Podcast appearances and mentions of jimmy donaldson

It's MrBeast week on The Worst of All Possible Worlds. To kick it off we're unlocking a premium episode we released in March 2023 where THE LADS WATCH 8 MRBEAST VIDEOS AND THEN TALK ABOUT THEM (Not Clickbait). Topics include MrBeast's humble origins, the monetization of altruism, and the internet's canonization of St. Jimmy Donaldson. Want more TWOAPW? Get access to our full back catalogue of premium and bonus episodes by subscribing for $5/month at Patreon.com/worstofall! Media Referenced in this Episode: The Beast Playlist “Is MrBeast for Real? Inside the Outrageous World of YouTube's Cash-Happy Stunt King” by EJ Dickson, Rolling Stone April 9th, 2022. “Mr. Beast, YouTube Star, Wants to Take Over the Business World” by Taylor Lorenz, NYTimes, May 4th, 2021. The CouRage and Nadeshot Show, Episode 38: "MrBeast Shares His BIGGEST YouTube Secrets!" The Joe Rogan Experience, Episode #1788: MrBeast. TWOAPW theme by Brendan Dalton: Patreon // brendan-dalton.com // brendandalton.bandcamp.com

(I'm typing thing recap while watching Love Island, so please excuse any typos.) ON TODAY'S WHO? WEEKLY we've got a cameo from Mr. Beast! (Mariah, how could you!) Plus, Sabrina Elba's bag (and what's in it), Bonnie McKee's DailyMail #exclusive, the El Moussas sell lip gloss and the men of Smartless start... a telecom business? Jeff Lewis' exes are engaged, Madelyn Cline cozies up to Timmy's enemy, Zach Bia, Huma Abedin and Alison Mack get married (separately), Taron's on Raya (and so is Peter Facinelli)... Rita's hanging out with Will Smith! To make music! Platonically! As always, call in at 619.WHO.THEM to leave questions, comments & concerns for a future episode of Who's There?. Get a ton of bonus content over on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patreon.com/WhoWeekly⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

Behind the scenes with Steph Curry, MrBeast, Kevin Durant, and more. Get 2 months free of Linktree Pro for creators: http://linktr.ee/youshaeiGet my free YT Thumbnail Hackpack: ⁠⁠⁠YTHackPack.com/Thumbnail⁠⁠⁠Join my Discord of 5,500 creators helping each other grow: ⁠⁠⁠⁠⁠⁠⁠Discord ⁠⁠⁠⁠⁠⁠⁠Join my newsletter for latest creator news & tips: ⁠⁠⁠⁠⁠⁠⁠⁠⁠created.news⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe for more videos: ⁠⁠⁠⁠⁠⁠⁠⁠⁠tr.ee/YoushaeiYT⁠We went behind-the-scenes with Jimmy Donaldson (aka MrBeast) as he shot his next video in the "Beat Ronaldo, Win $1,000,000" series of Pros vs Joes except this time the video featured the Golden State Warrior's Steph Curry alongside Neymar, Giannis Antetokounmpo, Damian Lilliard, and more in a video titled "Beat Neymar, Win $1,000,000." Beyond the shoot, we also spend time with the NBA's biggest stars, podcasts like All The Smoke co-hosts Matt Barnes and Stephen Jackson and journalists like Shams Charania to find out just how much YouTube has changed the NBA culminating in MrBeast's $100,000 halftime show where a lucky fan hit a halfcourt shot.

A los 26 años, MrBeast es el creador de contenido más exitoso del planeta: 400 millones de suscriptores, más de $700 millones de dólares anuales en ingresos y una obsesión que raya en la locura. En este episodio te contamos cómo pasó de ser un niño solitario y enfermo en Carolina del Norte a convertirse en el primer billonario de YouTube. ¿Cuál fue su fórmula? ¿Cuál es el verdadero costo de triunfar en la economía de la atención?

TWS News 1: Cell Phone Myths – 00:26 Last Thing You Bought Off Social Media – 3:45 Father’s Day Giveaway – 10:55 TWS News 2: Disney Updates – 15:05 Are You Kidding Me: Your Perfect Vacation – 19:06 TMI – 24:16 Unusual Signs of Divorce – 27:05 Rock Report: Mr. Jimmy Donaldson – 37:02 Was I Wrong – 41:18 Famous Ages – 43:51 You can join our Wally Show Poddies Facebook group at www.facebook.com/groups/WallyShowPoddies

Det här är berättelsen om hur Jimmy Donaldson från North Carolina knäckte koden och blev världens största youtuber. Nya avsnitt från P3 ID hittar du först i Sveriges Radio Play. Jimmy Donaldson (f. 1998) vill aldrig bli något annat än youtuber. Att spela in videor, som ska få så många klick som möjligt, är hans drömtillvaro. Som barn lär han sig om internets logik och hur man kan få algoritmerna att jobba för en. 2017 blir han viral med ett klipp där han räknar till 100 000. Idag är han störst på plattformen Youtube och följs av nästan 400 miljoner människor världen över. P3 ID om MrBeast handlar om ett barn av sin tid, som fyller flödena med snabba, färgglada och dramatiskt musiklagda klipp. Men också om youtubern som filantrop, där filmade donationer av hundratusentals dollar till sämre bemedlade människor och djur kritiseras som ”välgörenhetsporr”. Jimmy Donaldson, som 2024 tog klivet utanför youtube med tv-programmet och gameshowen ”Beast Games”, har hamnat i många blåsväder. Han anklagas bland annat för att ha ”plågat” sina tävlingsdeltagare. När plattformen TikTok ligger ute till försäljning på den amerikanska marknaden, nämns youtuberns namn som en av de potentiella köparna. Hör tech-reportrarna Björn Jefferey och Evelina Galli om hur MrBeast blev störst - och rikast - på sociala medier.I avsnittet hörs klipp från: MrBeasts, Oompavilles, PewDiePies, The Colin och Samir Shows och Mrwhosetheboss Youtube-kanaler, This Past Weekend with Theo Von, The Joe Rogans Experience, BeastGames på Amazon Prime, ABC News och CBS News Toronto.

Marc Hustvedt shares insights from helping to scale Mr. Beast to 350 million YouTube subscribers and building a global creator brand, revealing the unique combination of traits that separates extraordinary creators from everyone else.  Check out Contrarian Thinking and Codie Sanchez to learn more about Marc's current work helping people buy and grow small businesses.• Creator-led businesses eliminate the power imbalance found in traditional media• The most successful YouTube integrations feel natural and appeal to the global audience• Small, targeted audiences of 10,000 engaged followers can build lucrative businesses• The future of media belongs to individual creators who own their businesses• Work ethic and obsessive dedication to the craft set apart successful creators• Jimmy Donaldson (Mr. Beast) studied YouTube content for years before finding success• High appetite for risk and willingness to bet on yourself is critical• Single-minded focus on "making the best YouTube videos in the world" drove their strategy• The $3.5 million Squid Game recreation video was an inflection point for the brand• Traditional production methods create unnecessary friction for creatorsEver wonder what makes certain creators explode while others struggle to gain traction? Marc Hustvedt, former president of Mr. Beast, pulls back the curtain on how they built one of the most successful creator brands in history expanding into ventures like Feastables Chocolate and Mr. Beast Burger.What truly sets apart the 0.1% of creators from everyone else? Marc reveals it's a unique combination of traits: relentless work ethic, obsessive attention to detail, willingness to take massive risks, and laser-sharp focus on a singular mission. Jimmy Donaldson didn't just appear out of nowhere – he spent years making videos that barely got views, studying YouTube's ecosystem, and constantly iterating. The breakthrough came from understanding that success requires both quantity and quality, paired with the courage to bet everything on yourself repeatedly.  The $3.5 million Squid Game recreation video serves as a perfect case study of their approach. While traditional media might have stretched production over months of planning and bureaucracy, the Mr. Beast team compressed timelines dramatically. They secured one of the largest brand deals ever for a single YouTube video by creating a sense of FOMO around cultural moments. Most importantly, they eliminated the traditional production hierarchy: "We were both the studio and the network," Marc explains, allowing for nimble decision-making that put creative vision first.Marc is now bringing his digital expertise to Contrarian Thinking as their president, where he's exploring the intersection of education and creator economy. His observation? "You don't need a hundred million people to watch a video. If you've got the right 10,000 people, you can have a very lucrative business." This shift toward highly engaged, targeted communities represents the next evolution in digital content creation.  Listen now for insights on building digital businesses, recognizing talent, and creating content that captivates audiences in today's fragmented landscape.Marc Hustvedt: https://www.linkedin.com/in/marchustvedt/Marc was the President of Mr. Beast from 2021 to 2024.  After graduating from the University of Michigan, he co-founded Tubefilter and the Streamy Awards.Website: https://www.position2.com/podcast/Rajiv Parikh: https://www.linkedin.com/in/rajivparikh/Sandeep Parikh: https://www.instagram.com/sandeepparikh/Email us with any feedback for the show: spark@postion2.com

MrBeast, also known as Jimmy Donaldson, operates a highly successful YouTube channel with 372 million subscribers. His chocolate brand, Feastables, has outperformed his media business, generating $251 million in sales and over $20 million in profit last year. In contrast, his media business reported $246 million in sales but suffered an $80 million loss due to high production costs for the Amazon Prime reality show "Beast Games." The show attracted 50 million viewers but incurred over $100 million in production expenses. Feastables focuses on ethically sourced chocolate bars and sells a king-size pack for $35. The brand is available in major retailers across North America and other countries. Projections for 2025 include anticipated revenues of $288 million from YouTube, $520 million from Feastables, and $105 million from other ventures. Feastables launched in January 2022 with a $5 million investment and has steadily increased sales, reaching $96 million in 2023. MrBeast plans to raise significant funds for expansion into video games and wellness, with Beast Industries valued at approximately $5 billion following a recent funding round. He generates $600 million to $700 million in annual revenue, reinvesting in his businesses. Learn more on this news visit us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Photographer and host of The Salt Box Matthew Denicola joins to discuss the mimetic nightmare, ecstasy and American maximalism of Jimmy Donaldson, aka MR. BEAST + his stupefying reality competition game show BEAST GAMES (2024年〜). Follow Matthew on X: https://x.com/mattiopattio (ISP S5.E13)

MrBeast's reality show "Beast Games" featured contestants competing for a cash prize of $10 million, the largest in television history. The first season aired on Prime Video, attracting 50 million viewers within 25 days. Despite achieving high viewership, creator Jimmy Donaldson reported "tens of millions" in losses, with production costs exceeding a $100 million budget. The first two episodes alone incurred over $29 million in expenses. Donaldson emphasized quality delivery for the season and recognized the difficulties YouTube creators face when entering the streaming domain. The success of "Beast Games" has led to new opportunities for other content creators. Although he holds billionaire status, Donaldson keeps under $1 million in his bank account and reinvests his earnings into his brand. The show currently brings in about 700,000 new unique viewers daily. Learn more on this news visit us at: https://greyjournal.net/ Hosted on Acast. See acast.com/privacy for more information.

With millions of fans and record-breaking giveaways, MrBeast has redefined YouTube success. Victor Varnado, KSN, and Rachel Teichman, LMSW, take a closer look at how Jimmy Donaldson went from making simple videos to running a massive online empire. His viral stunts and philanthropy have made him a standout in the digital world. Join the hosts as they explore the rise of this internet sensation!Produced and hosted by Victor Varnado & Rachel TeichmanFull Wikipedia article here: https://en.wikipedia.org/wiki/MrBeastSubscribe to our new newsletter, WikiWeekly at https://newsletter.wikilisten.com/ for a fun fact every week to feel smart and impress your friends, and MORE! https://www.patreon.com/wikilistenpodcastFind us on social media!https://www.facebook.com/WikiListenInstagram @WikiListenTwitter @Wiki_ListenGet bonus content on Patreon Hosted on Acast. See acast.com/privacy for more information.

Check out Spotter Studio: https://partner.spotterstudio.com/colinandsamir In our fifth interview with Jimmy Donaldson, we break down MrBeast's biggest year yet. From launching the most expensive unscripted reality show of all time—Beast Games on Prime Video—to scaling Feastables, and pushing the limits of YouTube, Jimmy opens up about the challenges of such fast-paced growth. In the interview, we cover the relationship between creators and Hollywood, his mission to disrupt Big Chocolate, how he continues to innovate on YouTube and more.

Después de la prórroga de 75 días que Donald Trump le dio a Tik Tok, han salido muchos compradores a la palestra. Inversores que quieren aprovechar la oportunidad de hacerse con la red social que más ha crecido desde la pandemia. Aunque de momento, Byte Dance, propiedad del canal de emisión de vídeos cortos, es reacia a vender. Bill Ford, miembro del consejo de la compañía, le dijo a Bloomberg Television que pueden existir soluciones “sin necesidad de vender”. Una de las opciones que está cogiendo más fuerza en los últimos días es la de Elon Musk. El magnate de Tesla ampliaría así su dominio en el campo de las redes sociales aunque también le situaría en una posición demasiado monopolística. Aunque también es cierto que goza de una gran amistad con Donald Trump. Incluso algunos analistas se han aventurado a decir que tendría la aprobación del gobierno chino. Trump también estaría dispuesto a la compra por parte de Larry Ellison. Ellison también tiene estrechos lazos con Trump. Hay que recordar que su compañía Oracle, junto a SoftBank y a OpenAl están detrás de la multimillonaria inversión de Stargate. Además, los antecedentes dan más oportunidades a esta teoría. Hace cuatro años, cuando el presidente estadounidense quiso vender Tik Tok, Oracle fue una de las seleccionadas. Además, la empresa almacena los datos de la aplicación en Estados Unidos. Estas son las opciones más reales. Otra de las posibilidades es Microsoft. Pero hay más. Una de las llamativas es que la acabe adquiriendo Mr Beast, el youtuber más popular del mundo. Con casi 350 millones de seguidores, se calcula que tiene un patrimonio de 1 billón de dólares, según Celebrity Net Worth. No lo haría solo eso sí. Jimmy Donaldson, que así es como se llama en realidad, estaría iniciando una ronda de inversión con varios grupos. Así ve la posibilidad de hacerse con Tik Tok el propio creador de contenido.

Ralph and Lauren aren't just chatting about the weather; they're dissecting the art of achieving massive success by thinking bigger and setting ambitious goals, all based on the wisdom of "10X Is Easier Than 2X." They're dishing out their own experiences and providing examples to prove that aiming high, playing to your strengths, and assembling a dream team can lead to mind-blowing results. Get ready to ditch those tiny, yawn-inducing goals and embrace exponential growth in 2025 – Ralph and Lauren's pep talk will leave you feeling inspired and ready to conquer the world!Chapters:00:00:00 - Foggy Views, Frozen Pipes, and Podcast Vibes00:00:40 - When Life Gives You Challenges, Make Content00:01:41 - Delegation: That Thing We Love to Hate00:04:23 - 10X Thinking: Because 2X is Boring00:06:12 - Zodiac Signs, Salesforce, and Big Ideas00:11:07 - Eckhart Tolle, Present Moments, and Toilet Epiphanies00:16:39 - Turning Dreams Into Actionable Strategies00:23:15 - Falling Forward: Embracing Intelligent Failure00:28:37 - Jimmy Donaldson's Genius and Your Next Move00:32:04 - Big Goals, Bigger Futures—The Wrap-UpLINKS AND RESOURCES:10x Is Easier Than 2x: How World-Class Entrepreneurs Achieve More by Doing LessDan Sullivan(YouTube)Is Delegation Your Kryptonite? Conquer the 4 Levels and Free Up Your Time | Perpetual Traffic EP 591(Website)Episode 591: Is Delegation Your Kryptonite? Conquer the 4 Levels and Free Up Your TimeChris Mercer on LinkedInHow MrBeast Grew His ChannelGood to Great: Why Some Companies Make the Leap...And Others Don't (Good to Great, 1)Tier 11 JobsPerpetual Traffic on YouTubeTiereleven.comMongoose MediaPerpetual Traffic SurveyPerpetual Traffic WebsiteFollow Perpetual Traffic on TwitterConnect with Lauren on Instagram and Connect with Ralph on LinkedInThanks so much for joining us this week. Want to subscribe to Perpetual Traffic? Have some feedback you'd like to share? Connect with us on

MrBeast a plus de 340 millions d'abonnés. Il est Américain et a mis en œuvre une stratégie finement pensée pour conquérir la planète. En France, sa chaîne est même celle qui a gagné le plus d'abonnés en 2024, juste derrière celle d'Inoxtag, le YouTubeur français qui a grimpé l'Everest. Alors, quels sont les secrets de cette réussite ?Dans ce nouvel épisode de Questions Tech, Chloé Woitier, journaliste Tech, vous dit tout sur Jimmy Donaldson, le YouTubeur le plus suivi au monde.Vous pouvez retrouver Questions Tech sur Figaro Radio, le site du figaro.fr et sur toutes les plateformes d'écoute. Si cet épisode vous a plu, n'hésitez pas à vous abonner et donner votre avis !Montage : Astrid LandonHébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Welcome back, Spun Today listeners! I'm your host, Tony Ortiz, and you're tuning into episode 276—our final episode of 2024. Today we've got a jam-packed show celebrating creativity, ethical influence, and a look back on some standout moments from the past year.   First up, we'll also journey into the supernatural with a deep dive into Beetlejuice 2, the Tim Burton-esque sequel we've all been waiting for. From its returning stars to new additions like Jenna Ortega, this film has stirred up quite a buzz. We'll unpack plot points, character arcs, and those signature Burton elements that make it a must-watch.   It wouldn't be a Spun Today episode without some comedy! I'll share my thoughts on Tom Papa's latest special "Home Free" and other comedy hits of the year. We'll talk themes, standout bits, and the unique comedic styles that make each special worth watching.   In our "Goats Doing Goat $hit" segment, we're highlighting Mr. Beast again, but this time for his incredible philanthropy. We dive into the world of sustainable chocolate with Mr. Beast's (Jimmy Donaldson) venture, Feastables. We'll explore how his company is setting new ethical standards in cocoa sourcing, and his mission to push Big Chocolate toward more responsible practices.   And finally, I'll reflect on the ups and downs of 2024 and look ahead with optimism to 2025.   Stick around to hear how you can support the podcast—whether through our merch store or by rating and reviewing the show. Plus, I'll share some tips for budding podcasters inspired to start their own journey. Let's get those creative juices flowing, and dive right in!

What happens inside a Hollywood smear machine? This week Blake Lively filed a legal complaint which revealed private messages detailing an alleged campaign to tarnish her, after she accused director and co-star Justin Baldoni of misconduct on the set of “It Ends With Us.” Where does this leave both parties and what does it tell us about the dark arts in Hollywood PR? MrBeast aka Jimmy Donaldson released the much anticipated Beast Games on Amazon. Known for his big budget YouTube gameshows, Amazon gave him a reported $100 million to create a gameshow. Is it good? Does he manage to transfer from YouTube to streaming? What does it tell us about not just entertainment, but the world in which we find ourselves? And lastly, some festive cheer. Richard and Marina look back on how linear TV bagged huge audiences as we all came together for Xmas viewing. Will Gavin and Stacey roll back the clock and deliver a multi-million audience? Recommendations: Marina - Inside No 9: The Party's Over (iPlayer) Richard - Black Doves (Netflix) Join The Rest Is Entertainment Club for ad free listening and access to bonus episodes: www.therestisentertainment.com Sign up to our newsletter: www.therestisentertainment.com Twitter: @‌restisents Instagram: @‌restisentertainment YouTube: @‌therestisentertainment Email: therestisentertainment@gmail.com Producers: Neil Fearn + Joey McCarthy Executive Producers: Tony Pastor + Jack Davenport Learn more about your ad choices. Visit podcastchoices.com/adchoices

Josh and Lauren are back to regale us with tales of Thanksgiving and Black Friday (including one creator who made serious bank!) before diving into the headlines du jour. Up first: judging from TikTok Shop and a new beta on Facebook... are we about to enter the "monetize everything" era of content? Then, Cameo cozying up to creators and the Beast Games PR push begins.Here are the main topics we covered this week:A creator earned $2 million during one single Black Friday livestream on TikTok - TubefilterHas "micro-creator monetization" come to Facebook? - TubefilterCameo welcomes over 31,000 self-enrolled creators with CameoX | Marketing DiveAmazon's 'Beast Games' PR sweep brings Jimmy Donaldson to Roblox, chart-topping podcasts - TubefilterAlso, platforms are beginning to unleash their year-end reviews. Have you checked out your Spotify Wrapped? Well, now they have one for creators.Our Wrapped for Creators Experience Toasts 2024 Podcast ListeningAnd YouTube, of course, has its own wrap-up. (Pour one out for Rewind.)The Year on YouTube: Takeaways from Trends Around the World Creator Upload Socials:YOUTUBEINSTAGRAMTIKTOK

MrBeast (aka Jimmy Donaldson) is a content creator, entrepreneur, philanthropist, and considered by many to be the biggest YouTuber in the world.  MrBeast joins Theo to talk about the insane scope and stakes of his new show “Beast Games”, how he struggled for years on YouTube before taking off, and the motivation behind some of his most talked-about philanthropy projects.  MrBeast: https://www.instagram.com/mrbeast ------------------------------------------------ Tour Dates! https://theovon.com/tour New Merch: https://www.theovonstore.com ------------------------------------------------- Sponsored By: Celsius: Go to the Celsius Amazon store to check out all of their flavors. #CELSIUSBrandPartner #CELSIUSLiveFit https://amzn.to/3HbAtPJ  Prize Picks: First time users, download the PrizePicks app, use code THEO and PrizePicks will instantly give you $50 on your first lineup of $5 or more. https://www.prizepicks.com Kraken: Download the Kraken App!

We are recording our next big recap episode and taking questions! Submit questions and messages on Speakpipe here for a chance to appear on the show!Also subscribe to our calendar for our Singapore, NeurIPS, and all upcoming meetups!In our first ever episode with Logan Kilpatrick we called out the two hottest LLM frameworks at the time: LangChain and Dust. We've had Harrison from LangChain on twice (as a guest and as a co-host), and we've now finally come full circle as Stanislas from Dust joined us in the studio.After stints at Oracle and Stripe, Stan had joined OpenAI to work on mathematical reasoning capabilities. He describes his time at OpenAI as "the PhD I always wanted to do" while acknowledging the challenges of research work: "You're digging into a field all day long for weeks and weeks, and you find something, you get super excited for 12 seconds. And at the 13 seconds, you're like, 'oh, yeah, that was obvious.' And you go back to digging." This experience, combined with early access to GPT-4's capabilities, shaped his decision to start Dust: "If we believe in AGI and if we believe the timelines might not be too long, it's actually the last train leaving the station to start a company. After that, it's going to be computers all the way down."The History of DustDust's journey can be broken down into three phases:* Developer Framework (2022): Initially positioned as a competitor to LangChain, Dust started as a developer tooling platform. While both were open source, their approaches differed – LangChain focused on broad community adoption and integration as a pure developer experience, while Dust emphasized UI-driven development and better observability that wasn't just `print` statements.* Browser Extension (Early 2023): The company pivoted to building XP1, a browser extension that could interact with web content. This experiment helped validate user interaction patterns with AI, even while using less capable models than GPT-4.* Enterprise Platform (Current): Today, Dust has evolved into an infrastructure platform for deploying AI agents within companies, with impressive metrics like 88% daily active users in some deployments.The Case for Being HorizontalThe big discussion for early stage companies today is whether or not to be horizontal or vertical. Since models are so good at general tasks, a lot of companies are building vertical products that take care of a workflow end-to-end in order to offer more value and becoming more of “Services as Software”. Dust on the other hand is a platform for the users to build their own experiences, which has had a few advantages:* Maximum Penetration: Dust reports 60-70% weekly active users across entire companies, demonstrating the potential reach of horizontal solutions rather than selling into a single team.* Emergent Use Cases: By allowing non-technical users to create agents, Dust enables use cases to emerge organically from actual business needs rather than prescribed solutions.* Infrastructure Value: The platform approach creates lasting value through maintained integrations and connections, similar to how Stripe's value lies in maintaining payment infrastructure. Rather than relying on third-party integration providers, Dust maintains its own connections to ensure proper handling of different data types and structures.The Vertical ChallengeHowever, this approach comes with trade-offs:* Harder Go-to-Market: As Stan talked about: "We spike at penetration... but it makes our go-to-market much harder. Vertical solutions have a go-to-market that is much easier because they're like, 'oh, I'm going to solve the lawyer stuff.'"* Complex Infrastructure: Building a horizontal platform requires maintaining numerous integrations and handling diverse data types appropriately – from structured Salesforce data to unstructured Notion pages. As you scale integrations, the cost of maintaining them also scales. * Product Surface Complexity: Creating an interface that's both powerful and accessible to non-technical users requires careful design decisions, down to avoiding technical terms like "system prompt" in favor of "instructions." The Future of AI PlatformsStan initially predicted we'd see the first billion-dollar single-person company in 2023 (a prediction later echoed by Sam Altman), but he's now more focused on a different milestone: billion-dollar companies with engineering teams of just 20 people, enabled by AI assistance.This vision aligns with Dust's horizontal platform approach – building the infrastructure that allows small teams to achieve outsized impact through AI augmentation. Rather than replacing entire job functions (the vertical approach), they're betting on augmenting existing workflows across organizations.Full YouTube EpisodeChapters* 00:00:00 Introductions* 00:04:33 Joining OpenAI from Paris* 00:09:54 Research evolution and compute allocation at OpenAI* 00:13:12 Working with Ilya Sutskever and OpenAI's vision* 00:15:51 Leaving OpenAI to start Dust* 00:18:15 Early focus on browser extension and WebGPT-like functionality* 00:20:20 Dust as the infrastructure for agents* 00:24:03 Challenges of building with early AI models* 00:28:17 LLMs and Workflow Automation* 00:35:28 Building dependency graphs of agents* 00:37:34 Simulating API endpoints* 00:40:41 State of AI models* 00:43:19 Running evals* 00:46:36 Challenges in building AI agents infra* 00:49:21 Buy vs. build decisions for infrastructure components* 00:51:02 Future of SaaS and AI's Impact on Software* 00:53:07 The single employee $1B company race* 00:56:32 Horizontal vs. vertical approaches to AI agentsTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:11]: Hey, and today we're in a studio with Stanislas, welcome.Stan [00:00:14]: Thank you very much for having me.Swyx [00:00:16]: Visiting from Paris.Stan [00:00:17]: Paris.Swyx [00:00:18]: And you have had a very distinguished career. It's very hard to summarize, but you went to college in both Ecopolytechnique and Stanford, and then you worked in a number of places, Oracle, Totems, Stripe, and then OpenAI pre-ChatGPT. We'll talk, we'll spend a little bit of time about that. About two years ago, you left OpenAI to start Dust. I think you were one of the first OpenAI alum founders.Stan [00:00:40]: Yeah, I think it was about at the same time as the Adept guys, so that first wave.Swyx [00:00:46]: Yeah, and people really loved our David episode. We love a few sort of OpenAI stories, you know, for back in the day, like we're talking about pre-recording. Probably the statute of limitations on some of those stories has expired, so you can talk a little bit more freely without them coming after you. But maybe we'll just talk about, like, what was your journey into AI? You know, you were at Stripe for almost five years, there are a lot of Stripe alums going into OpenAI. I think the Stripe culture has come into OpenAI quite a bit.Stan [00:01:11]: Yeah, so I think the buses of Stripe people really started flowing in, I guess, after ChatGPT. But, yeah, my journey into AI is a... I mean, Greg Brockman. Yeah, yeah. From Greg, of course. And Daniela, actually, back in the days, Daniela Amodei.Swyx [00:01:27]: Yes, she was COO, I mean, she is COO, yeah. She had a pretty high job at OpenAI at the time, yeah, for sure.Stan [00:01:34]: My journey started as anybody else, you're fascinated with computer science and you want to make them think, it's awesome, but it doesn't work. I mean, it was a long time ago, it was like maybe 16, so it was 25 years ago. Then the first big exposure to AI would be at Stanford, and I'm going to, like, disclose a whole lamb, because at the time it was a class taught by Andrew Ng, and there was no deep learning. It was half features for vision and a star algorithm. So it was fun. But it was the early days of deep learning. At the time, I think a few years after, it was the first project at Google. But you know, that cat face or the human face trained from many images. I went to, hesitated doing a PhD, more in systems, eventually decided to go into getting a job. Went at Oracle, started a company, did a gazillion mistakes, got acquired by Stripe, worked with Greg Buckman there. And at the end of Stripe, I started interesting myself in AI again, felt like it was the time, you had the Atari games, you had the self-driving craziness at the time. And I started exploring projects, it felt like the Atari games were incredible, but there were still games. And I was looking into exploring projects that would have an impact on the world. And so I decided to explore three things, self-driving cars, cybersecurity and AI, and math and AI. It's like I sing it by a decreasing order of impact on the world, I guess.Swyx [00:03:01]: Discovering new math would be very foundational.Stan [00:03:03]: It is extremely foundational, but it's not as direct as driving people around.Swyx [00:03:07]: Sorry, you're doing this at Stripe, you're like thinking about your next move.Stan [00:03:09]: No, it was at Stripe, kind of a bit of time where I started exploring. I did a bunch of work with friends on trying to get RC cars to drive autonomously. Almost started a company in France or Europe about self-driving trucks. We decided to not go for it because it was probably very operational. And I think the idea of the company, of the team wasn't there. And also I realized that if I wake up a day and because of a bug I wrote, I killed a family, it would be a bad experience. And so I just decided like, no, that's just too crazy. And then I explored cybersecurity with a friend. We're trying to apply transformers to cut fuzzing. So cut fuzzing, you have kind of an algorithm that goes really fast and tries to mutate the inputs of a library to find bugs. And we tried to apply a transformer to that and do reinforcement learning with the signal of how much you propagate within the binary. Didn't work at all because the transformers are so slow compared to evolutionary algorithms that it kind of didn't work. Then I started interested in math and AI and started working on SAT solving with AI. And at the same time, OpenAI was kind of starting the reasoning team that were tackling that project as well. I was in touch with Greg and eventually got in touch with Ilya and finally found my way to OpenAI. I don't know how much you want to dig into that. The way to find your way to OpenAI when you're in Paris was kind of an interesting adventure as well.Swyx [00:04:33]: Please. And I want to note, this was a two-month journey. You did all this in two months.Stan [00:04:38]: The search.Swyx [00:04:40]: Your search for your next thing, because you left in July 2019 and then you joined OpenAI in September.Stan [00:04:45]: I'm going to be ashamed to say that.Swyx [00:04:47]: You were searching before. I was searching before.Stan [00:04:49]: I mean, it's normal. No, the truth is that I moved back to Paris through Stripe and I just felt the hardship of being remote from your team nine hours away. And so it kind of freed a bit of time for me to start the exploration before. Sorry, Patrick. Sorry, John.Swyx [00:05:05]: Hopefully they're listening. So you joined OpenAI from Paris and from like, obviously you had worked with Greg, but notStan [00:05:13]: anyone else. No. Yeah. So I had worked with Greg, but not Ilya, but I had started chatting with Ilya and Ilya was kind of excited because he knew that I was a good engineer through Greg, I presume, but I was not a trained researcher, didn't do a PhD, never did research. And I started chatting and he was excited all the way to the point where he was like, hey, come pass interviews, it's going to be fun. I think he didn't care where I was, he just wanted to try working together. So I go to SF, go through the interview process, get an offer. And so I get Bob McGrew on the phone for the first time, he's like, hey, Stan, it's awesome. You've got an offer. When are you coming to SF? I'm like, hey, it's awesome. I'm not coming to the SF. I'm based in Paris and we just moved. He was like, hey, it's awesome. Well, you don't have an offer anymore. Oh, my God. No, it wasn't as hard as that. But that's basically the idea. And it took me like maybe a couple more time to keep chatting and they eventually decided to try a contractor set up. And that's how I kind of started working at OpenAI, officially as a contractor, but in practice really felt like being an employee.Swyx [00:06:14]: What did you work on?Stan [00:06:15]: So it was solely focused on math and AI. And in particular in the application, so the study of the larger grid models, mathematical reasoning capabilities, and in particular in the context of formal mathematics. The motivation was simple, transformers are very creative, but yet they do mistakes. Formal math systems are of the ability to verify a proof and the tactics they can use to solve problems are very mechanical, so you miss the creativity. And so the idea was to try to explore both together. You would get the creativity of the LLMs and the kind of verification capabilities of the formal system. A formal system, just to give a little bit of context, is a system in which a proof is a program and the formal system is a type system, a type system that is so evolved that you can verify the program. If the type checks, it means that the program is correct.Swyx [00:07:06]: Is the verification much faster than actually executing the program?Stan [00:07:12]: Verification is instantaneous, basically. So the truth is that what you code in involves tactics that may involve computation to search for solutions. So it's not instantaneous. You do have to do the computation to expand the tactics into the actual proof. The verification of the proof at the very low level is instantaneous.Swyx [00:07:32]: How quickly do you run into like, you know, halting problem PNP type things, like impossibilities where you're just like that?Stan [00:07:39]: I mean, you don't run into it at the time. It was really trying to solve very easy problems. So I think the... Can you give an example of easy? Yeah, so that's the mass benchmark that everybody knows today. The Dan Hendricks one. The Dan Hendricks one, yeah. And I think it was the low end part of the mass benchmark at the time, because that mass benchmark includes AMC problems, AMC 8, AMC 10, 12. So these are the easy ones. Then AIME problems, somewhat harder, and some IMO problems, like Crazy Arm.Swyx [00:08:07]: For our listeners, we covered this in our Benchmarks 101 episode. AMC is literally the grade of like high school, grade 8, grade 10, grade 12. So you can solve this. Just briefly to mention this, because I don't think we'll touch on this again. There's a bit of work with like Lean, and then with, you know, more recently with DeepMind doing like scoring like silver on the IMO. Any commentary on like how math has evolved from your early work to today?Stan [00:08:34]: I mean, that result is mind blowing. I mean, from my perspective, spent three years on that. At the same time, Guillaume Lampe in Paris, we were both in Paris, actually. He was at FAIR, was working on some problems. We were pushing the boundaries, and the goal was the IMO. And we cracked a few problems here and there. But the idea of getting a medal at an IMO was like just remote. So this is an impressive result. And we can, I think the DeepMind team just did a good job of scaling. I think there's nothing too magical in their approach, even if it hasn't been published. There's a Dan Silver talk from seven days ago where it goes a little bit into more details. It feels like there's nothing magical there. It's really applying reinforcement learning and scaling up the amount of data that can generate through autoformalization. So we can dig into what autoformalization means if you want.Alessio [00:09:26]: Let's talk about the tail end, maybe, of the OpenAI. So you joined, and you're like, I'm going to work on math and do all of these things. I saw on one of your blog posts, you mentioned you fine-tuned over 10,000 models at OpenAI using 10 million A100 hours. How did the research evolve from the GPD 2, and then getting closer to DaVinci 003? And then you left just before ChatGPD was released, but tell people a bit more about the research path that took you there.Stan [00:09:54]: I can give you my perspective of it. I think at OpenAI, there's always been a large chunk of the compute that was reserved to train the GPTs, which makes sense. So it was pre-entropic splits. Most of the compute was going to a product called Nest, which was basically GPT-3. And then you had a bunch of, let's say, remote, not core research teams that were trying to explore maybe more specific problems or maybe the algorithm part of it. The interesting part, I don't know if it was where your question was going, is that in those labs, you're managing researchers. So by definition, you shouldn't be managing them. But in that space, there's a managing tool that is great, which is compute allocation. Basically by managing the compute allocation, you can message the team of where you think the priority should go. And so it was really a question of, you were free as a researcher to work on whatever you wanted. But if it was not aligned with OpenAI mission, and that's fair, you wouldn't get the compute allocation. As it happens, solving math was very much aligned with the direction of OpenAI. And so I was lucky to generally get the compute I needed to make good progress.Swyx [00:11:06]: What do you need to show as incremental results to get funded for further results?Stan [00:11:12]: It's an imperfect process because there's a bit of a... If you're working on math and AI, obviously there's kind of a prior that it's going to be aligned with the company. So it's much easier than to go into something much more risky, much riskier, I guess. You have to show incremental progress, I guess. It's like you ask for a certain amount of compute and you deliver a few weeks after and you demonstrate that you have a progress. Progress might be a positive result. Progress might be a strong negative result. And a strong negative result is actually often much harder to get or much more interesting than a positive result. And then it generally goes into, as any organization, you would have people finding your project or any other project cool and fancy. And so you would have that kind of phase of growing up compute allocation for it all the way to a point. And then maybe you reach an apex and then maybe you go back mostly to zero and restart the process because you're going in a different direction or something else. That's how I felt. Explore, exploit. Yeah, exactly. Exactly. Exactly. It's a reinforcement learning approach.Swyx [00:12:14]: Classic PhD student search process.Alessio [00:12:17]: And you were reporting to Ilya, like the results you were kind of bringing back to him or like what's the structure? It's almost like when you're doing such cutting edge research, you need to report to somebody who is actually really smart to understand that the direction is right.Stan [00:12:29]: So we had a reasoning team, which was working on reasoning, obviously, and so math in general. And that team had a manager, but Ilya was extremely involved in the team as an advisor, I guess. Since he brought me in OpenAI, I was lucky to mostly during the first years to have kind of a direct access to him. He would really coach me as a trainee researcher, I guess, with good engineering skills. And Ilya, I think at OpenAI, he was the one showing the North Star, right? He was his job and I think he really enjoyed it and he did it super well, was going through the teams and saying, this is where we should be going and trying to, you know, flock the different teams together towards an objective.Swyx [00:13:12]: I would say like the public perception of him is that he was the strongest believer in scaling. Oh, yeah. Obviously, he has always pursued the compression thesis. You have worked with him personally, what does the public not know about how he works?Stan [00:13:26]: I think he's really focused on building the vision and communicating the vision within the company, which was extremely useful. I was personally surprised that he spent so much time, you know, working on communicating that vision and getting the teams to work together versus...Swyx [00:13:40]: To be specific, vision is AGI? Oh, yeah.Stan [00:13:42]: Vision is like, yeah, it's the belief in compression and scanning computes. I remember when I started working on the Reasoning team, the excitement was really about scaling the compute around Reasoning and that was really the belief we wanted to ingrain in the team. And that's what has been useful to the team and with the DeepMind results shows that it was the right approach with the success of GPT-4 and stuff shows that it was the right approach.Swyx [00:14:06]: Was it according to the neural scaling laws, the Kaplan paper that was published?Stan [00:14:12]: I think it was before that, because those ones came with GPT-3, basically at the time of GPT-3 being released or being ready internally. But before that, there really was a strong belief in scale. I think it was just the belief that the transformer was a generic enough architecture that you could learn anything. And that was just a question of scaling.Alessio [00:14:33]: Any other fun stories you want to tell? Sam Altman, Greg, you know, anything.Stan [00:14:37]: Weirdly, I didn't work that much with Greg when I was at OpenAI. He had always been mostly focused on training the GPTs and rightfully so. One thing about Sam Altman, he really impressed me because when I joined, he had joined not that long ago and it felt like he was kind of a very high level CEO. And I was mind blown by how deep he was able to go into the subjects within a year or something, all the way to a situation where when I was having lunch by year two, I was at OpenAI with him. He would just quite know deeply what I was doing. With no ML background. Yeah, with no ML background, but I didn't have any either, so I guess that explains why. But I think it's a question about, you don't necessarily need to understand the very technicalities of how things are done, but you need to understand what's the goal and what's being done and what are the recent results and all of that in you. And we could have kind of a very productive discussion. And that really impressed me, given the size at the time of OpenAI, which was not negligible.Swyx [00:15:44]: Yeah. I mean, you've been a, you were a founder before, you're a founder now, and you've seen Sam as a founder. How has he affected you as a founder?Stan [00:15:51]: I think having that capability of changing the scale of your attention in the company, because most of the time you operate at a very high level, but being able to go deep down and being in the known of what's happening on the ground is something that I feel is really enlightening. That's not a place in which I ever was as a founder, because first company, we went all the way to 10 people. Current company, there's 25 of us. So the high level, the sky and the ground are pretty much at the same place. No, you're being too humble.Swyx [00:16:21]: I mean, Stripe was also like a huge rocket ship.Stan [00:16:23]: Stripe, I was a founder. So I was, like at OpenAI, I was really happy being on the ground, pushing the machine, making it work. Yeah.Swyx [00:16:31]: Last OpenAI question. The Anthropic split you mentioned, you were around for that. Very dramatic. David also left around that time, you left. This year, we've also had a similar management shakeup, let's just call it. Can you compare what it was like going through that split during that time? And then like, does that have any similarities now? Like, are we going to see a new Anthropic emerge from these folks that just left?Stan [00:16:54]: That I really, really don't know. At the time, the split was pretty surprising because they had been trying GPT-3, it was a success. And to be completely transparent, I wasn't in the weeds of the splits. What I understood of it is that there was a disagreement of the commercialization of that technology. I think the focal point of that disagreement was the fact that we started working on the API and wanted to make those models available through an API. Is that really the core disagreement? I don't know.Swyx [00:17:25]: Was it safety?Stan [00:17:26]: Was it commercialization?Swyx [00:17:27]: Or did they just want to start a company?Stan [00:17:28]: Exactly. Exactly. That I don't know. But I think what I was surprised of is how quickly OpenAI recovered at the time. And I think it's just because we were mostly a research org and the mission was so clear that some divergence in some teams, some people leave, the mission is still there. We have the compute. We have a site. So it just keeps going.Swyx [00:17:50]: Very deep bench. Like just a lot of talent. Yeah.Alessio [00:17:53]: So that was the OpenAI part of the history. Exactly. So then you leave OpenAI in September 2022. And I would say in Silicon Valley, the two hottest companies at the time were you and Lanktrain. What was that start like and why did you decide to start with a more developer focused kind of like an AI engineer tool rather than going back into some more research and something else?Stan [00:18:15]: Yeah. First, I'm not a trained researcher. So going through OpenAI was really kind of the PhD I always wanted to do. But research is hard. You're digging into a field all day long for weeks and weeks and weeks, and you find something, you get super excited for 12 seconds. And at the 13 seconds, you're like, oh, yeah, that was obvious. And you go back to digging. I'm not a trained, like formally trained researcher, and it wasn't kind of a necessarily an ambition of me of creating, of having a research career. And I felt the hardness of it. I enjoyed a lot of like that a ton. But at the time, I decided that I wanted to go back to something more productive. And the other fun motivation was like, I mean, if we believe in AGI and if we believe the timelines might not be too long, it's actually the last train leaving the station to start a company. After that, it's going to be computers all the way down. And so that was kind of the true motivation for like trying to go there. So that's kind of the core motivation at the beginning of personally. And the motivation for starting a company was pretty simple. I had seen GPT-4 internally at the time, it was September 2022. So it was pre-GPT, but GPT-4 was ready since, I mean, I'd been ready for a few months internally. I was like, okay, that's obvious, the capabilities are there to create an insane amount of value to the world. And yet the deployment is not there yet. The revenue of OpenAI at the time were ridiculously small compared to what it is today. So the thesis was, there's probably a lot to be done at the product level to unlock the usage.Alessio [00:19:49]: Yeah. Let's talk a bit more about the form factor, maybe. I think one of the first successes you had was kind of like the WebGPT-like thing, like using the models to traverse the web and like summarize things. And the browser was really the interface. Why did you start with the browser? Like what was it important? And then you built XP1, which was kind of like the browser extension.Stan [00:20:09]: So the starting point at the time was, if you wanted to talk about LLMs, it was still a rather small community, a community of mostly researchers and to some extent, very early adopters, very early engineers. It was almost inconceivable to just build a product and go sell it to the enterprise, though at the time there was a few companies doing that. The one on marketing, I don't remember its name, Jasper. But so the natural first intention, the first, first, first intention was to go to the developers and try to create tooling for them to create product on top of those models. And so that's what Dust was originally. It was quite different than Lanchain, and Lanchain just beat the s**t out of us, which is great. It's a choice.Swyx [00:20:53]: You were cloud, in closed source. They were open source.Stan [00:20:56]: Yeah. So technically we were open source and we still are open source, but I think that doesn't really matter. I had the strong belief from my research time that you cannot create an LLM-based workflow on just one example. Basically, if you just have one example, you overfit. So as you develop your interaction, your orchestration around the LLM, you need a dozen examples. Obviously, if you're running a dozen examples on a multi-step workflow, you start paralyzing stuff. And if you do that in the console, you just have like a messy stream of tokens going out and it's very hard to observe what's going there. And so the idea was to go with an UI so that you could kind of introspect easily the output of each interaction with the model and dig into there through an UI, which is-Swyx [00:21:42]: Was that open source? I actually didn't come across it.Stan [00:21:44]: Oh yeah, it wasn't. I mean, Dust is entirely open source even today. We're not going for an open source-Swyx [00:21:48]: If it matters, I didn't know that.Stan [00:21:49]: No, no, no, no, no. The reason why is because we're not open source because we're not doing an open source strategy. It's not an open source go-to-market at all. We're open source because we can and it's fun.Swyx [00:21:59]: Open source is marketing. You have all the downsides of open source, which is like people can clone you.Stan [00:22:03]: But I think that downside is a big fallacy. Okay. Yes, anybody can clone Dust today, but the value of Dust is not the current state. The value of Dust is the number of eyeballs and hands of developers that are creating to it in the future. And so yes, anybody can clone it today, but that wouldn't change anything. There is some value in being open source. In a discussion with the security team, you can be extremely transparent and just show the code. When you have discussion with users and there's a bug or a feature missing, you can just point to the issue, show the pull request, show the, show the, exactly, oh, PR welcome. That doesn't happen that much, but you can show the progress if the person that you're chatting with is a little bit technical, they really enjoy seeing the pull request advancing and seeing all the way to deploy. And then the downsides are mostly around security. You never want to do security by obfuscation. But the truth is that your vector of attack is facilitated by you being open source. But at the same time, it's a good thing because if you're doing anything like a bug bountying or stuff like that, you just give much more tools to the bug bountiers so that their output is much better. So there's many, many, many trade-offs. I don't believe in the value of the code base per se. I think it's really the people that are on the code base that have the value and go to market and the product and all of those things that are around the code base. Obviously, that's not true for every code base. If you're working on a very secret kernel to accelerate the inference of LLMs, I would buy that you don't want to be open source. But for product stuff, I really think there's very little risk. Yeah.Alessio [00:23:39]: I signed up for XP1, I was looking, January 2023. I think at the time you were on DaVinci 003. Given that you had seen GPD 4, how did you feel having to push a product out that was using this model that was so inferior? And you're like, please, just use it today. I promise it's going to get better. Just overall, as a founder, how do you build something that maybe doesn't quite work with the model today, but you're just expecting the new model to be better?Stan [00:24:03]: Yeah, so actually, XP1 was even on a smaller one that was the post-GDPT release, small version, so it was... Ada, Babbage... No, no, no, not that far away. But it was the small version of GDPT, basically. I don't remember its name. Yes, you have a frustration there. But at the same time, I think XP1 was designed, was an experiment, but was designed as a way to be useful at the current capability of the model. If you just want to extract data from a LinkedIn page, that model was just fine. If you want to summarize an article on a newspaper, that model was just fine. And so it was really a question of trying to find a product that works with the current capability, knowing that you will always have tailwinds as models get better and faster and cheaper. So that was kind of a... There's a bit of a frustration because you know what's out there and you know that you don't have access to it yet. It's also interesting to try to find a product that works with the current capability.Alessio [00:24:55]: And we highlighted XP1 in our anatomy of autonomy post in April of last year, which was, you know, where are all the agents, right? So now we spent 30 minutes getting to what you're building now. So you basically had a developer framework, then you had a browser extension, then you had all these things, and then you kind of got to where Dust is today. So maybe just give people an overview of what Dust is today and the courtesies behind it. Yeah, of course.Stan [00:25:20]: So Dust, we really want to build the infrastructure so that companies can deploy agents within their teams. We are horizontal by nature because we strongly believe in the emergence of use cases from the people having access to creating an agent that don't need to be developers. They have to be thinkers. They have to be curious. But anybody can create an agent that will solve an operational thing that they're doing in their day-to-day job. And to make those agents useful, there's two focus, which is interesting. The first one is an infrastructure focus. You have to build the pipes so that the agent has access to the data. You have to build the pipes such that the agents can take action, can access the web, et cetera. So that's really an infrastructure play. Maintaining connections to Notion, Slack, GitHub, all of them is a lot of work. It is boring work, boring infrastructure work, but that's something that we know is extremely valuable in the same way that Stripe is extremely valuable because it maintains the pipes. And we have that dual focus because we're also building the product for people to use it. And there it's fascinating because everything started from the conversational interface, obviously, which is a great starting point. But we're only scratching the surface, right? I think we are at the pong level of LLM productization. And we haven't invented the C3. We haven't invented Counter-Strike. We haven't invented Cyberpunk 2077. So this is really our mission is to really create the product that lets people equip themselves to just get away all the work that can be automated or assisted by LLMs.Alessio [00:26:57]: And can you just comment on different takes that people had? So maybe the most open is like auto-GPT. It's just kind of like just trying to do anything. It's like it's all magic. There's no way for you to do anything. Then you had the ADAPT, you know, we had David on the podcast. They're very like super hands-on with each individual customer to build super tailored. How do you decide where to draw the line between this is magic? This is exposed to you, especially in a market where most people don't know how to build with AI at all. So if you expect them to do the thing, they're probably not going to do it. Yeah, exactly.Stan [00:27:29]: So the auto-GPT approach obviously is extremely exciting, but we know that the agentic capability of models are not quite there yet. It just gets lost. So we're starting, we're starting where it works. Same with the XP one. And where it works is pretty simple. It's like simple workflows that involve a couple tools where you don't even need to have the model decide which tools it's used in the sense of you just want people to put it in the instructions. It's like take that page, do that search, pick up that document, do the work that I want in the format I want, and give me the results. There's no smartness there, right? In terms of orchestrating the tools, it's mostly using English for people to program a workflow where you don't have the constraint of having compatible API between the two.Swyx [00:28:17]: That kind of personal automation, would you say it's kind of like an LLM Zapier type ofStan [00:28:22]: thing?Swyx [00:28:22]: Like if this, then that, and then, you know, do this, then this. You're programming with English?Stan [00:28:28]: So you're programming with English. So you're just saying, oh, do this and then that. You can even create some form of APIs. You say, when I give you the command X, do this. When I give you the command Y, do this. And you describe the workflow. But you don't have to create boxes and create the workflow explicitly. It just needs to describe what are the tasks supposed to be and make the tool available to the agent. The tool can be a semantic search. The tool can be querying into a structured database. The tool can be searching on the web. And obviously, the interesting tools that we're only starting to scratch are actually creating external actions like reimbursing something on Stripe, sending an email, clicking on a button in the admin or something like that.Swyx [00:29:11]: Do you maintain all these integrations?Stan [00:29:13]: Today, we maintain most of the integrations. We do always have an escape hatch for people to kind of custom integrate. But the reality is that the reality of the market today is that people just want it to work, right? And so it's mostly us maintaining the integration. As an example, a very good source of information that is tricky to productize is Salesforce. Because Salesforce is basically a database and a UI. And they do the f**k they want with it. And so every company has different models and stuff like that. So right now, we don't support it natively. And the type of support or real native support will be slightly more complex than just osing into it, like is the case with Slack as an example. Because it's probably going to be, oh, you want to connect your Salesforce to us? Give us the SQL. That's the Salesforce QL language. Give us the queries you want us to run on it and inject in the context of dust. So that's interesting how not only integrations are cool, and some of them require a bit of work on the user. And for some of them that are really valuable to our users, but we don't support yet, they can just build them internally and push the data to us.Swyx [00:30:18]: I think I understand the Salesforce thing. But let me just clarify, are you using browser automation because there's no API for something?Stan [00:30:24]: No, no, no, no. In that case, so we do have browser automation for all the use cases and apply the public web. But for most of the integration with the internal system of the company, it really runs through API.Swyx [00:30:35]: Haven't you felt the pull to RPA, browser automation, that kind of stuff?Stan [00:30:39]: I mean, what I've been saying for a long time, maybe I'm wrong, is that if the future is that you're going to stand in front of a computer and looking at an agent clicking on stuff, then I'll hit my computer. And my computer is a big Lenovo. It's black. Doesn't sound good at all compared to a Mac. And if the APIs are there, we should use them. There is going to be a long tail of stuff that don't have APIs, but as the world is moving forward, that's disappearing. So the core API value in the past has really been, oh, this old 90s product doesn't have an API. So I need to use the UI to automate. I think for most of the ICP companies, the companies that ICP for us, the scale ups that are between 500 and 5,000 people, tech companies, most of the SaaS they use have APIs. Now there's an interesting question for the open web, because there are stuff that you want to do that involve websites that don't necessarily have APIs. And the current state of web integration from, which is us and OpenAI and Anthropic, I don't even know if they have web navigation, but I don't think so. The current state of affair is really, really broken because you have what? You have basically search and headless browsing. But headless browsing, I think everybody's doing basically body.innertext and fill that into the model, right?Swyx [00:31:56]: MARK MIRCHANDANI There's parsers into Markdown and stuff.Stan [00:31:58]: FRANCESC CAMPOY I'm super excited by the companies that are exploring the capability of rendering a web page into a way that is compatible for a model, being able to maintain the selector. So that's basically the place where to click in the page through that process, expose the actions to the model, have the model select an action in a way that is compatible with model, which is not a big page of a full DOM that is very noisy, and then being able to decompress that back to the original page and take the action. And that's something that is really exciting and that will kind of change the level of things that agents can do on the web. That I feel exciting, but I also feel that the bulk of the useful stuff that you can do within the company can be done through API. The data can be retrieved by API. The actions can be taken through API.Swyx [00:32:44]: For listeners, I'll note that you're basically completely disagreeing with David Wan. FRANCESC CAMPOY Exactly, exactly. I've seen it since it's summer. ADEPT is where it is, and Dust is where it is. So Dust is still standing.Alessio [00:32:55]: Can we just quickly comment on function calling? You mentioned you don't need the models to be that smart to actually pick the tools. Have you seen the models not be good enough? Or is it just like, you just don't want to put the complexity in there? Like, is there any room for improvement left in function calling? Or do you feel you usually consistently get always the right response, the right parametersStan [00:33:13]: and all of that?Alessio [00:33:13]: FRANCESC CAMPOY So that's a tricky product question.Stan [00:33:15]: Because if the instructions are good and precise, then you don't have any issue, because it's scripted for you. And the model will just look at the scripts and just follow and say, oh, he's probably talking about that action, and I'm going to use it. And the parameters are kind of abused from the state of the conversation. I'll just go with it. If you provide a very high level, kind of an auto-GPT-esque level in the instructions and provide 16 different tools to your model, yes, we're seeing the models in that state making mistakes. And there is obviously some progress can be made on the capabilities. But the interesting part is that there is already so much work that can assist, augment, accelerate by just going with pretty simply scripted for actions agents. What I'm excited about by pushing our users to create rather simple agents is that once you have those working really well, you can create meta agents that use the agents as actions. And all of a sudden, you can kind of have a hierarchy of responsibility that will probably get you almost to the point of the auto-GPT value. It requires the construction of intermediary artifacts, but you're probably going to be able to achieve something great. I'll give you some example. We have our incidents are shared in Slack in a specific channel, or shipped are shared in Slack. We have a weekly meeting where we have a table about incidents and shipped stuff. We're not writing that weekly meeting table anymore. We have an assistant that just go find the right data on Slack and create the table for us. And that assistant works perfectly. It's trivially simple, right? Take one week of data from that channel and just create the table. And then we have in that weekly meeting, obviously some graphs and reporting about our financials and our progress and our ARR. And we've created assistants to generate those graphs directly. And those assistants works great. By creating those assistants that cover those small parts of that weekly meeting, slowly we're getting to in a world where we'll have a weekly meeting assistance. We'll just call it. You don't need to prompt it. You don't need to say anything. It's going to run those different assistants and get that notion page just ready. And by doing that, if you get there, and that's an objective for us to us using Dust, get there, you're saving an hour of company time every time you run it. Yeah.Alessio [00:35:28]: That's my pet topic of NPM for agents. How do you build dependency graphs of agents? And how do you share them? Because why do I have to rebuild some of the smaller levels of what you built already?Swyx [00:35:40]: I have a quick follow-up question on agents managing other agents. It's a topic of a lot of research, both from Microsoft and even in startups. What you've discovered best practice for, let's say like a manager agent controlling a bunch of small agents. It's two-way communication. I don't know if there should be a protocol format.Stan [00:35:59]: To be completely honest, the state we are at right now is creating the simple agents. So we haven't even explored yet the meta agents. We know it's there. We know it's going to be valuable. We know it's going to be awesome. But we're starting there because it's the simplest place to start. And it's also what the market understands. If you go to a company, random SaaS B2B company, not necessarily specialized in AI, and you take an operational team and you tell them, build some tooling for yourself, they'll understand the small agents. If you tell them, build AutoGP, they'll be like, Auto what?Swyx [00:36:31]: And I noticed that in your language, you're very much focused on non-technical users. You don't really mention API here. You mention instruction instead of system prompt, right? That's very conscious.Stan [00:36:41]: Yeah, it's very conscious. It's a mark of our designer, Ed, who kind of pushed us to create a friendly product. I was knee-deep into AI when I started, obviously. And my co-founder, Gabriel, was a Stripe as well. We started a company together that got acquired by Stripe 15 years ago. It was at Alain, a healthcare company in Paris. After that, it was a little bit less so knee-deep in AI, but really focused on product. And I didn't realize how important it is to make that technology not scary to end users. It didn't feel scary to me, but it was really seen by Ed, our designer, that it was feeling scary to the users. And so we were very proactive and very deliberate about creating a brand that feels not too scary and creating a wording and a language, as you say, that really tried to communicate the fact that it's going to be fine. It's going to be easy. You're going to make it.Alessio [00:37:34]: And another big point that David had about ADAPT is we need to build an environment for the agents to act. And then if you have the environment, you can simulate what they do. How's that different when you're interacting with APIs and you're kind of touching systems that you cannot really simulate? If you call it the Salesforce API, you're just calling it.Stan [00:37:52]: So I think that goes back to the DNA of the companies that are very different. ADAPT, I think, was a product company with a very strong research DNA, and they were still doing research. One of their goals was building a model. And that's why they raised a large amount of money, et cetera. We are 100% deliberately a product company. We don't do research. We don't train models. We don't even run GPUs. We're using the models that exist, and we try to push the product boundary as far as possible with the existing models. So that creates an issue. Indeed, so to answer your question, when you're interacting in the real world, well, you cannot simulate, so you cannot improve the models. Even improving your instructions is complicated for a builder. The hope is that you can use models to evaluate the conversations so that you can get at least feedback and you could get contradictive information about the performance of the assistance. But if you take actual trace of interaction of humans with those agents, it is even for us humans extremely hard to decide whether it was a productive interaction or a really bad interaction. You don't know why the person left. You don't know if they left happy or not. So being extremely, extremely, extremely pragmatic here, it becomes a product issue. We have to build a product that identifies the end users to provide feedback so that as a first step, the person that is building the agent can iterate on it. As a second step, maybe later when we start training model and post-training, et cetera, we can optimize around that for each of those companies. Yeah.Alessio [00:39:17]: Do you see in the future products offering kind of like a simulation environment, the same way all SaaS now kind of offers APIs to build programmatically? Like in cybersecurity, there are a lot of companies working on building simulative environments so that then you can use agents like Red Team, but I haven't really seen that.Stan [00:39:34]: Yeah, no, me neither. That's a super interesting question. I think it's really going to depend on how much, because you need to simulate to generate data, you need to train data to train models. And the question at the end is, are we going to be training models or are we just going to be using frontier models as they are? On that question, I don't have a strong opinion. It might be the case that we'll be training models because in all of those AI first products, the model is so close to the product surface that as you get big and you want to really own your product, you're going to have to own the model as well. Owning the model doesn't mean doing the pre-training, that would be crazy. But at least having an internal post-training realignment loop, it makes a lot of sense. And so if we see many companies going towards that all the time, then there might be incentives for the SaaS's of the world to provide assistance in getting there. But at the same time, there's a tension because those SaaS, they don't want to be interacted by agents, they want the human to click on the button. Yeah, they got to sell seats. Exactly.Swyx [00:40:41]: Just a quick question on models. I'm sure you've used many, probably not just OpenAI. Would you characterize some models as better than others? Do you use any open source models? What have been the trends in models over the last two years?Stan [00:40:53]: We've seen over the past two years kind of a bit of a race in between models. And at times, it's the OpenAI model that is the best. At times, it's the Anthropic models that is the best. Our take on that is that we are agnostic and we let our users pick their model. Oh, they choose? Yeah, so when you create an assistant or an agent, you can just say, oh, I'm going to run it on GP4, GP4 Turbo, or...Swyx [00:41:16]: Don't you think for the non-technical user, that is actually an abstraction that you should take away from them?Stan [00:41:20]: We have a sane default. So we move the default to the latest model that is cool. And we have a sane default, and it's actually not very visible. In our flow to create an agent, you would have to go in advance and go pick your model. So this is something that the technical person will care about. But that's something that obviously is a bit too complicated for the...Swyx [00:41:40]: And do you care most about function calling or instruction following or something else?Stan [00:41:44]: I think we care most for function calling because you want to... There's nothing worse than a function call, including incorrect parameters or being a bit off because it just drives the whole interaction off.Swyx [00:41:56]: Yeah, so got the Berkeley function calling.Stan [00:42:00]: These days, it's funny how the comparison between GP4O and GP4 Turbo is still up in the air on function calling. I personally don't have proof, but I know many people, and I'm probably part of them, to think that GP4 Turbo is still better than GP4O on function calling. Wow. We'll see what comes out of the O1 class if it ever gets function calling. And Cloud 3.5 Summit is great as well. They kind of innovated in an interesting way, which was never quite publicized. But it's that they have that kind of chain of thought step whenever you use a Cloud model or Summit model with function calling. That chain of thought step doesn't exist when you just interact with it just for answering questions. But when you use function calling, you get that step, and it really helps getting better function calling.Swyx [00:42:43]: Yeah, we actually just recorded a podcast with the Berkeley team that runs that leaderboard this week. So they just released V3.Stan [00:42:49]: Yeah.Swyx [00:42:49]: It was V1 like two months ago, and then they V2, V3. Turbo is on top.Stan [00:42:53]: Turbo is on top. Turbo is over 4.0.Swyx [00:42:54]: And then the third place is XLAM from Salesforce, which is a large action model they've been trying to popularize.Stan [00:43:01]: Yep.Swyx [00:43:01]: O1 Mini is actually on here, I think. O1 Mini is number 11.Stan [00:43:05]: But arguably, O1 Mini has been in a line for that. Yeah.Alessio [00:43:09]: Do you use leaderboards? Do you have your own evals? I mean, this is kind of intuitive, right? Like using the older model is better. I think most people just upgrade. Yeah. What's the eval process like?Stan [00:43:19]: It's funny because I've been doing research for three years, and we have bigger stuff to cook. When you're deploying in a company, one thing where we really spike is that when we manage to activate the company, we have a crazy penetration. The highest penetration we have is 88% daily active users within the entire employee of the company. The kind of average penetration and activation we have in our current enterprise customers is something like more like 60% to 70% weekly active. So we basically have the entire company interacting with us. And when you're there, there is so many stuff that matters most than getting evals, getting the best model. Because there is so many places where you can create products or do stuff that will give you the 80% with the work you do. Whereas deciding if it's GPT-4 or GPT-4 Turbo or et cetera, you know, it'll just give you the 5% improvement. But the reality is that you want to focus on the places where you can really change the direction or change the interaction more drastically. But that's something that we'll have to do eventually because we still want to be serious people.Swyx [00:44:24]: It's funny because in some ways, the model labs are competing for you, right? You don't have to do any effort. You just switch model and then it'll grow. What are you really limited by? Is it additional sources?Stan [00:44:36]: It's not models, right?Swyx [00:44:37]: You're not really limited by quality of model.Stan [00:44:40]: Right now, we are limited by the infrastructure part, which is the ability to connect easily for users to all the data they need to do the job they want to do.Swyx [00:44:51]: Because you maintain all your own stuff.Stan [00:44:53]: You know, there are companies out thereSwyx [00:44:54]: that are starting to provide integrations as a service, right? I used to work in an integrations company. Yeah, I know.Stan [00:44:59]: It's just that there is some intricacies about how you chunk stuff and how you process information from one platform to the other. If you look at the end of the spectrum, you could think of, you could say, oh, I'm going to support AirByte and AirByte has- I used to work at AirByte.Swyx [00:45:12]: Oh, really?Stan [00:45:13]: That makes sense.Swyx [00:45:14]: They're the French founders as well.Stan [00:45:15]: I know Jean very well. I'm seeing him today. And the reality is that if you look at Notion, AirByte does the job of taking Notion and putting it in a structured way. But that's the way it is not really usable to actually make it available to models in a useful way. Because you get all the blocks, details, et cetera, which is useful for many use cases.Swyx [00:45:35]: It's also for data scientists and not for AI.Stan [00:45:38]: The reality of Notion is that sometimes you have a- so when you have a page, there's a lot of structure in it and you want to capture the structure and chunk the information in a way that respects that structure. In Notion, you have databases. Sometimes those databases are real tabular data. Sometimes those databases are full of text. You want to get the distinction and understand that this database should be considered like text information, whereas this other one is actually quantitative information. And to really get a very high quality interaction with that piece of information, I haven't found a solution that will work without us owning the connection end-to-end.Swyx [00:46:15]: That's why I don't invest in, there's Composio, there's All Hands from Graham Newbig. There's all these other companies that are like, we will do the integrations for you. You just, we have the open source community. We'll do off the shelf. But then you are so specific in your needs that you want to own it.Swyx [00:46:28]: Yeah, exactly.Stan [00:46:29]: You can talk to Michel about that.Swyx [00:46:30]: You know, he wants to put the AI in there, but you know. Yeah, I will. I will.Stan [00:46:35]: Cool. What are we missing?Alessio [00:46:36]: You know, what are like the things that are like sneakily hard that you're tackling that maybe people don't even realize they're like really hard?Stan [00:46:43]: The real parts as we kind of touch base throughout the conversation is really building the infra that works for those agents because it's a tenuous walk. It's an evergreen piece of work because you always have an extra integration that will be useful to a non-negligible set of your users. I'm super excited about is that there's so many interactions that shouldn't be conversational interactions and that could be very useful. Basically, know that we have the firehose of information of those companies and there's not going to be that many companies that capture the firehose of information. When you have the firehose of information, you can do a ton of stuff with models that are just not accelerating people, but giving them superhuman capability, even with the current model capability because you can just sift through much more information. An example is documentation repair. If I have the firehose of Slack messages and new Notion pages, if somebody says, I own that page, I want to be updated when there is a piece of information that should update that page, this is not possible. You get an email saying, oh, look at that Slack message. It says the opposite of what you have in that paragraph. Maybe you want to update or just ping that person. I think there is a lot to be explored on the product layer in terms of what it means to interact productively with those models. And that's a problem that's extremely hard and extremely exciting.Swyx [00:48:00]: One thing you keep mentioning about infra work, obviously, Dust is building that infra and serving that in a very consumer-friendly way. You always talk about infra being additional sources, additional connectors. That is very important. But I'm also interested in the vertical infra. There is an orchestrator underlying all these things where you're doing asynchronous work. For example, the simplest one is a cron job. You just schedule things. But also, for if this and that, you have to wait for something to be executed and proceed to the next task. I used to work on an orchestrator as well, Temporal.Stan [00:48:31]: We used Temporal. Oh, you used Temporal? Yeah. Oh, how was the experience?Swyx [00:48:34]: I need the NPS.Stan [00:48:36]: We're doing a self-discovery call now.Swyx [00:48:39]: But you can also complain to me because I don't work there anymore.Stan [00:48:42]: No, we love Temporal. There's some edges that are a bit rough, surprisingly rough. And you would say, why is it so complicated?Swyx [00:48:49]: It's always versioning.Stan [00:48:50]: Yeah, stuff like that. But we really love it. And we use it for exactly what you said, like managing the entire set of stuff that needs to happen so that in semi-real time, we get all the updates from Slack or Notion or GitHub into the system. And whenever we see that piece of information goes through, maybe trigger workflows to run agents because they need to provide alerts to users and stuff like that. And Temporal is great. Love it.Swyx [00:49:17]: You haven't evaluated others. You don't want to build your own. You're happy with...Stan [00:49:21]: Oh, no, we're not in the business of replacing Temporal. And Temporal is so... I mean, it is or any other competitive product. They're very general. If it's there, there's an interesting theory about buy versus build. I think in that case, when you're a high-growth company, your buy-build trade-off is very much on the side of buy. Because if you have the capability, you're just going to be saving time, you can focus on your core competency, etc. And it's funny because we're seeing, we're starting to see the post-high-growth company, post-SKF company, going back on that trade-off, interestingly. So that's the cloud news about removing Zendesk and Salesforce. Do you believe that, by the way?Alessio [00:49:56]: Yeah, I did a podcast with them.Stan [00:49:58]: Oh, yeah?Alessio [00:49:58]: It's true.Swyx [00:49:59]: No, no, I know.Stan [00:50:00]: Of course they say it's true,Swyx [00:50:00]: but also how well is it going to go?Stan [00:50:02]: So I'm not talking about deflecting the customer traffic. I'm talking about building AI on top of Salesforce and Zendesk, basically, if I understand correctly. And all of a sudden, your product surface becomes much smaller because you're interacting with an AI system that will take some actions. And so all of a sudden, you don't need the product layer anymore. And you realize that, oh, those things are just databases that I pay a hundred times the price, right? Because you're a post-SKF company and you have tech capabilities, you are incentivized to reduce your costs and you have the capability to do so. And then it makes sense to just scratch the SaaS away. So it's interesting that we might see kind of a bad time for SaaS in post-hyper-growth tech companies. So it's still a big market, but it's not that big because if you're not a tech company, you don't have the capabilities to reduce that cost. If you're a high-growth company, always going to be buying because you go faster with that. But that's an interesting new space, new category of companies that might remove some SaaS. Yeah, Alessio's firmSwyx [00:51:02]: has an interesting thesis on the future of SaaS in AI.Alessio [00:51:05]: Service as a software, we call it. It's basically like, well, the most extreme is like, why is there any software at all? You know, ideally, it's all a labor interface where you're asking somebody to do something for you, whether that's a person, an AI agent or whatnot.Stan [00:51:17]: Yeah, yeah, that's interesting. I have to ask.Swyx [00:51:19]: Are you paying for Temporal Cloud or are you self-hosting?Stan [00:51:22]: Oh, no, no, we're paying, we're paying. Oh, okay, interesting.Swyx [00:51:24]: We're paying way too much.Stan [00:51:26]: It's crazy expensive, but it makes us-Swyx [00:51:28]: That's why as a shareholder, I like to hear that. It makes us go faster,Stan [00:51:31]: so we're happy to pay.Swyx [00:51:33]: Other things in the infrastack, I just want a list for other founders to think about. Ops, API gateway, evals, you know, anything interesting there that you build or buy?Stan [00:51:41]: I mean, there's always an interesting question. We've been building a lot around the interface between models and because Dust, the original version, was an orchestration platform and we basically provide a unified interface to every model providers.Swyx [00:51:56]: That's what I call gateway.Stan [00:51:57]: That we add because Dust was that and so we continued building upon and we own it. But that's an interesting question was in you, you want to build that or buy it?Swyx [00:52:06]: Yeah, I always say light LLM is the current open source consensus.Stan [00:52:09]: Exactly, yeah. There's an interesting question there.Swyx [00:52:12]: Ops, Datadog, just tracking.Stan [00:52:14]: Oh yeah, so Datadog is an obvious... What are the mistakes that I regret? I started as pure JavaScript, not TypeScript, and I think you want to, if you're wondering, oh, I want to go fast, I'll do a little bit of JavaScript. No, don't, just start with TypeScript. I see, okay.Swyx [00:52:30]: So interesting, you are a research engineer that came out of OpenAI that bet on TypeScript.Stan [00:52:36]: Well, the reality is that if you're building a product, you're going to be doing a lot of JavaScript, right? And Next, we're using Next as an example. It's

Shoot us a Text.It's the last Friday of the month, and today we're covering new US tariffs on all sorts of Chinese goods, how OEMs are flocking back to television ads on NFL games, and Mr. Beast's manifesto on how to build a successful company. Show Notes with links:New tariffs go into effect today, as the Biden Administration targets Chinese-built EVs and other imports. The 100% tariff on Chinese EVs escalates trade tensions between the US and China and follows months of negotiation.The tariffs affect 14 categories of Chinese goods, including 50% on solar cells and 25% on key materials like steel, aluminum, and lithium-ion batteries.Tariffs on Chinese semiconductors are expected in 2025 and electronics such as laptops and cell phones are set for 2026.Chinese EVs only account for 2% of US EV imports.China has threatened retaliation, labeling the tariffs as “bullying” and escalating the global trade war.Automakers are ramping up their advertising spend during the NFL season after a few years of reduced activity. Brands like Toyota, Hyundai, and Detroit automakers are betting big on live sports, especially NFL games, to capture consumer attention.Automotive advertising increased by 17% over the last two NFL seasons and is expected to keep growing.Last NFL season, 44% of automotive ad spend on national TV was dedicated to NFL programming, compared to 31% across all sectors, according to Guideline.Toyota has become the “Official Automotive Partner of the NFL,” and GM plans to boost ad spending by $400 million in the second half of the year to promote new vehicle launches.NFL broadcasts garnered 17.9 million viewers on average last season, with the Super Bowl drawing 123.7 million.Toyota's Dedra DeLilli said, “The most appealing aspect of this partnership is we have access to 218 million highly diverse, highly engaged fans of the NFL.”MrBeast, YouTube's biggest star, is now making waves as a thought leader in the tech world. A leaked workplace guide by Jimmy Donaldson has caught the attention of Silicon Valley, sparking conversations on leadership, productivity, and intense work expectations.The 36-page guide emphasizes hiring "A-Players" who are obsessive, coachable, and the best in their field.Donaldson expects workers to be laser-focused, demanding radical accountability and a "no excuses" approach to their work.It's been compared to "founder mode," a hands-on management style popular in the tech industry.While some praise its intensity, others criticized it for promoting a grueling work culture.Investor Marc Cohen said, “It feels like it's written by a person rather than a corporation,”Hosts: Paul J Daly and Kyle MountsierGet the Daily Push Back email at https://www.asotu.com/ JOIN the conversation on LinkedIn at: https://www.linkedin.com/company/asotu/ Read our most recent email at: https://www.asotu.com/media/push-back-email

The girls talk about the one and only Mr. Beast. His rise to fame and recent scandals that have recently plagued him. Is it possible his charity work is staged? How much of his facade is real? Former employees have been putting out some wild accusations and some have wild accusations of their own. Do we think that the former employees are leeching off him for clout?  Do we think these accusations are true? Mae and Laura chime in with their two cents, and also point out that Mr. Beast is 6'5" .. so that might count for something? Good for you Thea Booyson.  Follow us @risquebusinessnews @laurasogar @mae_planert and write us a kind review for a good old fashioned in the mail gift! https://forms.gle/gRZ1j9vEwoGYhWQR9 #mrbeast #podcastepisode #podcast #womenpodcasters #womenpodcast #comedy #comedypodcast #scandalexposed #theabooyson  

REPLAY - In Ep. 71 of Earned, Conor sits down with Marc Hustvedt, president of the largest YouTube channel in America (137 MILLION subscribers at time of recording, now 312 MILLION): MrBeast. Marc's impressive resume also includes founding ventures like Tubefilter, Supergravity Pictures, and the Streamy Awards. We start the episode by learning why Marc enjoys building new companies, and hear why he's particularly interested in YouTubers. We ask Marc about the core elements that make a piece of content successful, and the reasons behind MrBeast's most recent explosion in (billions of) views. Marc shares how the MrBeast team has leaned into TikTok, before explaining the importance of viewer acquisition with captivating video titles and thumbnails, and a few of the ways MrBeast optimizes its content for performance. We hear Marc's take on the current monetization models of social platforms like YouTube and TikTok, and how MrBeast's side ventures like Feastables and MrBeast Burger are now contributing a larger slice of the revenue pie. To close the show, we discuss the impact that MrBeast, aka Jimmy Donaldson, has had on his hometown of Greenville, North Carolina, and hear what's in store for the company over the next 20 years. In this episode, you will learn:  1. The keys to creating a successful piece of content  2. The importance of catchy video titles and thumbnails for viewer acquisition  3. How MrBeast grew a following of 213 million subscribers (and counting!)   Resources: MrBeast Youtube: @mrbeast MrBeast Twitter: @mrbeast MrBeast Instagram: @mrbeast Shop MrBeast Merch: https://shopmrbeast.com/   Connect with the Guest: Marc's LinkedIn   Connect with Conor Begley & CreatorIQ: Conor's LinkedIn - @conormbegley CreatorIQ LinkedIn - @creatoriq   Follow us on social: CreatorIQ YouTube - @TribeDynamics CreatorIQ Instagram - @creatoriq CreatorIQ TikTok - @creator.iq CreatorIQ Twitter - @CreatorIQ

The mainstream media have been wanting to take down YouTube for years, and now they're using the biggest battering ram yet -- the MrBeast allegations. Could the accusations against Jimmy Donaldson have severe repercussions for ALL of YouTube, not just MrBeast? ➡️ Tip Jar and Fan Support: http://ClownfishSupport.com ➡️ Official Merch Store: http://ShopClownfish.com ➡️ Official Website: http://ClownfishTV.com ➡️ Audio Edition: https://open.spotify.com/show/6qJc5C6OkQkaZnGCeuVOD1 The media is using MrBeast's controversy to attack YouTube and potentially hurt content creators, suggesting that his success is not as organic as it seems and blaming the platform for various issues. 00:00 The media is using MrBeast's controversy to attack YouTube and potentially hurt content creators. 01:56 Media using MrBeast controversy to attack all of YouTube and alternative media. 04:58 Media using MrBeast's controversy to attack all of YouTube, concerns about compromised content and censorship, timing of controversy is suspicious. 08:11 The media targets MrBeast and other big YouTubers, suggesting his altruism is for views and money, not genuine. Expand

The MrBeast allegations are costing Amazon dearly, according to insiders. They claim Amazon paid Jimmy Donaldson $100 million for "Beast Games" out of desperation to boost their Prime Video numbers, and gave the 26-year-old multimillionaire complete control over everything -- including safety and advertising. Now those insiders are claiming that advertisers are not lining up to back the show due to the controversy. In fact, the insiders ae claiming that this might spawn a "Fyre Festival" type documentary instead, and that the show is being delayed in the hopes that the controversy dies down. YIKES. ➡️ Tip Jar and Fan Support: http://ClownfishSupport.com ➡️ Official Merch Store: http://ShopClownfish.com ➡️ Official Website: http://ClownfishTV.com ➡️ Audio Edition: https://open.spotify.com/show/6qJc5C6OkQkaZnGCeuVOD1 MrBeast's $100 million deal with Amazon for a reality show has turned into a PR disaster, raising concerns about safety, financial investments, and potential negative impact on independent content creators. 00:00 MrBeast's Amazon project is compared to Fyre Festival and considered a disaster, potentially impacting independent content creators on YouTube and Amazon. 02:43 MrBeast's massive deal with Amazon for a game show has turned into a PR disaster, with safety concerns and negative press. Thisreportedly led to a power struggle and the termination of his management company. 05:56 Amazon gave MrBeast $100 million to do whatever he wants, but their reality show partnership turned into a PR disaster and they can't sell advertising. 08:51 Amazon Insider reveals potential controversies surrounding MrBeast's business dealings and financial investments in other YouTubers. 10:15 MrBeast and friend's $100 million Amazon event involving medication and underwear was a disaster, with safety concerns and questions about viewer popularity. 13:01 Amazon's show with MrBeast is facing execution problems and negative press attention, which could potentially lead to a documentary if it turns into a disaster. 14:57 MrBeast's involvement with Amazon raises safety and financial concerns, highlighting the need for better risk assessment and checks for their reality show. 17:39 Amazon is worried about the cost of MrBeast's show and the potential impact on their budget and advertising revenue, comparing it to the Fyre Festival. About Us: Clownfish TV is an independent, opinionated news and commentary channel that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. Disclaimer: This series is produced by Clownfish Studios and WebReef Media, and is part of ClownfishTV.com. Opinions expressed by our contributors do not necessarily reflect the views of our guests, affiliates, sponsors, or advertisers. ClownfishTV.com is an unofficial news source and has no connection to any company that we may cover. This channel and website and the content made available through this site are for educational, entertainment and informational purposes only. These so-called “fair uses” are permitted even if the use of the work would otherwise be infringing. #MrBeast #Amazon #Streaming #Hollywood #News #Commentary #Reaction #Podcast #Comedy #Entertainment #Hollywood #PopCulture #Tech

Hey, beautiful people! It's your favorite podcast host, Levi, back with another episode of “What Are We Doing?”—the podcast where we dig into the weirdest, wildest, and most absurd corners of the internet. And trust me, Episode 153 is an absolute rollercoaster. First up, we dive headfirst into the bizarre and slightly disturbing world of MrBeast, aka Jimmy Donaldson, who's basically the fairy godmother of YouTube...if fairy godmothers were known for their multi-million dollar stunts and borderline dangerous challenges. This guy has been giving away everything from private islands to stacks of cash, but it turns out his latest venture, “Beast Games,” wasn't all sunshine and rainbows. Think “Hunger Games” meets “Squid Game” but with a sprinkle of YouTube insanity. Contestants were practically signing up for a buffet of suffering, where the menu included starvation, injury, and the occasional stretcher ride. All for a shot at 5 million bucks. But hey, who doesn't love a good public display of questionable ethics, right? Then, we switch gears and jet over to Vienna, where a pair of not-so-savvy villains had some twisted plans for a Taylor Swift concert. Yeah, you heard that right. These wannabe terrorists were gearing up for a massive attack at Tay-Tay's show, aiming to outdo the worst tragedies of the past decade. But plot twist—thanks to some sharp-eyed U.S. intelligence, their sinister scheme was foiled before anyone could say “Shake It Off.” The concert? Canceled. The fans? Devastated. The suspects? Rotting away in a cold Austrian cell. Karma, as they say, is real. And finally, we wrap up with the wackiest story of them all—RFK Jr. and his dead bear debacle in Central Park. Yes, RFK Jr., the guy who thinks vaccines are the devil's work and has a talent for stumbling into controversy like it's his day job, decided to spice things up by dumping a bear carcass in Central Park. Why, you ask? Apparently, he thought it would be “funny” to make it look like a cyclist killed the bear. Spoiler alert: it wasn't. But hey, at least it wasn't another conspiracy theory, right? So grab your favorite drink, get comfy, and prepare for a deep dive into the minds of people who probably shouldn't be left unsupervised. This episode is packed with jaw-dropping moments, a touch of sarcasm, and that sexy Levi flair you know and love. Don't miss it—Episode 153 is live now! And remember, if you think your life is weird, just wait until you hear what we're doing. --- Support this podcast: https://podcasters.spotify.com/pod/show/what-are-we-doing-pod/support

MrBeast, whose real name is Jimmy Donaldson, has recently been at the center of controversies due to past inappropriate language and accusations of racist comments. These issues have surfaced in the media, leading to discussions about his potential "cancellation" Despite these controversies, MrBeast's immense popularity and influence, especially among young audiences, make it unlikely that these issues will significantly impact his career. With a reported $700 million empire, MrBeast's brand is deeply entrenched in the YouTube ecosystem, and his philanthropic efforts have garnered him a loyal fan base.In the world of social media, where public figures often face backlash, MrBeast's situation highlights a common phenomenon: the difficulty of "canceling" someone with such a vast and dedicated following. His ability to continue business as usual, despite the controversies, underscores the resilience of his brand and the challenges of holding influential figures accountable in the digital age.

**MrBeast Reaches Historic 300 Million Subscribers, Becomes Most Subscribed YouTube Channel Ever** In an unprecedented milestone, YouTube sensation MrBeast has reached 300 million subscribers, making history as the first channel to achieve this monumental figure. Known for his extravagant challenges, philanthropic endeavors, and viral content, MrBeast, whose real name is Jimmy Donaldson, continues to captivate audiences worldwide. The journey to this historic milestone has been nothing short of extraordinary. Since launching his channel in 2012, MrBeast has consistently pushed the boundaries of online content creation. His videos, which range from giving away millions of dollars to staging elaborate stunts, have garnered billions of views, establishing him as a dominant force on the platform. This achievement solidifies MrBeast's position as the most subscribed YouTube channel, surpassing previous record holders and setting a new standard for success in the digital age. His innovative approach to content, combined with a genuine passion for making a positive impact, has resonated deeply with viewers, contributing to his rapid subscriber growth. In response to this historic achievement, MrBeast expressed his gratitude to his fans and supporters. "Reaching 300 million subscribers is a dream come true," he said. "I couldn't have done this without the incredible support of my fans and the dedication of my team. This is just the beginning, and I'm excited for what's next." As MrBeast continues to break barriers and redefine the landscape of online entertainment, his 300 million subscribers mark a new era in YouTube history. His success serves as an inspiration to aspiring content creators, proving that with creativity, determination, and a genuine connection with the audience, the sky's the limit. Stay tuned for more groundbreaking content from MrBeast as he continues to entertain, inspire, and give back to the community, one epic video at a time.

This episode explores the remarkable journey of Jimmy Donaldson, better known as MrBeast, from his humble beginnings to becoming a YouTube sensation. It covers his rise to fame, innovative content creation, philanthropic efforts, and business ventures. Listeners will gain insights into MrBeast's impact on digital culture and his ongoing influence in the world of online entertainment and charitable giving.

A nonprofit founded by right-wing billionaire Charles Koch was a "key contributor" to a recent video produced by the popular content creator MrBeast. The video, titled "We Schooled Hundreds of Teachers," was uploaded on April 30th to "Beast Philanthropy," a second channel MrBeast uses to showcase charity work. MrBeast, whose real name is Jimmy Donaldson, lists the organization Stand Together as one of the main financial sponsors. Stand Together was founded by Koch in 2003 as The Seminar Network and it describes itself as a "philanthropic community" dedicated to tackling the "country's biggest problems," which range from education initiatives to what it calls "freedom-minded solutions." But we wanted to focus on how MrBeast's corporate structure operates, so Fast Company contributing writer Ryan Broderick joined us to chat about it. Then, Fast Company associate editor David Salazar had a conversation with Vault founder David Greenstein and musician James Blake, who was the first artist on Vault and has been vocal about its potential as a new way for fans to engage with their favorite artists. They chatted about experimenting with new ways to monetize music.

A nonprofit founded by right-wing billionaire Charles Koch was a "key contributor" to a recent video produced by the popular content creator MrBeast. The video, titled "We Schooled Hundreds of Teachers," was uploaded on April 30th to "Beast Philanthropy," a second channel MrBeast uses to showcase charity work. MrBeast, whose real name is Jimmy Donaldson, lists the organization Stand Together as one of the main financial sponsors. Stand Together was founded by Koch in 2003 as The Seminar Network and it describes itself as a "philanthropic community" dedicated to tackling the "country's biggest problems," which range from education initiatives to what it calls "freedom-minded solutions." But we wanted to focus on how MrBeast's corporate structure operates, so Fast Company contributing writer Ryan Broderick joined us to chat about it. Then, Fast Company associate editor David Salazar had a conversation with Vault founder David Greenstein and musician James Blake, who was the first artist on Vault and has been vocal about its potential as a new way for fans to engage with their favorite artists. They chatted about experimenting with new ways to monetize music.

A nonprofit founded by right-wing billionaire Charles Koch was a "key contributor" to a recent video produced by the popular content creator MrBeast. The video, titled "We Schooled Hundreds of Teachers," was uploaded on April 30th to "Beast Philanthropy," a second channel MrBeast uses to showcase charity work. MrBeast, whose real name is Jimmy Donaldson, lists the organization Stand Together as one of the main financial sponsors. Stand Together was founded by Koch in 2003 as The Seminar Network and it describes itself as a "philanthropic community" dedicated to tackling the "country's biggest problems," which range from education initiatives to what it calls "freedom-minded solutions." But we wanted to focus on how MrBeast's corporate structure operates, so Fast Company contributing writer Ryan Broderick joined us to chat about it. Then, Fast Company associate editor David Salazar had a conversation with Vault founder David Greenstein and musician James Blake, who was the first artist on Vault and has been vocal about its potential as a new way for fans to engage with their favorite artists. They chatted about experimenting with new ways to monetize music.

Something happened on the internet this week that was at once HUGE and also kind of a foregone conclusion. Jimmy Donaldson better known as Mr. Beast has been for many years basically the king of YouTube. But, as of this week, Mr Beast is now officially the most subscribed YouTuber in the world with 271 million followers at time of recording. His clickbaity game-show style videos, with their extravagant sets and giant payouts, have come to define this era of the site. Remember Squid Game, the Korean Netflix sensation? That show got around 265 million views. Mr Beast's “real life” Squid Game video got 616 million views. That's why he's number 1. And there's actually a very interesting history of jockeying for YouTube's top spot. Mr. Beast has overtaken a giant Indian entertainment company, T-series (266 million subscribers) which had reigned unchallenged for years. In 2019, Micah worked with Brooke on a piece about the last time a big Western YouTuber went head to head with T-series. Back then it was a guy who was sort of the Mr Beast of that time, a youtuber known as PewDiepie. On the Media is supported by listeners like you. Support OTM by donating today (https://pledge.wnyc.org/support/otm). Follow our show on Instagram, Twitter and Facebook @onthemedia, and share your thoughts with us by emailing onthemedia@wnyc.org.

En privat ø i Caribien, et fly og 26 Teslaer. Bare lige for at nævne et par af de mange vilde ting, han forærer væk. Det bliver kaldt stunt-filantropi, når verdens største Youtuber MrBeast giver hundredtusindvis af kroner væk til vildt fremmede. Men da han som en anden Youtube-Jesus slår sig på at kurere tusind mennesker for blindhed og bygger brønde i Afrika, så begynder kritikken at pible frem. For selvom den unge Jimmy Donaldson lyder som lidt af en helgen, så bliver han beskyldt for at bedrive usmagelig, selvcentrerende velgørenhed, følelsesporno og for at optræde som en hvid frelser. I dagens Genstart fortæller tech-journalist på Zetland Frederik Kulager om, hvordan MrBeast har formået at sætte viralitet på formel, og hvorfor hans selviscenesatte filantropiske videoer også forarger. Vært: Simon Stefanski. Program publiceret i DR Lyd d. 20/5.

In this episode of Capital Hacking, we dive deep into the world of mindset and entrepreneurship and explore the concept of 10x growth and how it can transform your business and personal life. Kevin Taylor takes us on a journey inspired by the success story of Jimmy Donaldson, better known as Mr. Beast.Kevin introduces us to Mr. Beast, a 25-year-old YouTube sensation with over 231 million subscribers and a net worth of half a billion dollars. Through the power of 10x growth, Mr. Beast has achieved remarkable success in just six years, focusing on quality over quantity and becoming the best in the world at what he does.Drawing insights from the book "10x is Easier than 2x" by Dan Sullivan and Benjamin Hardy, Kevin emphasizes the importance of qualitative growth over quantitative measures. He highlights the key principles of 10x, such as focusing on the 20% that yields 80% of results, simplifying before multiplying, and developing a deeper level of mastery in your craft.Mr. Beast / Benjamin Hardy:https://youtu.be/XR_vtnIcz2c Ultimate Show notes:[00:01:25] Mindset Monday with Kevin Taylor[00:02:30] Review of Morgan Housel's Podcast[00:04:06] Perpetual Moving Goalpost[00:05:10] Talent Clustering[00:06:15] Periods of Excess[00:07:42] Economic Policy Debate[00:09:21] Social Problems and SolutionsTurn your unique talent into capital and achieve the life you were destined to live. Join our community! We believe that Capital is more than just Cash. In fact, Human Capital always comes first before the accumulation of Financial Capital. We explore the best, most efficient, high-integrity ways of raising capital (Human & Financial). We want our listeners to use their personal human capital to empower the growth of their financial capital. Together we are stronger. LinkedinFacebookApple Podcast

I interviewed Jimmy Donaldson aka MrBeast about how to grow on YouTube, his creative process, and I even asked Jimmy to roast his own videos so you can learn how to improve. During this interview, Jimmy also discussed Feastables, ideas he rejected, what really happens on shoot days, his biggest arguments with his team, and how the Beast team comes up with videos that get hundreds of millions of views like Squid Game in Real Life, $1 vs $500,000 Plane Ticket, and I Spent 50 Hours Buried Alive. Special thank you to Derral Eves and the VidSummit team for this opportunity and providing footage, Jimmy, Mateo, Chucky and the Beast team for helping me prepare, Firebrand Media for filming onsite, DrewTheDirector and Lumen Studios for post production, and everyone who helped make this possible. Took a village.  Chapters: 0:00 - 1:15 Intro 1:15 - 2:37 MrBeast's Creative Process 2:37 - 6:23 How MrBeast "Storyboards" Thumbnails 6:23 - 7:23 Crafting Perfect Intro 7:23 - 12:15 What MrBeast Learned from Thumbnail Tests 12:15 - 16:45 MrBeast Roasts His Worst & Best Videos 16:45 - 19:18 Video Ideas That MrBeast Rejected...And Why 19:18 - 22:04 MrBeast breaks down his best Shorts 22:04 - 24:32 MrBeast's schedule (removing yourself as bottleneck) 24:32 - 25:40 Recent Disagreements with Team 25:40 - 26:46 Advice and ideas for smaller YouTubers 26:46 - 29:11 How AI & Deepfakes Impact Jimmy's Production 29:11 - 31:22 Jimmy's Documentary & Talking with Streaming Platforms 31:22 - 32:17 What Jimmy watches on YT  32:17 - 34:39 Final Tips for Creators Subscribe for more videos ►⁠ https://bit.ly/YouTubeYoushaei⁠ Join my Discord with 4,500 creators helping each other grow:⁠⁠ https://discord.com/invite/created ⁠⁠ Sign up for my cartoon newsletter for creators:⁠⁠ https://www.created.news⁠⁠ Join waitlist for my Insider Accelerator:⁠⁠ https://www.insider.school⁠⁠ Follow me on: ⁠YouTube⁠ ⁠TikTok⁠ ⁠LinkedIn⁠ ⁠Twitter⁠ ⁠Instagram⁠ ⁠Threads

This is an unlocked bonus episode. You can find many more like it at https://www.patreon.com/10kpostspodcast -------- Ryan comes back on the podcast this week to explain Jimmy Donaldson's – aka "Mr Beast"- new antic, which seems to involve ignoring local government protocol in East Africa to build wells and electricity pylons. Though presented as an altruistic act, Ryan explains how these stunts feed into the expansive "Mr Beast" brand, at a time when it looks like Donaldson is running out of ideas. Ryan also explains Donaldson's failed attempts to be accepted by mainstream culture and how, despite his massive following, it seems unlikely he'll ever make a break into mainstream TV. Read and subscribe to Garbage Day here: https://www.garbageday.email/ -------- PALESTINE AID LINKS As the humanitarian crisis continues to unfold in Gaza, we encourage anyone who can to donate to Medical Aid for Palestinians. You can donate using the links below. https://www.map.org.uk/donate/donate https://www.savethechildren.org.uk/how-you-can-help/emergencies/gaza-israel-conflict -------- PHOEBE ALERT Can't get enough Phoebe? Check out her Substack Here! -------- Ten Thousand Posts is a show about how everything is posting. It's hosted by Hussein (@HKesvani), Phoebe (@PRHRoy) and produced by Devon (@Devon_onEarth).

MrBeast aka 25-year-old Jimmy Donaldson is the biggest solo performer on YouTube. He's followed by over 400 million people who watch his insane stunts and his enormous acts of philanthropy. So, who is this introverted twenty-five-year-old and what makes him tick? Did he really figure out the YouTube recommendation algorithm, and what makes him different from thousands of similar creators on YouTuber? For answers we contacted a writer who for three days, embedded herself with MrBeast, in his enormous compound, in Greenville, North Carolina…Ej Dickson. Ej is a Senior Writer for Rolling Stone and covers internet culture. Her work has appeared in the New York Times, New York Magazine, Elle, GQ, and Playboy, among others. She's also been featured on NPR's All Things Considered and This American Life, as well as the Hulu docuseries "Only Fans: Selling Sexy," The New Yorker once called her "ineffably cool."   ON THIS EPISODE: YouTube was birthed after a wardrobe malfunction. What's MrBeast's strategy for becoming the biggest YouTuber? How he created an old-school, Hollywood-style, studio system. MrBeast's parents, his house, and seeing 1 million dollars in cash. MrBeast cracked the YouTube algorithm. Or did he? Some call MrBeast's charitable videos - “inspiration porn” Is it? MrBeast and Tyler Perry - What they have in common. How long can MrBeast dominate? Ej introduces us to the odd world of Skibidi Toilet. Really??? Googleheim: The most popular social media star across ALL platforms is… *** FOLLOW EJ: Web RollingStone.com Podcast: Pod…CATS with Ej Dickson Instagram @ejdickson X @ejdickson *** FOLLOW REALLY NO REALLY: www.reallynoreally.com Instagram YouTube TikTok Facebook Threads XSee omnystudio.com/listener for privacy information.

MrBeast (aka Jimmy Donaldson) has more than 300 million total subscribers on YouTube and some of his videos have more viewers than the Super Bowl. He's opened restaurants. He's launched his own food company called Feastables. The MrBeast logo is now on the jerseys of the Charlotte Hornets. Also, MrBeast is 25-years-old. This multi-million dollar YouTube media empire? It's based in Greenville, North Carolina of all places. So how did that happen? In this episode, journalist Jeremy Markovich talks to Washington Post tech reporter Drew Harwell about what happens when a big time influencer sets up shop in a small town. To get more stories like this, subscribe to the North Carolina Rabbit Hole, the reader-supported newsletter that powers this podcast. Get a free-if-you-want-it-to-be subscription at ncrabbithole.com.

IF YOU ENJOY THIS PODCAST PLEASE CONSIDER SUPPORTING ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠HERE⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Join us as we delve into the stories behind today's biggest influencers and innovators. In this episode, we explore the extraordinary rise of "Mr. Beast," the internet sensation who went from humble beginnings to a net worth exceeding $500 million. We uncover the fascinating journey of Jimmy Donaldson, aka Mr. Beast, tracing his roots and examining the strategies he employed to amass a massive audience. From his early days creating content on YouTube to his groundbreaking philanthropic initiatives and viral stunts, we dissect the key moments that propelled Mr. Beast to internet stardom. Join us as we unravel the secrets behind his success and explore the impact of his generosity-driven approach to content creation. But that's not all—our discussion doesn't stop there. We also tackle the latest buzz surrounding tech mogul Elon Musk, who recently made headlines by challenging traditional advertising norms. Musk's bold stance, including his controversial remarks directed at companies like Disney, sheds light on the evolving landscape of digital media and the power dynamics at play in platforms such as "X" (formerly known as Twitter). Join us as we navigate through the intriguing intersections of digital culture, entrepreneurship, and media dynamics in this thought-provoking episode of Pocket Watch Podcast. #pocketwatchpodcast #mrbeast #x #twitter #elonmusk ---------------------------------- Follow Us On IG, YouTube, FaceBook, and More Here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@PocketWatchPodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Special Thanks To Our Audio Engineer Jean Lorenzo: IG-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@JeanLorenzoG⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Special Thanks To Our Designer In Visual Creative Marketing: IG-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@Graphics_ByTiago⁠ --- Send in a voice message: https://podcasters.spotify.com/pod/show/pocketwatchpodcast/message Support this podcast: https://podcasters.spotify.com/pod/show/pocketwatchpodcast/support

趕上理財新時尚！新光證券龍躍千金定期定額再升級，手續費1,000元紅包免費送！定期定額筆筆回饋怎樣扣都可以～立即點擊資訊欄連結 > https://fstry.pse.is/5lefxp —— 以上為播客煮與 Firstory Podcast 廣告 —— ------------------------------- 通勤學英語VIP加值內容與線上課程 ------------------------------- 通勤學英語VIP訂閱方案：https://open.firstory.me/join/15minstoday 社會人核心英語有聲書課程連結：https://15minsengcafe.pse.is/554esm ------------------------------- 15Mins.Today 相關連結 ------------------------------- 歡迎針對這一集留言你的想法： 留言連結 主題投稿/意見回覆 : ask15mins@gmail.com 官方網站：www.15mins.today 加入Clubhouse直播室：https://15minsengcafe.pse.is/46hm8k 訂閱YouTube頻道：https://15minsengcafe.pse.is/3rhuuy 商業合作/贊助來信：15minstoday@gmail.com ------------------------------- 以下是此單集逐字稿 (播放器有不同字數限制，完整文稿可到官網) ------------------------------- 國際時事跟讀 Ep.K727: MrBeast's $250,000 X Experiment: Unveiling the Power of Creator Monetization Highlights 主題摘要：MrBeast's X Experiment Success: MrBeast's X video experiment demonstrated the significant earnings potential for major influencers, earning over $250,000 and showcasing the power of creator monetization.X's Monetization Challenges: Despite MrBeast's success, disparities and concerns arise within X's creator program, with varying payouts and fears of inflammatory content linked to premium subscribers.Platform-Specific Monetization: MrBeast's unique success on X is attributed to his massive following, pre-roll video ads, and Shopify's involvement, emphasizing the importance of platform-specific monetization strategies.Influencer MrBeast, known off-screen as Jimmy Donaldson, recently tested Elon Musk's theory on X, the social media platform owned by Musk. Surprisingly, MrBeast revealed that a single video he posted on X earned him over $250,000, showcasing the potential windfall for major internet personalities participating in the platform's new ad revenue sharing program. 知名網紅MrBeast，本名Jimmy Donaldson，最近在由Elon Musk擁有的社群平台X上測試了Musk的理論。令人驚訝的是，MrBeast透露，他在X上發布的一個影片讓他賺了超過25萬美元，展示了參與該平台新的廣告收益分潤計劃的主要網紅可能獲得的潛在豐厚利潤。 Elon Musk introduced this program in July, aiming to share revenue from ads displayed in response to high-performing content. The catch: creators must subscribe to X's premium service. This initiative not only aligns X with competitors like Meta, YouTube, and TikTok but also strives to boost the platform's ad business. Elon Musk於七月推出了這項計劃，目的是回饋表現成果優異的內容，並分出廣告收益。問題是：創作者必須訂閱X的高級服務。這個行動不僅使X與Meta、YouTube和TikTok等競爭對手保持一致，還努力提振了平台的廣告業務。 Despite MrBeast's initial reluctance to regularly post on X, he decided to experiment by uploading an older video about testing expensive cars. Surpassing 160 million views, the video demonstrated the potential for meaningful earnings through X's nascent ad revenue sharing program. However, MrBeast cautioned that results might not be uniform for all creators. 儘管MrBeast最初不願意定期在X上貼文，但他決定上傳一個有關測試昂貴汽車的舊影片來進行實驗。該影片的觀看次數超過了1.6億次，展示了透過X新興的廣告收益分潤計劃實現有意義收入的潛力。然而，MrBeast警告說，結果可能不能套用到所有創作者上。 MrBeast's substantial payout can be attributed to his massive X following of over 27.5 million users. The influencer's engagement likely contributed to higher revenue per view, as advertisers sought the attention his content was garnering. MrBeast巨額的收入歸功於他在X上的龐大追隨者，超過2,750萬用戶。這位網紅的參與可能有助於提高每次觀看的收入，因為廣告商們都在尋求他的內容所帶來的關注度。 While MrBeast celebrated his success, not all creators on X share the same sentiment. Conservative commentator Dan Bongino received a mere $379, sparking criticism of X's creator program. Some fear that paying X Premium subscribers, whose posts are already boosted by the algorithm, might encourage inflammatory content. 儘管MrBeast慶祝他的成功，但並非所有X上的創作者都有相同的感受。保守評論員丹·邦吉諾僅收到379美元，引發對X創作者計劃的批評。有人擔心訂閱X高級服務後，他們的貼文會立刻被演算法提升，可能會促使發表更具爭議或冒犯性的內容。 While MrBeast's experiment may bring him considerable earnings, it's essential to note that X's monetization rules count views only from Premium subscribers. This subscription-based model may limit broader creator monetization experiences on X. The platform's payout system has faced scrutiny, with disparities observed between payouts for favored accounts and other users. 雖然MrBeast的實驗可能為他帶來可觀的收益，但需要注意的是，X的盈利規則僅計算高級訂戶的觀看次數。這種基於訂閱的模式可能會限制在X平台上更廣泛的創作者盈利體驗。該平台的付款制度受到質疑，因為可以觀察到對喜愛帳戶和其他用戶之間的分潤存在差異。 In conclusion, MrBeast's $250,000 X experiment sheds light on the potential earnings for major internet personalities, but it also underscores the challenges and disparities within X's creator monetization landscape. 總之，MrBeast的25萬美元X平台實驗，顯示了重點網紅的潛在收益，但也凸顯了X的創作者盈利環境中的挑戰與差異。 Keyword Drills 關鍵字：Initiative (i-ni-ti-a-tive): Elon Musk introduced this initiative in July, aiming to share revenue from ads displayed in response to high-performing content.Demonstrated (de-mon-strat-ed): Surpassing 160 million views, the video demonstrated the potential for meaningful earnings through X's nascent ad revenue sharing program.Commentator (com-men-ta-tor): Conservative commentator Dan Bongino received a mere $379, sparking criticism of X's creator program.Inflammatory (in-flam-ma-to-ry): Some fear that paying X Premium subscribers might encourage inflammatory content.Monetization (mon-e-ti-za-tion): MrBeast's success on X sheds light on the potential earnings for major internet personalities and the challenges within X's creator monetization landscape. Reference article: 1. https://edition.cnn.com/2024/01/23/tech/mr-beast-x-video-worth/index.html 2. https://mashable.com/article/mrbeast-x-video-upload-experiment

MrBeast is looking to get even richer. Amazon is reportedly going to throw $100 million at the YouTuber... after laying off HUNDREDS of employees at Twitch and Prime Video. Cue the outrage in 5... 4... 3... 2... 1... ➡️ Tip Jar and Fan Support: http://ClownfishSupport.com ➡️ Official Merch Store: http://ShopClownfish.com ➡️ Official Website: http://ClownfishTV.com ➡️ Audio Edition: https://open.spotify.com/show/6qJc5C6OkQkaZnGCeuVOD1 ➡️ Gaming News: https://open.spotify.com/show/0A7VIqE3r5MQkFgL9nifNc Additional Context: It's a tale of big deals and big cuts in the world of digital media. On one hand, we have MrBeast, a.k.a. Jimmy Donaldson, a YouTube sensation famed for his extravagant stunts and philanthropy, who is reportedly on the verge of inking a massive $100 million deal with Amazon Prime Video. This deal is set to bring his unique brand of content to a wider audience on one of the world's leading streaming platforms. On the other hand, Amazon, the parent company of Prime Video, is simultaneously making headlines for laying off a significant number of employees at its streaming and film subsidiaries, including Prime Video and Twitch. This move is part of a broader trend of layoffs in the tech and media industries, as companies adjust to changing market conditions and strategic priorities. The juxtaposition of these two stories — the lucrative deal with a YouTube star and the layoffs at Amazon's streaming services — highlights the dynamic and often unpredictable nature of the digital entertainment industry. While platforms are willing to invest heavily in popular content creators like MrBeast to attract and retain viewers, they are also making tough decisions to streamline operations and manage costs. This situation underscores the challenges faced by tech and media companies as they navigate the rapidly evolving landscape of online entertainment. The investment in MrBeast's content signifies a recognition of the power and influence of social media personalities in shaping viewer preferences, while the layoffs reflect the harsh realities of business economics in a highly competitive industry​​​​​​​​​​. About Us: Clownfish TV is an independent, opinionated news and commentary channel that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles. Disclaimer: This series is produced by Clownfish Studios and WebReef Media, and is part of ClownfishTV.com. Opinions expressed by our contributors do not necessarily reflect the views of our guests, affiliates, sponsors, or advertisers. ClownfishTV.com is an unofficial news source and has no connection to any company that we may cover. This channel and website and the content made available through this site are for educational, entertainment and informational purposes only. These so-called “fair uses” are permitted even if the use of the work would otherwise be infringing. #MrBeast #Amazon #Twitch #News #Commentary #Reaction #Podcast #Comedy #Entertainment #Hollywood #PopCulture #Tech

Jimmy Donaldson, better known as MrBeast, is one of YouTube's most popular creators. His videos rack up hundreds of millions of views. Elon Musk has encouraged Donaldson to post his videos on X, which would be a boon for the platform. But Donaldson has rebuffed him, saying that the math doesn't work for him on X. WSJ columnist Tim Higgins joins host Alex Ossola to talk about why, what it means for the future of the platform and what it reveals about the creator economy. Learn more about your ad choices. Visit megaphone.fm/adchoices

This is a preview of a bonus episode. Listen to the full episode on Patreon at www.patreon.com/10kpostspodcast. -------- Ryan comes back on the podcast this week to explain Jimmy Donaldson's – aka "Mr Beast"- new antic, which seems to involve ignoring local government protocol in East Africa to build wells and electricity pylons. Though presented as an altruistic act, Ryan explains how these stunts feed into the expansive "Mr Beast" brand, at a time when it looks like Donaldson is running out of ideas. Ryan also explains Donaldson's failed attempts to be accepted by mainstream culture and how, despite his massive following, it seems unlikely he'll ever make a break into mainstream TV. Read and subscribe to Garbage Day here: https://www.garbageday.email/ -------- PALESTINE AID LINKS As the humanitarian crisis continues to unfold in Gaza, we encourage anyone who can to donate to Medical Aid for Palestinians. You can donate using the links below. https://www.map.org.uk/donate/donate https://www.savethechildren.org.uk/how-you-can-help/emergencies/gaza-israel-conflict Currently, Aid is not getting through. You can donate with the knowledge that these funds will be used the instant Aid is allowed into Gaza, but please do make your voice heard in other ways about this. Showing up at protest marches and writing letters to elected MPs are useful acts of solidarity. Please encourage your elected representatives to support calls for a ceasefire, while still bearing in mind a ceasefire is temporary and represents the bare minimum. -------- PHOEBE ALERT Can't get enough Phoebe? Check out her Substack Here! -------- Ten Thousand Posts is a show about how everything is posting. It's hosted by Hussein (@HKesvani), Phoebe (@PRHRoy) and produced by Devon (@Devon_onEarth).

Tom and Jim discuss the Liberty-Aces WNBA Finals action, and all the crucial lessons of Week 5 in the NFL. Then, they are joined by YouTube sensation Mr. Beast to discuss his road to 200 million subscribers, his first viral video, what sets him apart from other YouTubers, and why influencer marketing is underrated.

Jimmy Donaldson, better known as MrBeast, has become a sensation on YouTube for ostentatious and sometimes absurd acts of altruism.Today, Max Read, a journalist and contributor to The Times, discusses what the rise of one of YouTube's most popular star tells us about the platform and its users.Max Read is a contributor to The New York Times Magazine and writes about technology and internet culture in his newsletter “Read Max.”Background reading: Why do so many people think Mr. Donaldson is evil?MrBeast is out to become the Elon Musk of online creators.For more information on today's episode, visit nytimes.com/thedaily. Transcripts of each episode will be made available by the next workday.

Jimmy Donaldson, better known as MrBeast, has over 147 million subscribers, and is a favorite among American families everywhere. Until recently, Jimmy has remained fairly neutral when it comes to politics and religion. That all changed when one of his best friends and costars Chris Tyson, recently announced that he is transitioning from male to female. Since then, Jimmy has ardently defended his friend's choices and his Wikipedia page now describes him as an “American YouTuber, philanthropist, and transgender rights activist.” Today, Natasha and Alisa discuss what it looks like to talk with our kids about sensitive issues like transgenderism, and when their favorite youtube celebrity goes woke.

The Deep State Runs Deep. Mike Morrell, former Deputy Director at the CIA, now admits he got some 50 ex spies to write the letter slamming Hunter Biden laptop report as 'Russian disinformation' to help Joe Biden win the election. And it gets worse. You're not going to believe WHO asked Morrell to orchestrate the letter. Remember: this same cast of spooks and political hacks not only wanted America to think that the Biden laptop was FAKE, they also wanted America to think the so-called "Trump Dossier" was REAL. And you wonder why Americans have no faith in government right now?  As Donald Trump soars past all others in a brand new WSJ poll, one must wonder: will the same group of intelligence professionals in the Democrat Party once again prevent Americans from knowing the truth?  Trump is the front runner for Republicans amid growing frustration with Ron DeSantis. DeSantis is embroiled in a massive brawl with Disney...and, a growing number of conservatives are questioning his motives.  The transgender scandal is consuming Youtube with parents worrying their children may become 'indocrinated' as the trans issue is a new theme in popular shows. Cast in point? The world's most popular YouTube star is a 24 year old named "Mr Beast". (His real name is Jimmy Donaldson.) Mr Beast has built a $500 million empire around his schtick as a regular guy hanging out with his regular guy friends and doing incredible things. Well, one of those guys... Chris Tyson...is now, Christina. It could be a big problem for the business (unless, of course, Bud Light is the sponsor) but, the bigger concern for the conservative community is how the show will play with kids.  Bud Light shares continue their stuggle. On Friday, shares of Anheuser-Busch closed nearly flat, while shares of Bud's rival Molsen Coors rallied again. So, while AB Inbev has lost billions in market cap since the Dylan Mulvaney promotion became know earlier this month, Bud's rival keeps seeing a higher share price. I'll explain what's going on.  And - yes - Biden's woke agenda comes with financial penalites for hard working Americans that pay their bills on time. A new Biden plan begins May 1st and will result in people with more than 680 on their credit score paying an additional fee of an estimated $40 per month to secure a home loan. The Biden admnistration is doing this to help offset the costs of borrowers with bad credit scores. Sounds a little like Venezuela-style economics to me! All that said - you still need to pay your bills on time which is why I'm thrilled to welcome Rachel Cruz on the program today. Rachel is the daughter of financial planning guru Dave Ramsey and a financial planning and budget expert herself. She's the author of several books and host of The Rachel Cruz Show. She joins me today with advice for EVERYONE about budgeting, planning, and making the most of your money. Don't forget to rate my podcast, follow my podcast on Apple, and WATCH my podcast on Youtube.  Sign up for my free newsletter at https://TrishReganShow.com. Today's show is sponsored by advertisers: https://LegacyPMInvestments.com https://RuffGreens.com/Trish  Support the show: https://trishregan.store/See omnystudio.com/listener for privacy information.

Special Guest: Kirk Cameron, Actor and Author, "As You Grow" In today's show, I'm sharing new details from the Chicago weekend 'Teen Takeover' that local lawmakers want to ignore. Lori Lightfoot said there was no mayhem. I beg to differ. Plus, Bud Light is trying out a new 'patriotic' marketing campaign in an effort to win back customers. Newsflash: Anheuser-Busch is not an 'American' company, it's a multi-national global conglomerate. I'll explain.  And - not even Mr. Beast can escape the story du jour.. Mr. Beast, aka Jimmy Donaldson, is the most famous Youtube personality. His empire and his show centers around him and his 'bros'...but, one of the bros isn't feeling so manly anymore. What will that mean for Mr. Beast? I'm asking one of our producers Simeon Harris (a Mr. Beast fan) who lays out the challenge for the Youtuber. Meanwhile, with 'cancel culture' reigning supreme, I'm joined by Kirk Cameron, who says he's feeling its effects in his library reading tour across the country. The actor best known for his role of Mike Seaver in the 1980s sit-com "Growing Pains", is out will a new book for children called "As You Grow." He says he and his publisher (Brave Books) are struggling to ensure his message of God and Family are heard because too many libraries don't like what he represents.  The culture war is raging. Join me for a front seat look at the challenges for our country, our economy, our children, and ourselves. Plus, don't forget to subscribe to my newsletter at https://TrishReganShow.com. Today's show is brought to you by advertisers including: https://LegacyPMInvestments.com https://RuffGreens.com/Trish      Support the show: https://trishregan.store/See omnystudio.com/listener for privacy information.

Dave Rubin of “The Rubin Report” talks about Jimmy Donaldson, better known as MrBeast, being attacked by Forbes and the liberal media for his “1,000 Blind People See For The First Time" video, where he pays for the manual small incision cataract surgery of 1,000 people; the Babylon Bee's perfect satire of the MrBeast controversy; and the media silence on Leonardo DiCaprio's latest liberal hypocrisy surrounding his climate change activism. Dave also does a special “ask me anything” question-and-answer session on a wide-ranging host of topics, answering questions from the Rubin Report Locals community. WATCH the MEMBER-EXCLUSIVE segment of the show here: https://rubinreport.locals.com/