Podcasts about Loop

Chicago can feel up to 8 degrees hotter than suburbs and rural areas because there are more buildings and asphalt and fewer plants to aid the natural process of keeping an area cool. Parts of the city with the least greenspace, tree canopy and more industry can be even hotter than the city average temperature. In the Loop explores  efforts by residents and officials to identify the city's hotter areas and help residents adapt to the extreme heat.GUESTS: Max Berkelhammer, professor, earth and environmental sciences UIC Lindy Wordlaw, Assistant Commissioner, Department of EnvironmentNedra Sims-Fears, executive director, Greater Chatham InitiativeFor a full archive of In the Loop interviews, head over to wbez.org/intheloop. 

Jury selection resumes this morning, The Red Sox continue their winning streak, US Forces wrap up a 10th straight night of airstrikes in Iran. Stay in "The Loop" with WBZ Newsradio.See omnystudio.com/listener for privacy information.

Have you ever caught yourself thinking, I know better than this — so why do I keep doing it? Whether you stay up too late scrolling or you snap at people you leave, we all do things that don't help sometimes. Most of us assume we need to create a lifestyle overhaul to fix the issue. But my guest today says that's exactly why so many of us stay stuck. He says a series of tiny shifts is the key to breaking free from emotional loops. My guest is Dr. Elisha Goldstein, a clinical psychologist and the host of the Emotional Longevity podcast. His new book is Tiny Shifts. Some of the things we discuss are: What an emotional loop is — and how the four parts of it (thoughts, emotions, sensations, actions) trap you before you notice Why your most convincing thoughts are often the least accurate — and how your brain evolved to make it that way Why overhauling your life almost never works — and what "tiny shifts" do instead The 4R method and how Dr. Goldstein used it in real time when he got scammed on Facebook Why overthinking is a nervous system problem Why shame is baked into our culture and how to work with it instead of getting trapped by it The "slippery moment" when one loop turns into another and you assume nothing's working Why healing your childhood wounds doesn't require years of therapy The Therapist's Take: My top three strategies for making tiny shifts starting today Related Episodes ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ 305 — How to Make the Biggest Changes With the Smallest Steps With Author Eric Zimmer ⁠194 — The Science of Small Wins: Why They Matter More Than Big Goals Links & Resources ⁠Tiny Shifts⁠ Connect with the Show Buy a copy of⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠13 Things Mentally Strong People Don't Do⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Connect with Amy on Instagram —⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@AmyMorinAuthor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Sign Up for My Newsletter —⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://amymorinlcsw.com/newsletter⁠⁠⁠⁠⁠⁠⁠ Sponsors Helix Sleep —Go to ⁠⁠⁠⁠⁠⁠⁠⁠helixsleep.com/STRONGER⁠⁠⁠⁠⁠⁠⁠⁠ to get 20% off sitewide   AirDoctor — Head to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AirDoctorPro.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠and use promo code STRONGER to get UP TO $300 off today! One Skin — Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠oneskin.co/STRONGER⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and use code stronger to get up to 30% off your first 3 subscription orders Quince — Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Quince.com/stronger⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for free shipping on your order and 365 day returns! Boll & Branch — Get 20% off sitewide at ⁠⁠⁠BollAndBranch.com/stronger⁠⁠⁠ with code stronger. Subscribe to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mentally Stronger Premium⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for exclusive content like weekly bonus episodes, mental strength challenges, and office hours with me. Learn more about your ad choices. Visit megaphone.fm/adchoices

Melisa and Alli explain why your brain naturally ruminates after a breakup. It's not because you're "stuck," but because your mind is trying to make sense of what happened and solve an unresolved problem.Become a supporter of this podcast: https://www.spreaker.com/podcast/lesbian-chronicles-coming-out-later-in-life--5601514/support.

Flooding has persistently plagued Chicago, the city famously built on a swamp. However, as storms become more severe and more frequent, residents will likely have to deal with flooded basements, streets and other damage more often.  With climate change driving worsening weather, Chicagoans are rolling up their sleeves to prepare. In the Loop hears from local organizations working to make communities more flood-resilient.GUESTS: Zach Wirtz, director of the Chicago Region Trees Initiative, Morton Arboretum Nina Idemudia, CEO, Center for Neighborhood TechnologyBre'Shaun Reddick, Local Partnership Manager, Alliance for the Great Lakes For a full archive of In the Loop interviews, head over to wbez.org/intheloop. 

There's a specific kind of heartbreak that doesn't happen the moment a promise is broken — it happens later, when you realize they were never who they said they were. And it doesn't just take your future; it rewrites your past. In this episode, Robin sits with the two kinds of broken promises — the ones others make to us, and the ones we quietly break to ourselves — and the one thing they both erode: self-trust. You'll hear: • Why the deepest wound isn't when they lied — it's the moment you knew and didn't listen • How to read the pattern: believe patterns, not promises • Why we override our own knowing (and what it costs us) • How to rebuild self-trust — starting with the smallest promises to yourself The takeaway: self-trust isn't the absence of people who let you down. It's the presence of a self you refuse to abandon. This week's invitation: make one small promise to yourself — and keep it.

Habits - Trapped in the Comparison Loop - Bishop Kevin ForemanSupport the show

Habits - Trapped in the Comparison Loop - Live from ATL - Bishop Kevin ForemanSupport the show

A pause on the Paramount Skydance acquisition of Warner Brothers. Oil and gas prices are on the rise. Dog complaints are growing in Boston. Stay in "The Loop" with #iHeartRadio.See omnystudio.com/listener for privacy information.

Jury selection begins this morning in the murder trial of Lindsay Clancy, Another week of no Jackpot winner for Megabucks, Gas prices are averaging 4-dollars a gallon again. Stay in "The Loop" with WBZ Newsradio.See omnystudio.com/listener for privacy information.

More on the cyclospora outbreak. Additional airstrikes between the U.S and Iran. A Boston city council calls out dog owners for not following leash laws. Stay in "The Loop" with #iHeartRadio. See omnystudio.com/listener for privacy information.

In this episode of Digi-Tools in a Cruel World, host Indi is joined by global accounting-tech heavyweights Heather Smith (dialling in from a beautiful corner of the UK Cotswolds) and Kendrick Hair (live from the US). Together, the team tears down the massive wave of announcements out of Xerocon London and digs into the platform's aggressive structural shift under CEO Sukinder Singh Cassidy. We dive deep into Xero Ultra, the brand-new plan aimed at the "messy middle"—scaling businesses with 20-200 employees who need multi-entity reporting and predictive cash flows without the massive price tag of a traditional enterprise ERP move. The team also breaks down Xero's massive leap into "Accountable Intelligence." With Xero crossing the 5-million customer milestone, we look at JAX (automated document capture replacing decade-old OCR technology), real-time automatic bank reconciliation, and Xero Force—a game-changing, no-code environment for accountants to build custom AI agents using natural language. Finally, we discuss the ripple effects across the ecosystem, from Dext CEO Sabby Gill's viral LinkedIn post on why they skipped sponsoring the event, to the realities of implementing Silicon Valley-style performance cultures in the accounting tech space. This episode is brought to you by The Loop and sponsored by Employment Hero. Switch to an AI-powered platform that actually takes the pain out of manual HR and payroll admin. Learn more at employmenthero.co.uk. Chapter Markers 00:00 - Welcome & Episode Intro: Global Tech Roundup with Heather and Kendrick 01:45 - Ecosystem Spotlights: Deep-diving into niche cloud tracking and regional jurisdictional differences 06:22 - Sponsor Break: Employment Hero 07:12 - Post-Conference Reflections: The evolution of Xerocon London 08:35 - Xero goes agentic, and hits five million customers doing it 10:10 - The New Toolkit: Breaking down JAX, Smart Document Capture, and Xero Force agents 11:58 - Xero builds a Lite-ERP tier for the businesses it keeps losing 14:15 - The Mid-Market Shift: Managing transaction limits, consolidation, and the "messy middle" subscription gap 18:50 - Corporate Culture & App Relations: Debating Sabby Gill's Dext announcement and Xero's internal "trim down" 23:10 - Closing Thoughts & Post-Show Banter

Welcome back to Season 16 of the Neuroscience Meets Social and Emotional Learning Podcast. I'm Andrea Samadi, and this is where we bridge neuroscience, social and emotional learning, and human performance so we can create measurable improvements in our well-being, achievement, leadership, productivity, and results. In this episode 403 The Movement Loop: How Movement Activates the Brain for Learning, Adaptation & Performance we will cover: ✔ Why movement is the biological input that prepares the brain for learning, adaptation, and performance. ✔ How Dr. Chuck Hillman's groundbreaking neuroscience research explains what happens inside the brain during and immediately after exercise. ✔ Why even one bout of physical activity can improve attention, executive function, and learning readiness. ✔ How Paul Zientarski translated neuroscience into practice through Naperville Central High School's internationally recognized Zero Hour PE Program. ✔ The science behind brain activation, neuroplasticity, positive stress, and cognitive reserve. ✔ Why movement isn't a break from learning—it's preparation for learning. ✔ How teachers, coaches, parents, and workplace leaders can use movement to improve focus, decision-making, creativity, and performance. ✔ How to apply the Movement Loop in everyday life using practical strategies you can begin today. ✔ Why movement is the input, adaptation is the process, and performance is the result. If you're just joining us, Season 16 explores Phase 3 of the Brain's Operating System for Human Performance—the Movement Loop. This season asks one central question: How does movement change the brain, adaptation change the body, and together create measurable improvements in performance? If you'd like the full overview of the five-phase framework, I encourage you to start with Episode 402. In Episode 402[i], we introduced Phase 3 of the Brain's Operating System for Human Performance—where we introduced The Movement Loop. We discovered one powerful idea. Movement is the input. When you move, your body sends a powerful biological signal to the brain that says: "Prepare. Activate. Adapt." Adaptation is the process. With movement, repetition, and recovery, the brain and body strengthen, rewire, and become more efficient. Performance is the output. The changes become visible in everyday life—clearer thinking, better decisions, stronger health, and improved performance. Then the loop begins again. Today we're asking the next question. Why does movement come first? Not simply because exercise is good for our bodies. But because movement is one of the most powerful biological signals we can send to the brain. Before we can focus... Before we can learn... Before we can adapt... The brain must first be activated. That's why movement isn't the outcome. It's the beginning. When we first interviewed Dr. Chuck Hillman on Episode 123[ii], (back in April 2021) I wanted to understand how his neuroscience research inspired Paul Zientarski's revolutionary Zero Hour PE Program at Naperville Central High School. It was Naperville's extraordinary performance on the Trends in International Mathematics and Science Study (TIMSS) that caught the eye of Dr. John Ratey. Naperville District 203 entered the international comparison as if it were its own "country" and ranked #1 in the world in science and #6 in the world in math. Those results made Dr. Ratey ask, "What are they doing differently?" That question ultimately led him to Naperville, (IL) where he discovered Zientarksi's Zero Hour PE initiative and made it the opening case study in  his book Spark. We will cover this story on our next episode Then years later, we revisited that conversation in Episode 397[iii] through the lens of motivation—exploring how movement increases engagement and helps us want to move more. But looking back through the lens of Phase 3, I realized I had been asking the wrong question. The bigger question isn't whether movement motivates us. The bigger question is: What happens inside the brain the moment we begin to move? That's the question we're answering today. And it's the reason movement sits at the beginning of the Brain's Operating System for Human Performance. Let's begin where every performance story begins... with movement. As we begin Phase 3, Dr. Chuck Hillman's research helps us understand why movement is the first step in the Movement Loop. In our interview, he mentioned that his lab has consistently demonstrated that even a single bout of physical activity can improve attention, cognition, and academic performance. But what fascinated me most wasn't simply that exercise works. It was why it works. Listen carefully as Dr. Hillman explains how movement acts as a positive biological stressor that prepares the brain for learning. EP 403 — Clip 1 Summary Movement is the First Signal As we begin Phase 3 of the Brain's Operating System, the first question is simple: Why does movement come first? Dr. Chuck Hillman has spent his career studying how physical activity changes the brain—from childhood through older adulthood. His research doesn't just focus on exercise, but on understanding how different life experiences shape brain health across the lifespan. One insight from our conversation completely changed how I think about movement. Dr. Hillman explained that even a single session of exercise can improve attention, cognition, and academic performance in children. His team is now studying why that happens, exploring how exercise acts as a positive stressor that activates biological pathways supporting brain function. But what struck me even more was why he chose to study children in the first place. Rather than waiting until someone reaches their 60s and begins experiencing cognitive decline, his goal is to help children build a stronger brain from the very beginning—to create what he calls a cognitive reserve that may protect brain health throughout life. That idea captures exactly what Phase 3 is all about. Movement isn't simply something we do to stay fit. It's one of the earliest investments we can make in the future health and performance of our brain. ______ This is the first piece of the Movement Loop. Movement isn't valuable because we eventually become fitter. This is what many of us used to think? I can't be alone in this…I used to move for fitness alone. Until I studied the brain…and learned that: It's valuable because it immediately changes the state of the brain. Before learning improves... Before memory improves... Before performance improves... The brain has to be activated. That's exactly what movement does. Key Takeaways from Dr. Hillman There are three ideas I'd like all of us to remember from this first clip. Movement is Information The Neuroscience Every movement you make sends information throughout your nervous system. As your muscles contract, they don't just move your body—they communicate with your brain. Your heart pumps faster, blood flow increases, oxygen and glucose are delivered more efficiently, and neurons begin communicating more actively. Movement also changes your brain's neurochemistry. Exercise increases the release of neurotransmitters like dopamine, norepinephrine, and serotonin, which help regulate attention, motivation, mood, and learning. At the same time, it stimulates the production of growth factors such as brain-derived neurotrophic factor (BDNF), which supports neuroplasticity—the brain's ability to form and strengthen new neural connections. That's why I like to think of movement as sending a message to the brain: "Wake up. Pay attention. Get ready to learn." Movement isn't simply burning calories. It's providing your brain with the biological conditions it needs to perform at its best. Stress Isn't Always Harmful The Neuroscience When we hear the word stress, we usually think of chronic stress—too much cortisol, too little recovery, and the negative effects that follow. But the brain actually needs short, manageable challenges to become stronger. Exercise is an example of what's often called eustress, or positive stress. When you exercise, your body briefly increases stress hormones like cortisol and activates your sympathetic nervous system. At the same time, it stimulates beneficial signaling molecules and growth factors that help the brain adapt. Dr. Hillman's research explores how these temporary changes—including biomarkers like cortisol and salivary alpha-amylase—may help explain why even a single bout of exercise improves attention and cognition. The important point is that the stress is temporary. When it's followed by recovery, your brain and body adapt. Without recovery, stress becomes harmful. With recovery, stress becomes the signal that drives growth. That's why adaptation sits in the middle of the Movement Loop. The challenge isn't the goal. The adaptation is. Activation Comes Before Performance The Neuroscience One of the biggest misconceptions about learning is that we can simply decide to pay attention. The brain doesn't work that way. Before the prefrontal cortex—the part of the brain responsible for focus, planning, decision-making, and executive function—can perform well, it must first be activated. Movement helps create that activation. Exercise increases cerebral blood flow, raises arousal to an optimal level, and improves communication across large-scale brain networks involved in attention and cognitive control. Think of it like warming up an orchestra before a concert. Before the musicians can perform together, they first need to tune their instruments. Movement helps the brain tune itself. That's why research consistently shows improvements in attention and executive function immediately after moderate physical activity. Performance doesn't begin when you sit down to work. Performance begins the moment you prepare your brain. That's why movement sits at the beginning of the Movement Loop. One final thought Movement isn't magic. It changes the brain because it changes the brain's biology. It increases blood flow. It changes neurochemistry. It activates neural networks. It stimulates neuroplasticity. And when those changes are repeated over time—and supported by recovery—they become adaptation. That's why movement is the input. Adaptation is the process. Performance is the result. TIPS to IMPLEMENT So what can we do with this research? Here are three simple ways to put the Movement Loop into practice this week. ✅ 1. Move with purpose. Before asking your brain to perform... Prepare it. Take a 10–20 minute walk. Ride your bike. Do a short workout. Climb the stairs instead of taking the elevator. Don't think of movement as taking time away from your work. Think of it as the first step toward doing your best work. I always park my car at the back of the parking lot, not afraid add bits of movement into the day. ✅ 2. Prepare the brain before expecting learning. Whether you're a parent... A teacher... A coach... Or a leader... Create opportunities to move before asking people to think. Movement isn't a reward after learning. It's preparation before learning. I discovered this one early on with this podcast. I can't focus on this topic, without moving first. ✅ 3. Start measuring brain performance—not just physical performance. The next time you move, don't just ask: "How many calories did I burn?" Ask: Did I think more clearly today? Did I focus more easily? Did I feel more energized? Did I recover better tonight? Am I becoming more resilient? Those are the real returns on your investment. Movement for me, is at the root of each of these questions. If I lacked movement (and sometimes I can't fit it in) I'll notice it show up in one of these questions, and know how important it is to get it back on track. Final Reflection Remember... Movement is the input. It's what we do. Every step you take sends a message to your brain: "Prepare. Activate. Adapt." The more consistently you send that signal, the more opportunities your brain and body have to change. Over time, those changes become visible. You think more clearly. You learn more effectively. You recover more efficiently. You perform at a higher level. But here's the part I find most fascinating. When I look at the Movement Loop... I don't stop at Performance. I look at the final circle. Confidence. Because confidence is what keeps the loop alive. I'll be the first to say I can't always do this. Something gets in the way. It's not always easy to move. Some days we're busy. Some days we're tired. Some days we simply don't feel like it. But every time you complete the loop, you collect another piece of evidence. "I showed up." "I felt better afterward." "I handled that challenge differently." "I'm becoming stronger than I was yesterday." Those moments become evidence. Evidence builds confidence. And confidence makes it easier to begin again tomorrow. That's how lasting change happens. Not through one workout. Not through one perfect week. But by repeating the loop. Again. And again. And again. Because every movement today is an investment in the person you're becoming tomorrow. Movement is the input. Adaptation is the process. Performance is the result. And confidence is what inspires us to begin again. Clip 2 Summary Brain Activation Creates Readiness to Learn Paul Zientarski explains how Naperville Central High School achieved remarkable academic results through its Zero Hour PE Program and the use of heart rate monitoring. His work around getting students to move was inspired by Dr. Hillman's research. In this clip, Paul Zientarski describes how Dr. Chuck Hillman's research transformed physical education from something schools did for physical health into something they could use to improve learning itself. The research that ignited him showed that after just 20 minutes of walking, students demonstrated measurable increases in brain activation and performed better on cognitive tasks. For Paul, this wasn't simply evidence that exercise works. It explained why movement belongs at the beginning of the school day. Movement wasn't taking time away from learning. It was preparing the brain to learn. Looking at this through the lens of the Movement Loop, we can now see what was happening biologically. Movement activated the brain. An activated brain was better able to pay attention. Better attention improved learning. Repeated learning created adaptation. And over time, those adaptations translated into stronger academic and life performance. That's why movement is the first step in Phase 3. Key Takeaways ✔ 1. Brain activation is the bridge between movement and performance. Movement doesn't improve learning—or performance—directly. It first changes the brain into a state that's ready to focus, solve problems, make decisions, and learn. Whether you're in a classroom, on the athletic field, or leading a team at work, performance begins with brain activation. ✔ 2. Readiness comes before instruction/teaching/learning. One of Paul Zientarski's greatest insights was that not every student needed more movement. Sometimes they needed more recovery. By monitoring students' heart rates, he learned to recognize when a student was physiologically ready to learn and when the brain and body needed time to recover. That's a lesson for all of us. Teachers can observe energy before beginning a lesson. Coaches can balance challenge with recovery. Leaders can recognize when their teams need a reset before asking for peak performance. The question isn't simply, "What am I teaching?" It's, "Are people ready to learn?" ✔ 3. Small actions create lasting adaptation. One walk. One movement break. One active lesson. One walking meeting. None of these seem significant on their own. But repeated consistently, they become the biological signals that drive adaptation. Small actions, repeated often, create lasting change. ✔ 4. Performance begins long before the moment that matters. A student's success doesn't begin when they open the exam. An athlete's success doesn't begin when the whistle blows. A leader's success doesn't begin when the meeting starts. Performance begins much earlier— when we prepare the brain to perform. ✔ 5. Movement isn't a break from work. It's preparation for better work. Whether you're teaching a lesson... Coaching a team... Leading a meeting... Or solving a complex problem... Movement should be viewed as part of the performance process—not time taken away from it. Tips to Implement Prepare the brain before expecting performance. Before teaching... Before coaching... Before presenting... Before solving difficult problems... Ask one question: "Is the brain ready to perform?" Sometimes the best way to improve performance is to begin with five to ten minutes of purposeful movement. Build movement into the rhythm of the day. Instead of viewing movement as a separate activity, make it part of how you work and learn. Teachers can begin lessons with movement. Coaches can use dynamic warm-ups with a cognitive focus. Leaders can replace one seated meeting each week with a walking meeting. Movement becomes the signal that says, "It's time to focus." Balance challenge with recovery. Paul didn't simply encourage students to move more. He also recognized when they needed recovery. High performance isn't created by constant effort. It's created by alternating challenge with recovery. That's true in classrooms. It's true in sports. And it's true in the workplace. Think beyond fitness. Don't ask, "How do I fit exercise into my day?" Instead ask, "How can movement improve everything I do afterward?" That one shift changes movement from another task on your calendar... into the first step toward better thinking, better learning, better leadership, and better performance. Closing Reflection Every time we move... We send a message to the brain. "Prepare. Activate. Adapt." That's why movement isn't just something we do for our bodies. It's one of the most practical performance tools available to teachers, coaches, leaders, parents, and anyone who wants to think more clearly, learn more effectively, and perform at a higher level. Because in the end... Movement is the input. (What we do.) Adaptation is the process. (How the brain and body change.) Performance is the result. (What we're capable of becoming.) Paul Zientarski didn't change physical education at Naperville HS. He changed the purpose of physical education. Instead of asking, "How do we build fitter students?" he helped us ask, "How do we build brains that are ready to learn?" EP 403 Review and Conclusion As we review and close out this episode, I'd like to leave you with one final thought. When we first interviewed Dr. Chuck Hillman, I was fascinated by what exercise did for the brain. When we interviewed Paul Zientarski, I was inspired by how he translated that research into a school that changed lives. But looking back through the lens of Phase 3, I realize they were teaching us something even bigger. They were teaching us where human performance begins. Not with talent. Not with intelligence. Not with motivation. It begins with a biological signal. Movement. Every step you take... Every walk... Every workout... Every hike... Every time you choose to move... You're telling your brain: "Prepare. Activate. Adapt." That's the first instruction your brain receives. From there, everything else becomes possible. Movement activates the brain. An activated brain pays attention. Attention makes learning possible. Learning creates adaptation. Adaptation improves performance. And performance builds confidence. Confidence is what inspires us to move again tomorrow. That's the Movement Loop. A Personal Journey This season has become personal for me. For years, I interviewed experts and shared their research. Over the past year, I began testing these ideas (their research) in my own life. Not to become a better athlete. Not to chase perfect numbers on my wearable device (as much as I would like to have them). But to see whether the neuroscience actually showed up in everyday life. It did. The more consistently I moved... The more consistently I recovered... The more my brain and body adapted. My recovery improved. My resting heart rate dropped. My hikes became easier. My thinking became clearer. Those weren't isolated improvements. They were evidence that the Movement Loop was working. And with every small improvement... my confidence grew. Not because someone told me I was improving. Because I could see it. I could measure it. I could feel it. That confidence became more than the result of movement. It became something I began carrying into other areas of my life. Into my work. Into my relationships. Into the challenges I was willing to take on. And that's when I realized something... Confidence doesn't end the Movement Loop. It becomes the beginning of another part of the Brain's Operating System. But... that's a conversation for another episode.   Your challenge for the week So before our next episode, I'd like to leave you with a simple challenge. Don't focus on changing your life overnight. Just begin the loop. Tomorrow morning... Before work... Before studying... Before an important conversation... Move. Even if it's just for ten or twenty minutes. Then ask yourself one question: How did that movement prepare my brain today? Start paying attention. Notice your focus. Notice your energy. Notice how you think. Because once you begin looking for the changes... You'll start seeing them everywhere. Write down what you notice…because if you don't write things down, you won't remember that the change occurred.   Looking ahead In our next episode, we'll continue exploring Phase 3 with one of the pioneers in this field, Dr. John Ratey, author of Spark. If Dr. Hillman showed us that movement activates the brain... Dr. Ratey helps us understand why exercise has been called "Miracle-Gro for the brain" and how movement literally changes the brain's ability to learn, adapt, and grow. We'll continue building the Movement Loop... one step at a time.   Until next time... Remember... Movement is the input. It's what we do. Adaptation is the process. It's how the brain and body change. Performance is the result. It's the measurable improvement we experience in our thinking, learning, health, leadership, and daily lives. And perhaps most importantly... Confidence is what keeps the loop alive. Because every step you take today is building the brain you'll depend on tomorrow. By the end of this season, my hope is that we don't just understand the neuroscience of human performance—we put it into practice. That we begin moving with purpose. That we notice how our brain and body respond. That we embrace adaptation as part of the process. And that we experience measurable improvements in our thinking, learning, health, leadership, and everyday performance.   I'm Andrea Samadi, and this has been the Neuroscience Meets Social and Emotional Learning Podcast. I'll see you next time.   EP 403: How did movement prepare my brain today? EP 404: How did today's movement help my brain learn? EP 405: How did recovery help my brain adapt? EP 406: How did attention improve my performance? EP 407: How will I keep the loop going?   RESOURCES:   Full interview Dr. Chuck Hillman https://www.youtube.com/watch?v=6ip5s2iDFLE&list=PLb5Z3cA_mnKhiYc5glhacO9k9WTrSgjzW&index=88   Full interview Paul Zientarski https://www.youtube.com/watch?v=UHYNEhxkxfE   Paul Zientarski's clip https://www.youtube.com/shorts/DmtkP_licdA   REFERENCES:   [i] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 402 “Phase 3 Intro: How Movement Builds the Brain https://andreasamadi.podbean.com/e/movement-loop-how-everyday-action-rewires-your-brain-and-boosts-performance/   [ii] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 123 https://andreasamadi.podbean.com/e/northeastern-university-professor-chuck-hillman-phd-on-the-impact-of-exercise-on-the-brain-and-learning/   [iii] Neuroscience Meets Social and Emotional Learning Podcast EPISODE 397 https://andreasamadi.podbean.com/e/move-to-learn-how-movement-activates-the-brain-and-fuels-motivation/                                

Police are asking for the publics help after vandalism on the Cape. A swim in the Charles River? Wildfire smoke disrupts flights at Logan Airport. Stay in "The Loop" with WBZ NewsRadio.See omnystudio.com/listener for privacy information.

A body found at a Mattapan home, owned by Congresswoman Ayanna Pressley's husband. A three-day Festival in Roxbury celebrates one of the city's oldest neighborhoods. The U.S. launches new air strikes overnight in response to the killing of two American Troops at a base in Jordan. Stay in "The Loop" with #iHeartRadio.See omnystudio.com/listener for privacy information.

A body found at a home in Mattapan. President Trump is threatening to punish Canada due to their wildfires. And, starbucks rats. Stay in "The Loop" with #iHeartRadio.See omnystudio.com/listener for privacy information.

Was passiert eigentlich, wenn Mensch und Maschine gemeinsam Entscheidungen treffen sollen und wer trägt am Ende die Verantwortung? Meike spricht mit Dr. Maurice Stenzel vom Alexander von Humboldt Institut für Internet und Gesellschaft über sein Forschungsprojekt „Human in the Loop". Es geht um Automation Bias, die doppelte Unsicherheit zwischen Befolgen und Überschreiben von KI-Empfehlungen, warum das Wegautomatisieren von Juniorjobs ein langfristiges Eigentor ist und was wir von der Luftfahrt über eine offene Fehlerkultur lernen können. Eine Folge für alle, die gerade an Digitalisierungs- oder KI-Projekten arbeiten und wissen wollen, warum es vor allem auf den zugrunde liegenden Prozess ankommt.

Jeff Urso slides back into the chair at Duffer's fresh off opening Hatricks in Mundelein right as the insane July heatwave hit. Half the AC working, belts snapping, fresh air intake fighting for its life — and he still got the place dialed in while running the kitchen and learning new wing tricks from his own chef.From bar ownership war stories (400-degree pizza ovens in 2010, ice makers holding up, bodies pumping out more heat than the equipment) the conversation rolls into what's actually happening in Lake Forest right now: the caucus interviewing mayoral hopefuls, the Block the Box fallout still costing the city, Jed Morris recusal drama, and whether anyone can run without splitting the vote.From there it's everywhere: Lake Forest Day parade route changes, the American Legion endowment debate, craft beer dying while big boys and infusions take over, fantasy football drafts on slow nights, Vegas stadium vibes, and why politicians sound different depending on who's in the room.So is the mayoral field already locked in with two caucus candidates?Will Lake Forest Day finally move to the weekend or stay mid-week for the bars?And who's actually paying attention to the charter, the pensions, and the block parties?We document. You decide.

Another arrest in last month's armed robbery at a children's Lemonade Stand in Southie. Boston Police still investigating a deadly shooting in Allston last night. After a TikTok video of rats running rampant goes viral, a Starbucks has temporarily closed. Stay in "The Loop" with #iHeartRadio.See omnystudio.com/listener for privacy information.

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

U.S. Central Command says two American service members have been killed in Iranian strikes on Jordan. The Ex-Wife of the Ice Agent who shot and killed a man in Maine last week says he has a history of violence and mental health issues and never should have been given a gun and badge. The Starbucks at South Station is now closed after a shocking discovery. Stay in "The Loop" with #iHeartRadio.See omnystudio.com/listener for privacy information.

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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

Chicago gets a one-two punch with another heat wave combined with dangerous air quality. A North Side Trader Joe's becomes the first of the grocery chain's Illinois locations to unionize. And the Shedd Aquarium agrees to take in abandoned beluga whales from a closed marine park in Canada. In the Loop breaks down those stories and more in the Weekly News Recap.GUESTS: Carrie Shepherd, Axios Chicago reporter Quinn Myers, Block Club Chicago reporter covering Wicker Park, West Town & Bucktown Bob Herguth, Chicago Sun-Times investigative reporterFor a full archive of In the Loop interviews, head over to wbez.org/intheloop. 

It’s time for Denver to be the Last Goddamn Dinosaur. Look at being SO goddamn the last dinosaur and shit. He rides a skateboard! He plays the guitar! He has opposable thumbs and may fight Lavos at the end of the adventure! We don’t know! He is simply too radical for us lowly humans. We… Read more S10X:E14 – “Denver, The Last Dinosaur” Opening Theme

Bottleneck Management was founded in 2001 by two college friends, Jason Akemann and Nathan Hilding, when they opened their first restaurant, Trace, in Wrigleyville. Since then, they’ve grown into a multi-brand hospitality company, with restaurants across the Chicago area, including Sweetwater Tavern & Grille on Michigan Ave, South Branch on the Chicago River, City Works, […]

Die Amerikaner, Jackson Suber, loop op vyf onder baansyfer 65 voor na die eerste rondte van die 154ste Ope Kampioenskap op Royal Birkdale in Engeland. Dis die 26-jarige Suber se ope-debuut en eerste besoek aan Europa. Hy is een hou voor Dan Brown van Engeland en die Suid-Koreaan, Im Sung-jae. Die verdedigende kampioen, Scottie Scheffler van Amerika, het geopen met twee onder 68. Rory McIlroy van Noord-Ierland het sy poging vir ʼn sewende major-titel met ʼn teleurstellende twee oor 72 begin. Suber sê hy het die eerste rondte geniet:

Governor Healey backs cheaper ticket prices around the state, the CDC is still following the Cyclospora outbreak here in Massachusetts, and Senate Democrats a new economic development bill. Stay in "The Loop" with WBZ NewsRadio.See omnystudio.com/listener for privacy information.

In this episode, Dr Elle Wadsworth talks to Professor Rasmon Kalayasiri, Head of the Department of Psychiatry at the Faculty of Medicine, Chulalongkorn University, Thailand. The interview covers Rasmon's editorial that asks the question: Can Thailand replace a commercialised cannabis market for adult use with a medical prescription model? Thailand's cannabis policy [01:15]Thailand's legal non-medical market between 2022 and 2025 [03:12]The non-medical market after the policy reversal in 2025 [04:50] Reasons for the reversal of non-medical cannabis policy [06:25]The role of the research community [07:58]The key data gaps in cannabis research in Thailand [09:19]Whether Thailand can replace a commercialised cannabis market for adult use with a medical prescription model [11:11]The drafting of Thailand's cannabis law [13:25]The take home messages [14:25]The lessons to learn from Thailand's experience [16:27]About Elle Wadsworth: Elle is an academic fellow with the Society for the Study of Addiction. She is based at the University of Bath with the Addiction and Mental Health Group, and her research interests include drug policy, cannabis legalisation, and public health. Elle holds a voluntary role at The Loop, a non-profit service provider of drug checking in the UK. About Rasmon Kalayasiri: Rasmon is the Head of the Department of Psychiatry at the Faculty of Medicine, Chulalongkorn University, in Bangkok, Thailand. She also serves as the Director of the Centre for Addiction Studies (CADS) and the Alcohol Helpline (1413), both supported by the Thai Health Promotion Foundation (ThaiHealth). In addition, she is the Chair of the Board of Examiners for the Addiction Psychiatry Training Program of the Royal College of Psychiatrists of Thailand.Declarations of interest: None Original editorial: Can Thailand replace a commercialised cannabis market for adult use with a medical prescription model? https://doi.org/10.1111/add.70499The opinions expressed in this podcast reflect the views of the host and interviewees and do not necessarily represent the opinions or official positions of the SSA or Addiction journal.The SSA does not endorse or guarantee the accuracy of the information in external sources or links and accepts no responsibility or liability for any consequences arising from the use of such information.Music by Jack Shakespeare. Hosted on Acast. See acast.com/privacy for more information.

Chicago Mayor Brandon Johnson is once again in the hot seat, answering our questions – and yours – during our monthly public forum “Ask The Mayor.” For a full archive of In the Loop interviews, head over to wbez.org/intheloop. 

Seth, Sean and Cole dive into takeaways from the Lance McCullers trade, assess how he stacks up among all-time Astros pitchers, talk about an elderly Florida lady's disgruntlement with her license plate, if a good run will propel the Texans to the Super Bowl, and see what Reggie and Lopez have coming up on In the Loop.

Sean's back. Seth and Sean, along with Cole Thompson, talk about the cringeworthy ESPYs, Lance McCullers being traded to the Brewers, if the Texans' offensive line has improved or is just older and costs more, go through the day's Headlines, discuss it seeming like the AFC South's "stock" is at an all-time high, dive into some fun stories they missed, why Tari Eason is starting to get on their nerves, accept the reality that CJ Stroud very likely won't make the NFL Top 100, assess an anonymous executive's notion that Calen Bullock is a top safety in the NFL, if the Texans should look at bringing back Stefon Diggs, dive into takeaways from the Lance McCullers trade, assess how he stacks up among all-time Astros pitchers, talk about an elderly Florida lady's disgruntlement with her license plate, if a good run will propel the Texans to the Super Bowl, and see what Reggie and Lopez have coming up on In the Loop.

Send us Fan MailSummer Vibes & Strip SpotsCamp Palms: The ultimate adult-only summer hangout is happening right now at Palms Casino Resort! Catch the camp-themed poolside fun before it packs up on July 31st.Palms Retail Find: Next to check-in, check out the Palms Store. They are fully stocked with high-quality sports gear—everything from UNLV, Dodgers, A's, and Raiders apparel. It's a little on the pricey side, but the quality of the hats, shirts, and hoodies is top-tier if you're looking for great Vegas fan gear.Moving Around TownVegas Loop Airport Expansion: The Loop is heading to the airport! We are going to test it out firsthand very soon and report back on how it handles the trek.Rideshare Reality: With Uber and Taxi prices continuing to tick upward, alternative transit options like the Loop are quickly becoming a must-watch.Big Venue Updates & RefreshesSkyfall Panoramic Bar & Lounge (W Las Vegas): One of the best views of the Strip is getting a makeover. Skyfall will temporarily close from July 20th through October 4th for a complete, contemporary redesign to align closer with the global W Hotels look. Mark your calendars for an early October return! Miss Loretta's Coming to Resorts World: A brand new country soul destination is opening this fall. Details are being kept under tight wraps by "Miss Loretta" herself, but expect handcrafted cocktails, dancing, plenty of rhinestones, and plenty of late-night energy.Big Headliners & Ticket DropsRicky Martin at Fontainebleau: Tickets are officially on sale! Catch the superstar live at the BleauLive Theater inside Fontainebleau Las Vegas on Friday, Oct. 30 and Saturday, Oct. 31 at 8:00 PM.Backstreet Boys Headline Official F1 Afterparty: Formula 1 weekend just got a major pop twist. Vibee announced the Backstreet Boys will headline the official post-race show at Sphere on November 21st. Note: Tickets are exclusively available to Formula 1 Heineken Las Vegas Grand Prix ticket holders or via Vibee experience packages.Neighborhood Spotlight: The Crown & Anchor Address is Back!The beloved former Crown & Anchor space at 1350 E. Tropicana Ave. has officially evolved into The Irish Spot. Managed by Kelcie Alder, the neighborhood venue keeps its strong local sports identity (including European football, hurling, and Gaelic football) with a fresh, community-focused Irish pub vibe.On the Menu: High-quality comfort food under $20, including Beer-Battered Fish & Chips ($19) and traditional Bangers & Mash ($16).Entertainment & Celebrity NewsNBA Summer League: Vegas is absolutely packed this week with basketball elite. Keep your eyes peeled—player sightings are happening all over the valley.Emmy Love for Vegas Filmed Shows: Big shoutout to the television productions filmed right here in Las Vegas pulling in major Emmy nominations, including Hacks, Margo's Got Money Troubles, and Pluburis!

What if the greatest gift we could offer someone wasn't fixing their challenges—but helping them discover new possibilities? In this inspiring episode of The Motivatarian Exchange, Dionne Woods sits down with occupational therapist, author, speaker, and founder of Pam's Den of Creative Fun, Pamela Fox Denzler, to explore what it truly means to help people of all abilities thrive. Pam has spent more than 40 years supporting adults with developmental disabilities while empowering caregivers, activity professionals, educators, organizations, and families with practical, creative strategies that encourage participation, confidence, communication, and joy. Known as a Possibility Architect, Pam shares how her personal journey—including growing up as a twin alongside her sister Paula, who had cerebral palsy—shaped her lifelong mission of helping people discover purpose, adapt creatively, and recognize that every individual has unique gifts to offer. Together, Dionne and Pam explore: • The Possibility Loop and how it helps us move forward when we feel stuck • Why every person deserves to be seen, heard, and valued • Creative wellness and the power of meaningful participation • Helping caregivers find practical, low-cost solutions • Adaptability as a pathway to confidence • Occupational therapy beyond traditional healthcare • Quieting your mind through creativity and intentional pauses • The importance of inclusion, encouragement, and human connection • Why creativity is one of the greatest tools for transformation Whether you're a caregiver, occupational therapist, educator, parent, creative entrepreneur, or someone searching for renewed purpose, this conversation will leave you inspired to see new possibilities—in yourself and in others. Learn more about Pamela Fox Denzler Website Pam's Den of Creative Fun Facebook Pam's Den of Creative Fun Instagram Pamela Fox Denzler (@pamsdenofcreativefun) • Instagram profile YouTube Pam's Den of Creative Fun If this conversation encouraged you, please subscribe, share this episode with someone who needs it, and leave a comment sharing one possibility you see differently today. ** Fun fact! Pam is a twin, and she married a twin! #OccupationalTherapy #Caregivers #SpecialNeeds #CreativeWellness #Inclusion #CreativeActivities #DisabilityAwareness #MotivatarianExchange #PamelaFoxDenzler #CreativeLiving

This episode features Ed Cohen and Mike Friedberg from Smart Start MN, The Loop's Kevin Cusick, The Sports Professor Rick Horrow, Erin Wondra, Rod from Tech Warrior, and psychic Ruth Lordan.

President Trump gets set to address the nation, The Feds will be casting a close eye on Mass election day, A surprise discovery in the waters off Winthrop. Stay in "The Loop" with WBZ Newsradio.See omnystudio.com/listener for privacy information.

Today, we are dropping our final episode in our series The AI Control Loop, How enterprises govern the AI they've already deployed - sponsored by our friends at Wallarm.Wallarm is the AI Control Platform for Enterprise AI, protecting every AI workload, API, and application in production, giving CISOs the governance they need and CIOs the speed they demand. Organizations choose Wallarm for a complete inventory of APIs, AI agents, and AI apps, patented AI/ML-based threat detection and blocking that operates at production traffic speeds.In our final episode, we are joined by Shayne Higdon, Wallarm CEO, who closes the series by examining what the accountability moment demands from enterprise leaders, what a mature AI governance model needs to prove rather than promise, and what the next 12 to 24 months look like for organizations that get this right.QuestionsWhy is now the accountability moment for enterprise AI?What has changed between the early days of AI experimentation and today's enterprise AI deployments that makes accountability such a pressing issue?When we talk about AI accountability, what does that actually mean in practical terms? Are we talking about visibility, auditability, enforcement, ownership—or all of the above?As organizations race to deploy AI, how should CIOs balance the speed of transformation with the responsibility to govern it effectively?Why are traditional governance and security models struggling to keep pace with the way AI is being adopted across the enterprise?Given those challenges, how should boards and executive teams evaluate whether their organizations are truly ready to scale AI safely and responsibly?And once an organization believes it's ready, what does a mature AI governance model actually need to prove - not just promise?From an operational standpoint, how do capabilities like discovery, runtime monitoring, and enforcement come together to create a closed-loop approach to AI accountability?Stepping back and looking across this entire conversation, what's the one mindset shift every enterprise leader needs to make when it comes to AI security and accountability?And finally, as listeners think about what's ahead, what should they expect the future of AI security and accountability to look like over the next 6, 12, or even 24 months?Linkshttps://www.wallarm.com/https://www.linkedin.com/in/shaynehigdon/Full AbstractAbstract: Join Shayne Higdon, Wallarm CEO, for this episode, which closes the series by examining what the accountability moment demands from enterprise leaders, what a mature AI governance model needs to prove rather than promise, and what the next 12 to 24 months look like for organizations that get this right.AI deployment is not waiting for governance to catch up. Across most enterprises, the gap between how fast AI is being adopted and how well it is being governed is widening every quarter. CIOs and CISOs are not debating whether to govern AI. They are trying to figure out how, under real organizational pressure, with tools and frameworks that were built for a different threat model.That pressure is coming from every direction at once. Boards want AI transformation to move fast. Regulators want documented evidence that it is under control. Security teams want runtime visibility and enforcement capabilities that most of their current tools do not provide. And the AI systems themselves are not waiting: they are accessing data, calling external services, and making decisions continuously, in ways that after-the-fact governance cannot meaningfully constrain.This is the accountability moment. Not because the risk is new, but because the consequences of undermanaged AI are now concrete enough to land on a board agenda, an audit report, and a regulatory deadline at the same time. What accountability actually requires in practice is the full AI control loop: knowing what AI is running across the enterprise, seeing what it is doing at runtime, enforcing policy before damage compounds, and generating continuous evidence that the governance is real and not retroactive. Organizations that can demonstrate all four are in a fundamentally different position than those still assembling audit evidence from spreadsheets the week before a review.Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://cash.app* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Weight-loss drugs like Ozempic and Wegovy are one of the fastest growing pharmaceuticals. But as newer GLP-1 medications hit the market, questions remain about side effects, health equity and the cultural impact. On today's In the Loop, we ask: what have we learned so far about the popular drug? And how have GLP-1s changed the way we think about health and weight loss?GUESTS: Dr. Micah Eimer, cardiologist, clinical assistant professor of medicine in the division of cardiology at Northwestern University Feinberg SchoolDavid D. Kim, PhD, assistant professor of medicine and public health sciences at University of ChicagoRachelle Lacroix Mallick, registered dietician and nutritionist with a focus on reproductive healthFor a full archive of In the Loop interviews, head over to wbez.org/intheloop.

​We discussed a few things including: 1. Tom's entrepreneurial journey 2. The 25 year TerraCycle growth story 3. Lessons Learned 4. Future vision for the company 5. Outlook for the recyclables industry Tom Szaky is founder and CEO of TerraCycle, an international leader in recycling, recycled content, and reuse. TerraCycle operates in 18 countries, working with some of the world's largest brands, retailers, and other stakeholders to create national platforms to recycle products and packaging that otherwise go to landfill or incineration. Szaky and TerraCycle also created Loop, the circular reuse platform that enables consumers to purchase products in reusable packaging. Tom and TerraCycle have received hundreds of awards and recognition from organizations including the United Nations, World Economic Forum, Fortune Magazine and U.S. Chamber of Commerce.  Tom is the author of four books, Revolution in a Bottle, Outsmart Waste, Make Garbage Great and The Future of Packaging. #podcast #AFewThingsPodcast

The obsession with content creation and a marketing-first emphasis as an artist creates pressure and an incentive structure that leads artists away from themselves.Birocratic reminds us of the truth: There is no better marketing plan than making good music. Nothing will endear you to potential fans more than that.It's so simple and easy to forget this in the noisy world of content. If art is the artist's product, then the rule of marketing is no different than for any other business. If the product isn't of quality, no amount of marketing will develop a real relationship. Best case scenario, you sell one product, get one listen. That person's trust is lost forever because of a hollow promise.Music is not content. Content is a container for your music. If you want to be an artist, yes you have to share your music, but you do not have to become a content creator. In the same way your art develops over time, so will the way that you deliver it.If you want to make music privately, you are not any less of an artist.If you want to be a content creator who makes music, go right ahead.If you want to build an audience, how will anyone discover your music if it isn't consistently shared?If you want to be an artist, put your message in a bottle and send it out to sea. Over and over again. With practice, you will learn to adapt to the ever changing movement of the water.Subscribe for free and immediately receive Sonic Stimulus Vol. 1 (a sample pack), Invisible Instruments, and access to The Pocket (in-studio production videos from guests). Get full access to ProducerHead at producerhead.substack.com/subscribe

Are we really trapped in a permanent political doom loop, or is the two-party duopoly actually more vulnerable than it looks? We often look at heavily gerrymandered, "safe" districts and hyper-partisan outrage and assume nothing can change until we get massive, far-off structural reform. But what if the major parties have aggressively optimized the system for a zero-sum, two-player game—and that very optimization has left them completely exposed to independent challenges? This week, David sits down with political campaign consultant Nathan Smolensky to break down the raw, data-driven mechanics of the independent landscape. Looking past the national presidential spectacle, Nathan explains why local and state races are a completely different playground for independent success, how candidates can flip the "spoiler effect" narrative on its head, and how scalable new campaign technologies are dismantling the traditional fundraising barrier to entry.Text me your feedback and leave your contact info if you'd like a reply (this is a one-way text). Thanks, DavidSupport the showShow Notes:https://outrageoverload.net/ Contact me, David Beckemeyer by email outrageoverload@gmail.com. Follow the show on Instagram @OutrageOverload. We are also on Facebook /OutrageOverload. Check out our Subtstack https://outrageoverload.substack.comHOTLINE: 925-552-7885Got a Question, comment or just thoughts you'd like to share? Call the O2 hotline and leave a message and you could be featured in an upcoming episodeIf you would like to help the show, you can contribute here. Tell everyone you know about the show. That's the best way to support it.Rate and Review the show on Podchaser: https://www.podchaser.com/OutrageOverloadAlso check out our companion podcasts, This Week in Outrage and Outrage Science Bites.Intro music and outro music by Michael Ramir C.Many thanks to my co-editor and co-director, Austin Chen.Outrage Overload, a Conners Institute podcast, is part of The Democr...

This article is for educational purposes only and is not a substitute for individualized medical advice. Always talk to your own healthcare provider before changing your diet, supplements, or medications.Unlocking the Secrets of Ferritin: What Your Iron Levels Are Telling YouYour “normal” bloodwork might be hiding the real reason you're exhausted, foggy, and losing hairTL;DR: * Ferritin is your iron savings account — and most labs only flag it as “abnormal” once it's nearly empty. * A level of 14 or 22 ng/mL might get a “you're fine” from your doctor, but optimal energy, mood, cognition, and hair growth usually need ferritin closer to 70–100 ng/mL. * Low ferritin can come from menstrual blood loss, poor absorption (celiac disease, low stomach acid, H. pylori), or inflammation-driven hepcidin blocking iron uptake.* If you're fatigued, foggy, cold, or shedding hair, ask for a full iron panel — not just a ferritin number — and talk through the results with your doctor.There's an old Japanese proverb: “When the body speaks, the wise person listens. When the body whispers, the fool waits for it to scream.” In health diagnostics, one of the quietest whispers is your ferritin level. It's often overlooked, yet it can be the missing link behind exhaustion, hair loss, brain fog, or the frustrating experience of bloodwork that comes back “normal” while you still feel terrible.What Is Ferritin?Ferritin is your body's iron storage protein. Think of your iron levels like a financial setup: hemoglobin is your checking account, drawn on daily. Ferritin is your savings account, tapped only when things get tight. Under stress, your body will drain the savings account long before it lets the checking account — hemoglobin — run low. That's why you can have “normal” hemoglobin and still be iron-depleted. A low ferritin level means your reserves are running out, and that shows up as fatigue, brain fog, mood changes, and thinning hair.Normal vs. OptimalMost labs flag ferritin as “normal” above roughly 10–20 ng/mL. That threshold mostly means you're not in immediate danger — not that you're thriving. Levels associated with feeling genuinely well tend to run from 70 to 100 ng/mL. So if you've been told your ferritin of 14 or 22 is fine, but you still feel wiped out, you're not imagining it — you're just being measured against a bar set for avoiding crisis, not for feeling good.Why Your Ferritin Might Be Low* Menstrual blood loss. For many women, the cumulative loss over months and years outpaces dietary iron intake, slowly draining reserves.* Absorption issues. Even a solid iron intake doesn't help if it isn't absorbed. Silent celiac disease, low stomach acid, or an H. pylori infection can quietly block uptake for years.* Inhibitors and hepcidin. Coffee, tea, and dairy consumed close to meals can inhibit iron absorption. Separately, inflammation can push your liver to produce hepcidin, a hormone that shuts down iron uptake even when you're eating enough.Symptoms to WatchPersistent fatigue, thinning hair, feeling cold more easily than others, and brain fog are the classic signs. If two or more of these sound familiar, it's worth getting your ferritin checked specifically — not just assumed to be fine because your CBC looked normal.The Bigger PictureIron does far more than carry oxygen. It's involved in thyroid hormone conversion, dopamine production, mitochondrial energy synthesis, and hair follicle health. That means low ferritin can produce symptoms that look a lot like depression or hypothyroidism — even when your thyroid panel and mood screening come back clean.Taking Action* Review your bloodwork. Look specifically at ferritin. Anything under 70 ng/mL is worth a conversation with your doctor.* Ask for a full iron panel. Ferritin alone isn't the whole story — request serum iron, total iron binding capacity (TIBC), transferrin saturation, and CRP (to rule out inflammation skewing the picture).* Adjust absorption habits. Space coffee and tea away from meals, lean into iron-rich foods, and avoid taking calcium and iron supplements together.* Choose the right supplement, if needed. Ferrous sulfate is harsh on the gut for many people. Iron bisglycinate is gentler and pairs well with vitamin C for better absorption — but check with your provider before starting, especially if high ferritin is a concern.* Loop in a professional. This is especially important before making changes if you suspect elevated ferritin, since iron overload carries its own risks.Listening to Your Body's WhisperYour body is constantly sending signals. Ignored long enough, whispers become screams. Taking ferritin seriously — not just as a checkbox on a lab report, but as a meaningful marker — is one concrete way to catch a problem while it's still easy to fix.Final ThoughtsMonitoring and optimizing ferritin can meaningfully change how you feel day to day. It starts with a simple ask: get the right test, read the number in context, and act on what it's telling you. If you want a more personalized look at your own levels and symptoms, consider scheduling a comprehensive health session.Stay informed, stay healthy, and listen closely to what your body is telling you. Until next time, take care.References* Camaschella, C. (2015). Iron-deficiency anemia. New England Journal of Medicine.* WHO guidance on serum ferritin concentrations for the assessment of iron status.* Clinical literature on ferritin thresholds and symptomatic iron deficiency without anemia.* Hepcidin and inflammation's role in iron regulation — recent reviews in Blood and Haematologica. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit tripleplaydoc.substack.com/subscribe

In this eye-opening episode, Dr. Scott Watier and Tommy Welling unpack the cortisol-dopamine-insulin loop — the hidden neurochemical cycle that drives cravings, emotional eating, and the "one bad decision snowball" that so many fasters experience. They explain why this isn't a willpower problem but a hormonal and behavioral feedback loop rooted in stress, sleep deprivation, and the brain's hardwired search for quick energy and dopamine reward. The hosts walk through each stage of the loop and share practical pattern interrupts — from breathwork and environment changes to proper mineral and electrolyte support — that can break the cycle before it restarts. A key insight from the episode is the cortisol-sodium connection, and how simply prioritizing proper hydration with unrefined salts first thing in the morning can calm the entire stress response system and make fasting feel dramatically easier throughout the day. The conversation wraps with a reminder that protecting sleep, eating earlier in the day, and building consistent fasting windows are the long-game levers that finally let your physiology work for you instead of against you — and this week's action step is to identify where you tend to enter the loop and apply just one targeted tool to interrupt it. ⁠⁠⁠Take the NEW FASTING PERSONA QUIZ! - The Key to Unlocking Sustainable Weight Loss With Fasting!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Resources and Downloads: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠SIGN UP FOR THE DROP OF THE ULTIMATE GUIDE TO BLOOD SUGAR CONTROL⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠GRAB THE OPTIMAL RANGES FOR LAB WORK HERE! - NEW RESOURCE! ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠FREE RESOURCE - DOWNLOAD THE NEW BLUEPRINT TO FASTING FOR FAT LOSS!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠SLEEP GUIDE DIRECT DOWNLOAD⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠DOWNLOAD THE FASTING TRANSFORMATION JOURNAL HERE!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Partner Links: Get your⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ FREE BOX OF LMNT⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ hydration support for the perfect electrolyte balance for your fasting lifestyle with your first purchase⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ here!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Get ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠25% off a Keto-Mojo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ blood glucose and ketone monitor (discount shown at checkout)! ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Click here!⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Our Community: Let's continue the conversation. Click the link below to JOIN the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Fasting For Life Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, a group of like-minded, new, and experienced fasters! The first two rules of fasting need not apply! If you enjoy the podcast, please tap the stars below and consider leaving a short review on Apple Podcasts/iTunes. It takes less than 60 seconds, and it helps bring you the best original content each week. We also enjoy reading them! Article Link: https://drjockers.com/electrolytes/

What would you do if you noticed a piano sitting outside your favorite cafe, or walked past someone tickling the ivories as you left the grocery store? John McCarthy, founder of the nonprofit Chicago Plays, is betting people are likely to come together as a community alongside an impromptu and free public performance. Chicago Plays is spicing up the city's public spaces with free piano concerts courtesy of your own neighbors. In the Loop sits with the man behind the music. GUESTS: John McCarthy, founder, Chicago PlaysWon Kim, artist and chefFor a full archive of In the Loop interviews, head over to wbez.org/intheloop.

As AI systems start acting on their own, the hardest security problem is no longer approving what a model outputs but governing the conditions under which it is allowed to act at all. Michael Chavira, co-founder of Axiologic Solutions, argues that most organizations confuse governance with security, writing policies and standing up AI councils that describe intent while leaving the enforcement layer, where a model actually touches data, underbuilt. His answer is to move humans out of the approve-every-action role and onto the loop, where they define the boundaries an agentic system runs inside and watch for it to step out.  That control is only as good as the data underneath it, and for most enterprises, the data is a weak point. Chavira makes the case that AI security is ultimately data security, since a model is only as trustworthy as the data feeding it, and he walks through why shadow AI, unlabeled data and untracked workarounds quietly undermine even well-governed systems. For tools that answer the same question differently each time, he recommends harness engineering and continuous data operations as the way to secure the system rather than chase every output. For links and resources discussed in this episode, please visit our show notes at https://www.forcepoint.com/resources/podcast/agentic-ai-humans-on-the-loop

Plus: combatting loneliness, turning down the volume on social comparison, and why mattering to a few people beats outperforming everyone. Gordon Flett is a Professor in the Department of Psychology at York University where he has served as the Director of the LaMarsh Centre for Child and Youth Research. Dr. Flett is most known for his influential research on perfectionism in health and mental health and his more recent work on the psychology of mattering.  In this episode we talk about: The difference between perfectionism and healthy striving How perfectionism functions as a coping mechanism for unmet needs for love, belonging, and significance What "mattering" actually means Antimattering (and our fear of not mattering) Why feeling like you matter to others can loosen the grip of perfectionism Why people consistently underestimate how much they matter to others Practical ways to help others feel like they matter Why helping others feel like they matter boomerangs back and boosts your own sense of significance The fear of not mattering in the age of AI Get the 10% with Dan Harris app here Sign up for Dan's free newsletter here Follow Dan on social: Instagram, TikTok Subscribe to our YouTube Channel Additional Resources:  Gordon's books: The Psychology of Mattering, Mattering as a Core Need in Children and Adolescents, and Perfectionism: Theory, Research, and Treatment Join Dan, Sebene Selassie, and Jeff Warren for Meditation Party, a 3-day immersive retreat at the Omega Institute in Rhinebeck, NY, October 16–18. Grab your in-person spot here, or sign up to livestream here! This episode is sponsored by: Quo – Try Quo for free, plus get 20% off your first six months when you go to quo.com/happier.  BetterHelp — Online therapy, matched to your needs. Get 10% off your first month at https://www.betterhelp.com/happier Warby Parker — Prescription glasses with virtual try-on. Buy one prescription pair and get 20% off additional prescription pairs at https://www.warbyparker.com/happier IQBAR:   To get twenty percent off all IQBAR products, including the ultimate sampler pack, plus free shipping, text DAN to 64000. To advertise on the show, contact sales@advertisecast.com or visit https://advertising.libsyn.com/10HappierwithDanHarris

In this enlightening episode of the "Transform Your Mind" podcast, host Myrna Young is joined by Dr. Hector Rodriguez, a renowned expert in brain science and emotional healing. Through their engaging dialogue, they explore the intricate workings of the brain in response to trauma and stress, revealing that trauma rewires the brain but can also be reversed. This episode serves as a guide to understanding how our brains are shaped by experiences and how they can be retrained for resilience and growth.Dr. Hector dives deep into brain patterns associated with trauma, explaining how our reactions are not just psychological but deeply rooted in neurobiology. Highlighting compelling insights into the survival mechanisms of the brain, he discusses how emotional resilience can be cultivated. With thought-provoking discussions around spirituality and faith, Dr. Hector illustrates how these elements aid in healing trauma, offering listeners a multidimensional perspective on emotional recovery. This episode is packed with thoughtful expertise and transformative ideas for anyone interested in personal growth and mental wellness.Key Takeaways:Trauma impacts brain wiring, but healing is possible through understanding and retraining brain patterns.Both major and minor life stressors affect the brain similarly, creating survival patterns that can hinder emotional well-being.Visualization techniques using SPECT imaging reveal that emotional trauma is a biological process affecting various brain functions, not a moral failing.Spirituality and belief in a higher power play a critical role in sustaining hope and resilience throughout the healing process.Self-awareness and understanding of personal behavior patterns are vital steps towards healing and disentangling from trauma-induced loops.Resources:Dr. Hector Rodriguez's Website: whitebutterflyclinic.comSponsor For this Episode Get started with Square and build a setup that works the way you do. Right now, listeners can get up to $200 off Square hardware when you sign up at square.com/go/transform Link to the transcript https://www.buzzsprout.com/1761155/19471788-breaking-the-loop-how-trauma-rewires-the-brain-and-how-to-heal/transcriptSee this video on The Transform Your Mind YouTube Channel https://www.youtube.com/@MyhelpsUs/videosTo see a transcripts of this audio as well as links to all the advertisers on the show page https://myhelps.us/Follow Transform Your Mind on Instagram https://www.instagram.com/myrnamyoung/Follow Transform Your mind on Facebookhttps://www.facebook.com/profile.php?id=100063738390977Please leave a rating and review on iTunes https://podcasts.apple.com/us/podcast/transform-your-mind/id1144973094Feedspot Top 100 Mental Health Podcast For sponsored Brand interviews and sponsorship inquires please visit Partner With The Transform Your Mind Podcast | Myrna Young Life Coach

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Economist Arpit Gupta joins Aaron to break down the future of American cities after remote work, office real estate collapse, and the rise of AI.From the “Urban Doom Loop” — where falling commercial property values crush city tax revenue and trigger a vicious spiral — to surging suburban housing prices, office-to-residential conversions, and the transformative potential of autonomous vehicles, this conversation covers the biggest forces reshaping urban America.Will AI supercharge white-collar job growth and rescue downtowns, or accelerate the displacement of routine office work? Are superstar cities like New York and San Francisco pulling ahead while second-tier cities struggle? And how will self-driving cars radically expand commuting patterns and city sprawl?CHAPTERS:(00:00 – Introduction & The Urban Doom Loop Explained)(03:20 – Post-COVID Office Market Recovery (or Lack Thereof))(07:45 – Why Housing Prices Are Soaring While Offices Collapse)(12:10 – Remote Work's Lasting Impact on City Centers)(15:30 – AI's Dual Effect on White-Collar Jobs & Office Demand)(22:40 – Can Tourism or “Playground Cities” Replace Commerce?)(26:15 – Agglomeration Effects in the Age of Zoom & AI)(31:40 – Advice for Young People Entering the Job Market)(36:50 – The Game-Changer: Autonomous Vehicles & the Future of Sprawl)(43:20 – Will Most Cities Decline or Adapt?)ARPIT GUPTA LINKS:

Illinois residents have lost nearly $8 billion gambling last year, according to a new report from the Illinois Answers Project. And for many, gambling is not easy to resist, thanks to the increase of slot machines in everyday establishments, new casinos, and mobile sports betting apps that puts the casino in people's pockets. In fact, more than one million Illinois adults either have a gambling addiction or are at risk of developing one.Who is losing the most money, and what is the state doing to help people at risk of addiction? In The Loop finds out more about what guardrails exist – or don't – when it comes to gambling in Illinois, and what state lawmakers are doing about it. GUESTS: Casey Toner, reporter, Illinois Answers Elizabeth Thielen, senior director at Nicasa Behavioral Health ServicesFor a full archive of In the Loop interviews, head over to wbez.org/intheloop.