Podcast appearances and mentions of Paul Graham

The saying goes that you haven't worked in radio, until you've worked in Winnipeg.Personalities like Brother Jake Edwards, Terry DiMonte, Don Percy, and many others gathered June 12-14 for the Winnipeg Radio Reunion. It saw colleagues who've worked in the market, both past and present, reminisce and reconnect.Broadcast Dialogue caught up with organizer Ralph “Racoon” Carney – an alum of CKRC and 1290 AM (CFRW-AM) Winnipeg, among other stations - to talk about bringing the event to life. He shares some weekend highlights, alongside Paul Graham, Courtney James, Jim Goddard, and Don Percy.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

"Si tu veux réussir en entrepreneuriat, c'est l'obsession client, c'est le service client."Je reçois Florian Boret - serial entrepreneur et fondateur d'Achille, sa troisième startup.On parle de son premier échec avec WeHome à la création de Wedoogift où il a appliqué religieusement les principes de Paul Graham et Y Combinator, et comment il en est venu à monter Achille.Achille c'est une startup qui mêle humain et IA pour gérer votre support client. Parce que selon Florian, si t'as un bon support client, t'es au max !Florian c'était mon boss chez Wedoogift (sa 2e startup, mon 2e stage lol) - et là-bas on était obligé de faire un peu de SAV justement.Il m'explique justement d'où lui vient ce customer obsession" à la française, et comment il a fait passer le NPS de Wedoogift de négatif à +40 en 3 ans.On parle aussi de sa stratégie "Do things that don't scale", de l'approche tech-enabled vs full IA, de ses "super agents" humains qui augmentent l'IA plutôt que d'être remplacés par elle - et évidemment, d'entrepreneuriat.Bonne écoute !

Découvrez comment Sam Altman, surnommé “le roi de l'IA” et “l'Oppenheimer des temps modernes,” est en train de bouleverser l'avenir de l'humanité. Visionnaire pour certains, manipulateur pour d'autres, il ambitionne de créer une intelligence plus puissante que nous tous.Mais jusqu'où ira-t-il ? Et pouvons-nous lui faire confiance ?

DEMAIN je reçois Florian Boret - serial entrepreneur et fondateur d'Achille, sa troisième startup.On parle de son premier échec avec WeHome à la création de Wedoogift où il a appliqué religieusement les principes de Paul Graham et Y Combinator, et comment il en est venu à monter Achille.Achille c'est une startup qui mêle humain et IA pour gérer votre support client. Parce que selon Florian, si t'as un bon support client, t'es au max !Florian c'était mon boss chez Wedoogift (sa 2e startup, mon 2e stage lol) - et là-bas on était obligé de faire un peu de SAV justement.Il m'explique justement d'où lui vient ce customer obsession" à la française, et comment il a fait passer le NPS de Wedoogift de négatif à +40 en 3 ans.On parle aussi de sa stratégie "Do things that don't scale", de l'approche tech-enabled vs full IA, de ses "super agents" humains qui augmentent l'IA plutôt que d'être remplacés par elle - et évidemment, d'entrepreneuriat.Bonne écoute !

Your traffic isn't the problem. Your ads aren't broken. Your positioning is off. And that's why you're stuck.In this solo episode, Jim Huffman shares a live coaching session that breaks down how to actually fix brand positioning — using case studies from Neat Apparel, GrowthHit, and breakout brands like Rocket Money, Warby Parker, and Figma. You'll learn how better positioning unlocks conversion rate gains, improves messaging, and becomes the foundation for scalable growth.This is the episode for Shopify founders ready to get serious about messaging, differentiation, and the real reasons customers buy.Key Topics Covered:Why your brand pitch likely isn't working (and how to fix it)Two frameworks to improve your positioning todaySpeaking to 3 customer types: informed, afflicted, and obliviousCase studies: Spanx, Truvani, Adam Shoes, Rocket Money & moreReal examples from GrowthHit and Neat Apparel's positioning playbookLearn live from Shopify experts. Join our biweekly AI-powered growth sessions - free for founders and marketers - https://shopifygrowthschool.com/ Resources:Shopify Growth SchoolGrowth Marketing OS (Operating System) GrowthHitJim Huffman websiteJim's LinkedinJim's Twitter Additional episodes you might enjoy:Startup Ideas by Paul Graham (#45)Nathan Barry: How to Bootstrap a Company to $30M in a Crowded Market (#41)How I Met My Biz Partner and Less Learned Hitting $2M ARR (#44)Ryan Hamilton on his Netflix special, touring with Jerry Seinfeld, & how to write a joke (#10)How We're Validating Startup Ideas (#51) 

What if the polished ads you spent thousands on were actually hurting your brand?In this episode, we explore how real, imperfect content is outperforming big-budget campaigns — and why most Shopify founders are still getting it wrong.Jim Huffman sits down with William Gasner, co-founder of Stack Influence, to unpack the real power behind UGC (user-generated content) and why it's become a secret weapon for smart eCommerce brands. From influencer seeding to ad fatigue, they get brutally honest about what actually drives conversions in 2025 — and how founders can stop wasting money and start scaling with authenticity.Key Topics CoveredWhy UGC outperforms professional ads in today's marketThe overlooked value of product seeding (and how to do it right)How to build a content machine that fuels both ads and organic growthWhy creative freedom often leads to better results than tight scriptsThe truth about influencers, follower counts, and what really matters Resources:Jim Huffman websiteJim's TwitterGrowthHitThe Growth Marketer's Playbook Additional episodes you might enjoy:Startup Ideas by Paul Graham (#45)Nathan Barry: How to Bootstrap a Company to $30M in a Crowded Market (#41)How I Met My Biz Partner and Less Learned Hitting $2M ARR (#44)Ryan Hamilton on his Netflix special, touring with Jerry Seinfeld, & how to write a joke (#10)How We're Validating Startup Ideas (#51) 

Việc điều hành một startup vô cùng khó khăn; khoảng một nửa số công ty được Y Combinator tài trợ được kỳ vọng sẽ thành công, có nghĩa là một nửa sẽ thất bại. Thành công thường đến từ việc "chỉ cần tránh chết"— sống sót cho đến khi đạt được thành công tài chính. Nguyên nhân chính dẫn đến cái chết của startup thường là hết tiền hoặc nhà sáng lập quan trọng bỏ cuộc, nhưng nguyên nhân sâu xa hơn thường là sự nản chí. Để đối phó, hãy duy trì liên lạc thường xuyên, liên tục lặp lại sản phẩm, tạo ra thứ mà ít nhất một số người thực sự yêu thích, và quan trọng nhất là tránh làm những việc khác gây xao nhãng. Hãy nhớ rằng cảm giác tồi tệ và cảm thấy những gì đang làm không hiệu quả là bình thường; những điều tồi tệ chắc chắn sẽ xảy ra, nhưng đừng nản chí và đừng bỏ cuộc. Khi tranh luận, đặc biệt là trực tuyến, hãy sử dụng Hệ thống phân cấp bất đồng quan điểm (Disagreement Hierarchy), cố gắng tránh lăng mạ (DH0) và công kích cá nhân (DH1), hướng đến các cấp độ cao hơn như phản biện (DH4) và bác bỏ (DH5, DH6) bằng lý lẽ, bằng chứng, và tập trung vào điểm chính. Điều này không chỉ làm cuộc trò chuyện tốt hơn mà còn khiến những người tham gia hạnh phúc hơn vì sự cay nghiệt tập trung ở các cấp độ thấp hơn. Quá trình học hỏi liên quan đến việc huấn luyện "mô hình thế giới" trong tâm trí bạn; ngay cả khi quên chi tiết, tác động vẫn còn lại, cho thấy việc đọc lại những cuốn sách quan trọng là rất đáng giá.Về tư duy và triết học, triết học truyền thống được xem là gặp vấn đề do sự mờ nhạt của các khái niệm hàng ngày, khiến nhiều tranh luận bị chi phối bởi sự nhầm lẫn về từ ngữ. Quan điểm cho rằng Aristotle đã sai lầm khi coi tri thức lý thuyết vô dụng là cao quý. Thay vì tìm kiếm "những sự thật chung nhất" mà không màng đến tính hữu dụng, cách tiếp cận tốt hơn là tìm kiếm "trong số những điều hữu ích mà chúng ta có thể nói, điều nào là chung nhất?", nhằm tạo ra những quan sát chung khiến người đọc hành động khác đi. Triết học được xem là một lĩnh vực rất trẻ, vẫn đang vật lộn với sự không chính xác của từ ngữ. Tư duy độc lập (vừa đúng vừa mới lạ) rất quan trọng trong một số lĩnh vực. Mặc dù có thể thiên về bẩm sinh, nó có thể được tăng cường hoặc không bị kìm hãm bằng cách giao lưu với những người có tư duy độc lập, nuôi dưỡng thái độ hoài nghi ("Điều đó có thật không?"), và quan sát các "thời trang trí tuệ" để tìm kiếm những ý tưởng chưa được khám phá. Tư duy độc lập có ba thành phần: sự kỹ lưỡng về sự thật, sự chống lại việc bị bảo phải nghĩ gì, và sự tò mò. Sự tò mò là nguồn gốc của những ý tưởng mới lạ và có thể được trau dồi bằng cách "chiều chuộng nó".Chàng-Ngốc-Già mong muốn xây dựng cùng mọi người một cộng đồng, một school:https://prime.changngocgia.com/feedTham vấn 1:1https://shorturl.at/E8lWC To hear more, visit changngocgia.substack.com

Cuộc đời là một hành trình phức tạp, đặc biệt khi nói đến việc tìm kiếm một công việc ý nghĩa và đạt được sự thịnh vượng. Nhiều người ao ước một "công thức" đơn giản, nhưng thực tế cho thấy con đường này đầy rẫy những ngã rẽ, áp lực xã hội và những cạm bẫy tinh vi. Dựa trên những phân tích sâu sắc từ các nguồn được cung cấp, chúng ta có thể phác thảo một bức tranh tổng thể về cách tiếp cận thách thức này, tập trung vào việc tìm kiếm công việc mình yêu thích, tầm quan trọng của lao động chăm chỉ, bản chất của sự giàu có, và cách tránh lãng phí những nguồn lực quý giá.Để tìm thấy sự viên mãn và thành công, chúng ta cần đi ngược lại nhiều bài học và áp lực đã ăn sâu trong tâm trí. Hãy tìm kiếm công việc mà bạn thực sự yêu thích, công việc đủ hấp dẫn để bạn sẵn sàng làm việc chăm chỉ và trung thực. Hiểu rằng sự giàu có đến từ việc tạo ra giá trị cho người khác, và những con đường như khởi nghiệp có thể khuếch đại nỗ lực của bạn thông qua đo lường và đòn bẩy.. Quan trọng nhất, hãy cảnh giác với những cạm bẫy của sự giả tạo và trì hoãn, đặc biệt là dạng "công việc giả", vốn là kẻ thù nguy hiểm nhất của thời gian và tiềm năng. Bằng cách tập trung vào công việc thực tế, chân thật và không ngừng học hỏi, bạn sẽ không chỉ có cơ hội đạt được thành công lớn lao mà còn trải nghiệm sự hài lòng sâu sắc từ chính quá trình làm việc đó. To hear more, visit changngocgia.substack.com

Liz Long, co-founder of Nearshore.ai, shares her extensive knowledge on transforming innovative ideas into actual products. Liz discusses the critical steps in product design, manufacturing, and supply chain management, offering invaluable tips for startups and established businesses alike. With a background in launching successful brands and an accelerator, Liz provides a wealth of practical advice, from navigating production phases to leveraging global supply chains. She also explores current trends, the importance of specialized products, and effective inventory management strategies. Whether you are a budding entrepreneur or a seasoned professional, this episode is packed with actionable insights to help you thrive in the e-commerce and manufacturing spaces. TOPICS DISCUSSED IN TODAY'S EPISODEStarting a Brand: The Initial StepsDesigning Your Product: Key ConsiderationsFinding the Right Factory and Supply ChainBudgeting and Cost ManagementNavigating Production and Quality ControlChoosing Manufacturing LocationsManaging Inventory and Cash FlowFinal Mile: Shipping and FulfillmentExploring Current Trends in Fashion and Product SpecializationThe Rise of the Drop Model and Its ChallengesLiz's Journey: From Reusable Shopping Bags to Product AcceleratorThe Transition to Nearshore and Its ImpactBalancing Work and Family: Liz's Personal InsightsConclusion and Final ThoughtsResources:Jim Huffman websiteJim's TwitterGrowthHitThe Growth Marketer's Playbook  Additional episodes you might enjoy:Startup Ideas by Paul Graham (#45)Nathan Barry: How to Bootstrap a Company to $30M in a Crowded Market (#41)How I Met My Biz Partner and Less Learned Hitting $2M ARR (#44)Ryan Hamilton on his Netflix special, touring with Jerry Seinfeld, & how to write a joke (#10)How We're Validating Startup Ideas (#51) 

What if your hardest season became your biggest breakthrough?Ming Zhao built a $24M skincare brand in just 2 years—while pregnant and going through Y Combinator. This isn't just a startup story. It's a story of pressure, clarity, and purpose colliding at once.In this raw and revealing episode, Ming Zhao, CEO and co-founder of PROVEN Skincare, shares how she transformed corporate burnout into one of the fastest-growing beauty startups—scaling to $24 million in just 24 months. She opens up about building a data-driven business during one of the most demanding periods of her life, and how AI, science, and personal insight helped reshape the future of skincare.Key Topics Covered:How AI and data are transforming skincareThe Skin Genome Project explainedNavigating Y Combinator while pregnantBuilding a business model rooted in scienceLiving the “Love your job, live your life” mindsetResources:ProvenJim Huffman websiteJim's TwitterGrowthHitThe Growth Marketer's Playbook Additional episodes you might enjoy:Startup Ideas by Paul Graham (#45)Nathan Barry: How to Bootstrap a Company to $30M in a Crowded Market (#41)How I Met My Biz Partner and Less Learned Hitting $2M ARR (#44)Ryan Hamilton on his Netflix special, touring with Jerry Seinfeld, & how to write a joke (#10)How We're Validating Startup Ideas (#51) 

Much has been made of the hallucinatory qualities of OpenAI's ChatGPT product. But as the Wall Street Journal's resident authority on OpenAI, Keach Hagey notes, perhaps the most hallucinatory feature the $300 billion start-up co-founded by the deadly duo of Sam Altman and Elon Musk is its attempt to be simultaneously a for-profit and non-profit company. As Hagey notes, the double life of this double company reached a surreal climax this week when Altman announced that OpenAI was abandoning its promised for-profit conversion. So what, I asked Hagey, are the implications of this corporate volte-face for investors who have poured billions of real dollars into the non-profit in order to make a profit? Will they be Waiting For Godot to get their returns?As Hagey - whose excellent biography of Altman, The Optimist, is out in a couple of weeks - explains, this might be the story of the hubristic 2020's. She speaks of Altman's astonishingly (even for Silicon Valley) hubris in believing that he can get away with the alchemic conceit of inventing a multi trillion dollar for-profit non-profit company. Yes, you can be half-pregnant, Sam is promising us. But, as she warns, at some point this will be exposed as fantasy. The consequences might not exactly be another Enron or FTX, but it will have ramifications way beyond beyond Silicon Valley. What will happen, for example, if future investors aren't convinced by Altman's fantasy and OpenAI runs out of cash? Hagey suggests that the OpenAI story may ultimately become a political drama in which a MAGA President will be forced to bail out America's leading AI company. It's TikTok in reverse (imagine if Chinese investors try to acquire OpenAI). Rather than the conveniently devilish Elon Musk, my sense is that Sam Altman is auditioning to become the real Jay Gatsby of our roaring twenties. Last month, Keach Hagey told me that Altman's superpower is as a salesman. He can sell anything to anyone, she says. But selling a non-profit to for-profit venture capitalists might even be a bridge too far for Silicon Valley's most hallucinatory optimist. Five Key Takeaways * OpenAI has abandoned plans to convert from a nonprofit to a for-profit structure, with pressure coming from multiple sources including attorneys general of California and Delaware, and possibly influenced by Elon Musk's opposition.* This decision will likely make it more difficult for OpenAI to raise money, as investors typically want control over their investments. Despite this, Sam Altman claims SoftBank will still provide the second $30 billion chunk of funding that was previously contingent on the for-profit conversion.* The nonprofit structure creates inherent tensions within OpenAI's business model. As Hagey notes, "those contradictions are still there" after nearly destroying the company once before during Altman's brief firing.* OpenAI's leadership is trying to position this as a positive change, with plans to capitalize the nonprofit and launch new programs and initiatives. However, Hagey notes this is similar to what Altman did at Y Combinator, which eventually led to tensions there.* The decision is beneficial for competitors like XAI, Anthropic, and others with normal for-profit structures. Hagey suggests the most optimistic outcome would be OpenAI finding a way to IPO before "completely imploding," though how a nonprofit-controlled entity would do this remains unclear.Keach Hagey is a reporter at The Wall Street Journal's Media and Marketing Bureau in New York, where she focuses on the intersection of media and technology. Her stories often explore the relationships between tech platforms like Facebook and Google and the media. She was part of the team that broke the Facebook Files, a series that won a George Polk Award for Business Reporting, a Gerald Loeb Award for Beat Reporting and a Deadline Award for public service. Her investigation into the inner workings of Google's advertising-technology business won recognition from the Society for Advancing Business Editing and Writing (Sabew). Previously, she covered the television industry for the Journal, reporting on large media companies such as 21st Century Fox, Time Warner and Viacom. She led a team that won a Sabew award for coverage of the power struggle inside Viacom. She is the author of “The King of Content: Sumner Redstone's Battle for Viacom, CBS and Everlasting Control of His Media Empire,” published by HarperCollins. Before joining the Journal, Keach covered media for Politico, the National in Abu Dhabi, CBS News and the Village Voice. She has a bachelor's and a master's in English literature from Stanford University. She lives in Irvington, N.Y., with her husband, three daughters and dog.Named as one of the "100 most connected men" by GQ magazine, Andrew Keen is amongst the world's best known broadcasters and commentators. In addition to presenting the daily KEEN ON show, he is the host of the long-running How To Fix Democracy interview series. He is also the author of four prescient books about digital technology: CULT OF THE AMATEUR, DIGITAL VERTIGO, THE INTERNET IS NOT THE ANSWER and HOW TO FIX THE FUTURE. Andrew lives in San Francisco, is married to Cassandra Knight, Google's VP of Litigation & Discovery, and has two grown children. Full TranscriptAndrew Keen: Hello, everybody. It is May the 6th, a Tuesday, 2025. And the tech media is dominated today by OpenAI's plan to convert its for-profit business to a non-profit side. That's how the Financial Times is reporting it. New York Times says that OpenAI, and I'm quoting them, backtracks on plans to drop nonprofit control and the Wall Street Journal, always very authoritative on the tech front, leads with Open AI abandons planned for profit conversion. The Wall Street Journal piece is written by Keach Hagey, who is perhaps America's leading authority on OpenAI. She was on the show a couple of months ago talking about Sam Altman's superpower which is as a salesman. Keach is also the author of an upcoming book. It's out in a couple weeks, "The Optimist: Sam Altman, OpenAI and the Race to Invent the Future." And I'm thrilled that Keach has been remarkably busy today, as you can imagine, found a few minutes to come onto the show. So, Keach, what is Sam selling here? You say he's a salesman. He's always selling something or other. What's the sell here?Keach Hagey: Well, the sell here is that this is not a big deal, right? The sell is that, this thing they've been trying to do for about a year, which is to make their company less weird, it's not gonna work. And as he was talking to the press yesterday, he was trying to suggest that they're still gonna be able to fundraise, that these folks that they promised that if you give us money, we're gonna convert to a for-profit and it's gonna be much more normal investment for you, but they're gonna get that money, which is you know, a pretty tough thing. So that's really, that's what he's selling is that this is not disruptive to the future of OpenAI.Andrew Keen: For people who are just listening, I'm looking at Keach's face, and I'm sensing that she's doing everything she can not to burst out laughing. Is that fair, Keach?Keach Hagey: Well, it'll remain to be seen, but I do think it will make it a lot harder for them to raise money. I mean, even Sam himself said as much during the talk yesterday that, you know, investors would like to be able to have some say over what happens to their money. And if you're controlled by a nonprofit organization, that's really tough. And what they were trying to do was convert to a new world where investors would have a seat at the table, because as we all remember, when Sam got briefly fired almost two years ago. The investors just helplessly sat on the sidelines and didn't have any say in the matter. Microsoft had absolutely no role to play other than kind of cajoling and offering him a job on the sidelines. So if you're gonna try to raise money, you really need to be able to promise some kind of control and that's become a lot harder.Andrew Keen: And the ramifications more broadly on this announcement will extend to Microsoft and Microsoft stock. I think their stock is down today. We'll come to that in a few minutes. Keach, there was an interesting piece in the week, this week on AI hallucinations are getting worse. Of course, OpenAI is the dominant AI company with their ChatGPT. But is this also kind of hallucination? What exactly is going on here? I have to admit, and I always thought, you know, I certainly know more about tech than I do about other subjects, which isn't always saying very much. But I mean, either you're a nonprofit or you're a for-profit, is there some sort of hallucinogenic process going on where Sam is trying to sell us on the idea that OpenAI is simultaneously a for profit and a nonprofit company?Keach Hagey: Well, that's kind of what it is right now. That's what it had sort of been since 2019 or when it spun up this strange structure where it had a for-profit underneath a nonprofit. And what we saw in the firing is that that doesn't hold. There's gonna come a moment when those two worlds are going to collide and it nearly destroyed the company. To be challenging going forward is that that basic destabilization that like unstable structure remains even though now everything is so much bigger there's so much more money coursing through and it's so important for the economy. It's a dangerous position.Andrew Keen: It's not so dangerous, you seem still faintly amused. I have to admit, I'm more than faintly amused, it's not too bothersome for us because we don't have any money in OpenAI. But for SoftBank and the other participants in the recent $40 billion round of investment in OpenAI, this must be, to say the least, rather disconcerting.Keach Hagey: That was one of the biggest surprises from the press conference yesterday. Sam Altman was asked point blank, is SoftBank still going to give you this sort of second chunk, this $30 billion second chunk that was contingent upon being able to convert to a for-profit, and he said, quite simply, yes. Who knows what goes on in behind the scenes? I think we're gonna find out probably a lot more about that. There are many unanswered questions, but it's not great, right? It's definitely not great for investors.Andrew Keen: Well, you have to guess at the very minimum, SoftBank would be demanding better terms. They're not just going to do the same thing. I mean, it suddenly it suddenly gives them an additional ace in their hand in terms of negotiation. I mean this is not some sort of little startup. This is 30 or 40 billion dollars. I mean it's astonishing number. And presumably the non-public conversations are very interesting. I'm sure, Keach, you would like to know what's being said.Keach Hagey: Don't know yet, but I think your analysis is pretty smart on this matter.Andrew Keen: So if you had to guess, Sam is the consummate salesman. What did he tell SoftBank before April to close the round? And what is he telling them now? I mean, how has the message changed?Keach Hagey: One of the things that we see a little bit about this from the messaging that he gave to the world yesterday, which is this is going to be a simpler structure. It is going to be slightly more normal structure. They are changing the structure a little bit. So although the non-profit is going to remain in charge, the thing underneath it, the for-profit, is going change its structure a little bit and become kind of a little more normal. It's not going to have this capped profit thing where, you know, the investors are capped at 100 times what they put in. So parts of it are gonna become more normal. For employees, it's probably gonna be easier for them to get equity and things like that. So I'm sure that that's part of what he's selling, that this new structure is gonna be a little bit better, but it's not gonna be as good as what they were trying to do.Andrew Keen: Can Sam? I mean, clearly he has sold it. I mean as we joked earlier when we talked, Sam could sell ice to the Laplanders or sand to the Saudis. But these people know Sam. It's no secret that he's a remarkable salesman. That means that sometimes you have to think carefully about what he's saying. What's the impact on him? To what extent is this decision one more chip on the Altman brand?Keach Hagey: It's a setback for sure, and it's kind of a win for Elon Musk, his rival.Andrew Keen: Right.Keach Hagey: Elon has been suing him, Elon has been trying to block this very conversion. And in the end, it seems like it was actually the attorneys general of California and Delaware that really put the nail in the coffin here. So there's still a lot to find out about exactly how it all shook out. There were actually huge campaigns as well, like in the streets, billboards, posters. Polls saying, trying to put pressure on the attorney general to block this thing. So it was a broad coalition, I think, that opposed the conversion, and you can even see that a little bit in their speech. But you got to admit that Elon probably looked at this and was happy.Andrew Keen: And I'm sure Elon used his own X platform to promote his own agenda. Is this an example, Keach, in a weird kind of way of the plebiscitary politics now of Silicon Valley is that titans like Altman and Musk are fighting out complex corporate economic battles in the naked public of social media.Keach Hagey: Yes, in the naked public of social media, but what we're also seeing here is that it's sort of, it's become through the apparatus of government. So we're seeing, you know, Elon is in the Doge office and this conversion is really happening in the state AG's houses. So that's what's sort interesting to me is these like private fights have now expanded to fill both state and federal government.Andrew Keen: Last time we talked, I couldn't find the photo, but there was a wonderful photo of, I think it was Larry Ellison and Sam Altman in the Oval Office with Trump. And Ellison looked very excited. He looked extremely old as well. And Altman looked very awkward. And it's surprising to see Altman look awkward because generally he doesn't. Has Trump played a role in this or is he keeping out of it?Keach Hagey: As far as my current reporting right now, we have no reporting that Trump himself was directly involved. I can't go further than that right now.Andrew Keen: Meaning that you know something that you're not willing to ignore.Keach Hagey: Just I hope you keep your subscription to the Wall Street Journal on what role the White House played, I would say. But as far as that awkwardness, I don't know if you noticed that there was a box that day for Masa Yoshison to see.Andrew Keen: Oh yeah, and Son was in the office too, right, that was the third person.Keach Hagey: So it was a box in the podium, which I think contributed to the awkwardness of the day, because he's not a tall man.Andrew Keen: Right. To put it politely. The way that OpenAI spun it, in classic Sam Altman terms, is new funding to build towards AGI. So it's their Altman-esque use of the public to vindicate this new investment, is this just more quote unquote, and this is my word. You don't have to agree with it. Just sales pitch or might even be dishonesty here. I mean, the reality is, is new funding to build towards AGI, which is, artificial general intelligence. It's not new funding, to build toward AGI. It's new funding to build towards OpenAI, there's no public benefit of any of this, is there?Keach Hagey: Well, what they're saying is that the nonprofit will be capitalized and will sort of be hiring up and doing a bunch more things that it wasn't really doing. We'll have programs and initiatives and all of that. Which really, as someone who studied Sam's life, this sounds really a lot like what he did at Y Combinator. When he was head of Y Combinator, he also spun up a nonprofit arm, which is actually what OpenAI grew out of. So I think in Sam's mind, a nonprofit there's a place to go. Sort of hash out your ideas, it's a place to kind of have pet projects grow. That's where he did things like his UBI study. So I can sort of see that once the AGs are like, this is not gonna happen, he's like, great, we'll just make a big nonprofit and I'll get to do all these projects I've always wanted to do.Andrew Keen: Didn't he get thrown out of Y Combinator by Paul Graham for that?Keach Hagey: Yes, a little bit. You know, I would say there's a general mutiny for too much of that kind of stuff. Yeah, it's true. People didn't love it, and they thought that he took his eye off the ball. A little bit because one of those projects became OpenAI, and he became kind of obsessed with it and stopped paying attention. So look, maybe OpenAI will spawn the next thing, right? And he'll get distracted by that and move on.Andrew Keen: No coincidence, of course, that Sam went on to become a CEO of OpenAI. What does it mean for the broader AI ecosystem? I noted earlier you brought up Microsoft. I mean, I think you've already written on this and lots of other people have written about the fact that the relationship between OpenAI and Microsoft has cooled dramatically. As well as between Nadella and Altman. What does this mean for Microsoft? Is it a big deal?Keach Hagey: They have been hashing this out for months. So it is a big deal in that it will change the structure of their most important partner. But even before this, Microsoft and OpenAI were sort of locked in negotiations over how large and how Microsoft's stake in this new OpenAI will be valued. And that still has to be determined, regardless of whether it's a non-profit or a for-profit in charge. And their interests are diverging. So those negotiations are not as warm as they maybe would have been a few years ago.Andrew Keen: It's a form of polyamory, isn't it? Like we have in Silicon Valley, everyone has sex with everybody else, to put it politely.Keach Hagey: Well, OpenAI does have a new partner in Oracle. And I would expect them to have many more in terms of cloud computing partners going forward. It's just too much risk for any one company to build these huge and expensive data centers, not knowing that OpenAI is going to exist in a certain number of years. So they have to diversify.Andrew Keen: Keach, you know, this is amusing and entertaining and Altman is a remarkable individual, able to sell anything to anyone. But at what point are we really on the Titanic here? And there is such a thing as an iceberg, a real thing, whatever Donald Trump or other manufacturers of ontologies might suggest. At some point, this thing is going to end in a massive disaster.Keach Hagey: Are you talking about the Existence Force?Andrew Keen: I'm not talking about the Titanic, I'm talking about OpenAI. I mean, Parmi Olson, who's the other great authority on OpenAI, who won the FT Book of the Year last year, she's been on the show a couple of times, she wrote in Bloomberg that OpenAI can't have its money both ways, and that's what Sam is trying to do. My point is that we can all point out, excuse me, the contradictions and the hypocrisy and all the rest of it. But there are laws of gravity when it comes to economics. And at a certain point, this thing is going to crash, isn't it? I mean, what's the metaphor? Is it Enron? Is it Sam Bankman-Fried? What kind of examples in history do we need to look at to try and figure out what really is going on here?Keach Hagey: That's certainly one possibility, and there are a good number of people who believe that.Andrew Keen: Believe what, Enron or Sam Bankman-Fried?Keach Hagey: Oh, well, the internal tensions cannot hold, right? I don't know if fraud is even necessary so much as just, we've seen it, we've already seen it happen once, right, the company almost completely collapsed one time and those contradictions are still there.Andrew Keen: And when you say it happened, is that when Sam got pushed out or was that another or something else?Keach Hagey: No, no, that's it, because Sam almost got pushed out and then all of the funders would go away. So Sam needs to be there for them to continue raising money in the way that they have been raising money. And that's really going to be the question. How long can that go on? He's a young man, could go on a very long time. But yeah, I think that really will determine whether it's a disaster or not.Andrew Keen: But how long can it go on? I mean, how long could Sam have it both ways? Well, there's a dream. I mean maybe he can close this last round. I mean he's going to need to raise more than $40 billion. This is such a competitive space. Tens of billions of dollars are being invested almost on a monthly basis. So this is not the end of the road, this $40-billion investment.Keach Hagey: Oh, no. And you know, there's talk of IPO at some point, maybe not even that far away. I don't even let me wrap my mind around what it would be for like a nonprofit to have a controlling share at a public company.Andrew Keen: More hallucinations economically, Keach.Keach Hagey: But I mean, IPO is the exit for investors, right? That's the model, that is the Silicon Valley model. So it's going to have to come to that one way or another.Andrew Keen: But how does it work internally? I mean, for the guys, the sales guys, the people who are actually doing the business at OpenAI, they've been pretty successful this year. The numbers are astonishing. But how is this gonna impact if it's a nonprofit? How does this impact the process of selling, of building product, of all the other internal mechanics of this high-priced startup?Keach Hagey: I don't think it will affect it enormously in the short term. It's really just a question of can they continue to raise money for the enormous amount of compute that they need. So so far, he's been able to do that, right? And if that slows up in any way, they're going to be in trouble. Because as Sam has said many times, AI has to be cheap to be actually useful. So in order to, you know, for it to be widespread, for to flow like water, all of those things, it's got to be cheap and that's going to require massive investment in data centers.Andrew Keen: But how, I mean, ultimately people are putting money in so that they get the money back. This is not a nonprofit endeavor to put 40 billion from SoftBank. SoftBank is not in the nonprofit business. So they're gonna need their money back and the only way they generally, in my understanding, getting money back is by going public, especially with these numbers. How can a nonprofit go public?Keach Hagey: It's a great question. That's what I'm just phrasing. I mean, this is, you know, you talk to folks, this is what's like off in the misty distance for them. It's an, it's a fascinating question and one that we're gonna try to answer this week.Andrew Keen: But you look amused. I'm no financial genius. Everyone must be asking the same question.Keach Hagey: Well, the way that they've said it is that the for-profit will be, will have a, the non-profit will control the for profit and be the largest shareholder in it, but the rest of the shares could be held by public markets theoretically. That's a great question though.Andrew Keen: And lawyers all over the world must be wrapping their hands. I mean, in the very best case, it's gonna be lawsuits on this, people suing them up the wazoo.Keach Hagey: It's absolutely true. You should see my inbox right now. It's just like layers, layers, layer.Andrew Keen: Yeah, my wife. My wife is the head of litigation. I don't know if I should be saying this publicly anyway, I am. She's the head of Litigation at Google. And she lost some of her senior people and they all went over to AI. I'm big, I'm betting that they regret going over there can't be much fun being a lawyer at OpenAI.Keach Hagey: I don't know, I think it'd be great fun. I think you'd have like enormous challenges and have lots of billable hours.Andrew Keen: Unless, of course, they're personally being sued.Keach Hagey: Hopefully not. I mean, look, it is a strange and unprecedented situation.Andrew Keen: To what extent is this, if not Shakespearean, could have been written by some Greek dramatist? To what extend is this symbolic of all the hype and salesmanship and dishonesty of Silicon Valley? And in a sense, maybe this is a final scene or a penultimate scene in the Silicon Valley story of doing good for the world. And yet, of course, reaping obscene profit.Keach Hagey: I think it's a little bit about trying to have your cake and eat it too, right? Trying to have the aura of altruism, but also make something and make a lot of money. And what it seems like today is that if you started as a nonprofit, it's like a black hole. You can never get out. There's no way to get out, and that idea was just like maybe one step too clever when they set it up in the beginning, right. It seemed like too good to be true because it was. And it might end up really limiting the growth of the company.Andrew Keen: Is Sam completely in charge here? I mean, a number of the founders have left. Musk, of course, when you and I talked a couple of months ago, OpenAI came out of conversations between Musk and Sam. Is he doing this on his own? Does he have lieutenants, people who he can rely on?Keach Hagey: Yeah, I mean, he does. He has a number of folks that have been there, you know, a long time.Andrew Keen: Who are they? I mean, do we know their names?Keach Hagey: Oh, sure. Yeah. I mean, like Brad Lightcap and Jason Kwon and, you know, just they're they're Greg Brockman, of course, still there. So there are a core group of executives that have that have been there pretty much from the beginning, close to it, that he does trust. But if you're asking, like, is Sam really in control of this whole thing? I believe the answer is yes. Right. He is on the board of this nonprofit, and that nonprofit will choose the board of the for-profit. So as long as that's the case, he's in charge.Andrew Keen: How divided is OpenAI? I mean, one of the things that came out of the big crisis, what was it, 18 months ago when they tried to push him out, was it was clearly a profoundly divided company between those who believed in the nonprofit mission versus the for-profit mission. Are those divisions still as acute within the company itself? It must be growing. I don't know how many thousands of people work.Keach Hagey: It has grown very fast. It is not as acute in my experience. There was a time when it was really sort of a warring of tribes. And after the blip, as they call it, a lot of those more safety focused people, people that subscribe to effective altruism, left or were kind of pushed out. So Sam took over and kind of cleaned house.Andrew Keen: But then aren't those people also very concerned that it appears as if Sam's having his cake and eating it, having it both ways, talking about the company being a non-profit but behaving as if it is a for-profit?Keach Hagey: Oh, yeah, they're very concerned. In fact, a number of them have signed on to this open letter to the attorneys general that dropped, I don't know, a week and a half ago, something like that. You can see a number of former OpenAI employees, whistleblowers and others, saying this very thing, you know, that the AG should block this because it was supposed to be a charitable mission from the beginning. And no amount of fancy footwork is gonna make it okay to toss that overboard.Andrew Keen: And I mean, in the best possible case, can Sam, the one thing I think you and I talked about last time is Sam clearly does, he's not driven by money. There's something else. There's some other demonic force here. Could he theoretically reinvent the company so that it becomes a kind of AI overlord, a nonprofit AI overlord for our 21st century AI age?Keach Hagey: Wow, well I think he sometimes thinks of it as like an AI layer and you know, is this my overlord? Might be, you know.Andrew Keen: As long as it's not made in China, I hope it's made in India or maybe in Detroit or something.Keach Hagey: It's a very old one, so it's OK. But it's really my attention overlord, right? Yeah, so I don't know about the AI overlord part. Although it's interesting, Sam from the very beginning has wanted there to be a democratic process to control what decision, what kind of AI gets built and what are the guardrails for AGI. As long as he's there.Andrew Keen: As long as he's the one determining it, right?Keach Hagey: We talked about it a lot in the very beginning of the company when things were smaller and not so crazy. And what really strikes me is he doesn't really talk about that much anymore. But what we did just see is some advocacy organizations that kind of function in that exact way. They have voters all over the world and they all voted on, hey, we want you guys to go and try to that ended up having this like democratic structure for deciding the future of AI and used it to kind of block what he was trying to do.Andrew Keen: What are the implications for OpenAI's competitors? There's obviously Anthropic. Microsoft, we talked about a little bit, although it's a partner and a competitor simultaneously. And then of course there's Google. I assume this is all good news for the competition. And of course XAI.Keach Hagey: It is good news, especially for a company like XAI. I was just speaking to an XAI investor today who was crowing. Yeah, because those companies don't have this weird structure. Only OpenAI has this strange nonprofit structure. So if you are an investor who wants to have some exposure to AI, it might just not be worth the headache to deal with the uncertainty around the nonprofit, even though OpenAI is like the clear leader. It might be a better bet to invest in Anthropic or XAI or something else that has just a normal for-profit structure.Andrew Keen: Yeah. And it's hard to actually quote unquote out-Trump, Elon Musk on economic subterfuge. But Altman seems to have done that. I mean, Musk, what he folded X into XAI. It was a little bit of controversy, but he seems to got away with it. So there is a deep hostility between these two men, which I'm assuming is being compounded by this process.Keach Hagey: Absolutely. Again, this is a win for Elon. All these legal cases and Elon trying to buy OpenAI. I remember that bid a few months ago where he actually put a number on it. All that was about trying to block the for-profit conversion because he's trying to stop OpenAI and its tracks. He also claims they've abandoned their mission, but it's always important to note that it's coming from a competitor.Andrew Keen: Could that be a way out of this seeming box? Keach, a company like XAI or Microsoft or Google, or that probably wouldn't happen on the antitrust front, would buy OpenAI as maybe a nonprofit and then transform it into a for-profit company?Keach Hagey: Maybe you and Sam should get together and hash that out. That's the kind ofAndrew Keen: Well Sam, I'm available to be hired if you're watching. I'll probably charge less than your current consigliere. What's his name? Who's the consiglieri who's working with him on this?Keach Hagey: You mean Chris Lehane?Andrew Keen: Yes, Chris Lehane, the ego.Keach Hagey: Um,Andrew Keen: How's Lehane holding up in this? Do you think he's getting any sleep?Keach Hagey: Well, he's like a policy guy. I'm sure this has been challenging for everybody. But look, you are pointing to something that I think is real, which is there will probably be consolidation at some point down the line in AI.Andrew Keen: I mean, I know you're not an expert on the maybe sort of corporate legal stuff, but is it in theory possible to buy a nonprofit? I don't even know how you buy a non-profit and then turn it into a for-profit. I mean is that one way out of this, this cul-de-sac?Keach Hagey: I really don't know the answer to that question, to be honest with you. I can't think of another example of it happening. So I'm gonna go with no, but I don't now.Andrew Keen: There are no equivalents, sorry to interrupt, go on.Keach Hagey: No, so I was actually asking a little bit, are there precedents for this? And someone mentioned Blue Cross Blue Shield had gone from being a nonprofit to a for-profit successfully in the past.Andrew Keen: And we seem a little amused by that. I mean, anyone who uses US health care as a model, I think, might regret it. Your book, The Optimist, is out in a couple of weeks. When did you stop writing it?Keach Hagey: The end of December, end of last year, was pencils fully down.Andrew Keen: And I'm sure you told the publisher that that was far too long a window. Seven months on Silicon Valley is like seven centuries.Keach Hagey: It was actually a very, very tight timeline. They turned it around like incredibly fast. Usually it'sAndrew Keen: Remarkable, yeah, exactly. Publishing is such, such, they're such quick actors, aren't they?Keach Hagey: In this case, they actually were, so I'm grateful for that.Andrew Keen: Well, they always say that six months or seven months is fast, but it is actually possible to publish a book in probably a week or two, if you really choose to. But in all seriousness, back to this question, I mean, and I want everyone to read the book. It's a wonderful book and an important book. The best book on OpenAI out. What would you have written differently? Is there an extra chapter on this? I know you warned about a lot of this stuff in the book. So it must make you feel in some ways quite vindicated.Keach Hagey: I mean, you're asking if I'd had a longer deadline, what would I have liked to include? Well, if you're ready.Andrew Keen: Well, if you're writing it now with this news under your belt.Keach Hagey: Absolutely. So, I mean, the thing, two things, I guess, definitely this news about the for-profit conversion failing just shows the limits of Sam's power. So that's pretty interesting, because as the book was closing, we're not really sure what those limits are. And the other one is Trump. So Trump had happened, but we do not yet understand what Trump 2.0 really meant at the time that the book was closing. And at that point, it looked like Sam was in the cold, you know, he wasn't clear how he was going to get inside Trump's inner circle. And then lo and behold, he was there on day one of the Trump administration sharing a podium with him announcing that Stargate AI infrastructure investment. So I'm sad that that didn't make it into the book because it really just shows the kind of remarkable character he is.Andrew Keen: He's their Zelig, but then we all know what happened to Woody Allen in the end. In all seriousness, and it's hard to keep a straight face here, Keach, and you're trying although you're not doing a very good job, what's going to happen? I know it's an easy question to ask and a hard one to answer, but ultimately this thing has to end in catastrophe, doesn't it? I use the analogy of the Titanic. There are real icebergs out there.Keach Hagey: Look, there could be a data breach. I do think that.Andrew Keen: Well, there could be data breaches if it was a non-profit or for-profit, I mean, in terms of this whole issue of trying to have it both ways.Keach Hagey: Look, they might run out of money, right? I mean, that's one very real possibility. They might run outta money and have to be bought by someone, as you said. That is a totally real possibility right now.Andrew Keen: What would happen if they couldn't raise any more money. I mean, what was the last round, the $40 billion round? What was the overall valuation? About $350 billion.Keach Hagey: Yeah, mm-hmm.Andrew Keen: So let's say that they begin to, because they've got, what are their hard costs monthly burn rate? I mean, it's billions of just.Keach Hagey: Well, the issue is that they're spending more than they are making.Andrew Keen: Right, but you're right. So they, let's say in 18 months, they run out of runway. What would people be buying?Keach Hagey: Right, maybe some IP, some servers. And one of the big questions that is yet unanswered in AI is will it ever economically make sense, right? Right now we are all buying the possibility of in the future that the costs will eventually come down and it will kind of be useful, but that's still a promise. And it's possible that that won't ever happen. I mean, all these companies are this way, right. They are spending far, far more than they're making.Andrew Keen: And that's the best case scenario.Keach Hagey: Worst case scenario is the killer robots murder us all.Andrew Keen: No, what I meant in the best case scenario is that people are actually still without all the blow up. I mean, people are actual paying for AI. I mean on the one hand, the OpenAI product is, would you say it's successful, more or less successful than it was when you finished the book in December of last year?Keach Hagey: Oh, yes, much more successful. Vastly more users, and the product is vastly better. I mean, even in my experience, I don't know if you play with it every day.Andrew Keen: I use Anthropic.Keach Hagey: I use both Claude and ChatGPT, and I mean, they're both great. And I find them vastly more useful today than I did even when I was closing the book. So it's great. I don't know if it's really a great business that they're only charging me $20, right? That's great for me, but I don't think it's long term tenable.Andrew Keen: Well, Keach Hagey, your new book, The Optimist, your new old book, The Optimist: Sam Altman, Open AI and the Race to Invent the Future is out in a couple of weeks. I hope you're writing a sequel. Maybe you should make it The Pessimist.Keach Hagey: I think you might be the pessimist, Andrew.Andrew Keen: Well, you're just, you are as pessimistic as me. You just have a nice smile. I mean, in all reality, what's the most optimistic thing that can come out of this?Keach Hagey: The most optimistic is that this becomes a product that is actually useful, but doesn't vastly exacerbate inequality.Andrew Keen: No, I take the point on that, but in terms of this current story of this non-profit versus profit, what's the best case scenario?Keach Hagey: I guess the best case scenario is they find their way to an IPO before completely imploding.Andrew Keen: With the assumption that a non-profit can do an IPO.Keach Hagey: That they find the right lawyers from wherever they are and make it happen.Andrew Keen: Well, AI continues its hallucinations, and they're not in the product themselves. I think they're in their companies. One of the best, if not the best authority, our guide to all these hallucinations in a corporate level is Keach Hagey, her new book, The Optimist: Sam Altman, Open AI and the Race to Invent the Future is out in a couple of weeks. Essential reading for anyone who wants to understand Sam Altman as the consummate salesman. And I think one thing we can say for sure, Keach, is this is not the end of the story. Is that fair?Keach Hagey: Very fair. Not the end of the story. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit keenon.substack.com/subscribe

In this engaging conversation, Brett McCollum interviews Paul H. Graham, an entrepreneur with a diverse background in various industries. They discuss the importance of maintaining a childlike wonder and belief in oneself, especially in the realm of entrepreneurship and investing. Paul shares his journey, insights on balancing finances, and the significance of mindset in achieving success. The conversation emphasizes the value of curiosity, collaboration, and the potential for personal growth at any age. In this conversation, Paul Graham and Brett McCollum explore the themes of overcoming limitations, the journey to joy in investing, finding peace and purpose in investments, and the importance of community. They discuss the mindset needed to succeed in real estate and investing, emphasizing curiosity, self-worth, and the human element in financial transactions. The dialogue encourages listeners to focus on the process rather than just the outcomes, fostering a deeper understanding of personal growth and fulfillment in their endeavors.   Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true ‘white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a “mini-mastermind” with Mike and his private clients on an upcoming “Retreat”, either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas “Big H Ranch”? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

Loved this one with Aussie Donnie Troy from Coqfighter.The early stage of the brand building journey is fun.you're so naive,everything is neweverything is freshIt's better than your “…old boring corporate job”But, at some point, it becomes hard.Unimaginably hard.Doubt soars in.Confidence wanesThe joy fades a little.Paul Graham calls it The Trough of SorrowThis episode will help you through Your Trough of SorrowWith a healthy seasoning of marketing, fried chicken and Lynard Skynard “free bird”Huge thanks to my boy for setting this one up.ON THE MENU:Why going ALL IN on your life's work will feel like “Work feels like play but also pain”Nassim Taleb's Barbell strategy for successful fried chicken shop: Micro/Macro, Systems/customer flairZen and The Art of Motorcycle Maintenance: why you need peace from mind vs. peace of mindWhy you must treat every part of your business as a “community”Too Many Stickers Rule: Why Bottom Up Leadership ALWAYS beats Top DownSeth Godin Competitors vs Colleagues Rule: Don't hate your competitorsWhy London is The Best City to Launch a Food & Drink BrandA La Carte Fried Chicken Menu + Alex Smith's Unique Points of Disagreement RuleBrand Building Lessons from Harley Davidson “offer people Status and Affiliation”

Flexport was a breakout success—reimagining global trade with tech at its core. But when the freight market cooled and efficiency overtook service, things started to unravel. Founder Ryan Petersen stepped aside, handing the CEO role to former Amazon exec Dave Clark. Months later, he was back at the helm.In this episode, Ryan explains what went wrong, how he's rebuilding Flexport—cutting $300M in costs, restoring customer focus—and why promoting from within beats chasing outside stars. He also weighs in on Trump's proposed tariffs and what they could mean for the future of global trade.Chapters: 00:00 Trailer00:31 Introduction02:07 Meeting smart people, seeing the world03:40 Eroded margins09:52 Charismatic and overconfident15:32 Not an overnight decision20:08 The founder has returned23:10 Redoing the hiring26:38 No substitute for passion31:00 Working for and with my brother37:28 Working with forwarders42:14 Being a founder can be lonely47:49 Life's work54:06 The right person for the job1:00:55 19 countries1:04:57 Blowing people up1:07:24 Work and being a good dad1:08:34 Not doing it for money and loving money1:17:52 Import and export tariffs1:22:57 De minimis1:25:54 Panama and the Suez Canal1:36:50 Going public1:42:24 Who Flexport is Hiring 1:42:42 What "grit" means to Ryan1:43:06 OutroMentioned in this episode: Founders Fund, Amazon, Toyota Motor Corporation, Slack, Brex, Pedro Franceschi, Henrique Dubugras, United States Customs and Border Protection, ImportGenius, Michael Kanko, Y Combinator, Paul Graham, Intel Corporation, Shopify, Geely Holding (Zhejiang Geely Holding Group Co., Ltd.), The Volvo Group, Intuit TurboTax, David Petersen, BuildZoom, TechCrunch, Google, Figma, Barack Obama, Donald Trump, Jimmy Carter, Panama Canal Authority, United States Navy, Coinbase, Uber, AirbnbLinks:Connect with RyanXLinkedInConnect with JoubinTwitterLinkedInEmail: grit@kleinerperkins.comLearn more about Kleiner Perkins

Sunday, March 23 2025

As VP of Google Labs, Josh Woodward leads teams exploring the frontiers of AI applications. He shares insights on their rapid development process, why today's written prompts will become outdated and how AI is transforming everything from video generation to computer control. He reveals that 25% of Google's code is now written by AI and explains why coding could see major leaps forward this year. He emphasizes the importance of taste, design and human values in building AI tools that will shape how future generations work and create. Mentioned in this episode: Notebook LM: Personal research product based on Gemini 2 (previously discussed on Training Data.) Veo 2: Google DeepMind's new video generation model. Paul Graham on X replying to Aaron Levie's post that “One approach to take in building in AI is to do something that's too expensive to be reasonably practical right now, and just bet that the costs will drop by 10X or 100X over time. The cost curve is on your side.” Where Good Ideas Come From: Book on the history of innovation by Steven Johnson. Project Mariner: Google DeepMind's research prototype exploring human-agent interaction starting with browser use. Replit Agent: Josh's favorite new AI app The Lego Story: Book on the history of Lego. Hosted by: Ravi Gupta and Sonya Huang, Sequoia Capital 

In this episode, we explore the essential steps to take an idea and grow it to a seven or eight-figure brand, particularly focusing on Shopify and direct-to-consumer markets. Key points include optimizing product offerings, building a strong business model, creating a unique category, investing in conversion rate optimization, leveraging paid and organic traffic, and more. This comprehensive guide is based on years of experience and is packed with actionable insights aimed at helping you build and scale your Shopify store effectively. TOPICS DISCUSSED IN TODAY'S EPISODEGet the Product RightLaunch with a Flagship ProductSolve a Real ProblemEarly adoptersChoose Your VillainGet the Business Model RightCreate a MovementCreate Your Own CategoryPick a FightBuild a CommunityIrresistible offerTest Activation TacticsTurn Customers into MarketersInvest in Organic TrafficRetention is KeyInvest in Conversion Rate OptimizationMaster Meta and InstagramOptimize Google AdsUse Proven Tech StackKnow Your NumbersLaunch StrategyCalendar Your Marketing Resources:Jim Huffman websiteJim's TwitterGrowthHitThe Growth Marketer's Playbook  Additional episodes you might enjoy:Startup Ideas by Paul Graham (#45)Nathan Barry: How to Bootstrap a Company to $30M in a Crowded Market (#41)How I Met My Biz Partner and Less Learned Hitting $2M ARR (#44)Ryan Hamilton on his Netflix special, touring with Jerry Seinfeld, & how to write a joke (#10)How We're Validating Startup Ideas (#51) 

Guest: Blake Scholl, Founder & CEO of Boom Supersonic“Passion and drive trumps knowledge and experience,” says Boom Supersonic CEO Blake Scholl. Long before he was running Boom — which earlier this year successfully tested the world's first privately-developed supersonic jet — he was enabling “the world's most obnoxious spam cannon” at Groupon, or designing a barcode-scanning game for retail shoppers.But eventually, Blake found the courage to be more audacious and do something closer to his lifelong love of aviation. He began educating himself about things he had never thought to learn, and tapping his LinkedIn network to get intros to the smartest people in the industry. “If you imagine yourself on like the day of IPO, 99 percent of what you needed to know to get to that day, you didn't know on day one,” he says. “So, why not take 99 percent to 99.5 percent, and work on the thing you really want to exist, even if you don't know anything about it yet?”Chapters: (01:07) - Blake on Boom's beginnings (01:52) - Breaking the sound barrier (05:23) - Concorde's legacy (09:36) - Navigating regulations (12:08) - Boomless supersonic flight (16:48) - The test flight (20:11) - Day-of nervousness (24:26) - Carrying passengers (26:55) - Cost & wi-fi (30:19) - “No middle seats” (32:35) - Hard tech (36:48) - What if Apple made a plane? (39:08) - Blake's career journey (43:29) - The risk of failure (49:12) - Finding the courage (52:49) - Balancing life with Boom (56:42) - Learning how to build a jet (01:00:20) - The power of LinkedIn (01:02:38) - Y Combinator Demo Day (01:08:24) - Richard Branson (01:11:38) - Dividing yourself (01:14:19) - Being a focused dad (01:20:05) - Exuberance vs. fear (01:24:15) - Hiring slowly (01:27:17) - What “grit” means to Blake Mentioned in this episode: Chuck Yeager, ChatGPT, the Apollo program, Elon Musk, SpaceX and Falcon 1, Boom Overture, Starlink, Boeing, Airbus, iPhone, Jony Ive, Uber, Airbnb, Anduril, United Airlines, American Airlines, Eclipse Aviation, Tesla, Scott Kirby, Mike Leskinen, Inktomi, Yahoo!, Amazon, Pelago, Google Ads, Kima Labs, Barcode Hero, Groupon, iPad, Eric Schmidt, Steve Jobs, Khan Academy, Sam Altman, Loopt, Virgin Atlantic, Paul Graham, Michael Seibel, Ashlee Vance, Bloomberg, Hacker News, Jared Friedman, Sen. Mark Kelly, SV Angel, Ron Conway, Virgin Galactic, Lockheed Martin, Gulfstream, Jeff Bezos, Jeff Holden, and How It's Made.Links:Connect with BlakeTwitterLinkedInConnect with JoubinTwitterLinkedInEmail: grit@kleinerperkins.com Learn more about Kleiner PerkinsThis episode was edited by Eric Johnson from LightningPod.fm

Jim is joined by Craig Swanson as they delve into the world of custom GPTs and their applications. They discuss how to utilize AI for tasks like copywriting and strategy development, and learn about the advancements in AI tools such as ChatGPT and Claude. They also showcase practical implementations, from setting up knowledge bases to effective prompt engineering. Tune in to see real-world examples of how custom GPTs can enhance productivity and creativity, and get insights on upcoming AI innovations and tools. TOPICS DISCUSSED IN TODAY'S EPISODEDevelopment of AI ToolsSelecting Appropriate AI ToolsCustom GPTs: Adding Personalization to AITechniques for Effective Prompt EngineeringUses of Custom GPTsCreating a Knowledge Base for GPTsExamples and Case Studies from Real-World ScenariosAdvanced AI Functions and In-depth ResearchExpanding AI in Teams and Future StrategiesConclusion and Future PerspectivesResources:Jim Huffman websiteJim's TwitterGrowthHitThe Growth Marketer's PlaybookAdditional episodes you might enjoy:Startup Ideas by Paul Graham (#45)Nathan Barry: How to Bootstrap a Company to $30M in a Crowded Market (#41)How I Met My Biz Partner and Less Learned Hitting $2M ARR (#44)Ryan Hamilton on his Netflix special, touring with Jerry Seinfeld, & how to write a joke (#10)How We're Validating Startup Ideas (#51) 

In this episode of the Men of Influence Podcast, host Tim Holloway sits down with Paul Graham, an entrepreneur, investor, and advocate for personal growth. Paul shares his inspiring journey from a childhood filled with curiosity and invention to becoming a key player in the world of private investments, oil and gas, and real estate syndications. His mission? To educate others on building wealth while embracing joy, purpose, and intentional living.The conversation dives deep into entrepreneurship, personal discipline, and influence, highlighting how consistent action, accountability, and mindset shifts are critical for success. Paul and Tim discuss the importance of mentorship, personal branding, and the power of storytelling to inspire others. They also explore the impact of fitness challenges like 75 Hard, revealing how physical discipline translates into business and life transformation.Key Takeaways from This Episode:✔️ Entrepreneurship is more than business—it's about creating value and impact✔️ Why consistency beats motivation in building lasting success✔️ How mentorship can radically shift your future✔️ The connection between physical fitness and mental resilience✔️ The power of taking action: why consuming knowledge isn't enough✔️ How to use storytelling to build influence and inspire othersIf you're looking to elevate your mindset, grow your influence, and take massive action in your business and life, this episode is packed with practical strategies and real-life insights to get you there.

In this episode, we dive into Paul Graham’s essay on how wealth creation has evolved over the past few decades. We explore why inherited wealth is on the decline, what industries are driving the newest fortunes, and how compounding still remains a powerful path to wealth. Then, Simon gives his key takeaways from Warren Buffett’s latest letter to shareholders. Including why Buffett prefers to invest in companies rather than to hold bonds, his concerns about the currency and how the underlying businesses that Berkshire owns have been doing. Tickets of stocks/ETFs discussed: BRK-B Check out our portfolio by going to Jointci.com Our Website Canadian Investor Podcast Network Twitter: @cdn_investing Simon’s twitter: @Fiat_Iceberg Braden’s twitter: @BradoCapital Dan’s Twitter: @stocktrades_ca Want to learn more about Real Estate Investing? Check out the Canadian Real Estate Investor Podcast! Apple Podcast - The Canadian Real Estate Investor Spotify - The Canadian Real Estate Investor Web player - The Canadian Real Estate Investor Asset Allocation ETFs | BMO Global Asset Management Sign up for Finchat.io for free to get easy access to global stock coverage and powerful AI investing tools. Register for EQ Bank, the seamless digital banking experience with better rates and no nonsense.See omnystudio.com/listener for privacy information.

SBS Finance Editor Ricardo Gonçalves speaks with Australia Post CEO Paul Graham about how gig economy and new start-ups are threatening his thriving parcels business and the reforms needed to secure its future. Plus Julia Lee from FTSE Russell on the day's market action, including the latest woes from Star Entertainment.

Understanding human nature is critical for mastery. This week I’m exploring the lessons of Robert Greene and what he can teach us about emotional states, crossroads, and getting more by doing less. Listen in as I share the most profound lessons from his books and how you can apply them to utilize the power of seduction strategy in your own life. Robert Greene is the author of the New York Times bestsellers The 48 Laws of Power, The Art of Seduction, The 33 Strategies of War, and The 50th Law. His highly anticipated fifth book, Mastery, examines the lives of great historical figures such as Charles Darwin, Mozart, Paul Graham and Henry Ford and distills the traits and universal ingredients that made them masters. In addition to having a strong following within the business world and a deep following in Washington, DC, Greene’s books are hailed by everyone from war historians to the biggest musicians in the industry (including Jay-Z and 50 Cent). Greene attended U.C. Berkeley and the University of Wisconsin at Madison, where he received a degree in classical studies. He currently lives in Los Angeles. You can see a full collection of Greene’s work here: https://www.amazon.com/stores/author/B001IGV3IS/allbooks?ingress=0&visitId=33a4f706-a8ef-4d25-8162-c1f038681070 Learn more about the gift of Adversity and my mission to help my fellow humans create a better world by heading to www.marcusaureliusanderson.com. There you can take action by joining my ANV inner circle to get exclusive content and information. See omnystudio.com/listener for privacy information.

In today's podcast, we dive deep into the journey of high achievers with Sherif Shakar, the CEO and founder of CEOS (Chief Executive Operating System). Sharif shares invaluable insights on how mindfulness can significantly benefit business leaders, emphasizing the importance of being intentional and creating space between stimulus and response. He elaborates on the challenges faced by CEOs managing large teams and offers tactical advice on managing time, focusing on the right tasks, and leading effectively. Sharif also discusses the transition from running small businesses to scaling up and the crucial role of a strong hiring process. If you're aiming to take your company to the next level and seek fulfillment in the process, this episode is a must-listen.TOPICS DISCUSSED IN TODAY'S EPISODEHappiness and AchievementMindfulness for Business LeadersAM and PM Bookends for SuccessCEOS: Chief Executive Operating SystemScaling Up: From Project Manager to CEOSharif's Journey and InsightsResources:CEO-SSherif Sakr LinkedInGrowth Marketing OS (Operating System) GrowthHitJim Huffman websiteJim's LinkedinJim's Twitter  Additional episodes you might enjoy:Startup Ideas by Paul Graham (#45)Nathan Barry: How to Bootstrap a Company to $30M in a Crowded Market (#41)How I Met My Biz Partner and Less Learned Hitting $2M ARR (#44)Ryan Hamilton on his Netflix special, touring with Jerry Seinfeld, & how to write a joke (#10)How We're Validating Startup Ideas (#51) 

In this episode, Jim and Jordan dive deep into creating ads that convert. Starting from asset essentials, they discuss product photography, lifestyle images, and UGC videos. They emphasize the significance of understanding customer personas and value propositions before creating ads. The video covers frameworks for ad testing, the importance of visual variety, and features real-world case studies, including e-commerce and B2B examples.Tune in for insights and strategies to boost your ad performance.  TOPICS DISCUSSED IN TODAY'S EPISODEIntroduction and Agenda OverviewEssential Assets for Effective Ad CreativeFrameworks for Crafting Targeted AdsVisual Variety and Testing StrategiesIterating and Optimizing Ad PerformanceCase Studies and Real-World ExamplesAI Tools for Ad CreationConclusion and Final ThoughtsResources:Jim Huffman websiteJim's TwitterGrowthHitThe Growth Marketer's Playbook Additional episodes you might enjoy:Startup Ideas by Paul Graham (#45)Nathan Barry: How to Bootstrap a Company to $30M in a Crowded Market (#41)How I Met My Biz Partner and Less Learned Hitting $2M ARR (#44)Ryan Hamilton on his Netflix special, touring with Jerry Seinfeld, & how to write a joke (#10)How We're Validating Startup Ideas (#51) 

It's no secret that the alternative protein startups are struggling these days. A combination of lower revenue, intense competition, and less available venture capital is leading to a contraction in the sector, with countless alt-meat and dairy companies conducting layoffs, declaring bankruptcy, and even folding altogether.  Enter Ahimsa Companies, a newly formed investment group acquiring promising but distressed plant-based brands. This isn't charity, though. Ahimsa's belief is that, with their consolidation strategy and pooled resources, these brands that are built on a strong underlying product can become profitable under the Ahimsa umbrella. As Ahimsa CEO Matt Tullman says in this conversation, pendulums swing, and many of these companies can be brought to profitability, meaning they could ultimately be sold at a much higher price than their valuation during this period in which plant-based products are in the valley. So far the company has acquired Wicked Foods, Simulate's Nuggs, Blackbird Pizza, an Ohio plant-based foods manufacturing plant, and more.  Matt is a man of many talents, as you'll hear in this episode. In addition to being CEO of Ahimsa Companies, he founded and sold his own education tech company, and is also the co-founder and CEO of Outlier Health, the parent company of supplement company Complement and of No Meat Athlete. He's both a missionary for plant-based lifestyles and a mercenary seeking to combine his passion for plant-based foods with profit.  Discussed in this episode Ahimsa Companies is looking for plant-based startups to acquire. Here's an analysis of their strategy and history. Interview with Matt in which he describes Ahimsa's goal by declaring that “we've got to step up and try to help these companies survive.” Story about Ahimsa's acquisition of the Plant Plant in Ohio.  Both Paul and Matt are interested in AI's potential to enable human-nonhuman communication, something Noa Weiss discussed on a past episode. Matt recommends reading both The Hard Thing about Hard Things and The Surrender Experiment. Matt also recommends Paul Graham's essay, Do Things That Don't Scale. More about Matt Tullman Matt is the co-founder and Group CEO of the Ahimsa Companies – a private equity firm acquiring and operating best-in-class plant-based food and manufacturing companies. He is also the co-founder and CEO of Outlier Health, the parent company of  Complement and No Meat Athlete, which have served nearly 13 million people just in the past three years. He's also an investor in health/food/bev startups, and a contributor to Inc. Magazine.  Previously Matt founded a ed-tech firm that was ultimately acquired by Stride Education (NYSE: LRN). Matt is most proud of bootstrapping a business that was named to the Inc. 500 list of fastest growing companies in 2021. He has dedicated his career to growing nutrition-first health companies to help catalyze the movement to a plant-based lifestyle for the mainstream consumer.

In this live session, Jim delves into the buzzword of the year: AI and its growth potential. We explore 11 AI prompts and tools designed to help businesses grow by working faster and smarter. Jim discusses strategic uses of custom GPTs, text and image generation, video creation, analytics, reporting, and website performance. We also share our experiences, both successes and roadblocks, in implementing these tools at our agency. Learn how these AI innovations can inspire and transform your business strategies, and discover practical steps to leverage AI effectively. TOPICS DISCUSSED IN TODAY'S EPISODEIntroduction and OverviewThe Power of AI in Marketing and GrowthCustom GPTs for Strategic GrowthEfficient Email Marketing with AIAI-Driven Image GenerationVideo Generation with AIAI for Analytics and ReportingOptimizing Website Performance with AIIntegrating AI Tools for Seamless WorkflowConclusion and Future InsightsResources:Growth Marketing OS (Operating System) GrowthHitJim Huffman websiteJim's LinkedinJim's Twitter Additional episodes you might enjoy:Startup Ideas by Paul Graham (#45)Nathan Barry: How to Bootstrap a Company to $30M in a Crowded Market (#41)How I Met My Biz Partner and Less Learned Hitting $2M ARR (#44)Ryan Hamilton on his Netflix special, touring with Jerry Seinfeld, & how to write a joke (#10)How We're Validating Startup Ideas (#51) 

Replit is one of the most visible and exciting companies reshaping how we approach software and application development in the Generative AI era. In this episode, we sit down with its CEO, Amjad Masad, for an in-depth discussion on all things AI, agents, and software. Amjad shares the journey of building Replit, from its humble beginnings as a student side project to becoming a major player in Generative AI today. We also discuss the challenges of launching a startup, the multiple attempts to get into Y Combinator, the pivotal moment when Paul Graham recognized Replit's potential, and the early bet on integrating AI and machine learning into the core of Replit. Amjad dives into the evolving landscape of AI and machine learning, sharing how these technologies are reshaping software development. We explore the concept of coding agents and the impact of Replit's latest innovation, Replit Agent, on the software creation process. Additionally, Amjad reflects on his time at Codecademy and Facebook, where he worked on groundbreaking projects like React Native, and how those experiences shaped his entrepreneurial journey. We end with Amjad's view on techno-optimism and his belief in an energized Silicon Valley. Replit Website - https://replit.com X/Twitter - https://x.com/Replit Amjad Masad LinkedIn - https://www.linkedin.com/in/amjadmasad X/Twitter - https://x.com/amasad FIRSTMARK Website - https://firstmark.com X/Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ X/Twitter - https://twitter.com/mattturck (00:00) Intro (01:36) The origins of Replit (15:54) Amjad's decision to restart Replit (19:00) Joining Y Combinator (30:06) AI and ML at Replit (32:31) Explain Code (39:09) Replit Agent (52:10) Balancing usability for both developers and non-technical users (53:22) Sonnet 3.5 stack (58:43) The challenge of AI evaluation (01:00:02) ACI vs. HCI (01:05:02) Will AI replace software development? (01:10:15) If anyone can build an app with Replit, what's the next bottleneck? (01:14:31) The future of SaaS in an AI-driven world (01:18:37) Why Amjad embraces techno-optimism (01:20:36) Defining civilizationism (01:23:11) Amjad's perspective on government's role

She's an economist, an institution-builder, an ecosystem-nurturer and one of our finest thinkers. Shruti Rajagopalan joins Amit Varma in episode 410 of The Seen and the Unseen to talk about her life & times -- and her remarkable work. (FOR FULL LINKED SHOW NOTES, GO TO SEENUNSEEN.IN.) Also check out: 1. Shruti Rajagopalan on Twitter, Substack, Instagram, her podcast, Ideas of India and her own website. 2. Emergent Ventures India. 3. The 1991 Project. 4. Life Lessons That Are Priceless -- Episodes 400 of The Seen and the Unseen. 5. Other episodes of The Seen and the Unseen w Shruti Rajagopalan, in reverse chronological order: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18. 6. The Day Ryan Started Masturbating -- Amit Varma's newsletter post explaining Shruti Rajagopalan's swimming pool analogy for social science research. 7. A Deep Dive Into Education -- Episode 54 of Everything is Everything. 8. Fixing Indian Education — Episode 185 of The Seen and the Unseen (w Karthik Muralidharan). 9. Population Is Not a Problem, but Our Greatest Strength -- Amit Varma. 10. Our Population Is Our Greatest Asset -- Episode 20 of Everything is Everything. 11. Where Has All the Education Gone? -- Lant Pritchett. 12. Lant Pritchett Is on Team Prosperity — Episode 379 of The Seen and the Unseen. 13. The Theory of Moral Sentiments — Adam Smith. 14. The Wealth of Nations — Adam Smith. 15. Commanding Heights -- Daniel Yergin. 16. Capitalism and Freedom -- Milton Friedman. 17. Free to Choose -- Milton Friedman and Rose Friedman. 18. Economics in One Lesson -- Henry Hazlitt. 19. The Road to Serfdom -- Friedrich Hayek. 20. Four Papers That Changed the World -- Episode 41 of Everything is Everything. 21. The Use of Knowledge in Society -- Friedrich Hayek. 22. Individualism and Economic Order -- Friedrich Hayek. 23. Understanding the State -- Episode 25 of Everything is Everything.  24. Richard E Wagner at Mercatus and Amazon. 25. Larry White and the First Principles of Money -- Episode 397 of The Seen and the Unseen. 26. Fixing the Knowledge Society -- Episode 24 of Everything is Everything. 27. Marginal Revolution. 28. Paul Graham's essays. 29. Commands and controls: Planning for indian industrial development, 1951–1990 -- Rakesh Mohan and Vandana Aggarwal. 30. The Reformers -- Episode 28 of Everything is Everything. 31. India: Planning for Industrialization -- Jagdish Bhagwati and Padma Desai. 32. Open Borders: The Science and Ethics of Immigration -- Bryan Caplan and Zach Weinersmith. 33. Cows on India Uncut. 34. Abdul Karim Khan on Spotify and YouTube. 35. The Surface Area of Serendipity -- Episode 39 of Everything is Everything. 36. Objects From Our Past -- Episode 77 of Everything is Everything. 37. Sriya Iyer on the Economics of Religion -- The Ideas of India Podcast. 38. Episodes of The Seen and the Unseen with Ramachandra Guha: 1, 2, 3, 4, 5, 6. 39. Episodes of The Seen and the Unseen with Pratap Bhanu Mehta: 1, 2. 40. Rohit Lamba Reimagines India's Economic Policy Emphasis -- The Ideas of India Podcast. 41. Rohit Lamba Will Never Be Bezubaan — Episode 378 of The Seen and the Unseen. 42. The Constitutional Law and Philosophy blog. 43. Cost and Choice -- James Buchanan. 44. Philip Wicksteed. 45. Pratap Bhanu Mehta on The Theory of Moral Sentiments -- The Ideas of India Podcast. 46. Conversation and Society — Episode 182 of The Seen and the Unseen (w Russ Roberts). 47. The Common Sense of Political Economy -- Philip Wicksteed. 48. Narendra Shenoy and Mr Narendra Shenoy — Episode 250 of The Seen and the Unseen. 49. Sudhir Sarnobat Works to Understand the World — Episode 350 of The Seen and the Unseen. 50. Manmohan Singh: India's Finest Talent Scout -- Shruti Rajagopalan. 51. The Importance of the 1991 Reforms — Episode 237 of The Seen and the Unseen (w Shruti Rajagopalan and Ajay Shah). 52. The Life and Times of Montek Singh Ahluwalia — Episode 285 of The Seen and the Unseen. 53. The Forgotten Greatness of PV Narasimha Rao — Episode 283 of The Seen and the Unseen (w Vinay Sitapati). 54. India's Massive Pensions Crisis — Episode 347 of The Seen and the Unseen (w Ajay Shah & Renuka Sane). 55. The Life and Times of KP Krishnan — Episode 355 of The Seen and the Unseen. 56. Breaking Through — Isher Judge Ahluwalia. 57. Breaking Out — Padma Desai. 58. Perestroika in Perspective -- Padma Desai. 59. Shephali Bhatt Is Searching for the Incredible — Episode 391 of The Seen and the Unseen. 60. Pics from the Seen-Unseen party. 61. Pramod Varma on India's Digital Empowerment -- Episode 50 of Brave New World. 59. Niranjan Rajadhyaksha Is the Impartial Spectator — Episode 388 of The Seen and the Unseen. 60. Our Parliament and Our Democracy — Episode 253 of The Seen and the Unseen (w MR Madhavan). 61. Episodes of The Seen and the Unseen with Pranay Kotasthane: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13. 62. The Overton Window. 63. When Ideas Have Sex -- Matt Ridley. 64. The Three Languages of Politics — Arnold Kling. 65. Arnold Kling and the Four Languages of Politics -- Episode 394 of The Seen and the Unseen. 66. The Double ‘Thank You' Moment — John Stossel. 67. Economic growth is enough and only economic growth is enough — Lant Pritchett with Addison Lewis. 68. What is Libertarianism? — Episode 117 of The Seen and the Unseen (w David Boaz). 69. What Does It Mean to Be Libertarian? — Episode 64 of The Seen and the Unseen. 70. The Libertarian Mind: A Manifesto for Freedom -- David Boaz. 71. Publish and Perish — Agnes Callard. 72. Classical Liberal Institute. 73. Shruti Rajagopalan's YouTube talk on constitutional amendments. 74. What I, as a development economist, have been actively “for” -- Lant Pritchett. 75. Can Economics Become More Reflexive? — Vijayendra Rao. 76. Premature Imitation and India's Flailing State — Shruti Rajagopalan & Alexander Tabarrok. 77. Elite Imitation in Public Policy — Episode 180 of The Seen and the Unseen (w Shruti Rajagopalan and Alex Tabarrok). 78. Invisible Infrastructure -- Episode 82 of Everything is Everything. 79. The Sundara Kanda. 80. Devdutt Pattanaik and the Stories That Shape Us -- Episode 404 of The Seen and the Unseen. 81. Y Combinator. 82. Space Fields. 83. Apoorwa Masuk, Onkar Singh Batra, Naman Pushp, Angad Daryani, Deepak VS and Srijon Sarkar. 84. Deepak VS and the Man Behind His Face — Episode 373 of The Seen and the Unseen. 85. You've Got To Hide Your Love Away -- The Beatles. 86. Caste, Capitalism and Chandra Bhan Prasad — Episode 296 of The Seen and the Unseen. 87. Data For India -- Rukmini S's startup. 88. Whole Numbers And Half Truths — Rukmini S. 89. The Moving Curve — Rukmini S's Covid podcast, also on all podcast apps. 90. The Importance of Data Journalism — Episode 196 of The Seen and the Unseen (w Rukmini S). 91. Rukmini Sees India's Multitudes — Episode 261 of The Seen and the Unseen (w Rukmini S). 92. Prosperiti. 93. This Be The Verse — Philip Larkin. 94. The Dilemma of an Indian Liberal -- Gurcharan Das. 95. Zakir: 1951-2024 -- Shruti Rajagopalan. 96. Dazzling Blue -- Paul Simon, featuring Karaikudi R Mani. 97. John Coltrane, Shakti, Zakir Hussain, Ali Akbar Khan, Pannalal Ghosh, Nikhil Banerjee, Vilayat Khan, Bismillah Khan, Ravi Shankar, Bhimsen Joshi, Bade Ghulam Ali Khan, Nusrat Fateh Ali Khan, Esperanza Spalding, MS Subbulakshmi, Lalgudi Jayaraman, TN Krishnan, Sanjay Subrahmanyan, Ranjani-Gayatri and TM Krishna on Spotify. 98. James Buchanan, Gordon Tullock, Israel Kirzner, Mario Rizzo, Vernon Smith, Thomas Schelling and Ronald Coase. 99. The Calculus of Consent -- James Buchanan and Gordon Tullock. 100. Tim Harford and Martin Wolf. 101. The Shawshank Redemption -- Frank Darabont. 102. The Marriage of Figaro in The Shawshank Redemption. 103. An Equal Music -- Vikram Seth. 104. Beethoven: Symphony No. 7 - Zubin Mehta and the Belgrade Philharmonic. 105. Pyotr Ilyich Tchaikovsky's violin concertos. 106. Animal Farm -- George Orwell. 107. Down and Out in Paris and London -- George Orwell. 108. Gulliver's Travels -- Jonathan Swift. 109. Alice in Wonderland and Through the Looking Glass -- Lewis Carroll. 110. One Day in the Life of Ivan Denisovich -- Aleksandr Solzhenitsyn. 111. The Gulag Archipelago -- Aleksandr Solzhenitsyn. 112. Khosla Ka Ghosla -- Dibakar Banerjee. 113. Mr India -- Shekhar Kapur. 114. Chalti Ka Naam Gaadi -- Satyen Bose. 114. Finding Nemo -- Andrew Stanton. 115. Tom and Jerry and Bugs Bunny. 116. Michael Madana Kama Rajan -- Singeetam Srinivasa Rao. 117. The Music Box, with Laurel and Hardy. 118. The Disciple -- Chaitanya Tamhane. 119. Court -- Chaitanya Tamhane. 120. Dwarkesh Patel on YouTube. Amit Varma and Ajay Shah have launched a new course called Life Lessons, which aims to be a launchpad towards learning essential life skills all of you need. For more details, and to sign up, click here. Amit and Ajay also bring out a weekly YouTube show, Everything is Everything. Have you watched it yet? You must! And have you read Amit's newsletter? Subscribe right away to The India Uncut Newsletter! It's free! Also check out Amit's online course, The Art of Clear Writing. Episode art: ‘Learn' by Simahina.

Wednesday January 29th, 2025

Stories from a longtime television producer who is responsible for such events as the World Junior Hockey Championship on TSN, who also happens to know Bob from their past. Learn more about your ad choices. Visit megaphone.fm/adchoices

Hear from our NHL insider John Shannon on the 6-2 Oilers win over Vancouver, Paul Graham's legacy as a live sports producer and much more. Plus, an audio recap of the Oilers and Canucks and post-game insight from Head Coach Kris Knoblauch and forward Jeff Skinner. Learn more about your ad choices. Visit megaphone.fm/adchoices

Michael Strong has spent decades quietly revolutionizing education by designing innovative schools and programs built around agency, critical thinking, entrepreneurship and creativity. He is the founder and CEO of The Socratic Experience, a virtual school that equips students for lifelong happiness and success through Socratic dialogue. Alongside his work in the US, he has educational consulting experience in multiple developing nations. And… he's a fellow Minnesotan! Michael joins the show to discuss whether Socratic education can scale, the benefits of the Mormon model, why high agency is the default, and MUCH more! I hope you enjoy this conversation as much as I did. For the full transcript, episode takeaways, and bucketloads of other goodies designed to make you go, “Hmm, that's interesting!”, check out our Substack. Important Links: Michael's Substack Twitter The Socratic Experience Show Notes: One book a night and mental chess - a Minnesotan childhood. Can Socratic education scale? Are we entrenching a new elite? Why high agency is the default Creating new subcultures & the benefits of the Mormon model Experimenting our way to prosperity Tearing down the citadel, secret censorship & claiming the moral high ground Prediction markets & why we should be betting on our reputation The heroic tradition of reason Michael as World Emperor MORE! Books Mentioned: Dr. Semmelweis vs. the World (Infinite Loops Substack) Ignore. Fight. Ridicule (Infinite Loops Substack) The Habit of Thought: From Socratic Seminars to Socratic Practice; by Michael Strong Be the Solution: How Entrepreneurs and Conscious Capitalists Can Solve All the World's Problems; by Michael Strong and John Mackey The Case Against Adolescence: Rediscovering the Adult in Every Teen; by Robert Epstein The Status Game: On Human Life and How to Play It; by Will Storr The New Inquisition: Irrational Rationalism and the Citadel of Science; by Robert Anton Wilson Hierarchy in the Forest: The Evolution of Egalitarian Behavior; by Christopher Boehm Collective Illusions: Conformity, Complicity, and the Science of Why We Make Bad Decisions; by Todd Rose Can Gambling Save Science? Encouraging an Honest Consensus; by Robin Hanson Skin in the Game: Hidden Asymmetries in Daily Life; by Nassim Nicholas Taleb Hothouse Earth: An Inhabitant's Guide; by Bill McGuire Think in Bets: Making Smarter Decisions When You Don't Have All the Facts; by Annie Duke The Ultimate Resource; by Julian L. Simon Keep Your Identity Small; by Paul Graham

In this episode, Jim and Jordan dive deep into the essentials of scaling Facebook and Instagram ads effectively, from a budget of $10 a day to $1,000 a day. The discussion covers critical aspects such as product-market fit, optimizing your website for conversions, segmenting ad campaigns, the importance of creative content, and more. Jordan also shares insider tips on managing advertising expectations, understanding conversion events, and a disciplined approach to budget increment. Practical examples, such as a case study with the brand 'Neat', are provided to illustrate successful strategies and approaches. TOPICS DISCUSSED IN TODAY'S EPISODESetting ExpectationsCommon Questions and Scaling AdsPreparing for Ad LaunchOrganic Sales and Business AnalysisMarket Positioning and Product FitWebsite Optimization for ConversionsLaunching Your Ad CampaignCreative Strategies and TestingScaling and Iterating Your CampaignFinal Thoughts and Next StepsResources:Growth Marketing OS (Operating System) GrowthHitJim Huffman websiteJim's LinkedinJim's Twitter Additional episodes you might enjoy:Startup Ideas by Paul Graham (#45)Nathan Barry: How to Bootstrap a Company to $30M in a Crowded Market (#41)How I Met My Biz Partner and Less Learned Hitting $2M ARR (#44)Ryan Hamilton on his Netflix special, touring with Jerry Seinfeld, & how to write a joke (#10)How We're Validating Startup Ideas (#51) 

                                      Listen in as Erin and Paul discuss: Transitioning from active income to passive income through strategic property investments and short-term rentals Focusing on maximizing cash flow and long-term appreciation by combining short-term rentals with additional land investments Emphasizing finding joy and purpose rather than chasing money alone Advocating blending financial growth with meaningful relationships and experiences How wealth-building isn't just about accumulating assets but creating time freedom to enjoy life Focusing on emotional transformation, valuing purpose-driven decisions over purely financial gains Encouraging celebrating wins and milestones, no matter how small, to reinforce positive habits Taking breaks, setting boundaries, and avoiding burnout to maintain productivity and personal well-being. …and much more!                                         About Paul H. Graham is a former commercial real estate broker who transitioned to building software in the real estate and fintech spaces. He used his salaries to purchase properties in the greater Austin, TX area to escape the everyday rat race. After his personal experience of being lost in the wealth-building trap, he started the podcast The Investor's Guide to Joy, talking to accredited investors about their mindset, lifestyle, and perspective. If you are a high-income earner looking to offset your tax liabilities, learn about investing in Oil and Gas syndications. How to Connect With Paul Website: https://investorsguidetojoy.com/ LinkedIn: https://www.linkedin.com/in/paulhgraham/ Facebook: https://www.facebook.com/itspaulgraham/ Instagram: https://www.instagram.com/itspaulgraham/ Twitter/X: https://x.com/pgra_ham

Join me as I reflect on 2024, discussing the challenges of delegation, business growth, and personal development. I'll share my key takeaways from managing Growth Hit and Neat, including the importance of consistency and overcoming setbacks. Discover my top five mindset drivers, favorite books, and new strategies for achieving goals in 2025. From implementing 'the one thing' daily to planning dad-daughter dates, get inspired to make this year impactful and rewarding! TOPICS DISCUSSED IN TODAY'S EPISODEThe Challenges of DelegationReflecting on 2024Growth Hit's Year of ConsistencyMindset Shifts and Key LearningsNeat's First Year Under New ManagementEntrepreneurial Adventures and Speaking EngagementsLooking Ahead to 2025Personal Goals and Simplifying LifeResources:My 2024 RecapGrowth Marketing OS (Operating System) GrowthHitJim Huffman websiteJim's LinkedinJim's Twitter Additional episodes you might enjoy:Startup Ideas by Paul Graham (#45)Nathan Barry: How to Bootstrap a Company to $30M in a Crowded Market (#41)How I Met My Biz Partner and Less Learned Hitting $2M ARR (#44)Ryan Hamilton on his Netflix special, touring with Jerry Seinfeld, & how to write a joke (#10)How We're Validating Startup Ideas (#51) 

Get more notes at https://podcastnotes.org Product and Media are the Leverage of the New Wealth (Listen) | Episode 21* The most important form of leverage is the idea of products which have no marginal cost of replication (aka product leverage)* You can replicate your efforts without having to involve other humans* Ex. – A podcast* Long ago, to get similar reach, you would have had to give a public lecture* 30-40 years ago – you would have had to get on TV* But today, thanks to the internet, anyone can launch a podcast* Product leverage is how fortunes will be made in the digital age – using things like code or media* Ex. of people who utilized code-based product leverage – Mark Zuckerberg, Jeff Bezos, Sergey Brin* Ex. of media-based product leverage – Joe Rogan, PewDiePie* Combining labor leverage, capital leverage, and product leverage is a magic combination for tech startups (for more on labor and capital leverage, check out these Podcast Notes)* You use the minimum, highest output labor – engineers and product developers* You add capital which you can use for marketing, advertising, and scaling* You then add lots of code, media, and content to get everything out there* Product and media leverage are permisionless – they don't require someone else's permission for you to use them or succeed* For labor leverage – someone has to decide to follow you* For capital leverage – someone has to give you money* But coding, writing tweets, making podcasts, YouTubing – these are permissionless* The robot revolution has already arrived – we just keep them in data centers/servers* Think – every great software developer has an army of robots working for him/her at night, while they sleep, after they've written the code and they're just cranking away* Robots do web searching for you* Robots handle customer service inquiries* Over time, this will progress to autonomous vehicles/planes/trucks* Coding is a superpower because it allows you to speak the language of the robots and tell them what to doProduct Leverage is Egalitarian in its Outputs (Listen) | Episode 22* Product (both code-based and media-based) leverage is egalitarian in its outputs* Compare this to labor and capital leverage – which are much less egalitarian* In general – the more of a human element there is in providing a service, the less egalitarian it is* “It's the nature of code and media output that the same product is accessible to everybody…The best products tend to be at the center, at the sweet spot of the middle class, rather than being targeted to the upper class.” – Naval Ravikant* For example:* Things like Netflix and Facebook – everybody can use* Compare this to Rolex watches or a Lamborghini – using/owning them is much more related to status-seeking* As the forms of leverage have gone from being labor-based and capital-based to being more product/code/media-based – “Most of the goods and services that we consume are becoming much more egalitarian in their consumption”* Things like food – rich people don't eat better food* Technology and media products have amazing scale economies* “If you care about ethics in wealth creation, it's better to create your wealth using code and media as leverage. Then those products are equally available to everybody as opposed to trying to create your wealth through labor or capital.” – Naval Ravikant* “If you're wealthy today, for large classes of things, you tend to spend your money on signaling goods to show other people that you are wealthy, and you try and convert them to status as opposed to actually consuming the goods for their own sake” – Naval RavikantBusiness Models Have Their Own Leverage (Listen) | Episode 23* Some business models give you “free leverage” – Examples:* Scale economies = the more you produce of something, the cheaper it gets to make* Technology and media products have this great quality where they have zero marginal cost of reproduction* Thinks like podcasts and YouTube videos* Ex. – Joe Rogan is working no harder now than he was on podcast #1, but it's now generating millions more* Then there are network effects businesses* A network effect is when each additional user adds value to the existing user base* Like language – The language becomes more valuable the more people who speak it* “Long-term, the entire world is probably going to end up speaking English and Chinese” – Naval Ravikant* It's thought that the value of a network is proportional to the square of the number of nodes of the network* A network of size 10 would have a value of 100, while a network of size 100 would have a value of 10,000* “You want to be in a network effects business” – Naval Ravikant* Things like Facebook, Uber, Twitter, YouTube, Google* “You should always be thinking about how your users or customers can add value to each other because that is the ultimate form of leverage” – Naval Ravikant* When you're picking a business model, aim to pick one where you can benefit from network effects, low marginal costs, and scale economiesAn Example: From Laborer to Real Estate Tech Company (Listen) | Episode 24* An example from the real estate business* A day laborer on a construction site, unless you're in a skilled trade, doesn't have specific knowledge* Even if you're a carpenter or electrician, other people can be trained to do your job – you can probably be replaced* You don't have much accountability – “You're a faceless cog in the construction crew”* They don't have much, if any, leverage* A general contractor, who someone hires to come and fix/repair their house, has a little more accountability* They'll make more money than a day laborer, but they take more risk (if the project runs over budget, they'll eat the loss)* The accountability gives them more potential income* They have labor leverage (people working for them)* A property developer is one level above a general contractor – these are people who go around looking for beaten-down properties which have potential and then buy them to fix them up* They can make a healthy profit by selling a building for 2-3x what they bought it for* A developer has more accountability/risk and much more specific knowledge* They have to know which neighborhoods are worth buying in, which lots are good/bad, and what makes/breaks a specific property* They have capital leverage and labor leverage* Beyond the property developer might be a famous architect/developer where just having your name on a property increases its value* Above that might be a property developer who builds entire communities* Above that – someone who funds real estate through an investment trust* Beyond that – someone (or a team of people) who understands the real estate market and the tech business (how to code/recruit developers/build a good product), and knows how to raise money from VCs* Think – something like Zillow* This team/individual would have all forms of leverage – labor (people working for him/her), code, capital (money from investors)* As you climb the chain – You layer in more knowledge which can only be gained on the job, more accountability/risk-taking, more capital, and more laborJudgment Is the Decisive Skill in an Age of Infinite Leverage (Listen) | Episode 25* First aim to get leverage, and once you have leverage – your judgment becomes the most important skill* How do you get leverage?* Get it permisionlessly – learn to code, create podcasts, become a good writer* Through permission – get people to work for you, or raise capital* “All the great fortunes are created through leverage” – Naval Ravikant* In high leverage positions (like a CEO), most of the time you're paid based on your judgment ability* Definitions:* Wisdom is knowing the long-term consequences of your actions* Judgment is wisdom on a personal domain (wisdom applied to external problems)* True judgment ability comes from experience* “Intellect without any experience is often worse than useless” – Naval Ravikant* You get the confidence that intellect gives you along with some credibility, but because you had no skin in the game and no real experience….”you're just throwing darts”* The people with the best judgment are actually among the least emotional* “The thing that prevents you from seeing what's actually happening are your emotions; our emotions are constantly clouding our judgment” – Naval Ravikant* Let's sum up:* First, you're accountable for your judgment* Judgment is the exercise of wisdom* Wisdom comes from experience* That experience can be accelerated through short iterations* “Investment books are sort of the worst place to learn about investment”* To get good at investing, you need broad-based judgment and thinking – the best way to obtain this is to study everything (including a lot of philosophy)* Philosophy makes you more stoic/less emotional and more likely to make better decisions (so you have better judgment)* The more outraged somebody gets, the worse their judgment probably is* “If someone's constantly tweeting political outrage and seems like an angry person, you don't want to hand them the keys to your car let alone the keys to your company”Set and Enforce an Aspirational Hourly Rate (Listen) | Episode 26* “No one is going to value you more than you value yourself” – Naval Ravikant* So set a high personal hourly rate and stick to it* Always factor your time into any decision (as well as your personal hourly rate)* So if your personal hourly rate is $60, and you estimate it will take you an hour and a half to return a $40 product, it's not worth it* You have a finite amount of high-output mental hours each day – “Do you want to use them to run errands and solve little problems or do you want to save them for the big stuff?”* “You can spend your life however you want, but if you want to get rich, it has to be your number one overwhelming desire” – Naval Ravikant* This means it has to come before ANYTHING else* Advice – Look forward to the future and set an aspirational hourly rate* Way back, Naval's aspirational hourly rate was $5,000/hour (even though he was only making a fraction of this at the time)* Today, Naval estimates he's actually beaten his goal* “It should seem and feel absurdly high. If it doesn't, it's not high enough.” – Naval Ravikant* If you can outsource something for less than your hourly rate, outsource it* Even for things like cooking* Paul Graham has said (directed to Y Combinator startups):* “You should be working on your product, getting product-market fit, exercising, and eating healthy. That's it. That's kind of all you have time for while you're on this mission.”Work as Hard as You Can (Listen) | Episode 27* “If getting wealthy is your goal, you're going to have to work as hard as you can” – Naval Ravikant* BUT – “Hard work is absolutely no substitute for who you work with and what you work on”* The hierarchy of importance:* “What you work on is probably the most important thing” – Naval Ravikant* AKA Product-Market-Founder fit (how well you personally are suited to a business”* Next – Picking the right people to work with* Third – How hard you work* But – they're like 3 legs of a stool, if you shortchange any one of them the whole stool is gonna fall down* The order of operations when building a business/career:* First – Figure out what you should be doing* Is there a market that's emerging that you're interested in?* Is there a product you could build which would fall in line with your specific knowledge?* Second – Surround yourself with the best people possible* “No matter how high your bar is, raise your bar” – Naval Ravikant* “You can never be working with other people who are great enough. If there's someone greater out there to work with, you should go work with them.” – Naval Ravikant* A good tip on deciding which startup to work for – Pick the one that will have the best alumni network for you in the future* Third – Work as hard as you can (AFTER you've picked the right thing to work on and the right people to work with)* “Nobody really works 80-120 hours a week sustainably at high-output with mental clarity” – Naval Ravikant* Knowledge workers tend to sprint while they're working on something that they're inspired/passionate about and then they rest* Sprint —> Rest —> Re-asses —> Try Again* (You end up building a marathon of sprints)* Inspiration is perishable* When you have the inspiration, act on it right then and there – otherwise you probably won't do it* Be impatient with actions and patient with results* “If I have a problem that I discover in one of my businesses that needs to be solved, I basically won't sleep until the resolution is in motion” – Naval RavikantBe Too Busy to “Do Coffee” (Listen) | Episode 28* Naval once tweeted – “You should be too busy to do coffee while keeping an uncluttered calendar”* The ONLY way to stay focused and be able to do the most high-impact work/what you're most inspired about is to constantly, RUTHLESSLY, decline meetings* It's fine to make connections and “do coffee” early in your career when you're exploring* But later in your career when you're exploiting – “You have to ruthlessly cut meetings out of your life”* If someone wants to have a meeting, suggest a phone call* If they want a phone call, suggest an email* When you do have meetings, make it a walking meeting (or a standing meeting), keep them short, and keep them small* “Any meeting with 8 people in it sitting around a conference table – nothing is getting done in that meeting, you're literally just dying one hour at a time” – Naval Ravikant* When you've done something important or valuable, busy people will meet with you* Suggest – “Hey, here's what I've done. Here's what I can show you. Let's meet and I'll be respectful of your time if this is useful to you.”* You HAVE to come with a proper calling card* “Product progress is the resume for the entrepreneur” – Naval Ravikant* You NEED proof of work to get a meeting with a busy person* “A busy calendar and a busy mind will destroy your ability to do great things in this world” – Naval Ravikant* If you want to be able to do great things you need free time and you need a free mind.Keep Redefining What You Do (Listen) | Episode 29* Naval tweeted – “Become the best in the world at what you do. Keep redefining what you do until this is true.”* “If you really want to get paid in this world, you want to be number one at whatever it is you're doing” – Naval Ravikant* Some of the most successful people in the world get paid for just being “them”* Oprah, Joe Rogan, etc. – they're being authentic to themselves* But – keep changing what you do until you're number one* It should be something that aligns with your specific knowledge, skill sets, interest, and capabilities* You should be thinking:* “I want to be the best at what I do”* “What I do is flexible, so that I'm the best at it”* (It's not an overnight discovery, it's a long journey)* A company should search for product-market fit* An entrepreneur should search for founder-product-market fitEscape Competition Through Authenticity (Listen) | Episode 30* Humans are highly memetic creatures – we tend to copy what everybody else is doing, including our desires* Very often, you get trapped in the wrong game because you're competing* The best way to escape competition is to just be authentic to yourself* If you're building and marketing something which is an extension of who you are, no one can compete with you on that* Think – It's near IMPOSSIBLE to compete with someone like Joe Rogan or Scott Adams* This is easiest to see in art, but even entrepreneurs are authentic (the businesses and product they create should be authentic to their desires and means)* “Authenticity naturally gets you away from competition” – Naval Ravikant* In entrepreneurship, the masses are never right* “If the masses knew how to build great things and create great wealth we'd all already be done. We'd all already be rich by now.” – Naval Ravikant* “Generally, most people will make the mistake of paying too much attention to the competition and being too much like the competition and not being authentic enough” – Naval Ravikant* The great founders tend to be authentic iconoclasts* As Robert Frost said – “Combine your vocation and your avocation” (what you love to do and what you do)* Long term, if you're good and successful at what you do, you'll find you're pretty much doing your hobbies for a living* “Ideally you want to end up specializing in being you” – Naval RavikantPlay Stupid Games, Win Stupid Prizes (Listen) | Episode 31* When you're being authentic, competition matters a whole lot less* Silicon Valley tech industry businesses tend to be winner take all* When you see competition, this can make you fly into a rage* You're often 1 step away from a completely different business, and sometimes you need to take that one step* But you won't be able to take it if you're fighting over a booby prize (aka playing a stupid game), blinded by competition* A personal example from Naval:* He was running Epinions (an online product review site independent of Amazon) a while back…* The space eventually turned into Trip Advisor and Yelp* “This is where we should have gone. We should have done more local reviews. There's more value to having a review for a scarce item (like a local restaurant) than some camera which might have 1,000 reviews on Amazon. But before we could get there, we got caught up in the whole comparison shopping game.” – Naval Ravikant* The whole space went to 0 as Amazon ended up winning the online retail game* “We should have been looking at what the consumer really wanted, and stayed authentic to ourselves – which is reviews, not price comparison” – Naval Ravikant* “We should have gone more and more into esoteric items that needed to be reviewed where customers had less and less data and wanted reviews more badly”* “If we stayed authentic to ourselves, we would have done better” – Naval RavikantEventually, You Will Get What You Deserve (Listen) | Episode 32* Naval tweeted – “Apply specific knowledge with leverage and eventually you'll get what you deserve”* (You could also add to that, apply: judgment or accountability)* Results take TIME* “If you're counting, you'll run out of patience before it actually arrives” – Naval Ravikant* Everybody wants results immediately, but you have to put in the hours* Put yourself in a good position with the specific knowledge, the accountability, the leverage, and your authentic skill set which allows you to be the best in the world at what you do (but you have to enjoy it)* Then just keep doing it, doing it, and doing it, and don't keep track, and don't keep count* “On a long enough time scale, you do get paid, but it can easily be 10 or 20 years” – Naval Ravikant* In entrepreneurship, you just have to be right ONCE* And the good news is you can take as many shots on goal as you want (usually every 3-5 years, 10 at the slowest)* Nivi has an equation:* Your eventual outcome = (the distinctiveness of your specific knowledge) x (how much leverage you apply) x (how often your judgment is correct) x (how accountable you are for the outcome) x (how much society values what you're doing) x (how long you can keep doing it) x (your improvement rate with learning and reading)* But the thing that matters most – find something you're good at that the market values* If you're good at it – you'll keep it up, develop the judgment, and eventually take on accountability (all the other variables fall into place)* “Product-market fit is inevitable if you're doing something you love to do and the market wants it” – Naval RavikantReject Most Advice (Listen) | Episode 33* “Avoid people who got rich quickly, they're just giving you their winning lottery ticket numbers” – Naval Ravikant* “The best founders I know listen to and read EVERYONE, but then they ignore everyone and make up their own mind” – Naval Ravikant* They have their OWN internal model of how to apply things to their situation and don't hesitate to discard information if necessary* Remember – “If you survey enough people, all the advice will cancel to 0”* When you hear a piece of advice/information, ask yourself:* “Is this true?”* “Is this true outside of the context of what that person applied it in?”* “Is it true in my context?”* “Do I want to apply it?”* Reject most advice, but remember you have to listen to/read enough of it to know what to reject and what to accept* Here's how Naval views the purpose of advice:* “I view it as helping me have anecdotes and maxims that I can then later recall when I have my own direct experience and say, ‘Ah, that's what that person meant.'” – Naval Ravikant* “90% of my tweets are just maxims that I carve for myself that are then mental hooks to remind me when I'm in that situation again” – Naval Ravikant* Like Naval's tweet – “If you can't see yourself looking with someone for life, then don't work with them for a day”Read the Full Notes at Podcast Notes Thank you for subscribing. 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Applications close Monday for the NYC AI Engineer Summit focusing on AI Leadership and Agent Engineering! If you applied, invites should be rolling out shortly.The search landscape is experiencing a fundamental shift. Google built a >$2T company with the “10 blue links” experience, driven by PageRank as the core innovation for ranking. This was a big improvement from the previous directory-based experiences of AltaVista and Yahoo. Almost 4 decades later, Google is now stuck in this links-based experience, especially from a business model perspective. This legacy architecture creates fundamental constraints:* Must return results in ~400 milliseconds* Required to maintain comprehensive web coverage* Tied to keyword-based matching algorithms* Cost structures optimized for traditional indexingAs we move from the era of links to the era of answers, the way search works is changing. You're not showing a user links, but the goal is to provide context to an LLM. This means moving from keyword based search to more semantic understanding of the content:The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share... but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways.All of this is now powered by a $5M cluster with 144 H200s:This architectural choice enables entirely new search capabilities:* Comprehensive result sets instead of approximations* Deep semantic understanding of queries* Ability to process complex, natural language requestsAs search becomes more complex, time to results becomes a variable:People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned... But what if searches can take like a minute or 10 minutes or a whole day, what can you then do?Unlike traditional search engines' fixed-cost indexing, Exa employs a hybrid approach:* Front-loaded compute for indexing and embeddings* Variable inference costs based on query complexity* Mix of owned infrastructure ($5M H200 cluster) and cloud resourcesExa sees a lot of competition from products like Perplexity and ChatGPT Search which layer AI on top of traditional search backends, but Exa is betting that true innovation requires rethinking search from the ground up. For example, the recently launched Websets, a way to turn searches into structured output in grid format, allowing you to create lists and databases out of web pages. The company raised a $17M Series A to build towards this mission, so keep an eye out for them in 2025. Chapters* 00:00:00 Introductions* 00:01:12 ExaAI's initial pitch and concept* 00:02:33 Will's background at SpaceX and Zoox* 00:03:45 Evolution of ExaAI (formerly Metaphor Systems)* 00:05:38 Exa's link prediction technology* 00:09:20 Meaning of the name "Exa"* 00:10:36 ExaAI's new product launch and capabilities* 00:13:33 Compute budgets and variable compute products* 00:14:43 Websets as a B2B offering* 00:19:28 How do you build a search engine?* 00:22:43 What is Neural PageRank?* 00:27:58 Exa use cases * 00:35:00 Auto-prompting* 00:38:42 Building agentic search* 00:44:19 Is o1 on the path to AGI?* 00:49:59 Company culture and nap pods* 00:54:52 Economics of AI search and the future of search technologyFull YouTube TranscriptPlease like and subscribe!Show Notes* ExaAI* Web Search Product* Websets* Series A Announcement* Exa Nap Pods* Perplexity AI* Character.AITranscriptAlessio [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 [00:00:10]: Hey, and today we're in the studio with my good friend and former landlord, Will Bryk. Roommate. How you doing? Will, you're now CEO co-founder of ExaAI, used to be Metaphor Systems. What's your background, your story?Will [00:00:30]: Yeah, sure. So, yeah, I'm CEO of Exa. I've been doing it for three years. I guess I've always been interested in search, whether I knew it or not. Like, since I was a kid, I've always been interested in, like, high-quality information. And, like, you know, even in high school, wanted to improve the way we get information from news. And then in college, built a mini search engine. And then with Exa, like, you know, it's kind of like fulfilling the dream of actually being able to solve all the information needs I wanted as a kid. Yeah, I guess. I would say my entire life has kind of been rotating around this problem, which is pretty cool. Yeah.Swyx [00:00:50]: What'd you enter YC with?Will [00:00:53]: We entered YC with, uh, we are better than Google. Like, Google 2.0.Swyx [00:01:12]: What makes you say that? Like, that's so audacious to come out of the box with.Will [00:01:16]: Yeah, okay, so you have to remember the time. This was summer 2021. And, uh, GPT-3 had come out. Like, here was this magical thing that you could talk to, you could enter a whole paragraph, and it understands what you mean, understands the subtlety of your language. And then there was Google. Uh, which felt like it hadn't changed in a decade, uh, because it really hadn't. And it, like, you would give it a simple query, like, I don't know, uh, shirts without stripes, and it would give you a bunch of results for the shirts with stripes. And so, like, Google could barely understand you, and GBD3 could. And the theory was, what if you could make a search engine that actually understood you? What if you could apply the insights from LLMs to a search engine? And it's really been the same idea ever since. And we're actually a lot closer now, uh, to doing that. Yeah.Alessio [00:01:55]: Did you have any trouble making people believe? Obviously, there's the same element. I mean, YC overlap, was YC pretty AI forward, even 2021, or?Will [00:02:03]: It's nothing like it is today. But, um, uh, there were a few AI companies, but, uh, we were definitely, like, bold. And I think people, VCs generally like boldness, and we definitely had some AI background, and we had a working demo. So there was evidence that we could build something that was going to work. But yeah, I think, like, the fundamentals were there. I think people at the time were talking about how, you know, Google was failing in a lot of ways. And so there was a bit of conversation about it, but AI was not a big, big thing at the time. Yeah. Yeah.Alessio [00:02:33]: Before we jump into Exa, any fun background stories? I know you interned at SpaceX, any Elon, uh, stories? I know you were at Zoox as well, you know, kind of like robotics at Harvard. Any stuff that you saw early that you thought was going to get solved that maybe it's not solved today?Will [00:02:48]: Oh yeah. I mean, lots of things like that. Like, uh, I never really learned how to drive because I believed Elon that self-driving cars would happen. It did happen. And I take them every night to get home. But it took like 10 more years than I thought. Do you still not know how to drive? I know how to drive now. I learned it like two years ago. That would have been great to like, just, you know, Yeah, yeah, yeah. You know? Um, I was obsessed with Elon. Yeah. I mean, I worked at SpaceX because I really just wanted to work at one of his companies. And I remember they had a rule, like interns cannot touch Elon. And, um, that rule actually influenced my actions.Swyx [00:03:18]: Is it, can Elon touch interns? Ooh, like physically?Will [00:03:22]: Or like talk? Physically, physically, yeah, yeah, yeah, yeah. Okay, interesting. He's changed a lot, but, um, I mean, his companies are amazing. Um,Swyx [00:03:28]: What if you beat him at Diablo 2, Diablo 4, you know, like, Ah, maybe.Alessio [00:03:34]: I want to jump into, I know there's a lot of backstory used to be called metaphor system. So, um, and it, you've always been kind of like a prominent company, maybe at least RAI circles in the NSF.Swyx [00:03:45]: I'm actually curious how Metaphor got its initial aura. You launched with like, very little. We launched very little. Like there was, there was this like big splash image of like, this is Aurora or something. Yeah. Right. And then I was like, okay, what this thing, like the vibes are good, but I don't know what it is. And I think, I think it was much more sort of maybe consumer facing than what you are today. Would you say that's true?Will [00:04:06]: No, it's always been about building a better search algorithm, like search, like, just like the vision has always been perfect search. And if you do that, uh, we will figure out the downstream use cases later. It started on this fundamental belief that you could have perfect search over the web and we could talk about what that means. And like the initial thing we released was really just like our first search engine, like trying to get it out there. Kind of like, you know, an open source. So when OpenAI released, uh, ChachBt, like they didn't, I don't know how, how much of a game plan they had. They kind of just wanted to get something out there.Swyx [00:04:33]: Spooky research preview.Will [00:04:34]: Yeah, exactly. And it kind of morphed from a research company to a product company at that point. And I think similarly for us, like we were research, we started as a research endeavor with a, you know, clear eyes that like, if we succeed, it will be a massive business to make out of it. And that's kind of basically what happened. I think there are actually a lot of parallels to, of w between Exa and OpenAI. I often say we're the OpenAI of search. Um, because. Because we're a research company, we're a research startup that does like fundamental research into, uh, making like AGI for search in a, in a way. Uh, and then we have all these like, uh, business products that come out of that.Swyx [00:05:08]: Interesting. I want to ask a little bit more about Metaforesight and then we can go full Exa. When I first met you, which was really funny, cause like literally I stayed in your house in a very historic, uh, Hayes, Hayes Valley place. You said you were building sort of like link prediction foundation model, and I think there's still a lot of foundation model work. I mean, within Exa today, but what does that even mean? I cannot be the only person confused by that because like there's a limited vocabulary or tokens you're telling me, like the tokens are the links or, you know, like it's not, it's not clear. Yeah.Will [00:05:38]: Uh, what we meant by link prediction is that you are literally predicting, like given some texts, you're predicting the links that follow. Yes. That refers to like, it's how we describe the training procedure, which is that we find links on the web. Uh, we take the text surrounding the link. And then we predict. Which link follows you, like, uh, you know, similar to transformers where, uh, you're trying to predict the next token here, you're trying to predict the next link. And so you kind of like hide the link from the transformer. So if someone writes, you know, imagine some article where someone says, Hey, check out this really cool aerospace startup. And they, they say spacex.com afterwards, uh, we hide the spacex.com and ask the model, like what link came next. And by doing that many, many times, you know, billions of times, you could actually build a search engine out of that because then, uh, at query time at search time. Uh, you type in, uh, a query that's like really cool aerospace startup and the model will then try to predict what are the most likely links. So there's a lot of analogs to transformers, but like to actually make this work, it does require like a different architecture than, but it's transformer inspired. Yeah.Alessio [00:06:41]: What's the design decision between doing that versus extracting the link and the description and then embedding the description and then using, um, yeah. What do you need to predict the URL versus like just describing, because you're kind of do a similar thing in a way. Right. It's kind of like based on this description, it was like the closest link for it. So one thing is like predicting the link. The other approach is like I extract the link and the description, and then based on the query, I searched the closest description to it more. Yeah.Will [00:07:09]: That, that, by the way, that is, that is the link refers here to a document. It's not, I think one confusing thing is it's not, you're not actually predicting the URL, the URL itself that would require like the, the system to have memorized URLs. You're actually like getting the actual document, a more accurate name could be document prediction. I see. This was the initial like base model that Exo was trained on, but we've moved beyond that similar to like how, you know, uh, to train a really good like language model, you might start with this like self-supervised objective of predicting the next token and then, uh, just from random stuff on the web. But then you, you want to, uh, add a bunch of like synthetic data and like supervised fine tuning, um, stuff like that to make it really like controllable and robust. Yeah.Alessio [00:07:48]: Yeah. We just have flow from Lindy and, uh, their Lindy started to like hallucinate recrolling YouTube links instead of like, uh, something. Yeah. Support guide. So. Oh, interesting. Yeah.Swyx [00:07:57]: So round about January, you announced your series A and renamed to Exo. I didn't like the name at the, at the initial, but it's grown on me. I liked metaphor, but apparently people can spell metaphor. What would you say are the major components of Exo today? Right? Like, I feel like it used to be very model heavy. Then at the AI engineer conference, Shreyas gave a really good talk on the vector database that you guys have. What are the other major moving parts of Exo? Okay.Will [00:08:23]: So Exo overall is a search engine. Yeah. We're trying to make it like a perfect search engine. And to do that, you have to build lots of, and we're doing it from scratch, right? So to do that, you have to build lots of different. The crawler. Yeah. You have to crawl a bunch of the web. First of all, you have to find the URLs to crawl. Uh, it's connected to the crawler, but yeah, you find URLs, you crawl those URLs. Then you have to process them with some, you know, it could be an embedding model. It could be something more complex, but you need to take, you know, or like, you know, in the past it was like a keyword inverted index. Like you would process all these documents you gather into some processed index, and then you have to serve that. Uh, you had high throughput at low latency. And so that, and that's like the vector database. And so it's like the crawling system, the AI processing system, and then the serving system. Those are all like, you know, teams of like hundreds, maybe thousands of people at Google. Um, but for us, it's like one or two people each typically, but yeah.Alessio [00:09:13]: Can you explain the meaning of, uh, Exo, just the story 10 to the 16th, uh, 18, 18.Will [00:09:20]: Yeah, yeah, yeah, sure. So. Exo means 10 to the 18th, which is in stark contrast to. To Google, which is 10 to the hundredth. Uh, we actually have these like awesome shirts that are like 10th to 18th is greater than 10th to the hundredth. Yeah, it's great. And it's great because it's provocative. It's like every engineer in Silicon Valley is like, what? No, it's not true. Um, like, yeah. And, uh, and then you, you ask them, okay, what does it actually mean? And like the creative ones will, will recognize it. But yeah, I mean, 10 to the 18th is better than 10 to the hundredth when it comes to search, because with search, you want like the actual list of, of things that match what you're asking for. You don't want like the whole web. You want to basically with search filter, the, like everything that humanity has ever created to exactly what you want. And so the idea is like smaller is better there. You want like the best 10th to the 18th and not the 10th to the hundredth. I'm like, one way to say this is like, you know how Google often says at the top, uh, like, you know, 30 million results found. And it's like crazy. Cause you're looking for like the first startups in San Francisco that work on hardware or something. And like, they're not 30 million results like that. What you want is like 325 results found. And those are all the results. That's what you really want with search. And that's, that's our vision. It's like, it just gives you. Perfectly what you asked for.Swyx [00:10:24]: We're recording this ahead of your launch. Uh, we haven't released, we haven't figured out the, the, the name of the launch yet, but what is the product that you're launching? I guess now that we're coinciding this podcast with. Yeah.Will [00:10:36]: So we've basically developed the next version of Exa, which is the ability to get a near perfect list of results of whatever you want. And what that means is you can make a complex query now to Exa, for example, startups working on hardware in SF, and then just get a huge list of all the things that match. And, you know, our goal is if there are 325 startups that match that we find you all of them. And this is just like, there's just like a new experience that's never existed before. It's really like, I don't know how you would go about that right now with current tools and you can apply this same type of like technology to anything. Like, let's say you want, uh, you want to find all the blog posts that talk about Alessio's podcast, um, that have come out in the past year. That is 30 million results. Yeah. Right.Will [00:11:24]: But that, I mean, that would, I'm sure that would be extremely useful to you guys. And like, I don't really know how you would get that full comprehensive list.Swyx [00:11:29]: I just like, how do you, well, there's so many questions with regards to how do you know it's complete, right? Cause you're saying there's only 30 million, 325, whatever. And then how do you do the semantic understanding that it might take, right? So working in hardware, like I might not use the words hardware. I might use the words robotics. I might use the words wearables. I might use like whatever. Yes. So yeah, just tell us more. Yeah. Yeah. Sure. Sure.Will [00:11:53]: So one aspect of this, it's a little subjective. So like certainly providing, you know, at some point we'll provide parameters to the user to like, you know, some sort of threshold to like, uh, gauge like, okay, like this is a cutoff. Like, this is actually not what I mean, because sometimes it's subjective and there needs to be a feedback loop. Like, oh, like it might give you like a few examples and you say, yeah, exactly. And so like, you're, you're kind of like creating a classifier on the fly, but like, that's ultimately how you solve the problem. So the subject, there's a subjectivity problem and then there's a comprehensiveness problem. Those are two different problems. So. Yeah. So you have the comprehensiveness problem. What you basically have to do is you have to put more compute into the query, into the search until you get the full comprehensiveness. Yeah. And I think there's an interesting point here, which is that not all queries are made equal. Some queries just like this blog post one might require scanning, like scavenging, like throughout the whole web in a way that just, just simply requires more compute. You know, at some point there's some amount of compute where you will just be comprehensive. You could imagine, for example, running GPT-4 over the internet. You could imagine running GPT-4 over the entire web and saying like, is this a blog post about Alessio's podcast, like, is this a blog post about Alessio's podcast? And then that would work, right? It would take, you know, a year, maybe cost like a million dollars, but, or many more, but, um, it would work. Uh, the point is that like, given sufficient compute, you can solve the query. And so it's really a question of like, how comprehensive do you want it given your compute budget? I think it's very similar to O1, by the way. And one way of thinking about what we built is like O1 for search, uh, because O1 is all about like, you know, some, some, some questions require more compute than others, and we'll put as much compute into the question as we need to solve it. So similarly with our search, we will put as much compute into the query in order to get comprehensiveness. Yeah.Swyx [00:13:33]: Does that mean you have like some kind of compute budget that I can specify? Yes. Yes. Okay. And like, what are the upper and lower bounds?Will [00:13:42]: Yeah, there's something we're still figuring out. I think like, like everyone is a new paradigm of like variable compute products. Yeah. How do you specify the amount of compute? Like what happens when you. Run out? Do you just like, ah, do you, can you like keep going with it? Like, do you just put in more credits to get more, um, for some, like this can get complex at like the really large compute queries. And like, one thing we do is we give you a preview of what you're going to get, and then you could then spin up like a much larger job, uh, to get like way more results. But yes, there is some compute limit, um, at, at least right now. Yeah. People think of searches as like, oh, it takes 500 milliseconds because we've been conditioned, uh, to have search that takes 500 milliseconds. But like search engines like Google, right. No matter how complex your query to Google, it will take like, you know, roughly 400 milliseconds. But what if searches can take like a minute or 10 minutes or a whole day, what can you then do? And you can do very powerful things. Um, you know, you can imagine, you know, writing a search, going and get a cup of coffee, coming back and you have a perfect list. Like that's okay for a lot of use cases. Yeah.Alessio [00:14:43]: Yeah. I mean, the use case closest to me is venture capital, right? So, uh, no, I mean, eight years ago, I built one of the first like data driven sourcing platforms. So we were. You look at GitHub, Twitter, Product Hunt, all these things, look at interesting things, evaluate them. If you think about some jobs that people have, it's like literally just make a list. If you're like an analyst at a venture firm, your job is to make a list of interesting companies. And then you reach out to them. How do you think about being infrastructure versus like a product you could say, Hey, this is like a product to find companies. This is a product to find things versus like offering more as a blank canvas that people can build on top of. Oh, right. Right.Will [00:15:20]: Uh, we are. We are a search infrastructure company. So we want people to build, uh, on top of us, uh, build amazing products on top of us. But with this one, we try to build something that makes it really easy for users to just log in, put a few, you know, put some credits in and just get like amazing results right away and not have to wait to build some API integration. So we're kind of doing both. Uh, we, we want, we want people to integrate this into all their applications at the same time. We want to just make it really easy to use very similar again to open AI. Like they'll have, they have an API, but they also have. Like a ChatGPT interface so that you could, it's really easy to use, but you could also build it in your applications. Yeah.Alessio [00:15:56]: I'm still trying to wrap my head around a lot of the implications. So, so many businesses run on like information arbitrage, you know, like I know this thing that you don't, especially in investment and financial services. So yeah, now all of a sudden you have these tools for like, oh, actually everybody can get the same information at the same time, the same quality level as an API call. You know, it just kind of changes a lot of things. Yeah.Will [00:16:19]: I think, I think what we're grappling with here. What, what you're just thinking about is like, what is the world like if knowledge is kind of solved, if like any knowledge request you want is just like right there on your computer, it's kind of different from when intelligence is solved. There's like a good, I've written before about like a different super intelligence, super knowledge. Yeah. Like I think that the, the distinction between intelligence and knowledge is actually a pretty good one. They're definitely connected and related in all sorts of ways, but there is a distinction. You could have a world and we are going to have this world where you have like GP five level systems and beyond that could like answer any complex request. Um, unless it requires some. Like, if you say like, uh, you know, give me a list of all the PhDs in New York city who, I don't know, have thought about search before. And even though this, this super intelligence is going to be like, I can't find it on Google, right. Which is kind of crazy. Like we're literally going to have like super intelligences that are using Google. And so if Google can't find them information, there's nothing they could do. They can't find it. So, but if you also have a super knowledge system where it's like, you know, I'm calling this term super knowledge where you just get whatever knowledge you want, then you can pair with a super intelligence system. And then the super intelligence can, we'll never. Be blocked by lack of knowledge.Alessio [00:17:23]: Yeah. You told me this, uh, when we had lunch, I forget how it came out, but we were talking about AGI and whatnot. And you were like, even AGI is going to need search. Yeah.Swyx [00:17:32]: Yeah. Right. Yeah. Um, so we're actually referencing a blog post that you wrote super intelligence and super knowledge. Uh, so I would refer people to that. And this is actually a discussion we've had on the podcast a couple of times. Um, there's so much of model weights that are just memorizing facts. Some of the, some of those might be outdated. Some of them are incomplete or not. Yeah. So like you just need search. So I do wonder, like, is there a maximum language model size that will be the intelligence layer and then the rest is just search, right? Like maybe we should just always use search. And then that sort of workhorse model is just like, and it like, like, like one B or three B parameter model that just drives everything. Yes.Will [00:18:13]: I believe this is a much more optimal system to have a smaller LM. That's really just like an intelligence module. And it makes a call to a search. Tool that's way more efficient because if, okay, I mean the, the opposite of that would be like the LM is so big that can memorize the whole web. That would be like way, but you know, it's not practical at all. I don't, it's not possible to train that at least right now. And Carpathy has actually written about this, how like he could, he could see models moving more and more towards like intelligence modules using various tools. Yeah.Swyx [00:18:39]: So for listeners, that's the, that was him on the no priors podcast. And for us, we talked about this and the, on the Shin Yu and Harrison chase podcasts. I'm doing search in my head. I told you 30 million results. I forgot about our neural link integration. Self-hosted exit.Will [00:18:54]: Yeah. Yeah. No, I do see that that is a much more, much more efficient world. Yeah. I mean, you could also have GB four level systems calling search, but it's just because of the cost of inference. It's just better to have a very efficient search tool and a very efficient LM and they're built for different things. Yeah.Swyx [00:19:09]: I'm just kind of curious. Like it is still something so audacious that I don't want to elide, which is you're, you're, you're building a search engine. Where do you start? How do you, like, are there any reference papers or implementation? That would really influence your thinking, anything like that? Because I don't even know where to start apart from just crawl a bunch of s**t, but there's gotta be more insight than that.Will [00:19:28]: I mean, yeah, there's more insight, but I'm always surprised by like, if you have a group of people who are really focused on solving a problem, um, with the tools today, like there's some in, in software, like there are all sorts of creative solutions that just haven't been thought of before, particularly in the information retrieval field. Yeah. I think a lot of the techniques are just very old, frankly. Like I know how Google and Bing work and. They're just not using new methods. There are all sorts of reasons for that. Like one, like Google has to be comprehensive over the web. So they're, and they have to return in 400 milliseconds. And those two things combined means they are kind of limit and it can't cost too much. They're kind of limited in, uh, what kinds of algorithms they could even deploy at scale. So they end up using like a limited keyword based algorithm. Also like Google was built in a time where like in, you know, in 1998, where we didn't have LMS, we didn't have embeddings. And so they never thought to build those things. And so now they have this like gigantic system that is built on old technology. Yeah. And so a lot of the information retrieval field we found just like thinks in terms of that framework. Yeah. Whereas we came in as like newcomers just thinking like, okay, there here's GB three. It's magical. Obviously we're going to build search that is using that technology. And we never even thought about using keywords really ever. Uh, like we were neural all the way we're building an end to end neural search engine. And just that whole framing just makes us ask different questions, like pursue different lines of work. And there's just a lot of low hanging fruit because no one else is thinking about it. We're just on the frontier of neural search. We just are, um, for, for at web scale, um, because there's just not a lot of people thinking that way about it.Swyx [00:20:57]: Yeah. Maybe let's spell this out since, uh, we're already on this topic, elephants in the room are Perplexity and SearchGPT. That's the, I think that it's all, it's no longer called SearchGPT. I think they call it ChatGPT Search. How would you contrast your approaches to them based on what we know of how they work and yeah, just any, anything in that, in that area? Yeah.Will [00:21:15]: So these systems, there are a few of them now, uh, they basically rely on like traditional search engines like Google or Bing, and then they combine them with like LLMs at the end to, you know, output some power graphics, uh, answering your question. So they like search GPT perplexity. I think they have their own crawlers. No. So there's this important distinction between like having your own search system and like having your own cache of the web. Like for example, so you could create, you could crawl a bunch of the web. Imagine you crawl a hundred billion URLs, and then you create a key value store of like mapping from URL to the document that is technically called an index, but it's not a search algorithm. So then to actually like, when you make a query to search GPT, for example, what is it actually doing it? Let's say it's, it's, it could, it's using the Bing API, uh, getting a list of results and then it could go, it has this cache of like all the contents of those results and then could like bring in the cache, like the index cache, but it's not actually like, it's not like they've built a search engine from scratch over, you know, hundreds of billions of pages. It's like, does that distinction clear? It's like, yeah, you could have like a mapping from URL to documents, but then rely on traditional search engines to actually get the list of results because it's a very hard problem to take. It's not hard. It's not hard to use DynamoDB and, and, and map URLs to documents. It's a very hard problem to take a hundred billion or more documents and given a query, like instantly get the list of results that match. That's a much harder problem that very few entities on, in, on the planet have done. Like there's Google, there's Bing, uh, you know, there's Yandex, but you know, there are not that many companies that are, that are crazy enough to actually build their search engine from scratch when you could just use traditional search APIs.Alessio [00:22:43]: So Google had PageRank as like the big thing. Is there a LLM equivalent or like any. Stuff that you're working on that you want to highlight?Will [00:22:51]: The link prediction objective can be seen as like a neural PageRank because what you're doing is you're predicting the links people share. And so if everyone is sharing some Paul Graham essay about fundraising, then like our model is more likely to predict it. So like inherent in our training objective is this, uh, a sense of like high canonicity and like high quality, but it's more powerful than PageRank. It's strictly more powerful because people might refer to that Paul Graham fundraising essay in like a thousand different ways. And so our model learns all the different ways. That someone refers that Paul Graham, I say, while also learning how important that Paul Graham essay is. Um, so it's like, it's like PageRank on steroids kind of thing. Yeah.Alessio [00:23:26]: I think to me, that's the most interesting thing about search today, like with Google and whatnot, it's like, it's mostly like domain authority. So like if you get back playing, like if you search any AI term, you get this like SEO slop websites with like a bunch of things in them. So this is interesting, but then how do you think about more timeless maybe content? So if you think about, yeah. You know, maybe the founder mode essay, right. It gets shared by like a lot of people, but then you might have a lot of other essays that are also good, but they just don't really get a lot of traction. Even though maybe the people that share them are high quality. How do you kind of solve that thing when you don't have the people authority, so to speak of who's sharing, whether or not they're worth kind of like bumping up? Yeah.Will [00:24:10]: I mean, you do have a lot of control over the training data, so you could like make sure that the training data contains like high quality sources so that, okay. Like if you, if you're. Training data, I mean, it's very similar to like language, language model training. Like if you train on like a bunch of crap, your prediction will be crap. Our model will match the training distribution is trained on. And so we could like, there are lots of ways to tweak the training data to refer to high quality content that we want. Yeah. I would say also this, like this slop that is returned by, by traditional search engines, like Google and Bing, you have the slop is then, uh, transferred into the, these LLMs in like a search GBT or, you know, our other systems like that. Like if slop comes in, slop will go out. And so, yeah, that's another answer to how we're different is like, we're not like traditional search engines. We want to give like the highest quality results and like have full control over whatever you want. If you don't want slop, you get that. And then if you put an LM on top of that, which our customers do, then you just get higher quality results or high quality output.Alessio [00:25:06]: And I use Excel search very often and it's very good. Especially.Swyx [00:25:09]: Wave uses it too.Alessio [00:25:10]: Yeah. Yeah. Yeah. Yeah. Yeah. Like the slop is everywhere, especially when it comes to AI, when it comes to investment. When it comes to all of these things for like, it's valuable to be at the top. And this problem is only going to get worse because. Yeah, no, it's totally. What else is in the toolkit? So you have search API, you have ExaSearch, kind of like the web version. Now you have the list builder. I think you also have web scraping. Maybe just touch on that. Like, I guess maybe people, they want to search and then they want to scrape. Right. So is that kind of the use case that people have? Yeah.Will [00:25:41]: A lot of our customers, they don't just want, because they're building AI applications on top of Exa, they don't just want a list of URLs. They actually want. Like the full content, like cleans, parsed. Markdown. Markdown, maybe chunked, whatever they want, we'll give it to them. And so that's been like huge for customers. Just like getting the URLs and instantly getting the content for each URL is like, and you can do this for 10 or 100 or 1,000 URLs, wherever you want. That's very powerful.Swyx [00:26:05]: Yeah. I think this is the first thing I asked you for when I tried using Exa.Will [00:26:09]: Funny story is like when I built the first version of Exa, it's like, we just happened to store the content. Yes. Like the first 1,024 tokens. Because I just kind of like kept it because I thought of, you know, I don't know why. Really for debugging purposes. And so then when people started asking for content, it was actually pretty easy to serve it. But then, and then we did that, like Exa took off. So the computer's content was so useful. So that was kind of cool.Swyx [00:26:30]: It is. I would say there are other players like Gina, I think is in this space. Firecrawl is in this space. There's a bunch of scraper companies. And obviously scraper is just one part of your stack, but you might as well offer it since you already do it.Will [00:26:43]: Yeah, it makes sense. It's just easy to have an all-in-one solution. And like. We are, you know, building the best scraper in the world. So scraping is a hard problem and it's easy to get like, you know, a good scraper. It's very hard to get a great scraper and it's super hard to get a perfect scraper. So like, and, and scraping really matters to people. Do you have a perfect scraper? Not yet. Okay.Swyx [00:27:05]: The web is increasingly closing to the bots and the scrapers, Twitter, Reddit, Quora, Stack Overflow. I don't know what else. How are you dealing with that? How are you navigating those things? Like, you know. You know, opening your eyes, like just paying them money.Will [00:27:19]: Yeah, no, I mean, I think it definitely makes it harder for search engines. One response is just that there's so much value in the long tail of sites that are open. Okay. Um, and just like, even just searching over those well gets you most of the value. But I mean, there, there is definitely a lot of content that is increasingly not unavailable. And so you could get through that through data partnerships. The bigger we get as a company, the more, the easier it is to just like, uh, make partnerships. But I, I mean, I do see the world as like the future where the. The data, the, the data producers, the content creators will make partnerships with the entities that find that data.Alessio [00:27:53]: Any other fun use case that maybe people are not thinking about? Yeah.Will [00:27:58]: Oh, I mean, uh, there are so many customers. Yeah. What are people doing on AXA? Well, I think dating is a really interesting, uh, application of search that is completely underserved because there's a lot of profiles on the web and a lot of people who want to find love and that I'll use it. They give me. Like, you know, age boundaries, you know, education level location. Yeah. I mean, you want to, what, what do you want to do with data? You want to find like a partner who matches this education level, who like, you know, maybe has written about these types of topics before. Like if you could get a list of all the people like that, like, I think you will unblock a lot of people. I mean, there, I mean, I think this is a very Silicon Valley view of dating for sure. And I'm, I'm well aware of that, but it's just an interesting application of like, you know, I would love to meet like an intellectual partner, um, who like shares a lot of ideas. Yeah. Like if you could do that through better search and yeah.Swyx [00:28:48]: But what is it with Jeff? Jeff has already set me up with a few people. So like Jeff, I think it's my personal exit.Will [00:28:55]: my mom's actually a matchmaker and has got a lot of married. Yeah. No kidding. Yeah. Yeah. Search is built into the book. It's in your jeans. Yeah. Yeah.Swyx [00:29:02]: Yeah. Other than dating, like I know you're having quite some success in colleges. I would just love to map out some more use cases so that our listeners can just use those examples to think about use cases for XR, right? Because it's such a general technology that it's hard to. Uh, really pin down, like, what should I use it for and what kind of products can I build with it?Will [00:29:20]: Yeah, sure. So, I mean, there are so many applications of XR and we have, you know, many, many companies using us for very diverse range of use cases, but I'll just highlight some interesting ones. Like one customer, a big customer is using us to, um, basically build like a, a writing assistant for students who want to write, uh, research papers. And basically like XR will search for, uh, like a list of research papers related to what the student is writing. And then this product has. Has like an LLM that like summarizes the papers to basically it's like a next word prediction, but in, uh, you know, prompted by like, you know, 20 research papers that X has returned. It's like literally just doing their homework for them. Yeah. Yeah. the key point is like, it's, it's, uh, you know, it's, it's, you know, research is, is a really hard thing to do and you need like high quality content as input.Swyx [00:30:08]: Oh, so we've had illicit on the podcast. I think it's pretty similar. Uh, they, they do focus pretty much on just, just research papers and, and that research. Basically, I think dating, uh, research, like I just wanted to like spell out more things, like just the big verticals.Will [00:30:23]: Yeah, yeah, no, I mean, there, there are so many use cases. So finance we talked about, yeah. I mean, one big vertical is just finding a list of companies, uh, so it's useful for VCs, like you said, who want to find like a list of competitors to a specific company they're investigating or just a list of companies in some field. Like, uh, there was one VC that told me that him and his team, like we're using XR for like eight hours straight. Like, like that. For many days on end, just like, like, uh, doing like lots of different queries of different types, like, oh, like all the companies in AI for law or, uh, all the companies for AI for, uh, construction and just like getting lists of things because you just can't find this information with, with traditional search engines. And then, you know, finding companies is also useful for, for selling. If you want to find, you know, like if we want to find a list of, uh, writing assistants to sell to, then we can just, we just use XR ourselves to find that is actually how we found a lot of our customers. Ooh, you can find your own customers using XR. Oh my God. I, in the spirit of. Uh, using XR to bolster XR, like recruiting is really helpful. It is really great use case of XR, um, because we can just get like a list of, you know, people who thought about search and just get like a long list and then, you know, reach out to those people.Swyx [00:31:29]: When you say thought about, are you, are you thinking LinkedIn, Twitter, or are you thinking just blogs?Will [00:31:33]: Or they've written, I mean, it's pretty general. So in that case, like ideally XR would return like the, the really blogs written by people who have just. So if I don't blog, I don't show up to XR, right? Like I have to blog. well, I mean, you could show up. That's like an incentive for people to blog.Swyx [00:31:47]: Well, if you've written about, uh, search in on Twitter and we, we do, we do index a bunch of tweets and then we, we should be able to service that. Yeah. Um, I mean, this is something I tell people, like you have to make yourself discoverable to the web, uh, you know, it's called learning in public, but like, it's even more imperative now because otherwise you don't exist at all.Will [00:32:07]: Yeah, no, no, this is a huge, uh, thing, which is like search engines completely influence. They have downstream effects. They influence the internet itself. They influence what people. Choose to create. And so Google, because they're a keyword based search engine, people like kind of like keyword stuff. Yeah. They're, they're, they're incentivized to create things that just match a lot of keywords, which is not very high quality. Uh, whereas XR is a search algorithm that, uh, optimizes for like high quality and actually like matching what you mean. And so people are incentivized to create content that is high quality, that like the create content that they know will be found by the right person. So like, you know, if I am a search researcher and I want to be found. By XR, I should blog about search and all the things I'm building because, because now we have a search engine like XR that's powerful enough to find them. And so the search engine will influence like the downstream internet in all sorts of amazing ways. Yeah. Uh, whatever the search engine optimizes for is what the internet looks like. Yeah.Swyx [00:33:01]: Are you familiar with the term? McLuhanism? No, it's not. Uh, it's this concept that, uh, like first we shape tools and then the tools shape us. Okay. Yeah. Uh, so there's like this reflexive connection between the things we search for and the things that get searched. Yes. So like once you change the tool. The tool that searches the, the, the things that get searched also change. Yes.Will [00:33:18]: I mean, there was a clear example of that with 30 years of Google. Yeah, exactly. Google has basically trained us to think of search and Google has Google is search like in people's heads. Right. It's one, uh, hard part about XR is like, uh, ripping people away from that notion of search and expanding their sense of what search could be. Because like when people think search, they think like a few keywords, or at least they used to, they think of a few keywords and that's it. They don't think to make these like really complex paragraph long requests for information and get a perfect list. ChatGPT was an interesting like thing that expanded people's understanding of search because you start using ChatGPT for a few hours and you go back to Google and you like paste in your code and Google just doesn't work and you're like, oh, wait, it, Google doesn't do work that way. So like ChatGPT expanded our understanding of what search can be. And I think XR is, uh, is part of that. We want to expand people's notion, like, Hey, you could actually get whatever you want. Yeah.Alessio [00:34:06]: I search on XR right now, people writing about learning in public. I was like, is it gonna come out with Alessio? Am I, am I there? You're not because. Bro. It's. So, no, it's, it's so about, because it thinks about learning, like in public, like public schools and like focuses more on that. You know, it's like how, when there are like these highly overlapping things, like this is like a good result based on the query, you know, but like, how do I get to Alessio? Right. So if you're like in these subcultures, I don't think this would work in Google well either, you know, but I, I don't know if you have any learnings.Swyx [00:34:40]: No, I'm the first result on Google.Alessio [00:34:42]: People writing about learning. In public, you're not first result anymore, I guess.Swyx [00:34:48]: Just type learning public in Google.Alessio [00:34:49]: Well, yeah, yeah, yeah, yeah. But this is also like, this is in Google, it doesn't work either. That's what I'm saying. It's like how, when you have like a movement.Will [00:34:56]: There's confusion about the, like what you mean, like your intention is a little, uh. Yeah.Alessio [00:35:00]: It's like, yeah, I'm using, I'm using a term that like I didn't invent, but I'm kind of taking over, but like, they're just so much about that term already that it's hard to overcome. If that makes sense, because public schools is like, well, it's, it's hard to overcome.Will [00:35:14]: Public schools, you know, so there's the right solution to this, which is to specify more clearly what you mean. And I'm not expecting you to do that, but so the, the right interface to search is actually an LLM.Swyx [00:35:25]: Like you should be talking to an LLM about what you want and the LLM translates its knowledge of you or knowledge of what people usually mean into a query that excellent uses, which you have called auto prompts, right?Will [00:35:35]: Or, yeah, but it's like a very light version of that. And really it's just basically the right answer is it's the wrong interface and like very soon interface to search and really to everything will be LLM. And the LLM just has a full knowledge of you, right? So we're kind of building for that world. We're skating to where the puck is going to be. And so since we're moving to a world where like LLMs are interfaced to everything, you should build a search engine that can handle complex LLM queries, queries that come from LLMs. Because you're probably too lazy, I'm too lazy too, to write like a whole paragraph explaining, okay, this is what I mean by this word. But an LLM is not lazy. And so like the LLM will spit out like a paragraph or more explaining exactly what it wants. You need a search engine that can handle that. Traditional search engines like Google or Bing, they're actually... Designed for humans typing keywords. If you give a paragraph to Google or Bing, they just completely fail. And so Exa can handle paragraphs and we want to be able to handle it more and more until it's like perfect.Alessio [00:36:24]: What about opinions? Do you have lists? When you think about the list product, do you think about just finding entries? Do you think about ranking entries? I'll give you a dumb example. So on Lindy, I've been building the spot that every week gives me like the top fantasy football waiver pickups. But every website is like different opinions. I'm like, you should pick up. These five players, these five players. When you're making lists, do you want to be kind of like also ranking and like telling people what's best? Or like, are you mostly focused on just surfacing information?Will [00:36:56]: There's a really good distinction between filtering to like things that match your query and then ranking based on like what is like your preferences. And ranking is like filtering is objective. It's like, does this document match what you asked for? Whereas ranking is more subjective. It's like, what is the best? Well, it depends what you mean by best, right? So first, first table stakes is let's get the filtering into a perfect place where you actually like every document matches what you asked for. No surgeon can do that today. And then ranking, you know, there are all sorts of interesting ways to do that where like you've maybe for, you know, have the user like specify more clearly what they mean by best. You could do it. And if the user doesn't specify, you do your best, you do your best based on what people typically mean by best. But ideally, like the user can specify, oh, when I mean best, I actually mean ranked by the, you know, the number of people who visited that site. Let's say is, is one example ranking or, oh, what I mean by best, let's say you're listing companies. What I mean by best is like the ones that have, uh, you know, have the most employees or something like that. Like there are all sorts of ways to rank a list of results that are not captured by something as subjective as best. Yeah. Yeah.Alessio [00:38:00]: I mean, it's like, who are the best NBA players in the history? It's like everybody has their own. Right.Will [00:38:06]: Right. But I mean, the, the, the search engine should definitely like, even if you don't specify it, it should do as good of a job as possible. Yeah. Yeah. No, no, totally. Yeah. Yeah. Yeah. Yeah. It's a new topic to people because we're not used to a search engine that can handle like a very complex ranking system. Like you think to type in best basketball players and not something more specific because you know, that's the only thing Google could handle. But if Google could handle like, oh, basketball players ranked by like number of shots scored on average per game, then you would do that. But you know, they can't do that. So.Swyx [00:38:32]: Yeah. That's fascinating. So you haven't used the word agents, but you're kind of building a search agent. Do you believe that that is agentic in feature? Do you think that term is distracting?Will [00:38:42]: I think it's a good term. I do think everything will eventually become agentic. And so then the term will lose power, but yes, like what we're building is agentic it in a sense that it takes actions. It decides when to go deeper into something, it has a loop, right? It feels different from traditional search, which is like an algorithm, not an agent. Ours is a combination of an algorithm and an agent.Swyx [00:39:05]: I think my reflection from seeing this in the coding space where there's basically sort of classic. Framework for thinking about this stuff is the self-driving levels of autonomy, right? Level one to five, typically the level five ones all failed because there's full autonomy and we're not, we're not there yet. And people like control. People like to be in the loop. So the, the, the level ones was co-pilot first and now it's like cursor and whatever. So I feel like if it's too agentic, it's too magical, like, like a, like a one shot, I stick a, stick a paragraph into the text box and then it spits it back to me. It might feel like I'm too disconnected from the process and I don't trust it. As opposed to something where I'm more intimately involved with the research product. I see. So like, uh, wait, so the earlier versions are, so if trying to stick to the example of the basketball thing, like best basketball player, but instead of best, you, you actually get to customize it with like, whatever the metric is that you, you guys care about. Yeah. I'm still not a basketballer, but, uh, but, but, you know, like, like B people like to be in my, my thesis is that agents level five agents failed because people like to. To kind of have drive assist rather than full self-driving.Will [00:40:15]: I mean, a lot of this has to do with how good agents are. Like at some point, if agents for coding are better than humans at all tests and then humans block, yeah, we're not there yet.Swyx [00:40:25]: So like in a world where we're not there yet, what you're pitching us is like, you're, you're kind of saying you're going all the way there. Like I kind of, I think all one is also very full, full self-driving. You don't get to see the plan. You don't get to affect the plan yet. You just fire off a query and then it goes away for a couple of minutes and comes back. Right. Which is effectively what you're saying you're going to do too. And you think there's.Will [00:40:42]: There's a, there's an in-between. I saw. Okay. So in building this product, we're exploring new interfaces because what does it mean to kick off a search that goes and takes 10 minutes? Like, is that a good interface? Because what if the search is actually wrong or it's not exactly, exactly specified to what you mean, which is why you get previews. Yeah. You get previews. So it is iterative, but ultimately once you've specified exactly what you mean, then you kind of do just want to kick off a batch job. Right. So perhaps what you're getting at is like, uh, there's this barrier with agents where you have to like explain the full context of what you mean, and a lot of failure modes happen when you have, when you don't. Yeah. There's failure modes from the agent, just not being smart enough. And then there's failure modes from the agent, not understanding exactly what you mean. And there's a lot of context that is shared between humans that is like lost between like humans and, and this like new creature.Alessio [00:41:32]: Yeah. Yeah. Because people don't know what's going on. I mean, to me, the best example of like system prompts is like, why are you writing? You're a helpful assistant. Like. Of course you should be an awful, but people don't yet know, like, can I assume that, you know, that, you know, it's like, why did the, and now people write, oh, you're a very smart software engineer, but like, you never made, you never make mistakes. Like, were you going to try and make mistakes before? So I think people don't yet have an understanding, like with, with driving people know what good driving is. It's like, don't crash, stay within kind of like a certain speed range. It's like, follow the directions. It's like, I don't really have to explain all of those things. I hope. But with. AI and like models and like search, people are like, okay, what do you actually know? What are like your assumptions about how search, how you're going to do search? And like, can I trust it? You know, can I influence it? So I think that's kind of the, the middle ground, like before you go ahead and like do all the search, it's like, can I see how you're doing it? And then maybe help show your work kind of like, yeah, steer you. Yeah. Yeah.Will [00:42:32]: No, I mean, yeah. Sure. Saying, even if you've crafted a great system prompt, you want to be part of the process itself. Uh, because the system prompt doesn't, it doesn't capture everything. Right. So yeah. A system prompt is like, you get to choose the person you work with. It's like, oh, like I want, I want a software engineer who thinks this way about code. But then even once you've chosen that person, you can't just give them a high level command and they go do it perfectly. You have to be part of that process. So yeah, I agree.Swyx [00:42:58]: Just a side note for my system, my favorite system, prompt programming anecdote now is the Apple intelligence system prompt that someone, someone's a prompt injected it and seen it. And like the Apple. Intelligence has the words, like, please don't, don't hallucinate. And it's like, of course we don't want you to hallucinate. Right. Like, so it's exactly that, that what you're talking about, like we should train this behavior into the model, but somehow we still feel the need to inject into the prompt. And I still don't even think that we are very scientific about it. Like it, I think it's almost like cargo culting. Like we have this like magical, like turn around three times, throw salt over your shoulder before you do something. And like, it worked the last time. So let's just do it the same time now. And like, we do, there's no science to this.Will [00:43:35]: I do think a lot of these problems might be ironed out in future versions. Right. So, and like, they might, they might hide the details from you. So it's like, they actually, all of them have a system prompt. That's like, you are a helpful assistant. You don't actually have to include it, even though it might actually be the way they've implemented in the backend. It should be done in RLE AF.Swyx [00:43:52]: Okay. Uh, one question I was just kind of curious about this episode is I'm going to try to frame this in terms of this, the general AI search wars, you know, you're, you're one player in that, um, there's perplexity, chat, GPT, search, and Google, but there's also like the B2B side, uh, we had. Drew Houston from Dropbox on, and he's competing with Glean, who've, uh, we've also had DD from, from Glean on, is there an appetite for Exa for my company's documents?Will [00:44:19]: There is appetite, but I think we have to be disciplined, focused, disciplined. I mean, we're already taking on like perfect web search, which is a lot. Um, but I mean, ultimately we want to build a perfect search engine, which definitely for a lot of queries involves your, your personal information, your company's information. And so, yeah, I mean, the grandest vision of Exa is perfect search really over everything, every domain, you know, we're going to have an Exa satellite, uh, because, because satellites can gather information that, uh, is not available publicly. Uh, gotcha. Yeah.Alessio [00:44:51]: Can we talk about AGI? We never, we never talk about AGI, but you had, uh, this whole tweet about, oh, one being the biggest kind of like AI step function towards it. Why does it feel so important to you? I know there's kind of like always criticism and saying, Hey, it's not the smartest son is better. It's like, blah, blah, blah. What? You choose C. So you say, this is what Ilias see or Sam see what they will see.Will [00:45:13]: I've just, I've just, you know, been connecting the dots. I mean, this was the key thing that a bunch of labs were working on, which is like, can you create a reward signal? Can you teach yourself based on a reward signal? Whether you're, if you're trying to learn coding or math, if you could have one model say, uh, be a grading system that says like you have successfully solved this programming assessment and then one model, like be the generative system. That's like, here are a bunch of programming assessments. You could train on that. It's basically whenever you could create a reward signal for some task, you could just generate a bunch of tasks for yourself. See that like, oh, on two of these thousand, you did well. And then you just train on that data. It's basically like, I mean, creating your own data for yourself and like, you know, all the labs working on that opening, I built the most impressive product doing that. And it's just very, it's very easy now to see how that could like scale to just solving, like, like solving programming or solving mathematics, which sounds crazy, but everything about our world right now is crazy.Alessio [00:46:07]: Um, and so I think if you remove that whole, like, oh, that's impossible, and you just think really clearly about like, what's now possible with like what, what they've done with O1, it's easy to see how that scales. How do you think about older GPT models then? Should people still work on them? You know, if like, obviously they just had the new Haiku, like, is it even worth spending time, like making these models better versus just, you know, Sam talked about O2 at that day. So obviously they're, they're spending a lot of time in it, but then you have maybe. The GPU poor, which are still working on making Lama good. Uh, and then you have the follower labs that do not have an O1 like model out yet. Yeah.Will [00:46:47]: This kind of gets into like, uh, what will the ecosystem of, of models be like in the future? And is there room is, is everything just gonna be O1 like models? I think, well, I mean, there's definitely a question of like inference speed and if certain things like O1 takes a long time, because that's the thing. Well, I mean, O1 is, is two things. It's like one it's it's use it's bootstrapping itself. It's teaching itself. And so the base model is smarter. But then it also has this like inference time compute where it could like spend like many minutes or many hours thinking. And so even the base model, which is also fast, it doesn't have to take minutes. It could take is, is better, smarter. I believe all models will be trained with this paradigm. Like you'll want to train on the best data, but there will be many different size models from different, very many different like companies, I believe. Yeah. Because like, I don't, yeah, I mean, it's hard, hard to predict, but I don't think opening eye is going to dominate like every possible LLM for every possible. Use case. I think for a lot of things, like you just want the fastest model and that might not involve O1 methods at all.Swyx [00:47:42]: I would say if you were to take the exit being O1 for search, literally, you really need to prioritize search trajectories, like almost maybe paying a bunch of grad students to go research things. And then you kind of track what they search and what the sequence of searching is, because it seems like that is the gold mine here, like the chain of thought or the thinking trajectory. Yeah.Will [00:48:05]: When it comes to search, I've always been skeptical. I've always been skeptical of human labeled data. Okay. Yeah, please. We tried something at our company at Exa recently where me and a bunch of engineers on the team like labeled a bunch of queries and it was really hard. Like, you know, you have all these niche queries and you're looking at a bunch of results and you're trying to identify which is matched to query. It's talking about, you know, the intricacies of like some biological experiment or something. I have no idea. Like, I don't know what matches and what, what labelers like me tend to do is just match by keyword. I'm like, oh, I don't know. Oh, like this document matches a bunch of keywords, so it must be good. But then you're actually completely missing the meaning of the document. Whereas an LLM like GB4 is really good at labeling. And so I actually think like you just we get by, which we are right now doing using like LLM

Drew Houston is the co-founder and CEO of Dropbox. Under his leadership, Dropbox has grown from a simple idea to a service used by over 700 million registered users globally, with a valuation exceeding $9 billion. Drew has led Dropbox through multiple phases, from explosive viral growth, to battling all the tech giants at once, to reinventing the company for the future of work. In our conversation, he opens up about:• The three eras of Dropbox's growth and evolution• The challenges he's faced over the past 18 years• What he learned about himself• How he's been able to manage his psychology as a founder• The importance of maintaining your learning curve• Finding purpose beyond metrics and growth• The micro, macro, and meta aspects of building companies• Much more—Brought to you by:• Paragon—Ship every SaaS integration your customers want• Explo—Embed customer-facing analytics in your product• Vanta—Automate compliance. Simplify security—Find the transcript at: https://www.lennysnewsletter.com/p/behind-the-founder-drew-houston-dropbox—Where to find Drew Houston:• X: https://x.com/drewhouston• LinkedIn: https://www.linkedin.com/in/drewhouston/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Drew and Dropbox(04:44) The three eras of Dropbox(07:53) The first era: Viral growth and early success(14:19) The second era: Challenges and competition(20:49) Strategic shifts and refocusing(29:36) Personal reflections and leadership lessons(40:19) Unlocking mindfulness and building support systems(43:14) The Enneagram test(50:35) The challenges of being a founder CEO(58:11) The third era: Rebooting the team and core business(01:22:41) Lessons and advice for aspiring founders(01:27:46) Balancing personal and professional growth(01:42:38) Final reflections and future outlook—Referenced:• Dropbox: https://www.dropbox.com/• Y Combinator: https://www.ycombinator.com/• Paul Graham's website: https://www.paulgraham.com/• Hacker News: https://news.ycombinator.com/• Arash Ferdowsi on LinkedIn: https://www.linkedin.com/in/arashferdowsi/• Sequoia Capital: https://www.sequoiacap.com/• Pejman Nozad on LinkedIn: https://www.linkedin.com/in/pejman/• Mike Moritz on LinkedIn: https://www.linkedin.com/in/michaelmoritz/• TechCrunch Disrupt: https://techcrunch.com/events/tc-disrupt-2024/• Dropbox viral demo: https://youtu.be/7QmCUDHpNzE• Digg: https://digg.com/• Reddit: https://www.reddit.com/• Hadi and Ali Partovi: https://www.partovi.org/• Zynga: https://www.zynga.com/• Steve Jobs announces Apple's iCloud: https://www.youtube.com/watch?v=ilnfUa_-Rbc• Dropbox Carousel: https://en.wikipedia.org/wiki/Dropbox_Carousel• Dropbox Is Buying Mega-Hyped Email Startup Mailbox: https://www.businessinsider.com/dropbox-is-buying-mega-hyped-email-startup-mailbox-2013-3• 5 essential questions to craft a winning strategy | Roger Martin (author, advisor, speaker): https://www.lennysnewsletter.com/p/the-ultimate-guide-to-strategy-roger-martin• Intel: https://www.intel.com/• Gordon Moore: https://en.wikipedia.org/wiki/Gordon_Moore• Netscape: https://en.wikipedia.org/wiki/Netscape• Myspace: https://en.wikipedia.org/wiki/Myspace• Bill Campbell: https://en.wikipedia.org/wiki/Bill_Campbell_(business_executive)• Enneagram type descriptions: https://www.enneagraminstitute.com/type-descriptions/• The Myers-Briggs Type Indicator: https://www.themyersbriggs.com/en-US/Products-and-Services/Myers-Briggs• Brian Chesky's new playbook: https://www.lennysnewsletter.com/p/brian-cheskys-contrarian-approach• Ben Horowitz on X: https://x.com/bhorowitz• Why Read Peter Drucker?: https://hbr.org/2009/11/why-read-peter-drucker• GitLab: https://about.gitlab.com/• Automattic: https://automattic.com/• Dropbox Dash: https://www.dash.dropbox.com/• Welcome Command E to Dropbox: https://blog.dropbox.com/topics/company/welcome-command-e-to-dropbox-• StarCraft: https://en.wikipedia.org/wiki/StarCraft_(video_game)• Procter & Gamble and the Beauty of Small Wins: https://hbr.org/2009/10/the-beauty-of-small-wins• Teaching Smart People How to Learn: https://hbr.org/1991/05/teaching-smart-people-how-to-learn—Recommended books:• Guerrilla Marketing: Easy and Inexpensive Strategies for Making Big Profits from Your Small Business: https://www.amazon.com/Guerilla-Marketing-Inexpensive-Strategies-Business/dp/0618785914• Playing to Win: How Strategy Really Works: https://www.amazon.com/Playing-Win-Strategy-Really-Works/dp/142218739X• High Output Management: https://www.amazon.com/High-Output-Management-Andrew-Grove/dp/0679762884/• Only the Paranoid Survive: How to Exploit the Crisis Points That Challenge Every Company: https://www.amazon.com/Only-Paranoid-Survive-Exploit-Challenge/dp/0385483821• Zone to Win: Organizing to Compete in an Age of Disruption: https://www.amazon.com/Zone-Win-Organizing-Compete-Disruption/dp/1682302113• Warren Buffett's books: https://www.amazon.com/warren-buffett-Books/s?k=warren+buffett&rh=n%3A283155• Poor Charlie's Almanack: The Essential Wit and Wisdom of Charles T. Munger: https://www.amazon.com/Poor-Charlies-Almanack-Essential-Charles/dp/1953953239• Invent and Wander: The Collected Writings of Jeff Bezos: https://www.amazon.com/Invent-Wander-Collected-Writings-Introduction/dp/1647820715/• The 15 Commitments of Conscious Leadership: A New Paradigm for Sustainable: https://www.amazon.com/15-Commitments-Conscious-Leadership-Sustainable-ebook/dp/B00R3MHWUE—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

In this episode, we explore 10 powerful leverage points that can transform your business and double your revenue. From optimizing ad creative and pricing strategies to innovative marketing tactics and global expansion, discover actionable insights and real-world case studies that demonstrate the impact of pulling the right levers. Learn how companies like Hotmail, Dollar Shave Club, Airbnb, and others achieved massive success through smart leverage points. Perfect for entrepreneurs and business leaders looking to work smarter, not harder. TOPICS DISCUSSED IN TODAY'S EPISODETripling Revenue with Simple StrategiesMindset Shift: Focusing on High-Impact ActivitiesIdentifying Leverage Points in Your BusinessHigh-Leverage Actions: Ad Creative, Pricing, and OffersCase Studies: Hall of Fame Leverage PointsPractical Examples: Implementing Leverage PointsGrowth Hit: Leveraging Content and OffersConclusion: Recap and Final Thoughts Resources:Growth Marketing OS (Operating System) GrowthHitJim Huffman websiteJim's LinkedinJim's Twitter Additional episodes you might enjoy:Startup Ideas by Paul Graham (#45)Nathan Barry: How to Bootstrap a Company to $30M in a Crowded Market (#41)How I Met My Biz Partner and Less Learned Hitting $2M ARR (#44)Ryan Hamilton on his Netflix special, touring with Jerry Seinfeld, & how to write a joke (#10)How We're Validating Startup Ideas (#51) 

Want to Start or Grow a Successful Business? Schedule a FREE 13-Point Assessment with Clay Clark Today At: www.ThrivetimeShow.com   Join Tim Tebow, LIVE and in-person at Clay Clark's December 5th & 6th 2024 Thrivetime Show  Business Workshop!!! Learn Branding, Marketing, SEO, Sales, Workflow Design, Accounting & More. **Request Tickets & See Testimonials At: www.ThrivetimeShow.com  **Request Tickets Via Text At (918) 851-0102   See the Thousands of Success Stories and Millionaires That Clay Clark Has Helped to Produce HERE: https://www.thrivetimeshow.com/testimonials/ Download A Millionaire's Guide to Become Sustainably Rich: A Step-by-Step Guide to Become a Successful Money-Generating and Time-Freedom Creating Business HERE: www.ThrivetimeShow.com/Millionaire   See Thousands of Case Studies Today HERE: www.thrivetimeshow.com/does-it-work/  

How do leaders attract and keep high performers? Brett Hagler, Y Combinator grad and New Story Founder, shares a young leader's counterintuitive advice on how to attract and keep high performers. Plus, Brett discusses getting better before getting bigger, and he and Carey share their thoughts on Paul Graham and Brian Chesky's Founder Mode theory.

En este episodio hablamos de cómo aplicar el Principio de Pareto para maximizar tu productividad. Hablamos de la diferencia entre hacer mucho y hacer lo CORRECTO para ver la mayor cantidad de resultados. Te llevo en el paso a paso de por qué decidimos borrar más de 500 pedazos de contenido de la cuenta de Isa García Corp y cómo encontrar las estrategias más efectivas para tu negocio digital. Para desbloquear un cupón de $111 USD para la Academia de Empresarias Digitales haz click aquí (www.isagarcia.online/academia) Para leer el artículo Maker Schedule vs. Manager Schedule por Paul Graham haz click aquí. Para leer el libro 10x Is Easier Than 2x por Dan Sullivan y Dr. Benjamin Hardy haz click aquí.

Jeff Bezos on retirement being lame, AI, the electricity metaphor for AI, the good fortune of being alive during multiple golden ages, long term life long passions, refusing to underestimate opportunity, dancing with curiosity, inventing, wandering, crisp documents and messy meetings, willing to be misunderstood, and why he doesn't do many interviews. This episode is what I learned from reading and watching Jeff Bezos at DealBook Summit and Jeff Bezos: The Electricity Metaphor. Another excellent Jeff Bezos interview on Lex Fridman Listen to more Founders episodes on Jeff Bezos: #321 Working with Jeff Bezos and #282 Jeff Bezos's Shareholder Letters----Ramp gives you everything you need to control spend, watch your costs, and optimize your financial operations —all on a single platform. Make history's greatest entrepreneurs proud by going to Ramp and learning how they can help your business control your costs and save more. ----Join my free email newsletter to get my top 10 highlights from every book----“I have listened to every episode released and look forward to every episode that comes out. The only criticism I would have is that after each podcast I usually want to buy the book because I am interested so my poor wallet suffers. ” — GarethBe like Gareth. Buy a book: All the books featured on Founders Podcast 

Get our Business Monetization Playbook: https://clickhubspot.com/monetization Episode 658: Sam Parr ( https://x.com/theSamParr ) and Shaan Puri ( https://x.com/ShaanVP ) talk to Replit founder Amjad Masad ( https://x.com/amasad ) about the massive opportunities with AI Agents.  — Show Notes:  (0:00) Replit origin story   (9:27) Replit's 10-year overnight success (12:27) Rejected 4x by YC (17:28) Personal essays from Paul Graham (20:17) "i hacked into my university to change my grades" (25:55) Rickrolling into YC (35:25) Shaan builds a food tracking app in 30 seconds (43:19) Magic School: An AI application for educators 4M users in 1 year (47:31) Amjad on Agents (49:53) Building moats in a goldrush (54:53) Replit is Shopify for software creators (1:05:11) The most gangster story in Silicon Valley — Links: • Amjad essays - https://amasad.me/  • Replit - https://replit.com/  • Codeacademy - https://www.codecademy.com/  • Do What Makes The Best Story - https://amasad.me/story  • Magic School AI - https://www.magicschool.ai/  • 11x AI - https://www.11x.ai/  • Synthesis Tutor - https://www.synthesis.com/tutor  • The Sovereign Individual - https://tinyurl.com/4w6ns7b2  • 7 Powers - https://tinyurl.com/382ch557  — Check Out Shaan's Stuff: Need to hire? You should use the same service Shaan uses to hire developers, designers, & Virtual Assistants → it's called Shepherd (tell ‘em Shaan sent you): https://bit.ly/SupportShepherd — Check Out Sam's Stuff: • Hampton - https://www.joinhampton.com/ • Ideation Bootcamp - https://www.ideationbootcamp.co/ • Copy That - https://copythat.com • Hampton Wealth Survey - https://joinhampton.com/wealth • Sam's List - http://samslist.co/ My First Million is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano

Amjad Masad is the co-founder and CEO of Replit, a browser-based coding environment that allows anyone to write and deploy code. Replit has 34 million users globally and is one of the fastest-growing developer communities in the world. Prior to Replit, Amjad worked at Facebook, where he led the JavaScript infrastructure team and contributed to popular open-source developer tools. Additionally, he played a key role as a founding engineer at the online coding school Codecademy. In our conversation, Amjad shares:• A live demo of Replit in action• How Replit's AI agent can build full-stack web applications from a simple text prompt• The implications of AI-powered development for product managers, designers, and engineers• How this might reshape companies and careers• Why being “generative” will become an increasingly valuable skill• “Amjad's law” and how learning to debug AI-generated code is becoming ever more valuable• Much more—Brought to you by:• WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs• Persona—A global leader in digital identity verification• LinkedIn Ads—Reach professionals and drive results for your business—Find the transcript at: https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad—Where to find Amjad Masad:• X: https://x.com/amasad• LinkedIn: https://www.linkedin.com/in/amjadmasad/• Website: https://amasad.me/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Amjad Masad and Replit(02:41) The vision and challenges of Replit(06:50) Replit's growth and user stories(10:49) Demo of Replit's capabilities(16:51) Building and iterating with Replit(25:04) Real-world applications and use cases(30:13) The technology stack(33:48) The evolution of Replit and its capabilities(39:36) The future of AI in software development(44:04) Skills for the future: generative thinking and coding(47:26) Amjad's law(50:36) Replit's new developments and future plans—Referenced:• Replit: https://replit.com/• Cursor: https://www.cursor.com• Aman Mathur on LinkedIn: https://www.linkedin.com/in/aman-mathur/• Node: https://nodejs.org/en• Claude: https://claude.ai/• Salesforce: https://www.salesforce.com/• Wasm: https://webassembly.org/• Figma: https://www.figma.com/• Codecademy: https://www.codecademy.com/• Hacker News: https://news.ycombinator.com/news• Paul Graham's website: https://www.paulgraham.com/• Jevons paradox: https://en.wikipedia.org/wiki/Jevons_paradox• Anthropic: https://www.anthropic.com/• Open AI: https://openai.com/• Amjad's tweet about “society of models”: https://x.com/amasad/status/1568941103709290496• About HCI: https://www.designdisciplin.com/p/hci-profession• Taylor Swift's website: https://www.taylorswift.com/• Andrew Wilkinson on LinkedIn: https://www.linkedin.com/in/awilkinson/• Haya Odeh on LinkedIn: https://www.linkedin.com/in/haya-odeh-b0725928/• Amjad's law: https://x.com/snowmaker/status/1847377464705896544• Ray Kurzweil's website: https://www.thekurzweillibrary.com/• God of the gaps: https://en.wikipedia.org/wiki/God_of_the_gaps—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Episode 648: Shaan Puri ( https://x.com/ShaanVP ) talks to Flexport founder Ryan Petersen ( https://x.com/typesfast ) about playing both games: bootstrapping a startup to millions and raising venture capital to build a multi-billion dollar company.  — Show Notes:  (0:00) Import Genius (5:36) Paul Graham's superpower (9:34) Data-as-a-service framework (13:51) Charlie Munger's worldly wisdom (19:45) Prioritizing adventure (24:09) The paradox of wealth (28:51) Charlie Munger's student experiment (31:00) Negotiation masterclass (37:23) Inside Founders Fund (43:16) Being in a crowd v following a crowd (46:29) Highs and lows (48:52) "You can just do things" (50:16) Unseen arbitrages (53:00) $50M Phone booths — Links: • Flexport - https://www.flexport.com/  • ImportGenius - https://www.importgenius.com/  • Schlep Blindness - https://paulgraham.com/schlep.html • Poor Charlie's Almanack - https://www.stripe.press/poor-charlies-almanack  • Founders Fund - https://foundersfund.com/  — Check Out Shaan's Stuff: Need to hire? You should use the same service Shaan uses to hire developers, designers, & Virtual Assistants → it's called Shepherd (tell ‘em Shaan sent you): https://bit.ly/SupportShepherd — Check Out Sam's Stuff: • Hampton - https://www.joinhampton.com/ • Ideation Bootcamp - https://www.ideationbootcamp.co/ • Copy That - https://copythat.com • Hampton Wealth Survey - https://joinhampton.com/wealth • Sam's List - http://samslist.co/ My First Million is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano

Episode 642: Sam Parr ( https://x.com/theSamParr ) and Shaan Puri ( https://x.com/ShaanVP ) talk to Garry Tan ( https://x.com/garrytan ), CEO of Y Combinator, about the sauce that makes YC outperform the rest of Silicon Valley plus lessons from Paul Graham, Peter Thiel and Garry's first million.  — Show Notes:  (0:00) Winning the game (3:16) Being the Harvard of startups (7:31) How YC outperforms most Silicon Valley investors (9:36) Spotting extreme winners (14:14) Capital-as-a-service (16:34) How Garry hustled at 14 to get his into financial security (23:04) Turning down Peter Thiel's offer to start Palantir (32:20) Garry's first million (44:57) Early days at YC (51:31) The edge of startups with a 2-pizza team (54:36) Advice for founders in AI (1:05:57) The spoon-bending story — Links: • YC - https://www.ycombinator.com/ • Founding Sales - https://www.foundingsales.com/ — Check Out Shaan's Stuff: Need to hire? You should use the same service Shaan uses to hire developers, designers, & Virtual Assistants → it's called Shepherd (tell ‘em Shaan sent you): https://bit.ly/SupportShepherd — Check Out Sam's Stuff: • Hampton - https://www.joinhampton.com/ • Ideation Bootcamp - https://www.ideationbootcamp.co/ • Copy That - https://copythat.com • Hampton Wealth Survey - https://joinhampton.com/wealth • Sam's List - http://samslist.co/ My First Million is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano

Today, I'm talking with Airbnb CEO Brian Chesky, who is only the second person to be on Decoder three times — the other is Meta CEO Mark Zuckerberg. Brian made a lot of waves earlier this year when he started talking about something called “founder mode,” or at least, when well-known investor Paul Graham wrote a blog post about Brian's approach to running Airbnb that gave it that name. Founder mode has since become a little bit of a meme, and I was excited to have Brian back on to talk about it, and what specifically he thinks it means. Talking to Brian is a ride, but I think I held my own, and I think you'll really like this one. Links: Founder Mode | Paul Graham Airbnb CEO Brian Chesky is taking it back to basics (2023) | Decoder Why the future of work is the future of travel, with Airbnb's Brian Chesky (2021) | Decoder Airbnb CEO Brian Chesky: ‘I Never Called it Founder Mode' | Skift Why Silicon Valley is abuzz over ‘Founder Mode' | NYT After Apple, Jony Ive Is Building an Empire of His Own | NYT Airbnb can now help you find somebody to manage your listing | The Verge Airbnb creates new chief business officer role | Reuters Why Jeff Bezos Says Your Goal Is to Make 3 Good Decisions per Day | Inc Taking the Mystery out of Scaling a Company | Ben Horowtiz Transcript: https://www.theverge.com/e/24043611 Credits:  Decoder is a production of The Verge and part of the Vox Media Podcast Network. Our producers are Kate Cox and Nick Statt. Our editor is Callie Wright. Our supervising producer is Liam James. The Decoder music is by Breakmaster Cylinder. Learn more about your ad choices. Visit podcastchoices.com/adchoices