Podcasts about prompts

Paid Ads Rebuild: 3 Workflows + 8 Prompts to Fix What AI Broke: https://clickhubspot.com/jgvk Ep. 442 50% of Google searches now result in zero clicks, how does that affect your 2027 ad budget? Kipp dives into how AI has completely disrupted the paid ads landscape, changing where you should spend your marketing dollars and what those ads actually cost. Learn more on which ad channels have become more expensive, where you can still find underpriced opportunities like YouTube Shorts, and why most 2027 marketing budgets are already broken if they don't account for these rapid changes. Mentions Loop: Outlearn. Outmarket. Outgrow https://www.hubspot.com/loop-marketing-book HeyGen https://app.heygen.com/ Captions https://captions.ai/ Canva https://www.canva.com/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@marketingatg Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

What if the biggest difference between AI helping your child... or holding them back... wasn't the technology at all? What if it came down to HOW they use it? In this episode, I'm sharing one of the most practical lessons in the entire series: simple changes that can completely transform the way children interact with AI and the value they get from it. If your child is already using AI, or will be soon, this is a conversation you'll want to have before it becomes a habit.

Coming to you from Whidbey Island, Washington this is Stories From Women Who Walk with the final Motivate Your Monday episode and your host, Diane Wyzga. As you know, the ending of every story holds the beginning of a new one. I'm retiring 60 Seconds and Motivate Your Monday episodes. Instead of ending these changes are evolving to Begin Anew. I launched my podcast, Stories From Women Who Walk in April 2020 as a platform for conversations with women who were walking their lives until their lives were flipped upside down by something unexpected, how they met the challenges, and changed to becoming stronger, even more confident women. I loved every single moment of listening stories out of my guests! COVID flipped our lives upside down, too. We were no longer commuting to work over long distances or in cars. Who would have the time to listen to a 30 or 40-minute conversation? What to do? Pivot to 60 Seconds. No one was offering this format. Turned out that I was really good at distilling topics, ideas and stories into soundbytes to inform, illuminate, delight, and comfort my listeners. At the same time I created a Listener Location Shout-out to kick off each episode to acknowledge and thank you for listening. How did I know where you were listening? The Simplecast platform provides a global map with yellow dots that light up the villages, towns and cities where your feet touch the ground all around the world. Each shout-out included a URL linked to the location so that other listeners could “armchair travel” to that place. Imagine how much I learned about my global audience. Some 1,330 episodes and close to 250,000 plays later it's time to do something different. But what? Running like a river beneath the surface of the lived experience moments of our lives is a deeper, truer one waiting to be acknowledged. How do I acknowledge that deeper truth? What will I hear when I let my Life speak? Where will it lead me next so that I can make the transitions I seek while inviting you to come along with me on the journey? I promise you this: the voice you have come to know and trust for over 6 plus years on the airwaves, along with the stories, insights, questions and prompts that speak to each of us as if to say, “What! You, too? I thought I was the only one.” [C.S.Lewis] I'd like to focus my creative efforts on producing “Diane on Mic” short-form episodes—maybe 7 minutes or so of spoken word—for Wednesdays on Whidbey and Story Prompt Friday. CTA: Let's continue making the very most of the time we have together, uncovering, discovering, shaping and sharing our stories. Let me know what you think. If there is something you're curious about, something you'd like to hear as my podcast begins anew, please email me and let me know: info@quartermoonstoryarts.net or subscribe on Substack. Comments Create Community. We are in this together for the long haul. And thank you for continuing to listen. You're invited: “Come for the stories - stay for the magic!” Speaking of magic, subscribe & spread this episode  with a generous 5-star review & comment—it helps us all—& join us next time! AND!  Stop by my Quarter Moon Story Arts website during reconstruction, email me [info@quartermoonstoryarts.net] to arrange a no-obligation Discovery Conversation, and stay current with me as Quarter Moon Story Arts on SUBSTACK. Stories From Women Who Walk Production Team Podcaster: Diane F Wyzga & Quarter Moon Story Arts Music: Entering Erdenheim by Steve Schuch & Night Heron Music ALL content and image © 2019 to Present Quarter Moon Story Arts. All rights reserved. Enjoy my work? Share & attribute it to Diane Wyzga of Stories From Women Who Walk podcast with a link back to the original source.

This is the introductory episode for a special mini series of the Make Movement Matter podcast. It's called, ‘The Everyday Experiment'. It's seven days of small, playful prompts designed to help you notice the movement that's already hidden in your ordinary life. No routine to follow, no right or wrong, and nothing to prove. Just a week of looking at your day through a slightly different lens. If movement has started to feel like something you ‘should' do, rather than something you enjoy, this might be exactly the gentle re-set week you need.Your next stepIf this week has left you curious about just how much more movement is possible in your everyday life, I always recommend starting in the same place... the ground.My free guide, Making the Ground Your Friend for Life, builds on everything you've been noticing this week, helping you rediscover the ground as a place of confidence, choice and possibility for years to come.Get your free copy here:https://movewith.reclaimmovement.co.uk/making-the-ground-your-friend-for-lifeTo find out more about today's guest and for the full show notes, visit:⁠The Make Movement Matter Podcast⁠If you enjoy this podcast, please press Follow and Rate and Review.  To support the show and help me to keep making the podcast, please contribute by ⁠Buying Me a Coffee...or Tea! Connect with your host, Wendy, founder of Reclaim Movement, on:⁠Instagram⁠ | ⁠YouTube⁠ | ⁠Website⁠

Prompts That Pay – https://www.marketingsharks.com/prompts-that-pay-to-sell-on-promptbase/Prompts That Pay teaches everyday people to turn ChatGPT image prompts into small digital products and sell them on PromptBase, the biggest AI-prompt marketplace, with 310,000+ listings and 500,000+ buyers already on it. No design skills, no audience. The creator has been on PromptBase about two years and selling his own listings for the last few months, already with a product sitting in the site's featured row and real payment emails to show for it.I went through PromptBase niche by niche, mapped every corner that actually has buyers sitting in it, and built a separate custom GPT for each one. That came out to 112 machines, and the third upgrade adds 57 more for two other PromptBase categories, so the full stack reaches 169. Not rewordings of one prompt. 169 different machines, each built for its own niche, each one handing the buyer a finished product plus the listing text to go with it, ready to upload the same day.And yes, you read that number right. Ten machines come with the first upgrade, the other 102 with the second, so a buyer who takes both walks away with the complete set for less than the price of one freelance design job.

Episode SummaryIn this episode, Dr. Candida Fink and special educator Jo-Ann Berry dive into the nuances of supporting students with ADHD in the classroom. Moving beyond repetitive "pay attention" reminders, they explore the "curious approach" to prompting—asking students if they are stuck or simply thinking. The conversation covers practical classroom strategies for high schoolers, the importance of student autonomy, and how simple adjustments like doodling or movement breaks can transform a student's ability to engage with challenging or tedious tasks.Key Points & HighlightsThe Power of Curiosity: Replacing standard redirections with curious questions (e.g., "Are you thinking or are you stuck?") helps students re-engage without feeling singled out or shamed.The "Neuro-Spicy" Classroom: Strategies like doodling, fidgets, and varied seating (wobbly stools, yoga balls, or spin chairs) are essential tools that help ADHD brains "reset" their attentional systems.Autonomy in High School: Giving older students the choice to opt-out or delay a task often leads to better engagement, as it shifts the dynamic from compliance to personal responsibility.Functional Writing Skills: For 11th and 12th graders, the focus shifts from academic perfection to functional communication, such as emailing a doctor or writing a job application.The Flaw in IEP Goals: Jo-Ann critiques the common IEP phrasing "the student needs to..." and argues that goals should reflect what the educator wants to see, rather than placing the "need" solely on the student.Takeaways & Practical TipsFor Teachers: Use neutral "check-ins" like "Are you with me?" rather than demanding eye contact. If a student is looking away, they may still be listening.For Students: Identify which sensory "muscle" helps you focus—whether it's a specific fidget, standing up for a minute, or taking a strategic "mental break" by looking out the window.Movement as a Tool: Understand that movement helps "wire" the brain to focus. Even a brief walk to the restroom can serve as a necessary cognitive reset.Writing Prompts: Use low-stakes, non-daily writing prompts to build the "writing muscle" without the pressure of a major grade.Resources MentionedRoss Greene's Collaborative & Proactive Solutions (CPS) Model: Referenced for its "What's up?" approach to problem-solving.Classroom Tools: Wobbly stools, yoga balls, spin chairs, and fidget bins.Digital Tools: iPads/Tablets for doodling during lessons or, even better, paper and pencil.Connect With UsWe want to hear from you! What strategies have worked in your classroom or for your child?Website: MentalHealthGoesToSchool.com.Social Media: Watch on YouTube and be sure to follow us on Instagram for more tips and "good things" from our travels and teaching.Timeline[00:00] Intro: Welcome to Episode 32.[02:15] The frustration of repetitive ADHD prompts in the classroom.[05:40] Strategies for high school: Doodling, fidgets, and movement breaks.[09:20] The "Are you stuck or are you thinking?" technique.[12:10] The importance of student autonomy and choice.[16:45] Reimagining IEP goals: Moving away from "Student needs to..."[20:30] Functional writing skills for upperclassmen.[24:50] "One Good Thing": Recapping our trip to Iceland and the beauty of the glaciers.If you enjoy our content, please like and follow - and review if you can!

Artificial intelligence is rapidly changing healthcare, but is it making us healthier or simply giving us faster answers? In this episode of Innovate & Elevate, Sharon Kedar sits down with Harvard-trained internist Dr. Lucy McBride to explore how AI is reshaping the patient experience and why asking better questions may be more important than finding quicker answers. Together, they discuss the growing gap between modern healthcare and the wellness industry, the role of primary care physicians in an AI-enabled world, and why personalized medicine will always require understanding the person behind the diagnosis.This Episode Is For You If:- You're curious about how AI tools like ChatGPT and Claude can help you make better healthcare decisions.- You want to become a more informed, empowered patient without relying solely on the internet for medical advice.- You're interested in the future of women's health, personalized medicine, and why better questions often lead to better care.Connect with Dr. Lucy McBride, MD:- Website: https://www.lucymcbride.com- LinkedIn: https://www.linkedin.com/in/lucymcbride/- Instagram: https://www.instagram.com/lucymcbride/- (Pre)Order Beyond the Prescription: https://www.beyondtheprescriptionbook.comConnect with Sharon:- Connect with Sharon on LinkedIn: https://www.linkedin.com/in/sharonkedar/- Learn more about Innovate and Elevate: https:// innovateandelevatepodcast.com- Join the newsletter to receive the latest episodes in your inbox: https://innovateandelevatepodcast.com/emailThe content shared in this episode is for informational purposes only and does not constitute medical, financial, or investment advice. Please seek guidance from your own qualified professionals before making decisions.Timestamps(00:00) Why Patients Turn to AI(02:20) Treating People, Not Diseases(03:48) Women's Health Is Unique(06:25) AI Enters the Exam Room(08:58) Why Better Questions Matter(11:12) When AI Gets It Wrong(13:58) Where AI Can Improve Healthcare(15:09) How Patients Should Use AI(17:28) Teaching the Next Generation(18:50) Beyond the PrescriptionAbout Our Guest: Dr. Lucy McBride, MD, is a Harvard-trained internist, practicing physician, and nationally recognized expert in whole-person health. She is the founder of Lucy McBride Integrative Medicine, creator of the Are You Okay? movement, and author of Beyond the Prescription. Drawing on more than 25 years of clinical experience, Dr. McBride helps patients better understand the connections between physical health, mental wellbeing, lifestyle, and everyday decision-making while advocating for a more personalized approach to modern medicine.About Sharon: Sharon Kedar is a co-founder and partner at Northpond Ventures, a multi-billion-dollar science-driven venture capital firm. Sharon holds an MBA from Harvard Business School and is a CFA charter holder. She lives in the Washington, DC area with her husband, Greg, their three kids, and their dog Bo.Podcast production by Jamie Brooke.

If you've ever stared at a blank screen wondering how to update your resume, write a cover letter, or prepare for an interview, you're not alone. ChatGPT can help eliminate the blank-page effect and speed up your job search—as long as you're using it as a starting point, not a substitute for your own experience.Here are 10 prompts to help you work smarter throughout your job search. Hosted on Acast. See acast.com/privacy for more information.

KI-Agenten sollen vor allem Kosten senken – und werden damit unter Wert verkauft. Der eigentliche Hebel liegt woanders: im Wachstum. Julian Kramer ist AI Evangelism Leader EMEA bei Adobe und bringt Unternehmen bei, wie sie KI sinnvoll einsetzen. In dieser Folge bricht er mit Joel herunter, was es wirklich braucht, damit KI-Agenten dein Unternehmen hebeln – jenseits von Prompts und Tool-Tutorials. Wir sprechen darüber, warum du Fähigkeiten aufbaust statt Stellen zu streichen, wie du KI-Autonomie mit Explainability by Design kontrollierbar machst, welche Teamprofile (Stichwort: W-Shaped People) du künftig brauchst – und wie deine Marke in der KI-Suche überhaupt auffindbar bleibt. Du erfährst... … warum KI-Agenten kein Sparprogramm sind, sondern dir Fähigkeiten schenken, die dein Unternehmen nie hatte … wie du KI-Autonomie zulässt, ohne die Kontrolle zu verlieren … warum du künftig W-Shaped Personalities brauchst … wie deine Marke in der KI-Suche auffindbar bleibt und warum Chatbot-Traffic 34 % besser konvertiert __________________________ ||||| PERSONEN |||||

MD-Dateien: Die Infrastruktur, die KI-Entwicklung von Anfang an richtig macht   Nicht bessere Prompts, sondern eine durchdachte Infrastruktur aus MD-Dateien macht KI-Entwicklung konsistent und skalierbar. TJ zeigt, wie sein Team der KI alles Nötige einmalig mitgibt — Umsysteme, Coding-Style, Corporate Identity, Engineering- und Design-Prinzipien — sodass sie von Schritt eins an qualitativ hochwertige Anwendungen baut. Warum das Prinzip auf einer IBM-Entwicklerkonferenz aus den 90ern fußt und was du davon heute direkt ableiten kannst, hörst du in dieser Folge.   Die Lego-Platte: Was MD-Dateien überhaupt leisten MD-Dateien sind keine Notizzettel — sie sind die strukturelle Grundlage, auf der KI-gestützte Entwicklung zuverlässig funktioniert. TJ beschreibt sie als das „Habitat" der KI: ein klar definierter Rahmen, der der KI mitteilt, in welcher Welt sie arbeitet. Wer diesen Rahmen einmal sauber aufsetzt, spart sich bei jeder neuen Anwendung das mühsame Wiederholen von Regeln, Kontexten und Präferenzen. Die KI kennt das Spielfeld — und entwickelt darin. Das ist der eigentliche Hebel: nicht mehr erklären, sondern dokumentiert haben.   Umsysteme und Coding-Style: Was die KI über deinen Stack wissen muss Im Koerting Institute sind aktuell 23 Umsysteme dokumentiert — von Klick-Tipp über Zoom bis Stripe und LexOffice. Das klingt nach Verwaltungsaufwand, ist aber entscheidend: Weil die KI weiß, dass Stripe das Abrechnungssystem ist, schlägt sie nie Digistore vor. Sie denkt im richtigen Stack. Ergänzt wird das durch den Coding-Style-Guide, der festlegt, wie Code dokumentiert wird, welche Praktiken vermieden werden sollen und wie qualitativ hochwertige Entwicklung im Team aussieht. Beides sind eigenständige MD-Dateien — und beides muss nur einmal geschrieben werden.   Corporate Identity und Dokumentationsrichtlinien: Konsistenz ohne Kontrolle Damit alle Anwendungen einheitlich wirken, steckt die Corporate Identity ebenfalls in einer MD-Datei: Farbgebung, Klammer-Syntax, visuelles Erscheinungsbild. Die KI greift automatisch darauf zurück. Parallel dazu regeln Dokumentationsrichtlinien über das Tool Linear, wie Features, Bugs und Release Notes festgehalten werden. Das klingt nach Bürokratie — ist aber das Gegenteil. Wer einmal definiert, wie Dinge dokumentiert werden, befreit sein Team davon, es jedes Mal neu aushandeln zu müssen. Struktur schafft Spielraum.   Engineering-Prinzipien: Eine IBM-Lektion, die 30 Jahre Gültigkeit hat Vor 30 Jahren lernte TJ auf einer IBM-Entwicklerkonferenz in Keystone, Colorado von Paul Giangara — damals einer der Chefentwickler bei IBM — zehn Prinzipien für performante Anwendungen. Eines davon: Denke den nächsten Nutzerschritt voraus und führe ihn im Hintergrund schon aus. Wenn der Nutzer dann klickt, ist das Ergebnis sofort da. Ein weiteres Prinzip ist der Zwei-Fakten-Check bei kritischen Berechnungen. Beide Prinzipien stehen jetzt in der Engineering-MD-Datei — und die KI baut sie automatisch in jede Anwendung ein. Kein Erinnern, kein Nachfragen. Einfach drin.   Design-Prinzipien und Fazit: Anwendungen, die kein Handbuch brauchen Die jüngste Ergänzung sind Design-Prinzipien — nicht im grafischen Sinne, sondern auf der Ebene der Usability. Wie werden Buttons angeordnet? In welcher Reihenfolge führt man Menschen durch Eingabemasken? Die Antworten stehen in der MD-Datei, damit Anwendungen selbsterklärend sind. Kein Handbuch für Melissa, Nina oder Mia — einfach öffnen und verstehen. Das ist die eigentliche Qualität dieser Infrastruktur: Nicht die KI wird besser, weil man besser prompts. Sie wird besser, weil sie von Anfang an den richtigen Kontext hat. Einmal aufsetzen. Immer profitieren.     Das nimmst du mit: • Wenn du deine Umsysteme — zum Beispiel Stripe, Klick-Tipp oder n8n — als MD-Datei mitgibst, schlägt die KI dir nie wieder Lösungen vor, die nicht in deinen Stack passen. • Engineering-Prinzipien wie der Zwei-Fakten-Check gehören in eine MD-Datei: Dann baut die KI sie automatisch in jede Anwendung ein, ohne dass du sie jedes Mal neu erklärst. • Das IBM-Prinzip von Paul Giangara aus Keystone, Colorado gilt noch heute: Denke den nächsten Nutzerschritt voraus, führe ihn im Hintergrund aus — und das Ergebnis ist sofort da, wenn der Nutzer klickt. • Design-Prinzipien in MD-Dateien sorgen dafür, dass Anwendungen selbsterklärend sind und kein Handbuch brauchen — ein konkreter Qualitätsgewinn für alle, die täglich damit arbeiten. • Eine MD-Datei-Infrastruktur ist keine einmalige Fleißarbeit, sondern die Voraussetzung dafür, dass KI-Entwicklung konsistent, skalierbar und vom ersten Schritt an qualitativ hochwertig bleibt.   Kapitel: 00:00 Einstieg: Was steckt in den MD-Dateien? 01:31 Umsysteme: Welche Tools kennt die KI? 02:07 Coding-Style-Guide: Wie wird bei euch entwickelt? 02:57 Corporate Identity & Dokumentationsrichtlinien 03:38 Engineering-Prinzipien: IBM-Lektion aus Keystone, Colorado 05:17 Design-Prinzipien: Usability statt Handbuch     Noch mehr von den Koertings ... Das KI-Café ... jede Woche Mittwoch (>350 Teilnehmer) von 08:30 bis 10:00 Uhr ... online via Zoom .. kostenlos und nicht umsonst Jede Woche Mittwoch um 08:30 Uhr öffnet das KI-Café seine Online-Pforten ... wir lösen KI-Anwendungsfälle live auf der Bühne ... moderieren Expertenpanel zu speziellen Themen (bspw. KI im Recruiting ... KI in der Qualitätssicherung ... KI im Projektmanagement ... und vieles mehr) ... ordnen die neuen Entwicklungen in der KI-Welt ein und geben einen Ausblick ... und laden Experten ein für spezielle Themen ... und gehen auch mal in die Tiefe und durchdringen bestimmte Bereiche ganz konkret ... alles für dein Weiterkommen. Melde dich kostenfrei an ... www.koerting-institute.com/ki-cafe/ Mit jedem Prompt ein WOW! ... für Selbstständige und Unternehmer Ein klarer Leitfaden für Unternehmer, Selbstständige und Entscheider, die Künstliche Intelligenz nicht nur verstehen, sondern wirksam einsetzen wollen. Dieses Buch zeigt dir, wie du relevante KI-Anwendungsfälle erkennst und die KI als echten Sparringspartner nutzt, um diese Realität werden zu lassen. Praxisnah, mit echten Beispielen und vollständig umsetzungsorientiert. Das Buch ist ein Geschenk, nur Versandkosten von 9,95 € fallen an. Perfekt für Anfänger und Fortgeschrittene, die mit KI ihr Potenzial ausschöpfen möchten. Das Buch in deinen Briefkasten ... https://koerting-institute.com/shop/buch-mit-jedem-prompt-ein-wow/ Die KI-Lounge ... unsere Community für den Einstieg in die KI (>2800 Mitglieder) Die KI-Lounge ist eine Community für alle, die mehr über generative KI erfahren und anwenden möchten. Mitglieder erhalten exklusive monatliche KI-Updates, Experten-Interviews, Vorträge des KI-Speaker-Slams, KI-Café-Aufzeichnungen und einen 3-stündigen ChatGPT-Kurs. Tausche dich mit über 4.000 KI-Enthusiasten aus, stelle Fragen und starte durch. Initiiert von Torsten & Birgit Koerting, bietet die KI-Lounge Orientierung und Inspiration für den Einstieg in die KI-Revolution. Hier findet der Austausch statt ... www.koerting-institute.com/ki-lounge/ Starte mit uns in die 1:1 Zusammenarbeit Wenn du direkt mit uns arbeiten und KI in deinem Business integrieren möchtest, buche dir einen Termin für ein persönliches Gespräch. Gemeinsam finden wir Antworten auf deine Fragen und finden heraus, wie wir dich unterstützen können. Klicke hier, um einen Termin zu buchen und deine Fragen zu klären. Buche dir jetzt deinen Termin mit uns ... www.koerting-institute.com/termin/ Weitere Impulse im Netflix Stil ... Wenn du auf der Suche nach weiteren spannenden Impulsen für deine Selbstständigkeit bist, dann gehe jetzt auf unsere Impulseseite und lass die zahlreichen spannenden Impulse auf dich wirken. Inspiration pur ... www.koerting-institute.com/impulse/ Die Koertings auf die Ohren ... Wenn dir diese Podcastfolge gefallen hat, dann höre dir jetzt noch weitere informative und spannende Folgen an ... über 500 Folgen findest du hier ... www.koerting-institute.com/podcast/ Wir freuen uns darauf, dich auf deinem Weg zu begleiten!

From the Mythos preview system card (emphasis mine): We ran an automated review of model behavior during training, sampling several hundred thousand transcripts from across much of the training process. We used recursive-summarization-based tools backed by Claude Opus 4.6 to summarize the resulting transcripts. [...] The most notable finding was that the model occasionally circumvented network restrictions in its training environment to access the internet and download data that let it shortcut the assigned task—a form of reward hacking. While highly concerning, this behavior was rare, even in settings where it could have been viable and helpful, with attempts appearing in about 0.05% of all training episodes and successful attempts appearing in about 0.01% of episodes. The technique matched the sandbox-escape incident that we separately elicited in our automated behavioral audit when we had an investigator model explicitly ask Claude Mythos Preview to find such a bypass. In every observed instance, the model used this access solely for completing the task. More broadly, we observed the model escalating its access within its execution environment when blocked: reaching a shell from restricted GUI computer-use interfaces, injecting commands through tool-call arguments, or recovering information the task had deliberately hidden. Prompts asking [...] ---Outline:(03:00) Thoughts and reflections about this probable fact(04:14) Estimating how many RL rollouts went into Mythos Preview The original text contained 3 footnotes which were omitted from this narration. --- First published: July 27th, 2026 Source: https://www.lesswrong.com/posts/QKDoZe6EKhxnFjLWK/is-mythos-good-at-cyber-because-it-kept-hacking-anthropic --- Narrated by TYPE III AUDIO.

Technikwoche.de ▹ Energie, Open Weight AI, AI Media, Social Media, OWAI, FOSAI, China AI ▹ eicker.TVDie https://Technikwoche.de von https://eicker.TV mit allen Kurzvideos an einem Stück und als YouTube Podcast:#AI Größenkampf ⚡ #China und die #USA dominieren den weltweiten #Energie und #CleanTech Wettlauf, doch China baut Stromerzeugung, Solar-, Wind- und Batteriespeicher deutlich schneller aus und investiert inzwischen mehr als jede andere Volkswirtschaft.Daten statt Geld

In EVN Report's news roundup for the week of July 24: Armenia's newly elected parliament to convene first session on August 2; Narek Karapetyan charged with coercing participation in rallies and money laundering; another non-combat death in the armed forces prompts renewed calls for reforms to prevent violence and deaths among soldiers and more.

In EVN Report's news roundup for the week of July 24: Armenia's newly elected parliament to convene first session on August 2; Narek Karapetyan charged with coercing participation in rallies and money laundering; another non-combat death in the armed forces prompts renewed calls for reforms to prevent violence and deaths among soldiers and more. The post Non-combat Death Prompts Renewed Calls for Reforms appeared first on EVN Report.

Grab the Secondary Teacher Systems Toolkit here: https://khristenmassic.thrivecart.com/systemstoolkit/?ref=pod Too many preps and not enough time? Let's make your planning period actually work for you. Reserve your spot in the Unit Planning Lab here: https://khristenmassic.thrivecart.com/unit/?ref=podcastPlanning for the next school year? If your day is organized by class period, your planning calendar should be too. Grab my Editable Class Period Calendar here: https://khristenmassic.com/secondarycalendarpodGet the Planning Period Reset Toolkit—a free set of quick-start tools to help you protect your time, focus faster, and finally finish something… even during chaotic school days. https://khristenmassic.com/resetShop my Teachers Pay Teachers store: https://www.teacherspayteachers.com/Store/Khristen-Massic-Cte-Teacher-CoachLet's talk about “teaching tips for classroom culture with easy class prompts”—because building a strong secondary classroom culture isn't about nailing one perfect icebreaker on day one. It's about threading purposeful classroom routines through your whole year. You know what phrase lands teachers in hot water? “Turn and talk.” Say those words and you risk either unlocking thoughtful discussion or kicking off thirty seconds of dead silence where one kid does all the heavy lifting. If you're nodding along, you're not alone.Here's the trap: we tell kids “go on, discuss,” and hope magic happens. For years, it seemed like think-pair-share should do the trick, but as revealed in this episode, just telling students to find an elbow partner and chat often flops. The primary keyword phrase—“what makes a first-week activity worth repeating”—shows up early for a reason: because summer icebreakers and one-and-done games are the junk food of classroom culture, and you deserve better.A better way? Lock in a discussion routine that actually gives every secondary student a real shot. The secret sauce described by host Khristen Massic isn't some expensive tech or complicated protocol. It's about adding one overlooked step to your discussion moves: writing. Not just thinking quietly, not just turning and talking, but jamming a quick “write” step right before the share. That move is a game changer for kids who freeze when put on the spot: multilingual learners, quieter kids, those who need extra processing time, and even the ones who get flustered and forget their ideas as soon as the spotlight's on them.Here's how it sounds in real life: students read or hear a prompt, and—wait for it—just think. No pressure. Then, every kid writes down a response, even if it's just a scribbled sentence or a couple of words. Now when they turn to discuss, nobody's scraping for something to say. It doesn't matter if they're extroverted or barely audible—the writing step puts everyone on a level field. That's how you move past, “Some kids just can't discuss,” and shift toward, “I've actually taught them how to have a conversation in this classroom.”It's not just the prompt structure either. Host Khristen Massic shares that the prompts themselves are everything. Forget, “What did you do this summer?” That kind of prompt shuts conversation down before it starts. Swapping in prompts like, “A class feels easier for me when…” or “One thing I want this class to feel like is…” pulls kids into thinking and sharing about things that matter—building connections inside the secondary classroom, not just filling airtime.A small tweak packs an extra punch: after pairing up and sharing, have kids jot down one thing they learned from their partner. It's subtle, but now kids actually listen all the way through—not sitting there waiting to talk, but seeking to really understand. That's real secondary teacher magic, quietly training students to listen with their eyes up and brains on.This discussion routine isn't a first-week stunt—it's a reusable system made for the real, messy arc of the school year. Why reinvent every week? Keep your classroom routines sturdy, just rotate your prompts. Use it for “getting to know each other,” reflecting on learning, or anchoring right to content when the time comes. Once kids know the steps, all their energy goes into the discussion instead of figuring out what the heck they're supposed to be doing.For any middle or high school teacher—especially if you juggle multiple preps—this approach can save your energy and theirs. Tired of hunting down dazzling new activities just to fill time? Swap in a reliable routine, change up your prompts, and focus on what works. The discussion explored why “think-write-pair-share” isn't just a fix for quiet kids or English language learners; it's the backbone for secondary classroom conversations that stick.If your students seem “bad at discussion,” this episode argues that maybe they never saw a real conversation routine modeled, let alone practiced. It's about changing who gets to participate and making classroom culture accessible, not exclusive. This isn't about teaching to the test or droning through a syllabus—this is about building work life balance for teachers by installing systems you can lean on all year, freeing you up for the real work of teaching.So, are your students struggling with conversation, or did they simply never have the structure? One small step—like adding that “write”—can change the way an entire room communicates.Teaching in the secondary world shouldn't mean doing all the talking. Set up the right classroom routines once, keep your prompts fresh, and watch your students finally do the heavy lifting.Light up your classroom with better conversations—ditch the dead air and teach like you mean it.

Most marriages don't unravel because of one major conflict; they slowly drift apart through avoidance, unresolved tension, and the stories we tell ourselves about our partner. In this episode, we explore how confirmation bias, emotional triggers, and taking personal responsibility can transform conflict from something that divides us into something that deepens connection.If you want healthier conversations, faster repair, and a stronger relationship, this episode is for you._______

Referentin Simone Bogner zeigt in diesem Webinar, warum KI-Content zwar schnell entsteht, aber oft trotzdem austauschbar bleibt. Sie erklärt, wie Marken KI mit exakten Zielen, Tonalität, Best-Practice-Beispielen und klaren Prioritäten füttern, damit daraus Inhalte entstehen, die nicht generisch wirken, sondern zur Marke passen und Ergebnisse bringen. Im Webinar geht es um 5 typische Fehler im Umgang mit AI-Content – von fehlender Zielsetzung über Masse statt Strategie bis hin dazu, dass erfolgreiche Formate und Learnings aus Performance-Daten nicht konsequent in die nächste Content-Planung übernommen werden. Dazu gibt Simone tiefere Einblicke in das Prompting-Game sowie einfache Modelle und Checklisten mit auf den Weg, mit denen sich KI gezielter und wirksamer steuern lässt. Folgendes hast Du nach dem Webinar gelernt: - Welche fünf typischen Fehler im Umgang mit AI-Content vermieden werden sollten - Warum KI-Content oft gut klingt, aber trotzdem keine klare Haltung transportiert - Wie aus einzelnen Prompts ein systematischer Ansatz wird - Wie Inhalte so gesteuert werden, dass sie nicht nur produziert, sondern gezielt für Marke, Relevanz und Wirkung entwickelt werden - Wie Content-Entscheidungen vom Bauchgefühl zu einem klaren, wiederholbaren Prozess werden Zielgruppe: - Social Media Manager, die Content, Community und Performance zusammenbringen. - CMOs und Marketing-Leads, die KI strategisch statt nur als Textmaschine nutzen wollen. - Content-Nerds mit Fokus auf Qualität, Relevanz und Markenprofil. - Marketing-Teams, die ihre Content-Prozesse smarter und skalierbarer aufstellen möchten. - Alle, die KI im Content nicht nur ausprobieren, sondern wirklich wirksam einsetzen wollen.

Send us Fan MailJane Bertch left corporate banking, moved through London into Paris, and built La Cuisine Paris into the largest English language French cooking school in the city, despite having no culinary background and no perfect plan. In this conversation, she shares why clarity comes through action, how community turns an experience based business into something durable, and why women should stop waiting for permission to begin again.Show NotesJane's story is a masterclass in founder reinvention, not as a polished leap from certainty to success, but as a lived process of testing, adapting, listening to clients, and building community around an idea that once felt wildly impractical.Clarity comes through action: Jane makes a powerful case for practicing in public, reminding women that waiting for complete readiness often delays the very learning that creates confidence. A lasting business is built through responsiveness: La Cuisine Paris grew through relationships, word of mouth, client trust, and constant adaptation, proving that beautiful ideas only survive when they meet real market desire. Community is not a soft asset: From her cooking school to her retreats and future work with women entrepreneurs, Jane shows how the right people, in the right environment, can help women see and step into a larger version of themselves. Reinvention belongs at every age: At 50, Jane is expanding beyond La Cuisine Paris into women's development, retreats, writing, and entrepreneurial support, carrying the message that it is never too late to plant the tree. Guest Contact & ConnectJane Bertch is the founder of La Cuisine Paris, author of The French Ingredient, and creator of women centered experiences through JCMB Consulting, including retreats, entrepreneurial programs, and her Substack, Prompts from Paris.Website: La Cuisine Paris Email: contact@janebertch.com Phone: 00 33 6 25 58 80 83 LinkedIn: Jane Bertch on LinkedIn Instagram: @janebertch Substack: Prompts from ParisJoin the proveHER community and access the companion blogcast for deeper insights from this episode.---Subscribe and ReviewIf you loved this episode, drop us a review, share it with a badass woman in your life, and subscribe to Badass Women in Business wherever you get your podcasts.Stay badass. Stay bold. Build it your way.Keep up with more content from Aggie and Cristy here:Facebook: Empowered Women Leaders  Instagram: @badass_women_in_businessLinkedIn: ProveHer - Badass Women in BusinessWebsite: Badasswomeninbusinesspodcast.comAthena: athenaac.com

In dieser Episode spricht Erik Siekmann mit Sebastian Denef, Mitgründer und CEO von AGENTS.inc, über den Einzug von autonomen KI-Agenten im Marketing und die Abgrenzung von Hype und Realität. Sebastian erklärt, warum herkömmliche Copiloten oft nur reaktive „Anstupser-Systeme“ sind und wie echte, vollautonome Agenten im Hintergrund arbeiten, während wir schlafen. Ein Highlight der Folge ist der tiefe Einblick in das Thema „Enterprise Agents“: AGENTS.inc widmet sich bereits seit 2016 – lange vor dem aktuellen Hype – ausschließlich hochgradig kontrollierten Agenten-Infrastrukturen. Erfahre, wie Unternehmen durch vollautonomes Monitoring globale Märkte und Kontexte in Echtzeit analysieren, warum einfache Prompts bei ChatGPT für geschäftliche Zwecke scheitern und wie KI-Agenten heute sogar hochkomplexe Werbematerialien bis hin zu druckfertigen Adobe InDesign-Layouts völlig selbstständig generieren. Über Sebastian Denef: Sebastian Denef ist Mitgründer und CEO von AGENTS.inc, einem Pionier-Unternehmen für KI-Agenten, das er aus seiner Forschungsarbeit am Fraunhofer-Institut heraus gründete. Seit vielen Jahren erforscht er die Human-Computer-Interaction und die Zukunft der Arbeit. Sebastian gilt als absoluter Vordenker im Bereich der Mensch-Maschine-Kollaboration und ist Autor des zukunftsweisenden Buchs „The Last Boss: How AI Agents Will Unlock Artificial General Intelligence“. Sein pragmatischer Ansatz bei AGENTS.inc zeigt, wie Unternehmen komplexe Datenströme bändigen, Halluzinationen in Geschäftsprozessen durch kontrollierte Agent-Workflows eliminieren und echte Software-Autonomie gewinnbringend im Enterprise-Alltag etablieren können. Der Marketing Transformation Podcast wird produziert von TLDR Studios.

In a bid to turn around its most dismal voter turnout since it was set up Auckland Council is considering introducing general election-style polling booths. Jessica Hopkins reports.

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

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

The abuse and neglect of a child in Charlotte prompted state lawmakers to pass stronger child welfare regulations as part of the newly enacted budget bill.The Dominique Moody Safety Act was passed in the state House and later added to the state budget bill. It addresses the death of six-year-old Dominique Moody, who was abused and neglected while living with a relative despite visits to the home by social workers and law enforcement. WUNC News sat down with the bill's sponsor, Rep. Allen Chesser, R-Nash, to learn more about how the legislation aims to prevent future tragedies and put more safeguards into the child welfare system. Chesser also spoke about new legislation on blockchain and digital currency issues and a proposed sales tax exemption for diapers.

Stephen Grootes speaks to Dr Saul Levin, Executive Director at economic policy research institute, Trade and Industrial Policy Strategies, about South Africa’s push for a new WTO safeguard to shield the struggling steel industry from a surge of imports, as manufacturers grapple with mounting job losses, shrinking production capacity, and growing concerns over the sector’s long-term viability. In other interviews, Robyn Hugo, Director for Climate Change Engagement at Just Share talks about growing calls for greater transparency and accountability around corporate lobbying in South Africa. While lobbying is a legitimate part of the democratic process, Just Share argues that the bigger risk is "policy capture", where well-resourced private interests quietly shape legislation to favour commercial objectives over the public interest. The Money Show is a podcast hosted by well-known journalist and radio presenter, Stephen Grootes. He explores the latest economic trends, business developments, investment opportunities, and personal finance strategies. Each episode features engaging conversations with top newsmakers, industry experts, financial advisors, entrepreneurs, and politicians, offering you thought-provoking insights to navigate the ever-changing financial landscape.    Thank you for listening to a podcast from The Money Show Listen live Primedia+ weekdays from 18:00 and 20:00 (SA Time) to The Money Show with Stephen Grootes broadcast on 702 https://buff.ly/gk3y0Kj and CapeTalk https://buff.ly/NnFM3Nk For more from the show, go to https://buff.ly/7QpH0jY or find all the catch-up podcasts here https://buff.ly/PlhvUVe Subscribe to The Money Show Daily Newsletter and the Weekly Business Wrap here https://buff.ly/v5mfetc The Money Show is brought to you by Absa     Follow us on social media   702 on Facebook: https://www.facebook.com/TalkRadio702 702 on TikTok: https://www.tiktok.com/@talkradio702 702 on Instagram: https://www.instagram.com/talkradio702/ 702 on X: https://x.com/CapeTalk 702 on YouTube: https://www.youtube.com/@radio702   CapeTalk on Facebook: https://www.facebook.com/CapeTalk CapeTalk on TikTok: https://www.tiktok.com/@capetalk CapeTalk on Instagram: https://www.instagram.com/ CapeTalk on X: https://x.com/Radio702 CapeTalk on YouTube: https://www.youtube.com/@CapeTalk567 See omnystudio.com/listener for privacy information.

Stephen Grootes speaks to Dr Saul Levin, Executive Director at economic policy research institute, Trade and Industrial Policy Strategies, about South Africa’s push for a new WTO safeguard to shield the struggling steel industry from a surge of imports, as manufacturers grapple with mounting job losses, shrinking production capacity, and growing concerns over the sector’s long-term viability. The Money Show is a podcast hosted by well-known journalist and radio presenter, Stephen Grootes. He explores the latest economic trends, business developments, investment opportunities, and personal finance strategies. Each episode features engaging conversations with top newsmakers, industry experts, financial advisors, entrepreneurs, and politicians, offering you thought-provoking insights to navigate the ever-changing financial landscape.    Thank you for listening to a podcast from The Money Show Listen live Primedia+ weekdays from 18:00 and 20:00 (SA Time) to The Money Show with Stephen Grootes broadcast on 702 https://buff.ly/gk3y0Kj and CapeTalk https://buff.ly/NnFM3Nk For more from the show, go to https://buff.ly/7QpH0jY or find all the catch-up podcasts here https://buff.ly/PlhvUVe Subscribe to The Money Show Daily Newsletter and the Weekly Business Wrap here https://buff.ly/v5mfetc The Money Show is brought to you by Absa     Follow us on social media   702 on Facebook: https://www.facebook.com/TalkRadio702 702 on TikTok: https://www.tiktok.com/@talkradio702 702 on Instagram: https://www.instagram.com/talkradio702/ 702 on X: https://x.com/CapeTalk 702 on YouTube: https://www.youtube.com/@radio702   CapeTalk on Facebook: https://www.facebook.com/CapeTalk CapeTalk on TikTok: https://www.tiktok.com/@capetalk CapeTalk on Instagram: https://www.instagram.com/ CapeTalk on X: https://x.com/Radio702 CapeTalk on YouTube: https://www.youtube.com/@CapeTalk567 See omnystudio.com/listener for privacy information.

This masterclass dives into how to turn your existing content into a true client acquisition engine instead of a vanity-metric machine. Jason walks through a complete system for creating content that pre-sells your offers by shifting core buyer beliefs, clarifying the identity journey of your ideal clients, and using a structured "10 P's" content mix. You'll also learn how to create powerful hooks, position your unique mechanism, and adopt the right energetic posture so your content attracts true fans and ready-to-buy prospects, not just likes.    "Teaching creates learners. Influence creates buyers."   Hear more about: The four jobs of content, and which one creates buyers The five beliefs every buyer must hold first Speaking to current vs. aspirational identity The 10 P's content mix for belief-shifting posts Hooks and high-value keywords that attract buyers Naming and explaining your unique mechanism Using story and proof to build authority and trust A one-to-many cadence for repurposing content How your energetic posture affects conversions Prompts to make content more fun and more you Connect with Jason Meland: Email: jason@goliveonlinemastermind.com Website: https://www.growmyvisibility.com/ Instagram: @coachjasonmeland Facebook: Jason Meland - In Demand Coach LinkedIn: Jason Meland

Modelle wie die von OpenAI oder Anthropic werden zur Commodity. Wer ein bisschen wartet, bekommt die gleiche Qualität ohnehin von mehreren Anbietern. Was bleibt, ist die Architektur drumherum, der Harness. Oliver und Alois erklären, wie Unternehmen General-Purpose-Modelle mit Speicher, Prompts und Agenten so einbetten, dass ein verlässliches eigenes System entsteht. Für manche Startups ist das schon die neue Core IT der Firma.Ein Thema ist das Geschäftsmodell dahinter: günstige Einstiegszugänge für 100 bis 300 Dollar im Monat binden Early Adopter in Teams, bis am Ende die großen Enterprise-Tickets über die API laufen.Lizenzchaos als Symptom: Kontingente wechseln wöchentlich, Power User werden automatisch gedrosselt, SLAs sind kaum planbar. Was früher eine feste DSL-Leitung war, ist heute eine Blackbox mit wechselnder Durchsatzrate. Wer eine verlässliche Entwicklungsstrecke bauen will, baut sie momentan auf Treibsand.Für Oliver und Alois ist das ein Argument für Dezentralisierung: eigene On-Prem-Architekturen, eingefrorene Modelle für stabile Use Cases, das Frontier-Modell nur noch als Dispatcher. Gerade im Verteidigungssektor, mit verteilten Datenquellen und Sensorik, wird daraus ein hierarchischer oder asymmetrischer Harness aus Spezialmodellen.China im Vergleich: weniger Brute Force, mehr Smart Force, dazu eine Open-Source-Szene, die mit weniger Rechenleistung mehr rausholt. Für europäische Anwender sind chinesische und amerikanische Modelle inzwischen ähnlich compliant. Bei sensiblen Workloads bleibt trotzdem die eigene Infrastruktur die sicherere Wahl.Für den Einstieg raten beide zu fertigen Plattformlösungen mit fester Modellauswahl. Das senkt die Hürde und liefert schnell ein erstes Ergebnis. Je klarer der eigene Use Case wird und je mehr proprietäre Daten und Geschäftslogik einfließen, desto mehr lohnt sich der Wechsel zum eigenen Harness statt der fremden One-Click-Lösung.Fazit: Harnessing ist am Ende eine Make-or-Buy-Entscheidung. Die großen Anbieter liefern den Göffel, die Mischung aus Löffel und Gabel für fast alles. Wer sein eigenes Besteck zusammenstellt, tauscht die stumpfe Gabel gegen ein schärferes Werkzeug, sobald der Use Case es verlangt. Wenn du wissen willst, wie das geht, ist diese Folge für dich.

Coverage that provides news and analysis of national issues significant to regional Australians.

The Mystic Fire started just after 4 p.m. near Mystic Court and White Canoe Lane in Nevada County on July 11th. Forward progress was stopped almost two hours later but not before zone NCO-E076 was flagged for an evacuation warning. A good reminder to Know Your Zone. Volunteers with Nevada County Mutual Aid organized a Community Care Day on Sunday, July 12, at the Grass Valley Elks Lodge. The free event featured locally sourced food, free books, face painting, a clothing swap, and a build-your-own care kit station featuring hygiene, first-aid and harm reduction items as well as Narcan and sexual health kits. You can listen to a tour given to KVMR News by one of the event organizers here.

June 10, 2026 ~ Phoebe Wall Howard, contributing columnist for The Detroit News, to talk about her new column: "Stellantis is looking for designers... NOT AI." Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

95% of the time, it's not a people problem. It's a process problem. So I set out to fix the process and in doing it, I found a way to clone my best employees. How skills took my X account from ~100K to 3M+ monthly impressions The finance skill that found $500K in hidden costs in 30 minutes Our internal "Skills Dojo" - think GitHub meets an app store for your team's best skills How evals and agentic loops turn one person into the work of 5-10 Chapters 00:00 Clone Your Best Employees With Skills 00:25 The Results: 3M+ Views on X 00:43 Building & Forking Skills 02:00 Fat Skills, Thin Harness 02:13 Skills That Drive Real Leads 02:48 The AI Marketing Skills Repo (2,600 Stars) 04:00 The Finance Skill That Saved $500K 04:53 X Long-Form Writer + Humanizer 05:39 Inside Our Skills Dojo 08:04 Publishing & Sanitizing Skills 09:00 Most Problems Are Process Problems 10:48 Speaking at MozCon NYC 11:26 Training The Team Like Jedi 12:03 The Person Of One 14:05 What Makes A Real Skill 14:18 What Is A Good Eval? 15:04 Agentic Loops Explained 16:45 Daisy-Chaining Skills Into Loops

On this podcast learn the top 100 ai prompts every wholesaler needs in 2026!FREE DOWNLOAD: 100 AI Prompts For Real Estate Investorshttps://flippingmastery.com/lp2019/100ai/With over 500,000 subscribers, this is the #1 channel on YouTube for all things wholesaling and flipping. SUBSCRIBE NOW! https://www.youtube.com/@FlippingMastery Podcast fan? Listen to your favorite Flipping Mastery TV videos on your favorite podcast platform! http://FlippingMasteryPodcast.com Jerry Norton went from digging holes for minimum wage in his mid 20's to becoming a millionaire by the age of 30. Today he's the nation's leading expert on flipping houses and has taught thousands of people how to live their dream lifestyle through real estate. **NOTE: To Download any of Jerry's FREE training, tools, or resources… Click on the link provided and enter your email. The download is automatically emailed to you. If you don't see it, check your junk/spam folder, in case your email provider put it there. If you still don't see it, contact our support at: support@flippingmastery.com or 888) 958-3028.Get Access to Unlimited Free Property Searches and Downloads: https://flippingmastery.com/propwireWholesaling & House Flipping Software: https://flippingmastery.com/flipsterpodMake $10,000 Finding Deals: https://flippingmastery.com/10kpodGet 100% funding for your deals: https://flippingmastery.com/fspodMentoring Program: https://flippingmastery.com/ftpodFREE 8 Week Training Program: https://flippingmastery.com/8wpodGet Paid $8700 To Find Vacant Lots For Jerry: https://flippingmastery.com/lfpodFREE 30 Day Quickstart Kit https://flippingmastery.com/qkpodFREE Virtual Wholesaling Kit: https://flippingmastery.com/vfpodFREE On-Market Deal Finder Tool: https://flippingmastery.com/dcpodFREE Wholesaler Contracts: https://flippingmastery.com/wcpodFREE Comp Tool: https://flippingmastery.com/compodFREE Funding Kit: https://flippingmastery.com/fkpodFREE Agent Offer Sheet & Scripts: https://flippingmastery.com/aspodFREE Cash Buyer Scripts: https://flippingmastery.com/cbspodFREE Best Selling Wholesaling Ebook: https://flippingmastery.com/ebookpodFREE Best Selling Fix and Flip Ebook: https://flippingmastery.com/ebpodFREE Rehab Checklist: https://flippingmastery.com/rehabpod LET'S CONNECT! FACEBOOK http://www.Facebook.com/flippingmastery INSTAGRAM http://www.instagram.com/flippingmastery

Sometimes your business can be working and still not feel like you. Maybe you have had your head down, doing the work, achieving the goals, and keeping things moving, only to look up and realize you feel a little lost. In this episode, I'm sharing powerful prompts for coming back to yourself, your work, and the reason you became a coach in the first place. These questions are designed to help you reconnect with what feels true, what matters most, and the kind of business you actually want to be building.      For full show notes, transcript, and to join the waitlist for Reimagine, go to: lindsaydotzlafcoaching.com/297                 Learn more about The Complete Coach here: lindsaydotzlafcoaching.com/the-complete-coach                 Follow along over on Instagram: instagram.com/lindsaydotzlaf

In this episode, I share ten easy-to-use AI prompts for families to try for everyday activities, from planning day trips and creating bedtime stories to helping with homework and weekly meal planning. You'll also hear how to model responsible AI use with your kids and customize each prompt for better results. If you're looking for simple ways to start using AI as a family, this episode has you covered! Show notes: https://classtechtips.com/2026/07/07/ai-prompts-for-families-378/ Sponsored by my Easy EdTech Club: https://EasyEdTechClub.com Follow Monica on Instagram: https://www.instagram.com/classtechtips/  Take your pick of free EdTech resources: https://classtechtips.com/free-stuff-favorites/   

Welcome back to another episode where we're diving deep into the next big shift in Amazon advertising: Sponsored Prompts. You've probably seen those little blue AI questions popping up right under your product images, and guess what? Amazon is officially letting us track them with the new Prompt Report inside Campaign Manager. Today, I'm hanging out with Tyler from Pilot House to break down exactly what this means for your brand. We talk about how Amazon's AI shopping assistant is taking over customer sessions and how your ad budget might be bleeding into these prompts without you even knowing it. Tyler drops some amazing strategies on how to find the new report, pause the prompts that aren't working, and a killer hack using ChatGPT or Gemini to see exactly how AI reads your product images. You definitely want to get ahead of this curve before these new placements start eating up your budget. We'll see you in The PPC Den!

Series Part 5 Final: Reflections & Prompts — Applying the Wisdom of A Spirit's Journey This final episode will invite listeners to turn inward through reflections, questions, and spiritual practices from the book that help integrate these concepts into everyday life, deepen their intuition, and use If it's Light, it's Right™ as a validating tool in their own spiritual journey. Cheryl Bradley has a profound gift for connecting with Spirit, helping others find peace, clarity, and connection through her work as a spiritual medium and teacher. From her Soul, to a Book to NBC 10 Television!! As a vessel for Spirit, Cheryl receives and delivers messages uniquely meant for each client, fostering a safe, nurturing environment for healing and growth.  She encourages and teaches those she works with to trust their intuition, embracing what feels light and right for them. Cheryl shares her expansive spiritual knowledge and passion empowering others to connect with their higher selves and enhance their intuitive abilities through group sessions, workshops, and spiritual coaching. Whether sharing messages from loved ones in Spirit or gently guiding others on their soul's journey, Cheryl's sessions are always filled with light and love, offering guidance to brighten your path and bring peace to your heart. In A Spirit's Journey, Cheryl Bradley invites you into the sacred rhythm of the soul — from the peace of the Other Side, through the choices made before birth, into the human experience, and eventually home again. This is not just a book about the afterlife. It is not just a book about spiritual awakening. It is a gentle welcome to remember. Through the lens of reincarnation, soul contracts, karma, and past lives, you'll begin to see how your relationships, challenges, and turning points were never random — but part of a larger unfolding guided by love and purpose. Inside these pages, you will explore: Life after death and the deeper meaning of the afterlife How soul contracts and karma shape the people who cross your path The dance between ego and higher self — and the power of free will How to strengthen your intuition, psychic abilities, and connection to spirit guides Reflection journal prompts designed to support healing, personal growth, and inner peace Rooted in New Age spirituality and mysticism, yet grounded in practical self-help, this book bridges spiritual insight with everyday living. If you have ever felt a quiet knowing that there is more… If you are moving through loss, awakening, or a desire for deeper healing… If you long to understand your soul's purpose and guide your path with clarity… This book meets you there. A Spirit's Journey is not my story. It is our story — the story of remembering who we were before we arrived, and who we will be when we return. You have not found this book by accident. You have been gently led here. Welcome home. Her approach is rooted in her guiding principle: “If it's Light, it's Right™ Website Come Stay in Touch by signing up for Cheryl's Newsletter https://cherylbradley.com/ Instagram https://www.instagram.com/cherylbradleymedium/ Facebook https://www.facebook.com/cheryl.p.bradley FBSpiritual Medium Page https://www.facebook.com/cherylbradleymedium/ Youtube Channel https://www.youtube.com/channel/UCzIs0s9ITFJbpxqWoXzrBgA Call In and Chat with Deborah during Live Show: 833-220-1200 or 319-527-2638 Learn more about Deborah here:  www.lovebyintuition.com

Most business owners are losing 20+ hours every single week to work that AI could be doing for them right now, and they don't even realize it.In this episode, I show you exactly how I use AI to claw back at least 20 hours a week so I can run over a dozen companies while traveling and barely touching my phone.Stick around because the way most founders are using AI today is the exact reason they're still drowning in their calendar.Prompts from the episode (run these on your AI with your calendar connected):1) "Audit the last 2 weeks of my calendar. Based on my quarterly goals doc in Notion, tell me what meetings I shouldn't be in, what stuff is below my pay grade, and where I'm wasting time. Ask me questions first to get better context."2) "Okay, based on that, build me a mock calendar of what I should actually be spending my time on. And walk me through your reasoning."3) "Now tell me the top 3 action items I need to focus on to make this change happen. Be specific. Who on my team is involved, what's the impact, what's the plan."▸▸ Subscribe to The Martell Method Newsletter: https://bit.ly/3XEBXez▸▸ Get My New Book (Buy Back Your Time): https://bit.ly/3pCTG78IG: @danmartell

After 13 years of marriage and four kids under 11, John and Kathy thought they just had a "parenting problem" — until they realized parenting stress had turned them into opponents instead of teammates. In this episode, Julia Woods breaks down how this couple uncovered the fear hiding underneath John's anger and the emotional shutdown hiding underneath Kathy's silence, and the practical communication tools that got them working as a team again in just six weeks. If you've ever fought about the kids and ended up fighting about each other, this one's for you._______

Most Marketers are using AI to generate. The ones winning right now are using it to interrogate. Jay breaks down a two-minute tactic he's been running with clients: go to all five major LLMs (ChatGPT, Claude, Gemini, Copilot, and Grok) and ask each one for the top 50 questions people are asking about your product or service. Then cross-reference the outputs to find what shows up on every platform. Those overlapping questions are your go-to-market. Your blog titles, your podcast topics, your offer angles, your webinar hooks — all of it, handed to you by the market itself. Daniel adds his own layer: before you let AI anywhere near your copy, write it yourself first. Then feed it in and ask AI to critique it through the lens of the greatest copywriters and strategists who ever lived. What would the legends say about your hook? Your offer? Your opening line? That's how you use AI as a refinement partner instead of a crutch — and the difference in output is night and day. The bigger idea: AI isn't the shortcut most Marketers think it is. Used right, it's the smartest pressure test in the room. If you want AI prompts that actually change what you ship, this is the episode for YOU. Follow Jay: LinkedIn: https://www.linkedin.com/in/schwedelson/ Podcast: Do This, Not That Follow Daniel: YouTube: https://www.youtube.com/@themarketingmillennials/featured Twitter: https://www.twitter.com/Dmurr68 LinkedIn: https://www.linkedin.com/in/daniel-murray-marketing Sign up for The Marketing Millennials newsletter:www.workweek.com/brand/the-marketing-millennials Daniel is a Workweek friend, working to produce amazing podcasts. To find out more, visit:www.workweek.com

Mark Butcher is joined by Dinesh Karthik and Kumar Sangakkara to recap all the action from day four of the third Test between England and New Zealand at Trent Bridge.•You can watch the cricket action live on Sky Sports. If you're not already a Sky customer, you can stream Sky Sports on your terms with a NOW membership. Sign up to NOW here: www.nowtv.com/membership/watch-sky-sports?DCMP=ilc_skysports_podcastlink•Listen to every episode of the Sky Sports Cricket Podcast here: www.skysports.com/podcasts/36578/11933948/sky-sports-cricket-podcast-with-nasser-hussain-and-michael-atherton•You can listen to the Sky Sports Cricket Podcast on your smart speaker by asking it to "play Sky Sports Cricket Podcast".•For all the latest cricket news, head to www.skysports.com/cricket•For advertising opportunities email: skysportspodcasts@sky.uk

Watch this on YouTube: https://youtu.be/gGbzHhN24E8Prompts:Together we could re-enact the spicy pages from your favorite romance novelI go crazy for a good ol' fashioned cuddle

How can sysadmins help software developers work securely and make more secure applications? While at NDC in Toronto, Richard sat down with Tanya Janca of SheCodesPurple to discuss what admins can do to help address the security challenges software developers face. Tanya talks about securing development environment and pipelines - developers routinely work from high privilege accounts because their tools require it, and as a result, have become the targets of black hats to get access to accounts, keys, and other exploitable resources. There are plenty of tools available to help work through the issues, including the latest AI-powered tools. LLMs can also help generate more secure code in the first place, and Tanya has created a set of prompts you can use to create more secure software. The threat landscape is shifting with these tools, and we need to act quickly to resist the new attacks! Links SheHacksPurple Canadian Guidance on Resisting Supply Chain Attacks OWASP Top 10 Security Risks for 2025 Prompts for Generating Secure Code Recorded May 8, 2026

Qué tal, queridos Curiosinautas. Bienvenidos a un nuevo CuriosiMartes, el resumen semanal de noticias tecno del tío Fabián.Esta semana viene cargada: el regreso inesperado de Commodore con un teléfono retro pensado para comunicarse sin caer en redes sociales, los problemas de Android 17 en los propios Pixel, la nueva guerra tecnológica entre China, Estados Unidos y la Unión Europea, y un cambio clave en Apple con la posible llegada de una etapa más enfocada en diseño y hardware.Además, hablamos de inteligencia artificial en serio: la salida de figuras clave de Google DeepMind y Meta, las advertencias de Sam Altman sobre una IA que podría superar intelectualmente a los humanos, el concepto de “rendición cognitiva” y el riesgo de dejar de pensar por depender demasiado de los chatbots.También exploramos el futuro de la robótica versátil junto a DEEPRobotics, con avances en robots cuadrúpedos, embodied AI, automatización y nuevos formatos como el M20 y su pequeño compañero robótico.La robótica ya no es solo industrial: empieza a mezclarse con asistencia, autonomía, seguridad, compañía y nuevas formas de interacción con el mundo físico.Y para cerrar, una noticia que parece ciencia ficción: Midjourney Medical trabaja en un sistema de escaneo corporal con medio millón de sensores ultrasónicos, pensado para analizar el cuerpo completo en menos de 60 segundos y ayudar en la detección temprana de enfermedades.

It wasn't a great weekend for fires around the state -- several fires popped up and even forced evacuations. In the hills above Salt Lake City, residents are urged to be on alert and ready to go at a moment's notice should the Bonneville Fire shift directions. But in Juab County, the entire town of Eureka remains under a mandatory evacuation thanks to the 24,000+ acre Iron Fire. No buildings burned at this point, but Highway 6 remains closed in the area. Utah Firewatch -- Inside Sources gets updates on those two fires from wildfire PIOs Sierra Hellstrom and Toby Weed. KSL Meteorologist Matt Johnson joins to talk about the forecast and the current air quality around the Wasatch Front. Benjamin Donner, Executive Director of the American Red Cross Central and Southern Utah Chapter joins to talk about the things homeowners can do to best prepare for what's to be a rough fire season.

Judge rules on some issues in Charlie Kirk murder case Your Voice, Your Vote: Final day before primary election day Fewer teens getting driver's licenses leads to decrease in voter registration among young people US-Iran peace negotiations continue Reflecting pool problems Data center pushback: GOP talking points, fears vs. knowledge America250: Should young people have hope in the future of the country? GOOOOOOAL: The World Cup continues

Episode SummaryIn this episode, Dr. Candida Fink and special educator Jo-Ann Berry dive into the nuances of supporting students with ADHD in the classroom. Moving beyond repetitive "pay attention" reminders, they explore the "curious approach" to prompting—asking students if they are stuck or simply thinking. The conversation covers practical classroom strategies for high schoolers, the importance of student autonomy, and how simple adjustments like doodling or movement breaks can transform a student's ability to engage with challenging or tedious tasks.Key Points & HighlightsThe Power of Curiosity: Replacing standard redirections with curious questions (e.g., "Are you thinking or are you stuck?") helps students re-engage without feeling singled out or shamed.The "Neuro-Spicy" Classroom: Strategies like doodling, fidgets, and varied seating (wobbly stools, yoga balls, or spin chairs) are essential tools that help ADHD brains "reset" their attentional systems.Autonomy in High School: Giving older students the choice to opt-out or delay a task often leads to better engagement, as it shifts the dynamic from compliance to personal responsibility.Functional Writing Skills: For 11th and 12th graders, the focus shifts from academic perfection to functional communication, such as emailing a doctor or writing a job application.The Flaw in IEP Goals: Jo-Ann critiques the common IEP phrasing "the student needs to..." and argues that goals should reflect what the educator wants to see, rather than placing the "need" solely on the student.Takeaways & Practical TipsFor Teachers: Use neutral "check-ins" like "Are you with me?" rather than demanding eye contact. If a student is looking away, they may still be listening.For Students: Identify which sensory "muscle" helps you focus—whether it's a specific fidget, standing up for a minute, or taking a strategic "mental break" by looking out the window.Movement as a Tool: Understand that movement helps "wire" the brain to focus. Even a brief walk to the restroom can serve as a necessary cognitive reset.Writing Prompts: Use low-stakes, non-daily writing prompts to build the "writing muscle" without the pressure of a major grade.Resources MentionedRoss Greene's Collaborative & Proactive Solutions (CPS) Model: Referenced for its "What's up?" approach to problem-solving.Classroom Tools: Wobbly stools, yoga balls, spin chairs, and fidget bins.Digital Tools: iPads/Tablets for doodling during lessons or, even better, paper and pencil.Connect With UsWe want to hear from you! What strategies have worked in your classroom or for your child?Website: MentalHealthGoesToSchool.com.Social Media: Watch on YouTube and be sure to follow us on Instagram for more tips and "good things" from our travels and teaching.Timeline[00:00] Intro: Welcome to Episode 32.[02:15] The frustration of repetitive ADHD prompts in the classroom.[05:40] Strategies for high school: Doodling, fidgets, and movement breaks.[09:20] The "Are you stuck or are you thinking?" technique.[12:10] The importance of student autonomy and choice.[16:45] Reimagining IEP goals: Moving away from "Student needs to..."[20:30] Functional writing skills for upperclassmen.[24:50] "One Good Thing": Recapping our trip to Iceland and the beauty of the glaciers.If you enjoy our content, please like and follow - and review if you can!

Watch the episode on YouTube so you can see Jillian generate 3 product ideas optimized to sell.   One of the biggest mistakes creators make is thinking they need to be famous before anyone will buy from them. But a digital product business does not require you to be a guru, a huge content creator, or a polished expert with a massive audience. It requires you to be useful. And most people are already useful. Every job, business, hobby, and life experience gives you knowledge someone else wants because they are a few steps behind you. The question is not, "Am I expert enough?" The better question is, "What problem can I help someone solve faster than they could solve it alone?" This is the same idea Jillian teaches in how to turn your personal experience into income with AI. Your experience becomes valuable when you package it around a specific result. Show Notes: MiloTree Start with a free MiloTree account Upgrade to a paid MiloTree plan Get the 3 Product Ideas AI Prompts Watch this episode on YouTube Join The Blogger Genius Newsletter   In this episode, Jillian show how to figure out what to sell from your own experience, how to price it correctly, and how MiloTree's AI Product Finder and AI Product Roadmap can help you go from "I have no idea what to sell" to a product ready to launch. Jillian also created a free PDF called Three AI Prompts to Find and Launch Your Digital Product Before Your Competition Does. The prompts walk you through the exact thinking process behind this episode. Prompt 1 finds your product idea using a before-and-after transformation. This helps you stop selling a topic and start selling the result your buyer wants. Prompt 2 identifies the dominant buying trigger. Is your product helping someone make money, save money, save time, reduce pain, move toward happiness, or raise status? Prompt 3 locks in the price, format, and product scope. This keeps you from turning a simple product into a six-month project. The goal is not to build something huge. The goal is to build something clear enough to finish and useful enough to sell.   Other Episodes You Will Like: How to turn personal experience into a digital product people buy How to find your first digital product idea in 3 questions 5 AI prompts to build a digital product business from scratch Why ebooks are dead and transformational products sell better How to build a digital product stack from one offer  

Grace starts the hour discussing Scott Pelley's temper tantrum while meeting his new boss. Then, a Boston City Councilor wants to defund the police and another wrong way crash has happened.  Visit the Howie Carr Radio Network website to access columns, podcasts, and other exclusive content.