BILLIONS

Guillaume Moubeche
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  • Entrepreneurship

After building my company to a $150M valuation in 4 years, I had one question left: How do you build a billion-dollar company? I’m Guillaume Moubeche, and on the BILLIONS Podcast, I’m taking you inside the room with the world’s most iconic builders, founders, and investors to find the answer. This is more than just another startup podcast; it’s a masterclass in high-growth SaaS, AI implementation, and wealth creation. From SaaS growth strategies and AI Agent pivots to the raw truth behind venture capital and exit strategies, we go where others don't. What you’ll learn on BILLIONS: SaaS Scal

  1. 3d ago

    He co-founded a $25B company. Then built a $7.2B one - Arvind Jain [Glean]

    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

  2. Oct 13

    How do you go from 25 customers to $110M ARR and a $500M deal? - Daniel Chait [Greenhouse]

    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

  3. Oct 6

    140M visits lost, 10x conversion gains: HubSpot’s AI growth playbook - Kieran Flanagan [HubSpot]

    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

  4. Sep 29

    How to redesign an $800M-a-year software giant for the AI era - Dan Rogers [Asana]

    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/

  5. Sep 15

    From 100 sales meetings a week to $600M ARR - Carles Reina [ElevenLabs]

    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

  6. Jul 9

    Why the "SaaS is dead" narrative is completely wrong - Aaron Levie [box]

    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

    Why the "SaaS is dead" narrative is completely wrong - Aaron Levie [box]

  7. Jul 2

    Why focus on only ONE product built a $1B super-brand (on just $6,000) - Pete Maldonado [Chomps]

    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

    Why focus on only ONE product built a $1B super-brand (on just $6,000) - Pete Maldonado [Chomps]

  8. Jun 25

    The man who built a bank for people banks don't want - Jason Wilk [Dave]

    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

    The man who built a bank for people banks don't want - Jason Wilk [Dave]
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About

After building my company to a $150M valuation in 4 years, I had one question left: How do you build a billion-dollar company? I’m Guillaume Moubeche, and on the BILLIONS Podcast, I’m taking you inside the room with the world’s most iconic builders, founders, and investors to find the answer. This is more than just another startup podcast; it’s a masterclass in high-growth SaaS, AI implementation, and wealth creation. From SaaS growth strategies and AI Agent pivots to the raw truth behind venture capital and exit strategies, we go where others don't. What you’ll learn on BILLIONS: SaaS Scal

Information

  • Creator
    Guillaume Moubeche
  • Years Active
    2025 - 2026
  • Episodes
    34
  • Rating
    Explicit
  • Copyright
    © Guillaume Moubeche
  • Show Website
    BILLIONS

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