Podcasts about Colossus

Send us Fan Mail#387 - Every year when my Sober Birthday comes around, I like to talk about it on the show. I pick a different theme or aspect of sobriety and what it means to me. There are thousands of us sober/clean runners out there. I can think of few activities that are as great for those getting sober or clean than running. My theme this year is being honest with ourselves. This applies to both running and the rest of our lives. Many times we're not honest with ourselves in our running. It's time to be honest. If you're injured, then get help. If you're slow and you run, you're a runner. And so on..... give yourself props, you deserve it.  Fit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts SpotifySupport the showMartha Runs the World websitehttps://www.buzzsprout.com/248027Email:martharunstheworld@gmail.comYouTube:https://www.youtube.com/@martharunstheworldInstagram:https://www.instagram.com/martharunstheworld/#

SpaceX just made history, raising $75 billion in the largest IPO the stock market has ever seen, now trading on NASDAQ at a $1.8 trillion valuation. 7investing's Simon Erickson break downs what you actually need to know as an investor. The SpaceX empire spans X (formerly Twitter, 600M users), xAI (the Grok-powering AI infrastructure running out of the 2-gigawatt Colossus data center), and 10,000 Starlink satellites serving 10 million subscribers across 164 countries. The scale is genuinely unprecedented.But the numbers tell a more complicated story. SpaceX did $20 billion in revenue last year, pricing it at 90x trailing sales, and generated just $1 billion in Q1 operating cash flow against $10 billion in quarterly capital expenditures. The company is burning cash aggressively, and the entire long-term thesis rests on Elon Musk executing on missions no company has ever attempted: orbital data centers, Starship, and eventually a Mars colony. This isn't a software company where you flip a switch and double revenue. These are physical, capital-intensive bets measured in decades.Simon and Heather are both passing on the IPO. The key man risk alone, Elon simultaneously running SpaceX, Tesla (NASDAQ:TSLA), X, and xAI, is the largest concentration of founder dependency in stock market history. Tesla (NASDAQ:TSLA) fans know this playbook: extraordinary vision, breakthrough results, but timelines that consistently slip years past what Elon says publicly. Full self-driving still isn't there. Orbital data centers won't be either, at least not on the schedule the prospectus implies.Near term, Starlink is the real business the only one generating meaningful cash flow and it's what will sustain SpaceX while Elon bets big on everything else. Expect another capital raise in 2026 and again in 2027. The real question for investors isn't whether SpaceX can change the world. It probably will. The question is whether a $1.8 trillion valuation gives you any margin of safety while it gets there. Right now, Simon and Heather say no.Join the conversation on the 7investing discord: https://discord.com/invite/PT9ZQqdXXSWant access to all our investing content? Join at 7investing.com/subscribe Stocks & Companies Mentioned:SpaceX (NASDAQ: SPCX)Tesla (NASDAQ:TSLA)Rocket Lab (NASDAQ:RKLB)xAI — private (subsidiary within SpaceX conglomerate)X (formerly Twitter) — private (subsidiary within SpaceX conglomerate)OpenAI — private#SpaceX #SpaceXIPO #ElonMusk #Starlink #IPOInvesting #SpaceStocks #TechIPO #GrowthStocks #StockMarket #StocksToWatch #TechStocks #SpaceInvesting #InvestingIn2026 #7investing #Simonerickson

The drama around Anthropic's Fable 5 model clogged our collective attention spans.

The trailer for X-Men '97 Season 2 has finally dropped! We'll discuss that as well as all of the X-Men comics that came out for the Month in X for May 2026. Month in X - May 2026 Wade Wilson: Deadpool #4 Sai: Dimensional Rivals #5 Wolverine: Weapons of Armageddon #4 Psylocke: Ninja #5 Jubilee: Deadly Reunion #1 Rogue #5 Cyclops #4 Moonstar #3 Magik and Colossus #4 Storm: Earth's Mightiest Mutant #4 Generation X-23 #4 Inglorious X-Force #5 Wolverine #20 (LGY #412) X-Men United #3 Uncanny X-Men #28 (LGY #728) X-Men #29 (LGY #338)  X-Men #30 (LGY #339)

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Send us Fan MailFit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts Spotify

De slotkoers van de grootste beursgang ooit is bekend. 160 dollar en 95 cent. SpaceX is 2.1 biljoen dollar waard en Elon Musk is biljonair. Bij OpenAI en Anthropic kunnen ze rustig ademhalen, want de markt is niet stuk. Integendeel: beleggers hebben opnieuw betaald voor de mythe van Musk en tonen zich bereid om verregaande bedragen te steken in de bizarre waarderingen van AI-bedrijven die dit jaar naar de beurs gaan. Tijdens het laatste uur van de beursdag maakten Donner Bakker, Jochem Visser en hun gasten een extra uitzending richting die laatste koers op de borden. Gast Johannes Smit, portfoliomanager bij het Centive Global Equity Fund van IBS, legt uit wat dit betekent voor de markt en voor beleggers. Hij bespreekt het verdere verloop van de koers nu er aandelen kunnen worden verkocht door insiders, terwijl indexen juist gedwongen gaan kopen. En hij legt uit waarom de verregaande zorgen van indexbeleggers wat hem betreft onterecht zijn. Gasten Joe van Burik en Ben van der Burg, techcommentatoren van BNR en makers van De Grote Tech Show, bespreken hoe dit bizarre bedrijf nu in elkaar steekt en hoe dat zo is gekomen. Natuurlijk moet Musk zelf ook nog even langs de lat worden gelegd. Is zijn effect op het universum nou netto positief, of negatief? Hint: er is een goeie discussie over te voeren. BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Je hoort hem ook in de BNR-podcast Moerdijk: dorp van de rekening. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie. Van Musk tot Microsoft en van Ahold tot ASML. Wij vertellen je wat beleggers bezighoudt, wie de markten in beweging zet en wat dat betekent voor jouw beleggingsportefeuille.See omnystudio.com/listener for privacy information.

De slotkoers van de grootste beursgang ooit is bekend. 160 dollar en 95 cent. SpaceX is 2.1 biljoen dollar waard en Elon Musk is biljonair. Bij OpenAI en Anthropic kunnen ze rustig ademhalen, want de markt is niet stuk. Integendeel: beleggers hebben opnieuw betaald voor de mythe van Musk en tonen zich bereid om verregaande bedragen te steken in de bizarre waarderingen van AI-bedrijven die dit jaar naar de beurs gaan. Tijdens het laatste uur van de beursdag maakten Donner Bakker, Jochem Visser en hun gasten een extra uitzending richting die laatste koers op de borden. Gast Johannes Smit, portfoliomanager bij het Centive Global Equity Fund van IBS, legt uit wat dit betekent voor de markt en voor beleggers. Hij bespreekt het verdere verloop van de koers nu er aandelen kunnen worden verkocht door insiders, terwijl indexen juist gedwongen gaan kopen. En hij legt uit waarom de verregaande zorgen van indexbeleggers wat hem betreft onterecht zijn. Gasten Joe van Burik en Ben van der Burg, techcommentatoren van BNR en makers van De Grote Tech Show, bespreken hoe dit bizarre bedrijf nu in elkaar steekt en hoe dat zo is gekomen. Natuurlijk moet Musk zelf ook nog even langs de lat worden gelegd. Is zijn effect op het universum nou netto positief, of negatief? Hint: er is een goeie discussie over te voeren. BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Je hoort hem ook in de BNR-podcast Moerdijk: dorp van de rekening. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie. Van Musk tot Microsoft en van Ahold tot ASML. Wij vertellen je wat beleggers bezighoudt, wie de markten in beweging zet en wat dat betekent voor jouw beleggingsportefeuille.See omnystudio.com/listener for privacy information.

Invest Like the Best: Read the notes at at podcastnotes.org. Don't forget to subscribe for free to our newsletter, the top 10 ideas of the week, every Monday --------- My guest today is Alex Sacerdote, founder of Whale Rock Capital Management.  Whale Rock is a technology focused investment firm that manages more than $17 billion across hedge fund, long only, and hybrid strategies. Over the past three years it has been one of the best performing hedge funds, compounding at roughly 44 percent a year. Alex invests through a single lens that he has refined over twenty years. He looks for technology S-curves, durable competitive advantages, and underappreciated earnings power.  This conversation is a tour through how he applies that framework right now. We start with his highest conviction position, which is Anthropic, and use it to work through the entire AI stack from chips to models to applications.  Please enjoy my conversation with Alex Sacerdote. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:00:00) Welcome to Invest Like The Best (00:02:29) Alex Sacerdote (00:03:08) Anthropic: Highest Conviction Position (00:13:23) Investing in Private Markets at Scale (00:19:08) S-Curves: The Full Framework (00:25:08) When to Buy Tech Companies (00:30:20) Identifying the Leader from the Pack (00:34:04) Anthropic & OpenAI's Competitive Moats (00:37:31) AI's Threat to Enterprise Software (00:43:18) Network Effects in the Agent Era (00:44:22) The Hardware Renaissance: Chips & Infrastructure (00:53:56) Why So Few Investors Get This Right (00:55:36) Key Risks to the AI Bull Case (00:57:47) The Application Layer (00:59:40) How AI Is Changing Research at WhaleRock (01:02:53) The Role of Investor Networks & Idea Sharing (01:03:40) Building a Multi-Product Firm (01:07:58) WhaleRock as a Learning Machine (01:09:15) The Kindest Thing

Angie Jackson returns to The Everyday Ironman Podcast for her third appearance, joining us just six days after crossing the finish line at Ironman Jacksonville. In this inspiring conversation, Angie shares her race-day experience, the challenges she faced, and the lessons learned from another incredible Ironman journey.We also dive into an important discussion about representation in endurance sports. As a Black female athlete, Angie offers her perspective on competing in a sport where athletes who look like her remain significantly underrepresented. Her insights highlight both the progress being made and the opportunities that still exist to make triathlon more inclusive.In addition, we discuss injuries, recovery, and the physical and mental resilience required to stay consistent through setbacks. Whether you're training for your first sprint triathlon or your next Ironman, this episode delivers motivation, perspective, and practical takeaways for every age-group athlete.#TheEverydayIronman #IronmanTriathlon #TriathlonLife #AgeGroupAthlete #EnduranceSports #IronmanJacksonville #TriathlonTraining #SwimBikeRunFit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts SpotifySupport the show

Send us Fan MailThis week on Running with James… Coach Carlos is BACK!We dive into the current state of HYROX and there's a lot to unpack:

Hey folks, Alex here, and welcome to a BIG MODEL week! We finally got Mythos (well almost)! Let me catch you up! This week started with WWDC26 from Apple, and Max Weinbach, who was in the room at Apple Park and actually has access to some of the new features including an all new SIRI AI, joined us to break down what could be the most used AI in the world very soon. At first I was skeptical, but he convinced me that the new Siri is actually good! Then, we saw the ultimate model drop: Anthropic finally shipped Mythos (X, my system card thread, benchmarks). Same weights, two names: Mythos 5 is the unrestricted version that only Project Glasswing partners get, Fable 5 is what the rest of us get, wrapped in the heaviest guardrails I've ever seen ship on a frontier model. It's state of the art on nearly every benchmarkThe model that was “too dangerous to release” is now... well, released, but with the heaviest guardrails we've seen. More on this later. Peter Gostev from Arena.ai joined us to break down the new model. Last but definitely not least, Google released a real-time translation model, that our friend Thor Schaeff from DeepMind demoed live, while we all spoke in different languages and it translated us in REAL TIME. It was really cool, definitely check that out. There's quite a few more things, like Loop Engineering Alpha, Swyx came by to talk about FrontierCode, OpenAI confirmed our suspicions that the anti-datacenter social media posts could be a concerted effort by groupds links to the Chinese government and much more. Let's dive in! ThursdAI - Let me catch you up, every week!

his week on Thumb Cramps, 150,000 video games have just been announced AND it's another episode of 92ne. Hmmmm. To celebrate, they're joined by Chloe Appleby to check out all their favourite announcements from the Directs, State of Plays, Showcases and Festivals then they review Castlevania: Aria of Sorrows for the Game Boy Advance, Turok 3: Shadow of Oblivion for the PC and Shadows of the Colossus for the PS4. This is a very long and very normal episode. Please follow Chloe and Thumb Cramps on Instagram and then send us $400. Thank you.Get Thumb Cramps Long Sleeve Shirts HereThumb Cramps+ has launched! Ad-free podcasts and a bonus monthly episode of Speedrunning Television; a brand new podcast that innovates how to watch television as gamers. Subscribe now on Sanspants Plus OR Apple Podcasts! Use the discount code Joel OR Jacksom for 10% off. Only applies to subscriptions through sanspantsradio.com.Email us at ThumbCrampsPod@gmail.com Find us on Instagram;Jackson | Duscher | Thumb Cramps | Chloe You can physically send us stuff to PO BOX 7127, Reservoir East, Victoria, 3073.Join our facebook group here or join our Discord here.Theme music by Benny Davis! You can find all his stuff at his website or check out his YouTube channel.Parts of this episode were recorded and produced on Wurundjeri land, we respectfully acknowledge the Wurundjeri People of the Kulin Nation, pay our respect to their Elders past and present, and recognise that sovereignty was never ceded. Hosted on Acast. See acast.com/privacy for more information.

On today's episode, we discuss the wild world of crypto, focusing on Bitcoin's recent price slide, why it remains a long‑term bet for many investors, and how upcoming regulation like the Clarity Act could reshape the market by allowing banks and exchanges to pay interest and hold Bitcoin as collateral. The hosts explain why they see most smaller coins and meme tokens eventually going to zero, while a handful of ISO 20022‑compliant projects such as XRP, XLM, Algorand, and HBAR may survive because of their real‑world payment use cases and regulatory clarity. They contrast the speculative upside of digital currencies with traditional safe‑haven assets like gold and silver, arguing that in a world of bots, instant settlement, and agent‑to‑agent transactions, only crypto can move value fast enough to power future financial systems. The conversation then shifts to the exploding demand for AI compute, comparing Elon Musk's Colossus data centers with Meta's massive new facilities, and exploring how companies are racing to refit industrial sites and even consider space‑based data centers to keep up. Throughout the episode, they emphasize that none of this is personal financial advice, urge listeners not to risk money they need for essentials, and keep things lively with jokes, personal anecdotes, and friendly back‑and‑forth about banks, bots, and “fart coin". Don't miss it!

My guest today is Alex Sacerdote, founder of Whale Rock Capital Management.  Whale Rock is a technology focused investment firm that manages more than $17 billion across hedge fund, long only, and hybrid strategies. Over the past three years it has been one of the best performing hedge funds, compounding at roughly 44 percent a year. Alex invests through a single lens that he has refined over twenty years. He looks for technology S-curves, durable competitive advantages, and underappreciated earnings power.  This conversation is a tour through how he applies that framework right now. We start with his highest conviction position, which is Anthropic, and use it to work through the entire AI stack from chips to models to applications.  Please enjoy my conversation with Alex Sacerdote. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:00:00) Welcome to Invest Like The Best (00:02:29) Alex Sacerdote (00:03:08) Anthropic: Highest Conviction Position (00:13:23) Investing in Private Markets at Scale (00:19:08) S-Curves: The Full Framework (00:25:08) When to Buy Tech Companies (00:30:20) Identifying the Leader from the Pack (00:34:04) Anthropic & OpenAI's Competitive Moats (00:37:31) AI's Threat to Enterprise Software (00:43:18) Network Effects in the Agent Era (00:44:22) The Hardware Renaissance: Chips & Infrastructure (00:53:56) Why So Few Investors Get This Right (00:55:36) Key Risks to the AI Bull Case (00:57:47) The Application Layer (00:59:40) How AI Is Changing Research at WhaleRock (01:02:53) The Role of Investor Networks & Idea Sharing (01:03:40) Building a Multi-Product Firm (01:07:58) WhaleRock as a Learning Machine (01:09:15) The Kindest Thing

Individually they were just like those guys who like to hang around the comic book shop and talk comics but together they form EMX! Check out Thacher's books a DemonWeaselStudios.com In this eXplicit, uncut and unedited episode of EMX we review Marvel Comics X-Men books of April 2026: Cyclops #3 Generation X-23 #3 Inglorious X-Force #4 Logan - Black, White and Blood #4 Magik and Colossus #3 Moonstar #2 Psyclocke - Ninja #4 Rogue #4 Storm - Earth's Mightiest Mutant #3 Uncanny X-Men #26-27 Uncanny X-Men Annual #1 Wolverine #18-19 X-Men #28 X-Men United #2 [RSS] Subscribe [RSS] EMX Subscribe [Apple Podcasts] Subscribe [Google Podcast] Subscribe All Podcasts  Email: EMP@EarthsMightiestPodcast.com Website: http://www.EarthsMightiestPodcast.comFacebook Group: http://facebookgroup.earthsmightiestpodcast.com/Viet's Website: http://www.comedianviet.comThacher's Website: http://www.DemonWeasel.com  

Send us Fan Mail#386 - What do runners think about while we're running? I ask this question and try to answer it with things that I've thought about over the years and I compare that to a study of runners that was done. What do you think about during your runs? Pace, form, food, or maybe all of the above? I also review Haruki Murakami's book "What I Talk About When I Talk Bout Running" that I finally read. In fact his book gave me the idea for this episode. So have fun and and enjoy your next run! Fit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts SpotifySupport the showMartha Runs the World websitehttps://www.buzzsprout.com/248027Email:martharunstheworld@gmail.comYouTube:https://www.youtube.com/@martharunstheworldInstagram:https://www.instagram.com/martharunstheworld/#

Thank you For Listening. Click here to Send us a comment if you have any thoughts on the episode! The word “Mormon” can end a conversation before it even starts, especially in the Bible Belt where labels often come loaded. We sit down with Frank Sikes (our Baptist friend) alongside Kenny O'bara and Joey Cornette to talk plainly about what people mean when they say “Mormon,” why The Church of Jesus Christ of Latter-day Saints has pushed back on the nickname in recent years, and how identity language can either build a bridge or throw up a wall. If you've ever wondered whether Latter-day Saints see themselves as Christian, or why the name matters so much, you'll get a clear answer from local Latter-Day Saints living in the south.From there we go deeper than vocabulary. We talk about the difference between sincere questions and “gotcha” debates, how to stay calm when someone attacks your faith, and why judging by “fruits” (love, service, sacrifice, integrity) is a better test than rumor. We also unpack why the LDS Church can look “too organized” to outsiders, how ministering and priesthood blessings work in real life, and why that structure often shows up as practical care when someone is struggling.We also tackle a common flashpoint: Galatians 1:8. We discuss context, what Paul is correcting, what “another gospel” means, and why ongoing revelation is part of the New Testament world Paul lived in. And we spend time on missionaries: not as online debate objects, but as young people making a serious sacrifice for Jesus Christ and learning life skills through constant rejection and service.If you enjoy thoughtful interfaith dialogue, Christian unity conversations, and real-world discussions about LDS beliefs and misconceptions, subscribe, share this with a friend, and leave a review so more people can find it. What's your experience with the term “Mormon,” and has it ever changed how you saw someone's faith?Beyond The BeaconJoin Bishop Kevin Sweeney for inspired interviews with Catholics living out our faith!Listen on: Apple Podcasts Spotify Beyond The BeaconJoin Bishop Kevin Sweeney for inspired interviews with Catholics living out our faith!Listen on: Apple Podcasts Spotify Fit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts Spotify Fit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts SpotifySupport the showThanks for listening!  Keep on Striving!Don't Forget to leave a review and rating.  Let us know your thoughts about the episode.  You can also follow on the following:YouTubehttps://www.youtube.com/@thejacksonhowellpodcastFacebookhttps://www.facebook.com/TheJacksonHowellPodcastTik Tokhttps://www.tiktok.com/@thejacksonhowellpodcastInstagramhttps://www.instagram.com/jacksonhowell5/

Send us Fan MailFit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts Spotify

Was zeigt Apple auf der WWDC über die Siri AI? OpenAI kündigt IPO-Filing an. Was Apples Restaurantrechnungs-Feature mit dem DMA zu tun hat und warum es in der EU nicht laufen wird. Im IPO-Corner stehen jetzt SpaceX, OpenAI und Anthropic gleichzeitig. SpaceX schließt zwei Milliarden-Cloud-Deals mit Anthropic und Google, ist beim Börsengang am Freitag aber nur doppelt überzeichnet. Goldman Sachs erwartet eine Verhundertfachung der KI-Sparte bis 2030. The Information enthüllt: xAI trainierte Grok monatelang auf Claude. Moonshot AI macht eine Achtfach-Runde. Meta zieht den Google-Move mit eigener Kapitalerhöhung. Bending Spoons (Komoot, AOL, Evernote, WeTransfer) plant einen Nasdaq-IPO. Meta bildet eigene Data-Center-Bauarbeiter aus. Chinas Exporte fallen. Landgericht Frankfurt verhängt Ordnungsgeld gegen Meta. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) WWDC: Apple-Refactoring & Siri AI (00:11:26) DMA-Stopp: Apple AI nicht für die EU (00:19:58) IPO-Corner: SpaceX, OpenAI, Anthropic (00:24:24) Anthropic + Google mieten Colossus (00:27:37) SpaceX Lock-up: Sale ab August (00:31:39) Goldman: SpaceX-AI 100x bis 2030 (00:36:33) SpaceX nur 2x überzeichnet (00:40:15) Retail-Offensive: Trade Republic, Revolut & Co. (00:55:44) SpaceX-Disclaimer & Kraken 5x Perp (00:59:21) XAI trainierte GROK auf Claude (01:02:39) Moonshot AI bei $30 Mrd. (01:04:51) Kalshi zahlt Influencer für Wahl-Narrative (01:07:12) Meta zieht den Google-Move (01:12:17) Bending Spoons plant Nasdaq-IPO (01:16:37) Meta Workforce Academy (01:18:22) Google AI Plus auf $4,99 (01:31:34) Pik-Temu: Chinas Exporte fallen (01:33:10) Landgericht Frankfurt straft Meta Shownotes Apple verschiebt Siri AI in der EU wegen DMA - apple.com OpenAI reicht IPO-Filing vertraulich ein - bloomberg.com SpaceX-IPO 2-fach überzeichnet, Orders schließen Mittwoch - bloomberg.com Google mietet SpaceX-Compute für $920 Mio. pro Monat - bloomberg.com SpaceX signs $30bn deal to lease computing capacity to Google - ft.com Goldman Sachs expects SpaceX's AI revenue to increase 100-fold by 2030 - ft.com Cursor erreicht $4 Mrd. annualisierten Umsatz - forbes.com SpaceX-IPO belebt europäisches Retail-Investing - reuters.com Kraken launcht SpaceX 5x Leverage Perp - blog.kraken.com XAI trainierte GROK monatelang auf Claude-Outputs - the-decoder.com Moonshot AI sucht $30 Mrd. Bewertung - bloomberg.com Kalshi: Bezahlte Influencer sollen LA-Wahl-Posts löschen - semafor.com Meta weighs big equity raising after blockbuster Google deal - ft.com Bending Spoons reicht US-IPO ein - reuters.com Meta launcht Workforce Academy für Data-Center-Bauer - wsj.com Google senkt AI-Plus-Preis auf $4,99 - 9to5google.com Chinas E-Commerce-Export stockt durch Iran-Krieg - reuters.com Landgericht Frankfurt: Ordnungsgeld gegen Meta - spiegel.de

Welcome to Wyllin's Gulch!Join us as we check out Daggerheart and give the Colossus of the Drylands campaign frame a go.Lore Master: IzziPlayers: Cuba as Blue Belly Bill, Adam as Ash Reddick, Amanda as Kitswizzle Wingdings, and D as HelveticaContent Warnings: Mention of suicideJoin our Patreon to get fun perks and early access to the podcast/VODs: ⁠⁠⁠https://www.patreon.com/dicedragonsguild⁠⁠⁠We've got MERCH: ⁠⁠⁠⁠https://tinyurl.com/ddgmerch⁠⁠⁠--MUSIC & SFX--"Combative Strings" and "Battlefield Gulch II", as well as additional music & SFX from Monument Studios via Fantasy+ ⁠⁠⁠(⁠⁠⁠⁠https://www.fantasy-plus.com/)⁠⁠⁠ Royalty-Free License. Music by Alexandre Miller - The Boy King of Idaho (⁠⁠⁠⁠⁠⁠⁠https://open.spotify.com/artist/0WvWTz5TPYOuoZ77e2iIX8?si=bhT8sX2gS_e8huPQnWd81Q⁠⁠⁠) Licensed under the Creative Commons 3.0: By Attribution license.Music & Ambient sounds by Michael Ghelfi. Please support him at his Patreon (⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/MichaelGhelfi⁠⁠⁠) and like and subscribe to his YouTube channel (⁠⁠⁠   / @michaelghelfistudios  ⁠⁠⁠)"Smoking Gun", "Cowboy Sting", "Pennsylvania Rose" by Kevin MacLeod (⁠⁠⁠incompetech.com⁠⁠⁠) Licensed under Creative Commons: By Attribution 4.0 License ⁠⁠http://creativecommons.org/licenses/by/4.0/⁠⁠ "Two Guns, One Destiny" by Shane Ivers (⁠⁠silvermansound.com⁠⁠), licensed under CC BY 4.0"Banjos, Unite!" by Alexander Nakarada is under a Creative Commons BY-SA 3.0 license (⁠⁠https://www.creatorchords.com⁠⁠)Select sound effects from ⁠⁠ZapSplat.com⁠⁠ (⁠⁠⁠https://www.zapsplat.com⁠⁠⁠)

SpaceX is set to go public on June 12, 2026 at a $1.75 trillion valuation, the largest IPO in history. The company is targeting a $75 billion raise at $135 per share. But the S-1 filing reveals a contradiction: Starlink generates billions while the company posts a net loss, driven by the xAI merger and a massive bet on AI compute. This episode breaks down the SpaceX IPO filing. xAI posted a $2.47 billion operating loss in Q1 2026, and Starlink revenue is covering most of it. Then two compute deals changed the math. Anthropic agreed to pay $1.25 billion a month to rent xAI's Colossus 1 data center, and Google signed a $920 million per month deal, both running through 2029. Together that's about $75 billion in contracted future revenue. We cover how SpaceX shifted from running GPUs internally for Grok to operating as an AI cloud infrastructure provider, the multi-class share structure that keeps Elon Musk in control, the possible Tesla merger tying together chips, data centers, and robotics, and the FCC filing for a million-satellite "space cloud." Plus where the $600-700 billion premium above Starlink and launch is actually coming from, and what a generational liquidity event means for employees and VC backers. SpaceX IPO 2026, xAI merger, Starlink revenue, Elon Musk, $1.75 trillion valuation, Google compute deal, Anthropic Colossus, AI infrastructure, orbital computing.

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On today's episode, we discuss where AI, robots, Bitcoin, and Elon Musk might take us by 2030—and whether that future looks more like abundance or a robot‑policed dystopia. Mark kicks things off with the “2030 is the new 1969” thesis, tying together Bitcoin's recent slump, capital rotating into hot AI IPOs like Anthropic, and Musk's massive Colossus data centers, which were built in about a year to power his accelerated Grok training. The crew then unpacks new “Starfall” re‑entry capsules for returning space‑manufactured goods, the prospect of zero‑gravity factories, and already‑deployed painting robots that can handle large commercial jobs—and soon, perhaps, precarious Victorian roofs. They debate whether AI really destroys jobs or just reshuffles them, joking about future workers guarding job‑stealing robots, DOT work‑zone bots causing head‑on collisions, and World Cup venues patrolled by robodogs that can probably “smell” contraband better than real dogs. Throughout, they circle back to the psychological and ethical side of persistent AI—“psychoanalyst” chatbots that remember everything, AI‑induced delusions, and the risk that powerful, amoral actors could weaponize autonomous systems—while still sounding genuinely awed at how fast all of this is arriving. Don't miss it!

DJ Humphreys is back — and a lot has changed.

Send us Fan MailThis week on the Running with James Podcast, we're joined by new friend Christian Rhodes—ultra runner, Ironman finisher, owner of Rhodes Run Lab, and a guy whose mission goes far beyond selling running shoes.Christian shares the story behind Rhodes Run Lab, how he got started in endurance sports, and the experiences that shaped his personal motto: “Bigger Than Me.”We dive into:His journey into ultramarathons and Ironman racingThe purpose that fuels his training and businessWhy helping people is about more than finding the right shoeBuilding community through runningLessons learned from pushing physical and mental limitsThe impact a local run specialty store can have on an athlete's journeyPlus, we check out some of the latest performance footwear from PUMA and talk about what's new, what's exciting, and what runners should have on their radar.Whether you're chasing a PR, training for your first race, or looking for a little inspiration, this episode is a reminder that the miles we run can be about something much bigger than ourselves.Run hard. Serve others. Think bigger than Fit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts SpotifySupport the showBecome a member and support the show:https://patreon.com/RunningwithJames?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=creatorshare_creator&utm_content=join_link

Most of us know what healthy eating looks like. The challenge is sticking to it when life gets busy, stress levels rise, and convenience wins out.So what can we learn from the way elite athletes fuel their bodies, and from the systems that make healthy choices easier?In this conversation, Mario Limaduran, Culinary Director for Trifecta Nutrition, explores the realities of meal planning, the role of discipline and habits, and why perfection is often the enemy of long-term success. His message is simple: healthy eating doesn't have to be complicated, but it does need to be sustainable.Explore Trifecta Nutrition mealsDisclosure: Live Long has an affiliate relationship with Trifecta Nutrition. If you make a purchase through our link, we may receive a small commission at no additional cost to you. Your support helps fund the podcast and keeps all episodes free to access.Fit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts SpotifySupport the showThe Live Long and Master Aging (LLAMA) podcast, a HealthSpan Media LLC production, shares ideas but does not offer medical advice.  If you have health concerns of any kind, or you are considering adopting a new diet or exercise regime, you should consult your doctor.

My guest today is Dara Khosrowshahi, the CEO of Uber. Before Uber, Dara ran Expedia for thirteen years. We start with why he took this job in 2017, and a big part of that story is Daniel Ek, who told him that life is not about happiness, it is about impact. We talk about what the chaos felt like on day one, and how his family leaving Iran when he was nine shaped the way he handles pressure today.  We spend most of our time on autonomous vehicles and Uber's role as the demand aggregator in a world of physical AI. Dara explains why Uber is a supply-led company, what it will take to win, and why he expects many winners in AVs rather than one.  We also discuss Uber's $10 billion in free cash flow, the push toward a single app for everything, and what he has learned from Allen & Co, Barry Diller and Reed Hastings. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:29) Intro to Dara Khosrowshahi (00:03:37) How Daniel Ek Convinced Dara to Take the Uber Job (00:06:54) Bringing Order to Chaos (00:09:20) Managing Stress as a Leader (00:11:22) The Chip on His Shoulder (00:12:53) Parenting Lessons (00:17:01) Mandate for AI Adoption (00:21:21) Uber's Role in Physical AI (00:22:48) Winning the AV Demand Race (00:27:41) Partnering vs. Competing with Waymo (00:32:05) AV Success Unlocks New Markets (00:35:09) Why Drones Haven't Arrived Yet (00:36:27) Regional AV Rollout Differences (00:37:35) Uber Eats International Winning Formula (00:39:44) Key to Aggregating Supply Well (00:44:34) Adding Hotels to Uber Platform (00:50:46) Lessons in Marketing at Scale (00:52:59) Apps vs. AI Agents in Seven Years (00:54:08) What Dara Learned from Barry Diller (00:56:52) What Dara Learned from Allen & Co (01:00:09) Buybacks vs. Growth Investing (01:04:17) Lessons from Reed Hastings (01:05:49) The Kindest Thing

Follow The Girl Gainz Podcast on Instagram https://www.instagram.com/thegirlgainzpodcast/Email: girlgainzpodcast@gmail.comWatch episodes on Youtube. Don't forget to subscribe! https://www.youtube.com/@TheGirlGainzPodcastFollow Alexis on Instagram: @the_alexis_nicoleFollow Amber on Instagram: @amberpacholokFor posing @posing_with_alexis https://posing-with-alexis.square.site/ Stage glam @amberpbeauty  https://bookamberpbeauty.square.site/Join The Biblical Bodybuilder Community https://thebiblicalbodybuilder.circle.so/join?invitation_token=d427c648eec129a5d19de8f065b66f87b6f1ceaf-dfbfe452-f8e1-4da7-831c-94bd5a656bb2CODES:KH Customs AlexisA or AmberpBlack Girl Vitamins: AmberpThe Shoe Fairy Code: AlexisFree Spirit Outlet: afitKH Customs AlexisA Pro Tan: Alexis Raw Nutrition: ALEXISRevive: ALEXISTLF: TLF-DRURYFit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts Spotify

Send us Fan Mail#385 - We runners love a challenge! So this week, I'm talking about 12 of the most dangerous trails in the US. They are dangerous for different reasons; some trails have huge elevation gains in short distances, while others have tricky and possibly dangerous trails while others are home to dangerous animals. Which one of these trails frighten you the most? You'll find out which one scares me the most! PS: I didn't include those trails that could be considered mountain climbing as that's not hiking or trail running. Also, I have individual episodes on the longest trails in the US and will continue those episodes on separate episodes. Fit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts SpotifySupport the showMartha Runs the World websitehttps://www.buzzsprout.com/248027Email:martharunstheworld@gmail.comYouTube:https://www.youtube.com/@martharunstheworldInstagram:https://www.instagram.com/martharunstheworld/#

Send us Fan MailFit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts Spotify

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

Thousands of years ago, Ancient Greek travellers created a list of the most extraordinary sights they encountered: the Seven Wonders of the Ancient World. These included places like the Hanging Gardens of Babylon, the Lighthouse of Alexandria, and the Colossus of Rhodes. Today though, only one still survives: the Great Pyramid of Giza in Egypt. So, in the early 2000s, a huge international campaign called the “New Seven Wonders of the World” aimed to create a modern list of wonders. Some governments launched campaigns encouraging citizens to vote for their country's monument. Critics argued that countries with larger populations had an unfair advantage. And UNESCO, the United Nations organisation responsible for protecting world heritage sites, was not involved in the project. So today, I want to explore the story behind the New Seven Wonders of the World. What are they? Why were they chosen? And do they really deserve the title of “wonder”? And we will do all of this while learning some new vocabulary and practicing your English listening comprehension. Conversation Club - ⁠https://thinkinginenglish.blog/patreon/conversation-clubs/⁠⁠ TRANSCRIPT - https://thinkinginenglish.blog/2026/06/01/390-what-are-the-new-seven-wonders-of-the-world-english-vocabulary-lesson/ AD Free Episode - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/thinkinginenglish⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Patreon - ⁠https://www.patreon.com/thinkinginenglish⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ YouTube Channel -⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@thinkinginenglishpodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ INSTAGRAM - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/thinkinginenglishpodcast/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠) $10 Free Credits on iTalki (Affiliate Link) - https://www.italki.com/affshare?ref=af17506448 ⁠⁠⁠⁠⁠⁠⁠ My Editing Software (50 % Discount Affiliate Link) - https://descript.cello.so/BgOK9XOfQdD Borough by Blue Dot Sessions Contact ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠advertising@airwavemedia.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to advertise on Thinking in English. Thinking in English is part of the ⁠⁠⁠⁠⁠Airwave Media podcast network.⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

For første gang siden 1980 koster det nesten ingenting å forsikre porteføljen mot et fall. Peter forklarer hvorfor skjevheten i opsjonsmarkedet har snudd, og hva det egentlig sier om optimismen der ute. Pluss: SpaceX er ikke et rakettselskap, det er et datasenterselskap i forkledning. Og vi trekker linjene tilbake til krakket i 1987 og portfolio insurance, denne gangen med agentisk handel i hovedrollen.00:03 Intervjuet på YouTube med E-tjenestens Mikael Schjelderup00:05 Volatility smirk fra 1987: Peter forklarer skjevheten i opsjonsmarkedet00:16 25 delta og risk reversal: hvordan man leser nedsiden00:17 Skjevheten har snudd: billig fallforsikring for første gang siden 198000:30 Punktene i rapporten: feila statsauksjoner og margingjeld opp 53 prosent00:32 SK Hynix 2X tidoblet på et halvår, og kapitalflukt fra Sør-Korea00:34 Resesjonsvarsel fra lastebilene og de tomme oljelagrene00:42 Hvilken bransje er SpaceX egentlig i?00:44 Avtalen med Anthropic og Colossus: en skjult hyperscaler til 1,25 milliarder i måneden00:48 Anthropic slipper Opus 4.8, og compute-regningen som skremmer folk00:52 Micron over 1000 milliarder dollar og DRAM-ETF-en opp 65 prosent00:55 Mangel på kritisk tenkning og agentisk token-forbruk01:02 SpaceX-lockup, Tesla-investorbasen og indeksforvalterens fangens dilemma01:07 Portfolio insurance og krakket i 1987: hva agentisk handel kan gjenta01:18 Sponsa tema: gull, Incrementum-grafen og veien tilbake til 1980-nivåene01:23 India ber folk slutte å kjøpe gull01:30 Howard Lindzen og second order-effekten av inflasjonens andre bølge01:38 Ukens markeder: tankrater opp 711 prosent, Nikkei på ny all time high, Oslo Børs01:40 Ukraina: Madyar, dronekrigen og det fire mil brede ingenmannslandet01:52 Hypersoniske missiler mot sivile og Lavrov som ber ambassadene evakuere02:00 Nagasaki, taktiske atomvåpen og Putins 15 000 livvakterEpisoden presenteres av Skygard. Norsk datalagring i Norge. skygard.no Hosted on Acast. See acast.com/privacy for more information.

Thank you For Listening. Click here to Send us a comment if you have any thoughts on the episode! The hardest part of a mission isn't always the heat, the long days, or the doors that never open. Sometimes it's the quiet moment in an airport when you realize you're about to leave a life that changed you, and you're scared of slipping back into the old version of yourself. Spencer Hutcheson just returned from serving as a missionary for The Church of Jesus Christ of Latter-day Saints in Tucson, Arizona, and he joins me while the experience is still fresh, honest, and emotionally real.  We talk through the chaos of getting home, the reunion with family, and the line that has stayed with me ever since: “I don't want to go back to my nets.” From there, we get into what that means for returned missionaries and for anyone trying to live as a disciple of Jesus Christ when life gets busy. Spencer opens up about starting his mission without a strong personal testimony, leaning on his parents' faith at first, and why he chose to serve anyway because he needed to know for himself.  We also hit the practical side: work and school after a mission, job shadowing, resisting comparison, and building simple routines that keep spiritual momentum alive like early mornings, scriptures before the phone, prayer morning and night, service, and consistent temple worship. Spencer shares an MTC night he wanted to quit, the prayer that changed everything, and why going “all in” is what brings the deep feeling of accomplishment at the end.  If you feel nervous about serving, coming home, or helping someone you love who's struggling, this conversation is for you. Subscribe, share this with a friend, and leave a review on Apple Podcasts or Spotify with your biggest takeaway.Beyond The BeaconJoin Bishop Kevin Sweeney for inspired interviews with Catholics living out our faith!Listen on: Apple Podcasts Spotify Fit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts SpotifySupport the showThanks for listening!  Keep on Striving!Don't Forget to leave a review and rating.  Let us know your thoughts about the episode.  You can also follow on the following:YouTubehttps://www.youtube.com/@thejacksonhowellpodcastFacebookhttps://www.facebook.com/TheJacksonHowellPodcastTik Tokhttps://www.tiktok.com/@thejacksonhowellpodcastInstagramhttps://www.instagram.com/jacksonhowell5/

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Today, we are breaking down Toast, a name we have covered before but are revisiting because the story has changed enough to be worth telling again. Most listeners will have tapped a Toast terminal without thinking much about the business behind it.  Our guest is Sean Barrett, founder, managing partner, and chief investment officer of Counter Global, who holds Toast as one of his largest positions and walks us through how a restaurant point of sale company became the operating system that runs the restaurant. He argues that Toast is best understood as the operating system for the restaurant rather than a payments terminal with software attached, and that the business grows as fast and as profitably as it does because the company spent years building purpose-built hardware, a multi-tenant software platform, and a sales force on the ground before it moved into new markets across grocery, enterprise, hospitality, and international.  We also discuss why a business winning roughly half of new restaurant openings in the United States still trades at a multiple that looks closer to a mature company than a category killer. Please enjoy this Breakdown of Toast. For the full show notes, transcript, and links to the best content to learn more, check out the episode page⁠⁠⁠⁠⁠⁠⁠ here.⁠⁠⁠⁠⁠⁠⁠ ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- This episode is brought to you by⁠⁠⁠⁠ ⁠Portrait Analytics⁠⁠⁠⁠⁠ - your centralized resource for AI-powered idea generation, thesis monitoring, and personalized report building. Built by buy-side investors, for investment professionals. We work in the background, helping surface stock ideas and thesis signposts to help you monetize every insight. In short, we help you understand the story behind the stock chart, and get to "go, or no-go" 10x faster than before. Sign-up for a free trial today at ⁠⁠⁠⁠⁠portraitresearch.com⁠⁠⁠⁠⁠ ----- Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps (00:00:00) Welcome to Business Breakdowns (00:03:19) Toast Business Overview & Financials (00:06:31) Recurring vs. Reoccurring Gross Profit (00:07:39) Nuance on Revenue Semantics (00:10:05) Transformation from 2020 to Today (00:11:51) Full Product Offering Overview (00:14:13) Revenue Model — Recurring vs. Transaction-Based (00:16:08) Net Take Rate (00:17:22) Software Side of Revenue (00:18:49) Hardware & SaaSpocalypse Connection (00:22:31) AI Offering & What They're Shipping (00:27:01) Impact of 8% Revenue Uplift for Restaurants (00:27:12) Competitive Landscape (00:32:44) Switching & Churn Dynamics (00:34:52) Competitive Advantage & Moat (00:37:43) Management Team & Culture (00:39:57) $10B Gross Profit TAM & Runway (00:44:01) Valuation Approach (00:45:53) Key Risks (00:48:32) Key Lessons

Ashley Raines and Chris Curtis sit down with Jill Bartholomew — veteran triathlete, certified triathlon coach, professional bike fitter, and host of the Be Fierce & Tri Podcast. Jill brings a wealth of experience to the conversation, sharing insights from years of racing and coaching age group athletes at every level. In 2026, she's taking on an incredible challenge: four full Ironman races, including Ironman Jacksonville. Whether you're chasing your first finish line or hunting a PR, Jill's passion for the sport and her expertise in run Disney events, bike fitting, and endurance coaching will leave you fired up and ready to train. Don't miss this one!

Send us Fan MailWith Special Guest Co-Host Leah Wyatt & Coach LaurenEpisode DescriptionIn the world of running, HYROX, and endurance sports, we spend a lot of time talking about training plans, workouts, nutrition, recovery, and race day strategies. But what about the people who help make it all possible?This week on The Performance Lab, James is joined by Coach Lauren and special guest co-host Leah Wyatt for a conversation about the often-overlooked side of performance: relationships, support systems, community, and the environment that shapes every athlete.Together, they discuss:Why high performance is rarely a solo effortThe impact of spouses, family, friends, and training partnersBalancing ambitious goals with everyday lifeWhen training becomes identityThe difference between commitment and obsessionHow community helps athletes stay consistent through hard seasonsWhether you're chasing a marathon PR, preparing for your next HYROX, or simply trying to become a healthier version of yourself, this episode is a reminder that the people around you play a bigger role in your success than you may realize.Because behind every athlete is a story—and often, a team of people helping them become who they're meant to be.High performance isn't built alone.  Fit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts SpotifySupport the showBecome a member and support the show:https://patreon.com/RunningwithJames?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=creatorshare_creator&utm_content=join_link

My guest today is Dan Loeb, the founder and CEO of Third Point.  Dan started Third Point in 1995 with a few million dollars, and today the firm manages over 24 billion across equities, corporate and structured credit, venture, and insurance.  He is best known for his activist work at companies like Sotheby's, Sony, and Yahoo, and for the public letters he has written to boards over the years. What I find most interesting about Dan is how much his approach has evolved across thirty years.  He came up as a credit and event-driven investor at Warburg Pincus and Jefferies, built Third Point, then layered in quality investing, thematic technology investing, and now a very large credit business that sits alongside the hedge fund. We cover how he thinks about the AI stack and the companies inside it he believes matter most, the difference between good and bad governance, what FTX taught him about due diligence, the Sony and Sotheby's stories, and the power of writing. Please enjoy my conversation with Dan Loeb. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:29) Dan Loeb (00:03:21) Mental Models Information Overload (00:06:50) Dan's Identity as an Investor (00:11:24) The End of Classic Event-Driven Investing (00:13:52) Evolving Strategy Over 30 Years (00:17:48) Return Opportunities in Today's Market (00:21:12) Sources of Alpha for Fundamental Investors (00:22:10) Good vs. Bad Governance (00:26:17) Writing as an Investing Tool (00:27:29) The Sotheby's Story (00:30:04) Activism Opportunities Today (00:31:03) Third Point's Evolution to 60% Credit (00:36:10) Dan as Sole Portfolio Manager (00:38:09) Value Investor Perspective on Today's Market (00:39:23) Investing Outside the US (00:40:33) The Sony Activism Story (00:43:59) Lessons from 30 Years of Investing (00:46:26) Danaher and Operational Excellence (00:48:48) Building the Insurance Liability Business (00:51:19) The FTX Story (00:53:07) Leading a Team Through Uncertainty (00:54:29) Where Third Point Is Most Contrarian (00:56:22) What Makes a Great Analyst Today (00:58:12) The Next 10 Years (01:00:24) The Kindest Thing

In this episode of The Brainstorm, Brett, Nick and Sam are joined by Daniel Maguire and Tasha Keeney to discuss the expected June 12th initial public offering (IPO) of SpaceX. SpaceX's recently filed S-1 revealed a significant opportunity across launch, Starlink, AI compute, and orbital data centers. The team unpacks the bull and bear cases, Starship's role, and whether SpaceX could become the backbone of future AI infrastructure.Key Points From This Episode:SpaceX's S-1 reframed the company as an AI infrastructure story, with xAI, Colossus data centers, and the Anthropic deal suggesting a major opportunity beyond launch and Starlink.Starship is the key unlock, potentially driving launch costs below $100/kg, accelerating Starlink bandwidth deployment, and making orbital data centers economically viable.The biggest risks are execution and monetization, including Starship reusability timelines, turning bandwidth into revenue, staying competitive at the AI frontier, and managing potential future integration with Tesla.If you know ARK, you know we focus on long-term innovation. But that doesn't mean we ignore breaking news. Every day, we debate the latest developments in tech and markets. Now, we're bringing those conversations to you in “The Brainstorm,” a co-production from ARK, WOLF, and Public. Tune in weekly for our quick takes on what's shaping innovation right now.Learn more about WOLF: https://wolf.financialLearn more about Public: https://public.com/Disclosure: http://arkinv.st/39rzF94

(1) Sam Hardiman, Daily Memphian Enterprise Reporter, on SpaceX, Colossus & 901 (2) Sens. Cruz & Cantwell detail their legislation College Sports

My guest today is Darren Farber, and this is his second appearance on the show. Darren is a Managing Partner of Albion River, a defense-focused investment firm and he previously served as a special advisor to the Deputy Under Secretary of Defense. We recorded this conversation in the middle of the Iranian contingency, and we spent most of our time on what winning actually means in a theater like Iran. We discuss why magazine depth matters for the American industrial base, lessons from Ukraine, and what the rise of neo-prime defense companies will require from Congress. Please enjoy my second conversation with Darren Farber. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:29) Darren Farber Intro (00:02:59) Defining What Winning Looks Like in Iran (00:12:16) The Strait of Hormuz (00:13:27) Eisenhower vs. Taylor: Two Military Doctrines Explained (00:17:12) US Military Readiness vs. the Pentagon Era (00:20:05) America's Magazine Depth (00:21:36) China's Vulnerability (00:25:28) Trading Freedom for Security (00:27:31) Today's Industrial Base (00:29:30) Lessons from the Ukraine War (00:31:11) Impact of Iran Conflict on Taiwan Risk (00:33:02) What Neo-Prime Defense Companies Need to Succeed (00:39:53) Can We Win Without Full Regime Change in Iran? (00:45:46) AI's Impact on Modern Warfare

Elyrea sells a kind of tour no traveler would think to search for, and Jean-Vladimir Deniau built the whole company around that fact.Jean-Vladimir Deniau is the founder of Elyrea, a French company that builds character-based immersive performances for the tourism market. The format is specific: a professional actor embodies a historical figure, Coco Chanel near Place Vendôme, Hemingway around Montparnasse, a GI on Omaha Beach, and walks a small group through that figure's neighborhood telling the story of their life. Deniau does not call himself a tour operator. He calls Elyrea a "Lego brick" that DMCs and tour operators build into the experiences they sell. The company has 15 of these performances running, almost all in France, and there is a structural problem at the center of it: nobody knows to ask for a tour with Coco Chanel, so the business cannot wait for B2C search demand. That one fact shapes how Elyrea picks its characters, how it sells, and how it funds itself.Mitch and Deniau cover the business behind the tours. Why Elyrea sells to the trade first and keeps its strongest tours off OTAs entirely. The capital-light model that built 15 tours with no outside investor. The four design rules behind a 90-minute performance, starting with the claim that you win or lose the audience in the first minute. And the recruitment problem of training an actor who learns the whole show, performs twice, and quits because the street is not the theater. Deniau also names the advice he would give any operator building an emotional experience: stay true to the place, do not overplay it, and keep the technology out of the way.Resources:Elyrea: elyrea.comLive actor booking for trade partners: elyrea.com/booking"The Colossus of Marousi" by Henry Miller, the travel book Deniau cites as the original spark

Send us Fan Mail#384 - This week we're having fun talking all about science in running! It's a hodge podge of different science facts about running: how we runners are smarter than non-runners (it's true, I think!), how your attitude will determine if you get sick or stay well after a race and so much more! So let's have fun with Running Science! Fit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts SpotifySupport the showMartha Runs the World websitehttps://www.buzzsprout.com/248027Email:martharunstheworld@gmail.comYouTube:https://www.youtube.com/@martharunstheworldInstagram:https://www.instagram.com/martharunstheworld/#

Send us Fan MailFit, Healthy & Happy Podcast Welcome to the Fit, Healthy and Happy Podcast hosted by Josh and Kyle from Colossus...Listen on: Apple Podcasts Spotify

SpaceX filed its public S-1 with the SEC, revealing 2025 revenue of $18.7 billion — up 33% year over year — anchored by Starlink's $11.4 billion connectivity segment. The Goldman-led syndicate is targeting a $1.75 to $2 trillion valuation, more than double the December 2025 tender offer mark, with a Nasdaq debut under SPCX as early as June. If it prices at range, it will be the largest IPO in history.Cerebras just had one of the biggest tech IPO debuts in years. The AI chip company listed at $185, opened at $350, and closed up 68% at $311 — giving it a roughly $95 billion valuation and making it the largest U.S. tech IPO since Uber. The AI hardware window is officially open, and the market is now treating non-NVIDIA AI infrastructure as a real public-market category.Cisco shocked the market with a major AI infrastructure guide. Revenue hit $15.84 billion, AI infrastructure orders were lifted from $5 billion to $9 billion for fiscal 2026, and the stock jumped 15%. The same day, Cisco cut 4,000 jobs to fund the pivot. The AI capex boom is no longer just NVIDIA — it is spreading into networking, optics, security, and the second layer of the infrastructure stack.The Trump-Xi Beijing summit ended without a formal AI deal. The U.S. cleared major Chinese companies including Alibaba, Tencent, ByteDance, JD, and Lenovo to buy up to 75,000 NVIDIA H200 chips each, but Beijing paused the orders almost immediately. AI infrastructure is no longer just a company-level decision — it is now a geopolitical bargaining chip.Google disclosed the first confirmed AI-built zero-day exploit used in the wild. The attack targeted a two-factor authentication flow in a widely used open-source system administration tool, and Google says the planned mass exploitation event was stopped before it scaled. The cybersecurity impact of AI is no longer theoretical — AI is now accelerating both offense and defense.Inflation came in hot again. April CPI rose 0.6% month over month, the Fed held rates at 3.50-3.75%, and markets are now pricing a higher chance of a rate hike than a cut. And yet the S&P 500 still closed above 7,500, while the Nasdaq and Dow also hit major levels. The AI trade is overpowering the macro signal — for now.Runner-up: Anthropic and the Gates Foundation signed a $200 million four-year partnership directing grants, Claude credits, and engineering support toward global health, K-12 tutoring, and smallholder-farm agronomy. The deal lands the same week Anthropic absorbed Colossus 1 and signed Google for $200 billion in TPUs. The model lab is becoming an infrastructure-scale institution.Runner-up: VoltaGrid raised $1 billion from Blackstone and Halliburton at a $10 billion-plus valuation to build behind-the-meter power systems for AI data centers. Power, not just chips, is becoming one of the biggest constraints in the AI boom.Runner-up: Amazon is reportedly preparing another 14,000 corporate layoffs, which would bring 2026 reductions to roughly 30,000 jobs if confirmed. The AI labor reduction cycle is widening across Big Tech.Runner-up: A former Google engineer was convicted of stealing TPU trade secrets after transferring more than 500 confidential files tied to Google's AI chip architecture and software stack. It is one of the clearest legal templates yet for AI-era intellectual property enforcement.If you want a prize, send us a DM:instagram.com/rickerandbontiktok.com/@rickerandbonyoutube.com/@rickerandbon

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SpaceX filed publicly for its IPO on Nasdaq, revealing $18.7B in 2025 revenue, billions in losses, and Musk's 85.1% voting control. Anthropic pays SpaceX $1.25B per month for compute. Nvidia beat estimates again, Spotify launches Reserved ticketing, and Waymo suspends service over flooding. SpaceX files publicly for its IPO, choosing Nasdaq to make its debut under the symbol SPCX; Elon Musk's shares give him 85.1% of the voting power in the company (Bloomberg) SpaceX's S-1 reveals Anthropic is paying $1.25B per month through May 2029 under their Colossus compute deal, with a 90-day termination clause (The Verge) Spotify partners with Live Nation to launch Reserved, a new feature that sets aside tickets for the most dedicated fans, starting with Premium users in the US (Hollywood Reporter) Spotify debuts a desktop app for creating personal podcasts, competing with Google's NotebookLM, with support for daily briefings based on email and calendar (TechCrunch) Nvidia reports Q1 revenue up 85% YoY to $81.62B, above $78.86B est., Data Center revenue up 92% YoY to $75.2B, and announces an $80B share repurchase program (Nvidia) Waymo suspends operations in Atlanta and San Antonio as its robotaxis struggle with flooded roads and says it has yet to develop a "final remedy" for flooding (TechCrunch) Learn more about your ad choices. Visit megaphone.fm/adchoices

My guest today is Gavin Baker, founding partner and CIO of Atreides Management, and this is our sixth conversation. The central theme is watts and wafers, the two physical constraints that in Gavin's view will dictate the next phase of AI. On power, he thinks the near-term shortage starts to ease in 2027 and 2028 as new sources of energy come online, and that orbital compute solves it in the long term. On wafers, he explains what is different this time from the dotcom bubble and why TSMC's capacity decisions may be the single most important variable to watch. We also discuss Elon's Terrafab, the disaggregation of GPUs, the role of new chip companies, and whether the economic value of AI will keep accruing to frontier models. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:29) Gavin Baker Intro (00:03:32) Anthropic's Record ARR Growth (00:11:49) Should OpenAI and Anthropic Raise at a Much Higher Valuation? (00:13:23) How Elon Preserves Investor Trust (00:14:00) Watts & Wafers (00:15:45) Data Centers in Space Explained (00:20:51) Orbital Compute's Impact on Terrestrial Data Centers (00:26:24) TSMC Supply Discipline & Bubble Risk (00:30:50) Demand for Frontier Tokens & The Bitter Lesson (00:35:33) Continual Learning & Memory (00:40:01) New Chip Companies & Startups (00:42:49) Prefill vs. Decode Disaggregation (00:48:40) AI-Native Founders: Different & Hard (00:51:27) Token Path & Application Layer (00:56:13) How Gavin Uses AI in Atreides (01:00:06) Signs of a Diversity Breakdown (01:05:42) Google, Meta, Amazon, Microsoft (01:11:42) Broader Knock-On Effects of AI