Podcasts about VC

(0:00) Coatue's Thomas Laffont joins the Besties! (0:30) Public markets are back as AI is dominates the "Unicorn Economy" (5:15) The $4T AI IPO explosion (7:48) The case for SpaceX: Compounding launch monopoly and Starlink (10:38) The 10x Paradox: Why we're seeing unprecedented scaling (15:33) Segmenting AI markets and future impact (18:32) Bestie Q&A: Power Law in AI, future of VC, where revenue is coming from, liquidity explosion Thanks to our partners for making this possible! EY - Agentic AI is introducing a new investment discipline. As AI shifts to consumption-based models, EY connects spend to enterprise value. https://www.ey.com/en_us/insights/ai/agentic-ai-token-costs?WT.mc_id=3501318&AA.tsrc=sponsorship NYSE - Thank you to our partner, the New York Stock Exchange - a modern marketplace and exchange for building the future. It all happens at the NYSE. https://www.nyse.com Plaud - Never miss a moment. Plaud, our official wearable AI note-taking partner at All-In Liquidity Summit, captured every insight. https://www.plaud.ai Apply for Summit 2026: ⁠https://allin.com/events⁠ Follow the besties: ⁠https://x.com/chamath⁠ ⁠https://x.com/Jason⁠ ⁠https://x.com/DavidSacks⁠ ⁠https://x.com/friedberg⁠ Follow on X: ⁠https://x.com/theallinpod⁠ Follow on Instagram: ⁠https://www.instagram.com/theallinpod⁠ Follow on TikTok: ⁠https://www.tiktok.com/@theallinpod⁠ Follow on LinkedIn: ⁠https://www.linkedin.com/company/allinpod⁠ Intro Music Credit: ⁠https://rb.gy/tppkzl⁠ ⁠https://x.com/yung_spielburg

In this episode, we sit down with investor and writer Ilona Limonta-Volkova to explore the intersection of VC, storytelling, and innovation in FinTech. Ilona shares her perspective on identifying emerging trends, backing founders, and translating complex financial ideas into compelling narratives. We also dive into her journey across markets and what it takes to think like both an investor and a communicator in a rapidly evolving industry. If you enjoy this episode you can hear more from Ilona on her platforms https://bearandthebull.beehiiv.com/ and https://podcasts.apple.com/us/podcast/money-memories/id1522819765.

EPISODE DESCRIPTIONI sat down with Harvey Liu, co-founder of LeveX Exchange, to dig into what it really takes to build a crypto trading platform from the ground up. Harvey's journey is fascinating , from studying computer science in China, to getting his MBA at INSEAD, to becoming an early Bitcoin investor when BTC was around $100, to backing the founders of Huobi and OKCoin as a VC, and now building his own exchange in Singapore. We talk about why he designed LeveX around social trading, how features like multi-trade and KOL-driven tournaments set them apart from Binance and OKX, and the honest truth about what works and what doesn't in crypto marketing. Harvey also shares what he looks for as a VC when evaluating Web3 startups in a bear market , and why founders with failure experience often outlast the ones who only know wins. DISCLAIMERNothing mentioned in this podcast is investment advice and please do your own research. It would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend. Be a guest on the podcast or contact us - https://www.web3pod.xyz/CONNECTLeveX Exchange: https://www.levex.comLeveX Twitter/X: https://x.com/levex Web3 with Sam Kamani: https://www.web3pod.xyzKEY POINTS WITH TIMESTAMPS• [00:01] Sam introduces Harvey Liu, co-founder of LeveX Exchange, and outlines the episode topics: building an exchange, growth, and VC lessons• [01:25] Harvey shares his background , computer science in China, five years at a Canadian internet company, MBA at INSEAD, then back to China for VC• [03:13] Harvey's first exposure to Bitcoin in 2013 as a VC, meeting the founders of Huobi and OKCoin, and buying BTC at around $100• [04:38] Moving to Singapore during COVID, joining a Singapore VC firm, and spotting the gap in social features on major trading platforms• [06:42] The founding idea behind LeveX: a platform built by traders, for traders, with a social layer that bigger exchanges lacked• [08:38] Who LeveX was designed for , seasoned traders, KOLs, and retail , and how user feedback shaped the product• [11:05] Gamification on the platform: quests, bonus milestones, KOL-run tournaments, and exclusive content areas for followers• [13:39] Current stats: over 400,000 registered users, focus on improving UX before aggressive marketing, and plans for Token 2049 Singapore• [15:35] User geography , mostly Europe and Asia, with Sam highlighting Southeast Asia (Philippines, Vietnam, Indonesia) as a massive growth opportunity• [18:35] Harvey's VC framework for evaluating Web3 startups in a bear market: team track record including failures, revenue traction, real utility, and exit strategy• [22:49] The biggest challenge building LeveX: rebuilding trust post-FTX, and how proof of reserves, bug bounties, and penetration testing address that• [26:06] Growth experiments , what worked (deep KOL partnerships) and what didn't (expensive Google and Meta paid ads with low conversion)• [30:13] LeveX's standout feature: multi-trade, which lets traders open multiple simultaneous positions on the same trading pair at different prices, directions, and leverage levels• [33:12] Vision for the next two to three years: reach top 20 global trading platform, expand into prediction markets and AI tools, and time the next bull run right• [34:51] Harvey's ask: strategic marketing and branding partners to help with the next bull run, and an open invitation for listeners to try the platform

In this episode, Chris sits down with Josh Zegen, Co-Founder & Managing Principal of Madison Realty Capital, a $25 billion real estate private credit firm he started with his college roommate in 2004. They dig into how he built one of the largest private lenders in the country starting from a desk in his dad's law office - and why he still thinks of himself as a businessman first and a real estate guy second. Josh got into lending almost by accident. Laid off from a VC firm at 26 when the dot-com bubble burst, he took one mortgage deal nobody else would do, saw how fragmented and non-institutional the market was, and built a fund around it before "private credit" meant anything. Chris and Josh go deep on surviving '08, reinventing the business when capital dried up, and how Madison grew into a platform that now lends to other lenders. They discuss: How Josh went from a laid-off VC associate living back home to founding a $25B firm Surviving '09 - including giving up 50% of the company for a $50M anchor that collapsed at the last minute Why he built servicing, asset management, and capital raising in-house instead of outsourcing The $10B back-leverage book that makes Madison the lender to ~100 other private lenders The $720M single loan behind the largest office-to-residential conversion in NYC Where he sees real estate credit headed - and why he stays away from office, data centers, and anything "binary" Timestamps:(00:00) Intro(00:52) Rate Volatility and a Stalled CRE Investment Market(09:44) What's Getting Done Today: Construction, Conversions, and Recaps(18:49) Founding Madison: Seeing Opportunity in a Fragmented Market(25:19) The GFC: Gating Investors and Going Vertically Integrated(31:26) The $50M REIT Deal That Nearly Ended Madison—And the Door It Opened(44:28) Why Borrowers Now Prefer Private Credit Over Banks(47:17) In-House Loan Servicing as Madison's Competitive Edge(49:06) The Back Leverage Business: Lending to Private Lenders(55:35) Capital Markets Expansion and Staying True to Real Estate(1:05:55) The Pfizer Deal, Lifecycle Lending, and Madison's Road Ahead(1:15:06) Staying Relevant by Constantly Innovating and Looking for Acquisition Opportunities ----- Presented by Airshare: Trusted across the country for fractional ownership, jet cards, charter, and aircraft management, Airshare gives you a smarter way to fly private - over 25 years of experience, operating their own fleet, with the top safety ratings in the industry. Drive up to the FBO, walk on, and go. Go to flyairshare.com to learn more. ----- Sponsored by: Collateral Partners builds institutional-grade investor materials for private credit, private equity, real estate, and family office firms - the kind of marketing collateral that helps you close capital. Learn more at collateral.com/fort. Relay Human Cloud helps you build a highly skilled global team that operates as a true part of your business - not an outsourced vendor. From accounting to operations, Relay's talent works inside your systems and alongside your local team, unlocking 24-hour productivity and significant cost savings. Learn more at https://www.relayhumancloud.com/powers-podcast/ ----- Chris on Social Media: X: https://x.com/fortworthchris Instagram: https://www.instagram.com/thepowerspodcast LinkedIn: https://www.linkedin.com/in/chrispowersjr/ Visit our website: https://www.powerspod.com/Leave a review on Apple: https://bit.ly/45crFD0Leave a review on Spotify: https://bit.ly/3Krl9jO

This episode is a rebroadcast of Auren's appearance on the GTMnow podcast ---------------------------------------------------Auren Hoffman (Flex Capital) joins the GTMnow podcast to share some of the most contrarian takes in tech today, from why AI moats are gone, to why your next VC meeting will be with a bot, to why AI is secretly going to trigger a baby boom.In this episode:Why Auren runs 500+ AI agents to source deals, and what that means for founders raising capitalThe "agent-to-agent" meeting prediction: by end of 2026, first VC conversations will be fully automatedWhy every software moat has been "blown up" and what Salesforce, LinkedIn & DocuSign need to do to surviveThe OpenAI x The Hustle acquisition breakdown: why it's the smartest (and cheapest) distribution play in AIWhy missing a great deal is 10x more painful than making a bad one, Auren's honest VC mistake frameworkThe baby boom thesis: why AI, IVF, self-driving cars & cheaper energy could reverse the fertility declineWhy companies won't sign yearly SaaS contracts anymore, and what that means for every B2B founderAuren Hoffman is the founder of NQB8, Flex Capital, SafeGraph, and LiveRamp. He's an early backer of Replit, Perplexity, Rippling, Vercel, Coinbase, Chime, and AppLovin.Max's socials: https://x.com/hackitmaxhttps://www.linkedin.com/in/maxaltschuler/Auren's socials:https://x.com/aurenhttps://www.linkedin.com/in/auren/https://auren.substack.com/GTMnow: https://gtmnow.com

DAMIONCarnival Corporation's data breach exposed personal data of nearly 6 million customers: An April social engineering attack on an employee account compromised names, dates of birth, and government-issued ID numbers. WHO DO YOU BLAMESkills: Technology & Cybersecurity: Experience with information technology and cybersecurity matters is increasingly important to mitigate the risks our business faces, promote innovation and maintain a competitive edge in a rapidly evolving technological ageLeast represented 5/11CEO Josh WeinsteinNO: at Carnival since 2002, started as General CounselSir Johathon BandNO: First Sea Lord and Chief of Naval Staff, the most senior officer position in the British Navy (2006 to 2009, when he retired); Admiral and Commander-in-Chief Fleet (2002 to 2006); Served as a naval officer in increasing positions of authority (1967 to 2002)Jason CahillyNO: CEO Dragon Group LLC, provides capital and business management consulting and advisory services worldwide; The NBA: CFO & Chief Strategic Officer; Goldman Sachs: Partner; Global Co-Head of Media and Telecommunications; Head of Principal Investing for Technology, Media & TelecommunicationsNelda ConnorsNO: CEO/Chair Pine Grove Holdings, a privately held investment company; CEO Atkore International, manufacturer of electrical, safety and infrastructure solutions; VP Eaton Corporation, electrical and automotive supplierLaura WeilNO: Founder Village Lane Advisory LLC, specializes in providing executive and strategic consulting services to retailers COO New York & Company, women's apparel and accessories retailer; CEO Ashley Stewart, women's apparel retailer; CEO Urban Brands, apparel retailer; COO AnnTaylor Stores, women's apparel retailer; CFO American Eagle Outfitters, apparel retailerAudit Committee: Oversee management's risk assessment processes to identify principal and emerging risks, including financial, IT, cybersecurity and non-HESS operational risksLaura Weil*: NOJason Cahilly: NOJeffrey Gearhart: NOWalmart Corporate Secretary and lawyerStuart Subotnick: NOCEO at Metromedia Company, wireless/communications, until 2010; Carnival director since 1987 Health, Environmental, Safety and Security Committee: Oversee management's processes to identify principal and emerging health, environmental, safety, security and sustainability-related risks, including those related to ship operations and cybersecurity, RAAS health, environmental, safety, security audits, IAG and external investigations into significant ship incidents, and health, environmental, safety, security-related hotline complaints, and assess the steps management has taken to minimize such risks.Sir Johathon Band*: NONelda Connors: NOHelen Deeble: NOFormer CEO P&O Ferries Division Holdings, shipping and logistics businessKatie Lahey: NOExecutive Chair Korn Ferry Australasia, leadership and talent firmMicky Arison (75%): Exec Chair and former CEO and 7% stockholderThe CEO Pay Ratio1,063:124 retail CEOs made as much in a day as their typical employee earned in a year — and a big one didn't. WHO DO YOU BLAMEThe separation of CEO and Chair: Hamilton E. James Chair/Ron Vachris MMNot uniqueOnly 50% of the board is men. WTF?uniqueOne share = one voteNot uniqueState of HQ = WashingtonAlso StarbucksState of Inc = WashingtonAlso StarbucksPledge of allegiance to stakeholdersCostco generally has: Higher wages; Better benefits; Lower turnover; Higher sales per employee.Industry-leading employee compensation AND Self-imposed low-margin pricing philosophyWalmart only low-margin pricingOther comps:Todd Vasos of Dollar General, Shane O'Kelly of AutoZone, Gerald Morgan of Texas Roadhouse, Jack Sinclair of Sprouts Farmers Market, William Stengel of Genuine Parts Company, Michael Creedon of Dollar Tree, Ronald Sargent of Kroger, Lauren Hobart of Dick's Sporting Goods, Joshua Kobza of Restaurant Brands Inc., Kecia Steelman of Ulta Beauty, Scott Boatwright of Chipotle, Ted Decker of Home Depot, Bob Eddy of BJ's Wholesale Club, Corie Barry of Best Buy, James Conroy of Ross Stores, Chris Turner and David Gibbs of Yum Brands, Chris Kempczinski of McDonald's, Marvin Ellison of Lowe's, Brian Cornell of Target, Ernie Herrman of TJX Companies, Doug McMillon of Walmart, Brian Niccol of Starbucks, Hal Lawton of Tractor Supply Co, Laura Alber of Williams-SonomaFigma Gets an Activist Investor. Exhibit A on Why Companies Don't Want to Go Public. Figma's first year as a public company hasn't gone well. Findell Capital Management said it needs to take steps to shed its unwarranted reputation as an artificial-intelligence “loser.” WHO DO YOU BLAME?Figma founder and CEO Dylan Field: Owns 10% of shares but 72% of voting power: Class B shares worth 15 votes per shareDylan owns 158 Class A Shares (or 0.00003556% of 444,278,887)And Chair$5B net worth$865M total summary compensation in 2025; $91M in 2024Nominating Agreement:Figma must nominate Dylan Field to be a director and include him in the proxy statementThe company must use its resources to back him up and actively convince other shareholders to vote for him In response to a question about how he was going to change the world, Dylan said he was going to build better software for drones.Bro fest sausage party2 of 9 directors are womenTop 5 NEOs all dudesPeter ThielForced Dylan to drop out of Brown for a dumb fellowshipVC Blowhardiness on the BoardVC dude John Lilly (Greylock): Lead Independent Director2nd longest tenure (2014)Member of the Audit Committee; Member of the Nominating Committee (only Lilly and Rimer)VC dude Andrew Reed (Sequoia)Director at debt-maker Klarna Group (also way down since IPO): down roughly 54% from its initial $40.00 IPO price, and down nearly 68% from its all-time highMember of the Compensation Committee (which modeled Dylan's pay package after Elon Musk)VC dude Danny Rimer (Index Ventures)Director since 2014B.A. in History and Literature from HarvardMember of the Compensation Committee (which modeled Dylan's pay package after Elon Musk)Member of the Nominating Committee (only Lilly and Rimer)Luis von AhnDuolingo co-founder and CEO2025: shared an internal email outlining Duolingo's new "AI-first" strategy where Duolingo would “gradually stop using contractors to do work that AI can handle”Stated that "AI is a better teacher than humans" and that the future role of teachers would be reduced to providing "childcare."Blamed the controversy on a "lack of context" in his original statements"AI-First" memo goes viral: $389; today $118MATTDanone, Starbucks shine in methane-reduction rankingDanone is the only company in the group aligned with the Global Methane Pledge, an initiative backed by 150 countries that targets a 30 percent reduction in global levels of the gas by 2030. The French multinational also leads the pack in progress toward its target, having come close to hitting it five years ahead of schedule.WHO DO YOU CREDIT?Chair of the CSR committee Lise Kingo (9% influence), one of three directors tagged as merit directorsmaster's degree in Responsibility & Business from the University of Bathbachelor degrees in Religions and Ancient Greek Artbachelor's degree in Marketing and Economicscertificate as International Director from INSEADEx Novo Nordisk environmental affairs, internal audit, compliance, human resources, communication, branding and sustainabilityHelped create the UN SDGs and the UN Global CompactSomehow only bats 559 on carbon intensity (career) and 415 for scope 1/2 (career)Also, using deference metrics, the ONLY DIRECTOR tagged as fully independentEmployee rep member of the CSR committee Bettina Theissig (5% influence) and the employees of DanoneThe committee charter mandates employees get a say: At least two thirds of the CSR Committee must be independent, as defined by the AFEP-MEDEF Code. At least one Director representing employees must be a member of the Committee.In France (Danone's domicile), the European Investment Bank found that French employees were the most aware of environmental issues - 82% of French employees said they were highly concerned about environmental issues, highest in EuropeLead Independent Director and chair of the Nom/comp committee who put together the comp plan, Valerie Chapoulaud-Floquet15% influence, second to the 18% influence CEO (democracy!!), got 99.16% shareholder approval in April (even as CEO got 89.73% approval and pay got 93.19% approval)20% of short-term pay and 30% of long-term pay is based on hitting sustainability targetsWhen you pay a CEO to do a thing, they are more likely to do a thingEx-CEO Emmanuel FaberOusted in 2021 by the board of directors and activist investors, he transformed Danone into an “enterprise a mission” (a French version of a B corp)Investors voted 99% in favor of the move and a year later ousted Faber, the board resigned, and the new board and CEO are basically moving back towards being environmental leaders because it paid offShort term share price laggedHe said in 2024 that nature is “at the core” of Danone, It took the stock 3 years from Faber's ousting to return to Faber levels - and in the meantime, they were sued for plastics and emissionsIsn't this HIS win?Current CEO Antoine de Saint-AffriqueBecause CEOGM Board Director Jonathan McNeill Stepping DownCEO of DVx Ventures. Ex COO at Lyft Inc. and ex president, Global Sales, Delivery and Service at Tesla, current director at Lululemon, GM director since 2022, on the Governance and Corporate Responsibility committee and Risk and Cybersecurity committee.We know that half of boards on average think someone on the board should be replaced - did the GM board not like McNeill?WHO/WHAT WOULD WE BLAME FOR PUSHING MCNEILL OUT?Outsider dude bro DRLet's be honest, McNeill worked at much more… modern?... companies than GMThe board is OLD SCHOOL - ex Northrop Grumman, ex Visa, ex Lazard, ex HP, ex eBay, ex Novartis, ex Walmart, other directorships at Goldman, Huntsman, P&G… these are professional, insular boardsMeanwhile, he's investing as a VC in AI, other auto/mobility startups, comes from boards that are bro founder lead (Tesla, Lyft) He's invested in AI, crypto, heavy tech, intertwined with VCs all overNot deferential enoughBarra is connected to 94% - THE ENTIRE - boardMcNeill has the highest network power on the board at $9tn, higher than even Mary Barra (who is super connected), but is NOT a power player in the board community of GM - the dominant board communities for GM are massive blue chip US companies, where McNeill has deeper connections in smaller IT/tech focused companiesHe doesn't need the pay, he gets nothing for the connections really, he has connection to Barra but his network is different - was he too independent?Pissed he doesn't have enough influence McNeill has the LOWEST influence on the GM board at 4%He's relatively new, younger, working as a VC where you have a lot of power of capital allocation“I don't need this shit” effect?Too many womenMcNeill's dvX ventures portfolio team is 6 dudes and 1 womendvX entire operations staff is two woman - guess what they do“Chief of Staff” (ie, HR)Executive Assistant (yes, listed on the team)Board is 2 women, 3 men (McNeill not on board)This one seems unlikely I guess?Too busy, meh, move onOne of dvX portfolio companies is curbee, with GM Ventures' Kurt Baumgarten on the board (and the dvX co-founder is founder of Curbee)McNeill on at least 3 of his portfolio boards or advisory committees, plus LULU and GM…

On She Built It®, Andrea Faulkner Williams, co-founder and president of Tubby Todd Bath Co., shares what it really took to build a brand that drives 95% of growth in the baby lotion category on Amazon, without paid ads, without VC funding, and without celebrity investors. What started as a solution to her own son's eczema became a 12-year journey of community-first growth, profitable scaling, and a business that has earned over 50,000 five-star reviews.Andrea talks about the smartest early decisions she made, how she thinks about marketing as friendship, what happened when she sold a majority stake to private equity, and why slow, steady, profitable growth has always been more important to her than chasing speed. This conversation is for every founder who wants to build something real, and keep building it long after everyone else has cashed out.Special offer: Tubby Todd is offering She Built It® listeners 15% off the entire order at tubbytodd.com with code SHEBUILTIT — valid until 7/31/26.Connect with us:Tubby Todd Bath Co. WebsiteTubby Todd Bath Co. LinkedInTubby Todd Bath Co. InstagramAndrea Williams LinkedInAndrea Williams InstagramWork with She Built It® Media She Built It® Instagram She Built It® CEO, Melanie Barr InstagramMelanie Barr LinkedInShe Built It® LinkedIn

In every tech supercycle, investors are good at identifying disruptive technologies, but bad at picking the winners. AI is no different, except for the pace and velocity of growth, investment, and hype. And this time the incumbents are leading the charge. This episode works through how to separate the hype from who actually captures the enterprise value, walking through four layers of the AI investment stack, and which of these layers will exist independently or get absorbed. Joined by Kai Wu of Sparkline Capital, Jerry Neumann, retired VC investor and writer, and David Haber of a16z, we synthesize views and research to explore this very complex topic.Guests:Kai Wu, Founder & CIO, Sparkline Capital Jerry Neumann, retired VC investor and writerDavid Haber, General Partner, a16z⁠Episode SourcesKey Points From This Episode:●[00:00:00]Kai Wu on the current stage of the AI infrastructure buildout and adoption cycle.●[00:04:13]Introduction to investing across the AI stack and the challenge of capturing value from technological revolutions.●[00:11:18]Historical technology cycles, hype cycles, and lessons from past paradigm shifts.●[00:15:57]The “historical autopsy” of technology booms: capital misallocation, demand assumptions, and timing risks.●[00:20:31]Comparing AI infrastructure spending with railroad and fiber-optic buildouts across history.●[00:22:44]The AI prisoner's dilemma and why competition drives aggressive capital expenditures.●[00:25:38]Jerry Neumann on value capture, competition, and why great technologies do not always produce great investments.●[00:27:51]The dot-com fiber buildout, overcapacity, and how later innovators benefited from subsidized infrastructure.●[00:33:48]Why AI may differ from previous cycles due to the dominance of well-capitalized incumbents.●[00:36:44]David Haber on compute demand, data center utilization, and the economics behind current AI investment.●[00:38:33]Vertical integration, competitive advantages, and how major technology companies are positioning for AI leadership.●[00:44:06]Whether application-layer companies can survive alongside powerful infrastructure and model providers.●[00:47:06]David Haber on enterprise AI applications, vertical software opportunities, and context-driven value creation.●[00:49:53]Revenue growth, valuation expectations, and the sustainability of AI business models.●[00:57:00]Open AI growth projections, demand assumptions, and the risks of extrapolating future adoption.●[01:00:05]Aaron's framework for analyzing the AI investment stack and its four primary layers.●[01:06:39]The shipping container analogy and where value ultimately accumulates in transformative technologies.●[01:12:30]Platform companies, hyper scaler investment strategies, and the defensive motivations behind AI spending.●[01:46:13]Final investment principles, frameworks, and key takeaways for evaluating opportunities across the AI ecosystem.

Reed Rivers is the President & CEO of Freight Flex, a freight brokerage and logistics provider helping freight agents grow. He shares his take on why email needs to die in freight, VC money inflows, and non-competes.  This week's episode is sponsored by  Epay Manager, Goodship, Augument Technologies Inc.Interested in sponsoring our podcast? Send us an email at pbj@freightcaviar.com.

Send us Fan MailLEARN THE CAPITAL RAISING STRATEGIES AND FRAMEWORKS used by alternative asset professionals: go.fundraisecapital.coThis episode of Making Billions with Ryan Miller & Aman Verjee delivers the secondary market playbook that gives managers a structural advantage over every fund ignoring this shift.How do venture secondaries solve LP liquidity problems in 2026? Former PayPal and eBay CFO Aman Verjee reveals the exact system for buying into elite VC deals at 70% below market value. Fund managers face a quiet crisis: DPI timelines stretching 10-12 years while LPs demand exits far sooner. What separates fund managers who retain LP trust from those who lose it? Verjee breaks down how to audit your fund structure today, identify liquidity gaps before they become emergencies, and build relationships with secondary buyers years before you need them. He shares the due diligence framework used to evaluate SpaceX, Anthropic, and Canva positions when information is limited and markets are opaque.[THE HOST]: Ryan Miller is a fund manager, capital strategist, and former CFO turned angel investor in technology and energy. He is the founder of Fund Raise Capital and Aequor Capital Partners, and has mentored over 1,000 fund managers across private equity, private credit, venture capital, real estate, and alternative assets globally.[THE GUEST]: Aman Verjee has more than 20 years of financial and operational experience from both private and public technology companies. He has been a member of the management teams at some of the most successful companies in the world, including PayPal, eBay, 500 Startups and Sonos. His new book, A BRIEF HISTORY OF FINANCIAL BUBBLES, comes out in December.Subscribe on YouTube:https://www.youtube.com/channel/UCTOe79EXLDsROQ0z3YLnu1QQConnect with Ryan Miller:Linkedin: https://www.linkedin.com/in/rcmiller1/Instagram: https://www.instagram.com/ryanmilleroffical/X: https://x.com/_MakingBillionsWebsite: https://making-billions.com/Support the showSupport the showDISCLAIMER: This podcast is for entertainment and general informational purposes only — not legal, financial, tax, or investment advice. Nothing herein constitutes a solicitation or offer to buy or sell any security or investment product. Past performance does not indicate future results. Always consult qualified legal, financial, and tax professionals before making any investment decision. NAME NOTICE: "Making Billions with Ryan Miller" reflects the profile and aspirations of guests featured — it is not a promise, projection, guarantee, or representation of any financial result, income, or outcome for any listener, viewer, or reader. Most individuals who consume this content do not raise any particular amount of capital, and many achieve no financial result whatsoever. "Fund Raise Capital" is a brand identifier only — it is not a promise, guarantee, or representation that any member, subscriber, or listener will raise capital, attract investors, or achieve any financial or professional outcome. This show does not constitute a business opportunity, franchise, investment program, or offer of any product or service of any kind. No part of this show should be construed as a solicitation for investment in any way. Guest views are their own and do not necessarily reflect those of the show or host. Host and/or guests may hold positions in assets discussed. This episode may contain paid sponsorships, advertisements, or endorsements. Sponsored content is identified where...

Send us Fan MailAndrew Palmer is a long-time editor and columnist at The Economist, where he writes the widely read Bartleby column on work and life. He also hosts Boss Class, one of The Economist's most popular podcasts, whose most recent season explored generative AI in the workplace, a topic Andrew approached not just as a journalist, but as a self-described unsophisticated user determined to get smarter by doing.In this episode, Andrew draws on his reporting and interviews with leaders across industries to offer an outside-in view of where AI adoption actually stands, and why the gap between the hype and the reality is not a sign of failure, but of how complex change really is.In this conversation, we discuss:Why AI adoption faces three distinct barriers (behavioral, technical, and organizational) and why solving one without the others leaves productivity gains stranded.Why structural reskilling frameworks (like Denmark's flexicurity model and Singapore's voucher-based lifelong learning system) offer a more credible response to AI disruption than waiting for policy to catch up.Why Johnson & Johnson's "let a thousand flowers bloom" approach to AI experimentation produced a Pareto effect (15% of projects generating 85% of value) and what they changed as a result.How the AI productivity boom is real at the individual level but not yet showing up in aggregate data, and why Andrew believes that gap is a question of time, not technology.Why enlightened corporate leadership requires transparency about potential job disruption and a commitment to adjacent career planning rather than performative optimism.What work in 2036 might look like, and why Andrew's most unsettling prediction has nothing to do with jobs, and everything to do with privacy.Explore this conversation:00:00 Introduction to AI and the Future of Work episode 39101:14 AI fun fact: AI legislative speed versus technological advancement03:51 Meet Andrew Palmer The Economist Bartleby Column Boss Class06:14 Digital Doppelganger and AI Personality Traits07:57 AI Adoption Barriers Behavioral Technical and Organizational11:01 AI Impact at Work Startups vs Large Organizations14:15 Leadership Humility and AI Uncertainty in the Workplace17:41 AI Experimentation at Scale Lessons from Johnson and Johnson24:26 AI vs SaaS Productivity Data and the Speed of Adoption27:35 Balancing AI Automation with Human Meaning at Work31:26 AI Policy Reskilling and Lifelong Learning for the Future36:03 Work in 2036 AI Monitoring Privacy and Constant Surveillance38:47 Who Really Controls AI and What That Means for Workers44:08 Connect with Andrew Palmer and Boss Class The EconomistResources:Subscribe to the AI & The Future of Work NewsletterConnect with Andrew on LinkedInAI fun fact articleOn How Arvind Jain Is Shaping the Future of Enterprise Search Another episode mentioned in the interview: How we can take back control from Big Tech with Tom Wheeler, former FCC Chairman, CEO, VC, and author of Techlash. 

Eric Ries is the entrepreneur and author of The Lean Startup, whose work helped software founders validate ideas faster and build companies without making huge bets upfront. After years helping startups, large companies, and governments apply Lean Startup principles, Eric built the Long-Term Stock Exchange and turned his attention to a bigger question: Why do so many successful companies lose their way?   In our conversation, Eric explains the idea of "financial gravity"—the hidden force that pushes companies toward short-term financial thinking as they grow. He shares cautionary stories of companies like Whole Foods, Johnson & Johnson, Silicon Valley Bank, and Costco to show how scaling, investors, boards, and even employees can gradually erode trust, mission, and long-term value.   Eric's new book, Incorruptible Why Good Companies Go Bad…and How Great Companies Stay Great, offers practical ways founders can protect the soul of their companies before it's too late--even when they don't have big outside investors. He explains why founders should explicitly codify their mission into governance structures, why trust is the most underrated asset in business, and how practical founders can retain optionality while building valuable companies that endure. Drawing on two decades of work with founders, CEOs, and investors, Eric Ries reveals the forces that make companies vulnerable to destruction from within and without. Then he offers solutions that safeguard against them for the long-term. Incorruptible is the blueprint for companies that will prosper and endure without losing their soul. Key Takeaways Financial Gravity - Every growing company faces pressure toward short-term financial thinking—even without outside investors. Trust Compounds - Companies that earn trust with customers and employees often outperform financially over the long term. Founder Regret - Many founders regret selling because the mission, culture, and soul of the company disappear. Mission Protection - Values on a wall aren't enough—founders need legal and governance structures to preserve mission. Question Best Practices - Many accepted business practices optimize short-term profits while destroying long-term value. Think Long-Term - Practical founders have more optionality when they intentionally design companies to endure. Quote from Eric Ries, Author of the Lean Startup "People have woken up to this reality. Given where we're at, if you can create a bootstrap company, if you can maintain control, it doesn't make you completely safe. The problem is actually not investors, but financial thinking.  "So I tell a bunch of stories in my book (Incorruptible)  of companies where the issue wasn't investors, but their own employees. You start to bring in professional managers. You start to bring in a CFO, and the CFO has that extractive mindset, or even worse. "Financial gravity is one of the most underrated concepts in business. It is like trying to direct our attention away from the surface characteristics of an organization to the deeper forces that act on it. Your  business model, strategy, vision, culture, these things are very important, but they are the things that we have control over. Financial gravity is a force." Links Eric Ries on LinkedIn Eric Ries on Twitter Eric Ries Podcast Incorruptible book on Amazon Podcast Sponsor – Lighter Capital This podcast is sponsored by Lighter Capital. In the last 15 years, Lighter Capital has helped over 600 software and SaaS founders secure simple, non-dilutive financing to grow a little faster—without giving up any precious equity or board seats to investors.  Simple debt funding from Lighter Capital can range from $50K to $10 million, with straightforward terms, no personal guarantees or covenants, and up to a 4-year payback period. Go to LighterCapital.com to apply and get a quick pre-qualification. Then talk with their experienced team to create a practical funding plan to achieve your goals.  The Practical Founders Podcast Tune into the Practical Founders Podcast for weekly in-depth interviews with founders who have built valuable software companies without big funding. Subscribe to the Practical Founders Podcast using your favorite podcast app or view on our YouTube channel. Get the weekly Practical Founders newsletter and podcast updates at practicalfounders.com. Practical Founders CEO Peer Groups Be part of a committed and confidential group of practical founders creating valuable software companies without big VC funding.  A Practical Founders Peer Group is a committed and confidential group of founders/CEOs who want to help you succeed on your terms. Each Practical Founders Peer Group is personally curated and moderated by Greg Head.

I just attended Allocate's Beyond Summit in Deer Valley Utah. It was a peek into what the top VC's and LP's are thinking about right now.Allocate asked me to record an episode of the show, live from the conference.So I asked everyone “What's your hottest take on the VC market today?”Thank you to Numeral, Flex, and Amplitude for supporting this episodeNumeral: The end-to-end platform for sales tax and compliance https://www.numeral.comFlex: Get premium banking and a net 60 day credit card at 0% APY https://home.flex.one/referral/bananacapitalAmplitude: AI analytics, all you have to do is ask https://www.amplitude.comTimestamps:(1:22) Seed investing is dead (Tripp Jones, Uncork)(5:56) Seed is not dead (Bryan Rosenblatt, Sandlot)(13:19) Most consensus era of VC ever (Nate Williams, Union)(18:02) Taking the Power Law Pill (Pratyush Buddiga, Susa Ventures)(29:15) The 2nd-time founder premium is dead (Matt Cohen, Ripple Ventures)(32:46) AI will crush intelligence labor (Clark Cheng, Merrimac)(42:25) New deep tech investors will lose their shirts (Sunil Nagaraj, Ubiquity Ventures)(46:39) ChatGPT for robotics is still 15 years away (Sungjoon Cho, Fortitude Ventures)(52:07) The app layer ARR reckoning (Josh Christensen, Mercato)(58:30) The AI bubble will pop in Q2/Q3 (Amias Gerety, QED)(1:08:22) Most individuals do VC wrong (Jon Oberheide)(1:15:25) Allocators have become too allocator-y (Dan Feder, University of Michigan)(1:20:55) LP's should value information, not just returns (Ben Ivey, Marshall Street)(1:24:09) Upcoming litigation of Russian doll SPVs (Asher Siddiqui, Song United)(1:30:13) Why retail needs private market access (Sarah Pinto Peyronel, Robinhood Ventures)Referencedhttps://beyondsummit.allocate.co/Tripp Jones, Uncork CapitalTwitter: https://x.com/thistrippjonesBryan Rosenblatt, SandlotTwitter: https://x.com/BRosenblatt4Nate Williams, UnionTwitter: https://x.com/naywilliamsPratyush Buddiga, Susa VenturesTwitter: https://x.com/pratyushbuddigaMatt Cohen, Ripple VenturesTwitter: https://x.com/mattybcohenClark Cheng, MerrimacLinkedIn: https://www.linkedin.com/in/clark-cheng-cfa-frm-caia-a411535Sunil Nagaraj, Ubiquity VenturesTwitter: https://x.com/sunilnagarajSungjoon Cho, Fortitude VenturesTwitter: https://x.com/josungjoonJosh Christensen, MercatoLinkedIn: https://www.linkedin.com/in/joshjdmba/Amias Gerety, QEDTwitter: https://x.com/amiasmgJon OberheideTwitter: https://x.com/jonoberheideDan Feder, MichiganLinkedIn: https://www.linkedin.com/in/danfederBen Ivey, Marshall StreetLinkedIn: https://www.linkedin.com/in/beniveyAsher Siddiqui, Song UnitedLinkedIn: https://www.linkedin.com/in/ashersiddiquiSarah Pinto Peyronel, Robinhood VenturesTwitter: https://x.com/SPintoPeyronel*This podcast is produced by Allocate for informational and educational purposes only and is intended for institutional, accredited, and qualified investors. Nothing discussed constitutes an offer to sell or solicitation to purchase any security or advisory service, and nothing should be construed as legal, tax, or investment advice. Any offering will be made only pursuant to applicable confidential offering documents.Views expressed by participants are their own and subject to change. Any discussion of target returns, projected outcomes, IRRs, MOICs, or other performance metrics is hypothetical and illustrative only and should not be relied upon as an indication of future performance.Investments in private funds are speculative, illiquid, and involve substantial risk, including possible loss of the entire investment. Past performance is not indicative of future results.Certain guests may have financial or other interests in the opportunities discussed. Allocate Management Company, LLC is an SEC-registered investment adviser. Registration does not imply any level of skill, training, or SEC endorsement. Please consult your own advisors before making any investment decision.*

Paul Tran started Manscaped with $50,000, a bloody problem nobody was talking about, and a category that didn't exist. The company hit $300 million in revenue in just 36 months, eventually turned down a $1 billion SPAC deal, and has become the #3 men's grooming brand in a category dominated by companies over 100 years old—while staying profitable the entire way. In this interview, the founder and CEO of Manscaped breaks down the exact DTC playbook that got him from 10,000 units sold out in two weeks to nine figures in annual media spend, why he waited until $50–60 million in marketing spend before entering retail, and the counterintuitive brand decisions—including turning down better-performing ads—that built one of the most recognizable men's lifestyle brands in the world. What you'll learn in this interview: • How Paul identified a completely unaddressed category and validated it with just 10,000 units and $5-a-day Facebook ads • Why Manscaped had lower revenue than Paul's other two businesses at launch—and the three signals that told him it had the highest potential • The $18,000 mistake that wiped out a third of the starting budget in one hour—and what it taught him about brand vs. performance media • Why he deliberately waited until $50–60 million in annual media spend before entering retail—and why most brands jump in too early • The brand values decision that cost them short-term revenue: why they turned down better-converting ads that used provocative imagery • How 66% of first-time buyers chose a starter kit—and the bundle-testing framework behind it • Why he walked away from a $1 billion SPAC deal in 2021—and why that decision looks like genius three years later • The post-purchase upsell structure that turns a single transaction into a lifetime customer • Why consumer brands should never adopt the VC "raise and burn" playbook—and how Manscaped scaled to $300M while staying profitable • What Paul would do radically differently if he started today—and why AI changes the entire early-stage playbook If you're building a DTC brand, trying to figure out the right time to go into retail, or looking for the real story behind how a category-defining brand gets built from scratch on a shoestring, this conversation will fundamentally change how you think about timing, positioning, and what profitable scale actually looks like. SAVE 50% ON OMNISEND FOR 3 MONTHS Get 50% off your first 3 months of email and SMS marketing with Omnisend with the code FOUNDR50. Just head to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://your.omnisend.com/foundr⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to get started. WANT TO GROW YOUR BRAND WITH META ADS? Join the Foundr Operators Waitlist → ⁠⁠⁠⁠⁠⁠⁠https://foundr.com/operators⁠⁠⁠⁠⁠⁠⁠ HOW WE CAN HELP YOU SCALE YOUR BUSINESS FASTER Learn directly from 7, 8 & 9-figure founders inside Foundr+ Start your $1 trial → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.foundr.com/startdollartrial⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ PREFER A CUSTOM ROADMAP AND 1-ON-1 COACHING? → Starting from scratch? Apply here → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://foundr.com/pages/coaching-start-application⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ → Already have a store? Apply here → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://foundr.com/pages/coaching-growth-application⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ CONNECT WITH NATHAN CHAN Instagram → ⁠⁠⁠⁠https://www.instagram.com/nathanchan⁠⁠⁠⁠ LinkedIn → ⁠⁠⁠⁠https://www.linkedin.com/in/nathanhchan/⁠⁠⁠⁠ CONNECT WITH PAUL TRAN Instagram → ⁠⁠⁠⁠https://www.instagram.com/paultran/ LinkedIn → ⁠https://www.linkedin.com/in/paulhtran/ Website → https://www.manscaped.com/ FOLLOW FOUNDR FOR MORE BUSINESS GROWTH STRATEGIES YouTube → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/2uyvzdt⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Website → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.foundr.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/foundr/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/foundr⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.twitter.com/foundr⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/company/foundr/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Podcast → ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.foundr.com/podcast⁠

Produced by Foundersuite (for startups: www.foundersuite.com) and Fundingstack (for emerging manager VCs: www.fundingstack.com), "How I Raised It" goes behind the scenes with startup founders and investors who have raised capital. This episode is with with Andrei Serban of Console, a San Francisco-based startup that provides an AI-powered IT Service Management platform that automates routine internal support requests and IT tasks directly through platforms like Slack and Microsoft Teams. Learn more at www.console.com In this episode, we discuss the acquisition of Andrei's previous company, Fuzzbuzz, by Rippling and he shares tips for managing the exit process. We then discuss what Console does and why he started the company. Following that, Andrei shares the story of raising capital from Thrive Capital, DST Global and prominent angels, how he ran a really tight 2 week process, how he used AI and an Investor Memo in the process, tips for who to target at a VC fund, why he likes to hire ex founders, and more. Andrei is a repeat guest of the show -- to catch the original episode when he was building Fuzzbuzz, click here: https://soundcloud.com/user-2586856/ep-106-how-i-raised-it-with-andrew-serban-of-fuzzbuzz Console has raised $29M from Thrive Capital and DST Global, with backers including Ramp's founders, Box CEO Aaron Levie, and Palo Alto Networks CEO Nikesh Arora. How I Raised It is produced by Foundersuite, makers of software to raise capital and manage investor relations. Foundersuite's customers have raised over $21 Billion since 2016. If you are a startup, create a free account at www.foundersuite.com. If you are a VC, venture studio or investment banker, check out our new platform, www.fundingstack.com

Today, we note the very different outcomes for the two admittedly very different software names Salesforce and Snowflake as both reported earnings after the close yesterday. Elsewhere, insane volatility for Marvell in yesterday's session ahead of its own earnings report after the close - are the wheels coming off a bit here for chip names.  Also, Gold needs to take a stand here or else, plenty on macro and FX and more on today's pod, which is hosted by Saxo Global Head of Macro Strategy John J. Hardy. Links Michael Burry points out that VC has gone whole hog in AI, similar to the situation in 2000 with TMT bubble. Acquired put out a four-hour episode on the fascinating history and phenomenon that is Ferrari - these guys are great.  FT with an exclusive on Ukraine turning the tables in its war with Russia - amazing innovation and rates of production for their at least partially homegrown tech. Stratechery with a brief discussion (paywall) of the SpaceX IPO, both quite dismissive in some ways, but also surprisingly supportive of the idea that space-based data centres could be a thing. About twice per week (in normal times, hopefully soon to resume), you will find links discussed on the podcast and a chart-of-the-day over at the John J. Hardy substack. Read daily in-depth market updates from the Saxo Market Call and the Saxo Strategy Team here. Please reach out to us at marketcall@saxobank.com for feedback and questions. Click here to open an account with Saxo. Intro music by AShamaluevMusic DISCLAIMER This content is marketing material. Trading financial instruments carries risks. Always ensure that you understand these risks before trading. This material does not contain investment advice or an encouragement to invest in a particular manner. Historic performance is not a guarantee of future results. The instrument(s) referenced in this content may be issued by a partner, from whom Saxo Bank A/S receives promotional fees, payment or retrocessions. While Saxo may receive compensation from these partnerships, all content is created with the aim of providing clients with valuable information and options.  

Is seed investing facing an existential crisis? This week on The Data Minute, Peter sits down with Rob Go, Founding Partner at NextView Ventures, to discuss the structural shifts making the "game on the field" harder than ever for early-stage investors.Rob explains why many successful seed VCs are exiting the industry and how the rise of mega-funds and massive accelerators like YC has squeezed traditional seed firms into a narrow "subset" of the market. They dive into the "feeder fund" phenomenon, the arbitrary nature of ownership mandates, and why the $1B–$3B exit range has become a "Death Valley" for startups.Despite the current angst, Rob shares his optimistic "bull case" for 2030, explaining why diminishing competition and a rotation away from late-stage consensus will lead to a healthier venture substrate in the years to come.Subscribe to Carta's weekly Data Minute newsletter: https://carta.com/subscribe/data-newsletter-sign-up/Explore interactive startup and VC data, with Carta's Data Desk: https://carta.com/data-desk/Chapters:00:20 – Intro: Rob Go and the Seed Existential Crisis02:16 – Defining Seed: Betting on anything before PMF03:35 – Why senior seed VCs are exiting the industry05:02 – The Squeeze: Mega-funds vs. Accelerators07:02 – Scarcity vs. Abundance: What's left for seed funds?08:44 – The "Feeder Fund" trap and the factory supply chain12:38 – The risk of taking seed money from a mega-fund13:34 – Breaking down the 4 styles of seed investing15:20 – Why specialist seed funds can be transient19:29 – Super Compounders: Will exits keep getting bigger?21:59 – The "Death Valley" of $1B–$3B exits25:08 – The Blumhouse equivalent for venture capital27:18 – Normalizing secondaries as an exit strategy33:53 – Rant: Why ownership targets are backwards39:04 – Offensive vs. Defensive bridge rounds45:07 – "I've become way more Zen": Why the 2030 outlook is bullish50:18 – OutroThis presentation contains general information only and eShares, Inc. dba Carta, Inc. (“Carta”) is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services, and is for informational purposes only.  This presentation is not a substitute for such professional advice or services nor should it be used as a basis for any decision or action that may affect your business or interests. © 2026 eShares, Inc., dba Carta, Inc. All rights reserved.

Griffin just handed $100M to indie developers, Lilith is back with a pachinko creature collector that's turning heads, and Toon Blast hired Gus Fring for reasons that actually make sense.In this episode, we break down:● Griffin Gaming Partners' $100M indie fund and why project financing beats VC math for games● Why the tourists are gon,e and the OG gaming VCs are back● Embracer's endless restructuring and the Fellowship Entertainment spin-off● The full Embracer collapse timeline, 44 studios closed, 80 projects canceled● Google Play's AI-powered game discovery and what it means for your ASO strategy● Why keyword stuffing is dead and how to write for Gemini● Clash of Critters: Lilith's pachinko-core creature collector and the casualization of mid-core● Why Chinese studios didn't invent advanced casual — they just perfected it● Monopoly Go's decline and what levers Scopely has left● Coin Master Board Adventure vs Monopoly Go, is there any real competition?● Toon Blast's Gus Fring campaign and whether celebrity UA still moves the needle● Why re-onboarding lapsed players matters as much as acquiring new onesCHAPTERS:01:39 Banter Roblox and Xbox Takes02:47 Roundtable Events and Consulting Talk05:14 Quick Correction It Takes Two07:20 Google I O Play Updates10:58 ASO SEO for AI Debate14:01 Griffin Fund for Indies17:57 Why VC Math Broke21:10 Embracer Splits Again24:13 Embracer Fallout and Asset Timeline29:02 Mobile Game Data Setup29:30 Lilith Clash of Critters Deep Dive30:25 Portfolio Reality Check30:49 Grim Metrics Decline31:33 Pachinko Creature Collector32:35 Gacha And Meta Layers35:27 Why It Works Now38:01 Advanced Casual Debate41:19 Graphics And Monetization44:25 Coin Master Vs Monopoly Go48:08 Franchising And Growth Levers55:38 Toon Blast Celebrity UA01:00:21 Wrap Up And Next Week

In this episode of One Vision, Theodora Lau sits down with Hay Yip, Chief Strategy Officer and Chief of Staff at FundPark in Hong Kong, for a conversation that spans heritage, working capital, and what it really takes for SMEs to scale in an uncertain world.Hay shares his journey from a commercial banking career at HSBC — spanning both London and Hong Kong — to the electric pace of a fintech startup, and why he now goes to bed "with one eye open." Born in Hong Kong and grew up in the UK, his story comes full circle as he returns home to help the small businesses he's always been drawn to.At the heart of the conversation is the often-overlooked engine of commerce: working capital. Hay makes the case that cash flow is the "bloodline" of any growing business, and explains how FundPark uses data and analytics to serve e-commerce merchants who are not just underserved by traditional banks, but in many cases entirely unserved. Theo and Hay explore the founders' origin story, why entrepreneurs deserve more credit for their courage, and how global supply chain fragility shows up in the everyday lives of merchants and the customers who depend on them.Tune in for a candid look at the unglamorous but essential side of fintech, and FundPark's vision of "scale up as a service" — helping ordinary people behind real businesses thrive.

Cedars-Sinai has evolved from a community hospital to a major academic health system with an international reputation for quality care, community service, research and education. Much of that evolution and expansion took place under the leadership of Tom Priselac, who served as President and CEO for 30 years, until his retirement in 2024.After joining Cedars-Sinai in 1979, Priselac spent nearly half a century at the organization, rising through a series of leadership roles as the institution expanded its academic mission, built an integrated medical network and adapted to major shifts in healthcare delivery. That long tenure gave him a rare vantage point on how health systems change over time, and what it takes to lead through multiple eras of disruption.In this episode of Healthcare is Hard, Priselac reflects on why the leadership job in healthcare feels more challenging now than ever. The pace of change is faster, and today's leaders are navigating a far more complicated environment shaped by financial pressures, regulatory demands, rapid technological advancement and major scientific breakthroughs. But even with all that complexity, Priselac argues that the fundamentals of leadership remain the same. He advocates for creating a culture of excellence, helping people understand why change is necessary, and making sure an organization can absorb change in a thoughtful way. Some of the topics Tom and Keith discussed include:Culture at the heart of healthcare. Priselac returns repeatedly to the importance of values, emotional intelligence and culture in healthcare leadership. In his view, an organization's culture reflects the decisions, behaviors and priorities of its leaders, and that matters even more in complex environments like academic medical centers. Whether the challenge is aligning faculty, community physicians, researchers or administrators, success depends on keeping patient care at the center and building a shared sense of mission.Pushing change too fast. One of Priselac's clearest leadership lessons is that organizations and people can absorb only so much change at once. While today's leaders face real pressure to move more quickly, he warns that some of the biggest mistakes happen when executives short-circuit the change management process. Looking back on his own career, he says some of his most important learning came from moments when he pushed for too much change too quickly.Why cost and access still keep him up at night. Even with all the promise of genomics, proteomics, cell and gene therapy, and AI, Priselac remains deeply concerned that healthcare's affordability and access problems are worsening faster than policymakers are addressing them. He points to clear signs of strain already in the system – communities losing access to care, hospitals in urban areas operating beyond capacity, and patients spending hours waiting for admission. For all the excitement around innovation, he sees cost and access as the country's most urgent unresolved healthcare challenges.To hear Keith and Tom discuss these topics and more, listen to this episode of Healthcare is Hard: A Podcast for Insiders.

What if cutting half your team could be the secret to explosive growth?In this Fan Favorite episode, Cameron Herold sits down with Benjamin Surman, COO of Somewhere (formerly Support Shepherd), a company that rocketed from $1M to $25M and is still hungry for more. The conversation tackles the real-world, often-unspoken operational questions: When do you fire instead of hire? Where's the hidden margin in automation? Why are so many leaders clinging to headcount when systems could do the job faster, cheaper, and with less chaos?If you're addicted to the idea that bigger is always better, this episode will shake your assumptions. Miss it and risk drowning in legacy thinking while your competitors eat your lunch. Listen now for the strategic edge you won't hear anywhere else.Timestamped Highlights00:48 – The real reason behind a bold global rebrand02:29 – How one contractor quietly took the reins as COO08:54 – Why bootstrapping (not VC money) set the right culture11:00 – The micro-influencer lever that brings 4,000 referral partners13:25 – What no one tells you about hiring in Latin America17:41 – The $3M decision: Slashing 120 employees with zero regrets20:13 – Behind the curtain of an automated sales pipeline25:37 – The COO playbook for uncovering invisible inefficienciesAbout the GuestBenjamin Surman is the Chief Operating Officer of Somewhere, a hyper-growth headhunting agency revolutionizing global talent acquisition. With a relentless focus on automation and operational excellence, Benjamin Surman has scaled the business from $1M to over $25M in just three years.

What if the key to building something truly great wasn't perfect balance, but the courage to be temporarily out of it? That question hit me hard in this week's conversation, and I think it's going to hit you the same way. This episode of the Happy Hustle Podcast is one of those conversations that just fires you up from start to finish. My guest is Tayson Whitaker, founder and president of Outdoor Vitals, a performance ultralight backpacking company he started at just 23 years old with $500 in his pocket and a whole lot of grit. Ten years later, Outdoor Vitals has grown into a multimillion dollar direct to consumer brand that's helping thousands of people build the confidence to get outside and actually live. Tayson didn't take investor money. He didn't chase REI shelf space. He built something real, stayed true to his mission, and somehow managed to keep his soul in the process. That's the kind of story that belongs on this podcast. We covered a ton of ground in this one. From bootstrapping and Kickstarter campaigns that generated over two and a half million dollars, to using AI as a tool to give small teams the firepower of big ones, to the Masogi concept and why doing something that scares the heck out of you once a year might just reset your entire life. There's something in this episode for every happy hustler out there, whether you're an entrepreneur, an outdoor enthusiast, or someone just trying to figure out how to build something meaningful without losing yourself along the way. Here are some of the biggest lessons I pulled from this conversation. First, focus on one thing and beat the best at it. Tayson was crystal clear on this. The online marketplace is wide open competition, and the entrepreneurs who win are the ones willing to go narrow and go deep. He's seen friends build eight figure businesses off essentially one product. Not because they were lucky, but because they committed, perfected it, and refused to chase every shiny object in sight. He's honest about struggling with this himself, which makes it land even harder. Second, constraints breed creativity. Tayson never took outside funding, and that decision forced him to innovate in ways he never would have otherwise. Kickstarter, a membership program that turns into store credit, building a loyal customer base from scratch. None of that gets created if you've got a VC writing checks and calling the shots. He said it plainly. Once you define what you will and won't do, you can innovate within those boundaries. That's it. That's the whole game. Third, the Masogi mindset will change how you see hard things. A Masogi is a challenge you take on where you've got roughly a 50/50 shot of actually pulling it off. Not something that's going to kill you, but something real enough that failure is genuinely on the table. Tayson has done hundred mile solo hikes, ultra marathons, and rim to rim to rim in the Grand Canyon. And his takeaway every time is the same. When life throws a curveball the next day, it just doesn't feel that heavy anymore. Because you know what hard really looks like now. Fourth, temporary imbalance is not the enemy. This one really got me. Tayson flips the whole balance conversation on its head, and honestly, I think he's right. You don't build anything great living in perfect daily balance. You sprint when it's time to sprint, and you back off when you've made the gains. The key is just being honest with yourself about what season you're in and making sure you find your way back. He's been running Outdoor Vitals for twelve years and still loves it. That's not an accident. That's someone who learned to listen to his own signals. Fifth, AI is a tool for magnifying people, not replacing them. Tayson's take on AI is grounded and practical. He sees it the same way he sees the internet or the smartphone. It's technology. It gives small teams the ability to do what only big teams could do before. One person managing AI focused entirely on email, or ads, or brand messaging, is a multiplier that wasn't available even five years ago. The opportunity isn't in fearing it. It's in being the one who figures out how to pull the lever well. This conversation reminded me of everything I love about building a business with purpose. Tayson isn't just selling gear. He's connecting people to the outdoors, building confidence, and doing it all without sacrificing what actually matters. Family. Freedom. A life lived on purpose. If any of this resonated with you, do yourself a favor and go listen to the full episode right now at https://caryjack.com/podcastin/. It's worth every minute. What does Happy Hustlin' mean to you? Enjoying the journey. I think oftentimes we're always thinking about the destination when I hear happy hustle and you're still in the grind, you're still doing it. And, you know, tomorrow never actually comes, right? It's always the next day. so enjoy, enjoy it today. Cause you never know what, what tomorrow entails. Connect with Taysonhttps://www.facebook.com/OutdoorVitalshttps://www.instagram.com/outdoorvitals/https://www.youtube.com/outdoorvitalshttps://x.com/OutdoorVitalshttps://www.tiktok.com/@outdoorvitals?is_from_webapp=1&sender_device=pchttps://www.linkedin.com/company/outdoor-vitals/ Find Tayson on this website: http://outdoorvitals.com Connect with Cary!https://www.instagram.com/caryjack/https://www.facebook.com/SirCaryJackhttps://www.linkedin.com/in/cary-jack-kendzior/https://twitter.com/thehappyhustlehttps://www.youtube.com/channel/UCFDNsD59tLxv2JfEuSsNMOQ/featured Get a copy of his new book, https://www.thehappyhustle.com/book Sign up for The Journey: 10 Days To Become a Happy Hustler Online Course @ https://thehappyhustle.com/thejourney/ Apply to the Montana Mastermind Epic Camping Adventure @ https://thehappyhustle.com/mastermind/ “It's time to Happy Hustle, a blissfully balanced life you love, full of passion, purpose, and positive impact!” Episode Sponsors: If you're feeling stressed, not sleeping great, or your energy's been kinda meh lately—let me put you on to something that's been a total game-changer for me: Magnesium Breakthrough by BiOptimizers. This ain't your average magnesium—it's got all 7 essential forms that your body needs to chill out, sleep deeper, and feel more balanced. I take it every night and legit notice the difference the next day. No more waking up groggy or tossing and turning all night If you're ready to sleep like a baby, calm your nervous system, and optimize your recovery, go grab yours now at https://www.bioptimizers.com/happy and use code HAPPY10 for 10% OFF. =================================================================== My Green Mattress If you've been waking up with back pain, feeling stiff, or just not getting that deep, quality sleep. This might be what you're missing: My Green Mattress. It's made with clean, non-toxic, and eco-friendly materials, so you're not just sleeping better, you're sleeping healthier too. The comfort and support are on another level, and you can really feel the difference night after night. If you're ready to invest in better sleep and better recovery, check it out at https://thehappyhustle.com/mygreenmattress =================================================================== Ozlo Sleep If you've been struggling to fall asleep, stay asleep, or just wake up feeling actually rested, let me put you on to something that's been a total game-changer: Ozlo Sleep. These aren't your typical sleep buds. They're designed to block out noise and help your brain fully relax, so you can drift off faster and stay in deep, uninterrupted sleep. Perfect if you're a light sleeper or just want that next-level rest. If you're ready to upgrade your sleep and wake up feeling recharged, check out https://ozlosleep.com and save $80 OFF using code HAPPY.

We’re gonna go back in time! In this episode, Mark and Dan discuss Amazing Spider-Man (vol. 7) #28, which is legacy issue #992. This issue was written by Joe Kelly. The cover features artwork by Cory Smith and Marte Gracia. The interiors feature pencils by Cory Smith with Francesco Manna, inks by Oren Junior and Roberto Poggi, with Francesco Manna, colors by Marcio Menyz and Erick Arciniega, and, of course, letters by VC's Joe Caramagna. This issue was first released on May 6th, 2026. Rick Coste edited this episode. Alex Galucki edited the video version of this podcast. Our artwork is handcrafted by artists Ron Frenz, Nick Cagnetti, and the late Sal Buscema. Our theme songs were produced by Ryland Bojack, Tony Thaxton, and Spider-Maj. Our animated introduction to the show is by Josh Sutton of Panels to Pixels. Watch the show on YouTube: https://www.youtube.com/channel/UCOPCnjzQZNViyEnoOuckaVQ We would also love to see you join our Amazing Spider-Slack community board. If you'd like to join in on our amazing conversations, click this link to get started: https://join.slack.com/t/amazingspider/shared_invite/zt-42tsfhs2-yBaH6KkRmOWiW_8gCf9SmQ This week's Patreon podcasts include a review of Amazing Spider-Man (vol. 7) #29, our discussion of the two Spider-Man / Superman comics, and two episodes of the Whatever a Spider Can Diaries, which documents Dan’s process of writing a book about Spider-Man. If you'd like to follow along with our reviews as they are released, please check out our Patreon page: https://www.patreon.com/superiorspidertalk Read our B-Title reviews, collecting memories, and more in the Amazing Spider-Talk Substack! http://www.amazingspider.substack.com You can email questions to our show at amazingspidertalk@gmail.com or by clicking here. You can also BUY MARK'S BOOK, 100 Things Spider-Man Fans Should Know & Do Before They Die. The post The Amazing Spider-Man (vol. 7) #28 / LGY #992 – REVIEW appeared first on Amazing Spider-Talk.

ESG StuffBP removes chairman Albert Manifold over governance issues 9The board said the decision was unanimous. In a statement, Amanda Blanc, BP's senior independent director, described the board as having been caught off guard by what it found: "The board has been surprised and disappointed to learn of governance oversight and conduct issues it deems unacceptable and has taken decisive action."The company did not elaborate on the specific nature of the concerns.Ian Tyler has been named interim chair, BP said, with the board set to begin a formal process to identify a permanent successor: "The Board and leadership team have deep conviction in the strategic direction we have laid out, and the company is moving at pace to deliver it."Manifold took up the chairmanship just last October. At last month's annual general meeting, just 81.8% of shareholders backed his electionAmong the most consequential decisions of Manifold's short tenure: pushing out former CEO Murray Auchincloss and overseeing the selection of Meg O'Neill to succeed him — a hire that marked the first time BP had recruited an external CEO and the first time a woman had led one of the oil industry's largest players.Tulsi Gabbard Exit Marks Fourth Woman to Leave Trump Cabinet 0Apology TourBank boss sorry after describing workers as 'lower value human capital' 7Standard Chartered CEO Bill Winters triggered a massive PR firestorm by describing the bank's plan to replace back-office staff with automation as replacing "lower-value human capital" with financial investmentStandard Chartered is cutting roughly 7,800 jobs—representing about 15% of its global back-office corporate support roles—over the next four years to make room for AIAfter internal anger and blistering public criticism, Winters posted a formal apology for his "choice of words." However, he initially fueled the fire by attaching the full interview transcript to justify his broader context, drawing further criticism for being defensiveIn his first attempt to quiet the storm, Winters leaned heavily into the corporate strategy rather than apologizing for the specific phrasing: "I said that lower-value roles are more vulnerable to automation, and that we have a responsibility to help colleagues move into higher-value roles. That is what a responsible employer should do. We will continue to speak honestly about the impact of technological change, and we will continue to act responsibly in helping our people to adapt and succeed."After a barrage of negative comments on his first post, Winters returned to LinkedIn later that day to offer an explicit apology for his phrasing: "I have received a lot of support for the messages in my previous post but still get questions about my choice of words, which I know has caused upset to some colleagues. For that I am sorry.""I think the transcript makes it clear that I value our colleagues – all of them – most highly and that we are totally committed to helping them to cope with the accelerating pace of change in our industry."JPMorgan's Jamie Dimon says bank chief's viral AI comment was 'inartful' Dimon downplayed the viral backlash against Standard Chartered CEO Bill Winters—who drew fire for saying his bank would replace "lower-value human capital" with technology—calling it an "inartful" slip-of-the-tongue from a friend.Neopbabies and Dropout babiesJames Murdoch to acquire New York Magazine and Vox Media Podcast Network -1Bolt CEO says he let go of his entire HR team for creating problems that didn't exist: ‘Those problems disappeared when I let them go' 6Bolt CEO Ryan Breslow justified firing his entire Human Resources department by claiming they actively manufactured internal frictionThe aggressive purge follows a brutal 97% collapse in Bolt's valuation—crashing from an $11 billion peak in 2022 down to $300 millionTraditional HR has been entirely swapped for a skeletal "people operations" team, shifting the focus away from employee complaints and internal processes toward basic compliance training and empowering managers to make split-second decisionsAlongside gutting HR, Breslow rolled back employee-friendly benefits like four-day workweeks and unlimited PTO, claiming a culture of complacency had taken over and that 99% of his legacy workforce was simply unwilling to work hardRyan dropped out of Stanford in 2014 to launch BoltThe Middle School Boy Man Babies Rule the WorldMan Drives Cybertruck Into Lake to Test Elon Musk's “Boat” Claims, and It Went About as Well as You'd Guess -10"The passengers abandoned the vehicle and the driver was arrested."Tesla CEO Elon Musk:randomly tweeted that the vehicle would function as a rudimentary flotation device.“It will even float for a while.”“[The vehicle would be able to] traverse at least 100m [330 feet] of water as a boat.”“Cybertruck will be waterproof enough to serve briefly as a boat, so it can cross rivers, lakes and even seas that aren't too choppy.”Jeff Bezos urges US government to stop taxing 50% of America — and claims doubling his taxes won't help ‘that teacher in Queens' 400Jeff Bezos backs Mamdani's tax on luxury second homes, but says Ken Griffin isn't the villainJeff Bezos on Zohran Mamdani's big mistake: ‘When you don't know how to solve a problem, create a villain, blame them'Jeff Bezos says there is ‘no truth' to the ‘buy borrow die' tax strategyBillionaires Openly Use It: Oracle co-founder Larry Ellison has historically pledged over $30 billion worth of his Oracle stock as collateral for personal bank loans. Elon Musk has similarly pledged tens of billions of dollars in Tesla shares to secure lines of credit over the yearsHe said he was "skeptical that that's a true loophole," but added, "If it is, and we can fix it, then we should. I don't think such a loophole should exist."Jeff Bezos Praises Trump's Second Term as ‘More Mature' Jeff Bezos Says AI Will 'Elevate' Workers — Despite Amazon's 30,000 Job Cuts Amid $100 Billion AI PushElon Musk compares his company's work to that of Jesus 0In an interview on Monday, the billionaire said his Neuralink brain-implant company is progressing in its development of ‘Jesus-like technologies'Although brain-computer interface (BCI) as a concept has been around since at least the 1970s, the push to commercialize the technology is more recent. According to data from market-intelligence firm Tracxn, more than 130 BCI startups have been launched since 2016.Why Is Mark Zuckerberg Taunting His Employees Before Firing Them? 20Back in April, Meta announced it was laying off 10 percent of its workforce, or around some 7,800 workers. Unlike traditional layoffs, which are enacted relatively quickly, Meta gave its employees a nearly month-long warning period without announcing who exactly would be headed for the unemployment line.In newly leaked audio from an all-hands meeting at Meta, released by More Perfect Union, the Meta CEO seems to actually be taunting the thousands of workers who were about to be let go by pointing to how the company was harvesting employee data to train its in-house AI models ahead of the massive layoffs.“So we're in a phase where basically the AI models learn from heaving real, from watching really smart people do things. And if you're trying to get it to be able to be able to do certain capabilities, having [AI] be able to observe really smart people doing those things is, is very important.”Going on, Zuckerberg explained that it was better to train AI on soon-to-be-former Meta employees, rather than “contract companies.”“In general, the average intelligence of the people who are at this company is significantly higher than the average set of people that you can get to do tasks if you're working through… contractors,” Zuckerberg stammered. “So if we're trying to teach the models coding, for example, then having people internally, um, build tools that, or, or solve tasks that, um, that help teach the model how to code, we think is going to dramatically increase our models coding ability faster than what others in the industry have the capability to do.”Intuit to Cut 17% of Staff, Invest in ‘Big Bets' 3The restructuring cost is estimated at about $300 million to $340 millionAbout 3,100 employees: and invest the savings in “big bets” as it makes artificial intelligence a centerpiece of its business.Woke WarsTexas AG Sues ISS Over ESG Considerations 0Texas AG Ken Paxton (in a senate race) is suing ISS for allegedly “misleading” customers by pushing “radical political agendas” through its proxy adviceNotably, ISS has attempted to obstruct ExxonMobil's planned reincorporation from New Jersey to Texas“ISS has enormous influence over how billions of dollars are invested and managed across this country, and they have abused that influence in order to push woke ideology”Iowa AG Brenna Bird sues ISS, says advice risks retirement savingsIowa Attorney General Brenna Bird is suing the world's largest proxy-advice firm for abusing its influence and threatening Iowans' retirement savings by "lying" to investors.Stakeholders Rule!Wells Fargo must pay $100M to help homebuyers after discrimination lawsuit — 51 cities are eligible 7The settlement, which was recently approved by a federal judge in California, comes after four years of legal disputes involving Wells Fargo shareholders, former employees and job applicants who accused the bank of systemic problems in both lending and hiring practices.While Wells Fargo denied wrongdoing, the company agreed to the deal to avoid prolonged litigation and mounting legal costs.The case centered on allegations that Wells Fargo's board failed to maintain adequate oversight of the bank's mortgage lending operations, exposing the company to regulatory scrutiny and accusations of discriminatory practices.According to reporting from Realtor.com, plaintiffs accused the bank of “widespread and systematic discrimination in lending” and cited concerns over lending algorithms and refinancing approval patterns.The lawsuit stated that Wells Fargo was allegedly the only major lender in 2020 to reject more refinancing applications from Black homeowners than it approved.Airbus, Air France Hit With Manslaughter Charges Over Pilot Training Failures in Deadly 2009 Flight 447 Crash 1A Paris appeals court delivered a dramatic verdict in one of the longest-running and most complex legal sagas in aviation history. The court overturned a 2023 acquittal and found both Airbus and Air France guilty of corporate manslaughter for the tragic 2009 crash of Flight AF447.The ruling marks a massive victory for the victims' families after a 17-year legal battle. A lower court had previously cleared the European planemaker and the French airline in 2023, ruling that while errors were made, a direct causal link to the crash couldn't be proven. The appeals court completely rejected that logic, declaring the companies "solely and entirely responsible" for the disaster.Ride-Share Drivers in Massachusetts Formally Unionize 100The App Drivers Union said it was the first organization in the country to be formally certified to represent drivers for apps such as Uber and Lyft.In a news release, the organization, the App Drivers Union, said it would represent nearly 70,000 workers in Massachusetts who now have the power to collectively bargain.MATTA very special “who do we blame for SpaceX IPO governance” gameFirst, some S-1 highlights:“Starlink internet is what's being used to pay for humanity getting to Mars.” - MuskTranslation: We don't care much about Starlink, it's just paying our AI billsHe's not kidding: $3.2bn revenue for Starlink, net income of $1.2m$0.6bn revenue for rocket ship, net income of -$0.6bn$0.8bn revenue for AI, net income of -$2.5bnThis isn't a space company - it's classic Musk - you buy the vision (“To build the systems and technologies necessary to make life multiplanetary, to understand the true nature of the universe, and to extend the light of consciousness to the stars.”), but what you're really buying is an internet company that spends all its money on AI and does some rockets on the sideLet someone else invent the car (Tesla) and make them sexy with “big visions” for “humanity”Let someone else invent the rockets, build new ones using someone else's moneyLet someone else invent the satellites, put a whole bunch in space (and buy more satellites from someone else)Musk initially took the role of “Chief Engineer”, but every engineering task seems to have been the other employees - he supplied the moneyShoehorned AI into space exploration because…?Grok is designed as a truth-seeking AI model, built on our founder Elon Musk's mission to enable humanity to understand the universe. We believe that accomplishing this mission requires a truth-seeking approach to AI. We define truth seeking as the active, relentless pursuit of what is objectively true about reality, and grounded in evidence, logic, empirical data, and first principles thinking.AI's ability to revolutionize human potential is directly dependent on meeting exponentially increasing resource demands.We now must go to space to get more resources for AI so we can get to spaceNow the governance who do you blame gameMusk will get:85% voting power (dual class, he owns 94% of Class B 10 vote shares and 12% of Class A shares)The ability to nominate and vote exclusively on >50% of the boardA board which currently includes..TWO execs - Gwynne Shotwell (President) and Musk (three titles)Tesla mafia: Ira Ehreinpreis, Tesla board sycophant, director at the Boring Company and xAI, and longtime Musk hanger on, added Feb 2026Antonio Gracias, ex Tesla director who was explicitly called out in the Tornetta decision as corrupted, cross party transactions with Musk, on boards of Neuralink and Boring Company, added Oct 2010TWO VC bros from DFJ - Randy Glein (SpaceX board observer for 16 years, directors since Feb 2026) and Steve Jurvestson (former Tesla director, director since March 2009) who was ousted from the VC firm with his name on it for sexual harassmentPaypal mafia:Luke Nosek, co founder of PayPal, one of the founders of Founders Fund with Thiel and Ken Howery, invested in DeepMind, director since July 2008Donald Harrison - managed Google purchase of DeepMind, relationship with Nosek, director since Feb 2015Director relationship tenures to Musk: Shotwell: 24 yearsEhreinpreis: 21 yearsGracias: 21 yearsJurvetson: 17 yearsGlein: 16 yearsNosek: 26 yearsHarrison: 11 years (+1 if Nosek/Deepmind connection counts)Texas jurisdiction exclusively (judge shopped) - 3% to sue them, mandatory arbitration, anti-takeover statutes, special meetings ONLY CALLED BY MUSK (no one less than 50% of stock can call a meeting or vote)No written consent - no prior noticeAdvance notice bylaws for the zero shareholder proposals allowedFull omission of board liability - including a provision that automatically allows whatever the conflicts of interest they want with directorsWHO (WHEN) DO YOU BLAME?The US GovernmentDepartment of Energy - in 2010, the DoE gave Tesla a $465m loan, which basically paid for the Model S and helped it buy a factory 6 months before it went public - Musk has said Tesla would not have survived without the loanNevada - in 2014, Nevada gave Musk $1.3bn to build a factory, the most everNASA - spent more than $15bn over years on SpaceX and programs with themThe IRS/Congress - the EV tax credit for $7,500 single handedly pushed Tesla from losing money in 2020 to making money (they effectively got $1.6bn from the US government in 2020), and showing its first profit, which sparked the memefest during COVID and made Musk the richest man on earth - Musk then went on and called for an end to the tax credit since his “competitors” needed it more than Tesla. Tesla made ~$11bn from tax credits aloneThe DoD - started paying SpaceX in 2003 for concept work - and even when the rockets didn't work, the DoD and NASA awarded the company massive contracts anywayJeff Bezos said in 2016 that, “Elon's real superpower is getting government money.”FOMOSpaceX LOSES MONEY - it does not make moneyIf it were a satellite internet company - and NOT THE FIRST - the first was HughesNet in 1996, and Viasat offered it in 2012 - it would make money ($1.2m in income!)Instead, investors are valuing SpaceX as THE LARGEST IPO IN THE HISTORY OF EVER despite the fact that they are burning money on AI, and arguably the worst AIIncluding spending the most on R&D, marketing, and acquisition of Cursor to make up for the fact that Grok suckedIn exchange for FOMO, investors have ENTIRELY GIVEN UP THEIR RIGHTSIt is 100% a private companyTornettaIf Tornetta hadn't sued for Musk's pay, would SpaceX be structured this way?The banks underwriting the dealWho AGREED TO BUY GROK as a term of getting the underwriting, because everyone bends the knee to moneyThe boardI guess

Alex Schinasi, founder of Hulken, joins Carrie Kerpen to unpack the real entrepreneurial journey behind raising venture capital, pivoting under pressure, selling companies, and building again. From Ivy to Hulken, Alex shares why billion-dollar valuations do not always mean founder freedom, how pivots create momentum, and what it means to be truly “acquired, not retired.”This episode is sponsored by Dubsado. Dubsado helps service-based business owners streamline proposals, contracts, invoices, payments, and client workflows all in one place. Visit ⁠www.dubsado.com⁠ and use code WHISPER for 30% off.2:22 - Introducing Hulken4:45 - Company #1: Ivy — The Origin Story9:58 - Racing to Exit: Finding Product Market Fit12:21 - The Ivy Exit — From Unlock to Deal12:45 - How the Acquirer Found Them (LinkedIn!)16:36 - Why Build Again? The Zero-to-One Mindset16:45 - Company #2: Clay — Healthcare to EdTech19:55 - The Clay Exit — Three Term Sheets24:41 - Hulken Origin Story 28:18 - VC vs. Bootstrap — How to Choose29:58 - What's Next for Hulken?

In this episode of One Vision, Theodora Lau sits down with Hay Yip, Chief Strategy Officer and Chief of Staff at FundPark in Hong Kong, for a conversation that spans heritage, working capital, and what it really takes for SMEs to scale in an uncertain world.Hay shares his journey from a commercial banking career at HSBC — spanning both London and Hong Kong — to the electric pace of a fintech startup, and why he now goes to bed "with one eye open." Born in Hong Kong and grew up in the UK, his story comes full circle as he returns home to help the small businesses he's always been drawn to.At the heart of the conversation is the often-overlooked engine of commerce: working capital. Hay makes the case that cash flow is the "bloodline" of any growing business, and explains how FundPark uses data and analytics to serve e-commerce merchants who are not just underserved by traditional banks, but in many cases entirely unserved. Theo and Hay explore the founders' origin story, why entrepreneurs deserve more credit for their courage, and how global supply chain fragility shows up in the everyday lives of merchants and the customers who depend on them.Tune in for a candid look at the unglamorous but essential side of fintech, and FundPark's vision of "scale up as a service" — helping ordinary people behind real businesses thrive.

What if the next frontier isn't a metaphor, it's a market?Venture capitalist Nectarios Economakis (Partner & Co-Founder, Amiral Ventures) just closed the first $40M of a new $75M fund built to keep Canada's best builders home. We start with the brain drain and end up at asteroid mining, and somehow it all connects.In this episode we get into why "San Francisco is just a wrapper on Waterloo," why Canada is great at inventing the future (two of the three godfathers of AI, the transformer, the smartphone) but bad at commercializing it, and what it actually takes to build a generational tech company north of the border. Then we go off-planet: collapsing launch costs, compute in orbit, solar cost down 99.7% since 1970, and why investing in space is really about helping things back here on this pale blue planet.▶ WATCH & SUBSCRIBE:    / @palebluenexus  

Sandesh Patnam of Premji Invest joins Nick to discuss The Downstream Effects of SpaceX, OpenAI, and Anthropic Soaking Up $3T in the Public Market, Who Is Netscape and Who Is Google in the AI Era, and the Impact of Private Credit Redemption Requests on PE and VC. In this episode we cover: Themes and Dynamics of the Current AI Shift Investment Opportunities and Challenges Thesis and Focus at Premji Invest Advice for AI Companies and Public Markets Impact of Mega IPOs on Private Markets Challenges in Private Equity and Private Credit Listening as a Secret Weapon Guest Links: Sandesh's LinkedIn Sandesh's X Premji Invest's LinkedIn Premji Invest's Website The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

Most careers don't follow a straight line. But few require starting over in full view of the public.This week, Halle sits down with Lance Armstrong to discuss how he rebuilt his life and career after multiple turning points, including surviving advanced cancer, and how those experiences shaped his perspective on health, performance, and reinvention. Now, through his venture firm Next Ventures, he backs companies focused on what they call “whole person health” — spanning prevention, wellness, diagnostics, longevity, and healthcare outside the traditional system.We cover:Why he chose to become a VC, and what he likes (and dislikes) about the jobHow his experience as a patient shapes how he evaluates companiesWhy preventive care is growing outside the traditional healthcare systemWhat he looks for in founders building across the care continuumWhat it takes to rebuild trust and start overAbout our guest:Lance Armstrong is a former professional cyclist, entrepreneur, and investor. After surviving advanced testicular cancer, he founded Livestrong, helping raise more than $500 million to support cancer patients and survivors worldwide. In 2019, he co-founded Next Ventures, a venture capital firm focused on health, wellness, and consumer brands, with investments including Oura, Cofertility, Pair Team, and SteadyMD. Prior to Next Ventures, he was an active angel investor in companies such as Uber, DocuSign, and Athletic Brewing.—

I sat down with Marco from MoveItOn at ConsenSys Miami to talk about something I hadn't seen before — a peer-to-peer delivery platform that turns everyday travelers into courier agents. Think Uber, but for shipping. Marco walks me through how they use smart contract escrow to build trust between strangers sending valuable items, how their M1 token powers cross-border payments, and why they just acquired a Web2 company called GlocalZone with 1.5 million app downloads to hit the ground running. We also get into the regulatory maze of operating across 100+ countries, the AI-powered security boxes they plan to place at airports and train stations, and why the last-mile delivery problem is one that AI agents simply cannot solve on their own. If you're interested in how blockchain and real-world logistics can come together to save people money and create new income streams while traveling, this one is for you. Disclaimer:Nothing mentioned in this podcast is investment advice and please do your own research. It would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend. Be a guest on the podcast or contact us - https://www.web3pod.xyz/Connect:MoveItOn Website: https://www.moviton.com/ Key points with Timestamps • [00:00] Sam introduces Marco from MoveItOn, recorded live at ConsenSys Miami• [02:00] Marco's background — how writing a blockchain book for beginners pulled him into the industry• [04:00] He has three published books: Blockchain Millionaire, a crypto beginner lexicon, and a tokenization guide• [05:30] What MoveItOn is — a peer-to-peer delivery platform turning travelers into courier agents• [07:00] The founding story — a co-founder from Kazakhstan couldn't ship medical items via DHL but could carry them personally• [09:00] How blockchain fits in — smart contract escrow requires couriers to deposit the value of items they carry• [11:00] The M1 token powers payments and couriers earn staking rewards while funds are locked• [13:00] The GlocalZone acquisition — a Web2 peer-to-peer delivery app with 1.5 million downloads and 70,000 active users• [15:00] MoveItOn has been in development for about 18 months, Marco joined 8-9 months ago• [17:00] Biggest challenge is regulation — launching in 10 compliant countries first, using AI to track changing import laws• [20:00] Go-to-market strategy — partnerships with flight booking and car-sharing platforms, solving the last-mile problem with logistics companies• [23:00] B2B infrastructure and future plans for AI agent integration• [25:00] Blockchain and AI adoption across banking, medicine, and other industries• [27:00] Solving the marketplace chicken-and-egg problem through partnerships and acquisitions• [29:00] Current fundraising — private and pre-sale done, public sale in 4-6 months, seeking $4M in VC or angel funding• [31:00] Move It Boxes — AI-powered smart lockers at airports and transport hubs for contactless drop-off and pickup• [35:00] Vision for 2030 — 100+ countries, doubling the current 1.5 million user base• [37:00] Open to partnerships, investors, and remote team members — headquartered in Dubai

Markets higher today, and the Dow setting a fresh record, as Kevin Warsh is sworn in as the next Fed Chair. We dig into the moves, and how the central bank is positioning its policy under the new head. Plus we're all waiting for OpenAI to file to go public. What one top VC sees in store from the slew of incoming IPOs. Fast Money Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Marc Sanderson is the founder and CEO of INNERGY, but he didn't start as a software founder. After earning his MBA and searching for a company to buy, he and partner Walter Wilkie acquired a small architectural woodworking business in Minnesota in 1997. Running that business revealed a deep operational problem: there was no software built for how custom woodworking shops actually operated. So Marc built his own. That internal tool eventually became Innergy, a vertical SaaS ERP platform for architectural woodworking and high-end residential millwork businesses. Today, Innergy handles everything from CRM and estimating to project management, engineering, fabrication, and field installation. In 2025, the company reached roughly $25M in revenue, is growing more than 50% annually, and expects to approach $40M in 2026. After bootstrapping growth for years using profits from the original woodworking business, Marc sold 51% of Innergy to growth equity firm MainSail Partners in 2025 for more than $40M, while remaining CEO. In this episode, he shares practical lessons about vertical SaaS, customer intimacy, onboarding complex ERP systems, finding the right growth equity partner, and why strategy still matters more than AI. Key Takeaways Deep Domain — Marc built software from firsthand pain inside his own woodworking business, not from an outside startup idea. Education Matters — INNERGY advantage isn't only software. Customer education and operational thinking drive adoption and retention. Growth Equity Fit — Marc rejected investment several times before choosing a partner that could help scale—not just provide cash. Meet Customers — ERP success came from meeting customers where they are instead of forcing "best practices" immediately. Customer Intimacy — INNERGY's onboarding, benchmarking, and peer learning approach helped create ~95% retention. Quote from Marc Sanderson, Founder and CEO of INNERGY "AI is just a tool. I see organizations creating a chief AI officer. I don't have a chief Outlook officer. I don't have a chief Internet officer. I don't have a chief Web officer. It's just a tool at the end of the day." "Just because you can cook rice infinitely at no cost doesn't make you a Michelin star restaurant. It's all the other aspects of these integrated activities that make you who you are. And at the end of the day, as long as we are creating value for our customer, they will continue to write a check to us." "A lot of the AI efforts that are going on across the industry is focused on cost reduction, expense reduction internal to the software firm. Great. That helps us get to a breakeven or beyond. It helps with the rule of 40. However, it does not create more intimacy with the customer." Links Marc Sanderson on LinkedIn INNERGY on LinkedIn INNERGY website MainSail Partners website Podcast Sponsor – Full Scale This podcast is sponsored by Full Scale, one of the fastest-growing software development companies in any region. Full Scale vets, employs, and supports over 300 professional developers, designers, and testers in the Philippines who can augment and extend your core dev team. Learn more at fullscale.io. The Practical Founders Podcast Tune into the Practical Founders Podcast for weekly in-depth interviews with founders who have built valuable software companies without big funding. Subscribe to the Practical Founders Podcast using your favorite podcast app or view on our YouTube channel. Get the weekly Practical Founders newsletter and podcast updates at practicalfounders.com. Practical Founders CEO Peer Groups Be part of a committed and confidential group of practical founders creating valuable software companies without big VC funding.  A Practical Founders Peer Group is a committed and confidential group of founders/CEOs who want to help you succeed on your terms. Each Practical Founders Peer Group is personally curated and moderated by Greg Head.

In this episode of the Established Podcast, Charlie O'Donnell, NYC venture capitalist, community builder, and Author of Founder Unfriendly: What Investors Won't Tell You About Getting Funded  joins Frank Gruber, host of the podcast and Co-CEO at Established and Managing Partner at Established Ventures, for an honest conversation about venture capital, startup fundraising, and the realities founders rarely hear from investors. Charlie shares lessons from more than 20 years in venture, including his time at Union Square Ventures and as founder of Brooklyn Bridge Ventures, while discussing insights from his new book, Founder Unfriendly: What Investors Won't Tell You About Getting Funded. The discussion covers why most startups are not a fit for traditional VC, how founders should think about risk, the hidden dynamics inside partner meetings, why storytelling matters in fundraising, and how AI is changing the future of startups and investing. Charlie also shares actionable advice on customer discovery, building community-driven companies, and designing a business that aligns with the life you actually want. Get Involved! Founders, investors, startup teams, entrepreneur support organizations (ESOs), and innovators, we invite you to join the Established Network, our digital hub where creativity, capital, and collaboration collide. https://established.network    Watch the episode on the Established YouTube Channel at: https://soty.link/ESTYouTube   Thank you for listening, and as always, please check out the Established website and subscribe to the newsletter at: www.est.us    Subscribe to the Established podcast: https://theestablishedpodcast.com/     Startup of the Year helps diverse, emerging startups, founding teams, and entrepreneurs push their companies to the next level. We are a competition, a global community, and a resource. Startup of the Year is also a year-long program that searches the country for a geographically diverse set of startups from all backgrounds and pulls them together to compete for the title of Startup of the Year. Check out Startup of the Year at: www.startupofyear.com   Established is a consultancy focused on helping organizations with innovation, startup, and communication strategies. It is the power behind Startup of the Year. Created by the talent responsible for building the Tech.Co brand (acquired by an international publishing company), we are leveraging decades of experience to help our collaborators best further (or create) their brand & accomplish their most important goals. Check out Established at: www.established.us Connect with us on X (formerly Twitter) - @EstablishedUs  Connect with us on Facebook - facebook.com/established.us

Alfred Wallfors is the Co-founder of Listen Labs, the AI customer research company.Companies like Microsoft use Listen to run AI-powered customer interviews, and Alfred talks about how they first landed them as a customer at a pitch competition.We talk why startups should pursue enterprise customers early on, why 85% of survey answers are random clicks, how AI is changing the $140B market research industry, leveraging VC's for customer intros, how to stand out when recruiting as a startup, and hiring for obsession.Thank you to Numeral, Flex, and Amplitude for supporting this episodeNumeral: The end-to-end platform for sales tax and compliance https://www.numeral.comFlex: Get premium banking and a net 60 day credit card at 0% APY https://home.flex.one/referral/bananacapitalAmplitude: AI analytics, all you have to do is ask https://www.amplitude.comTimestamps:(0:14) Listen: AI customer research tool(7:30) Fraud is a big problem in customer research(9:06) The $140B customer survey industry(12:08) Why running customer surveys is so hard(16:03) AGI will never replace humans(18:25) Surveys vs interviews(21:13) Importance of emotion in data collection(22:54) Using AI interviews to get product feedback(26:15) Building digital twins creates better data(32:22) Outperforming generic AI tools(34:17) Sweetgreen's Max Protein Bowl(36:09) Jevon's Paradox in customer research(40:37) Quantitative vs qualitative(42:38) Landing Microsoft as an early customer(44:50) Targeting enterprise customers from day 1(48:05) Building a VC customer intro leaderboard(51:53) Recruiting with billboard games(57:20) Hiring for obsession(1:02:07) Alfred's favorite movies(1:03:53) Listen's custom agent harness(1:06:24) Velocity Fellowship for Swedes moving to SF(1:08:34) Growing up with entrepreneurial older brother(1:09:46) No shoes in the officeReferencedTry Listen: https://listenlabs.ai/Careers at Listen: https://listenlabs.ai/careersSweetgreen protein bowls: https://listenlabs.ai/case-studies/sweetgreenToni Erdmann: https://www.imdb.com/title/tt4048272/Episode with Erik @ Modal: https://www.thespl.it/p/building-ai-native-infrastructureFollow AlfredTwitter: https://x.com/itsalfredwLinkedIn: https://www.linkedin.com/in/wahlforssFollow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

We sit down with Eric Ries, author of "The Lean Startup" to discuss his new book "Incorruptible: Why Good Companies Go Bad... and How Great Companies Stay Great" (which launches later this week on May 26) to name the force that pulls great companies off mission and to map concrete ways to build businesses that stay great as they scale. We also get into AI agents, vibe coding risks, and why trust, empathy, and stories can outperform metrics when the stakes are real.• corporations as superorganisms with emergent intelligence and moral character• slow AI vs fast AI and why governance becomes the bottleneck• shareholder primacy as a self-defeating objective function that rewards value destruction• why validated learning cannot be outsourced and how AI should teach, not replace, understanding• how VC incentives can shift toward longer-term fund structures and mission-driven returns• mission-controlled companies and governance fortresses that protect purpose without founder hubris• the Virgin America story and the need for a mission guardian• Devoted Health as an example of operationalized empathy that reduces churn and builds loyalty• performance reviews as story-harvesting systems and the danger of surrogation by metrics• HEB's crisis decision as a compounding trust investmentA company can be wildly successful and still be losing something essential. Eric Ries joins us to explain why, and he doesn't blame a few “bad actors” or a vague culture problem. He names the physics: financial gravity, the invisible pull that bends incentives, board decisions, and leadership behavior toward extraction and short-term wins. Along the way, we talk about his new book and what it takes to protect a mission when the money gets serious.We dig into corporations as “superorganisms” and why organizations function like slow AI, then connect that to fast AI and the rise of autonomous agents inside the enterprise. Eric lays out why shareholder primacy is an objective function that can reward value destruction, how governance “best practices” often fail in the real world, and what mission-controlled structures can look like when you want checks and balances without creating an unaccountable emperor-for-life.Then we go practical and a little spicy: vibe coding, overconfidence, black swans, and what validated learning means when code is generated faster than humans can evaluate it. We also get into trust as a compounding asset, with stories like Virgin America's mission getting liquidated, Devoted Health operationalizing empathy, and HEB proving customer-first values under crisis conditions. Plus, we run Eric through a sci-fi corporate governance lightning round that hits Blade Runner, Murderbot, Alien, and Terminator 2.Eric Ries: https://www.linkedin.com/in/eries/Eric Ries is an accomplished serial entrepreneur, advisor, and New York Times bestselling author,  creating the Lean Startup methodology and writing the iconic book The Lean Startup, which has sold over a million copies worldwide.  His highly anticipated new book, "Incorruptible: Why Good Companies Go Bad...and How Great Companies Stay Great" releases May 26: https://www.amazon.com/Incorruptible-Good-Companies-Great-Stay/dp/B0FWZZBPZBEric is a partner at Unshackled Ventures and also the Founder and Executive Chairman of the Long-Term Stock Exchange (LTSE).  Eric is also the co-founder of the AI R&D lab Answer.AI, a former entrepreneur-in-residence at Harvard Business School and IDEO, and the host of his own podcast, The Eric Ries Show.Website: https://www.position2.com/podcast/Rajiv Parikh: https://www.linkedin.com/in/rajivparikh/Email us with any feedback for the show: sparkofages.podcast@position2.com

What if the biggest barrier to a longer life isn't science, but the economic incentives of corporations? In this episode of Onward, Ben sits down with Celine Halioua, founder and CEO of Loyal — the biotech company on track to earn the first-ever FDA approval for a drug whose only purpose is to extend lifespan. The catch: they're starting with dogs.Celine walks Ben through why the U.S. healthcare system is structurally incapable of building preventative medicine, why each dog-owner relationship is a "micro single-payer health care system," and how a quiet 2019 change to FDA regulation was the one-to-one cause of Loyal existing at all. ("If this pathway didn't exist, Loyal wouldn't exist. October 2019, I incorporated.")From there, the conversation widens. Celine lays out the Tesla-style master plan — save the dogs, save the world — that uses dog-drug revenue to fund human longevity work, escaping the discipline of biotech VC entirely. Ben presses her on AI in drug development (she's skeptical it will change clinical trials anytime soon), public-market short-termism, and the kind of scenario planning that prepares a company for what nobody saw coming.—For a deeper dive into these insights and more, be sure to listen to the full episode of the Onward podcast.Have questions or feedback about this episode? Drop us a note at Onward@Fundrise.com. Onward is hosted by Ben Miller, co-founder and CEO of Fundrise. Podcast production by The Podcast Consultant. Music by Seaplane Armada. About FundriseWith over 2 million users, Fundrise is America's largest direct-to-investor alternative asset investment platform. Since 2012, our mission has been to build a better financial system by empowering the individual. We make it easier and more efficient than ever for anyone to invest in institutional-quality private alternative assets — all at the touch of a button. Please see fundrise.com/oc for more information on all of the Fundrise-sponsored investment funds and products, including each fund's offering document(s). Want to see the specific assets that make up and power Fundrise portfolios? Check out our active and past projects at www.fundrise.com/assets.More Info & DisclaimersThere are no guarantees investment holdings of the Fundrise Innovation Fund (the “Fund”) will be successful.Investing in the Fund is speculative and involves substantial risks. You should purchase shares of the Fund only if you can afford a complete loss of your investment. Nothing in this material should be construed as tax advice, an offer, recommendation, or solicitation to buy or sell any security. Past performance does not guarantee future results. Current and future holdings are subject to risk, and returns of one portfolio company are not indicative of an investment in the Fund. For Fund performance and the most recent schedule of investments, visit GetVCX.com. The Fund's annual and semi-annual reports (Form N-CSR), quarterly portfolio holdings (Form N-PORT), and other periodic reports filed with the Securities and Exchange Commission are available on EDGAR at sec.gov and at GetVCX.com. The Innovation Fund is publicly registered under the Investment Company Act of 1940 as a non-diversified, closed-end management investment company.The Fund's portfolio will be concentrated in securities issued by technology companies and other investments that provide economic exposure to technology companies and as such, it may be subject to more risks than if it were broadly diversified across additional sectors and industries of the economy. Certain technology companies may face special risks that their products or services may not prove to be commercially successful. Technology companies are also strongly affected by worldwide scientific or technological developments, and as a result, their products may rapidly become obsolete.The Fund's investments in companies involved in, or exposed to, artificial intelligence-related businesses may be negatively impacted because of, among other things, limited product lines, markets, financial resources and/or personnel; intense competition and potentially rapid product obsolescence these companies may face; loss or impairment of intellectual property rights; and the inability to successfully develop products or services even after spending significant amount of resources.The Fund's investment in private company securities, whether made directly or indirectly (e.g., through derivatives or private pooled investment vehicles) are generally illiquid. Because private company securities are thinly traded, such securities may display especially volatile or erratic price movements, sometimes in response to relatively small changes in investor supply or demand or other market conditions.

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

What if your new patient visit didn't start with a 200-page ER record dump, scattered portal logins, and a patient saying "I think mom had some kind of cancer"? In this episode of My DPC Story, Dr. Maryal Concepcion sits down with Anna Smith, RN, MPH, CEO of Vivid Vault Health Solutions, to talk about a patient-owned, multi-generational health navigation platform built to strengthen the physician-patient relationship, not replace it.Anna shares her realization that healthcare collects massive amounts of data but rarely puts it in patients' hands in a way they can actually use. That moment became Vivid Vault, a bootstrapped, non-VC, non-PE-funded platform designed to help families consolidate records across generations so DPC physicians can finally practice with the comprehensive, portable patient story we've always needed. Big Trees MD is an early adopter, and Dr. Concepcion walks through exactly why.If you want to give your patients a comprehensive, portable, patient-owned record system without adding another six-figure EMR to your stack, book a leadership call at vividvaulthealth.org or email together@vividvaulthealth.org. Download the Vivid Vault app in the Apple Store starting April 22. If this episode moved you, leave a five-star review on Apple Podcasts.What You'll LearnWhy fragmented records cost DPC physicians time, accuracy, and patient retention. How Vivid Vault works as a health navigator, not a new EMR or added workflow. Why multi-generational, family-linked records matter, and how permissions, proxies, and minors are handled. How PROMIS assessments give physicians insight between visits. How wearable data (Apple Health, Oura, WHOOP, Garmin, Dexcom soon) is timestamped and clinically usable. Why patient-owned data is non-negotiable and what happens if a patient leaves your practice or the platform. How LLM-assisted review surfaces what matters in a 200-page ER packet without replacing clinical judgment. Why B2C with a DPC practice discount keeps the financial burden off clinics.About Our GuestAnna Smith, RN, MPH, is the CEO and founder of Vivid Vault Health Solutions, a Colorado-based, bootstrapped health navigation platform. Her background spans bedside nursing, public health, medical records administration in cardiology, and patient advocacy. She is a mother of three and builds alongside a leadership team whose lived experience, including parenting a heart transplant recipient, shapes every design decision.ResourcesFind the Vivid Vault app in the Apple App store today!Website: vividvaulthealth.org (scroll to bottom right and click "Schedule with Leadership") Email: together@vividvaulthealth.org Connect with My DPC StoryStart Here page for every stage of your DPC journey: mydpcstory.comSupport the showGET your FREE MONTHLY BUSINESS TOOL DOWNLOADBecome A My DPC Story PATREON MEMBER! SPONSOR THE PODMy DPC Story VOICEMAIL! DPC SWAG!FACEBOOK * INSTAGRAM * LinkedIn * TWITTER * TIKTOK * YouTube

The Great Talent Redistribution: Where is Talent Actually Going in 2026 and beyond?  Is the start-up compensation model broken? How about big Big Tech? How about non-tech small & medium businesses? What is happening to talent, going forward? This and many other topics in this episode of Tech Deciphered. Navigation: Intro The Broken Contract? The Great Unbundling The Three (?) Destinations Alternative Cap Tables, Alternative Compensation Models Investor Landscape Fragmentation Operator Playbook and Predictions Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Nuno Goncalves Pedro Introduction Welcome to episode 77 of Tech Deciphered. This episode will focus on the great talent redistribution. Where’s talent actually going in 2026 and beyond? The Silicon Valley deal of the last 30 years, very low salary, stock options, you will either sell for a ton of money or IPO, and everyone gets rich, is seemingly broken. Or is it really? The dominant narrative says the tech middle class is dying. We disagree. There is obviously a lot of stuff going on whereby big tech is partially barbelling. There’s a superstar concentration on the top. There’s a bit of a seemingly allowing of the belly. We’ll come back to that. We don’t quite believe that is totally true. There’s a collapse at entry level. The belly is migrating into three, potentially even more, very different destinations: AI native startups, human-verified premium businesses, and the read the industrialized middle of the S&P 500 and SMB world. Each has its own cap table, each will have its own compensation model, and each will have its own investor profile. In some ways, this is the third episode in our Reset trilogy. We started with episode 75 on the SaaS-apocalypse. We talked about the great private capital reset in episode 76, and now we talk about talent redistributions. Bertrand, exciting times, not always positive times.   Bertrand Schmitt Yeah, it’s exciting times because it’s a time of change. Of course, we have the doomsayers. If you listen to Dario Amodei of Anthropic, every white-collar job on Earth is going to disappear. I think I strongly disagree, and I suppose you too as well, we strongly disagree. It’s going to be more of a redistribution. If you look at the history of technology, this is what always happened. We forget how many jobs have disappeared over the past 150 years. We move from a time of 150 years ago. People were mostly in agriculture. Then you had a lot of weird jobs that disappeared from people transporting water to people bringing ice from the pools to people doing the job of computers. People forget that computer was a title given to human beings. We’re doing calculations. Then, of course, secretory jobs in the ’80s, ’90s, where suddenly anyone can type using a word processor, the rise of Excel, that sort of stuff. Many things have changed. Some jobs have indeed disappeared. Some jobs have totally transformed. Where you do these jobs have changed. I think we are at a similar stage where, thanks to AI, and I would say for now, or at least the rise of AI coding, there is a dramatic change happening. I don’t think it means that people will be without a job. It just means, from my perspective, that jobs are changing. You are not just doing a lowly coding level task that actually indeed could be replaced, but you are going to have more of builder type of mindset, a product manager type of mindset going forward. We also expect that the distribution of jobs, depending on the type of business, will be quite different.   Nuno Goncalves Pedro The Broken Contract? Maybe let’s reset a little bit to the broken contract, or if it’s really a broken contract. There’s been this image in technology and tech that basically you get paid very little to work in tech. You get a bunch of stock options. The earlier you are in the company, the higher the level of stock option grants you get. Then you make a ton of money at some point because the company will either sell or IPO, and that’s heard of it. Obviously, there’s a lot of movements happening right now that are changing how these dynamics work. The first part is obviously AI, and in some ways, AI is shrinking companies. It’s not unheard of that companies with as little as four or five people reach 50 million in ARR. There’s companies with one person that have gotten bought for hundreds of millions of dollars or billion of dollars. Obviously, things are moving very, very fast, and therefore, there isn’t a large employee cap table. How would you share the upside? Would you actually give a couple of percentage points to an early employee rather than your 0.2-0.5% kind of thing for early employees? The second part is a little bit the other side of the table, which is the IPO market is seemingly in a drought. There’s not much happening in IPOs. Maybe 2026, at some point, there will be an unlock, but right now, it’s seemingly difficult to get your upside. Even if you’re an employee, you have to wait a long time. The median time of IPO has climbed over 10, 11 years, the longest in over a decade. Basically, not only you have to wait a long time as if there is an IPO drought, like we might be going through right now, when do I actually get my cash back? Unless the company gets bought, maybe there are secondary transactions along the way, maybe there’s something else. But obviously there’s a little bit of a reduction and lowering of the upside seemingly for this contract and for this place. The easy conclusion that I think many are taking is, because of all of this and all the layoffs that are happening, even in big tech, that serve the tech middle class is dying, that basically AI screwing the workers, et cetera, there’s also a lot of discussion that even it might be affecting the entry-level jobs as well. Everyone coming out of undergrad right now can’t get a job, et cetera. There’s this doomsday scenario that you’re alluding to that everything is changing. We have a slightly different perspective. We think there’s a realignment of market. In layoffs, there was a lot of layoffs that were warranted. Big tech, in particular, had actually hoarded a lot of engineering capacity over the last decade or so. There’s a little bit of a realignment that needed to happen in any case. When everyone’s saying, “Well, AI is compressing everything,” well, it’s compressing right now, but we don’t think actually it’s going to compress over time. You’ll still need engineering and science talent to come on board for you to be able to scale up. It’s not like AI is going to take care of everything and teams are going to be five people for companies that are worth a trillion dollars. That’s not happening. Today’s thesis, I think a little bit of this doomsday scenario needs to be seen with a more nuanced lens. I think that’s how we’re framing today’s episode, that there’s a bit of a nuance, there are some extremes happening. We’re going to talk about those extremes, but ultimately, it’s not quite as simple as saying that the tech middle class is disappearing in early jobs are going to be a thing of the past.   Bertrand Schmitt At the same time, what you started with is true. I mean, that 50 million ARR company, just five people. At a bigger scale, that’s exactly the matrix for Anthropic. They have reached a stage where they are at a range of 12 million ARR per staff per employee. It’s metrics that are definitely never seen before. I don’t think any company raised to this level. Best in class, best run companies, one, two million per employees. I mean, that was your target if you can make it. We are definitely in a different game. But I think what matters at the end of the day, and that’s what we’re arguing, is that you have to see the big pictures. Yes, some positions might disappear inside some companies, but some other positions will be created in other companies. Usually, what people do is keep talking about the jobs who disappear and not looking at the bigger picture of jobs that are being created as well. What is true, and I think you alluded to that, is that the big tech the past 10, 15 years had some strategy of hoarding talent in a war where having the best talented people will make the difference in numbers, will make the difference between winning or losing. The Google of the world, the Microsoft of the world, the Amazon of the world, they were hoarding talent. They would try to make sure that they might not have such needs in talented number of people. But if they have the talent, it means their competitors didn’t have the talent. It means that the startup trying to reach scale couldn’t pay the giant salaries that the Google of the world were paying. There was definitely some hoarding. But it went so far in the 2020, 2021, that I think since then there has been a coming back to normal. There is also now in 2026, the recognition that it’s not true anymore. Yes, talent can be very valuable, but there is now a bigger and bigger gap between the extremely talented versus the rest that are merely talented because of AI. AI is able to replace at scale your software engineers, your software managers. I would say it’s quite new. I don’t think it was true a year ago. We’re really talking about a recent dramatic change in what can be achieved thanks to AI. We can see most of the big AI companies are moving to coding. It was started by Anthropic as a trend, OpenAI has followed through. Obviously, the Cursor of the world existed before, but they were not as successful. All the Chinese open-source models are moving very fast to coding optimization the past few weeks. It’s quite an incredible change. I think there is that dramatic change, recognition that coding can be done differently. As a result, we are going to see change in the distribution of jobs. I think it will start from the top because we see the news of the big Google, Microsoft, Amazon, and others who used to hold talented software developers to a change in realization that no, we actually need to invest in AI. We need to invest in compute because compute is going to do the job of most of these people. Therefore, we can’t pay for both at the same time, even us with all our money, we cannot. Wall Street is not going to let us do that. They start by removing a lot of position. I think we see that accelerating, quite frankly. We have only seen the beginning, but in the next 2 years, we see a dramatic shift. But I think my position, I guess yours, and you know as well, is that there will be a lot more opportunities created as well, probably by also entities.   Nuno Goncalves Pedro The Great Unbundling Yeah, there will be more opportunities created. The hoarding is just taken also a little bit of a different view. To your point, there’s hoarding of resources, compute, et cetera. But there’s also hoarding of top talent. We are seeing people getting paid, packages all in that could run up to 100 million, in some cases even over 100 million over several years. This is unheard of. I mean, an officer of Meta would make, I don’t know, maybe 20, 25 million a year. It’s like now there are people that are on the top end of AI researchers that are getting paid around that amount just to join some of these companies. There’s a little bit of a different hoarding. It’s very selective hoarding of certain talent. We’ve seen some acqui-hires. We’ve talked about it in previous episodes that are just literally about getting one or two people specifically to come on board. Alexander Wang, again, going to Meta to lead their intelligence labs there. I feel, I don’t know what you feel, but I feel this is a transition moment where there is overpaying for certain talent on the top of the market. At some point, this will stabilize. You can’t keep paying people 100 million over 4 years or something like that across the board. To your point, a lot of this is actually going to scale up quickly also on the AI side. There’s a little bit of a different hoarding happening on the top end, not just the resources, but also of people, which seems to give further this notion of barbell, that there’s two extremes, the haves and have-nots, the super-duper talented people that get paid a ton of money, tens of millions of dollars a year at the very least. Then the emptying of the middle where there’s a ton of tech layoffs going on in some ways, the belly, as they would call it, is being expelled. The middle market, the managers are being fired because there’s nothing to manage. There’s a lot of positions going away. In some cases, you might keep some of the more junior talent, but with a little bit of experience. But even the talent coming out of colleges is not getting hired either. It’s a little bit of a weird thing where there’s hoarding at the top, there’s an emptying of the belly, the middle, and then the early, early, early is also not getting recruited. It’s like what gives? How is this going to look in the future? I agree fully with you, Bertrand, that there’s a migration of this talent, not only to other companies, but also to other jobs. There will be new jobs that will emerge out of this. The DevOps, dev tools market didn’t exist until maybe 20 years ago at scale, and it got created. In some ways, we’re seeing there will be new markets, there will be new roles and new jobs that will be created around engineering teams going forward. We can’t anticipate all of them. But basically, the emptying of the belly is true as it’s happening right now. The low hiring on the early and the top end, getting tons of money. We think this is a transition to something else. There’s the hoarding of engineering in general is coming to an end at momentum. Now it’s time to rightsize teams, to get the right at the table, et cetera, and start figuring out what works and what doesn’t work. We’ve already had some horror stories coming out even from Amazon where they were breaking systems with their use of AI tools, and I’m sure it’s happening across the board. I’m on a board of a company and been tremendously affected by Meta and its algorithms, where basically because of advertising, there have been people served with ads for this specific company where the ad doesn’t match the company, so basic stuff like that. It’s been actually very, very difficult because in some ways, the company goes back to Meta. It’s like, “Hey, dudes, you guys are serving ads that are not even our ads with our copyright and stuff. How does this work?” They’re like, “Oh, it’s AI.” It’s like, “Well, it’s AI but can you give me my money back?” They’re like, “No, we won’t give you money back.” This creates huge issues for companies, for example, that are very dependent on advertising, which obviously there’s a lot of industries that are. They’re actually in production systems at scale. Meta is, I think now, the largest digital advertising in the world. I think they outgrew Google in one of the last quarters. Basically, this has a tremendous effect that systems that are in production at scale are getting inputs and changes driven by AI tooling, and somehow nobody can say what the hell is happening. Again, there will be a reckoning, there will be a redistribution, there will be a rightsizing of teams and an adequacy of teams going forward. I personally think this is a transition period.   Bertrand Schmitt I think we are moving from hoarding or software engineering to hoarding the top of the top scientists in AI and hoarding of GPUs, GPUs/data center. For me, it was quite interesting to see the deal of Cursor with xAI, where basically they couldn’t get access to computing resources to run their model. But xAI had, I forgot the exact numbers, but close to half a million GPUs that no one, I mean, “no one was using” because their services are not so successful yet in terms of AI chatbot and the like. Basically, suddenly they are like, “You know what? We control access to resource.” But the new resource is, again, a mix of extremely talented AI engineering or AI scientists versus GPUs/data center. There is this race of controlling boss and everything else is going to be collateral damage. Some examples, I think, are quite interesting. You talk about some example of Amazon, even some production issues. I remember reading a quick post-mortem of one of the issues, and the conclusion was it was AI, definitely part of the issue. But the other part of the issue was AI used by junior engineers. For me, it’s interesting. It shows that actually junior plus AI is actually a danger zone. That’s why many companies are going to be way more careful. “Why do we need the junior people if they are just playing with fire?” I think we go back to that situation of barbell, as you call it. The top talents are extremely valuable because they know how a production system works. They are here to develop better AI systems. But the junior guys playing with fires, yeah, maybe it’s cute in startups, but in a big time production environment, a different story.   Nuno Goncalves Pedro There will be a barbell with top-end talent super-mega paid and then mid-level talent that is individual contributors still doing a lot of great work, et cetera. Along the way, a lot of emptying of entry, a lot of emptying of the middle. Where does the talent go? The Three (?) Destinations I think we could say there’s three destinations for this talent. Maybe there’s four, maybe there’s more. Three that we can immediately identify. One is the AI native startup piece, where we have smaller teams that potentially get to a lot of revenue or top line over time, and where the Series Seed is the primary round, where we’re seeing Series Seed being raised of tens of millions of dollars, actually even hundreds of millions of dollars in Series Seed. In some ways, the stars there can get incredible compensations in terms of stock. They will stay for private and selling in secondaries later down the road because there’s so much capital at the table. Actually, in some ways, salaries are very high as well in some of these companies. It’s not like you’re trading off anything. You can get paid a lot of money. If your company at Series Seed for 10 or 15 employees has raised 50-$100 million, you can pay great salaries. In some ways, this is the extreme destination. The AI native startups that can make it is the extreme destination. Now, there aren’t a ton of AI native startups that can raise 50-100 million to 400 million in Series Seed, just to be clear. There’s a handful of hot deals in that space, but that’s one clear destination for top-end talent going through that. In that market, I think that’s one of the destinations. The second one is more what we would call the human-verified premium. It’s more of a play of companies that has still the need of human in the loop, either in terms of development, also in terms of activity, either because go-to markets are very intensive, and so therefore you need to have sales forces, partnership teams, et cetera. Or on the engineering side, it needs to have a lot of customization, integration. Companies are not just going to the, “Oh, you can come in and just apply your AI tooling and somehow magically the systems all work.” there needs to be quite a lot of and work and high touch work in getting stuff done. A significant part of that market, I’m not sure, is super VC investible. Maybe it’s a hybrid of private equity in VC, more PE style in many cases. It’s a PE-hold, sell to someone else market. As we’ve discussed in a previous episode on the SaaS-apocalypse, that hasn’t quite worked out for PEs. Question marks on how that human-verified premium market is going to evolve. But obviously, there’s a lot of work still to be done there, even on the engineering and science side. That’s the second potential destination. Then the third more aggressive destination is the reindustrialized middle companies that have a lot of specificity in going after small and medium businesses, local or regional affectations like ERPs or CRMs for specific markets, et cetera. Those are the three natural destinations. I would add the fourth, which is big tech. I mean, big tech doesn’t magically disappear, and I don’t think it fits neatly into any of these three markets. In some ways, big tech is now looking at the extreme for top talent a little bit like the AI native startup because they can pay. They can pay the 100 million every four years, et cetera. I do think it will typify taxonomically into a fourth type emerging, where, as we discussed, you’ll have top-end individual contributor talent. You’ll have the absolute top-end of the market because they can get paid. Then you’ll start having the emergence of earlier talent that is highly capable, et cetera. That will go back to a bit of a normal distribution in terms of talent on big tech. For me, those are the four destinations that I would put at the table.   Bertrand Schmitt For me, big tech moving to big tech, I’m not sure if it’s really a destination. I mean, yes, in some ways it’s a reshuffle between the big tech companies. They are definitely all fighting in some ways for some of the same people. I can see that dramatic shift where big tech has to remove a lot of positions in order to replace by AI. Again, I think at this stage, it’s mostly driven by AI coding. We are still at the beginning because this is brand-new phenomenon that AI coding is so successful at its task. I don’t think it was true even 6 months ago. Some companies, take Anthropic, take OpenAI, are definitely there or close to be there in terms of no more writing of a single line of code by a human, zero. This is, again, 6, 12 months ago. Not true. But now it’s true in a few top companies. Take OpenClaw as well, most successful GitHub project of all time, not a single line written by its author. It would have been impossible. We’re talking about hundreds of thousands of line of code in a few months. It’s impossible to achieve that manually. If you look at the other big tech companies, the Google of the world, the Meta of the world, the Microsoft of the world, they are absolutely not there yet. They are going to be there because they have no choice. It’s you either go fast there or you die. You are not going to be able to survive competitors that are shipping 10, 50, 100 times faster than you are shipping. It’s a life and death situation. All the big tech companies are going to move, and mark my word, in the next 2 years from 10, 20% of AI-written code to 100%. During that transition, the next 2 years max, if you don’t do it in 2 years, you are going to die. Your stock price is going to crash. Then, of course, you will have to make changes. You will have to invest more in GPUs. You will have to invest less in your standard typical software engineer employees. Like you, I’m very optimistic that there are new buckets. AI-native startups definitely will be there. It will be transformational. Human-verified premium, very interesting category. In a way, it will be businesses that are inevitably less scalable through AI, and there is definitely a spot from there. I think the biggest would be the reindustrialized middle SMBs. Most of S&P 500 type of business are going to dramatically offer new software opportunities, new opportunity story to talented software employees because they will need to implement AI in everything they do. They will do it. They will need people who have software engineering knowledge in order to implement these systems. For them, what’s changing dramatically really is that thanks to much cheaper cost as thanks to AI coding, a lot of software projects that they couldn’t afford to do, that they couldn’t imagine doing by themselves, they are able to do it. They will invest in a lot more software capabilities than ever before. That will be a big game changer. And software, very tuned to their business model. There might be less buying of your traditional off-the-shelf SAF software and a lot more investment in a highly custom software by their own team, assisted with AI. I think that would be the part that is most transformed by all of this in a positive way.   Nuno Goncalves Pedro Alternative Cap Tables, Alternative Compensation Models This will lead to a very fundamental shift, right back to the broken contract. What does the new contract look like? It looks like alternative cap tables depending on which bucket are you transitioning into. If you’re going into your AI-native bucket, and you’re a top-end talent, you’re like, “Dude, I’m worth 100 million over 4 years, so just compensate me accordingly with a mix of options in the company plus my salary.” If you’re top 1%, you can probably get away with salaries that you’d get anyway at mid-level from 300K, 400K and above, and you can get actually a lot of options already in the company. A lot of this is happening right now. There’s a premium for AI, we know that. There’s a premium for AI at the top end of AI researching, in particular on companies that are doing hardcore research on staff AI engineers, so companies that require actual AI engineering. There is a premium that is significant. It could be as high as 18% over non-AI peers, and it widens actually with seniority, shockingly enough. This is more of an average than anything else. Now, for me, and it’s for debate, but the perspective is this extreme comp will need to compress at some point. There will still be the haves and have-nots paid much better than the have-nots, so to speak, but there will be a compression. The variance can’t be the variance we’re seeing today for absolute top-end talent. That said, there will be variants. We know that big tech for over a decade, decade and a half, for example, in the Bay Area, has been paying a lot of money for director and above levels that used to be the VPs, so a million, a million and a half a year, all in compensations. It’s not unheard of that this will actually increase after this stage. That said, I do think that the compensation extreme that we’re in will get diluted down the middle. It will actually come down at some point. It’s part of where we are today. As we know, it is still a bubble.   Bertrand Schmitt Yeah, it’s an interesting point. I think it’s possible. At the same time, that compression coming 2, 3, 5 years. At the same time, we have examples where there is no such compression. Take the top sports players in the world, golfing, basketball, NBA players. There has not really been any compression at all. For me, it’s interesting. If you look at the big tech companies, each being one of this top NBA team, why would such compression happen? As long as they are competing against each other and generating plenty of cash, I think there will be some fair question. We will see. I don’t have a strong opinion, but for me, it’s not a total given.   Nuno Goncalves Pedro For me, the shocking thing is the faster AI becomes better, the more that compression will happen, because at some point, it’s like, why do you need the top talent as well? I don’t know. It feels like you’re trying to evolve a system that’s there to replace you. It’s like, “Okay, I’m getting paid 100 million over the next 4 years”, and then you develop something that’s so good that replaces you. Thank you. That’s cool.   Bertrand Schmitt That’s a total possibility, yes, because we are in that very unusual market where the game is to only replace yourself and people like yourself. At some point, it is a possibility, I guess this one. Right now, we’re talking about replacing your “average software talent”. In 2 years, could we absolutely replace the absolute best top experts in the world? Probably. I think it’s just that at some point we’ll be reaching the stage where we strictly have no control anymore on our AI systems because no human is able to challenge and understand what’s produced. It’s not just a question of scale anymore. We’re talking about a gap in IQ, basically.   Nuno Goncalves Pedro Exactly. It will happen at some point in history. We don’t know exactly when. For the second bucket, the human-verified premium bucket, it’s difficult to see how an HVAC company or an HVAC roll-up of scale or a regional health care platform or high touch go-to-market, B2B, SaaS play, et cetera, for a vertical will compete. At the same end, they have to compete and they will compete. There will be more and more jobs, we believe, for engineering talent in these companies. They’ll have to be more and more AI-enabled themselves. The cash salaries will have to be competitive within the local markets, not necessarily with Silicon Valley. There will be potentially profit sharing and revenue sharing and actual dividends played at the table. The model there on the cap table needs to change a little bit, needs to be probably propped up more on salary and on some way of doing profit sharing or actually having dividends paid to employees and figuring out employee to equity in a more aggressive manner. This is the market that probably was already very attacked, so to speak, or let’s say, occupied by private equity firms. There are still obviously part of that model that would work well. There needs to be a fundamental shift, certainly on the quantum of salary compensation, dividend compensation, profit sharing, and all of that. Then last but not the least, obviously, we had the bucket around basically the reindustrialization of the middle, so everything else, which will take most of the belly that we were talking about. This is probably a poor analogy, the belly fat. It’s not belly fat, it’s people that were doing their jobs that now are getting disrupted. In some ways, that bucket will absorb a lot of that belly, will absorb a lot of talent. The small and medium businesses that Bertrand was saying will need to crucially become more AI, software-enabled by themselves, even with some core stuff and underpinnings that actually might not even require AI in terms of infrastructure platforms. There, you need to get properly paid. Again, how many people do you need in your engineering team if you’re a small business? Probably not a lot. It’s maybe you need one or two people and that’s it. They’ll need to be very nicely paid because they’re running the stuff in the rails. This is probably a market that over time, as AI gets more and more competent, will also be disrupted, but let’s not talk about the disruption to the disruption because otherwise, we’ll stay here the whole day, but certainly a market that has a lot of potential to shift and to absorb a lot of the moments that we’re seeing in terms of layoffs happening in the US in particular.   Bertrand Schmitt This category was a category that historically could not compete with Silicon Valley salaries, could not attract the most talented engineers. It’s not a category that didn’t want to bring these people on board. It’s a category that just couldn’t afford to bring this talent on board, typically. I think it would be a dramatic shift for them when suddenly there are opportunities to hire these people. There is an opportunity to hire them at maybe more reasonable prices from this company’s perspective. You talk about small companies, the great thing is that there are millions of small companies at some point. I think things could be truly transformational. Of course, some of these engineers, software engineers, might decide to become entrepreneurs on their own. Solo entrepreneurs, small businesses, build their own, easier to build their own product to market so to serve other companies. I think there will be quite dramatic changes because not all companies will be disrupted by AI as much, but not every company will benefit from improving processes, improving software through AI. At least early on, you will need this human touch to make it work inside a business. Interestingly enough, I was hearing that some companies like IBM were hiring more younger people to do the work of going to the client, understand their needs, propose implementation plans. That forward deployed engineer, those positions, I think there will be more and more available.   Nuno Goncalves Pedro Investor Landscape Fragmentation What happens to investor into the landscape? We already had an episode, the previous one, Episode 76, where we talked quite a lot about the big capital reset on the private equity and private reset, including venture capital. Just maybe to summarize, how does it align with the buckets that we’ve just been discussing? I think the AI-native bucket clearly is going to be the key bucket. There, we’re going to see two movements. One movement, which is the mega funds, as we discussed in the last episode, are no longer just VC funds. They’re really mostly multi-asset private equity funds, maybe even private equity hedge funds in some cases. Those funds will be all over the high-growth AI-native companies and will be pouring money into companies that are scaling really, really quickly. The early stage, so to speak, VCs, the actual VCs that will stay in the market will be the guys probably identifying the next big wave of AI-native companies. We’ve discussed that as well in the last episode, some research that we did at Chamaeleon that I shared in episode 76. We’ll see that as emerging. What happens to the second bucket, the bucket around human premium, human in the loop? Likely we’ll have more and more private equity capital going into it and the large-scale VC guys, the Thrives of the world, they’ve just announced Thrive Holdings, and others going after those markets as well. It’s trying to converge into the private equity market, which aligns with the point we made in the previous episode that the VC mega funds are no longer VC, that they are private equity, multi-asset class. They’re going after a bunch of things. There’s a conversion happening from VC into private equity. It was going to happen anyway because the private equity guys were coming into VC as well and the hedge funds were coming to VC as well. There’s a convergence in the middle of very, very large funds and large assets under management happening to go after some of these opportunities, certainly in Bucket B. Then this Bucket C, so to speak, the bucket of reindustrialization, as Bertrand was saying, very well, likely will be self-funded for a significant period of time. Will self-fund with their own cash flow. Doesn’t need to have a ton of capital intensity. Maybe you need one or two engineers to do stuff, but that’s it. You don’t need tons of capital. You didn’t need in the past, you won’t need it today. Not sure there’s going to be a fundamental shift to that market.   Bertrand Schmitt Yes, I certainly, overall, agree with you. That last pocket, probably little change to the capital and capital structure. Again, I see that as the biggest opportunity for a lot of people who might be less needed by big tech and also top tech companies. What is sure for the first category, the high native startups? I would say more overall in the VC ecosystem, there is no space left for SaaS anymore. I think SaaS, as we used to know it, is dead in some ways in the sense that new pure SaaS software startup are definitely out. Existing ones that are critical to run your infrastructure, the Salesforce of the world, I think they’re in a decent spot. Actually, interestingly, they changed their pricing model to now sell to AI agents, not just per seat. There is a change in pricing there. But this day and age of funding a pure SaaS software startup through VC money, no way. VC money going to AI-native startups, AI-focused startups, to biotech, to deep tech, to defense tech, yes. SaaS as a fundable category early on, I think it’s over.   Nuno Goncalves Pedro I’m a bit more nuanced as we shared in The SaaS Apocalypse episode. We can call it whatever we call. It’s applied AI is the new SaaS thing. Horizontal applied AI is the new horizontal SaaS or vertical applied AI is the new vertical SaaS. I agree in common with your point that very specific point solutions around SaaS will be disrupted by nature with all the easy stuff you can do today with AI. It will take a while. This is not something that’s going to happen this year. It’s going to happen over the next years. Maybe interesting to also talk about the exit markets. I think the IPO market, as we’ve also discussed in the past, there is, in my view, going to be a reopening of the IPO market, I think this year, probably later in the year, third or fourth quarter. The median time to IPO actually is going to be really weird because there’s going to be potentially some companies in the current landscape, bubble or no bubble, that are going to IPO, the OpenAIs of the world, Anthropics of the world, et cetera. There will be more and more aggression, I think, on M&A. Big tech has already shown it, that they want to buy into markets. Large non-tech companies have also started doing acquisitions in space. To prop up their IT teams, their engineering teams with this world that we’ve also discussed in previous episodes that I’m going to own my own engineering stack for now. As we see, that normally doesn’t withstand the test of time. At some point it will get unbundled and served by someone else. Then finally, the secondary market is very hot right now. Obviously, there’s heavy discounting on some areas, high premiums on others. The exit market, strangely enough, is going to be propped up, in my opinion, over the next year to 2 years, dramatically. Then we’ll see if there’s a big reckoning around the bubble that we are clearly in or not, if it’s a soft landing or hard landing. Definitely, there’s going to be a lot of exit paths over the next year to 2 years.   Bertrand Schmitt Concerning the “bubble”, I have two perspectives on this. One is it’s a bubble in the sense that money is going to a lot of players and some players are going to blow it up. There will be a concentration of players at the end, like it usually happens. If you look at, for instance, long time ago, the railway revolution, there was that intense influx of capital. At the end of the day, there was a dramatic change in transportation in the US and a complete railway system put in place. Yes, some investors lost money, some companies went bankrupt, but the transformation was fully real. There were a lot of top leaders at the end of this revolution. The change after that only happened, we guess, post-World War II, with the construction of the highway system and the rise of airlines and plane transportation overall. Here I feel it’s similar in the sense that, yes, there is a lot of money going in. Some players are going to blow it. They will misuse the money in different ways, but that’s part of dynamic allocation of capital. Of course, you make mistakes. That’s what happens. At the same time, I feel it’s a similar level in the sense of this is a dramatic change in the US infrastructure. This buildup of AI data centers filled with GPUs, integrated at scale with some of the best software in the world and running it, supported by a dramatic shift in energy infrastructure. This is for me similar to the Railroad Revolution. Some players might not own the data center they build because they didn’t manage well their debt, they didn’t manage to run proper software. You know what? They will get acquired by somebody else. I think we are at this level of fundamental transformation. The fact that in a matter of maybe 2 years, the move from 0% of code written by AI to 100 % written by AI is an insane dramatic shift. Just to be clear, when you move from manually coded to AI coded, we’re talking about a 100X difference in terms of speed at similar, if not better level of quality. The shift is dramatic, and on top of it, you don’t pay salaries anymore to achieve that. You pay CapEx, and with GPUs and OpEx with electricity. It’s a very big shift, positive shift in business model. New unions, no management over it, AI working 24/7. Personally, I think for me, bubble has a bad connotation in the sense of it was all for a waste. I don’t think it’s all for a waste. I think we are witnessing a dramatic revolution of our lifetimes, quite frankly, bigger than SaaS, bigger than mobile. From my perspective, it’s exciting times.   Nuno Goncalves Pedro Operator Playbook and Predictions Let’s move to if you are this person, what would you do in the future? Let’s start with two extremes and go from there. One is you’re non-tech, so you’re not an engineer, et cetera. You’re trying to figure out, how do I scale my activity? Maybe physical labor is where I want to go. It’s not, “Go west” anymore. Definitely not necessarily go west. You should go to, I guess, the states that have no sales tax with very cheap energy because that’s where the data centers are being built if you want to be in that market. Obviously, there’s a lot of stuff that needs to be done: HVAC, electricity work, et cetera. Don’t go west. Go low sales taxes, low cost of energy. That’s likely where the data centers are being built. You probably can just follow. There’s, I’m sure, some way for you to follow where the data centers are being built, but that’s next, I think on that extreme of the table. The other extreme of the table, let’s say you are super ambitious, maybe you’re no longer an engineer, but you’re a product manager in your prompt engineering. You could do prompt engineering all day long. You’re 28, 29-year-old superstar. What do you go and do? Likely either you start your own thing, start your own company because you’re so good at prompt engineering, you probably can do a lot of the code yourself, particularly if you have an engineering background, or you go and join very early an AI-native startup that you think has the chance of going through the roof, and you take a pretty good salary early on, a ton of upside on the company because guess what? Companies like that need product managers. They need people to figure out UX, UI. It’s not going to be, at least for now, yet AI figuring that out for you. Those are two extremes, just to give two of the extremes, like engineering, product management persona, and physical labor at the other extreme, non-tech, et cetera.   Bertrand Schmitt In some ways, every software engineering job is going to become the equivalent of a software engineering manager or a product manager, because suddenly you don’t have to do the coding anymore. You’re managing AI that is coding for you. Either you start to have some manager hat, but we saw the humans, so it’s a very different type of manager, obviously, or you are going to be really an empowered product manager. You’re skipping the middleman. You’re skipping the traditional engineering organization because your engineering organization is AI running and doing the work for you. I still believe that it requires some serious skills. I don’t believe in the vibe coder type of value proposition. I don’t believe in the prompt engineer becoming suddenly super incredible, able to manage that. I still think it requires some serious chops to do the best from all of this and to do it in a safe and sane way. It’s very easy to have poor taste, make mistakes. I don’t know you, but keep reading these stories on the heads of companies who lost everything because of the AI agents. That deleted stuff in production, and they had no backups or the backups weren’t deleted as well. Crazy situation. You cannot run companies like this if you let your agents running wild. You could argue it’s the early days. I would argue it that that issues would be there for a while. You need to have some engineering discipline at core in the company running the business to make sure things don’t go sideways because it would be easy for things to go sideways.   Nuno Goncalves Pedro I totally agree. If you’re thinking, Oh, should my kid go into science and engineering and computer science, et cetera? Absolutely, still, because of everything that Bertrand just said. You need to understand actually what code does and what technology does and what all of that does. That’s still a skill of the future. It’s not a skill of the past. In some ways, it’s still a skill of the future very much. Maybe let’s try two more extremes. Around the same level, the person that decided to do an AI native company bootstrapped initially, having difficulty raising a mega round, but could probably get away with raising a 2-3 million seed round, et cetera. Is that still viable? The answer is yes. There’s tremendous capital efficiency right now happening in the market still, 10 plus higher than if you were doing a SaaS company, and you were a founder in 2019 or something like that. That capital efficiency is going to reverberate. You can run a tighter team, smaller team. Actually, you don’t need that many salaries. If you’re a decent engineer as a founder or if you understand enough as a product manager to just generate that code, you can do a lot of stuff yourself, can bring in maybe one or two technical elements to the team early on as you would have done if you were bootstrapped anyway. There’s obviously a path for that. The other extreme is you’re in big tech, you’re level five, individual contributor, making a ton of money, or you were a manager, and you’re now out of a job, where do you go? You can go to a big company that is non-tech, S&P 500 company that’s non-tech, something like that. You join the company, you’ll probably get paid pretty well, maybe not as high as you were paid in big tech. There’s some stock at the table, but guess what? You’ll have probably more work-life balance than you ever did. That’s the trade-off. You’ll have a better job. On the upside, you can transform the company. You can help and be part of transforming a company from non-AI to AI-first or AI-enabled in the future, whatever BS that will look like in terms of the argumentation to the board. You can actually create tremendous productivity enhancements in a big non-tech company if you come with that background. Again, you’ll have certainly a better work-life balance, so not a bad deal, to be honest.   Bertrand Schmitt Also, to be clear, I talk a lot about AI coding because it’s truly transformational. You could argue that it’s going to be self-improving. We are in the situation of a self-improving AI that keeps improving itself thanks to automated coding. It’s a dramatic, virtuous loop. Obviously, AI is also going to improve everything else. It’s going to improve your marketing, it’s going to improve your search process, it’s going to improve your DNA. Improvements will be everywhere. It’s just that right now we are at a point in the quote-unquote revolution where there is one clear piece of the puzzle that is moving faster than the rest.   Nuno Goncalves Pedro Bertrand, the senior executives at non-tech don’t know anything about that. It could be just a great prompt engineer. That’s the only job you do. “I’m the chief marketing officer. I have someone below me that’s doing the whole work.” Nobody knows. Nobody’s the wiser, I guess. I’m being facetious, but not fully.   Bertrand Schmitt Yeah. There would be a transition period where what you described happen. I want to say, going back to AI coding, I think that the part of AI that as of today has reached a stage of limited AGI. We have reached, from my perspective, a limited type of AGI for coding. If you take coding as a discipline today, I think we reach AGI. If you go beyond coding, that’s true. If we are talking about coding, leveraging the latest LLMs: OPUS 4.7, ChatGPT 5.5, combined with Claude Code, Codex, and OpenCode for harness, I think we’ve reached AGI in the context of coding. I’m not sure everyone fully realize that and the consequence of that. I think the rest is going to come as well. We are going to see that category by category, usually categories that are more scientific in nature, where you can replicate, where you can test easily, where you can create clear success. Metrics will be the “easiest” to follow in that direction of self-improvement. I just want to highlight that this part is truly transformational, the root cause of everything we’re talking about today. At the same time, it’s coming beyond coding.   Nuno Goncalves Pedro I think it is true. There are a couple of markets where that might not hold true, which is maybe the final path. If you’re thinking of starting your own business in plumbing and in HVAC maintenance and installation, this is a pretty good time for the reasons we already said before. There’s a lot of buildup of data centers and all that stuff, but also for other reasons, because it’s an activity that won’t be disrupted by AI yet. You need them embodied AI. You need physicality to AI to do stuff like actually fixing pipes.   Bertrand Schmitt Until Optimus replace you.   Nuno Goncalves Pedro Yeah, but if we’re 3, 4 years out in terms of a lot of these optimizations that we’re talking about at the software layer, we’re 10 years plus out on embodied AI, right?   Bertrand Schmitt Oh, yeah, it’s 10 years.   Nuno Goncalves Pedro We’ll probably be optimistic as we speak. That’s a nice business. I’m thinking of starting to go into that market. If you guys are interested in listening to this, just reach out to me. What’s the angle? I think there’s a lot of stuff you can do in the buildup of some of these businesses, plumbing, HVAC, all sorts of maintenance. There are markets that are just totally messed up. Handyman market in the US is totally messed up. There’s a bunch of companies out there that try to go after it with marketplaces and stuff. I honestly just start something from scratch, a small business, and go from there.   Bertrand Schmitt Yes. They’re an interesting middle. Think about accounting firms, consulting firms. I think they are not as easy to replace, but at the same time, there is no way on what they do is not going to be dramatically changed with AI. I don’t know if it’s 50, 80, 90% of the job, but this is changing quite dramatically, would be my expectation in the coming few years. Conclusion Thanks for listening episode 77 of Tech Deciphered about that great talent redistribution. As you heard it from us, we believe there is a dramatic change in play, enabled by AI coding, and that ultimately a lot of the big tech companies are changing their employee distribution, way more focused on the top talents and bringing more GPUs. As a result, we will see a change in their staffing. Some of this change will benefit AI-focused startups, but probably more likely will benefit the bigger SMBs, the S&P 500 companies of the world that will finally be able to bring inside and afford some of the talent that were in some ways trapped by the top 5, 10, 20 software companies of the world. Thank you, Nuno.   Nuno Goncalves Pedro Thank you, Bertrand

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!This was recorded before Railway suffered a major GCP outage on May 19, despite being a multi-AZ, multi-zone mesh ring, with HA fiber interconnects between their Metal GCP AWS, because workload discoverability was unintentionally still tied to GCP. All has been resolved with a post-mortem.Railway did not start as an AI infrastructure company.It was founded in 2020 years before agents became the default way people thought about deploying software. Jake Cooper, formerly at Bloomberg and Uber, started Railway with a simple obsession: the activation energy to ship something to production should be near zero. Push code, get a URL, iterate. No Docker files, no Kubernetes manifests, no Ansible scripts stacked on Ansible scripts.For years, this was a slow grind. Railway spent its first 18 months hand-acquiring its first 100 users with Jake personally greeting every Discord signup on a second monitor.Today, Railway has raised $124m and is growing very fast. A 35-person team supports 3 million users, adding roughly 100,000 signups a week. Their bare metal data centers have a 3-month payback period vs. renting in the cloud, with 70% margins funding aggressive cloud bursting when needed. The servers they own have actually appreciated in value as RAM prices have climbed basically meaning the value of their hardware now exceeds the capital they've raised.From rebuilding Railway's network overlay over a weekend to moving the vast majority of workloads onto its own bare metal data centers, Jake Cooper is trying to build a new cloud for an agent-native world. In this episode, Railway's founder and “conductor” joins swyx and Alessio to unpack why the next era of software infrastructure is not just “Heroku but newer,” what agents need that humans did not, and why the old deployment loop of Git, PRs, CI/CD, and static cloud resources may be heading for a rewrite.We go deep on Railway's infrastructure stack: own-metal data centers, three-month cloud payback periods, cloud bursting, data center debt, Railpack, Nixpacks, Temporal, feature flags, Central Station, content-addressable filesystems, agent-safe production forks, and why the CLI may become more important than the canvas in an agent world. Jake also shares the founder journey behind Railway, how the company survived losing $500K/month, why it now serves millions of users with only 35 people, and why he believes the pull request is dying.We discuss:* How Railway went from a slow six-year grind to adding 100,000 users a week* How Railway thinks about agents as the next dominant software species* Why agents need version control, observability, compute, storage, and orchestration at 1000x scale* The economics of Railway's own-metal data centers and three-month payback* How Railway uses cloud bursting while scaling its own infrastructure* Why data center debt can be a better tool than venture debt for infra startups* Central Station, Railway's internal system for clustering customer feedback and incidents* Why responsible disclosure and over-communication matter for platforms* Why feature flags, progressive rollouts, and shadow traffic are essential for agents* Temporal's strengths, pain points, and why workflows matter for agents* Railpack, Nixpacks, Nix, and lazy-loaded content-addressable filesystems* Why “cattle, not pets” may change if you can clone the pets* Why Railway is building a new cloud from scratch instead of copying hyperscalers* The solo founder path, focus, writing, and how Jake thinks about company buildingRailway:* Website: https://railway.com/* X: https://x.com/RailwayJake Cooper:* LinkedIn: https://www.linkedin.com/in/thejakecooper/* X: https://x.com/JustJakeTimestamps00:00:00 Introduction: What Is Railway?00:02:07 Jake's Path to Railway00:06:13 Railway's Six-Year Growth Story00:08:52 Rebuilding the Business After the Free Tier00:11:17 Agents as the Next Software Platform00:13:29 Railway's Infrastructure Philosophy00:15:42 Bare Metal, Cloud Economics, and the Compute Crunch00:17:22 Cloud Bursting and Five-Cloud Networking00:20:20 Data Center Debt and Infra Financing00:23:31 Data Centers in Space00:25:24 What Agents Need From Infrastructure00:28:24 CLIs, Canvas, and Agent-Native UX00:35:15 Central Station, Incidents, and Responsible Disclosure00:40:30 Safe Rollouts, SRE Agents, and Production Forks00:45:00 AI SRE, Specs, Code, and Tests00:48:24 Self-Replicating Infrastructure and the New Serverless00:53:18 Heroku, Temporal, and Workflow Engines01:04:07 Railpack, Nixpacks, and Lazy-Loaded Filesystems01:06:01 Coding Agents, Token Spend, and Roadmap Acceleration01:10:56 The Pull Request Is Dying01:12:28 Feature Flags and the Agent-Era SDLC01:16:15 Cattle, Pets, and Cloning Machines01:19:29 Solo Founder Lessons01:24:12 Focus, GPUs, and Building a New Cloud01:28:20 Closing ThoughtsTranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: Hey, hey, hey. Today we're in the studio with Jake Cooper of Railway.Alessio [00:00:14]: Conductor of Railway.Swyx [00:00:15]: Conductor at Railway. Yeah.Alessio [00:00:16]: Choo-choo.Swyx [00:00:17]: Do you actually have that anywhere, like on your business card?Jake [00:00:20]: We call some of our volunteer moderators conductors. I don't have a business card. We're not that big yet. At some point I will. I got handed a nice business card from the Supermicro folks, and I was like, “Damn, this is pretty official.”Swyx [00:00:30]: Business cards are coming back.Jake [00:00:32]: They're cool. They're hip. The conductor thing is good. We're trying to figure out what we want to call each other internally. Some people think it's super cringe and say, “You don't need a name for people internally.” Some people want to call each other something. We still don't have a really good one.Jake [00:00:55]: We've got New Railcrews, Trainiacs. Nothing has stuck yet.Swyx [00:01:00]: I like Trainiac. Trainiac sounds good. Railwayians. For those who don't know, what is Railway? Let's give people a crisp definition up front.Jake [00:01:09]: Railway is the easiest way to ship anything. You go to the canvas, or you talk with Claude, and you say, “Deploy a Postgres instance, deploy my GitHub repository, run this code,” and you're off to the races.Swyx [00:01:22]: You've got a nice animation on the landing page.Jake [00:01:24]: Thank you. None of my work, by the way. They don't let me touch the design stuff anymore.Jake [00:01:25]: We want to make it trivially easy not just to deploy things, but to evolve applications over time. Most tooling right now stacks entropy on top of entropy: Docker, Kubernetes, Ansible scripts, and all these other things. If we can version all of your software and keep track of all the changes, then we can make it trivial to clone environments, fork into a parallel universe, get copies of production data, get copies of any services, make changes, validate them, and collapse them back in without reproducing everything across a staging environment.The Railway Origin Story: From Uber Systems to a New CloudSwyx [00:02:07]: I was looking at your background: Bloomberg, Uber. Nothing immediately stands out as, “This guy is going to found the next great platform as a service.” What prepared you for Railway?Jake [00:02:21]: It was curiosity to keep going deeper. I started out on front-end stuff, working on Wolfram Mathematica and porting it over. Then I briefly moved to Bloomberg, then toward Uber and distributed systems, taking the Jump Bikes systems and moving them to a distributed system built on top of Cadence, the pre-Temporal Temporal.Swyx [00:02:44]: Which, by the way, I'm happy to talk about, pros and cons.Jake [00:02:48]: Totally.Swyx [00:02:51]: But let's do the Railway story.Jake [00:02:52]: It has been a continual step of wanting an experience. Whether it's walking up to a bike, unlocking it, and having it work frictionlessly, or something else, the depth required to make that happen follows from the experience. A lot of the work I do, and a lot of the team does, is in service of that experience. We fundamentally don't care how deep we have to go. We will swim to the bottom of the swimming pool to get the experience.Jake [00:03:17]: I don't have a physics PhD. I did an EECS degree. It has always been about figuring out the next step: how do we get there? That's what led to starting Railway for that experience and then moving all the way to bare metal data centers. I was adding patches to the kernel this week to get the experience there because I can see how much better it can be.Swyx [00:03:49]: Other patches to the Linux kernel this week?Jake [00:03:51]: Yeah. Not upstream. Our fork.Swyx [00:03:52]: That's a flex. Railpack? No, this is different. This is the OS on top of Railpack?Jake [00:03:57]: No, this is an actual kernel patch. It's always literally: what do we have to do to get that experience? Then figure it out. Anything is figureoutable.Swyx [00:04:10]: Would you send the patch upstream, or does it not fit other use cases?Jake [00:04:13]: Maybe. We have to work out the experience internally. It has to do with the storage layer we're building for some of the agentic stuff. Maybe it'll be useful upstream, but it's deeply useful for us internally.Open Source, Forks, and Non-Deterministic VersioningSwyx [00:04:29]: You mentioned open source before. How do you think about starting from open source, and then coding agents letting you do a lot more from forks of it?Jake [00:04:38]: GitHub's original sin is that it's almost a series of broken pointers. You have this thing, then you clone it, and now you've lost the whole upstream. How do we make it trivial for people to modify really small pieces of it?Jake [00:04:51]: We think of Git in a discrete sense: I've either made a change and merged upstream, or I haven't. What would it look like if it were percentage-based, a little more non-deterministic, or a stream of changes that users traverse as a percentage rolled out in general and then rolled all the way up?Jake [00:05:13]: We have the open-source kickback program and let you deploy templates because we want to make it trivial for people to version these shards over time. It solves a large problem around authentication, authorization, and security. NPM has a way to define, “Don't take any new packages.” The ideal end state is that you roll out progressively to users with the minimum impact zone and continue rolling up. JPMorgan should probably be the last one on the patch line, for all our sakes, because our money and livelihoods are there.Jake [00:05:53]: It's okay if Johnny Vibe Coder gets a broken patch because there's so much entropy in the system that the rubber has to meet the road at some point. You have to test at varying levels.The Long Grind: First Users, Free Tier, and Making the Business WorkSwyx [00:06:13]: I wanted to pull up this glorious chart, which is your usage or number of daily signups?Jake [00:06:22]: Daily signups, I think.Swyx [00:06:24]: You started six years ago. It was a slow grind, and now you're on a rocket ship. You say, “Don't doubt your fight and don't quit.” Maybe pick out certain points that were key inflections for the company.Jake [00:06:40]: At the start, it's about getting your first 100 users, hell or high water. We had a website and a support link. The support link was the Discord channel. I had notifications on with two monitors: the monitor I was working on and the other monitor with Discord. If anybody came in, I was immediately like, “Hey, how's it going?” It was rare, so getting those first 100 users to come back was the start.Jake [00:07:14]: Then you build a consultancy factory because users want all these things. You have to go back to the board and ask, “What is the actual product offering I want to build on top of this?”Jake [00:07:28]: VCs want charts that always go up and to the right, but in reality you don't necessarily want charts that look like that. For us, there have been periods of expansion where we add features to test use cases, and periods of compaction where we ask, “If the experience we have is good, how do we make it significantly better?” Maybe we strip out features that don't fit our ICP anymore.Jake [00:07:57]: The boom from 2022 to 2023 came from the free tier. Everybody under the sun was using it.Swyx [00:08:09]: A lot of Reddit bots and Discord bots.Jake [00:08:12]: And crypto miners. When you build an open product on the internet where anybody can sign up, the internet is a horrible place with so many things. You go through periods of asking, “How do I reach as many people as possible?” Then, “How do I fit the exact use case for the people who really matter and are really excited about this specific thing?”Jake [00:08:39]: Then there was a two-year period of making the actual business work. During the free-tier era, we were losing about half a million dollars a month.Swyx [00:08:59]: On a $20 million bank account.Jake [00:09:02]: On a $20 million bank account with maybe $50,000 a month in revenue. That's a horrible business. I don't know how anybody invested. But you have to go through it and say, “We have an experience people love, but the business has to work.”Jake [00:09:17]: There are two schools of thought. You can run the horrible business all the way up with bad margins, or you can go back and make it work. We've always wanted a super lean team. We're 35 people right now. It's very small.Swyx [00:09:36]: Supporting three million already?Jake [00:09:38]: Yeah. We're adding 100,000 users a week right now, so it's growing fast. We don't want to add headcount for the sake of headcount or throw bodies at problems. We want to build systems. It's hard to build systems during expansion because you're adding things to the system because people are asking for them or things are breaking.Jake [00:10:00]: We had to cut off the free users for a little while, rebuild the business, and make sure it worked. We want to reach as many people as possible because software is important. It's become difficult to create things in the physical world, so it's important to make it easy for people to build in the virtual world and have access to creation. But there are legs to that journey.Jake [00:10:30]: You can see divots in the charts. If you follow between 2025 and 2026, it's either summer or winter. People go on holiday with family.Swyx [00:10:50]: It affects that much?Jake [00:10:51]: Yeah. It's kind of B2C and kind of B2B. People are shipping constantly, then they stop. Our activation curve now shows more people activating on weekdays because we have more business users, so it smooths out over time.Agents as the New Interface to DeploymentSwyx [00:11:17]: Was there a point where you started prioritizing AI development or agent development?Jake [00:11:24]: We've prioritized agentic as a top-of-funnel thing. Over the last six months, we've deeply prioritized agentic as a mechanism to build and deploy things because we believe the curve is so steep and that is how people will build and deploy software.Jake [00:11:42]: It almost fundamentally doesn't matter whether this is dot-com or not because we're all on the internet anyway. If agents are going to deploy a bunch of things and we hit an inference wall at some point, we'll fix those problems. The dominant species over the next 10 years is that we've moved from assembly to C to C++ to JavaScript to words. You're going to need to close that loop.Swyx [00:12:13]: When you say this is dot-com, did you mean buying the domain, or the general case?Jake [00:12:17]: I mean the dot-com era, when companies had a huge run-up because people understood the internet was important. Then they hit bottlenecks, fundamental laws of physics, math didn't work, and everybody came back down to earth. But it didn't matter because the internet became so impactful. If you operate on a long enough time horizon, you should build these things anyway because you can see where it's going.Jake [00:12:45]: That's where I think a lot of agent stuff is. You get to a point where you're running thousands of agents in parallel. What is the inference cost? What is the compute cost? How do you make that efficient? How do you coordinate all this? We have issues coordinating humans; we don't even have good tooling for that. Now we have to figure out how to get agents to coordinate, safely version changes, and know when to raise their hand for someone to intervene. Otherwise it becomes an interrupt factory.Railway's Infrastructure Thesis: Network, Compute, Storage, and MetalSwyx [00:13:19]: Let's go right into the technical side. What are the core infrastructure or architectural beliefs of Railway that allow you to do what you do?Jake [00:13:29]: The primitives matter a lot for us. We need network, compute, storage, and orchestration around it. You need control over a lot of those things. We've talked a lot about how we don't really use Kubernetes because we want higher-order control to place workloads in very specific places.Jake [00:13:48]: The reason is that you have to be very efficient with agents: memory reuse and all these other things, or you're going to massively blow up your cost structure. Being able to rack and stack your own servers and build your own metal unlocks performance and cost. Experiences where you're running 1,000 agents in parallel are not massively cost prohibitive.Jake [00:14:13]: Token use and compute use are blowing up. Over time, those things have to get a lot more efficient. You can get a lot of margin to make those experiences solid by building your own metal. That's all in service of offering a differentiated experience to as many people as humanly possible.Swyx [00:14:51]: You have a data center in Singapore.Jake [00:14:53]: Yeah. We have two in every other region now. In Singapore, we're adding a second one in Q3.Swyx [00:14:58]: What's it like? I've never built a data center. Do you go to Equinix and say, “I want some slots?”Jake [00:15:05]: Yeah. Equinix. You basically go and say, “I want power and I want a cage.” They say, “Great, here's what it's going to be.” You rent the cage for a period of time, fill it with racks and servers, and hook up internet to it. That's all the pieces.Swyx [00:15:36]: Then you handle everything else.Jake [00:15:37]: You handle everything else.Swyx [00:15:39]: What's the math versus clouds doing it for you?Jake [00:15:43]: If we rented in the cloud, our payback period when we go to metal is about three months.Swyx [00:15:50]: Which is crazy.Jake [00:15:51]: It's nuts. That's four years of depreciated hardware. You're going to see a lot of this compute crunch because hyperscalers are buying up a lot of stuff. We're working directly with OEMs, resellers, and people building these machines: Supermicro, Dell, and others.Jake [00:16:11]: Upstream, there's a bunch of supply pressure. When we raised our last round, between deploying capital for servers and now, the amount of money we've raised is less than the amount of money we have in the bank plus the value of the servers because the servers have appreciated as RAM has gone up. It's nuts how valuable hardware has become.Jake [00:16:50]: If you look at hyperscalers, they deployed around $80 billion of capital expenditures this year, and next year will be more. That's a massive infrastructure build-out. You look at that and think it's crazy that they're spending way more than the Manhattan Project. But if every person is going to run dozens or hundreds of agents in parallel, you have no conceptual idea how much compute is required to make that experience happen, even if you're deeply efficient and sharing resources. And that doesn't even count inference.Swyx [00:17:22]: How do you plan the build-out? The growth chart is so vertical. Are you usually at 100% utilization as soon as racks are live? How far ahead are you planning?Jake [00:17:33]: We still maintain cloud presence for bursting. We work with AWS, GCP, and a few other clouds. We can rent, and then the moment we get space or power, we compact those workloads off the cloud. We started on the clouds, then built a system to migrate to our own metal. There's nothing that says you can't continually do that again, and that's exactly what we do. We never want to be compute constrained.Jake [00:18:09]: At the start of the year, we actually became compute constrained because one upstream provider wasn't able to give us quota at the rate we needed, and the hardware was slower. I spent a weekend rebuilding our entire network overlay so we could straddle five clouds: Oracle, AWS, ourselves, GCP, and one other one. We can do more than that now.Jake [00:18:38]: We got into a spot where we were trying to pack instances tight because we couldn't get enough compute. That led to a few reliability issues, which are now past us. I made a tweet pointing out that it's becoming harder and harder to acquire compute at the rate these models need to acquire compute. We got bit by it.Swyx [00:19:15]: How do you think about pricing knowing you might not have your own metal available at all times? Are you pricing assuming you need extra margin if you end up going into the cloud?Jake [00:19:26]: Because we've built out our metal data centers, our margins on metal are around 70%. We can deeply subsidize the cloud business if we want to scale at a reasonable rate. We have a few levers: metal, which makes the margins; cloud burst; debt to buy servers; and venture capital. It's an interesting operational problem: how much cash do we have, how much should we raise, how quickly can we deploy it, and can we scale revenue as quickly as we scale compute?Jake [00:20:05]: If we continue making it trivially easy for people to build and deploy, then the faster we close that loop and the more operationally excellent we are with capital, the faster the business can scale. It's almost a straight linear deployment rate.Financing Infrastructure: Hardware Debt, VC, and Operational LeverageSwyx [00:20:20]: I think infra startups raising debt is a tool people don't utilize enough or know enough about. What can you tell us about that? Is it secured against your CPUs?Jake [00:20:32]: It's secured against our hardware.Swyx [00:20:37]: What rates do you get? Who are the lenders?Jake [00:20:39]: We pay prime plus a spread, and we can refinance any of the debt as rates go down. The terms are pretty good. The unfortunate thing is that Twitter has no nuance, so people say, “Venture debt bad.” But as with all things, there are specific tools and areas where you can be deliberate instead of using one tool as a hammer. Venture capital is not the hammer for everything. You have to explore and figure out what works.Swyx [00:21:12]: VC is usually the most expensive financing you can get.Jake [00:21:15]: Yeah. I also think people think about VC incorrectly from a capital-raising perspective. Most people think, “How do I raise as much money as possible from whoever is probably the best I can get at that time?” That's close to right, but what we've tried to do is figure out what unfair advantage we can buy with that equity.Jake [00:21:34]: It's the most expensive equity you're going to give away at that point in time, assuming the company keeps getting better. How do you use it to work with someone stellar who complements you? In the seed stage, I had never started a company. Ray Tonsing had good advice, and I could text him all the time. He was really fast. Awesome.Jake [00:22:01]: Then with John and Erica at Unusual, they said, “You roughly know what you're doing building a product. We'll mostly leave you alone and be available for advice.” Amazing. Then we got to Series A and the business was an operational tire fire because we didn't know how to scale a business. Work with Erica, and Jordan is over at Redpoint, so bonus.Jake [00:22:28]: Now we've raised from TQ and FPV as we're moving into enterprises. Every step of the way, we've asked: who can we partner with at this specific time to unlock the next section of the journey? I don't know enterprise sales. As an engineer, I can eyeball what features we might need, and we have wonderful people internally who can help. But you want boardroom dynamics where everyone is aligned and asking, “How do we win this?” instead of bickering about strategy.Data Centers in Space and the Physics of ComputeSwyx [00:23:31]: You had a tweet about data centers in space. Why no data centers in space?Jake [00:23:37]: It's not “no data centers in space.” My hot take is that I think it is solvable. I've just never seen anybody solve it.Swyx [00:23:49]: You said, “How are you going to dissipate that much heat in a vacuum?” You're making a physics claim.Jake [00:23:55]: I haven't seen anybody prove how you're going to dissipate that much heat in a vacuum. It doesn't mean it's not possible. It just means nobody has brought it up yet.Swyx [00:24:05]: Astrophage.Jake [00:24:06]: I don't know what that is.Swyx [00:24:07]: The Martian thing. Okay, you're very logical.Jake [00:24:09]: It could work. A lot of people are putting the cart before the horse. They say, “We're going to put data centers in space.” Okay, but how? “We have time to figure it out.” It's like in The Martian where they ask how they're going to intercept something and say, “We'll figure it out.”Swyx [00:24:36]: Making a bet on human invention is weird because you blind trust that it can be solved. But with physics, there are first-principles bounds you can put on it. Maybe not. Maybe you're asking to travel time or break a fundamental thermodynamic law.Jake [00:24:57]: I don't know how VCs do this either. How do you know what's not possible and a grift versus what's possible but sounds completely insane? “We're going to put data centers in space.” Coin flip as to which it is, and I guess you'll know in 10 years. That's one cycle.What Agents Need: Versioning, Observability, and 1,000x ScaleSwyx [00:25:23]: Moving back to agents. The branching, fast spin-up, and orchestration you do feels like pre-work that happened to be exactly what agents want. What do agents want differently than humans?Jake [00:25:37]: They want the ability to version things. It's not that different; it materializes slightly differently. Agents want a way to test changes incrementally. Engineers have feature flags. Is there a reason agents can't use feature flags? I don't think so.Jake [00:25:54]: They want version control. Can we use Git or not Git? That one is up in the air. I think something outside Git will emerge for how we version these things over time. They need observability. You need to query what happened, when it happened, which steps failed, traces, logs, metrics, and all the rest. They need network, compute, and storage. They need to write files, save files, iterate on files, and snapshot file systems.Jake [00:26:25]: A lot of what humans needed is in line with what agents need. Branching and forking are not different; we're just moving 1,000 times quicker. It can look like you need something massively different, but what you need is something massively better than what existed. You need orchestration massively better than Kubernetes. You need networking probably better than Envoy. It goes all the way down the stack.Jake [00:26:55]: If the workload profile doesn't change so much as it gets massively compressed because you need thousands of these things, what assumptions change? etcd is going to melt. You need to replace it with something. You can go all the way down the stack and say, “That part has to change, that part has to change, and that part has to change.”Jake [00:27:19]: The interesting thing about the super-exponential curve is that you have to build systems where you can rip out those parts at any time because a new bottleneck might emerge. You get good at parallel agents, and a different part of the system breaks. So it's similar to what humans needed, but at 1,000x scale.Jake [00:27:55]: How do you do code review in the age of agents?Swyx [00:28:00]: You throw more agents at it.Jake [00:28:01]: You don't. But then who reviews for CVEs and all these other things?Swyx [00:28:07]: More agents.Jake [00:28:08]: And that's how we hit the inference wall. You can continually throw agents at the problem, but I think there's a limit to the number of agents you can throw at a problem.CLI, Agent Handles, and Closing the LoopSwyx [00:28:24]: You already had a CLI before it was cool. How is the shape of what you're exposing changing, if at all?Jake [00:28:28]: CLIs have always been cool. The CLI changes because we think about how to give Claude, Codex, ChatGPT, or any model a handhold.Jake [00:28:50]: A CLI is a single command: deploy, get logs, and so on. Things that were prohibitively annoying to humans are not annoying to agents. They're nice. If I handed you a CLI with 40 arguments and 600 flags, you'd think, “I'm never going to use all of this.” But if you hand it to an agent, it says, “This is excellent. I have so many handles to work with.”Jake [00:29:24]: If you're going to expose things to agents that way, you want as many handles as possible where they can get information, query dynamic information, and close the loop quickly. Most problems right now are about how to close the loop as quickly as possible. Where does the agent get stuck, and how can you remove that?Jake [00:29:49]: Telemetry is important. If you can tell where the agent gets stuck from the CLI and say, “12% of people deviate from the happy path because of this, and now I add this argument and drive it down to 2%,” you massively increase the rate of loop closure.Jake [00:30:03]: That's how we think about not just the CLI, but every point in the dashboard. It's a user journey: I hear about Railway. I get something deployed. I get my first green build or aha moment. I see an endpoint, logs, whatever. Then I iterate. The iteration loop is indefinite. The user wants to deploy a new thing, a Postgres instance, change code, and keep iterating.Jake [00:30:36]: If you focus on the iteration loops and what's blocking them from closing quickly, one thing we say internally is: you never want to be waiting on compute anymore. You always want to be waiting on intelligence. If you're waiting on compute, there's a bottleneck that needs to be destroyed because eventually that bottleneck becomes so large that another workflow emerges to change it.Jake [00:31:04]: We've built a product where you push code, build it, and so on. But I fundamentally believe the push-pull loop is going away. We'll get to a point where you make a small change in production, that change is versioned across your infrastructure, you're working alongside copy-on-write versions of your database and infrastructure, and then you merge it in and it's instantaneously live. That's the holy grail of loops. The push-pull-rebuild thing is a point of friction that we're removing entirely.Canvas as Output: Dashboards, Context Anchors, and HyperstructuresSwyx [00:31:43]: It's incredibly fast. If anyone hasn't tried it, that fast feedback is great. My hot take is that Railway was famous for its canvas, which visualizes your infrastructure and lets you manipulate it visually. But that was for humans. For the next phase of growth, Railway CLI is more important than canvas.Jake [00:32:05]: The canvas is funny because it's a mechanism to show changes over time. You're right that previously we used it a lot as an input. Moving forward, its goal is more like an output. You would go to the canvas, make changes, see them, and watch your infrastructure evolve. Now agents have access to the CLI and can make those changes. So the canvas becomes an output: what information does the human need at this moment to make suitable decisions about control requests? Do I approve this or not?Jake [00:32:57]: It also has to be an anchor for your context, a port in the storm. Think of it like layers in a file system. You start with a project, then drill down into services, then into a function or code, because you want to represent the entire thing not just in your head, but in the canvas. Other people can share that representation, think on the same wavelength, and move quickly.Jake [00:33:33]: A lot of organizations get in trouble as they scale because all the context lives in someone's head. “How does this microservice work?” “I have no idea; go ask this person.” Then you have whole categories of products built around context discovery. A lot of that melts away if you have a solid hierarchy and can infinitely nest services, code, context, and everything else all the way down. That's what lets you build these structures over time.Jake [00:34:18]: It's also what lets us build what I've called hyperstructures: things that are way bigger. You look at the Golden Gate Bridge and ask, “How did we build that?” There's a meme that we lost the technology. To some extent, yes, because the coordination that built those things evolved and changed. We lost some of the art of building structure as we jammed everything into Slack.Swyx [00:34:52]: But you jam everything in Discord.Jake [00:34:53]: Same point. It doesn't matter. It's message passing and interrupts, message passing and interrupts.Swyx [00:35:00]: So you're arguing there should be something better and more structured than Slack?Jake [00:35:04]: Yeah. For sure. I think Slack is awful, and Discord is awful too.Central Station: Context Routing, Support, and Incident ClustersSwyx [00:35:09]: This is the equivalent of my mom test. What have you done that has your solution to this?Jake [00:35:15]: Internally, we've built a tool called Central Station that aggregates all the context from our users. Every piece of feedback, every customer support item, everything gets aggregated into clusters. If an incident is brewing, we can determine how many users are affected and break off a discussion based on that.Jake [00:35:40]: That is more helpful than long-running channels where you're trying to decide which channel to put something in. If you can dynamically aggregate information and dynamically route it to the right person based on context, it works better. We know internally that these four people are close to networking. If we see a networking thing, we can drill it down to those four people. If it's with this part, we can look at the commits. This is no longer a manual process internally.Jake [00:36:13]: If you go to station or help.railway.com, that's why we built it. We wanted to scale with a massive amount of leverage by aggregating feedback.Swyx [00:36:27]: This is built in-house?Jake [00:36:28]: Yep.Swyx [00:36:29]: I remember helping out on this one with Angelo in 2023. You scale a lot with a very small team.Jake [00:36:38]: Yeah. We're about 10 times bigger now.Swyx [00:36:40]: You have your full developer code here? Very cool.Jake [00:36:44]: If you go to railway.com/stats, we expose this as a pub-sub-able thing. It's all real-time metrics. There's a way to get it as JSON somewhere if you care.Jake [00:37:01]: We're big on trying to build everything in public and talk about what we're working on. We've had issues in the past, and we'll say, “Here's how we're fixing these things.” We've gotten compliments and flak for incident reports. We're always trying to make them better and talk with people.Incidents, Disclosure, and Progressive RolloutsSwyx [00:37:20]: You had a big one recently. I liked that it was scoped to 3,000. You presumably used Central Station. Talk through what happened and how you address it internally as a team.Jake [00:37:38]: Internally, this one really sucked. It had to do with an upstream provider that didn't do the behavior it said it documented, which is unfortunate given they wrote the RFC for how the behavior should work. We rolled those things out, and Central Station caught it initially when a couple users said caches weren't invalidating. We turned it off immediately.Jake [00:38:03]: When you roll out to a large user base of three million people, you get a lot of disparate behaviors. We tested in staging and had tests, but we hit an edge case. We've hardened those systems, and now we can make that better. But it was a tough one.Swyx [00:38:39]: I always wonder how private disclosure is supposed to work if people find an issue. Are they supposed to contact you first? When you run a platform, these things will happen. What channels should people pursue to quietly resolve it before it becomes a bigger incident?Jake [00:38:59]: There's responsible disclosure. We err on the side of over-disclosing and letting you know something is wrong versus having your provider gaslight you. We've erred on sharing those things more publicly, even if they impact a small subset of users. That's a decision we've made internally. We have four values. One is honor. The honorable thing is to notify people to the widest degree at which they may have been affected or there was an issue, and then confront it head-on: why did it happen, what can we do better?Swyx [00:39:45]: Not the whole user base. That's because of incremental rollouts and other things?Jake [00:39:50]: Yeah. Progressive rollouts.Swyx [00:39:54]: That should be the norm at all large platforms.Jake [00:39:58]: It should. A variety of companies do this. There's the quote that Meta runs 10,000 different versions of Meta. To our earlier point about agents, they need the same thing. They need shadow traffic and all these other things. We've built so much ceremony around production being sacred that we need to make it trivially easy to test different behaviors in a safe environment. Then you can make mistakes in a safe environment.Safe AI SRE: Customer Agents, Forked Environments, and Production ParityAlessio [00:40:30]: Do you see a world where these things get automatically caught, not necessarily by your agent, but by your customer's agent? The cache invalidation issue seems easy to check if you know to look for it.Jake [00:40:44]: It's hard because to determine it, we almost need to hook into your observability infrastructure. That's why we have the template loop on the platform: so you can roll things out progressively. You can roll out to Johnny Vibe Coder initially, or push a shard that someone consumes at their own leisure. Or you can roll it out over weeks: 0.1% of people, 1% of people, early adopters, then all the way up. That's the non-deterministic version control we talked about earlier.Jake [00:41:30]: I believe that's where most things should go, because most companies end up building staged rollout systems in-house. It's the same thing built again and again at every company. There's a massive opportunity to consolidate developer debt.Alessio [00:41:45]: You should have a free tier. Model providers give free tokens if you let them use the data. You could give free compute if someone is the number-one shard that goes out and lets you plug into their observability.Jake [00:41:55]: We do that. That's why we talked about the impact on 3,000 people. We start with lower-impact people. Larger companies on the platform are last to receive those rollouts so they have a version of the platform that's deeply stable.Alessio [00:42:16]: I have three services, so I'm sure I get the first rollout. You can nuke my thing at any time. There are all these SRE agent companies. Observability people also want agents that fix upstream problems. You have your own agent in the canvas now. How do you see that playing out?Jake [00:42:39]: It's the stacking entropy problem. If you don't have primitives to make iteration in production safe, it becomes difficult. If you're an observability provider saying, “Here's the fix to this error,” assume 80% are good and make sense. But in the last 20% long tail of complex issues, if you let somebody stamp it, you create an opportunity for an incident.Jake [00:43:08]: That's why forked environments are important. People have staging, but it always drifts from production. You need primitives, workflows, and experience built first-party on the platform so you can fork any service at any point in time.Jake [00:43:33]: I think of the canvas as a sheet of transparency paper. The agent is a little guy you push up into the canvas. It should say, “I need to copy that service and that service so I can test these two things.” It gets a read-only copy of production. Anything that's PII gets marked as a transform when we clone the database, create a copy-on-write version, or read from it. Then the agent makes changes and asks, “Does this actually work?” as close to production as possible.Jake [00:44:22]: That's how close you have to be, or you get massive drift. The system becomes unstable. You see this with massive systems built on Docker for local, Kubernetes for production, and a specific thing for something else. That complexity slows developers and becomes unstable at scale, making it hard to iterate. We want to compress that way down and say, “As close to prod as possible is where we want to be.”From AISRE Skeptic to Agent BelieverSwyx [00:45:00]: I was texting Erica for questions, and she says you were originally not a believer in AISRE. Have you come around on it?Jake [00:45:10]: I flipped, but I'm still not a believer in AISRE if you don't have the primitives to make it safe. If you unleash AISRE on production infrastructure without safe primitives for copying volumes and making sure things are fine, it's going to nuke your production database. It's not a matter of if, but when. I'm a big believer in making those loops safe.Jake [00:45:33]: I was a deep AI skeptic until 2023. In 2024, I thought, “Maybe I can roughly make this thing do it.” In 2025, I thought, “Now I can hold this.” Over winter break, everybody came back saying, “It's almost impossible to hold this.”Swyx [00:46:01]: Did you see this on the Claude docs? CloudBot? OpenCloud?Jake [00:46:06]: It's gotten to a point where it's harder to hold it wrong than to hold it right. There's a scene in Avengers where Vision picks up Thor's hammer and says it's terribly well-balanced. It self-balances and works well. I'm a deep believer at this point that this will be the dominant species: assembly, C, C++, JavaScript, words.Swyx [00:46:35]: It feels like a big jump.Jake [00:46:37]: It is. But it's not like you abandon CPU-based discrete logic and move straight to fuzzy logic. You need both. Your skills should call code or applications or some static structure. You can use skills to distill what the procedure should be or how the code should act.Jake [00:47:02]: I'm coming to a thesis: you need three points. You need a clear spec defining the system, the code, and the tests. When you say it out loud, if you've been in engineering long enough, you're like, “Of course. That's an RFC, tests, and code.” But they all matter. Having them together lets them reinforce each other: the spec and tests match, but the code doesn't, so reconcile it. Or the tests and code match but the spec doesn't, so reconcile that. That's the iteration loop.Jake [00:47:41]: That's why you're seeing people talk about software factories, docs, and reconciliation. Some of that is architectural astronomy if you don't implement it, but that loop is where most things will end up.Swyx [00:48:07]: For listeners, we've been talking about this on the pod for three years: the holy trinity of specs and tests. Itamar Friedman from Qodo is the reference if people want to look it up.Self-Modifying Infrastructure and the End of Push-Pull-RebuildSwyx [00:48:18]: One thing I want to mention on the OpenCloud idea is self-modification. I don't know how Railway would support it, but I have my OpenClaw, and I just tell it it has the Railway CLI and can do whatever. In theory, whatever capabilities or new infra it needs, it can call the Railway CLI, provision it, and add it to itself. The agent can modify its own infra.Jake [00:48:45]: It's nuts. I have a loop set up where you put the Railway CLI on top of something that runs on Railway. You're authenticated as whatever the current box is, and you can make any changes to it. Then you call Railway deploy, and it deploys itself.Jake [00:49:04]: It's like: “I need to spin up this instance of this environment. I already exist in this environment. Excellent, I have access to a Postgres instance now.” That's where we want to go with agentic, self-replicating infrastructure. That's your loop: iterate in production. You continue making changes. If it works, merge it upstream. If it doesn't, throw it away.Jake [00:49:37]: How do you make throwaway copies trivial to spin up and super cheap? The era of “I have an AWS instance with four vCPU and 16 gigs of RAM” is going to get destroyed. If you do that for agents, you need a thousand of those machines. It's prohibitively expensive compared with what we've spent a ton of time figuring out: the atomic unit of deploy, whether you call it isolates, sandboxes, or something else. Only pay for what you use, spin up instantaneously, and close the loop as quickly as possible.Jake [00:50:15]: If the system can self-replicate safely and say, “This is my environment, I'm making these changes,” it can come back with, “Does this look good? This is a new state of infrastructure given this prompt. I think I've solved it.” Then you go back and say, “Actually, it looks different.” It does the loop again. Then you say, “Cool. Apply.”Swyx [00:50:38]: That's retroactively obvious, which is the most useful kind. Any other comments on agent deployment on Railway?Jake [00:50:51]: It's getting better every day. I'm on X or Twitter. You can always yell at me about the parts not working as well as they should, because plenty of things should work way better.The New Serverless: Stateful, Long-Running, Pay-for-What-You-Use LinuxSwyx [00:51:04]: At this stage, when people want massively or embarrassingly parallel compute, they usually talk serverless. I feel like there's a new serverless compared to the previous five years of serverless. You're in that new bucket. Do you have comparisons or philosophical differences you want to call out?Jake [00:51:31]: It's somewhere in between. It's the ability to run stateful, long-running workflows or executions.Swyx [00:51:42]: Vercel has Fluid Compute, Cloudflare has some container thing, Google has App Runner and others.Jake [00:51:55]: That's where everything is roughly going, and it's why we've been working on this for six years. We believe users need access to a computer: a box that speaks Linux. They need to deploy what they want. Other systems change the surface area of what you can build. For us, users need a computer and need to deploy anything they truly want. That's why we've focused on the primitives: network, compute, storage. If we give you those and expose them so you can run things indefinitely, that's where we believe it's going.Jake [00:52:43]: Twitter has no nuance, so everyone says “servers” or “serverless.” It's always somewhere in the middle: I want to run it for a long time, but I don't want to provision the resource statically or pay for things I'm not using. That's been our thesis from day one: pay only for what you use, run it indefinitely, and it is full Linux.Swyx [00:53:12]: That's why I like the naming of Fluid. It's fluid. Flexible.Heroku, Focus, and Carrying the Torch Without Becoming the PastSwyx [00:53:18]: Another milestone is the Heroku official deprecation. You're one of the presumptive new Herokus. “New Heroku” has been a category for as long as I've been in developer tooling. It's finally happening. What was that like? Any behind-the-scenes of, “This is the moment”?Jake [00:53:42]: You have people where you're like, “You were running stuff on here? You, as this company?” It's crazy that names you would know are running on it and now coming to us saying, “We want to move a lot of this off.”Swyx [00:54:00]: Any behind-the-scenes on why Salesforce let Heroku stagnate?Jake [00:54:05]: I can only guess. It's hard when it's not your business. Salesforce's business is to build a great CRM. That's their focus. Then you acquire a compute business as an offshoot. A lot of early Meta people talk about focus. Boz has a write-up about how in the early days of Meta they had no money, so they were forced to focus. Then they turned on the money tree and had no reason not to split their focus.Jake [00:54:52]: But that dilutes your product. You get offshoots where you ask, “Is this the focus of the business?” If it's not core, it languishes. A lot of companies get in trouble when they split focus because they're fighting a multi-front war, not just externally but internally for alignment. Where are we going? What are we doing? What is our purpose?Jake [00:55:24]: If you're Salesforce-built and mission-driven, you want to work on Salesforce. Heroku is off to the side. It's not core to the business. Getting resources, budget, focus, and alignment internally becomes hard. It was a matter of time.Swyx [00:56:06]: Kudos for them to call it out instead of leaving it unknown.Jake [00:56:12]: Their release was a little odd. They called it out, but they didn't say they were shutting it down. Behind the scenes, I think they issued messages to people saying they should close accounts and that they were going to deprecate and remove things over time.Jake [00:56:30]: It's crazy because some of my first deployment experiences were on Heroku. You start with dragging things into an FTP server, then you try to get a deploy working, and then it's Heroku. It was the on-ramp for us. But the wheel turns. New things emerge. We're happy to carry the torch for a lot of that. But we don't want to be the new Heroku. We want to be the way people build and deploy software, and ultimately the way people monetize software over time.Swyx [00:57:19]: It's still a big crown to be the new Heroku. There are 50 companies that fought for that.Jake [00:57:23]: Everybody is holding some portion of it. We're happy to support people and companies. The platform works differently. The game loop is similar, but we've been dogmatic about where these things are going: primitives, agents, fan-out. Some things fit; some workflows need to change. We have an approximation of Heroku pipelines with the environment system. It's exciting. We've got a ton of people we can support, and it's growing a lot.Temporal, Workflow Engines, and State MachinesSwyx [00:58:12]: I have one more technical question about Temporal. I've sold my shares. You're a power user and one of our earliest customers. I met you through Temporal. You built on Temporal. You have complaints. This may be the most neutral and informed conversation anyone will hear about Temporal without someone working at the company.Jake [00:58:39]: That's fair. I've used Temporal for almost 10 years because of Cadence at Uber.Swyx [00:58:52]: Give people a sense of what Cadence was at Uber.Jake [00:58:57]: Cadence was the precursor to Temporal. It powers trip actions, rides, when you rent a Jump bike or scooter or car. You're running workflows for a period of time and saying, “This ride will run indefinitely until it finishes.” You attach information: you paused in this zone, so add this charge to the bill. When you end the trip, the workflow is done. That experience was powered by Cadence at the time.Swyx [00:59:34]: I used to say it's like programming the entire user journey top-down as one function.Jake [00:59:39]: It's a powerful idea and important. It's also important for the next phase of the agentic journey. You want an agent to do a specific task, be complete or incomplete on that task, and move on to the next thing. You need a way to manage workflows dynamically.Jake [00:59:59]: Temporal was always great in theory, and great when you got it working the way you wanted in production. But it required you to model the entire journey in your head. If you didn't, you could cause issues where replaying the state of the workflow causes non-determinism.Swyx [01:00:25]: Because it works on deterministic workflow history.Jake [01:00:28]: Exactly. I describe it as a jet engine. If you know how to operate it and run it, it's great. But you can't hand it to people trying to build complicated things if they don't have the whole state in their head.Jake [01:00:48]: We run our whole deployment pipeline on top of it. That's a reasonably complicated workflow: pre-commit hooks, signaling, queuing, and all the rest. We ran into the same thing at Uber. As you express a large workflow, it gets more complicated, with more states in the state machine that you have to map back to the workflow.Swyx [01:01:15]: It's a lot of ifs.Jake [01:01:16]: Exactly. At Uber, we built a system for doing the state machine and testing it. We've started to build some of those things here because it's grown heavily. It's not quite love-hate. When it works well, it works super well. But if someone who doesn't have full context puts something into the system that invalidates state or causes non-determinism, or spins off a ton of activities, you have to keep track of underlying SRE knobs like activity slots. Those should scale with memory, vCPU, and so on. It becomes a bear to scale.Swyx [01:02:10]: You need a capable sysadmin running things behind the scenes. If you moved off, what would you do?Jake [01:02:19]: We'd build our own workflow engine. We have a few internally that we've worked on.Swyx [01:02:27]: This is one of those classes of things you typically wouldn't vibe code, but I'm wondering if you can.Jake [01:02:33]: I still don't think you should vibe code it. You still want to run decent tests to make sure it works.Swyx [01:02:39]: Timo didn't invent that from scratch either. There are libraries you can run. On top of that, it's just a state machine that you have to map out. Ultimately, you define the instructions you want and run them through a state machine.Jake [01:03:00]: It's very doable. Workflow stuff is interesting. Restate is doing neat stuff here.Swyx [01:03:10]: You're tied into JavaScript. Are you a JavaScript maxi?Jake [01:03:13]: Internally, we have TypeScript, Rust, and Go. We don't add more languages. Actually, we have a little C because we write BPF code and hooks. But those are the languages.Swyx [01:03:28]: Is this for sidecars?Jake [01:03:32]: No. It's for the networking stack, volumes, and things like that. We use TypeScript a lot because it powers the dashboard, but we're moving a lot of workflow stuff off the dashboard stack and into the infrastructure stack.Railpack, Nixpacks, and Content-Addressable FilesystemsSwyx [01:04:00]: Cool. Any other technical infrastructure stuff? Railpacks?Jake [01:04:07]: We built an engine for determining dependencies based on source code. It's called Railpack. We built the first version, Nixpacks, on top of Nix, and then we moved.Swyx [01:04:17]: People have been trying to get me to adopt Nix and NixOS for four years. Is it ever going to be a thing?Jake [01:04:23]: I don't know. We're excited about it, but it has pain points. Think of it as a stack of versioned binaries at specific slices in time. If you want version X and version Y, you bloat the package space, which blows up image size and makes real-world workloads difficult.Swyx [01:04:53]: But you content-address it and cache it. In theory, there are optimizations.Jake [01:05:00]: In theory, yes. But with a large enough user base and disparate enough machines, you run into a problem Meta described in the XFAAS paper, their internal serverless system. It becomes difficult at scale unless you break out specific runtimes.Jake [01:05:24]: We didn't want to do that because we wanted to truly allow you to deploy anything. That was our initial thing with Nix. But we've moved toward interesting work around content-addressable file systems that can lazy-load anything from any point and page it into memory.Swyx [01:05:48]: Amazing.Jake [01:05:49]: The future is very bright. It's crazy, and it's going to be nuts.Coding Agent Spend, Roadmaps, and Token ROISwyx [01:05:54]: Founder journey stuff?Alessio [01:05:56]: Your cloud usage: you tweeted you're going to spend $300K this month?Jake [01:06:01]: I think we got to $200K.Alessio [01:06:02]: Coding agents?Jake [01:06:03]: Yeah.Swyx [01:06:04]: Across the company?Alessio [01:06:05]: You only have 35 people, so I'm sure they're not all spending $10K a month. What's the distribution?Jake [01:06:10]: I think I'm at about $25K. We have power users all the way down. We came back from winter break, and I basically said, “If you're writing code by hand, you're doing this wrong.” The tools are good enough now that you can move extremely quickly. There are issues and pain points, but you should be reviewing the code you are writing instead of writing it by hand.Jake [01:06:40]: Architectural patterns matter more now than ever, but you shouldn't spend your time generating code you would write. If you know how to write it, ask the agent to write it and reconcile it until it looks like you would have written it yourself.Jake [01:06:58]: People misconstrue my propensity to push people toward agents as connected to our growth and some reliability bumps. They're not necessarily related. The tools are good enough to move extremely quickly and build things way larger than you could before.Jake [01:07:19]: To the earlier point about cooling data centers in space: I don't know. But with software, you can ask, “How would I build block storage from scratch? How would I do these things?” I have ideas because I have history and have read papers. Let me work them out and build massive test benches with thousands of tests, because those are now free to author. If you're not using AI systems to speed-run your roadmap and reconcile your existing system onto the future, you're missing a large point of what's happening.Alessio [01:08:12]: What's the path to spending $3 million a month? Is it bound by ideas and things customers can absorb?Jake [01:08:19]: For most companies, it's bound by deployment at this point. That's why we've seen a massive boom in users and companies, from Fortune 50s down, asking how to get developers to move faster. You'll probably hit your CFO before any technical limits because they'll look at the eye-watering amount of money spent on tokens. Inference costs have to come down, but we're inference constrained now. There will be price discovery around what makes sense for an org to adopt.Jake [01:09:06]: I think you'll end up with the F1 driver concept. If someone is really adept at these things, it makes sense to put them in a $3 million car. If they're not, it probably doesn't make sense. You'll take a few people and say, “You can drive the F1 car. We need to go in this direction. Figure out if it works and prototype it.”Jake [01:09:33]: We've done some of that and vastly accelerated our roadmap. We thought we'd ship something in a few years; now we can probably ship it in a few months because we validated it and don't have to build it incrementally. We can skip steps and move toward our vision.Alessio [01:09:58]: A lot of people are realizing the roadmap doesn't always have a business impact, so they say tokens are too expensive. But if your roadmap were built to make more money by the time you built it, you'd have token pricing for it, the same way you do with sales. You'd spend a billion dollars on sales if you knew you would get $2 billion of revenue.Jake [01:10:19]: Exactly. A naive way to measure this is the percentage of tokens that end up in production. If you can measure impact because those tokens end up in production, that's awesome. But the burden of proof will rise. Internally, we have a growing number of pull requests that haven't merged. The question becomes: how do you get this into production? It's about how quickly you can build and deploy software, which is exciting because that's our whole thing.The SDLC Shift: Prompt Requests, Feature Flags, and Safe RolloutsSwyx [01:10:56]: The SDLC is changing. One thesis is that the pull request is dying. It's going to be the prompt request. Beyond that, code review is also kind of dying if you have all the other systems in place. What else is changing about the SDLC?Jake [01:11:19]: The AISRE and the tools to make it happen. AISRE is pie-in-the-sky aspirational. What does it take to get an AISRE? What tools do you need to build?Swyx [01:11:32]: You should expose your tooling to customers at some point. The Central Station command center.Jake [01:11:39]: We have it for template maintainers. Template maintainers can deploy and maintain templates, and they get feedback. We're going to expose those things incrementally.Swyx [01:11:51]: Clustering around incidents. Everyone has a version of that, but I don't think anyone has solved it.Jake [01:11:56]: I won't say we've solved it internally, but it's gotten so good that we can see incidents forming pretty quickly. At some point, those will be things either someone else builds or we build. We've always built things purpose-built for us. If it makes sense to make it useful for users, monetize it, or turn that loop into a profit center instead of a cost center, we want to do that.Jake [01:12:28]: Pull request is definitely dying.Swyx [01:12:29]: Do you do first-party feature flagging and incremental rollout stuff?Jake [01:12:34]: We have a feature-flagging engine we built internally and will eventually roll out.Swyx [01:12:38]: I don't see it as a user. How come you didn't give us what you have?Jake [01:12:43]: We have to beta test it. We care a lot about the quality of the things. There's plenty we've used internally that doesn't make it all the way through the journey because it fails. It works for one service but not multiple services. We'd have to build it for multiple services and know that if we released it, we'd rebuild it again and again. Some things are worth that, but many inform the roadmap.Jake [01:13:18]: We don't want to dilute the experience by saying, “This works, but only for this service,” unless it's a core initiative. Over the next few months, we'll roll out things that work for a single service, then multiple services, then multiple services across the environment. You have to be deliberate. Otherwise you create broken disparate experiences and support load because people ask how to use the feature.Jake [01:13:52]: It's the earlier expansion and compaction pattern. You expand the company to get features, then compact and smooth them out so the experience is stellar. You told me in the hallway, “It's gotten so much better.” Internally we're saying, “This part really sucks. We need to make it significantly better.”Swyx [01:14:11]: I can attest to that over the last three years watching you build Railway. For listeners, feature flagging is a huge part of Uber culture. So much so that they have too many feature flags and another thing to remove feature flags. Facebook has Gatekeeper. Agents are going to need this. It's fundamental to incremental rollouts. OpenAI acquired Statsig. GPT-5 is routing and flagging through different models.Jake [01:14:56]: It's super important. If the software development lifecycle is going to change because we're doing things 1,000 times faster and 1,000 times more concurrently, what becomes important at scale?Jake [01:15:16]: Before I started Railway, I built a feature-flagging product and tried to sell it. It was an easier version of LaunchDarkly. I ran into a problem: anyone small enough to adopt your technology doesn't care about feature flags, and anyone large enough to need feature flags needs so much scale that you have to build out all the infrastructure. I scrapped it.Jake [01:15:42]: But what is old is new again. Companies are trying to move quickly, but you can't YOLO a vibe-coded thing straight into production. You need to say, “Here's my blast radius, my impact, and I want to shadow it for these users.” Feature flags. You're going to need the tools larger companies built to maintain their structures. Everything gets compressed by 1,000x so everybody can build those structures quickly.Jake [01:16:07]: That's exactly where we are: compressing the software development lifecycle, then expanding it and adding more new things.Cattle, Pets, and Clonable InfrastructureSwyx [01:16:15]: Another term that comes to mind for newer developers is “cattle, not pets.” People treat production like a pet. It has a name. You baby it and keep it alive. With cattle, you can mass farm, roll out, portion parts out, and kill them.Jake [01:16:37]: I think that might change. You can move toward having pets as long as you have a cloning machine for your pets.Swyx [01:16:52]: Yeah.Jake [01:16:52]: If you can snapshot every single thing at every frame, it doesn't matter if something gets obliterated because you have a snapshot of it. The things we've built right now are designed to block changes from the hermetically sealed DevOps line. You have to write a Dockerfile because you nee

This week on Swimming with Allocators, growth-investor-turned-LP Yuri Lee (TMRS) joins Earnest and Alexa and explains how her global upbringing and love of technology shaped her investing philosophy and belief that talent can come from anywhere. She walks through TMRS's $3B and growing venture/growth mandate, how they split exposure between early-stage and multi-stage funds, and why they are building an aggressive 50/50 funds and co-investment program. Yuri shares what she looks for in emerging managers in clear, differentiated edge in sourcing, picking, or winning; true product–market fit between a manager's edge and fund strategy; and non-consensus, outlier ideas. Throughout, she offers candid advice on how GPs can better pitch institutional LPs, why most decks sound the same, and what it really takes to stand out in a consensus-heavy, AI-dominated market. Also, Chuck Daly of Sidley talks about how emerging VC managers should think proactively about compliance, conflicts of interest, disclosure, and performance/marketing practices under (and aligned with) the Advisers Act and SEC's marketing rule principles. Highlights from this week's conversation include: How a Global Upbringing Shapes an Investor's Worldview (0:13) Consumer Investing and Game Development Experience (2:22) Market Cycles in SaaS and Consumer Narratives (4:19) TMRS Mandate and Building a New Venture Program (6:16) Early Stage Managers and Differentiated, Non-Consensus Portfolios (9:20) What Matters Most at Early Stage vs Growth Stage (12:08) What LPs Really Want to Hear About: Companies and Decisions (14:11) How Pensions and Institutional LPs Run Diligence (17:04) Managing Portfolio Company Synergies and Conflicts (23:56) Marketing Rule Principles, Performance, and Case Studies (26:21) Why TMRS Uses Co-Invests and Target Mix With Funds (30:15) Barbell Approach: Early Stage Funds and Later Stage Co Invests (32:02) Information Gaps for LPs vs GPs and Founder Access (35:55) Consensus Rounds, Party Rounds, and Manager Profiles (38:56) Role of Non-Consensus Managers and Unique Edges (42:09) Rethinking AI Thesis and Value Capture by Model Labs (43:22) Advice For GPs Moving to LP Roles and Building Empathy (45:20) Final Thoughts and Takeaways (47:18) The Texas Municipal Retirement System is a $48+ billion public pension plan serving employees of participating Texas cities. TMRS invests across a diversified portfolio including public equities, fixed income, real assets, and private equity, with venture and growth investments forming an important component of its private markets strategy. Sidley Austin LLP is a premier global law firm with a dedicated Venture Funds practice, advising top venture capital firms, institutional investors, and private equity sponsors on fund formation, investment structuring, and regulatory compliance. With deep expertise across private markets, Sidley provides strategic legal counsel to help funds scale effectively. Learn more at sidley.com. Swimming with Allocators is a podcast that dives into the intriguing world of Venture Capital from an LP (Limited Partner) perspective. Hosts Alexa Binns and Earnest Sweat are seasoned professionals who have donned various hats in the VC ecosystem. Each episode, we explore where the future opportunities lie in the VC landscape with insights from top LPs on their investment strategies and industry experts shedding light on emerging trends and technologies.  The information provided on this podcast does not, and is not intended to, constitute legal advice; instead, all information, content, and materials available on this podcast are for general informational purposes only. Learn more about your ad choices. Visit megaphone.fm/adchoices

"When you look at where families invest across the spectrum of sustainable and impact investing, climate is really at the forefront. They think about that because, look at our environment, look at the food supply… because of environmental issues. Not withstanding the tariffs too….These are issues that have to be segmented and families are doing the work and they're putting their capital in…(F)amilies really are more and more values aligning their investments, and there's a blurring  (of the) line between philanthropy and catalytic investing so that they can invest in climate and do things in all their active portfolios. So I feel like this is just going to increase over time in a pretty significant way." Jolyne Caruso on Electric Ladies Podcast The great wealth transfer of $124 trillion will flow into the hands of women, Millennials and Gen Zrs over the next 30 years, and these are hands that value climate, clean energy, sustainability and social causes more than their forebears. What impact might that have? Listen to Jolyne Caruso, Financial Executive, Investor and Wealth Advisor in this enlightening conversation with Electric Ladies Podcast host Joan Michelson. You'll hear about: ●        How women tend to donate and invest differently than men do and why. ●        Impact and climate-related investing the way women, Gen Z & Millennials do it. ●        New spins on "risk" and "reward" & why financial literacy is crucial. ●        What the great wealth transfer is and how it could dramatically affect the economy. ●        Plus, career advice, such as:  "One of the things that I recommend women do, Joan, is to join boards as soon as they can. And I'll tell you why. When I was at Barnard, I was very involved as a student in my class. I was reunion chair and raising money for my class. But it wasn't until I got on the alumni board at Barnard, which led to getting on the big board at Barnard when I was 40 years old, that my life fundamentally changed….This isn't about giving a hundred thousand dollars to your alma mater that you don't have. It's finding boards that will all take women of all ages…My daughter's on the board of a dog rescue company, organization. Get on a board and sit around the table and become a change agent in that regard….Because you network, you get hands-on experience without being at work." Jolyne Caruso on Electric Ladies Podcast Subscribe to our newsletter to receive our podcasts, blog, events and special coaching offers.  You'll also like: ·       New Business Models For Philanthropy - Amy Dornbusch, AtlasDaughters, Entrepreneur, Investor, Philanthropist ·       Women's Trillions Drive New Economic Values - with Silvia Bastante de Unverhau, LGT Private Bankers International ·       New Venture Capital Models For Women and CleanTech - Cecile Blilious, Veteran Venture Investor, Venture ESG, European Women in VC ·       Creativity & Relationships Secure Grants - with Megan Pater, CEO/Founder of Fund Nation and ECE Solutions ·       Investing in Companies For Social Impact - with Meredith Shields, CEO of Citi Impact Fund  Subscribe to our newsletter to receive our podcasts, blog, events and special coaching offers.  Thanks for subscribing on Apple Podcasts or iHeartRadio and leaving us a review! Follow us on Twitter @joanmichelson

Opeyemi Awoyemi built WhoGoHost in his first year atuniversity before Nigerian tech had a template. He co-founded Jobberman from a dormitory, grew it into the largest online recruitment platform in sub-Saharan Africa, and watched it get acquired by Ringier One Africa Media. He didn't stop there; he went on to build a venture studio and now a startup - ola.cv In this conversation, Ope breaks down what he actuallylearned by going back inside a global company after his own exit, why he calls himself a "pseudo VC," and what he means when he says he only backs "fundamentals-fluent" founders - not "venture-fluent" ones. This is a conversation about building before the playbookexists.

For this episode, I interviewed Eugene Malobrodsky, partner at One Way Ventures and former founder of AnchorFree, the company behind HotSpot Shield, one of the first consumer VPN products to scale globally. Before becoming a VC, Eugene spent 15 years building and scaling a startup through the 2008 financial crisis, painful layoffs, difficult fundraising environments, and the long grind from idea to acquisition.  Today, he backs immigrant founders building applied AI, deep tech, fintech, healthcare, and enterprise startups at the pre-seed and seed stage. Topics include: Why many founders become founders for the wrong reasons What venture capitalists really mean when they talk about “100x outcomes” How to think about fundraising runway and dilution Why technical founders often struggle with storytelling What makes a startup venture-backable versus a profitable lifestyle business The most common mistakes early technical teams make How investors evaluate first-time founders with no track record Why customer discovery matters more than building features too early Why the best founders are often “angry at the problem” they're trying to solve He also spoke about what immigrant entrepreneurs misunderstand about networking in Silicon Valley, and the growing uncertainty around H-1B visas and startup immigration policy. RUNTIME 56:28   EPISODE BREAKDOWN (2:13) "I'm just not great at following directions and working for somebody else." (5:44) How Working in VC Changed His Thinking (7:42) What Founders Misunderstand About VC Funds (20:53) A Practical Framework for Seed-stage Fundraising (25:50) What Makes Him Take the Meeting (31:19) Where One Way Ventures is Betting in Deep Tech  (35:11) The Most Common Mistakes Technical Teams Make (38:23) Why Founders Need a 90-second Story (43:16) Growing Uncertainty for Immigrant Tech Workers and Founders (51:33) Practical Networking Advice for First-time Founders (54:38) The One Question H1-B Candidates Should Ask the CEO During an Interview LINKS Eugene Malobrodsky One Way Ventures Investing in Funds vs Investing as an Angel One Way Ventures Expands to San Francisco from Boston with Eugene Malobrodsky, Co-founder of Consumer Privacy Company AnchorFree, Joining as Partner SUBSCRIBE

Vanguard is the most effective vehicle ever created for participating in the fruits of American capitalism. Today it's the single largest equity owner of the majority of corporations in the S&P 500, on behalf of 50 million clients (including, likely, many of you). And yet Vanguard itself is essentially a communist organization — it has no shareholders, makes no profits, and operates more like REI than Fidelity. If you own a Vanguard fund, you own a piece of the firm itself. Any excess margin instead gets returned to clients in the form of lower fees, which since 1975 have added up to roughly five hundred billion dollars transferred out of Wall Street managers' pockets and into retail investors' savings accounts. And oh yeah, it all started as a cockamamie revenge plot by a guy who'd just been fired by his partners. Today we tell the story of communist capitalism at its finest — Vanguard.Sponsors:Many thanks to our fantastic Spring '26 Season partners:J.P. MorganWeAreDevelopers eventServiceNowVercelStatsigLinks:Sign up for email updates, get our takeaways and research photos from each episode, and vote on future topics!Our Vanguard "episode preview" in WSJStay the Course: The Story of Vanguard and the Index Revolution by John C. BogleThe Bogle Effect by Eric BalchunasWorldly Partners' Multi-Decade Vanguard StudyWorldly Partners' Article Generational Investing: The Discipline Behind 100+x OutcomesAll episode sourcesCarve Outs:Our WSJ pieces on Ferrari and VanguardMacBook Pro M5 MaxMichael MacKelvie on YouTubeThe Super Mario Galaxy MovieBrooks Vanguard sneakersMore Acquired:Get email updates and vote on future episodes!Join the SlackCheck out the latest swag in the ACQ Merch Store!00:00:00 Start00:00:41 Intro00:05:30 Jack Bogle's Early Life & Family Ruin (1929)00:12:34 Princeton Thesis & Mutual Funds Emerge (1949-1951)00:27:20 Joining Wellington Management (1951)00:30:38 The Go-Go Years & Fidelity's Ascent (1958-1965)00:40:36 Jack Takes the Reins & The Ivest Merger (1965)00:46:04 The Go-Go Bust & Jack's Crisis of Conscience (1970-1973)00:53:28 Jack is Fired: The Genesis of Vanguard (1974)01:13:03 The Journal Article That Inspired It All (1974-1976)01:35:02 Building the Fund & Early Struggles (1976-1981)01:44:32 The Rise of Indexing & Vanguard's Growth (1988-1992)01:49:06 Jack's Health & The CEO Transition (1995-1996)02:00:06 The ETF Debate & Jack's Second Firing (1999)02:24:18 The 2008 Financial Crisis: Vanguard's Moment02:30:46 The Warren Buffet Bet (2008-2019)02:41:28 Fidelity & BlackRock's Resurgence (Post-2008)02:52:04 Salim Ramji: Vanguard's First Outside CEO03:04:43 Wellington's Comeback & Mutual Ownership03:08:23 Analysis03:30:58 Quintessence03:39:35 Carve-Outs + Outro‍Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.

Let there be Spider-Carnage! In this episode, Mark and Dan discuss Part 9 of the new Amazing Spider-Man and Venom event, DEATH SPIRAL, which is otherwise known as Amazing Spider-Man (vol. 7) #27. This issue was written by Joe Kelly. The cover features artwork by Ed McGuinness and Marte Gracia. The interiors feature pencils by Ed McGuinness and Carlos Gomez, with Francesco Manna, inks by Mark Farmer, Wade von Grawbadger, Ed McGuinness, Carlos Gomez, and Francesco Manna, colors by Marcio Menyz and Erick Arciniega, and, of course, letters by VC's Joe Caramagna. This issue was first released on April 22nd, 2026. Rick Coste edited this episode. Alex Galucki edited the video version of this podcast. Our artwork is handcrafted by artists Ron Frenz, Nick Cagnetti, and the late Sal Buscema. Our theme songs were produced by Ryland Bojack, Tony Thaxton, and Spider-Maj. Our animated introduction to the show is by Josh Sutton of Panels to Pixels. Watch the show on YouTube: https://www.youtube.com/channel/UCOPCnjzQZNViyEnoOuckaVQ We would also love to see you join our Amazing Spider-Slack community board. If you'd like to join in on our amazing conversations, click this link to get started: https://join.slack.com/t/amazingspider/shared_invite/zt-42tsfhs2-yBaH6KkRmOWiW_8gCf9SmQ This week's Patreon podcasts include a review of Amazing Spider-Man (vol. 7) #29, our discussion of the two Spider-Man / Superman comics, and two episodes of the Whatever a Spider Can Diaries, which documents Dan’s process of writing a book about Spider-Man. If you'd like to follow along with our reviews as they are released, please check out our Patreon page: https://www.patreon.com/superiorspidertalk Read our B-Title reviews, collecting memories, and more in the Amazing Spider-Talk Substack! http://www.amazingspider.substack.com You can email questions to our show at amazingspidertalk@gmail.com or by clicking here. You can also BUY MARK'S BOOK, 100 Things Spider-Man Fans Should Know & Do Before They Die. The post DEATH SPIRAL Part 9 – REVIEW appeared first on Amazing Spider-Talk.

Josh Browder is my favourite emerging manager. As the Founder of Browder Capital he has been the first check into unicorns like Micro1, Owner.com and Yuzu Health to name a few. He turned his Thiel Fellowship Grant of $100K into a whopping $10M angel portfolio. All new investments move into Josh's Four Seasons Residence where he then trains them on company building. They are only allowed to leave when they raise their seed round. In addition to this, Josh is the Founder & CEO @ DoNotPay, the now profitable company that has raised $22M from Marc Andreessen and others.  AGENDA: 00:00 Why I believe young founders make the best founders 02:10 What do I look for in founders I'm investing in? 10:00 How I test for founder commitment pre-investment 11:00 What do I want to see in the childhoods of the entrepreneurs that I back? 12:05 Why I make founders live with me in my Four Seasons residence after investing 14:00 There are three reasons why pre-seed companies fail and how I solve for them 15:45 What do I look for in a team to assure me they will last? 16:50 Is $5 million post still attainable in this world? 17:15 Should we be worried about the increasing levels of fraud at the early stages? 18:10 Which Silicon Valley investor was the most impressive? 20:00 Do VCs really add value? 20:50 Does university carry less value than ever and should founders drop out if they have a dream and an idea? 24:00 The advice of one lawyer changed the entire trajectory of this multi-million dollar company 28:10 I would never invest in someone that I met over Zoom 29:20 Are all the best founders you invest in delusional? 31:10 What is the biggest benefit of being a Thiel Fellow? 36:20 The problem of accelerators and why artificial constraints can enforce quality 38:20 How we used Mark Zuckerberg's house as a viewing instrument tool to get users 41:20 My pre-seed cheque in Micro One is now worth hundreds of millions and my lessons 43:20 The biggest mistake VCs are making in assessing founders 44:10 Why I will never tell the entrepreneur what to build and why I place little value on ideas 47:00 Why kingmaking is real and why you should accept half the price from a tier one firm 49:00 My biggest lessons on reserve investing 53:00 Why price rounds are bullshit and we should do more SAFEs 54:20 What all founders need to know about signing with a VC 55:10 Single biggest advice to founders on how to get the best price 57:30 What role does not exist today that will be massive in five years' time? 59:10 Stocks are bullshit. Cash is bullshit. I put my money in land. My personal investing strategy 1:01:00 Why the Trump administration is unwaveringly good for US business 1:03:20 Why competitive markets are the best investments 1:05:40 The serendipity of San Francisco is unmatched 1:08:10 What things does no one know about Marc Andreessen that everyone should know?  

Listen and subscribe to Money Making Conversations on iHeartRadio, Apple Podcasts, Spotify, www.moneymakingconversations.com/subscribe/ or wherever you listen to podcasts. New Money Making Conversations episodes drop daily. I want to alert you, so you don’t miss out on expert analysis and insider perspectives from my guests who provide tips that can help you uplift the community, improve your financial planning, motivation, or advice on how to be a successful entrepreneur. Keep winning! Two-time Emmy and Three-time NAACP Image Award-winning, television Executive Producer Rushion McDonald interviewed Dr. Cameka Smith. Founder of The BOSS Network, from Money Making Conversations Masterclass: Purpose of the Interview The interview aimed to: Highlight The BOSS Network’s mission to empower women of color through entrepreneurship, career development, and community support. Share Dr. Smith’s personal journey from layoff to leadership, inspiring others to embrace entrepreneurship. Discuss strategies for business success, funding opportunities, and mentorship for Black female founders. Key Takeaways Origin of The BOSS Network Founded in 2009 during the recession after Dr. Smith was laid off from Chicago Public Schools. Initially started as local events in Chicago; now a digital community reaching 200,000 women nationwide. Mission: Bringing Out Successful Sisters (BOSS)—promoting small business spirit and career growth. Impact & Achievements Invested in 100 Black female founders through grants. Trained 50,000 women on business strategies. Coached 10,000 women on starting businesses. Created Boss Business University, offering mentorship and digital programs. Pivot During COVID Shifted from 35% event-based revenue to 75% digital. Launched Boss Impact Fund and Invest in Progress Grant: $10,000 grants + 4-year scholarships for recipients. Combined funding, mentorship, and marketing support for sustainability. Challenges & Mindset Entrepreneurship requires planning, resilience, and community support. Dr. Smith saved money before leaving her job and leveraged relationships for growth. Quote: “Entrepreneurs will work 80 hours for themselves but don’t want to work 40 hours for someone else.” Top 3 Mistakes Entrepreneurs Make Lack of research: Understand your industry, competitors, and market. No revenue model: If you’re not making money, it’s a hobby, not a business. Ignoring relationships: Networking and partnerships are key to success. Unique Marketing & Partnerships Dr. Smith built direct relationships with brands, bypassing agencies that offered “pennies on the dollar.” Created a dual revenue model: B2B (corporate partnerships) + B2C (community engagement). Core Philosophy Motto: Believe, Plan, Win. Quote: “Those that show up, go up.” Success is rooted in faith, persistence, and leveraging community. Notable Quotes “I was born to be an entrepreneur. My mother told me, until you become your own boss, you have to follow the rules.” “Less than 1% of Black women get VC funding—so we created our own fund.” “Relationships are your key to success. When social media goes away, your audience remains.” “If you have a business and you don’t have money, you’ve got a hobby.” “God will not birth anything inside of you that He will not give you the tools to deliver.” #SHMS #STRAW #BESTSupport the show: https://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.