Podcasts about define

Liz Peek. Peek critiques New York politician Zohran Mamdani for failing to define the working class, arguing the DSAprimarily represents white, college-educated liberals. She also discusses Kevin Warsh's influence and the likelihood of the Federal Reserve maintaining interest rates despite inflationary pressures and global instability in the Middle East. (2)1900

This Episode is Sponsored by StayFi Your ultimate tool for Vacation Rental WiFi marketing allowing you to collect guest emails automatically via custom captive WiFi login splash pages. Drive repeat direct bookings and convert your OTA bookings to book direct for their next visit. Visit https://stayfi.com/vrsuccess/ and use code VRSUCCESS for 50% off 3 months of StayFi service. ________________________________________________________________________________________________________________________________________ Paul Anderson first appeared on the Vacation Rental Success Podcast in 2021, when he was running a guest house in Oxford and building a following on Instagram. Five years on, he returns as a coach and strategist working across dozens of short-term rental operators, and now a commissioned officer in the Royal Air Force reserves. That mix of experience gives this conversation its shape: what the military taught him about briefing, decision-making and slowing down, and how those same lessons apply to the way operators are using AI in their marketing. The central argument is one Paul delivered on stage at Scale in Brighton, and it is worth sitting with. AI did not break marketing, it exposed the people who were never really marketing in the first place. When content becomes cheap and fast to produce, the operators who understood their audience all along pull ahead, and the ones who mistook activity for strategy get found out. Heather and Paul talk through his 10-80-10 protocol, his SMERLAC briefing structure borrowed from military planning, and why the real work in AI happens before you ever ask it to write a word. It is a practical, funny and genuinely useful conversation for any operator who has looked at their AI-generated captions and felt that something was missing. Key takeaways AI has not broken marketing. It has exposed the operators who were producing content without ever really marketing, because publishing posts, writing blogs and appearing in magazines are not the same as moving a guest from "I might visit" to "I want to stay there." The real work happens before you prompt. Paul's 10-80-10 protocol puts the first 10 percent into defining exactly who you want and briefing the AI properly, lets AI do 80 percent of the heavy lifting, then reserves the final 10 percent for inspecting and personalising the output. Define your perfect potential guest in granular detail. Go beyond age and income to how many cars they have, where they shop, even the colour of their hair, so you can picture one real person on the other side of the screen rather than a demographic. Use a proper brief, not a wish. "Write me 10 captions about my holiday cottage" is a wish. Paul's SMERLAC structure (Situation, Mission, Execution, Resources, Limitations, Ask questions, Check understanding) gives the AI what it needs to produce something useful. Followers are not the same as money. A client with 28,000 followers built through giveaways had almost no buying intent, while 52 people who genuinely want to book beat 52,000 who just think you are funny. Feed AI your real voice. Heather uploaded the handwritten manuscript of a book she wrote in 2005, unedited, and it changed everything the AI produced. Your idiolect, the phrases only you use, is what cuts through the sameness. ________________________________________________________________________________________________________________________________________

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Attorney Tessie D. Edwards, Founder of a multi-million-dollar family law firm in Atlanta, Georgia Purpose of the Interview The interview highlights Tessie Edwards' remarkable journey from teen mother and high school dropout to successful attorney, law firm owner, and community advocate. The conversation serves as a motivational success story focused on perseverance, personal accountability, education, entrepreneurship, and overcoming adversity. The discussion explores: Her childhood dream of becoming an attorney. The challenges of teen motherhood. Her path through higher education and law school. Building a successful law practice. The role of family and support systems. Advice for individuals pursuing ambitious goals. Executive Summary Tessie Edwards knew from the third grade that she wanted to become an attorney because she loved debate, argument, and advocacy. Growing up in a low-income community in Florida, she witnessed many people struggling with the criminal justice system and wanted to become a voice for those who felt unheard. Her journey was anything but traditional. She became pregnant at 15, had her first child at 16, dropped out of high school, earned her GED, attended community college, briefly joined the military, completed her bachelor's degree, and eventually graduated from law school while raising five children. Today, she is the owner of a multi-million-dollar law firm in Atlanta and the mother of seven children. Throughout the interview, Edwards attributes her success to clear goals, relentless determination, family support, and an unwavering commitment to her vision. Key Takeaways 1. Success Starts with a Clear Vision Edwards emphasized that she always knew what she wanted to become and remained focused despite numerous obstacles. Lesson: Clearly defining your goals helps guide decision-making and keeps you moving forward when challenges arise. 2. Your Beginning Does Not Determine Your Ending Despite becoming a teen mother, dropping out of school, and growing up in poverty, she refused to let her circumstances define her future. Lesson: Adversity can become motivation rather than limitation. 3. Parenthood Became Her Greatest Motivation A transformative moment occurred when someone told her that her infant daughter looked just like her. Edwards realized her daughter might want to become like her, which inspired her to create a life worthy of being a role model. Lesson: Responsibility can become a powerful catalyst for personal growth and achievement. 4. Persistence Beats Obstacles Edwards faced countless setbacks but continued pursuing her goals regardless of challenges. Lesson: Success often comes from refusing to quit rather than avoiding adversity. 5. Support Systems Matter She credited much of her success to her husband, children, siblings, and professional team. Lesson: Surrounding yourself with people who believe in your vision can make a significant difference in achieving your goals. 6. Stop Waiting for the Perfect Time When people advised her to delay law school until her children were older, she rejected the idea and chose to move forward immediately. Lesson: There is rarely a perfect time to pursue your dreams. 7. Dreams Require Action Rushion McDonald repeatedly emphasized that Edwards didn't merely dream about success—she consistently took action to achieve it through education, persistence, and hard work. Lesson: Ambition without action accomplishes little; success requires both vision and execution. Notable Quotes On becoming an attorney "Some people learn to litigate. I feel like I was born to litigate." On achieving success "Nothing other than dreaming it, seeing it, and then persistently going after it time and time and time again." On determination "The aggression will never stop. If I want it, I am absolutely going to give it everything in my power to go get it." On what changed her life "If she looked like me, she may want to be like me." On delaying goals "What am I waiting for? The same four years are going to go by whether she's in kindergarten or not." On success "Be clear about what it is you want. Be driven, be focused, be hungry." On support systems "Surround yourself with people that believe in you." On purpose "I feel like my life was so divinely chosen." On her current mindset "I feel thankful but not satisfied." Overall Message Tessie Edwards' interview is ultimately a story about resilience, determination, and possibility. Her journey demonstrates that difficult beginnings do not prevent extraordinary outcomes. Through focus, education, persistence, and the support of loved ones, she transformed her life and built a thriving legal practice. The interview encourages listeners to define their goals, work relentlessly toward them, and never allow current circumstances to dictate their future. #SHMS #BEST #STRAWSupport the show: https://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Attorney Tessie D. Edwards, Founder of a multi-million-dollar family law firm in Atlanta, Georgia Purpose of the Interview The interview highlights Tessie Edwards' remarkable journey from teen mother and high school dropout to successful attorney, law firm owner, and community advocate. The conversation serves as a motivational success story focused on perseverance, personal accountability, education, entrepreneurship, and overcoming adversity. The discussion explores: Her childhood dream of becoming an attorney. The challenges of teen motherhood. Her path through higher education and law school. Building a successful law practice. The role of family and support systems. Advice for individuals pursuing ambitious goals. Executive Summary Tessie Edwards knew from the third grade that she wanted to become an attorney because she loved debate, argument, and advocacy. Growing up in a low-income community in Florida, she witnessed many people struggling with the criminal justice system and wanted to become a voice for those who felt unheard. Her journey was anything but traditional. She became pregnant at 15, had her first child at 16, dropped out of high school, earned her GED, attended community college, briefly joined the military, completed her bachelor's degree, and eventually graduated from law school while raising five children. Today, she is the owner of a multi-million-dollar law firm in Atlanta and the mother of seven children. Throughout the interview, Edwards attributes her success to clear goals, relentless determination, family support, and an unwavering commitment to her vision. Key Takeaways 1. Success Starts with a Clear Vision Edwards emphasized that she always knew what she wanted to become and remained focused despite numerous obstacles. Lesson: Clearly defining your goals helps guide decision-making and keeps you moving forward when challenges arise. 2. Your Beginning Does Not Determine Your Ending Despite becoming a teen mother, dropping out of school, and growing up in poverty, she refused to let her circumstances define her future. Lesson: Adversity can become motivation rather than limitation. 3. Parenthood Became Her Greatest Motivation A transformative moment occurred when someone told her that her infant daughter looked just like her. Edwards realized her daughter might want to become like her, which inspired her to create a life worthy of being a role model. Lesson: Responsibility can become a powerful catalyst for personal growth and achievement. 4. Persistence Beats Obstacles Edwards faced countless setbacks but continued pursuing her goals regardless of challenges. Lesson: Success often comes from refusing to quit rather than avoiding adversity. 5. Support Systems Matter She credited much of her success to her husband, children, siblings, and professional team. Lesson: Surrounding yourself with people who believe in your vision can make a significant difference in achieving your goals. 6. Stop Waiting for the Perfect Time When people advised her to delay law school until her children were older, she rejected the idea and chose to move forward immediately. Lesson: There is rarely a perfect time to pursue your dreams. 7. Dreams Require Action Rushion McDonald repeatedly emphasized that Edwards didn't merely dream about success—she consistently took action to achieve it through education, persistence, and hard work. Lesson: Ambition without action accomplishes little; success requires both vision and execution. Notable Quotes On becoming an attorney "Some people learn to litigate. I feel like I was born to litigate." On achieving success "Nothing other than dreaming it, seeing it, and then persistently going after it time and time and time again." On determination "The aggression will never stop. If I want it, I am absolutely going to give it everything in my power to go get it." On what changed her life "If she looked like me, she may want to be like me." On delaying goals "What am I waiting for? The same four years are going to go by whether she's in kindergarten or not." On success "Be clear about what it is you want. Be driven, be focused, be hungry." On support systems "Surround yourself with people that believe in you." On purpose "I feel like my life was so divinely chosen." On her current mindset "I feel thankful but not satisfied." Overall Message Tessie Edwards' interview is ultimately a story about resilience, determination, and possibility. Her journey demonstrates that difficult beginnings do not prevent extraordinary outcomes. Through focus, education, persistence, and the support of loved ones, she transformed her life and built a thriving legal practice. The interview encourages listeners to define their goals, work relentlessly toward them, and never allow current circumstances to dictate their future. #SHMS #BEST #STRAWSee omnystudio.com/listener for privacy information.

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Attorney Tessie D. Edwards, Founder of a multi-million-dollar family law firm in Atlanta, Georgia Purpose of the Interview The interview highlights Tessie Edwards' remarkable journey from teen mother and high school dropout to successful attorney, law firm owner, and community advocate. The conversation serves as a motivational success story focused on perseverance, personal accountability, education, entrepreneurship, and overcoming adversity. The discussion explores: Her childhood dream of becoming an attorney. The challenges of teen motherhood. Her path through higher education and law school. Building a successful law practice. The role of family and support systems. Advice for individuals pursuing ambitious goals. Executive Summary Tessie Edwards knew from the third grade that she wanted to become an attorney because she loved debate, argument, and advocacy. Growing up in a low-income community in Florida, she witnessed many people struggling with the criminal justice system and wanted to become a voice for those who felt unheard. Her journey was anything but traditional. She became pregnant at 15, had her first child at 16, dropped out of high school, earned her GED, attended community college, briefly joined the military, completed her bachelor's degree, and eventually graduated from law school while raising five children. Today, she is the owner of a multi-million-dollar law firm in Atlanta and the mother of seven children. Throughout the interview, Edwards attributes her success to clear goals, relentless determination, family support, and an unwavering commitment to her vision. Key Takeaways 1. Success Starts with a Clear Vision Edwards emphasized that she always knew what she wanted to become and remained focused despite numerous obstacles. Lesson: Clearly defining your goals helps guide decision-making and keeps you moving forward when challenges arise. 2. Your Beginning Does Not Determine Your Ending Despite becoming a teen mother, dropping out of school, and growing up in poverty, she refused to let her circumstances define her future. Lesson: Adversity can become motivation rather than limitation. 3. Parenthood Became Her Greatest Motivation A transformative moment occurred when someone told her that her infant daughter looked just like her. Edwards realized her daughter might want to become like her, which inspired her to create a life worthy of being a role model. Lesson: Responsibility can become a powerful catalyst for personal growth and achievement. 4. Persistence Beats Obstacles Edwards faced countless setbacks but continued pursuing her goals regardless of challenges. Lesson: Success often comes from refusing to quit rather than avoiding adversity. 5. Support Systems Matter She credited much of her success to her husband, children, siblings, and professional team. Lesson: Surrounding yourself with people who believe in your vision can make a significant difference in achieving your goals. 6. Stop Waiting for the Perfect Time When people advised her to delay law school until her children were older, she rejected the idea and chose to move forward immediately. Lesson: There is rarely a perfect time to pursue your dreams. 7. Dreams Require Action Rushion McDonald repeatedly emphasized that Edwards didn't merely dream about success—she consistently took action to achieve it through education, persistence, and hard work. Lesson: Ambition without action accomplishes little; success requires both vision and execution. Notable Quotes On becoming an attorney "Some people learn to litigate. I feel like I was born to litigate." On achieving success "Nothing other than dreaming it, seeing it, and then persistently going after it time and time and time again." On determination "The aggression will never stop. If I want it, I am absolutely going to give it everything in my power to go get it." On what changed her life "If she looked like me, she may want to be like me." On delaying goals "What am I waiting for? The same four years are going to go by whether she's in kindergarten or not." On success "Be clear about what it is you want. Be driven, be focused, be hungry." On support systems "Surround yourself with people that believe in you." On purpose "I feel like my life was so divinely chosen." On her current mindset "I feel thankful but not satisfied." Overall Message Tessie Edwards' interview is ultimately a story about resilience, determination, and possibility. Her journey demonstrates that difficult beginnings do not prevent extraordinary outcomes. Through focus, education, persistence, and the support of loved ones, she transformed her life and built a thriving legal practice. The interview encourages listeners to define their goals, work relentlessly toward them, and never allow current circumstances to dictate their future. #SHMS #BEST #STRAWSteve Harvey Morning Show Online: http://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.

The next generation of financial advisors and leaders are not asking for a job, they're asking for a future. Ray Sclafani explores why career pathing has evolved from a nice-to-have benefit into the most critical retention lever advisory firms have. Drawing on Deloitte's 2025 research showing only 6% of Gen Z and Millennials prioritize reaching a leadership position, Ray unpacks what ambition actually looks like today: growth, meaning, money, well-being, and a thoughtful pace of development.For advisory firm owners and leaders, the implications are direct. A firm with no clear development path doesn't stand still, it falls behind. This episode provides a five-part framework for building career pathways that work. Ray then shares a practical starting point: a single career conversation in the next 60 to 90 days that changes how your people feel about their future with your firm.The firms that provide honest visibility of a future worth building will retain more top talent, develop better leaders, and build more durable businesses.WHAT YOU'LL LEARN IN THIS EPISODEWhy the next generation defines ambition differently and what that means for retention strategyHow to define roles with clarity and purpose so every position has a visible pathwayThe single biggest mistake firms make when building career paths and how to avoid itWhy addressing AI's role impact directly is now a core part of career developmentHow to eliminate ambiguity around partnership so people stop guessing what it meansTHE FIVE-PART CAREER PATHING FRAMEWORKDefine the Roles. Establish clear purpose, expectations, and required skills for each position. Map progression pathways for advisors (from client service associate to enterprise leader), operations (specialist to enterprise operator), and leadership (people manager to executive leader).Define the Progression. Specify what it takes to move from one role to the next: technical skills, client relationship management, leadership capabilities, business development expectations, decision rights, and cultural behaviors. Specificity builds trust.Connect to Actual Development. Attach real development objectives to each progression step. Identify specific competencies that need improvement, not vague hopes. The manager's job is connecting today's work to tomorrow's opportunity.Address AI's Impact. Clarify which skills become more valuable (empathy, judgment, planning, decision making, communication, relationship leadership) and commit to training people to use AI responsibly. Don't let people wonder alone.Make Ownership Expectations Clear. Define passages to partnership, distinguish between producing and nonproducing partners, clarify income versus equity partnership, and spell out what business development, client retention, leadership, and enterprise thinking mean for ownership.REFLECTION QUESTIONS FOR YOUR LEADERSHIP TEAMCan every high potential employee at your firm see a future worth working toward?Where are career paths clearly defined, and where are they implied but not yet documented?Which roles will AI reshape first? And how are you preparing your team for that shift?Who needs a development conversation before they start taking calls from another firm?RESOURCES MENTIONEDDeloitte 2025 Gen Z and Millennial SurveySchwab 2025 Career Pathing ResearchCFP Board Career Pathway ResourcesClientWise Business Builders Academy™ClientWise Executive Coaching and Team DevelopmentBuilding the Billion Dollar Business is hosted by Ray Sclafani, founder and CEO of ClientWise, the financial services industry's leading executive coaching and team development firm for elite advisors and wealth management teams.Find Ray and the ClientWise Team on the ClientWise website or LinkedIn | Twitter | Instagram | Facebook | YouTubeBuilding The Billion Dollar Business

TW: This episode contains devastating conversations around child loss from a traumatic event, and themes of grief. Please do take care of yourself while listening, and know that it's completely okay to step away and come back when you're ready.In this week's episode, Gi sits down with Jen Stancombe to honour the life of her daughter, Elsie Dot Stancombe.In July 2024, seven-year-old Elsie was one of three young girls killed in the Southport attack. Jen joins Gi to share who Elsie truly was beyond the headlines: a joyful, caring, and inspirational big sister, who brought light to everyone she met.Jen speaks about navigating the unimaginable grief alongside her husband David and their younger daughter, while sharing how love for Elsie continues to shape their family every day. She also reflects on the strength they've found in keeping Elsie's memory alive and the purpose they've discovered through the foundation created in her name.We are incredibly grateful to Jen for trusting us with Elsie's story. To find out more about the incredible work being carried out in Elsie's memory, or to support the foundation, please visit Elsie's Story. Hosted on Acast. See acast.com/privacy for more information.

Book Alan's Business Breakthrough Session. Your first 30-minute coaching call is FREE. Learn how to prioritize success and let your quality of life become the byproduct. - https://calendly.com/alanlazaros/30-minute-breakthrough-sessionCome get jacked with the Next Level Fitness Accountability Group. Reach out to Kevin or Alan on Instagram:Kevin: https://www.instagram.com/neverquitkid/Alan: https://www.instagram.com/alazaros88/If you want to start, grow, scale, or monetize your podcast. Join the Next Level Podcast Accelerator.  Starting July 28, 2026, 5pm EST. Use promocode: NLULISTENER for 30% off - https://www.nextleveluniverse.com/group-coaching/_______________________How much better could you become if you stopped needing to look good while you learn?In today's episode, Kevin and Alan break down why real mastery requires you to move beyond what feels safe. Most people want confidence before they take a risk, but skill is built through testing your limits, getting feedback, correcting mistakes, and accepting that you may fall short in public.Drawing from their experiences in podcasting, speaking, fitness, and coaching, they examine deliberate practice, long-term consistency, and the difference between performing well once and building skills that hold up under pressure. Easy wins may protect your confidence, but they can also limit your growth. Define your standard, practice with intention, and earn your next level._______________________NLU is more than a podcast. From the Next Level Dreamliner to Group Coaching, we provide tools and communities to help you grow with more clarity, consistency, and accountability.Visit our website and socials through the links below.

In this  episode of The Business of Happiness, Dr. Tarryn MacCarthy offers a powerful new way for dentists, doctors, and high-achieving healthcare professionals to think about wealth. Beyond income, practice growth, and financial freedom, she asks what truly makes a life feel rich.Through a personal story and honest reflections from her years in dentistry, Dr. Tarryn shares why success can sometimes feel like pressure, how “golden handcuffs” can limit freedom, and why time, health, relationships, creativity, and choice may matter more than the next number in your bank account. This episode is a thoughtful reset for anyone who wants more happiness, fulfillment, and freedom without walking away from ambition.Show notes:(0:56) Rethinking wealth beyond money and assets(7:25) Deep relationships shape lasting happiness and health(9:38) Time, freedom, and golden handcuffs(11:21) A self-alignment framework to help dentists and healthcare professionals to reconnect with themselves beyond burnout and outside expectations. https://truetomemethod.com/(14:14) Let your definition of wealth change(16:25) Freedom comes from daily choices(18:38) Choose where your focus goes(20:51) Gratitude shifts a dental practice(23:41) Define wealth on your own terms(25:04) Outro_______________________IMPORTANT LINKS:Empower Her Retreat:Dates: October 1–4, 2026Location: Taos, New MexicoWebsite: empowerherretreat.orgConnect with Dr. MacCarthy:Email: tarryn@drtarrynmaccarthy.comBook a call with Tarryn:https://api.leadconnectorhq.com/widget/bookings/happiness-and-prosperity-strategy-callUnlock your inner peace and reclaim joy in your profession with the Nervous System Regulation For Dentists Course: https://www.thebizofhappiness.com/calmPlease join my Facebook group, Business Of Happiness Hive, so we can all take this journey to find fulfillment and happiness together. Click here: https://www.facebook.com/groups/2047152905700283Where to find me:Website: www.thebizofhappiness.comFacebook: facebook.com/thebusinessofhappinessIG: @thebizofhappinessIt would mean the world to me if you subscribe, leave a review, and share this podcast with your friends, co-workers, and families. This will help the trajectory of this podcast and allow others who are seeking true happiness to find the podcast.

Pastor Jake brings a powerful message this week called Define the Relationship, diving into what it looks like to move from simply attending church to becoming part of a committed, connected family. We'll talk about why committing to our church family matters, how God uses the local church to strengthen our faith, and what it means to invest in one another with purpose. It's the perfect week to lean in and discover how you fit into the story God is writing here at Manna.Website: https://mannastafford.church/Find us on: Facebook:   / mannastafford  Instagram:   / manna.stafford  TikTok:   / manna.stafford

Full article: Attenuation- and Entropy-Based Habitat Imaging for High-Risk Features in Lung Adenocarcinoma Presenting as a Part-Solid Nodule on CT: A Multicenter Study Can habitat imaging guide lung nodule evaluation? Osvaldo Berlina, MD, discusses this article by Li et al. that uses habitat imaging based on attenuation and entropy values to predict high-risk features in lung cancer presenting as a part-solid nodule.

"Everybody has a plan until they get punched in the face." Mike Tyson's infamous warning for his opponents could easily apply to the new occupant of Number 10.Giving his first speech outside Downing Street, the new prime minister hinted at what his priorities will be – from easing the cost-of-living crisis to keeping commitments on defence spending.But what happens when those plans make contact with reality? Can Mr Burnham's "Manchesterism" work on a national level? And will international events interfere with his domestic agenda?Niall Paterson is joined by Sky's chief political correspondent Jon Craig to assess what might happen in his first few weeks and months in office.Have you got a question for Niall? Email us: why@sky.uk

Our laws still don't clearly define what consent is. Instead, they focus on what consent is not.

Este episodio está impulsado por CheckToBuild, una plataforma que automatiza el control de ejecución mediante la comparación entre los modelos BIM y la realidad capturada en obra.En este episodio compartimos la 4ª Mesa Tecnológica ConTech: La nueva era de la ejecución de obras, organizada por butic The New School. Una conversación moderada por David Barco con Alejandro Ruiz Lara, CEO de CheckToBuild; Carolina Ramírez, directora de CRa-HUB; Fernando Valderrama, referente en costes AECO y cocreador de Presto; y Miguel Villamor, director general de AEC-ON.Algunos de los topics sobre los que hemos debatido son:Qué queremos controlar realmente durante la ejecución de una obra.Cómo reducir la distancia entre planificación y realidad ejecutada.Captura de la realidad mediante escáneres 3D, drones, cámaras y robótica.Comparación automática entre el modelo BIM y la obra construida.Control de tolerancias, replanteos, planitud e instalaciones MEP.Seguimiento de producción, mediciones y certificaciones basadas en datos.Inteligencia artificial y automatización aplicadas al control de calidad.Integración de BIM 4D y 5D, CDE, Lean Construction y Business Intelligence.Barreras culturales y organizativas para implantar estos procesos.Reflexionamos sobre tecnologías y conceptos clave como:Reality Capture: Documenta el estado real de la obra y genera evidencias verificables sobre su avance.BIM aplicado a obra: Convierte el modelo en una referencia para comprobar geometría, planificación, mediciones y costes.Inteligencia artificial: Automatiza la detección de desviaciones y el análisis de la información capturada.Gobernanza del dato: Define cómo capturar, estructurar, validar y distribuir la información de obra.As-built digital: Facilita una documentación final actualizada y respaldada por evidencias.

You just spent two years writing a book. Now what? That's the question at the center of this conversation about book publicity, expectations, and the long game of building a body of work. Bill Sherman sits down with Sandy Smith, CEO of Smith Publicity, to unpack what a smart book launch actually looks like — and why most of the excitement (and anxiety) authors feel in the 60 days around publication misses the point. Sandy's first move with any client is to ask what success looks like a year out. That reframe — from launch week to long horizon — shapes everything else: how to define goals, how to find the right audience for a big idea, and why a book should be treated less like a product drop and more like a two-to-three-year (or longer) relationship-building asset. The conversation moves through sharp, practical territory: the difference between an audience that's ready for your message and one that isn't (yes, even when they need it most), how publicists match authors to podcasts, bylined articles, and expert commentary opportunities, and why revisiting an author's earlier books can sometimes be more valuable than pushing the new release. Sandy and Bill also talk through the emotional arc of a launch — the pre-launch momentum, the 60-day flurry, and the letdown that can follow once the initial push ends. Underneath the tactics is a bigger idea both return to again and again: books create relationships. They let readers spend time with an author's thinking in a low-stakes, high-trust way, and that trust compounds over years, not weeks. The episode also touches on what makes an idea worth an audience's attention in the first place, and why generosity — not ego — tends to separate thought leaders who build lasting influence from those who burn out chasing a moment. Whether you're planning a first book launch or trying to get more mileage out of one published years ago, this episode offers a grounded, experience-based look at what actually works. Three Key Takeaways: • A launch is a beginning, not a finish line. Treat the book as a two-to-three-year (or longer) asset rather than a 60-day sprint — the real payoff comes from what you do with it after publication week ends. • Define your goal before you plan tactics. Whether the aim is sparking conversation, building visibility, or attracting clients, that goal should shape everything from media targets to messaging. • Audience readiness matters more than audience size. The right strategy meets people who are already primed to hear your idea, then lets them bring others along — rather than forcing a conversation an audience isn't ready for. This episode landed right in the middle of launch week for The Thought Leadership Handbook — the new book from Bill Sherman, Peter Winick, and Naren Aryal — and everything Sandy Smith unpacked about goals, audience readiness, and building a lasting asset is exactly what's behind it. If you want to see those ideas in practice, head to thoughtleadershiphandbook.com for a free excerpt, your own thought leadership avatar, and tools to sharpen your strategy. Then preorder your copy today at Amazon, Barnes & Noble, Bookshop, or Amplify — and become part of the conversation this book is meant to start.

From Big Tech Burnout to Land Flipping Freedom: Mike Deaton's “DIRT” Framework Host John Duffin interviews Mike Deaton, who spent over 20 years in big tech supply chain roles before being laid off in 2016 on the same day as his wife, Ligia. Imagine the feeling when both partners endure a serious career change on the same day!! Instead of returning to corporate work, they mapped their values and “perfect day,” hired a coach, and used a land-flipping toolkit Mike had previously purchased to build a business focused on buying vacant land at deep discounts and reselling without renovations. Mike explains the simplicity of land compared to other real estate models, early challenges around mindset and sales, and the importance of combining systems with soft skills. He outlines his coaching approach and DIRT framework—Define strategy, Identify markets, Reach out to owners, and perform Transactions—anchored in each person's “why,” celebrating small wins and leveraging different forms of capital, such as relationships, time, and know-how.The impact of pivoting and change, especially when forced, is difficult. But what I got from Mike was the clarity that when a new situation presents itself, and it feels true to you, you have to seize that moment. To learn more about Mike Deaton and purchase his book, click the links below. https://flippingdirt.us/https://www.linkedin.com/in/michaelbdeaton/00:00 Love of Transformation00:36 Podcast Intro and Guest03:06 From Big Tech to Layoffs05:33 Choosing Freedom Over Following the Herd5:57 Discovering Land Flipping09:03 Career Highs and Burnout15:14 Resisting the Safe Default17:27 Building a Life Together19:44 Coaching and Mindset Shift21:18 Land Flipping Explained22:09 Messy Middle and First Win22:50 First Deal Momentum23:14 Coaching Done Differently24:43 Soft Skills Origins29:14 Transformation Through Wins31:34 Land Business Basics34:17 Capital Time Matrix39:01 Network As Capital41:56 Values Then Action46:55 Outro And Resources

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

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

Discipline, as a virtue, is related to the other virtues. By controlling our urges or wants or lifestyle, we're actually in a better position to be courageous or just.

Lambda lambda lambda, nerds! Neste NerdCast, é hora de reunir o time de ciência para uma conversa sobre comunicação e as diferentes formas de linguagem que guiam as nossas vidas (o que, parando pra pensar, por si só já é um exemplo, hehehe).  No episódio, Alottoni e Azaghal recebem André Souza, Ana Arantes e Altay de Souza para discutir como a percepção de o que é "linguagem" é o fruto de diversos fatores que você talvez nem imagina. Mas não antes de um debate extremamente necessário sobre o motel favorito dos cientistas, que já virou também o nosso. NerdCon Estão abertas as vendas da PRIMEIRA NERDCON! O evento acontece em 20 de setembro de 2026, no Memorial da América Latina, em São Paulo. Confira todas as informações e compre seus ingressos (com direito a meia-entrada para TODO MUNDO!), no site: https://nerdcon.com.br/ Jovem Nerd Esporte Clube #FicaJNEC Acompanhe os últimos jogos da copa com episódios inéditos no feed do JNEC + live da final neste domingo (19 de julho). Conheça todos os canais e redes sociais do programa em: https://linktr.ee/jnesporteclube CONFIRA OS OUTROS CANAIS DO JOVEM NERD E-MAILS Mande suas críticas, elogios, sugestões e caneladas para nerdcast@jovemnerd.com.br APP JOVEM NERD: Google Play Store | Apple App Store ARTE DA VITRINE: Randall Random Baixe a versão Wallpaper da vitrine EDIÇÃO COMPLETA POR RADIOFOBIA PODCAST E MULTIMÍDIA Learn more about your ad choices. Visit megaphone.fm/adchoices

In the one-hundred and eighteenth episode, Roy and Rob play a fun game of 20 Questions, but instead of your whimsical kiddish game, the guys dive into questions that will the define the Steelers 2026 Training Camp and preseason. Follow Roy on X⁠⁠ @PreacherBoyRoy⁠⁠ or on Instagram⁠⁠ @bigcountryscoutingllc⁠⁠ Follow Robert Robinson on X⁠⁠ @RobRobGraphics⁠⁠ New Centerville Church of God Service⁠ ⁠link⁠⁠. Information about⁠⁠ The Heyward House⁠⁠ Information about Craig Wofley's Foundation - ⁠Wolf911⁠ If you are keeping tabs on all things football related go check out my website, ⁠https://www.prospectencyclopedia.com/⁠ Go check out my work, as well as Jim Wexell and all the great staff at Steel City Insider on⁠⁠ ⁠⁠⁠⁠247sports.com⁠⁠ ⁠Order⁠ Jim Wexell's latest book, If These Walls Could Talk: Stories from the Sideline, Locker Room, and Press Box, that features the late Craig Wolfley. Stay Humble, and Be A Blessing! Learn more about your ad choices. Visit megaphone.fm/adchoices

Most people want the opportunity—but few are willing to become the person who is ready for it.▶️ Join the Better Than Best Brotherhood: https://www.skool.com/better-than-best-academy-5909/aboutYears ago, I sat behind a drum kit trying to prove I was ready to become our church's next drummer. I reached the end of the song, hit the crash cymbal…and the cymbal came crashing down into my lap.My worship leader gave me an honest answer: “Not yet. Keep practicing.”That moment could have convinced me to put the dream away forever. Instead, I learned that feedback is not always rejection. Sometimes feedback is a map showing you the distance between where you are and where you need to be.In this video, we explore:00:00 Imperfect Yet Present01:04 Cymbal Crash Lesson02:12 Talent Versus Price03:30 Dream Shelved Early05:12 Private Practice Season06:54 Prices Law Explained09:59 Feedback Not Rejection11:22 Work Works On You14:21 Repeatability And Trust17:39 Time Is Limited19:15 Five Steps To Excellence21:49 Take The Dream Back22:37 Final Charge And SendoffYou may not control when your next opportunity arrives, but you can control who you become while you wait.Choose your arena. Define the repetition. Invite honest feedback. Stay consistent while you are hidden. Then try again.What dream have you placed on the shelf? Share it in the comments—and decide what action you will take over the next 30, 60, or 90 days.Subscribe for more videos about faith, purpose, discipline, leadership, personal growth, and becoming who God has called you to be.#PersonalGrowth #ChristianMotivation #ExcellenceWHO AM IHey, I'm Red Wallace, a former rapper(10 year career) current drummer turned personal development coach. Through podcast(mostly on YouTube) and 1on1/group coaching, I provide guidance to help you chisel away the parts that aren't you revealing your true identity, empowering you to live your God given purpose!

Go to www.Blackriflecoffee.com (http://www.blackriflecoffee.com/) and get premium coffee! True Gold Republic put together a 2026 Precious Metals Kit exclusively for this audience. Go to http://goldwithgraham.com (http://goldwithgraham.com/) or call 800-628-GOLD to claim yours. Go get your NEVER WOKE merch at https://neverwokeapparel.com/ Follow Us on Social Media:
 • Twitter :https://twitter.com/GrahamAllen • Instagram :https://www.instagram.com/grahamallen1
 •Facebook :https://www.facebook.com/GrahamAllenOfficial/
 • TikTok :https://www.tiktok.com/@thegrahamallen
 • Rumble :https://rumble.com/user/GrahamAllenOfficial

My guest on this episode is Ben Cajee - CBeebies and CBBC presenter, BBC Sport broadcaster and now debut children's picture book author. His book, The Panda-Badger, is an uplifting story about a little character who doesn't quite fit into a world of pandas or badgers - and what happens when he realises that being both is actually his greatest strength. It's inspired by Ben's own experience of growing up mixed heritage, and it is already a firm favourite with my kids.Ben chats to me about what it was like to be asked, in 2020, to speak publicly about his own experiences of racism. He explains how the viral CBeebies link he wrote himself, came about, and the harder, more personal one that followed... and the online pile-on that came with it. Ben also talks about:Being physically sick with nerves before a live half-hour BBC broadcast - in a wetsuit, in October, with nowhere to hidePresenting Blue Peter, achieving the thing he'd always aimed for, and sitting on his sofa afterwards thinking it didn't feel how he thought it wouldWhy he refuses to let anyone describe him as just one thing (and the conversation with a former NBA star that helped him see why)What it felt like watching his own links back to improve, and why he thinks not everything should be perfectly polishedThe celebrity author narrative, and why he's not remotely bothered by itFind Ben on Instagram: https://www.instagram.com/ben_cajee/?hl=en-gb Buy The Panda-Badger: https://www.amazon.co.uk/Panda-Badger-Ben-Cajee/dp/0241562600If you enjoy the podcast, please do leave a rating and review - it really helps other people find it. Not Another Mummy Podcast is brought to you by me, journalist and author Alison Perry. I'm a mum of three and I love interviewing people about parenthood and confidence on the podcast. You can check out my other episodes and come chat to me on Instagram: @iamalisonperry or on Threads: @iamalisonperry. You can buy my book OMG It's Twins now.Music: Epidemic SoundArtwork: Eleanor BowmerSupport this show http://supporter.acast.com/notanothermummy. Hosted on Acast. See acast.com/privacy for more information.

If you've been handed a diagnosis and quietly accepted it as your future, this is a different way to understand how the body actually heals. For seven years, Celinne chased cystic acne across dermatologists, elimination diets, and every remedy she could find. Never knowing the real driver was undiagnosed tick-borne illness. When the diagnosis finally came (Lyme, Bartonella, and Babesia), she'd already been told what most people are told: this takes years to heal, if you heal at all. Eighteen months later, her labs came back with no trace of it — a result her former client and doctor, naturopathic physician and tick-borne specialist Dr. Carrie Chojnowski, says she'd never seen. What changed the biology wasn't the medicine alone. She treated the illness as a teacher and cleared the story underneath the symptoms while the science did its work. In this conversation, Celinne and Dr. Carrie open the full case — how tick-borne illness hides and gets missed. Why a diagnosis was never meant to be a prognosis, and how changing the energetic blueprint of a story can reorganize what shows up in the body. You'll hear what becomes possible when you stop handing your future to a diagnosis and start listening to what your body is actually saying. Healing Lyme Disease. ON THIS EPISODE: 00:00 Hear the lab result that stopped her doctor cold 06:17 Trace how seven years of cystic acne hid an undiagnosed infection 12:41 Learn why so much tick-borne illness goes undiagnosed 15:04 Meet Lyme, Bartonella, and Babesia as personalities at a party 26:07 Unpack the "bomb and shrapnel" reason people are told they can't heal 32:53 Sit with the belief that we choose our illnesses as teachers 40:53 Define the metaphysical — and how it lands in the body 56:42 Reclaim intuition: why no one should hand you your prognosis 1:07:00 Get the testing behind a result that read as "never had it" KEY IDEAS:

Favour Obasi-ike, MBA, MS breaks down the debate between content quality and velocity. He argues that while velocity drives visibility, quality is the foundation that keeps audiences engaged and builds trust.High-quality content must be readable, understandable, and digestible, utilizing clear formatting and strong internal linking. By establishing a proof of concept and adhering to Google's E-E-A-T guidelines, creators can scale their publishing frequency sustainably.Who Is This For?This episode is for entrepreneurs, marketers, SEO professionals, bloggers, and creators trying to build search visibility. It serves anyone looking to publish consistently without falling into the trap of low-quality content overload.Key Moments & Timestamps[00:03] - Introduction: Content quality versus content velocity.[02:38] - Quality is not only what you say; it is how it is presented.[05:03] - Headings and the “readable, understandable, digestible” standard.[12:10] - Quotes, testimonials, strong titles, and the content experience.[15:13] - Self-audit: Would you watch, read, or listen to your own content?[21:00] - Attention, engagement, and quality as audience priorities.[26:54] - Google E-E-A-T: experience, expertise, authority, and trust.[29:39] - The debate: Evergreen content versus timely updates.[45:03] - Updating old articles and leveraging the modified date.[47:26] - Content velocity, manual indexing, and internal linking.[52:02] - Two top-quality articles beat ten subpar pieces.[53:35] - Building a controlled cadence to avoid audience burnout.[59:42] - Establish quality and a proof of concept before scaling output.[64:00] - Researching ranked content to evaluate the competitive landscape.[67:17] - Aligning keywords, titles, URLs, and opening lines with intent.[68:42] - Best strategy: High-quality content posted consistently.[77:41] - Final takeaway: Quality trumps velocity; scale only with quality.Memorable Quote"Quality trumps velocity—and if you're going to do velocity, make sure you do it with quality."Frequently Asked Questions (FAQs)What makes content high quality?High-quality content is useful, trustworthy, well-researched, and structured. It should be easy to read and act on, utilizing proper formatting to guide the user experience.Does publishing more content improve SEO?Publishing volume helps only when relevance, quality, internal linking, and audience value remain strong. Velocity without quality leads to subpar results.Should old content be updated?Yes. Update older content when facts, tools, algorithms, or market conditions change. This preserves its evergreen foundation while signaling freshness.Is evergreen or fresh content better?Both serve a purpose. Evergreen content supports lasting discovery, while fresh content is necessary for news, trends, and platform updates.How often should a brand publish?Choose a publishing cadence your team can sustain without lowering standards. It is better to publish fewer high-quality pieces than to overwhelm your audience.Action StepsAudit one of your existing pieces for readability, formatting, and search intent.Fix heading hierarchy, paragraph length, text emphasis, visuals, and calls to action.Refresh a strong older article with current evidence, updated links, and fresh examples.Define a minimum quality checklist and test it until it becomes a repeatable process.Set a sustainable publishing cadence based on audience behavior and team capacity.Scale your output only after your quality, engagement, and workflow are stable.For more strategies, connect directly via the podcast resources shared at the conclusion of the episode.

Árið 1996 höfðu vinkonurnar Tiffany og Melissa báðar starfað á ljósabekkjastofu sem var sérstaklega fyrir karlmenn - en þar gátu þeir sótt sér auka þjónustu sem ekki bauðst á hefðbundnum ljósabekkjastofum.  En það var þar sem að lík þeirra beggja áttu eftir að finnast einn góðan veður dag en þær höfðu báðar verið stungnar til bana.  Við tók rannsókn sem að leiddi lögreglu í margar áttir og enn er mörgum spurningar ósvarað.   Þátturinn er í boði  Define the Line Sport  Heimaskipulag   Komdu í áskrift!  www.pardus.is/mordskurinn 

Send us a MessageIn this episode of Culture Change RX, Sue welcomes back Doug Morse, Principal of Strategic Planning at Capstone Leadership Solutions, for a conversation about a different approach to strategy.Rather than trying to be slightly better than the hospital down the road, Doug challenges organizations to ask a much bigger question:What unique value can only your organization provide?Together, Sue and Doug explore why differentiation isn't enough, why rural hospitals must identify their unique value, and how strategic clarity creates the focus, alignment, and momentum needed for long-term success.The conversation also introduces one of Doug's most powerful questions:"What are you willing to promise your patients?"For leaders feeling overwhelmed by competing priorities, long to-do lists, and initiative fatigue, this episode offers a refreshing perspective on strategy, execution, and the future of rural healthcare.Doug's past podcast episodes:Growth and Culture Accelerators:  Nimble Planning and Continuous LearningClarity Over Chaos: Making Strategy Work in Rural HealthcareConnect with Doug:Doug@CapstoneLeadership.netDoug's LinkedIn[FREE Webinar] A 5-Step Approach to Achieving Nursing Excellence in Rural Hospitals:  Aug 11 at Noon ETMany rural hospitals need a proven approach for creating and sustaining nursing excellence. Join Capstone for a special 1-hour webinar where we will overview our proven 5-step approach for achieving nursing care improvements that take hold and last.Register here: https://capstoneleadership-net.zoom.us/meeting/register/gCRrqIc2QeK7mj-1gzIJsgHi! I'm Sue Tetzlaff. I'm a culture and execution strategist for small and rural healthcare organizations - helping them to be the provider and employer-of-choice so they can keep care local and margins strong.For decades, I've worked with healthcare organizations to navigate the people-side of healthcare, the part that can make or break your results. What I've learned is this: culture is not a soft thing. It's the hardest thing, and it determines everything.When you're ready to take your culture to the next level, here are three ways I can help you:1. Listen to the Culture Change RX PodcastEvery week, I share conversations with leaders who are transforming healthcare workplaces and strategies for keeping teams engaged, patients loyal, and margins healthy. 2. Subscribe to our Email NewsletterGet practical tips, frameworks, and leadership tools delivered right to your inbox—plus exclusive content you won't find on the podcast.

https://teachhoops.com/ Every single coach in the country is sitting on one. One conversion they know they absolutely need to have, but continue to push off. A player who needs a heavy dose of the truth regarding their body language. An assistant coach who is quietly slipping below the program's operational standard. A parent whose unrealistic expectations need an immediate, firm boundary reset. A team leader who has quietly drifted away from the collective vision. And yet, the conversation waits. Tomorrow becomes next week, next week becomes next month, and a minor operational leak slowly turns into an unmanageable crisis. In this episode, we step directly into the "Truth Room" to confront the psychology of delayed candor. We pull back the curtain on why coaches avoid these high-friction moments. It isn't a communication problem; it is a fear problem. We unpack how hiding from discomfort under the guise of "protecting the relationship" is actually an act of self-preservation that destroys your culture. Discover how to balance personal care with direct challenge, and learn why unspoken truth quietly becomes accepted behavior inside a level 4 championship program. True, transformational program building requires a leader to navigate the tight space between supporting an individual and demanding adherence to the program's unyielding Standard of Tolerance. The Fallout: When you prioritize an athlete's short-term comfort or fear their defensive reaction, you choose silence. This passive avoidance creates a massive cultural drift. Your silence actively teaches the rest of the roster that your stated standard is flexible when the confrontation becomes uncomfortable. The Execution: The absolute best coaches do not look at a difficult conversation as an act of criticism; they view it as an investment of Trust Capital and an act of absolute belief. If you challenge an assistant or a player through the exhaust, it is because you care too much about their long-term growth to let them settle for mediocrity. When you step into the room to address a boundary line that has been crossed, bypass emotional lectures and utilize this high-signal, socratic framework to maintain absolute control of the environment: Step 1: State the Objective Observation ───► "I see this specific behavior occurring on the floor..." Step 2: Define the Functional Impact ───► "It is actively hurting your development and stalling our team's Next Play Speed..." Step 3: Mandate the Explicit Correction ───► "This is the exact structural adjustment that needs to change immediately..." Step 4: Reaffirm Unshakable Belief ───► "I am holding you to this line because I know you are capable of leading this program." Coach's Note: "Delayed candor always increases the cost. Every single day you choose to look the other way because a conversation feels too heavy or uncomfortable, you are actively training your gym to accept a lower standard. Stop letting fear manage your program's ceiling. Step up, look them in the eye, care personally, but challenge directly. Speak the truth through the exhaust, and let your culture carry the weight." Title Ideas: Are You Delaying the Conversation Your Basketball Team Needs Most? Why Avoiding Hard Conversations is Silently Destroying Your Culture How Elite Basketball Coaches Deliver Honest Feedback Without Losing the Team The Hidden Danger of Delayed Candor in a Basketball Program Primary Keywords: Handling difficult conversations in basketball, building a basketball program culture, TeachHoops, Coach Collins, basketball coaching staff communication, standard of tolerance, coach-player accountability workflows. Secondary Keywords: Next play speed resilience, own the room coaching language, active density practice scripts, Types of Coaches (3).pdf, effective field goal percentage focus, decision IQ constraints, socratic coaching method, player-led team autonomy. Description Snippet: "Are you holding back from having a difficult, honest conversation with a shifting player, a passive assistant, or an overreaching parent? In this podcast episode, Coach Collins breaks down why avoiding tough feedback is a fear problem, not a communication problem. Discover how unspoken truth quickly becomes accepted behavior inside a gym, and learn a simple 4-step candor framework to challenge your roster directly while building unshakeable trust capital." Suggested Tags:#BasketballCoaching #TeachHoops #CoachCollins #CoachingPhilosophy #LeadershipTips #TeamCulture #SportsLeadership #HighSchoolBasketball #CoachingCommunication Are you preparing to have this critical candor conversation with a key varsity player whose poor body language has been creating an environmental leak during your July tournament workouts, or are you looking to realign your assistant coaching staff before your official pre-season onboarding schedule begins this fall? Show NotesThe Leadership Balance: Care vs. Candor [HIGH CHALLENGE] │ │ Championship Standard Harsh & Abrasive │ (Care Personally + (Truth Without │ Challenge Directly) Care) │ │ ───────────────────────┼─────────────────────── [HIGH CARE] │ Passive Avoidance │ Weak Compliance (The Fear Trap) │ (Care Without │ Truth) │ 1. The Danger of Care Without Truth (The Compliance Leak)2. The Power of Truth Delivered with Care (The Championship Standard)The 4-Step Candor FrameworkThe Candor Audit: Delayed Fear vs. Immediate StandardLeadership VariableThe Delayed Fear Trap (Level 2 Leak)The Immediate Candor Standard (Level 4)Primary MotivationProtecting yourself from temporary relational discomfortProtecting the long-term integrity of the program's brandCultural ResultUnspoken truth quietly becomes accepted behaviorAccountability forces a rapid Next Play Speed resetStaff AlignmentAllowing an assistant's low edge to slide; passive frictionAddressing slipping metrics early to maintain a unified staffLocker Room VibeCoach-Fed frustration; athletes sensing a double standardPlayer-Led clarity; the roster knows exactly where it standsYouTube SEO Strategy Learn more about your ad choices. Visit podcastchoices.com/adchoices

What if the reason your business feels stuck isn't because you need a better strategy—but because you don't have a movement? In this episode, Dielle Charon shares the exact framework she used to completely rebrand her business, launch a thriving membership, redesign her offers, and create a new vision after hitting a plateau. She walks through the 10-question "Movement Business Plan" she uses to make major business decisions—from refining your messaging and offers to designing a business model that supports the life you actually want. If you're thinking about rebranding, changing your niche, creating new offers, or scaling to your next revenue milestone, this episode will help you build a business with intention instead of reacting to what's happening around you. Timestamps 00:00 Intro + Why every business needs a movement 01:30 How Dielle rebuilt her business after plateauing 03:00 Why desire isn't enough without a plan 05:00 The difference between short-term planning and a movement plan 06:45 When it's time to rethink your business model 08:45 Question 1: Define your movement word 11:00 Question 2: Map your skills to your clients' identity 15:30 Question 3: Create a company direction, not just a revenue goal 17:30 Question 4: The Want Match exercise 22:30 Building offers your clients want—and you love delivering 24:30 Question 5: Let your movement drive every business decision 27:00 Question 6: Decide how you want to receive money 29:15 Question 7: Design your schedule before your business designs it 33:30 The hidden sacrifices behind every business model 35:45 Question 8: Decide what you're willing to leave behind 38:15 Question 9: Build values that support your movement 40:30 Question 10: Visualize how your movement shows up in your marketing 43:00 Becoming a "video-first" business 44:45 The full 10-question Movement Business Plan recap 47:00 Final thoughts: Build a movement, not just a business Website: forthe23percent.com Instagram: @forthe23percent Membership: forthe23percent.com/membership

Your life is the sum of your choices. Not one decision.Not one moment.But the daily choices that quietly shape who you become. On this episode of Like It Matters Radio, Mr. Black reveals the five choices that ultimately define every life. They may seem ordinary, but together they determine your direction, your influence, and your legacy. The five choices are: How you spend your timeThe attitude you chooseWhat you focus onThe story you tell yourselfWhat you believe about God, yourself, and your purpose Drawing from neuroscience, psychology, Scripture, and leadership principles, Mr. Black explains why our minds are constantly filtering information through deletion, distortion, and generalization—and why the stories we repeat to ourselves eventually become the lives we live. This episode also explores: How your Reticular Activating System (RAS) shapes what you notice and pursueWhy beliefs at the identity level create lasting transformationThe Sovereignty Triangle found in Jeremiah 29:11, Romans 8:28, and Ephesians 2:10The difference between living for Survival, Success, or Significance Because success isn’t the highest goal. Significance is. It’s not about what you accumulate. It’s about who you build. It’s not about making a name for yourself. It’s about making a difference in the lives of others. This is an Hour of Power designed to help you examine the choices you’re making today—because today’s choices become tomorrow’s character, and tomorrow’s character becomes your legacy. The time is now. Choose wisely. Inspiration. Education. Application. When you live your life like it matters… it does.See omnystudio.com/listener for privacy information.

As we celebrate, each in our own way, the 250th anniversary of these United States, we have to admit: We don't seem to be so united anymore. United or not, though, we Americans are a reflection of the movies that we watch. On this week's show, The Movies 101 hosts will be discussing their choices for the movies that, for now at least, define what America is.

  Maurice discusses the hidden costs of living on autopilot, urging listeners to ask hard questions and make intentional decisions. This episode explores how comfort can lead to complacency, impact personal growth, and hinder purpose. Learn to take ownership of your choices for a more fulfilling life. In This Episode:00:00 Habit Versus Intention03:40 Comfort Over Clarity07:42 Defining Your Why10:08 WYN Inner Circle Key Takeaways:Question decisions made out of habit rather than intention.Recognize how comfort can lead to complacency and cloud clarity.Understand the importance of asking “why” to avoid borrowed ideas and gain ownership.Define your “why” to prevent life from defining it for you.Embrace mistakes as learning opportunities for personal growth.

...

Tus resultados no solo dependen de lo que sabes hacer, también están profundamente relacionados con la imagen que tienes de ti mismo.En este episodio analizamos siete conceptos que suelen confundirse, pero que influyen directamente en tu comportamiento y en la forma en la que vendes: autoconcepto, autodefinición, autoimagen, autoevaluación, autoeficacia, autoestima y autovalía.Porque cuando te repites que eres malo prospectando, que no sabes negociar o que no tienes seguridad para hablar con ciertos clientes, terminas actuando de una manera que confirma esas ideas. No necesariamente porque sean verdad, sino porque las convertiste en parte de tu identidad.También te comparto un ejercicio para reconocer las etiquetas que has construido alrededor de ti y empezar a decidir, de manera consciente, qué quieres escribir después de las palabras: “Yo soy…”MENOS CURSITIS Y MÁS RESULTADOS DE VENTAS Regístrate en el Top Team de Ventashttps://www.detonadoresdevalor.com/top Hosted on Acast. See acast.com/privacy for more information.

Veterans report July 25th. First practice July 26th. And for the first time all offseason, we stop talking about what the 49ers could be and start finding out what they actually are. We're going LIVE to break down every major storyline heading into 49ers training camp — and there are a lot of them. 

Who Gets to Define the Next Generation? Gen Z Finds Its Voice—and Its Anchor—in Christ Is Gen Z lost in the noise of culture, media, and politics—or are they quietly forging a radical new path of faith? In this powerful episode, Catherine sits down with high school senior and gifted poet Charis Shoemaker to explore how today’s young Christians are wrestling with identity, truth, and calling in an ever-fractured world. If you wonder what the next generation truly believes, and how Christian parents can encourage their kids to pursue God in the midst of cultural chaos, this is an episode you cannot afford to miss. About Our Guest:Charis Shoemaker is no ordinary teenager. A state award-winning actress, an eloquent poet, and—most importantly—a passionate follower of Christ, she represents a new wave of young believers speaking up for authentic faith. Charis’ original poem, “They Say,” recently brought a packed house to their feet at her homeschool graduation, voicing the hopes and struggles of a generation determined to follow Christ—no matter the cost. What You’ll Discover in This Episode: Gen Z Under the MicroscopeListen as Charis shares firsthand what it’s like to grow up constantly defined—often unfairly—by societal labels, politics, and the relentless barrage of social media. In an era of fractured opinions, what do young people really hear about who or what they’re supposed to be? The Power of Godly InfluenceCharis opens up about her own journey, including profound family challenges and the steady faith of her mother, a night-shift nurse who made time for Scripture and prayer no matter how hard life got. Her story testifies to the quiet resilience that shapes faith behind the scenes. A Poetic Anthem for Gen ZHear Charis' captivating poem, “They Say,” in full. Her words challenge generational stereotypes and culture’s attempts to define identity—while serving as an anthem for those who choose to be defined not by people, but by the voice of God. “If my life is to revolve around opinions, I would much rather they come from a God who is preeminent than a people who are insignificant... I would much rather they be words spoken by the Word who became flesh for me.” – Charis Shoemaker, 19:15 Dealing with Church Hurt and PainCharis recounts personal battles—including abuse and disappointment with the Church—and how those wounds could have derailed her faith. Yet, she found hope, healing, and her truest identity in Jesus. Parents will find wisdom here for helping kids process pain with grace rather than anger or withdrawal. Who Gets to Define Us?The conversation zeroes in on the theme of authority, urging listeners to ask tough questions: Is our identity determined by culture, by the Church, by ourselves—or solely by God, the Creator who names us and calls us His own? Hope Rising in Gen ZContrary to stereotypes, Charis reveals that a growing number of her peers are returning to church and hungry for real, unfiltered truth. She challenges parents not to underestimate Gen Z's capacity—and desire—for deep faith, and encourages both generations to risk bold, loving conversations about what matters most. “There’s a generation coming up after you that wants truth…Be there for us. Give us that thing to hold on to.” – Charis Shoemaker, 53:38 Why This Matters:In a season when so much is said about the failures and fears of the next generation, this episode highlights a surprising undercurrent: Gen Z isn’t just walking away—they’re wrestling, seeking, and often returning to the God who defines them. Catherine and Charis Shoemaker’s honest exchange offers hope to every parent praying for their child’s faith journey. Memorable Moments: The story of Charis Shoemaker's friend whose final words were simply, “Trust Jesus”—a moving reminder of the enduring legacy of authentic faith (35:33). An inspiring encounter with a peer who worships “mother earth” and how open conversation sows seeds of truth and friendship (52:53). The recognition that every generation faces labels, but the only voice that matters is God’s. Modern Application:Christian parents, take heart! The next generation is listening, watching, and longing for truth—especially from you. Your example, prayers, and honest conversations matter more than ever. As you foster their search for identity, remember: God alone must anchor our children in a world desperate to define them. Call to Action:How are you helping your children—or the young people in your life—listen to God’s voice above all others? This week, try having an open conversation about identity and faith at home. Share your own story, and invite them to share theirs—uncut, unfiltered, and real. Resources Mentioned: “Gay Girl, Good God” by Jackie Hill Perry The Narrow Road (song by Reagan James Boone) Let us know: How are you equipping your family to anchor their identity in Christ, not in what “they say”? Share your thoughts and experiences with us on social media, or email your parenting questions for future episodes! Discover more Christian podcasts at lifeaudio.com and inquire about advertising opportunities at lifeaudio.com/contact-us.

Don't Let Yesterday Define Todayhttps://lifemotivationdaily.blogspot.com/https://www.youtube.com/@liveandlaugh2025-y8l

Workers Are Feeling the AI Squeeze: How It Could Define the Next Housing CyclePodcast Description If you ask the average American, AI is taking over, as are the headlines warning that it's coming for our jobs. Open LinkedIn, and you'll see stories about chatbots replacing employees, hiring freezes, and departments being downsized. But when you dig into the actual data, it's murky at best. So, what's really happening, and how should real estate investors prepare?   On one hand, unemployment remains relatively low, and layoffs aren't surging across the U.S.—not yet at least. In fact, many economists are still projecting positive job growth in the short term. On the other hand, you have growing concerns among what seems like most American workers. Fear about job displacement. Career uncertainty. The pressure to stay employable.   Then there's the trickle-down impact on the housing market. Rising unemployment affects the biggest renter demographic in the nation. Do real estate investors need to temper expectations for rental demand and rent growth for the foreseeable future? Does “conservative” investment analysis need to go to another level? We're breaking it all down, plus much more, on today's show. In This Episode We Cover What to make of “murky” data surrounding AI's impact on the U.S. job market  Why Americans are becoming increasingly worried about AI-caused layoffs (despite “positive” forecasting) Two ways that widespread adoption of AI could affect the housing market Why real estate investors should prepare for lower rental demand and rent growth Which real estate markets are the best long-term bets as AI reshapes the economy And So Much More! Links from the Show Join the Future of Real Estate Investing with Fundrise Join BiggerPockets for FREE Join us at the BiggerPockets Conference October 2-4 in Orlando. Buy tickets Sign Up for the Investor Brief Newsletter Find an Investor-Friendly Agent in Your Area Worried About AI? Here's How Real Estate Is Changing Faster Than Ever Dave's BiggerPockets Profile World Economic Forum: The Future of Jobs Reports 2025 U.S. Bureau of Labor Statistics (BLS): Employment Situation Summary Mercer: Global Talent Trends 2026. Solving the Human-Machine Equation Resume Now: AI Disruption: 9 in 10 Workers Fear Job Loss to Automation Challenger, Gray, & Christmas: Challenger Report December 2025 CNBC: Satya Nadella Says as Much as 30% of Microsoft Code Is Written by AI McKinsey Global Institute: Agents, Robots, and Us: Skill Partnerships in the Age of AI National Bureau of Economic Research (NBER): Firm Data on AI Buy the Book, Recession-Proof Real Estate Investing Check out more resources from this show on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BiggerPockets.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and https://www.biggerpockets.com/blog/on-the-market-441. Interested in learning more about today's sponsors or becoming a BiggerPockets partner yourself? Email ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠advertise@biggerpockets.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

Chris Welsh is joined by Jake Ciely and Tera Roberts to break down the toughest running back decisions you'll face during 2026 fantasy football drafts. From the elite first-round tier featuring Bijan Robinson, Jahmyr Gibbs and Christian McCaffrey to the biggest mid-round values and late-round sleepers, the crew debates every major RB decision fantasy managers will have to make. Timestamps: (May be off due to ads) Intro - 0:00:00 Bijan Robinson vs. Jahmyr Gibbs - 0:02:38 Christian McCaffrey vs. Jonathan Taylor vs. James Cook - 0:06:06 Store Code and FP Premium Giveaway - 0:12:07 Ashton Jenty vs. De’Von Achane - 0:12:48 Chase Brown vs. Saquon Barkley vs. Omarion Hampton - 0:18:14 Derrick Henry vs. Kenneth Walker vs. Jeremiyah Love - 0:22:54 Hard Rock Bet - 0:28:44 Kyren Williams vs. Breece Hall vs. Josh Jacobs - 0:30:42 Quinshon Judkins vs. Bucky Irving vs. D’Andre Swift vs. TreVeyon Henderson - 0:35:25 Bhayshul Tuten vs. Jadarian Price - 0:40:36 FantasyPros Draft Simulator - 0:45:50 Lightning Round - 0:46:48 Helpful Links: Hard Rock Bet - Sign up for Hard Rock Bet and make a $5 bet and you'll get $150 in bonus bets if you win. Head over to Hard Rock Bet, sign up and make your first deposit today. Payable in bonus bet(s). Not a cash offer. Offered by the Seminole Tribe of Florida in FL. Offered by Seminole Hard Rock Digital, LLC, in all other states. Must be 21+ and physically present in AZ, CO, FL, IL, IN, NJ, OH, TN or VA to play. Terms and conditions apply. Concerned about gambling? In FL, call 1-888-ADMIT-IT. In IN, if you or someone you know has a gambling problem and wants help, call 1-800-9-WITH-IT. GAMBLING PROBLEM? CALL 1-800-GAMBLER (AZ, CO, IL, NJ, OH, TN, VA) Draft Wizard - Dominate your fantasy football draft with Draft Wizard. Run fast mock drafts, test different strategies, build custom cheat sheets, get pick-by-pick draft advice, and learn your leaguemates' tendencies before draft day. Just download the FantasyPros App or head to fantasypros.com/draftwizard Follow us on Twitch - The team here at FantasyPros is taking questions all week, every week on Twitch. Follow us on Twitch at twitch.tv/fantasypros and never miss a stream! Discord – Join our FantasyPros Discord Community! Chat with other fans and get access to exclusive AMAs that wind up on our podcast feed. Come get your questions answered and BE ON THE SHOW at fantasypros.com/chat Leave a Review – If you enjoy our show and find our insight to be valuable, we’d love to hear from you! Your reviews fuel our passion and help us tailor content specifically for YOU. Head to Apple Podcasts, Spotify, or wherever else you get your podcasts and leave an honest review. Let’s make this show the ultimate destination for fantasy football enthusiasts like us. Thank you for watching and for showing your support – https://fantasypros.com/review/ BettingPros Podcast – For advice on the best picks and props across both the NFL and college football each and every week, check out the BettingPros Podcast at bettingpros.com/podcast, our BettingPros YouTube channel at youtube.com/bettingpros, or wherever you listen to podcasts.See omnystudio.com/listener for privacy information.

President Trump's new “Trump Accounts” may become one of the most practical, forward-looking ideas Washington has produced in a generation.In this episode of The Right Side with Doug Billings, Doug explains why these child-focused investment accounts could change the financial future of millions of young Americans by giving them something far more powerful than another government promise: ownership.With a $1,000 government seed contribution for eligible children, private-sector investment support, and the potential for long-term wealth creation, Trump Accounts represent a direct challenge to the dependency mindset pushed on younger generations for decades.Doug breaks down why this idea matters, why it could help shift Gen Z and future generations from grievance to ownership, and why this may prove to be a Mount-Rushmore-qualifying event for President Trump's legacy.This is about more than money. It's about responsibility, freedom, opportunity, and giving the next generation a real stake in America's success.We're in this together. Believe it. For the Republic! Cheers.#TrumpAccounts #MillionaireMakers #AmericaFirst #FinancialFreedom #NextGeneration #OwnershipEconomy #SocialSecurity #GenerationalWealth #SelfReliance #DougBillings #TheRightSideSupport the show

In this solo episode, Michele Lamoureux dives into the world of labels — the words we use to define ourselves and others — and explores how they can both empower and limit us. Whether you've been labeled by society, a relationship, or your own inner critic, this episode unpacks the how our opinions of ourselves are formed, the impact of self-labeling, and how to reclaim your personal narrative on your own terms. If you've ever felt boxed in by a title, a role, or someone else's perception of who you are, this conversation will challenge you to think differently about the power of words and identity. Tune in to discover how to use labels as tools for growth and self-awareness rather than barriers to your true potential.    KEY TOPICS Introduction And Overview Of Labels (00:07) Examples Of Common Personal Labels And Their Power (01:30) Impact Of Labels From Parents, Teachers, And Others On Children (03:10) Reflection Exercise: Whiteboard Of Self Labels And Positive Identity (04:30) Michele's Personal Labels And Being Your Own Champion (06:00) Harmful External Labels: "Exotic," "Nihilist," And Misaligned Identities (09:35) The "Karen" Label And Using Names As Derogatory Terms (09:35) Emotional Impact On Real People Named Karen (11:30) Agency In Choosing, Reframing, And Reclaiming Labels (17:10) Closing: Invitation To Kindness, Self Acceptance, And Upcoming Episodes (26:36)   RESOURCES MENTIONED Join The Newsletter Subscribe on YouTube Follow on APPLE PODCASTS Follow on SPOTIFY PODCASTS Book: Design a Life You Love   *The Good Life with Michele Lamoureux podcast and content provided by Michele Lamoureux is for educational and entertainment purposes only. It does NOT constitute medical, mental health, professional, personal, or any kind of advice or serve as a substitute for such advice. The use of information on this podcast or materials linked from this podcast or website is at the user's own risk. Always consult a qualified healthcare or trusted provider for any decisions regarding your health and wellbeing. This episode may contain affiliate links.

A motivated team can still miss the mark when direction is fuzzy.     Joe Rotella, chief value officer at Delphia Consulting, reveals how misalignment masquerades as progress and why status updates create a dangerous sense of security. Explore concrete tactics: Define “what it takes to win,” replace status checks with “What are you doing now, what's next, and how can I help?,” and learn how to switch from feedback to feed forward.     Walk away with a script bank, a feedback framework, and a simple opening question that instantly improves results.    Shorter blurb (for newsletter):  Is your team busy but off target? Joe Rotella, chief value officer at Delphia Consulting, LLC, outlines clarity-first planning, future-focused feedback, and remote habits that support without smothering. Learn the value of a feedback framework, outcome-first agendas, and the phrase that instantly improves results.  Subscribe to the All Things Work newsletter to get the latest episodes, expert insights, and additional resources delivered straight to your inbox: https://shrm.co/fg444d    ---  Explore SHRM's all-new flagships. Content curated by experts. Created for you weekly. Each content journey features engaging podcasts, video, articles, and groundbreaking newsletters tailored to meet your unique needs in your organization and career. Learn More: https://shrm.co/coy63r 

Fr. Elias Mary Mills, F.I., served as Rector of the Shrine Church at the Shrine of Our Lady of Guadalupe in Lacrosse, Wisconsin, from 2016 to 2021. He was ordained in May of 2000. In Today's Show: Has there been any movement in the Church regarding the apparitions of Our Lady of Akita as far as it being officially confirmed as an apparition? What is your take on Medjugorje? Is it fake or is it the real deal? What is a good way to define mortal sin? What tasks do you think we will have in Heaven? And more. Visit the show page at thestationofthecross.com/askapriest to listen live, check out the weekly lineup, listen to podcasts of past episodes, watch live video, find show resources, sign up for our mailing list of upcoming shows, and submit your question for Father!

Who will define the 2026 fantasy football season? Joe Pisapia, Derek Brown, and Andrew Erickson each reveal their Mount Rushmore, the four players they believe will have the biggest impact on the NFL and fantasy football this year. From league-winning running backs to MVP candidates and breakout stars, the crew plants their flags on the names you need to know before your drafts. Before unveiling their Mount Rushmores, the guys break down George Pickens' outlook for 2026, discuss whether he can repeat last season's breakout, react to the latest training camp buzz surrounding Cam Skattebo, Omarion Hampton, Ladd McConkey, and cover all the biggest fantasy football headlines! Timestamps: (May be off due to ads) Intro - 0:00:00 Pristine Auction Giveaway - 0:02:37 Headlines - 0:03:25 Cam Skattebo - 0:03:43 Omarion Hampton - 0:09:53 Ladd McConkey - 0:14:38 Chris Rodriguez - 0:21:34 Hard Rock Bet - 0:27:19 George Pickens Deep Dive - 0:28:13 Players Who Could Define the NFL Season - 0:39:31 Joe’s List - 0:39:56 Erickson’s List - 0:42:24 D-Bro’s List - 0:45:32 Outro - 0:51:49See omnystudio.com/listener for privacy information.

"Even a dog can wag its tail when it passes you on the street." Howard H White In this colorful episode of The Pivot Podcast, we have. the privilege of sitting with the legendary Howard H White, Nike executive, Jordan Brand ambassador, master storyteller, and one of the most influential figures in sports culture. H shares his remarkable journey from humble beginnings to becoming one of the most respected leaders at Nike and the Jordan Brand. He opens up about the pivotal moments that shaped his career, the obstacles he overcame, and the mindset that helped him turn purpose into impact. The conversation dives into his lifelong relationships with some of the biggest names in sports, including Michael Jordan, Tiger Woods, Derek Jeter, Charles Barkley, Serena Williams and countless other athletes and leaders who have helped shape generations both on and off the court. Howard offers a rare behind-the-scenes perspective on the trust, loyalty, and leadership that define those relationships. He shares personal experiences and never-heard-before stories of having a front row seat to the greatest athletes in history. More than a conversation about sports and sneakers, this episode is packed with timeless lessons on leadership, resilience, faith, mentorship, and building a legacy that extends far beyond personal success. This is a masterclass in character from a man who has spent decades inspiring some of the world's greatest athletes—and proving that the most powerful legacy isn't what you accomplish, but who you help along the way. Learn more about your ad choices. Visit megaphone.fm/adchoices

Which NFL narratives are worth believing—and which ones are complete offseason overreactions? Joe Pisapia, Chris Welsh, and Seth Woolcock break down the biggest storylines heading into the 2026 NFL season, including whether the Rams deserve to be NFC favorites, if the Bengals are legitimate Super Bowl contenders, and whether Patrick Mahomes will actually be ready for Week 1. The crew also debates Brandon Aiyuk's latest social media antics, Dak Prescott's passing upside, Jaxon Smith-Njigba's breakout season, the Bears' expectations under Ben Johnson, and whether the Chiefs are finally vulnerable in the AFC West. Plus, they rank every last-place NFL team based on their chances to go from worst to first in 2026. Timestamps: (May be off due to ads) Intro - 0:00:00 Pristine Auction Giveaway - 0:02:30 Camp Buzz: BUY OR SELL - 0:02:56 Brandon Aiyuk - 0:04:20 Patrick Mahomes - 0:12:20 Can Dak go for 5000 Passing Yards? - 0:17:22 Michael Wilson goes for 1000 Yards - 0:23:28 Hard Rock Bet - 0:27:54 True or False Off-Season Narratives - 0:30:25 Ranking Worst To First Candidates: 0:52:37See omnystudio.com/listener for privacy information.

Gaius and Germanicus discuss the concept of "honest graft" in the American republic from their perspective in 92 AD Londinium. They define honest graft as the legal wealth gained through government office, citing Donald Trump's crypto coin venture as a modern example. Germanicus argues that such graft acts as a lubricant for a republic, much like Roman consular armies sought wealth in foreign expeditions. Unlike a monarchy with a pre-funded ruler, republican officials must enrich themselves during their tenure to maintain the system. The conversation shifts to the "unseemly" nature of this wealth, which Germanicus claims is only treated as immoral when used as a political cudgel by opposing factions. They critique the American tendency toward moralism, which they trace back to a fading Protestant ethic. Germanicus suggests that modern moral outrage is situational, exemplified by the "woke" movement and cancel culture, which serve as power tools rather than reflections of a firm moral compass. They contrast the "blind trusts" of past presidents with Trump's transparent financial trades, concluding that the American system is essentially a client-patron network disguised by flimsy, situational moralism. (1)1945

Sponsor: Hit the link for early access to zen.ai -  bit.ly/zenAI_bpp_irish Sign up now to access daily Notre Dame news and recruiting scoop on the Four Horsemen Lounge, plus all premium Notre Dame stories on IrishIllustrated.com!Get your first month for only $1.00 -- sign up today. What's on your mind?Talk about it at the Four Horsemen Lounge Sign up for our FREE Notre Dame Newsletter Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Six million dollars sounds like enough to retire. But that number alone does not answer the question.In this episode, Ari responds to a real listener wondering if they can retire at 59 with significant savings. The surprising truth is that the portfolio is not the starting point. Spending is.Ari walks through a simple way to reverse engineer retirement. Define what your lifestyle actually costs, layer in healthcare, travel, and one time expenses, then work backward to see what your portfolio needs to support. Without that clarity, it is easy to keep chasing a bigger number and delay retirement longer than necessary.The numbers matter. But so does the life you are trying to fund. What you are retiring from and what you are retiring to can change the answer just as much as any spreadsheet.Because retirement is not about hitting a number. It is about knowing what that number needs to do for you.--Advisory services are offered through Root Financial Partners, LLC, an SEC-registered investment adviser. This content is intended for informational and educational purposes only and should not be considered personalized investment, tax, or legal advice. Viewing this content does not create an advisory relationship. We do not provide tax preparation or legal services. Always consult an investment, tax or legal professional regarding your specific situation.The strategies, case studies, and examples discussed may not be suitable for everyone. They are hypothetical and for illustrative and educational purposes only. They do not reflect actual client results and are not guarantees of future performance. All investments involve risk, including the potential loss of principal.Comments reflect the views of individual users and do not necessarily represent the views of Root Financial. They are not verified, may not be accurate, and should not be considered testimonials or endorsementsParticipation in the Retirement Planning Academy or Early Retirement Academy does not create an advisory relationship with Root Financial. These programs are educational in nature and are not a substitute for personalized financial advice. Advisory services are offered only under a written agreement with Root Financial.Create Your Custom Early Retirement Strategy HereGet access to the same software I use for my clients and join the Early Retirement Academy hereAri Taublieb, CFP ®, MBA  is the Chief Growth Officer of Root Financial Partners and a Fiduciary Financial Planner specializing in helping clients retire early with confidence.