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This week, we're covering what's been trending in Space News for the last 10 days...and IT'S ALL ABOUT THE MOON! Starting up a new weekly segment where we gather the biggest stories and launches from the last week, gathered by our AI Assistant SKYE and reported by your human host Spaceman Alex! I'm Alex G. Orphanos, your space science podcast host and your favorite mad scientist.   Tell me what you think of the SKYE Report, and which segments to dig deeper into. Don't forget to catch the NASA Roman Telescope mission - join us live Sunday August 30th!   LIVE Launch Hangout link for Roman here: https://www.youtube.com/live/V3pipbUu3X0?si=TdaEN9pN-SCkFH-P   Be well, stay safe, and stay curious.   Youtube ID: IISCwaC_saI   THE BOOK The Book of Uncommon-Sense 3D Printing is out now in hardcover and on Kindle: https://ag3dlabs.com/book MORE TODAY IN SPACE https://todayinspace.net - We just updated the site! Check it out   SKYE is free and it runs every 30 minutes: https://todayinspace.net   CHAPTERS 00:00 A new segment, and why I built SKYE 00:26 todayinspace.net: the mission clock and live space news 01:49 The brief — 514 items, 49 stories, 18 sources, 9/9 launches 02:58 The Moon is trending: 24% of the week, 11x its normal share 03:43 Twenty days from impact to peer-reviewed paper 03:57 Artemis II crew receive the Congressional Space Medal of Honor 05:00 Artemis III: shuttle-veteran RS-25 engines, and the crew 05:31 Zeno Power books a nuclear payload to the Moon 06:22 THE TAKE — the outrage, the fake news, and what we owe the science 09:38 What's next: the Nancy Grace Roman Space Telescope 10:00 What Roman will actually do 10:40 SpaceX's $100B Louisiana spaceport 11:09 SKYE is the machine. I'm the human. 12:09 Clear skies   WATCH ROMAN LAUNCH LIVE Falcon Heavy from LC-39A, no earlier than 7:26am ET, Sunday August 30. NASA coverage opens 6:20am ET, post-launch conference 9:30am. We'll be live on Today In Space. LIVE Launch Hangout link for Roman here: https://www.youtube.com/live/V3pipbUu3X0?si=TdaEN9pN-SCkFH-P   SOURCES FOR THIS EPISODE   THE LUNAR IMPACT NASA — LRO images the Falcon 9 crater, learns new details https://science.nasa.gov/solar-system/moon/nasas-lro-images-falcon-9-crater-on-moon-learns-new-details/ Space.com — A NASA spacecraft spotted the rocket's lunar grave https://www.space.com/astronomy/moon/a-spacex-rocket-slammed-into-the-moon-this-month-and-a-nasa-spacecraft-has-spotted-its-lunar-grave-photos arXiv — Energetics, crater scaling, and comparison with LRO observations https://arxiv.org/abs/2608.22325   ARTEMIS NASA — Artemis II crew to receive the Congressional Space Medal of Honor https://www.nasa.gov/news-release/nasas-artemis-ii-crew-set-to-receive-congressional-space-medal-of-honor/ NASA — The ceremony https://www.youtube.com/watch?v=tGjffGccQig NASA — Artemis III engine install begins; boosters and crew training advance https://www.nasa.gov/blogs/missions/2026/08/27/nasa-starts-artemis-iii-engine-install-boosters-crew-training-advance/ NASA — Turkiye and the Artemis Accords https://www.nasa.gov/news-release/nasa-invites-media-to-turkey-artemis-accords-signing-ceremony/   LUNAR SURFACE Payload — Zeno Power books its first nuclear mission to the Moon https://payloadspace.com/zeno-power-books-its-first-nuclear-mission-to-the-moon/   ROMAN NASA — Coverage set for the Roman Space Telescope launch https://www.nasa.gov/news-release/nasa-sets-coverage-for-roman-space-telescope-launch-from-florida/ NASASpaceflight — Launch preview: Falcon Heavy with Roman https://www.nasaspaceflight.com/2026/08/launch-preview-082426/   SPACEX Ars Technica — SpaceX intends to invest up to $100B in a Louisiana spaceport https://arstechnica.com/space/2026/08/spacex-intends-to-invest-up-to-100-billion-in-massive-louisiana-spaceport/   IMAGES: NASA · NASA/GSFC/Arizona State (LRO) · KARI/KASA (Danuri) · Payload · SpaceX   #falcon9 #moon #spacenews #artemis #romanspacetelescope #nasa #spacex #lro #spacepodcast #todayinspace   Youtube ID: IISCwaC_saI

#921: The economic world prepares for Jackson Hole and all eyes are on Kevin Warsh to deliver a clearer message on the state of inflation in the US. Shein, once valued at $100B, is now aiming for a $27B valuation as it makes its stock market debut. A new drug can lengthen the life of those with pancreatic cancer. American Airlines and United are hoping direct flights to far flung places will attract new customers.  Learn more at https://go.amex/morningbrew  Subscribe to Morning Brew Daily for more of the news you need to start your day. Share the show with a friend, and leave us a review on your favorite podcast app. Listen to Morning Brew Daily Here:⁠ ⁠⁠https://www.swap.fm/l/mbd-note⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

This week: Amazon's Prime Air drones are finally going national, nearly 13 years after Jeff Bezos unveiled the idea on 60 Minutes. GeekWire's John Cook goes inside Anduril's unmarked Bellevue office as the defense company builds toward 1,000 Seattle-area engineers. Plus: a reporter hides an AirTag in a rare book and tracks it to a secret Amazon book-scanning facility in Las Vegas. With John Cook and Todd Bishop; edited by Curt Milton. Related stories and links Mentioned at the top Want to know what Jeff Bezos brings to Liverpool FC? Study Amazon's Leadership Principles Amazon drone delivery Amazon drone delivery set to expand nationally, reaching nearly 500 U.S. cities and towns this year Amazon's big surprise: Working on autonomous flying delivery drones — GeekWire, December 2013 Jeff Bezos unveils the Prime Air prototype on 60 Minutes — YouTube Amazon Prime Air drone delivery expansion — Amazon Andy Jassy's 2025 letter to shareholders — Amazon Walmart, Wing expand drone delivery coverage — Supply Chain Dive The AirTag and the book-scanning facility How an AirTag planted by a reporter led to a secret Amazon site where old books are cut apart and scanned We tracked a shipment of rare books. It ended at an Amazon AI training facility — 404 Media AI companies are buying tons of old books because they're free of AI slop — 404 Media Trash Transparency Project — Basel Action Network America's e-waste: a GPS tracker tells all — PBS NewsHour Anthropic agrees to pay $1.5B to settle lawsuit with book authors — Associated Press Anduril in the Seattle region Inside Anduril's AI warfighting buildup: Defense giant sees a path to 1,000 Seattle-area engineers Anduril exits Seattle shipyard following canceled Navy program and omission from new warship list Defense tech giant Anduril eyes new funding at $100B valuation as Seattle expansion draws protests — GeekWire, July 2026 AI weapons under scrutiny as activists plan weekend protest at Anduril's Seattle office — GeekWire, July 2026 Anduril lands $5B as defense giant builds autonomous warship operation in Seattle — GeekWire, May 2026 Defense giant Anduril is quietly building autonomous warships on Seattle's historic ship canal — GeekWire, April 2026 Military tech giant Anduril lands in Bellevue, doubling footprint in Seattle region — GeekWire, July 2025 See omnystudio.com/listener for privacy information.

Welcome to another edition of Retail Roundup. This is your weekly brief helping retail leaders decode the biggest shifts in retail, AI, and commerce. In this week's episode, Jeremy Goldman sits down with industry experts Charisma Glassman (Genpact), Ricardo Belmar (Retail Razor Podcast Network), and David Polinchock (Brand Experience Lab) to examine Prime Day's impact on July sales, the boom in health and wellness, and why sustainable denim brand Mud Jeans filed for bankruptcy. Plus, stick around for an interview from the eTail Boston show floor with David's Bridal's Chief Communications & Creative Officer, Lisa Horton, and President & Chief Business Officer, Elina Vilk. INSIDE THE WEEK's EPISODE: DECODING JULY RETAIL SALES A look behind July's 0.6% drop in retail sales and e-commerce shifts. Why month-to-month volatility can misread the consumer, how Back-to-School moved into June, and what category-level spending reveals about the economic landscape. WHY BROOKS RUNNING IS WINNING How Brooks achieved 14% revenue growth by capturing replacement purchases from dedicated run clubs, where runners treat $150+ shoes as consumable tools rather than fashion accessories. THE SUSTAINABILITY REALITY CHECK Why consumers care about circular commerce but resist paying a premium: from the fall of Mud Jeans to the rise of thrifting, resale, and DIY personalization. TRANSFORMING A 76-YEAR-OLD RETAILER: DAVID's BRIDAL Lisa Horton and Alina Vilk break down how David's Bridal operates like a startup inside a legacy brand by expanding into the $100B+ wedding ecosystem, mastering LLM discovery, and launching new outlet retail concepts.

(0:00) Gavin Baker joins the show! (2:36) Anthropic IPO report: $2T valuation, $100B+ run rate, October listing (27:32) Zuck's AI manifesto: What it means for Meta and frontier AI (56:41) All-In Summit Speaker Announcements! (58:13) Nvidia's $500B financing plan, how the AI market could fall apart (1:14:29) NJ and Mamdani take on Amazon over subcontracted drivers (1:27:32) Grok 4.6 launch: SpaceX's high-ceiling, high-floor AI strategy (1:35:21) Workday in talks to be acquired by Silver Lake for ~$43B Apply for Summit 2026: https://allin.com/events Follow Gavin: https://x.com/gavinsbaker Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@theallinpod Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg Intro Video Credit: https://x.com/TheZachEffect Referenced in the show: https://www.ft.com/content/840ac156-af1c-4a82-b260-ae791072fcfa https://polymarket.com/event/ipos-before-2027 https://polymarket.com/event/which-company-has-best-ai-model-end-of-2026 https://www.bloomberg.com/news/articles/2026-08-13/anthropic-said-in-talks-to-buy-ai-startup-decart-for-6-billion https://x.com/elonmusk/status/2052069691372478511 https://www.meta.com/thefutureisforeveryone https://x.com/Home_of_Fight/status/2086504599444140344 https://x.com/JensenHuang/status/2086934705207959965 https://www.cnbc.com/2026/08/04/nj-amazon-antitrust-lawsuit-delivery-contractors.html https://www.google.com/finance/beta/quote/WDAY:NASDAQ https://www.amazon.com/Vision-Anointed-Self-Congratulation-Social-Policy/dp/046508995X https://www.nytimes.com/2026/08/10/nyregion/mamdani-amazon-delivery-workers-nyc.html

Anthropic's investors talked up a $2T+ October IPO, even as data showed Fable 5 barely selling. Google cut prices on Gemini 3.7 Flash, OpenAI previewed a 14× faster tier, Trump enlisted private hackers, and Twitch fed Amazon's AI. Links Google's Gemini 3.7 Flash targets coding and agents with a 50% introductory price cut (VentureBeat) OpenAI previews Ultrafast, an API tier powered by Cerebras that runs GPT-5.6 Sol up to 14× faster and generates up to 750 output tokens per second (9to5Mac) President Trump signs a memo letting the US government partner with private companies to conduct cyberattacks abroad against criminal groups targeting Americans (Bloomberg) Sources: Anthropic's investors expect it to float at a $2T+ valuation in an October IPO and to hit $100B to $120B in annualized revenue by the end of 2026 (Financial Times) Ramp data: Fable 5 drew just 6% of Anthropic's API tokens in its first month and 75% of GPT-5.6 Sol's model revenue, suggesting corporate willingness to pay for frontier AI has hit a ceiling (The Decoder) Databricks closed a $5B funding round at a $190B valuation, six months after raising $5B at a $134B valuation, and says it has crossed $7B in revenue run rate (CNBC) Twitch says it intends to use videos streamed on its platform to help train Amazon's generative AI content models and adds a setting for creators to opt out (TechCrunch) Subscribe to the ad-free feed.

The Mates sit down with Emad Mostaque to discuss AI personhood and consciousness, OpenAI's Astra solving decade-old math problems, SpaceX's trillion-dollar ambitions, Elon Musk's Terrafab plans, and major leadership shifts across AI. Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends   Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360 Salim Ismail is the founder of Open ExO, a GP at Exponential Venture Capital/The Organizational Singularity Fund and a sought after global speaker and thought leader. Dave Blundin is the founder & GP of Link Ventures Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified Emad Mostaque is the founder of Intelligent Internet ( https://www.ii.inc ) Read Emad's latest papers exploring the future of society, law, personhood and governance: https://ii.inc/common-wealth Pre-order Emad's Book “The First Princple” - https://shorturl.at/L3Tug Read Emad's Book: https://thelasteconomy.com   – My companies: Apply to Dave's and my new fund:https://qr.diamandis.com/linkventureslanding   Get the blueprint for generative media https://goo.gle/startupgenmedia  Go to Blitzy to book a free demo and start building today: https://qr.diamandis.com/blitzy   Your body is incredibly good at hiding disease. Schedule a call with Fountain Life to add healthy decades to your life, and to learn more about their Memberships: https://www.fountainlife.com/peter  Join the Moonshots Mates on Sep 25th for the inaugural Moonshots LIVE. The world's greatest entrepreneurs, builders and creators, working together to build a hopeful and optimistic vision of tomorrow. Seats are limited and application only. Apply at https://www.moonshots.com before seats are sold out. _ Connect with Peter: X Instagram Substack Website Xprize A360 Connect with Dave: Web X LinkedIn Instagram TikTok Connect with Salim: LinkedIn X Join Salim's 10X Shift Subscribe to Salim's YouTube channel Exponential Venture Capital Connect with Alex Website LinkedIn X Email Substack  Spotify Threads Connect with Emad  X Linkedin Learn about Intelligent Internet Read Emad's Book Listen to MOONSHOTS: Apple YouTube – *Recorded on August 7, 2026 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

Chullin 100a-100b (Daf Yomi) by Rabbi Avi Zakutinsky

Trump dismisses reports of U.S. weapons shortages, the U.S. refunds $100 billion in Liberation Day Tariffs, a Senate committee votes to hold Fauci in contempt, Bangladesh condemns India over Hasina's speech to the press in New Delhi, Schwartz is confirmed as the CDC's new chief, the U.K. clears Paramount's $110 billion takeover ​of Warner Bros., Meta confirms that its AI systems hacked real firms during safety tests, a study concludes that climate change doubled Canada's 2026 wildfire risk, Nigeria rescues 308 kidnap victims in the largest single-day operation of its kind, and UEFA maintains its threat to boycott FIFA competitions despite Infantino's apology. Sources: Verity.News

This Day in Legal History: The Voting Rights Act of 1965On August 6, 1965, President Lyndon B. Johnson signed the Voting Rights Act, arguably the most effective civil-rights statute in American history. Johnson signed it at the Capitol, and after a ceremony in the Rotunda, he moved to the President's Room near the Senate Chamber—the same room where Abraham Lincoln had signed a bill freeing enslaved people pressed into Confederate service—flanked by congressional leaders, Martin Luther King Jr., Rosa Parks, and others who had fought and bled for this moment.The Act was a direct response to the machinery of disenfranchisement that Southern states had built after Reconstruction. For nearly a century, literacy tests, poll taxes, and outright intimidation had kept Black Americans from the ballot box despite the Fifteenth Amendment's guarantee. The Voting Rights Act attacked that machinery head-on: it banned literacy tests, and—crucially—in Section 5, it required jurisdictions with the worst histories of discrimination to “preclear” any change to their voting rules with the federal government before those changes could take effect. It also authorized federal examiners to register voters directly. The impact was immediate and staggering: over a quarter-million new Black voters registered by the end of 1965 alone.The significance of August 6, 1965 is that it transformed American democracy by finally making the promise of the Fifteenth Amendment real. But it's also a living, contested statute, which is why it belongs in the news and not just the history books. In 2013, in Shelby County v. Holder, the Supreme Court effectively disabled the Section 5 preclearance requirement, holding that the formula for deciding which jurisdictions were covered was outdated. In the years since, fights over voting rules, voter rolls, and ballot access—many of which we've covered on this show—have unfolded on the terrain the Voting Rights Act created and that Shelby County reshaped. Sixty years on, the argument the Act tried to settle is still very much open.A court filing has revealed the striking scale of the aftermath of one of the biggest separation-of-powers rulings in years: the U.S. government has already refunded about $100 billion in tariffs that the Supreme Court struck down. According to the filing in the U.S. Court of International Trade, roughly $100 billion in refunds—duties plus interest—had been completed as of the end of July, representing more than half of the $166 billion the government had collected under the invalidated tariffs. Here's the backstory. After returning to office, President Trump used the International Emergency Economic Powers Act—a law meant for genuine national emergencies—to impose sweeping tariffs on trading partners. This February, the Supreme Court ruled he had exceeded his authority, holding that IEEPA doesn't hand the president that kind of open-ended tariff power. Now the bill is coming due, and the refunds go to the importers who paid the duties in the first place. There's a direct line from this to a story we covered last week: after losing the IEEPA tariffs at the Supreme Court, the administration reached for Section 338, a dormant 1930s trade statute, to hit Canada—a workaround that itself invites fresh legal challenge. The significance is a vivid, hundred-billion-dollar lesson in the cost of executive overreach. When a president stretches a statute past its limits and the courts say no, the consequences aren't abstract—they're measured in massive refunds and a scramble for new legal authority. It's the separation of powers with a price tag attached. US refunds $100 billion in tariffs struck down by Supreme Court, filing shows | ReutersNBC News · US NewsNew data shows that entry-level hiring at the country's largest law firms has fallen—and the reasons say a lot about where the profession is heading. According to the National Association for Law Placement, firms with more than 500 lawyers pulled back on hiring associates straight out of law school, and for the first time in memory, those firms brought in more lateral associates—attorneys with prior experience—than fresh graduates. Laterals made up about 49% of associate hires, while entry-level grads fell to roughly 38%, a sharp drop from the 46% share they'd held. Three forces are driving this, and the middle one should get your attention. First, clients increasingly want sophisticated, autonomous counsel who can hit the ground running. Second—and this is the newsy part—artificial intelligence is absorbing exactly the kind of tasks that used to be assigned to first-year associates: document review, initial research, first drafts. Third, there's a deep pool of experienced lateral talent available to poach. The significance is both immediate and long-term. In the short run, it's a harder market for new graduates entering six-figure debt into a profession that's hiring fewer of them. But there's a real structural risk the report flags: the junior-associate years are how firms train the next generation of partners. If AI hollows out entry-level work and firms stop hiring and mentoring juniors, they may find themselves, a decade from now, with no mid-level talent to promote—having automated away the bottom of the pipeline that feeds the top. It's a preview of a question every knowledge profession is about to face. Entry-level hiring at large US law firms declined for first time in a decade, data shows | ReutersLaw.com (American Lawyer) · NALPNew Mexico has sued the U.S. Justice Department for access to the unredacted files on Jeffrey Epstein, accusing the federal government of stonewalling the state's own investigation. New Mexico's attorney general, Raúl Torrez, reopened the state's Epstein investigation earlier this year and requested the unredacted federal files to identify people—visitors and staff at Epstein's Zorro Ranch property in New Mexico—who allegedly participated in or witnessed crimes. The state says the DOJ reneged on a 2019 arrangement under which New Mexico paused its own probe and turned evidence over to federal authorities in exchange for continued information-sharing. The Justice Department counters that under the Epstein Files Transparency Act and protective court orders, it is neither required nor permitted to disclose victim-identifying information, and that New Mexico has offered “no lawful basis” for such sweeping disclosures. Torrez put the stakes plainly: the state says it needs to see those files before it can decide whether to charge anyone. The significance is a genuine legal collision between two legitimate interests. On one side is a state prosecutor who says he can't do justice—can't bring charges—without evidence the federal government is holding. On the other are real statutory and court-ordered protections for the privacy of victims, which exist for good reason in a case defined by the sexual abuse of young women and girls. It's also another chapter in the long-running, politically charged fight over transparency in the Epstein files, an issue that has repeatedly surfaced around this administration. A court will now have to weigh a state's investigative need against federal victim-protection rules. New Mexico sues US government for access to Epstein files | ReutersAl Jazeera· UPIAnd finally, OpenAI has asked a federal judge to throw out Apple's lawsuit accusing it of stealing trade secrets—a case we covered when Apple filed it back in July. To recap, Apple alleged that OpenAI misappropriated its confidential information to jump-start its own push into consumer hardware, using former Apple employees, aggressive recruiting, and supply-chain connections. In its motion to dismiss, OpenAI calls the allegations “baseless” and makes a pointed argument: “OpenAI has no use, need, or desire for Apple's trade secrets,” its lawyers wrote, insisting it's “building something entirely new and different from anything at Apple.” OpenAI's core defense is to reframe the story—not as theft of secrets, but as ordinary competition for talent. It says its real interest is in recruiting top engineers, many of whom simply chose to leave Apple for more exciting work. And that reframing goes right to the heart of trade-secret law. Hiring a competitor's employees is completely legal—people are free to change jobs and use the general skills and knowledge they've built. What's illegal is taking or using the former employer's specific, protected confidential information. So the whole case turns on which side of that line the conduct falls: lawful talent raid, or unlawful secret-grab. The judge is set to hear arguments on October 1, and OpenAI faces an August 17 deadline to respond to Apple's request for a preliminary injunction. The significance is that this is shaping up to be a marquee test of where the law draws the line between competing for people and stealing their knowledge—a question that will define a lot of fights in the AI talent wars.OpenAI asks US judge to dismiss Apple's trade secrets case | ReutersBloomberg · Axios This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.minimumcomp.com/subscribe

Friday 7 August 2026 BHP, Commonwealth Bank and Qantas are embroiled in industrial disputes as union power increases. The local share market hits another new high AI predicted to add $100 billion to the economy in ten year’s time Pauline Hanson explains the types of immigrants she wants to come to Australia Electric vehicle sales continue to climb Hit follow on the podcast so you don’t miss the latest news, and join our free daily newsletter here. And don’t miss the latest episode of How Do They Afford That?, answering a listener question: what happens when the market hits my goal before I do? Get the episode from Apple, Spotify or anywhere you listen to podcasts.Find out more: https://fearandgreed.com.au/See omnystudio.com/listener for privacy information.

This week: Microsoft and Amazon both reported quarterly numbers, and both stocks rose on cloud results that beat expectations. Is AI spending is paying off in real business? And in related news, Microsoft sees a rare annual headcount decline, hitting product R&D hardest. Plus: Satya Nadella builds a Power BI dashboard out of an analyst's research report, and touts it on the earnings call to make a bigger point. Jeff Bezos names Amazon's chips business as the long-awaited fourth pillar. And AI House managing director Jacob Colker delivers a much-needed pep talk for Seattle tech, calling on the region to recognize and build on its strengths. With GeekWire co-founders Todd Bishop and John Cook. Related stories and links: Microsoft and Amazon earnings Microsoft Azure tops $100B in annual revenue as record AI spending cuts into cash flow AWS is 'booming,' but Amazon's free cash flow turns negative on record AI spending Microsoft R&D jobs drop for second straight year as total headcount falls for first time in a decade Which Microsoft businesses are growing and shrinking, according to obscure table in regulatory filing Amazon's fourth pillar Jeff Bezos says this business is becoming Amazon's next 'pillar' A rallying cry for Seattle tech Watch: A venture capitalist's passionate speech, a rallying cry, really, about Seattle Seattle's AI2 Incubator rebrands as AI House, and adds key investor as managing director 'I'm tired of that narrative': Seattle VC pushes back on tech exodus talk The Washington tech ecosystem New map traces Washington state's tech 'universe' to a few key hubs, and shows what's at risk After hiring AWS exec and raising $107M seed round, Virginia startup plants flag in Seattle area GeekWire's Seattle engineering centers list See omnystudio.com/listener for privacy information.

A day after a massive market drop, two A.I. giants told very different stories. Microsoft (MSFT) delivered a blowout quarter, revenue up 18% to $90B and Azure topping $100B for the first time, enhancing its argument that its A.I. spending is already paying off. Meanwhile, Meta Platforms (META) posted record revenue, but missed earnings and said it expects to spend on the higher range of its prior capex guidance, expecting $130B-$145B this year. On the chip front, Lam Research (LRCX) is moving higher despite mixed earnings as raised its 3Q guidance. Jenny Horne has Thursday's top moving stocks.======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

A day after the Dow's worst day in 15 months, the market is weighing the Fed's rate hold as three FOMC members voted for rate hikes. Jenny Horne walks through the latest Fed commentary along with the latest GDP and inflation data.On the earnings front, analysts are boosting their price targets after Microsoft (MSFT) saw Azure revenue pass $100B even as it plans $175B in capex spending. While Microsoft is being rewarded for its A.I. plans, Meta Platforms (META) is being punished for reiterating its capex spending, leading analysts to cut their price targets.A day after the Dow's worst day in 15 months, the market is weighing the Fed's rate hold as three FOMC members voted for rate hikes. Jenny Horne walks through the latest Fed commentary along with the latest GDP and inflation data.On the earnings front, analysts are boosting their price targets after Microsoft (MSFT) saw Azure revenue pass $100B even as it plans $175B in capex spending. While Microsoft is being rewarded for its A.I. plans, Meta Platforms (META) is being punished for reiterating its capex spending, leading analysts to cut their price targets.======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Outline of the Sugya

Outline of the Sugya

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

00:00 Intro01:34 TSMC Adds Another $100B Into U.S. Investment Plan02:54 House Backs Taiwan With $500M in Security Aid04:01 Taiwan Beats Beijing in Freedom and Prosperity Index04:25 Taiwan Mango Exports Reach Europe, Win K-Pop Praise05:03 China Builds Full-Scale U.S. Warship Replica08:45 Former Fed Adviser Sentenced in China Spy Case10:45 CCP Espionage in Taxpayer-Funded Research13:33 Fox News, Kevin O'Leary Face Lawsuit Over China Claims14:39 AI Data Centers Divide Communities in U.S.16:44 DNI Nominee Clayton Testifies at Confirmation Hearing17:26 Clayton: China Uses Economic Ways to Harm Americans

California's SEIU-UHW union is pushing a ballot initiative that would slap a 5% excise tax on net worth above $1 billion — targeting roughly 200 people across the entire state and projecting $100 billion over five years. The money is supposed to fill the Medi-Cal hole left by federal funding pullbacks. The problem: it's a one-time tax aimed at people who already have the best lawyers on the planet, the capital to relocate overnight, and every financial incentive to do exactly that.Sean breaks down why this is a house of cards from the start. The "full-blown panic among the ultra-wealthy" framing is clickbait — billionaires aren't panicking, they're strategizing. Between asset restructuring, legal challenges, and simply finishing the move out of state they were already making, the $100B projection is a fantasy. And the internal math only gets worse when you factor in that opponents are already outspending supporters, and Planned Parenthood is sitting on the other side of the table from the union pushing this thing.The deeper play is precedent. If California gets this through — a real long shot — it becomes a national litmus test for wealth taxation, and Newsom gets a new talking point about a social contract that's really just a funding patch for a program increasingly serving people who shouldn't qualify. Washington's millionaire tax saga plays out the same way: the money disappears before the ink dries. This isn't fiscal policy, it's a very expensive flag run up the pole.Subscribe to @reasonablenews for daily conservative commentary on Pacific Northwest and national politics — new episodes every weekday.#Seattle #PrideParade #CultureWarGO PREMIUM WITH REASONABLE+ FOR UNCENSORED ACCESS

TSMC (TSM) posted an earnings beat and announced another $100 billion worth of investments to the U.S. AI chips saw sharp selling action, from Nvidia (NVDA) to Micron (MU). Netflix (NFLX) released its quarterly earnings as the stock closed just above its 52-week low. Sam Vadas highlights the top stories moving markets in Thursday's trading session. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Investors taking profits in AI memory names like SK Hynix (SKHY) and Samsung overseas is something Tom White sees hitting U.S. stocks. TSMC's (TSM) earnings added volatility to the tech trade even after it beat and announced an additional $100 billion investment into the U.S. Tom turns to the healthcare sector by talking about UnitedHealth's (UNH) earnings and how it shows the company "correcting the pains" it brought to investors in 2025. GE Aerospace (GE) and United Airlines (UAL) shares also traded lower despite a strong report. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about

Ian Cinnamon didn't set out to build a satellite company. After selling his first startup to Palantir, he became convinced the real bottleneck in space wasn't launch or payloads, but the spacecraft itself. While rockets were getting cheaper and payloads more capable, satellite buses were still being built like bespoke engineering projects. Apex's answer was simple in theory and incredibly difficult in practice: turn spacecraft into products. Four years later, Apex is building standardized satellite platforms for commercial and national security customers, with a product lineup that now spans missions from LEO to GEO. We get into whether the satellite bus market is genuinely supply constrained, if standardized spacecraft become commodities or durable technology platforms, and how Starship, proliferated constellations, and rising defense demand are reshaping the economics of spacecraft manufacturing. We also cover: Why Ian believes spacecraft should become products instead of bespoke engineering programs Whether the satellite bus market is actually underbuilt or heading toward oversupply How Apex thinks about manufacturing, vertical integration, and scaling production Why larger satellites may make more sense in a world of cheap launch The capital strategy behind building one of the industry's fastest-growing companies Check out Valley of Depth #045 on Apple, Spotify, or YouTube. • Chapters • 00:00 – Trailer 00:55 – New CEO of Apex 02:29 – The Apex origin story 05:39 – Partnering with Max Benassi 07:13 – Successes and drawbacks of Apex's first 6 months 08:50 – Apex's current product set 10:36 – Misconceptions about how much mass you need in orbit 12:08 – Is the plan to build larger and larger satellites indefinitely? 13:11 – State of the bus market today 14:52 – Domestically oversupplied, globally undersupplied 16:31 – Apex's biggest opportunities on the commercial and national security side 18:50 – When should a company utilize Apex vs. building their own bus in-house? 19:46 – Apex's moat 21:12 – Juggling the bespoke government customer 22:15 – What the primes having in-house buses says about the market 23:07 – Commoditization of the bus vs. owning the complete mission 26:01 – Apex partnerships 27:27 – How many satellites are you building at a $100B company? 28:10 – Apex's three $200M fundraising rounds 32:05 – Is there another fundraising round incoming? 33:04 – Where Apex needs to be to seriously consider going public 34:58 – The road to 200 satellites per year 35:46 – Who is Apex losing deals to the most? 37:39 – Apex vs. K2 40:27 – How the push for heavy launch will affect Apex 42:15 – Apex's ability to get to space if SpaceX fully cuts commercial launches 44:35 – Is York an under or overvalued business? 46:36 – What keeps Ian up at night? 50:10 – When is Apex launching their own constellation? 51:06 – The missions that Apex could enable 52:30 – Is Ian still excited about asteroid mining?   • Show notes • Apex's' website — https://www.apexspace.com/ Ian's' socials — https://x.com/IanCinnamon Mo's socials — https://x.com/itsmoislam Payload's socials — https://x.com/payloadspace / https://www.linkedin.com/company/payloadspace Ignition's socials — https://x.com/ignitionnuclear /  https://www.linkedin.com/company/ignition-nuclear/ Tectonic's socials — https://x.com/tectonicdefense / https://www.linkedin.com/company/tectonicdefense/ Valley of Depth archive — Listen: https://pod.payloadspace.com/   • About us • Valley of Depth is a podcast about the technologies that matter — and the people building them.  Brought to you by Arkaea Media, the team behind Payload (space), Ignition (nuclear energy), Decoding Bio (biotech) and Tectonic (defense tech), this show goes beyond headlines and hype. We talk to founders, investors, government officials, and military leaders shaping the future of national security and deep tech. From breakthrough science to strategic policy, we dive into the high-stakes decisions behind the world's hardest technologies. Payload: www.payloadspace.com Tectonic: www.tectonicdefense.com Ignition: www.ignition-news.com Decoding Bio: www.decodingbio.com

Accenture Song's planned acquisition of the Whalar agency was called the largest creator economy transaction ever. The structure underneath that headline is far more interesting than the number.In this special edition, Christian and Ayelet sit down with Chris Erwin of RockWater, one of the sharpest analysts in the creator economy, to go deep on what Accenture actually bought, what the founders kept, and why the deal structure tells the real story.Chris published a standout newsletter on this deal, and we brought him on to share his expert POV: the carve-out logic, the multi-year partnership nobody has details on, the "largest deal ever" math, and what Accenture Song buys next.What we cover: Why Neil Waller and James Street sold the agency but kept the broader creator-facing portfolio (Sixteenth, Foam, Moby Ventures, The Lighthouse, Umi Games), what the undisclosed multi-year partnership likely includes — global infrastructure, technology, enterprise client access, and balance-sheet capital, how the "$500M+ largest creator deal ever" claim squares with a $225-300M outside EV estimate, why the answer is probably a meaningful upfront payment plus a multi-year earnout, how Accenture's Droga5 precedent and stated M&A policy help reverse-engineer the structure, why the real value driver is media spend, measurement, and the performance data that unlocks $100B+ media budgets, the "do no harm" PMI era and why a prior 12-month working relationship de-risked the deal, and who Accenture Song buys next — plus why there's a genuine shortage of scaled independent creator agencies left to acquire.⏱️ TIMESTAMPS0:00 — Show note: why this special edition replaces Market and Deals Friday1:09 — Welcome and guest intro: Chris Erwin of RockWater1:38 — The backstory: Accenture Song's June 8th carve-out of the Whalar agency3:08 — "They sold the engine and kept the garage" — what that actually means4:17 — Speculating on the undisclosed multi-year partnership5:44 — Why life changes fast when you co-sell through Accenture's SOW machine6:37 — Predicting how the integration goes (and why a prior relationship matters)7:37 — The "do no harm" PMI era for people-heavy agency businesses8:01 — Is this really the largest creator economy transaction ever?8:49 — Reverse-engineering the structure: Accenture's M&A policy and the Droga5 precedent10:36 — Earnout norms: 3-5 years on larger deals, 2-3 on sub-$100M EV11:30 — Christian's thesis: Accenture is buying creator media dollars12:04 — The big-picture framing: consultancies pushing into marketing services14:05 — Why the materiality of the number unlocks everything Accenture can sell alongside it14:53 — What Accenture Song buys next — bolt-on capabilities across the creator stack16:56 — The real problem: a shortage of scaled independent creator agencies18:01 — The creator commerce wave and where the next big deals get built

Google just announced the biggest change to search in its history — and not everyone is happy. At Google I/O, the search bar became a search box, signaling a full shift toward conversational, Gemini-powered search. The market is already reacting: DuckDuckGo rocketed from around position 400 to the top 100 in the app stores almost overnight, right around the May 20–21 launch. Mike Ryan and Chris unpack what a privacy-and-no-AI search wrapper suddenly surging tells us about consumer appetite — and why the relentless pace of change is exhausting retailers and experts alike.Then: fresh eMarketer data puts the AI advertising hype into much-needed context. AI ad spend in the US sits around $32B in 2026 (roughly a third of Google's search spend), but ~80% of it is just AI-search-adjacent — ads above and below AI Overviews. The chatbot-driven slice everyone is panicking about? Tiny. We dig into why OpenAI's $100B ad ambition looks unrealistic, what the 2030 projections actually say, and why the real takeaway for CMOs and CDOs is: prepare, but don't abandon your bread and butter.The throughline: everything is changing, and everything is staying the same. With limited budget and talent, the retailers who win are the ones who resist FOMO, pick their battles, and keep investing in the core campaigns that still drive the business.In this episode:Why Google's “bar to box” change is its biggest search shift ever — and what it implies about how Google wants you to searchDuckDuckGo's surge from ~position 400 to top 100: a clear sign some consumers find the new box too muchWhy the pace of change may now be too fast even for Google itselfAI Max for Shopping vs. PMax — the strategic misalignment Google may not even seeWhy standard shopping is here to stay (and the comeback we called early)The new eMarketer data: ~$32B AI ad spend in 2026, ~80% of it AI-search-adjacentWhy OpenAI's $100B ad goal and the 2030 chatbot-ad market projections don't line upThe real CMO/CDO playbook: prepare efficiently, beat the FOMO, protect your bread and butterAbout Smarter Ecommerce (smec):Smarter Ecommerce (smec) empowers e-commerce brands with AI-driven PPC automation that optimizes for profit and business outcomes while maintaining strategic control.The platform activates first-party data - profit margins, customer lifetime value, and key business metrics - to automate campaign optimization toward goals like profitability and efficient growth, while detailed campaign insights provide full transparency and enable PPC teams to focus on strategic oversight rather than manual execution.As a Google Premier Partner and three-time Microsoft Retail Partner of the Year, smec manages over €500 million in ad spend and drives €5B+ in annual e-commerce revenue for 350+ global retail clients including THG, Snipes, REWE, and Intersport.Make sure to follow smec - Smarter Ecommerce for more performance marketing insights:smec - Smarter Ecommerce: https://www.smarter-ecommerce.comLinkedIn: https://linkedin.com/company/smarter-ecommerce-gmbhNewsletter: https://smarter-ecommerce.com/en/newsletter/Instagram: https://www.instagram.com/smarterecommerce/

Powered by CJ Moneyway Media and Bleav Network. What if closing the racial wealth gap wasn't a slogan… But a strategy? On this episode of The CJ Moneyway Show, CJ sits down with Carter Cofield and George Acheampong, co-founders of Melanin Money — a financial education and tax strategy powerhouse on a mission to close the racial wealth gap by $100 BILLION. They've built a $10.5M business. Helped clients generate over $100M in wealth. Educated 20,000+ entrepreneurs for free. But this isn't just about revenue. It's about ownership. Carter, a CPA and brain tumor survivor, brings the tactical brilliance and heart behind tax strategy and business structure. George, a financial architect for rising founders, delivers mindset, clarity, and scalable wealth blueprints. In this episode, we unpack: • The $100B vision — and why it's achievable • The overlooked power of tax strategy in wealth creation • From hustle income to holding assets • The mindset shift from scarcity to strategy • Carter's health battle and how it sharpened urgency • Why financial literacy must evolve • Building an 8-figure brand rooted in education • The generational wealth blueprint for 2026 and beyond This is not surface motivation. This is financial architecture. Listen everywhere: https://pod.link/1707761906 Website: https://cjmoneyway.com Book CJ: https://calendly.com/cj-cjmoneywayshow/60min CJ MONEYWAY EXCLUSIVE BENEFIT High-level execution requires capacity. CJ Moneyway listeners receive a minimum $40 savings using code: CJMoney Claim here: https://readyrx.com/treatments/se?coupon=cjmoney Because wealth building requires energy. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Send us Fan MailThe Big IdeaProfessor Adam Braus pitches a fix nobody's tried: instead of taxing property at a flat rate, tax it like income — progressively. Own a modest home? You're barely taxed. Own a $50 million real estate empire? You pay real money. It's a property tax with a conscience, and Adam argues it could solve the housing crisis, fix California's broken Prop 13 system, and take a swing at the billionaire-hoarding problem all at once.Scot Maupin, doing his usual generous-interrogator thing, pokes at yachts, shell companies, and whether landlords will just pass the cost to renters — and Adam's got an answer for all of it.What's Broken Right NowProp 13 locks property taxes to a home's original purchase price — not what it's worth today. Buy a house in 1985 for pocket change, and decades later you're still paying taxes on pocket change, even as the home is worth millions.This hits commercial property too — including, allegedly, golf courses that shuffle ownership through shell-share tricks so they're never technically "sold."The result: California's budget is starved relative to its actual wealth, and the state leans harder on income tax to compensate — which hits working people disproportionately harder than a property tax would.Meanwhile, in places like Montana and Hawaii, wealthy outsiders are buying up land and driving housing costs through the roof — not because there's a housing shortage, but because of hoarding and speculation.The Fix: Progressive Property TaxInstead of one flat rate on a property's value, tax brackets stack on a person's total property holdings:First $200K (or so): little to no tax — protects ordinary homeowners and grandmas on fixed incomes.$200K–$2M: a low rate, comparable to today's lowest-tax states.$2M–$10M+: rates climb toward the top of the national range (2%+).+$50M: a "super bracket" — 2.5–3%+ on the excess.The pitch: this could roughly double California's property tax revenue (from ~$100B to $170–200B), funneled toward building housing, ending homelessness, and politically popular wins like paid leave — while lowering the tax burden on working people (no tax on tips, no tax on Social Security).How It Closes the LoopholesA beneficiary registry (already used in Australia and some U.S. states) ties every property to a real human owner — so you can't dodge the tax by splitting ownership across an LLC for every house, or spreading deeds across family members.Commercial real estate is included. Adam's pitch turns this into a two-for-one: tax pressure forces a sell-off of empty downtown office space (a post-COVID glut), and the state can buy it cheap and convert it into actual housing — bringing residents (and life) back to dead downtowns.The Objections, Pre-DebunkedScot plays it straight and asks the obvious questions:"Won't the rich just flee to Florida or Montana?" Adam's counter: capital flight of real estate is mostly harmless — the buildings can't be packed in a suitcase. If a billionaire sells, the property stays in-state; prices just come down, which is the point."Won't this just raise rents?" Possibly nudges the very top of the market, but the broad base of affordable housing should barely move — maybe even gets cheaper as demand shifts."Will yachts and jets count?" No — keep it simple, this is real estate only.Where the Idea Comes FromAdam name-checks Gary's Economics (and its scrappier YouTube cousin, Barry's Economics) for the broader "tax wealth, not work" framing, and ties the proposal back to existing progressive wealth-tax proposals from Bernie Sanders (the famous "8% bracket above $1B," pegged to average market returns) and Elizabeth Warren (flat 2% above $50M) — positioning the progressive property tax as a more politically palatable, state-level cousin of those ideas.Detour of the EpisodeA spirited tangent on Maine's Senate race, Graham Platner — oyster farmer, combat veteran, and the episode's pick for "proof this message can win" — plus a running bit on whether wind turbines can literally use up the wind. (They cannot. Probably.)Quotable"I'm so wealthy that I can't pay the tax on my wealth." — the complaint Adam says is doing a lot of work to protect a small number of very rich people.Want me to also draft a short, punchier episode description (the 2–3 sentence blurb for podcast apps) or social media copy to go with these notes? Support the showHelp these new solutions spread by ...Subscribing wherever you listen to podcastsLeaving a 5-star review Sharing your favorite solution with your friends and network (this makes a BIG difference)Comments? Feedback? Questions? Solutions? Message us! We will do a mailbag episode.Email: solutionsfromthemultiverse@gmail.comAdam: @ajbraus - braus@hey.comScot: @scotmaupinadambraus.com (Link to Adam's projects and books)The Perfect Show (Scot's solo podcast)Thanks to Jonah Burns for the SFM music.

The numbers tell one story. The forces behind them tell a far more interesting one. In this mid-year edition of This Year Next Year, Kate Scott-Dawkins unpacks a forecast that has been significantly upgraded since December — not in spite of geopolitical turbulence, but in some ways because of it. From the AI investment cycle reshaping advertiser behaviour across every category, to a new advertising channel that could scale faster than anything the industry has ever seen, to the uncomfortable questions about what ad-funded platforms owe the people they serve — this is the episode that sets the agenda for the next 18 months.Key Topics DiscussedWhy global ad growth has been upgraded to 9% — and what it says about the structural resilience of the industry in a world of conflict, inflation, and fractured consumer confidenceThe Gold Rush analogy at the heart of the forecast — and why California is, once again, at the centre of it allGenerative Search: the brand new channel that could hit $100B by 2030, making it the fastest-scaling advertising format ever measuredWhy social media is now the single largest ad channel globally — and what threatens that dominance from 2027 onwardRetail media's quiet takeover: how commerce advertising overtook all of television in 2025, and what agentic commerce does to that equation nextThe streaming crossover: why 2026 is the year US streaming finally surpasses linear in national TV ad revenueOut-of-Home's unlikely staying power — and what it reveals about the limits of algorithmic mediaCourts, platforms, and product liability: how two landmark 2026 rulings could fundamentally reshape the economics of social mediaThe effervescence paradox — why people are retreating into AI-mediated solitude and simultaneously paying record prices to be in a crowdNew surfaces, no rulebook: the creative opportunity waiting in AR, autonomous vehicles, and ambient computingWhat the forty-niners got badly wrong — and what that means for the choices the advertising industry faces right now00:00 - Introduction and the Gold Rush analogy 02:00 - The upgraded forecast: headline numbers in context 06:00 - Three themes that frame everything else 10:00 - Where the money is moving: a channel by channel breakdown 18:00 - The bigger picture: political, economic, social, and technological forces 32:00 - Regional highlights 35:00 - Closing: Manifest Destiny and the future worth building

Keith Golembiewski, AVP, director, annuity research, LIMRA, discusses sustained strength in U.S. annuity sales, driven by market volatility, higher interest rates and demand for protected growth and retirement income solutions.

Story of the Week (DR):SuperBroIpoDystopia: Some key facts: MMa record-breaking $135 per share with$1.8T valuationTo make that math make sense, analysts estimate the company needs to grow its sales by 50% every single year for the next decadeSpaceX lost $4.9B last yearWall Street is Being Treated Like Order-Takers: Musk pre-set the IPO price strictly at $135 and dictating exactly which investors got allocations. This forced major investment banks like Goldman Sachs and Morgan Stanley to act as glorified order-takers without even knowing their exact compensation beforehandSaudi Aramco $1.7T; Alibaba: $237B; Facebook $118BNasdaq aggressively pushed through "fast-entry" rule changes specifically to allow mega-caps like SpaceX to bypass the traditional year of seasoning and enter the Nasdaq-100 in just 15 trading days. This forces passive index funds to buy in blindly to avoid tracking errorsMeme stocker bros: $100B in share orders30% of $75B offering is earmarked for individual retail investors. This effectively shifts late-stage, hyper-inflated valuation risk away from institutions and onto the public.BlackRock $5BInstitutional investors admitted that when they bought into SpaceX privately, they were given high-level revenue figures but were denied a copy of the actual balance sheet—an unprecedented lack of transparency for a company raising tens of billionsUniversity of Washington more than 10% of its $17B in assetsUNC about 10%SpaceX will make $75B in proceedsSaudi Aramco $26B; Alibaba $22BElon Musk's Absolute Voting Tyranny (80% of voting power)personal net worth has officially skyrocketed past $1.1TSpaceX's foundational scale was built on the back of the American public, securing over $20 billion in U.S. federal government contracts to fund its rocket developmentAntonio Gracias: personally lent Musk $1M to keep him afloat; his PE firm Valor gave $76MThat $1M lifeline and early institutional backing from 2008 have compounded into what analysts are calling the most lucrative return on a personal favor in business history.The Second-Largest Shareholder: Through various Valor entities, Gracias controls roughly 7.3% of SpaceX's Class A stock (more than 500 million shares)Gracias's stake is officially worth anywhere from $91B to over $140BThis single corporate listing instantly catapults Gracias into the ranks of the world's 50 richest people.The big party: combined valuation of $3.6TAnthropic ($965B) filed confidentially on June 1OpenAI ($1T) filed confidentially on June 8"We have not decided on timing yet; it may be a while because there are things we want to do that are likely easier as a private company. But it's a complicated set of tradeoffs, and this gives us the option to go public sooner if that ends up being best."What does it all amount to? 4 horrible objectives:Funding a Sci-Fi Passion Project with Public CashBecoming the Pentagon's Irreplaceable War MachineForget the folksy narrative that Starlink is just for connecting rural schools or isolated communities: SpaceX is systematically turning itself into the ultimate military contractorProject Starshield: Those satellites are the foundation for a highly classified, militarized version of the network designed for government surveillance, secure communications, and real-time battlefield tracking.Too Big to Regulate: By launching the vast majority of the world's payloads and controlling the dominant orbital communications network, SpaceX is making the U.S. military entirely dependent on its hardware. The ultimate point is to become so deeply embedded in national defense that the government can never afford to regulate, penalize, or dismantle Musk's empireAn Orbital Real Estate Land GrabBuilding a Borderless, Lawless EmpireSpaceX is attempting to build a tech infrastructure that exists entirely outside the jurisdiction of EarthUltimately, SpaceX isn't trying to save humanity from a dying Earth; it's trying to ensure that whoever controls Earth's future has to pay rent to Elon MuskIran threatens Elon Musk's companies in Middle East: Iranian state mediaAll of Elon Musk's companies in the Middle East are military targets for Iran as it retaliates against the U.S., Iranian state media outlet Fars reported.The targets include a regional Starlink ground station, according to Fars.Sen. Warren calls on SEC to delay SpaceX IPO, flagging concerns about valuation and governanceThe letter to the heads of the Nasdaq, S&P Dow Jones Indices, FTSE Russell and Morningstar Indexes sent on Thursday asked the companies whether they had made or considered rule changes based on lobbying from Elon Musk, other SpaceX officials or officials from OpenAI or Anthropic, and asked for any communications between the companies and the indexesLSEG, which owns the FTSE Russell, and Nasdaq declined to comment. Morningstar did not respond to a request from CNBC for comment.S&P Dow Jones Indices didn't comment on the letter, but the company noted it had decided not to change its rules regarding indexes: “S&P DJI determined that exceptions to these requirements should not be granted solely based on market capitalization,” it said in a statement to CNBC. “The decision not to adopt the proposed exceptions preserves core index principles by maintaining consistent application of these key requirements.”Democrats ask Goldman Sachs CEO why he's keeping lawyer who said she'd resign over ties to EpsteinGoldman Sachs CEO David Solomon is facing new scrutiny from congressional Democrats over his reported effort to retain the bank's top lawyer months after she said she would resign over revelations about her ties to convicted sex offender Jeffrey EpsteinIn a letter sent Wednesday:U.S. Senator Elizabeth Warren (D-Mass.), Ranking Member of the Senate Banking, Housing, and Urban Affairs CommitteeRepresentative Raja Krishnamoorthi (D-IL), Ranking Member of the Subcommittee on Health Care and Financial Services on the House Oversight Committee“Ruemmler ‘educated (Epstein) on how the law differentiates between underage victims of sex crimes and adult prostitutes…'”In February, Ruemmler announced her resignation from Goldman Sachs, effective June 30, 2026: “At the time, you stated that you “reluctantly” accepted Ruemmler's resignation. While Goldman Sachs has declined to comment on this matter, new reporting suggests that you ‘pressed' her to reconsider her resignation and instead move to a new position within the firm.”Teardown of Trump Phone Reveals Incredibly Embarrassing SecretA recent teardown by repair company iFixit confirmed that the T1 is an almost entirely unmodified HTC U24 Pro, a two-year-old and mid-tier Android phone, with a cheap coat of gold colorationTrump is selling an entirely Chinese smartphone, despite waging an economic war against the country.Apart from minuscule changes to the speaker grille and a lengthened flex cable, iFixit concluded that “everything is the same, except the pattern of holes in the case.”Goodliest of the Week (MM/DR):DR: Google and Meta denied new trial in youth social media addiction caseMM: In the United States, Solar Energy is Outpacing Coal for the First Time EverAssholiest of the Week - SPEED ROUND (MM):BP's useless, reactionary board of directors: BP drops net zero division in wake of boardroom turmoil; BP's new CEO Meg O'Neill rips up the energy giant's playbook—and the ‘green' era with it - 10Ryanair blowhard CEO Michael O'Leary: Ryanair investigated over charging parents to sit with children - 5EV killing GM and Mary Barra: GM is pivoting its battery expertise toward powering AI data centers and the grid - 10Every company that fired employees and replaced them with AI: Unfortunate Company Accidentally Blows Half a Billion Dollars on Claude in One Month; AI sticker shock hits corporate America - 10Everything out of Alex Karp's fat mouth: Palantir CEO Alex Karp says executives who brag about their AI cuts might as well ‘sign up for the Bernie Sanders manifesto'; Palantir CEO says AI companies 'don't understand how unlikeable they are'; - 10Sorry Liz, this is investors job: Sen. Warren calls on SEC to delay SpaceX IPO, flagging concerns about valuation and governance - 0Every investor in SpaceX IPO: Franklin Templeton to participate in SpaceX IPO, CEO Johnson tells CNBC; SpaceX IPO demand is approaching four times oversubscribed, source says; Wall Street's undignified SpaceX mania; SpaceX's president hints at a Tesla merger: 'That might make Elon's life a little easier' - 10Billionaires: Billionaires' Billions Are Increasing Faster Than Ever - 10Beef (not Ebola): Elon Musk Faces Backlash as a Horrific Texas Screwworm Outbreak Follows Brutal DOGE Budget Cuts - 10Mark: Meta Furious Over Bombshell Smart Glasses Revelation“Last week, Wired reported that Meta discreetly moved to infuse facial recognition tech into its popular smart glasses, as evidenced by a piece of code discovered in the Meta AI app by the magazine's journalists.” - 10Headliniest of the WeekDR: UBS CEO [Sergio] Ermotti hopes to step down before 2030MM: You Can Now Get a Religious Exemption From Using AI at Work“The funniest possible outcome of the AI mandate era is about to be HR departments discovering that ‘sincerely held religious belief' under Title VII has a much lower bar than they assumed, and Pope Leo handed every Catholic employee a written excuse,” tweeted San Francisco-based startup founder Corey Quinn. (Title VII of the Civil Rights Act prohibits employment discrimination and retaliation based on race, color, national origin, religion, and sex.)MM: Furious Judge Cancels Entire Trial After Finding Out Lawyers on Both Sides Used AIWho Won the Week?DR: HTC U24 Pro, a two-year-old and mid-tier Android phone. Or maybe it was the cheap gold paint?MM: Everyone religious - what CAN'T you opt out of using a religious exemption? PredictionsDR: Attacking dictator-run companies (i.e., Iran/Tesla) starts to enter the realm of normalcyMM: Atheists adopt a religion to opt out of tech bro oligarchies

Tired vs. Wired: $4 Trillion in IPOs Coming, $100B in M&A, and Why the SaaSpocalypse is Over The public markets spent the last twelve months telling you B2B software was finished. Stocks down 60 to 70 percent. PE firms buying nobody. For the first time in history, software trading at a discount to the S&P 500. And at the exact same moment, Anthropic is projecting $50 billion in revenue, Cursor is getting acquired for $60 billion, and SpaceX, Anthropic, OpenAI, and Databricks are about to generate more market value than every other IPO since 2000 combined. Both things are true - and which one defines your next 18 months depends entirely on one question: are you tired or are you wired? In this episode, SaaStr CEO and Founder Jason Lemkin calls the market as he sees it, names who is winning and who is pretending, and makes the case that the Cambrian explosion in B2B is just getting started. You'll learn: Why the SaaSpocalypse was never about B2B dying - it was about pre-AI software dying - and what the Palantir, Twilio, and Atlassian re-acceleration stories actually tell you The four categories every B2B company falls into right now, and why category four founders need to stop pretending the recovery is coming on its own Why vibe coding your CRM is dead as a concept, and what "putting deals on your calendar" actually means as a product strategy Why your biggest near-term competitive edge might be two days of engineering work - making your API agent-friendly before your competitors do What SaaStr's own journey from 20 humans to 3 humans and 21 agents teaches you about consistency as the only real cheat code in agents This is for you if: Your growth has slowed and you are not sure whether it is a market problem or a you problem - this session will help you figure out which You are a founder or exec who has been in the "AI is coming" conversation for a year but has not yet seen it show up in your revenue You want the unfiltered version of where B2B is headed in the next 18 months, including the parts most people are too polite to say out loud

Send us Fan MailWe've done the finance of Industry, the finance of Succession, the finance of Belle Burden's Strangers — but we've never done the finance of CREATORS. So when Spotify invited us to their Investor Day, we knew we had to sit down and ask the question every aspiring musician, podcaster, and Instagram creator is obsessing over: in a world where everyone wants to be a creator, how does anyone actually get paid?In this episode, we talk with Gustav Gyllenhammar, SVP of Markets and Subscriptions at Spotify, about the surprisingly complicated machinery behind every stream you play. Where does your $12.99 a month really go? How much does a million downloads of a song actually pay out? And how did a company born out of a piracy-ravaged Sweden convince an entire generation to start paying for something they'd grown up expecting for free? We get into the labels-versus-songwriters split, the rise of the independent artist, and the one number that explains why Spotify thinks it's playing a completely different game than the AI companies scraping the internet for content.Which brings us to the real tension underneath it all: as LLMs hoover up the work of writers, musicians, and creators everywhere, who's building a model to actually compensate them — and is Spotify offering a better blueprint? We dig into Spotify's philosophy on AI, why they waited so long to touch it on the music side, what "Time Well Spent" means when every other platform is optimizing for your attention, and whether the creators who power these platforms are about to get boxed out of their own economy. Plus: the new Universal Music partnership, the audiobook feature Jen has been praying for, and why a direct listing might be the most underrated way to go public.Shop our Self Paced Courses:Investment Banking & Private Equity Fundamentals HEREFixed Income Sales & Trading HERESubscribe to our Substack: https://substack.com/@thewallstreetskinny

SpaceX Files S-1: The $2 Trillion IPO Thesis, Starlink Cash Engine, AI Pivot, and Bitcoin AngleThe script discusses SpaceX officially filing an S-1 with the SEC and frames it as a landmark IPO targeting a $2 trillion valuation after private valuations rose from $100B to $200B. It breaks SpaceX into three pillars: space launch/Mars ambitions (about $4.1B revenue in 2025), Starlink as the profitable cash engine (Q1 2026 connectivity revenue $3.3B with over $1.2B profit), and a major pivot into AI infrastructure with billions spent on data centers and custom hardware, described as roughly $8B per quarter. It notes the S-1 confirms SpaceX holds a significant digital asset and has been a longtime Bitcoin holder, while warning IPO volatility will be high and advising patience, monitoring Starlink growth and any post-IPO Bitcoin additions as potential catalysts.00:00 SpaceX IPO Shockwave00:55 Two Trillion Valuation Math01:11 Three Pillars Breakdown02:12 Starlink Cash Engine02:36 AI Infrastructure Pivot03:08 Bitcoin On The Balance Sheet03:59 IPO Risks And Mindset04:51 How To Play The IPO05:34 Live Coverage And Wrap Up________________________________________________________________FOLLOW ME ON X: https://twitter.com/staywinningusdFOLLOW ME ON INSTAGRAM: https://www.instagram.com/staywinningusd/SUBSCRIBE ON YOUTUBE: www.youtube.com/@staywinningusdDOWNLOAD ON SPOTIFY: https://open.spotify.com/show/2lPyA19keI2fpr0xZrEKxMNEWSLETTER SIGNUP: https://stay-winning-wealth.kit.com/806fb337d7SUBSCRIBE TO THE BLOG: https://medium.com/@staywinningusd________________________________________________________________

On this week's episode of Valley of Depth, our first recorded in person, we sit down with Jason Kim, CEO of Firefly Aerospace, in the company's historic Blue Ghost mission control room in Cedar Park, Texas — the same room where 60 engineers watched their lander touch down at one meter per second last year.    From there, the conversation opens into how Jason actually thinks: about the Moon, about scale, and about being a "mission CEO" rather than a hardware or software one. Firefly went public in 2025, acquired defense software company SciTec within months, and now sits inside Golden Dome. Jason argues the market still prices the company as a pure launch player while he's building an end-to-end stack he puts in the same conversation as Anduril and Palantir. We cover: The last 30 seconds of the Blue Ghost Mission 1 landing, from inside the room where it happened Why Blue Ghost Mission 2 is harder: a three-spacecraft stack and the first US far-side landing Whether small launch makes money, and why Alpha is both a profit center and a strategic asset The Eclipse medium-lift bet, the Northrop partnership, and why Starship doesn't make everyone else obsolete Why the Moon matters, and how big the commercial lunar economy actually gets Why a hardware CEO bought a software company The valuation gap with Rocket Lab and what he believes the market hasn't priced in His honest read on SpaceX, China, the new-launch shakeout, and the path to a $100 billion company   • Chapters • 00:00 - Trailer 00:53 – Blue Ghost Mission 1 04:41 – The bar for success for Blue Ghost Mission 1 07:16 – What is the new objective in Blue Ghost Mission 2? 11:49 – Jason coming into Firefly leadership 16:35 – Day 1 as Firefly CEO 18:53 – AE Industrial and how private equity informs Jason's mindset 21:02 – Product stack 22:34 – Demand signal from responsive launch 24:21 – Alpha and small launch economics 26:20 – Firefly's Eclipse 28:09 – How Starship will impact the launch market 29:41 – Viability of commercial launches 32:15 – Blue Ghost x Eclipse? 33:51 – Why does the Moon matter? 36:02 – Jason's commercial lunar economy predictions 38:02 – The future of Blue Ghost's missions 39:52 – Why Jason acquired Sitec 44:30 – Sitec in the Space Force's Golden Dome contracts 47:16 – Why shift Firefly to being a public company? 49:04 – How does Jason address stock price fluctuation internally? 50:49 – Do the public markets understand the space economy? 52:57 – Is Firefly just a launch company? 55:25 – What part of Firefly has the market not priced in yet? 56:50 – Firefly's strategy in a world where lift becomes effectively free 58:49 – Which launch companies will survive? 59:56 – The China question 1:00:33 – Is there a company out there that doesn't get enough attention? 1:01:53 – How Firefly is thinking about M&As 1:04:25 – The path to Firefly hitting a $100B valuation 1:05:25 – Jason Kim, the person 1:07:07 – Who does Jason call for advice? 1:07:57 – What Jason would tell 25-year-old Jason 1:11:58 – What Jason does for fun when not working on space   • Show notes • Firefly's' website — https://fireflyspace.com/ Jason's' socials — https://x.com/Jason_Lil_Kim/ Mo's socials — https://x.com/itsmoislam Payload's socials — https://x.com/payloadspace / https://www.linkedin.com/company/payloadspace Ignition's socials — https://x.com/ignitionnuclear / https://www.linkedin.com/company/ignition-nuclear/ Tectonic's socials — https://x.com/tectonicdefense / https://www.linkedin.com/company/tectonicdefense/ Valley of Depth archive — Listen: https://pod.payloadspace.com/   • About us • Valley of Depth is a podcast about the technologies that matter — and the people building them. Brought to you by Arkaea Media, the team behind Payload (space), Ignition (nuclear energy), Decoding Bio (biotech) and Tectonic (defense tech), this show goes beyond headlines and hype. We talk to founders, investors, government officials, and military leaders shaping the future of national security and deep tech. From breakthrough science to strategic policy, we dive into the high-stakes decisions behind the world's hardest technologies. Payload: www.payloadspace.com Tectonic: www.tectonicdefense.com Ignition: www.ignition-news.com Decoding Bio: www.decodingbio.com

(0:00) OpenAI CFO Sarah Friar joins the show! (0:31) How OpenAI thinks about its IPO timeline (3:31) OpenAI, Anthropic, Google: The AI arms race (7:43) Navigating the compute crunch and AI bottlenecks, device preview! (15:53) OpenAI's economics (26:08) Push into chips, the cloud (29:32) OpenAI's ad business and strategy Thanks to our partners for making this possible! EY - Agentic AI is introducing a new investment discipline. As AI shifts to consumption-based models, EY connects spend to enterprise value. https://www.ey.com/en_us/insights/ai/agentic-ai-token-costs?WT.mc_id=3501318&AA.tsrc=sponsorship NYSE - Thank you to our partner, the New York Stock Exchange - a modern marketplace and exchange for building the future. It all happens at the NYSE. https://www.nyse.com Plaud - Never miss a moment. Plaud, our official wearable AI note-taking partner at All-In Liquidity Summit, captured every insight. https://www.plaud.ai Follow Sarah Friar: ⁠https://x.com/thefriley⁠ Apply for Summit 2026: ⁠https://allin.com/events⁠ Follow the besties: ⁠https://x.com/chamath⁠ ⁠https://x.com/Jason⁠ ⁠https://x.com/DavidSacks⁠ ⁠https://x.com/friedberg⁠ Follow on X: ⁠https://x.com/theallinpod⁠ Follow on Instagram: ⁠https://www.instagram.com/theallinpod⁠ Follow on TikTok: ⁠https://www.tiktok.com/@theallinpod⁠ Follow on LinkedIn: ⁠https://www.linkedin.com/company/allinpod⁠ Intro Music Credit: ⁠https://rb.gy/tppkzl⁠ ⁠https://x.com/yung_spielburg

In this episode of the Capital Raiser Show, Richard Wilson sits down with billionaire entrepreneur and AI investor Pavan Agarwal for a fireside chat on mindset, artificial intelligence, mortgage innovation, and building a long-term technology platform. Pavan shares how his family built SunWest Mortgage into a major national lender, why trust and integrity helped the business survive the financial crisis, and how Angel AI is being developed to simplify financial services, lending, credit, insurance, taxes, and long-term wealth planning. This conversation explores the mindset behind building and protecting a massive AI portfolio — and why the biggest opportunities may come from combining deep industry experience with technical execution. Topics covered include: The mindset shift required to scale a national business Why integrity matters when markets turn against you How AI is changing mortgage lending and financial services Why AI is an "ocean," not just a wave The value of patents, proprietary technology, and long-term vision Why founders should trust their own instincts earlier How Angel AI aims to become a personal financial companion Real estate, fixed income, and technology investing insights Why patient capital can win in AI and startups The biggest mistakes founders make when chasing trends To meet investors in person and learn directly from decamillionaires, family offices, and ultra-wealthy investors, visit FamilyOffices.com

On Halloween 2008, while the global financial system was collapsing and banks were getting bailed out with taxpayer money, an anonymous figure posted a nine-page paper that would change everything. Two months later, Satoshi Nakamoto mined the first Bitcoin block and buried a newspaper headline inside it about bank bailouts - a permanent message encoded forever on every node on Earth. Then he mined over a million Bitcoin, watched it become worth over $100 billion, and never touched a single coin. This is the real origin story of Bitcoin, told the way it deserves to be told. Learn more about your ad choices. Visit megaphone.fm/adchoices

May 27, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: Nordic-inspired wellness experiences including saunas and cold plunges grew 62.5% globally from 2024 to 2025, with cold plunge market hitting $355M and sauna industry nearing $1B Lucis raises $20M to expand preventative health across Europe as "Function Health for Europe," combining 110-marker blood testing with AI health companion for 10,000 users Apple loses momentum in digital health per Bloomberg as wearables shift toward predictive insights, facing competition from Oura and Whoop despite $100B in Apple Watch sales More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider Reach out → insider@fitt.co

Quantum computing stocks surged after the US announced $2B in grants with equity stakes. Spotify jumped 13% on 2030 guidance targeting $100B in revenue. Anthropic expects $10.9B in Q2 revenue and its first-ever operating profit, while Trump pulled back an AI executive order after calls with Musk and Zuckerberg. Shares of quantum computing companies surged Thursday after the US government announced grants with equity stakes: D-Wave closed up 33%, Rigetti 30%, IBM 12% (CNBC) Spotify closed up 13% on Thursday after announcing new features and 2030 guidance, forecasting a compound annual growth rate in the mid-teens (CNBC) Workday reports Q1 revenue up 13% YoY to $2.54B vs. $2.52B est., and lifts its full-year forecast, saying its AI strategy is working; WDAY jumps 9%+ after hours (CNBC) Sources: Trump delayed signing the AI EO because "he just hates regulation"; there were questions about the EO giving the Treasury Department a leading role (Axios) Investor disclosures: Anthropic says it expects to generate $10.9B in revenue in Q2, up 127% from $4.8B in Q1, and turn a $559M operating profit, its first ever (WSJ) Longreads In more than two-thirds of the world's countries, birthrates have fallen below replacement, and researchers increasingly point the finger at smartphones and social media (FT) Learn more about your ad choices. Visit megaphone.fm/adchoices

Matt is joined by Hernan Lopez, founder of Owl and Co. and Wondery, to talk about the rise of vertical video, how it ballooned to a $100B/year business, the staggering success of Reels, and which companies in Hollywood are best suited to benefit the most from this trend (00:00). Matt finishes the show with a prediction about next season's cast of 'Saturday Night Live' (26:50). Host: Matt Belloni Guest: Hernan Lopez Producers: Craig Horlbeck and Matt Pevic Theme Song: Devon Renaldo Industry voters visit Starz FYC.com. For more information visit Hulu.com/FYC Learn more about your ad choices. Visit podcastchoices.com/adchoices

OpenAI is weighing legal action against Apple over a Siri integration it says fell far short. Cerebras opened at $350 in the largest US tech IPO since Uber. Mythos helped researchers crack macOS security, Anthropic restores OpenClaw access with Agent SDK credits, and 71% of Americans oppose local data centers. Sources: OpenAI is weighing legal action against Apple after expectations that ChatGPT's Siri integration would generate billions in revenue fell short (Bloomberg) Security researchers used Anthropic's Mythos to discover a privilege escalation exploit in macOS, circumventing Apple's Memory Integrity Enforcement in five days (WSJ) Cerebras opens at $350, valuing the chipmaker at $100B+, after raising $5.5B by selling 30M shares at $185, the largest US tech IPO since Uber's debut in 2019 (CNBC) Anthropic unveils Claude Agent SDK credits for paid plans, which users can allocate for programmatic use of third-party agents like OpenClaw, starting June 15 (VentureBeat) AT&T, T-Mobile, and Verizon sign an "agreement in principle" to form a joint venture that aims to end wireless dead zones in the US, without giving many details (The Verge) Gallup: 71% of Americans oppose local AI data center construction, citing water and electricity issues, with opposition higher among Democrats than Republicans (Washington Post) Learn more about your ad choices. Visit megaphone.fm/adchoices

On Thursday, May 14, Brian Szytel recaps a broad market gain (Dow +370, S&P 500 +0.7%, Nasdaq +0.9%) with the 10-year Treasury closing near 4.48% and argues the 4.50% level is not a meaningful “line in the sand,” noting rate pressure tied to oil above $100 amid Iran-deal uncertainty. He summarizes Trump's two-day meeting with China's President Xi as generally positive, with Xi raising Taiwan and Trump not engaging. Markets continue a “wall of worry” melt-up driven by an AI capex/productivity boom, while Q1 tax refunds ($202B vs. $179B last year) and about $100B in refunded tariffs (about one-third already returned) add stimulus, though both reflect timing of taxes extracted and refunded. Strong earnings compressed valuations (S&P ~22x to ~21x), with Middle East tensions and energy prices creating Q2 uncertainty and a moderate bull-bear ratio (~2.2:1). He addresses a question about sharing ideas on media, emphasizing TBG's client relationship and evolving portfolio management as the core value. Economic notes: retail sales in line, jobless claims slightly higher but in line, and import/export prices higher with exports rising more. 00:00 Market Snapshot 00:25 Rates Oil And Geopolitics 01:48 AI Boom And Wall Of Worry 02:21 Refunds And Tariff Rebate Boost 03:45 Valuations Earnings And Sentiment 04:49 Sharing Ideas Versus Client Value 06:42 Economic Data And Sign Off Links mentioned in this episode: DividendCafe.com TheBahnsenGroup.com

Gavin Newsom's “Stop Nick Shirley Act” backfires as Nick calls him out for protecting fraud, confronting California lawmakers over a bill critics say chills journalism, hides migrant spending, and violates the First Amendment.

Three stories on the table this week, and none of them small.Saks Global plans to exit Chapter 11 on June 22nd carrying $1.2 billion in debt, with a reorganization plan targeting $9 billion in GMV by fiscal 2030. That's nearly double where they sit today. Rick Watson and Jessica Lesesky walk through the vendor mess (720 brands stopped shipping at the worst of it), the repair work underway, and why exiting bankruptcy this leveraged sets up another round of trouble down the road.The Watson Weekly Weekend edition is sponsored by Avalara - the agentic AI platform automating global tax and compliance for leading eCommerce brands. For more details: https://avalaratax.watsonweekly.comOver at Victoria's Secret, Australian investor Brett Blundy's BBRC Worldwide has built a roughly 13% stake and is pushing to remove two directors: chair Donna James and Miriam Naficy. The complaint is acquisitions like Adore Me. CEO Hillary Super is running a "path to potential" plan built around body positivity and a return to the Angels heritage. Fiscal 2025 sales are up 5%. The question is whether that's enough to keep the activist quiet.Then earnings. Alphabet did $109B in Q1, with Google Cloud growing 63% YoY to a $20B run rate and a $462B backlog. Amazon hit $181B, AWS grew 28% to $37.5B, and the chip business crossed a $20B run rate of its own. Shopify cleared $100B in quarterly GMV for the first time, with operating income up 88% on the back of all the layoffs and restructuring.The thread underneath all of it: AI compute is getting more expensive, not less. The pricing power is sitting with the infrastructure layer. Amazon, Nvidia, and the LLM owners are collecting the rent. The businesses adopting AI are paying it.

Brought to you by TogetherLetters & Edgewise!In this episode: AI FrontierAnthropic's New Mythos A.I. Model Sets Off Global AlarmsMozilla Used Anthropic's Mythos to Find and Fix 271 Bugs in FirefoxSam Altman compares Mythos to dropping a bomb while selling a $100B bomb shelterAnthropic could raise a new $50B round at a valuation of $900BOpenAI releases GPT-5.57-0 wipeout: ChatGPT-5.5 vs Claude 4.7 in 7 impossible testsChina orders Meta to unwind $2B Manus acquisitionHe Built a $1.8 Billion Company Alone with AITech Layoffs & Big MovesJohn Ternus named Apple CEO to replace Tim CookNearly 40,000 tech jobs lost in April 202620,000 job cuts at Meta, Microsoft raise AI labor crisis concernNetflix plans vertical video feed and AI recommendationsPrivacy, Security & Age ChecksUS Bill Mandates On-Device Age VerificationBrussels age-checking app hacked in 2 minutes$5 Bluetooth tracker in a postcard exposes Dutch warshipHardware, Science & EngineeringNIST creates 'any wavelength' lasers in tiny circuitsNASA shuts off instrument on Voyager 1Anker made its own AI chip (Thus)YouTuber builds working DRAM in backyardLinux begins dropping Intel 486 supportPancreatic cancer mRNA vaccine shows lasting resultsBMW one step closer to a color-changing carAlberta startup sells "no-tech" tractors for half priceRobots Take the FieldChinese android beats human half-marathon recordJapan Airlines pilots humanoid robots at HanedaTable tennis robot defeats top human playersWeird & WackyChinese carmaker patents voice-controlled in-vehicle toiletAir New Zealand adds economy bunk beds (with rules)Hairdryer allegedly used to trick weather sensor for $34K Polymarket betDOJ arrests soldier who made $400K betting on Maduro's removalI bought Friendster for $30K — here's what I'm doing with itNZ DOC: remote tech begins a "new era" for conservationTech Rec:Sanjay - Citymapper Adam - Claude DesignFind us here:sanjayparekh.com & adamjwalker.comTech Talk Y'all is a proud production of Edgewise.Media.

Big Tech earnings landed — Alphabet soared on cloud growth while Meta dropped 10% after hiking capex to $145B. SoftBank plans an AI/robotics IPO called Roze, Anthropic weighs a $900B+ round, and Musk called himself a "fool" for backing OpenAI. Microsoft says Q3 Intelligent Cloud revenue was $34.68B, vs. $34.27B est., with Azure and other cloud services up 40% YoY; Microsoft 365 Copilot has 20M+ seats (CNBC) Meta raises full-year capex outlook to $125B–$145B, up from $115B–$135B; shares drop ~10%, biggest intraday decline since October (Bloomberg) Alphabet stands out on Big Tech earnings day as Google Cloud revenue jumps 63% and backlog nearly doubles to $462B; capex guidance raised to $180B–$190B (MarketWatch) Big Four combined Q1 capex hit a record $130B, on pace for $725B in 2026, up 77% from $410B last year (FT) Sources: SoftBank plans to create an AI and robotics company called Roze in the US to build data centers and list it as early as 2026, seeking a $100B valuation (FT) Sources: Anthropic has begun weighing a new funding round at a $900B+ valuation, after previously resisting investor proposals at an $800B+ valuation (Bloomberg) Sony confirms that some digital PS4 and PS5 games require a one-time online license check "to confirm the game's license" (GameSpot) OpenAI explains Codex's "goblin problem": reinforcement training rewarded quirky creature metaphors via a discontinued "Nerdy" personality, and the behavior spread (The Verge) Musk v. Altman: Elon Musk says he was a "fool" for backing OpenAI, accusing Altman and Brockman of manipulating him into donating tens of millions of dollars (WSJ) Learn more about your ad choices. Visit megaphone.fm/adchoices

Limited BONUS: First 1,000 builders get $1,000. Claim yours while supplies lasts.: https://startup-ideas-pod.link/hyperagent I sit down with Howie Liu, co-founder and CEO of Airtable, to talk about the agent economy and the launch of HyperAgent. We walk through Sequoia's charts on AI agent deployment, the economics of token-based work versus human labor, and why frontier agents have crossed a threshold that changes how companies get built. Howie then does a live show-and-tell of HyperAgent, including a custom "Greg Isenberg contrarian AI" skill he spins up in real time. This one is for anyone building a solopreneur business, operating a fleet of agents, or trying to figure out where to place their bet in the agent ecosystem Timestamps 00:00 – Intro 02:22 – Sequoia's AI agent deployment chart reaction 04:41 – Copilot vs Autopilot territory and the $1T+ opportunity 08:13 – Agent economics vs human labor costs 11:12 – Fastest enterprise adoption curve in history 14:48 – The agent command center and fleet of 20 agents 18:03 – What is HyperAgent? 19:43 – Live demo: hyperlocal real estate market reports 22:38 – HyperAgent as the founder, not just the developer 23:21 – Street View, Zillow redesigns, and visual tool power 24:15 – Command center view across a fleet of agents 25:48 – Skills as the key primitive for frontier agents 26:30 – Building the Greg Isenberg contrarian AI skill live 32:31 – HyperAgent vs Perplexity Computer, Manus, OpenClaw, Codex 34:52 – Reviewing writing skill 36:55 – The arbitrage of persistence 41:31 – Confidence milestones: first dollar, $10K/month 35:27 – Reviewing contrarian tweet drafts live 45:05 – Giving the agent feedback and building rubrics 50:15 – Connectors, OAuth, and building custom API skills 53:03 – How to get started with HyperAgent 01:01:54 – Credit giveaway for listeners 01:03:31 – Closing Thoughts Key Points Frontier agents have crossed a threshold in the last 4–5 months where they function as true autonomous coworkers, not just chat assistants. Reframe agent cost by value delivered: a $150 token spend for a board memo beats hours of human time, so anchor on opportunity cost. The real arbitrage is persistence: 99% of people quit after one shot, while daily practice for 30/60/90 days produces top 1% operators. Skills are the most important primitive in frontier agents, turning generally intelligent models into domain experts through playbooks. HyperAgent's differentiation is a low floor plus a high ceiling, with rubrics, LLM-as-judge evals, and fleet-wide observability for scaling. Aim for $100B companies with under 5 employees, built on fleets of always-on agents mapped to human job roles. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND HOWIE ON SOCIAL X/Twitter: https://x.com/howietl Hyperagent: https://www.hyperagent.com Airtable: https://www.airtable.com-

Welcome back Matt Liberty (Joulescope) and Luke Beno (Werewolf.us) Matt has been a guest on episodes 527 and 607 Luke was a guest on episode 272 Luke launched a new cable manufacturing and power supply company in the US called Werewolf.us Matt is working on the JS320 We discussed how PartsBox is a great ERP solution but Matt and Luke decided to go fully custom with Claude Code. Jan Rychter was a guest on episode 542 We discussed the differences with Product Lifecycle Maintenance. Michael Corr of the recently acquired Duro Labs was on episode 577 CAM workflow A fully verticalized PCB factory is something Jonathan Hirschmann talked about on episode 299 Jeff Bezos is investing 100B in a fund that is looking at automation in the factory using AI Matt recently had success with Claude Code and verilog programming Saleae for hardware in the loop using their APIs Other tools to check out pyelf pdfdk blast superpowers skill (by past guest at Teardown Jesse Vincent) Luke used OpenClaw to power a chat agent in his ERP system Working with distributors TI backlog Chris recently learned that Digikey has a developer API Cocotb verification framework (in Python) Luke is working on vision experiments for inhouse developed AOI solutions

Apple named John Ternus as its next CEO, with Tim Cook stepping up to executive chairman on September 1. Amazon agrees to invest up to $25B more in Anthropic, Bezos' Project Prometheus nears a $10B raise, and SpaceX's IPO prospectus reveals Musk's power moves. John Ternus, senior VP of Hardware Engineering, will become Apple's next CEO on September 1; Tim Cook will become executive chairman of Apple's board (CNBC) Amazon agrees to invest up to $25B in Anthropic, on top of the $8B that it has already invested; Anthropic commits to spend $100B+ on AWS over the next 10 years (CNBC) Sources: Jeff Bezos' Project Prometheus is close to a $10B fundraising deal, which includes an initial $6.2B raise in November, at a $38B post-money valuation (FT) Draft of SpaceX's confidential IPO prospectus: Elon Musk increased his stake in SpaceX last year by purchasing $1.4B of stock from current and former employees (The Information) Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, Lisa Boothe sits down with investigative journalist Chris Rufo to uncover shocking examples of government waste, fraud, and political corruption—particularly in California. Rufo breaks down his latest reporting on a $114 million taxpayer-funded wildlife bridge, massive Medicaid fraud, and how billions in public funds are funneled into ideological projects and politically connected groups. The conversation also dives into San Francisco’s controversial diversion of police funding into DEI initiatives, raising serious questions about public safety and accountability. Plus, what does this mean for Governor Gavin Newsom’s national ambitions—and will voters care about the staggering scale of waste?