Fazit zu den WM-Wochen: Welche politischen Themen standen etwas im Hintergrund und kommen jetzt wieder nach vorne?   Neben Fußball geht es in dieser Folge von Amerikanische Zustände um die Fragen:   Wie wichtig ist der Tod von Senator Lindsey Graham? Hat Trump neue Ideen für den Irankrieg? Wie gefährlich war seine Rede zur Sicherheit von Wahlen? Welche neue Debatte entflammt um die Einwanderungsbehörde ICE?   Zur WM-Bilanz (ab 38:50) haben wir außerdem einen tollen Gast: Deana Rohlinger ist Soziologin an der Florida State University und blickt mit uns darauf, wie die WM-Wochen dem Land möglicherweise zu einem neuen Selbstbild verholfen haben. Kann die Freude an der Gastfreundschaft vielleicht sogar ein wenig über die tiefen Gräben der Gesellschaft hinweghelfen? Mehr über ihre Arbeit steht unter: http://www.deanarohlinger.com/   Mit ihr probieren wir etwas Neues aus: In unserer Folge gibt es das Gespräch mit ihr im englischen Original, aber wir haben auch eine übersetzte Version erstellt. Dafür haben wir unsere Fragen nachträglich noch einmal auf Deutsch eingesprochen und Rohlinger von einer KI übersetzen lassen. Diese deutschsprachige Version gibt es von 1:08:35 bis 1:32:50.   Falls Ihr Meinungen oder Anregungen zu diesem Aufbau habt, erreicht Ihr uns gerne unter amerikanischezustaende@gmail.com.

Hier geht's zu unserem Angebot für Firmenkurse: oaws.de/firmenkurse. Ohne Aktien-Zugang ist's schwer? Starte jetzt bei unserem Partner Scalable Capital. Mit eigenem KI-Chatbot, der dir alle Fragen rund ums Investieren beantwortet. Alle weiteren Infos gibt's hier: scalable.capital/oaws. Iran-Krise drückt Ryanair. Chinas Moutai hebt Preise wegen schwacher Nachfrage. PKW-Verkäufe in China sollen 14% fallen. AMD, Broadcom und Intel legen zu. Prysmian liefert Kabel für 6 Mrd. $. Die Odyssee macht 264 Mio. $. AMC & IMAX profitieren. Eni (WKN: 897791) fördert billig Öl und wächst. Jeder Dollar mehr beim Ölpreis bringt 100 Mio. € Cashflow. Dazu das Satellitenmodell: Töchter wie Enilive und Plenitude sind allein 20 Mrd. € wert. Knapp 5% Dividendenrendite obendrauf. Nichts mit Sportwagen. Ferrari Group (WKN: A4122L) transportiert Schmuck, Taschen und Uhren für über 100 Luxusmarken. 15% Nettomarge, 4% Dividende, 100 Mio. $ Nettocash. Eine Wette auf den gesamten Luxusmarkt ohne Einzelmarken-Risiko. Diesen Podcast vom 21.07.2026, 3:00 Uhr stellt dir die Podstars GmbH (Noah Leidinger) zur Verfügung. Learn more about your ad choices. Visit megaphone.fm/adchoices

In dieser Folge sprechen wir über ein SUV aus Japan, das die wenigsten von euch auf dem Zettel haben. Ein Auto, das bis heute wirkt wie ein serienreifes Concept Car – und mit weniger als 6.000 gebauten Exemplaren sogar seltener ist als ein Ferrari Testarossa. Die Rede ist vom Isuzu VehiCROSS. Als er 1993 auf der Tokyo Motor Show vorgestellt wird, sieht er aus, als wäre er direkt einem Science-Fiction-Film entsprungen. Mit seinen markanten, unlackierten Kunststoffbeplankungen und den kantigen Formen hebt er sich deutlich vom SUV-Einerlei seiner Zeit ab. In Europa begegnet man dem VehiCROSS nur selten. Wenn doch einmal einer auftaucht, ist er erstaunlicherweise gar nicht so teuer – schlicht, weil kaum jemand gezielt nach ihm sucht. Verdient der Isuzu VehiCROSS deutlich mehr Aufmerksamkeit? Darüber sprechen diesmal Frank Otero Molanes, Jens Seltrecht und Lukas Hambrecht.Executive Producer: Christoph Falke & Ruben Schulze-FröhlichProjektleitung: Lukas HambrechtSounddesign & Produktion: Fabian SchäfflerMarketing und Ansprechpartner: Bastian SchonauerCopyrights Cover: www.oldtimer-markt.de Hosted on Acast. See acast.com/privacy for more information.

Reizüberflutung, ständige Erreichbarkeit und zu wenig Erholung gehören für viele zum Alltag. Doch unser Gehirn ist darauf biologisch nicht ausgelegt. Schon kleine Störungen im Schlaf oder in der Energieversorgung können spürbare Folgen haben. Warum mentale Erschöpfung oft missverstanden wird – und welche Signale darauf hindeuten, dass das Gehirn an seine Grenzen gerät, erklärt die Neurowissenschaftlerin Dr. Teresa Cramer. Sie untersucht am MIT in Boston die Mechanismen von Lern- und Anpassungsfähigkeit im Gehirn und gibt in dieser Folge auch Tipps, wie wir unser Gehirn schützen können. Produktion: Lilian Hoenen Redaktion: Antonia Beckermann Noch mehr "Aha!"- Folgen gibt es bei WELTplus und Apple Podcasts. Alle zwei Wochen am Montag eine neue Folge. Hier bei WELT hören: https://www.welt.de/podcasts/aha-zehn-minuten-alltags-wissen/plus246844328/Noch-mehr-Alltagswissen-Aha-Bonus-Folgen-fuer-Abonnenten-Podcast.html. "Aha! Zehn Minuten Alltags-Wissen" ist der Wissenschafts-Podcast von WELT. Wir freuen uns über Feedback an wissen@welt.de. Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

En nuestro episodio 522 conversamos con Leopoldo Fergusson, economista, profesor de los Andes, doctor del MIT sobre: + El estudio sobre cómo el acento determina tu éxito + La importancia de combatir la desigualdad. + La igualdad es el medio pero no el fin. + La paradoja de la meritocracia. + Cómo se percibe la meritocracia en Latam. + El problema de la meritocracia. + La cultura del trabajo duro. + La investigación sobre la norma social "no sea sapo". + Cómo reducir la informalidad. + Cómo lidiar y superar el fracaso. ______________________________________________________________________

Inklusive Kommunikation klingt für manche nach Stilfrage, für andere nach Sprachpolizei und für wieder andere einfach nach gesundem Menschenverstand. Aber was passiert eigentlich, wenn Sprache nicht nur beschreibt, sondern Bilder im Kopf erzeugt, Teams prägt und sogar beeinflusst, wer sich auf einen Job bewirbt oder in einer Tech Community willkommen fühlt? Genau darum geht es in dieser Episode.Zu Gast ist Lukas Kahwe Smith, Softwareentwickler, Open Source Veteran aus dem PHP-Community und ehemaliger CTO von Witty Works. Mit ihm sprechen wir über inklusive Sprache in der Softwareentwicklung, über Gendern, geschlechtsneutrale Begriffe, diskriminierende Sprachmuster, problematische Formulierungen in Stellenausschreibungen und die Frage, warum Begriffe wie Main Branch, Master Branch, Whitelist, Blacklist oder Leader Follower in der Tech-Welt mehr sind als nur Benennungssache. Außerdem schauen wir auf Open Source, Teamkommunikation, Leadership-Kommunikation, Diversity in Communities und darauf, wie Witty Works als Browser Extension und Lerntool für inklusivere Kommunikation funktioniert hat.Besonders spannend wird es beim Blick auf KI und LLMs. Helfen ChatGPT, Claude und Co. dabei, inklusiver zu schreiben, oder zementieren sie am Ende nur die Sprache der Vergangenheit? Wenn du wissen willst, warum Kontext wichtiger ist als starre Regeln, wie Sprache Kultur verändert und weshalb kleine Formulierungen große Wirkung haben können, dann ist diese Folge genau dein Ding. Bonus: Ein racist soap dispenser und ein Git Branch haben heute mehr gemeinsam, als dir vielleicht lieb ist.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

Alle reden davon, dass KI die Einstiegsjobs frisst. Virginia Sondergeld von Indeed schaut in die Daten — und die zeigen etwas anderes.Die Einstiegsjobs gehen zurück, das stimmt. Aber der Rückgang begann schon im Frühjahr 2022, und in den Daten lässt sich KI bisher nicht als Ursache isolieren. Sobald man Remote-Arbeit mitrechnet, verschwindet der KI-Effekt. Virginia erklärt, warum Unternehmen trotzdem gern "wegen KI" entlassen — und was dieses Signaling mit Angst und Unsicherheit unter Arbeitnehmenden macht.Dazu geht es um den Mismatch am Arbeitsmarkt (genug Bewerbungen, aber nicht die richtigen), den demografischen Wandel mit drei Millionen Menschen, die bis 2040 aus dem Erwerbsalter ausscheiden, und darum, warum Frauen KI seltener nutzen als Männer — und was das langfristig für ihre Karrieren bedeutet.Über den Gast: Virginia Sondergeld ist Arbeitsmarktökonomin bei Indeed und arbeitet mit Indeed-Daten zur Zukunft der Arbeit.Studien und Daten aus der Folge:- Canaries in the Coal Mine (Brynjolfsson u.a.): https://digitaleconomy.stanford.edu/app/uploads/2025/11/CanariesintheCoalMine_Nov25.pdf- The Broken Ladder (Lambert & Schindler): https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6787638- Indeed Datenportal: https://data.indeed.com/- Frauen und Männer in der KI-Transformation: https://www.hiringlab.org/de/blog/2026/04/21/frauen-und-maenner-in-der-ki-transformation-gleiches-potenzial-ungleiche-nutzung/Mehr HR Weekly:- YouTube: https://www.youtube.com/@HRWeekly- beyobie: https://www.beyobie.comWERBEPARTNERUnser heutiger Partner ist die Union Krankenversicherung.Mit dem Vorsorgetarif erhalten Mitarbeitende bis zu 500 € pro Jahr für Vorsorgeuntersuchungen – darunter Blutbild, Lungenfunktion, Hautkrebsscreening und viele weitere Checks.Besonders praktisch: Einige Untersuchungen, wie das Hautkrebsscreening, können digital und ohne lange Wartezeiten durchgeführt werden.Außerdem gibt es keine Gesundheitsprüfung und keinen umfangreichen Fragenkatalog zu Vorerkrankungen. Auch Mitarbeitende mit Vorerkrankungen können im Kollektiv mitversichert werden.Mehr Informationen findet ihr hier: https://www.ukv.de/v/krankenversicherung/betriebliche-krankenversicherung.htmlbeyobieDatenchaos im HR-Team? beyobie bringt Klarheit.Wenn es einen Frust hinter Datenanalysen im HR gibt, dann ist es dieser: zu viel Aufwand, zu wenig Aussagekraft. Mit beyobie verbindet ihr ganz einfach eure HR-Systeme und verwandelt eure HR-Daten im Handumdrehen in starke Argumente fürs Management – ganz ohne Excel-Marathon.

In der heutigen Folge spricht Michél mit Robert Itschner, dem CEO der BKW Gruppe – einem der größten Energie- und Infrastrukturunternehmen Europas aus der Schweiz.  Mit über 12.000 Mitarbeitenden schafft BKW rund 5 Milliarden Schweizer Franken Umsatz (Ca. 5,4 Mrd. €). Gemeinsam sprechen sie über die Energiewende, den Ausbau von Netzen und erneuerbaren Energien sowie den Blick der Schweiz auf Deutschland. Außerdem geht es um Unternehmertum, Führung, die Zukunft der europäischen Energieversorgung und warum Optimismus für den Wirtschaftsstandort entscheidend ist. _________________________________________________ Die BLACK BOX auf der BAU 2027 ist das neue Innovationsformat der Messe München und DIGITALWERK – mit Livedemos, Diskussionen, Networking und digitalem Content.  Das geht nur mit Unternehmen, die ihre Innovationen nicht nur ausstellen, sondern sichtbar machen. Mehr Infos und Kontaktmöglichkeiten gibt's unter https://www.digitalwerk.io/dw-events/blackbox  _________________________________________________ VESTIGAS ist die All-in-One-Lösung für die digitale Baulieferkette – vom digitalen Lieferschein über KI-gestützte Rechnungsprüfung bis zum smarten Rechnungsworkflow. Weniger Papierkram, rechtssichere Prozesse und mehr Zeit für die wirklich wichtigen Aufgaben auf der Baustelle und im Büro. Mehr erfahrt ihr unter www.vestigas.com  _________________________________________________ 00:00 – Darum gehts in der Folge 02:54 – Vom Mechaniker zum CEO 06:17 – Die BKW Gruppe erklärt 17:41 – Das Geschäftsmodell der Zukunft 21:15 – Schweiz vs. Deutschland: Energiepolitik 29:28 – Warum Infrastruktur schneller werden muss 31:58 – Die Energiewende braucht Tempo 33:56 – Führung in unsicheren Zeiten 40:09 – Warum eine Rolle rückwärts Milliarden kostet 41:40 – Der Schweizer Blick auf Deutschland

In dieser besonderen Folge von GOAL IN ONE begrüßen Yannick Rosenberger und Patrick Emery den neuesten Coach im Team von Dein Golf Coach: Alexander Stohr.Alexander bringt über 20 Jahre Coaching-Erfahrung mit und teilt in dieser Episode seine Philosophie rund um modernes Golftraining, effektives Lernen und die häufigsten Fehler, die ambitionierte Golfer auf der Driving Range und auf dem Platz machen.Gemeinsam sprechen wir darüber:⛳ Wie Alexander zum Golfsport gekommen ist⛳ Warum er sich für Dein Golf Coach entschieden hat⛳ Wie er Spieler analysiert und individuelle Lösungen entwickelt⛳ Warum Qualität im Training wichtiger ist als möglichst viele Range-Bälle⛳ Die größten Denkfehler beim Putten und kurzen Spiel⛳ Weshalb Gefühl und Eigenwahrnehmung entscheidender sind als das reine Nachahmen von Schwungpositionen⛳ Was Mitglieder und Golfer künftig von Alexander im Online-Coaching und vor Ort erwarten dürfenFreu dich auf ein persönliches Kennenlernen, spannende Einblicke in modernes Golfcoaching und viele praktische Impulse, die dir helfen, dein Golfspiel nachhaltig zu verbessern.Über Dein Golf CoachDein Golf Coach ist die Online-Golfplattform für ambitionierte Golferinnen und Golfer, die ihr Handicap nachhaltig verbessern möchten. Mit den Experten Yannick Rosenberger, Patrick Emery und jetzt auch Alexander Stohr erhältst du professionelles Golf Online Coaching in den Bereichen Schwungtechnik, kurzes Spiel, Putting, Platzstrategie und Mentaltraining.

Wie denkt einer der erfolgreichsten Unternehmer unserer Zeit? Und was ist das wahre Erfolgsgeheimnis hinter Sir Richard Branson? In dieser exklusiven Folge nehme ich dich mit hinter die Kulissen von Necker Island, der Privatinsel von Sir Richard Branson auf den British Virgin Islands. Vier Tage voller intensiver Begegnungen, inspirierender Gespräche und überraschender Erkenntnisse – doch die wichtigste Lektion hatte nichts mit Business, Strategie oder Leadership zu tun. Du erfährst: •⁠  ⁠warum außergewöhnlicher Erfolg nicht aus Verbissenheit entsteht, •⁠  ⁠welche Rolle Lebensfreude, Neugier und Intuition wirklich spielen, •⁠  ⁠was ich auf Necker Island über Erfolg, Erfüllung und authentische Führung gelernt habe, •⁠  ⁠und wie du diese Erkenntnisse direkt auf dein eigenes Leben und Business übertragen kannst. Denn vielleicht folgt Erfolg nicht den Menschen, die ihm hinterherjagen – sondern denen, die das Leben mit ganzem Herzen leben.   Erfolg ist kein Zufall. Phoenix Mindset auch nicht.

Achtung (Werbung in eigener Sache):  Jetzt mein neues Buch kaufen (in Co-Produktion mit Prof. Dr. Johanna Bath): "Die perfekte Employee Journey & Experience" (erschienen im Oktober 2025): Springer: https://link.springer.com/book/9783662714195 Amazon: https://bit.ly/44aajaP Thalia: https://www.thalia.de/shop/home/artikeldetails/A1074960417 Dieses Fachbuch stellt die wichtigsten Elemente der Employee Journey vor – vom Pre-Boarding bis zum Offboarding – und erläutert, wie Verantwortliche in Unternehmen eine gelungene Employee Experience realisieren und nachhaltig verankern können. Mein Gast: Christiane Räbiger Christiane Räbiger ist Unternehmerin, Beraterin und Expertin für wirksame Unternehmenssteuerung sowie nachhaltige Führung. Seit vielen Jahren begleitet sie Unternehmen und Führungskräfte dabei, Strategien erfolgreich umzusetzen und Organisationen so auszurichten, dass messbare Ergebnisse entstehen. Im Mittelpunkt ihrer Arbeit steht dabei nicht die reine Kennzahlenorientierung, sondern die Frage, wie Führung, Zusammenarbeit und Unternehmenssteuerung intelligent zusammenspielen. Sie zeigt, warum nachhaltiger Erfolg entsteht, wenn Führungskräfte Verantwortung teilen, Wirkzusammenhänge verstehen und Kennzahlen als Steuerungsinstrument statt als Selbstzweck begreifen. Mit ihrem Beratungsunternehmen unterstützt sie Organisationen dabei, Strategien in wirksames Handeln zu übersetzen, KPI-Systeme sinnvoll zu entwickeln und Führungskräfte in ihrer Rolle als Gestalter nachhaltiger Ergebnisse zu stärken. In der GainTalents Folge 458 spreche ich mit Christiane Räbiger (Gründerin und Inhaberin von Twinnovativ) darüber, warum wirksame Führung weit mehr ist als das Erreichen von Kennzahlen. Es geht um Selbstverortung, den ehrlichen Blick auf die eigenen Limitierungen und die Frage, wie Kennzahlen zum Steuerungsinstrument statt zum Selbstzweck werden. Herzlichen Dank an Christiane für die vielen wertvollen Impulse und praxisnahen Beispiele! Nachhaltig erfolgreiche Führung beginnt bei der eigenen Haltung: Führungskräfte müssen sich selbst reflektieren, Verantwortung übernehmen und auch unangenehme Wahrheiten aushalten können. Gleichzeitig braucht es verständliche Ziele, transparente Kennzahlen und Klarheit darüber, welchen Beitrag jedes Teammitglied leisten kann. Die wichtigsten Erkenntnisse aus dem Gespräch mit Christiane: Nachhaltige Führung beginnt bei der Frage „Wer bin ich?" (Selbstführung und Selbstreflexion) – tiefe Wurzeln geben Halt und Verlässlichkeit im Team Kennzahlen sind Leitplanken, dürfen aber kein Selbstzweck sein: Sie geben Klarheit, wenn jeder seinen konkreten Beitrag versteht Klarheit, Struktur und Wahrheit schlagen jeden Egotrip – gerade im Mittelstand und in Familienunternehmen Wirksame Führung verbindet Strategie und Zahlen mit Vertrauen, Kommunikation und psychologischer Sicherheit   #GainTalentsPodcast #Leadership #Unternehmenssteuerung #Strategie #KPI #PerformanceManagement #Organisationsentwicklung   Shownote-Links Christiane Räbiger LinkedIn: https://www.linkedin.com/in/christiane-r%C3%A4biger/ Website: https://www.twinnovativ.de/ Themenseite: https://www.twinnovativ.de/unternehmenssteuerung/ Link zum CxO-Event:https://www.twinnovativ.de/landingpage/cxo-event-generationenwechsel/ https://www.baerenherz.org   Links Hans-Heinz Wisotzky:  Website: https://www.gaintalents.com/podcast und https://www.gaintalents.com/blog Podcast: https://www.gaintalents.com/podcast Bücher: Neu (jetzt überall zu kaufen): Die perfekte Employee Journey und Experience https://link.springer.com/book/9783662714195 Erste Buch: Die perfekte Candidate Journey und Experience https://www.gaintalents.com/buch-die-perfekte-candidate-journey-und-experience LinkedIn https://www.linkedin.com/in/hansheinzwisotzky/ LinkedIn https://www.linkedin.com/company/gaintalents XING https://www.xing.com/profile/HansHeinz_Wisotzky/cv Facebook https://www.facebook.com/GainTalents    

In 2007, two Harvard researchers added a splash of balsamic vinegar to beer.  When drinkers knew the beer contained vinegar, they hated it.  But when the researchers told them “this is our MIT brew," the majority loved it.  The study reveals the secrets behind marketing.  On today's episode of Nudge with award-winning science writer David Robson, we explain how good marketing can shape our perception.  Link to the bonus episode: ⁠https://nudge.kit.com/86d3e39845⁠  Become an FSB member: https://get.fsb.org.uk/nudge/ Read the Expectation Effect: https://amzn.to/4dvkb3s  David's website: https://davidrobson.me/  David's Substack: https://davidarobson.substack.com/  ---  Today's sources:  Amar, M., Ariely, D., Bar-Hillel, M., Carmon, Z., & Ofir, C. (2011). Brand names act like marketing placebos. Hebrew University of Jerusalem. Clark, A. (2016). Surfing uncertainty: Prediction, action, and the embodied mind. Oxford University Press. Crum, A. J., & Langer, E. J. (2007). Mind-set matters: Exercise and the placebo effect. Psychological Science, 18(2), 165–171. Faasse, K., Martin, L.R., Grey, A., Gamble, G., and Petrie, K.J. (2016). Impact of brand or generic labeling on medication effectiveness and side effects. Health Psychology, 35(2), 187. Grabenhorst, F., Rolls, E. T., & Bilderbeck, A. (2008). How cognition modulates affective responses to taste and flavor: Top-down influences on the orbitofrontal and pregenual cingulate cortices. Cerebral Cortex, 18(7), 1549–1559. Lee, L., Frederick, S., & Ariely, D. (2006). Try it, you'll like it: The influence of expectation, consumption, and revelation on preferences for beer. Psychological Science, 17(12), 1054–1058. Lidstone, S. C., Schulzer, M., Dinelle, K., Mak, E., Sossi, V., Ruth, T. J., & Stoessl, A. J. (2010). Effects of expectation on placebo-induced dopamine release in Parkinson disease. Archives of General Psychiatry, 67(8), 857–865. Plassmann, H., O'Doherty, J., Shiv, B., & Rangel, A. (2008). Marketing actions can modulate neural representations of experienced pleasantness. Proceedings of the National Academy of Sciences, 105(3), 1050–1054. Quattrone, A., Barbagallo, G., Cerasa, A., & Stoessl, A. J. (2018). Neurobiology of placebo effect in Parkinson's disease: What we have learned and where we are going. Movement Disorders, 33(8), 1213–1227. Robson, D. (2022). The expectation effect: How your mindset can transform your life. Canongate. Spence, C., & Piqueras-Fiszman, B. (2014). The perfect meal: The multisensory science of food and dining. John Wiley & Sons.

Edward Woodford is the co-founder and CEO of zerohash, a premiere crypto-infrastructure provider, powering digital asset products for platforms across the financial services sector. Edward earned an MBA from MIT, and has led zerohash to support a broad ecosystem of exchanges, neobanks, and payment networks, with API-first solutions for custody, settlement, and compliance.Follow To Dine For:Official Website: ToDineForTV.comFacebook: Facebook.com/ToDineForTVInstagram: @ToDineForTVEmail: ToDineForTV@gmail.com Thank You to our Sponsors!American National InsuranceNotre Dame Family WinesNouvieFollow Our Guest:Official Site: zerohash.comLinkedIn: Edward Woodford Hosted on Acast. See acast.com/privacy for more information.

Kann ein Politiker in der Öffentlichkeit gegen etwas sein und privat darauf zurückgreifen? Diese Frage beschäftigt gerade unsere Nachbarn in Deutschland. Denn während der ehemalige CDU-Gesundheitsminister Jens Spahn, öffentlich in der Vergangenheit gegen die Leihmutterschaft aufgetreten ist, hat er gemeinsam mit seinem Partner genau so jetzt ein Kind bekommen. Mit einer Leihmutter aus den USA. Spahn musste inzwischen seinen Posten räumen. Doppelmoral oder sollte die Politik vom Privatleben stärker getrennt werden? Darüber sprechen wir heute mit Lenz Jacobsen, er arbeitet für die Zeit im politischen Feuilleton in Berlin und ist Podcast-Co-Host von "Servus. Grüezi. Hallo".

Herzlich willkommen zu Ihrem morgendlichen Newsletter! Jens Spahns Rücktritt ist mehr als eine Personalie: Er zeigt, wie eng politische Glaubwürdigkeit und privates Handeln miteinander verknüpft sind. Spahn hatte sich politisch gegen Leihmutterschaft in Deutschland ausgesprochen, nutzte selbst aber die Möglichkeit im Ausland und brachte damit viele Parteifreunde in Erklärungsnot. Der Druck aus der Union und aus dem Kanzleramt machte schließlich deutlich, dass sein Amt kaum noch vom Konflikt zwischen Parteiposition und persönlicher Entscheidung zu trennen war. Mit der schnellen Trennung signalisiert die Partei, dass sie an ihrem familienpolitischen Kurs festhält. Zugleich muss sie Antworten darauf finden, wie sie künftig mit modernen Familienmodellen umgehen will, ohne das Vertrauen in ihre Grundsätze weiter zu strapazieren.

MARK 10:13-31 "[13] And they were bringing children to him that he might touch them, and the disciples rebuked them. [14] But when Jesus saw it, he was indignant and said to them, “Let the children come to me; do not hinder them, for to such belongs the kingdom of God. [15] Truly, I say to you, whoever does not receive the kingdom of God like a child shall not enter it.” [16] And he took them in his arms and blessed them, laying his hands on them. [17] And as he was setting out on his journey, a man ran up and knelt before him and asked him, “Good Teacher, what must I do to inherit eternal life?” [18] And Jesus said to him, “Why do you call me good? No one is good except God alone. [19] You know the commandments: ‘Do not murder, Do not commit adultery, Do not steal, Do not bear false witness, Do not defraud, Honor your father and mother.'” [20] And he said to him, “Teacher, all these I have kept from my youth.” [21] And Jesus, looking at him, loved him, and said to him, “You lack one thing: go, sell all that you have and give to the poor, and you will have treasure in heaven; and come, follow me.” [22] Disheartened by the saying, he went away sorrowful, for he had great possessions. [23] And Jesus looked around and said to his disciples, “How difficult it will be for those who have wealth to enter the kingdom of God!” [24] And the disciples were amazed at his words. But Jesus said to them again, “Children, how difficult it is to enter the kingdom of God! [25] It is easier for a camel to go through the eye of a needle than for a rich person to enter the kingdom of God.” [26] And they were exceedingly astonished, and said to him, “Then who can be saved?” [27] Jesus looked at them and said, “With man it is impossible, but not with God. For all things are possible with God.” [28] Peter began to say to him, “See, we have left everything and followed you.” [29] Jesus said, “Truly, I say to you, there is no one who has left house or brothers or sisters or mother or father or children or lands, for my sake and for the gospel, [30] who will not receive a hundredfold now in this time, houses and brothers and sisters and mothers and children and lands, with persecutions, and in the age to come eternal life. [31] But many who are first will be last, and the last first.” ESV LESSON NOTES Harvard University, California Institute of Technology, Stanford University, MIT, and Princeton University have some of the lowest college acceptance rates in our country (3-4%). In many different fields, being outstanding seems to be the benchmark for entry (colleges, athletics, promotions, jobs). Within these realms, and others, personal achievements/accolades are the pathway to acceptance (Achievements = Acceptance) Children are great examples of people who are helpless, defenseless, and needy. Unlike adults, children come with no pretense, no artificial or adopted behaviors to impress people. Children come empty handed and they know how to ask for help. And they don't just ask for help, they eagerly receive it. Because of this, Jesus uses these little ones as examples of how one should receive the Kingdom. In Jesus' words in verses 14-15, we see two realities about entering the kingdom of God: 1.) Entrance into the Kingdom of God is received. 2) Entrance into the Kingdom of God is for the child-like. These two realities complement one another because if we are not child-like then we will not receive. In contrast to the child-like reception Jesus speaks about in verses 14-15, we see the rich man perceive eternal life as a matter of personal achievement. Within his conversation with Jesus, Jesus gives the man this command, "... go, sell all that you have and give to the poor, and you will have treasure in heaven; and come, follow me.” (vs. 21) Going, selling, and giving were the means by which Jesus told this man to detach himself from his current master, his idol of possessions. These commands ultimately led to the ultimate command: "come, follow me". Yet, upon hearing this, the man walks away. From here we see a difficult truth: entrance into the Kingdom of God is not accepted by everyone. In the disciples day, those who were rich were considered to be blessed by God. Since this was the dominating ideology, those who were rich were virtually guaranteed a place in the kingdom of God because their wealth was evidence of God's blessing. This is why the disciples react so stunned after hearing Jesus' teachings in verses 23-26. They ask Jesus a sincere question ("Then who can be saved?") and Jesus responds with the perfect answer (“With man it is impossible, but not with God. For all things are possible with God.”) Jesus lovingly reminds His disciples that entrance into the kingdom of God is completely beyond the sphere of human possibility. What we can't do in our humanity, God did in the life, death, and resurrection of Jesus. There is an ultimate connection between the child-likeness of verse 15 and the rich man of verse 22. This connection is seen in 3 aspects: Their records. Their responses. What they receive.  1.) Record - Child-like: No Significant Record / Rich man: Significant Record 2.) Response - Child-like: Eager Reception / Rich man: Sorrowful Rejection 3.) Receive - Child-like: Salvation / Rich man: Separation God restores Gospel goodbyes. When we leave good things and say goodbye to dear people for the sake of the Gospel, God does not leave us empty-handed. The God who is able “to do far more abundantly than all that we ask or think” does not overlook our goodbyes when they are rooted in the name of Jesus. Entrance into the Kingdom of God gives the children of God hope for what is to come.  DISCUSSION QUESTIONS 1. As you remember your process of choosing where to go to college, what were some of the schools you were considering? Why? Did you end up at your "dream school"? 2. In your life, what environments or settings do you feel the most tempted/feel the most pressure to relay your personal accomplishments or accolades? Why those environments? 3. Receiving is different than earning. Why is this difference such a big deal in our relationship with God? In your walk with God, do you find it difficult to receive rather than earn? If so, in what aspects is it the most difficult? 4. Have you had any experience with someone walking away/rejecting Jesus? If so, what was that like for you to experience? As you read about this man's decision, what do you notice about Jesus' reaction/response? Does his response capture your attention in any way? 5. Are there any difficult statements/teachings from Jesus that you have had to wrestle with in your walk with Him? If so, what did/does that process look like for you? 6. As you process through this connection between the childlikeness of verse 15 and the rich man of verse 22, what sticks out the most to you? What speaks most deeply to your heart? 7. Have you ever had to say a "gospel goodbye" to something or someone? If so, how difficult was that? How has God shown up since then?

In unserem Sommerspezial von "Mit den Waffeln einer Frau", wird es geheimnisvoll: Privatdetektivin Lena Körner hat sich aus unserer Community bei uns gemeldet und nimmt Barbara mit in eine Welt voller heimlicher Treffen, verräterischer Kontobewegungen und einer roten Rose auf einem Friedhof, die zu einem ziemlich unerwarteten Ergebnis geführt hat. Außerdem erzählt Lena, wie sie einen von Polizei und Gericht gesuchten Mann im Ausland aufgespürt hat und warum man seiner besten Freundin besser nicht jede Affäre anvertraut. Viel Spaß mit unserem Sommerspezial!

Mit zunehmender Taktzahl überlegt und beschließt die Bundesregierung Streichungen und Kürzungen – als Reformen bezeichnet – staatlicher Leistungen in vielen Politikbereichen sowie Beitragserhöhungen (Renten-, Pflege- und Krankenkassenbeiträge) und längere Lebensarbeitszeiten. Ist der Sozialstaat nicht mehr finanzierbar? Sind Leistungseinbußen auf der einen und Beitragserhöhungen auf der anderen Seite unumgänglich, um den Sozialstaat mit Verfassungsrang auf einemWeiterlesen

After 104 games and a month full of unforgettable soccer moments, Spain is world champion of the 2026 FIFA World Cup. With a 1-0 win over Argentina in the final, La Roja secures world soccer's biggest title for the second time. At the same time, Spain is making history: the country is the first nation to hold the men's and women's world championship titles at the same time. - Nach 104 Spielen und einem Monat voller unvergesslicher Fußballmomente ist Spanien Weltmeister der FIFA Fußball-Weltmeisterschaft 2026. Mit einem 1:0-Sieg gegen Argentinien im Finale sichert sich La Roja zum zweiten Mal den größten Titel des Weltfußballs. Gleichzeitig schreibt Spanien Geschichte: Als erste Nation hält das Land nun gleichzeitig die Weltmeistertitel der Männer und der Frauen.

In der heutigen Folge sprechen die Finanzjournalisten Lea Oetjen und Moritz Seyffarth über Chips im Bärenmarkt, Boeings neue Hoffnung und was in dieser Woche sonst noch wichtig wird. Außerdem geht es um Apple, Nvidia, Meta, Alphabet, Netflix, Uber, Delivery Hero, Intuitive Surgical, Microsoft, Amazon, Alibaba, Baidu, Cisco, Micron Technology, AMD, Broadcom, TSMC, Qualcomm, Intel, Arm, Leverage Shares 2x Micron Technology ETP Securities (WKN: A3GUFU), Leverage Shares -3x Short Semiconductors (WKN: A4A5UP) und Leverage Shares -3x Short SpaceX (WKN: A4AVLN). Das ist der Link zum Artikel von Daniel: https://www.welt.de/wirtschaft/plus6a59e8adc72232627649939a/etps-auf-einzelaktien-die-479-prozent-wette-auf-den-ki-boom.html Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Hier könnt ihr den AAA-Newsletter abonnieren: https://www.welt.de/newsletter/article232797673/Alles-auf-Aktien-Der-taegliche-Boersen-Newsletter-fuer-WELTplus-Abonnenten.html Und – ganz neu: AAA gibt es jetzt auch auf Instagram: https://www.instagram.com/alles_auf_aktien/ Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Anzeige: Diese Folge enthält Werbung für Smartbroker+. Depot eröffnen, 30 € ETF als Bonus sichern und aus tausenden ETFs wählen. Smartbroker+ macht Investieren einfach. Alle Informationen gibt es unter: https://get.smartbrokerplus.de/triple-aaa-podcast2/ Anzeige: Eight Sleep: Der Pod 5 reguliert die Temperatur im Bett automatisch, trackt Schlaf- und Gesundheitswerte ohne Wearable und kann so zu besserem Schlaf beitragen. Mit dem Code ALLESAUFAKTIEN erhaltet ihr auf https://www.eightsleep.com/allesaufaktien bis zu 350 Euro Rabatt. Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

  Was wäre, wenn Intuition keine geheimnisvolle Gabe, sondern eine Fähigkeit wäre, die sich trainieren lässt? Gemeinsam mit Martin Zoller spreche ich über Remote Viewing, die Ursprünge dieser Methode, ihre Verbindung zu geheimdienstlicher Forschung und die Frage, ob jeder Mensch lernen kann, seiner Wahrnehmung stärker zu vertrauen. Mich interessiert besonders eure Meinung: Habt ihr schon einmal etwas gespürt oder geahnt, das sich später bewahrheitet hat?   Das reine Creawin Kreatin Monohydrat von Annapurna in geprüfter Kölner-Liste-Qualität erhöht deine körperliche Leistung bei intensiven Schnellkraftbelastungen im Training. Mit dem Gutscheincode bio360 sparst du auch noch bares Geld. >>>Jetzt anschauen

Mitä seuraavaksi tapahtuu? Mitä toivon tapahtuvan? Mitä pelkään tapahtuvan?Mielemme rakentaa jatkuvasti käsitystä todellisuudesta aistihavaintojen, aiempien kokemusten ja kehon viestien pohjalta. Mielemme muodostamat odotukset eivät ainoastaan pohjaudu aiempiin kokemuksiimme, vaan ne vaikuttavat myös siihen, miten tulkitsemme itseämme, muita ihmisiä ja ympäröivää maailmaa. Siksi ne ohjaavat myös sitä, miten toimimme ja millaisia valintoja teemme.Mutta miksi odotamme joidenkin tilanteiden menevän pieleen, vaikka emme oikeasti tiedä, mitä tulee tapahtumaan? Miksi mieli tuntuu niin helposti uskovan omia ennusteitaan?Psykologi Nina Lyytisen asiantuntijavieraana on psykologi, kouluttajapsykoterapeutti ja tietokirjailija Janna Meunier. Nina ja Janna keskustelevat siitä, miten mielemme rakentaa todellisuutta ja kuinka odotukset vaikuttavat ajatuksiimme, tunteisiimme ja käyttäytymiseemme. Janna on kirjoittanut kirjan Odotusten voima – Tarina, jonka kerromme itsellemme.Mieli pyrkii jatkuvasti vähentämään epävarmuutta ennakoimalla tulevaa. Se on tärkeä selviytymisen kannalta, mutta toisinaan odotukset voivat myös rajoittaa elämäämme, lisätä huolia ja saada meidät uskomaan asioita, jotka eivät vielä ole totta.Tässä jaksossa kuulet muun muassa:Miten mieli rakentaa todellisuutta?Miten odotukset rakentuvat?Miksi odotukset voivat tuntua faktoilta?Miten odotukset voivat tukea tai heikentää hyvinvointia?Voiko omia odotuksia oppia tunnistamaan ja muuttamaan?Lisätietoja:Janna Meunier kotisivut jannameunier.fiJanna Meunierin IG @JannameunierJanna Meunierin linkedln https://www.linkedin.com/in/jannameunier/Janna Meunierin facebook https://www.facebook.com/PsychotherapyJannaMeunier/Linkki Janna Meunierin kirjaan Odotuksen voima https://jannameunier.fi/odotustenvoima/#kirjat‍Haluatko antaa palautetta? Vinkata aiheita tai vieraita? Tee se täällä: psykopodiaa.fi/palaute tai suoraan Spotify-sovelluksessa, jos tätä sitä kautta kuuntelet.Jos pidit tästä jaksosta olisin kiitollinen, jos jaat sen somessa kavereillesi ja jos jätät arvion siinä palvelussa missä tätä kuunteletkin! Muista myös tilata Psykopodiaa, niin et missaa uusia jaksoja!Psykopodiaa-podcastin kaupallisista kumppanuuksista vastaa Suomen Podcastmedia: https://www.podcastmedia.fi/Psykologi- ja koulutuspalvelut Nina Lyytinen OyTarjoan psykologin keskustelutukea aikuisille erilaisissa elämäntilanteissa ja kriiseissä.Voit olla yhteydessä, kun kaipaat apua esimerkiksi:• elämäntilanteen jäsentämiseen tai kriisien käsittelyyn• itsekriittisten ajatusten ja vaativuuden työstämiseen• vuorovaikutushaasteisiin, ihmissuhdeongelmiin, masennukseen, ahdistukseen tai työuupumukseenPsykologin vastaanottoni Saraste Mielen klinikalla.Etsittekö organisaatiolle kokenutta puhujaa tai psykologivalmentajaa?

Mit dem Final zwischen Spanien und Argentinien ist die grösste Fussball-WM der Geschichte zu Ende gegangen. 104 Partien wurden in den USA, Kanada und Mexiko ausgetragen. Neben den sportlichen Leistungen prägten auch politische Nebengeräusche das Turnier, etwa um US-Präsident Donald Trump. Weitere Themen: Kilometerlange Staus, lange Wartezeiten und Ausweichverkehr sorgen am Gotthard regelmässig für Ärger. Nach Protesten von Anwohnerinnen und Anwohnern im Kanton Uri wächst der Druck auf die Politik. Doch welche Massnahmen liegen überhaupt auf dem Tisch? Die USA und der Iran setzen ihre gegenseitigen Angriffe fort und stellen die Absichtserklärung für Friedensgespräche infrage. Die Sorge vor einer Ausweitung des Kriegs wächst. Nahost-Korrespondent Thomas Gutersohn ordnet die Lage und die Strategie der Konfliktparteien ein.

Eine Folge zwischen Abschied, Aufbruch und jeder Menge HSV. Zu Beginn nehmen wir Abschied von Kevin Keegan. Mit seinem Tod verliert der HSV einen seiner größten Spieler aller Zeiten. Als Deutscher Meister und Ballon-d'Or-Gewinner prägte eine der erfolgreichsten Epochen unseres Vereins. Ruhe in Frieden, Kevin.

Mit dem Final zwischen Spanien und Argentinien ist die grösste Fussball-WM der Geschichte zu Ende gegangen. 104 Partien wurden in den USA, Kanada und Mexiko ausgetragen. Neben den sportlichen Leistungen prägten auch politische Nebengeräusche das Turnier, etwa um US-Präsident Donald Trump.

Die erste Stadt verbietet Rechenzentren jetzt, die lokale Infrastruktur für Abwasser zu nutzen. Aber es ist nicht nur das Abwasser, KI-Unternehmen brauchen auch Gas. Musk übernimmt das Business jetzt direkt – ihr merkt, wir drehen die zweite Runde in “Rechenzentren vs. the people”. Aber jetzt, wo die WM vorbei ist, fragen wir die Expertin direkt auch mal: Wie war es denn? Also nicht der Fußball, sondern alles drumherum. ➡️ Mit der "Haken Dran"-Community ins Gespräch kommen könnt ihr am besten im Discord: http://hakendran.org ✔️ Jetzt abstimmen für den Grauen Haken 2026 und den Goldenen Haken 2026 - am besten per Mail an grauerhaken@hakendran.org

Im bislang eher schwächelnden Blockbuster-Jahr liefert Christopher Nolan ein Mega-Spektakel für Riesenleinwände. "The Odyssey", frei nach Homer, vereint Superlativen: Überwiegend an Originalschauplätzen rund um die Welt gedreht, mit IMAX-Kameras, möglichst wenig Computereffekten und einem beeindruckenden Star-Ensemble. Ob es dem Regisseur tatsächlich gelungen ist, atemberaubende Schauwerte mit existentiellem Tiefgang zu verknüpfen, darüber diskutieren Christian Fuchs und Jan Hestmann. Mit dabei: ORF-Historienspezialist Hans Wu, der über geschichtliche Hintergründe und den Kulturkampf rund um den Film berichtet. Eine epische Folge, beim Zeus! Sendungshinweis: FM4 Film Podcast, 20.07.2026, 0 Uhr

Guten Morgen! Jens Spahns Rücktritt ist mehr als eine Personalie: Er zeigt, wie eng politische Glaubwürdigkeit und privates Handeln miteinander verknüpft sind. Spahn hatte sich politisch gegen Leihmutterschaft in Deutschland ausgesprochen, nutzte selbst aber die Möglichkeit im Ausland und brachte damit viele Parteifreunde in Erklärungsnot. Der Druck aus der Union und aus dem Kanzleramt machte schließlich deutlich, dass sein Amt kaum noch vom Konflikt zwischen Parteiposition und persönlicher Entscheidung zu trennen war. Mit der schnellen Trennung signalisiert die Partei, dass sie an ihrem familienpolitischen Kurs festhält. Zugleich muss sie Antworten darauf finden, wie sie künftig mit modernen Familienmodellen umgehen will, ohne das Vertrauen in ihre Grundsätze weiter zu strapazieren.

Desde los Comités de Influencia Especial del Kremlin hasta la búsqueda de archivos secretos sobre fenómenos anómalos en el MIT, este episodio recorre las fronteras de la verdad y la manipulación. Hablamos de cómo se construye una mentira a escala global, del impacto del miedo social en nuestras vidas y de la obsesión por descifrar un futuro que, a menudo, solo existe en nuestra imaginación. Un viaje por los rincones más oscuros de la red y las salas de control del poder, donde la burocracia y el misterio se entrelazan para ocultar —o revelar— lo que realmente ocurre cuando nadie nos mira. Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Dagmar Rosenfeld und Robin Alexander unterbrechen ihren Sommerurlaub für eine Sonderfolge: Jens Spahn ist im Privaten für den Wunsch, mit seinem Ehemann eine Familie zu gründen, einen Weg gegangen, den seine Partei und auch er selbst im Politischen ablehnen: die Erfüllung des Kinderwunschs durch Leihmutterschaft. Ein Weg, der in Deutschland verboten ist. Die Diskrepanz zwischen persönlichem Handeln und politischer Haltung sowie der Widerspruch, als Spitzenpolitiker die hierzulande geltende Rechtslage zu umgehen, haben ihn eingeholt. Jens Spahn ist von seinem Amt als Fraktionsvorsitzender zurückgetreten. Mit ihm geht einer, der sich zuletzt als Stabilisator der Koalition erwiesen hat, auch wenn sein politischer Werdegang alles andere als frei von Fehltritten war. Was bedeuten die Geschehnisse für die CDU? Was spielte sich in den vergangenen Tagen hinter den Kulissen ab? Und wie geht es nun weiter? Darüber sprechen Dagmar Rosenfeld und Robin Alexander in dieser Sonderfolge von Machtwechsel. –––– Machtwechsel befindet sich in der Sommerpause. Neue Folgen gibt es ab Mittwoch, 5. August. –––– Machtwechsel live in Hamburg: Am 1. Oktober 2026 diskutieren Dagmar Rosenfeld und Robin Alexander im Kleinen Saal der Elbphilharmonie. Die Veranstaltung ist ausverkauft. Für alle, die keine Tickets mehr bekommen haben: 2027 geht es mit weiteren Live-Shows quer durch Deutschland. –––– Erhalten Sie einen exklusiven 15% Rabatt auf Saily Datenpakete! Nutzen Sie den Code Machtwechsel beim Checkout vor Ihrer Reise. Downloaden Sie jetzt die Saily App oder gehen Sie zu https://saily.com/machtwechsel Sie möchten mehr über unsere Werbepartner erfahren? Hier finden Sie alle Infos & Rabatte: https://linktr.ee/Machtwechsel Sie möchten Werbung in diesem Podcast schalten? Dann erfahren Sie hier mehr über die Werbemöglichkeiten bei Seven.One Audio: https://www.seven.one/portfolio/sevenone-audio Sie möchten “Machtwechsel” komplett werbefrei hören und mitdiskutieren? Dann kommen Sie zu uns in die Machtwechsel-Community auf CampfireFM und werden hier Unterstützer des Podcasts! In der Podcast-Community-App vertiefen wir Themen aus den Episoden, diskutieren aktuelle Entwicklungen und kommen mit Ihnen ins Gespräch: https://www.joincampfire.fm/api/download-app „Letzte Chance – Der neue Kanzler und der Kampf um die Demokratie“ von Robin Alexander ist im Siedler Verlag erschienen und hier erhältlich: https://www.penguin.de/buecher/robin-alexander-letzte-chance/buch/9783827502001. Machtwechsel ist eine Produktion von Dagmar Rosenfeld und Robin Alexander, in Zusammenarbeit mit Studio Bummens.

Der heisse und trockene Sommer führt in weiten Teilen der Schweiz zu grosser Waldbrandgefahr. Sie liegt mittlerweile in den meisten Regionen auf der zweithöchsten Stufe. Ein Zustand, der in künftigen Sommern zur neuen Normalität werden könnte. Weitere Themen: Argentinien ist im Fussballfieber und heute spielt die Nationalmannschaft im WM-Final. Das Land ist sozusagen im Ausnahmezustand. Trotzdem oder gerade deswegen lief der politische Betrieb während der WM auf Hochtouren weiter und Präsident Javier Milei drückte verschiedene unpopuläre Vorhaben durch. Im französischen Lorient ist ein Forschungsschiff zu einer aussergewöhnlichen Arktis-Mission aufgebrochen. Es soll sich im Packeis einfrieren lassen und rund acht Monate durch eine kaum erforschte Region treiben. Mit an Bord ist auch die Schweizer Ozeanografin Nina Schuback vom Schweizer Polarinstitut.

The great lie of the Epstein scandal isn't just what he did, but how the powerful around him suddenly claimed they couldn't remember him at all. Presidents, princes, billionaires, academics, bankers, and celebrities who once courted his money and shared his jets all reached for the same script when the walls closed in: I barely knew him. It was a coordinated act of survival, not an accident. Institutions like Harvard, MIT, Deutsche Bank, and JP Morgan played the same game, pretending they never saw the red flags. Legacy media, instead of hammering the contradictions, often published these denials straight, allowing amnesia to masquerade as truth. Forgetting became strategy, and strategy became cover.But memory leaves evidence. Flight logs, photographs, donations, and testimonies remain, and every denial only underscores the complicity of those who looked away. The survivors don't get to forget; they live with scars while the powerful rewrite history. What the amnesia act reveals is cowardice: a willingness to erase reality to protect reputation. Epstein built his empire on memory, yet his circle tried to survive through erasure. In the end, their denials brand them more deeply than their associations ever could—because the attempt to forget is itself proof they remembered perfectly well.to contact me:bobbycapucci@protonmail.comBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-moscow-murders-and-more--5852883/support.

Bundeskanzler Friedrich Merz hat den zurückgetretenen Unions-Fraktionshef Spahn für dessen Informationspolitik kritisiert. Er habe die Partei und die Fraktion erst sehr, sehr spät über alles in Kenntnis gesetzt, so Merz im ZDF. Mit mehr Zeit hätte man alles in Ruhe besprechen können, aber die Zeit habe es nicht gegeben. Dass die Elternschaft über eine Leihmutter aus den USA eine solche Empörung auslöse, habe er aber nicht erwartet, so Merz. Nun wolle er sich mit CSU-Chef Markus Söder besprechen und relativ bald über eine Nachfolge entscheiden. In Berlin kursieren mehrere Namen: Kanzleramtschef Frei, Bundesinnenminister Dobrindt, Gesundheitsministerin Warken und CDU-Generalsekretär Linnemann könnten demnach den Unionsfraktionsvorsitz übernehmen. Merz denkt nach eigenen Worten auch über eine Regierungsumbildung nach.

Anna Schubert ist Tierrechts-Aktivistin. Im Mai 2024 ist sie für eine Recherche in einen Schlachthof eingebrochen und hat eine Kamera an einem Pater Noster installiert. Dieser transportiert Schweine vor der Schlachtung in einen Schacht, in den CO2 einströmt. Die Betäubung mit CO2 ist in Deutschland gängige Praxis: 35 Millionen Tiere pro Jahr werden so betäubt und anschließend getötet. Die Aufnahmen verbreitet sie im Anschluss im Internet, um über das aufzuklären, was in Deutschland ganz legal jeden Tag praktiziert wird. Richtig viel Aufmerksamkeit bekommen die Aufnahmen aber erst, als der Schlachthof Anna Schubert und Hendrik Haßel verklagt, weil ihm durch die Verbreitung der Aufnahmen Schäden entstanden seien. Der „Schlachthof Prozess“ ist seitdem in allen Medien. Wie es für Anna und Hendrik weitergeht und warum sie ihr Ziel trotz der Zivilklage und dem Strafprozess, der noch auf sie zukommt erreicht haben, das erzählt Anna in dieser Folge von Fairquatscht. Mehr Infos zum Prozess findet ihr hier: https://schlachthofprozess.org Die Energiewende-News werden von Polarstern Energie präsentiert. Polarstern ist ein echter Ökoenergieanbieter, der sich als Sozialunternehmen glaubhaft für eine bessere Wirtschaft einsetzt. Mit meinem Code "marisa20" spart ihr 20 Euro auf eure erste Jahresrechnung, wenn ihr zu Polarstern wechselt. Mehr Infos gibt es hier: https://www.polarstern-energie.de/?utm_source=mysustainableme-podcast&utm_medium=audio&utm_campaign=podcast-mysustainableme-Juni

Mit 30 sollte man doch eigentlich alles im Griff haben… oder? In dieser Folge sprechen wir über die Erwartungen, die viele an die 30er haben und wie die Realität oft aussieht. Von Freundschaften und Beziehungen über Vergleiche mit anderen bis hin zu gesellschaftlichem Druck und dem Gefühl, seinen eigenen Weg finden zu müssen. Eine ehrliche Folge über das Erwachsenwerden - ganz ohne ohne Filter✨Hier findet ihr uns auf Social Media: @lizzi1402 & @fit.by.leni

Michael präsentiert im SHOCK2 Wochenstart zum 392. Mal das Serviceformat für die SHOCK2-Community! Mit dabei Tristan von Siren Games mit einem neuen Brettspiel-Tipp. Wir wünschen euch viel Spaß beim Hören und freuen uns auch dieses Mal besonders auf euren Input, Ideen & Feedback im Forum. Aus produktionstechnischen Gründen gibt es dieses mal keine Kapitelmarken! SHOCK2 Newsflash (Whatsapp) https://whatsapp.com/channel/0029VbDhIwKF6smrXxXtDL26 SHOCK2.CLUB Community https://shock2.club/ SHOCK2 auf Youtube https://www.youtube.com/@shock275 Partner: Siren Games – Tabletop und Brettspiele https://www.sirengames.at/ Brettspiel: Northgard: Uncharted Lands https://www.sirengames.at/de/northgard-uncharted-lands-de Möglichkeiten, SHOCK2 zu unterstützen! https://shock2.club/t/jetzt-moeglichkeiten-shock2-zu-unterstuetzen/8350 Release Liste: Welche Spiele erscheinen nächste Woche? https://mag.shock2.info/?s=Releaseliste Streaming-Tipps: Die TV & Film-Highlights der nächsten Woche! https://mag.shock2.info/tag/streaming-tipps/ TOP 10: Das waren die meistgelesenen SHOCK2-Artikel der letzten Woche https://mag.shock2.info/?s=SHOCK2+TOP+10 Patreon SHOCK2 VIP Inhalte auf Spotify https://shock2.club/t/shock2-bei-patreon-steady/229/481

Wiederholt hat der amerikanische Präsident die Übernahme Grönlands gefordert – so auch beim Nato-Gipfel im Juli 2026 in Ankara. „Das sollte von den Vereinigten Staaten kontrolliert werden, nicht von Dänemark“, sagte Donald Trump zum türkischen Präsidenten Erdoğan. Dänemarks Regierungschefin Mette Frederiksen wies die Forderung erneut zurück. Europa reagierte reflexartig, mit Empörung und dem üblichen Verweis auf die Souveränität Dänemarks. Die eigentliche Frage wird dabei aber übersehen: Ist Europa überhaupt in der Lage, die geostrategisch vielleicht wichtigste Region der 2030er Jahre zu sichern? Oder benennt Trump – undiplomatisch, aber analytisch treffend – nur das Vakuum, das China und Russland nur allzu gern füllen würden?Der Wettlauf um die Arktis könnte der bedeutendste geopolitische Umbruch unserer Zeit sein. Das Meereis schmilzt viermal schneller als das Eis auf dem Rest der Erde, neue Handelsrouten öffnen sich, riesige Vorkommen an Öl, Gas und Seltenen Erden werden zugänglich. Russland und China bauen ihre Präsenz systematisch aus. Und Europa steckt einmal mehr im „geopolitischen Schraubstock“, wie das FERI Cognitive Finance Institute in seiner Arktis-Analyse formuliert.Daniel Stelter spricht mit Dr. Heinz-Werner Rapp, Gründer und Leiter des FERI in Bad Homburg, über den Klimawandel als geopolitischen Katalysator, über die mehr als 36 Millionen Tonnen Seltene Erden, die Grönland beherbergt, über die Nordostpassage, die die Handelsroute vom chinesischen Ningbo nach England um zwanzig Tage verkürzt und über die Frage, ob Europa die Tragweite dieser Umbrüche überhaupt begreift.Hinweis ABSTURZ – So retten wir Deutschland: das neue Buch von Daniel Stelter. Jetzt überall, wo es Bücher gibt. Auch bestellbar bei Thalia, Amazon, geniallokal.HörerserviceCognitive Briefing Kampf um die Arktis — Globaler Wettlauf um Rohstoffe, Macht und strategische Dominanz (12-2025) des FERI Cognitive Finance Institute: https://tinyurl.com/2kuaws2w beyond the obvious – Neue Analysen, Kommentare und Einschätzungen zur Wirtschafts- und Finanzlage finden Sie unter think-bto.com.Newsletter – Den monatlichen bto-Newsletter abonnieren Sie hier.Redaktionskontakt – Wir freuen uns über Ihre Meinungen, Anregungen und Kritik unter podcast@think-bto.com.Handelsblatt – Ein bisschen mehr Luft im Sommer? Dann ist jetzt die perfekte Zeit, Ihrem Wissensvorsprung ein Upgrade zu geben – mit dem Handelsblatt. Das liefert Ihnen verlässliche Fakten, exklusive Hintergründe und eine klare Einordnung wirtschaftlicher Entwicklungen. Mit unserem Sommer-Special ist der Einstieg supereinfach: Sie erhalten jetzt 6 Wochen für nur 1 Euro vollen Zugriff auf alle digitalen Inhalte. Sichern Sie sich dieses besondere Angebot unter handelsblatt.com/sommer26 Werbepartner – Weitere Informationen zu den Angeboten unserer aktuellen Werbepartner finden Sie hier. Hosted on Acast. See acast.com/privacy for more information.

Michaela und John sind frisch verheiratet. Für ihre Flitterwochen haben sie sich einen ganz besonderen Ort ausgesucht: die malerischen Strände von Mauritius. Doch als sie dort ankommen, ahnen sie noch nicht, dass sie nicht den Rest ihres Lebens miteinander verbringen werden. Einer der Beiden wird allein nach Hause zurückkehren. ! TRIGGER-WARNUNG ! Mord (an einem Ehepartner), Erdrosselung, JustizirrtumWIR GEHEN LIVE!! CONTRA CREATE präsentiert: Überdosis Crime LIVE 2026

In dieser Samstagsfolge von Alles auf Aktien reden wir einfach mal über alles. Über das Leben in Dubai, den harten Job des Investmentbankers und die konkrete Formel für finanzielle Freiheit. Heute ist jemand bei uns zu Gast, der all das erlebt hat und immer noch erlebt. Sie hat früh alles gewagt und dann genau das bekommen, wonach sie strebte. Erst wickelte sie Milliarden-Deals für Morgan Stanley ab, dann ging sie zu Delivery Hero und AboutYou. Heute macht sie ihr eigenes Ding – von Dubai aus. Ihr erfahrt von ihr außerdem, wie eine 100-Stunden-Woche aussieht, warum man besser keine Anleihen hält, wie man 100.000 Euro investiert und wie nah die Drohneneinschläge wirklich waren. Ein Gespräch mit Julia Stötzel. Mit dem Code „AAAFRIENDS“ kannst du 50 Prozent auf deine Tickets sparen – aber nur unter folgendem Link. https://veranstaltung.businessinsider.de/event/financesummit26/summary?rp=c6dc55d6-6f4f-4fb4-b75f-3f3501d84859 Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Hier könnt ihr den AAA-Newsletter abonnieren: https://www.welt.de/newsletter/article232797673/Alles-auf-Aktien-Der-taegliche-Boersen-Newsletter-fuer-WELTplus-Abonnenten.html Und – ganz neu: AAA gibt es jetzt auch auf Instagram: https://www.instagram.com/alles_auf_aktien/ Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

In Spaniens Großstädten setzen digitale Nomaden den Wohnungsmarkt unter Druck. In einigen Dörfern sind sie dagegen Hoffnungsträger. Mit schnellem Internet und günstigen Wohnungen werben Gemeinden gezielt um Menschen, die ortsunabhängig arbeiten. Anika Reker www.deutschlandfunkkultur.de, Die Reportage

Endlich Sommerferien. Die verbringt Selma auf dem Land, getrennt von ihrer Freundin Emmi. Sie schreiben einander Tagebuch, damit jede nachlesen kann, was die andere erlebt hat. Selma erzählt vom Waschbären, von der tauben Katze und von ihrer Bande. Mit der erlebt sie viele Abenteuer. Am Ende ist Selma traurig und glücklich zugleich, nach Hause zu fahren. Alle 7 Folgen der OHRENBÄR-Hörgeschichte: Selmas Sommertagebuch von Jeanette Wagner. Es liest: Marion Elskis. ▶ Mehr Hörgeschichten empfohlen ab 6: https://www.ohrenbaer.de/podcast/empfohlen-ab-6.html ▶ Mehr Infos unter https://www.ohrenbaer.de & ohrenbaer@rbb-online.de

"Eruptions-Wetter" wie starker Regen hat die Kraft, Vulkane ausbrechen zu lassen. Mit dem Klimawandel wird das häufiger. Auch die Gletscherschmelze kann Magma mobilisieren. Umgekehrt beeinflussen Vulkane das Klima. Wie explosiv ist diese Beziehung? Ein Podcast von Renate Ell.

Machen wir uns mal nichts vor. Bei aller wirklich großen Liebe zur deutschen Sprache, die jedenfalls von meiner Seite ausgeht, schmiert ein „Hey, Leute“ gegen ein „Ciao, Raggazzi“ natürlich ab. Doppelt gut finde ist es allein schon deswegen, weil es sich auch als Verabschiedung nutzen lässt, aber - hey. Wir stehen hier ja erst am Anfang. Heute ist der wunderbare Musiker Francesco Wilking zu Gast, dessen großartige Songs Ihr kennt oder bitte unbedingt kennenlernen müsst. Francescos aktuelles Projekt ist die Crucchi Gang, liebe ich, Tele kennt Ihr oder Die Höchste Eisenbahn. Francesco wurde 1974 in Freiburg geboren, Mutter Römerin, Vater Bremer. Abitur, Zivildienst, davor und danach: nichts als Musik. Mit seiner Frau Anke und den 2 Kindern lebt er seit 2001 in Berlin. In Toast Hawaii erzählt mir Francesco u.a. von Onkel Tonino, den Artischocken und frischen Venusmuscheln, die direkt aus dem Meer kommen, von Nudeln mit Kichererbsen und Bohnen, wir sprechen über echten Feta und frischen Mozzarella, Risotto aus Milchreis und die Maxime, sich beim Pastakochen niemals vom Herd wegzubewegen. *** WERBUNG Toast Hawaii wird unterstützt von dmBio, die Bio-Lebensmittelmarke von dm-drogerie markt. Ganz nach dem Motto „Natürlich lecker erleben“ bietet dmBio mit mehr als 550 Produkten eine vielfältige Auswahl – von leckeren Snacks für zwischendurch bis hin zu original italienischen Tomatensaucen. Haben auch Sie eine dmBio-Geschichte, die im Podcast erzählt werden soll? Dann schreiben Sie uns gerne unter rustberlin@icloud.com ÖKO-Kontrollstelle: DE-ÖKO-007

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

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