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Today on BILLIONS, I’m sitting down with Aytekin Tank, founder and CEO of Jotform. Over the last 20 years, he built Jotform from a one-person bootstrapped startup into a global software platform used by more than 40 million people. He never raised outside capital. He still owns 100% of the company. And after reaching nine figures in revenue, Jotform is now going through one of the biggest transformations in its history: rebuilding its products around AI. Aytekin’s story is a masterclass in patience. For the first two years, he worked alone. For the first five years, the company basically grew one employee at a time. Then came Google Forms, Typeform, multiple rewrites of the core product, SEO shifts, new products, and now AI. Through all of it, one principle stayed the same: A founder’s job isn’t just to start the company. It’s to keep founding its next chapter. In this conversation, we break down: → How Aytekin bootstrapped Jotform to 40M+ users without raising a dollar → Why he believes founders should keep “founding” their company every day → Why stepping away from the product almost hurt Jotform twice → How Google Forms forced him to rethink his role as founder → Why Jotform has rewritten its core forms product four times — and is doing it again with AI → How customer interviews helped Jotform become a multi-product company → The SEO strategy that helped Jotform grow through thousands of highly specific use cases → Why losing Google traffic barely affected Jotform’s real business metrics → How Jotform maintained ~50% growth for years → Why Aytekin still owns 100% of the business → His two financial rules: one year of payroll in the bank and ~25% margins → Why your best employees should feel “painfully expensive” → Why AI could make great employees 10x — and eventually 100x — more valuable → Why Aytekin doesn’t believe AI will replace people → Why he thinks SaaS companies could actually grow MORE because of AI → Why vibe-coding your own SaaS may cost far more than simply buying it → His advice for anyone trying to bootstrap a company today One of my favorite ideas from the conversation: AI can automate the work. But it cannot take responsibility for the outcome. And for Aytekin, that means great people become more valuable — not less — as AI gets better. If you want to understand what 20 years of patient, profitable, founder-led compounding looks like, this is the episode. 00:32 – Aytekin Tank: 20 years bootstrapping Jotform to 40M+ users 01:07 – The different stages of building Jotform 03:14 – Why founders must keep “founding” their company 06:08 – Delegating everything to focus on one thing 09:06 – Rewriting the product again and again 11:26 – How Jotform became a multi-product company 12:57 – The real growth engine behind Jotform 16:08 – What happened when Google traffic started falling 19:19 – Why Jotform never shares its revenue publicly 21:58 – Growing 100%, then 50%, without outside capital 23:05 – Owning 100% of the company and taking dividends 24:08 – Aytekin’s 2 financial rules for bootstrapping 26:18 – How to retain your best employees without equity 29:41 – Will AI destroy SaaS? 31:08 – Why AI can’t replace people 32:04 – From 1x to 10x to 100x employees with AI factories 33:39 – Why vibe-coding won’t kill SaaS 37:30 – Does Aytekin ever want to sell Jotform? 38:49 – His advice to bootstrap founders today 40:46 – Where to follow Aytekin and Jotform

Today on BILLIONS, I’m sitting down with David Steinberg, a seven-time founder who has spent more than 30 years building companies. He has sold four, taken two public, and today leads Zeta Global, an intelligent AI infrastructure company worth around $8 billion. But one of the most important decisions of his career came in 2017. Zeta was on track to generate around $50 million in EBITDA. David went to his board and told them he wanted to effectively give up profitability for the next two to three years, put 400 engineers on a new platform, and rebuild the company around data and artificial intelligence. The board meeting became one of the most contentious of his career. Then, after one investor pushed back hard against two acquisitions David wanted to make, he decided to buy both. That decision helped create the Zeta that exists today. In this conversation, we break down: → What David learned building companies through the internet, mobile and AI revolutions → Why proprietary data and distribution become MORE valuable during technology shifts → Why he believes giving company data to frontier models can destroy your competitive advantage → The decision to sacrifice ~$50M of EBITDA and rebuild Zeta from the ground up → The board meeting that ended with David deciding to buy two companies instead of one → How Zeta turned data and AI into the foundation of its platform → Why its customers generate roughly 600–700% return on marketing spend → How 89.6% of Zeta’s new code is now generated automatically → Why a platform that required 400 engineers in 2017 could potentially be built with 40 today → Why enterprises pay for fighter jets but often only know how to fly prop planes → How Athena is changing the way customers interact with enterprise software → Why Zeta’s net retention rate is above 120% → Why David believes small inference models can outperform frontier models on business tasks → What the agentic internet could look like → The 5 rules Zeta uses before acquiring a company → How Zeta completed 19 major acquisitions with 18 considered successful → Why so many of Zeta’s senior executives came through M&A → David’s pitch for turning entrepreneurs into “intrapreneurs” We also discuss OpenAI, Anthropic, Palantir, data sovereignty, AI agents, the future of enterprise software and what 30+ years of building companies has taught David about surviving technology revolutions. If you want to understand how a serial founder thinks about rebuilding companies before technology forces them to, this is the episode. 00:00 – David Steinberg: 7 startups, 4 exits and 2 IPOs 00:32 – Meet the founder behind Zeta Global 01:01 – What the internet, mobile and AI revolutions have in common 03:24 – Why proprietary data is your biggest competitive advantage 04:53 – Why David sacrificed $50M in EBITDA to rebuild Zeta 08:03 – The two acquisitions that transformed Zeta 09:58 – Inside Zeta's most contentious board meeting 12:23 – From the 2017 pivot to AthenaOS 13:20 – How Zeta delivers 600–700% return on marketing spend 15:33 – Why Zeta is expanding beyond marketing 17:45 – How AI automates 89.6% of Zeta's new code 18:54 – Why enterprises buy fighter jets but fly prop planes 21:13 – Athena's adoption: 40% of major clients and 83% voice usage 21:45 – How proprietary context drives 120%+ net retention 24:35 – Why inference models could outperform frontier models 27:39 – The future of AI agents and agentic commerce 29:27 – What David learned from 19 acquisitions 30:53 – Zeta's 5 non-negotiable rules for M&A 32:35 – How to turn acquired founders into intrapreneurs 33:12 – David's most important job as a founder 34:11 – Final thoughts and where to follow David Steinberg

Today on BILLIONS, I’m sitting down with George Bonaci, VP of Growth & Demand Gen at Ramp. Before Ramp, George led growth at Gong and helped scale Samsara from under $100M ARR to more than $650M ARR through its IPO. Today, he’s helping build the growth engine behind Ramp — the finance platform valued at $44 billion and used by more than 70,000 companies. And his approach to growth is pretty unconventional. Ramp intentionally sets aside budget with zero expectations of return. Why? Because if every experiment works, you’re probably not taking big enough swings. One of those swings involved spending six figures advertising Ramp inside fortune cookies. George thought it sounded insane. It worked. In this conversation, we break down: → How Ramp keeps operating like a startup at a $44B valuation → Why George believes most growth teams don’t fail enough → The six-figure fortune cookie experiment that actually worked → Why learning in one week can be worth spending 5x more → How Ramp structures small autonomous “pods” to move faster → How George allocates talent and budget across growth bets → What Ramp’s spending data reveals about the AI model wars → Why companies investing heavily in AI are actually hiring MORE people → Why AI could make great operators 10x — or even 100x — better → Why George refuses to automate away the jobs that train future experts → Why the “SaaS apocalypse” narrative may be completely wrong → Why Ramp prefers using AI to create new tools instead of replacing existing SaaS → What happens when AI agents start buying from other AI agents → Why AEO could become much bigger than “SEO for ChatGPT” → How products will need to become discoverable, understandable and usable by agents → The one bet Ramp absolutely cannot afford to get wrong We also discuss why AI-generated 40-page documents are usually a sign of poor thinking, why the best candidates simply say “let me show you,” how work becomes invisible inside AI-native products, and why speed may still be the greatest competitive advantage a startup has. If you want to understand how one of the fastest-growing fintech companies in the world thinks about growth, AI and experimentation, this is the episode. 00:00 – George Bonaci: Samsara, Gong and $44B Ramp 00:46 – Why your growth bets should fail 01:58 – Ramp’s six-figure fortune cookie experiment 03:38 – How much should you spend on an experiment? 07:11 – How Ramp still operates like a startup at $44B 08:46 – Building autonomous growth pods 11:38 – Taking a new Ramp product from zero to growth 14:00 – How to allocate your best people across bets 15:45 – What Ramp’s data reveals about the AI model wars 17:13 – Why AI adopters may actually hire MORE people 18:23 – Is AI making us work harder instead of less? 21:12 – Hiring great people in the age of AI slop 22:16 – Why great thinking should fit on one page 25:01 – What happens to jobs when AI does the execution? 28:06 – Will AI kill entry-level jobs? 30:38 – Is the “SaaS apocalypse” actually happening? 32:28 – Using AI to build vs. replacing existing software 34:03 – When AI agents start buying from AI agents 35:28 – How to make your product discoverable by AI 37:24 – Giving AI agents the ability to spend money 41:03 – Ramp vs. incumbents like American Express 42:24 – Ramp’s long-term vision 43:15 – What George would do differently 44:08 – The one bet Ramp cannot get wrong

Today on BILLIONS, I’m sitting down with Arvind Jain, founder and CEO of Glean. Before Glean, Arvind spent years at Google working on search, then co-founded Rubrik, which became a multi-billion-dollar public company.Instead of retiring, he started again from scratch. Today, Glean has crossed $300M ARR and was last valued at $7.2B. And Arvind has some very strong views on where AI is going next.He believes open-source models will ultimately win for most technologies, and says they can already handle 80–90% of what enterprises want to do with AI. He also believes frontier models will increasingly become indistinguishable from one another.Which raises a much bigger question:If the model itself becomes a commodity, where does the real value live? For Arvind, the answer is context. In this conversation, we break down: → Why Arvind started again after already building a multi-billion-dollar company → How a problem inside Rubrik led to the creation of Glean → Why enterprise context may matter more than the underlying AI model → How Glean reduces hallucinations by validating AI outputs against company data → Why enterprises need more than “zero data retention” to safely deploy AI → Why Arvind believes open source will ultimately win → Why open-source models can already handle 80–90% of enterprise AI tasks → Why frontier models could become increasingly commoditized → How Glean routes every task to the right model based on quality and cost → Why specialized smaller models can outperform frontier models on specific tasks → Why AI may actually make us work MORE, not less → Whether intelligence still matters when everyone has access to AI → How AI agents could fundamentally change the way the internet is designed → Why autonomous agents could potentially spend hundreds of thousands of dollars per day without the right guardrails → What Arvind thinks Google and Gemini need to do next → The biggest mistake he made while building Glean One of the most interesting parts of the conversation:Glean was already training transformer-based language models for individual enterprise customers in 2019, years before ChatGPT launched. If you want to understand where enterprise AI is really going, beyond the hype around the latest models, this is the episode. TIMELINE: 00:26 – Arvind Jain: from Rubrik to $7.2B Glean 00:52 – Why start again after already building a huge company 02:24 – How Glean works and why enterprise context matters 04:57 – Solving AI hallucinations with validation 06:28 – Keeping enterprise data safe with AI 09:43 – Why Arvind believes open source will win 11:57 – If AI models become commodities, where is the moat 14:47 – Why specialized AI models will outperform general models 16:50 – How Glean chooses the best model for every task 19:00 – How to actually measure whether AI did a good job 21:13 – Will AI make us work less… or more? 23:42 – What happens when intelligence becomes abundant 26:41 – How AI agents will change the internet 29:24 – Building at AI speed inside slow-moving enterprises 34:09 – Arvind’s take on Google, Gemini and frontier models 36:16 – The one thing he would do differently building Glean 37:28 – Where to follow Arvind and Glean

Today on BILLIONS, I’m sitting down with Daniel Chait, co-founder and CEO of Greenhouse, the hiring platform used by more than 7,000 companies. Daniel has spent more than 27 years as an entrepreneur and built Greenhouse from an idea VCs initially dismissed into a $100M+ ARR company. Now, hiring is being disrupted again. Candidates are using AI to apply to more jobs and make every resume look better. Companies are responding by using AI to filter thousands of increasingly similar applications. Daniel calls it the “AI doom loop.” And Greenhouse is betting that the answer to AI breaking hiring… might actually be better AI. In this masterclass, we break down: → The 25-customer test Greenhouse used to prove product-market fit → The fundraising hack that turned investor rejection into demand → Why Greenhouse cut 30% of its team during COVID — only for hypergrowth to return weeks later → How the company crossed $100M ARR and brought in TPG as a majority investor → Why AI has made hiring worse for both candidates and recruiters → Why resumes are becoming a worse signal for identifying great talent → Greenhouse’s acquisition of Ezra AI Labs and its bet on Voice AI interviews → Why Daniel believes every candidate could eventually get a first conversation → How AI could actually remove some of the most biased parts of traditional hiring → Why SaaS pricing is moving from seats, to usage, and eventually outcomes → Why founders shouldn’t hire a sales leader before figuring out how to sell → How to hire great executives for jobs you’ve never done yourself We also discuss startup hustle, acquisitions, surviving market cycles, the future of resumes, whether candidates will accept being interviewed by AI, and what the next generation of hiring software could look like. If you want to understand how AI is changing hiring — and how a $100M+ SaaS company is trying to reinvent itself before someone else does — this is the episode. TIMELINE: 00:45 – Building Greenhouse and why hiring was broken 03:41 – Getting rejected by VCs and ignoring startup hype cycles 06:34 – The fundraising hack that got investors to say yes 09:40 – The job fair hustle that gave Greenhouse its first insights 11:29 – The 25-customer product-market-fit test 13:14 – Cutting 30% during COVID — then hypergrowth came back 16:45 – Buy vs. build: how Greenhouse thinks about acquisitions 18:14 – Acquiring Ezra AI Labs and betting on Voice AI 20:21 – The “AI doom loop” breaking hiring 23:25 – Do we still need resumes and job applications? 25:35 – 3 bets made by Greenhouse 27:45 – Will candidates accept being interviewed by AI? 31:55 – From SaaS seats to usage and outcome-based pricing 33:55 – Bigger mistake : why founders hire sales leaders too early 35:45 – How to hire for a job you’ve never done yourself

Today on BILLIONS, I’m sitting down with Kieran Flanagan, SVP of Agentic GTM & Systems at HubSpot. Kieran spent 10 years helping build one of the most copied growth engines in SaaS, left to become CMO of Zapier, and then came back to HubSpot to help rebuild its entire go-to-market playbook around AI. And they had no choice but to rethink it. In just one year, HubSpot lost 140 million visits — around 80% of its organic traffic — as Google and AI completely changed how people search. Yet the company kept growing. Some of HubSpot’s AI-powered experiments increased conversion rates by 3x, 5x, and even 10x. Today, HubSpot serves more than 300,000 customers, generates more than $3B in revenue, and is rebuilding the growth engine that made it famous for an AI-first world. In this masterclass, we break down: → How HubSpot lost 80% of its organic traffic and still kept growing → The AI experiments that generated up to 10x conversion improvements → Why Kieran believes informational SEO is being killed by AI → How HubSpot is optimizing for ChatGPT, Gemini and answer engines → Why “taste and judgment” are becoming more valuable as AI gets better → How to build context layers that make AI agents smarter over time → Why junior employees may actually struggle more with AI than experts → How HubSpot built a network of 350 creators to replace part of its search-driven growth → Why some shows inside that network grew 5x → How more than half of HubSpot’s revenue now comes from customers who started as free users → The new distribution channels and ecosystems HubSpot is betting on next We also discuss why AI is creating a “sea of sameness”, why perfectly polished content can now be a disadvantage, how creators can influence visibility inside AI engines, and what happens to Google when users no longer need to click on links. If you want to understand how one of the biggest growth companies in SaaS is rebuilding its playbook for the AI era, this is the episode. TIMELINE: 00:50 – Building a billion-dollar company before vs. after AI03:14 – Using AI to personalize emails at scale05:40 – How HubSpot lost 140M organic visits08:26 – The new AI-first marketing playbook11:55 – Building taste and judgment into AI15:12 – Kieran’s AI Content OS18:11 – Why AI-generated content all looks the same23:48 – Replacing SEO with new distribution channels25:28 – HubSpot’s 350-creator network27:18 – How this creator network works?31:28 – Winning inside ChatGPT and answer engines34:50 – HubSpot’s next big growth bets37:10 – AI, agencies and HubSpot’s partner ecosystem39:28 – Kieran’s new book, Loop

Today on BILLIONS, I'm sitting down with Dan Rogers, CEO of Asana, to discuss AI agents, the future of SaaS, and what comes after the chatbot. Dan took over an established software business with around $800 million in revenue. His next challenge: turning a platform built for human collaboration into an operating system for teams of humans and AI agents. A model can generate an answer. But who needs to approve it? Which team acts next? What has already been decided? Dan's argument is that this context and coordination - not just the intelligence of the model - will determine whether AI can do useful work inside a company. At the time of our conversation, AI products were contributing 25% of Asana's net new ARR. We get into the decisions behind that shift: charging for fulfilled requests rather than tokens, giving users an initial AI allowance at no extra cost, and buying Stack AI to accelerate the roadmap. We also discuss keeping teams focused through market volatility, integrating an acquired company without losing its people, and why past success can become a liability when you're taking over a new business. In this masterclass, we break down: Beyond the Chatbot: Why generating an answer is different from coordinating work, and what changes when your team includes AI agents.The Request-Based Pricing Bet: Why Asana is hiding token complexity from customers and charging around the fulfillment of a request.Value Before the Sale: The decision to give users an initial AI allowance, help them discover the value, and let adoption drive further spending.Buying Time: How the Stack AI acquisition accelerates Asana's roadmap, and why the small details of integration matter as much as the big strategic decisions.The Model Moat Test: Does your business become more or less valuable when AI models improve? Dan explains why he believes Asana benefits.Leading Through the Noise: Listening to customers, finding wins beyond the daily stock price, and questioning the playbook that made you successful before. SUBSCRIBE for more episodes of BILLIONS. Follow meLinkedIn: https://www.linkedin.com/in/-g-/X: https://twitter.com/GuillaumeMbhTikTok: https://www.tiktok.com/@guillaumemoubecheInstagram: https://www.instagram.com/guillaume_moubeche/

Today on BILLIONS, I'm sitting down with Carles Reina, the first investor and fourth employee at ElevenLabs, to unpack the AI bubble, the rise of Chinese models, and Europe's fight to stay in the race. Carles helped build ElevenLabs' go-to-market engine on the road to over $600 million in annual recurring revenue. As the founder of Baobab Ventures, he also sees the other side of the boom: the funding rounds that may be getting ahead of reality. Before the scale came 80 to 100 sales meetings a week, a Google Sheets CRM, and months of testing who would actually pay. Then came a sales model most teams would question: quotas set at 20 times base salary, uncapped commissions, and average quota attainment of around 167%. But this conversation goes beyond the ElevenLabs growth story. We debate the commercial barriers Carles sees around Chinese AI models, why he believes Europe is not investing seriously enough in AI infrastructure, and what it takes to build a global company from day one. He also explains why extraordinary growth and an AI bubble can exist at the same time - and why he expects some aggressively valued startups to face a funding reality check within six to nine months of this conversation. In this masterclass, we break down: The 100-Meeting Week: How Carles tested his way from creator demand to a repeatable sales motion, then hired reps who started closing deals within weeks.The 20x Quota Rule: Why ElevenLabs set targets at 20 times base salary, rewarded overperformance with commission accelerators, and adjusted expectations when markets proved tougher.Permission to Fail: Why Carles encouraged experiments with company money - and only needed one idea out of 100 to unlock the next stage of growth.The AI Adoption Gap: Why strong Chinese models do not automatically translate into easy enterprise adoption, and why Europe needs more than technical talent to compete.The Operator-Investor Playbook: Why Carles kept Baobab small despite $23M in commitments, and why he wants to return capital to investors along the way.The Valuation Reality Check: How real AI demand can coexist with overheated funding rounds, and why a high valuation can become a liability at the next raiseGlobal from Day One: Why Carles challenges the one-market-at-a-time playbook, pushes founders to hire go-to-market talent early, and wants investors to do more than write checks. TIMELINE 00:00 - Intro - from first investor to fourth employee at ElevenLabs 03:25 - 80-100 sales meetings a week: finding what actually sells 06:46 - Hiring "icebreakers" vs. scaling the sales team 09:36 - Selling the future: from developers to enterprise 12:25 - Building a sales team that experiments with AI 17:05 - The 20x quota rule, uncapped commissions & 167% attainment 21:46 - The incentive mistake that held back enterprise sales 24:13 - Chinese AI models: great technology, harder enterprise adoption 34:01 - Europe's AI infrastructure problem 34:38 - Hugging Face, Nvidia & the European funding gap 37:37 - Building Baobab Ventures: fundraising and early results 43:37 - AI bubble or real growth? The next funding reality check 47:53 - Three lessons for founders building globally SUBSCRIBE👇https://www.youtube.com/c/GuillaumeMoubeche?sub_confirmation=1 Join the lemlist family 🚀 https://community.lemlistfamily.com/join?invitation_token=9db20d2c4707b75ebb065462261c4665a3c35a59-aad63a7f-90a5-452e-a628-4ccbcbaa84ca Follow me https://www.linkedin.com/in/-g-/ https://twitter.com/GuillaumeMbh https://www.tiktok.com/@guillaumemoubeche https://www.instagram.com/guillaume_moubeche/ Learn how to book meetings with 30%+ of your prospects (for free): https://www.lemlistfamily.com/multichannel-masterclass?mtm_campaign=5008&mtm_source=organic&mtm_medium=youtube

Today on BILLIONS, I'm sitting down with Aaron Levie, the co-founder and CEO of Box, who has rapidly transitioned his enterprise platform from a pure SaaS model into a cutting-edge playground for autonomous AI agents. Aaron has a masterclass view of the data infrastructure that legacy tech giants wish they controlled. In this conversation, we pull back the curtain on the high-stakes battle for sovereign AI. We unpack the real fallout of the U.S. government's unprecedented export controls on Anthropic's frontier models, why data platforms like Snowflake are posting blockbuster quarters amidst the AI boom, and how specialized tools like Cursor show that the future of intelligence is multifaceted, not winner-take-all. If you want to understand where the real economic value of applied AI resides over the next decade, this is the blueprint. In this masterclass, we break down: The Export-Control Precedent: Inside the unprecedented restriction of Anthropic's frontier model from non-US users and why Aaron calls it a brand-new moment in AI regulation.The Safety-Rhetoric Boomerang: How AI safety messaging scared the government into a model-approval pipeline — and why some safety advocates may quietly prefer that outcome.China's $50B Scenario: Why Aaron believes China can simply throw $50B at compute to stay in the race and why France, Japan, the UK, and Germany may be forced to build their own sovereign models.The Multifaceted Intelligence Future: Why the AI market won't be "winner-take-all," and how Cursor's applied-layer harness (routing tasks across cheap and premium models) became the template.Building for Machine Users: How Box adapted its file system MCP server, CLI, Markdown editing, HTML compatibility so agents and people work off the same data.Why More Agents Make SaaS More Valuable: Why deploying 100x more agents than employees increases the value of the underlying CRM, ERP, and content systems instead of killing them.The CS-Grad Dislocation: A grounded look at the shifting job market away from Big Tech layoffs and into AI startups and industries like life sciences and manufacturing.TIMELINE : 00:00 – Turning Box from SaaS into an AI agent platform03:00 – Sovereign AI: why countries will build their own models05:51 – Can open and Chinese models catch up to the frontier?08:51 – The chip embargo debate and Jensen Huang's argument14:09 – AI safety, regulation, and government model approval18:29 – Why AI won't be winner-take-all (the Cursor case study)21:33 – How Box built a file system for AI agents27:17 – Is SaaS dead? Why agents make software more valuable30:48 – Will AI replace jobs? The truth about CS grads

Today on BILLIONS, I'm sitting down with Pete Maldonado, the visionary who took $6,000, one failed food venture behind him, and zero institutional backing and built a dominant food empire that's approaching $1 billion in revenue this year. Pete and his co-founder Rashid bet everything on a category the entire industry assumed was dead: the gas-station meat stick. For nearly ten years they bootstrapped taking less than $1M in primary capital prioritizing extreme operational focus and deleting complexity at every corner. They stayed so maniacally disciplined that they only ever scaled one product format : meat sticksusing just 12 core recipes to capture market share from corporate giants. But extreme efficiency comes with massive friction. Pete opens up about the devastating reality of underestimating their explosive demand curve, which cost them 9 figures in lost revenue last year alone, the inside story of surviving an overnight COVID collapse with Trader Joe's, and why he chose to step down as CEO to hand over the keys to his co-founder. In this masterclass, we break down: The $6,000 Side Hustle Genesis: How a personal trainer used early Shopify tools and a $99 Photoshop Elements subscription to design a world-class brand from his desk.The Rule of Deleting Complexity: Why going deep on a single SKU beats going wide, and how relentless simplicity early on became the reason they could scale at all.The 9-Figure Forecasting Nightmare: The brutal operational pain of undershooting cultural shifts and cutting massive amounts of purchase orders when demand outpaces supply.The Over-the-Register Museum Trap: How an unexpected plexiglass policy at Trader Joe's wiped out retail sales overnight during COVID—and the pivot that saved the team from layoffs.The Hidden Weight of Personal Guarantees: Moving past bank-debt structures that put family homes on the line to engineer a 100% secondary private equity deal with Stride Consumer Partners.Stepping Down at the Peak: Pete's candid psychological transition from active day-to-day CEO to hands-off Chairman to protect his family time and scale the company further. TIMELINE : 00:00 – Building Chomps on $6,000: a $6,000 food brand from nothing 09:46 – Brand awareness vs. distribution: never hit a shelf before the customer knows you 11:53 – Riding the diet tribes: CrossFit, Paleo, Whole30, Keto, and now GLP-1 19:13 – The 2016 Trader Joe's inbound: staying methodical and rejecting advisor pressure to over-expand 28:05 – Forecasting demand and surviving COVID33:31 – The plexiglass "museum": surviving canceled COVID orders with zero layoffs 37:17 – Personal guarantees & the 100% secondary raise: de-risking the families 40:46 – Casting a wider net: breaking the bottom-of-funnel ROAS trap to unlock top-of-funnel scale 46:15 – Stepping down: from CEO to Chairman REFERENCES Rashid Ali  Noah Kagan  Tim Ferriss  Liz Carter  The Million Dollar Weekend  Nutrisystem  Jenny Craig Trader Joe's Whole Foods Sprouts Thrive Market Jack Link's Slim Jim Stride Consumer Partners Shopify WordPress Amazon  Whole30 Approved  CrossFit  Paleo  Keto  GLP-1

On this episode of BILLIONS, I'm sitting down with Jason Wilk, four-time founder and CEO of Dave, the neobank built to take on the predatory overdraft fees that quietly bleed billions a year from the Americans who can least afford them. Jason's story is one of the wildest comebacks in fintech. After going public via SPAC in January 2022, Dave hit a $5 billion valuation, then the macro turned. Rates spiked, growth capital dried up, and within nine months the stock had collapsed 98%, dragging the company's market cap down to roughly $50 million, less than the cash sitting on its own balance sheet.Most teams would have panicked, slashed headcount, or sold cheap. Jason did the opposite: he froze hiring, refused layoffs, killed every non-core product, and put the entire company behind one number, unit economics. Today Dave is back to a nearly $4 billion market cap, with 2026 guidance of over $700M in revenue and over $300M in EBITDA, a ~$400M earnings swing in just a few years. In this masterclass, we break down: The $75 microloan bet : how Dave used cash-flow data instead of FICO to underwrite the smallest loan in the country, importing a model that worked in India and Africa but no one had cracked in the US.120 meetings for a Series A : why traditional VCs had never even heard of overdraft fees, and what it took to finally get the check.Surviving a 98% wipeout : the operational playbook Jason ran when growth capital ground to a halt and raising more was off the table.Making millionaires at the bottom : how a Performance Stock Unit structure turned the crash into the biggest wealth-creation event in the company's history."VC money is just very high-APR debt" : why Jason wishes he'd taken his $10M Series A as venture debt and kept the equity.Eating other people's margin : Dave's new credit card and multi-product roadmap, aimed at the $100B+ a year Americans pay in credit card APRs and late fees.TIMELINE : 00:00 – Why he declared war on the $34 overdraft fee01:55 – The $75 microloan that ignores your credit score04:26 – 120 investor meetings to close the Series A09:48 – Going public via SPAC at a $5B valuation11:15 – How the stock crashed 98% in 9 months16:19 – Making employees millionaires at rock bottom19:38 – From burning $100M to $300M in EBITDA23:17 – Why VC money is worse than a loan shark27:00 – The new credit card attacking a $100B market43:29 – Running a $4B company with 300 people REFERENCES : Jason Wilk SV Angel  Ron Conway Paul Graham GoBuyside / « Gocleff »  Dave  Plaid Acorns  BankSimple  Norwest Venture Partners  Tiger Global  Y Combinator

On this episode of BILLIONS, I'm sitting down with Peter Singlehurst, who built the private companies team from scratch at legendary investment firm Baillie Gifford, deploying billions into more than 100 of the most important private companies on the planet. Peter operates on a timeline that makes typical venture capitalists look shortsighted. From backing Tesla in 2013 at a $3B market cap to entering SpaceX at a $30B valuation, his strategy completely bypasses the short-term noise of quarterly earnings. In this masterclass, he breaks down why optimizing for the highest possible price at an IPO is a lethal mistake, the massive arbitrage hidden within the world's most misunderstood tech giant (ByteDance), and the raw post-mortem of their highest-profile mistake: Northvolt. We break down: The Philosophy Swerve: How a philosophy graduate skipped a PhD to build a multi-billion dollar growth engine and why Baillie Gifford deliberately hires people with no finance background.The Death of the IPO Monopoly: Why the world's most valuable hyper-growth companies no longer need public exchanges to unlock liquidity.Debt Kills, Dilution Doesn't: Peter's contrarian warning to scaling founders on why leverage is a ticking time bomb for pre-profitable businesses.The ByteDance Arbitrage: The inside story of buying shares at ~4x free cash flow while Western investors ran away.The Northvolt Post-Mortem: A transparent breakdown of their highest-profile mistake and how to spot a venture-stage asset masquerading as a growth-stage giant.Disrupting the 2-and-20 Norm: How Baillie Gifford structures an ultra-LP-friendly 1-and-10 fee model charged on invested capital, not committed capital.TIMELINE : 00:00 – "You get the shareholders you deserve": the long-term underwriting mindset00:53 – From philosophy to growth equity: why Baillie Gifford avoids finance backgrounds05:44 – Entry mechanics: Tesla's $3B public entry vs SpaceX's $30B private scale08:23 – The leverage trap: why a little dilution never killed a business, but debt does13:50 – Democratizing elite assets: how the Schiehallion Fund opens up Stripe, SpaceX & Databricks to everyday savers23:15 – Designing the ideal IPO: why chasing the highest possible price destroys public-market trust30:07 – The founder risk matrix: Bezos' 1997 shareholder letter & Musk's "bet the house" blueprint35:30 – The ByteDance arbitrage: buying shares at ~4x free cash flow52:47 – Flipping the venture fee model: the LP-friendly 1-and-10 on invested capital56:09 – The Northvolt post-mortem: growth equity risk vs venture equity risk REFERENCES Elon Musk  Jeff Bezos  Jeff Bezos’s letter to his shareholders in 1997 Warren Buffett  Larry Ashbrook Hendrick Borginon  Baillie Gifford  Tesla  SpaceX  ByteDance  Amazon  Alibaba  Anduril  Bending Spoons Airbnb Spotify  Stripe Databricks Affirm Wise Tempus Klarna  Figma Northvolt Uber Lyft Meta (Facebook)

On this episode of BILLIONS, I'm sitting down with elite finance operator Martino Cadoni, current CFO of DeepL (one of Europe's leading AI companies) and the veteran strategist behind one of Central Europe's biggest banking IPOs. Martino cut his teeth in corporate America's legendary "CFO factory" the grueling General Electric leadership program, working relentless 996 schedules to solve high-stakes financial fires across the globe. From being flown to Budapest on a day's notice to re-evaluate multi-million dollar reserves in two weeks during a currency crisis, to managing capital and balance-sheet strategy as deputy CFO at HSBC, Martino knows exactly what it takes to build institutional readiness.In this masterclass, he pulls back the curtain on private vs. public markets, exposing the lethal structural mistakes tech unicorns make during down rounds and explaining exactly how legacy banking giants accidentally funded their own demise. In this masterclass, we break down: The GE CFO Factory: Inside the intense 996 operational rotation program that builds high-agency, first-principles problem solvers.The "Zombie Corn" Trap: Why raising capital at massive valuations with high preferred share thresholds completely paralyzes tech startups when the market turns.The Balance Sheet Obsession: Why optimizing accounts payable, invoice due dates, and basic payment timing yields instant cash flow wins that most tech companies completely step over.The AI Billing Paradigm Shift: Navigating the massive industry uncertainty around revenue models—from traditional per-seat subscriptions to usage and outcome-based billing.How Traditional Banking Lost: Why legacy financial institutions wasted billions on stock buybacks and short-term dividends instead of innovating, allowing Revolut and Nubank to ruthlessly strip away their market share. TIMELINE : 00:00 – Finance as an Accelerator: The core value-creation thesis + who Martino is (from a $749M banking IPO to DeepL).01:26 – The GE "CFO Factory": Rotations, the 996 schedule, the Budapest currency emergency, and the Toyota/agile principles behind high-agency finance.10:34 – The Balance Sheet Obsession: Why tech over-indexes on the P&L, and the easy cash wins in accounts payable, invoice timing, and FX.17:48 – Public Markets, SOX & the Cost of Capital: Building precise guidance, how interest-rate cycles dictate fintech lending, and Revolut's revenue diversification.27:49 – The "Zombie Corn" Trap: How high preferred-share thresholds freeze startups when the market turns and the common-share advantage.35:12 – Down Rounds & Cap Table Management: Pitching fresh investors a path to upside, and why a concentrated cap table lets you manage LPs one-on-one.41:26 – Why DeepL & the AI Billing Shift: Joining Europe's AI race, and the industry struggle to move from per-seat to usage and outcome-based pricing.50:13 – Defensibility & the Short-Term Trap: Avoiding FOMO, building embedded moats, and how legacy banks burned billions on buybacks while Revolut and Nubank ate their lunch. REFERENCES Thierry Pieton Kazuma Shipchandler John Elkann  Jarek Kutylowski Matt Cohler  Danny Rimer General Electric (GE)  Converteam  Moneta Money Bank Klarna  DeepL  Revolut  Benchmark Index Ventures IVP Iconic B2Venture GIC ATomico Ontario Teachers' Pension Plan

Is traditional international banking officially obsolete? On this episode of BILLIONS, I'm sitting down with Steven Schwartz, co-founder of Whop, the internet marketplace that is quietly constructing a brand-new financial infrastructure for global creators.What started as a simple $75 sneaker bot sold in Facebook groups has exploded into a massive global network. Today, Whop is a powerhouse where nearly 40,000 people earn over $300 million every single month, and the platform processes nearly $1 million daily in physical product sales alone. Steven breaks down how they partnered with stablecoin giant Tether to unlock open financial networks for unbanked regions across the globe, why they are deliberately driving traditional credit card processing fees to zero, and why 70% of his entire corporate team consists of former founders. In this masterclass, we break down: The Facebook Group Bootstrapping: How Steven and his co-founder Cameron built an iOS app to flip Yeezys and turned a micro-win into an addiction.The Core Pivot: Moving from niche downloadable software keys to hosting full community ecosystems with integrated chat rooms and video courses.The Midtown Micro-Payments Project: The wild story of building laminated QR code profiles to route Apple Pay and Venmo donations directly to the homeless.Commoditizing the Gatekeepers: Why Whop refuses to profit off standard credit card processing and is actively racing payment margins to zero.The On-Chain Future: How stablecoin rails let an entrepreneur in Nigeria, Thailand, or anywhere without reliable banking infrastructure send and receive money across borders, building a real business without ever needing a legacy bank account.Hiring High Agency: Why Whop acquires companies first and foremost for talent, bringing in former founders who are high-agency, obsessive, and have already built zero-to-one products, rather than chasing headcount. TIMELINE 00:00 – The $75 Sneaker Bot: monitoring Nike's Twitter to auto-buy rare drops 03:09 – The Pivot: from niche software keys to courses & private communities 05:34 – Payments Philosophy: the homeless QR-code project & racing fees to zero 08:34 – The Tether Bet: stablecoins & bypassing legacy banks for the unbanked 19:32 – "Powered by Whop": the viral loop & the zero-fee ecosystem 23:48 – Acquiring for Talent: why 70% of the team are former founders 40:04 – From Alabama Construction to $180K/Month: real-world impact 42:38 – "Eating Glass": the perseverance to deliver a global mission

On this episode of BILLIONS, I’m sitting down with Sandy Diao, an elite growth operator who has been remarkably right about major market trends long before the rest of the ecosystem. Sandy helped scale products to 200 million users by leading early growth efforts as employee number 30 at Pinterest. She then joined Descript as their first ghost hire, architecting an automated affiliate model that drove 25% of all new users completely self-service. Her thesis is a warning to every modern SaaS operator: the siloed channel specialist is obsolete. In a world flooded by AI-generated content, traditional acquisition paths are collapsing. The future belongs to full-stack, unified operators who realize that trust is the only channel that compounds. In this masterclass, we break down: The Pinterest Support Trench: How responding to raw customer tickets unlocked the insights to rewrite onboarding and drive massive user activation. Data-Inspired vs. Data-Driven: Why chasing exact precision can paralyze early growth, and why directional insights are the secret to building high-velocity engines. The Descript Affiliate Machine: How to structure automated, self-service loops that scale acquisition without expanding headcount. The Death of Growth Moats: Why traditional software channels are decaying and how to transition to a unified growth framework. Auditing Your Engine: Sandy's precise multi-step diagnostic process for troubleshooting an underperforming distribution strategy. TIMELINE : 00:00 – Why most growth moats won't survive the AI era 01:03 – The Support Trench: How customer tickets rewrote Pinterest's onboarding 10:00 – Overcoming Team Friction: How to align engineering with rapid growth experiments 16:06 – From B2C to B2B: Spotting high-intent institutional signals in consumer data 18:17 – Data-Inspired vs. Data-Driven: Why chasing absolute precision kills execution velocity 25:09 – The Descript Affiliate Loop: Building a self-service machine that drove 25% of new users 38:00 – Retention in the AI Era: Maintaining product durability when switching costs drop 41:10 – The Growth Collapse: Why the siloed channel specialist is officially obsolete 44:03 – The Growth Audit: Her foundational framework to diagnose an underperforming engine 47:02 – Adaptive Moats & Unfair Advantages: Why the permanent distribution moat is dead REFERENCES : Ben Silbermann  Evan Sharp  Pinterest  Descript  Indiegogo The ONE Smart Piano  TeachShare  Adobe  Facebook/Meta  Twitter (X)  Coca-Cola  Burt's Bees Stripe Google Ads Meta Pixel (ex-Facebook Pixel) Claude  ChatGPT Gemini  Power law outcomes  GEO (Generative Engine Optimization)