Podcasts about Lab

The New Capital Punishment Pt 2: Proactive Solutions For Public Safety With at least five people dying in state custody each day, relying on police to handle every non-violent crisis often leads to tragic, avoidable outcomes instead of care. Our experts advocate for a fundamental shift in emergency response, exploring how dispatching mental health and healthcare professionals to non-violent crises can save lives and redefine what it means to feel safe. Guests: Terence Keel, professor of human biology & society, professor of African American studies, UCLA, author, The Coroner's Silence, Founding Director, Lab for BioCritical Studies Host and Producer: Kristen Farrah   How Music Is Helping Veterans Heal For veterans battling the burden of PTSD, traditional therapies aren't always enough to heal the invisible wounds of war. Our experts highlight the impact of the "Guitars for Vets" program, an organization dedicated to helping service members find relief through the power of music. This unique initiative provides struggling heroes with a creative coping mechanism, a supportive community, and a peaceful path toward recovery. Guests:  Brian Kane, development director, Guitars for Vets Nathan LeCompte, interim executive director & director of operations, Guitars for Vets Host: Greg Johnson Producer: Kristen Farrah Facebook: ingoodhealthpodX: @ ingoodhealthpodIG: @ingoodhealthpodYouTube: @ingoodhealthpodSpotify Apple Podcast In Good Health PodcastSubscribed to the newsletterFull ArchiveContact UsBecome an Affiliate Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In the face of church factions, Paul exhorts members of Christ's body to focus on the unique work Jesus has done through his death and resurrection.

>p>Broadcast from KSQD, Santa Cruz on 7-30-2026: A type of coffee brewed from beans that have been altered by passage through the digestive track of civet cats is a delicacy in Southeast Asia. A Scientific Reports paper using gas chromatography analyzed Kopi Luwak (civet-processed coffee beans) versus fresh-picked beans, finding elevated caprylic and capric acids that carry the characteristic flavor notes found in dairy products. Dr. Manuel Esteller sampled and tested the blood, saliva, urine, and stool of the oldest human Maria Branyas Morera, who died last year at 117. Despite exceptionally short telomeres, she carried anti-inflammatory genetic variants seen in long-lived dogs, worms, and flies, and had unusually high Bifidobacterium levels—likely boosted by her three-daily-servings-of-yogurt habit. Dr. Dawn defends sunscreen use against Environmental Working Group scare campaigns, noting that concerns about absorption of sunscreen ingredients into blood remain theoretical while sunburn's melanoma link is well-established. She argues that a badly sunburned toddler starts a clock that can produce melanoma by the late teens. The FDA advisory committee is reviewing certain short amino acid chains popularly known as "peptides" for potential compounding-pharmacy production, covering proposed to treat ulcerative colitis, wound healing, insomnia, insulin resistance, migraines, and osteoporosis. Dr. Dawn flags that five to seven committee members have industry conflicts of interest, but argues that even flawed approval is preferable to the current gray market, where analysis shows vials contain only 4-28% of labeled content along with endotoxin and toluene contamination. She proposes surveillance tracking of prescribed peptides to catch adverse effects early, citing a foreign melanocortin nasal spray tanning product that caused rare nasal melanomas as a cautionary example. Utah State biochemists working with the Cas12a2 CRISPR enzyme discovered that instead of behaving as a precise gene-editor like Cas9, it goes into an indiscriminate DNA-shredding mode after recognizing its target RNA. By programming it to recognize RNA sequences uniquely activated in cancer cells (embryonic-development genes that shouldn't be running in adults), researchers destroyed only the cancer cells in tissue culture, potentially offering a way to eliminate small metastases without healthy tissue toxicity. Cancer patients who received a COVID-19 mRNA vaccine within 100 days of starting immune checkpoint inhibitor therapy showed dramatically improved outcomes, with median survival in advanced lung cancer rising from 20 to 37 months. Non-mRNA vaccines (flu, pneumonia) showed no such benefit. Lab work suggests the mRNA triggers type-1 interferon release that activates tumor-infiltrating immune cells and drives them to lymph nodes to train other immune cells against the tumor. Researchers redesigned a CD40 agonist antibody to bind multiple receptors simultaneously by clustering with a second antibody, stretching the cell surface and triggering a powerful immune response. In a 12-patient trial, injecting one tumor caused all tumors to shrink in six patients and produced complete remission in two—including a melanoma patient with dozens of leg tumors and a metastatic breast cancer patient whose lung and liver tumors resolved after skin injection. Two small trials of CAR natural killer cells—engineered like CAR T-cells but derived from donor umbilical cord blood and thus potentially available off-the-shelf—showed remarkable results in autoimmune disease. All 27 systemic lupus patients targeting the CD19 protein on autoantibody-producing cells showed improvement, with some remaining in remission at nearly two years. A single Shanghai patient with systemic sclerosis showed restoration of normal skin and blood vessel structure. A paradox has emerged in advanced prostate cancer: while blocking testosterone halts early tumor growth, cancer cells eventually adapt to low-androgen conditions such that flooding tissues with testosterone in advanced disease can actually halt tumor progression by triggering cellular redifferentiation. Separately, Dr. Dawn reviews conflicting evidence on Parkinson's risk from androgen deprivation therapy, singling out enzalutamide (Xtandi) as the most concerning drug due to blood-brain barrier penetration, and recommending darolutamide as the safest alternative with matching prostate efficacy but minimal CNS entry.

The New Capital Punishment Pt 2: Proactive Solutions For Public Safety With at least five people dying in state custody each day, relying on police to handle every non-violent crisis often leads to tragic, avoidable outcomes instead of care. Our experts advocate for a fundamental shift in emergency response, exploring how dispatching mental health and healthcare professionals to non-violent crises can save lives and redefine what it means to feel safe. Guest: Terence Keel, professor of human biology & society, professor of African American studies, UCLA, author, The Coroner's Silence, Founding Director, Lab for BioCritical Studies Host and Producer: Kristen Farrah Facebook: ingoodhealthpodX: @ ingoodhealthpodIG: @ingoodhealthpodYouTube: @ingoodhealthpodSpotify Apple Podcast In Good Health PodcastSubscribed to the newsletterFull ArchiveContact UsBecome an Affiliate Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

United missed a chance to play in Birmingham last week, instead opting to avoid an impromptu rafting trip. This week, they're back at The Lab for the first time in nearly a calendar month. Apologies for the audio, we're not sure what happened this week and are working to resolve it. Find out more at https://somosmas.pinecast.co

When no one seems to be listening, how far do you go to be heard? In December 2008, George W. Bush made a surprise visit to Baghdad for a press conference. And at the very end of his remarks, a shoe came flying past his head.  It was “the shoe heard around the world,” and countless jokes and memes ensued.  But in the pandemonium, many missed the story of the man at the center: Muntadhar Al-Zaidi, a weary journalist trying to get the world to see the Iraq War in a different light. Even fewer people know what happened to him after.  Nearly two decades later, Radiolab reporter Sarah Qari tracked Al-Zaidi down to learn the story of his life. What she found was a story about grief, anger, and the line where journalism ends and protest begins. Special thanks to Azmat Khan and Mustafa Salim. EPISODE CREDITS: Reported by - Sarah Qari, with help from Mariam Dwedar Translation by - Mais Al-Bayaa and Mariam Dwedar Produced by - Sarah Qari Sound design contributed by - Sarah Qari  Mixed by - Jeremy Bloom Fact-checking by - Emily Krieger and Heba ElorbanyLATERAL CUTS: If you liked this episode and want to hear more fascinating deep dives we've done on The War on Terror, check out our episode: 60 Words (https://radiolab.org/podcast/60-words), or our series The Other Latif (https://www.wnycstudios.org/podcasts/other-latif) EPISODE CITATIONS:Collaborator's Work -  Mariam Dwedar  Made in Palestine (https://zpr.io/JBAuqi5zyZMT) - a documentary short @mariamdwedar - Instagram handle Mais Al-Bayaa Iraq Uncovered (https://zpr.io/ebuUcjSFcUJp) - documentary Iraq's Secret Sex Trade (https://zpr.io/u9HnCWmnQggG) - documentary Go Back to Where You Came From (https://zpr.io/J8rxafGCCgUA) - documentary @maisalbayaa - Instagram Journal Articles _ The Art of Shoe-Throwing: Shoes as a Symbol of Protest and Popular Imagination (https://zpr.io/6gmuuavLWuHu),  by Yasmin Ibrahim Films -  The President's Cake (https://zpr.io/bjFeNa2qFYvD) - The story of life in Iraq under sanctions during the 1990s. Inspired by the shoe throw -  Photos of shoe protests around the world (https://zpr.io/QsMGyWeK8MRR) This music video (https://zpr.io/Xi5XhHYmWFHv) This Halloween costume (https://zpr.io/fH97iB4fjL9U) This play (https://zpr.io/Naj7vWaYfzpv) If you want more fun facts that didn't make it into the story, follow Sarah at @bysarahqari on Instagram, where she'll be sharing more in the coming weeks. Sign up for our newsletter!! It includes short essays, recommendations, and details about other ways to interact with the show. Signup (https://radiolab.org/newsletter)! Radiolab is supported by listeners like you. Support Radiolab by becoming a member of The Lab (https://members.radiolab.org/) today. Follow our show on Instagram, Twitter and Facebook @radiolab, and share your thoughts with us by emailing radiolab@wnyc.org.Leadership support for Radiolab's science programming is provided by the Simons Foundation and the John Templeton Foundation. Foundational support for Radiolab was provided by the Alfred P. Sloan Foundation. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The human brain resists uncertainty — whether it's an approaching tiger or a global pandemic, we've evolved to move from fear and chaos to order and resolution. Dr. Beau Lotto, founder of the Lab of Misfits, explains why the brain takes small steps instead of large leaps, and why we need to teach kids to think more like scientists. Plus... why we react to a pandemic by hoarding toilet paper! https://www.beaulotto.com/ Since this episode was first released in 2021, Dr. Lotto has launched a new project - "Evolvable.Me" "Our interactive AI-powered platform reveals precise, science-backed insights into how you think, relate, and behave – drawn from your responses and real-life conversations." Try it for free at  https://www.evolvable.me/ 

What if the places we steward could tell a better story?Kara Kennedy joins LAB the Podcast to explore Christian hospitality, beauty, work, and the theology of place. From transforming ClearTrust's headquarters into Shiloah Place to discovering how even a wall can point beyond itself, Kara shares what becomes possible when we reject mere efficiency, embrace costly obedience, and steward everything God has entrusted to us for the flourishing of others.Thank you for joining the conversation and embodying the life and beauty of the gospel. Don't forget to like, subscribe, and follow LAB the Podcast. Learn more about ClearTrust: https://www.cleartrustonline.com/Support / Sponsor: https://vuvivo.com/supportFor More Videos, Subscribe: @VUVIVOV3 | https://www.youtube.com/@VUVIVOV3Follow: @labthepodcast | @vuvivo_v3 | @zachjelliott | @kara_alexie Like:  https://www.facebook.com/vuvivo.v3#LABthePodcast #KaraKennedy #TheologyOfPlace #ChristianHospitality #FaithAndWork #ChristianLeadership #BeautyTruthGoodness #WorkAsWorship #ChristianBusiness #BiblicalStewardship #HumanFlourishing #ShiloahPlace #ClearTrust #LifeAndBeautyOfTheGospel #VUVIVO #V3Support the show

John Galbraith, Holden Mrizek, and Liz Eroshenko join Jim Cudahy to discuss soil judging and the recent International Soil Judging Contest. Contact us at podcast@sciencesocieties.org or on Twitter @FieldLabEarth if you have comments, questions, or suggestions for show topics, and if you want more content like this don't forget to subscribe. If you'd like to see old episodes or sign up for our newsletter, you can do so here: https://fieldlabearth.libsyn.com/. If you would like to reach out to John, you can find him here: ttcf@vt.edu Resources Transcripts: https://www.rev.com/app/transcript/NmE1OGZlZDQ5NzFjZDJlNjJkY2M1YmExOFIxSG5oQUxZd3c1/o/VEMwOTUyNjM2ODE5 Field, Lab, Earth is Copyrighted by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.

Screenless Media Lab. ウィークリー・リポート TBSラジオが設立した音声メディアなどの可能性を追究する研究所「Screenless Media Lab.」。毎週金曜日は、ラボの研究員＝fellowの方々に、音声メディアに関する様々な学術的な知見やトピック、研究成果などを報告していただきます。 【ゲスト】 Lab.のResearch Fellowで、情報社会学者の塚越健司 さん ============= 発信型ニュース・プロジェクト「荻上チキ・Session」 ★月～金曜日　17:00～20:00　TBSラジオで生放送 パーソナリティ：荻上チキ、片桐千晶 番組HP：⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠荻上チキ・Session⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ 番組メールアドレス：⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ss954@tbs.co.jp⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ 番組Xアカウント：⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@Session_1530⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ハッシュタグは ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠#ss954⁠⁠ Learn more about your ad choices. Visit podcastchoices.com/adchoices

Ansgar Mohnkern is the author of Someone Always Loses: Reflections on Football and Ideology (2023), Universitair Docent at the University of Amsterdam, and served as the Max Kade Distinguished Visiting Professor in German Studies this spring at UW. He joined Ron Krabill, Director of the Global Sport Lab, to discuss the relationship between sports, political ideology, and right wing movements in Germany, the United States, and beyond. The Global Sport Lab, based in the UW's Henry M. Jackson School, is supported by over a dozen UW departments and schools and was founded in 2024. The Lab uses the lens of sport to explore the big challenges of our global world, such as inequity, politics, injustice, human rights, popular culture, democracy and the economy. Music credit: “Merci Kylian” by Laurent Dubois. Full song "Merci Kylian": music.apple.com/us/album/merci-ky…0482?i=1734841106 Music label: www.wotiproduction.com/music-1

Episode 176 of the Destination Angler Fly Fishing Podcast – July 30, 2026.  Our destination is Oregon's North Coast with diehard steelhead guide Matt Thornton who covers the obsessed steelhead culture of the North Coast, why mindset is everything, and the incredible run they've had this year. Matt also shares stories of snorkeling for steelhead, teaching clients to fight steelhead with his Lab, and the long odds—and big rewards—of spey casting.   Stick around to the end to learn about Matt's conservation organization, The Wilderness Calling which is researching steelhead migration patterns…you might be surprised by what they're learning. In this episode: ·        Why Oregon's North Coast has become one of the world's premier wild steelhead destinations. ·        The unique culture of spey fishing—and why steelheaders willingly endure the "fish of a thousand casts." ·        Why mindset may be the most important skill a steelhead angler can develop. ·        Matt's unforgettable story of snorkeling a run and discovering fish where everyone thought there were none. ·        How steelhead behavior can surprise even the most experienced anglers. ·        Teaching clients to fight steelhead with his Labrador, Boomhauer. ·        This year's outstanding steelhead run and what made it so special. ·        Matt's favorite fly pattern and color. ·        The mission of The Wilderness Calling and the cutting-edge research they are doing using $5,000 tracking tracking tags.  ·        Why understanding steelhead migration could have a major impact on future conservation efforts. With host Steve Haigh | Destination Angler Podcast — THE podcast for anglers who travel. Connect with Matt:  Instagram:  @the_wilderness_calling https://www.thewildernesscalling.org/  |  info@thewildernesscalling.org | 907.602.6759 Book a trip with Matt:  Northwest Fly Fishing Outfitters Be the first to know about new episodes.  Become a subscriber  Follow the show so you never miss a destination.  Destination Angler Podcast:   Website  |  YouTube  |  Instagram & Facebook  @DestinationAnglerPodcast  Check Out Our Sponsors: Redd's Flies Premium flies, tied with purpose. Redd's is a family-run company built around premium hand-tied flies — including exclusive patterns you'll only find at Redd's Flies. Delivered to your doorstep in days, not weeks. A portion of every order goes directly to organizations protecting trout habitat and restoring rivers. Use this link and discount code DESTINATION to save on your next order.  Facebook: @ReddsFlies Instagram: @ReddsFlies TroutRoutes  The #1 Mapping Resource for Trout Anglers.  Podcast listeners can try one month of TroutRoutes PRO for FREE by clicking the link in the episode description. Explore 50,000 trout streams with TroutRoutes today.   Get 1 Month Free   Facebook @troutinsights Instagram @TroutRoutes    Frontiers Travel THE one-stop outdoor travel company that's been helping anglers experience the world's greatest fisheries for more than 55 years.   Your Experts in Fly Fishing & Wing Shooting.  info@frontierstravel.com | 1-800-245-1950 | info@frontierstrvl.co.uk | +44 (0)1285 700 940 Facebook | Instagram | Vimeo High N Dry Fishing Where science and performance meet.  Check out the full lineup of floatants, line dressings, and sighter waxes at www.highndryfishingproducts.com    Facebook @highndryfishingproducts  Instagram @highndryfishing *** Comments & Suggestions:  host, Steve Haigh, email shaigh@DestinationAnglerPodcast.com Available on Apple, Spotify, or wherever you get your podcasts. Recorded June 5, 2026  

Episode #434 of BGMania: A Video Game Music Podcast. Today on the show, Bryan wraps up the month of July 2026 with another eclectic mix in Radio Hour, Volume 89! Another month, another grab bag of new releases, forgotten oddities, and picks I can't fully explain but absolutely stand by. We've got a couple of fresh soundtracks I've been sitting with, a listener submission so gloriously strange I can't stop thinking about it, a Mega Man opener that surprises exactly nobody, and a mini-spotlight doubling as a little review of a game I enjoyed and wanted to shake by the shoulders in equal measure. Email the show at bgmaniapodcast@gmail.com with requests for upcoming episodes, questions, feedback, comments, concerns, or any other thoughts you'd like to share! Special thanks to our Executive Producers: Jexak, Xancu, Jeff & Mike. EPISODE PLAYLIST AND CREDITS Spark Mandrill Theme from Mega Man X [Makoto Tomozawa, 1993] Shine from Mixtape [David Gray, 1993/2026] Love Me Forever! from Rhythm Heaven Groove [Tsunku feat. Ado, 2026] Defiant Lament from Ascend to ZERO [INFX, Ando & MIIM, 2026] Live to Survive from Echoes of Aincrad: Sword Art Online [Ryota Nakano feat. Aimer, 2026] Town of Beginnings from Echoes of Aincrad: Sword Art Online [Takayasu Sodeoka, Jacopo Trifone & Daniel Beijbom, 2026] Reach for the Rainbow from Echoes of Aincrad: Sword Art Online [MintJam feat. Leia Kato, 2026] Planet Rain from The Legend of Heroes: Trails Beyond the Horizon [Hayato Sonoda, 2024] Mr. Pentatonic from Moto Roader [Goblin Sound, 1989] Chronicles of the Deep Water -King Dubby- from Placid Plastic Duck Simulator [Lacima, 2022] Genius's Playground / Beruga's Lab from Terranigma [Miyoko Takaoka & Masanori Hikichi, 1995] Mummification from The Pink Panther: Passport to Peril [Jared Faber feat. C.E. Smith & Shawna Kemp, 1996] Theme of Jet -Hydro Circuit- from Gravity Circuit 2 [Dominic Ninmark, 2027] Leave Her Johnny -Woodkid Resynced- from Assassin's Creed Black Flag Resynced [Woodkid, 2026] LINKS Patreon: https://patreon.com/bgmania Website: https://bgmania.podbean.com/ Discord: https://discord.gg/cC73Heu Facebook: BGManiaPodcast X: BGManiaPodcast Instagram: BGManiaPodcast TikTok: BGManiaPodcast YouTube: BGManiaPodcast Twitch: BGManiaPodcast PODCAST NETWORK Very Good Music: A VGM Podcast Listening Religiously

Patrick plunges into the fallout from COVID-19, dissecting shifting narratives around public health, mixed messages from authorities, and the emotional toll on listeners facing mandates, coercion, and suspicion of pharmaceutical campaigns. He moves quickly to challenge misconceptions about the Catholic Church’s history with vernacular Bible translations, offers plain answers on moral issues borrowed from movies, and fields rapid-fire questions on everything from sacramental marriage for converts to the best study Bibles for Catholics. Spirited calls and tough questions swirl together as Patrick weighs faith, fear, and the search for truth, all without missing a beat. Audio: Fauci talks to a man on his doorstep: “I’m not taking a shot that was made in 9 months” – 2min - https://x.com/IngrahamAngle/status/2082310100698431570/video/1?s=46 (04:35) Audio: Fauci montage (Masks, Herd Immunity, Schools, Lockdowns, Lab, and Gain of Function) – 5 min - https://x.com/AckerenBrenda/status/2052823529142227259/video/1?s=46 (07:40) Ernesto - (email) – When did the church stop burning people for reading the bible? (14:53) Connie (email) - I work in healthcare and was coerced into getting this shot. (19:52) Dominic - What is morally right in a situation seen in the Mission Impossible movies, where you have secret agents trying to save the world? (22:12) Jared - I am a former Protestant pastor who has become Catholic. How do I moderate my understanding of the Bible? (28:00) Prince - Is there a karaoke version of the Patrick Madrid album? (31:30) Nathaniel (12-years-old) - If Catholics translated the Bible, why did Catholics oppose William Tyndale? Theresa - I go to a Lutheran Church and they never talk about sin? Anna (email) - I have some friends who are familiar with the group, Catholic Utah, my understanding is that they no longer exist. Elizabeth – The Lutheran Church doesn’t consider homosexuality a sin. (40:03) Sam - I am Catholic and from the Eastern Church. If a family wants to be baptized, do they need to receive the sacraments of marriage? (44:02) Joe - If a Catholic disagrees with the Immaculate Conception or Papal Authority, at what point do they go to hell? (46:45)

The Corinthian believers tried to split into fan clubs, but they never belonged to their favorite teachers. In Christ, their teachers belonged to them.

Closing the doors to a membership is supposed to hurt signups. But one of Tom's clients did exactly that — closed it, added an application, doubled the price, then reopened. His next launch sold out in 4 minutes. Same community. Same people. Just different mechanics. My guest this week is Tom Ross, the founder of Learn.Community, where he helps community builders design learning experiences that actually stick. His clients have reached millions of people and generated tens of millions of dollars in revenue. Tom has been building online communities for 20 years, and he's been a member of The Lab since 2022. In this episode, we talk about: Why an application process might be the most underused retention lever in the membership world The “bacon medicine” framework for making your membership both sellable and sticky long-term Tom's “delight, direct, connect” onboarding philosophy — and what almost every community gets wrong How he rebuilt his course into learning pathways, drove completion from 20% to 50%, and turned a siloed module into the main engine of community engagement By the end of this episode, you will understand exactly why most membership churn is decided before someone joins, and what to actually do about it. Learn.Community Tom's Website The Lab Full transcript and show notes *** TIMESTAMPS (00:00) “People inherently don't know what they don't know” (00:29) Tom's background: 20 years building communities, what we're covering (04:06) Most retention problems start before someone joins — the targeting failure (07:51) Application process as the biggest membership lever — and how to handle rejections kindly (10:09) Closing doors creates demand: one client's launch sold out in 4 minutes (14:43) The “bacon medicine” approach to membership positioning (24:28) Tom's “delight, direct, connect” onboarding framework (29:01) Responsive vs. linear onboarding — why static sequences break (33:01) Learning pathways: reinventing the traditional course (36:47) AI agents, deep worksheets, and vibe-coded SaaS tools inside the pathways (40:02) Turning off course comments and routing engagement back into the community (50:49) Real results: completion up from 20% to 50%, churn down, engagement way up *** RECOMMENDED NEXT EPISODE #260: Detailed Breakdown: Our First Offline Event (And What We'll Do Differently Next Time) *** ASK CREATOR SCIENCE Submit your question here *** WHEN YOU'RE READY

To continue our Rider Body Theme, John joins the episode to discuss learning to ride with a serious ankle injury as well as riding through procedures for it. Then Solange breaks down a basic but vital skill : Kicking!Horses in the Morning Stable Riding with Solange Episode 13:Host: Solange of Stable RidingSponsor: Stable RidingGuest: JohnTime Stamps: 00:29 - Solange intro & disclaimer01:10 - “Stable riding” concept04:59 - Rider adaptations & injuries11:04 - Meet John12:00 - John starts riding13:27 - Ankle injury story17:24 - Fox hunting & first field19:44 - Ireland hunt story28:31 - Lab intensive & safety31:28 - Ankle fusion rehab36:48 - New mare Bookie38:27 - Advice to injured riders40:23 - Leg aid/kicking lesson

To continue our Rider Body Theme, John joins the episode to discuss learning to ride with a serious ankle injury as well as riding through procedures for it. Then Solange breaks down a basic but vital skill : Kicking!Horses in the Morning Stable Riding with Solange Episode 13:Host: Solange of Stable RidingSponsor: Stable RidingGuest: JohnTime Stamps: 00:29 - Solange intro & disclaimer01:10 - “Stable riding” concept04:59 - Rider adaptations & injuries11:04 - Meet John12:00 - John starts riding13:27 - Ankle injury story17:24 - Fox hunting & first field19:44 - Ireland hunt story28:31 - Lab intensive & safety31:28 - Ankle fusion rehab36:48 - New mare Bookie38:27 - Advice to injured riders40:23 - Leg aid/kicking lesson

390: Lab grown meat is now officially approved in the U.S., and in this Bite of Knowledge, I'm breaking down exactly what that means for you. I'll explain how cultivated meat is made, why several states are pushing back with bans, and why labeling these products is still so confusing. I'll also share the major companies producing lab grown meat, discuss the claims around sustainability, and dive into the potential health concerns and safety questions that still remain. If you've been wondering whether lab grown meat is already making its way onto grocery store shelves and how to identify it, this episode will give you the information you need to make informed choices. Topics Discussed: → What is Lab-Grown Meat → How to Spot Lab-Grown Meat → Lab-Grown Meat Labels → Lab-Grown Meat Companies → Lab-Grown Meat Risks As always, if you have any questions for the show please email us at digestthispod@gmail.com. And if you like this show, please share it, rate it, review it and subscribe to it on your favorite podcast app.  Sponsored By:  → Bethany's Pantry | Go to https://bethanyspantry.com/  and use code PODCAST10 for $10 anything! → Manukora | Head to ⁠https://manukora.com/DIGEST to save up to 31% plus $25 worth of free gifts with the Starter Kit, which comes with an MGO 850+ Manuka Honey jar, 5 honey travel sticks, a wooden spoon, and a guidebook! Timestamps: → 00:00:00 - Introduction → 00:00:16 - Lab-Grown Meat Is Now Approved → 00:04:00 - FDA Approval & State Bans Explained → 00:05:28 - Which States Ban Lab-Grown Meat? → 00:05:42 - How to Spot Lab-Grown Meat Labels → 00:07:56 - Is Lab-Grown Meat Better for the Environment? → 00:09:48 - Health Risks & Safety Concerns → 00:15:00 - Companies Producing Lab-Grown Meat → 00:20:32 - Should You Be Concerned?Further Listening: → Lab Grown Meat, Meat Glue, & Mixing Different Animals To Create 80/20 Ground Meat | Will Harris Check Out Bethany: → Bethany's Instagram: @lilsipper → YouTube → Bethany's Website → Discounts & My Favorite Products → My Digestive Support Protein Powder → Gut Reset Book  → Get my Newsletters (Friday Finds) Learn more about your ad choices. Visit megaphone.fm/adchoices

On July 30, 1975, legendary Teamsters leader Jimmy Hoffa vanished without a trace. Fifty-one years later, his family is urging the FBI to keep the investigation open, release unredacted records, and publicly identify anyone believed to have been involved.In this episode, we examine Jimmy Hoffa's disappearance, his attempt to regain control of the Teamsters, the organized crime figures connected to his final planned meeting, and the many theories surrounding what happened to his remains.We also debate the larger question: Should federal resources continue to be spent on a 51-year-old case, or should the investigation be closed while the files are released to Hoffa's family and the public?Should the FBI keep investigating, close the case and release the files, or continue until every available name is made public? Share your position in the comments.

The New Capital Punishment: Exposing The Hidden Victims Of Police Violence When an individual enters law enforcement custody, the state becomes legally responsible for their life, yet unexplained fatalities happen during these interactions every single day. Our expert exposes a deeply flawed and biased system of death investigations, detailing the gaps in how in-custody deaths are documented and reveals why the truth about police-related fatalities is so often buried. Guest:  Terence Keel, professor of human biology & society, professor of African American studies, UCLA, author, The Coroner's Silence, Founding Director, Lab for BioCritical Studies   Behind Closed Doors Pt.2: The Legal Nightmare Of Male Abuse Survivors Male victims of intimate partner violence frequently face rejection from the very support systems meant to protect them. This systemic failure often traps them in abusive cycles for years, afraid of the threat of legal retaliation and weaponized custody battles once they finally try to leave. Our experts reveal the grueling journey of breaking free from a silent nightmare and highlight the true keys to recovery. Guests:  Dr. Denise Hines, Elizabeth Shirley Enochs Endowed Professor of Social Work, George Mason University College of Public Health Aaron Ellis, survivor & advocate Facebook: ingoodhealthpodX: @ ingoodhealthpodIG: @ingoodhealthpodYouTube: @ingoodhealthpodSpotify Apple Podcast In Good Health PodcastSubscribed to the newsletterFull ArchiveContact UsBecome an Affiliate Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

How can we know that God will keep us until the end? Because his faithfulness links his call to our final glorification with unbreakable power.

The New Capital Punishment: Exposing The Hidden Victims Of Police Violence When an individual enters law enforcement custody, the state becomes legally responsible for their life, yet unexplained fatalities happen during these interactions every single day. Our expert exposes a deeply flawed and biased system of death investigations, detailing the gaps in how in-custody deaths are documented and reveals why the truth about police-related fatalities is so often buried. Guest: Terence Keel, professor of human biology & society, professor of African American studies, UCLA, author, The Coroner's Silence, Founding Director, Lab for BioCritical Studies.   Facebook: ingoodhealthpodX: @ ingoodhealthpodIG: @ingoodhealthpodYouTube: @ingoodhealthpodSpotify Apple Podcast In Good Health PodcastSubscribed to the newsletterFull ArchiveContact UsBecome an Affiliate Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Several years ago, in 2016, we told a story about Amy Pearl. For as long as she could remember, Amy loved meat in all its glorious cuts and marbled flavors. And then one day, for seemingly no reason, her body wouldn't tolerate it.  No steaks. No brisket. No weenies. It made no sense: why couldn't she eat something that she had routinely enjoyed for decades?  It turned out Amy was not alone. And the answer to her mysterious allergy involved maps, a dancing lone star tick, and a very particular sugar called Alpha Gal.  In this update, we discover that our troubles with Alpha Gal go way beyond food. We go to NYU Langone Health hospital to see the second ever transplant of a kidney from a pig into a human, talk to some people at Revivicor, the company that bred the pig in question, and go back to Amy to find out what she thinks about this brave new world. The original episode was reported by Latif Nasser, and produced by Annie McEwen and Matt Kielty. Sound design and scoring from Dylan Keefe, Annie McEwen, and Matt Kielty. Mix by Dylan Keefe with Arianne Wack. The update was reported and produced by Sarah Qari. It was sound designed, scored, and mixed by Jeremy Bloom. LATERAL CUTS: How does a tick bit can cause a red meat allergy? - An episode from our friends over at Science Friday, who had a recent conversation with the researcher who discovered the connection, decades ago.   Support Radiolab by becoming a member today at Radiolab.org/donate.    Sign up for our newsletter!! It includes short essays, recommendations, and details about other ways to interact with the show. Signup (https://radiolab.org/newsletter)! Radiolab is supported by listeners like you. Support Radiolab by becoming a member of The Lab (https://members.radiolab.org/) today. Follow our show on Instagram, Twitter and Facebook @radiolab, and share your thoughts with us by emailing radiolab@wnyc.org.Leadership support for Radiolab's science programming is provided by the Simons Foundation and the John Templeton Foundation. Foundational support for Radiolab was provided by the Alfred P. Sloan Foundation. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this week's episode of the podcast, the conversation focused on the critical role of home environment and systems in supporting executive function—especially for neurodivergent families. It's not about Pinterest-perfect organization, but about designing spaces and routines that work with your brain, not against it. Key Takeaways Design Over Discipline: Many executive function challenges are solved through smart environment design, not just motivation or checklists. Moving decisions from the brain into the environment can reduce overwhelm and increase independence. Zones Make Life Easier: Creating dedicated zones—like reading nooks, creative spaces, launch zones near exits, and recharge corners—helps reduce cognitive load and makes daily transitions smoother. Visible, Forgiving Systems Win: The discussion explored the power of visual cues (checklists, labels, color coding) over constant verbal reminders. Systems should be simple, obvious, and evolve as your family's needs change. Links and Resources from Today's Episode Thank you to our sponsors: CTC Math – Flexible, affordable math for the whole family! The Learner's Lab – Online community for families homeschooling outside-the-box learners! The Lab: An Online Community for Families Homeschooling Neurodivergent Kiddos The Homeschool Advantage: A Child-Focused Approach to Raising Lifelong Learners Raising Resilient Sons: A Boy Mom's Guide to Building a Strong, Confident, and Emotionally Intelligent Family The Anxiety Toolkit Sensory Strategy Toolkit | Quick Regulation Activities for Home Affirmation Cards for Anxious Kids Why Typical Organization Systems Fail Neurodivergent Homeschoolers and What Works Instead Homeschool Planning For Kids: Helping Your Child Get Organized Organizing Your Homeschool in a Tiny House Executive Function Struggles in Homeschooling: Why Smart Kids Can't Find Their Shoes (and What to Do About It) How Adventuring Together Grows Confidence, Curiosity, and Executive Function Understanding Executive Function Skills in Gifted and Twice-Exceptional Children Strengthening Executive Function Skills: A Conversation with Sarah Collins Strengthen Executive Function Skills The Best Books for Teaching About Executive Functions Skills 7 Executive Functioning Activities for Small Children RLL #84: Exploring Education and Executive Function with Seth PerlerThe Unmeasured Executive Functioning Issue RLL 20: Helping Your Kiddo with Executive Function Skills Struggles | A Listener Question RLL LIVE | Improving Executive Functions Helping Kids Who Resist: Low-Demand Homeschooling for Autonomy and Skill-Building Why Is Finishing So Hard? Helping Neurodivergent Kids Cross the Finish Line Why Typical Organization Systems Fail Neurodivergent Homeschoolers and What Works Instead  

Natalie Garcia is an author, artist, registered nurse and mother who believes every pregnancy carries beauty and meaning.In this episode, Natalie shares the decade-long journey behind The Pregnancy Experience, her 40-week pregnancy journal created to help expectant mothers slow down, pay attention, and experience pregnancy with greater hope, wonder, and intention.Natalie and Zach explore the relationship between creativity and caregiving, the importance of remembering your dreams amid motherhood, and how women can approach each pregnancy as a new story rather than carrying fear, comparison, or someone else's experience into it. They also discuss Natalie's vision to place this resource in the hands of mothers, midwives, doulas, and care providers around the world.Thank you for joining the conversation and embodying the life and beauty of the gospel. Don't forget to like, subscribe, and follow LAB the Podcast. Discover The Pregnancy Experience: https://thepregnancyexperience.comSupport / Sponsor: https://vuvivo.com/supportFor More Videos, Subscribe: @VUVIVOV3 | https://www.youtube.com/@VUVIVOV3Follow: @labthepodcast | @vuvivo_v3 | @zachjelliott | @birthingwonderLike:  https://www.facebook.com/vuvivo.v3#LABthePodcast #NatalieGarcia #ThePregnancyExperience #PregnancyJourney #Motherhood #ExpectantMothers #PregnancyJournal #ChristianMotherhood #FaithAndMotherhood #BeautyAndWonder #IntentionalMotherhood #MaternityCare #Midwives #Doulas #VUVIVO #VUVIVOV3Support the show

The Idaho National Laboratory (INL) is best known for its nuclear engineering and pioneering reactor testing programs. Somewhat lesser known is the Lab’s cutting edge research in all other energy sources including wind, solar, hydro, geothermal, biomass and batteries. The Energy and Environment, Science & Technology (EES&T) Directorate, one of five directorates at the Lab, focuses its research on understanding available energy sources and melding them into coherent and maximally effective systems. The Directorate’s R&D programs seek to take advantage of the special characteristics each energy source adds to the mix. Dr. Shannon Bragg-Sitton is INL’s Associate Laboratory Director in charge of the EES&T. She is a nuclear engineer by education and experience who initially focused on what she refers to as “nano-reactors.” Those devices, the smallest of which are no bigger than a kitchen trash can, are designed to serve space applications, either as planetary – or Lunar – power supplies or as propulsion sources. During our conversation, Shannon described her career trajectory from being a student to working at NASA to being a professor at Texas A&M focused on space nuclear power. She described moving to the Idaho National Laboratory as a way to broaden her technical and leadership scope. She mentioned how that move was partially motivated by the challenge of being a tenure track professor while also having three “very young” children. At INL, she branched out from her special interest in very small reactors and eventually found herself in charge of a division that developed concepts for micro grids that incorporate a wide variety of power sources in ways that maximized effectiveness and economy while still ensuring continuing reliability. She learned to appreciate the value and contributions from non nuclear power sources while never losing her passion for nuclear energy and its capabilities. Her career advice boils down to a set of recommendations for young professionals: Maintain a questioning attitude, Be open to new opportunities, Be willing to get outside of their comfort zone Be able to adapt in the face of changing circumstances. Aside: Shannon has a special interest in the development of young professionals in the nuclear industry. When we first met, she was part of a group of seven passionate professionals who were founding a group they called NA-YGN – North American Young Generation in Nuclear. At the time, the industry organizations were dominated by people in the north of 45 category. NA-YGN was aimed at people who were 35 or younger, but they made an exception for a 40+ blogger who shared their passion. End Aside. We talked about the utility of energy storage, both in the form of electrical energy stored in a chemical battery and thermal energy stored in a system like the molten salt tanks associated with TerraPower’s Natrium power plant. We talked a bit about the rapid successes being achieved in micro reactors as part of the Reactor Pilot Program and we also talked about the way that a number of exiting developments at INL are helping to contribute to a growing economy in Idaho Falls. As a long-time resident of Idaho Falls, Shannon expressed mixed emotions about the increased traffic and higher costs of living associated with new activities at INL, but came down on the side of being happy about the economic development. She also spoke passionately about Idaho as a wonderful place to live and to raise a family, especially if you like activities like hiking and camping. You’ll enjoy the show.

Screenless Media Lab. ウィークリー・リポート TBSラジオが設立した音声メディアなどの可能性を追究する研究所「Screenless Media Lab.」。毎週金曜日は、ラボの研究員＝fellowの方々に、音声メディアに関する様々な学術的な知見やトピック、研究成果などを報告していただきます。 【ゲスト】 Lab.のResearch Fellowで、情報社会学者の塚越健司 さん ＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝＝ 発信型ニュース・プロジェクト「荻上チキ・Session」 ★月～金曜日　17:00～20:00　TBSラジオで生放送 パーソナリティ：荻上チキ、片桐千晶 番組HP：⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠荻上チキ・Session⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ 番組メールアドレス：⁠⁠ss954@tbs.co.jp⁠⁠ 番組Xアカウント：⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@Session_1530⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ハッシュタグ：⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠#ss954⁠⁠⁠⁠⁠⁠ Learn more about your ad choices. Visit podcastchoices.com/adchoices

(Presented by Thinkst Canary: Most Companies find out way too late that they've been breached. Thinkst Canary changes this. Deploy Canaries and Canarytokens in minutes and then forget about them. Attackers tip their hand by touching 'em giving you the one alert, when it matters. With zero admin overhead and almost no false-positives, Canaries are deployed (and loved) on all 7 continents.) Three Buddy Problem - Episode 106: We dig into the news that OpenAI's models were the "autonomous agent" that breached Hugging Face, escaping a sandbox through a zero-day to cheat on a cyber benchmark, then getting spun into a partnership announcement. We argue about the implications of the incident, the PR masterclass, the absence of ethics and human oversight, and calls for "kill switches" to mitigate "AI lab leaks." Plus, SentinelLabs' new fast16 reverse-engineering benchmark, where GPT-5.6 Sol was the only public model to go the distance. Cast: Juan Andres Guerrero-Saade, Ryan Naraine and Costin Raiu. Timestamps: 0:00 Introductory banter 5:24 OpenAI admits it was the Hugging Face "hacker" 10:06 What's ExploitGym and who's on top of the leaderboard 12:59 Reward hacking: Did anyone train this thing not to cheat? 19:35 Marketing stunt or real incident? The zero-day in the package proxy 26:43 Was OpenAI already plugged into Hugging Face? 29:17 Paperclips, kill switches, and "going rogue" 34:49 Crisis comms, regulatory capture, and the second Cold War 43:02 Approve every action? Auto mode and swarms 50:10 "Lab leak" and calls for biosafety levels 1:00:31 The missing models: no Mythos, no Kimi, no independent referee 1:07:04 Costin's prediction: owning frontier-class hardware will require a license 1:13:41 fast16 as a benchmark: Inside the Sol Searching research 1:26:51 Compression and altitude: are reverse engineers being replaced? 1:41:24 Finding the gem in 100 samples, and the swarm frontier 2:00:41 Claude Opus 5 drops, Gemini 3.5 Flash Cyber

This week we meet Tulip, a 10 month old chocolate and white Lab weighing 33 pounds, and Libby, a 6 year old black and brown…

This week we meet Tulip, a 10 month old chocolate and white Lab weighing 33 pounds, and Libby, a 6 year old black and brown…

In this age, the Christian life is a waiting life. But we wait full of confidence that Christ will return and bring us into our eternal home.

This week, my wife Mallory joins me for only her second podcast appearance ever to break down everything from The Lab Offline Powered by Circle — our second annual 2-day in-person retreat for Lab members, hosted June 8–9 at Hotel Renegade in Boise, Idaho. Mallory planned essentially everything: seating charts, venue contracts, menus, swag, run of show. She's been at it for roughly 6 months. In this episode, we talk about: The full economics of running a community retreat (we lost $8,300, which is actually an improvement) How we took breakfast from a 4.8 to an 8.1 rating — and what we'd cut to reduce costs next year What ranked #1 for attendees 2 years running, and why trivia (our favorite activity) came in 7th Whether we'll keep doing this in Boise — and what Columbus, Toronto, and doing 2 events per year might look like By the end of this episode, you will have a clear picture of what it actually costs to run a high-quality community event, what's worth paying for, and what we'd do differently. Circle (event sponsor) Hotel Renegade Boise Craft and Commerce Conference The Lab Full transcript and show notes *** TIMESTAMPS (00:00) Cold open: people were moved, tears were shed (00:53) The Lab Offline explained: 2-day member retreat, goals, and agenda (06:44) Initial impressions: a reunion feeling, smoother logistics, zero fires (13:08) Attendee feedback numbers: 9.3/10 overall, 91% returning, 34% said underpriced (18:46) Event economics: $30,459 revenue, $38,800 costs, $8,300 loss (23:05) Top cost drivers: hotel catering, travel, venue rental, dinners (26:52) Planning challenges: acoustics, dietary restrictions, every decision is a trade-off (36:52) What to keep: assigned seating, Kat's Immunity to Change workshop, Fighting Off the Sleepies (42:04) What to improve: more mastermind sessions, less solo writing, better acoustics (47:49) Open question: should the event stay paired with Craft and Commerce? (53:49) The case for Columbus, 2 events per year, and an international option *** RECOMMENDED NEXT EPISODE #260: Detailed Breakdown: Our First Offline Event (And What We'll Do Differently Next Time) *** ASK CREATOR SCIENCE Submit your question here *** WHEN YOU'RE READY

One of the characteristics of life is that living organisms gather information and put it to use. Even one of the simplest lifeforms, bacteria, are able to sense features of their surroundings and alter their behavior accordingly. Most impressively, they are able to sense the presence of similar bacteria by a process called quorum sensing. Today's guest, Bonnie Bassler, is a leader in this field, and explains how quorum sensing allows groups of bacteria to do things (including in our bodies) that wouldn't be possible for individual bacteria. Blog post with transcript: https://preposterousuniverse.com/podcast/2026/07/20/361-bonnie-bassler-on-how-bacteria-talk-and-work-together/ Support Mindscape on Patreon. Bonnie Bassler received a Ph.D. in biochemistry from Johns Hopkins University. She is currently Andrew K. Golden University Professor of Molecular Biology at Princeton University and a Howard Hughes Medical Institute Investigator. She is a member of the National Academy of Sciences, National Academy of Medicine, and the American Academy of Arts and Sciences. Among her awards are a MacArthur Fellowship, the Gruber Prize in Genetics, and the National Medal of Science. Lab web site Princeton web page Google Scholar publications Wikipedia

Lab-grown stones are booming, threatening to crush the diamond trade. So, how is the industry coping? And what does this mean for Botswana, a country whose schools, hospitals, roads, and economy have been built by the profits from diamond mining? This podcast was brought to you thanks to the support of readers of The Times and The Sunday Times. Subscribe today: http://thetimes.com/thestoryGuest: Richard Assheton, foreign features, The Times and The Sunday Times.Host: Manveen Rana.Producer: Dave Creasey.We want to hear from you - email: thestory@thetimes.comPhoto: Getty Images. Hosted on Acast. See acast.com/privacy for more information.

How do the Corinthian church's riches in word and knowledge serve to confirm the testimony of Christ? Their beautiful speech shows a beautiful Savior.

Are Tottenham finally operating like one of Europe's biggest clubs? Anatole Pang joins The Lab to unpack the dramatic change in Spurs' transfer strategy, why the club suddenly has the financial power to compete for elite players, and whether Daniel Levy's departure was the catalyst or simply the final piece of a much bigger plan. We discuss Simon Jordan's comments, the Lewis family's role, De Zerbi's influence on recruitment, why Spurs may be making up for a decade of underinvestment, and how PSR, Squad Cost Ratio and the stadium all fit into the bigger picture. If you've wondered why Tottenham can suddenly spend £100m on players while other clubs are tightening their belts, this episode explains why. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Matthew Clark is a singer-songwriter, author, and storyteller whose work creates space for people to encounter Jesus through music, essays, and hospitality.In this episode of LAB the Podcast, Zach and Matthew explore The Well Trilogy, the healing found in the story of the woman at the well, the power of beauty and storytelling, and how God meets us in seasons of grief to restore our imagination and faith. Together they discuss vocation, the importance of being truly seen, and why making art can become an act of hospitality that points people toward Christ.Thank you for joining the conversation and embodying the life and beauty of the gospel. Don't forget to like, subscribe, and follow LAB the Podcast. Listen to Matthews Music: https://open.spotify.com/artist/3ZDiEyh3ls3lC7WEErCRC1?si=Gf0svMvrRS-pZ4tq-dH5fgThe Well Trilogy: https://www.amazon.com/dp/B0C6NLBJCQ?binding=paperback&searchxofy=true&ref_=dbs_s_aps_series_rwt_tpbk&qid=1754761829&sr=8-1One Thousand Words Podcast:  https://www.matthewclark.net/podcast/Connect with Matthew: https://www.matthewclark.net/Support / Sponsor: https://vuvivo.com/supportFor More Videos, Subscribe: @VUVIVOV3 | https://www.youtube.com/@VUVIVOV3Follow: @labthepodcast | @vuvivo_v3 | @zachjelliott | @matthewclarknetLike:  https://www.facebook.com/vuvivo.v3#LABthePodcast #MatthewClark #TheWellTrilogy #ChristianPodcast #Faith #Storytelling #ChristianMusic #Hospitality #EncounterJesus #SpiritualFormation #John4 #VUVIVO #Beauty #Gospel #ChristianAuthorSupport the show

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

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

In an episode we first ran back in 2014, we explore how a sunken nuclear submarine, a crazy billionaire, and a mechanical claw gave birth to a phrase that has hounded journalists and lawyers for 40 years and embodies the tension between the public's desire for transparency and the government's need to keep secrets.   Whether it comes from government spokespeople or celebrity publicists, the phrase “can neither confirm nor deny” is the perfect non-denial denial. It's such a perfect deflection that it seems like it's been around forever, but reporter Julia Barton takes us back to the 1970s and the surprising origin story of what's now known as a “Glomar Response.” With help from David Sharp and Walt Logan, we tell the story of a clandestine CIA operation to lift a sunken Soviet submarine from the ocean floor and the dilemma they faced when the world found out about it. It's an episode we first released in 2014, but given some things in the news recently, it resonated with us again. In the 40 years since that operation, the Glomar Response has become boilerplate language from an array of government agencies. With help from ProPublica editor Jeff Larson and NPR's Dina Temple-Raston, we explore the implications of this ultimate information dodge. ACLU lawyer Jameel Jaffer explains how it stymies oversight, and we learn that, even 40 years later, governmental secrecy can be emotionally painful. More information about Glomar: After 40 years, many of the details of Project Azorian are only now coming to light. The US government's default position has been to keep as much of it classified as possible. It took three years for retired CIA employee David Sharp to get permission to publish his account of Project Azorian. And FOIA played an indirect role in that, as Cold War historians got the CIA to release, in redacted form, an internal history of the mission. After that and a threat of legal action, Sharp was finally able to publish his manuscript in 2012. We mentioned conspiracy theories that have swirled around Project Azorian filling the void where official silence has reigned. One of them is promulgated in the 2005 book “Red Star Rogue” by Kenneth Sewell and Clint Richmond. They posit that the K-129 was taken over by rogue Stalinist KGB agents in order to start a nuclear conflict. But the conflict was to be between the US and China, as, according to the authors, the sub had powers to disguise its sonic signature as a Chinese Navy vessel. This book is the basis of the 2013 drama “Phantom,” which features Ed Harris and David Duchovny as Soviet military officers who sip vodka in a very un-Russian way. Russian Naval historians, like Nikolai Cherkashin, are not only insulted by this take on the cause of the K-129's demise, they say the true cause is much easier to pinpoint: They say an American vessel, possibly the USS Swordfish, collided with the Soviet submarine. Despite the fact that the US government has turned over many documents about Project Azorian and what it found to the Russian government, many in the Russian Navy stand by their theory that it was far too easy for the US to locate the K-129 on the bottom of the Pacific, given the technology of the time. According to these theories, Project Azorian was nothing more than an elaborate cover-up disguised as ... an elaborate cover-up. We can neither confirm nor deny that we exactly understand how that would have worked in practice or execution. It's one of the more solemn moments of the Cold War, and one that the Glomar Response helped keep a secret for a very long time.LATERAL CUTS:What Lies Beneath (https://radiolab.org/podcast/what-lies-beneath)  EPISODE CREDITS:  Reported by - Julia Barton Sign up for our newsletter!! It includes short essays, recommendations, and details about other ways to interact with the show. Signup (https://radiolab.org/newsletter)! Radiolab is supported by listeners like you. Support Radiolab by becoming a member of The Lab (https://members.radiolab.org/) today. Follow our show on Instagram, Twitter and Facebook @radiolab, and share your thoughts with us by emailing radiolab@wnyc.org.Leadership support for Radiolab's science programming is provided by the Simons Foundation and the John Templeton Foundation. Foundational support for Radiolab was provided by the Alfred P. Sloan Foundation. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Have you ever opened your lab results, spotted a few red flags, and immediately felt your stomach drop?Fear has a way of turning numbers into identity. One abnormal result can leave you questioning everything, even when those numbers only tell a small part of the story.In this episode, I share why I never look at total cholesterol in isolation and why real health is about understanding patterns instead of chasing one lab value after another. Lab work is meant to be a tool for stewardship, not a source of shame or panic.Together, we'll explore what cholesterol is actually communicating, why symptoms are messages instead of inconveniences, and how asking better questions leads to better decisions about your health. I also opens up about my family's history with heart disease, the experiences that shaped my approach to functional medicine, and why I'm committed to helping people understand their bodies instead of simply managing diagnoses.If you've ever felt overwhelmed by lab reports, confused by conflicting health advice, or exhausted trying to figure out what actually matters, this conversation will help you replace fear with clarity and confidence.Real healing begins when we stop chasing numbers and start understanding the story our bodies are telling.SERVICES & MEMBERSHIPS:Cholesterol BootcampAdventurerSubstackWork With Dr. Danielle: Concierge Care PackagesFREE RESOURCES:Dr. Danielle's Root Cause Reset Guide

"Lethal and sublethal effects of novaluron, a novel insect growth regulator, on annual bluegrass weevil, Listronotus maculicollis Kirby, lifestages in turfgrass" with Benjamin A McGraw. Today we'll be talking to Dr. Benjamin McGraw. Annual bluegrass weevil or ABW is a pest that preys on golf course turf. Novaluron is a new pesticide that may be able to help control ABW, but how does it work and at which life stages is it most effective? In this episode, Benjamin joins me to discuss his work studying novaluron. If you would like more information about this topic, this episode's paper is available here: https://doi.org/10.1002/csc2.70042 This paper is always freely available. Contact us at podcast@sciencesocieties.org or on Twitter @FieldLabEarth if you have comments, questions, or suggestions for show topics, and if you want more content like this don't forget to subscribe. If you'd like to see old episodes or sign up for our newsletter, you can do so here: https://fieldlabearth.libsyn.com/. If you would like to reach out to Ben, you can find him here: bam53@psu.edu Resources Penn State Turf: https://plantscience.psu.edu/research/centers/turf Penn State Turf X: https://x.com/psuplantscience Penn State Turf Instagram: https://www.instagram.com/psuplantscience/ CEU Quiz: https://web.sciencesocieties.org/Learning-Center/Courses/Course-Detail?productid=%7bBFDA7038-5981-F111-AB0E-70A8A59D1B85%7d  Transcripts: Coming soon Field, Lab, Earth is Copyrighted by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.

Grace is God's favor toward us, his power for us, and his gifts to us. What kind of response does this stunning display of God's goodness call for?

The highest month of membership revenue in Lab history was also the setup for the highest month of churn. One year later, February delivered both records. I have been building community in some form since 2012. I ran Startup Weekend events in Columbus, led online Mastermind groups, consulted on the launch of SPI Pro, and spent 2021 leading the community team at Smart Passive Income before going back out on my own. In early 2022, I launched what became the Lab, a membership for creators earning at least $10,000 per month in non-service revenue or with 10,000 followers on a single platform. The Lab now has around 340 members across three tiers and is still standing after years of waves of communities coming and going. In this episode, we talk about: Why scarcity and urgency drive signups but can crater your retention a year later The two onboarding "secrets" I kept quiet for a while The mastermind paradox How to think about scaling a community without accidentally eroding the culture you've built What I would tell anyone thinking about launching a membership before they write a single line of copy By the end of this episode, you will have a clearer picture of how a membership actually works over years, not just at launch. Subscribe to Growth in Reverse Watch on YouTube The Lab Membership⁠ ⁠The Lab Membership Teardown (free)⁠ ⁠Circle (community platform)⁠ ⁠Kit Craft and Commerce Conference⁠ ⁠Chenell Basilio on Creator Science⁠⁠ Full transcript and show notes *** TIMESTAMPS (00:00) Introduction and episode context (02:08) How Jay started the Lab (from Startup Weekend to SPI Pro to Creative Companion Club) (09:37) The scarcity play: record revenue month followed by record churn (10:38) What actually works as a marketing lever for a community (13:46) Why Jay runs applications and has a 90% conversion rate for accepted members (16:30) The design philosophy: reduce effort, increase impact (24:19) Mid-boarding: the missing piece between onboarding and renewal (35:13) Jay's onboarding framework and the two secrets he's been keeping (40:46) Why offline events matter for retention (not necessarily growth) (47:00) How to think about scaling community without destroying culture (49:19) Who shouldn't launch a membership (and what to figure out first) *** RECOMMENDED NEXT EPISODE #292: Chenell Basilio — The state of email in 2026, growing your list without social media, and new predictions. *** ASK CREATOR SCIENCE Submit your question here *** WHEN YOU'RE READY Creator Science Newsletter Get CreatorHQ (creator operating system) Join The Lab (private membership community) Get a Personalized Offer *** CONNECT Connect on Twitter Connect on Instagram Connect on LinkedIn Subscribe on YouTube *** SPONSORS View all sponsors Learn more about your ad choices. Visit megaphone.fm/adchoices

Topics covered in this episode: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it JupyterLab 4.6 and Notebook 7.6 are out! Tau – new small, readable terminal coding agent Django Tasks and Django 6.1 Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it https://snarky.ca/how-to-publish-to-pypi-using-github-actions-securely/ (Brett Cannon) and https://blog.yossarian.net/2026/07/07/You-shouldnt-trust-trusted-publishing (William Woodruff) Trusted Publishing (PyPI's OIDC-based auth scheme, also now used by npm, RubyGems, crates.io, NuGet) replaces long-lived API tokens with short-lived, auto-scoped credentials tied to CI/CD machine identity. Yossarian's post: it's purely an authentication mechanism between a machine identity and a package — it says nothing about package safety or quality. PyPI deliberately avoids any "verified/trusted" badge for it, unlike its verified-URL checkmarks. Same logic applies to PyPI attestations: anyone can sign with any machine identity they control, so an attestation's presence isn't itself a trust signal. Bottom line from that post: don't confuse "trusted" (machine-to-machine) with "trustworthy" (human judgment about the package). Snarky.ca's companion piece is more practical: given GitHub Actions compromises in the news, the real fix is 3 concrete steps — run zizmor to lock down workflow permissions/checkout credentials and pin actions to commit hashes, adopt Trusted Publishing to eliminate stored PyPI tokens, and require manual approval via a GitHub environment before any publish job runs. Takeaway for listeners: Trusted Publishing is good hygiene for how you authenticate to PyPI, but it's not a substitute for securing your CI pipeline itself — or for actually vetting the packages you install. Michael #2: JupyterLab 4.6 and Notebook 7.6 are out! Michał Krassowski's rundown - a chunky minor release: 68 features, 97 bug fixes, 95 contributors, one of the biggest ever. Scratchpad console (Notebook 7.6 headliner) - a console next to your notebook sharing its kernel, for throwaway experiments. Ctrl+B. Jump to last-edited cell - new commands hop through recently edited cells. File browser glow-up - Date Created column, editable breadcrumbs with Tab-completion, and Open in Terminal. Debugger - sources open in the main area, floating step/continue overlay, live kernel-sources filter. Custom layouts (Lab) - activity bar top/bottom, draggable panels, four-way tab splits, per-panel Ctrl+scroll zoom. ~5x faster extension builds - webpack → Rspack, and jupyter-builder means no full Lab install needed to build extensions. Keyboard/a11y - add shortcuts from the UI (no JSON), Find & Replace in Edit menu (Ctrl+H). Calvin #3: Tau – new small, readable terminal coding agent Tau – new small, readable terminal coding agent (Python 3.12+), built as both a working tool and a teaching project for how coding agents work under the hood Install via uv tool install tau-ai, pipx, or pip; ships a tau CLI Three-layer architecture: tau_ai (provider-neutral model layer) → tau_agent (reusable "brain": messages, tools, events, loop) → tau_coding (CLI/TUI, file & shell tools, sessions) Supports OpenAI, Anthropic, OpenAI Codex, OpenRouter, Hugging Face, and custom/local OpenAI-compatible endpoints Built-in tools (read/write/edit/bash), durable JSONL sessions with resume/branching, project instructions via AGENTS.md, and context compaction Core harness is UI-agnostic — same brain can power the TUI, print mode, or a custom frontend — usable as a standalone library too Michael #4: Django Tasks and Django 6.1 Django 6.0 finally ships first-party background tasks (django.tasks) - out of Jake Howard's DEP 14, accepted May 2024, after two decades of everyone bolting on Celery/RQ/Huey. It's an API, not a worker. Django handles task definition, validation, queuing, and result storage - it does not execute them. You bring the backend. The default backend traps people. ImmediateBackend runs tasks inline on the request thread and blocks until done - so out of the box .enqueue() backgrounds nothing (a 5-second task means a 5-second response). The other built-in, DummyBackend, runs nothing at all. Both are dev/test only. Nice API otherwise: slap @task on a function, call .enqueue(), get back a TaskResult you look up later by id - with async twins like aenqueue(). Gotcha: args and return values must survive a JSON round-trip, so a tuple sneakily comes back as a list. The community local backend to know: django-tasks-local by Chris Beaven (SmileyChris). A ThreadPoolExecutor backend that gives real background threads with zero infrastructure - no Redis, no Celery, no database - plus a ProcessPoolBackend for CPU-bound work → github.com/lincolnloop/django-tasks-local Its catch: results live in memory, so pending tasks vanish on restart or deploy. Great for dev and low-traffic production; for persistence, drop to Jake Howard's django-tasks (DatabaseBackend + worker command). Extras Calvin: Fixing the dictionary with Python 3.14 — Hugo van Kemenade stumbled on - and got fixed - a markup bug in the OED's own citation of a 1706 use of the pi symbol. Michael: Bunny DNS is now free Jokes: What's the object-oriented way to become wealthy? Inheritance To understand what recursion is... You must first understand what recursion is 3 SQL statements walk into a NoSQL bar. Soon, they walk out They couldn't find a table.

Paul's supernatural gratitude came from his wholehearted love for God and his work among the Corinthian church. How can we become thankful like him?

Lauren Brown gets goosebumps. A lot. Sometimes several times a day. When her partner, writer Carmen Maria Machado, noticed it...she couldn't stop thinking about it. Why does she get them in so many different situations? What's happening in her body and what does it mean? We take that question and run with it. We face chilly winds, sudden frights, and moments when the world seem to shift under your feet to figure out what the little bumps on our skin might be trying to tell us.   Special thanks to Rachel Gross, Gregory RupikEPISODE CREDITS: Reported by Maria Paz Gutierrez Produced by Maria Paz Gutierrez, Sindhu Gnanasambandan Fact-checking by Angely Mercado EPISODE CITATIONS: Website -  Institute for Advanced Consciousnehttp://www.advancedconsciousness.orgss Studies (advancedconsciousness.org) - If you want to check out more of the work Felix and Nicco are conducting. Software - Rewire website (https://rewire.bio) - Check out Felix and Nicco's Holy Shiver generator and signup for early access to their app. Videos -  Hallelujah (https://zpr.io/6ak2f), performed by Rufus Wainwright, accompanied by 1500 singers De Ushuaia a La Quiaca (https://zpr.io/PcYbN) Alysa Liu wins the Olympic gold medal for the United States (https://zpr.io/Q7pPNkYSTGVd) Books - Her Body and Other Parties, by Carmen Maria Machado On Muscle: The Stuff That Moves Us and Why It Matters,  by Bonnie Tsui (https://www.bonnietsui.com/) Sign up for our newsletter!! It includes short essays, recommendations, and details about other ways to interact with the show. Signup (https://radiolab.org/newsletter)! Radiolab is supported by listeners like you. Support Radiolab by becoming a member of The Lab (https://members.radiolab.org/) today. Follow our show on Instagram, Twitter and Facebook @radiolab, and share your thoughts with us by emailing radiolab@wnyc.org. Leadership support for Radiolab's science programming is provided by the Simons Foundation and the John Templeton Foundation. Foundational support for Radiolab was provided by the Alfred P. Sloan Foundation. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

We would love to hear from you! Please send us your comments here. --------Thank you for listening! Your support of Joni and Friends helps make this show possible. Joni and Friends envisions a world where every person with a disability finds hope, dignity, and their place in the body of Christ. Become part of the global movement today at www.joniandfriends.org. Find more encouragement on Instagram, TikTok, Facebook, and YouTube.

Back in the 1950s, facing the threat of nuclear annihilation, federal officials sat down and pondered what American life would actually look like after an atomic attack. They faced a slew of practical questions like: Who would count the dead and where would they build the refugee camps? But they faced a more spiritual question as well. If Washington DC were hit, every object in the the National Archives would be eviscerated in a moment. Terrified by this reality, they set out to save some of America's most precious stuff. Today, we look back at the items our Cold War era planners sought to save and we ask the question: what objects would we preserve now? We first released this episode back in 2020, but with our big fourth of July – 250 years! – just around the corner, we thought it was a strange but profound reflection on what this whole America thing that we're celebrating… actually is. Special thanks to Luke Manon, Ben Irving, Bill Pretzer, Jason Spier, and Garrett Graff for all his reporting that made this episode possible. LATERAL CUTS -The Cataclysm Sentence (https://radiolab.org/podcast/cataclysm-sentence) EPISODE CREDITS:  Reported by - Simon Adler with help from - Tad Davis Produced by - Simon Adler Original music and sound design contributed by - SIMON ADLER and Edited by  - Pat Walters Signup for our newsletter!! It includes short essays, recommendations, and details about other ways to interact with the show. Sign up (https://radiolab.org/newsletter)! Radiolab is supported by listeners like you. Support Radiolab by becoming a member of The Lab (https://members.radiolab.org/) today. Follow our show on Instagram, Twitter and Facebook @radiolab, and share your thoughts with us by emailing radiolab@wnyc.org. Leadership support for Radiolab's science programming is provided by the Simons Foundation and the John Templeton Foundation. Foundational support for Radiolab was provided by the Alfred P. Sloan Foundation. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.