Podcasts about vcs

Mike Collins is the Founder and CEO of Alumni Ventures, a leading venture capital firm that enables individual accredited investors to access diversified venture portfolios and co-invest alongside top-tier VCs. He is a serial entrepreneur and experienced venture capitalist who has founded multiple companies, including Kid Galaxy, Big Idea Group, and RDM. He also launched Green D Ventures, Alumni Ventures' first alumni fund, where he oversaw the portfolio as Managing Partner. Mike has spent his career helping investors and entrepreneurs build innovative, high-growth businesses, and holds degrees from Dartmouth College and Harvard Business School. In this episode… What does it really take to succeed in venture capital, where uncertainty is constant, and failure is often part of the process? What separates investors who consistently build strong portfolios from those who don't? For Mike Collins, a seasoned venture capitalist and serial entrepreneur, success in venture capital comes from focusing on people over pure ideas and building disciplined, diversified portfolios to manage inevitable risk. He highlights that early-stage investing is less about certainty and more about judgment, trust, and pattern recognition developed over time. A key takeaway is that long-term success stems from striking a balance between conviction and humility and accepting that many investments will fail while a few yield outsized returns. He also emphasizes the value of co-investing and leveraging a global investor community to expand access and insight. In this episode of the Inspired Insider Podcast, host Dr. Jeremy Weisz sits down with Mike Collins, Founder and CEO of Alumni Ventures, to discuss venture capital, portfolio building, and entrepreneurial lessons. They talk about betting on people over ideas, managing risk through diversification, and lessons from sports and investing. Mike also shares insights on co-investing strategies and democratizing access to venture capital.

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…

In this episode, we discuss how some VCs and founders inflate or frame ARR to make startups look stronger for the press. We also look at why these numbers matter, how they shape public perception, and what it means for trust in the startup ecosystem. Our AI Hustle Skool Community: https://www.skool.com/aihustleGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

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 Monica Cornitcher. Entrepreneurial journey, the inspiration behind Medase Cocktails, and the realities of launching, funding, and scaling a premium nonalcoholic spirits brand in a highly competitive market. Purpose of the Conversation The purpose of the episode is to: Educate aspiring entrepreneurs on how to build a differentiated consumer brand Demonstrate the importance of storytelling, market clarity, and operational discipline Highlight the growth of the nonalcoholic / zero‑proof beverage movement Inspire founders—especially founders of color—to own their niche, seek capital strategically, and scale intentionally. Key Takeaways 1. Business Built from Personal Need and Purpose Medase Cocktails was co‑founded by Monica and her lifelong friend during her friend’s battle with breast cancer, a time when alcohol was no longer an option—but celebration still mattered. The brand was created to allow people to celebrate authentically without alcohol It carries emotional depth rooted in friendship, gratitude, and loss Monica continues the mission after her co‑founder passed away in 2024 Lesson: Purpose-driven businesses create deeper emotional connection and long-term brand equity. 2. Differentiation Is Everything Monica deliberately rejected the “sparkling water with flavor” model common in nonalcoholic drinks. Her differentiators include: Authentic cocktail taste (Old Fashioned, Margarita, Moscow Mule) Organic juices, not artificial flavors Bold packaging that stands out on shelves Drinks designed to smell, taste, and feel like real cocktails Lesson: Competing on authenticity—not cost—is how you carve out market share in crowded spaces. 3. Brand Names and Stories Matter The name “Medase” means “thank you” and reflects gratitude, friendship, and emotional support. Monica emphasizes: Every flavor name, color, and product decision has a story A strong brand narrative creates curiosity, loyalty, and investor interest Lesson: People invest in brands they feel—emotionally, not just intellectually. 4. Venture Capital Is Not Just About Numbers While financials matter, Monica stresses that VCs also invest in founders and stories. What helped her secure venture capital: A compelling personal story Relevant founder skill sets (M&A, law, operations) Clear understanding of the market opportunity Lesson: Early-stage funding often depends on who you are and why you’re building, not just revenue. 5. Research, Planning, and Discipline Before Launch Unlike many food startups, Medase did not begin in a kitchen. They: Conducted a feasibility study Built a formal business plan Worked with a Black female food scientist Set strict personal funding limits before seeking capital Lesson: Preparation reduces risk and builds long-term sustainability. 6. Scaling Requires Operational Maturity As sales increased—especially on Amazon—Monica emphasized the need to move from “hustle mode” to operational excellence. Key scaling principles: Understand unit economics Track ROI for events and activations Adjust pricing as volume increases Build strategy across marketing, operations, and distribution Lesson: Hustle starts the business; operations grow it. 7. Niche First, Expansion Later Medase does not try to be “everything to everyone.” Core customers include: People seeking a break from alcohol Health-conscious consumers Black men looking for alcohol replacements Consumers wanting cocktail taste without hangovers Lesson: Strong niches create loyal advocates who fuel organic growth. 8. Smart Distribution Strategy Rather than rushing into retail, Monica prioritized direct-to-consumer channels: Amazon (top-performing channel) Brand website TikTok Shop Only after 6–7 months of traction did retail expansion become viable. Lesson: Control your margins and demand before entering expensive retail environments. Memorable Quotes “I wanted an authentic cocktail without compromise.” “Everything we do has a story behind it.” “Sometimes it’s not about the financials—it’s about the founder and the story.” “Don’t be everything to everybody. Find your market and stick with your market.” “Hustle starts the business, but operations give you scale.” “If it tastes too much like alcohol and you gave me a one-star review—thank you. That means I did my job.” Overall Message This episode is a real-world entrepreneurial blueprint showing how clarity of vision, emotional authenticity, disciplined planning, and niche focus can turn a personal idea into a scalable national brand. Monica Cornitcher exemplifies the modern founder:visionary, data-aware, emotionally intelligent, and unapologetically authentic. #SHMS #BEST #STRAWSupport the show: https://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.

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 Monica Cornitcher. Entrepreneurial journey, the inspiration behind Medase Cocktails, and the realities of launching, funding, and scaling a premium nonalcoholic spirits brand in a highly competitive market. Purpose of the Conversation The purpose of the episode is to: Educate aspiring entrepreneurs on how to build a differentiated consumer brand Demonstrate the importance of storytelling, market clarity, and operational discipline Highlight the growth of the nonalcoholic / zero‑proof beverage movement Inspire founders—especially founders of color—to own their niche, seek capital strategically, and scale intentionally. Key Takeaways 1. Business Built from Personal Need and Purpose Medase Cocktails was co‑founded by Monica and her lifelong friend during her friend’s battle with breast cancer, a time when alcohol was no longer an option—but celebration still mattered. The brand was created to allow people to celebrate authentically without alcohol It carries emotional depth rooted in friendship, gratitude, and loss Monica continues the mission after her co‑founder passed away in 2024 Lesson: Purpose-driven businesses create deeper emotional connection and long-term brand equity. 2. Differentiation Is Everything Monica deliberately rejected the “sparkling water with flavor” model common in nonalcoholic drinks. Her differentiators include: Authentic cocktail taste (Old Fashioned, Margarita, Moscow Mule) Organic juices, not artificial flavors Bold packaging that stands out on shelves Drinks designed to smell, taste, and feel like real cocktails Lesson: Competing on authenticity—not cost—is how you carve out market share in crowded spaces. 3. Brand Names and Stories Matter The name “Medase” means “thank you” and reflects gratitude, friendship, and emotional support. Monica emphasizes: Every flavor name, color, and product decision has a story A strong brand narrative creates curiosity, loyalty, and investor interest Lesson: People invest in brands they feel—emotionally, not just intellectually. 4. Venture Capital Is Not Just About Numbers While financials matter, Monica stresses that VCs also invest in founders and stories. What helped her secure venture capital: A compelling personal story Relevant founder skill sets (M&A, law, operations) Clear understanding of the market opportunity Lesson: Early-stage funding often depends on who you are and why you’re building, not just revenue. 5. Research, Planning, and Discipline Before Launch Unlike many food startups, Medase did not begin in a kitchen. They: Conducted a feasibility study Built a formal business plan Worked with a Black female food scientist Set strict personal funding limits before seeking capital Lesson: Preparation reduces risk and builds long-term sustainability. 6. Scaling Requires Operational Maturity As sales increased—especially on Amazon—Monica emphasized the need to move from “hustle mode” to operational excellence. Key scaling principles: Understand unit economics Track ROI for events and activations Adjust pricing as volume increases Build strategy across marketing, operations, and distribution Lesson: Hustle starts the business; operations grow it. 7. Niche First, Expansion Later Medase does not try to be “everything to everyone.” Core customers include: People seeking a break from alcohol Health-conscious consumers Black men looking for alcohol replacements Consumers wanting cocktail taste without hangovers Lesson: Strong niches create loyal advocates who fuel organic growth. 8. Smart Distribution Strategy Rather than rushing into retail, Monica prioritized direct-to-consumer channels: Amazon (top-performing channel) Brand website TikTok Shop Only after 6–7 months of traction did retail expansion become viable. Lesson: Control your margins and demand before entering expensive retail environments. Memorable Quotes “I wanted an authentic cocktail without compromise.” “Everything we do has a story behind it.” “Sometimes it’s not about the financials—it’s about the founder and the story.” “Don’t be everything to everybody. Find your market and stick with your market.” “Hustle starts the business, but operations give you scale.” “If it tastes too much like alcohol and you gave me a one-star review—thank you. That means I did my job.” Overall Message This episode is a real-world entrepreneurial blueprint showing how clarity of vision, emotional authenticity, disciplined planning, and niche focus can turn a personal idea into a scalable national brand. Monica Cornitcher exemplifies the modern founder:visionary, data-aware, emotionally intelligent, and unapologetically authentic. #SHMS #BEST #STRAWSee omnystudio.com/listener for privacy information.

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 Monica Cornitcher. Entrepreneurial journey, the inspiration behind Medase Cocktails, and the realities of launching, funding, and scaling a premium nonalcoholic spirits brand in a highly competitive market. Purpose of the Conversation The purpose of the episode is to: Educate aspiring entrepreneurs on how to build a differentiated consumer brand Demonstrate the importance of storytelling, market clarity, and operational discipline Highlight the growth of the nonalcoholic / zero‑proof beverage movement Inspire founders—especially founders of color—to own their niche, seek capital strategically, and scale intentionally. Key Takeaways 1. Business Built from Personal Need and Purpose Medase Cocktails was co‑founded by Monica and her lifelong friend during her friend’s battle with breast cancer, a time when alcohol was no longer an option—but celebration still mattered. The brand was created to allow people to celebrate authentically without alcohol It carries emotional depth rooted in friendship, gratitude, and loss Monica continues the mission after her co‑founder passed away in 2024 Lesson: Purpose-driven businesses create deeper emotional connection and long-term brand equity. 2. Differentiation Is Everything Monica deliberately rejected the “sparkling water with flavor” model common in nonalcoholic drinks. Her differentiators include: Authentic cocktail taste (Old Fashioned, Margarita, Moscow Mule) Organic juices, not artificial flavors Bold packaging that stands out on shelves Drinks designed to smell, taste, and feel like real cocktails Lesson: Competing on authenticity—not cost—is how you carve out market share in crowded spaces. 3. Brand Names and Stories Matter The name “Medase” means “thank you” and reflects gratitude, friendship, and emotional support. Monica emphasizes: Every flavor name, color, and product decision has a story A strong brand narrative creates curiosity, loyalty, and investor interest Lesson: People invest in brands they feel—emotionally, not just intellectually. 4. Venture Capital Is Not Just About Numbers While financials matter, Monica stresses that VCs also invest in founders and stories. What helped her secure venture capital: A compelling personal story Relevant founder skill sets (M&A, law, operations) Clear understanding of the market opportunity Lesson: Early-stage funding often depends on who you are and why you’re building, not just revenue. 5. Research, Planning, and Discipline Before Launch Unlike many food startups, Medase did not begin in a kitchen. They: Conducted a feasibility study Built a formal business plan Worked with a Black female food scientist Set strict personal funding limits before seeking capital Lesson: Preparation reduces risk and builds long-term sustainability. 6. Scaling Requires Operational Maturity As sales increased—especially on Amazon—Monica emphasized the need to move from “hustle mode” to operational excellence. Key scaling principles: Understand unit economics Track ROI for events and activations Adjust pricing as volume increases Build strategy across marketing, operations, and distribution Lesson: Hustle starts the business; operations grow it. 7. Niche First, Expansion Later Medase does not try to be “everything to everyone.” Core customers include: People seeking a break from alcohol Health-conscious consumers Black men looking for alcohol replacements Consumers wanting cocktail taste without hangovers Lesson: Strong niches create loyal advocates who fuel organic growth. 8. Smart Distribution Strategy Rather than rushing into retail, Monica prioritized direct-to-consumer channels: Amazon (top-performing channel) Brand website TikTok Shop Only after 6–7 months of traction did retail expansion become viable. Lesson: Control your margins and demand before entering expensive retail environments. Memorable Quotes “I wanted an authentic cocktail without compromise.” “Everything we do has a story behind it.” “Sometimes it’s not about the financials—it’s about the founder and the story.” “Don’t be everything to everybody. Find your market and stick with your market.” “Hustle starts the business, but operations give you scale.” “If it tastes too much like alcohol and you gave me a one-star review—thank you. That means I did my job.” Overall Message This episode is a real-world entrepreneurial blueprint showing how clarity of vision, emotional authenticity, disciplined planning, and niche focus can turn a personal idea into a scalable national brand. Monica Cornitcher exemplifies the modern founder:visionary, data-aware, emotionally intelligent, and unapologetically authentic. #SHMS #BEST #STRAWSteve Harvey Morning Show Online: http://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.

In episode 665 of the New Media Show, hosted by 2017 Podcast Hall of Famer Rob Greenlee, Rob talks with Ashley Christenson, also known as Ashni, for a deep conversation about one of the most important questions facing podcasting, streaming, creator media, startups, and traditional media right now: What does “New Media” actually mean today? The term “New Media” has been around since the late 1990s, but its meaning is shifting again. What once described digital media outside traditional broadcast and print is now being used by creators, VCs, startups, streaming strategists, AI companies, and professional communities to refer to something more specific: creator-led media that builds trust, influence, industry position, and direct audience relationships. Ashley brings a unique perspective from 13 years in online media, Twitch streaming, YouTube education, startup marketing, community building, and creator strategy. She explains that she sees the creator economy as building an audience as the asset, whereas the emerging version of New Media is more about building status and position within an industry conversation. In her view, the key difference is not simply between consumer and professional audiences, but about what the media operation is designed to build and protect. Rob brings the longer history of podcasting and digital media into the discussion, asking whether podcasting was one of the first major expressions of New Media and whether it now sits within a much larger creator-led ecosystem. The conversation explores how podcasting, YouTube, streaming video, newsletters, live shows, X, AI-generated content, and Apple Podcasts' move toward HLS video streaming are all blurring the old lines between podcasting, creator media, and professional media. A major theme in this episode is whether podcasting is still its own category or has become a powerful format within the broader New Media industry. Rob argues that the word “podcast” is increasingly defined by audiences and platforms, while creators may need to think more broadly as show builders, media operators, and participants in the creator economy. Ashley and Rob also explore how X is becoming a real-time professional media layer, why founders, investors, executives, and AI builders are returning to the platform, and why companies are experimenting with live streaming, clipping, launch videos, short-form content, and creator-style formats to reach professional audiences. The episode also moves into AI-generated media, human-hosted content, AI clones, disclosure, and trust. Rob argues that human-created and AI-created content may both need clear labeling, while Ashley points out that long-form podcasts may remain more defensible because listeners often build real relationships with hosts over time. This conversation lands on a bigger media reality: New Media is no longer just a technology term. It is becoming a business category, a creator category, a trust category, and a professional influence category. Podcasting helped build the foundation, but the next version of New Media is broader, more video-driven, more AI-assisted, more platform-diverse, and more dependent on trust than ever before. Key Topics: What “New Media” means in 2026 Creator economy vs. New Media Audience as an asset vs. status as an asset Why podcasting helped define early New Media Whether podcasters should now think more like creators and show builders Apple Podcasts HLS video and the return of video podcasting YouTube, Spotify, X, and the platform shift around shows Why VCs and startups are using the term New Media X is a professional media and live content platform Traditional media is trying to become more internet-native AI-generated podcasts, AI clones, and synthetic media Human-hosted content, disclosure, and audience trust Why long-form podcasts may remain defensible in the AI era Chapter Markers: 00:00 Cold Open and Welcome 00:32 What Does New Media Mean 02:08 Podcasting Meets Multi Format 03:14 Meet Rob Greenlee 04:01 Introducing Ashley Christensen 04:53 Ashley’s Creator Economy Journey 08:26 AI Definitions of New Media 12:35 Creator Economy vs New Media 16:29 The Kill Switch Test 21:38 Is VC Rebranding New Media 24:10 Niche Status Media Examples 31:55 Traditional Media Goes Internet Native 34:59 Podcasting Identity and Convergence 41:35 Creator as a Catch-All Term 43:56 Naming New Media 46:11 Podcast Term Debate 51:02 X Shapes Media 55:35 X Video Creator Push 01:00:51 Twitter Podcast Roots 01:04:38 AI Flooding Podcasts 01:07:48 Human Trust Labels 01:11:34 Clones and Disclosure 01:17:49 Trust Factor Wrap 01:18:19 Closing and Where to Follow Guest and Host Links Guest: Ashley Christenson / Ashni Streaming strategist, creator economy, and new media operator X: https://x.com/ashnichrist YouTube: https://youtube.com/@ashnichrist Hype Partners: https://x.com/hypepartners Host: Rob Greenlee New Media Show: https://newmediashow.com Rob Greenlee: https://robgreenlee.com Podcast Hall of Fame: https://podcasthall.com Rob Greenlee on LinkedIn: https://www.linkedin.com/in/robgreenlee Rob Greenlee Booking: https://calendly.com/robgreenlee About the Host/Author: Rob Greenlee is a 2017 Podcast Hall of Fame inductee and Chair, a global new-media leader who bridges podcasting's human roots and its AI-driven future. As founder of Trust Factor Lab and host of the “New Media Show” and “Spoken Human”, Rob helps creators start, grow, monetize, and future-proof their content. He's held leadership roles at Microsoft, Spreaker, Libsyn, StreamYard, and PodcastOne, and serves as Chairperson of the Podcast Hall of Fame. Learn more at RobGreenlee.com and join the Trust Factor Lab Creator/Podcast Services. Personal/AI Disclosure Note: I used AI tools to help organize and edit this episode and generate show notes. I have many hand edits; the views, clarifications, responsibility, and industry perspective are mine and my guests’. I have been working in podcasting and platform adoption for more than two decades, and this article reflects my own position. The original word choice was mine, and so is the clarification. The post What Is New Media Now vs Podcasting? | Ashley Christenson / @Ashni #665 first appeared on New Media Show.

Did Daily Wire really torch $100 million on Bentkey? Marcus Pittman, CEO of LOOR TV, called the collapse of conservative streaming years before it happened - and he is back on the show to explain exactly why conservative entertainment keeps losing. Four years ago Marcus came on with a contrarian pitch that made VCs laugh in his face - Netflix meets Kickstarter, where subscribers decide what gets made. Today he has funded nearly 40 pieces of content for under a million bucks, the same number the venture guys swore would cost over $100 million. He bet against the room and the room was wrong. Here is the mistake that keeps sinking the right. Conservatives build content for the parent and forget the parent is not the consumer - the kid is. McDonald's figured this out with the Happy Meal in the 70s. Chuck E Cheese figured it out. Bentkey never did, ad the bill came due to the tune of nine figures. Then we get into the part nobody on our side wants to hear. Rumble keeps screaming "free speech platform" while quietly admitting they have no Mr. Beast. Microsoft lit $100 million on fire chasing Ninja over to Mixer. The pattern is always the same - buy the big name, wait for the audience to follow, watch it crash. Marcus lays out the open letter he wrote Chris Pavlovsky and the playbook that actually builds an audience from zero. We close on where this is all going - why Gen Z does not care about celebrities, why the next great Christian film might be a Ryan Gosling space movie, and why the human connection that AI can never fake is the whole ballgame. If you build, fund, or just consume content, this one will recalibrate how you think about the entire game. CHAPTERS: 00:00 - The $100M Question Hollywood Won't Answer 01:23 - The Guy Who Called Every Conservative Streaming Flop 03:41 - The Consumer vs. The Purchaser Mistake 05:36 - What the Happy Meal Teaches About Kids' Content 08:03 - How Daily Wire Torched $100 Million on Bentkey 09:52 - VeggieTales Did It Right (A Hammer In Search of a Nail) 11:28 - Build What People Actually Want 12:39 - The Cable TV Playbook Nobody Runs Anymore 15:23 - Is "Project Hail Mary" the Christian Film We've Been Waiting For? 17:46 - The Blind Spot: Nobody Builds For Young Men 18:14 - "There's No Market" Is a Lie (Tesla, Uber, the Wright Brothers) 21:37 - Rumble's Real Problem - Where's Their Mr. Beast? 24:26 - The Open Letter to Chris Pavlovsky 27:40 - The $100M Mixer Disaster 29:45 - Why Gen Z Doesn't Care About Celebrities Anymore 33:23 - The Human Connection AI Can't Fake 36:40 - Where to Find LOOR TV + Final Thoughts Today's Guest - Marcus Pittman / LOOR TV: LOOR.tv (join, or hit the Creator / Investor tabs before logging in): https://www.loor.tv Marcus on X: https://x.com/ImKingGinger LOOR on X: https://x.com/WatchLOOR Marcus's Substack ("Poorly Written"): https://substack.com/@marcuspittman Marcus's Federalist piece, "Project Hail Mary Is The Masculine Christian Film You've Been Waiting For": https://thefederalist.com/2026/04/07/project-hail-mary-is-the-masculine-christian-film-youve-been-waiting-for/ Marcus's first time on the show (Ep. 437, Feb 2022): https://www.briannicholsshow.com/437-fighting-the-culture-with-great-storytelling-with-loortv-ceo-marcus-pittman/ The Brian Nichols Show: Home base (listen + watch everywhere): https://www.briannicholsshow.com Apple Podcasts: https://podcasts.apple.com/us/podcast/the-brian-nichols-show/id1334346967 Rumble: https://rumble.com/c/TheBrianNicholsShow Follow Brian on X: https://www.briannicholsshow.com/twitter (@BNicholsLiberty) Follow Brian on Facebook: https://www.briannicholsshow.com/facebook More from Brian - CX Without the BS: https://podcasts.apple.com/us/podcast/cx-without-the-bs/id1747979147 Listener questions: brian@briannicholsshow.com Today's Sponsor - Cardio Miracle Support your heart health and the show - for 15% off: https://CardioMiracle.com/TBNS Learn more about your ad choices. Visit megaphone.fm/adchoices

Did Daily Wire really torch $100 million on Bentkey? Marcus Pittman, CEO of LOOR TV, called the collapse of conservative streaming years before it happened - and he is back on the show to explain exactly why conservative entertainment keeps losing. Four years ago Marcus came on with a contrarian pitch that made VCs laugh in his face - Netflix meets Kickstarter, where subscribers decide what gets made. Today he has funded nearly 40 pieces of content for under a million bucks, the same number the venture guys swore would cost over $100 million. He bet against the room and the room was wrong. Here is the mistake that keeps sinking the right. Conservatives build content for the parent and forget the parent is not the consumer - the kid is. McDonald's figured this out with the Happy Meal in the 70s. Chuck E Cheese figured it out. Bentkey never did, ad the bill came due to the tune of nine figures. Then we get into the part nobody on our side wants to hear. Rumble keeps screaming "free speech platform" while quietly admitting they have no Mr. Beast. Microsoft lit $100 million on fire chasing Ninja over to Mixer. The pattern is always the same - buy the big name, wait for the audience to follow, watch it crash. Marcus lays out the open letter he wrote Chris Pavlovsky and the playbook that actually builds an audience from zero. We close on where this is all going - why Gen Z does not care about celebrities, why the next great Christian film might be a Ryan Gosling space movie, and why the human connection that AI can never fake is the whole ballgame. If you build, fund, or just consume content, this one will recalibrate how you think about the entire game. CHAPTERS: 00:00 - The $100M Question Hollywood Won't Answer 01:23 - The Guy Who Called Every Conservative Streaming Flop 03:41 - The Consumer vs. The Purchaser Mistake 05:36 - What the Happy Meal Teaches About Kids' Content 08:03 - How Daily Wire Torched $100 Million on Bentkey 09:52 - VeggieTales Did It Right (A Hammer In Search of a Nail) 11:28 - Build What People Actually Want 12:39 - The Cable TV Playbook Nobody Runs Anymore 15:23 - Is "Project Hail Mary" the Christian Film We've Been Waiting For? 17:46 - The Blind Spot: Nobody Builds For Young Men 18:14 - "There's No Market" Is a Lie (Tesla, Uber, the Wright Brothers) 21:37 - Rumble's Real Problem - Where's Their Mr. Beast? 24:26 - The Open Letter to Chris Pavlovsky 27:40 - The $100M Mixer Disaster 29:45 - Why Gen Z Doesn't Care About Celebrities Anymore 33:23 - The Human Connection AI Can't Fake 36:40 - Where to Find LOOR TV + Final Thoughts Today's Guest - Marcus Pittman / LOOR TV: LOOR.tv (join, or hit the Creator / Investor tabs before logging in): https://www.loor.tv Marcus on X: https://x.com/ImKingGinger LOOR on X: https://x.com/WatchLOOR Marcus's Substack ("Poorly Written"): https://substack.com/@marcuspittman Marcus's Federalist piece, "Project Hail Mary Is The Masculine Christian Film You've Been Waiting For": https://thefederalist.com/2026/04/07/project-hail-mary-is-the-masculine-christian-film-youve-been-waiting-for/ Marcus's first time on the show (Ep. 437, Feb 2022): https://www.briannicholsshow.com/437-fighting-the-culture-with-great-storytelling-with-loortv-ceo-marcus-pittman/ The Brian Nichols Show: Home base (listen + watch everywhere): https://www.briannicholsshow.com Apple Podcasts: https://podcasts.apple.com/us/podcast/the-brian-nichols-show/id1334346967 Rumble: https://rumble.com/c/TheBrianNicholsShow Follow Brian on X: https://www.briannicholsshow.com/twitter (@BNicholsLiberty) Follow Brian on Facebook: https://www.briannicholsshow.com/facebook More from Brian - CX Without the BS: https://podcasts.apple.com/us/podcast/cx-without-the-bs/id1747979147 Listener questions: brian@briannicholsshow.com Today's Sponsor - Cardio Miracle Support your heart health and the show - for 15% off: https://CardioMiracle.com/TBNS Learn more about your ad choices. Visit megaphone.fm/adchoices

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

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

If you've been wanting to buy a business for years, get your ticket to Main Street Millionaire Live. You'll learn how to find the right business for you, make deals, evaluate them, finance them, and own the upside: https://contrarianthinking.biz/MSML26_BDYT You've got a product idea. Maybe you've even built the thing. But now you're stuck on the part that actually matters: getting people to give you money before you've spent a fortune building inventory, hiring a team, or raising venture capital that'll own your company before you do. Everett Taylor is the CEO of Kickstarter, the platform that's helped creators raise over $8 billion and launch everything from billion dollar companies to side hustles that print cash. He came up through the mailroom, got stabbed working a parking lot, and built his career by learning how to sell himself before he had anything to sell. Now he runs one of the most creative funding platforms in the world and he's breaking down exactly how to raise money without giving away equity, how to pitch without sounding desperate, and why most people fail before they even start. In this episode, you'll learn: Why selling yourself is the first skill every founder needs and how to build confidence even when you're starting from zero The pre-order pyramid: exclusivity, urgency, value, timeliness, and trust, and why video is the number one thing that makes or breaks a campaign How to calculate your real costs so you don't lose money on every sale and why you need a 25 to 30 percent buffer for the things you can't predict The biggest mistakes founders make when pitching: not being concise, not building trust, and not understanding the difference between pitching VCs and pitching customers Why raising venture capital means giving up control and how pre-orders let you test demand, keep equity, and build a real business on your terms ___________ (00:00:00) Introduction: How to Raise Your First 100K Without Connections (00:00:52) The Self-Selling Framework: Marketing Yourself Before Your Product (00:03:54) Mental Toughness and Emotional Regulation: The Two Traits You Need (00:08:24) Pre-Orders Over Venture Capital: The Kickstarter Advantage (00:10:21) The Pre-Order Pyramid: Exclusivity, Urgency, Value, and Trust (00:13:41) The Video-First Rule: Why Great Campaigns Start With 30 Seconds (00:17:11) The Biggest Pitch Mistakes: Get to the Point or Get Lost (00:20:20) When to Bootstrap vs When to Raise Millions (00:23:02) The Venture Capital Trap: Why a 250M Offer Got Rejected (00:26:29) Cost Management and Buffer Strategy: The 25-30 Percent Rule (00:32:46) Surprising Success Stories: 28M Board Games and Comic Book Empires (00:37:49) The CEO Playbook: Mental Toughness, Humility, and Rolling Up Your Sleeves (00:40:43) Remote Leadership: Trust, Aggressive Goals, and Performance Management (00:43:26) The Kickstarter Turnaround: Rejecting Mediocrity and Rebuilding Culture ___________ MORE FROM BIGDEAL

How is AI changing the way brands innovate, launch products, understand consumers, and build stronger relationships with their markets? In this episode of Grownlearn, host Zorina Dimitrova speaks with Shantanu Srivastava, a global marketing and innovation leader with more than two decades of experience across major consumer brands including Twinings, Danone, Sanofi, and Reckitt. Shantanu explains how AI is transforming marketing efficiency, product development, consumer research, compliance screening, and brand strategy inside large organizations. He also shares why human judgment remains essential, even as AI tools make innovation faster, cheaper, and more continuous. The conversation also explores the shift from traditional brand storytelling to story doing and story living, with examples from challenger and purpose-led brands such as Oatly, Tony's Chocolonely, Dove, and others. Shantanu also shares his personal transition from corporate brand leadership to entrepreneurship, including his work in health and wellness, lifestyle-related health management, and advisory work with startups and purpose-driven brands. In this episode, we discuss: • How AI is changing marketing and innovation • Why FMCG companies are rethinking consumer-led product development • How AI can reduce time and cost in research, marketing planning, and product launches • Why brand storytelling is evolving into story doing and story living • What startups can learn from global brands — and what big brands can learn from startups • How purpose-led brands create deeper consumer engagement • Why AI-driven innovation still needs human oversight • Shantanu's journey from global brand leadership to health and wellness entrepreneurship This conversation is especially relevant for founders, marketers, brand leaders, innovation teams, FMCG professionals, startup advisors, and anyone interested in the future of AI-driven brand growth.

In this episode, we sit down with Marius Smyth of the Digital Irish Venture Fund (DIVF) for a follow-up to our earlier conversations with Marty Loughlin. Where Marty walked us through how DIVF picks founders and the realities of pitching the fund, Marius zooms out to the layer underneath: how DIVF is building the Irish diaspora into a working venture ecosystem rather than relying on it as a goodwill network. He is leading the build-out of a physical “Green Room” for Irish founders in New York, architecting the fund's co-investment strategy, and thinking hard about where a fund of DIVF's size best fits in an industry increasingly polarised between mega-funds and solo angels.In this conversation, we get into:Why the Irish network is a sourcing engine, not just a hospitality network — and what DIVF is building to keep it commercial rather than a mutual appreciation society.How DIVF co-invests alongside other funds without becoming dependent on whoever is leading the round.Why the right place for an Irish venture fund right now is not at either end of the cheque-size spectrum, and where DIVF deliberately sits.The most practical first step for a founder coming out of Ireland with no US network, plus where the gaps still are in the Irish ecosystem itself.If you're an Irish founder building for the US market — or thinking about the next layer of infrastructure the diaspora needs — this episode is the strategic counterpart to Marty's tactical advice from the earlier episodes.About the Digital Irish Venture Fund (DIVF)DIVF is an early-stage venture firm focused on Irish and Irish diaspora founders building for global markets. The fund operates as “friendly operators” rather than traditional shark VCs — providing hands-on operating help, warm introductions, and access to a diaspora network across New York, London and Dublin after the cheque is written. DIVF co-invests alongside other funds and works alongside the broader Irish ecosystem — Enterprise Ireland, universities, accelerators, and angels — to bring strong Irish companies into the US market.Want to get in contact with the Digital Irish team? Email us at podcast@digitalirish.com

Most VCs think corporate venture capital is slower, more conflicted and structurally weaker than traditional venture firms. Marc Thom, Corporate Vice President and Head of Henkel Ventures, argues the opposite and explains why the best CVCs may actually outperform traditional VCs over time.In this episode, Marc joins Andreas Munk Holm and Jeppe Høier to discuss how Henkel built one of Europe's leading corporate venture platforms, why most startup-corporate partnerships fail and how corporates can create both strategic and financial advantage through venture investing.Topics coveredWhy the best CVCs can outperform VCsHow Henkel structures venture investing and partnershipsThe “holy bible” behind startup collaboration inside corporatesWhy most startup partnerships fail internallyThe role corporates should play on startup cap tablesHow AI is reshaping industrial R&D and materials scienceTimestamps(00:00) Why CVCs can outperform traditional VCs(04:00) How Henkel structures startup sourcing and partnerships(11:00) The use case framework behind Henkel Ventures(16:00) The “Role of Henkel” in startup investing(23:00) Why Henkel invested in ResearchGate(27:40) AI, chemistry and the future of industrial R&D(30:20) Why Marc believes CVCs can outperform VCs(36:00) How Henkel built internal alignment for venture investingSubscribe to EUVC, the home of European tech, for more insights.

(Presented by TLPBLACK: A cybersecurity intelligence platform focused on sharing curated, high-sensitivity threat insights and research with trusted security professionals.) Three Buddy Problem x Ekoparty Miami: SentinelLabs researcher Gabriel Bernadett-Shapiro hops on the mic to unpack who gets to define what "security" even means in the age of AI, why venture capital keeps funding the wrong things, and how the frontier labs quietly ate everyone's coding harness. Plus, how AI actually contributed to cracking the FAST 16 research, overcoming the guardrails, and why your domain expertise is the only thing keeping you out of full-blown rabbit-hole psychosis. Cast: Juan Andres Guerrero-Saade, Ryan Naraine and Gabriel Bernadett-Shapiro. Timestamps: 0:00 Introductory banter 4:55 Gabe returns: how the models got scary-good at code 8:45 Bay Area short-termism and the "10x in 18 months" trap 11:35 VCs as tastemakers, and why that's broken 13:00 The unpaid-labor pipeline into the AI labs 18:00 The real misunderstanding about security's moat 20:18 Bug bounties: a net negative for the industry? 22:20 The great vuln fire sale — find 50,000, fix zero 27:28 Who will maintain vetted open-source libraries? 29:29 FAST 16: how AI actually broke the case open 35:05 The rabbit-holing machine and the path to "AI psychosis" 41:05 Stuxnet, Kim Zetter, and the story we'll never be told

"You could start your day paddleboarding on Lake Ontario, then you could go to a workout class, then you could go to a session on what VCs want to hear." Last year, a decentralized community experiment led to 15,000 people attending over 300 events in the middle of June. With nearly 600 events welcoming people from around the world set to begin Monday, this year's Toronto Tech Week promises to be much bigger and bolder. Co-directors Julia Konefal and Mell Truong join The BetaKit Podcast to ensure you are eventmaxxing all week long in Toronto. -- Clio is proud to present the BetaKit Guide: Toronto Tech Week 2026. This guide is your ultimate shortcut to navigating all 600 events, from panels, mixers, and competitions, taking place across the city from May 25th to 29th. Read the full guide here.

Jianggan Li, Founder of Momentum Works, joins Jeremy Au to unpack the Trump Xi Beijing summit, the first US presidential visit to China since 2017. They decode the optics of Zhongnanhai Garden and the Temple of Heaven, Xi Jinping's Thucydides Trap reference, the 200 plane Boeing deal, and why the absence of major deliverables is itself a strategic win. The conversation dives deep into the AI chip war, why NVIDIA's market share in China collapsed from 95% to under 10%, how the US export ban accelerated Chinese semiconductor self-reliance, and DeepSeek's reported 50 billion RMB funding round with the founder personally contributing 20 billion. They examine why Jensen Huang was added to Trump's delegation last minute, Elon Musk's unique position with Tesla in China, how Chinese state subsidies flow through local governments, and why founders like the Manus team made costly domicile mistakes. For Southeast Asia founders, VCs, and operators in Singapore, Indonesia, Vietnam, Philippines, Thailand, and Malaysia, this episode reveals why both superpowers settled into managed competition rather than decisive split, and what it means for global supply chains, AI models, and capital flows in 2026. Watch, listen or read the full insight at https://www.bravesea.com/blog/trump-xi-summit Get transcripts, startup resources & community discussions at https://www.bravesea.com WhatsApp: https://whatsapp.com/channel/0029VakR55X6BIElUEvkN02e TikTok: https://www.tiktok.com/@jeremyau Instagram: https://www.instagram.com/jeremyauz Twitter X : https://x.com/jeremyau LinkedIn: https://www.linkedin.com/company/bravesea English: Spotify | YouTube | Apple Podcasts Bahasa Indonesia: Spotify | YouTube | Apple Podcasts Chinese: Spotify | YouTube | Apple Podcasts 00:00 Introduction 01:23 Why the Trump Xi summit was a strategic win 03:25 Key players and how China renamed Rubio 06:26 Xi's Thucydides Trap and the Athens Sparta lesson 09:11 Chinese self-media versus official narratives 12:10 Zhongnanhai Garden and Temple of Heaven optics 15:15 El Niño, food security and global risk 16:36 Boeing deal, Elon Musk and Tesla in China 19:04 Jensen Huang added last minute to the delegation 21:16 Why Chinese founders still domicile in Singapore 23:08 DeepSeek's 50 billion RMB funding anomaly 24:00 NVIDIA's China collapse and the backfired chip ban 26:32 DeepSeek and ByteDance: founder driven AI 29:34 How Chinese state subsidies actually work 32:17 DeepSeek's cost efficiency strategy 33:04 Future outlook: Xi's US visit and Taiwan

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

Jean-Baptiste Wautier spent nearly thirty years in private equity, including over two decades as Partner and CIO at BC Partners, one of Europe's leading buyout firms, where he helped manage approximately forty billion euros in institutional capital. Today he runs a consumer-focused family office and sits on the boards of Pershing Square Holdings and Howard Hughes Holdings.In this episode, JB breaks down what the best deals have in common before anyone knows they are great, why he believes diversification is insurance for investors who don't understand what they own, and how moving from institutional capital to family capital fundamentally changes your relationship to time, risk, and opportunity.We also get into the Iran war, volatile markets, and the S&P rallying twenty percent in a single month. JB makes a convincing bear case and an equally convincing bull case — and then tells you exactly what he is doing with his own capital right now.⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.comKey topics discussed:- From institutional PE to family capital: how removing mandate constraints and preset timelines changes the way you invest- The three ingredients every great deal has in common: moat, management, and optionality- Why diversification is insurance for investors who don't understand what they own, and why concentration is how alpha gets generated- The Iran war, volatile markets, and why you can never time the market — including JB's case for and against deploying right now- Buy well, own well, and the ancient Greek concept of Kairos as the art of knowing when to exitLinks:Wautier Family Office - https://wautier.co.uk/Connect with Jean-Baptiste Wautier - https://www.linkedin.com/in/jean-baptiste-wautier/Connect with Prashant: https://linkedin.com/in/choubeysahabSubscribe to VC10X newsletter - ⁠https://vc10x.beehiiv.com⁠Subscribe on YouTube - ⁠https://youtube.com/@VC10X ⁠Subscribe on Apple Podcasts - ⁠https://podcasts.apple.com/us/podcast/vc10x-investing-venture-capital-asset-management-private/id1632806986⁠Subscribe on Spotify - ⁠https://open.spotify.com/show/7F7KEhXNhTx1bKTBFgzv3k?si=WgQ4ozMiQJ-6nowj6wBgqQ⁠VC10X website - ⁠https://vc10x.comTimestamps:(00:00) - Preview(03:06) - Introduction to the guest, Jean-Baptiste Wautier (JB), and episode overview.(04:53) - The biggest change in private equity over the last 20 years.(06:56) - The three common traits of the best investment deals.(09:38) - Where private equity genuinely adds operational value.(12:42) - How investing family capital changes your relationship to time and risk.(16:50) - Are constraints helpful or harmful to an investor's performance?(20:48) - The biggest mistake investors make with consumer companies.(22:45) - Lessons for PE investors from Bill Ackman's public market style.(26:44) - The governance approach for a high-conviction, concentrated fund.(28:13) - Why the Berkshire Hathaway holding company model is so difficult to replicate.(31:04) - What an effective board does that shareholders never see.(32:49) - Early warning signs that a board is becoming ceremonial.(34:13) - A deep dive into the current investing environment and geopolitical risks.(37:30) - The detailed case against deploying capital now.(39:20) - The detailed case for deploying capital now.(42:19) - Is investment success from buying well, owning well, or behaving well?#PrivateEquity #FamilyOffice #VentureCapital #VC10X #Investing

Chris Holman welcomes Chris Rizik, Founder and CEO, Renaissance Venture Capital Fund, Ann Arbor, MI. Chris Rizik, founder and CEO of Renaissance Venture Capital Fund, joins Michigan Business Beat to discuss his firm's unique "fund of funds" model, which invests in venture capital funds nationwide and then works to attract those funds to invest in Michigan startups. He highlights the firm's signature "UnDemo Day" event — a speed-dating-style format connecting Michigan startups with out-of-state VCs through hundreds of pre-arranged one-on-one meetings, held virtually in the spring and in person at Ford Field each fall — which has helped channel roughly $3 billion in investment into Michigan companies. Rizik also pushes back on the old notion that most venture-backed startups are expected to fail, arguing that a well-matched ecosystem can produce a much higher success rate across a range of company sizes. He closes by sharing that he will receive the Venture Vanguard Award, the nation's top venture capital honor, crediting his team and the broader Michigan ecosystem — including the Michigan Venture Capital Association — for helping put Michigan on the national VC map. » Visit MBN website: www.michiganbusinessnetwork.com/ » Subscribe to MBN's YouTube: www.youtube.com/@MichiganbusinessnetworkMBN » Like MBN: www.facebook.com/mibiznetwork » Follow MBN: twitter.com/MIBizNetwork/ » MBN Instagram: www.instagram.com/mibiznetwork/

In Episode 7, of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Edward Chenard, Fractional Chief Data and Analytics Officer, where they discuss how data leaders can break free from the cost centre trap and drive measurable, quantifiable business value, having personally delivered over $2.5 billion in revenue across Fortune 500s and start-ups alike, which includes;How a career in product management at GE laid the foundation for an outcome-first approach to data leadership.Why not having a seat at the table is not an excuse and why the biggest commercial wins came from several levels below the C-suite.How to read the type of organisation you are in and choose your influence strategy accordingly.Why the shift from AI-as-tool to AI-as-strategy matters for how data leaders position themselves now.Why data teams are correctly perceived as overheads.Building a personalisation platform at Best Buy that generated over $1 billion in revenue.Why identifying the metrics the C-suite actually obsess over is the only way to get and keep their attention.Why delivering what the job description says is the riskiest career move a data leader can make.How a predictive shipping tool built in four months turned a century-old logistics company into a recognised innovator.Why agreeing attribution with business stakeholders before the work starts is the only way to get the credit you deserve.Why the individual contributor mindset of doing what you are told actively works against data leaders when they step into leadership roles.Why data leaders need to think like intrapreneurs, owning a P&L and speaking the language of finance, VCs, and private equity rather than just tech.Why a background in international business and theology turned out to be better preparation for data leadership than a technical degree.Why philosophy and physics majors often outperform computer science graduates in data roles because thinking through problems without solid facts is the real differentiator.Why IT cultures that lead with process are structurally incapable of delivering transformation.Why training your team on AI regardless of what the C-suite thinks is a leadership obligation, not insubordination.How Edwards frameworks for moving data teams from dashboard builders to decision-makers are publicly available.Why it is human as the loop, not human in the loop, and why AI will quickly expose those who have been faking it.Thanks to our sponsor, Data & AI Literacy Academy.Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first.At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme.They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.

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?  

I meet 1,000+ founders every year. Most are bad at fundraising.I also interview 100+ of the world's best founders on my podcast each year. Most are incredible at fundraising.One raised $14M in 17 days. another was 3x oversubscribed on a $3M round. another closed a seed in hours from a single X post. All are first-time, unproven founders.They don't waste time becoming "friends" with VCs. They have a business to build. They treat fundraising for what it is: a process where you manufacture FOMO as fast as possible, take the money, and move on.This video breaks down the 4 steps the best fundraisers use to raise fast. The same 4 steps taught at YC and 500 Startups (where i went). The same 4 steps you can run on thousands of VCs worldwide to close $2-3M in weeks not months.Why You Should ListenWhy you need to reach out to 50 VCs on the same day just to end up with three term sheets.How to engineer intro blurbs that make VCs feel like they're already late to the game.Why setting fake deadlines is the fastest way to destroy all your credibility with investors.How one founder raised $3M in five weeks by starting with a $1.5M target and driving FOMO.Keywords startup podcast, startup podcast for founders, product market fit, finding pmf, fundraising, raising a seed round, VC pitch, FOMO, startup fundraising playbook, term sheets, investor meetings, Pablo Srugo, venture capitalChapters00:00:00 Intro00:01:30 Step 1: Build a List of 50 Qualified VCs00:06:00 Step 2: Engineer the Intros00:14:00 Step 3: Compress the Timeline00:20:00 Step 4: Manufacture FOMO00:26:00 Three Rules to Never BreakSend me a message to let me know what you think!

Anshula Singh came into Foster's MBA program with five years of software engineering experience — building products at Salesforce and ServiceNow, working with machine learning, helping train early LLMs from the inside. She wasn't new to AI. She was already watching it closely. In her second winter quarter, she took Software Entrepreneurship — a course where students pitch ideas on day two, form teams, and spend ten weeks building a company. Anshula's team built Authscript, an AI platform to automate prior authorization forms in healthcare. They got far enough to pitch in front of VCs. Then new federal legislation made their product obsolete almost overnight. In this conversation, Anshula talks about what it takes to build an AI startup under time pressure, what the experience taught her about when to use AI and when not to, and her advice for business students figuring out where they fit in a landscape that keeps shifting. Key Takeaways Know when to kill the idea. Find your idea killer early — before you're burning capital defending a thesis that no longer holds. Customer discovery still beats AI research. The most valuable insights came from talking to people. AI supported the market sizing; the real signal came from humans in the room. Make AI work with how you already work. Don't reinvent yourself for the tool. Figure out where AI makes you more productive in your existing workflow, and start there. Keep your own voice. As models improve, your distinct perspective becomes more important, not less. AI is ambient infrastructure. The question isn't whether it changes your work — it will. The question is how you position yourself within that change. About Anshula Singh Anshula Singh is an MBA candidate at the UW Foster School of Business. Prior to Foster, she spent five years as a software engineer at Salesforce and ServiceNow, where she worked on machine learning applications and contributed to early large language model development. Subscribe Follow Conversations on Careers and Professional Life wherever you listen. Conversations on Careers and Professional Life is hosted by Gregory Heller and produced at the UW Foster School of Business.

"Pre-construction has become less efficient than construction itself."That is the claim Dustin Devan made on this week's Bricks, Bucks & Bytes after returning from Advancing Pre-Con. Martin, Patric Hellermann and Dustin also dug into the proposed California wealth tax, Bentley's Q1 earnings, and why VCs keep funding the 51st AI takeoff company.Tune in to find out about:✅ Why 80% of project cost is locked in before design is even 30% done✅ How the California wealth tax proposal would actually value private companies✅ Patric's case for indexing into "directionally right" early-stage bets✅ Why takeoff is a feature, not a companyListen now on Spotify, Apple Podcasts and YouTube.#aec #construction #constructiontech #bricksandbytes #bricksbucksandbytes #ai #vcOur Sponsors:BreadCrumb- 50,000+ projects globally. All running safer, faster, with Breadcrumb. - breadcrumb.coAphex is the multiplayer planning platform where construction teams plan together, stay aligned, and deliver projects faster – check out aphex.coArchdesk -  “The #1 Construction Management Software for Growing Companies - Manage your projects from Tender to Handover” check archdesk.comChapters00:00 Intro00:35 Introduction and Overview of Topics02:15 US Construction Spending Trends09:52 California Wealth Tax Proposal26:29 Advancing Pre-Construction Insights34:38 Building Cost Infrastructure and Pricing Engines40:30 The Role of Design in Construction Projects45:39 The Importance of Feasibility in Design50:46 The Impact of AI on Construction Estimation53:25 Navigating the Takeoff Tool Landscape57:02 Innovations in Construction Robotics 

A security agency tested 5,000 apps built with Lovable, Replit, Base44 and Netlify. Every single one had vulnerabilities — including apps that were live, charging customers, and handling personal data. Sophia Matveeva is joined by Rags Vadali — former Google engineer, Meta product lead who launched Instagram filters to 600 million people, and CEO of AI startup Floto — for an honest expert conversation about what AI tools can and cannot do for your product right now. You'll learn: Why a product can look finished while being fundamentally unsafe What VCs now do when they see a vibe-coded product Why Apple is rejecting AI-built apps from the App Store When to call in developers in the age of AI (and why what they do for you has changed) This is not an episode about why AI tools are bad. It is about knowing where the line is — so you can use them on the right side of it. Resource mentioned in this episode: Wired: Thousands of Vibe-Coded Apps Expose Corporate and Personal Data on the Open Web Ready to build your tech product the right way? Book a call: https://calendly.com/sophia-matveeva/new-meeting Timestamps: 00:00 - Introduction: VC walks away from vibe-coded startup 02:36 - Security breach: 5,000 AI-built apps had vulnerabilities 05:00 - The iceberg problem: What's hidden below the surface 08:35 - Every single app had security issues exposed 11:09 - Who gets sued: The platforms or the founders? 13:09 - VCs rejecting vibe-coded apps during due diligence 15:29 - Apple cracking down on AI-generated apps 18:21 - The maintenance nightmare: Adding features breaks everything 24:46 - What kind of engineer you actually need now 29:53 - Building isn't the constraint anymore - sales and marketing are 34:35 - Engineers' role is now strategic, not operational Free AI Mini-Workshop for Non-Technical Founders: Learn how to go from idea to a tested product using AI — in under 30 minutes. Get free access here: techfornontechies.co/aiclass Follow and Review: We'd love for you to follow us if you haven't yet. Click that purple '+' in the top right corner of your Apple Podcasts app. We'd love it even more if you could drop a review or 5-star rating over on Apple Podcasts. Simply select "Ratings and Reviews" and "Write a Review" then a quick line with your favorite part of the episode. It only takes a second and it helps spread the word about the podcast. Listen to our podcast on: Apple Spotify YouTube Audible Pandora Transcript: https://www.techfornontechies.co/blog/303-before-you-build-with-ai-what-every-non-technical-founder-needs-to-know

For more information and support, join us at https://thecirsgroup.com Uncover the truth behind chronic illnesses that are often misdiagnosed for years. If you've been told your symptoms are anxiety, IBS, or autoimmune, this episode explores a groundbreaking approach that could change everything. Learn how the Shoemaker Protocol, backed by clinical research, helps identify and treat CIRS (Chronic Inflammatory Response Syndrome) caused by biotoxin exposure like mold. In this episode, Barbara and Jacie walk through the first phase of the Shoemaker Protocol: determining whether CIRS is the root cause of your symptoms. You'll learn how to recognize key symptom patterns, use the validated VCS (Visual Contrast Sensitivity) test, and identify common exposure sources such as water-damaged buildings and tick bites. What you'll learn: • The 35 symptom clusters linked to CIRS and how to assess your own • How the VCS test can be an early screening tool • Common exposure triggers that are often overlooked • The role of genetics and why pattern recognition matters • The first steps to begin a clinically validated path to recovery CIRS can create widespread disruption in the body, from immune dysfunction to neurological symptoms, and is frequently mistaken for other conditions. This episode helps you connect the dots so you can move toward targeted, effective healing. Whether you're just starting your health journey or searching for answers after years of confusion, this is part one of a five-part series designed to bring clarity, direction, and hope. Explore more resources and support at https://thecirsgroup.com TIMESTAMPS 00:00 - Intro: an answer for healing 01:35 - Why we're covering the five phases of the Shoemaker Protocol 02:53 - What is CIRS? 04:09 - Common misdiagnoses 05:25 - Understanding CIRS Symptoms 08:23 - Four Pillars of CIRS Diagnosis: First, Symptom Clusters 09:00 - Second: Visual Contrast Sensitivity Test 12:48 - Third: Exposure History and Its Importance 16:25 - Fourth: Blood Work and Genetic Testing 19:20 - What CIRS is Not 20:30 - Next Steps for Diagnosis 22:15 - Personal Experiences and Hope for Healing 24:00 - Conclusion and Upcoming Episodes For more information and support, join us at https://thecirsgroup.com Order Jacie's book! The 30 Day Carnivore Bootcamp: https://a.co/d/7MgHrRs The CIRS Group: Support Community: https://thecirsgroup.com Instagram: https://www.instagram.com/thecirsgroup/ Find Jacie for carnivore, lifestyle and limbic resources: Jacie's book on the Carnivore diet!  https://a.co/d/8ZKCqz0 Instagram: https://www.instagram.com/ladycarnivory YouTube: https://www.youtube.com/@LadyCarnivory Blog: https://www.ladycarnivory.com/ Find Barbara for business/finance tips and coaching: Website: https://www.actlikebarbara.com/ Instagram: https://www.instagram.com/actlikebarbara/ YouTube: https://www.youtube.com/@actlikebarbara Jacie is a Shoemaker certified Proficiency Partner, NASM certified nutrition coach, author, and carnivore recipe developer determined to share the life changing information of carnivore and CIRS to anyone who will listen. Barbara is a business and fitness coach, CIRS and ADHD advocate, writer, speaker, and a big fan of health and freedom. Together, they co-founded The CIRS Group, an online support community to help people that are struggling with their CIRS diagnosis and treatment.

Most corporates struggle to move fast enough for AI and robotics. Toyota's answer is corporate venture.In this episode, Andreas Munk Holm and Jeppe Høier speak with Ro Gupta, CEO and Managing Director of Woven Capital, Toyota's growth-stage venture fund focused on mobility, AI, automation and climate technology.They discuss how Toyota uses CVC to identify frontier technologies, work with startups and think decades ahead.Ro shares how Woven Capital functions as agile “speedboats” around Toyota's industrial “supertanker”, scanning for strategic opportunities across AI, robotics, mobility, manufacturing and infrastructure.HighlightsWhy Toyota invests through both “inside-out” and “outside-in” modelsHow Woven evaluates AI, robotics and the future of labourWhy strategic engagement matters more than passive investingHow Toyota balances long-term thinking with venture-speed executionWhy Europe is becoming increasingly important for Toyota's innovation strategyTimestamps(00:00) Ro Gupta's founder background and move into Toyota(04:00) Startup operators vs corporate insiders in CVC(10:00) Toyota's multi-fund venture strategy(15:00) The “supertanker and speedboats” framework(18:00) AI hype cycles and long-term industrial strategy(23:00) AI, robotics and advanced manufacturing themes(27:00) What Woven Capital looks for in startups(30:00) What founders expect from CVC investors(33:00) How Woven collaborates with traditional VCs(35:00) Europe's role in Toyota's future strategy(39:00) Toyota's next 100-year opportunity(44:00) The “build, move and connect” frameworkSubscribe to EUVC, the home of European tech, for more insights: https://www.eu.vc/subscribe

Does it feel like your favorite AI tool is declared dead one week, only to be resurrected the next? This week, Andrew sits down with Bryan Bischof, Head of AI at Theory Ventures, to explore the hidden levers of inference systems and the industry's obsession with prematurely writing off useful tools. Bryan shares his experiences with why prompt optimization is mostly a dead end, the secret to building high-performing data agents, and how his team builds operational software for VCs. The two also break down the origins of the satirical rip-grep.com and drop hints about Bryan's highly anticipated next AI game show experiment. AI Council 2026: Catch Bryan's track on inference systems and get tickets.Read the guide: The APEX FrameworkFollow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's stories:Events: Catch Bryan's track live at the AI Council Conference.Writing: Check out the April Fools manifesto at rip-grep.comTheory Ventures: Learn more about the work happening at Theory Ventures.X/Twitter: @BEBischofLinkedIn: Bryan BischofOFFERSStart Free Trial: Get started with LinearB's AI productivity platform for free.Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era.LEARN ABOUT LINEARBAI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production.AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance.AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil.MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

Feeling overwhelmed by all the conflicting advice about startup funding? One person says bootstrap. Another says raise venture capital. Someone else swears crowdfunding is the smartest move. Then you hear angels are better, loans are risky, VCs are the only path, or that you should never give up equity. No wonder you feel stuck. In this episode, Jayla breaks down why funding advice feels so confusing for first-time entrepreneurs—and why the real question is not, "Which funding path is best?" The better question is: "Which funding path is best for me, my business, my stage, my goals, and my constraints?" If you are preparing to raise your first round of capital, this episode will help you stop collecting random opinions and start evaluating your options with a clearer decision-making framework. Jayla walks through the first thing every founder should define before looking for money: the job of the capital. What does the money actually need to do? Build a prototype? Buy inventory? Hire support? Prove demand? Reach a specific milestone? Because funding should not just reduce anxiety or sit in the bank. It should help your business create a measurable outcome. Then she shares a simple 4-part filter to help you decide whether bootstrapping, friends and family, crowdfunding, angel investors, loans, or venture capital actually make sense for your company. You'll learn how to evaluate funding based on: Business fit: Does this funding option make sense for your business model? Stage fit: Are you actually ready for this type of capital? Risk fit: Can you handle the pressure attached to this money? Goal fit: Does this path support the business—and life—you actually want? Jayla also shares a candid reflection from her own entrepreneurial journey, including what she wishes she had understood earlier about chasing someone else's version of success. Funding is powerful, but it comes with expectations. The capital path you choose can shape your company, your ownership, your pressure level, and your lifestyle. This episode is especially important right now because early founders are surrounded by more content, hot takes, and startup advice than ever. The goal is not to absorb every opinion. The goal is to build enough clarity to move forward confidently. If you are trying to figure out how to fund your startup, this episode will help you cut through the noise and choose the path that actually fits. In This Episode, You'll Learn Why conflicting funding advice is so common for early-stage founders Why "more money" is not always the answer How to define the job of capital before raising Why your business model should influence your funding path When bootstrapping, crowdfunding, angels, loans, or VC may make sense Why stage matters before approaching investors The hidden pressure that comes with debt, equity, crowdfunding, and bootstrapping How your personal goals should shape your capital strategy Why chasing someone else's definition of success can create regret How to evaluate advice instead of automatically absorbing it   About Your Host Jayla Siciliano is an entrepreneur with 25+ years in consumer brands, product, and marketing. After raising her first angel round against all odds and later appearing on Shark Tank, where she closed a deal with Mark Cuban, she now helps founders become fundable, confident, and ready to attract the right investors. Entrepreneurship changed her life, and she's on a mission to help first-time founders raise their first round of angel funding and change theirs too.   Disclaimer The information in this podcast is educational and general in nature and does not take into consideration the listener's personal circumstances. Therefore, it is not intended to be a substitute for specific, individualized financial, legal, or tax advice.

Most VCs talk about ownership. Few actually build it. Neil Sequeira, Co-Founder and General Partner at Defy, breaks down the unconventional strategies his firm uses to average 17 percent ownership across their seven highest marked portfolio companies — and why that number puts them up against any early stage manager in the country.Neil spent 12 years at General Catalyst before co-founding Defy a decade ago. In this conversation, he gets into why 75 percent of their deal flow never goes to market, how they made their biggest capital call on April 1st 2020 when venture investment was down 80 percent industry-wide, and why the most contentious deal at the partner meeting is usually the one that ends up doing the best.This is a masterclass in early stage conviction, portfolio construction, and what it actually means to partner with a founder for the long term.⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.comWe talk about -- Why Defy keeps their partnership small on purpose, and how that directly drives better early stage returns- The April 1st 2020 capital call: how they deployed 20% of Fund 2 in the quarter venture fell 80% industry-wide- The three-bucket framework for evaluating investments, and why the founder bucket outweighs market knowledge and hard work combined- How Defy averages 17% ownership across their seven highest marked companies using strategies most VCs never think to use- The one early signal that has predicted every failed investment in their portfolio, and why they no longer rationalize past it---Timestamps:(00:00) - Preview(00:28) - Introduction to Neil Sequeira and Defy(02:05) - How Decision-Making Quality Changes as VC Firms Scale(05:54) - The Speed of Conviction in Large vs. Small Firms(07:20) - The Power of Proprietary Deals(08:52) - Neil's Most Formative Investment Decisions(13:25) - Why the "Person" is the Most Critical Investment Factor(16:39) - Case Study: When an Investment Thesis Evolves Significantly(20:40) - Evolving Portfolio Construction Across Different Funds(22:30) - The Impact of AI on Investment Strategy and Check Size(24:10) - Building Company-Creation Platforms (US Defense, Crypto)(25:25) - How LPs React to Evolving Fund Strategies(28:20) - A Contrarian Approach: Investing When the Market Goes Dark(32:39) - Initial Bets vs. Doubling Down on Winners(34:35) - How Defy Owns 17% of Their Best Companies(37:34) - Patterns in Failed Investments: Lessons from Hindsight(38:25) - The Red Flag of Founder Integrity Issues(40:15) - The Danger of Market Noise and Not Controlling Your Destiny(44:03) - Start of Rapid Fire Round(44:19) - Sectors and Regions of Investment(44:53) - Typical Stage of Investment(45:47) - Leading Investment Rounds(46:28) - Typical Check Size and Ownership Goals(47:38) - How to Connect with Neil and Defy(48:45) - ConclusionLinks:Defy - https://defy.vc/Connect with Neil Sequeira - https://www.linkedin.com/in/neil-sequeira-76739a40/Connect with Prashant: https://linkedin.com/in/choubeysahabSubscribe to VC10X newsletter - ⁠https://vc10x.beehiiv.com⁠Subscribe on YouTube - ⁠https://youtube.com/@VC10X ⁠Subscribe on Apple Podcasts - ⁠https://podcasts.apple.com/us/podcast/vc10x-investing-venture-capital-asset-management-private/id1632806986⁠Subscribe on Spotify - ⁠https://open.spotify.com/show/7F7KEhXNhTx1bKTBFgzv3k?si=WgQ4ozMiQJ-6nowj6wBgqQ⁠VC10X website - ⁠https://vc10x.com#VentureCapital #EarlyStageInvesting #StartupFunding #VC10X #NeilSequeira #Defy #PortfolioConstruction #FounderAdvice #VCPodcast #StartupInvesting

https://youtu.be/aQyHwoGfy50 Max Kryzhanovskiy, President and CEO of MOS Creative, is driven by a desire to set an example for his children and show what's possible through technology, persistence, and innovation. As the leader of a tech-forward agency that builds websites, apps, and AI-enabled platforms, Max helps businesses move from idea to execution by creating digital products that solve real problems and scale over time. We explore Max's MVP Framework — Define the problem, Determine target market, Prototype the product, Build the MVP, Test and obtain feedback, Iterate — a practical approach for transforming ideas into scalable digital products. Max explains why founders should avoid overbuilding too early, how AI is accelerating prototyping and development, and why businesses must balance automation with authentic human connection. — Drive Growth Using AI Agents with Max Kryzhanovskiy  Good day, dear listeners. Steve Preda here with the Management Blueprint Podcast, and my guest today is Max Kryzhanovskiy, the President and CEO of MOS Creative, a company that builds websites and apps that drive growth. They were also the first company in Baltimore to launch a mobile site. Welcome to the show, Max.  Thank you for having me.  Let me ask you this—what is a mobile site? Is it a mobile phone site, or is it something different?  I mean, now it probably doesn't matter as much anymore, because everybody obviously has a website that works on a smartphone screen—or a responsive websites. But before mobile websites came out—or I should say, when smartphones first came out—we had to adjust for smaller screens. We were all used to bigger screens on a computer, and then once we started having different screen sizes come out before responsive, we were the first company to have a mobile website in Baltimore. And we actually built a web application specifically to create them ourselves, and then also went to market to offer it to other clients as well. So a mobile website is just like it sounds, a website that’s specifically designed for mobile.  That’s cool. So it sounds like you are very much a tech-forward company, and you are at the edge of technology. And as we were logging on, you said that you would be recording this on your phone because you actually have AI agents running on your computer. Does that mean you have AI agents as part of your team? What kind of agents do you have? Is it still an experiment, or is it already in execution mode?  It's in execution mode, but we're always experimenting. We like to think we're ahead of the curve, but with AI, we're all experimenting to a certain extent, right? Something new comes out, we try it out, see if it works, and see how it can be applied to your business—what kind of outcomes it can give you. So I'm all about AI. It's amazing. It's an amazing tool. But I think AI is becoming a lot more than we thought it was going to be—and also a lot less at the same time. Meaning, when AI launched—for example, when ChatGPT came out to the broader market—I mean, obviously AI had been around for a while—but when ChatGPT launched its chatbot platform publicly, we were amazed by how much work it could done. So it went from zero to a hundred. “Oh my God, it can do all of this,” right? But now, for example, with the more recent models—4.5, 5.0—the improvements are much smaller.  It's not a hundred percent or a thousand percent better anymore. Now it's maybe five or ten percent better, but the cost keeps increasing. I just read somewhere that even Claude said Claude Code won't be included much longer as part of the regular plan. So now it's only in the $200 higher-tier plan, plus you have to buy additional tokens. So it's really becoming more like, “Hey, yeah, we can do this for you—but you're going to end up paying something similar to what you'd pay a team.” At first, it was more like, “Let's get into the market. Let's get a lot of people interested.” But now, obviously, they have a lot of money behind them—investors, VCs, public market pressure—and they need to bring in revenue. So I think things are going to change very soon. AI is going to become a lot more expensive because the infrastructure and resources it requires are expensive. So eventually, those costs are going to be passed on to users. Yeah. And I noticed that ChatGPT started to do some ads as well. They’re probably going to go that direction, and who knows what that’s going to bring. But that's not our topic today. Today, it's about something else—frameworks. But before I go to the framework question, I'd like to ask you: what is your personal “why,” and how are you manifesting it at MOS Creative? Well, I'm a family man, so my “why” is to see my kids grow up to be amazing human beings—and hopefully to show them a great example of what can be accomplished in sports and in business. So my “why” is also to be a good person. Success can mean different things to different people, but for me, I love the hunt to get to a certain level of success. And then it's kind of like—us as humans, or at least a lot of people—we reach a certain level of success and we don't really celebrate it. It's more like, “Okay, let's get to the next level.” So my “why” is to show my kids that anything is possible if they really want it. Why I got into this space—it was exciting. You could see how quickly technology was moving, the kind of innovation that was possible, and it excited me. So that was one of the main reasons I got into technology. But the other reason was because I was in a different business, and we created technology that helped us grow. And I thought, “Oh wow, this is a completely different way to scale a business.” So technology became the direction we took. Yeah, I love it. I think inspiring our kids is a huge driver for many people, and it totally makes sense. Technology is exciting. I'd like to switch gears here and ask my other common question on this podcast, because this podcast is all about frameworks—business frameworks—how we can help listeners understand things, simplify things, and see different perspectives. So my question to you is: what is your favorite shortcut to success—or framework? And I don't mean “shortcut” in a negative sense, but rather a framework that allows you to understand things differently, make decisions, serve clients, and create valuable outcomes. Whatever it is—something that has worked for you, and is simple enough that you can explain it to listeners in three to five steps. Well, I believe in always being open to learning. It's not specifically a framework—it's more of a mindset: understanding that we don't know everything, especially now, with how quickly things are changing. I mean, a lot of people say that AI is going to make humanity a little dumber than we are. But actually, I learn a lot from it as well. If I'm doing something and I think, “Oh, this is a great way to speed up the process,” then I use it. So let's say, for example, a client asks me a question. There are different ways to approach it. If I already know the answer because I have specific experience with it, I can answer it, right? That doesn't always mean the answer is going to be correct.  I can research it, or I can get an answer from AI and then verify it through research and experience to make sure the outcome is actually what it says it's going to be. The learning part is making sure you're always open to figuring out whether the steps you've taken before are the right steps—or whether they can be optimized. I'm a big believer that everything can be optimized, especially now. There's almost no question that can't be answered quickly. Maybe there are some deep philosophical questions—but for the most part, especially in business, work, or even life, you can get answers very quickly. For example, I had a kind of vertigo-type feeling, and I was wondering what exactly it was. I entered specific prompts into ChatGPT, and it actually broke things down really well for me. Then I went to a doctor. First, I checked with a friend of mine who's a nurse, and she said, “This is probably what you have.” And she started asking me questions. I thought, “This is funny—these are exactly the same questions ChatGPT asked me.” And her husband said, “You know what? That proves that medicine is basically a set of questions. As you answer one question, it leads to the next.” So it's like a dynamic questionnaire. And by the time I got to the doctor, I already had a good idea of what it potentially was, and I knew what questions to ask so I could understand the next steps to fix it.  Yeah.  So what I'm saying is there’s always a way to improve. I'm a big believer in that. It doesn't matter what you're doing, because in this age, everything moves very fast—regardless of the business you're in. That's true. It's interesting that you say ChatGPT can answer any question. It's true—sometimes it hallucinates, but it still gives you an answer. Yesterday, I went to a presentation, and the president of Great Game of Business talked about this. He said, “Today, the answer is everywhere. So it's not a lack of answers—it's a lack of good questions.” So what we really have to come up with are good questions to ask. That's the bigger challenge now—not finding the answer. And I thought that was a really interesting insight. I agree. It's the same thing, right? It relates to prompts as well. If you have a good prompt, you're going to get a better answer. If you ask a good question, you're going to get a better answer. So yeah, I agree with you. Listen, AI isn't a complete solution, but it's a huge help—especially if you're just starting out. Yeah. So what drives your business? Is it technology? Is it trends? Is it something else? What drives it?  It's kind of a mix between technology and growth marketing. What that means is we work with clients all the way from ideation to scaling. We've also had several clients successfully exit. So clients come to us and say, “I have an idea. How do I take it to the next step?” Obviously now, there are AI builders and AI platforms that can help take a high-level idea and turn it into some kind of prototype—or at least a basic flow. But ideally, we work with clients from the idea stage all the way through design, development, launch, and driving traffic to the product. So the perfect client fits into that category. They might have an idea for a web application, mobile application, or software product.  They come to us and they're not really sure what the next steps are—or they've done some research For example, I spoke to a prospective client the other day. She worked with a developer who tried to build the product using an AI builder. For some reason, something didn't work out, and now she's back at square one. So now we have to review what she actually wants to build, determine the best approach, and figure out what phase one, phase two, and phase three should look like. So that's kind of how we work. For our clients, it's not just, “Let us develop it for you.” It's also about the creative side, the messaging, and the user experience. It's about making sure that when someone downloads the app—or visits the website or web application—it serves its purpose. It's a problem-solving product. It needs to solve a problem so users keep coming back again and again. And then we help grow it to new audiences. That's when it starts to scale and become exponential. Does that make sense? Yeah. So I’m wondering, you work from the idea forward, or you work from the outcome backwards? What’s the approach?  That's a great question. Not everyone knows the outcome right away. When someone has both an idea and a clear outcome, it works better, right? Because then you can help them get to that outcome. But overall, the outcomes are usually very high-level. You know: “I want to build this web application or software because I'm targeting this audience.” Okay—but what does that really mean? What problem are you solving? To be honest with you, ninety percent of people don't really know what problems they should be solving at the initial stage. So, talking about frameworks, we work with them to define which problems they should solve first. Because most startups—or even profitable companies trying to add new technology into their workflow or business—often don't know what one or two problems they should solve for the MVP before going all in. Yeah. Okay, so step one is to define the problem. What's step two?  Make sure you have the right audience for that problem. That's a big issue. A lot of times, people try to serve everyone. You don't want to go too broad, and you don't want to go too narrow. If you go too narrow, you're going to hit a ceiling before you even go to market.  So you determine the audience for the problem you're trying to solve, right?  Correct.  And then what's the next step?  Once you determine the audience and define the problem, the next best step is to create some kind of prototype and actually take it to that audience to test for product-market fit. Meaning: get feedback. Again, it doesn't have to be a fully working product. But go to that audience and get feedback like: “Yes, this solves my problem,” and “Yes, I would pay for it.” Or even better—for them to actually exchange some money to join a waitlist or gain access to an early version of the product, so they can test it and provide feedback. That's the best-case scenario. Because once you have that input, it becomes much easier to make adjustments. It doesn't matter whether those adjustments are in the design or in the actual working product—you're refining it for that niche audience. Yeah, that makes sense. So you design the prototype or minimum viable product, then you test it and get feedback. Then what do you do?  Well, I want to clarify something. Designing a prototype and having a minimum viable product can be two separate things.  Okay.  You can design a prototype. Again, it can be designed in Figma, using an AI builder, or even just as a workflow or user flow. Obviously now, things are a little different because you can build prototypes much faster. That doesn't mean they're going to be production-ready. But a minimum viable product is usually focused on solving one or two specific problems for that market. It's a problem-solving product that actually works—meaning it's much closer to being production-ready. Yeah.  So those are two separate things. There's a very big difference between them.  Yeah, because now you have vibe coding, and with tools like Lovable—or whatever platform you're using—you can create a prototype quickly. But it's not necessarily going to work, and then you still have to build the actual working product. Correct. Yes, I agree. Then you test it, expose it to the target market, and gather feedback. And then what do you do? Do you iterate? What's the next step? You iterate, yeah. So at that point, ideally, you have product-market fit, you've received great feedback from users, and—best-case scenario—they've even paid you some money. Then you either expand on what has already been built, or you go all in: invest more money into it and start building a production-ready product. And once you have that, you may realize that you also need to improve the user interface. That happens a lot—especially if you vibe-coded it. The output usually isn't the best when it comes to user interface design or user experience. So you may need to redesign the interface, properly develop it, and then take a production-ready application to market. And then it goes back into the cycle of iteration. Meaning, you keep gathering feedback. This is why I often recommend not adding too many features in the beginning. Focus on one or two core features—one or two main user flows within those features. That's it. Forget about everything else. Yeah. And then you can add features later.  You can always add features later. Most of the time, if you add too many features in the beginning, you'll probably end up cutting at least 40% of them because people just won't use them. And I'm not talking about core features like sign-up, sign-in, forgot password, onboarding, authentication—that kind of stuff. Obviously, you need those. But you still have to figure out who your audience is. Do you need SMS login? Do you need email login? Do you need both? Do you need social logins? You have to make sure you clearly understand your audience—but you don't need everything all at once. You may eventually need all of it, but not in the beginning. Yeah, that's true. So you've worked with other businesses, which means you're primarily a business-to-business agency, right?  Business-to-business, business-to-government—we've also built business-to-consumer apps as well. But usually, our client is a business-to-business.  Yeah. So here's my question: In B2B, how do you gain people's trust so they'll even engage with your product? I understand there's a funnel—but how do you get businesses into the top of that funnel? How do you create that initial trust so they engage? What does it take? Many things. Content helps, obviously. Creating content like this, creating videos—I create videos on a regular basis talking about what's out there, what's possible, what's good, what's bad. Kind of the everyday life of an agency, and the type of work we do. We also post projects on different directories and platforms. A lot of previous clients come back to us, and we get many client referrals. We rank pretty well for SEO and AEO, so a lot of people find us through ChatGPT. Especially because that's one of the services we offer. People find us when searching for things like “best app developers” or “best website designers” in our specific area. We're not targeting nationwide rankings—that's much harder and a much longer-term strategy. But in our area—Maryland, Howard County, Columbia—we rank very high.  And what does it take to rank high in AEO—in AI search?  It's the same approach we take to rank in Google. Google obviously owns Gemini, and now there's Google AI Overview. It's really a real-estate play. If you have a website that's properly structured for Google—with some adjustments for semantic search, like adding question-and-answer content to every page, especially product and service pages—you improve your chances significantly. You also need a properly configured robots.txt file with clear descriptions, so when search crawlers reach your site, they can immediately understand the structure and know where to go. When you see sources cited in AI search, that's exactly what those systems are reading from your site.  You also need the right technical setup: Your website has to be fast. You need proper H1, H2, and H3 structure across the site. So overall, it's about having a properly structured website. If you follow strong SEO fundamentals, with additional improvements specifically for AEO and GEO—because now it's not just SEO anymore, it's SEO, AEO, and GEO—you'll usually appear in ChatGPT, Google AI Overview, Gemini, Perplexity, and other AI search tools. And your Google Business Profile and Google Maps listing are properly optimized—which has changed a lot recently on Google's side as well—you'll also show up more often in local AI search results. So isn't it true that AI search looks for different kinds of signals than traditional SEO? I've heard, for example, that backlinks are less important in AI search than they used to be. They're not as important for AI search, but backlinks still carry a lot of weight. Again, you have to think about this as two separate systems, right? There's Google Search—with Google AI Overview and featured snippets—and then there's Google Maps. You don't need a website just to appear on Google Maps. You mainly need a properly optimized Google Business Profile. And you can still show up in AI search that way. Having a website does help, because it sends another signal to Google, but it's not as critical. The most important thing—and I'll answer your question for both cases—is consistency and structure. For Google Maps, if you have a properly maintained Google Business Profile with constant updates—blog posts, videos, photos, and business updates—that teaches Google AI what your business does. So you want updated product pages, images, descriptions, and location details if you're location-based.  All of that educates Google, which helps you rank higher on Google Maps. And like I said, Google Maps ranks very well in AI search. Now, if you also have a website, that's even better. And on your website, it helps to embed your Google Map as well, because that reinforces another signal from Google Maps. For example, some of our clients have multiple locations, so we include Google Maps with all their locations on the site—and that helps. Then you also create location pages, just like you create product pages or service pages. Google—and AI systems in general—don't really rank entire websites. They rank individual pages. That's why top-of-funnel content is usually blog posts or educational content answering someone's problem. Then that written or video content leads users to a service page or product page. That's basically how it works. Does that make sense? Yeah, that's very interesting. So if I want to increase my AI ranking… one of my clients told me that if your clients post about you on Reddit, that can be really powerful and help drive AI search visibility. Is that true? Reddit and Quora are very powerful. Very powerful. They rank very high. Listen, I'll give you a simple example that anybody can use. If you go to Quora or Reddit and look at the questions people are asking—for example, let's say you search for “app development”—you can filter by questions and literally see what people are asking. If you answer those questions in a natural way, related to your service or product, and include a backlink—not in a salesy way, but naturally—that's a very strong backlink. And speaking of backlinks: they're still relevant. Maybe they don't carry as much weight as they used to, but they're still very valuable.  Because when Google or AI systems evaluate content—and when you search in ChatGPT, Claude, or Gemini and see sources—those sources are essentially citations and backlinks. So if your website has strong citations and is properly structured, it absolutely helps you get discovered. You just need to make sure everything is set up correctly so Google—or any other search system—understands what your content means. But yes, to answer your question directly: Reddit and Quora are excellent for visibility because they're high-authority websites with massive traffic and very strong domain ratings. Yeah. That’s great. So Google Maps, Reddit, Quora, they are big drivers. That’s great.  Huge drivers. I mean, listen, there are many others—but social media has become huge over the past two years. Before, if you made a Reel on Instagram, you wouldn't be able to find it through Google search. But in the past couple of years, they opened that up. Why do you think they did that? Because they understand the value of content. Just like YouTube—where you can find videos through specific keywords—they want Instagram videos to be discoverable through Google Search and AI search. And then those searches lead people back to their platform. If someone who isn't already an Instagram user discovers content they like—a creator they like—they may sign up for Instagram because of it. So yeah, all of this ties back to backlinks and discoverability. It's really about how you use those backlinks. I mean, YouTube has been a huge driver for people looking for answers or trying to learn almost anything. So yeah, that's kind of how it works. It's one big spiderweb. Yes. It’s interesting. So basically, the more content I have and the more content other people post about me in credible sites, whether it’s Reddit, Quora, YouTube, social media, and they all point to my website or web pages, then the more it’s going to be discoverable by AI. That’s kinda makes sense.  You're definitely going to become more discoverable. But again, if it's just “Steve Preda,” that alone may not be valuable unless someone is specifically searching for your name. Now, if people are responding to or discussing how to apply a specific framework—and someone is searching for that framework that relates to your content—then it becomes relevant. Does that make sense?  Yeah. Yeah, understand. Yeah. Absolutely. Let me ask you this. If you could have a magic wand and fix one thing inside your company in the next 12 months, what would that be?  That’s an interesting question. I don’t know. I think I'd be very interested in applying more AI agents so they can help drive the business and support more growth. Overall, I just want healthy growth—making sure we're happy with the work we're doing, and that our clients are happy with the work we deliver. Because that leads to better outcomes, longer-term relationships, and healthier growth for the company. I mean, my ultimate goal at some point is probably to grow the company and eventually sell it. If we're happy with what we're doing, and our clients are happy with the work we're delivering, I think that growth will happen organically. Yeah. And what do you need to make the company sellable in your perspective?  Having strong, scalable systems—and AI is going to help with a lot of that.  So do you believe that a company with only AI employees—at the extreme—could still become a very valuable company? No, I'm not saying we should rely only on AI, and I'm definitely not planning to let go of any employees. What I'm saying is that AI can help with certain smaller tasks that sometimes get missed or forgotten. That's a perfect fit for AI. For example, even during conversations—if a project manager is handling several clients at once—we usually need updates on what was discussed. Yes, AI can record the conversation, but more importantly: what are the actionable next steps? And from those action items, what has already been completed, and what still needs to be done? Those are the kinds of things AI agents can help with—tasks that don't necessarily require a human. That way, time isn't wasted and can instead be used more effectively to make sure things are getting done and that we're reaching the outcome you mentioned earlier. What is your opinion about controlling AI agents? What is the level of risk? Not just about someone maybe doing a prompt injection and kind of hijacking your agents, but losing control of the agents in terms of complexity. So do you see a risk there that someone could kind of unleash these agents and somehow not be able to control them, or the quality of their work? Could they not control that? Or something changes and the agents get impacted—maybe a software update or something like that? Is this a thing, or is that not a concern? I think there should definitely always be guardrails. For example, right now we're building a platform with AI to gather RFPs, review them, score them, and actually create outputs—like the structure of the RFP. But before they get submitted, an actual person reviews them. I think there should always be final approval by a human—unless it becomes such a perfect system. I mean, it's software, right? At a certain point, can something go wrong? Yes. Especially with updates—unless you own the full process from beginning to end. Yeah, I think there's always a risk, but there's always a risk with software.  There should definitely be some guardrails, no doubt about it. I don't think it should be the last step before a human approves it and actually—for this RFP example—submits the response to whatever platform. I think a human should always review and approve it to make sure everything is working properly. But I think you can save a lot of time. For example, instead of us doing two or three RFPs a month, we can do ten or fifteen. I mean, the quality isn't really changing. It's structure. It's answering what they're asking for. So if it fits the criteria we're looking for, we still spend time reviewing it. I mean, we got an RFP the other day that was 150 pages. It would probably take two days just to read it. And at a certain point, you're like, “You know what? This isn't a good fit.” So it saves time. It just creates more efficiency. But there should definitely be guardrails and structure for sure, and a human should be involved in the loop. That I agree with you on. Okay. It's a big topic. One of the thoughts is that at some point AI is talking to AI. Like in hiring—you see these big recruiting companies using AI to filter resumes, and then applicants use AI to write resumes that fit what the filters are looking for. And at some point, the authenticity or credibility of those resumes begins to fade because it's all prearranged. So then the whole purpose of filtering employees starts to diminish. Do you think this kind of thing might happen with RFPs too? Maybe. Very possible. I wouldn't be surprised if it's not happening already. Yeah, I mean, it's definitely very possible. There are already several platforms that find RFPs. They work a little differently. We're building specifically for our own purpose. I do want to document the process to kind of show, “Hey, here's what can be done.” But yeah, it's very possible, for sure. Listen, if you're relying on a regular process to get a job, then you're probably not going to get the job. There are a lot more people looking for work right now. I don't know if you heard about Microsoft—and I think Tesla too—but companies are letting people go left and right. Microsoft is offering long-term employees buyouts. And by long-term employees, I mean people who are probably older and maybe not as knowledgeable or experienced with AI.  It's like, “Hey, let us buy you out so you can retire a little earlier.” So this is happening. If you're going through the same regular hiring process as everyone else, you're competing against 500 or 1,000 other people for the same job. Obviously, it's an employer's market right now, not an employee's market. If you're trying to get a job, it shouldn't just be through the regular process. It should be through people you know. Networking is going to have even more value. Personal connections matter, and people knowing, “Hey, this person actually spoke to me the right way.” You should also know how to use AI, because that's going to give you an edge in getting a job. But actually speaking to someone should happen through networking and connections. Yeah, that's my feeling too—that human interaction is actually going to increase dramatically in value. Because authenticity… that's really the only way to verify authenticity: being face-to-face with someone, a real physical person. That's fascinating. Yeah. But I'll tell you—like I said, I post videos on a regular basis. My mom asked me the other day, “Max, are you using AI, or is it really you?” I said, “No, it's really me. It's not AI.” So it's funny because AI is getting so good that you're not always sure what's real anymore. And even with RFPs—it's not just about submitting proposals or resumes. Personal and human connection is going to become more valuable than ever. If I personally knew every buyer putting out an RFP, I'd rather talk to them directly, one hundred percent. Because it becomes a completely different process.  Yeah, that's spot on. Love it. So, great information. I love the framework: define the problem, determine the audience, create a prototype, build the MVP, test it, and then iterate. That's how you build a digital product—whether it's a website or an app. So if you're out there looking for a solution, Max Kryzhanovskiy and MOS Creative may have the solution for you. So if people would like to connect with Max Kryzhanovskiy and MOS Creative, where can they reach you? People can reach us through our website: www.moscreative.com. They can also find me on LinkedIn under Max Kryzhanovskiy or MOS Creative. They can fill out a form on our website or email us at info@moscreative.com. Fantastic. So if you want an AI-driven platform, definitely reach out to Max. So Max, thank you for coming and sharing your ideas. And I love that you have such a strong vision for AI and that you're actively experimenting within your company, which means your clients will benefit from that as well. And if you enjoyed this conversation, then stay tuned, because every week a successful entrepreneur comes on the show and shares their ideas and frameworks. So thanks for coming, Max—and thank you for listening. Thank you. Important Links: Max's LinkedIn Max's website Max's email: info@moscreative.com

Why are gaming VCs shifting capital to consumer apps? In this episode, Henric Suuronen and Harri Manninen, founding partners of Play Ventures, one of gaming's top venture funds with $500 million under management and over 100 portfolio companies, break down how they evaluate founders, why they require a two-year vesting cliff, and what co-founder conflict really looks like from the investor side. We discuss the venture capital math behind gaming startup funding, why consumer apps trade at higher multiples than games, how to read founder chemistry during a pitch, and what Play Ventures looks for in early-stage teams from Istanbul to Singapore. A personal conversation between friends who funded my startup, Savage Game Studios, before its acquisition by PlayStation.CHAPTERS: 01:54 From Mobile Games to Apps08:04 Backing Younger Hungry Teams10:45 Founder Dynamics Due Diligence15:54 Reading Founder Chemistry20:44 Giving Clear Nos and Feedback26:22 Board Truths and Founder Conflict32:31 Impulsive Founder Decisions35:54 Early Stage VC Role41:48 Ecosystem Golden Eras45:25 Scaling the VC Firm49:41 Culture Without Hierarchy55:14 Closing, Thanks, and Wrap

This Week In Startups is made possible by:Grasshopper Bank - https://grasshopper.bank/twistPaperOS - https://paperos.com/twistLinkedIn Jobs - https://linkedIn.com/twistPlaud - https://Plaud.ai/twistThe top 5 U.S. venture firms captured 73% of all LP commits in Q1, and three veteran VCs say the math may have officially broken. Aleph's Michael Eisenberg argues we may be witnessing the end of a 60-year run for venture capital as a craft business. Maniv's Mike Granoff and Oxcart's Larry Covert push back, arguing it's merely splitting into two asset classes: "Consensus VC" and traditional VC. Either way, the implications for founders, LPs, and the next decade of innovation are enormous.TWiST is back on the beat with a venture round table discussing investment concentration, the IPO drought, "bullshit ARR" in the AI era, AI gross margins, the U.S.-China chip war, the Iran conflict's impact on defense tech, the death of NATO and the rise of allied supply chains, why Tel Aviv's stock exchange could become the next NASDAQ, and a lightning round on each VC's favorite portfolio company. Let's go!Timestamps:0:00 Intro + sponsor reads (Grasshopper Bank, PaperOS, LinkedIn Jobs)0:58 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at https://Plaud.ai/twist and use code TWIST for 10% off!2:13 Introductions: Eisenberg (Aleph), Granoff (Maniv), Covert (Oxcart)3:57 The impact of rising venture capital concentration6:43 "We may be witnessing the end of venture capital"9:17 Consensus VC vs. Traditional VC10:01 LinkedIn Jobs - Hire right, the first time. Post your first job and get $100 off towards your job post at https://LinkedIn.com/twist11:48 Why mid-size firms beat the behemoths on founder access19:35 Coining "Consensus Colossal Collaborative Capital" (CCCC)20:03 AngelList's USVC retail VC fund — does it help mid-size funds?20:18 PaperOS - Whether you're raising a round, launching a fund, or managing a venture portfolio, PaperOS can unlock simplicity and scale across your empire of capital, contracts, and companies. Claim your $10,000 credit at https://paperos.com/twist23:27 Are there more breakout startups today than 5 years ago?28:36 The "bullshit ARR" problem and AI gross margins30:03 Grasshopper Bank - Time is money. Don't waste either. Go to https://grasshopper.bank/twist and get an exclusive $500 cash bonus just for opening an account.32:08 Cursor's negative gross margins and the hyperscaler funding flywheel33:06 Are we all electron constrained?35:35 Are we headed for surge pricing on compute?40:22 Will anything replace NVIDIA? NextSilicon, Hailo & Israel's chip stack43:33 Distillation, small models, and Apple's edge advantage46:38 Public trust in AI: should government mandate Waymo & FSD?1:02:58 Defense tech: Saronic, Anduril & the coming defense M&A wave1:06:35 The Iran war timeline & supply chain impact1:18:52 Lightning round: Jiga, Divergent, Volaback, Firehawk, HarbingerSubscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisCheck out all our partner offers: https://partners.launch.co/Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.com

David covers three Wednesday stories: Coinbase laying off 14% of staff, a16z Crypto raising $2.2 billion, and Strategy losing $12.5 billion as CEO Phong Le floats selling bitcoin. We also unpack the AI-first layoff narrative, the potential return of the infra supercycle ($6B+ raised across crypto VCs in 2026), and why a Cambrian explosion of DATs and ETFs might absorb whatever Saylor has to offload. Enjoy! -- TIMESTAMPS: (00:00) Intro (01:17) Coinbase Layoffs (07:15) Nexo Ad (07:50) Coinbase Layoffs (Cont.) (10:30) a16z Crypto Fund 5 (15:17) Nexo Ad (16:09) a16z Crypto Fund 5 (Cont.) (22:30) Strategy Losses FOLLOW THE SHOW › David — https://x.com/dcanellis › The Breakdown — https://x.com/TheBreakdownBW SPONSORS › NEXO Nexo is the premier digital wealth platform. Receive interest on your crypto, borrow against it without selling, and trade a range of assets. Now available in the U.S with 30 days of exclusive privileges. Get started at http://nexo.com/breakdown Get top market insights and the latest in crypto news. Subscribe to the Blockworks Daily Newsletter: https://blockworks.co/newsletter/ DISCLAIMER As always, remember this podcast is for informational purposes only, and any views expressed by anyone on the show are solely their opinions, not financial advice.

For more information and support, join us at https://thecirsgroup.com Jacie and Barbara discuss chronic fatigue syndrome (ME/CFS), emphasizing it as a real, multisystem illness rather than “just being tired,” and describe hallmark symptoms like post-exertional malaise (PEM), unrefreshing sleep, brain fog/word-finding issues, orthostatic intolerance, sensory sensitivity, pain, headaches, and the push-crash cycle. They explain how ME/CFS is often hard to diagnose due to lack of a single definitive test, the use of exclusionary testing and symptom criteria, and how cardiopulmonary exercise testing can objectively demonstrate impaired recovery and reduced oxygen utilization, which is useful for disability or legal documentation. They argue the diagnosis is a label, not a root cause, and propose chronic inflammatory response syndrome (CIRS) from biotoxin exposure (often water-damaged buildings/mold) as a potential driver, citing Dr. Ming Dooley's review comparing ME/CFS research to CIRS treatment outcomes. They outline next steps: assess exposure history and symptom patterns, take symptom clusters and the VCS test, find a CIRS-literate practitioner, address current environments before protocols, and explore resources and their community membership. For more information and support, join us at https://thecirsgroup.com TIMESTAMPS 00:00 Intro to Chronic Fatigue / medical disclaimer 02:30 Chronic Fatigue Syndrome is real, let's ask WHY 04:26 What is ME/CFS? 06:40 The CFS lived experience 09:09 Getting a ME/CFS diagnosis 11:05 Labels vs root cause 12:08 What is actually happening when you have ME/CFS 13:01 How CIRS may cause ME/CFS: published research backed 15:29 Why doctors miss CIRS 17:14 Next steps if you want to seek treatment 20:10 Testing and find a practitioner that is Shoemaker Certified 21:04 Resources For more information and support, join us at https://thecirsgroup.com LINKS MENTIONED IN THIS EPISODE: Ming Dooley's published research connecting CFS and CIRS: https://pubmed.ncbi.nlm.nih.gov/40806417/ https://pmc.ncbi.nlm.nih.gov/articles/PMC11623837/ https://pubmed.ncbi.nlm.nih.gov/41265402/ VCS test and symptom questions for CIRS: https://www.survivingmold.com/store/online-vcs-screening Order Jacie's book! The 30 Day Carnivore Bootcamp: https://a.co/d/7MgHrRs The CIRS Group: Support Community: https://thecirsgroup.com Instagram: https://www.instagram.com/thecirsgroup/ Find Jacie for carnivore, lifestyle and limbic resources: Jacie's book on the Carnivore diet!  https://a.co/d/8ZKCqz0 Instagram: https://www.instagram.com/ladycarnivory YouTube: https://www.youtube.com/@LadyCarnivory Blog: https://www.ladycarnivory.com/ Find Barbara for business/finance tips and coaching: Website: https://www.actlikebarbara.com/ Instagram: https://www.instagram.com/actlikebarbara/ YouTube: https://www.youtube.com/@actlikebarbara Jacie is a Shoemaker certified Proficiency Partner, NASM certified nutrition coach, author, and carnivore recipe developer determined to share the life changing information of carnivore and CIRS to anyone who will listen. Barbara is a business and fitness coach, CIRS and ADHD advocate, writer, speaker, and a big fan of health and freedom. Together, they co-founded The CIRS Group, an online support community to help people that are struggling with their CIRS diagnosis and treatment.

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Have you ever received terrible advice from a "successful" investor or mentor? In this episode, Igor Taber explains why taking advice from VCs who built companies a decade ago might actually destroy your startup. Because the market has drastically changed, relying on outdated experience makes these investors "credible and wrong"—the most dangerous combination in the startup world. We also dive into the insane $1.7 trillion AI investment bubble , why giving non-technical founders AI coding tools is like giving them a bazooka , and the truth about how top VC firms actually generate deal flow through cold outreach. Finally, Igor breaks down the three ways to build a venture firm and why being humble is the ultimate survival skill in a market where nobody truly knows the future. Check out the company: https://cortical.vc----------------------------------------------------------------------------------------------------------------------------

https://youtu.be/oPA1dSUab9Y James Green, CEO of Cognome and former Pixar executive under Steve Jobs, is driven by a deep curiosity and a pull toward ideas that can create massive impact. From early internet ventures to mobile innovation and now AI in healthcare, James has consistently aligned himself with transformative trends. In this episode, he shares hard-earned lessons from scaling multiple companies and introduces a simple but powerful framework that explains why many startups struggle to grow beyond their early stages. We explore James' 3-Stage StartUp Growth Framework: Whiteboard Phase, PowerPoint Phase, PDF Phase—a model that captures how organizations must evolve as they scale. He explains why early-stage chaos is necessary, how structure begins to take shape in the middle phase, and why standardization becomes critical at scale. James also dives into the toughest leadership challenges—especially making difficult people decisions—and shares why aligning with strong market tailwinds and creating “pull” from customers is essential for sustainable growth. — Grow Your Business in 3 Phases with James Green  Good day, dear listeners. Steve Preda here with the Management Blueprint, and my guest today is James Green, the CEO of Cognome, a health tech company that is solving the problem of how to manage different AI models that are being deployed in healthcare today. Earlier, he worked as a vice president at Disney. He worked directly under Steve Jobs at Pixar, and he has had at least six other CEO roles in ed tech, media, and healthcare. Welcome to the show, James.  Thank you very much. Delighted to be here.  Yeah, super excited. And Steve Jobs—you don't often have people that have known Steve Jobs now even Tim Cook has resigned. Yeah. Yeah.  And it’s 13 years, I guess. Steve Jobs is being gone. So what was it like working with the man? Was he a difficult boss?  First of all, most of the things you hear about him are accurate. So it’s not one of these things where you hear a lot about Steve Jobs and actually the man was totally different. So most of what you’ve heard is true. And I’ll give you one short anecdote sort of before we go on, which is something that I always found incredibly impressive about him. When you work for him, if you disagreed and said, “Hey, you want it to be white, I want it to be black,” without hesitation he would say something like, “Here are seven reasons why you're wrong.” First of all, before we go into those seven reasons, what’s impressive about that is he had a number and he stuck with it.  And it happened in seconds and he didn’t know before. So if you think about that, it’s hard to keep all of that in your head. So the guy was just super, super clever. And then he would list them 1, 2, 3, 4, 5, 6, 7, and you’d be out. Like it’s done. It’s like, “Oh, damn.” So yeah, he was unbelievable human, and it was an honor and a privilege to have worked with him.  Yeah, well, that's awesome—to talk to you, having worked with him and having some direct experience. Definitely not an easy boss when he has seven guns to shoot you down. Yeah.  But there's a lot to learn. I mean, you learn the most from these kinds of bosses.  Yeah.  So let's get into the question—which is normally the first one, but this is the exception: What is your personal “why,” and how are you manifesting it in Cognome, James, and in your previous jobs?  Yeah, I've thought about this a lot. I've tried to come up with what my “why” really is. And what I’ve come up with is I can’t help myself. And I’m going to go through examples of it and what I mean by that. I pay a lot of attention to the world. I pay a lot of attention to what’s going on. I get very seduced by new ideas and new things and things that I think will have big impact. And once I start thinking about it and thinking about what that impact is, I cannot help but start getting involved in it. That sounds very abstract, so I want to try to make that super concrete. So when I was working at Pixar, for example—the internet was being born. This is the late '90s.  I couldn't help myself. I started an ad-serving company called Sabela Media. That company got sold to 24/7, then to DoubleClick, which later got acquired by Google. So the internet was there. I had to do it. I had to have something in it. Then after that, I was thinking about what to do next—and mobile phones, if you remember, were still flip phones, mostly used for texting. The second company that I did was putting content onto those phones. It just seemed obvious to me—I couldn't help myself. I saw the opportunity, and it clearly worked. That company was called GiantBear. It was sold to BlueCora. After that, there was this crazy innovation going on in television of all things with effects. Now, again, we take these things for granted. We’ve got AI creating things all day long, back in the day, we didn’t. So I ran a company called PVI, which is famous for inventing the first-down line you see in football games. So that was kind of the very first virtual object you saw in live things. Again, it may seem like, oh, that’s an everyday event, but back in the day it was totally not. And I think it opened up football to many more people—you no longer needed the chain crew to understand what was going on. And then if we fast-forward—there are a few things in the middle, but I don't want to bore everyone—to where I am today at Cognome. I even wore my little Cognome shirt so I could advertise it throughout the podcast.  Yeah, that's smart. I have to do that.  AI is clearly the big thing today. But for me, intellectually, it's not enough to just say, “I'll do an AI model,” like everyone else. For me, healthcare is one of the areas that AI will have the biggest impact with. Healthcare for a lot of reasons has been a laggard technologically for specific things about how they store data, so it hasn’t been adopted things like multi-tenant SaaS, because the data has to stay local and things like this. So AI will revolutionize it. And AI will make decisions about whether people live or die, right? So it's really consequential. And for me, the question is—how are you going to manage that? That's a super interesting intellectual opportunity. And so Cognome ExplainerAI. So my “why” is: what's going on, what's interesting, and what's changing the world? And the beautiful thing about that is you get a “rising tide lifts all boats” situation. You're not fighting against a trend—you're moving with it. The whole world is rising, and you can be part of that. That’s sort of my “why”.  Yeah, so basically—in other words—it's about coming up with revolutionary ideas and implementing them?  Yeah. I mean, I want to make an impact in the world. I want to make a difference. I'm not a very religious person—in fact, not at all. So I believe our time here is limited. I want to make a difference. I want to be part of what's going on. So yeah, that's my “why.”  Yeah—tapping into trends. Well, that’s great. I mean, don't know if it's a “why,” but making the most of the opportunity to be here and maximizing impact—that's a huge one. Love it.  Yeah.  STEVE PREDA: So let me segue to the next one. This podcast is all about frameworks. So the objective here is what’s a shortcut that you can teach the listeners that they can implement in their business? So what is your “shortcut” to success? Maybe “shortcut” is the wrong word. What is the framework you use to interpret the world, understand it better, and make decisions?  Yeah, this is another thing I struggled with a little bit. So I listened to your questions, and I tried to make my answers really personal. I'm trying to be authentic—this is what I actually do all the time, as opposed to this is what I’m doing at the moment, or this is what I did for a second. The truth is, frameworks come and go. There are a lot of frameworks out there. I've probably used 15 different sales frameworks. I mostly operate in the B2B world, so there are lots of frameworks you can use—for example, in sales. But I tried to think of something more consistent—a framework I've used across every company I've worked with, all the time. And the one I always come back to is about growth. So what I want to talk about is: how do you manage a company that's going through growth? Because it's not obvious—and I do have a framework for it. And unlike some of the other frameworks—like something McKinsey, Bain, or someone’s invented this framework and you are adopting it. This is really pretty personal to me, and I’ve adopted various little things about it. There are these two ideas that live in parallel. One is in the sales process, where I think companies go through this idea of, I call it a Whiteboard sales process, a PowerPoint sales process. And forgive me for being a little dated, but a PDF style process, something you can’t change. And at the same time, they go through these stages where you are a small company, a medium-sized company, and a larger company. Think of it roughly as fewer than 12 people, then 10 to 75, and then 75 to 100 and beyond. And I’ve managed all of these sizes. And what’s interesting about these is that if you don’t have a framework to manage yourself through these stages, you’re going to fail. You as a leader will be replaced. I personally have replaced leaders who cannot go through those kinds of things. One of the things I've done in my career is act as a sort of hired gun for VCs. They make an investment, and then they bring me in to replace the founder if they haven't been able to navigate that growth stage. And so the framework works like this. When you're starting a company—what I call the “whiteboard” phase—what you're selling is a little different every time. And the consequence of that inside the company is everyone is doing everything. It’s a little chaotic and it’s okay. Like, less than 10 people, it’s okay. It’s okay that the finance person is doing a little selling and the engineer is doing a little marketing. It’s okay, because you only have 10 people maybe. When you go into a client, you are sort of inventing yourself as you go. There's always that first client where you're saying, “I think we should do this. This is how I'm going to help you make money, save money, or do something better.”  You’re figuring things out. Yeah.  And maybe there's some pivots in there. Maybe there isn't. Not everyone gets to be Google and get it right the first time, but you’ll see. In the end, you start getting things right. And then you go through what I call the PowerPoint phase. So what this is—you now have more than 10 people. It kind of isn't okay that the sales guy is doing finance, or the engineer is doing marketing. You actually have people in their swim lanes. I call it the PowerPoint because you've built PowerPoints, so you’ve got slides that you can use and it’s replicable. Guess what? You tend to tweak them for each client. You are still—you know what—the way you're selling to… I don't want to make a stupid example up—Home Depot is still a little different than selling to Lowe's. You know that—even though it should be exactly the same—it's still a little different. You're tweaking it each time. You're moving slide three to slide seven. Sometimes you don't show slide 10. You're still tweaking it.  Yeah. I relate to that.  And your organization is structured, but not completely rigid. Everyone still knows each other in the company. It's up to maybe 50—I think it maxes out at about 75 people. But every single person in the company knows each other. They’re all collaborating. You don’t need a lot of structure inside the company because there’s sort of culture in there to hold everyone together, right? And then you get to the third stage, which I call the PDF stage—where you've figured it out. You sell the same thing. Maybe you have three PDFs because you're selling in three verticals. But you go into a client—this is the thing—and it never changes. Slide one is always slide one. Slide two is always slide two. Slide three is always slide three. And you have maybe a hundred people in your company. And by the way, now you have levels. So not everybody knows everybody. And as a CEO, I have my lieutenants. My lieutenants have people working for them. And I sort of feel like everyone can manage—I don't know—five, six, seven, eight people. More than that is difficult unless the roles are not very sophisticated. So you need this management layer, which separates the CEO from the rest of the organization. So you need a lot more structure. And as you go through these three phases—and they're really different—a tragic thing happens. It happens all the time. The person who was so helpful in the whiteboard phase, who was your go-to person, they don’t make it in the third phase because they’re a generalist. They liked the chaos. They liked being able to have their foot, and they’ll complain to you. They'll say, “Why aren't you listening to me?” It's an engineer saying, “Why isn't sales listening to me?” Dude, you're an engineer—stick to your knitting. Like, no. And this culture goes through every single company I’ve ever run. Most of them have gone through these three phases—small, medium, and large. And one of the things I try to do with employees in these phases—and this is part of the framework—is to give them a huge amount of latitude to see if they can succeed in the phase. So, to give them the freedom—if you're being blunt—to give them enough rope to hang themselves. And if you're being kind, to give them the freedom to be who they are, to be the best they can be, and to support them—not control them. And so, if you are aware of this framework as you grow, and you give that latitude, and you hire smart people, then you can see which ones you keep and which ones you don't. And honestly, the worst and hardest part of managing through growth is that selection and weeding-out process—of the people who worked in the first stage but don't work in the last stage. So that is the only kind of framework for me that has stood the test of time. It has worked in media, worked in healthcare, and worked in various other places. Does that make sense to you? Does it resonate with you?  Absolutely. And I was just working on a chapter in my new book, and I was actually writing about this very idea—why some companies are never able to grow, because they are not able to make these decisions, these painful decisions, as you described.  Super painful—the worst. It’s the worst part. Firing people is the worst part of being a CEO. If you enjoy that, you’re a bad CEO. You want to have a positive environment, so you want to everyone have a good time. And when there’s growth, usually there’s incredible optimism and great culture. So any CEO who enjoys that process is not a good CEO. Yeah, that’s so true. This is kind of a difficult thing. You have to be ruthless to some degree.  You do. Yeah. That's why this framework has helped me—and it's helped me be gracious and kind to people. Let's just call her Jane, right? A totally fictitious person. But you can go to Jane in stage three and say, “Jane, do you remember how much you loved it in the first phase?” I'm going to give you some time here. You are going to leave, but I'm going to give you some time to work on a special project. But you also need to find your next startup—because you love that environment. And I am going to put this bureaucracy in place, and you're going to fight it until the day you die. So I can't have you here—I just can't. I can give you this little thing to do and you can have some weeks to go do that and give you some time, but the framework helps you be gracious and helps you make those decisions as you grow. That’s an amazing framework. This is really unique. We've recorded, I think, close to 400 episodes with different frameworks—and this hasn't come up. Nothing similar has come up.  Woo-hoo.  Love it. So where are you now in your business? Which phase are you in?  I am in between the whiteboard and the PowerPoint phase. Maybe because I'm an optimist, I'm going to say I'm in the PowerPoint phase. But I know there's still part of me that's drawing things on the whiteboard. We have 12 people, so we're just at the edge of growing out of that phase. I don’t have that layer in the middle. We have half a dozen clients. I suspect that by the end of this year, we'll be fully in the PowerPoint phase. And it'll be another 18 months after that until we get to the next stage—and that's assuming we continue to grow. I mean, my whole raison d'être is to find these really special things, grow them, and make an impact. So let’s hope that happens. Yeah, well, you've had some practice in your previous six CEO positions, so I'm sure you'll figure this out. So what drives growth in your business?  Yeah, this goes a little bit back to phase one. So I've picked an area that's growing by itself. I mean, AI—there are more and more models being deployed in hospitals. Hospitals are growing. The number of models deployed in them is growing at about 2.2 times the rate of the general population. So good for me. There are federal regulations coming that say you need to control what your AI models are doing. That's also good for me. It's a lovely day when regulation is good for your business—it usually isn't. But it's not unusual in healthcare. If you look at electronic health records, that was driven by government regulation and funding. So this is a little bit like that. Federal, state, and other institutions are driving this trend. And then there are things happening inside healthcare organizations themselves that we can tap into. I always think that when you're selling, you should have a good story. So I'm going to tell you the kind of story we use.  When we meet with a chief information officer, we tell stories like the ones I'm about to share. And this really helps us tap into that growth. Because part of growth in a B2B environment is having a strong sales team, good engagement, and solid frameworks—like: do they have budget? Are you talking to the right decision-maker? All of those kinds of frameworks, which to me are more tactical—I've used a lot of them. But we go in and say things like: “Have you ever experienced a situation in radiology where a new model was released and no one told you about it—and now you have to monitor it?” This is happening. And they're like, “Oh my God—yes.” And then they tell you a story about it.  And then you say, “What about that note from CMS?”—that's the organization that runs Medicare and Medicaid, for those not in healthcare. “Did you hear that they're coming down to audit some of your peers?” And they're like, “Oh my God—we just got notice that we're being audited.” And then—how about your board? How's your board doing? Are they coming down and saying, “What are you doing in AI?” So you try to tell these stories and then you create this tension, where they have to grow and they have to control, and then that’s where we come in. We can help all of these companies manage all of these models. What we do—we have this product called ExplainerAI. We tap into the underlying data from the electronic health record—the EHR, or medical record. We tap into the models—the front end—and the logging files behind them. And then we can tell whether the model is exhibiting drift, and how it's performing across different areas. That could be geographic areas, or demographic areas. Is it performing the same with young men and older women? Is it performing the same over time? Is it degrading? Is it releasing personal health information when it shouldn't? Is it hallucinating, if it's an LLM? That’s what we do. And then we can send alerts out to people, saying, “Hey, listen, this model is making shit up right now, you need to deal with it.” And then they can talk to the vendor and handle it. So we're in a good space. And so growth is, to some extent, this idea of a rising tide lifting all boats. I've picked an area that's growing, so I can grow with it. And then part of it is being connected and having a good way of engaging with people who are buyers. And so we have these stories that we tell in our decks about how we help in these situations.  Have you had to pivot between the original idea and where you are?  Yeah, we have. And for anyone who's listening and thinking, “Oh my God, I'm going to have to pivot,” I use Google as my favorite example of someone who just got so lucky. They were like, “We're going to have this little thing that searches the internet,” and they never really changed—until they got so big they could do more. That is the exception, not the rule. And what’s interesting about the way we started is it’s still a core differentiator for us—we started with the ability to take data from an EHR, from a medical record, translate it, and store it in a common data model. It's called OMOP. It's the most common way that researchers structure this kind of data.  And we thought this technology would be widely adopted by researchers. We have contracts with people like Hopkins, Ohio State, NYU—big institutions—but it's not big enough. It’s not going fast enough. What it does do, though, is for our ExplainerAI, it gives us the technology—it's a moat—to connect to the source of truth, the electronic health record, so that you can get actual outcomes versus predictions. Many models cannot get the actual data out of the EHR. So they just say, “This is my prediction, this is my prediction, this is my prediction.” And over time—that's fine, those are predictions—but how do they actually compare to what really happened?  Yeah. What actually happened? And because of where we started, we have a way of efficiently and accurately getting that information. So it is still the bedrock. But it's definitely a pivot. And then you basically put an AI layer on top, and that's great. And how did you know when to pivot? How do you reach that tipping point? How do you know this is the moment—you have to pull the plug on this because it's not working?  First of all, I think on a personal level, I'm always late. So I think I could always have made this decision earlier. If I'm being self-critical at a high level. And I don't think I have a clean answer—but I'll tell you how I've done it. If you have a better way, I'd love to know. It’s about sales engagement. So you go to a hundred people, you have a hundred meetings, and you sell to two. That's not good enough. It's just not good enough. And those two are complaining. What you want to see in a product—and I think this is true of all great products, especially today—I use examples like Facebook and Tesla—is that products are pulled, not pushed. If you still find yourself, after nine months, pushing—and you don't have the momentum where your product is being pulled—you're wrong. You need your clients to be making referrals, and you need to be pulled into deals. In today's advertising and marketing world, it's too noisy.  Maybe back in the seventies you could do it, but now it's just too noisy—especially in B2B. There are so many people selling to the same buyers that they need to hear about your product from others, have people around them recommending it, and pulling you in. There's some time—and I usually take closer to a year, which is long. It would be better for me to do it in six months or even three months. I haven’t found a way to do that where you pivot if you’re just not getting traction, basically.  Yeah, okay. I love it. So what's one thing in your company that you're trying to figure out right now? One thing in my company that I'm trying to figure out right now is how to further ramp up sales. I'm cheating a little bit here, because I think we may already have it figured out—but leaving you with an unanswered question isn't very helpful. So we were having—and still are, to some extent—problems getting ExplainerAI rolled out. People were interested in it, but they wouldn't buy. So we tried to figure out why. And one of the things we found is this: For those of your listeners who may not know, healthcare is probably the largest portion of GDP in the country. Buyers are very large. We don't always think about it this way, but if you do—everyone goes to the doctor. It affects 100% of the population. And these large institutions—a hospital is usually a multi-billion-dollar organization—and there are about 6,500 of them in the country. So we've got 6,500 multi-billion-dollar companies in this country. It’s crazy, right? They don't want to buy from small companies—they want to buy from big companies. This is one of the things we found out. So we get to the finish line, they say yes—and then no one tells you the truth, right? No one says, “I'm not buying from you because you're small.” But we ended up figuring it out through triangulation. So we've been building partnerships. We started with Intel. We made some of our models work on Intel CPUs, and I'm actually pretty proud of that work. For the nerds out there—we're working on Xeon 6 chips, the Granite Rapids chips—running locally deployed LLM ensembles. Think of it as models like Qwen and LLaMA running inside their chips—what I'd call small-to-medium language models, not large language models.  Up to 32 billion parameters, running on a CPU, not a GPU. So that’s a big deal. Intel loves us, and we've been able to leverage their ecosystem to have their partners sell our product. So now you've got HPE selling ExplainerAI. You've got Lenovo selling ExplainerAI. And probably my favorite partner—love you, ePlus, if you're listening—I think you're the best. They're a Fortune 1000 reseller selling ExplainerAI. So now we have large companies selling our product, and that's starting to come to fruition. Now, it's not solved—my revenue isn't going boom yet—because if it were, I'd be firmly in the PowerPoint phase, heading toward the PDF phase. But it's looking really good, and I'm very excited.  Cognome Inside.  There you go. Cognome Inside—yes. Cognome Inside. Intel Inside—for those of you who remember. Yes.  Love it. Okay, so before we wrap up, I have one more question for you: What is a question that entrepreneurs should always be asking themselves?  I think the hardest thing about being an entrepreneur is dealing with the amplitude of the variance that happens inside it. There are incredibly high days, and there are incredibly low days. There are days when you don't even want to get out of bed in the morning. You don't have many clients, and one of them has just told you that you're a complete moron. Even if you've got the best product in the world, if you're in the whiteboard or PowerPoint phase, you're going to make mistakes. You just are. No one's perfect. And there are days when some combination of a client, an employee, or the product—something has failed, someone has left, something isn't working—and you feel awful. So what I'd say to entrepreneurs is this: if you really are an entrepreneur, it is your personality that you can still get through those and wake up in the morning and say, I believe in this. I know I can do it. I can keep doing it.  And one of the things that I think separates an entrepreneur from someone who isn't is this: When I go through these moments, I ask myself, “What's the worst that could happen?” And I usually start with: “Is anyone going to die?” And the answer is almost always no. No one's going to die. So it’s not that bad. And by the way, I remember giving that advice to a young person once—and I saw their face go white. And I thought, “Oh, that's not an entrepreneur.” That's the kind of person who hears that and thinks, “Oh my God, really? You think about the worst thing that could happen so you can deal with it?” And I'm like, yes.  Does that apply to the company itself? Is the company included in that “worst-case” question?  To me, the next step is: is an individual going to die? That's a higher stake than whether the company is going to die. But yes—is the company going to die? That's part of the thinking, because you're going through all the consequences. Am I going to lose all my money? Is the company going to fail? Those are escalations of that thinking. But to me, company death is less tragic than a human death.  Yeah, true.  Not everyone might agree with that, but I think so.  You can try again.  Yeah.  Start another company.  Yeah, exactly. Anyway, your question was: what is a question that an entrepreneur should always be asking themselves? For me, turning that upside down and inside out—it's: what's the worst that can happen, and can you get through it? Are you able to get through it? Do you have the drive and the imagination to keep going? That's the question I've continually found myself asking, as opposed to any other kind of existential question. And I think some of the other questions are not always the right way to look at it—like“Is this the best business?” Because there's a very big difference between an entrepreneur and an investor.  An entrepreneur has to keep going, while an investor might quit. Investors, they’re playing the portfolio game. They can say, “That's not working—I'm dropping that and keeping this.” As an entrepreneur, you can't really play that game with your time. I mean, Elon Musk is running four companies—so okay, fine—but most of us aren't. Most of us are running one or two, and we need more tenacity to make it work—to pivot or to find another path. That's a really big difference between an entrepreneur and other kinds of people. And it's why I've kept doing it. It comes back to the very first question: why do you do this? I can't help myself. I just can't. It's what I like to do.  Yeah, the contrast is addictive—the contrast between near-death and near-Nirvana, right? Yeah. I love it. I mean, you can't have euphoria without depression. You wouldn't know what it was—it would just seem normal.  Yeah, just a personal example of that—I was in Hungary, where I was born, for the election two weeks ago.  By the way, I'm so excited about that election, for many reasons.  The exhilaration that I felt—and that everyone else felt—was even greater than when the Berlin Wall came down, because the system was worse.  Yeah.  And if they hadn't lived through that for 16 years, they wouldn't have felt it. Now, we didn't experience it directly—but still.  But even I was paying attention to a lot of things, and I was following that one very closely. Even I felt that sense of euphoria. I was like, “That's great.” I was at the dinner table with my wife and kids—and I'm not Hungarian, it's not affecting me. I mean, Viktor Orbán isn't really having any effect on my life at all. Maybe he shows up at some conferences in the U.S., but still—not affecting me. But I'm sitting there at dinner like, “Did you hear what happened today? That's great.” Anyway.  Awesome. I'm glad you're on that side of the equation. James, if people would like to learn more—if they'd like to learn about Cognome and connect with you—where should they go? Where can they find you?  Yeah, so you can certainly go to cognome.com. You can email info@cognome.com. But if you've listened to this podcast, I'm always happy to hear from people. I answer every single email myself. And if you know my name—James Green—you can just put a dot in the middle and add @cognome.com at the end, and that will get to me. Delighted to hear from any of you—especially if you're a CIO in a hospital, you should reach out.  Well, all those hospital CIOs—please call James, or at least send him an email. And for those of you listening—this was an amazing framework: from whiteboard to PowerPoint to PDF. Definitely relatable. And remember—if no one's dying, it's okay. You can always pivot and live to fight another day. So, James, thanks for coming—and thank you for listening. Important Links: James' LinkedIn James' website James' email: info@cognome.com

Many AI startups funded in the last 18 months won't last three years - so what makes a business durable today?Yaniv Bernstein is joined by Marlon Nichols, co-founder and managing general partner of MaC Venture Capital - one of the most active seed-stage AI investors in the market, having scaled MaC to over $600M AUM across three funds in just four years. Marlon's portfolio includes Pipe, Stoke Space, Thrive Market, Chef Robotics, and exits like Wonder Dynamics to Autodesk and Gimlet Media to Spotify.In this conversation, Marlon uses his industry experience to explain the biggest threats to new AI startups, and what the key components of successful startups in the industry will be.In this episode, you will:Hear why Marlon thinks niche or mid-size foundational models are now prime acquisition targets for OpenAIDiscover why misreading traction is the #1 mistake VCs are making right nowLearn why Marlon is more excited about manufacturing-line prediction and grid-scale batteries than humanoid robotsExplore why founder unit economics need a complete rewrite when token costs replace SaaS-style marginal costUnderstand why software is no longer a moat — and why data access, deep customer integration, and speed are the only three durable advantages left at the application layerLearn the difference between AI-native companies and AI-bolted-on companies, and what a 5-year-old startup should do if it's on the wrong side of that lineTimestamps00:00 Coming Up01:07 On Today's Show: Durable Tech02:33 Meet Marlon Nichols03:25 Defining 'Durable' AI Startups05:23 Moats for Foundation Models07:15 Chef Robotics and AI Native vs AI Enabled09:24 Upgrading Legacy Startups10:55 Pipe's AI Pivot Case Study13:19 Winning at the App Layer15:56 Speed and Workflow Stickiness17:51 Investment Checklist and Team20:30 Automotive Digital Twins and Regulatory Testing24:27 Why Physical AI?26:09 Robotics In Manufacturing26:55 Energy Storage And Batteries28:30 Why Cheaper Builds Still Need Talent30:19 Where Traction Can Be Misleading33:41 Token Costs And Unit Economics36:46 Closing ThoughtsMentioned in this episodeMaC Venture Capital: https://macventurecapital.com/Marlon Nichols on LinkedIn: https://www.linkedin.com/in/marloncnichols/'The Bitter Lesson' by Rich Sutton: http://www.incompleteideas.net/IncIdeas/BitterLesson.htmlVera (Yaniv's startup, AI-supported guidance for people caring for ageing parents): https://vera.guide/The PactHonor the Startup Podcast Pact! If you have listened to TSP and gotten value from it, please:Follow, rate, and review us in your listening appSecure your official TSP merchandise at https://shop.tsp.show/Follow us here on YouTube: https://www.youtube.com/channel/UCNjm1MTdjysRRV07fSf0yGgGive us a public shout-out on LinkedIn or anywhere you have a social media followingKey linksThis episode of the Startup Podcast is sponsored by .tech domains. Forget weird prefixes and creative misspellings; the availability for .tech domains is simply way better than .com. For a clean name that highlights your tech credentials, get a .tech domain at your favorite registrar.This episode of the Startup Podcast is sponsored by Vanta. Vanta helps businesses get and stay compliant by automating up to 90% of the work for the most in demand compliance frameworks. With over 200 integrations, you can easily monitor and secure the tools your business relies on. For a limited time offer of US$1,000 off, go to ⁠⁠⁠⁠https://⁠www.vanta.com/tsp⁠⁠⁠⁠⁠The Startup Podcast website: https://www.tsp.show/episodes/Learn more about Chris and YanivWork 1:1 with Chris: http://chrissaad.com/advisory/Follow Chris on Linkedin: https://www.linkedin.com/in/chrissaad/Follow Yaniv on Linkedin: https://www.linkedin.com/in/ybernstein/Producer: Justin McArthur https://www.linkedin.com/in/justin-mcarthurAssistant Producer: Steph Hefferan https://www.linkedin.com/in/steph-heff/Intro Voice: Jeremiah Owyang https://web-strategist.com/

TCW Podcast Episode 257 - Nine Atari Games   The Atari VCS launched with nine games to entice consumers into the new gaming medium, and Combat set the trend of packing multiple "games" into a single cartridge. Featuring a wide array of game modes, Combat holds its own even today and served as the console's official tie-in title. Atari's partnership with Sears, however, rebranded all but one of the launch titles under different names, including Target Fun, released elsewhere as Air-Sea Battle. Many of the launch games carried multiple modes to stretch their value, and even these early titles pulled clever tricks to push past the VCS's five sprite limit. To justify the console's cost to families, Atari aimed squarely at adults with Blackjack while also including the educational game Basic Math. While many of these early titles were forgotten as more advanced games arrived over the course of the Atari VCS's life, their influence on a foundational system cannot be disputed.   Atari Archive: https://www.atariarchives.org/ Atari Archive (Youtube): https://www.youtube.com/c/AtariArchive Combat: https://www.youtube.com/watch?v=LNtI90yiOOU Air-Sea Battle: https://www.youtube.com/watch?v=0l0AQ5LuvQM Video Olympics: https://www.youtube.com/watch?v=rie4hsUeDuA Indy 500: https://www.youtube.com/watch?v=_POr4OdHhiI Street Racer: https://www.youtube.com/watch?v=TCSYY1kK9so Star Ship: https://www.youtube.com/watch?v=j4zySJ96a4U Blackjack: https://www.youtube.com/watch?v=_nvWBin7_Rc Basic Math: https://www.youtube.com/watch?v=lv7T4ya9hcg Surround: https://www.youtube.com/watch?v=dhZ1wfGLq1w How to do long form Square Roots: https://www.youtube.com/watch?v=6evC4klO_lI TCW 051 - Sons of Pong: https://www.theycreateworlds.com/episodes/TCW051 TCW 201 - Advanced Balls and Paddles Part 1: https://www.theycreateworlds.com/episodes/TCW201 TCW 202 - Advanced Balls and Paddles Part 2: https://www.theycreateworlds.com/episodes/TCW202 TCW 009 - History of Mediagenic Part 1: https://www.theycreateworlds.com/episodes/TCW009 TCW 010 - History of Mediagenic Part 2: https://www.theycreateworlds.com/episodes/TCW010   New episodes are on the 1st and 15th of every month!   TCW Email: feedback@theycreateworlds.com  BlueSky: @theycreateworlds.bsky.social Patreon: https://www.patreon.com/theycreateworlds Alex's Video Game History Blog: http://videogamehistorian.wordpress.com Alex's book: http://bit.ly/TCWBOOK1   Intro Music: Josh Woodward - Airplane Mode -  Music - "Airplane Mode" by Josh Woodward. Free download: http://joshwoodward.com/song/AirplaneMode  Outro Music: RoleMusic - Bacterial Love: http://freemusicarchive.org/music/Rolemusic/Pop_Singles_Compilation_2014/01_rolemusic_-_bacterial_love    Copyright: Attribution: http://creativecommons.org/licenses/by/4.0/

Does AI really break B2B positioning, or is it exposing deeper product problems? In this roundtable, Refine Labs' VP of Innovation Matt Sciannella sits down with Fletch PMM founders Anthony Pierri and Rob Kaminski to unpack what's actually happening when companies try to position themselves for the AI era.They cover why AI mandates from VCs create confusion (not clarity), how Intercom, Palantir, Salesforce, and Owner.com handle multi-product positioning, and why delegating positioning to LLMs is a race to mediocrity.What is product positioning in B2B SaaS?Product positioning defines who your product is for, what problem it solves, and why it's different from alternatives. It's the upstream decision that drives homepage messaging, paid media, and GTM clarity.How does AI affect B2B positioning strategy?AI doesn't break positioning fundamentals — it adds market uncertainty and product pressure. Companies still must answer: what problem do you solve, for whom, and better than what?Can AI write your positioning for you?No. LLMs can accelerate research and fill in details, but they can't generate non-obvious strategy from scratch. They're best used when humans provide 80% of the thinking first.Why do multi-product companies struggle with positioning?Most markets are fragmented. Customers think narrowly — they're not shopping for "everything." Leading with one clear use case (like Apple with iPhone, Owner.com with restaurant grading) outperforms breadth.What is a go-to-market positioning framework?A GTM positioning framework defines your category, ideal customer profile (ICP), competitive alternatives, differentiated value, and homepage message — in that order, before messaging or campaigns.#b2bmarketing #ProductPositioning #GTMStrategy #B2BSaaS #DemandGeneration #ProductMarketing #AIMarketing #ContentMarketing #SaaSMarketing #RefineLabsRoundtable #FletchPMM #MarketingStrategy #ICPMessaging #HomepageCopywriting #GoToMarket

Join Simtheory: https://simtheory.aiSo Chris, this week... a LOT has happened. We're back to regular programming (maybe), and back with our average takes. Nothing's changed.GPT-5.5 just dropped today - but you can't even use it in the API. Vaporware? OpenAI is charging MORE than Opus 4.7 and we haven't even tested it yet. Meanwhile Claude Opus 4.7 landed a couple weeks ago and... the vibes are off? Mike's actually going BACK to 4.6. Something's wrong.But the real star: OpenAI Image 2. This thing is genuinely terrifying. We committed what can only be described as "parody fraud" - faking a council letter so realistic Mike's own mother fell for it on a phone call. Then Chris posted a fake development approval with the mayor's real name into a local Facebook group and had to delete it when someone tagged the actual mayor. The forgery capabilities are absolutely unhinged.Also: GLM 5.1 is so good Mike forgot he switched to it. Kimi K 2.6 is criminally underrated. VCs are paying 70% of your real token costs. Consumers pay only 5.5% of actual cost. The everything app war is ON. The SaaS-pocalypse is real. And we made two new diss tracks.Chris made a graffiti sign in LA. It says "This Day in AI." It was the best artwork in the class. That tells you everything.CHAPTERS:0:00 - Intro & We're Back (Don't Over-Commit)1:14 - Overview: Everything That Dropped While We Were Gone2:56 - GPT-5.5: Vaporware? Not Even in the API4:57 - Benchmarks vs Reality: Nobody's Excited About OpenAI Models5:50 - GLM 5.1 & Kimi K 2.6: Secretly Just As Good?8:15 - The Everything App Race & Product Layer War8:56 - Token Economics: You're Only Paying 5.5% of Real Cost13:08 - We Burned $1.5M in Cloud Credits in 2 Months16:13 - "$30/Month Is Too Expensive" (It Actually Costs $700)19:25 - Where Is Google?? TPUs Should Flatten Everyone22:01 - Agentic Tasks Are 10-50x More Expensive Than Chat25:07 - OpenAI Workspace Agents: Glorified Zapier?27:01 - Single Agent vs Multi-Agent: How Do You Actually Work?33:06 - Building Automation Is HARD (Our Support Shame)35:33 - OpenAI Image 2: The Fraud Episode Begins44:16 - FRAUD DEMO: The Fake Council Letter (Mum Falls For It)49:16 - FRAUD DEMO 2: Chris Posts Fake Mayor Letter on Facebook52:17 - Fake Receipts, Bank Statements & Can Forgeries Be Detected?57:25 - Claude Opus 4.7: The Vibes Are Off59:51 - Mythos Preview: "Pics or It Didn't Happen"1:01:56 -

In this episode, we're talking with Neal Conlon, who has raised over $300 million across multiple companies, about the hidden levers that unlock eight-figure valuations. Neal reveals how a protein bar company with almost no revenue secured a $10M valuation and shares frameworks for building a business worth far more than you think. Work with the team on building businesses and exits at https://capitalism.com/partners Learn more at https://capitalism.com Timestamps (0:00) Intro - Your business may be worth more  (1:45) Neal's $300M+ fundraising background  (4:00) The protein bar company story - from almost (6:00) Key numbers that determine company value (8:00) Moving from founder-dependent to scalable (10:00) Revenue per seat and valuation multiples - new (12:00) Understanding comparable companies and what they've raised or (14:00) Using retention and unit economics to drive growth (16:00) The pitch deck - the first step in (18:00) Market saturation vs market opportunity (20:00) Types of investors — VCs, angel investors, and (22:00) Making it feel real with actual investor money (24:00) The importance of experiencing these concepts to truly (26:00) Building your product roadmap and demonstrating market validation (28:00) Collecting real feedback from potential buyers vs friends (30:00) Moving from $300K profit to $1.5M valuation through (32:00) The first lever — improving the offer and (34:00) Identifying multiple levers — bookkeeping and software platform (36:00) How small changes in processes can dramatically increase (38:00) The healthcare company example - valuing by brand (40:00) Empowering your team and moving yourself out of (42:00) Case study — going from $8M to $50M (44:00) Transitioning from trader to owner mindset - revaluing (46:00) Hiring the right people to replace founder dependencies (48:00) The power of hiring specialists to handle entire departments (50:00) Creating multiple revenue streams and business units (52:00) Portfolio approach — combining multiple businesses under one

Quantum computing risk, USDC vs. Tether drama after the Drift hack, and World Liberty Financial's governance circus take center stage as Haseeb, Tom, Tarun, and special guest Joshua Lim dissect market signals, institutional FUD, Trumpcoin shenanigans, and ask: is crypto VC dead or just getting started? Welcome to The Chopping Block — where crypto insiders Haseeb Qureshi, Tom Schmidt, Tarun Chitra, and Robert Leshner chop it up about the latest in crypto. This week, the crew is joined by special guest Joshua Lim, Head of Derivatives at FalconX (and self-described Quantum FUD Whisperer). Ever wondered what happens when a quantum computer finally threatens public key cryptography? We break down the real and imagined risks of “Q Day,” what markets are actually pricing in, and why watching for Satoshi's coins moving is still the ultimate market panic trigger. Next up, the hosts tackle the messiest storyline in stablecoins: the massive Drift hack, North Korea's role, and the blame game between USDC and Tether. Is Circle's “wait for the court order” approach defensible, or are PR wins up for grabs for whoever moves fastest? We would never forget the crypto car crash that is World Liberty Financial: from drama-filled governance votes that magically extend lockups, to Justin Sun's redemption arc versus Trumpcoin, to whale-scale DeFi leverage that could nuke a protocol. It's a masterclass in governance theater and permissioned shenanigans. Finally, we level with all the “crypto venture is dead” crowd — who's still building, where the real capital is now, and why bear markets always demand an extra shot of conviction. From quantum nightmares to meme coin melodrama, let's get into it. Listen to the episode on Apple Podcasts, Spotify, Pods, Fountain, Podcast Addict, Pocket Casts, Amazon Music, or on your favorite podcast platform. Show highlights

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 Monica Cornitcher. Entrepreneurial journey, the inspiration behind Medase Cocktails, and the realities of launching, funding, and scaling a premium nonalcoholic spirits brand in a highly competitive market. Purpose of the Conversation The purpose of the episode is to: Educate aspiring entrepreneurs on how to build a differentiated consumer brand Demonstrate the importance of storytelling, market clarity, and operational discipline Highlight the growth of the nonalcoholic / zero‑proof beverage movement Inspire founders—especially founders of color—to own their niche, seek capital strategically, and scale intentionally. Key Takeaways 1. Business Built from Personal Need and Purpose Medase Cocktails was co‑founded by Monica and her lifelong friend during her friend’s battle with breast cancer, a time when alcohol was no longer an option—but celebration still mattered. The brand was created to allow people to celebrate authentically without alcohol It carries emotional depth rooted in friendship, gratitude, and loss Monica continues the mission after her co‑founder passed away in 2024 Lesson: Purpose-driven businesses create deeper emotional connection and long-term brand equity. 2. Differentiation Is Everything Monica deliberately rejected the “sparkling water with flavor” model common in nonalcoholic drinks. Her differentiators include: Authentic cocktail taste (Old Fashioned, Margarita, Moscow Mule) Organic juices, not artificial flavors Bold packaging that stands out on shelves Drinks designed to smell, taste, and feel like real cocktails Lesson: Competing on authenticity—not cost—is how you carve out market share in crowded spaces. 3. Brand Names and Stories Matter The name “Medase” means “thank you” and reflects gratitude, friendship, and emotional support. Monica emphasizes: Every flavor name, color, and product decision has a story A strong brand narrative creates curiosity, loyalty, and investor interest Lesson: People invest in brands they feel—emotionally, not just intellectually. 4. Venture Capital Is Not Just About Numbers While financials matter, Monica stresses that VCs also invest in founders and stories. What helped her secure venture capital: A compelling personal story Relevant founder skill sets (M&A, law, operations) Clear understanding of the market opportunity Lesson: Early-stage funding often depends on who you are and why you’re building, not just revenue. 5. Research, Planning, and Discipline Before Launch Unlike many food startups, Medase did not begin in a kitchen. They: Conducted a feasibility study Built a formal business plan Worked with a Black female food scientist Set strict personal funding limits before seeking capital Lesson: Preparation reduces risk and builds long-term sustainability. 6. Scaling Requires Operational Maturity As sales increased—especially on Amazon—Monica emphasized the need to move from “hustle mode” to operational excellence. Key scaling principles: Understand unit economics Track ROI for events and activations Adjust pricing as volume increases Build strategy across marketing, operations, and distribution Lesson: Hustle starts the business; operations grow it. 7. Niche First, Expansion Later Medase does not try to be “everything to everyone.” Core customers include: People seeking a break from alcohol Health-conscious consumers Black men looking for alcohol replacements Consumers wanting cocktail taste without hangovers Lesson: Strong niches create loyal advocates who fuel organic growth. 8. Smart Distribution Strategy Rather than rushing into retail, Monica prioritized direct-to-consumer channels: Amazon (top-performing channel) Brand website TikTok Shop Only after 6–7 months of traction did retail expansion become viable. Lesson: Control your margins and demand before entering expensive retail environments. Memorable Quotes “I wanted an authentic cocktail without compromise.” “Everything we do has a story behind it.” “Sometimes it’s not about the financials—it’s about the founder and the story.” “Don’t be everything to everybody. Find your market and stick with your market.” “Hustle starts the business, but operations give you scale.” “If it tastes too much like alcohol and you gave me a one-star review—thank you. That means I did my job.” Overall Message This episode is a real-world entrepreneurial blueprint showing how clarity of vision, emotional authenticity, disciplined planning, and niche focus can turn a personal idea into a scalable national brand. Monica Cornitcher exemplifies the modern founder:visionary, data-aware, emotionally intelligent, and unapologetically authentic. #SHMS #BEST #STRAWSupport the show: https://www.steveharveyfm.com/See omnystudio.com/listener for privacy information.