Podcasts about Brex

Play Episode Listen Later Jun 11, 2026 35:55

In this episode of Run the Numbers, CJ breaks down Bending Spoons' F-1 filing and the acquisition machine behind AOL, Evernote, Vimeo, Eventbrite, and more. He unpacks the company's playbook: buy under-optimized digital businesses, transform operations, raise prices, reinvest earnings, and repeat — while asking the core question: how much was built, and how much was bought?—SPONSORS:RightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY has been part of Silicon Valley since it was just a valley, helping the most successful names in tech go from startup to exit to megacap. With teams across strategy, tax, audit, and transactions, EY helps you get your financials right early, long before your investors start asking for it. You build the next big thing, and EY will help you build it right. Learn more at https://www.ey.com/techstartupsSpendHound cuts your SaaS and AI spend by up to 30% using real pricing benchmarks across 10,000 vendors, so you always know what fair pricing looks like before your next renewal. Rated #1 on G2 in SaaS spend management, it's free forever for teams up to 1,000 employees. Sign up by June 12th and get $500 just for getting started. Go to https://www.spendhound.com/cjBrex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/run—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 What is Bending Spoons?1:03 The Internet's attic: the portfolio3:11 The metrics rundown5:44 Revenue: $1.3B, 95% growth6:04 82% of growth was bought, not built6:29 Gross margin: 66%6:50 Subscription mix and NRR7:33 Net income: basically zero8:00 Cash: $741M, debt: $4.4B8:35 Revenue per employee: $2.57M9:39 Sponsors — RightRev | Rillet | EY12:42 Organic growth is mostly price hikes13:50 A house of adjustments14:54 Add-backs bigger than the profit15:22 The reorganization line: cost of firing19:21 Sponsors — SpendHound | Brex | Aleph22:51 Does the playbook actually work?23:07 Evernote: the proof point23:45 Romini: the growth proof point24:10 AI in three directions at once25:45 The debt engine27:50 Red flag 1: material accounting weaknesses28:38 Red flag 2: pro forma numbers come with a confession29:00 Red flag 3: App Store dependency29:11 Red flag 4: no long-term contracts29:30 Red flag 5: foreign private issuer29:52 Red flag 6: they've never sold anything30:19 Cap table and board31:07 Valuation: 14–18x33:00 Bull vs. bear case33:55 Miscellaneous: the S1 is already stale35:25 Credits

The AI-Native GTM Playbook | Sam Blond, Monaco

The Peel

Play Episode Listen Later Jun 11, 2026 116:48

Sam Blond is the Co-founder and CEO of Monaco, the revenue engine for startups.Sam is one of the best sales operators in tech. He spent four years as CRO at Brex, where he helped scale it to a ~$12B valuation, ran sales at Zenefits before that, and got his start at EchoSign.If there's a modern GTM playbook, Sam helped write it. Our conversation walks through how AI has rewritten a big chunk of it. But most importantly, we talk about what hasn't changed.We get into the sales work AI is now better at than humans, and why Sam thinks 90% of startups misdiagnose their bottleneck as conversion when it's really demand gen.He explains why he doesn't measure early brand marketing at all and trusts anecdotes over attribution, walks through the full Monaco launch playbook including the Super Bowl box-truck story, and shares a rev-ops insight from Brex, including how they figured out a specific ICP converted at 4x the rate of another.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:(0:00) Scaling Brex to $12B(1:14) How AI speeds up prospecting and TAM building(5:19) Using AI to get more leverage(9:15) Incubating Monaco at Founders Fund(12:56) Innovator's dilemma in AI(15:57) Why AI companies build full platforms, not wedge products(23:30) Revenue is just a math equation(27:18) Two ways AI increases conversion rates(36:56) AI will never replace spending time with customers(39:46) Don't measure the impact of brand marketing(49:03) Your marketing must be different (and hard)(58:39) Customer discovery calls and working with design partners(1:03:03) The zero to 100 launch(1:11:00) Monaco's launch playbook(1:19:00) Send gifts that are unique and social(1:22:17) Naming your company(1:28:04) Founders should send early outbound(1:32:38) How multi-channel augments AI outbound(1:39:42) Using intent signals and outreach timing to increase conversions(1:43:28) Two common ways founders mess up when scaling revenue(1:50:22) Monaco's Forward Deployed AE'sReferencedTry Monaco: https://www.monaco.com/Careers at Monaco: https://jobs.ashbyhq.com/monacoSam's launch post: https://x.com/samdblond/status/2026420015793320129?s=20Follow SamTwitter: https://x.com/samdblondLinkedIn: https://www.linkedin.com/in/sam-blond-791026b/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/

Vercel's CFO Marten Abrahamsen: Move Fast or Fall Behind

Play Episode Listen Later Jun 8, 2026 53:42

CJ Gustafson sits down with Marten Abrahamsen, CFO of Vercel, at the NYSE to talk about running finance inside a hypergrowth AI company. They cover AI use cases in finance, rev rec, forecasting, KPI dashboards, PLG, consumption pricing, and Marten's “speeding tickets vs. parking tickets” framework for moving fast without losing control.—SPONSORS:Brex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY has been part of Silicon Valley since it was just a valley, helping the most successful names in tech go from startup to exit to megacap. With teams across strategy, tax, audit, and transactions, EY helps you get your financials right early, long before your investors start asking for it. You build the next big thing, and EY will help you build it right. Learn more at https://www.ey.com/techstartupsSpendHound cuts your SaaS and AI spend by up to 30% using real pricing benchmarks across 10,000 vendors, so you always know what fair pricing looks like before your next renewal. Rated #1 on G2 in SaaS spend management, it's free forever for teams up to 1,000 employees. Sign up by June 12th and get $500 just for getting started. Go to https://www.spendhound.com/cj—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/martenabrahamsen/Company: http://vercel.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Speeding tickets vs. parking tickets3:21 Visa IPO in the financial crisis5:09 Going public has changed6:45 Private market: 22–24 trillion9:03 More or fewer public companies?9:48 Sponsors — Brex | Aleph | RightRev13:04 KPI dashboard on your phone14:12 Revenue flux via Slack and Notion15:37 RevRec tool: green, yellow, red17:56 V0 is a job requirement19:43 Speeding tickets vs. parking tickets20:33 Sponsors — Rillet | EY | SpendHound23:49 Very few one-way doors25:02 Finance in hypergrowth25:39 Three-scenario planning27:00 Honest with the board31:00 PLG + consumption at Vercel33:32 What Marten checks every morning34:03 Why RPO doesn't work here35:36 Holiday usage is up37:10 ICP shifted to solo developer39:22 Capital allocation in a fast market41:32 Growth compounds; margin can't43:22 SaaS gross margins: spicy take44:24 Cash-burning AI: 2026 vs. 202147:29 Are some hypergrowth cos destroying value?50:00 Lightning round50:11 Bank of Ireland mix-up51:10 Don't punt problems forward52:04 Finance software stack52:38 Expensed an oven53:12 Credits

A CFO Explains Stock Exchanges

Play Episode Listen Later Jun 4, 2026 37:18

In this episode of Run the Numbers, CJ breaks down how stock exchanges became the operating system of modern capitalism. From ship captains raising voyage money, to the Dutch East India Company's first tradable shares, to coffee house traders, the Buttonwood Agreement, market crashes, Robinhood, and GameStop, this is the story of how markets turned ownership into something liquid, global, and very, very human.—SPONSORS:SpendHound cuts your SaaS and AI spend by up to 30% using real pricing benchmarks across 10,000 vendors, so you always know what fair pricing looks like before your next renewal. Rated #1 on G2 in SaaS spend management, it's free forever for teams up to 1,000 employees. Sign up by June 12th and get $500 just for getting started. Go to https://www.spendhound.com/cjBrex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY has been part of Silicon Valley since it was just a valley, helping the most successful names in tech go from startup to exit to megacap. With teams across strategy, tax, audit, and transactions, EY helps you get your financials right early, long before your investors start asking for it. You build the next big thing, and EY will help you build it right. Learn more at https://www.ey.com/techstartups—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.comSlacker Stuff: https://www.slackerstuff.com/Ben on LinkedIn: https://www.linkedin.com/in/slackerstuff/—RELATED EPISODES:A CFO Explains Secondarieshttps://youtu.be/pENvBuXhGukA CFO Explains the Diamond Industryhttps://youtu.be/fPrho7hvykAA CFO Explains Marketplaceshttps://youtu.be/LpbH9GpBrSY—TIMESTAMPS:0:00 The First IPO, and Why It Changed Everything2:50 Coffee, Buttonwood Trees, and the First Insider Trading Scandal6:31 The Railroads Built Your Month-End Close10:17 Sponsors — SpendHound | Brex | Aleph13:47 Buying Stocks on Credit, and How That Ended18:13 Merrill Lynch Goes to the Suburbs, and the Paper Almost Wins21:44 Sponsors — RightRev | Rillet | EY24:47 NASDAQ, Pets.com, and the Most Expensive Sock Puppet in History28:49 The Phone in Your Pocket Democratized Everything, For Better or Worse33:03 The Stock Market Was Never Really About Stocks36:47 Credits#RunTheNumbersPodcast #FinanceHistory #StockMarket #Investing #FinanceLeadership

P96. They Don't Need to Hack You Now. They Just Need to Wait. (with Kevin Kane)

Play Episode Listen Later Jun 2, 2026 26:02

Google has said to be concerned about quantum computing by 2029. Kevin Kane, Co-Founder and CEO of American Binary, argues that timeline is already too relaxed and that companies treating post-quantum as a future problem are the ones most exposed right now. He breaks down what a real quantum-resilient architecture takes, why formal verification matters, and what harvest attacks mean for every encrypted message sent today. Kevin Kane: www.linkedin.com/in/iamkevinpkane American Binary: https://www.ambit.inc Jon: www.linkedin.com/in/jon-mclachlan Sasha: www.linkedin.com/in/aliaksandr-sinkevich YSecurity: www.ysecurity.io

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Zapier's CFO on Closing the Books in 5 Days, AI Hiring Bars, and M&A Discipline

Play Episode Listen Later Jun 1, 2026 49:48

In this episode of Run the Numbers, CJ sits down with Ryan Roccon, CFO of Zapier, to cover AI hiring standards, automating month-end close, measuring ROI on AI spend, and why determinism still beats agents most of the time.—SPONSORS:EY has been part of Silicon Valley since it was just a valley, helping the most successful names in tech go from startup to exit to megacap. With teams across strategy, tax, audit, and transactions, EY helps you get your financials right early, long before your investors start asking for it. You build the next big thing, and EY will help you build it right. Learn more at https://www.ey.com/techstartupsSpendHound cuts your SaaS and AI spend by up to 30% using real pricing benchmarks across 10,000 vendors, so you always know what fair pricing looks like before your next renewal. Rated #1 on G2 in SaaS spend management, it's free forever for teams up to 1,000 employees. Sign up by June 12th and get $500 just for getting started. Go to https://www.spendhound.com/cjBrex is an intelligent finance platform with AI-powered agents that capture expenses automatically, enforce policy before the spend happens, and close your books in minutes instead of weeks. 35,000+ companies like OpenAI, Coinbase, Anthropic, and DoorDash already run on Brex. It's time to get Brex AF. Learn more at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform that lets your product team ship new pricing without asking finance for permission, and your sales team close deals without creating downstream chaos. Check out their free tool at calculator.rightrev.com It scores your rev rec process, shows what's exposing you to risk, and tells you exactly where to focus before it bites you in the rear end. Check it out at https://calculator.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cj—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/ryanroccon/Company: https://zapier.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:Ryan's First Appearancehttps://youtu.be/VIZ_RzfV78IChris Byington, Head of Data @ Superhumanhttps://youtu.be/ydH38JnWfww—TIMESTAMPS:0:00 Preview and intro2:11 Ryan's scope at Zapier3:17 Why the finance guy runs ecosystems6:19 AI is a required hire competency6:46 The four levels: unacceptable to transformative8:48 How Zapier tests for it11:50 Favorite interview question13:06 Sponsors — EY | SpendHound | Brex16:18 AI changes the hiring profile17:07 Support becomes customer-facing engineering18:44 Where AI beats deterministic Zaps21:00 80-90% of builds are deterministic22:41 Sponsors — Aleph | RightRev | Rillet26:00 Month end close on Zaps27:40 Time saved is a leading indicator29:53 AI token costs: COGS or investment?31:51 Performance issue or measurement issue?33:41 Partner ARR: chasing the wrong thing35:56 Build around what changes the decision37:17 The initiative sizing coach40:00 Escalate on magnitude, not certainty41:55 The 9,000 integration story44:02 M&A process and minimum model46:19 Cash vs. stock: incentive alignment48:06 CFO's role in M&A: show up early49:18 Credits

Navan CFO Aurélien Nolf on Resource Allocation, IR, and AI in Finance

Play Episode Listen Later May 28, 2026 49:47

In this episode of Run the Numbers, CJ sits down with Aurélien Nolf, CFO of Navan, to unpack how to pre-align before budgeting, how to think about portfolio construction inside a company, when to fund or kill internal bets, how IR is becoming more connected to FP&A, and where AI actually works inside finance teams.—SPONSORS:Rillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJ—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/aureliennolf5b716412/Company: https://navan.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro1:21 Welcome and guest intro3:06 100-mile ultramarathon at Lake Tahoe4:47 Resource allocation lessons from EA6:41 Bucketing bets: proven, intuition, moonshots7:43 Pre-alignment before budgeting9:58 The 70/20/10 framework10:35 Sponsors — Rillet | EY | SpendHound13:51 The common trap: chasing everything16:16 Lyft: $1B burning to $1B profitable18:11 Killing projects without crushing morale19:24 TAM as the planning foundation20:57 Navan's TAM: managed vs. unmanaged22:15 Sponsors — Brex | Aleph | RightRev25:48 Why go after the unmanaged segment28:24 Not all TAM dollars are equal29:26 How IR is evolving30:45 Why FP&A and IR belong together31:54 Metrics: disclose vs. guide34:04 Use internal metrics externally35:12 Communicating bad news to the market39:22 Earnings prep: the black book40:04 AI in finance: can't vibe code compliance41:31 Ava handles 55% of interactions43:08 AI ROI: same framework as anything else44:02 Why finance hasn't had its AI moment44:46 Lightning round44:56 Screwed up: wrong investor meeting45:23 Sunday planning ritual46:42 Advice to younger self47:29 Finance software stack48:34 Craziest expense: curtains at the hotel laundry49:17 Credits

SpaceX Is Going Public: Here's Everything You Need to Know

Latent Space: The AI Engineer Podcast â€” CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

Play Episode Listen Later May 25, 2026 42:27

In this episode of Run the Numbers, CJ breaks down SpaceX's S-1, unpacking what the filing reveals about Starlink, xAI, X, common control accounting, revenue, losses, CapEx, and Elon Musk's Mars-linked compensation structure.—SPONSORS:RightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/run—LINKS: CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 SpaceX S1 breakdown0:50 Elon's Mars colony comp plan2:03 Common control accounting: SpaceX + xAI + X3:04 What SpaceX actually does3:43 How reusable rockets work4:24 Launch cost curve: foundation of everything5:49 Launch services: $8B, 85% of global launches6:44 Starlink: $11.4B, 63% EBITDA margins7:36 xAI and X: burning $1B/month8:22 Sponsors — RightRev | Rillet | EY11:18 Colossus and orbital AI thesis11:41 Revenue, segments and CapEx breakdown14:54 RPO: $28.4B backlog15:18 Starlink subscribers and ARPU decline16:01 Target valuation: $1.5–1.75 trillion16:47 Starlink deep dive18:05 International pricing strategy21:38 The consolidated entity problem22:28 Related party section: nine pages22:32 Sponsors — SpendHound | Brex | Aleph26:15 Valor Equity: $20B in equipment leases27:05 Tesla cross-ownership and Terrafab28:23 R&D: $8.6B, 46% of revenue30:33 Starship: key risk and growth linchpin31:41 Red flag 1: CEO comp tied to Mars colony32:22 Red flag 2: Musk concentration risk32:49 Red flag 3: Cursor option — $10B downside33:36 Red flag 4: X advertising is shrinking34:06 IPO structure and SPCX ticker34:50 30% retail allocation, no lockups35:44 S&P 500 inclusion forces buying within 15 days37:54 Valuation: 60–70x forward revenue38:43 Peer comparison39:44 What you're buying at $1.5T40:53 CFO comp: the only sane plan in the filing41:25 Bitcoin on the balance sheet41:57 Credits

How Figure Is Cutting Mortgage Costs from $12,000 to $1,000, with CEO Michael Tannenbaum

Lend Academy Podcast

Play Episode Listen Later May 21, 2026 35:25

Michael Tannenbaum became CEO of Figure in early 2024, taking over from founder Mike Cagney and leading the company through its September 2025 IPO. In this conversation, we get into the mechanics of how Figure's blockchain-based platform competes with Fannie Mae and Freddie Mac, what it actually takes to cut mortgage origination costs from $12,000 to $1,000, and where the real opportunities in tokenization lie.What We CoveredTaking over as CEO from Mike Cagney and the Big Rocks frameworkHow Figure describes itself: building the future of capital markets on blockchainThe B2B partner network and how it compares to Fannie Mae's functionCutting mortgage origination costs from $12,000 to $1,000 and 45 days to fiveWhy Figure competes directly with Fannie Mae and Freddie MacHow blockchain eliminates third-party diligence and prevents loan double-pledgingThe Figure Connect marketplace and its rapid growth since June 2024Where tokenization adds real value — and where it doesn'tYLDS: Figure's SEC-registered yield-bearing stablecoin and its role in capital marketsThe timing and mechanics of Figure's September 2025 IPOBuilding a rate-agnostic business across different macro environmentsThree growth areas: consumer mortgages, Democratized Prime, and on-chain equitiesKey TakeawaysFigure's origination platform and its capital market are the same system — you can't separate them, and that's the competitive moat. Tokenization only creates liquidity when the underlying assets are standardized and fungible; putting unique assets on a blockchain doesn't conjure buyers. The recent fraud cases involving double-pledged loans (Tricolor, First Brands, MFS) have turned blockchain's immutability from a skeptic's objection into a selling point. And Figure is running at what Michael calls the rule of 150 — 100% year-over-year growth at 50% margins — in one of the most rate-sensitive and entrenched markets on earth.About Michael TannenbaumMichael Tannenbaum is the CEO of Figure, a blockchain-based capital markets company he took public on Nasdaq in September 2025. Before Figure, he was an early executive at both SoFi (Chief Revenue Officer) and Brex (COO), and sat on the Brex board when it was acquired by Capital One. He began his career in investment banking at J.P. Morgan.Connect with Fintech One-on-One:Tweet me @PeterRentonConnect with me on LinkedInFind previous Fintech One-on-One episodes

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Giving Agents Computers — Ivan Burazin, Daytona

Play Episode Listen Later May 21, 2026 70:27

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

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5 Ways CFOs Can Build a Better Sales Engine with Paul Stansik

Play Episode Listen Later May 21, 2026 42:41

In this episode of Run the Numbers, CJ sits down with ParkerGale Operating Partner Paul Stansik to break down five ways CFOs can help build a better sales engine: making the budget mean something, improving forecasting, sharpening metrics, getting involved in key RevOps moments, and building real trust with sales.—SPONSORS:Aleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metrics—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/paulstansik/Company: https://www.parkergale.com/Hello Operator: https://hellooperator.substack.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:Is a weekly martini ARR? | with Dave Kellogghttps://youtu.be/Yb1lUQLJ6qw—TIMESTAMPS:All verified. Here are the timestamps:0:00 Preview and intro2:27 Parker Gale and Paul's role3:52 Topic: how CFOs build a better sales engine6:21 1: Make the budget mean something8:11 Budget segmentation and cleaving the business10:54 Sponsors — Aleph | RightRev | Rillet14:08 2: Help emphasize forecasting17:23 Forecasting as non-threatening co-construction19:37 Sponsors — EY | SpendHound | Brex23:06 3: Lend a hand with data and metrics25:32 Walking sales through NDR levers27:16 Metrics tied to exit readiness28:00 4: Get involved in a few RevOps spots29:04 Pricing, proposals, and quoting31:22 Kill your SKUs32:51 Selling with certainty: quote formatting34:26 CFO letter for enterprise deals37:37 5: Build a great relationship with sales37:59 You can't fix a secret39:23 EQ over IQ for finance leaders40:41 Recap: all five tips42:11 Credits#RunTheNumbersPodcast #CFO #SalesStrategy #FinanceLeadership #RevenueOperations

95. Stop Saying No: How Security Leaders Enable AI Instead of Blocking It (with Pranava Adduri and George Gerchow)

Play Episode Listen Later May 19, 2026 39:50

Security incidents don't end when the threat is contained. They end when you can confirm no sensitive data left the building and most teams can't confirm that. Pranava Adduri and George Gerchow of Bedrock Data joined the show to talk through what data visibility actually looks like at enterprise scale, why the office of no is dead, and what a DBOM has to do with AI compliance. Together they make the case that data-first security isn't just a better posture, it's the only posture that survives an AI-driven enterprise. Pranava Adduri: www.linkedin.com/in/padduri George Gerchow: www.linkedin.com/in/georgegerchow Bedrock Data: www.bedrockdata.ai Jon: www.linkedin.com/in/jon-mclachlan Sasha: www.linkedin.com/in/aliaksandr-sinkevich YSecurity: www.ysecurity.io

ai apple microsoft security uber robin hood saying no blocking soc stop saying brex security leaders pranava enable ai

Figure CFO Macrina Kgil on Blockchain Lending, Stablecoins, and IPOs

Play Episode Listen Later May 18, 2026 52:49

In this episode of Run the Numbers, CJ sits down with Figure Technologies CFO Macrina Kgil to break down how Figure's business model works, why traditional lending remains so bloated, and how speed in origination and funding flows through financial performance. They also cover stablecoins, blockchain as invisible infrastructure, AI in accounting, and scaling finance with fewer than 35 people.—SPONSORS:Brex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cj—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/macrina/Company: https://www.figure.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro3:10 Working for a CEO who's a former CFO5:00 What Figure does and how it makes money6:57 Blockchain's first commercial use case8:17 50%+ margins, path to 60%9:23 Sponsors — Brex | Aleph | RightRev12:56 HELOC origination: 45 days to 5 days14:26 Where the lending system is bloated15:56 Credit and liquidity as core risks17:06 Blockchain makes the marketplace transparent18:10 Risk as a fintech CFO19:49 Sponsors — Rillet | EY | SpendHound22:58 Crypto on the balance sheet24:12 Blockchain becomes invisible like cloud25:44 Stablecoins explained27:47 YLDS: Figure's yield-bearing stablecoin29:02 Crawl, walk, run stablecoin strategy34:32 IPO process: what got easier35:03 What got harder: testing the waters37:12 Blockchain KPIs and investor conversations38:21 Finance team: 130 people down to 3540:00 SEC engagement: storytelling not financials40:49 IPO advice: pick durable KPIs42:05 First earnings after IPO: don't miss43:13 AI automation goal: 60% by 202646:11 Director of Finance Transformation hire47:40 30-60-90 for the transformation role48:58 Long-Ass Lightning Round52:20 Credits

Cerebras IPO: S1 Breakdown - The Giant Chip, the OpenAI Deal, and the $24B Backlog

Play Episode Listen Later May 14, 2026 46:40

Cerebras is going public with the largest commercial chip ever built, $510M in 2025 revenue, and a $24.6B backlog mostly tied to OpenAI. CJ breaks down the company's wafer-scale AI bet, why inference changed the story, the strange customer-investor-lender relationships behind the IPO, and the big question: is Cerebras the next NVIDIA-style infrastructure winner, or a concentrated hardware company with a very expensive cloud pivot?—SPONSORS:SpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartups—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro0:59 Cerebras: the dinner plate chip3:56 Why chip size matters for AI5:26 Old vs. new AI: inference is the bottleneck7:30 Revenue: 20x in 3 years7:49 Sponsors — SpendHound | Brex | Aleph11:33 Gross margin12:02 Net income: the one-time accounting trick12:41 Operating cash flow whipsaw13:24 RPO: $24.6B backlog13:58 Customer concentration: the UAE entities18:06 Sponsors — RightRev | Rillet | EY21:05 Cloud revenue: the inverse SaaS story22:23 Cloud gross margin collapse23:53 G42 warrants for pennies29:14 The OpenAI warrant: Funky Town31:08 $40B market cap milestone31:36 R&D and S&M breakdown33:15 Balance sheet and cash burn35:59 Red flag 1: accounting weaknesses36:37 Red flag 2: one foundry, no supply deal37:28 Red flag 3: UAE geopolitical risk38:10 Red flag 4: cloud is unproven39:02 Cap table: founders diluted40:43 Voting control: Class A, B, and N41:15 Valuation: 10–13x forward revenue41:48 Peer comparison43:47 CEO's prior issues46:10 CreditsNothing said or created by this podcast is business or investment advice#RunTheNumbersPodcast #IPO #Semiconductors #AIStrategy #FinanceLeadership

The Investor Behind Warby Parker, Harry's, and the Psychology of Consumer Growth | David Bell

ceo ai marketing compliance mercury edited reinventing warrants capital one credit unions brex

Play Episode Listen Later May 11, 2026 60:56

David Bell, co-founder of Idea Farm Ventures and early investor behind Warby Parker, Harry's, and Diapers.com, and CJ break down how consumer investing works. They cover why durable consumer companies require more than clean unit economics, how to apply SaaS-style thinking to businesses without contracts, and why the best opportunities often live in boring gray space.—SPONSORS:EY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cj—Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/david-bell-086820/Company: https://www.ideafarmventures.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro3:16 Edge: economics and psychology5:21 Best ideas in boring gray space5:41 Functional, emotional, and symbolic value7:08 Grüns gummies and divisibility9:02 Sponsors — EY | SpendHound | Brex12:31 Ideation: personal pain vs. market analysis13:17 Diapers.com and the Starbucks origin story16:00 Warby Parker: asking why16:33 LTV to CAC in D2C18:02 Retention math: 85 to 90% can double LTV19:00 Milkman and recurring vs. reoccurring20:42 Trust economics22:38 Warby stores boost online sales22:58 Sponsors — Aleph | RightRev | Rillet26:12 Away store as advertising27:46 Warby discovery: dots on a map30:10 Home try-on word of mouth value32:09 D2C unit economics mistakes34:55 Innovating on distribution35:50 Touchland in Sephora: right channel, right signal37:00 Capital allocation: margin and low CAC first39:18 Sequencing: people, brand, then inventory40:58 Product vs. brand: the 8x10 thought experiment42:23 Consumer monetization shifts45:45 The gravity framework50:32 Isolation principle: most underused lever52:36 Working backwards from exit at day zero57:23 What if your business isn't venture scale?59:32 Book plug: Founders Gold1:00:25 Credits

How Warrant Is Reinventing Marketing Compliance for Credit Unions

Fintech Combine

Play Episode Listen Later May 8, 2026 34:15

Kris Kovac sits down with Austin Carroll, founder and CEO of Warrant, to discuss how AI is transforming marketing compliance for banks, credit unions, and fintechs. Austin shares her journey from leading marketing at companies like Brex, Mercury, and Capital One to building Warrant — a platform designed to help financial institutions streamline approvals, reduce regulatory risk, and move at startup speed without sacrificing compliance. Follow the Pod:https://twitter.com/fintechcombineFollow Kris Kovacs:https://twitter.com/ManagementByteshttps://www.linkedin.com/in/kriskovacs/https://www.instagram.com/kriskovacs/The Fintech Combine is Produced and Edited by Anson Beckler-JonesFollow Anson Beckler-JonesInstagram - @ansonandcoYoutube - @ansonandco

Inside Mostly Media: The Team Behind Run the Numbers

Play Episode Listen Later May 7, 2026 53:10

CJ turns the mic on the people behind Mostly Media for a special behind-the-scenes episode of Run the Numbers. Michelle, Callie, Sarah, Matthew, Ben, and Steve share what it's like building the company, scaling media, talent, sales, production, and operations, and dealing with CJ's scooter lore, calendar quirks, and chaos along the way.—SPONSORS:Rillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJ—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro2:44 Show intro: meet the Mostly Media team3:37 Michelle Finn: accounting and ops4:28 Parts Tech days: CJ's first CFO role7:21 How the newsletter convinced Michelle to join9:02 Sponsors — Rillet | EY | SpendHound12:11 Callie Spillane: talent director13:17 Callie's background: HubSpot, Sneak, Superhuman13:40 Why Callie was hard to hire15:37 Snyk hypergrowth: 150 to 1,500 people16:59 Zero to one vs. one to ten18:20 9 of 20 roles filled: how it's going23:24 Sponsors — Brex | Aleph | RightRev26:58 Sarah Bousquet: media op27:45 Stay at home mom to ops lead33:48 CJ's schedule: American Psycho34:43 Matthew Mozzocchi: sales and partnerships35:41 Going full time with a newborn36:20 Product market fit signal38:07 Fewer, bigger bets on creators41:00 Podcast is the air game, newsletter is the ground game43:56 Ben Hillman and Steve Cerasoli: production47:46 Media in service of a product vs. the product itself48:37 Run the Numbers vs. Mostly Growth51:25 Where are we in three years?52:40 Credits#RunTheNumbersPodcast #CreatorEconomy #MediaBusiness #Entrepreneurship #ContentBusiness

Leah Solivan: Founder of TaskRabbit and Precedent.vc

Play Episode Listen Later May 5, 2026 58:48

This week, we chat with Leah Solivan!Leah is the founder of TaskRabbit, the pioneering on-demand marketplace that helped redefine the future of work and laid the foundation for what we now know as the gig economy. As CEO for eight years, she scaled TaskRabbit into an international business operating in more than 40 cities before overseeing its successful acquisition by IKEA in 2017.Today, Leah continues to build and back world-changing companies as Managing Director of Precedent.vc, General Partner at Fuel Capital, and founder of The Precedent Collective. Across her investing career, she's supported more than 150 companies spanning consumer technology, marketplaces, hardware, education, and retail, including companies like Nanit, Pacaso, Our Place, Good Dog, and Upwards.Before founding TaskRabbit, Leah began her career as a software engineer at IBM, where she worked on products like Lotus Notes and Domino, building the technical foundation that would later shape her journey as a founder and investor.Leah is also a passionate advocate for diversity in technology and entrepreneurship, proudly representing her Latina heritage and speaking globally on topics like the future of work, innovation, and leadership.✨ This episode is presented by Brex.Brex: brex.com/trailblazerspodThis episode is supported by RocketReach, Gusto, OpenPhone & Athena.RocketReach: rocketreach.co/trailblazersGusto: gusto.com/trailblazersQuo: Quo.com/trailblazersAthena: athenago.me/Erica-WengerFollow Us!Leah Solivan: @labunleashedBreaking Precedent Podcast: https://www.breakingprecedent.com/https://truthorlie.ai/@thetrailblazerspod: Instagram, YouTube, TikTokErica Wenger: @erica_wenger

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94. How one unsecured printer can take down 11,000 devices (with Jim LaRoe, Symphion, Inc.)

Play Episode Listen Later May 5, 2026 39:02

Your printers know your passwords. They store credentials for your email server, your file shares, and your LDAP. Jim LaRoe, founder of Symphion, explains why 99% of enterprise printers sit at factory defaults, and what a single forgotten device actually costs you. Jim: www.linkedin.com/in/jim-laroe Symphion: www.symphion.com Jon: www.linkedin.com/in/jon-mclachlan Sasha: www.linkedin.com/in/aliaksandr-sinkevich YSecurity: www.ysecurity.io

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Diamonds, De Beers, and the Death of Artificial Scarcity | A CFO Explains Diamonds

Play Episode Listen Later May 4, 2026 26:59

In this episode of Run the Numbers, CJ Gustafson breaks down the diamond industry as a business model: how De Beers controlled supply, engineered demand, and built one of the most powerful pricing machines in history. From the Central Selling Organization to “A Diamond Is Forever” to the rise of lab-grown diamonds, this episode unpacks monopolies, scarcity, pricing power, and what happens when technology forces transparency.—SPONSORS:RightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to replace NetSuite and close faster. With revenue recognition, close management, multi-entity support, and native Stripe and Salesforce integrations, Rillet helps scaling companies run their finance stack in one place. Hundreds of teams, including Windsurf and Mercor, use Rillet to make the zero-day close real. Book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/run—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:Why Uber Drivers Can't Escape the 30% Cuthttps://youtu.be/LpbH9GpBrSY—TIMESTAMPS:All verified. Here are the timestamps:0:00 Blood Diamond1:55 Show intro2:15 Diamond history: ancient India and riverbed origins3:56 Brazil supply shock and the mining era4:39 De Beers: Cecil Rhodes and the PE rollup6:47 Sponsors — RightRev | Rillet | EY9:49 The Central Selling Organization (CSO)10:39 The site system: invite-only, take-it-or-leave-it auctions12:22 Diamonds are more abundant than gold13:01 NW Ayer, Bernays, and engineering demand14:17 "A Diamond is Forever" campaign15:13 Lab grown diamonds: 20% of US purchases16:32 De Beers collapses: $3.1B operating loss17:18 Sponsors — SpendHound | Brex | Aleph21:07 Lesson 1: Rollups are powered by capital21:39 Lesson 2: Distribution beats mining22:11 Lesson 3: Artificial scarcity unravels22:55 Lesson 4: Price opacity is a temporary moat23:39 Lesson 5: Demand engineering24:30 Lesson 6: The real product was risk smoothing26:30 Credits#RunTheNumbersPodcast #BusinessStrategy #FinanceHistory #Pricing #Monopoly

Mariam Naficy: Founder of Minted and CEO at Arcade

Play Episode Listen Later Apr 28, 2026 67:00

This week, we chat with Mariam Naficy!A serial entrepreneur and visionary leader known for pioneering new ways to connect creativity, technology, and human expression. Mariam is the Founder and Chairman of Minted, and now the Founder and CEO of Arcade, a platform that uses AI to help people communicate visually — transcending geographic and language barriers to connect creators, artisans, and consumers worldwide.For Mariam, Arcade is a full-circle moment. Having grown up overseas exploring marketplaces and bazaars, she developed a lifelong love of handcrafted objects and the stories behind them. At Arcade, she's bringing that passion to life — using technology to foster deeper human connection. By training models for makers like Christofle, Bitossi Ceramiche, Cabana, and Anastasio Home, as well as manufacturers around the world, Arcade empowers anyone to create meaningful, personal objects that reflect individual taste and creativity.Before founding Arcade, Mariam built some of the Internet's most influential creative brands over the past several decades. She founded and built Minted, the design marketplace that revolutionized the creator economy and became a several hundred million dollar business, and co-founded Eve.com, the very first online cosmetics retailer in the world, in 1998.At the heart of her work is a belief that technology, when used thoughtfully, can bring meaning, beauty, and connection into people's lives—as well as create meaningful income for others, such as the Minted artist community.✨ This episode is presented by Brex.Brex: brex.com/trailblazerspodThis episode is supported by RocketReach, Gusto, OpenPhone & Athena.RocketReach: rocketreach.co/trailblazersGusto: gusto.com/trailblazersQuo: Quo.com/trailblazersAthena: athenago.me/Erica-WengerFollow Us!Mariam Naficy: @mnaficyArcade: arcade_ai@thetrailblazerspod: Instagram, YouTube, TikTokErica Wenger: @erica_wenger

ceo founders ai internet arcade gusto cabana brex minted mariam naficy

The North Face Former CFO: How Finance Builds Iconic Brands

Play Episode Listen Later Apr 27, 2026 51:20

In this episode of Run the Numbers, CJ Gustafson sits down with Angela Chen, former CFO of The North Face and Mars Veterinary Health, to unpack how finance leaders build and scale iconic consumer brands. Angela shares how to measure brand equity, why the CFO should act as an architect of growth, and how capital, talent, and strategy connect inside a scaling business. They also get into humanistic leadership, consumer-margin tradeoffs, and what a 39-cent Taco Bell taco can teach finance teams about growth.—SPONSORS:Aleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metrics—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/angelachensf/Company: https://sku.is/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:Minted's CFO: Half the Year Happens in One Monthhttps://youtu.be/hD4-exunKMo—TIMESTAMPS:0:00 Preview and intro2:45 Measuring brand equity5:09 CFO as architect of growth7:11 Connecting money, ideas, and talent7:52 North Face vs. Mars: where to invest10:52 Sponsors — Aleph | RightRev | Rillet14:14 Scaling North Face 5x without diluting the brand16:31 From technical brand to lifestyle brand19:30 Quality of revenue21:49 Seasonality and Q4 concentration24:01 Sponsors — EY | SpendHound | Brex27:14 Retailers shouldn't own factories31:30 Supply chain and 18-month product cycles33:23 Humanistic leadership in finance34:32 Purpose and profit are mutually reinforcing36:15 Purpose vs. profit in public companies38:05 Playing to Win framework39:47 Operational cadence and the gas gauge43:02 Killing bad projects: the battery jacket story44:16 Taco Bell and 39-cent margins47:10 Lightning round47:36 Screwed up: hiring on credentials48:27 Advice to younger self: network is the work50:07 Craziest expense story: snowcat ski trip50:50 Credits

How Great Deals Are Found, Evaluated, and Won | PSG's Chris Nesbitt

Play Episode Listen Later Apr 23, 2026 54:34

In this episode of Run the Numbers, CJ sits down with PSG Managing Director Chris Nesbitt to unpack how great deals are actually found, how investment decisions are really made, and why narrative often matters more than most investors admit. They also dig into forecasting, boardroom authenticity, simple vs. complex models, and the roles of market, product, and leadership in driving outcomes.—SPONSORS:Brex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cj—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/christophersnesbitt/Company: https://psgequity.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro2:44 PSG origin story4:01 Growth to 30B AUM5:07 Strategy: small software at scale5:50 Vertical SaaS treasure hunting8:10 Ministry Brands: software meets payments9:28 Sponsors — Brex | Aleph | RightRev12:46 Early M&A work and rollup strategy15:52 Sourcing is more competitive now18:28 Smoke signals and relationship sourcing21:22 Does brand get you in the room?22:15 Authenticity as a sourcing edge22:52 Sponsors — Rillet | EY | SpendHound26:09 Brand name of investor or deal partner?27:44 Investors are narrative driven animals29:18 Market, product, then execution31:26 Danger of falling in love with the narrative33:40 Operator AI pivot story: GRC company34:51 Keep it simple: one tab, five key inputs39:21 Forecasting confidence beyond 12-18 months41:51 What makes a useful board meeting45:01 Build vs. buy: the payments decision47:45 ARR vs. EBITDA multiples50:30 Lightning round50:34 Board materials: send 3 days in advance51:03 LTV to CAC and cap software debates51:32 First deal at PSG52:35 What young investors get wrong54:04 Credits

ai strategy growth brand board market numbers run authenticity danger investors falling in love smoke preview saas lightning designed crm ipo trusted cj doordash psg credits coinbase ey forecasting sourcing arr erp anthropic cfos fp cac ebitda ltv hootsuite aleph nesbitt grc great deals brex evaluated zoominfo vertical saas ministry brands

Marlon Nichols: Co-Founder of MaC Venture Capital

Play Episode Listen Later Apr 21, 2026 66:54

This week, we chat with Marlon Nichols!Marlon is the co-founder and managing general partner of MaC Venture Capital, a leading seed-stage firm backing visionary founders who are redefining industries and shaping the future. Under his leadership, MaC has grown into one of North America's largest seed-stage venture firms, with over $600 million in assets under management.He's backed an incredible portfolio of companies including MongoDB, Gimlet Media, Thrive Market, Blavity, and Pipe—consistently identifying cultural and technological shifts before they hit the mainstream. His ability to spot transformative opportunities has earned him recognition on Business Insider's Seed 100 and PitchBook's top VCs to watch.Before founding MaC Venture Capital, Marlon launched Cross Culture Ventures and served as an investment director at Intel Capital, developing a sharp lens on the intersection of culture, technology, and consumer behavior.A former professional athlete, Marlon brings a leadership style rooted in discipline and long-term vision, and is deeply committed to expanding access in venture capital through his work with Kauffman Fellows.✨ This episode is presented by Brex.Brex: brex.com/trailblazerspodThis episode is supported by RocketReach, Gusto, OpenPhone & Athena.RocketReach: rocketreach.co/trailblazersGusto: gusto.com/trailblazersQuo: Quo.com/trailblazersAthena: athenago.me/Erica-WengerFollow Us!Marlon Nichols @MarlonCNicholsMaC Ventures: MaCVentureCap@thetrailblazerspod: Instagram, YouTube, TikTokErica Wenger: @erica_wenger

co founders north america seed mac business insider pipe vcs gusto mongodb thrive market gimlet media brex blavity pitchbook intel capital marlon nichols mac venture capital cross culture ventures

93. The Conversation Nobody's Having About AI (with Jacob Andra and Stephen Karafiath)

Play Episode Listen Later Apr 21, 2026 42:50

The biggest AI mistake companies make isn't picking the wrong tool, it's not understanding the dependencies underneath it. Jacob and Stephen from Talbot West share how they map entire organizations to find the right AI entry point, why LLMs are overhyped, and what technologies are actually underrated right now. Jacob: www.linkedin.com/in/jacobandra Stephen: www.linkedin.com/in/stephenkarafiath Talbot West: www.talbotwest.com Jon: www.linkedin.com/in/jon-mclachlan Sasha: www.linkedin.com/in/aliaksandr-sinkevich YSecurity: www.ysecurity.io

ai conversations apple microsoft uber robin hood soc brex

Why Health Tech Doesn't Operate Like SaaS | Manu Diwakar, CFO of Virta Health

Play Episode Listen Later Apr 20, 2026 54:17

In this episode of Run the Numbers, CJ sits down with Manu Diwakar, CFO of Virta Health, to unpack why health tech breaks traditional SaaS thinking. They get into the realities of running a business where outcomes matter, half the company are medical professionals, and efficiency can't come at the expense of care. It's a conversation about sustainable scaling, smarter reinvestment, and building for durability over hype.—SPONSORS: SpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com/cjBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartups—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/manu-diwakar-1aa578/Company: https://www.virtahealth.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro1:18 Welcome and guest intro3:02 What sector is health tech?4:34 Virta go to market explained7:01 B2B to B2C model9:00 Value based care and ROI guarantees9:18 Half the company are medical professionals10:36 Sponsors — SpendHound | Brex | Aleph14:03 Annual planning process16:42 Shape of the curve17:33 TAM: metabolic health is massive20:45 Fee for service vs. value based care24:16 Sponsors — RightRev | Rillet | EY27:24 Unique costs of health tech: billing, compliance30:54 Corporate practice of medicine31:44 North star: members under management32:43 The flywheel: $250 charge, $500 saved33:28 Early stage CFO job is easy35:28 Bad habits baked in during high growth38:02 Choosing not to profit vs. not turning a profit41:15 Running a VC-backed business for sustainability42:07 People, tech and process framework44:31 Hiring philosophy: hard work, learning, curiosity48:26 Athletes vs. experts in hiring50:03 Clock hands interview question50:49 Lightning round51:09 Screwed up: having hard conversations late51:57 Advice to younger self52:15 Finance software stack52:42 Craziest expense story53:47 Credits

Inside Figma's Financial Playbook with CFO Praveer Melwani

Play Episode Listen Later Apr 16, 2026 46:29

In this episode of Run the Numbers, CJ sits down with Praveer Melwani, CFO of Figma, to unpack the financial model behind one of tech's most iconic product-led companies. They cover viral growth, forecasting without a traditional sales pipeline, AI credit pricing, margin tradeoffs, and the metric Praveer believes matters most in the long run: free cash flow per share.—SPONSORS:EY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.comBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cj—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/praveer-melwani/Company: https://www.figma.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro1:21 Welcome and guest intro2:55 From banking to Dropbox to Figma5:04 Inflection points: building trust early8:36 TAM expansion thinking10:31 Sponsors — EY | SpendHound | Brex13:38 Two-thirds of Figma users aren't designers14:36 Forecasting product led growth16:22 Cohorts and NDR discovery17:39 LTV to CAC philosophy18:57 Product signals for account expansion20:22 How Figma achieved hypergrowth with 90%+ margins22:14 AI expands TAM: time to hit the gas23:22 AI credit pricing model24:50 Sponsors — Aleph | RightRev | Rillet28:13 Outcome based pricing consideration29:00 Where AI margins settle: gross profit dollars30:08 Free cash flow per share as north star31:04 NDR and pricing volatility32:13 Bundled vs. unbundled seats34:50 Being engaged in sales to understand admin pain35:17 IPO day experience36:27 Keeping employees focused beyond the stock price37:19 Employee stock pressure and lockup reality39:37 Kitchen cabinet of advisors41:44 How to ask better questions of advisors41:47 Lightning round41:50 Advice to younger self42:47 Finance software stack44:09 Claude WTF moment: forecasting model throughput45:13 Craziest expense story: the haircut45:59 Credits

Paige Hendrix Buckner: CEO at All Raise

Play Episode Listen Later Apr 14, 2026 69:11

This week, we chat with Paige Hendrix Buckner!Paige is the CEO of All Raise, a nonprofit working to accelerate the success of women and non-binary venture capitalists and reshape the culture of the venture ecosystem. She previously served as All Raise's Chief of Staff and Interim CEO before stepping into the role full time.Before joining All Raise, Paige was the COO of Founder Gym, the largest online program training underrepresented founders on how to raise venture capital.Her career spans public service, entrepreneurship, and community building. She started at Teach For America, later became Policy Director for Multnomah County in Oregon, and went on to found her own venture, ClientJoy. She also developed the TIE Young Entrepreneurs program and was a founding board member of XXcelerate, initiatives focused on helping the next generation of entrepreneurs and women founders succeed.Paige has been featured in publications like Bloomberg, Forbes, Fortune, Entrepreneur, and TechCrunch, where she shares insights on venture capital, diversity in tech, and why building a more inclusive ecosystem is critical to innovation and long-term success.✨ This episode is presented by Brex.Brex: brex.com/trailblazerspodThis episode is supported by RocketReach, Gusto, OpenPhone & Athena.RocketReach: rocketreach.co/trailblazersGusto: gusto.com/trailblazersQuo: Quo.com/trailblazersAthena: athenago.me/Erica-WengerFollow Us!Paige: @PaigeHBucknerAll Raise: @AllRaise@thetrailblazerspod: Instagram, YouTube, TikTokErica Wenger: @erica_wenger

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TJC Debrief with Paulo Passoni: Only 4 US Companies Can IPO

The J Curve

Play Episode Listen Later Apr 14, 2026 87:02

Paulo Passoni, Managing Partner at Valor Capital, and Olga Maslikhova break down Claude 4.6, Brex's $5.15B sale to Capital One, and why only 4 US companies can IPO right now. This is the April 2026 edition of TJC Debrief — a monthly show covering tech, venture, and capital markets for Latin American founders and investors.We cover why Claude 4.6 was a bigger “aha moment” than the original ChatGPT for building companies and how it's rewiring CTO roles, org design, and the question of what a moat even is anymore, how Nubank, Revolut, Tether, and Plata are reshaping consumer finance and why Paulo thinks regional US banks are an “aberration” that shouldn't legally exist, the Brex x Capital One deal, Ramp's software multiple, and what the prof stack saving late-stage LPs means for every fintech exit going forward, Brazil's IPO window cracking open and Mexico's sudden flood of Sequoia, Founders Fund, and a16z capital, why Paulo thinks many employees are already “worse than AI” and why every salary should now come with a token budget, how he built a working marketplace in three hours on Perplexity Comet without writing a line of code, and the coming collapse of low-ROI universities and what it means for talent in LATAM.Subscribe to The J Curve Insider newsletter for deeper insights and follow Olga on LinkedIn and Instagram.

Wealthfront's CFO on Automation, Compounding Growth, and Going Public

Play Episode Listen Later Apr 13, 2026 53:42

In this episode of Run the Numbers, CJ sits down with Wealthfront CFO Alan Imberman to unpack automation as a strategy, the compounding power of retention and trust, and how to balance elite profitability with continued investment. They also discuss why Wealthfront went public earlier than many peers and what's really happening in fintech right now.—SPONSORS:Rillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.comBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJ—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/alan-imberman-cfa-aab2371/Company: https://www.wealthfront.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro1:37 Welcome and guest intro3:11 Wealthfront overview4:38 Automation as core philosophy6:50 55K clients per support rep example7:45 90% gross margins and $1M revenue per employee8:46 Optimization vs. exploration framework9:58 Data flywheel: the Path product10:43 Cash account insight from customer data11:26 Home lending insight from wire data13:33 Sponsors — Rillet | EY | SpendHound16:45 Product led growth and referrals18:11 Incentives vs. paid marketing20:46 Compounding philosophy and 120% NDR22:07 Long term thinking vs. public market pressure22:40 No guidance decision26:26 Sponsors — Brex | Aleph | RightRev29:44 Serving the wealth builder: 80/20 in wealth management31:45 Decision to go public at $339M revenue33:46 Does size matter for IPOs?34:46 Fintech's moment: Chime, Klarna, Circle36:49 Non-monetary benefits of going public38:30 Memos over slides40:31 Hedge fund early career: spreading 10-Ks in Excel46:45 Don't lose the forest for the trees in modeling48:31 Lightning round48:43 Screwed up: de-annualizing a fee rate49:55 Advice to younger self50:32 Finance software stack51:14 Craziest expense story: $100K coffee tab53:11 Credits#RunTheNumbersPodcast #CFO

How Strategic CFOs Get It Wrong

ceo canadian research fallout indiana jones phantom mermaid simplecast goldmine laci brex johnny pemberton

Play Episode Listen Later Apr 9, 2026 52:45

On this week's Run the Numbers, CJ sits down with Steve Isom of Bloomerang to break down what a “strategic CFO” really is. They cover the shift from reporting to operating, why customer orgs drive SaaS value, and how AI is reshaping the finance role. —SPONSORS:RightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.comBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/run—LINKS: Steve on LinkedIn: linkedin.com/in/steveisomjrCompany: https://bloomerang.com/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview1:25 Intro2:55 Welcome Steve Isom3:13 CFO to COO promotion5:02 Having a pulse on every function6:51 Defining "strategic CFO"7:19 Tying strategy to value creation9:27 Being embedded in the rhythm of the business10:55 Finance leaders as commentators vs. team captains12:25 Sponsors — RightRev | Rillet | EY15:33 Killing projects as a core skill18:14 Activity doesn't equal impact19:16 Project Steve killed20:39 Taking over the customer org at Bloomerang22:18 Why acquisition is a cash-losing exercise23:07 LTV lives post-sale24:39 Most vulnerable area: customer success25:24 Ruthless CSM segmentation26:30 Nonprofits don't think about your software29:21 Sponsors — Spendhound | Brex | Aleph32:43 CFO running ops35:52 Metrics vs. humans37:18 Skip levels and what they reveal39:01 Incentives drive the wrong outcomes40:22 Customer-introduced delays as a key sub-metric40:27 Unit economics become tangible when you're accountable42:17 Going deep on AI45:13 Motivating your team to experiment with AI46:02 AI for personal projects47:05 Resource allocation in a vibe-coding world48:42 Does AI efficiency just mean more work?50:32 What excites Steve about the future of finance leadership52:08 Finance leaders who don't use AI won't get hired52:13 Credits#RunTheNumbersPodcast #CFO #StrategicFinance #SaaSFinance #FinanceLeadership #CFOtoCOO

The Phantom Gold Mine Fallout w/ Johnny Pemberton

Scam Goddess

Play Episode Listen Later Apr 7, 2026 58:46

A broke CEO, the Indiana Jones of geologists, and a lovestruck mining prospector came together in the '90s and rocked the world with the largest gold-mining scam in history. Laci welcomes actor and comedian Johnny Pemberton (Fallout, Mermaid) to tell the true tale of the downfall of the Canadian penny-stock mining company Bre-X. Stay schemin'! Don't miss Johnny Pemberton's film: Mermaid CON-gregation, catch Scam Goddess LIVE in a city near you. Keep the scams coming and snitch on your friends by emailing us at ScamGoddessPod@gmail.com. Follow on Instagram: Scam Goddess Pod: @scamgoddesspod Laci Mosley: @divalaci Johnny Pemberton: @johnny_pemberton Research by Kathryn Doyle SOURCES https://www.atlasobscura.com/articles/cheating-wonders-the-6000000000-gold-mine-that-didnt-exist https://www.mining.com/web/bre-x-scandal-a-history-timeline/ https://www.bbc.com/news/world-us-canada-68987824 https://calgaryherald.com/news/local-news/bre-x-the-real-story-and-scandal-that-inspired-the-movie-gold https://www.youtube.com/watch?v=orFsbouXYUY https://slate.com/human-interest/2015/08/the-bre-x-gold-mining-scandal-turned-part-of-the-borneo-jungle-into-a-6-billion-lie.html Subscribe to SiriusXM Podcasts+ to listen to new episodes of Scam Goddess ad-free and a whole week early. Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Rob Schutz: Founder of Snagged

Latent Space: The AI Engineer Podcast â€” CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

Play Episode Listen Later Apr 7, 2026 67:20

This week, we chat with Rob Schutz!Rob is a serial entrepreneur, operator, and growth expert who's spent his career building and scaling technology businesses that challenge outdated systems. He's the founder of Snagged, where he helps entrepreneurs, startups, and established companies acquire premium domain names—and he's also the co-founder of Ro, the digital health platform that's transformed how millions of people access care and was last valued at $7 billion.Before Ro, Rob was employee number seven and later VP of Growth at Bark, where he helped take the company from zero to a $100 million run rate and ultimately to the public markets as NYSE:BARK. Earlier in his career, he worked in healthcare consulting, helping hospitals transition to digital EMRs, and even founded and sold a daily deals company called What's the Deal—a move that set the stage for his long-term focus on marketing, growth, and scaling consumer tech.At Ro, Rob played a key role in building a vertically integrated digital clinic—combining telemedicine, pharmacy, and logistics—to make healthcare more accessible, stigma-free, and user-first. His work has helped redefine what modern healthcare can look like, especially in areas like men's health, mental health, and chronic care.Rob is a Penn State graduate and currently lives in New Jersey with his wife, two young kids, and his dog.✨ This episode is presented by Brex.Brex: brex.com/trailblazerspodThis episode is supported by RocketReach, Gusto, OpenPhone & Athena.RocketReach: rocketreach.co/trailblazersGusto: gusto.com/trailblazersQuo: Quo.com/trailblazersAthena: athenago.me/Erica-WengerFollow Us!Rob Schutz: @robSnagged: @snagged@thetrailblazerspod: Instagram, YouTube, TikTokErica Wenger: @erica_wenger

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Giving Accountants Their Time Back with Cosmin Nicolaescu

The Unique CPA

Play Episode Listen Later Apr 7, 2026 36:18

Support for this episode comes from BILL. Simplify your workflows and accelerate your growth with BILL's accountant console. Take a demo today at BILL.com/UniqueCPA for a $250 gift card – terms apply. Cosmin Nicolaescu spent years at Stripe and Brex building the financial infrastructure that millions of businesses depend on. Now he's turned that experience toward a different problem: Why are accounting firms still operating like it's 1995? As CEO and co-founder of Accrual, Cosmin is building an AI platform designed to take the mechanical burden off CPAs and give them back time for the work that actually requires their judgment. Episode 258 marks part one of a two-part conversation on The Unique CPA where he talks with Randy about what drew him to the accounting profession specifically, how watching finance teams operate strategically at Stripe and Brex shaped his thinking, and what it looks like in practice when a firm cuts a 100-hour tax return down to 15. Get the full show notes and more resources at RandyCrabtree.com

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Extreme Harness Engineering for Token Billionaires: 1M LOC, 1B toks/day, 0% human code, 0% human review — Ryan Lopopolo, OpenAI Frontier & Symphony

Play Episode Listen Later Apr 7, 2026 72:43

We're proud to release this ahead of Ryan's keynote at AIE Europe. Hit the bell, get notified when it is live! Attendees: come prepped for Ryan's AMA with Vibhu after.Move over, context engineering. Now it's time for Harness engineering and the age of the token billionaires.Ryan Lopopolo of OpenAI is leading that charge, recently publishing a lengthy essay on Harness Eng that has become the talk of the town:In it, Ryan peeled back the curtains on how the recently announced OpenAI Frontier team have become OpenAI's top Codex users, running a >1m LOC codebase with 0 human written code and, crucially for the Dark Factory fans, no human REVIEWED code before merge. Ryan is admirably evangelical about this, calling it borderline “negligent” if you aren't using >1B tokens a day (roughly $2-3k/day in token spend based on market rates and caching assumptions):Over the past five months, they ran an extreme experiment: building and shipping an internal beta product with zero manually written code. Through the experiment, they adopted a different model of engineering work: when the agent failed, instead of prompting it better or to “try harder,” the team would look at “what capability, context, or structure is missing?”The result was Symphony, “a ghost library” and reference Elixir implementation (by Alex Kotliarskyi) that sets up a massive system of Codex agents all extensively prompted with the specificity of a proper PRD spec, but without full implementation:The future starts taking shape as one where coding agents stop being copilots and start becoming real teammates anyone can use and Codex is doubling down on that mission with their Superbowl messaging of “you can just build things”.Across Codex, internal observability stacks, and the multi-agent orchestration system his team calls Symphony, Ryan has been pushing what happens when you optimize an entire codebase, workflow, and organization around agent legibility instead of human habit.We sat down with Ryan to dig into how OpenAI's internal teams actually use Codex, why the real bottleneck in AI-native software development is now human attention rather than tokens, how fast build loops, observability, specs, and skills let agents operate autonomously, why software increasingly needs to be written for the model as much as for the engineer, and how Frontier points toward a future where agents can safely do economically valuable work across the enterprise.We discuss:* Ryan's background from Snowflake, Brex, Stripe, and Citadel to OpenAI Frontier Product Exploration, where he works on new product development for deploying agents safely at enterprise scale* The origin of “harness engineering” and the constraint that kicked off the whole experiment: Ryan deliberately refused to write code himself so the agent had to do the job end to end* Building an internal product over five months with zero lines of human-written code, more than a million lines in the repo, and thousands of PRs across multiple Codex model generations* Why early Codex was painfully slow at first, and how the team learned to decompose tasks, build better primitives, and gradually turn the agent into a much faster engineer than any individual human* The obsession with fast build times: why one minute became the upper bound for the inner loop, and how the team repeatedly retooled the build system to keep agents productive* Why humans became the bottleneck, and how Ryan's team shifted from reviewing code directly to building systems, observability, and context that let agents review, fix, and merge work autonomously* Skills, docs, tests, markdown trackers, and quality scores as ways of encoding engineering taste and non-functional requirements directly into context the agent can use* The shift from predefined scaffolds to reasoning-model-led workflows, where the harness becomes the box and the model chooses how to proceed* Symphony, OpenAI's internal Elixir-based orchestration layer for spinning up, supervising, reworking, and coordinating large numbers of coding agents across tickets and repos* Why code is increasingly disposable, why worktrees and merge conflicts matter less when agents can resolve them, and what it really means to fully delegate the PR lifecycle* “Ghost libraries”, spec-driven software, and the idea that a coding agent can reproduce complex systems from a high-fidelity specification rather than shared source code* The broader future of Frontier: safely deploying observable, governable agents into enterprises, and building the collaboration, security, and control layers needed for real-world agentic workRyan Lopopolo* X: https://x.com/_lopopolo* Linkedin: https://www.linkedin.com/in/ryanlopopolo/* Website: https://hyperbo.la/contact/Timestamps00:00:00 Introduction: Harness Engineering and OpenAI Frontier00:02:20 Ryan's background and the “no human-written code” experiment00:08:48 Humans as the bottleneck: systems thinking, observability, and agent workflows00:12:24 Skills, scaffolds, and encoding engineering taste into context00:17:17 What humans still do, what agents already own, and why software must be agent-legible00:24:27 Delegating the PR lifecycle: worktrees, merge conflicts, and non-functional requirements00:31:57 Spec-driven software, “ghost libraries,” and the path to Symphony00:35:20 Symphony: orchestrating large numbers of coding agents00:43:42 Skill distillation, self-improving workflows, and team-wide learning00:50:04 CLI design, policy layers, and building token-efficient tools for agents00:59:43 What current models still struggle with: zero-to-one products and gnarly refactors01:02:05 Frontier's vision for enterprise AI deployment01:08:15 Culture, humor, and teaching agents how the company works01:12:29 Harness vs. training, Codex model progress, and “you can just do things”01:15:09 Bellevue, hiring, and OpenAI's expansion beyond San FranciscoTranscriptRyan Lopopolo: I do think that there is an interesting space to explore here with Codex, the harness, as part of building AI products, right? There's a ton of momentum around getting the models to be good at coding. We've seen big leaps in like the task complexity with each incremental model release where if you can figure out how to collapse a product that you're trying to.Build a user journey that you're trying to solve into code. It's pretty natural to use the Codex Harness to solve that problem for you. It's done all the wiring and lets you just communicate in prompts. To let the model cook, you have to step back, right? Like you need to take a systems thinking mindset to things and constantly be asking, where is the Asian making mistakes?Where am I spending my time? How can I not spend that time going forward? And then build confidence in the automation that I'm putting in place. So I have solved this part of the SDLC.swyx: [00:01:00] All right.[00:01:03] Meet Ryan swyx: We're in the studio with Ryan from OpenAI. Welcome.Ryan Lopopolo: Hi,swyx: Thanks for visiting San Francisco and thanks for spending some time with us.Ryan Lopopolo: Yeah, thank you. I'm super excited to be here.swyx: You wrote a blockbuster article on harness engineering. It's probably going to be the defining piece of this emerging discipline, huh?Ryan Lopopolo: Thank you. It is it's been fun to feel like we've defined the discourse in some sense.swyx: Let's contextualize a little bit, this first podcast you've ever done. Yes. And thank you for spending with us. What is, where is this coming from? What team are you in all that jazz?Ryan Lopopolo: Sure, sure.Ryan Lopopolo: I work on Frontier Product Exploration, new product development in the space of OpenAI Frontier, which is our enterprise platform for deploying agents safely at scale, with good governance in any business. And. The role of VMI team has been to figure out novel ways to deploy our models into package and products that we can sell as solutions to enterprises.swyx: And you have a background, I'll just squeeze it in there. Snowflake, brick, [00:02:00] stripe, citadel.Ryan Lopopolo: Yes. Yes. Same. Any kind of customerswyx: entire life. Yes. The exact kind of customer that you want to,Vibhu: so I'll say, I was actually, I didn't expect the background when I looked at your Twitter, I'm seeing the opposite.Stuff like this. So you've got the mindset of like full send AI, coding stuff about slop, like buckling in your laptop on your Waymo's. Yes. And then I look at your profile, I'm like, oh, you're just like, you're in the other end too. Oh, perfect. Makes perfect.Ryan Lopopolo: I it's quite fun to be AI maximalist if you're gonna live that persona.Open eye is the place to do it. And it'sswyx: token is what you say.Ryan Lopopolo: Yeah. Certainly helps that we have no rate limits internally. And I can go, like you said, full send at this stay.swyx: Yeah. Yeah. So the Frontier, and you're a special team within O Frontier.Ryan Lopopolo: We had been given some space to cook, which has been super, super exciting.[00:02:47] Zero Code ExperimentRyan Lopopolo: And this is why I started with kind of a out there constraint to not write any of the code myself. I was figuring if we're trying to make agents that can be deployed into end to enterprises, they should be [00:03:00] able to do all the things that I do. And having worked with these coding models, these coding harnesses over 6, 7, 8 months, I do feel like the models are there enough, the harnesses are there enough where they're isomorphic to me in capability and the ability to do the job.So starting with this constraint of I can't write the code meant that the only way I could do my job was to get the agent to do my job.Vibhu: And like a, just a bit of background before that. This is basically the article. So what you guys did is five months of working on an internal tool, zero lines of code over a mi, a million lines of code in the total code base.You say it was cenex, more like it was cenex faster than you would've. If you had done it by end. SoRyan Lopopolo: yeah, thatVibhu: was the mindset going into this, right?Ryan Lopopolo: That's right.[00:03:46] Model Upgrades LessonsRyan Lopopolo: Started with some of the very first versions of Codex CLI, with the Codex Mini model, which was obviously much less capable than the ones we have today.Which was also a very good constraint, right? Quite a visceral feeling to ask the [00:04:00] model to build you a product feature. And it just not being able to assemble the pieces together.Which kind of defined one of the mindsets we had for going into this, which is whenever the model just cannot, you always pop open at the task, double click into it, and build smaller building blocks that then you can reassemble into the broader objective.And it was quite painful to do this. Honestly, the first month and a half was. 10 times slower than I would be. But because we paid that cost, we ended up getting to something much more productive than any one engineer could be because we built the tools, the assembly station for the agent to do the whole thing.[00:04:43] Model Generations, Build Systems & Background ShellsRyan Lopopolo: But yeah, so onward to G BT 5, 5, 1, 5, 2, 5, 3, 5 4. To go through all these model generations and see their kind of corks and different working styles also meant we had to adapt the code base to change things up when the model was revved. [00:05:00] One interesting thing here is five two, the Codex harness at the time did not have background shells in it, which means we were able to rely on blocking scripts to perform long horizon work.But with five, three and background shells, it became less patient, less willing to block. So we had to retool the entire build system to complete in under a minute and. This is not a thing I would expect to be able to do in a code base where people have opinions. But because the only goal was to make the Asian productive over the course of a week, we went from a bespoke make file build to Basil, to turbo to nx and just left it there because builds were fast at that point.swyx: Interesting. Talk more about Turbo TenX. That's interesting ‘cause that's the other direction that other people have been doing.Ryan Lopopolo: Ultimately I have. Not a lot of experience with actual frontend repo architecture.swyx: You're talking that Jessica built the sky. So I'm like, I know the NX team. I know Turbo from Jared [00:06:00] Palmer.And I'm like, yeah, that's an interesting comparison.[00:06:02] One Minute Build LoopRyan Lopopolo: The hill we were climbing right, was make it fast.swyx: Is there a micro front end involved? Is it how how complex reactRyan Lopopolo: electron base single app sort of thingswyx: And must be under a minute. That's an interesting limitation. I'm actually not super familiar with the background shelf stuff.Probably was talked about in the fight three release.Ryan Lopopolo: BA basically means that codex is able to spawn commands in the background and then go continue to work while it waits for them to finish. So it can spawn an expensive build and then continue reviewing the code, for example.swyx: Yeah.Ryan Lopopolo: And this helps it be more time efficient for the user invoking the harness.swyx: And I guess and just to really nail this, like what does one minute matter? Like why not five, okay, good. We want no. WeRyan Lopopolo: want the inner loop to be as fast as possible. Okay. One minute was just a nice round number and we were able to hit it.swyx: And if it doesn't complete, it kills it or some something,Ryan Lopopolo: No.We just take that as a signal that we need to stop what we're doing, double click, decompose a build graph a bit to get us to high back under so that we [00:07:00] can able the agent continue to operate.swyx: It's almost like you're, it's like a ratchet. It's like you're forcing build time discipline, because if you don't, it'll just grow and grow.That's right. And you mentioned that my current, like the software I work on currently is at 12 minutes. It sucks.Ryan Lopopolo: This has been my experience with platform teams in the past, where you have an envelope of acceptable build times and you let it go up to breach and then you spend two, three weeks to bring it back down to the lower end of the average low bed stop.But because tokens are so cheap Yeah. And we're so insanely parallel with the model, we can just constantly be gardening this thing to make sure that we maintain these in variants, which means. There's way less dispersion in the code and the SDLC, which means we can simplify in a way and rely on a lot more in variance as we write the software.[00:07:45] Observability, Traces & Local Dev StackVibhu: Lovely.[00:07:46] Humans Are BottleneckVibhu: You mentioned in your article, like humans became the bottleneck, right? You kicked off as a team of three people. You're putting out a million line of code, like 1500 prs, basically. What's the mindset there? So as much as code is disposable, you're doing a lot of review. A lot [00:08:00] of the article talks about how you wanna rephrase everything is prompting everything, is what the agent can't see.It's kind of garbage, right? You shouldn't have it in there. So what's like the high level of how you went about building it, and then how you address okay, humans are just PR review. Like how is human in the loop for this?Ryan Lopopolo: We've moved beyond even the humans reviewing the code as well.[00:08:19] Human Review, PR Automation & Agent Code ReviewRyan Lopopolo: Most of the human review is post merge at this point.But post, post merge, that's not even reviewed. That's justswyx: Oh, let's just make ourselves happy by YouRyan Lopopolo: haven't used fundamentally. The model is trivially paralyzable, right? As many GPUs and tokens as I am willing to spend, I can have capacity to work with my hood base.The only fundamentally scarce thing is the synchronous human attention of my team. There's only so many hours in the day we have to eat lunch. I would like to sleep, although it's quite difficult to, stop poking the machine because it makes me want to feed it. You have to step back, right?Like you need to take a systems thinking mindset to things and [00:09:00] constantly be asking where is the agent making mistakes? Where am I spending my time? How can I not spend that time going forward? And then build confidence in the automation that I'm putting in place. So I have solved this part of the SDLC, and usually what that has looked like is like we started needing to pay very close attention to the code because the agent did not have the right building blocks to produce.Modular software that decomposed appropriately that was reliable and observable and actually accrued a working front end in these things, right?[00:09:35] Observability First SetupRyan Lopopolo: So in order to not spend all of our time sitting in front of a terminal at most, doing one or two things at a time, invested in giving the model that observability, which is that that graph in the post here.swyx: Yeah. Let's walk through this traces and which existed firstRyan Lopopolo: we started with just the app and the whole rest of it. From vector through to all these login metrics, APIs was, I dunno, half an [00:10:00] afternoon of my time. We have intentionally chosen very high level fast developer tools. There's a ton of great stuff out there now.We use me a bunch, which makes it trivial to pull down all these go written Victoria Stack binaries in our local development. Tiny little bit of python glue to spin all these up. And off you go. One neat thing here is we have tried to invert things as much as possible, which is instead of setting up an environment to spawn the coding agent into, instead we spawn the coding agent, like that's the entry point.It's just Codex. And then we give Codex via skills and scripts the ability to boot the stack if it chooses to, and then tell it how to set some end variables. So the app and local Devrel points at this stack that it has chosen to spin up. And this I think is like the fundamental difference between reasoning models and the four ones and four ohs of the past, where these models could not think so you had to put them in [00:11:00] boxes with a predefined set of state transitions.Whereas here we have the model, the harness be the whole box. And give it a bunch of options for how to proceed with enough context for it to make intelligent choices. SoVibhu: sales, so like a lot of that is around scaffolding, right? Yes. Previous agents, you would define a scaffold. It would operate in that.Lube, try again. That's pivoted off from when we've had reasoning models. They're seeming to perform better when you don't have a scaffold, right? That's right.[00:11:28] Docs Skills GuardrailsVibhu: And you go into like niches here too, like your SPEC MD and like having a very short agent MG Agent md.swyx: Yes. Yes.Vibhu: Yeah. So you even lay out what it is here, but I likeswyx: the table contents.Vibhu: Yeah.swyx: Like stuff like this, it really helps guide people because everyone's trying to do this.Ryan Lopopolo: This structure also makes it super cheap to put new content into the repository to steer both the humans and the agents.swyx: You, you reinvented skills, right?Vibhu: One big agents andswyx: skills from first princip holdsRyan Lopopolo: all skills did not exist when we started doing this.Vibhu: You have a short [00:12:00] one 100 line overall table of contents and then you have little skills, right? Core beliefs, MD tech tracker. Yeah. Yeah. The scale is overRyan Lopopolo: The tech jet tracker and the quality score are pretty interesting because this is basically a tiny little scaffold, like a markdown table, which is a hook for Codex to review all the business logic that we have defined in the app, assess how it matches all these documented guardrails and propose follow up work for itself.Before beads and all these ticketing systems, we were just tracking follow up work as notes in a markdown file, which, we could spa an agent on Aron to burn down. There's this really neat thing that like the models fundamentally crave text. So a lot of what we have done here is figure out ways to inject textswyx: intoRyan Lopopolo: the system right when we get a page, because we're missing a timeout, for example.I can just add Codex in Slack on that page and say, I'm gonna fix this by adding a timeout. Please update our reliability documentation. To require that all network calls have [00:13:00] timeouts. So I have not only made a point in time fix, but also like durably encoded this process knowledge around what good looks like.swyx: Yeah.Ryan Lopopolo: And we give that to the root coding agent as it goes and does the thing. But you can also use that to distill tests out of, or a code review agent, which is pointed at the same things to narrow the acceptable universe of the code that's produced.swyx: I think one of the concerns I have with that kind of stuff is you think you're making the right call by making, it's persisted for all time across everything.Yes. But then you didn't think about the exceptions that you need to make, right? And that you have to roll it back.Vibhu: Part of it isswyx: also sometimes it can follow your s instructions too.Vibhu: It's somewhat a skill, right? So it determines when it uses the tools, right? Like it's not like it'll run outta every call.It'll determine when it wants to check quality score, right?Ryan Lopopolo: Yeah. And we do in the prompts we give these agents, allow them to push back,[00:13:51] Agent Code Review RulesRyan Lopopolo: When we first started adding code review agents to the pr, it would be Codex, CLI. Locally writes the change, pushes up a PR on [00:14:00] those PR synchronizations of review agent fires.It posts a comment. We instruct Codex that it has to at least acknowledge and respond to that feedback. And initially the Codex driving the code author was willing to be bullied by the PR reviewer, which meant you could end up in a situation where things were not converging. So yeah, we had to,swyx: he's just a thrash.Ryan Lopopolo: We had to add more optionality to the prompts on both of these things, right? The reviewer agents were instructed to bias toward merging the thing to not surface anything greater than a P two in priority. We didn't really define P two, but we gave it, youswyx: did define P two.Ryan Lopopolo: We gave it a framework within which to score its outputswyx: and then greater than P zero is worse, right?Yes. P two is very good.Ryan Lopopolo: P zero is you will mute the code place ifswyx: you merch thisRyan Lopopolo: thing, right?swyx: Yeah.Ryan Lopopolo: But also on the code authoring agent side, we also gave it the flexibility to either defer or push back against review feedback, right? This happens all the time, right? Like I happen to notice something and leave a code review, [00:15:00] which.Could blow up the scope by a factor of two. I usually don't mean for that to be addressed Exactly. In the moment. It's more of an FYI file it to the backlog, pick it up in the next fix it week sort of thing. And without the context that this is permissible, the coding agents are gonna bias toward what they do, which is following instructions.swyx: Yeah.[00:15:19] Autonomous Merging Flowswyx: I do wanted to check in on a couple things, right? Sure. All the coding review agent, it can merge autonomously. I think that's something that a lot of people aren't comfortable with. And you have a list here of how much agents do they do Product code and tests, CI configuration and release tooling, internal Devrel tools, documentation eval, harness review, comments, scripts that manage the repository itself, production dashboard definition files, like everything.Yes. And so they're just all churning at the same time, is there like a record that, that any human on the team pulls to stop everythingRyan Lopopolo: Because we are building a native application here. We're not doing continuous deploy. So there's still a human in the loop for cutting the release branch.I see. We require a blessed [00:16:00] human approved smoke test of the app before we promote it to distribution, these sort of things.swyx: So you're working on the app, you're not building like infrastructure where you have like nines of reliability, that kinda stuff?Ryan Lopopolo: That's correct. That's correct. Okay. And also like full recognition here that all of this activity took in a completely greenfield repository.There's. Should be no script that this applies generally toswyx: this is a production thing, you're gonna shipRyan Lopopolo: toswyx: customers. Of course. Yeah, of course. So this is realVibhu: And like one of the things there is, you mentioned you started this as a repo from scratch. The onboarding first month or so was pretty, it was like working backwards, right?Yeah. And then you had to work with the system and now you're at that point where you know, you're very autonomous. I'm curious like, okay, so what, how human in the loop is it? So what are the bottlenecks that you wish you could still automate? And part of that is also like, where do you see the model trajectory improving and offloading more human in the loop?We just got 5.4. It's a really good,Ryan Lopopolo: fantastic model, by the way.Vibhu: Yeah. Yeah. It's the first one that's merged. Top tier coding. So it's codex level coding and reasoning. So general reasoning both in one model. SoRyan Lopopolo: andVibhu: computer [00:17:00] use vision.Ryan Lopopolo: Now we now with five four, I can just have Codex write the blog post, whereas for this one I had to balance between chat.swyx: Oh, I need to, I might be out of a job. Oh my God.Ryan Lopopolo: Oh,swyx: I know. You just gave me an idea for a completely AI newsletter that five four could do. Yeah, I get it Now.Ryan Lopopolo: This sort of thing is just one example of closing the loop, right? Like the dashboard thing you mentioned. We have Codex authoring the Js ON, for the Grafana dashboards and publishing them and also responding to the pages, which means when it gets the page, it knows exactly which dashboards are defined and what alerts.What alert was triggered by which exact log in the code base. ‘cause all of this stuff is collated together.swyx: It has to own everything.Yes. Yeah. Yeah.Ryan Lopopolo: And it means that if we have an outage that did not result in a page. It has the existing set of dashboards available to it. It has the existing set of metrics and logs and can figure out where the gaps in the dashboard are or [00:18:00] in the underlying metrics and fix them in one go.In the same way, you would have a full stack engineer be able to drive a feature from the backend all the way to the front end.Vibhu: So it, it seems like a lot of the work you guys had to do was you as a small team are fully working for a way that the model wants the software to be written. It's like less human legible for better. Code legibility, agent legibility. How do you think that affects broader teams? So one at OpenAI, do liaison, like this is how software should be written. Like I can imagine, say you join a new team with this methodology, this mindset there's ways that, teams do code review, teams write code, like teams are structured and a lot of it is for human legibility.So should we all swap? Like how does this play back one broader into OpenAI and then like broader into the software engineering, right? Is it like teams that pick this up will it's pretty drastic, right? You have to make a pretty big switch. Should they just full send Yeah.Ryan Lopopolo: The mindset is very much that I'm removed from the process, right? I can't really have deep code level opinions about [00:19:00] things. It's as if I'm. Group tech leading a 500 person organization.Vibhu: Yeah.Ryan Lopopolo: Like it's not appropriate for me to be in the weeds on every pr. This is why that post merge code review thing is like a good analog here, right?Like I have some representative sample of the code as it is written, and I have to use that to infer what the teams are struggling with, where they could use help, where they're already moving quickly and I can pivot my focus elsewhere.Vibhu: Yeah.Ryan Lopopolo: So I don't really have too many opinions around the code as it is written.I do, however, have a command based class, which is used to have repeatable chunks of business logic that comes with tracing and metrics and observability for free. And the thing to focus on is not how that business logic is structured, but that it uses this primitive ‘cause I know that's gonna give leverage by default.Vibhu: Yeah.Ryan Lopopolo: Yeah, back to that sort of systems stinking,Vibhu: and you have part of that in your blog post, enforcing architecture and ta taste how you set boundaries for what's used. There's also a section on redefining [00:20:00] engineering and stuff, but yeah, it's just, it's interesting to hear,Ryan Lopopolo: and as the models have gotten better, they have gotten better at proposing these abstractions to unblock themselves, which again, lets me move higher and higher up the stack to look deeper into the future on what ultimately blocked the team from shipping.swyx: Yeah. You mentioned so you, this is primarily a, it is like a 1 million line of code base electron app. But it manages its own services as well, so it's like a backend for front end type thing.Ryan Lopopolo: We do have a backend in there, but that's hosted in the cloud.Yeah. This sort of structure is actually within the separate main and render processesWithin theswyx: electric.That's just how electronic works.Ryan Lopopolo: Yeah, of course. So have also treated like. MVC style decomposition with the same level of rigor, which has been very fun.swyx: I have a fun pun. This is a tangent, NVC is model view controller. Any sort of full stack web Devrel knows that.But my AI native version of this is Model view Claw, the clause the harness.Ryan Lopopolo: That's right. That's right. I do think that there is an interesting space to [00:21:00] explore here with Codex, the harness as part of building AI products, right? There's a ton of momentum around getting the models to be good at coding.We've seen big leaps in like the task complexity with each incremental model release where if you can figure out how to collapse a product that you're trying to build, a user journey that you're trying to solve into code, it's pretty natural to use the Codex Harness to solve that problem for you. It's done all the wiring and lets you just communicate and prompts to let the model cook.Yeah. It's been very fun. And there's also a very engineering legible way of increasing capabil. It's fantastic, right? Yeah. Just give you, just give the model scripts, the same scripts you would already build for yourself.swyx: Yeah.Yeah. So for listeners, this is Ryan saying that software engineering or coding against will eat knowledge work like the non-coding parts that you would normally think.Oh, you have to build a separate agent for it. No, start a coding agent and go out from there. Which open Claw has like it's pie Underhood.Ryan Lopopolo: [00:22:00] Yes.Vibhu: Basically define your task in code. Everything is a codingswyx: agent by the way. Since I brought it up, it's probably the only place we bring it up. Is any open claw usage from you?Any?Ryan Lopopolo: No. No. Not for me. I don't have any spare Mac Minis rattling around my house.swyx: You can afford it? No. I just, I'm curious if it's changed anything in opening eye yet, but it's probably early days. And then the other, the other thing I, I wanna pull on here is like you mentioned ticketing systems and you mentioned prs and I'm wondering if both those things have to go away or be reinvented for this kind of coding.So the git itself and is like very hostile to multi-agent.Ryan Lopopolo: Yeah. We make very heavy use of work trees.swyx: But like even then, like I just did a, dropped a podcast yesterday with Cursors saying, and they said they're getting rid of work trees ‘cause it still has too many merge conflicts.It's still un too un unintuitive. But go ahead.Ryan Lopopolo: The models are really great at resolving merge conflicts. Yeah. And to get to a state where I'm not synchronously in the loop in my terminal, I almost don't care that there are mergeswyx: with disposable.[00:23:00] Yeah.Ryan Lopopolo: We invoke a dollar land skill and that coaches codex to push the PR Wait for human and agent reviewers Wait for CI to be green.Fix the flakes if there are any merged upstream. If the PR comes into conflict, wait for everything to pass. Put it in the merge queue. Deal with flakes until it's in Maine. End. This is what it means to delegate fully, right? This is in a, very large model re probably a significant tax on humans to get PRS merged, but the agent is more than capable of doing this and I really don't have to think about it other than keep my laptop open.swyx: Yeah. I used to be much more of a control freak, but now I'm like, yeah, actually you could do a better job of this than me. Yeah. With the right context. Yes.[00:23:47] Encoding Requirementsswyx: Anything else in harness in general? Just this piece, I just wanna make sure we,Ryan Lopopolo: I think one thing that I maybe didn't make super clear in the article that I heard on Twitter as an interesting, that's respond [00:24:00]swyx: to them.What's the chatter and then what's your response?Ryan Lopopolo: Ultimately, all the things that we have encoded in docs and tests and review agents and all these things are ways to put all the non-functional requirements of building high scale, high quality, reliable software into a space that prompt injects the agent.We either write it down as docs, we add links where the error messages tell how to do the right thing. So the whole meta of the thing is to basically tease out of the heads of all the engineers on my team, what they think good looks like, what they would do by default, or what they would coach a new hire on the team to do to get things to merch.And that's why we pay attention to all the mistakes, mistakes that the agent makes, right? This is code being written that is misaligned with some as yet not written down, non-functional requirement.swyx: Sorry, what? Did the online people misunderstand orRyan Lopopolo: No,swyx: whatyouRyan Lopopolo: responded to? Somebody just literally said that.I was like, oh yeah,swyx: okay,Ryan Lopopolo: This is the [00:25:00] thing. This is what I've been doing. Oh, youswyx: agree? Yeah. I see. Interesting.Ryan Lopopolo: One other neat thing, which I did totally did not expect is folks were just. Taking the link to the article and giving it to pi or Codex and say, make my repo this,Vibhu: you achi a whole recursion.Ryan Lopopolo: And it was wildly effective. Really? It was wildly effective. NoVibhu: way. It just actually is something I tried with five, four yesterday. I didn't have time. Last time I was like out speaking of something, and this is one of my things, I was like, okay, I have this article. Can we just scaffold out what it would be like to run this?And I, I did it first as that and then I was like, okay, let me take another little side repo and say okay, if I was to fully automate this like this because I haven't written a line of code, it'sRyan Lopopolo: like over full, setVibhu: it right. The side thing I'm doing of voice. TTS I'm just like, slobbing out, whatever.It's nothing production. I'm like, how would I make this like this? And it's actually like a really good way. It's like a good way to learn what could be changed, what could be like, it's just a good analyzing, right? You give it all the codes, you give it all the context, you give it the article and it walks you through it very well.That's right. That's right.[00:25:57] Inlining Dependencies[00:25:57] Dependencies Going Away & Brett Taylor's Responseswyx: I guess one more thing before we go to Symphony is I wanted to cover [00:26:00] Brett Taylor's response. We had him on the show. He is your chairman, which is wild. Yeah. That he's reading your articles as well and like getting engaged in it. He says software dependencies are going away.Basically they can just be like vendored. Yes. Response.Ryan Lopopolo: Aswyx: hundred percent. A hundred percent agree. You still pro qr, you still pay Datadog. You still pay Temporal. Thank you.Ryan Lopopolo: Yep. The level of complexity of the dependencies that we can internalize is, I would say low, medium right now. Just based on model capability.What does the,swyx: what is medium?Ryan Lopopolo: I would say like a. A couple thousand line dependency is a thing that we could in-house No problem. Call in an afternoon of time. One neat thing about it is like probably most of that code you don't even need. Like by in-house and abstraction, you can strip away all the generic parts of it and only focus on what you need to enable the specific thing.Yes. You're building,swyx: I've been calling this the end of b******t plugins.Ryan Lopopolo: Yeah.swyx: Because there's so much when I published an open source thing, I want to accept everything, be liberal. I want to accept, this is post's law, but that means there's so much bloat. Yes. There's so much overhead.Ryan Lopopolo: One other neat thing about [00:27:00] this too is when we deploy Codex Security on the repo, it is able to deeply review and change. The internalized dependencies in a much lower friction way than it would be to like, push patches upstream, wait for them to be released, pull them down, make sure that's compatible with all the transitive I have in my repo and things like that.So it's also much lower friction to internalize some of these things if code is free. ‘cause the tokens are cheap sort of thing.swyx: Yeah. Yeah. I think like the only argument I have against this is basically scale testing, which obviously the larger pieces of software like Linux, MySQL, he calls up even the Datadog and Temporals and then maybe security testing where Yes.Classically, I think, is it linis tos, it said security open source is the best disinfectant.Ryan Lopopolo: Many eyes.swyx: Many eyes. And if inline your dependencies and code them up, you're gonna have to relearn mistakes from other people that Yep.Ryan Lopopolo: Yep. And to internalize that dependency, you're back to zero and you have to start.Reassembling all those bits and pieces to Yeah. Have [00:28:00] high confidence in the code as it is written. Yeah.Vibhu: Even part of the first intro of this, you basically mentioned like everything was written by codex, including internal tooling, right? So internal tooling, like when you're visualizing what's going on it's writing it for itself.swyx: Yeah. I'm built internal tools way I now, and like I just show them off and they're like, how long did you spend? And I didn't spend any time. I just prompted it,Ryan Lopopolo: very funny story here.swyx: Yeah, go ahead.Ryan Lopopolo: We had deployed our app to the first dozen users internally had some performance issues, so we asked them to export a trace for us get a tar ball, gave it to our on-call engineer, and he did a fantastic job of working with Codex to build this beautiful local Devrel tool, next JS app, the drag and drop the tar ball in, and it visualizes the entire trace.It's fantastic. Took an afternoon, but none of this was necessary. Because you could just spin up codex and give it the tar ball and ask the same thing and get the response immediately. So in a way, optimizing for human [00:29:00] legibility of that debugging process was wrong. It kept him in the loop unnecessarily when instead he could have just like Codex cooked for five minutes and gotten this same.swyx: Yeah, you verify your instincts here of this is how we used to do it. Or this is how I would have used to solve it.Ryan Lopopolo: Yeah. In this local observability stack. Like sure, you can de deploy Yeager to visualize the traces, but I wouldn't expect to be looking at the traces in the first place because I'm not gonna write the code to fix them.swyx: Yeah. So basically there needs to be like this kind of house stack and owning the whole loop. I think that is very well established. And it sounds like you might be like sharing more about that in the future, right?Ryan Lopopolo: Yeah. I think we're excited to do[00:29:36] Ghost Libraries Specs[00:29:36] Ghost Libraries & Distributing Software as SpecsRyan Lopopolo: We're gonna talk about Symphony in a little bit, but like the way we distribute it as a spec, which I think folks are calling Ghost Libraries on Twitter.This is like a such a cool name. It does mean it becomes much cheaper to share software with the world, right? You define a spec, how you could build your own specifying as much as is required for a coding agent to reassemble it [00:30:00] locally. The flow here is very cool. Like we have taken. All the scaffolding that has existed in our proprietary repo spun up a new one.Ask Codex with our repo as a reference. Write the spec. We tell it. Spin up a team ox spawn a disconnected codex to implement the spec. Wait for it to be done. Spawn another codex and another team ox to review the spec com or review the implementation compared to upstream and update the spec so it diverges less.And then you just loop over and over Ralph style until you get a spec that is with high fidelity able to reproduce the system as it is. It's fantastic.Vibhu: And you're basically, you're not really adding any of your human bias in there, right? That's correct. A lot of times people write a spec and be like, okay, I think it should be done this way, and you'll riff on something.And it's no, the agent could have just handled it like you're still scaffolding in a sense, right? I want it done this way. It can determine its spec better.swyx: That's right. That's right. Part of me it, I'm, I've been working a lot on evals recently, and part of me is wondering if [00:31:00] an agent can produce a spec that it cannot solve.Is it always capable of things that he can imagine or can you imagine things that it is impossible to do?Ryan Lopopolo: I think with Symphony, we, there's like this there's this axis where you have things that are easier, hard, or established or new, right? And I think things that are hard and new is still something that the models need humans.Yeah. Drive.swyx: Yeah. Yeah.Ryan Lopopolo: But I think those other quadrants are largely salt. Given the right scaffold and the right thing that's gonna drive the agent to completion,swyx: it's crazy that it solved,Ryan Lopopolo: but it means that the humans, the ones with limited time and attention get to work on the hardest stuff, like the problems where it's pure white space out in front. Or like the deepest refactorings where you don't know what the proper shape of the interfaces are. And this is where I wanna spend my time. ‘cause it lets me set up for the next level of scale.swyx: Yeah. Yeah. Amazing. Let's introduce Symphony.I think we've been mentioning it every now and then. Elixir. Interesting option.Ryan Lopopolo: Yeah.swyx: Yeah. I'm not,Ryan Lopopolo: again, like the [00:32:00] elixir manifestation here is just a derivative. Is it a modelswyx: chosen? Yeah.Ryan Lopopolo: Yeah. Yeah. And it chose that because the process supervision and the gen servers are super amenable to the type of process orchestration that we're doing here.You are essentially spinning up little Damons for every task that is in execution and driving it to completion, which. Means the mall gets a ton of stuff for free by using Elixir and the Beam.swyx: I had to go do a crash course in Beam and Elixir, and I think most people are not operating at that scale of concurrency where you need that.But it is a good mental model for Resum ability and all those things. And these are things I care about. But tell me the story, the origin story of Symphony. What do you use it for? Is this, how did it form maybe any abandoned paths that you didn't take?[00:32:46] Terminal Free Orchestration[00:32:46] Symphony: Removing Humans from the LoopRyan Lopopolo: At the end of December we were at about three and a half PRS per engineer per day.This was before five two came out in the beginning of January. Everyone gets back from holiday with five two and no other work [00:33:00] on the repository. We were up in the five to 10 PRS per day per engineer. And I don't know about y'all, but like it's very taxing to constantly be switching like that. Like I was pretty tapped out at the end of the day, again, where are the humans spending their time? They're spending their time context switching between all these active tmox pains to drive the agent forward.swyx: Yeah. No way. Yeah.Ryan Lopopolo: So let's again, build something to remove ourselves from the loop. And this is what frantic sprinted adapt here to find a way to remove the need for the human to sit in front of their terminal.So a lot of experimentation with Devrel boxes and, automatically spinning up agents, like it seems like a fantastic end state here, where my life is beach. I open live twice a day and say yes no to these things. Yeah. And this is again, a super, super interesting framing for how the work is done.Because I become more latency and sensitive. I have [00:34:00] way less attachment to the code as it is written. Like I've had close to zero investment in the actual authorship experience. So if it's garbage. I can just throw it away and not care too much about it. In Symphony, there's this like rework state where once the PR is proposed and it's escalated to the human for review, it should be a cheap review.It is either mergeable or it is not. And if it's not, you move it to rework. The elixir service will completely trash the entire work tree NPR and start it again from scratch. Okay. And this is that opportunity again to say, why was it trash right? What did the agent do that wasswyx: bad. Yeah.Ryan Lopopolo: Fix that before moving the ticket toswyx: endRyan Lopopolo: of progress again.swyx: Yeah. Why is this not in codex app? I guess this, you guys are ahead of Codex app,Ryan Lopopolo: yeah, so the way the team has been working is basically to be as AI pilled as possible and spread ahead. And a lot of the things we have worked on have fallen out [00:35:00] into a lot of the products that we have.Like we were in deep consultation with the Codex team to. Have the Codex app be a thing that exists, right? To have skills be a thing that Codex is able to use. So we didn't have to roll our own to put automations into the product. So all of our automatic refactoring agents didn't have to be these hand rolled control loops.It has been really fantastic to be, in a way, un anchored to the product development of Frontier and Codex and just very quickly try to figure out what works and then later find the scalable thing that can be deployed widely. It's been a very fun way to operate. It's certainly chaotic. I have lost track very often of what the actual state of the code looks like.‘cause I'm not in the loop. There was. One point where we had wired playwright directly up to the Electron app. With MCPM CCPs, I'm pretty bearish on because the harness forcibly injects all those tokens in the [00:36:00] context, and I don't really get a say over it. They mess with auto compaction. The agent can forget how to use the tool.There's probably only what three calls in playwright that I actually ever want to use. So I pay the cost for a ton of things. Somebody vibed a local Damon that boots playwright and exposes a tiny little shim CLI to drive it. And I had zero idea that this had occurred because to me, I run Codex and it's able to, it's oh, it's better.Yeah. Like no knowledge of this at all. Uhhuh.[00:36:30] Multi Human ChaosRyan Lopopolo: So we have had like in human space to spend a lot of time doing synchronous knowledge sharing. We have a daily standup that's 45 minutes long because we almost have to. Fan out the understanding of the current state.swyx: Yeah, I was gonna say this is good for a single human multi-agent, but multi human, multi-agent is a whole like po like explosion of stuff.Ryan Lopopolo: Yeah. And that this is fundamentally why we have such a rigid, like 10,000 [00:37:00] engineer level architecture in the app because we have to find ways to carve up the space so people are not trampling on each other.swyx: Sorry, I don't get the 10,000 thing. Did I miss that?Ryan Lopopolo: The structure of the repository is like 500 NPM packages.It's like architecture to the excess for what you would consider, I think normal for a seven person team. But if every person is actually like 10 to 50. Then the like numbers on being super, super deep into decomposition and sharding and like proper interface boundaries make a lot more sense.swyx: Yeah. To me, that's why I talked about Microfund ends and I, an anex is from that world, but Cool. It is just coming back to, to, to this I dunno if you have other, thoughts on. Orchestrating so much work coin going through this. Is this enough? Is this like any aha moments?Vibhu: It'll be interesting to see like where, okay, so right now you pick linear as your issue tracker, right?swyx: Or it's like a is it actually linear? This is actually linear.[00:37:55] Linear vs Slack WorkflowVibhu: Oh, that's linear. It's linear.swyx: Oh I never looked atVibhu: video. The demo video I had to download to [00:38:00] run.swyx: So I, because I'm a Slack maxie, but Yeah, linear. Linear is also really good. Yes,Ryan Lopopolo: we do make a good use of Slack. We we fire off codex to do all these lotion, elasticity, fix ups, the things that like sync that knowledge into the repository.It's super cheap. Yeah.swyx: Yeah.Ryan Lopopolo: Just do it in Codex.swyx: My biggest plug is OpenAI needs to build Slack. You need to own Slack. Build yours. Turn this into Slack.Ryan Lopopolo: I did read about it. Youswyx: did?Ryan Lopopolo: Yeah.[00:38:25] Collaboration Tools for AgentsRyan Lopopolo: I would say that if we think that we want these agents to do economically valuable work, which is like this is the mission, right?We want AI to be deployed widely, to do economically valuable work, then we need to find ways for them to naturally collaborate with humans, which means collaboration tooling, I think, is an interesting space to explore.swyx: Yeah, totally. Yeah. GitHub, slack, linear.Vibhu: Yeah, that was my thing. Okay, where do we see right now Codex has started Codex Model, then CLI, now there's an app, app can let me shoot off multiple Codex is in parallel, but there's no great team collaboration for Codex.And it [00:39:00] seems like your team had some say into what comes out, right? So you talked to ‘em, codex kind of was a thing. From there, if you guys are on the bound, what stuff that like, you might not focus on, but what do you expect other people to be building, right? So people that are like five x 50 Xing.Should you build stuff that's like very niche for your workflow, for your team? Should it be more general so other people can adopt? Is there a niche there? ‘Cause part of it is just okay, is everything just internal tooling? Do we have everything our own way? Like the way our team operates has our own ways that we like to communicate or is there a broader way to do it?Is it something like a issue tracker? Just thoughts if you wanna riff on that.[00:39:35] Standardizing Skills and CodeRyan Lopopolo: I think TBD we have not figured this out in a general way. I do think that there is leverage to be had in making the code and the processes as much the same as possible. If you think that code is context, code is prompts, it's better from the agent behavior perspective to be able to look in a package in directory X, Y, Z, and it not to have to page so [00:40:00] deeply into directory if you C, because they have the same structure, use the same language, they have the same patterns internally.And that same like leverage comes from aligning on a single set of skills that you're pouring every engineer's taste into to make sure that the agent is effective. So like in our code base, we have, I think, six skills. That's it. And if some part of the software development loop is not being covered, our first attempt is to encode it in one of the existing setup skills, which means that we can change the agent behavior.Yeah. More cheaply than changing the human driver behavior.swyx: Yeah.[00:40:39] Self Improvement via Logsswyx: Have you ever, have you experimented with agents changing their own behavior?Ryan Lopopolo: We do.swyx: Yeah. Or parent agent changing a subagents, behavior or something like that.Ryan Lopopolo: We have some bits for skill distillation. So for example, there's one neat thing you can do with Codex, which is just point it at its own session logs to ask it to tell you how you can use [00:41:00] the tool pedal better.swyx: It's like introspectionRyan Lopopolo: or ask it to do things. I useVibhu: this session better. What skills should Iswyx: high? I like the modification of, you can do, just do things to you can just ask agent to do things.Ryan Lopopolo: Yeah. You can just codex things. This is like a, this is like a silly emoji that we have, right? You can just codex things, you can just prompt things.It's really glorious future we live in, but okay, you can do that one-on-one. But we're actually slurping these up for the entire team into blob storage and. Running agent loops over them every day to figure out where as a team can we do better and how do we reflect that back into the repositories?Yes, though everybody benefits from everybody else's behavior for free. Same for like PR comments, right? These are all feedback. That means the code as written, deviated from what was good, a PR comment, a failed build. These are all signals that mean at some point the agent was missing context. We gotta figure out how toswyx: Yeah.Ryan Lopopolo: Slurp it up and put it back in the reboot.swyx: By the way, I do this exactly right. I used to, when I use cloud code for [00:42:00] knowledge work, cloud cowork is like a nice product, right? Yes. In I think you would agree. I always have it tell me what do I do better next time? And that's the meta programming reflection thing.So I almost think like you have six reflection extraction levels in symphony and almost like the zero of layer. So the six levels are PO policy, configuration, coordination, execution, integration, observability. We've talked about a couple of these, but the zero layer is like the, okay, are we working well?Can we improve how we work? Yes. Can I modify my own workflow without MD or something? I don't know.Ryan Lopopolo: Yeah, of course. Yeah, of course you can. Like this thing is also able to cut its own tickets ‘cause we give it full access.Yeah. Make it a ticket to have it cut. Tickets you can.Put in the ticket that you expect it to file as on follow up work,swyx: like Yeah. Self-modifying. Yeah.Ryan Lopopolo: Yeah.[00:42:44] Tool Access and CLI FirstRyan Lopopolo: Put, don't put the agent in a box. Give the agent full accessibility over it. Domain.swyx: I had a mental reaction when you said don't put the agent in a box. So I think you should put it in a box. Like it's just that you're giving the box everything it needs.Ryan Lopopolo: Yeah. Context and tools.swyx: But we're like, as developers, we're used to calling [00:43:00] out to different systems, but here you use the open source things like the Prometheus, whatever, and you run it locally so that you can have the full loop. I assume.Ryan Lopopolo: Yep.Vibhu: I think likeRyan Lopopolo: another, you wanna minimize cloud, cloud dependencies.Vibhu: You also want to make sure that you think about what the agent has access to. What does it see? Does it go back into the loop, like from the most basic sense of you let it see its own like calls, traces it can determine where it went wrong. But are you feeding that back in? So you know, just the most basic level of you wanna see exactly what's input output, like does the agent have access to.What is being outputted, right? It can self-improve a lot of these things. It's allRyan Lopopolo: text, right? My job is to figure out ways to funnel text from one agent to the other.swyx: It's so strange like way back at the start of this whole AI wave Andre was like, English is the hottest day programming language.It's here, it's just Yeah. The feature as well.Vibhu: A lot of, okay. Like a lot of software, a lot of stuff. There's a gui, it's made for the human. We're seeing the evolution of CLI for everything, right? All tools have CLIs. Your agents can use [00:44:00] them well, do we get good vision? Do we get good little sandboxes?Like right now? It's a really effective way, right? Models love to use tools. They love the best. They love to read through text. So slap a CLI let it go loose. That works for everything.Ryan Lopopolo: It does. Yeah. Yeah.[00:44:14] UI Perception and RasterizingRyan Lopopolo: We've also been adapting nont, textual things to that shape in order to improve model behavior in some ways, right?We want the agent to be able to see the UI agents do not perceive visually in the same way that we do. They don't see a red box, they see red box button, right? They see these things in latent space. So if we want, Hey, yeah, I do. We haveswyx: a ding if that goes off every time. Alien spaceRyan Lopopolo: ding.Anyway if we wanna actually make it see the layout, it's almost easier to rasterize that image to ask EOR and feed it in to the agent. Ha. And there's no reason you can't do both, right? To like further refine how the model perceives the object it's [00:45:00] manipulating.swyx: Cool. Could we, you wanna talk about a couple more of these layers that might bear more introspection or that you have personal passion for?[00:45:07] Coordination Layer with ElixirRyan Lopopolo: I will say that the coordination layer here was a really tricky piece to get right.swyx: Let's do it. Yep. I'm all about that. And this is Temporal core.Ryan Lopopolo: This is where when we turn the spec into Elixir, where like the model takes a shortcut, right? Like it's oh, I have all these primitives that I can make use of in this lovely runtime that has native process supervision.Which is I think, a neat way to have taken the spec and made it more choices achievable by making choices that naturally mapswyx: Yeah.Ryan Lopopolo: To the domain, right? In the same way that like you would prefer to have a TypeScript model repo if you are doing full stack web development, right? Because the ability to share types across the front end and backend reduces a lot of complexity.And becauseswyx: that's what graph kill used to be.Ryan Lopopolo: That's right. Andswyx: I don't know if it's still alive, butRyan Lopopolo: [00:46:00] no humans in the loop here. So like my own personal ability to write or not write elixir. Doesn't really have to bias us away from using the right tool for the job. It is just wild.swyx: Love it. I love it.Yeah. I wonder if any languages struggle more than others because of this? I feel like everyone has their own abstractions. That would make sense. But maybe it might be slower, it might be more faulty where like you'd have to just kick the server every now and then. I, I don't know. I think observability layer is really well understood.Integration layer, CP is dead. I think all these just like a really interesting hierarchy to travel up and down. It's common language for people working on the system to understandRyan Lopopolo: The policy stuff is really cool, right? Yeah. You don't really have to build a bunch of code to make sure the system wait for the, to passswyx: it's institutional knowledge.Ryan Lopopolo: Yeah. You just give it the G-H-C-L-I with some text that say CI has to pass. It makes the maintenance of these systems a lot easier.[00:46:57] Agent Friendly CLI Outputswyx: Do you think that CLI maintainers need to be [00:47:00] do anything special for agents or just as is? It's good because like I don't think when people made the G GitHub, CLI, they anticipated this happening.Ryan Lopopolo: That's correct. The GH CLI is fantastic. It's great super industry.swyx: Everyone go try GH repo create GH pull and then pull request number, right? GH HPR, like 1 53, whatever. And then it like pullsRyan Lopopolo: basically my only interaction with the GitHub web UI at this point is GH PR view dash web.Exactly. Glanceswyx: at the diffRyan Lopopolo: and be like Sure thing. Send it. Yeah. But the CLI are nice ‘cause they're super token efficient and they can be made more token efficient really easily. Like I'm sure you all have seen like I go to build Kite or Jenkins and I could just get this massive wall of build output.And in order to unblock the humans, your developer productivity team is almost certainly gonna write some code that parses the actual exception out of the build logs and sticks it in a sticky note at the top of the page. And you basically [00:48:00] want CLI to be structured in a similar way, right? You're gonna want to patch dash silent to prettier because the agent doesn't care that every file was already formatted.Just wants to know it's either formatted or not. So it can then go run a right command. Similarly, like in our PNPM distributed script runner, when we had one, when you do dash recursive, like it produces a absolute mountain of text. But all of that is for passing. Test suites. So we ended up wrapping all of this in another scriptswyx: to suppress the,Ryan Lopopolo: which you can vibe the channel only output the failing parts of the tests.swyx: You make a pipe errors versus the standard, standard out. I don't know. Okay. Whatever. Too much thinking have to do that. The CII used to maintain SCLI for my company and yeah, this is like core, very core to my heart. But you're vibing my job.Ryan Lopopolo: That's right.swyx: Cool. Any other things?This is a long spec. [00:49:00] I appreciate that. It's got a lot of strong opinions in here. Any other things that we should highlight? I think obviously you can spend the whole day going through some of these, but I do think that some of these have a lot of care or some of this you might wanna tell people, Hey, take this, but, make it your own.[00:49:15] Blueprint Spec and GuardrailsRyan Lopopolo: Fundamentally, software is made more flexible when it's able to adapt to the environment in which it is deployed, which means that things like linear or GitHub even are specified within the spec, but not required pieces of it. There's like a more platonic ideal of the thing that you could swap in like Jira or Bitbucket, for example.But being able to tightly specify things like the ID formats or how the Ralph Loop works for the individual agents. Basically means you can get up and running with a fully specified system quickly that you then evolve later on. I think we never intended for this to be a static spec that you can [00:50:00] never change.It's more like a blueprint to get something worth a starting point up and running.swyx: Yeah.Ryan Lopopolo: For you then to vibe later to your heart's content,swyx: you have like code and scripts in here where it's oh, I think this is a really good prompt. It's just a very long prompt.Ryan Lopopolo: Fundamentally, the agents are good at following instructions, so give them instructions.And it will, improve the reliability of the result. We, much like the way we use Symphony, we don't want folks to have to monitor the agent as it is vibing the system into existence. So being very opinionatedVery strict around what these success criteria are means that our deployment success rate goes up. Yeah. It means we don't have to get tickets on this thing.Vibhu: Think it all goes back to that like code to disposable, right? Like early on when you had CLI or you'd kick off a Codex run, it would take two hours. You would wanna monitor okay, I'm in the workflow of just using one.I don't want it to go down the wrong path. I'll cut it off and, just shoot off four, like that was my favorite thing of the Codex app, right? Yeah. Just Forex it like, [00:51:00] it's okay. One of them will probably be right, one of them might be better. Stop overthinking it. Like my first example was probably like deep research.When you put out deep research and I'd ask it something like, I asked it something about LLM, it thought it was legal something and spent an hour, came back with a report completely off the rails. And I was like, okay, I gotta monitor this thing a bit. No don't monitor it. Just you want to build it so it's that it, it goes the right way.And you don't wanna, you don't wanna sit there and babysit, right? You don't want to babysit your agentsRyan Lopopolo: with that deep research query that you made. Looking at the bad result, you probably figured out you needed to tweak your prompt Yeah. A bit, right? That's that guardrail that you fed back into the code base for the task, your prompt to further align the agent's execution.Same sort of concept supply there too.swyx: When you talk, how are the customers feelingRyan Lopopolo: for Symphony? I think we have none, right? This is a thing we have put out into theswyx: world. Symphony's internal, right? As long as you are happy, you are the customer. That'

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CFO Explains: The Rise of Secondaries and the Death of the IPO Path

Play Episode Listen Later Apr 6, 2026 38:04

Secondaries aren't a niche anymore — they're the main event. In 2025, the secondary market hit $233B, outpacing IPOs 5:1. This episode breaks down how we got here, why companies stay private longer, and how employees, investors, and CFOs actually navigate liquidity. From Facebook's wild west to structured tenders and continuation vehicles — this is the new playbook.—SPONSORS:Aleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.comBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metrics—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.comBen on LinkedIn: https://www.linkedin.com/in/slackerstuff/Slacker Stuff: https://www.slackerstuff.com/—RELATED EPISODES:IPOs Are Being Replacedhttps://youtu.be/oGuZf83pdREThe $10T Question: Who Needs Wall Street?" | Scott Vosshttps://youtu.be/szCIZmTs3SQDriving revenue without selling | Greg Henry of 1Passwordhttps://youtu.be/f5FsNoG8A3EFinance vs. Marketing: Who's Really Right About ROI? | Brandon Sullivanhttps://youtu.be/ivcht5N7uRQDo the things spreadsheets can't do | SeatGeek's Teddy Collinshttps://youtu.be/jn0L5KkmMI4—TIMESTAMPS:0:00 The $233B market replacing IPOs2:57 What is a secondary?4:24 The dot-com hangover5:48 The wild west era8:58 Sponsors — Aleph | RightRev | Rillet12:21 Why companies stay private longer13:56 The three stakeholders18:57 When is it too early?20:42 How to run one of these things23:57 Sponsors — EY | SpendHound | Brex27:04 Not all shares are created equal31:08 The good, the bad, and the ugly34:23 GP-led continuation vehicles36:19 The whole game changed37:34 Credits#RunTheNumbersPodcast #PrivateMarkets #SecondaryMarkets #VentureCapital #CFO

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Fanatics CFO on CAC, LTV, and Capital Allocation Across Verticals

Play Episode Listen Later Apr 2, 2026 44:54

In this episode of Run the Numbers, CJ sits down with Glenn Schiffman, CFO of Fanatics. They break down the economics of sports IP, how Fanatics approaches CAC, LTV, and capital allocation across merchandising, collectibles, and betting, and the negotiation lessons Glenn learned from decades in investment banking and leading finance at IAC.—SPONSORS:Brex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjEY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.com—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/glenn-h-s-51440521/Company: https://www.fanaticsinc.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro1:13 Welcome and guest intro2:38 Football at Duke and college businesses5:40 Predecessor to DoorDash story7:10 IP monetization explained9:22 All deals are snowflakes10:36 Partnership KPIs13:21 Sponsors — Brex | Aleph | RightRev16:40 Buy, bet, collect: CAC and LTV17:15 Single view of the fan18:39 Multi-business customers spend 4.7x20:32 Fanatics credit card launch22:13 AI for personalization23:01 DTC vs. wholesale margin profiles24:05 Budgeting process26:07 Infinite vs. finite: focus on revenue26:37 Sponsors — Rillet | EY | SpendHound29:47 Starve your losers, feed your winners32:58 Capital compounding: revenue, EBITDA, FCF33:56 Buybacks at IAC37:02 First negotiation at Lehman39:17 Outlasting the other side39:56 Listening in negotiations40:03 Long-Ass Lightning Round44:25 Credits#RunTheNumbersPodcast #CFO #SportsCommerce #CapitalAllocation #FinanceLeadership

David Shim and Rachana Rele: Read AI CEO and VP of Product Design for AI-native products at Adobe on amplifying creative work — not replacing it

Design Better Podcast

Play Episode Listen Later Apr 1, 2026 35:30

Today we have two guests from two different companies who have one shared conviction: AI works best when it amplifies people, not replaces them. Today we're joined by Rachana Rele, VP of Product Design for AI-native products at Adobe, and David Shim, co-founder and CEO of Read AI. Together, they're building very different products — but they share a vision of AI that removes the drudgery from creative work and makes room for the thinking that actually matters. In this conversation, we dig into some ideas that could genuinely change how you think about your work. David talks about this concept of “storage of intelligence” — the idea that your knowledge, your meeting history, your working style could all be captured and made available as a kind of digital twin that keeps working even when you're not in the room. And Rachana shares how Adobe is thinking about AI not as a one-shot creative output machine, but as a collaborative partner that helps teams break out of their own blind spots. We also push them on the harder questions — the job anxiety that's real right now in tech, the surveillance concerns that come with recording your work life, and where they each personally draw the line. Bios David Shim is Co-Founder and CEO of Read AI, an AI productivity platform focused on helping knowledge workers leverage the power of AI to improve how they collaborate, communicate, and get work done. The platform provides meeting insights, search, chat, and proactive recommendations for millions of professionals, integrating seamlessly with the tools teams already use. Read AI is pioneering the concept of the Digital Twin—AI that serves as a true extension of you, built on deep contextual understanding of how you work. Today, Read AI is trusted by teams at 90% of the Fortune 500 and in the past year, was recognized as a Top 10 AI Vendor for Enterprises by Brex, a Top 50 AI App by a16z and Mercury, and named one of Inc.'s Top 16 Companies to Watch Before founding Read AI, David served as CEO of Foursquare and previously founded Placed, which was acquired by Snap in 2017. In 2025, he was named CEO of the Year by Geekwire. Rachana Rele Rachana has spent 20+ years at the intersection of technology and human experience — figuring out not just what to build, but why it matters. At Adobe, she shapes the direction of new products, nurtures ideas from zero to something real, and helps early-stage businesses find their footing and grow. She's also a perpetual student — currently finishing an MBA at UC Berkeley's Haas School of Business, with an M.Eng. in HCI from Clemson and a B.E. in Industrial Engineering from the University of Mumbai.

Mayssa Chehata: Founder of Behave Candy

Play Episode Listen Later Mar 31, 2026 67:33

This week, we chat with Mayssa Chehata!Mayssa is the founder of BEHAVE Candy, the first low-sugar, clean-label candy designed not to spike blood sugar. Inspired by her love of sweets and her father's diabetes, she set out to create a better alternative—partnering with chefs and food scientists to develop candy that delivers on taste while helping reduce sugar in the food system.In addition to building BEHAVE, Mayssa is also the host of the ROCK BOTTOM podcast, and is a writer and speaker.Before starting her company, she built her career across a range of well-known organizations, including the NFL, Uber, Daily Harvest, and SoulCycle.And outside of all that, she also moonlights as a DJ.✨ This episode is presented by Brex.Brex: brex.com/trailblazerspodThis episode is supported by RocketReach, Gusto, OpenPhone & Athena.RocketReach: rocketreach.co/trailblazersGusto: gusto.com/trailblazersQuo: Quo.com/trailblazersAthena: athenago.me/Erica-WengerFollow Us!Mayssa Chehata: @mayssa_c@behavecandy@thetrailblazerspod: Instagram, YouTube, TikTokErica Wenger: @erica_wenger

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AI Pricing and the Hidden Growth Lever Most CFOs Ignore

Play Episode Listen Later Mar 26, 2026 55:22

In this episode of Run the Numbers, CJ sits down with Kunal Agarwal, CFO of Gorgias, to unpack how AI is reshaping pricing and operations. They discuss outcome-based pricing, how to forecast LLM-driven costs, and why order-to-cash isn't just back-office plumbing—it can be a true growth lever when designed correctly.—SPONSORS:Tabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome 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.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cj—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/agarwalk/Company: https://www.gorgias.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and Intro2:14 PE to venture incubator4:39 Operational empathy from zero-to-one4:45 First paying user feeling5:40 Jet ski customer support story8:49 Hanging with IC sales reps12:24 Sponsors — Tabs | Abacum | Brex15:44 Finance as the decision engine17:36 Gorgias overview20:20 Pricing structure and iteration22:11 Outcome based / resolution pricing24:48 AI success rate as key metric25:33 Sponsors — Metronome | RightRev | Rillet28:57 Pricing value split — $1 per resolution31:17 Vertical specificity as AI moat33:27 Managing LLM costs35:41 Falling token costs and model mix39:28 Order to cash as growth engine43:31 Auditing order to cash at 25M ARR44:09 Manual choke points46:32 Learning density over titles48:30 SurveyMonkey as the most formative period50:07 Lightning round50:17 Listening to respond vs. listening to learn51:04 Advice to younger self52:02 Finance software stack52:45 Cortex — internal AI decision tool54:21 Craziest expense story54:52 Credits

Satya Patel: Co-Founding Parter of Homebrew and Screendoor

Play Episode Listen Later Mar 24, 2026 62:11

This week we chat with Satya Patel! Satya is the co-founding General Partner of Homebrew, a seed-stage venture capital firm based in San Francisco. Through Homebrew, Satya has backed some of the most impactful startups of the past decade, including Chime, Cruise, Finix, Gusto, Habi, Honor, Plaid, and Shield AI.He's also the co-founder of Screendoor, an investment platform designed to support emerging fund managers by pairing mentorship from experienced GPs with anchor capital from world-class institutional LPs.Before becoming a venture capitalist, Satya served as VP of Product at Twitter, where he built and led the Product Management and User Services teams during a pivotal period of the company's growth. Prior to that, he held venture and product roles across the tech ecosystem, including serving as a Partner at Battery Ventures and as a product management leader at Google.Satya has been recognized on the Forbes Midas List Seed for his track record backing category-defining companies, particularly in fintech and AI. The U.S.-born son of Indian immigrants, he grew up in Las Vegas before building a career at the intersection of technology, product, and venture capital.✨ This episode is presented by Brex.Brex: brex.com/trailblazerspodThis episode is supported by RocketReach, Gusto, OpenPhone & Athena.RocketReach: rocketreach.co/trailblazersGusto: gusto.com/trailblazersQuo: Quo.com/trailblazersAthena: athenago.me/Erica-WengerFollow Us!Satya Patel: @satyap@thetrailblazerspod: Instagram, YouTube, TikTokErica Wenger: @erica_wenger

Why up to 50% of Savvy Wealth's marketing budget goes towards experimentation

Category Visionaries

Play Episode Listen Later Mar 24, 2026 24:35

Savvy Wealth is an AI-enabled platform for independent financial advisors — solo operators and small teams — that handles everything from CRM and billing to compliance, investment management, and financial planning. In this episode of BUILDERS, I sat down with Ritik Malhotra, Founder & CEO, to get into the GTM mechanics behind selling into one of the most trust-locked markets in financial services: advisors who don't just buy software — they move their entire business.Topics Discussed:What Ritik took — and deliberately inverted — from watching Brex scale from ~$5M to $100M in revenue in a single yearWhy Savvy's GTM motion is structurally closer to recruiting than B2B sales — and what that means for team designHow a data science-driven "likelihood to move" model shapes top-of-funnel targetingWhat's actually driving growth: brand trust and advisor word-of-mouth over outboundWhy cold email and conference booths underdelivered, and the experimentation framework Ritik runs insteadHow Savvy deliberately blends adjacent-industry sales talent with wealth management insidersWhy the "AI replaces the advisor" framing gets the value prop of human financial guidance fundamentally wrongThe long-term vision: a fully vertically integrated operating system for financial advisors, orchestrated by proactive AI agents// Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.ioThe 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

ceo founders ai europe wealth b2b crm 100m builders 5m front lines savvy 10m experimentation gtm marketing budgets brex whispered wrongthe 100m arr

What It Takes to Go Public Today | Inside the IPO Process with RBC's Federico Acabbi

Play Episode Listen Later Mar 23, 2026 56:38

In this episode of Run the Numbers, CJ sits down with Federico Acabbi, investment banker at RBC, to break down what's happening across cybersecurity, infrastructure, and the broader software market. They cover why horizontal software is under pressure while security spend holds up, what it really takes to go public today, and how the IPO process actually works behind the scenes—from diligence to pricing.—SPONSORS:Rillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome 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.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.com—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNFederico: https://www.linkedin.com/in/federicoacabbi/rbccm.comCJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro2:36 Software investability right now6:46 Cyber — CFO's #2 investment priority8:17 AI turbocharging attacks9:08 DevOps disruption10:30 Sponsors — Rillet | Tabs | Abacum13:55 IPO landscape overview14:33 Revenue and market cap benchmarks19:24 Founder share sales at IPO22:28 Secondary market vs. IPO market23:14 How many IPOs can the market digest?24:20 Investor mix for larger vs. smaller IPOs25:04 Sponsors — Brex | Metronome | RightRev28:23 Role of mutual funds in IPOs30:00 What banks actually do on an IPO31:49 Book building and allocation32:43 Research coverage35:52 NDRs and testing the waters38:59 Roadshow format — then vs. now40:41 Direct listings explained43:57 How banks make money on IPOs44:56 Fee splits between banks47:36 IPO vs. M&A — which is more attractive?51:02 Junior banking story — Nokia and the Vespa54:31 Craziest expense story56:08 Credits

The Anatomy of Financial Bubbles | Lessons From 200 Years of Market Panic

Play Episode Listen Later Mar 19, 2026 55:38

In this episode of Run the Numbers, CJ sits down with financial historian and PayPal Mafia member Aman Varejee to explore the recurring patterns behind economic bubbles. Drawing on research from his upcoming book “A Brief History of Financial Bubbles”, Aman explains what defines a bubble, the psychology that fuels speculative manias, and why major technological shifts—from railways to the internet to AI—often coincide with periods of extreme market speculation.—SPONSORS:RightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome 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 Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNAman: https://www.linkedin.com/in/aman-verjee/https://practicalvc.com/Aman's book: https://bigbubbletrouble.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro1:38 Welcome and guest intro3:48 Defining a bubble6:03 Cognitive biases behind bubbles10:27 Sponsors — RightRev | Rillet | Tabs13:56 Do bubbles produce breakthroughs?14:35 Dot-com bubble — winners and losers16:00 UK railway bubble21:24 Bubble warning signs25:30 Sponsors — Abacum | Brex | Metronome28:47 Do bubbles attract grifters?31:11 Role of government in bubbles35:59 Bubble duration vs. severity37:32 2008 — roots go back to 199240:27 Are bubbles connected through time?43:38 Are we in an AI bubble?45:06 Nvidia vs. Cisco valuation comparison46:52 Circular finance in AI49:16 FP&A at PayPal under Thiel and Musk52:02 PayPal's referral campaign and LTV/CAC54:40 Book plug55:09 Credits#RunTheNumbersPodcast #Fintech #EconomicBubbles #FinanceHistory #CFO

Kipp Bodnar: CMO at HubSpot

Play Episode Listen Later Mar 17, 2026 54:40

This week we chat with Kipp Bodnar!Kipp is the Chief Marketing Officer of HubSpot, the #1 CRM platform for scaling companies, where he leads the company's global marketing strategy—driving awareness, demand, and growth across one of the most influential software brands in the world.Before stepping into the CMO role, Kipp served as Vice President of Marketing at HubSpot, overseeing worldwide demand generation, building out the EMEA and APAC marketing teams, and managing field marketing, localization, strategic partnerships, and social media. He's helped shape how modern SaaS companies think about growth at scale.Beyond HubSpot, Kipp is a trusted advisor to leading SaaS companies like SimplyMeasured, InsightSquared, and Guidebook. He's also the co-author of The B2B Social Media Book, a playbook for marketers looking to generate real results through digital channels.An industry-leading speaker, blogger, and marketing strategist, Kipp combines storytelling with data-driven execution—and has been at the forefront of how B2B marketing has evolved over the past decade.This is going to be a masterclass in modern marketing.✨ This episode is presented by Brex.Brex: brex.com/trailblazerspodThis episode is supported by RocketReach, Gusto, OpenPhone & Athena.RocketReach: rocketreach.co/trailblazersGusto: gusto.com/trailblazersQuo: Quo.com/trailblazersAthena: athenago.me/Erica-WengerFollow Us!Kipp Bodnar: @kippbodnar@thetrailblazerspod: Instagram, YouTube, TikTokErica Wenger: @erica_wenger

marketing vice president b2b saas cmo crm chief marketing officers hubspot gusto emea apac guidebook brex kipp bodnar insightsquared

Marketplace Economics | Faire CFO Jason Lee

Play Episode Listen Later Mar 16, 2026 63:08

In this episode of Run the Numbers, CJ sits down with Jason Lee, CFO of Faire, to unpack the economics of wholesale marketplaces. They break down how Faire connects independent brands with local retailers, the key metrics that matter in marketplace businesses, and ROI as a decision-making framework. Jason also shares lessons from scaling Square through its IPO and how finance leaders navigate valuation resets in private markets.—SPONSORS:Metronome 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.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metrics—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNJason: https://www.linkedin.com/in/jason-lee-11787020/Faire: https://www.faire.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro3:01 How Faire serves retailers5:49 Revenue model and retention metrics8:21 Recurring vs. reoccurring revenue11:54 Sponsors — Metronome | RightRev | Rillet15:17 Input vs. output metrics17:13 The business equation19:08 Metric ownership22:07 Killing metrics: signal to noise23:21 800 metrics at Faire24:51 Customer count as a misleading metric26:04 Sponsors — Tabs | Abacum | Brex29:25 Planning cadences at Faire32:41 When to reallocate resources36:40 ROI as a framework40:25 Portfolio view of investments43:53 Discipline is knowing where to say yes47:11 Faire's valuation reset50:49 Tender offer mechanics55:33 Screwing up IR at Square IPO56:52 Advice to younger self58:44 AI at Faire59:32 Claude connected to data warehouse1:00:16 Tariff analysis with AI1:02:38 Credits#RunTheNumbersPodcast #Marketplace #CFO #SaaS #FinanceLeadership

Delian Asparouhov: Co-Founder of Varda

Play Episode Listen Later Mar 10, 2026 48:30

This week we chat with Delian Asparouhov!Delian is a Partner at Founders Fund and the President and Co-Founder of Varda Space Industries, one of the most ambitious companies redefining what's possible in space and manufacturing. Varda is pioneering the orbital economy by producing pharmaceuticals and advanced materials in microgravity—unlocking breakthroughs that simply can't be achieved on Earth.Before co-founding Varda, Delian earned a Thiel Fellowship while still an undergraduate, leaving MIT to move to Silicon Valley and build at the frontier of technology. Since then, he's become a leading voice at the intersection of startups, deep tech, and national ambition.Beyond venture and space manufacturing, Delian is also the co-creator of the Hill & Valley Forum, which brings together leaders from Washington and Silicon Valley to strengthen collaboration around technology, defense, and national security.Born abroad and raised in Salt Lake City, Delian's journey—from a young coder obsessed with space to backing and building category-defining companies—has been anything but conventional. Driven by intellectual curiosity and an appetite for hard problems, he's helping shape the future of both industry and exploration.✨ This episode is presented by Brex.Brex: brex.com/trailblazerspodThis episode is supported by RocketReach, Gusto, OpenPhone & Athena.RocketReach: rocketreach.co/trailblazersGusto: gusto.com/trailblazersQuo: Quo.com/trailblazersAthena: athenago.me/Erica-WengerFollow Us!Delian's Twitter: @zebulgar@thetrailblazerspod: Instagram, YouTube, TikTokErica Wenger: @erica_wenger

president earth washington co founders partner mit silicon valley driven salt lake city gusto varda founders fund brex thiel fellowship delian delian asparouhov varda space industries

Solo Episode: The Bucket Method (7-Step Time Management System)