Podcasts about PMF

Nuno Goncalves Pedro is the Founder and Managing Partner at Chamaeleon. He won't describe it this way, but I'd call Chamaeleon something like "The RenTech of VC."Chamaeleon is built around its proprietary data platform, Mantis, which borrows tools like factor analysis from public-market investors and operates more like a quant hedge fund than a traditional venture firm.We talk through a bunch of data that cuts against the common narrative in venture, including why repeat founders aren't always the safer bet, why sub-$100m funds catch the majority of fund-returning deals, and why 10x might be a better target than 100x.Thank you to Numeral, Flex, Amplitude, and Merge for supporting this episode.Numeral: The end-to-end platform for sales tax and compliance https://www.numeral.comFlex: Get premium banking and a net 60 day credit card at 0% APY https://home.flex.one/referral/bananacapitalAmplitude: AI analytics, all you have to do is ask https://www.amplitude.comMerge: Every modal. One API. Total control. Check out Merge's Agent Handler. merge.dev/turnerTimestamps:(1:00) When 1st time founders outperform serial entrepreneurs(8:05) Mantis: factor-driven quant model for VC(18:33) Why most VC's are not data-driven(22:28) Top 1% VC fund performance(27:41) Early customer sentiment stronger success indicator than PMF or Team(34:09) Importance of co-investors on performance(39:42) Sub-$100M funds capture 70% of fund-returning deals each year(43:53) The Neolab AI bubble(52:16) Marketing games that VC's play(55:22) Most investors are not high conviction(56:43) Startups not raising for at least 3 years are 5x less likely to succeed. 10x less likely at 5 years.(1:00:19) Emerging managers have lowest LP interest in the last 15 years(1:11:19) LP capital is much less concentrated than in 2011(1:16:28) The importance of remaining relevant(1:21:01) You must lean into your unique edge as an investor(1:23:18) Pros/Cons of an alumni network venture strategy(1:28:29) Specialist funds outperform generalists (with a catch)(1:35:22) The data says go for 10x, not 100x returns(1:41:41) Should you start or join a VC firm today?(1:48:07) Nuno's collection of 270+ phones(1:53:16) Racing cars (and winning championships)ReferencedChamaeleon: https://www.chamaeleon.vc/Tech Deciphered Podcast https://decipheredshow.com/Say It With Charts: https://www.amazon.com/Say-Charts-Executives-Visual-Communication/dp/007136997XHow To Lie With Charts: https://www.amazon.com/How-Charts-Gerald-Everett-Jones/dp/1419651439Redmagic Phone: https://redmagic.gg/ASUS Rog phone: https://rog.asus.com/phones/rog-phone-model/Follow NunoLinkedIn: https://www.linkedin.com/in/ngpedro/Follow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

EPISODE DESCRIPTIONI sat down with Amit Mahensaria, co-founder of Pred, to explore why the $500 billion sports betting industry is ripe for disruption. Amit isn't a typical Web3 founder , he came in as a degen, a 22-year sports trader who got tired of the house always winning. In this episode, we dig into how Pred is building a trustless, peer-to-peer sports prediction exchange on Base, why live sports demand a completely different architecture than general prediction markets like Polymarket, and what it really takes to build an on-chain order book that can keep up with a goal being scored in real time. We also get into the state of the prediction market industry, who's going to win the space, and why Amit believes the Hyperliquid of sports trading hasn't been built yet , until now. DISCLAIMERNothing mentioned in this podcast is investment advice and please do your own research. It would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend. Be a guest on the podcast or contact us - https://www.web3pod.xyz/CONNECTPred Website: https://www.pred.app/trade/fif-cdr-den-2026-06-03Twitter/X - Pred: https://x.com/predofficialWeb3 with Sam Kamani: https://www.web3pod.xyz/KEY POINTS WITH TIMESTAMPS• [00:02] Sam introduces Amit Mahensaria, co-founder of Pred, a sports-native prediction exchange at the intersection of AI, crypto, and blockchain• [01:11] Amit shares his background , not a typical Web3 founder, but a 22-year sports trader and DeFi degen since the 2020 DeFi Summer• [02:32] His co-founder is a Web3 OG and former product and design head of Binance India• [03:38] The origin story: Amit built a peer-to-peer sports trading community 7 years ago after getting frustrated with sportsbook middlemen always taking a cut• [05:43] The core thesis , middlemen are being removed from every industry, and sports betting is one of the last frontiers where the house still always wins• [07:16] Why general-purpose prediction markets like Polymarket and Kalshi are not designed for sports UX or speed• [10:27] The biggest technical challenges: building an off-chain order book with on-chain matching, achieving 10x lower latency than competitors, and managing correlated multi-outcome order books in real time• [14:44] The Venn diagram problem , crypto users and frequent sports traders overlap by around 40%, poker bettors and crypto users by 60%• [16:29] How Pred abstracts crypto complexity away for mainstream users, and partnerships with fund.xyz and swap.com for on-ramping• [17:47] Key product learnings from 200-250 beta users over 8 weeks , sports UX must look nothing like a financial trading terminal• [19:47] Why Pred chose to build on Base , speed via Flash Blocks, distribution, and a roadmap conversation with Jesse Pollak• [21:55] The prediction market landscape has over 120 projects, but the space is still very early , the Hyperliquid of prediction markets hasn't emerged yet• [25:54] Pred is coming out of invite-only beta and opening to the public by end of month, starting with soccer only• [28:46] Advice for Web3 founders , do not launch a points program before you have PMF; GTM too early will kill you• [32:22] Long-term vision: a trustless, globally accessible sports trading exchange where users own the platform and trust every trade• [34:09] Liquidity management strategy , a transparent algo-driven vault similar to Hyperliquid's HLP, plus easy API onboarding for sports-focused market makers• [38:20] Current asks: users who want to trade and give feedback, sports-focused market makers, and a larger fundraise planned post-public launch

Felipe Carvalho is Co-Founder and Chief Revenue Officer of Camu — an AI workflow automation platform. Previously, Felipe spent 10 years building the global go-to-market organization at Pipefy alongside founder Alessio Alionço, scaling a horizontal workflow platform that serves Volvo, Capgemini, IBM, Accenture, Visa, Santander, Itaú, and thousands of other SMBs and enterprises across Brazil, the US, and beyond. Before Pipefy, he built and scaled the fundraising function at Hospital Pequeno Príncipe — Brazil's largest children's hospital — raising over $20M and growing a team of 50.In this TJC Operators episode, Felipe shares the brutal sales lesson from Pipefy — why selling everything to everyone is a GTM trap that hides inefficiency through inbound demand, and why outbound exposed it overnight. He walks through the "who would be crazy not to buy this" framework from Seth Shaw (former CRO of Airtable) that reshaped Pipefy's outbound motion, how Camu got from 1–2% to 17% to 33% conversion by progressively narrowing focus to a single ERP (SAP Business One) and one specific workflow (invoice intake), why charging monthly with no strings attached was the cleanest way to validate true product-market fit, the Sean Ellis "very disappointed" PMF survey methodology and how Camu hit high-50s on a V1 product, why saying no to massive enterprise RFPs is a superpower in the early days, how Felipe now manages 68 active opportunities solo by using Claude and AI to automate 50–70% of sales back-office work (CRM updates, ROI calculations, proposal generation, deal-power scoring), the FCA (Fact, Cause, Action) framework Pipefy used to run monthly results meetings and why analyzing wins matters as much as analyzing losses, why "building a plane is different from flying a plane" — and why founders should nail the sales playbook themselves before hiring senior enterprise sellers, the shift from selling software-as-a-service to delivering recurring impact and how risk has moved from buyer to seller in the AI era, and the lesson he most wants Brazilian founders to learn about building credibility before the market gives it to you.Subscribe to The J Curve Insider newsletter for deeper insights and follow Olga on LinkedIn and Instagram.

Most founders treat 'scale' like a switch you flip after raising a round: hire 14 reps, 10x the ad spend, and pray. About half scale too early and burn the runway, while the other half scale too late and get caught by a more aggressive competitor. Almost nobody can tell you, in measurable terms, when they're actually ready.In this episode, Yaniv Bernstein is joined by Mark Roberge - founding CRO at HubSpot (where he scaled the company from $0 to $100M ARR), senior lecturer at Harvard Business School, cofounder of Stage 2 Capital, and author of the new book 'The Science of Scaling'. Mark walks Yaniv through his impressive data-driven framework for scaling that he's spent a decade refining, covering how to objectively define product-market fit, why customer retention is the only honest measure of PMF, and how to instrument a Leading Indicator of Retention you can act on in week one.In this episode, you will:Learn why retention is the only honest measure of product-market fit, and why most founders are flying blind without itDiscover Mark's framework for building a Leading Indicator of Retention (LIR) you can measure in week one, using Slack, HubSpot, and Facebook as worked examplesHear Mark coach Yaniv through Vera's LIR in real time, and pick up a repeatable method for designing one for your own businessLearn the 'Stay/Go/Slow' model for pacing hires and spend post-raise, and why startups should reassess monthly or quarterly rather than locking in an annual planGet Mark's take on why 'paranoid optimism' is the trait that correlates most strongly with founder success, and the link between that trait and founder mental healthTimestamps00:00 Coming Up00:26 On Today's Show: The Science of Scaling01:47 Guest Intro: Mark Roberge02:31 Why Scaling Needs Data04:20 Eric Ries and Product Market Fit06:56 Retention as a North Star10:15 What Makes a Good Leading Indicator?15:00 Case Study: Vera (Yaniv's Startup)17:41 Choosing Frequency and Event23:55 Instrumenting and Unique Value31:12 Blitzscaling and Defining PET34:41 ICP Denominator Rules37:28 Segmenting By Product40:40 Go To Market Fit45:25 Dealing with Revenue-Focused Investor Pressure50:33 The Pace of Scaling56:07 About the Book, The Science of Scaling57:45 Founder Mental Health01:02:28 Closing ThoughtsResources in this episode:Mark Roberge on LinkedIn: https://www.linkedin.com/in/markroberge/‘The Science of Scaling: Using Data to Decide When — and How Fast — to Scale Revenue' by Mark Roberge: https://www.amazon.com/Science-Scaling-Revenue-Mark-Roberge/dp/1394319428Stage 2 Capital (Mark's B2B SaaS-focused venture firm): https://www.stage2.capital/Vera (Yaniv's startup): 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 appFollow us on YouTube for full-video episodes: https://www.youtube.com/@startup-podcastGive 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 and memorable name, go to https://⁠get.tech/tspThis 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/tsp⁠⁠⁠⁠⁠ The Startup Podcast website: https://www.tsp.show/episodes/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/

Surojit spent 14 years at Google building mobile ads into a $100B+ business and then took Coinbase public as Chief Product Officer in 2021. In early 2023, before "agent" was even a word in AI papers, he started Ema in stealth—betting on a future where teams of AI agents would replace the "human glue" inside Fortune 500s.In this episode, Surojit breaks down how a Hitachi deployment across 55,000 employees became Ema's true PMF moment, why he spent the first year obsessed with SOC 2, ISO 42001, and air-gapped architecture before chasing revenue, and why one client just cut their HR team from 1,000 people to 550 by automating 65,000 monthly job changes.Why You Should ListenWhy true PMF is when your average salesperson can sell the product without you in the room.How a single Hitachi deployment unlocked credibility for every Fortune 500 deal that followed.Why a cold email—not a warm intro—turned into Ema's largest partner today.How partnering with PwC and KPMG became a faster wedge into the C-suite than any conference.Keywords startup podcast, startup podcast for founders, product market fit, finding pmf, AI agents, enterprise AI, AI employees, Fortune 500 sales, Surojit Chatterjee, Ema, agentic AI, enterprise softwareChapters00:00:00 Intro00:02:00 Hitachi Was the PMF Moment00:04:10 What Ema Actually Does00:11:48 From Coinbase to a Pre-ChatGPT Bet00:28:48 The Cold Email That Won a Top Partner00:30:52 Small Dinners Beat Massive Conferences00:36:11 The Moment of True Product Market FitSend me a message to let me know what you think!

Superhuman Mail users respond to 72% more emails per hour and save an average of four hours every week — numbers backed by a case study from one of the Big Three strategy consulting firms. Rahul Vohra, CEO at Superhuman Mail, built the world's fastest email engine over three years without launching, held the line until the product was ready, and then productized product-market fit into a repeatable, measurable science. Following Superhuman's acquisition by Grammarly in 2025, Rahul is now steering the company toward a unified AI-native productivity suite spanning email, calendar, tasks, and agents.What you'll learn:The 5-step PMF Engine: how to survey, segment, analyze, implement, and track your way to product-market fit with a numerical scoreWhy you should ignore the not disappointed and most somewhat disappointed users — and which signals actually tell you who to build forHow to use the High Expectation Customer (HXC) framework to narrow your market without changing your productWhy PMF is a moving target and how to defend it against commoditization and copy-cat competitionHow Rahul operates as the editor of the product — using 20 verbatim quotes to push PMs and designers to sharper decisionsKey takeaways:If more than 40% of your users would be very disappointed without your product, you have an initial PMF — and you can measure your way thereChanging your market is faster than changing your product — segmentation alone can jump your PMF score 10 points overnightBuilding for your highest-expectation customer is not the same as building for your ICP — confuse the two, and you'll optimize for the wrong signalCredits:Host: Carlos Gonzalez de VillaumbrosiaGuest: Rahul VohraSocial Links:Find out more about Product School hereFollow our Podcast on TikTok hereFollow Product School on LinkedIn here

Becca Lindquist is Head of Sales at Clay, one of the fastest-growing AI companies to reach $100M+ ARR. She previously helped scale dbt Labs into a category-defining data platform, building and leading high-performing sales teams. Before that, she was an early sales leader at Heap, where she played a key role in scaling the GTM motion. AGENDA: 00:00 Why most sales reps plateau—and when you should leave your company 02:45 Should you jump from SaaS to a hot AI startup right now? 05:30 How long is too long at one company—and what great tenure actually looks like 08:00 How to read a LinkedIn profile like a world-class sales leader 13:30 The biggest hiring mistake sales leaders make (and how to catch it fast) 16:00 Titles vs salary: what actually predicts a great hire 20:00 What early-stage founders must look for in their first sales hires 24:00 How to evaluate AI startups: PMF, retention, and real signals that matter 27:00 Sales comp decoded: quota, OTE, and how to design winning incentives 33:00 How to build a high-performance sales culture (and avoid a toxic one) 41:00 What makes a true champion—and why most reps get this wrong 45:00 Weekly forecasting: how elite sales teams run pipeline reviews 50:00 Are SDRs dead? How AI is really changing outbound sales 52:00 Why every rep owns pipeline—and how top teams generate it 54:30 The future of sales productivity: AI tools, workflows, and what actually works    

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From employee #16 to $1B ARR at Nutanix, then scaling ThoughtSpot to $150M ARR and a $4B+ valuation now building for a world where agents will drive the internet.Sudheesh Nair joins the Neon Show.The internet as we see it today was optimized around human strengths and weaknesses, using algorithms to monetize our greed and fear. But as agents take up more of the internet, that playbook starts to break. We are moving from a web of discovery to an outcome-driven internet, where agents care only about the destination, not the journey.As an operator who has scaled companies, Sudheesh believes sales is a noble profession where there is no middle ground. You are either a hero or a zero. Sales is not a function at the edge of the company, it is the primary job of every employee in a company. When that happens, teams stop acting like mercenaries chasing targets and start behaving like missionaries focused on customer outcomes.Beyond agents, we also discuss building companies and whether there are right or wrong reasons to start. Sudheesh's view is simple. There are no right or wrong reasons, but you have to be brutally honest with yourself about why you are doing it.This episode is one hour of clear thinking on agents, sales, and the realities of company building.00:00 – Trailer01:49 – What % of the internet is agents today?10:25 – How far are we from trillions of agents?12:47 – Why isn't the internet ready for agents?18:31 – Consumer is a tough game19:49 – Selling to enterprises = high value / low risk22:14 – A noble profession with only heroes or zeroes24:41 – Only 3 reasons why people buy anything26:14 – How we got Fortune 500 customers in just 18 months27:28 – The wrong reasons to start a company31:05 – Cursor vs Claude vs Codex34:30 – Do investors prefer failed founders over first-time founders?35:06 – 3 reasons why an enterprise will sign your startup39:52 – PMF has to be proven every day41:21 – What's the play b/w OpenAI, Google, and Anthropic?45:12 – Drivers vs passengers in companies47:17 – The muscles you build as an operator50:45 – Hire one person when you actually need four51:41 – Why is marketing the most in-demand skill?53:50 – Nutanix: from 0 to $1B ARR in 26 quarters54:55 – The hardest choice Nutanix made59:25 – Talent is universal. Opportunities are not01:04:08 – Selling is everyone's job01:05:58 – Passion comes from value creation-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

Most people know what they want. The problem is they keep waiting for certainty that never comes.Oolka founder, Utkrishta Kumar built India's first just-in-time fulfilment network at 27, helped scale Meesho through one of India's biggest social commerce pivots and then left before the IPO. Not because he had to, but because the regret of not starting felt heavier than the risk of failing.In this episode, Avnish and Utkrishta work through the questions early founders actually get stuck on:1. How do I know I'm ready to start up?2. How do you find PMF and is tracking PMF enough?3. How do I build an AI product that ChatGPT can't just replace tomorrow?4. If I've already made money, why does failure still terrify me?5. The conversation lands somewhere honest: you won't see the whole road. 6. You just need to be okay with the fogA new episode of Unstarted - every Thursday00:00  Leaving before the IPO00:56  Introduction: the one question every aspiring founder is asking 01:55  Growing up risk-averse 04:54 Q1: How do you know if starting up is the right move? 07:12 How to build a founder's operating system without an MBA 11:43 Q2: How do I know if I've reached PMF? 13:47 What Oolka does — and why every credit problem is individual 16:51 Why he left Meesho before the IPO — and the fear money doesn't fix 19:58 Q3: How do you build with AI without being replaced tomorrow? 26:49 Final advice: more than 70% never fire the bullet

Ever wonder why smart kids struggle so much with school even when they clearly understand the material? When bright kids freeze, avoid homework, or fall apart under pressure, it's often stress—not ability. Dr. Roseann Capanna-Hodge, founder of Regulation First Parenting™, helps parents calm dysregulation so learning can thrive. Many parents see their smart kids struggle and wonder if it's laziness, ADHD, or lack of effort—but often, the real challenge is a stressed, dysregulated nervous system.In this episode, I'll break down why smart kids struggle so much with school, explain why executive functioning shuts down under stress, and show you how to calm the brain first so your child can focus, follow through, and feel capable again. It's not bad parenting—it's a dysregulated brain.Why do smart kids struggle so much with school?Even gifted children who grasp concepts quickly can find starting, sustaining, or completing tasks overwhelming. This isn't about motivation—it's about executive functioning shutting down under stress. When a smart child's brain perceives threat, fight-or-flight takes over, and problem-solving skills go offline.Key takeaway: Bright kids may freeze or avoid tasks when their nervous system is activated. It's not that they can't do the work like all the other kids; it's that stress has pushed their brain into survival mode.Tip: Observe your child's stress signals rather than assuming defiance. When gifted kids struggle, behavior is communication—not laziness.Real-Life Example: A child who aces tests but struggles with daily homework isn't lazy—they're stressed and need regulation first.How does stress affect gifted students' executive functioning?Smart children often carry “full cups” of stress—academic pressure, social challenges, and sensory overload. When cortisol and adrenaline rise, prefrontal cortex activity drops, making focus, planning, and working memory nearly impossible.Tips:Prioritize calm before teaching new skills.Small, structured steps work better than charts or punishments.Real-Life Example: A first grader may experience a meltdown over a multi-step assignment not because they can't do it, but because their brain is overwhelmed by too much information at once.What are nervous system-friendly strategies for smart kids?You can help gifted kids access their natural abilities by regulating first, then teaching executive functioning skills.Visualize the end goal – Show them what success looks like for each task.Activate muscle memory – Warm-up activities or role-play create confidence.Map out the steps – Break homework or projects into micro-steps after stress is reduced.Tip: Use mind maps for visual learners—breaking a project into smaller bubbles reduces overwhelm.Parent scenario: A high school gifted child with dyslexia suggested a strategy to manage group work on their own, showing executive functioning emerging after nervous system regulation.Want to stay calm when your child pushes every button?Become a Dysregulation Insider VIP and get the FREE Regulation Rescue Kit—your step-by-step guide to stop oppositional behaviors without yelling or giving in.Go to www.drroseann.com/newsletter and grab your kit today.

What does it look like to run the same playbook across five venture funds?That is the bet Ashmeet Sidana has made at Engineering Capital. From Fund One to Fund Five, he has written the first check into founders solving problems with Technical insight.His portfolio includes Rubrik, now a public company, SignalFx which was acquired by Splunk for $1 billion, and CodeRabbit, last valued at $550 million. Ashmeet runs Engineering Capital as a solo GP and the fund has been oversubscribed since Fund One.Ashmeet says that the most common way technical founders fail is by “playing house.” Founders who build beautifully organized systems and clean processes, but don't obsessively seek product market fit. His view is that founders should ruthlessly prioritize finding PMF above everything else.Ashmeet is an investor who has seen enough cycles to know what actually compounds, and is still early-stage enough to care about the details that most people have moved past.00:00 – Trailer01:15 – Where does Engineering Capital place its bets?10:07 – How the VC landscape has evolved11:20 – Are technical founders the norm in AI?16:23 – Why the name Engineering Capital?16:50 – What every VC looks for in a founder21:34 – Why Founders Choose Your Term Sheet26:26 – Rule of 1-2 in-person meetings daily with founders31:42 – Does AI give younger founders an edge?32:59 – Founders must ruthlessly prioritize35:58 – The trap of “playing house”37:40 – PMF can change overnight, Ex: Facebook40:13 – 1 in 10 companies fail due to lack of PMF43:17 – The most valuable skill a founder can have44:19 – Why have a Chief Engineer at a VC firm?45:44 – The job of every CEO is to learn46:09 – Solo founders are much riskier48:15 – An accidental entry into VC49:52 – Solo GP: risks and rewards53:34 – $250M across funds54:43 – Why solo GPs work better in the US58:25 – Where Ashmeet's portfolio companies are located01:00:57 – Be very careful of vanity metrics01:02:15 – Vibe coding will change the face of software01:03:36 – Don't chase trends in how companies are built01:05:58 – $100M ARR is the outcome of a strong package01:06:35 – How affordable is Bay Area for young founders?01:11:32 – AlexNet, not ChatGPT, was the real AI inflection point01:12:57 – US Public Companies Are Down 50% in 40 years-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

Yogi spent 20 years living the nightmare of enterprise accounting. As a senior finance leader at Rubrik, he watched highly paid professionals spend three weeks every month manually wrangling data into spreadsheets—a problem that caused mass burnout and multi-million dollar stock corrections.When ChatGPT launched, Yogi knew the technology was finally ready to solve the problem. In this episode, he breaks down how he left his executive track to found Maxima, how he landed massive enterprises like Scale AI and Rippling as early design partners, and why he managed to raise $41M from top-tier VCs like Kleiner Perkins and Redpoint before he even had a pitch deck.Why You Should ListenHow a 1st-time founder raised an $11M Seed and a $30M Series A in a year.Why replacing accountants with AI is a bigger opportunity than replacing SaaS tools.How to use the "Design Partner Playbook" to secure Fortune 500 customers.Why charging for an MVP creates the friction you actually need to find true PMF.The difference between selling "digital shelves" and selling "folded laundry" in the age of AI.Keywordsstartup podcast, startup podcast for founders, AI in accounting, enterprise SaaS, product market fit, finding pmf, raising seed round, raising series a, B2B sales, design partners00:00:00 Intro00:07:37 Leaving a CFO Track to Become a Founder00:11:52 Raising an $11M Seed Round from Kleiner Perkins00:20:07 The Design Partner Playbook00:22:34 Why You Must Charge Your Early Design Partners00:28:36 The Aha Moment for Product Market Fit00:33:20 Selling "Folded Laundry" Instead of "Digital Shelves"00:36:47 Raising a $30M Series A Pre-EmptivelySend me a message to let me know what you think!

What does it actually take to go from employee to founder — after 8 years inside one of India's greatest startups?In Episode 5 of Unstarted, Avnish Bajaj sits down with Anil Goteti, CEO of Scapia, to talk about the real founder journey — not the highlight reel. From leaving McKinsey after just one year, to carrying a US loan back to India, to building and shutting down his first startup before finding PMF with Scapia.This episode covers:- Why entrepreneurs are made, not born- The Monday Morning Test — knowing when to quit- What 8 years at Flipkart actually teaches you- Why competition never killed a company — customers did- How to know when your product is (and isn't) working- The failure before Scapia and what it taught himIn the end, the destination was always clear and the route was never going to be linear. Finally, the Monday morning test doesn't lie.Unstarted is a podcast by Z47 - by founders, for founders. Whether you've started or you're yet unstarted.YT Chapters0:00 - Introduction & What is Unstarted1:45 - Meet Anil Goteti — IIT, McKinsey, Flipkart & Scapia4:00 - Are Entrepreneurs Born or Made?7:30 - The Kid Who Wanted a Product in Every Indian's Hand11:00 - IIT Electrical vs Computer Science — The First Detour14:00 - Leaving McKinsey After 1 Year: "I Want to Be the King"18:30 - Joining Flipkart - Taking One Notch of Risk22:00 - 8 Years at Flipkart: The Best Projects & Lessons31:00 - The Monday Morning Test35:30 - Who Inspires Anil Goteti?39:00 - How Do You Know When Your Product Is Working?45:00 - Competition Never Killed Anyone - Customers Did49:00 - The Failed Startup Before Scapia54:00 - What's Next: Building Scapia

//The Wire//1900Z March 25, 2026// //ROUTINE// //BLUF: WAR ESCALATES AGAIN IN THE MIDDLE EAST AS ISRAEL AND IRAN BEGIN MORE ROUTINE TARGETING OF EACH OTHERS NUCLEAR POWER PLANTS. MULTIPLE FPV DRONE ATTACKS REPORTED IN BAGHDAD. US ARMY RAISES ENLISTMENT AGE TO 42 FOR NEW RECRUITS.// -----BEGIN TEARLINE-----  -International Events-Middle East: Multiple escalations of the conflict took place overnight. Following Israeli/American bombing yesterday, Iranian forces retaliated by striking multiple US bases/positions in Kuwait. In Israel, strikes were also reported at the Orot Rabin Power Plant in Hadera, however the munition appears to have missed the generation facilities by a narrow margin. Another Iranian strike targeted fuel tanks at Kuwait International Airport overnight, and similar strikes were reported in Tel Aviv which resulted in unknown damage.Iraq: Multiple significant events occurred overnight as the PMF begins more deliberate offensive operations. Yesterday morning, the United States conducted airstrikes at the personal residences of PMF leadership in Al Habbaniya. This strike killed Saad Al Baiji (an operations chief), and subsequently resulted in an intensification of targeting efforts on American positions throughout Baghdad. As a result of this targeting of PMF leadership, Iraqi Prime ​Minister ‌Mohammed ⁠Shia Al Sudani has authorized PMF militia groups to retaliate against American forces.Analyst Comment: This is a major escalation that could effectively open up another front in the war. The Popular Mobilization Forces (PMF) are a semi-autonomous, highly-organized militia group that serves as one of Iran's major proxy groups outside their own borders. Technically, they are linked to the Iraqi government, but in practice they mostly just do what they want while being supplied by the Iranians. The official Iraqi government stating that they will let them off leash (while not entirely surprising) is in effect a return to GWOT era, but this time the Iraqi government is openly endorsing their attacks on Americans. So in effect, battle lines are being drawn and the Iraqis are now taking the side of Iran. Depending on how kinetic PMF operations become, American forces may face more contested airspace over not just Iran, but Iraq as well.Otherwise, this week has witnessed an escalation of the war as FPV drone attacks have become more commonly carried out at Camp Victory by insurgents targeting American forces. In a video released yesterday evening, one HH-60M helicopter was targeted, along with a Sentinel radar array.Analyst Comment: The success of these attacks is not known, but the Iraqi militias conducting these attacks have significantly improved their targeting efforts with lessons learned from Ukraine, or probably more accurately...Russia. One of the FPV drones recorded the other drone attacking the radar site, before flying off to find another target. However, while searching for other targets of opportunity, the idiots accidentally targeted a MEDEVAC helicopter, which is evidenced by the video being edited to blur out the giant Red Cross painted on the side of the aircraft. After reviewing the tape, the militia group probably realized that makes them look bad, so they blurred the footage themselves.Kuwait: Civil Defense authorities have begun producing informational videos for the general public, regarding what to do in the event of a nuclear incident at a power plant in a neighboring country, such as if Israeli/American forces were to target the active reactor building at Bushehr Nuclear Power Plant.Analyst Comment: All eyes are on Bushehr. The Israelis dropped munitions in the parking lot a few days ago, probably as a warning, but in retaliation the Iranians hit the residential buildings housing scientists at Dimona yesterday, and this morning they hit an unknown target immediately adjacent to the Hadera plant.

Live from the Vercel recording studio, Lindsey Simon (VP Engineering @ Vercel) joins us to deconstruct the evolution of management craft and career growth strategies! We dissect the practice of live all-hands demos as a tool for context, accountability and inspiration. Plus, Lindsey's "vote with your wallet" framework for career strategy, how Lindsey's open source project inspired him to apply to Vercel, and why the most effective VPs are building hobby projects to maintain AI competency and empathy for non-technical users.   ABOUT LINDSEY SIMON Lindsey Simon is VP of Engineering at Vercel. Making the Web better has been his lifelong career ambition. Prior to Vercel, Lindsey spent seven years at Google, where he helped launch App Engine as an original core team member, and worked as a tech lead on the Google Translate and Web Performance teams. Lindsey has lived in San Francisco for the past 15 years, and his creative hobbies (beyond coding) include writing music and hunting for wild mushrooms.   This episode is brought to you by xMatters! xMatters automates the entire incident lifecycle with their purpose-built AI powered workflow, giving your team the context they need to stop disruptions before they start and minimize resolution times. Head over to xmatters.com to learn more!   SHOW NOTES: The evolution of Vercel's all hands to demo days: using live show-and-tell to maintain context and inspire the team (2:4p) Accountability for what's real: Why live visual demos help engineering teams with real-time workflow adjustments (4:36) Strategies for creating a successful live visual demo without over-rehearsing (6:20) Lindsey's career inflection point: Navigating the transition from a large ecosystem at Salesforce to a mission-driven startup (10:08) Career advice: Vote with your wallet and go somewhere with pre-existing PMF that feeds your ambition (12:33) The "Janitor" Mindset: Why prioritizing the company's mission over a specific job title can lead to unique opportunities (14:36) How Lindsey's open source hobby project led to a code-first interaction with @ Vercel (19:17) Vercel's "Dig Deep" value: Breaking down the company culture and the importance of technical support for developers (21:26) Standing out in the interview process: Why managers must bring a strong "Point of View" on what a company should do differently (23:51). The Swiss Army Knife Manager: Why today's leaders must also be salespeople, PMs, and customer support engineers (24:46). The death of pure "people management": Re-centering on the IC craft and why managers must maintain AI competency (26:12). Adopting better IC skills: Building hobby projects for non-technical users to maintain empathy for the user experience (28:33) Management principles that remain true today (32:54) Combatting imposter syndrome: Building trust by being vulnerable and learning alongside your team (36:45). Interviewing trends: Assessing how candidates operate with and without AI tools (38:05). The return of "In Real Life" work: Why the Bay Area culture is refocusing on "sweating the details" in person (39:15) Rapid fire questions (41:33)   This episode wouldn't have been possible without the help of our incredible production team: Patrick Gallagher - Producer & Co-Host Jerry Li - Co-Host Noah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/ Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/ Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Pour l'épisode de cette semaine, je reçois Jean de Rauglaudre, le CEO de Collective.Collective est aujourd'hui une solution de sourcing et de recrutement dopée à l'IA, permettant aux recruteurs d'identifier les meilleurs talents — freelances comme CDI — grâce à des agents intelligents.Jean est déjà venu deux fois sur le podcast, et dans cet épisode, on se concentre sur l'évolution récente de Collective : d'une marketplace de freelances à un produit SaaS centré sur l'IA, avec une croissance très rapide à la clé.Au cours de cet épisode, nous avons parlé :des différents pivots opérés par Collective et de leur passage d'un modèle service à un pur SaaSde la recherche du Product Market Fit et des signaux concrets qui montrent qu'on y estdu lancement de leur agent IA “Sherlock” et de son rôle dans leur accélérationde leur croissance impressionnante (de 30k à 1M d'ARR en un an)de leur stratégie go-to-market entre inbound, outbound et influencede leur vision du futur du SaaS à l'ère de l'IA et de l'agentiquede leurs réflexions sur le pricing dans un monde post-SaaS traditionnelet enfin de leur ambition de devenir un acteur globalUn épisode très concret sur les pivots, le PMF et la nouvelle génération de produits SaaS propulsés par l'IA.Vous pouvez suivre Jean sur LinkedIn.Bonne écoute !Mentionnés pendant l'épisode :L'épisode #127 avec Jean de RauglaudreInside eFounders avec Jean de RauglaudrePour soutenir SaaS Connection en 1 minute⏱ (et 2 secondes) :Abonnez-vous à SaaS Connection sur votre plateforme préférée pour ne rater aucun épisode

What happens when you spend your whole career chasing external validation and it still isn't enough?Romita Mazumdar is the Founder and CEO of Foxtale, one of India's fastest-growing D2C beauty brands. But this conversation isn't about the business. It's about the years before it: the banking desk that made her feel seen for the wrong reasons, the VC firm where she was suddenly invisible, and the team dinner where someone asked a question she couldn't answer the expected way.In this episode, Avnish Bajaj and Romita attempt to answer: 1. When do you actually know it's time to leave? 2. How do you find PMF when you don't trust your own lens? 3. What does it mean to build for yourself, not for validation? 4. How do you know you have what it takes?5. What they land on: you either need self-belief, passion, or obsession. One 6. of the three. A hundred percent. That's the bar.Chapters00:00 The moment I decided to build a $100M consumer brand02:10 Overachiever mindset, validation & a confusing career path05:45 Losing confidence in VC & not feeling like I belonged09:05 Where my ambition really came from12:20 The turning point that made me choose entrepreneurship15:05 Q1: When do you know it's time to leave your job and start up?20:40 Q2: How do you know you've found product–market fit?26:50 Q3: How do you deal with conflict when working with family / co-founders?30:10 Company first: the rule I use to make hard decisions35:10  What ambition, duty & entrepreneurship really meanFollow Z47Website - https://www.z47.com/Instagram - https://www.instagram.com/z47.vc/LinkedIn - https://www.linkedin.com/company/z47-vc/

Ben Choi has spent three decades across the technology ecosystem—as a product leader, founder, and venture investor—and today serves as a senior leader at Next Legacy Partners, where he helps oversee $3.5B+ invested across premier venture capital firms and early-stage startups.In this episode of Investing in Integrity, our host Ross Overline and Ben navigate the intersection of venture capital, philanthropy, and moral leadership. Ben shares how Next Legacy's flagship model is designed to multiply capital—and then give it away.From there, the conversation goes deeper than mechanics. Ben outlines the values that shaped his leadership and why generosity is often driven not by one motivation, but by the shared joy of impact beyond yourself.Finally, Ross and Ben wrestle openly with capitalism—how it's the best economic system ever tested at scale, it can still evolve to be even better, and what responsibility future finance leaders carry to make that a reality.Whether you're a student trying to define success or a senior leader shaping institutions, this episode is a masterclass in using capital with clarity, humility, and purpose.Meet Ben ChoiBen Choi is a Managing Partner at Next Legacy. He manages $3.5B+ in investments with premier venture capital firms and directly into early-stage startups. His venture track record includes pre-PMF investments in Marketo (acquired for $4.75B) and CourseHero (last valued at $3.6B). He previously ran product for Adobe Creative Cloud offerings and founded CoffeeTable, raising venture financing before selling the company.Ben studied Computer Science at Harvard University and earned his MBA from Columbia Business School. He lives in Los Altos with his wife, Lydia, their three sons, and a ball python.

Turbopuffer came out of a reading app.In 2022, Simon was helping his friends at Readwise scale their infra for a highly requested feature: article recommendations and semantic search. Readwise was paying ~$5k/month for their relational database and vector search would cost ~$20k/month making the feature too expensive to ship. In 2023 after mulling over the problem from Readwise, Simon decided he wanted to “build a search engine” which became Turbopuffer.We discuss:• Simon's path: Denmark → Shopify infra for nearly a decade → “angel engineering” across startups like Readwise, Replicate, and Causal → turbopuffer almost accidentally becoming a company • The Readwise origin story: building an early recommendation engine right after the ChatGPT moment, seeing it work, then realizing it would cost ~$30k/month for a company spending ~$5k/month total on infra and getting obsessed with fixing that cost structure • Why turbopuffer is “a search engine for unstructured data”: Simon's belief that models can learn to reason, but can't compress the world's knowledge into a few terabytes of weights, so they need to connect to systems that hold truth in full fidelity • The three ingredients for building a great database company: a new workload, a new storage architecture, and the ability to eventually support every query plan customers will want on their data • The architecture bet behind turbopuffer: going all in on object storage and NVMe, avoiding a traditional consensus layer, and building around the cloud primitives that only became possible in the last few years • Why Simon hated operating Elasticsearch at Shopify: years of painful on-call experience shaped his obsession with simplicity, performance, and eliminating state spread across multiple systems • The Cursor story: launching turbopuffer as a scrappy side project, getting an email from Cursor the next day, flying out after a 4am call, and helping cut Cursor's costs by 95% while fixing their per-user economics • The Notion story: buying dark fiber, tuning TCP windows, and eating cross-cloud costs because Simon refused to compromise on architecture just to close a deal faster • Why AI changes the build-vs-buy equation: it's less about whether a company can build search infra internally, and more about whether they have time especially if an external team can feel like an extension of their own • Why RAG isn't dead: coding companies still rely heavily on search, and Simon sees hybrid retrieval semantic, text, regex, SQL-style patterns becoming more important, not less • How agentic workloads are changing search: the old pattern was one retrieval call up front; the new pattern is one agent firing many parallel queries at once, turning search into a highly concurrent tool call • Why turbopuffer is reducing query pricing: agentic systems are dramatically increasing query volume, and Simon expects retrieval infra to adapt to huge bursts of concurrent search rather than a small number of carefully chosen calls • The philosophy of “playing with open cards”: Simon's habit of being radically honest with investors, including telling Lachy Groom he'd return the money if turbopuffer didn't hit PMF by year-end • The “P99 engineer”: Simon's framework for building a talent-dense company, rejecting by default unless someone on the team feels strongly enough to fight for the candidate —Simon Hørup Eskildsen• LinkedIn: https://www.linkedin.com/in/sirupsen• X: https://x.com/Sirupsen• https://sirupsen.com/aboutturbopuffer• https://turbopuffer.com/Full Video PodTimestamps00:00:00 The PMF promise to Lachy Groom00:00:25 Intro and Simon's background00:02:19 What turbopuffer actually is00:06:26 Shopify, Elasticsearch, and the pain behind the company00:10:07 The Readwise experiment that sparked turbopuffer00:12:00 The insight Simon couldn't stop thinking about00:17:00 S3 consistency, NVMe, and the architecture bet00:20:12 The Notion story: latency, dark fiber, and conviction00:25:03 Build vs. buy in the age of AI00:26:00 The Cursor story: early launch to breakout customer00:29:00 Why code search still matters00:32:00 Search in the age of agents00:34:22 Pricing turbopuffer in the AI era00:38:17 Why Simon chose Lachy Groom00:41:28 Becoming a founder on purpose00:44:00 The “P99 engineer” philosophy00:49:30 Bending software to your will00:51:13 The future of turbopuffer00:57:05 Simon's tea obsession00:59:03 Tea kits, X Live, and P99 LiveTranscriptSimon Hørup Eskildsen: I don't think I've said this publicly before, but I just called Lockey and was like, local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you. But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working.So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people. We're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards. Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before.Alessio: Hey everyone, welcome to the Leading Space podcast. This is Celesio Pando, Colonel Laz, and I'm joined by Swix, editor of Leading Space.swyx: Hello. Hello, uh, we're still, uh, recording in the Ker studio for the first time. Very excited. And today we are joined by Simon Eski. Of Turbo Farer welcome.Simon Hørup Eskildsen: Thank you so much for having me.swyx: Turbo Farer has like really gone on a huge tear, and I, I do have to mention that like you're one of, you're not my newest member of the Danish AHU Mafia, where like there's a lot of legendary programmers that have come out of it, like, uh, beyond Trotro, Rasmus, lado Berg and the V eight team and, and Google Maps team.Uh, you're mostly a Canadian now, but isn't that interesting? There's so many, so much like strong Danish presence.Simon Hørup Eskildsen: Yeah, I was writing a post, um, not that long ago about sort of the influences. So I grew up in Denmark, right? I left, I left when, when I was 18 to go to Canada to, to work at Shopify. Um, and so I, like, I've, I would still say that I feel more Danish than, than Canadian.This is also the weird accent. I can't say th because it, this is like, I don't, you know, my wife is also Canadian, um, and I think. I think like one of the things in, in Denmark is just like, there's just such a ruthless pragmatism and there's also a big focus on just aesthetics. Like, they're like very, people really care about like where, what things look like.Um, and like Canada has a lot of attributes, US has, has a lot of attributes, but I think there's been lots of the great things to carry. I don't know what's in the water in Ahu though. Um, and I don't know that I could be considered part of the Mafi mafia quite yet, uh, compared to the phenomenal individuals we just mentioned.Barra OV is also, uh, Danish Canadian. Okay. Yeah. I don't know where he lives now, but, and he's the PHP.swyx: Yeah. And obviously Toby German, but moved to Canada as well. Yes. Like this is like import that, uh, that, that is an interesting, um, talent move.Alessio: I think. I would love to get from you. Definition of Turbo puffer, because I think you could be a Vector db, which is maybe a bad word now in some circles, you could be a search engine.It's like, let, let's just start there and then we'll maybe run through the history of how you got to this point.Simon Hørup Eskildsen: For sure. Yeah. So Turbo Puffer is at this point in time, a search engine, right? We do full text search and we do vector search, and that's really what we're specialized in. If you're trying to do much more than that, like then this might not be the right place yet, but Turbo Buffer is all about search.The other way that I think about it is that we can take all of the world's knowledge, all of the exabytes and exabytes of data that there is, and we can use those tokens to train a model, but we can't compress all of that into a few terabytes of weights, right? Compress into a few terabytes of weights, how to reason with the world, how to make sense of the knowledge.But we have to somehow connect it to something externally that actually holds that like in full fidelity and truth. Um, and that's the thing that we intend to become. Right? That's like a very holier than now kind of phrasing, right? But being the search engine for unstructured, unstructured data is the focus of turbo puffer at this point in time.Alessio: And let's break down. So people might say, well, didn't Elasticsearch already do this? And then some other people might say, is this search on my data, is this like closer to rag than to like a xr, like a public search thing? Like how, how do you segment like the different types of search?Simon Hørup Eskildsen: The way that I generally think about this is like, there's a lot of database companies and I think if you wanna build a really big database company, sort of, you need a couple of ingredients to be in the air.We don't, which only happens roughly every 15 years. You need a new workload. You basically need the ambition that every single company on earth is gonna have data in your database. Multiple times you look at a company like Oracle, right? You will, like, I don't think you can find a company on earth with a digital presence that it not, doesn't somehow have some data in an Oracle database.Right? And I think at this point, that's also true for Snowflake and Databricks, right? 15 years later it's, or even more than that, there's not a company on earth that doesn't, in. Or directly is consuming Snowflake or, or Databricks or any of the big analytics databases. Um, and I think we're in that kind of moment now, right?I don't think you're gonna find a company over the next few years that doesn't directly or indirectly, um, have all their data available for, for search and connect it to ai. So you need that new workload, like you need something to be happening where there's a new workload that causes that to happen, and that new workload is connecting very large amounts of data to ai.The second thing you need. The second condition to build a big database company is that you need some new underlying change in the storage architecture that is not possible from the databases that have come before you. If you look at Snowflake and Databricks, right, commoditized, like massive fleet of HDDs, like that was not possible in it.It just wasn't in the air in the nineties, right? So you just didn't, we just didn't build these systems. S3 and and and so on was not around. And I think the architecture that is now possible that wasn't possible 15 years ago is to go all in on NVME SSDs. It requires a particular type of architecture for the database that.It's difficult to retrofit onto the databases that are already there, including the ones you just mentioned. The second thing is to go all in on OIC storage, more so than we could have done 15 years ago. Like we don't have a consensus layer, we don't really have anything. In fact, you could turn off all the servers that Turbo Buffer has, and we would not lose any data because we have all completely all in on OIC storage.And this means that our architecture is just so simple. So that's the second condition, right? First being a new workload. That means that every company on earth, either indirectly or directly, is using your database. Second being, there's some new storage architecture. That means that the, the companies that have come before you can do what you're doing.I think the third thing you need to do to build a big database company is that over time you have to implement more or less every Cory plan on the data. What that means is that you. You can't just get stuck in, like, this is the one thing that a database does. It has to be ever evolving because when someone has data in the database, they over time expect to be able to ask it more or less every question.So you have to do that to get the storage architecture to the limit of what, what it's capable of. Those are the three conditions.swyx: I just wanted to get a little bit of like the motivation, right? Like, so you left Shopify, you're like principal, engineer, infra guy. Um, you also head of kernel labs, uh, inside of Shopify, right?And then you consulted for read wise and that it kind of gave you that, that idea. I just wanted you to tell that story. Um, maybe I, you've told it before, but, uh, just introduce the, the. People to like the, the new workload, the sort of aha moment for turbo PufferSimon Hørup Eskildsen: For sure. So yeah, I spent almost a decade at Shopify.I was on the infrastructure team, um, from the fairly, fairly early days around 2013. Um, at the time it felt like it was growing so quickly and everything, all the metrics were, you know, doubling year on year compared to the, what companies are contending with today. It's very cute in growth. I feel like lot some companies are seeing that month over month.Um, of course. Shopify compound has been compounding for a very long time now, but I spent a decade doing that and the majority of that was just make sure the site is up today and make sure it's up a year from now. And a lot of that was really just the, um, you know, uh, the Kardashians would drive very, very large amounts of, of data to, to uh, to Shopify as they were rotating through all the merch and building out their businesses.And we just needed to make sure we could handle that. Right. And sometimes these were events, a million requests per second. And so, you know, we, we had our own data centers back in the day and we were moving to the cloud and there was so much sharding work and all of that that we were doing. So I spent a decade just scaling databases ‘cause that's fundamentally what's the most difficult thing to scale about these sites.The database that was the most difficult for me to scale during that time, and that was the most aggravating to be on call for, was elastic search. It was very, very difficult to deal with. And I saw a lot of projects that were just being held back in their ambition by using it.swyx: And I mean, self-hosted.Self-hosted. ‘causeSimon Hørup Eskildsen: it's, yeah, and it commercial, this is like 2015, right? So it's like a very particular vintage. Right. It's probably better at a lot of these things now. Um, it was difficult to contend with and I'm just like, I just think about it. It's an inverted index. It should be good at these kinds of queries and do all of this.And it was, we, we often couldn't get it to do exactly what we needed to do or basically get lucine to do, like expose lucine raw to, to, to what we needed to do. Um, so that was like. Just something that we did on the side and just panic scaled when we needed to, but not a particular focus of mine. So I left, and when I left, I, um, wasn't sure exactly what I wanted to do.I mean, it spent like a decade inside of the same company. I'd like grown up there. I started working there when I was 18.swyx: You only do Rails?Simon Hørup Eskildsen: Yeah. I mean, yeah. Rails. And he's a Rails guy. Uh, love Rails. So good. Um,Alessio: we all wish we could still work in Rails.swyx: I know know. I know, but some, I tried learning Ruby.It's just too much, like too many options to do the same thing. It's, that's my, I I know there's a, there's a way to do it.Simon Hørup Eskildsen: I love it. I don't know that I would use it now, like given cloud code and, and, and cursor and everything, but, um, um, but still it, like if I'm just sitting down and writing a teal code, that's how I think.But anyway, I left and I wasn't, I talked to a couple companies and I was like, I don't. I need to see a little bit more of the world here to know what I'm gonna like focus on next. Um, and so what I decided is like I was gonna, I called it like angel engineering, where I just hopped around in my friend's companies in three months increments and just helped them out with something.Right. And, and just vested a bit of equity and solved some interesting infrastructure problem. So I worked with a bunch of companies at the time, um, read Wise was one of them. Replicate was one of them. Um, causal, I dunno if you've tried this, it's like a, it's a spreadsheet engine Yeah. Where you can do distribution.They sold recently. Yeah. Um, we've been, we used that in fp and a at, um, at Turbo Puffer. Um, so a bunch of companies like this and it was super fun. And so we're the Chachi bt moment happened, I was with. With read Wise for a stint, we were preparing for the reader launch, right? Which is where you, you cue articles and read them later.And I was just getting their Postgres up to snuff, like, which basically boils down to tuning, auto vacuum. So I was doing that and then this happened and we were like, oh, maybe we should build a little recommendation engine and some features to try to hook in the lms. They were not that good yet, but it was clear there was something there.And so I built a small recommendation engine just, okay, let's take the articles that you've recently read, right? Like embed all the articles and then do recommendations. It was good enough that when I ran it on one of the co-founders of Rey's, like I found out that I got articles about, about having a child.I'm like, oh my God, I didn't, I, I didn't know that, that they were having a child. I wasn't sure what to do with that information, but the recommendation engine was good enough that it was suggesting articles, um, about that. And so there was, there was recommendations and uh, it actually worked really well.But this was a company that was spending maybe five grand a month in total on all their infrastructure and. When I did the napkin math on running the embeddings of all the articles, putting them into a vector index, putting it in prod, it's gonna be like 30 grand a month. That just wasn't tenable. Right?Like Read Wise is a proudly bootstrapped company and it's paying 30 grand for infrastructure for one feature versus five. It just wasn't tenable. So sort of in the bucket of this is useful, it's pretty good, but let us, let's return to it when the costs come down.swyx: Did you say it grows by feature? So for five to 30 is by the number of, like, what's the, what's the Scaling factor scale?It scales by the number of articles that you embed.Simon Hørup Eskildsen: It does, but what I meant by that is like five grand for like all of the other, like the Heroku, dinos, Postgres, like all the other, and this then storage is 30. Yeah. And then like 30 grand for one feature. Right. Which is like, what other articles are related to this one.Um, so it was just too much right to, to power everything. Their budget would've been maybe a few thousand dollars, which still would've been a lot. And so we put it in a bucket of, okay, we're gonna do that later. We'll wait, we will wait for the cost to come down. And that haunted me. I couldn't stop thinking about it.I was like, okay, there's clearly some latent demand here. If the cost had been a 10th, we would've shipped it and. This was really the only data point that I had. Right. I didn't, I, I didn't, I didn't go out and talk to anyone else. It was just so I started reading Right. I couldn't, I couldn't help myself.Like I didn't know what like a vector index is. I, I generally barely do about how to generate the vectors. There was a lot of hype about, this is a early 2023. There was a lot of hype about vector databases. There were raising a lot of money and it's like, I really didn't know anything about it. It's like, you know, trying these little models, fine tuning them.Like I was just trying to get sort of a lay of the land. So I just sat down. I have this. A GitHub repository called Napkin Math. And on napkin math, there's just, um, rows of like, oh, this is how much bandwidth. Like this is how many, you know, you can do 25 gigabytes per second on average to dram. You can do, you know, five gigabytes per second of rights to an SSD, blah blah.All of these numbers, right? And S3, how many you could do per, how much bandwidth can you drive per connection? I was just sitting down, I was like, why hasn't anyone build a database where you just put everything on O storage and then you puff it into NVME when you use the data and you puff it into dram if you're, if you're querying it alive, it's just like, this seems fairly obvious and you, the only real downside to that is that if you go all in on o storage, every right will take a couple hundred milliseconds of latency, but from there it's really all upside, right?You do the first go, it takes half a second. And it sort of occurred to me as like, well. The architecture is really good for that. It's really good for AB storage, it's really good for nvm ESSD. It's, well, you just couldn't have done that 10 years ago. Back to what we were talking about before. You really have to build a database where you have as few round trips as possible, right?This is how CPUs work today. It's how NVM E SSDs work. It's how as, um, as three works that you want to have a very large amount of outstanding requests, right? Like basically go to S3, do like that thousand requests to ask for data in one round trip. Wait for that. Get that, like, make a new decision. Do it again, and try to do that maybe a maximum of three times.But no databases were designed that way within NVME as is ds. You can drive like within, you know, within a very low multiple of DRAM bandwidth if you use it that way. And same with S3, right? You can fully max out the network card, which generally is not maxed out. You get very, like, very, very good bandwidth.And, but no one had built a database like that. So I was like, okay, well can't you just, you know, take all the vectors right? And plot them in the proverbial coordinate system. Get the clusters, put a file on S3 called clusters, do json, and then put another file for every cluster, you know, cluster one, do js O cluster two, do js ON you know that like it's two round trips, right?So you get the clusters, you find the closest clusters, and then you download the cluster files like the, the closest end. And you could do this in two round trips.swyx: You were nearest neighbors locally.Simon Hørup Eskildsen: Yes. Yes. And then, and you would build this, this file, right? It's just like ultra simplistic, but it's not a far shot from what the first version of Turbo Buffer was.Why hasn't anyone done thatAlessio: in that moment? From a workload perspective, you're thinking this is gonna be like a read heavy thing because they're doing recommend. Like is the fact that like writes are so expensive now? Oh, with ai you're actually not writing that much.Simon Hørup Eskildsen: At that point I hadn't really thought too much about, well no actually it was always clear to me that there was gonna be a lot of rights because at Shopify, the search clusters were doing, you know, I don't know, tens or hundreds of crew QPS, right?‘cause you just have to have a human sit and type in. But we did, you know, I don't know how many updates there were per second. I'm sure it was in the millions, right into the cluster. So I always knew there was like a 10 to 100 ratio on the read write. In the read wise use case. It's, um, even, even in the read wise use case, there'd probably be a lot fewer reads than writes, right?There's just a lot of churn on the amount of stuff that was going through versus the amount of queries. Um, I wasn't thinking too much about that. I was mostly just thinking about what's the fundamentally cheapest way to build a database in the cloud today using the primitives that you have available.And this is it, right? You just, now you have one machine and you know, let's say you have a terabyte of data in S3, you paid the $200 a month for that, and then maybe five to 10% of that data and needs to be an NV ME SSDs and less than that in dram. Well. You're paying very, very little to inflate the data.swyx: By the way, when you say no one else has done that, uh, would you consider Neon, uh, to be on a similar path in terms of being sort of S3 first and, uh, separating the compute and storage?Simon Hørup Eskildsen: Yeah, I think what I meant with that is, uh, just build a completely new database. I don't know if we were the first, like it was very much, it was, I mean, I, I hadn't, I just looked at the napkin math and was like, this seems really obvious.So I'm sure like a hundred people came up with it at the same time. Like the light bulb and every invention ever. Right. It was just in the air. I think Neon Neon was, was first to it. And they're trying, they're retrofitted onto Postgres, right? And then they built this whole architecture where you have, you have it in memory and then you sort of.You know, m map back to S3. And I think that was very novel at the time to do it for, for all LTP, but I hadn't seen a database that was truly all in, right. Not retrofitting it. The database felt built purely for this no consensus layer. Even using compare and swap on optic storage to do consensus. I hadn't seen anyone go that all in.And I, I mean, there, there, I'm sure there was someone that did that before us. I don't know. I was just looking at the napkin mathswyx: and, and when you say consensus layer, uh, are you strongly relying on S3 Strong consistency? You are. Okay.SoSimon Hørup Eskildsen: that is your consensus layer. It, it is the consistency layer. And I think also, like, this is something that most people don't realize, but S3 only became consistent in December of 2020.swyx: I remember this coming out during COVID and like people were like, oh, like, it was like, uh, it was just like a free upgrade.Simon Hørup Eskildsen: Yeah.swyx: They were just, they just announced it. We saw consistency guys and like, okay, cool.Simon Hørup Eskildsen: And I'm sure that they just, they probably had it in prod for a while and they're just like, it's done right.And people were like, okay, cool. But. That's a big moment, right? Like nv, ME SSDs, were also not in the cloud until around 2017, right? So you just sort of had like 2017 nv, ME SSDs, and people were like, okay, cool. There's like one skew that does this, whatever, right? Takes a few years. And then the second thing is like S3 becomes consistent in 2020.So now it means you don't have to have this like big foundation DB or like zookeeper or whatever sitting there contending with the keys, which is how. You know, that's what Snowflake and others have do so muchswyx: for goneSimon Hørup Eskildsen: Exactly. Just gone. Right? And so just push to the, you know, whatever, how many hundreds of people they have working on S3 solved and then compare and swap was not in S3 at this point in time,swyx: by the way.Uh, I don't know what that is, so maybe you wanna explain. Yes. Yeah.Simon Hørup Eskildsen: Yes. So, um, what Compare and swap is, is basically, you can imagine that if you have a database, it might be really nice to have a file called metadata json. And metadata JSON could say things like, Hey, these keys are here and this file means that, and there's lots of metadata that you have to operate in the database, right?But that's the simplest way to do it. So now you have might, you might have a lot of servers that wanna change the metadata. They might have written a file and want the metadata to contain that file. But you have a hundred nodes that are trying to contend with this metadata that JSON well, what compare and Swap allows you to do is basically just you download the file, you make the modifications, and then you write it only if it hasn't changed.While you did the modification and if not you retry. Right? Should just have this retry loops. Now you can imagine if you have a hundred nodes doing that, it's gonna be really slow, but it will converge over time. That primitive was not available in S3. It wasn't available in S3 until late 2024, but it was available in GCP.The real story of this is certainly not that I sat down and like bake brained it. I was like, okay, we're gonna start on GCS S3 is gonna get it later. Like it was really not that we started, we got really lucky, like we started on GCP and we started on GCP because tur um, Shopify ran on GCP. And so that was the platform I was most available with.Right. Um, and I knew the Canadian team there ‘cause I'd worked with them at Shopify and so it was natural for us to start there. And so when we started building the database, we're like, oh yeah, we have to build a, we really thought we had to build a consensus layer, like have a zookeeper or something to do this.But then we discovered the compare and swap. It's like, oh, we can kick the can. Like we'll just do metadata r json and just, it's fine. It's probably fine. Um, and we just kept kicking the can until we had very, very strong conviction in the idea. Um, and then we kind of just hinged the company on the fact that S3 probably was gonna get this, it started getting really painful in like mid 2024.‘cause we were closing deals with, um, um, notion actually that was running in AWS and we're like, trust us. You, you really want us to run this in GCP? And they're like, no, I don't know about that. Like, we're running everything in AWS and the latency across the cloud were so big and we had so much conviction that we bought like, you know, dark fiber between the AWS regions in, in Oregon, like in the InterExchange and GCP is like, we've never seen a startup like do like, what's going on here?And we're just like, no, we don't wanna do this. We were tuning like TCP windows, like everything to get the latency down ‘cause we had so high conviction in not doing like a, a metadata layer on S3. So those were the three conditions, right? Compare and swap. To do metadata, which wasn't in S3 until late 2024 S3 being consistent, which didn't happen until December, 2020.Uh, 2020. And then NVMe ssd, which didn't end in the cloud until 2017.swyx: I mean, in some ways, like a very big like cloud success story that like you were able to like, uh, put this all together, but also doing things like doing, uh, bind our favor. That that actually is something I've never heard.Simon Hørup Eskildsen: I mean, it's very common when you're a big company, right?You're like connecting your own like data center or whatever. But it's like, it was uniquely just a pain with notion because the, um, the org, like most of the, like if you're buying in Ashburn, Virginia, right? Like US East, the Google, like the GCP and, and AWS data centers are like within a millisecond on, on each other, on the public exchanges.But in Oregon uniquely, the GCP data center sits like a couple hundred kilometers, like east of Portland and the AWS region sits in Portland, but the network exchange they go through is through Seattle. So it's like a full, like 14 milliseconds or something like that. And so anyway, yeah. It's, it's, so we were like, okay, we can't, we have to go through an exchange in Portland.Yeah. Andswyx: you'd rather do this than like run your zookeeper and likeSimon Hørup Eskildsen: Yes. Way rather. It doesn't have state, I don't want state and two systems. Um, and I think all that is just informed by Justine, my co-founder and I had just been on call for so long. And the worst outages are the ones where you have state in multiple places that's not syncing up.So it really came from, from a a, like just a, a very pure source of pain, of just imagining what we would be Okay. Being woken up at 3:00 AM about and having something in zookeeper was not one of them.swyx: You, you're talking to like a notion or something. Do they care or do they just, theySimon Hørup Eskildsen: just, they care about latency.swyx: They latency cost. That's it.Simon Hørup Eskildsen: They just cared about latency. Right. And we just absorbed the cost. We're just like, we have high conviction in this. At some point we can move them to AWS. Right. And so we just, we, we'll buy the fiber, it doesn't matter. Right. Um, and it's like $5,000. Usually when you buy fiber, you buy like multiple lines.And we're like, we can only afford one, but we will just test it that when it goes over the public internet, it's like super smooth. And so we did a lot of, anyway, it's, yeah, it was, that's cool.Alessio: You can imagine talking to the GCP rep and it's like, no, we're gonna buy, because we know we're gonna turn, we're gonna turn from you guys and go to AWS in like six months.But in the meantime we'll do this. It'sSimon Hørup Eskildsen: a, I mean, like they, you know, this workload still runs on GCP for what it's worth. Right? ‘cause it's so, it was just, it was so reliable. So it was never about moving off GCP, it was just about honesty. It was just about giving notion the latency that they deserved.Right. Um, and we didn't want ‘em to have to care about any of this. We also, they were like, oh, egress is gonna be bad. It was like, okay, screw it. Like we're just gonna like vvc, VPC peer with you and AWS we'll eat the cost. Yeah. Whatever needs to be done.Alessio: And what were the actual workloads? Because I think when you think about ai, it's like 14 milliseconds.It's like really doesn't really matter in the scheme of like a model generation.Simon Hørup Eskildsen: Yeah. We were told the latency, right. That we had to beat. Oh, right. So, so we're just looking at the traces. Right. And then sort of like hand draw, like, you know, kind of like looking at the trace and then thinking what are the other extensions of the trace?Right. And there's a lot more to it because it's also when you have, if you have 14 versus seven milliseconds, right. You can fit in another round trip. So we had to tune TCP to try to send as much data in every round trip, prewarm all the connections. And there was, there's a lot of things that compound from having these kinds of round trips, but in the grand scheme it was just like, well, we have to beat the latency of whatever we're up against.swyx: Which is like they, I mean, notion is a database company. They could have done this themselves. They, they do lots of database engineering themselves. How do you even get in the door? Like Yeah, just like talk through that kind of.Simon Hørup Eskildsen: Last time I was in San Francisco, I was talking to one of the engineers actually, who, who was one of our champions, um, at, AT Notion.And they were, they were just trying to make sure that the, you know, per user cost matched the economics that they needed. You know, Uhhuh like, it's like the way I think about, it's like I have to earn a return on whatever the clouds charge me and then my customers have to earn a return on that. And it's like very simple, right?And so there has to be gross margin all the way up and that's how you build the product. And so then our customers have to make the right set of trade off the turbo Puffer makes, and if they're happy with that, that's great.swyx: Do you feel like you're competing with build internally versus buy or buy versus buy?Simon Hørup Eskildsen: Yeah, so, sorry, this was all to build up to your question. So one of the notion engineers told me that they'd sat and probably on a napkin, like drawn out like, why hasn't anyone built this? And then they saw terrible. It was like, well, it literally that. So, and I think AI has also changed the buy versus build equation in terms of, it's not really about can we build it, it's about do we have time to build it?I think they like, I think they felt like, okay, if this is a team that can do that and they, they feel enough like an extension of our team, well then we can go a lot faster, which would be very, very good for them. And I mean, they put us through the, through the test, right? Like we had some very, very long nights to to, to do that POC.And they were really our biggest, our second big customer off the cursor, which also was a lot of late nights. Right.swyx: Yeah. That, I mean, should we go into that story? The, the, the sort of Chris's story, like a lot, um, they credit you a lot for. Working very closely with them. So I just wanna hear, I've heard this, uh, story from Sole's point of view, but like, I'm curious what, what it looks like from your side.Simon Hørup Eskildsen: I actually haven't heard it from Sole's point of view, so maybe you can now cross reference it. The way that I remember it was that, um, the day after we launched, which was just, you know, I'd worked the whole summer on, on the first version. Justine wasn't part of it yet. ‘cause I just, I didn't tell anyone that summer that I was working on this.I was just locked in on building it because it's very easy otherwise to confuse talking about something to actually doing it. And so I was just like, I'm not gonna do that. I'm just gonna do the thing. I launched it and at this point turbo puffer is like a rust binary running on a single eight core machine in a T Marks instance.And me deploying it was like looking at the request log and then like command seeing it or like control seeing it to just like, okay, there's no request. Let's upgrade the binary. Like it was like literally the, the, the, the scrappiest thing. You could imagine it was on purpose because just like at Shopify, we did that all the time.Like, we like move, like we ran things in tux all the time to begin with. Before something had like, at least the inkling of PMF, it was like, okay, is anyone gonna hear about this? Um, and one of the cursor co-founders Arvid reached out and he just, you know, the, the cursor team are like all I-O-I-I-M-O like, um, contenders, right?So they just speak in bullet points and, and facts. It was like this amazing email exchange just of, this is how many QPS we have, this is what we're paying, this is where we're going, blah, blah, blah. And so we're just conversing in bullet points. And I tried to get a call with them a few times, but they were, so, they were like really writing the PMF bowl here, just like late 2023.And one time Swally emails me at like five. What was it like 4:00 AM Pacific time saying like, Hey, are you open for a call now? And I'm on the East coast and I, it was like 7:00 AM I was like, yeah, great, sure, whatever. Um, and we just started talking and something. Then I didn't know anything about sales.It was something that just comp compelled me. I have to go see this team. Like, there's something here. So I, I went to San Francisco and I went to their office and the way that I remember it is that Postgres was down when I showed up at the office. Did SW tell you this? No. Okay. So Postgres was down and so it's like they were distracting with that.And I was trying my best to see if I could, if I could help in any way. Like I knew a little bit about databases back to tuning, auto vacuum. It was like, I think you have to tune out a vacuum. Um, and so we, we talked about that and then, um, that evening just talked about like what would it look like, what would it look like to work with us?And I just said. Look like we're all in, like we will just do what we'll do whatever, whatever you tell us, right? They migrated everything over the next like week or two, and we reduced their cost by 95%, which I think like kind of fixed their per user economics. Um, and it solved a lot of other things. And we were just, Justine, this is also when I asked Justine to come on as my co-founder, she was the best engineer, um, that I ever worked with at Shopify.She lived two blocks away and we were just, okay, we're just gonna get this done. Um, and we did, and so we helped them migrate and we just worked like hell over the next like month or two to make sure that we were never an issue. And that was, that was the cursor story. Yeah.swyx: And, and is code a different workload than normal text?I, I don't know. Is is it just text? Is it the same thing?Simon Hørup Eskildsen: Yeah, so cursor's workload is basically, they, um, they will embed the entire code base, right? So they, they will like chunk it up in whatever they would, they do. They have their own embedding model, um, which they've been public about. Um, and they find that on, on, on their evals.It. There's one of their evals where it's like a 25% improvement on a very particular workload. They have a bunch of blog posts about it. Um, I think it works best on larger code basis, but they've trained their own embedding model to do this. Um, and so you'll see it if you use the cursor agent, it will do searches.And they've also been public around, um, how they've, I think they post trained their model to be very good at semantic search as well. Um, and that's, that's how they use it. And so it's very good at, like, can you find me on the code that's similar to this, or code that does this? And just in, in this queries, they also use GR to supplement it.swyx: Yeah.Simon Hørup Eskildsen: Um, of courseswyx: it's been a big topic of discussion like, is rag dead because gr you know,Simon Hørup Eskildsen: and I mean like, I just, we, we see lots of demand from the coding company to ethicsswyx: search in every part. Yes.Simon Hørup Eskildsen: Uh, we, we, we see demand. And so, I mean, I'm. I like case studies. I don't like, like just doing like thought pieces on this is where it's going.And like trying to be all macroeconomic about ai, that's has turned out to be a giant waste of time because no one can really predict any of this. So I just collect case studies and I mean, cursor has done a great job talking about what they're doing and I hope some of the other coding labs that use Turbo Puffer will do the same.Um, but it does seem to make a difference for particular queries. Um, I mean we can also do text, we can also do RegX, but I should also say that cursors like security posture into Tur Puffer is exceptional, right? They have their own embedding model, which makes it very difficult to reverse engineer. They obfuscate the file paths.They like you. It's very difficult to learn anything about a code base by looking at it. And the other thing they do too is that for their customers, they encrypt it with their encryption keys in turbo puffer's bucket. Um, so it's, it's, it's really, really well designed.swyx: And so this is like extra stuff they did to work with you because you are not part of Cursor.Exactly like, and this is just best practice when working in any database, not just you guys. Okay. Yeah, that makes sense. Yeah. I think for me, like the, the, the learning is kind of like you, like all workloads are hybrid. Like, you know, uh, like you, you want the semantic, you want the text, you want the RegX, you want sql.I dunno. Um, but like, it's silly to like be all in on like one particularly query pattern.Simon Hørup Eskildsen: I think, like I really like the way that, um, um, that swally at cursor talks about it, which is, um, I'm gonna butcher it here. Um, and you know, I'm a, I'm a database scalability person. I'm not a, I, I dunno anything about training models other than, um, what the internet tells me and what.The way he describes is that this is just like cash compute, right? It's like you have a point in time where you're looking at some particular context and focused on some chunk and you say, this is the layer of the neural net at this point in time. That seems fundamentally really useful to do cash compute like that.And, um, how the value of that will change over time. I'm, I'm not sure, but there seems to be a lot of value in that.Alessio: Maybe talk a bit about the evolution of the workload, because even like search, like maybe two years ago it was like one search at the start of like an LLM query to build the context. Now you have a gentech search, however you wanna call it, where like the model is both writing and changing the code and it's searching it again later.Yeah. What are maybe some of the new types of workloads or like changes you've had to make to your architecture for it?Simon Hørup Eskildsen: I think you're right. When I think of rag, I think of, Hey, there's an 8,000 token, uh, context window and you better make it count. Um, and search was a way to do that now. Everything is moving towards the, just let the agent do its thing.Right? And so back to the thing before, right? The LLM is very good at reasoning with the data, and so we're just the tool call, right? And that's increasingly what we see our customers doing. Um, what we're seeing more demand from, from our customers now is to do a lot of concurrency, right? Like Notion does a ridiculous amount of queries in every round trip just because they can't.And I'm also now, when I use the cursor agent, I also see them doing more concurrency than I've ever seen before. So a bit similar to how we designed a database to drive as much concurrency in every round trip as possible. That's also what the agents are doing. So that's new. It means just an enormous amount of queries all at once to the dataset while it's warm in as few turns as possible.swyx: Can I clarify one thing on that?Simon Hørup Eskildsen: Yes.swyx: Is it, are they batching multiple users or one user is driving multiple,Simon Hørup Eskildsen: one user driving multiple, one agent driving.swyx: It's parallel searching a bunch of things.Simon Hørup Eskildsen: Exactly.swyx: Yeah. Yeah, exactly. So yeah, the clinician also did, did this for the fast context thing, like eight parallel at once.Simon Hørup Eskildsen: Yes.swyx: And, and like an interesting problem is, well, how do you make sure you have enough diversity so you're not making the the same request eight times?Simon Hørup Eskildsen: And I think like that's probably also where the hybrid comes in, where. That's another way to diversify. It's a completely different way to, to do the search.That's a big change, right? So before it was really just like one call and then, you know, the LLM took however many seconds to return, but now we just see an enormous amount of queries. So the, um, we just see more queries. So we've like tried to reduce query, we've reduced query pricing. Um, this is probably the first time actually I'm saying that, but the query pricing is being reduced, like five x.Um, and we'll probably try to reduce it even more to accommodate some of these workloads of just doing very large amounts of queries. Um, that's one thing that's changed. I think the right, the right ratio is still very high, right? Like there's still a, an enormous amount of rights per read, but we're starting probably to see that change if people really lean into this pattern.Alessio: Can we talk a little bit about the pricing? I'm curious, uh, because traditionally a database would charge on storage, but now you have the token generation that is so expensive, where like the actual. Value of like a good search query is like much higher because they're like saving inference time down the line.How do you structure that as like, what are people receptive to on the other side too?Simon Hørup Eskildsen: Yeah. I, the, the turbo puffer pricing in the beginning was just very simple. The pricing on these on for search engines before Turbo Puffer was very server full, right? It was like, here's the vm, here's the per hour cost, right?Great. And I just sat down with like a piece of paper and said like, if Turbo Puffer was like really good, this is probably what it would cost with a little bit of margin. And that was the first pricing of Turbo Puffer. And I just like sat down and I was like, okay, like this is like probably the storage amp, but whenever on a piece of paper I, it was vibe pricing.It was very vibe price, and I got it wrong. Oh. Um, well I didn't get it wrong, but like Turbo Puffer wasn't at the first principle pricing, right? So when Cursor came on Turbo Puffer, it was like. Like, I didn't know any VCs. I didn't know, like I was just like, I don't know, I didn't know anything about raising money or anything like that.I just saw that my GCP bill was, was high, was a lot higher than the cursor bill. So Justine and I was just like, well, we have to optimize it. Um, and I mean, to the chagrin now of, of it, of, of the VCs, it now means that we're profitable because we've had so much pricing pressure in the beginning. Because it was running on my credit card and Justine and I had spent like, like tens of thousands of dollars on like compute bills and like spinning off the company and like very like, like bad Canadian lawyers and like things like to like get all of this done because we just like, we didn't know.Right. If you're like steeped in San Francisco, you're just like, you just know. Okay. Like you go out, raise a pre-seed round. I, I never heard a word pre-seed at this point in time.swyx: When you had Cursor, you had Notion you, you had no funding.Simon Hørup Eskildsen: Um, with Cursor we had no funding. Yeah. Um, by the time we had Notion Locke was, Locke was here.Yeah. So it was really just, we vibe priced it 100% from first Principles, but it wasn't, it, it was not performing at first principles, so we just did everything we could to optimize it in the beginning for that, so that at least we could have like a 5% margin or something. So I wasn't freaking out because Cursor's bill was also going like this as they were growing.And so my liability and my credit limit was like actively like calling my bank. It was like, I need a bigger credit. Like it was, yeah. Anyway, that was the beginning. Yeah. But the pricing was, yeah, like storage rights and query. Right. And the, the pricing we have today is basically just that pricing with duct tape and spit to try to approach like, you know, like a, as a margin on the physical underlying hardware.And we're doing this year, you're gonna see more and more pricing changes from us. Yeah.swyx: And like is how much does stuff like VVC peering matter because you're working in AWS land where egress is charged and all that, you know.Simon Hørup Eskildsen: We probably don't like, we have like an enterprise plan that just has like a base fee because we haven't had time to figure out SKU pricing for all of this.Um, but I mean, yeah, you can run turbo puffer either in SaaS, right? That's what Cursor does. You can run it in a single tenant cluster. So it's just you. That's what Notion does. And then you can run it in, in, in BYOC where everything is inside the customer's VPC, that's what an for example, philanthropic does.swyx: What I'm hearing is that this is probably the best CRO job for somebody who can come in and,Simon Hørup Eskildsen: I mean,swyx: help you with this.Simon Hørup Eskildsen: Um, like Turbo Puffer hired, like, I don't know what, what number this was, but we had a full-time CFO as like the 12th hire or something at Turbo Puffer, um, I think I hear are a lot of comp.I don't know how they do it. Like they have a hundred employees and not a CFO. It's like having a CFO is like a runningswyx: business man. Like, you know,Simon Hørup Eskildsen: it's so good. Yeah, like money Mike, like he just, you know, just handles the money and a lot of the business stuff and so he came in and just hopped with a lot of the operational side of the business.So like C-O-O-C-F-O, like somewhere in between.swyx: Just as quick mention of Lucky, just ‘cause I'm curious, I've met Lock and like, he's obviously a very good investor and now on physical intelligence, um, I call it generalist super angel, right? He invests in everything. Um, and I always wonder like, you know, is there something appealing about focusing on developer tooling, focusing on databases, going like, I've invested for 10 years in databases versus being like a lock where he can maybe like connect you to all the customers that you need.Simon Hørup Eskildsen: This is an excellent question. No, no one's asked me this. Um, why lockey? Because. There was a couple of people that we were talking to at the time and when we were raising, we were almost a little, we were like a bit distressed because one of our, one of our peers had just launched something that was very similar to Turbo Puffer.And someone just gave me the advice at the time of just choose the person where you just feel like you can just pick up the phone and not prepare anything. And just be completely honest, and I don't think I've said this publicly before, but I just called Lockey and was like local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you.But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working. So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people and we're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards and.Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before. As I said, I didn't even know what a seed or pre-seed round was like before, probably even at this time. So I was just like very honest with him. And I asked him like, Lockie, have you ever have, have you ever invested in database company?He was just like, no. And at the time I was like, am I dumb? Like, but I think there was something that just like really drew me to Lockie. He is so authentic, so honest, like, and there was something just like, I just felt like I could just play like, just say everything openly. And that was, that was, I think that that was like a perfect match at the time, and, and, and honestly still is.He was just like, okay, that's great. This is like the most honest, ridiculous thing I've ever heard anyone say to me. But like that, like that, whyswyx: is this ridiculous? Say competitor launch, this may not work out. It wasSimon Hørup Eskildsen: more just like. If this doesn't work out, I'm gonna close up shop by the end of the mo the year, right?Like it was, I don't know, maybe it's common. I, I don't know. He told me it was uncommon. I don't know. Um, that's why we chose him and he'd been phenomenal. The other people were talking at the, at the time were database experts. Like they, you know, knew a lot about databases and Locke didn't, this turned out to be a phenomenal asset.Right. I like Justine and I know a lot about databases. The people that we hire know a lot about databases. What we needed was just someone who didn't know a lot about databases, didn't pretend to know a lot about databases, and just wanted to help us with candidates and customers. And he did. Yeah. And I have a list, right, of the investors that I have a relationship with, and Lockey has just performed excellent in the number of sub bullets of what we can attribute back to him.Just absolutely incredible. And when people talk about like no ego and just the best thing for the founder, I like, I don't think that anyone, like even my lawyer is like, yeah, Lockey is like the most friendly person you will find.swyx: Okay. This is my most glow recommendation I've ever heard.Alessio: He deserves it.He's very special.swyx: Yeah. Yeah. Yeah. Okay. Amazing.Alessio: Since you mentioned candidates, maybe we can talk about team building, you know, like, especially in sf, it feels like it's just easier to start a company than to join a company. Uh, I'm curious your experience, especially not being n SF full-time and doing something that is maybe, you know, a very low level of detail and technical detail.Simon Hørup Eskildsen: Yeah. So joining versus starting, I never thought that I would be a founder. I would start with it, like Turbo Puffer started as a blog post, and then it became a project and then sort of almost accidentally became a company. And now it feels like it's, it's like becoming a bigger company. That was never the intention.The intentions were very pure. It's just like, why hasn't anyone done this? And it's like, I wanna be the, like, I wanna be the first person to do it. I think some founders have this, like, I could never work for anyone else. I, I really don't feel that way. Like, it's just like, I wanna see this happen. And I wanna see it happen with some people that I really enjoy working with and I wanna have fun doing it and this, this, this has all felt very natural on that, on that sense.So it was never a like join versus versus versus found. It was just dis found me at the right moment.Alessio: Well I think there's an argument for, you should have joined Cursor, right? So I'm curious like how you evaluate it. Okay, I should actually go raise money and make this a company versus like, this is like a company that is like growing like crazy.It's like an interesting technical problem. I should just build it within Cursor and then they don't have to encrypt all this stuff. They don't have to obfuscate things. Like was that on your mind at all orSimon Hørup Eskildsen: before taking the, the small check from Lockie, I did have like a hard like look at myself in the mirror of like, okay, do I really want to do this?And because if I take the money, I really have to do it right. And so the way I almost think about it's like you kind of need to ha like you kind of need to be like fucked up enough to want to go all the way. And that was the conversation where I was like, okay, this is gonna be part of my life's journey to build this company and do it in the best way that I possibly can't.Because if I ask people to join me, ask people to get on the cap table, then I have an ultimate responsibility to give it everything. And I don't, I think some people, it doesn't occur to me that everyone takes it that seriously. And maybe I take it too seriously, I don't know. But that was like a very intentional moment.And so then it was very clear like, okay, I'm gonna do this and I'm gonna give it everything.Alessio: A lot of people don't take it this seriously. But,swyx: uh, let's talk about, you have this concept of the P 99 engineer. Uh, people are 10 x saying, everyone's saying, you know, uh, maybe engineers are out of a job. I don't know.But you definitely see a P 99 engineer, and I just want you to talk about it.Simon Hørup Eskildsen: Yeah, so the P 99 engineer was just a term that we started using internally to talk about candidates and talk about how we wanted to build the company. And you know, like everyone else is, like we want a talent dense company.And I think that's almost become trite at this point. What I credit the cursor founders a lot with is that they just arrived there from first principles of like, we just need a talent dense, um, talent dense team. And I think I've seen some teams that weren't talent dense and like seemed a counterfactual run, which if you've run in been in a large company, you will just see that like it's just logically will happen at a large company.Um, and so that was super important to me and Justine and it's very difficult to maintain. And so we just needed, we needed wording for it. And so I have a document called Traits of the P 99 Engineer, and it's a bullet point list. And I look at that list after every single interview that I do, and in every single recap that we do and every recap we end with.End with, um, some version of I'm gonna reject this candidate completely regardless of what the discourse was, because I wanna see people fight for this person because the default should not be, we're gonna hire this person. The default should be, we're definitely not hiring this person. And you know, if everyone was like, ah, maybe throw a punch, then this is not the right.swyx: Do, do you operate, like if there's one cha there must have at least one champion who's like, yes, I will put my career on, on, on the line for this. You know,Simon Hørup Eskildsen: I think career on the line,swyx: maybe a chair, butSimon Hørup Eskildsen: yeah. You know, like, um, I would say so someone needs to like, have both fists up and be like, I'd fight.Right? Yeah. Yeah. And if one person said, then, okay, let's do it. Right?swyx: Yeah.Simon Hørup Eskildsen: Um. It doesn't have to be absolutely everyone. Right? And like the interviews are always the sign that you're checking for different attributes. And if someone is like knocking it outta the park in every single attribute, that's, that's fairly rare.Um, but that's really important. And so the traits of the P 99 engineer, there's lots of them. There's also the traits of the p like triple nine engineer and the quadruple nine engineer. This is like, it's a long list.swyx: Okay.Simon Hørup Eskildsen: Um, I'll give you some samples, right. Of what we, what we look for. I think that the P 99 engineer has some history of having bent, like their trajectory or something to their will.Right? Some moment where it was just, they just, you know, made the computer do what it needed to do. There's something like that, and it will, it will occur to have them at some point in their career. And, uh. Hopefully multiple times. Right.swyx: Gimme an example of one of your engineers that like,Simon Hørup Eskildsen: I'll give an eng.Uh, so we, we, we launched this thing called A and NV three. Um, we could, we're also, we're working on V four and V five right now, but a and NV three can search a hundred billion vectors with a P 50 of around 40 milliseconds and a p 99 of 200 milliseconds. Um, maybe other people have done this, I'm sure Google and others have done this, but, uh, we haven't seen anyone, um, at least not in like a public consumable SaaS that can do this.And that was an engineer, the chief architect of Turbo Puffer, Nathan, um, who more or less just bent this, the software was not capable of this and he just made it capable for a very particular workload in like a, you know, six to eight week period with the help of a lot of the team. Right. It's been, been, there's numerous of examples of that, like at, at turbo puff, but that's like really bending the software and X 86 to your will.It was incredible to watch. Um. You wanna see some moments like that?swyx: Isn't that triple nine?Simon Hørup Eskildsen: Um, I think Nathan, what's calledAlessio: group nine, that was only nine. I feel like this is too high forSimon Hørup Eskildsen: Nathan. Nathan is, uh, Nathan is like, yeah, there's a lot of nines. Okay. After that p So I think that's one trait. I think another trait is that, uh, the P 99 spends a lot of time looking at maps.Generally it's their preferred ux. They just love looking at maps. You ever seen someone who just like, sits on their phone and just like, scrolls around on a map? Or did you not look at maps A lot? You guys don't look atswyx: maps? I guess I'm not feeling there. I don't know, butSimon Hørup Eskildsen: you just dis What about trains?Do you like trains?swyx: Uh, I mean they, not enough. Okay. This is just like weapon nice. Autism is what I call it. Like, like,Simon Hørup Eskildsen: um, I love looking at maps, like, it's like my preferred UX and just like I, you know, I likeswyx: lotsAlessio: of, of like random places, soswyx: like,youswyx: know.Alessio: Yes. Okay. There you go. So instead of like random places, like how do you explore the maps?Simon Hørup Eskildsen: No, it's, it's just a joke.swyx: It's autism laugh. It's like you are just obsessed by something and you like studying a thing.Simon Hørup Eskildsen: The origin of this was that at some point I read an interview with some IOI gold medalistswyx: Uhhuh,Simon Hørup Eskildsen: and it's like, what do you do in your spare time? I was just like, I like looking at maps.I was like, I feel so seen. Like, I just like love, like swirling out. I was like, oh, Canada is so big. Where's Baffin Island? I don't know. I love it. Yeah. Um, anyway, so the traits of P 99, P 99 is obsessive, right? Like, there's just like, you'll, you'll find traits of that we do an interview at, at, at, at turbo puffer or like multiple interviews that just try to screen for some of these things.Um, so. There's lots of others, but these are the kinds of traits that we look for.swyx: I'll tell you, uh, some people listen for like some of my dere stuff. Uh, I do think about derel as maps. Um, you draw a map for people, uh, maps show you the, uh, what is commonly agreed to be the geographical features of what a boundary is.And it shows also shows you what is not doing. And I, I think a lot of like developer tools, companies try to tell you they can do everything, but like, let's, let's be real. Like you, your, your three landmarks are here, everyone comes here, then here, then here, and you draw a map and, and then you draw a journey through the map.And like that. To me, that's what developer relations looks like. So I do think about things that way.Simon Hørup Eskildsen: I think the P 99 thinks in offs, right? The P 99 is very clear about, you know, hey, turbo puffer, you can't run a high transaction workload on turbo puffer, right? It's like the right latency is a hundred milliseconds.That's a clear trade off. I think the P 99 is very good at articulating the trade offs in every decision. Um. Which is exactly what the map is in your case, right?swyx: Uh, yeah, yeah. My, my, my world. My world.Alessio: How, how do you reconcile some of these things when you're saying you bend the will the computer versus like the trade

Scott Stevenson is the Co-founder and CEO of Spellbook.Spellbook is an AI copilot for contract review and drafting, essentially “Cursor for lawyers.” They have 4,000 customers in 80 countries, and to my knowledge is the fastest growing AI company in Canada, and the largest company in the world built on a Microsoft Word plugin.Scott has been building in legal AI longer than almost anyone. We talk about why legal software was essentially untouched before LLM's, why the market is so hot right now, if it's sustainable, and how Spellbook navigates product differentiation compared to horizontal AI products like ChatGPT.We talk about why fine-tuning your own models was one of the biggest mistakes early AI companies made, how to build a network effect as a vertical AI product, and Spellbook's philosophy of “Don't sharpen your axe when the chainsaw is coming out tomorrow”.Spellbook spent a few years finding PMF before really taking off in 2022, and Scott shares their playbook for launching over 100 product experiments in three years, how to know when to lean in, and what it's been like scaling Spellbook post-PMF.Thank you to Numeral and Flex for supporting this episode.Try Numeral, the end-to-end platform for sales tax and compliance: https://www.numeral.comSign-up for Flex Elite with code TURNER, get $1,000: https://form.typeform.com/to/Rx9rTjFzTimestamps:(0:30) Spellbook: “Cursor for Contracts”(3:08) Building the world's largest Microsoft Word plugin(14:06) Why legal software was untouched before LLMs(18:32) $30 trillion moves through contracts annually(20:51) Why ChatGPT won't replace vertical tools(25:15) Fine-tuning was the biggest mistake in AI(30:00) Differences between pro and amateur gamers(37:38) Top-down vs. bottoms-up in legal AI(42:27) The long-tail of legal AI software(47:24) Building for models that don't exist yet(51:20) Skating where the puck is going(1:01:35) The legal bill that cost 50% of his bank account(1:09:33) Testing 100 landing pages in 3 years(1:14:06) The moment Spellbook hit PMF(1:19:17) Building new brands for each product experiment(1:23:10) Raising a Series B with a tweet(1:27:41) What Scott learned from Keith Rabois(1:31:16) Scott's favorite new AI toolReferencedSpellbook: https://www.spellbook.legal/Careers at Spellbook: https://www.spellbook.legal/careersPlaying to Win by David Sirlin: https://www.amazon.com/Playing-Win-becoming-David-Sirlin/dp/1413498817Find the Fast Moving Water by NFX: https://www.nfx.com/post/find-the-fast-moving-waterSpellbook's case study with Replit: https://replit.com/customers/spellbookTwin: https://twin.so/Follow ScottTwitter: https://x.com/scottastevensonLinkedIn: https://www.linkedin.com/in/scottasBlog: https://blog.scottstevenson.net/Follow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

Ahmad Sadeddin is the founder and CEO of Corgea. Corgea provides the security tools to find, triage, and fix insecure code. Ahmad shares:- Why you don't need to raise much to find PMF - stay lean: you should surprise people with how few people you are.- What is a small amount to raise? And what team size do you need? - Pivoting during YC and how Corgea found their first customers and the signs of Product Market Fit- The journey to Product Market Fit never stops- How Corgea worked towards Product Market FitThis episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.Links:Ahmad Sadeddin https://www.linkedin.com/in/asadeddin/Corgea https://corgea.com/The Fatal Pinch by Paul Graham https://paulgraham.com/pinch.html

hema.to is building AI-powered diagnostic infrastructure for cytometry—a specialized area of laboratory medicine analyzing immune system data to detect blood cancers like leukemia and lymphoma. Unlike radiology or pathology where AI solutions are abundant, cytometry has remained largely untouched by the AI wave, creating both opportunity and isolation for the Munich-based company. In a recent episode of BUILDERS, we sat down with Karsten Miermans, CEO at hema.to GmbH, to discuss why they're deliberately keeping sales founder-led despite having paying customers, how South America became an unexpected beachhead market, and what it actually means to build infrastructure versus point solutions in healthcare. Topics Discussed:  From consulting project to venture-backed company: recognizing scalability in hindsight  The workflow integration problem killing healthcare AI implementations  Infrastructure versus technology: why healthcare AI isn't just about the algorithm  Learning ideal customer profile after 18 months of being "all over the place"  Why South America's governance structure enables faster adoption than the US  Resisting the urge to hire sales before achieving true repeatability  The 10-year vision: shifting from "watch and wait" to "predict and prevent" in immune disease GTM Lessons For B2B Founders: Pattern matching fails when you're an outsider—budget 18+ months to find your beachhead: Karsten assumed every application of their diagnostic method was the same and spent a year and a half "blue eyed" (naively optimistic) before identifying their true ICP. The outsider advantage lets you reimagine workflows insiders can't, but you'll incorrectly assume transferability across use cases. Don't expect repeatability in year one when entering regulated, workflow-dependent markets. Infrastructure requires multi-stakeholder orchestration—resource for enterprise complexity from day one: Karsten distinguishes technology (point solutions, single users) from infrastructure (shared resources requiring data exchange and workflow integration). In healthcare, this means integration into hospital systems, databases, and electronic health records across multiple stakeholders. "Every sale becomes enterprise sales" even for individual labs because of this infrastructure requirement. Founders building horizontal platforms should model sales cycles and resource requirements as enterprise from the start, regardless of deal size. Your ICP is cognitively overloaded—they won't understand your category innovation: Doctors are "under so much pressure that they just don't have any cognitive capacity left" to philosophically evaluate why AI might be difficult to implement or how infrastructure differs from technology. They need problems solved within their existing mental models. Skip the category education. Frame everything as workflow enhancement, not innovation. Let sophistication emerge through implementation, not pitch decks. Revenue doesn't equal repeatability—know when you're still in discovery mode: Despite having paying customers, Karsten explicitly states "we're not at product-market fit yet" because they're "discovering and learning things with every new laboratory hospital" around data privacy, integration, and AI deployment. The PMF signal isn't customer count or revenue—it's when the process becomes predictable, customers refer others, and you stop discovering new requirements. Hiring sales before this point scales complexity, not revenue. Regulatory friction determines market sequencing, not just market size: US governance complexity turns every deal into heavy enterprise sales with "many stakeholders," while South America proved "much more willing to move with fewer processes," making them "just much faster to adopt innovative technology." This wasn't strategy—Karsten's CTO speaks Spanish through a personal connection. But the lesson transfers: for infrastructure plays in regulated markets, test adoption velocity in lower-governance environments first to build proof points, even if TAM looks smaller on paper. In healthcare, marketing is clinical evidence—customer success creates your GTM flywheel: Karsten spends minimal time on marketing because beyond the first 5-10 users, doctors "want to see clinical evidence, they want to see papers, they want to see maybe that a friend of theirs is using it." Marketing in healthcare isn't content or demand gen—it's peer validation and published proof. Founders should structure early customer engagements to generate this evidence, not just revenue. The "marketing sales flywheel really does kick in much more once you have product market fit" because PMF enables the evidence generation required for credibility. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Didi spent five years building a product that no one really wanted. He raised $10 million, tried endless pivots, and was known as the "black sheep" of his investors' portfolio. Then, with his back against the wall, he made one final bet on a boring, unsexy market: FP&A for Excel users.In this episode, Didi breaks down how that final pivot turned into a rocket ship. He reveals why he sold cheap monthly contracts to prove demand, how he used his kids to automate LinkedIn outreach, and why targeting the market everyone else ignores (Excel lovers) was the key to unlocking massive growth.Why You Should ListenHow to survive 5 years of wandering before finding PMF.Why he sold $790/month contracts to validate a pivot.How to scale from $0 to $20M ARR with 100% outbound sales.Keywordsstartup podcast, startup podcast for founders, product market fit, finding pmf, pivot, B2B sales, outbound sales strategy, FP&A software, excel automation, Didi Gurfinkel00:00:00 Intro00:02:42 The First 5 Years of Wandering00:11:39 Being the "Black Sheep" of the Portfolio00:14:12 Identifying the FP&A Opportunity00:20:55 The Pivot: Selling $790/Month Contracts00:30:30 Scaling from $1M to $20M with Outbound00:33:18 Why the Mid-Market is Wide Open00:34:22 The Moment of True Product Market FitSend me a message to let me know what you think!

Empathy is pioneering bereavement care as an enterprise benefit, transforming how employers and financial institutions support employees during life's most challenging transitions. Working with 9 of the top 10 life insurance carriers in the US and Canada—covering over 40 million people—Empathy created a new category by combining grief support with practical logistics like probate navigation, account deactivation, and estate settlement. In a recent episode of BUILDERS, we sat down with Ron Gura, Co-Founder & CEO of Empathy, to learn how the company went from testing five verticals simultaneously to dominating life insurance, then leveraged the group life/employer overlap to expand into employee benefits. Topics Discussed: Testing five enterprise verticals simultaneously to find product-market fit Landing New York Life through their venture arm and innovation team Why life insurance carriers need to be risk-averse (and how to work with that reality) The strategic overlap between group life insurance and employee benefits Investing in brand at seed stage when your barrier to entry is psychological aversion Navigating dual audiences: decision-makers in their workday versus end users in crisis Expanding from loss to adjacent life transitions like disability leave and estate planning GTM Lessons For B2B Founders: Run parallel vertical tests with focus constraints, not sequential exploration: Ron identified 10+ potential verticals but intentionally tested exactly five simultaneously—hospices, funeral homes, employers, and two others before life insurance emerged as the winner at position five. This parallel testing with artificial constraints forces prioritization while dramatically compressing time-to-insight. Sequential testing would have meant potentially cycling through five failed pilots before discovering their strongest market. B2B founders with horizontal platforms should pick their top 3-5 verticals and run focused pilots in parallel, accepting that this burns more resources upfront but eliminates the risk of quitting before finding your wedge. Map the ecosystem overlap between buyer personas before choosing your wedge: Empathy's expansion from life insurance to employers wasn't growth strategy—it was recognizing an architectural reality. Half their carriers sell group life, meaning MetLife doesn't sell to consumers at metlife.com but exclusively to employer groups. When Amanda at Paramount loses her sister (not covered by insurance), she calls Paramount HR. When her husband dies (covered by MetLife group policy), the beneficiary calls MetLife. Same end user, two different enterprise entry points into the same moment. B2B founders should map these triangular relationships before choosing their wedge vertical. The question isn't just "who has budget?" but "who else touches this user in adjacent contexts?" Brand investment at seed stage is product strategy when fighting cognitive aversion: Ron's insight: "The barrier to entry isn't regulatory and isn't technology. It's us humans trying really hard not to think about our own mortality." This isn't a marketing problem—it's a fundamental go-to-market blocker. The company made what most would consider Series A investments (premium domain, design system, tone/voice framework) at seed stage specifically because brand reduces psychological friction to adoption. Contrast this with Monday.com starting as "daPulse" and rebranding years into success. B2B founders addressing taboo topics (death, mental health, financial distress, relationship issues) should model brand as a core distribution lever, not post-PMF polish. In deeply human categories, buyer's lived experience is your demo: Enterprise buyers at Citibank, MetLife, or Google aren't experiencing crisis during the sales cycle—they're evaluating ROI in their normal workday. But as Ron noted, "Everyone we're talking to...they're humans. They have parents, they had loss, they went through probate." The most common response after seeing the product: "Damn, I wish you called me a few months ago. I needed this a year ago with my mom." This turns product demo into personal recognition. B2B founders in universal human experience categories (caregiving, bereavement, parental leave, financial stress) should structure discovery and demo to activate buyer's memory of their own experience, not just their budget authority. Category creation is a resource-attraction strategy that trades speed for competitive exposure: Ron explicitly acknowledged: "There's pros and cons to defining a category. It's helpful when you attract resources, talent, capital. It also creates very fertile ground for a number two sympathy.com to come along and learn from this podcast...what to go after." Category leadership accelerates recruiting and fundraising by providing narrative clarity, but it simultaneously publishes your playbook. Every hiring blog post, podcast appearance, and positioning document teaches future competitors which verticals to target and which to avoid. B2B founders should treat category creation as a conscious bet: trade competitive opacity for talent/capital velocity. If you're not ready to defend your position, stay in stealth longer. Bridge new categories to existing budget lines through analogous benefits: When entering new verticals beyond life insurance, Ron doesn't educate from zero. With employers, he positions bereavement care alongside caregiving solutions, fertility programs, and parental leave: "This is a life transition happening in my own intimate house. Just like a new baby. I have new duties now." This isn't metaphor—it's budget mapping. Bereavement care gets evaluated against existing family benefits spending, not created from scratch. B2B founders in new categories should identify which existing line item their solution logically extends, then structure ROI narratives around reallocation, not net-new budget creation. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Miranda Nover is the Co-founder and CEO of Fort Health. Fort builds wearables that automatically track strength training for people who care about longevity.This is a new format I'm experimenting with. It's the first time I've had a Banana portfolio company founder on the show while they're still at the pre-seed stage. When I surveyed my subscribers a few weeks ago, you were most interested in more early stage VC-backed founders, and I'd love your feedback on what you think of this.Miranda is still very much working through the idea maze and iterating on the Fort product. We talk about the megatrends driving consumer health, why she's building a company that helps people get stronger, and everything she's learned getting a hardware company off the ground.She's also in the middle of the current YC batch, and gives an inside look at what it's been like and if she'd recommend it to other founders.Thank you to Numeral and Flex for supporting this episode.Try Numeral, the end-to-end platform for sales tax and compliance: https://www.numeral.comSign-up for Flex Elite with code TURNER, get $1,000: https://form.typeform.com/to/Rx9rTjFzTimestamps:(3:37) Importance of strength training(6:34) Benefits of being strong(10:37) Evolution of Fort's hardware(15:58) Automating workout tracking(19:29) Two types of strength trainers(25:30) Building the strength company(27:26) How healthcare is consumerizing(40:43) Lessons building batteries at Tesla(44:56) Hardest parts about building a hardware startup(51:01) Adventures in vibe coding(57:54) How to use Twitter as a founder(1:02:09) The launch video industrial complex(1:08:03) What it's like doing YC(1:10:19) Selling crayons in 3rd grade, Lemonade stands(1:14:41) Miranda's best vintage finds(1:16:44) How Turner evolved as a VC(1:22:22) Turner's early social media PMF(1:28:53) Inventing shitpostingReferencedTry Fort: https://www.fort.cx/Follow MirandaTwitter: https://x.com/mirandanoverLinkedIn: https://www.linkedin.com/in/mirandanoverFollow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

Jay was running a respectable AI startup with $3M ARR. But he knew it wasn't a venture-scale rocket ship. So, he decided to fire all his customers, pivot the entire company, and bet everything on a new vertical: legal AI for plaintiff attorneys.Eve went from zero to unicorn status in under two years, raising $100M at a $1B valuation. In this episode, Jay breaks down the brutal reality of pivoting a revenue-generating company, how to achieve "demo shock" in an antiquated industry, and why 4-hour user sessions were the first sign that he had struck gold.Why You Should ListenHow threatening to shut down your product can reveal PMF.Why firing all your existing customers might be the only way to scale.How to achieve a 40% conversion rate from cold outreach to demo.Why you should target mid market instead of enterprise if you want to deploy AI fast.Keywordsstartup podcast, startup podcast for founders, product market fit, finding pmf, pivot, legal tech, AI startup, B2B sales, unicorn startup, Jay Madheswaran, Eve00:00:00 Intro00:02:27 From VC to Founder00:08:42 The First Idea: RPA for NLP00:16:52 The Hard Decision to Pivot at 3M ARR00:24:26 Product Discovery While Still Supporting Old Customers00:33:56 40 Percent Conversion from Cold Outreach00:39:56 Firing Customers to Find True PMF00:41:06 The 4-Hour User Session Signal00:46:05 From 1M to 10M ARR in One Year00:49:11 The Moment of True Product Market FitSend me a message to let me know what you think!

David Choi and Conor Moore are CoFounders of Permian Labs, the builders behind USDai.AI infrastructure is projecting trillions of dollars in CapEx spend, but there's a problem: traditional finance can't keep up. Banks move too slow. Private credit funds can't scale. The most important commodity in the world has no liquid debt market.USDai is filling this gap by financing AI infrastructure with GPU-backed loans, offering stablecoin depositors 10-15% APR. David and Conor break down how they're using DeFi rails and tokenization to create liquid debt markets for GPUs, enabling institutional borrowers to access capital and retail users to earn yield on productive AI infrastructure.In this episode, we cover:+ Why trillions in AI CapEx can't get traditional financing+ How USDAI structures loans against GPUs, not businesses+ Why this could become "the interest rate of artificial intelligence"+ Their two-token model: USDai vs. sUSDai------

This episode breaks down an article by Jason Cohen, founder of WP Engine and SmartBear, outlining his step-by-step roadmap from idea to product-market fit (PMF) for startups, especially DevTools. His 8 step roadmap provides insights on personal fit, market validation, customer interviews, building an SLC (simple, lovable, complete) MVP, sales focus, retention, prioritization, and founder psychology, drawing from Cohen's unicorn success and pitfalls to avoid.Links:   • Jason Cohen    •  WP Engine   •  Smart Bear    •  Jason Cohen's articleThis episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs. 

This Week In Startups is made possible by:Quadratic - http://quadratic.ai/twistToday's show: Don't get distracted! Here are the MOST CRUCIAL aspects of running a startup, where founders need to keep their full and uninterrupted focus.- Make sure you're saving up your cash- Why you need to just get started and build SOMETHING- Trust and reliability is EVERYTHING for new products- Why distribution should be your top priorityDownload all this practical and tactical startup advice from seasoned veterans Jason Calacanis, Amanda Bradford, and William P. Barnes in this Tokyo edition of TWiST.Timestamps:(00:00) Amanda and Will's big takeaways from Founder U in Saudi Arabia and now Tokyo(3:19) Why cash flow management is so important before you find PMF(5:57) Why first-time founders get the order of operations wrong(6:45) Just build SOMETHING, even if it's taped together(11:19) “Focus is everything in the early stages”(14:17) From a wedge to a bridge(15:50) Quadratic - Bringing the productivity boost of AI into your spreadsheets. Visit http://quadratic.ai/twist to sign up and use the code TWIST to get one free month of their pro tier subscription.(18:45) What to put on your “Not Right Now” list(19:11) Embracing simplicity(22:00) The importance of trust and reliability (especially for Uber!)(28:12) Why innovation needs a constraining variable(33:40) To really drive word of mouth, you have to overdeliver(36:32) Distribution is the primary job of a founder/CEO(38:19) Some of the panel's favorite distribution hacks(45:54) Why Jason respects Japan's commitment to excellence and competency(49:38) Looking for “high slope” in early employees(53:11) Transitioning your team from early-stage startup to growth(57:45) Whoever writes it down gets credit for the idea(58:04) Q: Has the meaning of money changed for the panel now that they've had successful exits?*Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com/Check out the TWIST500: https://twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcp*Follow Lon:X: https://x.com/lons*Follow Alex:X: https://x.com/alexLinkedIn: https://www.linkedin.com/in/alexwilhelm/*Follow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanis/*Thank you to our partners:(15:50) Quadratic - Bringing the productivity boost of AI into your spreadsheets. Visit http://quadratic.ai/twist to sign up and use the code TWIST to get one free month of their pro tier subscription.Check out all our partner offers: https://partners.launch.co/

This episode breaks down Marc Andreessen's 2007 article on why market matters most in startups, plus some great wisdom from Michael Seibel on spotting real PMF through explosive growth and customer pull.Links:   •  Marc Andreessen's article   •  Michael Seibel's post   •  Product Market Fit collapseThis episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.

This is a special episode from the Neon Fund.In 2025, the US saw $1.8 trillion worth of M&A deals, around 25× more than India. But India's startup ecosystem is much younger, which makes every acquisition a playbook for founders on process, pricing leverage, and stakeholder management.Neon backed Zenduty in 2020, when the founders had been bootstrapping profitably for two years and were already growing at a pace many VC-backed startups aspire to.Today, founders Ankur Rawal and Vishwa Krishnakumar join Siddhartha, Partner at Neon, to discuss one of the most untalked acquisitions of 2025.Over a 10-year journey, Zenduty pivoted to SRE in 2020. Vishwa and Ankur also share insights on the future of the DevTools space, which they believe will always be a strong choice to build great products, because engineers are among the hardest end users to please.This episode is a founders' view on how acquisitions work in Indian SaaS.00:00 – Trailer01:00 – Initial years of a decade-long journey07:12 – How Zenduty chose its investors11:04 – How much should founders dilute?12:24 – Building with profitability before & after fundraise14:45 – Six years of survival before the pivot17:01 – Why the pivot to the SRE space?18:39 – How Zenduty differentiated from PagerDuty19:12 – End users are the toughest to please in engineering20:39 – Is market attractive if biggest player is valued only $1.5B?25:22 – Why acquisition and not a Series A?27:18 – The process before acquisition29:23 – How pricing negotiations work31:51 – Should devtool companies build from India or US?34:58 – Three types of connects at physical events37:06 – What physical presence at events signals39:06 – Founders' feedback on Neon Fund41:41 – “Don't build in silence”43:50 – How to build a core AI-native company today47:54 – Do first-time founders have an edge in the AI era?52:08 – Cost to PMF has drastically gone down54:48 – What hard problems are startups solving today?55:37 – Why are acquisitions rare in India?1:00:20 – How US investors are facilitating M&As1:01:14 – How to make your brand visible to potential acquirers-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

Think recovery tools only work if you use everything at once? Jake Ross breaks down how combining PEMF, far infrared, red light, & natural gemstones creates consistent gains in energy, nervous system balance, and performance, without complicated routines. Meet our guest Jake Ross is part of the leadership and growth marketing team at HealthyLine, where he helps translate advanced recovery technologies into real-world routines for athletes and health-focused individuals. He focuses on product education, customer experience, and how PEMF, infrared, red light, and gemstone therapies support recovery and longevity. Thank you to our partners Outliyr Biohacker's Peak Performance Shop: get exclusive discounts on cutting-edge health, wellness, & performance gear Ultimate Health Optimization Deals: a database of of all the current best biohacking deals on technology, supplements, systems and more Latest Summits, Conferences, Masterclasses, and Health Optimization Events: join me at the top events around the world FREE Outliyr Nootropics Mini-Course: gain mental clarity, energy, motivation, and focus Key takeaways NFL athletes like Christian Jones use jet mats to support daily recovery, showing recovery now ranks as high as training Pro athletes extend peak performance years by prioritizing recovery over more volume or intensity Consistent recovery routines drive measurable sleep score gains, rising from the low 70s to 86+ PMF devices emit controlled low-intensity waves tuned to biological function, unlike uncontrolled environmental EMFs PMF frequencies range from 0–15 Hz, with Earth's Schumann resonance at 7.83 Hz as a biological reference point Users tune PMF protocols for relaxation, grounding, productivity, or physical recovery based on nervous system needs HealthyLine Jet Series mats stack PMF, far infrared, red light, negative ions, & gemstone heat in one session Far infrared heat penetrates deeply to increase blood flow & support joints, tissues, & recovery Heated gemstones like amethyst, jade, & tourmaline release negative ions that improve air quality & relaxation Consistent stacked recovery beats one-off sessions, reinforcing the lesson that recovery works best as a habit, not a hack Episode highlights 00:00 EMFs as an invisible performance stressor in modern environments 04:28 PEMF explained and how controlled frequencies support recovery 10:58 Why recovery habits drive longevity and performance 22:17 Building sustainable recovery routines that actually stick 38:50 Stacking PEMF, infrared, red light, and natural elements 52:42 Long-term recovery mindset and avoiding quick-fix thinking   Links Watch it on YouTube: https://youtu.be/ruOssswPbo0  Full episode show notes: outliyr.com/246 Connect with Nick on social media Instagram Twitter (X) YouTube LinkedIn Easy ways to support Subscribe Leave an Apple Podcast review Suggest a guest Do you have questions, thoughts, or feedback for us? Let me know in the show notes above and one of us will get back to you! Be an Outliyr, Nick

Harman went from cold-calling hotels 100 times a day to building the category-defining guest management platform for the hospitality industry. Canary built a $600M company by first solving one tiny, annoying problem: paper credit card authorization forms.In this episode, Harman breaks down how a simple digital form became the wedge into thousands of hotels. He reveals why they stuck with outbound sales long after hitting millions in revenue, the terror of collecting physical checks during the first week of COVID, and the exact moment he knew they had hit product-market fit.Why You Should ListenThe "Activated Hair on Fire" framework: How to turn a latent problem into a must-have purchase.Why outbound sales (and cold calling) is often your top early growth channel.How to use a simple, "unscalable" wedge to unlock a massive market.Why you should celebrate the lows: A counterintuitive take on managing founder psychology.The story of signing 200+ customers in a single day (and finding true PMF).Keywordsstartup podcast, startup podcast for founders, product market fit, finding pmf, vertical saas, outbound sales, cold calling strategies, early stage growth, b2b sales, hospitality tech00:00:00 Intro00:02:13 From Management Consulting to Hotel Tech00:11:32 The Paper Form that Launched a Company00:17:35 The Activated Hair on Fire Framework00:24:26 Landing the First Customer via Cold Call00:28:21 Applying to YC 00:32:35 Making 100 Cold Calls a Day00:43:42 The COVID Cash Flow Panic00:48:27 Signing 200 Customers in One DaySend me a message to let me know what you think!

Jason Cohen is a four-time founder (including two unicorns, one being WP Engine) and an investor in over 60 startups, and has been sharing his lessons on company building at A Smart Bear for nearly 20 years. In this episode, Jason shares his methodical five-step framework for diagnosing stalled growth—a problem that faces almost every team.We discuss:1. Jason's five-step framework: logo retention, pricing, NRR, marketing channels, target market2. A small tweak that'll double response rates on your cancellation surveys3. Why “it's too expensive” is almost never the real reason customers cancel4. The “elephant curve” of growth5. How repositioning the same product can increase revenue 8x6. When to reconsider if growth is even the right goal for your business—Brought to you by:10Web—Vibe coding platform as an APIStrella—The AI-powered customer research platformBrex—The banking solution for startups—Episode transcript: https://www.lennysnewsletter.com/p/why-your-product-stopped-growing—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Jason Cohen:• Preorder Jason's book: https://preorder.hiddenmultipliers.com/• X: https://x.com/asmartbear• LinkedIn: https://www.linkedin.com/in/jasoncohen• Blog: https://longform.asmartbear.com• Website: https://wpengine.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Jason Cohen(05:19) Jason's writing journey(08:25) Questions to ask when your product stops growing(18:17) Getting real customer feedback(20:27) Analyzing cancellation reasons(26:54) Onboarding and activation(29:35) Quick summary(35:46) Revisiting pricing strategies(41:46) Positioning strategies(47:52) Why pricing is inseparable from your strategy(52:06) The importance of net revenue retention (NRR)(01:00:25) Asking whether or not this is good for the customer(01:04:34) Leveraging existing customers(01:06:42) Are your acquisition channels saturated? The “elephant curve”(1:09:41) Why all marketing channels eventually decline(01:12:04) Direct vs. indirect marketing channels(1:13:36) Getting creative with new channels(01:19:04) Do you actually need to grow?(01:25:57) Deciding when to quit(01:29:27) Book announcement(01:33:21) AI corner(01:34:35) Contrarian corner(01:37:43) Lightning round and final thoughts—Referenced:• Tyler Cowen's website: https://tylercowen.com• How to Perform a Customer Churn Analysis (and Why You Should): https://www.groovehq.com/blog/learn-from-customer-churn• Linear: https://linear.app• Jira: https://www.atlassian.com/software/jira• Patrick Campbell's post on X about pricing: https://x.com/Patticus/status/1702313260547006942• The art and science of pricing | Madhavan Ramanujam (Monetizing Innovation, Simon-Kucher): https://www.lennysnewsletter.com/p/the-art-and-science-of-pricing-madhavan• Pricing your AI product: Lessons from 400+ companies and 50 unicorns | Madhavan Ramanujam: https://www.lennysnewsletter.com/p/pricing-and-scaling-your-ai-product-madhavan-ramanujam• Pricing your SaaS product: https://www.lennysnewsletter.com/p/saas-pricing-strategy• M&A, competition, pricing, and investing | Julia Schottenstein (dbt Labs): https://www.lennysnewsletter.com/p/m-and-a-competition-pricing-and-investing• “Sell the alpha, not the feature”: The enterprise sales playbook for $1M to $10M ARR | Jen Abel: https://www.lennysnewsletter.com/p/the-enterprise-sales-playbook-1m-to-10m-arr• Buffer: https://buffer.com• AG1: https://drinkag1.com• How to find hidden growth opportunities in your product | Albert Cheng (Duolingo, Grammarly, Chess.com): https://www.lennysnewsletter.com/p/how-to-find-hidden-growth-opportunities-albert-cheng• How Duolingo reignited user growth: https://www.lennysnewsletter.com/p/how-duolingo-reignited-user-growth• The Elephant in the room: The myth of exponential hypergrowth: https://longform.asmartbear.com/exponential-growth• HubSpot: https://www.hubspot.com• Zigging vs. zagging: How HubSpot built a $30B company | Dharmesh Shah (co-founder/CTO): https://www.lennysnewsletter.com/p/lessons-from-30-years-of-building• Adjacency Matrix: How to expand after PMF: https://longform.asmartbear.com/adjacency/• Ecosystem is the next big growth channel: https://www.lennysnewsletter.com/p/ecosystem-is-the-next-big-growth• ChatGPT apps are about to be the next big distribution channel: Here's how to build one: https://www.lennysnewsletter.com/p/chatgpt-apps-are-about-to-be-the• 10 contrarian leadership truths every leader needs to hear | Matt MacInnis (Rippling): https://www.lennysnewsletter.com/p/10-contrarian-leadership-truths• Breaking the rules of growth: Why Shopify bans KPIs, optimizes for churn, prioritizes intuition, and builds toward a 100-year vision | Archie Abrams (VP Product, Head of Growth at Shopify): https://www.lennysnewsletter.com/p/shopifys-growth-archie-abrams• Geoffrey Moore on finding your beachhead, crossing the chasm, and dominating a market: https://www.lennysnewsletter.com/p/geoffrey-moore-on-finding-your-beachhead• ER on Prime Video: https://www.amazon.com/ER-Season-1/dp/B0FWK5WJQ4• The Pitt on Prime Video: https://www.amazon.com/The-Pitt-Season-1/dp/B0DNRR8QWD• Wispr Flow: https://wisprflow.ai• Anker: https://www.anker.com—Recommended books:• Will: https://www.amazon.com/Will-Smith/dp/1984877925• Monetizing Innovation: How Smart Companies Design the Product Around the Price: https://www.amazon.com/Monetizing-Innovation-Companies-Design-Product/dp/1119240867• Hidden Multipliers: Small Things That Accelerate Growth: https://preorder.hiddenmultipliers.com• On Writing Well: The Essential Guide to Mastering Nonfiction Writing and Effective Communication: https://www.amazon.com/Writing-Well-Classic-Guide-Nonfiction/dp/0060891548• Crossing the Chasm, 3rd Edition: The Updated Version of the Insightful Guide on Bringing Cutting-Edge Products to the Mainstream: https://www.amazon.com/Crossing-Chasm-3rd-Disruptive-Mainstream/dp/0062292986—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Amplio operates a two-sided marketplace that helps manufacturers monetize surplus inventory and decommissioned industrial equipment rather than writing off assets or paying for disposal. The company has won contracts with GM and SpaceX despite competing against liquidators with 30-year local relationships. In a recent episode of BUILDERS, we sat down with Trey Closson, Co-Founder and CEO of Amplio, to unpack how the company executed a complete business model pivot from supply chain risk software to marketplace, discovered that enterprise deals close faster than SMB despite conventional wisdom, and built repeatable GTM motions in a fragmented $100B+ market previously dominated by local operators. Topics Discussed: Executing Amplio's pivot from supply chain risk software to surplus inventory marketplace Moving four truckloads of inventory through a WeWork to prove the business model Closing GM and SpaceX inbound from Google Ads as the PMF validation signal Displacing 30-year incumbent relationships through corporate + local dual threading Why enterprise contracts closed faster than SMB deals in Amplio's specific context Scaling beyond founder-led sales to repeatable AE motions Operating a two-sided marketplace: supply acquisition strategy vs. demand conversion GTM Lessons For B2B Founders: Manual heroics prove economics before automation: When a customer offered Amplio $25 million in surplus inventory, Trey had no warehouse, no logistics infrastructure, and no playbook. What was supposed to be four pallets became four full truckloads delivered to their WeWork. Trey and one employee physically moved inventory boxes off pallets into their office space, then figured out how to sell it while the WeWork management threatened eviction. The core insight: "the first time solving a problem, it doesn't need to be an automated, efficient process, it just needs to be okay. A customer has a problem, we need to figure out a way to solve that problem." Only after proving they could profitably solve the problem multiple times did they invest in automation and efficiency. For founders, the implication is clear—delay infrastructure investment until you've manually proven unit economics and repeatability, even if execution requires unsustainable effort. True PMF signals come from zero-relationship wins: Trey leveraged 15 years of supply chain relationships to secure initial customers and build product infrastructure. But he identifies the precise PMF inflection point: "middle of last year, we had both GM and SpaceX respond to a Google Ad." These companies had zero connection to Trey or his co-founder, found Amplio through SEM, and chose them over traditional liquidators they'd worked with for years. This is the distinction between "my network will buy from me" and "the market will buy from us." Founders should use their Rolodex to achieve velocity and prove the concept, but recognize that true product-market fit only exists when customers with no founder relationship choose your solution over established alternatives. Enterprise velocity depends on payment direction and urgency profile: Amplio deliberately focused on enterprise after being told by multiple founders to avoid "hunting whales." They discovered enterprise closed faster than SMB for three structural reasons. First, SMBs had unrealistic recovery expectations—wanting $900K back on $1M inventory when market reality is cents on the dollar, creating unresolvable expectation gaps. Second, enterprises had the problem across 100+ facilities with no dedicated owner and urgent mandates from finance or supply chain leadership. Third, because Amplio pays customers rather than charging them, legal review velocity increased dramatically. As Trey explains: "the lawyers thankfully determine, because we're not getting paid by them, that there's low risk for them in terms of signing a contract with us." Founders should map their specific deal structure and customer urgency profile rather than defaulting to SMB-first based on generic advice. Displace entrenched relationships through dual-threading: The surplus liquidation market is hyper-fragmented with hundreds of thousands of local liquidators, many holding 30-year plant-level relationships. Amplio's breakthrough: "partnering together with that person at the corporate level we can indicate not only can we solve the problem locally, but we can also do it across the entire enterprise." They pair the local plant manager with corporate procurement or finance leadership, demonstrating local problem-solving plus enterprise-wide scalability that local liquidators cannot match. This dual-threading strategy neutralizes the incumbent's relationship advantage while showcasing the efficiency and consistency that corporate leadership values. For founders entering relationship-driven markets, identify the corporate stakeholder whose enterprise-wide objectives trump individual facility loyalty. Accelerate trust through predictable execution in low-NPS markets: Industrial liquidation is a "really low NPS industry—nobody loves working with their liquidator." In markets with poor customer satisfaction and commoditized offerings, trust accelerates when you focus on "say-do ratio"—if you commit to something, execute it. Amplio often solves adjacent problems outside their core offering and frequently removes inventory from warehouses faster than economically optimal to make customers "look like an absolute hero." This over-delivery in low-satisfaction markets creates disproportionate differentiation. The tactical implementation: understand what problems the organization is trying to solve beyond your core product, find ways to solve those problems even if not monetizable, and prioritize making your champion successful over optimizing every transaction. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Send us a textGuest: Kevin Jacobson, CEO at Foxen -- Most SaaS companies try to scale by adding headcount and channels. Foxen scaled by tightening fundamentals.In this episode, Kevin Jacobson, CEO of Foxen, joins host Ken Lempit to explain how an overlooked market — multifamily housing — became a durable SaaS growth opportunity through operational discipline and relationship-driven GTM.Kevin breaks down why traditional industries lag in SaaS adoption, why consistency matters more than speed, and how Foxen scaled through direct sales, referrals, and systems built to support growth. He also shares lessons from raising growth equity and why systems, not people, ultimately unlock scale.Key takeaways:Underserved markets reward execution over hypeConsistency precedes scalable SaaS growthDirect sales still win in relationship-driven marketsSystems, not headcount, enable scaleIf you're a SaaS leader selling into traditional industries or rethinking how growth really happens after PMF, this episode delivers a grounded, operator-first perspective.---Not Getting Enough Demos? Your messaging could be turning buyers away before you even get a chance to pitch.

My guests today are Tom Digan and Greg Stewart. Tom is the co-founder of Ladder, and Greg is its CEO. Ladder was my first angel investment. What followed over the next seven years is one of the most unlikely and dramatic business stories I've been a part of. Today, Ladder is the number one grossing fitness app in the App Store, approaching $100M in ARR with more than 300,000 paying members. But the path from near death to dominance involved debt collectors, leadership changes, and a full reset during the pandemic. Tom and Greg built Ladder by being relentlessly empirical about their customers, ruthless about prioritization, raising money wherever they could, and doing whatever it took when most founders would have quit. We cover the messy early years when survival meant negotiating creditors, how they found PMF by reading thousands of app store reviews, and how they built a TikTok growth engine with no performance marketing experience. They share their long-term vision for becoming the category winner for health and fitness and the impact of AI and GLP-1s on their business. This is a conversation about how hard it really is to build something valuable, told by two people who lived through all of it. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠ ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠Ramp⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠ to sign up for free and get a $250 welcome bonus. ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vanta. Trusted by thousands of businesses, Vanta continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Visit vanta.com/invest.  ----- This episode is brought to you by Rogo. Rogo is an AI-powered platform that automates accounts payable workflows, enabling finance teams to process invoices faster and with greater accuracy. Learn more at Rogo.ai/invest. ----- This episode is brought to you by ⁠WorkOS⁠. WorkOS is a developer platform that enables SaaS companies to quickly add enterprise features to their applications. Visit ⁠WorkOS.com⁠ to transform your application into an enterprise-ready solution in minutes, not months. ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ridgeline⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps (00:00:00) Welcome to Invest Like The Best  (00:04:26) Episode Intro (00:05:45) Ladder: The #1 Fitness App (00:09:28) The Messy, Early Years (00:14:47) Sponsors (00:16:20) The Darkest Point (00:18:17) Why Greg Joined Ladder (00:19:45) The Turning Point: Ladder 2.0 (00:21:57) The Key to Negotiating with Creditors (00:23:16) Fundraising Challenges and Strategies (00:25:50) Developing Ladder Teams (00:31:31) Listen to Your Customers (00:32:57) Launching Nutrition (00:38:53) Sponsors (00:39:31) Don't Listen to Investors on Product Feedback (00:40:18) The Cave Process (00:43:13) Crossing the Chasm (00:43:53) How to Crack TikTok (00:51:10) The Content Frontier (00:52:07) Controlled Bets at Scale (00:54:19) Why you should Build a B2C Company (00:57:37) The Impact of AI and GLP-1s (01:02:32) Sponsors (01:02:53) Staying Focused on the Core Product (01:05:00) Building the System of Record for Health and Fitness (01:09:45) What It's Like Talking to Investors Now (01:12:32) The Kindest Thing

Jake founded Serval in April 2024— by Dec 2025 he'd raised a $75M Series B from Sequoia at a $1B valuation.He didn't look for a "wedge" or a "niche." He looked at ServiceNow—a $160B, 20+ year-old incumbent that everyone IT team relies on—and rebuilt it from the ground up in a YEAR. In this episode, Jake reveals the audacity behind building a full-platform replacement from Day 1, why he spent months building in the dark with zero revenue, and how he achieved a 50% demo-to-close rate on six-figure enterprise deals.Why You Should ListenHow to go from incorporation to a $1B valuation in just 18 months.The psychological shift in sales calls that proves PMF.How to build a demo so compelling that 50% buy on the spot.Why you no longer need to find a small wedge to win post Gen AI.The specific question that stops customers from giving you generic feedback.Keywordsstartup podcast, startup podcast for founders, hypergrowth, zero to one, unicorn startup, Sequoia Capital, replacing legacy software, enterprise sales strategy, ServiceNow competitor, Jake Stauch00:00:00 Intro00:03:25 Why "Hair on Fire" Problems Matter00:06:58 Learning What Winning Feels Like at Verkada00:14:05 100+ Customer Discovery Calls00:18:12 The One Question That Unlocks Real Pain00:23:48 Why No-Code Workflows Fail00:28:45 Taking Risks on AI Model Improvements00:35:49 From $0 to Six-Figure ACVs in 6 Months00:39:00 The Strategy to Rip and Replace ServiceNow00:47:30 The "Rounding Up" Signal of PMFSend me a message to let me know what you think!

This week, Dougie DeLuca joins the Hivemind team to discuss his recent piece "Crypto Is Dead". We deep dive into the shift that is happening within crypto as we head into 2026, why is sentiment so bad, where to allocate in 2026, where crypto has found PMF and more. Enjoy! -- Follow Dougie: https://x.com/DougieDeLuca Follow Jose: https://x.com/ZeMariaMacedo Follow Jason: ⁠https://x.com/3xliquidated⁠ Follow Yan: https://x.com/YanLiberman Follow Empire: ⁠https://x.com/theempirepod⁠ Subscribe on YouTube: ⁠https://bit.ly/4jYEkBx⁠ Subscribe on Apple: ⁠https://bit.ly/3ECSmJ3⁠ Subscribe on Spotify: ⁠https://bit.ly/4hzy9lH⁠ -- Crypto Is Dead: https://x.com/DougieDeLuca/status/2000957512862884100 -- Get top market insights and the latest in crypto news. Subscribe to Blockworks Daily Newsletter: ⁠https://blockworks.co/newsletter/⁠ -- Timestamps: (0:00) Introduction (0:56) Is Crypto Dead? (12:20) Why Is Sentiment So Bad? (28:52) Crypto's Path Forward (34:21) Where To Allocate In 2026? -- Disclaimer: Nothing said on Empire is a recommendation to buy or sell securities or tokens. This podcast is for informational purposes only, and any views expressed by anyone on the show are solely our opinions, not financial advice. Santiago, Jason, the Hivemind team, and our guests may hold positions in the companies, funds, or projects discussed.

GreenLite delivers private construction plan review as an alternative to traditional city permitting processes. After spending six months testing both sides of the construction permitting transaction, the company identified owner-developers as their ICP and built a business model around Florida's privatization legislation—legislation that has now expanded to nine additional states including Texas, Tennessee, and California. In this episode of BUILDERS, we sat down with James Gallagher, CEO and Co-Founder of GreenLite, to explore how his fifth startup leveraged regulatory shifts, rejected workflow software in favor of outcomes, and scaled by targeting chief development officers at enterprise retailers struggling with permitting delays. Topics Discussed: How GreenLite discovered architects were heavy users but wrong customers due to two-part sales dynamics Why owner-developers became the ICP after six months of customer discovery across applicants and agencies The accidental discovery of private plan review through conversations with Fort Worth and Miami-Dade agencies GreenLite's platform combining regulatory permissions, licensed AEC professionals, and AI-augmented software How natural disasters and AEC talent shortages are accelerating privatization legislation nationwide Cold email strategies that converted enterprise retailers by surfacing acute pain points GTM Lessons For B2B Founders: Map two-sided markets to find where purchasing authority and pain intersect: GreenLite pitched a CTO at a major architecture firm who responded positively but said "I just need to talk to my client, my customer." This revealed architects required approval from owner-developers despite being the heaviest product users. James pivoted to owner-developers who "carry the land, carry the construction loans" and feel revenue delays most acutely. The lesson: usage intensity doesn't equal buyer authority. In complex ecosystems, systematically test which party controls budget and feels enough pain to sign contracts independently. Recognize when procurement cycles kill early-stage validation velocity: Cities explicitly told James their "crazy procurement cycles" made early partnership impractical despite genuine interest. State and local education and government sales require specialized expertise and extended timelines that prevent rapid iteration. James chose to prove the model with private sector customers first. For founders: government can be a lucrative eventual market, but unless you have sled sales expertise and 12+ month runway per deal, validate PMF elsewhere first. Capitalize on regulatory tailwinds before markets realize they exist: Only Florida permitted private plan review when GreenLite launched in July 2022. By late 2024, nine states passed enabling legislation driven by natural disaster reconstruction needs and talent shortages in city building departments. James positioned GreenLite to ride this wave rather than selling transformation to resistant agencies. Founders should monitor legislative and regulatory changes in their verticals—new compliance requirements or permissions can suddenly open massive TAMs with minimal incumbent competition. Enterprise cold email converts when you surface non-obvious acute pain: GreenLite cold emailed chief development officers at major retail chains and quick-service restaurants with "Are you missing your openings due to permitting?" The response rate validated that permitting delays—not site selection or construction costs—were a critical path blocker for store rollout velocity. James targeted CDOs rather than real estate or design teams because they own the full development timeline. For enterprise sales: identify the executive accountable for the metric your solution impacts, then lead with how you move that specific number. Validate outcome-based models before building sophisticated workflow tools: GreenLite's customers rejected "another workflow product or system of record" that required API integrations with their ERPs and construction management systems. Instead, they wanted "faster, more predictable, more transparent permits." James built a viable business delivering finished permits through licensed professionals augmented by software, with the AI sophistication coming later. The business was "super viable well before the product was" by early 2023. For founders in industries resistant to software adoption: test whether buyers want tools to operate or outcomes to purchase—outcome-based pricing can achieve PMF faster and command premium willingness-to-pay. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role.  Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

遊戲規則已經改變，未來的贏家將是最快察覺並回應產品市場契合度變化的公司。The rules of the game have changed, the companies that will win in the future detect and respond to changes in PMF the fastest. -- Hosting provided by SoundOn

Arthur Breitman, the co-founder of Tezos, is a computer scientist and entrepreneur. Arthur has a background in mathematics and computer science, and prior to the Tezos project, he worked in quantitative finance at Goldman Sachs and Morgan Stanley, and as a research engineer at Google and Waymo. Arthur graduated from the École Polytechnique and the Courant Institute of NYU where he studied applied mathematics. Arthur is a member of the Tezos Foundation Council and is also a director at Trilitech, a London-based adoption team for the Tezos blockchain. In this conversation, we discuss:- Tokenized uranium - Quantum's threat to Bitcoin - Deep dive on Tezos blockchain - Proof-of-stake is the best consensus - There is no PMF for security on blockchains - The emergence of Tezos as the artists' blockchain - EVM compatibility layer - 19 upgrades without a hard fork - The Data Availability Layer deep dive - The future of Tezos TezosX: @tezosWebsite: tezos.comLinkedIn: TezosArthur BreitmanX: @ArthurBLinkedIn: Arthur Breitman---------------------------------------------------------------------------------This episode is brought to you by PrimeXBT.PrimeXBT offers a robust trading system for both beginners and professional traders that demand highly reliable market data and performance. Traders of all experience levels can easily design and customize layouts and widgets to best fit their trading style. PrimeXBT is always offering innovative products and professional trading conditions to all customers.  PrimeXBT is running an exclusive promotion for listeners of the podcast. After making your first deposit, 50% of that first deposit will be credited to your account as a bonus that can be used as additional collateral to open positions. Code: CRYPTONEWS50 This promotion is available for a month after activation. Click the link below: PrimeXBT x CRYPTONEWS50FollowApple PodcastsSpotifyAmazon MusicRSS FeedSee All

Most conversations in startups begin at zero: what's the idea, who's the customer, how big is the market. But the stage before that, when you know you're ready to be a founder yet the direction is still completely undefined. That strange, uncomfortable, high-potential zone Aditya Agarwal calls “minus one.”In this episode, Aditya and Prateek Mehta breaks down what happens in this “figuring out” stage. The questions people avoid, the habits that matter, and why some of the best companies begin long before their founders have any conviction.We get into how this stage is evolving in the AI era. Exploration cycles are faster, technical founders can test more directions than ever, and the gap between “I'm experimenting” and “I'm running a real company” has narrowed. India's builder ecosystem is shifting too: more second-time founders, more people with real outcomes behind them, and far more comfort sitting with ambiguity.Aditya shares his own minus-one moment after Facebook, his startup acquisition, Dropbox's IPO, and Flipkart, and why that transitional period changed the way he thinks about early-stage startups. Prateek brings on-the-ground view from Bangalore, where ambition, technical depth, and the appetite to explore hard problems from robotics to voice models to AI infra are rising.This episode is for anyone who feels they're between missions. Anyone who wants to understand why the most important part of building a company might actually be the time you spend before you even know what you're building.00:00- Trailer01:06- Aditya's journey to starting SPC after Facebook & Dropbox 03:48- A “learning club” for people in figuring-out stage06:23- 3 Northstars of the SPC community07:02- How SPC evolved from a community to a fund10:32- Not everyone should be a founder11:51- 1% selection rate13:53- Building conviction in 1 of 3 outcomes16:36- SPC is at PMF stage18:38- Mismatch of traditional VC's v/s rapid pace startups19:04- How AI has impacted investing at SPC26:32- How AI has changed VC firms29:02- Axis of curiosity replacing thesis30:17- Star Companies of SPC US33:34- Binny Bansal's role in starting SPC India37:16- Questions & confusions as founders in early stage39:50- Number of great entrepreneurs is NOT small41:49- Talent density in India vs Bay Area44:04- Founders don't need a culture of permission45:08- India tier 2 and 3 does invest heavily in AI46:11- AI is truly democratizing tech49:09- Math gives India advantage in AI51:48- A lot of science fiction is coming true-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us a text

Zaid Rahman is the Co-founder and CEO of Flex.Flex is the AI native private bank for high net worth middle market business owners, headlined by it's 60-day interest free credit card for businesses.Flex just announced their $60 million Series B, as well as their new consumer product, Flex Elite, which pits it head-to-head against Amex for the consumer spending of some of the wealthiest people in America. It's products now spans from when a business owner first generates revenue, all the way to when they spend that cash personally.This conversation goes inside how the company scaled from zero to a $70 million revenue run rate in two years, and everything Zaid learned along the way.Thank you to Eric Bahn at Hustle Fund, Jeff Morris Jr. at Chapter One, Andrew Ziperski at General Catalyst, and Jared Thomas and Ewan Steel at Flex for helping brainstorming topics for the conversation.Timestamps:(1:44) Raising $60m to fix business finance(3:23) Flex Elite: Personal + Business banking(4:48) Jumbo shrimps: powering 40% of US payroll(9:16) The forgotten mid market business(14:01) “Flex fuels ambition”(16:08) How to serve entrepreneurs in middle America(22:58) Flex's 5-pillar product suite(27:12) Starting Flex to help construction companies(31:51) Using AI to lend to mid-market customers(40:22) Power of multi-product in fintech(43:53) Zero to $3B in volume in 18 months(44:43) Raising a bear market Series A in 2023(51:00) How referrals landed their first big customers(55:07) Flex's playbook for 85% organic growth(1:01:15) Dissecting various accents(1:04:22) Building a quiet luxury brand(1:09:33) Importance of customer happiness(1:12:43) Why CEO's should be the top sales person(1:13:58) Building lots of in-house software(1:24:33) PMF is like operating a popular restaurant(1:30:49) How to raise a debt facility(1:34:48) Recruiting is so crucial for startups(1:39:00) Why VC's hate lending businesses(1:45:14) Underserved vs Underbanked in fintech(1:48:02) Why business owners want personal + business banking(1:54:49) Acquiring Maza, leaning in to M&A(2:02:53) Most fintech companies look the same(2:08:35) Founder group therapy with Eric at Hustle Fund(2:11:50) The Thiel Fellowship's 10% unicorn hit rate(2:15:52) Lesson from the ruler of Dubai(2:19:24) Building Flex's risk underwriting engine(2:26:58) Flex's AI opportunityReferencedTry Flex: https://www.flex.oneCareers at Flex: https://jobs.lever.co/Flex/Basel III https://en.wikipedia.org/wiki/Basel_IIILinguistic TikTok account: https://www.tiktok.com/@zaydupreeLazy luxury: most worn shoes on private jets: https://www.wsj.com/style/fashion/lazy-luxury-sneakers-are-these-the-most-worn-shoes-on-private-jets-7801be30Follow ZaidTwitter: https://x.com/zaidrmnLinkedIn: https://www.linkedin.com/in/zaidrahmanFollow TurnerTwitter: https://twitter.com/TurnerNovakLinkedIn: https://www.linkedin.com/in/turnernovakSubscribe to my newsletter to get every episode + the transcript in your inbox every week: https://www.thespl.it/

This week, we're joined by Ishan Gupta, co-founder of Juicebox — a company redefining how recruiting works in an AI-native world.Ishan shares how they pivoted from earlier ideas to focus on the highest value part of hiring: identifying the right people and getting them into process. With Juicebox, you describe what you want in natural language and the platform searches more than 800 million professional profiles, surfaces the best matches, and engages candidates through recruiting agents.Their breakout feature, Autopilot, uses LLMs to semantically evaluate and stack rank candidates based on nuanced criteria, which quickly drove organic growth and strong PMF. We talk about why their data makes the product so sticky, how they see recruiting evolving over the next five years, what led them to raise their Series A, and the culture they are building around moving fast and being intellectually honest.Ishan also shares what they are building next with their memory layer, which will make it clear what the AI is learning over time and how it improves future searches.Episode chapters:1:44 - Competitive programming4:35 - Choosing the company name5:55 - YC and the pivot8:00 - How Juicebox differs from traditional recruiting9:51 - The killer feature14:30 - What makes the product sticky17:15 - Is recruiting a zero sum game19:10 - The macro view of hiring22:05 - Raising a later Series A24:10 - Product expansion26:45 - Quick fire round This episode is brought to you by Grata, the leading deal sourcing platform for private equity. Grata's AI powered search, investment grade data, and intuitive workflows help you find and win the right deals faster. Visit grata.com to book a demo.This episode is also sponsored by Overlap, the AI powered app that uses LLMs to surface the best moments from any podcast. Overlap reads full transcripts, finds the most relevant clips, and stitches them into a personalized stream of insights. Tap into podcasts as a real information source with Overlap 2.0, now available on the App Store.

Brian Balfour, Founder & CEO of Reforge and former VP of Growth at HubSpot, joins Mostly Growth to explore why product-market fit is a moving target. He introduces the concept of the Product-Market Fit Treadmill, a state where rising customer expectations and competitive pressure make it harder than ever to stay ahead. Brian breaks down how AI has accelerated PMF collapse, explains the hidden costs of product adoption, and shares how Reforge shipped five AI-native products with a team of just 20 people. Packed with frameworks, strategic insight, and startup realism, this episode is essential listening for product leaders, operators, and founders navigating the next wave of GTM.—SPONSORS:Pulley is the cap table management platform built for CFOs and finance leaders who need reliable, audit-ready data and intuitive workflows, without the hidden fees or unreliable support. Switch in as little as 5 days and get 25% off your first year: https://pulley.com/mostlymetricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.com—LINKS:Mostly Metrics: https://www.mostlymetrics.comCJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Growth Unhinged: https://www.growthunhinged.com/Kyle on LinkedIn: https://www.linkedin.com/in/kyle-poyar/Brian Balfour: brianbalfour.comBrian on LinkedIn: https://www.linkedin.com/in/bbalfour/Slacker Stuff: https://www.slackerstuff.com/Ben on LinkedIn: https://www.linkedin.com/in/slackerstuff/https://brianbalfour.com/four-fits-growth-frameworkhttps://x.com/amasad/status/1981201454032703662?s=46https://getlatka.com/companies/firefliesaihttps://x.com/rowancheung/status/1988218743952916537?https://gamma.app/insights/how-we-built-a-usd100m-business-differently—RELATED EPISODES:When the marketing math doesn't math | with Emily Kramerhttps://youtu.be/sSuoV_YSrlwWhy Founders Are Posting Sad Dinnershttps://youtu.be/Zl6NSIHF2Gk—TIMESTAMPS:00:00:00 Preview and Intro00:01:51 Sponsors – Pulley, Metronome00:04:11 Introducing Brian Balfour & Reforge background00:07:22 Evergreen frameworks & Four Fits resurgence00:11:01 PMF treadmill and rising expectations00:14:26 AI shocks and PMF collapse (Chegg)00:16:43 CRM expectations & AI-native workflows00:20:44 R&D as ongoing cost to serve00:22:26 Customers buying based on future product velocity00:24:32 Communicating rapid releases & driving adoption00:25:17 Reforge's expanding AI product suite00:27:52 Product delivery vs. product adoption bottlenecks00:29:32 Platform distribution shifts introduction00:30:51 Evaluating emerging platforms00:32:04 The open → close platform cycle00:33:31 Moats, escape velocity & platform dominance00:36:32 Choosing major vs. emerging platforms00:40:22 ChatGPT dominance in AI discovery00:42:16 Hiring, resumes & filtering AI-generated applications00:43:30 AI note-taking market & “Flintstoning”00:47:03 Trying Gamma & AI-generated presentation tools00:50:08 AI onboarding innovations (WhatsApp agent)#MostlyGrowthPodcast #ProductMarketFit #BrianBalfour #StartupStrategy #Reforge This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cjgustafson.substack.com

It's Etiquette Finishing School Day at Slow Ventures, Sam dials in from the Four Seasons in a Brioni suit to recap Slow's first-ever Etiquette School—covering caviar bumps, sommelier tips, and the “low heart rate” approach to leadership. The crew argue that etiquette now matters in tech because trust is scarce and “PMF-only” is an outdated YC-era story. Jess also unpacks details from Apple's Gemini deal, Sequoia's leadership shuffle, Anthropic's latest numbers, and crypto's meme-driven chaos. Watch till the end for free No Kings and Queens of Corbet protest tees from Sam.Chapters:02:33 Etiquette Day at Slow — Sam's recap from the Four Seasons07:00 Why etiquette matters for founders in 202513:20 Apple x Google: Gemini to power Siri17:24 Apple's AI strategy: Restricting Spend on AI20:04 LLMs vs search the new user behavior shift27:40 Sequoia's leadership handoff36:44 Meme coin corner Jelly's 400M rise and community-led products47:55 Waymo swarms El Camino AI meets the real world50:30 Sam's "No Kings and Queens" merchWe're also on ↓X: https://twitter.com/moreorlesspodInstagram: https://instagram.com/moreorlessYouTube: https://youtu.be/zv4VdtKpQQkConnect with us here:1) Sam Lessin: https://x.com/lessin2) Dave Morin: https://x.com/davemorin3) Jessica Lessin: https://x.com/Jessicalessin4) Brit Morin: https://x.com/brit

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, we're joined by Gordon Liao, Chief Economist at Circle, to dissect the Stablecoin Wars. From Circle's Arc and Stripe + Paradigm's Tempo, to Solana's native stablecoin push and Hyperliquid's deal, we unpack why everyone suddenly wants their own chain or branded stablecoin. Is this the future of crypto's monetary layer — or just a fragmentation nightmare? We dig into FX use cases, PMF for stablecoins, collective bargaining power of ecosystems, and whether “stablecoin-as-a-service” is the next killer primitive or a liquidity trap. Show highlights