BILLIONS

From Revolut employee #3 to building a £5B energy giant - Alan Chang [Fuse Energy]

On this episode of BILLIONS, I'm sitting down with Alan Chang, Co-Founder and CEO of Fuse Energy—a tech-driven energy company that has completely shattered the UK market. Alan was employee #3 and Chief Revenue Officer at Revolut. Instead of coasting on fintech success, he and his co-founder Charles took that hyper-growth playbook and weaponized it against traditional utility giants like British Gas and Octopus. In just three years, Fuse has exploded from £2M to £400M in annual revenue, achieving a £5 billion valuation. If you want to know how a lean tech team can buy a wind turbine, acquire a grid operator, and out-execute legacy multi-billion dollar incumbents, this is your blueprint. In this masterclass, we break down: * The Revolut Exit: Why Alan walked away from fintech because the problem was "largely solved". * The £1M MVP: How they bought an energy license for £50K and a single wind turbine for £750K, using a mix of their own capital and an early round. * Anti-Democracy Culture: Why running a startup by committee fails, and why top performers should be paid 10x more than bottom performers. * Full-Stack Infrastructure Control: Why Fuse is currently buying a grid operator to dominate supply. * Internal AI Weaponization: How Fuse is building internal AI agents (PR reviews and error-tracking) to keep their team incredibly small and efficient. TIMELINE 00:00 – Leaving Revolut: Moving from a "solved" fintech industry to an unsolved energy crisis. 04:20 – The £1M Full-Stack MVP: Door-knocking for a wind turbine and securing an energy license. 09:33 – The Efficiency War: Why European energy costs are 3x higher than China's. 13:31 – Controlling the Grid: Why Fuse is actively acquiring a grid operator. 17:05 – The Execution Layer: Rejecting complex designs and demanding simplicity. 22:36 – High-Performance Compensation: Why top engineers make 10x more than the bottom tier. 28:50 – VC Term Sheets: Setting absolute founder-control red lines with investors. 36:50 – The Main Job: Why recruiting absolute elite talent takes up the majority of a CEO's day. 43:52 – Product Design: Building beautiful micro-solar and balcony battery products for consumers. 46:05 – Weaponizing AI Internally: Building PR review and error-tracking agents to optimize code. REFERENCES * Nik Storonsky [https://uk.linkedin.com/in/nstoronsky] * Charles Orr [https://uk.linkedin.com/in/charles-orr] * Revolut [https://www.revolut.com/] (Antoine le Nel : Episode 9 [https://www.youtube.com/watch?v=qT6CMcUMO-s]) * British Gas [https://www.britishgas.co.uk/] * Octopus Energy [https://octopus.energy/] * Fuse Energy [https://fuseenergy.com/]

The fastest revenue engine in SaaS history: $5.4B run rate in 10 Years - Ron Gabrisko [Databricks]

Is the traditional "per-seat" SaaS model officially obsolete? In 2016, Ron Gabrisko [linkedin.com/in/ron-gabrisko-4a21a] joined a startup with less than $1M in ARR. It was a company of 50 engineers and a product beloved by developers who had never even spoken to a sales rep. Ten years later, Databricks is a $134B giant doing $5.4B in ARR and they are still growing at a staggering 65% year-over-year. No CRO in history has built a revenue engine this fast, from this early a starting point. Ron didn't do it by following the standard Silicon Valley playbook; he did it by pioneering Consumption-Based Pricing and leveraging Open Source as the ultimate top-of-funnel engine. In this masterclass, we break down: * Consumption vs. Seats: Why Databricks tied its pricing to the "most basic unit of value" and how it fueled a $100B+ valuation. * The Open Source Funnel: How to monetize a community without "locking them in". * Building Trust with Engineers: Why Ron hires "really technical sales folks" to add value rather than just pitching. * Scaling through Innovation: Why Databricks didn't stop at one product, but built a sticky ecosystem (Spark, Delta, MLflow). * The GenAI Future: Why owning and protecting your data is the "secret sauce" for the next decade of AI. Timeline : 00:00 – The $5.4B Machine: Intro01:20 – Joining Databricks at sub-$1M ARR with 7 PhD founders04:12 – Selling to engineers: hiring "really technical sales folks"06:29 – Killing the SaaS Seat: consumption and the "most basic unit of value"09:22 – Net retention 130%: the multi-product open source strategy14:53 – Planning 65% YoY: the science of forecasting19:03 – Structuring 5,000+ sellers: verticalization and outcome-based selling29:11 – "Don't give your data to us": the data ownership philosophy33:54 – Usage-based vs value-based: why pricing is public on the website

$100M ARR in 7 quarters: inside the $10B AI machine - Reggie Marable [Sierra]

On this episode of BILLIONS, I’m sitting down with Reggie Marable, the Chief Revenue Officer at Sierra. Reggie’s path wasn't a straight line. He went from being a professional linebacker in the CFL to working in a Sprint call center, and eventually rose to become the Head of Sales for Slack North America. After years at Salesforce, he walked away to join Sierra as employee #23. Founded by Bret Taylor (former Salesforce Co-CEO) and Clay Bavor, Sierra has reached a $10B valuation and is on a path to $100M ARR in just seven quarters. Their secret? A business model that should terrify every legacy SaaS founder: Outcome-Based Pricing. In this masterclass, we break down: * The Sierra Sprint: How to scale to a $10B valuation in record time. * Service as Software: Why Sierra only charges customers when a problem is actually resolved. * The Sales Shift: Why Reggie left a massive leadership role at Slack for an early-stage startup. * Hiring for the AI Era: Why Reggie looks for "humble, hardworking, and low-ego" talent over pedigree. * The Recovery: How getting fired earlier in his career became the foundation for his $10B mindset. TIMELINE : 00:00 – "If your dreams don't scare you...": The Muhammad Ali mindset. 01:17 – From the CFL to a Sprint call center: Reggie’s raw beginnings. 04:07 – The "fired" moment: How losing his job led to a total reinvention. 05:33 – The Salesforce & Slack era: Mentoring 200+ people and scaling Slack North America. 10:00 – Why Reggie left Slack to become employee #23 at Sierra. 12:23 – Outcome-based pricing: Why the "per seat" model is dying. 16:20 – AI agents in the wild: Real-world results for Cigna, Singtel, and Sonos. 19:00 – The resolution model: Charging for solved problems, not software access. 31:15 – Operational cadence: How a $10B startup manages its weekly rhythm. 35:50 – Hiring strategy: Why "humble and hardworking" beats high-ego sellers. 44:10 – The "diversity" advantage: Building high-performance teams through inclusion. 47:16 – Reggie’s final advice: "Success is a winding road." REFERENCES :

Creating a $140B market: The secondary market masterclass - Larry Aschebrook [G Squared]

Today on BILLIONS, I'm sitting down with Larry Aschebrook, the guy who invented a market that Wall Street didn't think existed. Larry started personally buying shares in Twitter and Uber on the side and he realized: there are thousands of employees sitting on life-changing paper gains, with zero liquidity, waiting for companies that might never IPO. So in 2011, he quit his safe university job and launched G Squared, a fund to solve that problem. Nobody took him seriously. The secondary market was "taboo." Companies thought selling shares meant you were failing.Today, he manages $5 billion. He turned a $150M Spotify bet into $1 billion. He made $800M on Coursera. And the market he built is now worth $140 billion a year. Larry, welcome to BILLIONS.TIMELINE : 00:00 : The psychology of the secondary market pioneer. 01:13 : Quitting a decade-long career for a "ghost" market. 03:23 : The Hustle: Cold-calling alumni for early Twitter and Uber shares. 05:41 : The $150 million Spotify bet and the $9M personal risk with zero collateral. 11:19 : Data Arbitrage: How Larry knew record labels were secretly buying Spotify. 15:43 : Scaling G Squared: From a $35 million pool to $7 billion AUM. 20:05 : Why DPI (Cash Back) is the only metric that matters, and why paper gains are a lie. 25:09 : The Scars: Learning from the "quick commerce" collapse and other losses. 37:09 : The Future: OpenAI, SpaceX, and the evolution of private liquidity. 53:12 : Advice for Founders: Why you must hire "grinders," not just pedigree. REFERENCES : * Daniel Ek [https://www.linkedin.com/in/daniel-ek-1b52093a/]  * Spencer McLeod [http://linkedin.com/in/spencermcleod] * Mitchell Green [https://leadedge.com/team/mitchell-green/] * Jim Simons [https://fr.wikipedia.org/wiki/Jim_Simons] * Elon Musk [https://x.com/elonmusk]  * Spotify [https://www.spotify.com/]  * Twitter [https://x.com/]  * Uber [https://www.uber.com/]  * Palantir [https://www.palantir.com/]  * Coursera [https://www.coursera.org/]  * Anthropic [https://www.anthropic.com/]  * OpenAI [https://www.openai.com/]  * SpaceX [https://www.spacex.com/]  * Stripe [https://www.stripe.com/]  * Wiz [https://www.wiz.io/]  * Toast [https://www.toasttab.com/]  * Netflix [https://www.netflix.com/] * Apple Music [https://www.apple.com/apple-music/]  * Instacart [https://www.instacart.com/]  * Postmates [https://www.postmates.com/]  * Meituan [https://www.meituan.com/]  * Alibaba [https://www.alibabagroup.com/]  * Turo [https://www.turo.com/]  * Getaround [https://www.getaround.com/] * Klarna [https://www.klarna.com/]  * Revolut [https://www.revolut.com/]  * Databricks [https://www.databricks.com/]  * 23andMe [https://www.23andme.com/]  * Gorillas  * Pagaya⁠ [https://www.pagaya.com/]

McKinsey's AI leader moved to head a $2B AI workforce - Matthew Fitzpatrick [Invisible]

Is the traditional SaaS model officially dead ? On this episode of BILLIONS, I’m sitting down with Matthew Fitzpatrick, the man Fortune 500 CEOs called when they didn’t know what to do with AI. Matthew walked away from one of the most prestigious roles in tech, leading 1 000 engineers at McKinsey’s QuantumBlack Labs to lead Invisible Technologies [https://invisibletech.ai/]. Invisible is the "invisible" engine behind the AI revolution. They don't just build software; they provide the RLHF (Reinforcement Learning from Human Feedback) and the data that trains the models the entire world is building on. With $100M raised at a $2B+ valuation, Matthew is proving that the future isn't in selling tools, but in selling outcomes. In this masterclass, we break down: * The McKinsey Exit: Why a top AI leader "jumped ship" for a $2B startup. * The Death of SaaS: Why "Outcome-based pricing" is replacing the subscription model. * The Enterprise Gap: Why 90% of companies are failing to get AI into production. * The Scaling Laws: The truth about data bottlenecks and the future of AI training. * Process as Code: How Invisible integrates human intelligence with AI to solve "impossible" problems. TIMELINE : 00:00 The data bottleneck: Why Enterprise AI is currently "stuck" 01:01 Why McKinsey’s AI chief left to lead a $2B unicorn 02:33 The "Four Platforms": How Invisible actually works 05:58 SaaS vs. Outcomes: The pricing model of the future 09:19 Why the "AI Bubble" reality check is coming 15:12 The "Capability Gap" holding back the Fortune 500 22:15 RLHF & Data: Building the workforce behind the major models 31:42 "Process is Code": The new architecture for billion-dollar companies 41:10 Matthew’s advice for founders: Don't just build a "wrapper" 48:20 The future of the "Invisible" empire REFERENCES : * Mary Meeker [https://www.linkedin.com/in/mary-meeker-5823ba48/] * Elon Musk [https://x.com/elonmusk] Étude MIT Sloan [https://sloanreview.mit.edu/article/beyond-the-hype-the-real-state-of-ai/] * Étude NBER (National Bureau of Economic Research) [https://www.nber.org/papers/w31161] * Article Bloomberg [https://www.bloomberg.com/news/articles/2019-03-06/mary-meeker-s-1999-internet-predictions-how-did-they-turn-out] * McKinsey & Company [https://www.mckinsey.com/]  * Quantum Black [https://www.mckinsey.com/capabilities/quantumblack/how-we-help-clients]  * Invisible Technologies [https://www.invisible.co/]  * SwissGear [https://www.swissgear.com/]  * Y Combinator [https://www.ycombinator.com/] * WeCP (We Create Problems) [https://www.wecreateproblems.com/] * Databricks [https://www.databricks.com/]  * Snowflake [https://www.snowflake.com/] * Jevons paradox [https://en.wikipedia.org/wiki/Jevons_paradox]  * Reinforcement learning from human feedback (RLHF) [https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback] * Chain-of-thought reasoning [https://arxiv.org/abs/2201.11903]  * Revolut [https://www.revolut.com/]