Podcasts about Salesforce

Daniel Mahncke and Shawn O'Malley take a deep dive into Wix.com — the Israeli website-building platform whose investment case now turns on two of the most debated questions in the stock today: whether the generative-AI wave that lets anyone spin up a site from a text prompt is the end of Wix or whether Wix is too sticky, and whether the Base 44 acquisition — Wix's bet on AI-powered app generation — is the next leg of the story or a distraction from the SMB infrastructure business the company already dominates. IN THIS EPISODE YOU'LL LEARN: (00:00:00) Intro (00:01:32) How Wix was founded (00:21:35) Why clients keep using Wix (00:28:05) How much of WIX is actually vulnerable to AI (00:37:07) Why Wix is more sticky than it seems (00:38:24) Whether vibecoding is likely to disrupt drag-and-drop website building (00:46:54) Why Base44 could change the entire investment case (01:06:24) How Wix could survive and turn into a multibagger (01:09:21) Valuation discussion of Wix (01:13:26) Whether Shawn and Daniel add Wix to the Intrinsic Value Portfolio BOOKS AND RESOURCES Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Track ⁠⁠⁠⁠The Intrinsic Value Portfolio⁠⁠⁠⁠. Portfolio Review Submit Tool. Value Investor Club Article. Chit Chat Stocks w/ Manuel Cunha. Future Investing Interview w/ Manuel Cunha. Rene Sellman Substack Article. Manuel Cunha Substack Article. Previous Intrinsic Value breakdowns: Figma, Microsoft, Salesforce, Adobe. Follow Shawn on ⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠. Follow Daniel on ⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠. Related ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠books⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ mentioned in the podcast. Ad-free episodes on our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Get smarter about valuing businesses through ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out ⁠⁠⁠⁠⁠⁠⁠⁠The Investor's Podcast Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our ⁠sponsors⁠: Plus500 Netsuite Shopify Vanta References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor's Podcast Network is not responsible for any claims made by them. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm

Gold is dipping even as the Middle East conflict intensifies and inflation worries mount — a counterintuitive move that has investors scratching their heads about where to find safety. We explore what's really driving gold prices and whether the precious metal still belongs in a defensive portfolio.Today's Stocks & Topics: Treasury Bill, Market Wrap, Schwab U.S. Dividend Equity ETF (SCHD), Martin Marietta Materials, Inc. (MLM), Gold's Confusing Signal: Why the Safe-Haven Trade Isn't Playing Out as Expected, Select STOXX Europe Aerospace & Defense ETF (EUAD), Salesforce, Inc. (CRM), Uranium Royalty Corp. (UROY), AstraZeneca PLC (AZN).Our Next Wealth Webinar: “Beyond the Yield: How to Invest for Your Income Needs” June 30th, 2026 - 12:00 pmTo sign up: https://us06web.zoom.us/webinar/register/5717793889555/WN_XuoDgMVwSv6wZXXurrZTLgOur Sponsors:* Check out Anthropic and use my code Claude.ai/invest for a great deal: https://www.anthropic.com* Check out Chilipad and use my code sleep.me/INVEST for a great deal: https://sleep.me* Check out Plaud AI and use my code INVEST for a great deal: https://plaud.ai* Check out Progressive: https://www.progressive.com* Check out Quince and use my code quince.com/invest for a great deal: https://www.quince.com* Check out Scribe and use my code scribe.how/invest for a great deal: https://scribe.com* Check out TaskRabbit and use my code INVEST for a great deal: https://taskrabbit.com* Check out TruDiagnostic and use my code INVEST20 for a great deal: https://www.trudiagnostic.comAdvertising Inquiries: https://redcircle.com/brands

This episode is a rebroadcast of Auren's appearance on the GTMnow podcast ---------------------------------------------------Auren Hoffman (Flex Capital) joins the GTMnow podcast to share some of the most contrarian takes in tech today, from why AI moats are gone, to why your next VC meeting will be with a bot, to why AI is secretly going to trigger a baby boom.In this episode:Why Auren runs 500+ AI agents to source deals, and what that means for founders raising capitalThe "agent-to-agent" meeting prediction: by end of 2026, first VC conversations will be fully automatedWhy every software moat has been "blown up" and what Salesforce, LinkedIn & DocuSign need to do to surviveThe OpenAI x The Hustle acquisition breakdown: why it's the smartest (and cheapest) distribution play in AIWhy missing a great deal is 10x more painful than making a bad one, Auren's honest VC mistake frameworkThe baby boom thesis: why AI, IVF, self-driving cars & cheaper energy could reverse the fertility declineWhy companies won't sign yearly SaaS contracts anymore, and what that means for every B2B founderAuren Hoffman is the founder of NQB8, Flex Capital, SafeGraph, and LiveRamp. He's an early backer of Replit, Perplexity, Rippling, Vercel, Coinbase, Chime, and AppLovin.Max's socials: https://x.com/hackitmaxhttps://www.linkedin.com/in/maxaltschuler/Auren's socials:https://x.com/aurenhttps://www.linkedin.com/in/auren/https://auren.substack.com/GTMnow: https://gtmnow.com

Send us Fan MailThis week's enterprise software announcements highlight how rapidly the market is evolving toward agentic architectures, semantic intelligence, and AI-driven operational orchestration. Anthropic expanded MCP with a framework designed for full-stack agentic applications, reinforcing the industry's push toward composable AI ecosystems. Meanwhile, Hubbl Technologies raised funding to position itself as an intelligence layer for the Salesforce agentic environment, while Salesforce continued broadening its AI footprint through Agentforce for Communications. Sage enhanced the Sage Intacct Suite with new capabilities focused on finance operations, and Sinch introduced a collection of AI agent features targeting customer engagement workflows. On the operational side, Typeface unveiled a marketing orchestration engine, while Blue Yonder announced new AI agents and mobile applications aimed at supply chain execution and workforce enablement. At the same time, Zendesk moved deeper into AI-powered customer support through its acquisition of Forethought, and Actian launched an AI analyst designed to transform business glossaries into a live semantic layer, signaling the growing importance of governed enterprise context for AI-native operations.In today's episode, we invited a panel of industry analysts for a live discussion on LinkedIn to analyze current enterprise software stories. We covered many grounds, including the direction and roadmaps of each enterprise software vendor. Finally, we analyzed future trends and how they might shape the enterprise software industry.Video: https://www.youtube.com/watch?v=NCxtpqQ_vIwQuestions for Panelists?

Thanks to Salesforce for Partnering on this episode, check out our new AI report here: https://www.jobsofthefuture.co/salesforce-ai-poll In this episode of Jimmy's Jobs of the Future, Jimmy interviews Zahra at Salesforce's new AI centre in London's Devonshire Square, discussing careers, AI adoption, and skills. Zahra shares her first job in a pizza place, an early ambition to become a diplomat blocked by 1990s eligibility rules, and a 22-year career at Andersen Consulting/Accenture before moving to Salesforce. They discuss polling with Focal Data showing 51% of people have received no employer AI training and concerns about a widening digital divide. Zahra explains Salesforce's focus on enterprise AI grounded in trusted company data with guardrails, the value of a physical AI centre for collaboration, and how she uses an internal AI Slackbot for daily task lists and meeting prep while staying critical and authentic. They cover AI benefits for mid-market firms, a Simplyhealth contact-centre example, and Salesforce's Future Trailblazers schools program and call for a national “front door” for skills like Trailhead. 00:00 Intro 00:50 Inside Salesforce's New AI Centre 01:41 My First Job: Making Pizzas for £5 an Hour 03:48 From Diplomacy Dreams to Big Tech 06:31 Britain's AI Training Problem 09:07 Why Salesforce Built an AI Hub 10:46 How I Use AI Every Day 14:21 Why Authenticity Still Matters 15:07 The Priorities Every CEO Must Balance 16:15 Is an AI Divide Emerging? 17:40 Why Businesses Still Don't Trust AI 19:01 The Companies Winning With AI 22:23 Buy Commodity, Build to Differentiate 23:16 Which Skills Will Matter in the AI Era? 26:24 What I Look For When Hiring 30:07 A National Plan for AI Skills 32:40 Sending 1,000 Employees Into Schools 34:12 My Unexpected Dream Job 35:42 Final Thoughts ********** Follow us on socials! Instagram: https://www.instagram.com/jimmysjobs Tiktok: https://www.tiktok.com/@jimmysjobsofthefuture Twitter / X: https://www.twitter.com/JimmyM Linkedin: https://www.linkedin.com/in/jimmy-mcloughlin-obe/ Want to come on the show? hello@jobsofthefuture.co Sponsor the show or Partner with us: sunny@jobsofthefuture.co Check out our clips channel here! ⬇️ https://www.youtube.com/@JimmysJobsClips Credits: Host / Exec Producer: Jimmy McLoughlin OBE Producer: Sunny Winter https://www.linkedin.com/in/sunnywinter/ Junior Producer: Thuy Learn more about your ad choices. Visit podcastchoices.com/adchoices

Bradley sits down with Emily Witucki, a Salesforce consultant and first-time mentor on the Trailblazer Mentorship Platform, alongside her three mentees: Andi Poulson, Ola Mostafa, and Monique Larroux. Together, they pull back the curtain on what mentorship in the Salesforce ecosystem actually looks like: the vulnerability, the breakthroughs, the LinkedIn glow-ups, and the genuine human connections that form along the way. Whether you're chasing your first certification, stuck in a job search rut, or an experienced pro wondering if mentorship still applies to you, this episode will meet you where you are. Spoiler: it does. And it's free. Episode Timestamps 01:16 - Welcome & Panel Introductions: Bradley welcomes mentor Emily Witucki and her three mentees: Andi, Ola, and Monique. 06:18 - Why Emily Became a Mentor: The Salesforce Fairy Godmother: Emily shares that her motivation to mentor came from a deep love of helping people and a desire to pay forward the sense of community she found in Talent Stacker. 08:22 - The Mentee Perspective. What Andi, Ola & Monique Were Looking For: Each mentee shares what drew them to the platform, from bombing interviews and feeling stuck, to needing direction after certification, to simply wanting to level up. 10:00 - How the Trailblazer Mentorship Platform Actually Works: Bradley clarifies the application and matching process, explains that membership in Talent Stacker is not required, and notes that 100% of applicants were accepted in the last two cohorts. 26:05 - Branding: Emily identified Andi's brand right away by seeing her pink hair. Emily's approach with all of them on social media and resume and portfolio, is to show people who they are, that who they are is already amazing. 30:14 – LinkedIn and Community: The panel discusses the importance of LinkedIn in the modern job search, but also the supportive community to be found there, along with the Salesforce Ohana and Talent Stacker. 42:08 - What's Next: Goals from Each Mentee: Andi aims for two new certifications and deeper community involvement, Ola is pursuing Salesforce legend status and Google Data Analytics, and Monique is weeks away from passing her admin exam. 51:50 - Advice for the "Mentorless" & How to Get Involved: The group encourages listeners to apply for mentorship without hesitation, with Emily also calling on companies to consider sponsoring the program through trailblazermentorship.com. Links & Resources Trailblazer Mentorship Platform: trailblazermentorship.com Talent Stacker: talentstacker.com

On this episode of What Are You Good At (a THINK Business & Sales series)… Jeff Gunsberg and I sat down with Henry King, author of Autonomous: Why the Fittest Businesses Embrace AI for Strategies and Digital Labor, for one of the most eye-opening AI conversations we've had on the show. Henry has spent years inside Accenture, Salesforce, and the consulting world — giving him a rare lens on how fast business is changing and what leaders must prepare for next. We talked about: Why the last 3–6 months of AI adoption have moved faster than the last few years. Why most people are still using ChatGPT like Google, and what that's costing them. Why the companies that win will be the ones bold enough to rethink everything: org charts, workflows, roles, and the relationship between humans and machines. And Henry breaks down exactly why we're about to see AI as digital colleagues, not just productivity tools. Top 3 Takeaways 1. Generative AI is not the finish line, it's the warm-up. The real disruption is agentic AI that performs work, makes decisions, and becomes part of the workforce. 2. The human advantage is shifting. We win with curiosity, imagination, empathy, and better questions, not effort or years of experience. 3. Leaders must design for movement, not stability. The dust isn't settling. The organizations that thrive will be the ones built for flexibility, experimentation, and constant reinvention. --- Henry King Innovation Strategist and Advisor | Co-author of best-selling books BOUNDLESS and AUTONOMOUS | ex-Salesforce, Deloitte Consulting, Accenture Leader in Innovation, Transformation and Technology. 30+ years experience helping companies meet their growth and transformation goals, both in the US and internationally, with expertise in i) Innovation Strategy and Management, ii) Digital Transformation, iii) Information Technology Leadership, Strategy, and Full Lifecycle Management iv)Thought Leadership, Writing and Teaching Co-author of the best selling book ""Boundless: A New Mindset for Unlimited Business Success"". Co-author of the upcoming book ""Autonomous: Why the Fittest Businesses Embrace AI-First Strategies and Digital Labor"". Specialties: Innovation Strategy, digital Transformation, Concept Development and Implementation, Customer Experience Optimization and Innovation, Capabilities Development, Organizing for Innovation, Innovation Management, Chief Information Officer, IT Strategy, Systems Development Connect with Jon Dwoskin: Twitter: @jdwoskin Facebook: https://www.facebook.com/jonathan.dwoskin Instagram: https://www.instagram.com/thejondwoskinexperience Website: https://jondwoskin.comLinkedIn: https://www.linkedin.com/in/jondwoskin Email: jon@jondwoskin.com Get Jon's Book: The Think Big Movement: Grow your business big. Very Big! Connect with Jeff Gunsberg:Website: https://title-connect.com Connect with Henry King:LinkedIn: https://www.linkedin.com/in/henry-king-88ba109/ *E - explicit language may be used in this podcast.  

A buyer in your niche just picked their SaaS partner. It wasn't you, and you never knew the deal was happening. In this episode I break down what Jay McBain, the most quoted channel analyst in the world, told me about the 28 moments that now decide every considered purchase, and why the deal is usually won or lost at moments 10 through 12, long before a salesperson knows it exists. I explain why waiting for the platform to send you a lead is finished, and I walk through the exact buying journey of a Salesforce buyer who books a call with a partner she has never spoken to. If you feel invisible while smaller, sharper partners keep landing the work you should be winning, this one is for you.Resources and LinksJay McBain on The Paul Higgins Podcast: Episode 683 - Get Specific, Get Rich, or Get Out with Jay McBainNeed help with your WHO and WHAT decisions? Apply for a FREE Multiplier CallBook a Decision Session herePrevious episode: 685 - Why Your Client Will Ask for 5 on 60 Next MonthCheck out more episodes of the Paul Higgins PodcastSubscribe to our YouTube channel: @PaulHigginsMentoringJoin our newsletterSuggested resources

What happens to a deposit when the account holder dies — and why are banks so unprepared for the one moment they know is coming?In this episode of the One Vision Podcast, Theodora Lau sits down with Martha Underwood, Founder and CEO of Prismm and author of the new release: The Death of Deposits. Drawing on 25+ years across IBM, Silicon Valley, and BBVA Compass, Martha talks about the unspoken assumption in banking — that the user will always be there — and how that assumption is now colliding with the largest generational wealth transfer in history.Together, Theo and Martha unpack the retention illusion, why the beneficiary field is the richest unused lead list sitting inside every bank's core, and why deposit attrition at death is an infrastructure problem, not a marketing one. They dig into the operational reality, the cultural reality, and the human reality, and why AI's real job is orchestration under pressure (not more automation). A deeply human conversation about deposits, design, and what it really means to extend a banking relationship beyond a single account holder.

Patrick Moorhead and Daniel Newman cover Daniel's acquisition of Enterprise Technology Research, IBM's historic $15 billion single-day commitment spanning quantum and open-source security, Anthropic's Claude Opus 4.8, and the heaviest single earnings night of the season featuring Dell, Marvell, Salesforce, Synopsys, Snowflake, HP, and Micron crossing $1 trillion in market cap. The handpicked topics for this week are: Anthropic Releases Claude Opus 4.8: Six Weeks After 4.7 Anthropic dropped Opus 4.8 just six weeks after 4.7, claiming it surpasses GPT-5.5 and Gemini 3.1 Pro on agentic coding, knowledge work, and computer use. Benchmark improvements across the board: agentic coding up from 64.3% to 69.2%, knowledge work from 1753 to 1890, agentic computer use from 82.8% to 83.4%. Three new features ship alongside it: Dynamic Workflows for multi-subagent orchestration inside Claude Code, Effort Control for managing token spend, and mid-task system messages via the API. Fast mode is now 2.5x faster and 3x cheaper. Pat's honest take: what it says on paper is good, particularly on tool triggering and citation precision, but he has lost significant trust in the company and is watching closely. (The Decode)   IBM Commits $10 Billion to Quantum: The Largest Single Quantum Bet in History IBM announced a $10 billion commitment over five years targeting a large-scale fault-tolerant quantum computer by 2029, landing the same day as the $5 billion Project Lightwell announcement for a single-day IBM strategic commitment of $15 billion. Pat has been calling 2029 to 2031 as the realistic commercial quantum window and calls this the strongest single corporate financial signal yet that the timeline is real. Daniel's framing: IBM wants to be the NVIDIA of quantum, and with a $10 billion commitment, it's sending a flare to the entire industry that pure-play quantum companies cannot compete at this balance sheet level. (The Decode)   IBM and Red Hat Launch Project Lightwell: $5B to Secure Open-Source Software IBM and Red Hat committed $5 billion and a global force of 20,000 engineers to secure open-source software for enterprises through frontier agentic AI, anchored by 11 of the largest US and Canadian banks including Bank of America, Goldman Sachs, JPMorgan Chase, Mastercard, and Visa. Pat's read: this is the productization answer to Anthropic Mythos. Mythos found the vulnerabilities. Lightwell is the industrial-scale patching and validation layer enterprises can actually buy on a subscription. Daniel adds that IBM is flexing its engineering talent base as a premium strategic asset, a direct counter to the narrative that AI replaces engineers. (The Decode)   Anthropic Project Glasswing: 23,000 Vulnerabilities Found Across 1,000 OSS Projects Anthropic's Claude Mythos scanned more than 1,000 widely deployed open-source projects and surfaced approximately 23,000 candidate vulnerabilities, with 1,094 confirmed as critical severity. The Cyber Verification Program now gates the strongest cyber-capable Claude variant behind vetted defenders only. While the tool creates real value, the surface of attack will likely grow as fast as any tool built to defend it. (The Decode)   Anthropic in Talks to Run Claude on Microsoft Maia 200 CNBC and The Information reported Microsoft is in active negotiations to supply Anthropic with its custom Maia 200 inference chip, which would make Anthropic the only frontier lab simultaneously running production workloads on four distinct silicon stacks: NVIDIA, AWS Trainium, Google TPU, and Microsoft Maia. Pat's context: Maia 200 delivers 30% better tokens per dollar than the latest Azure fleet per Satya Nadella, and this deal would be Maia's first major external deployment. Daniel's read: what can be built will be sold right now, and Anthropic chasing every available compute source is simply the structural reality of growing at 80x when you planned for 10x. (The Decode)   The Flip: Is AI CapEx Too Expensive to Earn Its Return? Pat takes the affirmative. With $725 billion in hyperscaler CapEx tracking for 2026, likely $1 trillion next year, memory has become the choke point making it even more expensive, and open-source models have closed enough of the quality gap for most enterprise tasks that the premium of frontier APIs is increasingly hard to justify. A recent Signal65 white paper shows on-prem payback at 18 months. Daniel's counter: Dell just booked $24 billion in AI orders in a single quarter. Agentforce crossed $1 billion ARR at 169% growth. NVIDIA guided to $91 billion. Only 20% of enterprises are using AI and only 2% of consumers. Both hosts admitted off the flip their notes looked nearly identical. (The Flip)   Micron Crosses $1 Trillion Market Cap Micron became the 12th US company ever to cross $1 trillion in market cap, surging 19% on May 26th as UBS raised its price target to $1,625, implying a $1.8 trillion market cap. Samsung's Q1 memory ASP jumped 146% year over year. DRAM spot prices spiked 55 to 60% quarter over quarter. Daniel has been pounding this call since sub-$100 and calls it a cycle elongated beyond anything seen in the 27 prior memory cycles, driven by HBM capacity reallocation away from consumer DRAM creating structural shortage. (Bulls and Bears)   Dell Technologies Q1 FY27: The Biggest Enterprise AI Infrastructure Print of 2026 Record $43.8 billion revenue, up 88% year over year, crushing the $35.7 billion consensus by $8 billion. AI-optimized servers at $16.1 billion, up 757% year over year. $24.4 billion in AI orders booked in a single quarter. FY27 AI server revenue guide raised from $50 billion to $60 billion. Non-GAAP EPS of $4.86 beat the $2.96 consensus by 64%. Stock up 18% after hours. Pat's framing: Dell was very clear about what they were going to do. Rack engineering, sales, and service. The basics. And they executed the basics at an extraordinary level while building a special relationship with NVIDIA who views Dell as a market maker for both enterprise and NeoCloud. Daniel's add: play nice and win. Michael Dell navigated the political landscape brilliantly and pulled the entire Dell brand along with him. (Bulls and Bears)   Marvell Technology Q1 FY27: Record Revenue, Data Center at 76% of Mix Record $2.418 billion revenue, up 28% year over year. Data center at $1.833 billion, up 27% year over year, now 76% of total revenue. Q2 guide of $2.7 billion at midpoint accelerates growth to 35% year over year. Operating cash flow a record $638.8 million. Daniel went on TV and said it's "written in the stars," arguing the market had misunderstood this one for too long by conflating its custom AI ASIC story with the full breadth of its connectivity and networking portfolio. Pat's closing: the shorts are eating it now and the custom AI ASIC versus merchant GPU debate is finally settling into the right answer, which is both in lockstep. (Bulls and Bears)   Salesforce Q1 FY27: Agentforce Crosses $1 Billion ARR Revenue $11.13 billion, up 13% year over year. Non-GAAP EPS of $3.88 crushed the $3.12 consensus by 24%. Agentforce ARR crossed $1 billion, up 169% year over year, with 28.6 trillion tokens processed, up 152% quarter over quarter. 50% of Agentforce bookings came from existing customers expanding. Daniel flagged the $25 billion accelerated buyback funded by new debt as an interesting signal worth watching. Pat's bottom line: it's not perfect, but certainly no "SaaSpocalypse" in those numbers. (Bulls and Bears)   Synopsys Q2 FY26: First Full Quarter With Ansys Integrated Revenue $2.276 billion, up 42% year over year, beating consensus. Non-GAAP EPS of $3.35 beat $3.15. FY26 guide raised to $9.665 billion midpoint. Daniel's framing: every chip runs through Synopsys tools, and the Ansys addition makes it the full-stack co-design platform Jensen Huang keeps talking about. Synopsys is not just the pick and shovel of current AI silicon. It is the pick and shovel of quantum, robotics, and space as well. (Bulls and Bears)   Snowflake Q1 FY27: Strongest Sequential Dollar Growth in Company History Product revenue $1.33 billion, up 34% year over year, the strongest sequential dollar growth in Snowflake history. Net revenue retention 126%. FY27 product revenue guide raised to $5.84 billion. Natoma acquisition announced for secure agentic enterprise connectivity. New $6 billion multi-year AWS commitment. Daniel's closing: proprietary unique data is the real moat of the agentic era, and that data has to live somewhere. It is going to go to platforms like Snowflake. (Bulls and Bears)   HP Inc. Q2 FY26: Eight Straight Quarters of Growth With AI PCs at 44% of Shipments Revenue $14.4 billion, up 9% year over year, the company marks its eighth consecutive quarter of top-line growth. Non-GAAP EPS of $0.86 beat the prior guide. Personal Systems at $10.2 billion, up 13%, with 30% operating profit growth. AI PCs jumped from 35% to 44% of shipments quarter over quarter, with HP guiding to 60 to 70% next fiscal year. FY26 EPS guide raised. Pat's note: they still need a permanent CEO, which would help investors sleep better at night. Daniel's add: the real explosive moment for device companies comes when AI moves to the edge and enterprises shift from expensive frontier model consumption to on-device inference. (Bulls and Bears)   Everpure Q1 FY27: Record Revenue, Rebrand Complete Record revenue of $1.1 billion, up 35% year over year. Product revenue $577 million, up 55%. Subscription ARR at $2 billion. FY27 guide raised to $4.41 to $4.51 billion. Pure Storage officially completed its rebrand to Everpure. Daniel's emerging thesis: the agentic era has focused enormous attention on memory and compute, but after the inference runs, the data has to sit somewhere. Storage has not seen its full inflection yet and Everpure is well positioned when that wave arrives. (Bulls and Bears)   The Decode Anthropic Releases Claude Opus 4.8 May 28  https://techcrunch.com/2026/05/28/anthropic-releases-opus-4-8-with-new-dynamic-workflow-tool/ IBM Commits $10B Over Five Years to Quantum Computing the Same Day as $5B Project Lightwell, Bringing IBM's One-Day AI https://www.barrons.com/articles/ibm-stock-quantum-computing-aafbb1eb IBM + Red Hat Announce Project Lightwell  https://newsroom.ibm.com/2026-05-28-ibm-and-red-hat-commit-5-billion-to-redefine-the-future-of-open-source-in-the-ai-era Anthropic Project Glasswing / Claude Mythos Finds 23,000 Potential Vulnerabilities Across 1,000+ Open-Source Projects https://www.securityweek.com/anthropic-mythos-detected-23000-potential-vulnerabilities-across-1000-oss-projects/ Anthropic Negotiating to Run Claude on Microsoft's Maia 200 AI Chips  https://www.cnbc.com/2026/05/21/anthropic-microsoft-maia-200-ai-chip.html OpenAI + Anthropic Walk Back the AI Jobs Apocalypse Ahead of IPOs https://finance.yahoo.com/sectors/technology/articles/ai-chiefs-walk-back-job-193605798.html https://x.com/RiskCentre/status/2059397756016611668 The Flip Is AI Capex Becoming Too Expensive to Earn Its Return — and Will the Result Be a Forced Shift to Open-Source and Smaller Use-Case-Specific Models, or a Continued $725B+ Hyperscaler Buildout That Vindicates the Capex on Productivity Gains? FOR:  The shift is to open-source + smaller use-case-specific models with better token economics, not away from AI https://x.com/danielnewmanUV/status/2059822712122400975 DeepSeek 75% permanent price cut + Anthropic Claude Code restriction reversal https://www.buildfastwithai.com/blogs/ai-news-today-may-26-2026 $190B Microsoft capex + $725B+ aggregate hyperscaler capex with no analog ROI yet  https://www.buildfastwithai.com/blogs/ai-news-today-may-26-2026   AGAINST:  Salesforce Agentforce ARR crossed $1B this quarter on 28.6T tokens processed  https://www.stocktitan.net/sec-filings/CRM/8-k-salesforce-inc-reports-material-event-3b8ead2852bb.html Lenovo +105% AI revenue, +84% Q4; Dell $43B AI backlog: the AI infrastructure flywheel is converting capex to revenue today https://investor.marvell.com/news-events/press-releases/detail/1023/marvell-technology-inc-reports-first-quarter-of-fiscal-year-2027-financial-results NVIDIA $91B Q2 guide + $1T Blackwell+Vera Rubin CY25-CY27 reaffirmed  https://www.cnbc.com/2026/05/20/were-raising-our-price-target-on-nvidia-after-another-knockout-quarter-and-guide-.html DeepSeek + Chinese price war is a Chinese export-controls story, not a US economic ceiling story https://www.cnbc.com/2026/05/21/anthropic-microsoft-maia-200-ai-chip.html   Bulls & Bears Micron (NASDAQ: MU) Crosses $1 TRILLION Market Cap for the First Time https://www.cnbc.com/2026/05/26/micron-stock-trillion-market-cap.html Dell Technologies Q1 FY27 ACTUALS  https://www.cnbc.com/2026/05/28/dell-q1-earnings-report-2027.html Marvell Technology Q1 FY27 ACTUALS https://investor.marvell.com/news-events/press-releases/detail/1023/marvell-technology-inc-reports-first-quarter-of-fiscal-year-2027-financial-results Salesforce CRM Q1 FY27 ACTUALS  https://investor.salesforce.com/financials/quarterly-results/ Synopsys SNPS Q2 FY26 ACTUALS https://investor.synopsys.com/events-and-presentations/events/event-details/2026/Q2-Fiscal-Year-2026-Earnings/default.aspx Snowflake SNOW Q1 FY27 ACTUALS  https://www.businesswire.com/news/home/20260527027931/en/Snowflake-Reports-Financial-Results-for-the-First-Quarter-of-Fiscal-2027 HP Inc. HPQ Q2 FY26 ACTUALS https://finance.yahoo.com/markets/stocks/articles/hp-q2-earnings-call-highlights-230459161.html Everpure (NYSE: P, formerly Pure Storage) Q1 FY27 ACTUALS  https://investor.salesforce.com/financials/quarterly-results/ Synopsys SNPS Q2 FY26 ACTUALS https://investor.synopsys.com/events-and-presentations/events/event-details/2026/Q2-Fiscal-Year-2026-Earnings/default.aspx Snowflake SNOW Q1 FY27 ACTUALS  https://www.businesswire.com/news/home/20260527027931/en/Snowflake-Reports-Financial-Results-for-the-First-Quarter-of-Fiscal-2027 HP Inc. HPQ Q2 FY26 ACTUALS  https://finance.yahoo.com/markets/stocks/articles/hp-q2-earnings-call-highlights-230459161.html Everpure (NYSE: P, formerly Pure Storage) Q1 FY27 ACTUALS https://www.prnewswire.com/news-releases/everpure-announces-first-quarter-fiscal-2027-financial-results-302783502.html

Favour Obasi-ike, MBA, MS provides a deep dive into the 2026 scheduling landscape, comparing Cal vs. Calendly. As a digital marketing expert, he explores the benefits of white-labeling, custom APIs, and recurring events. The discussion addresses the polarizing nature of scheduling links in professional networking and offers a guide on using these tools to maintain a long-term business presence. Favour emphasizes that while Calendly is a pioneer, Cal.com's open-source nature provides unique flexibility for modern entrepreneurs. Check Calendly.com vs Cal.com on G2 Reviews here.Who is this for?This content is tailored for entrepreneurs, small business owners, and digital marketers who want to streamline their online booking systems. It's for those deciding between established tools like Calendly and open-source alternatives like Cal.com to manage their time and client interactions more effectively.Key MomentsFavour introduces Cal.com as a powerful alternative to Calendly, highlighting that many features Calendly charges for are free on Cal (03:43). He breaks down a comparison chart, noting that Cal offers custom routing logic, custom domains, and two-way Salesforce/HubSpot synchronization—features often missing or restricted in Calendly (05:36, 08:33).A significant portion of the room debates the etiquette of scheduling links, with some participants viewing them as "self-serving," while others defend them as essential tools for protecting a creator's time (13:11, 16:22).Favour also explains the authenticity of Trustpilot reviews, emphasizing the platform's transparency in business validation (09:56).Timestamps00:18 – Introduction and shoutouts to the Business and Marketing House.02:26 – Discovering Cal.com: A backstory from LinkedIn to Berlin.04:29 – The legacy of Calendly and its impact on time management.05:36 – Feature Breakdown: Custom routing, domains, and API access.08:33 – Integration Deep Dive: Salesforce, HubSpot, and two-way sync.09:56 – Understanding Trustpilot: How to verify business authenticity.13:11 – The "Pain in the Ass" Factor: A debate on scheduling link etiquette.20:45 – Best practices for using schedulers in podcasting and networking.FAQsIs Cal.com really free? Favour notes that many features Calendly charges for are available for free on Cal.com.What is "white-labeling" in scheduling? It allows you to remove the platform's branding and use your own domain, a feature unique to Cal.com.Why do some people hate scheduling links? Some find them impersonal or "self-serving," preferring direct email or text communication to set meetings.How does Cal.com sync with my CRM? Unlike some tools that only offer one-way sync, Cal.com provides a two-way synchronization with platforms like Salesforce and HubSpot.Action StepsAudit Your Booking Flow: Review your current process and identify if you are paying for features that could be free elsewhere.Check the Comparison: Visit the Cal.com vs. Calendly chart to see which tool aligns with your technical needs.Verify Your Presence: Ensure your business is claimed on platforms like Trustpilot to build authentic social proof.Soft-Launch Your Link: Pair scheduling links with a personal note to avoid the "self-serving" perception.Test Custom Domains: If using Cal.com, explore custom domains to enhance your professional branding.Ready to Rank? Book Your SEO & Web Dev Services Today

Craig Rosenberg, Chief Platform Officer at Scale Venture Partners and co-founder of Topo, joins AJ Bruno and Asad Zaman to take on the question every founder is wrestling with: can you still build a world-class sales team when OpenAI and Anthropic are handing individual contributors $10 million equity packages? Craig argues you do not have to compete head-on, then lays out the hiring profile to chase instead, the quota-to-comp discipline that keeps packages sane, and why founder brand has become the most reliable pipeline play left as CAC keeps climbing. Topics include enterprise AE compensation, where private equity is still winning the GTM talent war, the Topo playbook for events and data-as-moat, and a bull-versus-bear debate on whether Gong goes public in the next 36 months. Plus, a Quiz Pro Quo on the real customer counts behind Salesforce, HubSpot, and ZoomInfo. Key Takeaways: - Rather than try to outbid OpenAI and Anthropic for talent, build your own farm system and develop people into the role. As Craig Rosenberg, Chief Platform Officer at Scale Venture Partners, put it: "You have to change your hiring profile to a unique profile that's unique to your business, but then you gotta coach 'em up." - A resume from a hot AI lab is not a guarantee of success at your company. As Craig Rosenberg noted, "The person that is going to do well at Anthropic may not do well at Series B," so hire for the stage and the hunger rather than the logo. - On compensation, Craig anchors the package to the role's real value: "you pay for what your wedge costs… if you feel like you have to pay $10 million, then you have a huge problem and you gotta go back to the drawing board." If the number runs away from you, the model is broken. - With CAC climbing and most channels breaking down, founder brand has become the highest-leverage pipeline play. As Craig Rosenberg said, "The value of building a founder brand, when you look at the data, it's amazing," pointing to gains in both pipeline and deal size. Connect with the Hosts & Guests: Host: AJ Bruno, CEO at QuotaPath - https://www.linkedin.com/in/ajbruno3/ Host: Asad Zaman, CEO at Sales Talent Agency - https://www.linkedin.com/in/azaman1/ Guest: Craig Rosenberg, Chief Platform Officer at Scale Venture Partners - https://www.linkedin.com/in/craigrosenberg/ Topline is more than a YouTube Channel: Subscribe to Topline Newsletter: https://toplinemedia.substack.com/ Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack Chapters: 00:00 Introducing Craig Rosenberg 02:34 Can Anyone Out-Hire The AI Labs? 04:33 Why Craig Isn't Worried 06:52 Enterprise AE Comp Is Climbing 08:21 Founders Overpay For Star CROs 10:53 Why AI Reps Struggle At Series B 14:00 Hire The Slighted CRO 14:42 Quota-To-Comp And Attainment 18:45 Can AI Labs Sustain Growth? 22:20 Where PE Still Wins GTM Talent 27:17 Major Runs Reshape GTM 32:36 The Topo GTM Playbook 37:55 Quiz Pro Quo 47:45 Founder Brand And Rising CAC 58:42 Bulls and Bears

Today we look at fresh blow-out enthusiasm for a major tech name reporting strong earnings that blasted its stock into the stratosphere after hours yesterday. We also note the bevy of interesting tech names reporting next week, run through some macro and FX observations, note the key technical hold in gold, the shifting forward curve in crude oil and more. Regarding today's episode title, consider Saxo's outrageous prediction for this year regarding the SpaceX IPO and one of the main things - or perhaps the main thing - Elon Musk hopes to achieve in going to Mars (link below). Today's pod hosted by Saxo Global Head of Macro Strategy John J. Hardy. Links We took a stab at predicting the scale of the SpaceX IPO  (undershooting badly!) and some of the true reasons for Elon Musk's passion for going to Mars in the Saxo Outrageous Predictions for 2026 released late last year. The Register discussed Salesforce' "waving bye-bye" to its User Interface as it opens up its data to a trillion API calls from other platforms, including increasingly Anthropic  via its Headless 360, a programmable agent platform. The constantly shade-throwing Futurism (ironically a bit Luddite at times) with an article questioning whether CEO's are suffering from AI psychosis as they are out of touch with the struggle for AI to get traction at ground level in their organisations. Is AI making some people feel and act dangerously over-competent and what are the risks if so? (HT FTAlphaville) About twice per week (in normal times, hopefully soon to resume), you will find links discussed on the podcast and a chart-of-the-day over at the John J. Hardy substack. Read daily in-depth market updates from the Saxo Market Call and the Saxo Strategy Team here. Please reach out to us at marketcall@saxobank.com for feedback and questions. Click here to open an account with Saxo. Intro music by AShamaluevMusic DISCLAIMER This content is marketing material. Trading financial instruments carries risks. Always ensure that you understand these risks before trading. This material does not contain investment advice or an encouragement to invest in a particular manner. Historic performance is not a guarantee of future results. The instrument(s) referenced in this content may be issued by a partner, from whom Saxo Bank A/S receives promotional fees, payment or retrocessions. While Saxo may receive compensation from these partnerships, all content is created with the aim of providing clients with valuable information and options.  

Everyone says AI is taking jobs. The data says something more complicated.In this episode of This Week in European Tech, Dan Bowyer and Mads Jensen of SuperSeed unpack the growing panic around AI-driven job losses, why junior hiring is falling across many industries and whether AI is actually the culprit.They explore new research suggesting remote work may be having a bigger impact on entry-level employment than AI, discuss the UK's record number of young people not in employment, education or training and examine what the data really shows about automation and labour markets.They also cover Anthropic's latest model release, the rise of AI application-layer companies, Europe's sovereignty debate, the economics of AI infrastructure and a zero-employee AI company that just raised $30 million.Topics coveredIs AI really replacing workers?Why junior hiring is fallingWhat the data says about AI and employmentAnthropic's rise and Opus 4.8Why the AI application layer is winningEurope's tech sovereignty dilemmaThe zero-employee AI company phenomenonAI infrastructure beyond GPUsTimestamps(00:00) The rise of the zero-employee AI company(04:50) Why AI applications are becoming more valuable(09:00) AI infrastructure moves beyond GPUs(16:00) Snowflake, Salesforce and enterprise AI adoption(24:00) Anthropic's latest model and valuation surge(27:00) Europe's sovereignty dilemma(33:00) The $30 million zero-employee AI startup(35:45) Is AI actually taking jobs?(38:00) What the data says about junior hiring(41:00) Why AI may not be the main cause(46:00) Predictions: which AI unicorn could fail next?(48:00) Deal of the week: Cognition and DevinFor more European venture, AI and startup insights, subscribe to EUVC, the home of European tech.

Cramer says it's worth it to stay long this software giant. Become an Investing Club member to go behind the scenes with Jim Cramer and Jeff Marks every day as they talk candidly about the market's biggest headlines, analyst calls and holdings in the Charitable Trust – and see up close how they decide when, and if, to take action on stocks. Sign up here:  cnbc.com/morningtake   CNBC Investing Club Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Carl Quintanilla, Jim Cramer and David Faber drilled down on tech and the AI trade: Salesforce shares fell in reaction to the company's mixed Q1 results and lighter-than-expected revenue guidance for the current quarter. Shares of Snowflake soared on news of its Q1 beat, raised outlook and a $6 billion commitment to its multiyear partnership with Amazon's AWS.  On the M&A front: Fertitta Entertainment agrees to buy Caesars Entertainment in an all-cash deal valued at $5.7 billion. Also in focus: Crude oil prices rise amid faltering hopes for a U.S.-Iran deal, Marvell extends its rally, Dell wins Pentagon contract, retailers surging on earnings, Meta launches subscriptions, Cramer's take on investing in Microsoft, economic data deluge. Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Today on the Salesforce Admins Podcast, we talk to Kate Lessard, Lead Admin Evangelist at Salesforce. Join us as we chat about why security is the foundation for how data, automation, and AI work together. You should subscribe for the full episode, but here are a few takeaways from our conversation with Kate Lessard. Why […] The post Salesforce Admins Can Reduce Risk With Proactive Security appeared first on Salesforce Admins.

Today, we note the very different outcomes for the two admittedly very different software names Salesforce and Snowflake as both reported earnings after the close yesterday. Elsewhere, insane volatility for Marvell in yesterday's session ahead of its own earnings report after the close - are the wheels coming off a bit here for chip names.  Also, Gold needs to take a stand here or else, plenty on macro and FX and more on today's pod, which is hosted by Saxo Global Head of Macro Strategy John J. Hardy. Links Michael Burry points out that VC has gone whole hog in AI, similar to the situation in 2000 with TMT bubble. Acquired put out a four-hour episode on the fascinating history and phenomenon that is Ferrari - these guys are great.  FT with an exclusive on Ukraine turning the tables in its war with Russia - amazing innovation and rates of production for their at least partially homegrown tech. Stratechery with a brief discussion (paywall) of the SpaceX IPO, both quite dismissive in some ways, but also surprisingly supportive of the idea that space-based data centres could be a thing. About twice per week (in normal times, hopefully soon to resume), you will find links discussed on the podcast and a chart-of-the-day over at the John J. Hardy substack. Read daily in-depth market updates from the Saxo Market Call and the Saxo Strategy Team here. Please reach out to us at marketcall@saxobank.com for feedback and questions. Click here to open an account with Saxo. Intro music by AShamaluevMusic DISCLAIMER This content is marketing material. Trading financial instruments carries risks. Always ensure that you understand these risks before trading. This material does not contain investment advice or an encouragement to invest in a particular manner. Historic performance is not a guarantee of future results. The instrument(s) referenced in this content may be issued by a partner, from whom Saxo Bank A/S receives promotional fees, payment or retrocessions. While Saxo may receive compensation from these partnerships, all content is created with the aim of providing clients with valuable information and options.  

Oil prices remain surprisingly muted despite months of disruption in the Strait of Hormuz, but energy markets may be running out of time before low inventories become a much bigger problem.Chuck Zodda and Mike Armstrong break down why crude oil has not surged as much as many expected, how China and the U.S. may be helping offset lost Middle East supply, and why major producers are warning that inventories could soon reach dangerously low levels. They also discuss new inflation data, why the Fed may still have little room to cut rates, signs that the labor market is stabilizing, and how the AI economy continues to drive growth through major cloud and semiconductor spending.The show also covers Snowflake's $6 billion deal with Amazon, Salesforce's struggle to prove its AI strategy can defend future growth, and Boston's logistical headaches as the World Cup approaches.

D.A. Davidson's Gil Luria and BEP Research's Ben Pouladian talk with TITV Host Akash Pasricha about Snowflake's accelerating revenue growth and Salesforce's disappointing Q2 guidance. We also talk with The Information's Apple reporter Aaron Tilley about Apple's strategy to shrink Google Gemini models onto devices, and Microsoft reporter Aaron Holmes about the company's plan to release homegrown coding models to protect GitHub Copilot from competitors like Cursor.Articles discussed on this episode: https://www.theinformation.com/articles/apple-renew-push-ai-runs-devices-instead-cloudhttps://www.theinformation.com/articles/meta-launches-new-enterprise-push-boost-business-adoption-ai-toolshttps://www.theinformation.com/newsletters/ai-agenda/microsoft-release-new-coding-model-next-week-comeback-attemptSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - Snowflake Shares Jump as AI Product Adoption Grows08:04 - Salesforce Earnings Reaction and M&A Strategy12:48 - Meta Launches New Enterprise Push & Paid AI Chatbot Subscriptions23:03 - Apple to Renew Push for AI That Runs on Devices28:26 - Microsoft to Release New Coding Model

Carl Quintanilla, Jim Cramer and David Faber led off the show with tech fueling stocks' record run: Micron rallies and lifts the memory chip makers after joining the $1 trillion valuation club.  The anchors reacted to Goldman Sachs' call to raise its year-end price target on the S&P 500 to 8,000. Also in focus: SpaceX's IPO and new "fast entry" rules as the company gets ready to go public, Zscaler plummets, GE Vernova CEO's take on data centers in space, software woes ahead of Salesforce's after-the-bell earnings, JPMorgan Chase CEO Jamie Dimon on "high" asset prices, retail earnings winners and losers, the SpaceX "halo effect" on space stocks.   Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this Watson Weekly interview episode, Rick Watson is joined by Lennart Stevens, VP of Product Management for Agentforce Commerce at Salesforce, who walks through Storefront Next, the latest evolution of Salesforce's commerce storefront.Storefront Next is built for developers and for a world where AI and agentic coding are the default. You can spin up a new storefront inside Business Manager with a click-based setup. Under the hood it runs on Salesforce's Managed Runtime as a hosted headless surface, with an enhanced SCAPI layer that lets apps, kiosks, and other channels pull from the same data. The stack standardizes on React, Shadcn, and Tailwind. Existing customers keep their catalogs, prices, and promotions and surface them through the new API.The Watson Weekly interview is sponsored by Avalara - the agentic AI platform automating global tax and compliance for leading eCommerce brands. For more details: https://avalaratax.watsonweekly.com.Lennart also gets into the agentic tooling (agent shopper, agentic merchandising), quiet AI like product readiness scores that flag missing info without nagging, reusable content blocks and embedded Page Designer components, and turnkey industry templates for retail, cosmetics, and furniture that convert well out of the box. He covers the upgraded CLI, the growing library of skills, and support for UCP as the channel-selling standard.The whole point: cut the standup busywork so developers spend time on what actually moves the business.#watsonweekly #agentforce #storefrontnext #agentic

Investors parse a critical wave of software earnings and shifting market leadership. Adam Crisafulli of Vital Knowledge breaks down the market's latest theme and explains what strong software results could mean for the broader rally. Our Leslie Picker reports on Jamie Dimon's latest comments around succession planning and what they signal for Wall Street leadership. Salesforce, Snowflake, HP, Marvell and Synopsys all report earnings giving investors a fresh read on enterprise spending, AI demand and infrastructure growth. Brent Thill of Jefferies reacts to the software results and explains where the sector goes next. Plus, the sharp drop in Zscaler and what it says about cybersecurity stocks and investor expectations with Evercore's Peter Levine. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

CRE brokerages have spent tens of thousands configuring Salesforce. Nobody uses it. The data is stale, the pipeline is fiction, and the analyst is entering last week's emails at 8pm to stay compliant.   Yaakov Zar, founder and CEO of Lev.com, has spent six years building a system to fix exactly that. AI-native deal management, not a retrofitted CRM. Lev ingests documents, extracts deal facts, resolves conflicts across sources, and surfaces a single source of truth - without manual data entry. The lender outreach problem is structural, not behavioral. Brokers don't fail to follow up because they're lazy; they fail because tracking 30 lender responses across email, Excel, and Salesforce is an analyst job that breaks at scale. Lev automates the intake, parsing, and quote matrix automatically. The platform is now open infrastructure. API, MCP connectors, and a CLI mean firms can build their own CRE operating system on top of Lev's deal data layer - rather than vibe-coding something that breaks under enterprise compliance requirements. Firms that keep running capital markets on spreadsheets are not just inefficient - they are building institutional knowledge in a format that walks out the door with every departing analyst. The structural advantage of organizations that systematize this data now will compound. For firms that wait, the gap will not close.   *** At GowerCrowd, we are bringing the most advanced AI tools to our clients for capital formation - and across other operational verticals too (like acquisitions). If you'd like to learn more about how we can assist you too, please reach out.   Subscribe to my newsletter and get access to this transformational intel before anyone else:  https://gowercrowd.com/subscribe Email: adam@gowercrowd.com Call: 213-761-1000  

Leaders are burning out quietly, and most organizations are still pretending it isn't happening. Mel Doman, organizational psychologist, author, and founder of the Workplace Mental Health Method, has spent years equipping leaders at Google, Microsoft, and Salesforce with the skills to have real conversations about mental health at work. In this episode, she breaks down why we've systematically dehumanized leaders, what that costs us, and what we can do about it right now. What you'll take away: Why leaders need a "fence, not a door" when it comes to emotional expression at work How gender-based emotion shaming affects everyone, regardless of gender identity What U.S. organizations can learn from the global concept of "duty of care" Timestamps 00:02 Intro: What is the HR Mixtape? 00:16 Mel's background: from clinical therapy to organizational psychology 01:06 The moment Mel decided to leave clinical work and why 03:38 Why shared language is the foundation of mental health conversations 04:48 Introducing Cornered Office and why she wrote it 08:20 Leaders are human too: the forgotten truth in organizations 09:30 The Nike/Air monologue and what it reveals about how we treat leaders 12:00 The fence, not a door: how leaders can navigate vulnerability and authority 13:03 Gender-based emotion shaming and why everyone is affected 18:54 What U.S. organizations can learn from global duty of care practices Guest Bio Mel Doman is an organizational psychologist, author, and founder of The Workplace Mental Health Method. A former licensed therapist, Mel made the shift from clinical practice in 2013 to tackle mental health at a systems level. She has since worked with global organizations including Google, Microsoft, and Salesforce, equipping leaders and teams with the conversational literacy they need to talk about mental health, communication, and team dynamics at work. She is the author of two books, including her latest, Cornered Office: Why We Need to Talk About Leadership Mental Health. Mel has lived and worked across the U.S., U.K., and internationally, giving her a uniquely global perspective on workplace culture. Brought to you by Paylocity Paylocity is the fasted growing unified platform for HR, Finance, and IT. Paylocity brings your people, processes, and data together in one place so HR leaders can spend less time managing systems and more time doing the work that actually moves their organizations forward. Learn more at paylocity.com Keywords: leadership mental health, workplace mental health, organizational psychology, HR podcast, Mel Doman, Cornered Office, duty of care, gender-based emotion shaming, employee wellbeing, people leaders, burnout, HR Mixtape, Paylocity

Send us Fan MailThis week's enterprise software announcements reveal how aggressively vendors are repositioning around AI agents, composable integration, and industry-specific workflows. NetSuite introduced its new integration platform to simplify connectivity across enterprise ecosystems, while Oracle expanded both its process manufacturing capabilities and AI agent portfolio inside Fusion Cloud Applications. QAD and Tata Consultancy Services strengthened their manufacturing operations strategy through the Redzone partnership, reinforcing the growing importance of connected frontline execution. Meanwhile, Intuit Mailchimp rolled out new e-commerce enhancements, and Seismic and Highspot announced a major merger that could reshape the sales enablement landscape. On the AI infrastructure side, Anthropic expanded MCP with a framework for full-stack agentic applications, while emerging vendors like Hubbl Technologies positioned themselves as orchestration layers for the Salesforce agentic ecosystem. Finally, Sage, Salesforce, and Sinch continued the broader trend of embedding AI agents deeper into finance, communications, and customer engagement workflows, signaling that the enterprise software market is rapidly shifting from passive systems of record toward autonomous systems of execution.In today's episode, we invited a panel of industry analysts for a live discussion on LinkedIn to analyze current enterprise software stories. We covered many grounds, including the direction and roadmaps of each enterprise software vendor. Finally, we analyzed future trends and how they might shape the enterprise software industry.Video: https://www.youtube.com/watch?v=i0Ktqz2rXh8Questions for Panelists?

In this episode of One Vision, Theodora Lau sits down with Hay Yip, Chief Strategy Officer and Chief of Staff at FundPark in Hong Kong, for a conversation that spans heritage, working capital, and what it really takes for SMEs to scale in an uncertain world.Hay shares his journey from a commercial banking career at HSBC — spanning both London and Hong Kong — to the electric pace of a fintech startup, and why he now goes to bed "with one eye open." Born in Hong Kong and grew up in the UK, his story comes full circle as he returns home to help the small businesses he's always been drawn to.At the heart of the conversation is the often-overlooked engine of commerce: working capital. Hay makes the case that cash flow is the "bloodline" of any growing business, and explains how FundPark uses data and analytics to serve e-commerce merchants who are not just underserved by traditional banks, but in many cases entirely unserved. Theo and Hay explore the founders' origin story, why entrepreneurs deserve more credit for their courage, and how global supply chain fragility shows up in the everyday lives of merchants and the customers who depend on them.Tune in for a candid look at the unglamorous but essential side of fintech, and FundPark's vision of "scale up as a service" — helping ordinary people behind real businesses thrive.

ERIK DODIER is a lifelong entrepreneur, EOS Implementer, and former CEO whose journey is defined by one core belief: the most meaningful success comes from playing the long game. Whether building a company, raising a family, investing in health, or redefining life after a major exit, Erik views every chapter through the lens of durability, intention, and personal evolution. He co-founded PixelMEDIA at 24 and bootstrapped it from a small Dover apartment into one of Salesforce's leading global commerce partners, later rebranding it as RafterOne and building a team of more than 500 people before its 2022 acquisition by IPG.   CONNECT WITH Erik Dodier Website: https://implementer.eosworldwide.com/erik-dodier/ LinkedIn: https://www.linkedin.com/in/erikdodier/     JOIN THE FIGHT AGAINST FOOD INSECURITY   Join the fight against food insecurity here in the U.S. DONATE TODAY at Meet the Streets Outreach, INC. to fight hunger!    Meet the Streets Outreach provides essential support to Houston's food-insecure communities by offering over 2,100 hot meals each month. With your help, we can continue to serve those in need. Your support ensures that we can continue to make a meaningful difference in the lives of Houston's most vulnerable residents. Thank you for considering this opportunity to invest in the well-being of our community.   Food Insecure Households For many families in the U.S., the past several years have been difficult. Higher food prices, economic instability, and other factors have made providing for a family even harder. 1 in 8 households in the U.S. is food insecure. That means these families don't have enough money or resources to buy enough food for everyone in their household. As recently as 2022, 7.3 million children lived in food insecure households. Also, 16.9% of children live in poverty.   SNAP Benefits More than 22 million U.S. households use SNAP benefits to help with food costs, as of April 2023. Sometimes known as "food stamps," SNAP is the federal Supplemental Nutrition Assistance Program. People who receive SNAP benefits can use it to buy groceries, seeds, and plants for food. SNAP cannot be used to purchase hot food or household items like cleaning supplies, vitamins, or diapers.   CONNECT WITH Cedric Francis Website: https://www.lead2greatness.com/ Facebook: https://www.facebook.com/cedricbfrancis X: https://twitter.com/cedricbfrancis Instagram: https://www.instagram.com/cedricbfrancis/ LinkedIn: https://www.linkedin.com/in/cedric-b-francis-a0544037/  

In this episode, Kimberly Snyder and Brooke Taylor explore the deep roots of success related struggles, sharing insights on how cultural and intergenerational beliefs shape our worth. Brooke's practical-spiritual approach offers tools to align your ambitions with your authentic self, ending the cycle of never feeling enough.Chapters00:00 Introduction to the Success Wound02:54 Cultural Influences on Self-Worth05:58 The Archetypes of Success Wound09:02 The Grinder and the Pleaser12:04 The Hider and the Impact of Motherhood15:06 Navigating Ambition and Personal Power18:08 Finding Your North Star19:32 The Journey of Writing and Self-Discovery22:05 Navigating Vulnerability and Intentions in Publishing25:49 The Archetypes of Success and Their Impact29:30 Healing the Success Wound: Emotional and Behavioral Strategies34:41 Finding Wholeness Through Connection and AbundanceSponsors: LMNTOFFER: Right now, for my listeners LMNT is offering a free sample pack with any LMNT drink mix purchase at DrinkLMNT.com/FEELGOOD. That's 8 single serving packets FREE with any LMNT any LMNT drink mix purchase. This deal is only available through my link so. Also try the new LMNT Sparkling — a bold, 16-ounce can of sparkling electrolyte water.USE LINK: DrinkLMNT.com/FEELGOODBrooke Taylor Resources: Book: Healing the Success Wound: Align Your Ambition, Find Lasting Career Fulfillment, and End the Cycle of Never-Enough Website: www.brooketaylorcoaching.comBio: Brooke Taylor is a globally recognized transformational career coach, keynote speaker, and the leading authority on a phenomenon she pioneered known as the “Success Wound™.” With a career that began in the high-pressure hallways of Silicon Valley, Brooke spent years as a Marketing Lead at Google, where she was honored with the Google Global Sales Award. Despite her outward accolades, she experienced firsthand the "manic ambition" and burnout that often plague high achievers, leading her to a profound personal transformation and the eventual founding of her coaching practice.Brooke's highly anticipated first book, "Healing the Success Wound" (published by Hachette, May 2026), which serves as a definitive guide for women to reclaim their identities from the "Success Wound". Today, Brooke is the secret weapon for executive women at the world's most influential firms, including Goldman Sachs, McKinsey, Uber, Salesforce, Coinbase, and Meta. She has helped over 5,000 leaders decouple their self-worth from their achievements, allowing them to trade chronic burnout and workplace anxiety for a career rooted in deep fulfillment and sustainable power.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI already knows your buyer better than most sales reps do. The question is — what are you going to do about it?In this episode, John is joined by Scott Gillum, author of The Hidden Buyer Journey, to unpack seven years of research on how buyers actually make decisions — and why our sales and marketing tools are barely scratching the surface of what's possible. From personality profiling and corporate culture mapping to the death of the sales stage, this conversation goes deep on what it really takes to sell in a world where the buyer is more informed, more protected, and more machine-assisted than ever before.If you're serious about staying relevant as a sales professional in an AI-first world, this one's a must-listen.Want to make sure you're equipped before the market moves on without you? Visit www.jbarrows.com and learn how you can Make It Happen.What You'll LearnWhy corporate culture predicts deal velocity better than any sales methodologyHow to use personality profiling tools to adapt your style to any buyerWhy 85% of the people who actually influence your deals aren't in your CRMThe one thing AI still can't replicate — and why it's your biggest competitive advantageWhy Return on Effort (ROE) is replacing ROI as the real measure of AI in salesScott Gillum is the Founder and CEO of Carbon Design. Prior to founding Carbon Design, he was the President of the Washington, DC office for gyro (a Dentsu agency), the world's largest B2B agency.His career follows the pipeline. Starting at the bottom closing deals as a sales rep. Then as a management consultant after graduate school, helping clients build and capture demand for sales and marketing channels. Advertising broadened his knowledge and experience in building brands and creating awareness.Along the way, he's been the head of marketing for an Inc. 500 company, and an interim CMO for a Fortune 500 company. Today, Scott helps clients improve the effectiveness of their marketing efforts up and down the funnel. From transitioning to digital to finding new ways to communicate, connect, and motivate audiences.Scott's a member of the Gartner for Marketing Leaders Council and he writes a regular column for MediaPost on business marketing. He's articles have appeared in leading publications such as Forbes, Fortune, Adage, the Huffington Post and he has contributed to various books on marketing. Additionally, his work on sales and marketing integration was made into a Harvard Business School Case Study and is taught at leading business schools across the nation.Website: https://carbondesign.com/scott-gillum/LinkedIn: https://www.linkedin.com/in/scottgillum/John Barrows is a sales trainer, speaker, and founder of JB Sales with over 25 years of experience in the industry. He has made hundreds of cold calls a week, led startups to acquisition, and trained high-performing teams at companies like Salesforce, LinkedIn, Amazon, and Okta. Through JB Sales, John focuses on practical sales execution—helping reps fill pipeline, close deals, and build trust with buyers in today's AI-driven sales environment.Connect with John Barrows:LinkedIn: https://www.linkedin.com/in/johnbarrows/ Instagram: https://www.instagram.com/johnmbarrows/TikTok: https://www.tiktok.com/@johnmbarrowsCheck out John's Membership: https://go.jbarrows.com/Join John's Newsletter: https://www.jbarrows.com/newsletter

In this episode, Simon and Dan break down why software stocks continue to struggle despite many companies showing little evidence of major AI disruption so far. They discuss the ongoing valuation reset across SaaS, the shift away from seat-based pricing, and why companies like Salesforce, ServiceNow, Adobe, Constellation Software, and Topicus may need more than solid earnings to regain investor confidence. They then turn to Bitcoin, looking at why it remains well below its roughly US$125,000 peak. Simon covers the role of ETF outflows, leverage, post-election crypto optimism fading, quantum computing fears, profit-taking, and higher bond yields as potential headwinds. To wrap up, they highlight stocks on their radar, including Visa and Mastercard as high-quality payment networks facing stablecoin disruption risk, and Waste Connections as a real-economy compounder trading at a more attractive valuation while the market remains focused on AI-related names. Tickers of stocks discussed: CRM, NOW, ADBE, CSU.TO, TOI.V, V, MA, WCN, WM, RSG, GFL.TO, BTC. Subscribe to our Our New Youtube Channel! Check out our portfolio by going to Jointci.com Our Website Our New Youtube Channel! Canadian Investor Podcast Network Twitter: @cdn_investing Simon’s twitter: @Fiat_Iceberg Braden’s twitter: @BradoCapital Dan’s Twitter: @stocktrades_ca Want to learn more about Real Estate Investing? Check out the Canadian Real Estate Investor Podcast! Apple Podcast - The Canadian Real Estate Investor Spotify - The Canadian Real Estate Investor Web player - The Canadian Real Estate Investor Asset Allocation ETFs | BMO Global Asset Management Sign up for Fiscal.ai for free to get easy access to global stock coverage and powerful AI investing tools. Register for EQ Bank, the seamless digital banking experience with better rates and no nonsense. See omnystudio.com/listener for privacy information.

A client called you last week. The report used to take an hour. Now it takes five minutes. They want to know why they're still paying for the hour, and the honest answer is you don't have one. In this episode, I break down why AI didn't break your pricing model, it just exposed what was already broken the day you started selling time instead of outcomes. I walk through what's happening across Salesforce, HubSpot, Zoho, Monday, NetSuite, and Acumatica partners right now, why the hour collapse means you never had pricing power, and the four moves that protect your revenue before that phone call lands. If you're still quoting in hours and hoping clients don't notice, this one's for you.Resources and LinksNeed help with your WHO and WHAT decisions? Apply for a FREE Multiplier CallBook a Decision Session herePrevious episode: 684 - 73 Minutes to Stop Running Two Businesses at OnceCheck out more episodes of the Paul Higgins PodcastSubscribe to our YouTube channel: @PaulHigginsMentoringJoin our newsletterSuggested resources

In this episode of the CPQ Podcast, Frank Sohn speaks with Rohit Chhabra, Chief Product Officer at Conga, about Conga's CPQ strategy, the role ofConga Smart CPQ, and how AI is becoming a broader part of the quote-to-cash and commercial operations landscape. Rohit shares insights from more than 25 years of business, engineering, and product leadership experience, including how his background shapes the way he works with teams, prioritizes product investments, and focuses on customer outcomes. He also discusses his involvement with the Juvenile Diabetes Research Foundation / Breakthrough T1D and how that personal connection has influenced his views on leadership, urgency, and mission-driven work. The conversation covers Conga's two CPQ product lines: Conga Advantage CPQ and Conga Smart CPQ. Rohit explains why the solutions address different customer segments and industry requirements, and why Conga plans to continue investing in both offerings rather than forcing them into a single product path. A key topic is the role of pricing optimization and pricing explainability, especially following Conga's acquisition of PROS. Rohit also discusses Conga's broader AI approach across CPQ, pricing, contract lifecycle management, and document automation, including sales agents, redline-related AI capabilities, admin AI for rule maintenance, and a unified AI technology stack. Listeners will also hear Rohit's perspective on integration strategy, including Conga's OEM relationship with Workato, expansion beyond Salesforce and Microsoft Dynamics, potential HubSpot CRM support, and how Conga prioritizes its roadmap using a business-outcome-based approach. This episode is especially relevant for anyone interested in CPQ software, pricing optimization, AI in quote-to-cash, revenue lifecycle management, CLM integration, and the future direction of Conga's CPQ portfolio.

Chad Peets is one of the most straight-talking, no BS sales leaders of our time. Today, he partners with founders of the fastest growing companies in the world, like Harvey, Factory to build the best sales teams in a world of AI.    Chris Degnan is a legendary technology sales leader who achieved the historic feat of scaling Snowflake from $0 to $4BN in ARR.  AGENDA: 00:00 – The $100M CRO Packages Nobody Believes Are Real 04:10 – Why Most "Elite" Salespeople Are Actually Just Order Takers 08:00 – The Secret to Hiring Killer Sales Talent at Early-Stage Startups 10:05 – 20x Quotas & The Death of Traditional Pipeline Generation 16:20 – The ARR Scam: Why Most AI Revenue Numbers Are Fake 17:45 – Why the Best Engineers Do Not Want to Be Forward Deployed Engineers 21:10 – Why Paying Everyone the Same Kills Great Sales Organisations 24:15 – Anthropic's Crazy Compensation Is Breaking the Entire Sales Market 29:00 – The Brutal Truth About Replacing CROs & Firing Sales Leaders 32:20 – Forecasting in AI Is Completely Broken 38:20 – The Fatal Mistake Founders Make Chasing Venture Valuations 39:40 – Why Most VCs Give Absolutely Terrible Sales Advice 42:10 – Global Sales From Day One: The New AI Go-To-Market Playbook 44:15 – "Anthropic Is a $5 Trillion Company" 47:40 – The Death of the Traditional SDR & The Rise of Full-Stack AI Sellers 49:00 – Consumption Pricing, Vertical AI & Why SaaS Is Getting Rewritten 52:00 – What the Best Sales Cultures Still Get Right in the AI Era    

Corrections! Not 10m Satellites: 10,000GolfI suck. But I will persevere. What is the point, if life is so stressful otherwise. GOOD Questions. PoliticsPlay You can't earn $1 billion.  AOC is so stupid and un motivating. Play. Bezos response529 Plans: Nuances of 529sGood problem to have. Dealing with Apartment costs exceeding “allowed” cost of housing. Total BS. MarketsBooming!!! ATH: 1 year: IGV up 18% since I bought: BOOM! Secondary Markets for Private CompaniesSPVs and LLCs buying interests with underlying shares in private companies is risky.  WSJ article here.My experience. Risk on, but understand it. Likely cash back, but may not get shares. If cash, taxable. SalesForce Mark is All-In on the All-in Podcast on AI. Play 40.00 Mark talking about the AI power of SalesForce, Slack, etc… FAR FROM DEAD.  Inspiration from Chamath on Time and Positivity. Key to winning:Choose to be positive and grateful. Then, just keep at it. Time is the great compounder and will do the rest. So many people just don't have the discipline to stay positive and grateful. Then time compounds the bitterness instead.Same with Finance and Investing. Tesla Tesla Berlin Manufacturing VideoTesla FSD Safety Data.SpaceXLaunch in a couple of hours!! IPO update. Allocation. $1.5-2. I'll bet it goes to 3t.  SpaceX website here. SpaceX and AnthropicDeal of $1.5b/ month for access to compute. AIPlay Jensen Huang on AI Driving GrowthOpenAI Lawsuit. Basically came down to SoL. Meaning he had 2 or 3 years to file. He knew about and went along with it for quite some time. Though I am a Musk fan, I agree with the simplicity of this. AI personal StoryGolf. Used Claude to interpret all stats and provide thoughts. PoliticsPlay Jeff Bezos on doing his job right and his for-profit companies will do more for societal good than any non-profit. Play. Jeff Bezos on what AI will do for jobs. Fraud: Kash Patel on arresting 15 people on Minnesota fraud of $90mAmy Bach 40+ years in prison. Feeding our Future. Here is another one $250m. Spencer Pratt

This episode of the How We Got There podcast features a conversation with Mark Rutledge, Chief Storyteller | Global ISV Partner Solutions at Salesforce. The builder advocacy program that he works on is one that every ISV should learn about and explore if they might be a fit for working with them.Fun fact - I worked with Mark Rutledge when he as a client of my former business, GTM Guides, while he was building on the Salesforce platform for at Moody's. Thanks to Codescience (now Bridgenext) for bringing us together.Mark shares his journey of being in the ecosystem to working at Salesforce to bring the human aspect to partners - contemplate who we are building for vs. just what we are building. PDOs are an essential part of the ecosystem as they have a view of the technology and the market to help an ISV's vision come to production - especially important for composite apps who don't have Salesforce dev and architecture talent internally.One of my favorite parts of the conversation was hearing Mark talk about constructing a hero story around the pain end customers will be feeling all the way to “the universe has been saved” in the narrative. It all starts with spending time listening to customers, approaching it with empathy. If you're just getting started, try to end a demo of a feature with “…so you can _________” as an easy way to integrate what it actually means to your customers.We touch on the shift from per-seat pricing to consumption-based pricing and how ISVs should be looking to adapt to the model where Salesforce is goring towards.This episode is brought to you by ISVApp. ISVapp the usage analytics platform built specifically for Salesforce ISV and OEM applications. ISVapp is your central toolbox for reducing churn, increasing renewals, uncovering upsell opportunities, and closing more deals. #salesforce #isv #gtm #salesforcepartners #appexchange

Most companies are using AI completely wrong. They use ChatGPT in isolation, run random prompts, and wonder why nothing compounds. In this video, I break down the exact Single Brain system we use to connect agents like OpenClaw, Hermes, and NemoClaw into one unified intelligence layer that helps teams move dramatically faster. We cover how these AI fleets plug into Slack, HubSpot, Salesforce, Google Search Console, analytics tools, ad accounts, and internal data systems to create a compounding workflow engine that actually generates revenue. I also walk through real examples including AI generated ad creatives, automated reporting, scaling top performing campaigns into hundreds of variants, reducing operational costs by $500,000, and how one person with agents can outperform entire traditional teams. If you want to understand where AI agents are actually heading and how businesses are using them to create leverage right now, this is the framework. Chapters: (00:00) Why most AI adoption fails (02:06) Connecting all your business tools into one brain (03:22) The AI org chart of the future (05:20) Why most teams are still using AI wrong (06:31) Human timelines no longer work (07:10) Building ad creatives with AI agents (08:06) Scaling campaigns into 200 variants automatically (08:47) How $7,500 in tokens saved $500,000 (10:02) Why AI agents will replace traditional workflows

Today on the Salesforce Admins Podcast, we talk to Nick McOwen, Senior Salesforce Administrator at Alpine Intel. Join us as we chat about his path to Salesforce and the TDX workshop he gave about the development lifecycle, sandboxes, and data masking. You should subscribe for the full episode, but here are a few takeaways from […] The post Can AI Help Salesforce Admins Build Apps More Efficiently? appeared first on Salesforce Admins.

Recorded live at the 2022 Live Unreal Retreat in Traverse City, Michigan, this is Part 2 of an exclusive keynote presentation featuring bestselling author and marketing expert Jay Baer. In this session, Jay Baer takes a deeper dive into the power of “Talk Triggers” and explains the five types of Talk Triggers. Drawing from decades of experience consulting major brands like Nike, Salesforce, and Oracle, Jay shares actionable strategies businesses can use to create unforgettable customer experiences that people can't stop talking about. In Part 2, Jay breaks down the 5 types of Talk Triggers: Generosity Speed Usefulness Empathy Attitude He explains how each one works, why they are so effective, and how businesses can implement them to increase referrals, improve customer loyalty, and stand out in crowded markets. Whether you're a real estate agent, entrepreneur, marketer, or business owner, this keynote is packed with practical insights on customer experience, referral marketing, and building a brand people naturally recommend.

Travis Hahler, Senior Director of Global Strategy & Transformation at Salesforce and founder of The Neurological Nomad, joins John Golden to explain why resistance to change is biology, not rebellion — and how leaders can read resistance as diagnostic information instead of personal pushback. Learn more at https://www.theneurologicalnomad.com/.

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

Mary Price grew up as a first-generation American, learning early lessons in grit, integrity, and trust from her family and her uncle's shoe repair shop. Today, as the founder of Penfield Talent, she brings that same people-first philosophy to talent leadership, executive search, and helping companies build high-trust teams. In this conversation, she shares how she balances empathy with high standards, what AI can and can't replace in recruiting, and the inside story of leading GTM hiring at Slack through the Salesforce acquisition, when her team doubled the sales organization in one year during COVID.Connect with host James Mackey on LinkedIn!Intro (00:00)Background (00:46)Career (09:02)TA (37:38) Thank you to our sponsor, SecureVision, for making this show possible!  Follow us:https://www.linkedin.com/company/82436841/SecureVision: #1 Rated Embedded Recruitment Firm on G2!https://www.g2.com/products/securevision/reviewsThanks for listening!

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

In the May 2026 Mailbag, Jason and Jeff tackle a series of compelling listener questions, starting with a deep dive into whether "vibe coding" and agentic AI are truly an existential threat to major SaaS companies like Salesforce and Snowflake. The duo also untangles the complicated corporate web of Brookfield Infrastructure (BIPC vs. BIP), exploring why the company might finally be moving to ditch its limited partnership structure. Later, they revisit the ongoing struggles at The Trade Desk, debate the concept of "regret minimization" when dealing with massive winners like Rocket Lab and Nvidia, and discuss how to properly balance single-company risk versus overall portfolio volatility. 00:48 West Coast Game Time Rant 02:46 Mailbag and Discord Shoutout 03:56 Ryan Email and Mascot Story 05:47 Baseball Chaos to Investing Lesson 07:47 AI Versus SaaS Debate 11:15 Pricing Models and Moats 22:11 Brookfield BIP Versus BIPC Explained 26:38 Why BIPC Dropped and Simplification 30:25 Discord Questions Teaser 30:52 Trade Desk Doubts 35:54 Valuation Reality Check 38:55 Execution Not Value 40:10 Rocket Lab Concentration 41:55 Regret Minimization 46:55 Singles vs Home Runs 49:22 Vinnie Risk Trimming 50:06 Three Types of Risk 54:50 Money Unplugged Reflections Companies mentioned: ADBE, ADSK, BAM, BEP, BEPC, BIP, BIPC, CRM, NBIS, NVDA, RKLB, SHOP, SNOW, TTD Find where to listen & subscribe,  portfolio contests, and contact information at https://investingunscripted.com ***************************************** To get 15% off any paid plan at fiscal.ai, visit https://fiscal.ai/unscripted Listen to the Chit Chat Stocks Podcast for discussions on stocks, financial markets, super investors, and more. Follow the show on Spotify, Apple Podcasts, or YouTube ***************************************** Join our PatreonSubscribe to our portfolio on Savvy Trader Learn more about your ad choices. Visit megaphone.fm/adchoices

The Great Talent Redistribution: Where is Talent Actually Going in 2026 and beyond?  Is the start-up compensation model broken? How about big Big Tech? How about non-tech small & medium businesses? What is happening to talent, going forward? This and many other topics in this episode of Tech Deciphered. Navigation: Intro The Broken Contract? The Great Unbundling The Three (?) Destinations Alternative Cap Tables, Alternative Compensation Models Investor Landscape Fragmentation Operator Playbook and Predictions Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Nuno Goncalves Pedro Introduction Welcome to episode 77 of Tech Deciphered. This episode will focus on the great talent redistribution. Where’s talent actually going in 2026 and beyond? The Silicon Valley deal of the last 30 years, very low salary, stock options, you will either sell for a ton of money or IPO, and everyone gets rich, is seemingly broken. Or is it really? The dominant narrative says the tech middle class is dying. We disagree. There is obviously a lot of stuff going on whereby big tech is partially barbelling. There’s a superstar concentration on the top. There’s a bit of a seemingly allowing of the belly. We’ll come back to that. We don’t quite believe that is totally true. There’s a collapse at entry level. The belly is migrating into three, potentially even more, very different destinations: AI native startups, human-verified premium businesses, and the read the industrialized middle of the S&P 500 and SMB world. Each has its own cap table, each will have its own compensation model, and each will have its own investor profile. In some ways, this is the third episode in our Reset trilogy. We started with episode 75 on the SaaS-apocalypse. We talked about the great private capital reset in episode 76, and now we talk about talent redistributions. Bertrand, exciting times, not always positive times.   Bertrand Schmitt Yeah, it’s exciting times because it’s a time of change. Of course, we have the doomsayers. If you listen to Dario Amodei of Anthropic, every white-collar job on Earth is going to disappear. I think I strongly disagree, and I suppose you too as well, we strongly disagree. It’s going to be more of a redistribution. If you look at the history of technology, this is what always happened. We forget how many jobs have disappeared over the past 150 years. We move from a time of 150 years ago. People were mostly in agriculture. Then you had a lot of weird jobs that disappeared from people transporting water to people bringing ice from the pools to people doing the job of computers. People forget that computer was a title given to human beings. We’re doing calculations. Then, of course, secretory jobs in the ’80s, ’90s, where suddenly anyone can type using a word processor, the rise of Excel, that sort of stuff. Many things have changed. Some jobs have indeed disappeared. Some jobs have totally transformed. Where you do these jobs have changed. I think we are at a similar stage where, thanks to AI, and I would say for now, or at least the rise of AI coding, there is a dramatic change happening. I don’t think it means that people will be without a job. It just means, from my perspective, that jobs are changing. You are not just doing a lowly coding level task that actually indeed could be replaced, but you are going to have more of builder type of mindset, a product manager type of mindset going forward. We also expect that the distribution of jobs, depending on the type of business, will be quite different.   Nuno Goncalves Pedro The Broken Contract? Maybe let’s reset a little bit to the broken contract, or if it’s really a broken contract. There’s been this image in technology and tech that basically you get paid very little to work in tech. You get a bunch of stock options. The earlier you are in the company, the higher the level of stock option grants you get. Then you make a ton of money at some point because the company will either sell or IPO, and that’s heard of it. Obviously, there’s a lot of movements happening right now that are changing how these dynamics work. The first part is obviously AI, and in some ways, AI is shrinking companies. It’s not unheard of that companies with as little as four or five people reach 50 million in ARR. There’s companies with one person that have gotten bought for hundreds of millions of dollars or billion of dollars. Obviously, things are moving very, very fast, and therefore, there isn’t a large employee cap table. How would you share the upside? Would you actually give a couple of percentage points to an early employee rather than your 0.2-0.5% kind of thing for early employees? The second part is a little bit the other side of the table, which is the IPO market is seemingly in a drought. There’s not much happening in IPOs. Maybe 2026, at some point, there will be an unlock, but right now, it’s seemingly difficult to get your upside. Even if you’re an employee, you have to wait a long time. The median time of IPO has climbed over 10, 11 years, the longest in over a decade. Basically, not only you have to wait a long time as if there is an IPO drought, like we might be going through right now, when do I actually get my cash back? Unless the company gets bought, maybe there are secondary transactions along the way, maybe there’s something else. But obviously there’s a little bit of a reduction and lowering of the upside seemingly for this contract and for this place. The easy conclusion that I think many are taking is, because of all of this and all the layoffs that are happening, even in big tech, that serve the tech middle class is dying, that basically AI screwing the workers, et cetera, there’s also a lot of discussion that even it might be affecting the entry-level jobs as well. Everyone coming out of undergrad right now can’t get a job, et cetera. There’s this doomsday scenario that you’re alluding to that everything is changing. We have a slightly different perspective. We think there’s a realignment of market. In layoffs, there was a lot of layoffs that were warranted. Big tech, in particular, had actually hoarded a lot of engineering capacity over the last decade or so. There’s a little bit of a realignment that needed to happen in any case. When everyone’s saying, “Well, AI is compressing everything,” well, it’s compressing right now, but we don’t think actually it’s going to compress over time. You’ll still need engineering and science talent to come on board for you to be able to scale up. It’s not like AI is going to take care of everything and teams are going to be five people for companies that are worth a trillion dollars. That’s not happening. Today’s thesis, I think a little bit of this doomsday scenario needs to be seen with a more nuanced lens. I think that’s how we’re framing today’s episode, that there’s a bit of a nuance, there are some extremes happening. We’re going to talk about those extremes, but ultimately, it’s not quite as simple as saying that the tech middle class is disappearing in early jobs are going to be a thing of the past.   Bertrand Schmitt At the same time, what you started with is true. I mean, that 50 million ARR company, just five people. At a bigger scale, that’s exactly the matrix for Anthropic. They have reached a stage where they are at a range of 12 million ARR per staff per employee. It’s metrics that are definitely never seen before. I don’t think any company raised to this level. Best in class, best run companies, one, two million per employees. I mean, that was your target if you can make it. We are definitely in a different game. But I think what matters at the end of the day, and that’s what we’re arguing, is that you have to see the big pictures. Yes, some positions might disappear inside some companies, but some other positions will be created in other companies. Usually, what people do is keep talking about the jobs who disappear and not looking at the bigger picture of jobs that are being created as well. What is true, and I think you alluded to that, is that the big tech the past 10, 15 years had some strategy of hoarding talent in a war where having the best talented people will make the difference in numbers, will make the difference between winning or losing. The Google of the world, the Microsoft of the world, the Amazon of the world, they were hoarding talent. They would try to make sure that they might not have such needs in talented number of people. But if they have the talent, it means their competitors didn’t have the talent. It means that the startup trying to reach scale couldn’t pay the giant salaries that the Google of the world were paying. There was definitely some hoarding. But it went so far in the 2020, 2021, that I think since then there has been a coming back to normal. There is also now in 2026, the recognition that it’s not true anymore. Yes, talent can be very valuable, but there is now a bigger and bigger gap between the extremely talented versus the rest that are merely talented because of AI. AI is able to replace at scale your software engineers, your software managers. I would say it’s quite new. I don’t think it was true a year ago. We’re really talking about a recent dramatic change in what can be achieved thanks to AI. We can see most of the big AI companies are moving to coding. It was started by Anthropic as a trend, OpenAI has followed through. Obviously, the Cursor of the world existed before, but they were not as successful. All the Chinese open-source models are moving very fast to coding optimization the past few weeks. It’s quite an incredible change. I think there is that dramatic change, recognition that coding can be done differently. As a result, we are going to see change in the distribution of jobs. I think it will start from the top because we see the news of the big Google, Microsoft, Amazon, and others who used to hold talented software developers to a change in realization that no, we actually need to invest in AI. We need to invest in compute because compute is going to do the job of most of these people. Therefore, we can’t pay for both at the same time, even us with all our money, we cannot. Wall Street is not going to let us do that. They start by removing a lot of position. I think we see that accelerating, quite frankly. We have only seen the beginning, but in the next 2 years, we see a dramatic shift. But I think my position, I guess yours, and you know as well, is that there will be a lot more opportunities created as well, probably by also entities.   Nuno Goncalves Pedro The Great Unbundling Yeah, there will be more opportunities created. The hoarding is just taken also a little bit of a different view. To your point, there’s hoarding of resources, compute, et cetera. But there’s also hoarding of top talent. We are seeing people getting paid, packages all in that could run up to 100 million, in some cases even over 100 million over several years. This is unheard of. I mean, an officer of Meta would make, I don’t know, maybe 20, 25 million a year. It’s like now there are people that are on the top end of AI researchers that are getting paid around that amount just to join some of these companies. There’s a little bit of a different hoarding. It’s very selective hoarding of certain talent. We’ve seen some acqui-hires. We’ve talked about it in previous episodes that are just literally about getting one or two people specifically to come on board. Alexander Wang, again, going to Meta to lead their intelligence labs there. I feel, I don’t know what you feel, but I feel this is a transition moment where there is overpaying for certain talent on the top of the market. At some point, this will stabilize. You can’t keep paying people 100 million over 4 years or something like that across the board. To your point, a lot of this is actually going to scale up quickly also on the AI side. There’s a little bit of a different hoarding happening on the top end, not just the resources, but also of people, which seems to give further this notion of barbell, that there’s two extremes, the haves and have-nots, the super-duper talented people that get paid a ton of money, tens of millions of dollars a year at the very least. Then the emptying of the middle where there’s a ton of tech layoffs going on in some ways, the belly, as they would call it, is being expelled. The middle market, the managers are being fired because there’s nothing to manage. There’s a lot of positions going away. In some cases, you might keep some of the more junior talent, but with a little bit of experience. But even the talent coming out of colleges is not getting hired either. It’s a little bit of a weird thing where there’s hoarding at the top, there’s an emptying of the belly, the middle, and then the early, early, early is also not getting recruited. It’s like what gives? How is this going to look in the future? I agree fully with you, Bertrand, that there’s a migration of this talent, not only to other companies, but also to other jobs. There will be new jobs that will emerge out of this. The DevOps, dev tools market didn’t exist until maybe 20 years ago at scale, and it got created. In some ways, we’re seeing there will be new markets, there will be new roles and new jobs that will be created around engineering teams going forward. We can’t anticipate all of them. But basically, the emptying of the belly is true as it’s happening right now. The low hiring on the early and the top end, getting tons of money. We think this is a transition to something else. There’s the hoarding of engineering in general is coming to an end at momentum. Now it’s time to rightsize teams, to get the right at the table, et cetera, and start figuring out what works and what doesn’t work. We’ve already had some horror stories coming out even from Amazon where they were breaking systems with their use of AI tools, and I’m sure it’s happening across the board. I’m on a board of a company and been tremendously affected by Meta and its algorithms, where basically because of advertising, there have been people served with ads for this specific company where the ad doesn’t match the company, so basic stuff like that. It’s been actually very, very difficult because in some ways, the company goes back to Meta. It’s like, “Hey, dudes, you guys are serving ads that are not even our ads with our copyright and stuff. How does this work?” They’re like, “Oh, it’s AI.” It’s like, “Well, it’s AI but can you give me my money back?” They’re like, “No, we won’t give you money back.” This creates huge issues for companies, for example, that are very dependent on advertising, which obviously there’s a lot of industries that are. They’re actually in production systems at scale. Meta is, I think now, the largest digital advertising in the world. I think they outgrew Google in one of the last quarters. Basically, this has a tremendous effect that systems that are in production at scale are getting inputs and changes driven by AI tooling, and somehow nobody can say what the hell is happening. Again, there will be a reckoning, there will be a redistribution, there will be a rightsizing of teams and an adequacy of teams going forward. I personally think this is a transition period.   Bertrand Schmitt I think we are moving from hoarding or software engineering to hoarding the top of the top scientists in AI and hoarding of GPUs, GPUs/data center. For me, it was quite interesting to see the deal of Cursor with xAI, where basically they couldn’t get access to computing resources to run their model. But xAI had, I forgot the exact numbers, but close to half a million GPUs that no one, I mean, “no one was using” because their services are not so successful yet in terms of AI chatbot and the like. Basically, suddenly they are like, “You know what? We control access to resource.” But the new resource is, again, a mix of extremely talented AI engineering or AI scientists versus GPUs/data center. There is this race of controlling boss and everything else is going to be collateral damage. Some examples, I think, are quite interesting. You talk about some example of Amazon, even some production issues. I remember reading a quick post-mortem of one of the issues, and the conclusion was it was AI, definitely part of the issue. But the other part of the issue was AI used by junior engineers. For me, it’s interesting. It shows that actually junior plus AI is actually a danger zone. That’s why many companies are going to be way more careful. “Why do we need the junior people if they are just playing with fire?” I think we go back to that situation of barbell, as you call it. The top talents are extremely valuable because they know how a production system works. They are here to develop better AI systems. But the junior guys playing with fires, yeah, maybe it’s cute in startups, but in a big time production environment, a different story.   Nuno Goncalves Pedro There will be a barbell with top-end talent super-mega paid and then mid-level talent that is individual contributors still doing a lot of great work, et cetera. Along the way, a lot of emptying of entry, a lot of emptying of the middle. Where does the talent go? The Three (?) Destinations I think we could say there’s three destinations for this talent. Maybe there’s four, maybe there’s more. Three that we can immediately identify. One is the AI native startup piece, where we have smaller teams that potentially get to a lot of revenue or top line over time, and where the Series Seed is the primary round, where we’re seeing Series Seed being raised of tens of millions of dollars, actually even hundreds of millions of dollars in Series Seed. In some ways, the stars there can get incredible compensations in terms of stock. They will stay for private and selling in secondaries later down the road because there’s so much capital at the table. Actually, in some ways, salaries are very high as well in some of these companies. It’s not like you’re trading off anything. You can get paid a lot of money. If your company at Series Seed for 10 or 15 employees has raised 50-$100 million, you can pay great salaries. In some ways, this is the extreme destination. The AI native startups that can make it is the extreme destination. Now, there aren’t a ton of AI native startups that can raise 50-100 million to 400 million in Series Seed, just to be clear. There’s a handful of hot deals in that space, but that’s one clear destination for top-end talent going through that. In that market, I think that’s one of the destinations. The second one is more what we would call the human-verified premium. It’s more of a play of companies that has still the need of human in the loop, either in terms of development, also in terms of activity, either because go-to markets are very intensive, and so therefore you need to have sales forces, partnership teams, et cetera. Or on the engineering side, it needs to have a lot of customization, integration. Companies are not just going to the, “Oh, you can come in and just apply your AI tooling and somehow magically the systems all work.” there needs to be quite a lot of and work and high touch work in getting stuff done. A significant part of that market, I’m not sure, is super VC investible. Maybe it’s a hybrid of private equity in VC, more PE style in many cases. It’s a PE-hold, sell to someone else market. As we’ve discussed in a previous episode on the SaaS-apocalypse, that hasn’t quite worked out for PEs. Question marks on how that human-verified premium market is going to evolve. But obviously, there’s a lot of work still to be done there, even on the engineering and science side. That’s the second potential destination. Then the third more aggressive destination is the reindustrialized middle companies that have a lot of specificity in going after small and medium businesses, local or regional affectations like ERPs or CRMs for specific markets, et cetera. Those are the three natural destinations. I would add the fourth, which is big tech. I mean, big tech doesn’t magically disappear, and I don’t think it fits neatly into any of these three markets. In some ways, big tech is now looking at the extreme for top talent a little bit like the AI native startup because they can pay. They can pay the 100 million every four years, et cetera. I do think it will typify taxonomically into a fourth type emerging, where, as we discussed, you’ll have top-end individual contributor talent. You’ll have the absolute top-end of the market because they can get paid. Then you’ll start having the emergence of earlier talent that is highly capable, et cetera. That will go back to a bit of a normal distribution in terms of talent on big tech. For me, those are the four destinations that I would put at the table.   Bertrand Schmitt For me, big tech moving to big tech, I’m not sure if it’s really a destination. I mean, yes, in some ways it’s a reshuffle between the big tech companies. They are definitely all fighting in some ways for some of the same people. I can see that dramatic shift where big tech has to remove a lot of positions in order to replace by AI. Again, I think at this stage, it’s mostly driven by AI coding. We are still at the beginning because this is brand-new phenomenon that AI coding is so successful at its task. I don’t think it was true even 6 months ago. Some companies, take Anthropic, take OpenAI, are definitely there or close to be there in terms of no more writing of a single line of code by a human, zero. This is, again, 6, 12 months ago. Not true. But now it’s true in a few top companies. Take OpenClaw as well, most successful GitHub project of all time, not a single line written by its author. It would have been impossible. We’re talking about hundreds of thousands of line of code in a few months. It’s impossible to achieve that manually. If you look at the other big tech companies, the Google of the world, the Meta of the world, the Microsoft of the world, they are absolutely not there yet. They are going to be there because they have no choice. It’s you either go fast there or you die. You are not going to be able to survive competitors that are shipping 10, 50, 100 times faster than you are shipping. It’s a life and death situation. All the big tech companies are going to move, and mark my word, in the next 2 years from 10, 20% of AI-written code to 100%. During that transition, the next 2 years max, if you don’t do it in 2 years, you are going to die. Your stock price is going to crash. Then, of course, you will have to make changes. You will have to invest more in GPUs. You will have to invest less in your standard typical software engineer employees. Like you, I’m very optimistic that there are new buckets. AI-native startups definitely will be there. It will be transformational. Human-verified premium, very interesting category. In a way, it will be businesses that are inevitably less scalable through AI, and there is definitely a spot from there. I think the biggest would be the reindustrialized middle SMBs. Most of S&P 500 type of business are going to dramatically offer new software opportunities, new opportunity story to talented software employees because they will need to implement AI in everything they do. They will do it. They will need people who have software engineering knowledge in order to implement these systems. For them, what’s changing dramatically really is that thanks to much cheaper cost as thanks to AI coding, a lot of software projects that they couldn’t afford to do, that they couldn’t imagine doing by themselves, they are able to do it. They will invest in a lot more software capabilities than ever before. That will be a big game changer. And software, very tuned to their business model. There might be less buying of your traditional off-the-shelf SAF software and a lot more investment in a highly custom software by their own team, assisted with AI. I think that would be the part that is most transformed by all of this in a positive way.   Nuno Goncalves Pedro Alternative Cap Tables, Alternative Compensation Models This will lead to a very fundamental shift, right back to the broken contract. What does the new contract look like? It looks like alternative cap tables depending on which bucket are you transitioning into. If you’re going into your AI-native bucket, and you’re a top-end talent, you’re like, “Dude, I’m worth 100 million over 4 years, so just compensate me accordingly with a mix of options in the company plus my salary.” If you’re top 1%, you can probably get away with salaries that you’d get anyway at mid-level from 300K, 400K and above, and you can get actually a lot of options already in the company. A lot of this is happening right now. There’s a premium for AI, we know that. There’s a premium for AI at the top end of AI researching, in particular on companies that are doing hardcore research on staff AI engineers, so companies that require actual AI engineering. There is a premium that is significant. It could be as high as 18% over non-AI peers, and it widens actually with seniority, shockingly enough. This is more of an average than anything else. Now, for me, and it’s for debate, but the perspective is this extreme comp will need to compress at some point. There will still be the haves and have-nots paid much better than the have-nots, so to speak, but there will be a compression. The variance can’t be the variance we’re seeing today for absolute top-end talent. That said, there will be variants. We know that big tech for over a decade, decade and a half, for example, in the Bay Area, has been paying a lot of money for director and above levels that used to be the VPs, so a million, a million and a half a year, all in compensations. It’s not unheard of that this will actually increase after this stage. That said, I do think that the compensation extreme that we’re in will get diluted down the middle. It will actually come down at some point. It’s part of where we are today. As we know, it is still a bubble.   Bertrand Schmitt Yeah, it’s an interesting point. I think it’s possible. At the same time, that compression coming 2, 3, 5 years. At the same time, we have examples where there is no such compression. Take the top sports players in the world, golfing, basketball, NBA players. There has not really been any compression at all. For me, it’s interesting. If you look at the big tech companies, each being one of this top NBA team, why would such compression happen? As long as they are competing against each other and generating plenty of cash, I think there will be some fair question. We will see. I don’t have a strong opinion, but for me, it’s not a total given.   Nuno Goncalves Pedro For me, the shocking thing is the faster AI becomes better, the more that compression will happen, because at some point, it’s like, why do you need the top talent as well? I don’t know. It feels like you’re trying to evolve a system that’s there to replace you. It’s like, “Okay, I’m getting paid 100 million over the next 4 years”, and then you develop something that’s so good that replaces you. Thank you. That’s cool.   Bertrand Schmitt That’s a total possibility, yes, because we are in that very unusual market where the game is to only replace yourself and people like yourself. At some point, it is a possibility, I guess this one. Right now, we’re talking about replacing your “average software talent”. In 2 years, could we absolutely replace the absolute best top experts in the world? Probably. I think it’s just that at some point we’ll be reaching the stage where we strictly have no control anymore on our AI systems because no human is able to challenge and understand what’s produced. It’s not just a question of scale anymore. We’re talking about a gap in IQ, basically.   Nuno Goncalves Pedro Exactly. It will happen at some point in history. We don’t know exactly when. For the second bucket, the human-verified premium bucket, it’s difficult to see how an HVAC company or an HVAC roll-up of scale or a regional health care platform or high touch go-to-market, B2B, SaaS play, et cetera, for a vertical will compete. At the same end, they have to compete and they will compete. There will be more and more jobs, we believe, for engineering talent in these companies. They’ll have to be more and more AI-enabled themselves. The cash salaries will have to be competitive within the local markets, not necessarily with Silicon Valley. There will be potentially profit sharing and revenue sharing and actual dividends played at the table. The model there on the cap table needs to change a little bit, needs to be probably propped up more on salary and on some way of doing profit sharing or actually having dividends paid to employees and figuring out employee to equity in a more aggressive manner. This is the market that probably was already very attacked, so to speak, or let’s say, occupied by private equity firms. There are still obviously part of that model that would work well. There needs to be a fundamental shift, certainly on the quantum of salary compensation, dividend compensation, profit sharing, and all of that. Then last but not the least, obviously, we had the bucket around basically the reindustrialization of the middle, so everything else, which will take most of the belly that we were talking about. This is probably a poor analogy, the belly fat. It’s not belly fat, it’s people that were doing their jobs that now are getting disrupted. In some ways, that bucket will absorb a lot of that belly, will absorb a lot of talent. The small and medium businesses that Bertrand was saying will need to crucially become more AI, software-enabled by themselves, even with some core stuff and underpinnings that actually might not even require AI in terms of infrastructure platforms. There, you need to get properly paid. Again, how many people do you need in your engineering team if you’re a small business? Probably not a lot. It’s maybe you need one or two people and that’s it. They’ll need to be very nicely paid because they’re running the stuff in the rails. This is probably a market that over time, as AI gets more and more competent, will also be disrupted, but let’s not talk about the disruption to the disruption because otherwise, we’ll stay here the whole day, but certainly a market that has a lot of potential to shift and to absorb a lot of the moments that we’re seeing in terms of layoffs happening in the US in particular.   Bertrand Schmitt This category was a category that historically could not compete with Silicon Valley salaries, could not attract the most talented engineers. It’s not a category that didn’t want to bring these people on board. It’s a category that just couldn’t afford to bring this talent on board, typically. I think it would be a dramatic shift for them when suddenly there are opportunities to hire these people. There is an opportunity to hire them at maybe more reasonable prices from this company’s perspective. You talk about small companies, the great thing is that there are millions of small companies at some point. I think things could be truly transformational. Of course, some of these engineers, software engineers, might decide to become entrepreneurs on their own. Solo entrepreneurs, small businesses, build their own, easier to build their own product to market so to serve other companies. I think there will be quite dramatic changes because not all companies will be disrupted by AI as much, but not every company will benefit from improving processes, improving software through AI. At least early on, you will need this human touch to make it work inside a business. Interestingly enough, I was hearing that some companies like IBM were hiring more younger people to do the work of going to the client, understand their needs, propose implementation plans. That forward deployed engineer, those positions, I think there will be more and more available.   Nuno Goncalves Pedro Investor Landscape Fragmentation What happens to investor into the landscape? We already had an episode, the previous one, Episode 76, where we talked quite a lot about the big capital reset on the private equity and private reset, including venture capital. Just maybe to summarize, how does it align with the buckets that we’ve just been discussing? I think the AI-native bucket clearly is going to be the key bucket. There, we’re going to see two movements. One movement, which is the mega funds, as we discussed in the last episode, are no longer just VC funds. They’re really mostly multi-asset private equity funds, maybe even private equity hedge funds in some cases. Those funds will be all over the high-growth AI-native companies and will be pouring money into companies that are scaling really, really quickly. The early stage, so to speak, VCs, the actual VCs that will stay in the market will be the guys probably identifying the next big wave of AI-native companies. We’ve discussed that as well in the last episode, some research that we did at Chamaeleon that I shared in episode 76. We’ll see that as emerging. What happens to the second bucket, the bucket around human premium, human in the loop? Likely we’ll have more and more private equity capital going into it and the large-scale VC guys, the Thrives of the world, they’ve just announced Thrive Holdings, and others going after those markets as well. It’s trying to converge into the private equity market, which aligns with the point we made in the previous episode that the VC mega funds are no longer VC, that they are private equity, multi-asset class. They’re going after a bunch of things. There’s a conversion happening from VC into private equity. It was going to happen anyway because the private equity guys were coming into VC as well and the hedge funds were coming to VC as well. There’s a convergence in the middle of very, very large funds and large assets under management happening to go after some of these opportunities, certainly in Bucket B. Then this Bucket C, so to speak, the bucket of reindustrialization, as Bertrand was saying, very well, likely will be self-funded for a significant period of time. Will self-fund with their own cash flow. Doesn’t need to have a ton of capital intensity. Maybe you need one or two engineers to do stuff, but that’s it. You don’t need tons of capital. You didn’t need in the past, you won’t need it today. Not sure there’s going to be a fundamental shift to that market.   Bertrand Schmitt Yes, I certainly, overall, agree with you. That last pocket, probably little change to the capital and capital structure. Again, I see that as the biggest opportunity for a lot of people who might be less needed by big tech and also top tech companies. What is sure for the first category, the high native startups? I would say more overall in the VC ecosystem, there is no space left for SaaS anymore. I think SaaS, as we used to know it, is dead in some ways in the sense that new pure SaaS software startup are definitely out. Existing ones that are critical to run your infrastructure, the Salesforce of the world, I think they’re in a decent spot. Actually, interestingly, they changed their pricing model to now sell to AI agents, not just per seat. There is a change in pricing there. But this day and age of funding a pure SaaS software startup through VC money, no way. VC money going to AI-native startups, AI-focused startups, to biotech, to deep tech, to defense tech, yes. SaaS as a fundable category early on, I think it’s over.   Nuno Goncalves Pedro I’m a bit more nuanced as we shared in The SaaS Apocalypse episode. We can call it whatever we call. It’s applied AI is the new SaaS thing. Horizontal applied AI is the new horizontal SaaS or vertical applied AI is the new vertical SaaS. I agree in common with your point that very specific point solutions around SaaS will be disrupted by nature with all the easy stuff you can do today with AI. It will take a while. This is not something that’s going to happen this year. It’s going to happen over the next years. Maybe interesting to also talk about the exit markets. I think the IPO market, as we’ve also discussed in the past, there is, in my view, going to be a reopening of the IPO market, I think this year, probably later in the year, third or fourth quarter. The median time to IPO actually is going to be really weird because there’s going to be potentially some companies in the current landscape, bubble or no bubble, that are going to IPO, the OpenAIs of the world, Anthropics of the world, et cetera. There will be more and more aggression, I think, on M&A. Big tech has already shown it, that they want to buy into markets. Large non-tech companies have also started doing acquisitions in space. To prop up their IT teams, their engineering teams with this world that we’ve also discussed in previous episodes that I’m going to own my own engineering stack for now. As we see, that normally doesn’t withstand the test of time. At some point it will get unbundled and served by someone else. Then finally, the secondary market is very hot right now. Obviously, there’s heavy discounting on some areas, high premiums on others. The exit market, strangely enough, is going to be propped up, in my opinion, over the next year to 2 years, dramatically. Then we’ll see if there’s a big reckoning around the bubble that we are clearly in or not, if it’s a soft landing or hard landing. Definitely, there’s going to be a lot of exit paths over the next year to 2 years.   Bertrand Schmitt Concerning the “bubble”, I have two perspectives on this. One is it’s a bubble in the sense that money is going to a lot of players and some players are going to blow it up. There will be a concentration of players at the end, like it usually happens. If you look at, for instance, long time ago, the railway revolution, there was that intense influx of capital. At the end of the day, there was a dramatic change in transportation in the US and a complete railway system put in place. Yes, some investors lost money, some companies went bankrupt, but the transformation was fully real. There were a lot of top leaders at the end of this revolution. The change after that only happened, we guess, post-World War II, with the construction of the highway system and the rise of airlines and plane transportation overall. Here I feel it’s similar in the sense that, yes, there is a lot of money going in. Some players are going to blow it. They will misuse the money in different ways, but that’s part of dynamic allocation of capital. Of course, you make mistakes. That’s what happens. At the same time, I feel it’s a similar level in the sense of this is a dramatic change in the US infrastructure. This buildup of AI data centers filled with GPUs, integrated at scale with some of the best software in the world and running it, supported by a dramatic shift in energy infrastructure. This is for me similar to the Railroad Revolution. Some players might not own the data center they build because they didn’t manage well their debt, they didn’t manage to run proper software. You know what? They will get acquired by somebody else. I think we are at this level of fundamental transformation. The fact that in a matter of maybe 2 years, the move from 0% of code written by AI to 100 % written by AI is an insane dramatic shift. Just to be clear, when you move from manually coded to AI coded, we’re talking about a 100X difference in terms of speed at similar, if not better level of quality. The shift is dramatic, and on top of it, you don’t pay salaries anymore to achieve that. You pay CapEx, and with GPUs and OpEx with electricity. It’s a very big shift, positive shift in business model. New unions, no management over it, AI working 24/7. Personally, I think for me, bubble has a bad connotation in the sense of it was all for a waste. I don’t think it’s all for a waste. I think we are witnessing a dramatic revolution of our lifetimes, quite frankly, bigger than SaaS, bigger than mobile. From my perspective, it’s exciting times.   Nuno Goncalves Pedro Operator Playbook and Predictions Let’s move to if you are this person, what would you do in the future? Let’s start with two extremes and go from there. One is you’re non-tech, so you’re not an engineer, et cetera. You’re trying to figure out, how do I scale my activity? Maybe physical labor is where I want to go. It’s not, “Go west” anymore. Definitely not necessarily go west. You should go to, I guess, the states that have no sales tax with very cheap energy because that’s where the data centers are being built if you want to be in that market. Obviously, there’s a lot of stuff that needs to be done: HVAC, electricity work, et cetera. Don’t go west. Go low sales taxes, low cost of energy. That’s likely where the data centers are being built. You probably can just follow. There’s, I’m sure, some way for you to follow where the data centers are being built, but that’s next, I think on that extreme of the table. The other extreme of the table, let’s say you are super ambitious, maybe you’re no longer an engineer, but you’re a product manager in your prompt engineering. You could do prompt engineering all day long. You’re 28, 29-year-old superstar. What do you go and do? Likely either you start your own thing, start your own company because you’re so good at prompt engineering, you probably can do a lot of the code yourself, particularly if you have an engineering background, or you go and join very early an AI-native startup that you think has the chance of going through the roof, and you take a pretty good salary early on, a ton of upside on the company because guess what? Companies like that need product managers. They need people to figure out UX, UI. It’s not going to be, at least for now, yet AI figuring that out for you. Those are two extremes, just to give two of the extremes, like engineering, product management persona, and physical labor at the other extreme, non-tech, et cetera.   Bertrand Schmitt In some ways, every software engineering job is going to become the equivalent of a software engineering manager or a product manager, because suddenly you don’t have to do the coding anymore. You’re managing AI that is coding for you. Either you start to have some manager hat, but we saw the humans, so it’s a very different type of manager, obviously, or you are going to be really an empowered product manager. You’re skipping the middleman. You’re skipping the traditional engineering organization because your engineering organization is AI running and doing the work for you. I still believe that it requires some serious skills. I don’t believe in the vibe coder type of value proposition. I don’t believe in the prompt engineer becoming suddenly super incredible, able to manage that. I still think it requires some serious chops to do the best from all of this and to do it in a safe and sane way. It’s very easy to have poor taste, make mistakes. I don’t know you, but keep reading these stories on the heads of companies who lost everything because of the AI agents. That deleted stuff in production, and they had no backups or the backups weren’t deleted as well. Crazy situation. You cannot run companies like this if you let your agents running wild. You could argue it’s the early days. I would argue it that that issues would be there for a while. You need to have some engineering discipline at core in the company running the business to make sure things don’t go sideways because it would be easy for things to go sideways.   Nuno Goncalves Pedro I totally agree. If you’re thinking, Oh, should my kid go into science and engineering and computer science, et cetera? Absolutely, still, because of everything that Bertrand just said. You need to understand actually what code does and what technology does and what all of that does. That’s still a skill of the future. It’s not a skill of the past. In some ways, it’s still a skill of the future very much. Maybe let’s try two more extremes. Around the same level, the person that decided to do an AI native company bootstrapped initially, having difficulty raising a mega round, but could probably get away with raising a 2-3 million seed round, et cetera. Is that still viable? The answer is yes. There’s tremendous capital efficiency right now happening in the market still, 10 plus higher than if you were doing a SaaS company, and you were a founder in 2019 or something like that. That capital efficiency is going to reverberate. You can run a tighter team, smaller team. Actually, you don’t need that many salaries. If you’re a decent engineer as a founder or if you understand enough as a product manager to just generate that code, you can do a lot of stuff yourself, can bring in maybe one or two technical elements to the team early on as you would have done if you were bootstrapped anyway. There’s obviously a path for that. The other extreme is you’re in big tech, you’re level five, individual contributor, making a ton of money, or you were a manager, and you’re now out of a job, where do you go? You can go to a big company that is non-tech, S&P 500 company that’s non-tech, something like that. You join the company, you’ll probably get paid pretty well, maybe not as high as you were paid in big tech. There’s some stock at the table, but guess what? You’ll have probably more work-life balance than you ever did. That’s the trade-off. You’ll have a better job. On the upside, you can transform the company. You can help and be part of transforming a company from non-AI to AI-first or AI-enabled in the future, whatever BS that will look like in terms of the argumentation to the board. You can actually create tremendous productivity enhancements in a big non-tech company if you come with that background. Again, you’ll have certainly a better work-life balance, so not a bad deal, to be honest.   Bertrand Schmitt Also, to be clear, I talk a lot about AI coding because it’s truly transformational. You could argue that it’s going to be self-improving. We are in the situation of a self-improving AI that keeps improving itself thanks to automated coding. It’s a dramatic, virtuous loop. Obviously, AI is also going to improve everything else. It’s going to improve your marketing, it’s going to improve your search process, it’s going to improve your DNA. Improvements will be everywhere. It’s just that right now we are at a point in the quote-unquote revolution where there is one clear piece of the puzzle that is moving faster than the rest.   Nuno Goncalves Pedro Bertrand, the senior executives at non-tech don’t know anything about that. It could be just a great prompt engineer. That’s the only job you do. “I’m the chief marketing officer. I have someone below me that’s doing the whole work.” Nobody knows. Nobody’s the wiser, I guess. I’m being facetious, but not fully.   Bertrand Schmitt Yeah. There would be a transition period where what you described happen. I want to say, going back to AI coding, I think that the part of AI that as of today has reached a stage of limited AGI. We have reached, from my perspective, a limited type of AGI for coding. If you take coding as a discipline today, I think we reach AGI. If you go beyond coding, that’s true. If we are talking about coding, leveraging the latest LLMs: OPUS 4.7, ChatGPT 5.5, combined with Claude Code, Codex, and OpenCode for harness, I think we’ve reached AGI in the context of coding. I’m not sure everyone fully realize that and the consequence of that. I think the rest is going to come as well. We are going to see that category by category, usually categories that are more scientific in nature, where you can replicate, where you can test easily, where you can create clear success. Metrics will be the “easiest” to follow in that direction of self-improvement. I just want to highlight that this part is truly transformational, the root cause of everything we’re talking about today. At the same time, it’s coming beyond coding.   Nuno Goncalves Pedro I think it is true. There are a couple of markets where that might not hold true, which is maybe the final path. If you’re thinking of starting your own business in plumbing and in HVAC maintenance and installation, this is a pretty good time for the reasons we already said before. There’s a lot of buildup of data centers and all that stuff, but also for other reasons, because it’s an activity that won’t be disrupted by AI yet. You need them embodied AI. You need physicality to AI to do stuff like actually fixing pipes.   Bertrand Schmitt Until Optimus replace you.   Nuno Goncalves Pedro Yeah, but if we’re 3, 4 years out in terms of a lot of these optimizations that we’re talking about at the software layer, we’re 10 years plus out on embodied AI, right?   Bertrand Schmitt Oh, yeah, it’s 10 years.   Nuno Goncalves Pedro We’ll probably be optimistic as we speak. That’s a nice business. I’m thinking of starting to go into that market. If you guys are interested in listening to this, just reach out to me. What’s the angle? I think there’s a lot of stuff you can do in the buildup of some of these businesses, plumbing, HVAC, all sorts of maintenance. There are markets that are just totally messed up. Handyman market in the US is totally messed up. There’s a bunch of companies out there that try to go after it with marketplaces and stuff. I honestly just start something from scratch, a small business, and go from there.   Bertrand Schmitt Yes. They’re an interesting middle. Think about accounting firms, consulting firms. I think they are not as easy to replace, but at the same time, there is no way on what they do is not going to be dramatically changed with AI. I don’t know if it’s 50, 80, 90% of the job, but this is changing quite dramatically, would be my expectation in the coming few years. Conclusion Thanks for listening episode 77 of Tech Deciphered about that great talent redistribution. As you heard it from us, we believe there is a dramatic change in play, enabled by AI coding, and that ultimately a lot of the big tech companies are changing their employee distribution, way more focused on the top talents and bringing more GPUs. As a result, we will see a change in their staffing. Some of this change will benefit AI-focused startups, but probably more likely will benefit the bigger SMBs, the S&P 500 companies of the world that will finally be able to bring inside and afford some of the talent that were in some ways trapped by the top 5, 10, 20 software companies of the world. Thank you, Nuno.   Nuno Goncalves Pedro Thank you, Bertrand

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!This was recorded before Railway suffered a major GCP outage on May 19, despite being a multi-AZ, multi-zone mesh ring, with HA fiber interconnects between their Metal GCP AWS, because workload discoverability was unintentionally still tied to GCP. All has been resolved with a post-mortem.Railway did not start as an AI infrastructure company.It was founded in 2020 years before agents became the default way people thought about deploying software. Jake Cooper, formerly at Bloomberg and Uber, started Railway with a simple obsession: the activation energy to ship something to production should be near zero. Push code, get a URL, iterate. No Docker files, no Kubernetes manifests, no Ansible scripts stacked on Ansible scripts.For years, this was a slow grind. Railway spent its first 18 months hand-acquiring its first 100 users with Jake personally greeting every Discord signup on a second monitor.Today, Railway has raised $124m and is growing very fast. A 35-person team supports 3 million users, adding roughly 100,000 signups a week. Their bare metal data centers have a 3-month payback period vs. renting in the cloud, with 70% margins funding aggressive cloud bursting when needed. The servers they own have actually appreciated in value as RAM prices have climbed basically meaning the value of their hardware now exceeds the capital they've raised.From rebuilding Railway's network overlay over a weekend to moving the vast majority of workloads onto its own bare metal data centers, Jake Cooper is trying to build a new cloud for an agent-native world. In this episode, Railway's founder and “conductor” joins swyx and Alessio to unpack why the next era of software infrastructure is not just “Heroku but newer,” what agents need that humans did not, and why the old deployment loop of Git, PRs, CI/CD, and static cloud resources may be heading for a rewrite.We go deep on Railway's infrastructure stack: own-metal data centers, three-month cloud payback periods, cloud bursting, data center debt, Railpack, Nixpacks, Temporal, feature flags, Central Station, content-addressable filesystems, agent-safe production forks, and why the CLI may become more important than the canvas in an agent world. Jake also shares the founder journey behind Railway, how the company survived losing $500K/month, why it now serves millions of users with only 35 people, and why he believes the pull request is dying.We discuss:* How Railway went from a slow six-year grind to adding 100,000 users a week* How Railway thinks about agents as the next dominant software species* Why agents need version control, observability, compute, storage, and orchestration at 1000x scale* The economics of Railway's own-metal data centers and three-month payback* How Railway uses cloud bursting while scaling its own infrastructure* Why data center debt can be a better tool than venture debt for infra startups* Central Station, Railway's internal system for clustering customer feedback and incidents* Why responsible disclosure and over-communication matter for platforms* Why feature flags, progressive rollouts, and shadow traffic are essential for agents* Temporal's strengths, pain points, and why workflows matter for agents* Railpack, Nixpacks, Nix, and lazy-loaded content-addressable filesystems* Why “cattle, not pets” may change if you can clone the pets* Why Railway is building a new cloud from scratch instead of copying hyperscalers* The solo founder path, focus, writing, and how Jake thinks about company buildingRailway:* Website: https://railway.com/* X: https://x.com/RailwayJake Cooper:* LinkedIn: https://www.linkedin.com/in/thejakecooper/* X: https://x.com/JustJakeTimestamps00:00:00 Introduction: What Is Railway?00:02:07 Jake's Path to Railway00:06:13 Railway's Six-Year Growth Story00:08:52 Rebuilding the Business After the Free Tier00:11:17 Agents as the Next Software Platform00:13:29 Railway's Infrastructure Philosophy00:15:42 Bare Metal, Cloud Economics, and the Compute Crunch00:17:22 Cloud Bursting and Five-Cloud Networking00:20:20 Data Center Debt and Infra Financing00:23:31 Data Centers in Space00:25:24 What Agents Need From Infrastructure00:28:24 CLIs, Canvas, and Agent-Native UX00:35:15 Central Station, Incidents, and Responsible Disclosure00:40:30 Safe Rollouts, SRE Agents, and Production Forks00:45:00 AI SRE, Specs, Code, and Tests00:48:24 Self-Replicating Infrastructure and the New Serverless00:53:18 Heroku, Temporal, and Workflow Engines01:04:07 Railpack, Nixpacks, and Lazy-Loaded Filesystems01:06:01 Coding Agents, Token Spend, and Roadmap Acceleration01:10:56 The Pull Request Is Dying01:12:28 Feature Flags and the Agent-Era SDLC01:16:15 Cattle, Pets, and Cloning Machines01:19:29 Solo Founder Lessons01:24:12 Focus, GPUs, and Building a New Cloud01:28:20 Closing ThoughtsTranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: Hey, hey, hey. Today we're in the studio with Jake Cooper of Railway.Alessio [00:00:14]: Conductor of Railway.Swyx [00:00:15]: Conductor at Railway. Yeah.Alessio [00:00:16]: Choo-choo.Swyx [00:00:17]: Do you actually have that anywhere, like on your business card?Jake [00:00:20]: We call some of our volunteer moderators conductors. I don't have a business card. We're not that big yet. At some point I will. I got handed a nice business card from the Supermicro folks, and I was like, “Damn, this is pretty official.”Swyx [00:00:30]: Business cards are coming back.Jake [00:00:32]: They're cool. They're hip. The conductor thing is good. We're trying to figure out what we want to call each other internally. Some people think it's super cringe and say, “You don't need a name for people internally.” Some people want to call each other something. We still don't have a really good one.Jake [00:00:55]: We've got New Railcrews, Trainiacs. Nothing has stuck yet.Swyx [00:01:00]: I like Trainiac. Trainiac sounds good. Railwayians. For those who don't know, what is Railway? Let's give people a crisp definition up front.Jake [00:01:09]: Railway is the easiest way to ship anything. You go to the canvas, or you talk with Claude, and you say, “Deploy a Postgres instance, deploy my GitHub repository, run this code,” and you're off to the races.Swyx [00:01:22]: You've got a nice animation on the landing page.Jake [00:01:24]: Thank you. None of my work, by the way. They don't let me touch the design stuff anymore.Jake [00:01:25]: We want to make it trivially easy not just to deploy things, but to evolve applications over time. Most tooling right now stacks entropy on top of entropy: Docker, Kubernetes, Ansible scripts, and all these other things. If we can version all of your software and keep track of all the changes, then we can make it trivial to clone environments, fork into a parallel universe, get copies of production data, get copies of any services, make changes, validate them, and collapse them back in without reproducing everything across a staging environment.The Railway Origin Story: From Uber Systems to a New CloudSwyx [00:02:07]: I was looking at your background: Bloomberg, Uber. Nothing immediately stands out as, “This guy is going to found the next great platform as a service.” What prepared you for Railway?Jake [00:02:21]: It was curiosity to keep going deeper. I started out on front-end stuff, working on Wolfram Mathematica and porting it over. Then I briefly moved to Bloomberg, then toward Uber and distributed systems, taking the Jump Bikes systems and moving them to a distributed system built on top of Cadence, the pre-Temporal Temporal.Swyx [00:02:44]: Which, by the way, I'm happy to talk about, pros and cons.Jake [00:02:48]: Totally.Swyx [00:02:51]: But let's do the Railway story.Jake [00:02:52]: It has been a continual step of wanting an experience. Whether it's walking up to a bike, unlocking it, and having it work frictionlessly, or something else, the depth required to make that happen follows from the experience. A lot of the work I do, and a lot of the team does, is in service of that experience. We fundamentally don't care how deep we have to go. We will swim to the bottom of the swimming pool to get the experience.Jake [00:03:17]: I don't have a physics PhD. I did an EECS degree. It has always been about figuring out the next step: how do we get there? That's what led to starting Railway for that experience and then moving all the way to bare metal data centers. I was adding patches to the kernel this week to get the experience there because I can see how much better it can be.Swyx [00:03:49]: Other patches to the Linux kernel this week?Jake [00:03:51]: Yeah. Not upstream. Our fork.Swyx [00:03:52]: That's a flex. Railpack? No, this is different. This is the OS on top of Railpack?Jake [00:03:57]: No, this is an actual kernel patch. It's always literally: what do we have to do to get that experience? Then figure it out. Anything is figureoutable.Swyx [00:04:10]: Would you send the patch upstream, or does it not fit other use cases?Jake [00:04:13]: Maybe. We have to work out the experience internally. It has to do with the storage layer we're building for some of the agentic stuff. Maybe it'll be useful upstream, but it's deeply useful for us internally.Open Source, Forks, and Non-Deterministic VersioningSwyx [00:04:29]: You mentioned open source before. How do you think about starting from open source, and then coding agents letting you do a lot more from forks of it?Jake [00:04:38]: GitHub's original sin is that it's almost a series of broken pointers. You have this thing, then you clone it, and now you've lost the whole upstream. How do we make it trivial for people to modify really small pieces of it?Jake [00:04:51]: We think of Git in a discrete sense: I've either made a change and merged upstream, or I haven't. What would it look like if it were percentage-based, a little more non-deterministic, or a stream of changes that users traverse as a percentage rolled out in general and then rolled all the way up?Jake [00:05:13]: We have the open-source kickback program and let you deploy templates because we want to make it trivial for people to version these shards over time. It solves a large problem around authentication, authorization, and security. NPM has a way to define, “Don't take any new packages.” The ideal end state is that you roll out progressively to users with the minimum impact zone and continue rolling up. JPMorgan should probably be the last one on the patch line, for all our sakes, because our money and livelihoods are there.Jake [00:05:53]: It's okay if Johnny Vibe Coder gets a broken patch because there's so much entropy in the system that the rubber has to meet the road at some point. You have to test at varying levels.The Long Grind: First Users, Free Tier, and Making the Business WorkSwyx [00:06:13]: I wanted to pull up this glorious chart, which is your usage or number of daily signups?Jake [00:06:22]: Daily signups, I think.Swyx [00:06:24]: You started six years ago. It was a slow grind, and now you're on a rocket ship. You say, “Don't doubt your fight and don't quit.” Maybe pick out certain points that were key inflections for the company.Jake [00:06:40]: At the start, it's about getting your first 100 users, hell or high water. We had a website and a support link. The support link was the Discord channel. I had notifications on with two monitors: the monitor I was working on and the other monitor with Discord. If anybody came in, I was immediately like, “Hey, how's it going?” It was rare, so getting those first 100 users to come back was the start.Jake [00:07:14]: Then you build a consultancy factory because users want all these things. You have to go back to the board and ask, “What is the actual product offering I want to build on top of this?”Jake [00:07:28]: VCs want charts that always go up and to the right, but in reality you don't necessarily want charts that look like that. For us, there have been periods of expansion where we add features to test use cases, and periods of compaction where we ask, “If the experience we have is good, how do we make it significantly better?” Maybe we strip out features that don't fit our ICP anymore.Jake [00:07:57]: The boom from 2022 to 2023 came from the free tier. Everybody under the sun was using it.Swyx [00:08:09]: A lot of Reddit bots and Discord bots.Jake [00:08:12]: And crypto miners. When you build an open product on the internet where anybody can sign up, the internet is a horrible place with so many things. You go through periods of asking, “How do I reach as many people as possible?” Then, “How do I fit the exact use case for the people who really matter and are really excited about this specific thing?”Jake [00:08:39]: Then there was a two-year period of making the actual business work. During the free-tier era, we were losing about half a million dollars a month.Swyx [00:08:59]: On a $20 million bank account.Jake [00:09:02]: On a $20 million bank account with maybe $50,000 a month in revenue. That's a horrible business. I don't know how anybody invested. But you have to go through it and say, “We have an experience people love, but the business has to work.”Jake [00:09:17]: There are two schools of thought. You can run the horrible business all the way up with bad margins, or you can go back and make it work. We've always wanted a super lean team. We're 35 people right now. It's very small.Swyx [00:09:36]: Supporting three million already?Jake [00:09:38]: Yeah. We're adding 100,000 users a week right now, so it's growing fast. We don't want to add headcount for the sake of headcount or throw bodies at problems. We want to build systems. It's hard to build systems during expansion because you're adding things to the system because people are asking for them or things are breaking.Jake [00:10:00]: We had to cut off the free users for a little while, rebuild the business, and make sure it worked. We want to reach as many people as possible because software is important. It's become difficult to create things in the physical world, so it's important to make it easy for people to build in the virtual world and have access to creation. But there are legs to that journey.Jake [00:10:30]: You can see divots in the charts. If you follow between 2025 and 2026, it's either summer or winter. People go on holiday with family.Swyx [00:10:50]: It affects that much?Jake [00:10:51]: Yeah. It's kind of B2C and kind of B2B. People are shipping constantly, then they stop. Our activation curve now shows more people activating on weekdays because we have more business users, so it smooths out over time.Agents as the New Interface to DeploymentSwyx [00:11:17]: Was there a point where you started prioritizing AI development or agent development?Jake [00:11:24]: We've prioritized agentic as a top-of-funnel thing. Over the last six months, we've deeply prioritized agentic as a mechanism to build and deploy things because we believe the curve is so steep and that is how people will build and deploy software.Jake [00:11:42]: It almost fundamentally doesn't matter whether this is dot-com or not because we're all on the internet anyway. If agents are going to deploy a bunch of things and we hit an inference wall at some point, we'll fix those problems. The dominant species over the next 10 years is that we've moved from assembly to C to C++ to JavaScript to words. You're going to need to close that loop.Swyx [00:12:13]: When you say this is dot-com, did you mean buying the domain, or the general case?Jake [00:12:17]: I mean the dot-com era, when companies had a huge run-up because people understood the internet was important. Then they hit bottlenecks, fundamental laws of physics, math didn't work, and everybody came back down to earth. But it didn't matter because the internet became so impactful. If you operate on a long enough time horizon, you should build these things anyway because you can see where it's going.Jake [00:12:45]: That's where I think a lot of agent stuff is. You get to a point where you're running thousands of agents in parallel. What is the inference cost? What is the compute cost? How do you make that efficient? How do you coordinate all this? We have issues coordinating humans; we don't even have good tooling for that. Now we have to figure out how to get agents to coordinate, safely version changes, and know when to raise their hand for someone to intervene. Otherwise it becomes an interrupt factory.Railway's Infrastructure Thesis: Network, Compute, Storage, and MetalSwyx [00:13:19]: Let's go right into the technical side. What are the core infrastructure or architectural beliefs of Railway that allow you to do what you do?Jake [00:13:29]: The primitives matter a lot for us. We need network, compute, storage, and orchestration around it. You need control over a lot of those things. We've talked a lot about how we don't really use Kubernetes because we want higher-order control to place workloads in very specific places.Jake [00:13:48]: The reason is that you have to be very efficient with agents: memory reuse and all these other things, or you're going to massively blow up your cost structure. Being able to rack and stack your own servers and build your own metal unlocks performance and cost. Experiences where you're running 1,000 agents in parallel are not massively cost prohibitive.Jake [00:14:13]: Token use and compute use are blowing up. Over time, those things have to get a lot more efficient. You can get a lot of margin to make those experiences solid by building your own metal. That's all in service of offering a differentiated experience to as many people as humanly possible.Swyx [00:14:51]: You have a data center in Singapore.Jake [00:14:53]: Yeah. We have two in every other region now. In Singapore, we're adding a second one in Q3.Swyx [00:14:58]: What's it like? I've never built a data center. Do you go to Equinix and say, “I want some slots?”Jake [00:15:05]: Yeah. Equinix. You basically go and say, “I want power and I want a cage.” They say, “Great, here's what it's going to be.” You rent the cage for a period of time, fill it with racks and servers, and hook up internet to it. That's all the pieces.Swyx [00:15:36]: Then you handle everything else.Jake [00:15:37]: You handle everything else.Swyx [00:15:39]: What's the math versus clouds doing it for you?Jake [00:15:43]: If we rented in the cloud, our payback period when we go to metal is about three months.Swyx [00:15:50]: Which is crazy.Jake [00:15:51]: It's nuts. That's four years of depreciated hardware. You're going to see a lot of this compute crunch because hyperscalers are buying up a lot of stuff. We're working directly with OEMs, resellers, and people building these machines: Supermicro, Dell, and others.Jake [00:16:11]: Upstream, there's a bunch of supply pressure. When we raised our last round, between deploying capital for servers and now, the amount of money we've raised is less than the amount of money we have in the bank plus the value of the servers because the servers have appreciated as RAM has gone up. It's nuts how valuable hardware has become.Jake [00:16:50]: If you look at hyperscalers, they deployed around $80 billion of capital expenditures this year, and next year will be more. That's a massive infrastructure build-out. You look at that and think it's crazy that they're spending way more than the Manhattan Project. But if every person is going to run dozens or hundreds of agents in parallel, you have no conceptual idea how much compute is required to make that experience happen, even if you're deeply efficient and sharing resources. And that doesn't even count inference.Swyx [00:17:22]: How do you plan the build-out? The growth chart is so vertical. Are you usually at 100% utilization as soon as racks are live? How far ahead are you planning?Jake [00:17:33]: We still maintain cloud presence for bursting. We work with AWS, GCP, and a few other clouds. We can rent, and then the moment we get space or power, we compact those workloads off the cloud. We started on the clouds, then built a system to migrate to our own metal. There's nothing that says you can't continually do that again, and that's exactly what we do. We never want to be compute constrained.Jake [00:18:09]: At the start of the year, we actually became compute constrained because one upstream provider wasn't able to give us quota at the rate we needed, and the hardware was slower. I spent a weekend rebuilding our entire network overlay so we could straddle five clouds: Oracle, AWS, ourselves, GCP, and one other one. We can do more than that now.Jake [00:18:38]: We got into a spot where we were trying to pack instances tight because we couldn't get enough compute. That led to a few reliability issues, which are now past us. I made a tweet pointing out that it's becoming harder and harder to acquire compute at the rate these models need to acquire compute. We got bit by it.Swyx [00:19:15]: How do you think about pricing knowing you might not have your own metal available at all times? Are you pricing assuming you need extra margin if you end up going into the cloud?Jake [00:19:26]: Because we've built out our metal data centers, our margins on metal are around 70%. We can deeply subsidize the cloud business if we want to scale at a reasonable rate. We have a few levers: metal, which makes the margins; cloud burst; debt to buy servers; and venture capital. It's an interesting operational problem: how much cash do we have, how much should we raise, how quickly can we deploy it, and can we scale revenue as quickly as we scale compute?Jake [00:20:05]: If we continue making it trivially easy for people to build and deploy, then the faster we close that loop and the more operationally excellent we are with capital, the faster the business can scale. It's almost a straight linear deployment rate.Financing Infrastructure: Hardware Debt, VC, and Operational LeverageSwyx [00:20:20]: I think infra startups raising debt is a tool people don't utilize enough or know enough about. What can you tell us about that? Is it secured against your CPUs?Jake [00:20:32]: It's secured against our hardware.Swyx [00:20:37]: What rates do you get? Who are the lenders?Jake [00:20:39]: We pay prime plus a spread, and we can refinance any of the debt as rates go down. The terms are pretty good. The unfortunate thing is that Twitter has no nuance, so people say, “Venture debt bad.” But as with all things, there are specific tools and areas where you can be deliberate instead of using one tool as a hammer. Venture capital is not the hammer for everything. You have to explore and figure out what works.Swyx [00:21:12]: VC is usually the most expensive financing you can get.Jake [00:21:15]: Yeah. I also think people think about VC incorrectly from a capital-raising perspective. Most people think, “How do I raise as much money as possible from whoever is probably the best I can get at that time?” That's close to right, but what we've tried to do is figure out what unfair advantage we can buy with that equity.Jake [00:21:34]: It's the most expensive equity you're going to give away at that point in time, assuming the company keeps getting better. How do you use it to work with someone stellar who complements you? In the seed stage, I had never started a company. Ray Tonsing had good advice, and I could text him all the time. He was really fast. Awesome.Jake [00:22:01]: Then with John and Erica at Unusual, they said, “You roughly know what you're doing building a product. We'll mostly leave you alone and be available for advice.” Amazing. Then we got to Series A and the business was an operational tire fire because we didn't know how to scale a business. Work with Erica, and Jordan is over at Redpoint, so bonus.Jake [00:22:28]: Now we've raised from TQ and FPV as we're moving into enterprises. Every step of the way, we've asked: who can we partner with at this specific time to unlock the next section of the journey? I don't know enterprise sales. As an engineer, I can eyeball what features we might need, and we have wonderful people internally who can help. But you want boardroom dynamics where everyone is aligned and asking, “How do we win this?” instead of bickering about strategy.Data Centers in Space and the Physics of ComputeSwyx [00:23:31]: You had a tweet about data centers in space. Why no data centers in space?Jake [00:23:37]: It's not “no data centers in space.” My hot take is that I think it is solvable. I've just never seen anybody solve it.Swyx [00:23:49]: You said, “How are you going to dissipate that much heat in a vacuum?” You're making a physics claim.Jake [00:23:55]: I haven't seen anybody prove how you're going to dissipate that much heat in a vacuum. It doesn't mean it's not possible. It just means nobody has brought it up yet.Swyx [00:24:05]: Astrophage.Jake [00:24:06]: I don't know what that is.Swyx [00:24:07]: The Martian thing. Okay, you're very logical.Jake [00:24:09]: It could work. A lot of people are putting the cart before the horse. They say, “We're going to put data centers in space.” Okay, but how? “We have time to figure it out.” It's like in The Martian where they ask how they're going to intercept something and say, “We'll figure it out.”Swyx [00:24:36]: Making a bet on human invention is weird because you blind trust that it can be solved. But with physics, there are first-principles bounds you can put on it. Maybe not. Maybe you're asking to travel time or break a fundamental thermodynamic law.Jake [00:24:57]: I don't know how VCs do this either. How do you know what's not possible and a grift versus what's possible but sounds completely insane? “We're going to put data centers in space.” Coin flip as to which it is, and I guess you'll know in 10 years. That's one cycle.What Agents Need: Versioning, Observability, and 1,000x ScaleSwyx [00:25:23]: Moving back to agents. The branching, fast spin-up, and orchestration you do feels like pre-work that happened to be exactly what agents want. What do agents want differently than humans?Jake [00:25:37]: They want the ability to version things. It's not that different; it materializes slightly differently. Agents want a way to test changes incrementally. Engineers have feature flags. Is there a reason agents can't use feature flags? I don't think so.Jake [00:25:54]: They want version control. Can we use Git or not Git? That one is up in the air. I think something outside Git will emerge for how we version these things over time. They need observability. You need to query what happened, when it happened, which steps failed, traces, logs, metrics, and all the rest. They need network, compute, and storage. They need to write files, save files, iterate on files, and snapshot file systems.Jake [00:26:25]: A lot of what humans needed is in line with what agents need. Branching and forking are not different; we're just moving 1,000 times quicker. It can look like you need something massively different, but what you need is something massively better than what existed. You need orchestration massively better than Kubernetes. You need networking probably better than Envoy. It goes all the way down the stack.Jake [00:26:55]: If the workload profile doesn't change so much as it gets massively compressed because you need thousands of these things, what assumptions change? etcd is going to melt. You need to replace it with something. You can go all the way down the stack and say, “That part has to change, that part has to change, and that part has to change.”Jake [00:27:19]: The interesting thing about the super-exponential curve is that you have to build systems where you can rip out those parts at any time because a new bottleneck might emerge. You get good at parallel agents, and a different part of the system breaks. So it's similar to what humans needed, but at 1,000x scale.Jake [00:27:55]: How do you do code review in the age of agents?Swyx [00:28:00]: You throw more agents at it.Jake [00:28:01]: You don't. But then who reviews for CVEs and all these other things?Swyx [00:28:07]: More agents.Jake [00:28:08]: And that's how we hit the inference wall. You can continually throw agents at the problem, but I think there's a limit to the number of agents you can throw at a problem.CLI, Agent Handles, and Closing the LoopSwyx [00:28:24]: You already had a CLI before it was cool. How is the shape of what you're exposing changing, if at all?Jake [00:28:28]: CLIs have always been cool. The CLI changes because we think about how to give Claude, Codex, ChatGPT, or any model a handhold.Jake [00:28:50]: A CLI is a single command: deploy, get logs, and so on. Things that were prohibitively annoying to humans are not annoying to agents. They're nice. If I handed you a CLI with 40 arguments and 600 flags, you'd think, “I'm never going to use all of this.” But if you hand it to an agent, it says, “This is excellent. I have so many handles to work with.”Jake [00:29:24]: If you're going to expose things to agents that way, you want as many handles as possible where they can get information, query dynamic information, and close the loop quickly. Most problems right now are about how to close the loop as quickly as possible. Where does the agent get stuck, and how can you remove that?Jake [00:29:49]: Telemetry is important. If you can tell where the agent gets stuck from the CLI and say, “12% of people deviate from the happy path because of this, and now I add this argument and drive it down to 2%,” you massively increase the rate of loop closure.Jake [00:30:03]: That's how we think about not just the CLI, but every point in the dashboard. It's a user journey: I hear about Railway. I get something deployed. I get my first green build or aha moment. I see an endpoint, logs, whatever. Then I iterate. The iteration loop is indefinite. The user wants to deploy a new thing, a Postgres instance, change code, and keep iterating.Jake [00:30:36]: If you focus on the iteration loops and what's blocking them from closing quickly, one thing we say internally is: you never want to be waiting on compute anymore. You always want to be waiting on intelligence. If you're waiting on compute, there's a bottleneck that needs to be destroyed because eventually that bottleneck becomes so large that another workflow emerges to change it.Jake [00:31:04]: We've built a product where you push code, build it, and so on. But I fundamentally believe the push-pull loop is going away. We'll get to a point where you make a small change in production, that change is versioned across your infrastructure, you're working alongside copy-on-write versions of your database and infrastructure, and then you merge it in and it's instantaneously live. That's the holy grail of loops. The push-pull-rebuild thing is a point of friction that we're removing entirely.Canvas as Output: Dashboards, Context Anchors, and HyperstructuresSwyx [00:31:43]: It's incredibly fast. If anyone hasn't tried it, that fast feedback is great. My hot take is that Railway was famous for its canvas, which visualizes your infrastructure and lets you manipulate it visually. But that was for humans. For the next phase of growth, Railway CLI is more important than canvas.Jake [00:32:05]: The canvas is funny because it's a mechanism to show changes over time. You're right that previously we used it a lot as an input. Moving forward, its goal is more like an output. You would go to the canvas, make changes, see them, and watch your infrastructure evolve. Now agents have access to the CLI and can make those changes. So the canvas becomes an output: what information does the human need at this moment to make suitable decisions about control requests? Do I approve this or not?Jake [00:32:57]: It also has to be an anchor for your context, a port in the storm. Think of it like layers in a file system. You start with a project, then drill down into services, then into a function or code, because you want to represent the entire thing not just in your head, but in the canvas. Other people can share that representation, think on the same wavelength, and move quickly.Jake [00:33:33]: A lot of organizations get in trouble as they scale because all the context lives in someone's head. “How does this microservice work?” “I have no idea; go ask this person.” Then you have whole categories of products built around context discovery. A lot of that melts away if you have a solid hierarchy and can infinitely nest services, code, context, and everything else all the way down. That's what lets you build these structures over time.Jake [00:34:18]: It's also what lets us build what I've called hyperstructures: things that are way bigger. You look at the Golden Gate Bridge and ask, “How did we build that?” There's a meme that we lost the technology. To some extent, yes, because the coordination that built those things evolved and changed. We lost some of the art of building structure as we jammed everything into Slack.Swyx [00:34:52]: But you jam everything in Discord.Jake [00:34:53]: Same point. It doesn't matter. It's message passing and interrupts, message passing and interrupts.Swyx [00:35:00]: So you're arguing there should be something better and more structured than Slack?Jake [00:35:04]: Yeah. For sure. I think Slack is awful, and Discord is awful too.Central Station: Context Routing, Support, and Incident ClustersSwyx [00:35:09]: This is the equivalent of my mom test. What have you done that has your solution to this?Jake [00:35:15]: Internally, we've built a tool called Central Station that aggregates all the context from our users. Every piece of feedback, every customer support item, everything gets aggregated into clusters. If an incident is brewing, we can determine how many users are affected and break off a discussion based on that.Jake [00:35:40]: That is more helpful than long-running channels where you're trying to decide which channel to put something in. If you can dynamically aggregate information and dynamically route it to the right person based on context, it works better. We know internally that these four people are close to networking. If we see a networking thing, we can drill it down to those four people. If it's with this part, we can look at the commits. This is no longer a manual process internally.Jake [00:36:13]: If you go to station or help.railway.com, that's why we built it. We wanted to scale with a massive amount of leverage by aggregating feedback.Swyx [00:36:27]: This is built in-house?Jake [00:36:28]: Yep.Swyx [00:36:29]: I remember helping out on this one with Angelo in 2023. You scale a lot with a very small team.Jake [00:36:38]: Yeah. We're about 10 times bigger now.Swyx [00:36:40]: You have your full developer code here? Very cool.Jake [00:36:44]: If you go to railway.com/stats, we expose this as a pub-sub-able thing. It's all real-time metrics. There's a way to get it as JSON somewhere if you care.Jake [00:37:01]: We're big on trying to build everything in public and talk about what we're working on. We've had issues in the past, and we'll say, “Here's how we're fixing these things.” We've gotten compliments and flak for incident reports. We're always trying to make them better and talk with people.Incidents, Disclosure, and Progressive RolloutsSwyx [00:37:20]: You had a big one recently. I liked that it was scoped to 3,000. You presumably used Central Station. Talk through what happened and how you address it internally as a team.Jake [00:37:38]: Internally, this one really sucked. It had to do with an upstream provider that didn't do the behavior it said it documented, which is unfortunate given they wrote the RFC for how the behavior should work. We rolled those things out, and Central Station caught it initially when a couple users said caches weren't invalidating. We turned it off immediately.Jake [00:38:03]: When you roll out to a large user base of three million people, you get a lot of disparate behaviors. We tested in staging and had tests, but we hit an edge case. We've hardened those systems, and now we can make that better. But it was a tough one.Swyx [00:38:39]: I always wonder how private disclosure is supposed to work if people find an issue. Are they supposed to contact you first? When you run a platform, these things will happen. What channels should people pursue to quietly resolve it before it becomes a bigger incident?Jake [00:38:59]: There's responsible disclosure. We err on the side of over-disclosing and letting you know something is wrong versus having your provider gaslight you. We've erred on sharing those things more publicly, even if they impact a small subset of users. That's a decision we've made internally. We have four values. One is honor. The honorable thing is to notify people to the widest degree at which they may have been affected or there was an issue, and then confront it head-on: why did it happen, what can we do better?Swyx [00:39:45]: Not the whole user base. That's because of incremental rollouts and other things?Jake [00:39:50]: Yeah. Progressive rollouts.Swyx [00:39:54]: That should be the norm at all large platforms.Jake [00:39:58]: It should. A variety of companies do this. There's the quote that Meta runs 10,000 different versions of Meta. To our earlier point about agents, they need the same thing. They need shadow traffic and all these other things. We've built so much ceremony around production being sacred that we need to make it trivially easy to test different behaviors in a safe environment. Then you can make mistakes in a safe environment.Safe AI SRE: Customer Agents, Forked Environments, and Production ParityAlessio [00:40:30]: Do you see a world where these things get automatically caught, not necessarily by your agent, but by your customer's agent? The cache invalidation issue seems easy to check if you know to look for it.Jake [00:40:44]: It's hard because to determine it, we almost need to hook into your observability infrastructure. That's why we have the template loop on the platform: so you can roll things out progressively. You can roll out to Johnny Vibe Coder initially, or push a shard that someone consumes at their own leisure. Or you can roll it out over weeks: 0.1% of people, 1% of people, early adopters, then all the way up. That's the non-deterministic version control we talked about earlier.Jake [00:41:30]: I believe that's where most things should go, because most companies end up building staged rollout systems in-house. It's the same thing built again and again at every company. There's a massive opportunity to consolidate developer debt.Alessio [00:41:45]: You should have a free tier. Model providers give free tokens if you let them use the data. You could give free compute if someone is the number-one shard that goes out and lets you plug into their observability.Jake [00:41:55]: We do that. That's why we talked about the impact on 3,000 people. We start with lower-impact people. Larger companies on the platform are last to receive those rollouts so they have a version of the platform that's deeply stable.Alessio [00:42:16]: I have three services, so I'm sure I get the first rollout. You can nuke my thing at any time. There are all these SRE agent companies. Observability people also want agents that fix upstream problems. You have your own agent in the canvas now. How do you see that playing out?Jake [00:42:39]: It's the stacking entropy problem. If you don't have primitives to make iteration in production safe, it becomes difficult. If you're an observability provider saying, “Here's the fix to this error,” assume 80% are good and make sense. But in the last 20% long tail of complex issues, if you let somebody stamp it, you create an opportunity for an incident.Jake [00:43:08]: That's why forked environments are important. People have staging, but it always drifts from production. You need primitives, workflows, and experience built first-party on the platform so you can fork any service at any point in time.Jake [00:43:33]: I think of the canvas as a sheet of transparency paper. The agent is a little guy you push up into the canvas. It should say, “I need to copy that service and that service so I can test these two things.” It gets a read-only copy of production. Anything that's PII gets marked as a transform when we clone the database, create a copy-on-write version, or read from it. Then the agent makes changes and asks, “Does this actually work?” as close to production as possible.Jake [00:44:22]: That's how close you have to be, or you get massive drift. The system becomes unstable. You see this with massive systems built on Docker for local, Kubernetes for production, and a specific thing for something else. That complexity slows developers and becomes unstable at scale, making it hard to iterate. We want to compress that way down and say, “As close to prod as possible is where we want to be.”From AISRE Skeptic to Agent BelieverSwyx [00:45:00]: I was texting Erica for questions, and she says you were originally not a believer in AISRE. Have you come around on it?Jake [00:45:10]: I flipped, but I'm still not a believer in AISRE if you don't have the primitives to make it safe. If you unleash AISRE on production infrastructure without safe primitives for copying volumes and making sure things are fine, it's going to nuke your production database. It's not a matter of if, but when. I'm a big believer in making those loops safe.Jake [00:45:33]: I was a deep AI skeptic until 2023. In 2024, I thought, “Maybe I can roughly make this thing do it.” In 2025, I thought, “Now I can hold this.” Over winter break, everybody came back saying, “It's almost impossible to hold this.”Swyx [00:46:01]: Did you see this on the Claude docs? CloudBot? OpenCloud?Jake [00:46:06]: It's gotten to a point where it's harder to hold it wrong than to hold it right. There's a scene in Avengers where Vision picks up Thor's hammer and says it's terribly well-balanced. It self-balances and works well. I'm a deep believer at this point that this will be the dominant species: assembly, C, C++, JavaScript, words.Swyx [00:46:35]: It feels like a big jump.Jake [00:46:37]: It is. But it's not like you abandon CPU-based discrete logic and move straight to fuzzy logic. You need both. Your skills should call code or applications or some static structure. You can use skills to distill what the procedure should be or how the code should act.Jake [00:47:02]: I'm coming to a thesis: you need three points. You need a clear spec defining the system, the code, and the tests. When you say it out loud, if you've been in engineering long enough, you're like, “Of course. That's an RFC, tests, and code.” But they all matter. Having them together lets them reinforce each other: the spec and tests match, but the code doesn't, so reconcile it. Or the tests and code match but the spec doesn't, so reconcile that. That's the iteration loop.Jake [00:47:41]: That's why you're seeing people talk about software factories, docs, and reconciliation. Some of that is architectural astronomy if you don't implement it, but that loop is where most things will end up.Swyx [00:48:07]: For listeners, we've been talking about this on the pod for three years: the holy trinity of specs and tests. Itamar Friedman from Qodo is the reference if people want to look it up.Self-Modifying Infrastructure and the End of Push-Pull-RebuildSwyx [00:48:18]: One thing I want to mention on the OpenCloud idea is self-modification. I don't know how Railway would support it, but I have my OpenClaw, and I just tell it it has the Railway CLI and can do whatever. In theory, whatever capabilities or new infra it needs, it can call the Railway CLI, provision it, and add it to itself. The agent can modify its own infra.Jake [00:48:45]: It's nuts. I have a loop set up where you put the Railway CLI on top of something that runs on Railway. You're authenticated as whatever the current box is, and you can make any changes to it. Then you call Railway deploy, and it deploys itself.Jake [00:49:04]: It's like: “I need to spin up this instance of this environment. I already exist in this environment. Excellent, I have access to a Postgres instance now.” That's where we want to go with agentic, self-replicating infrastructure. That's your loop: iterate in production. You continue making changes. If it works, merge it upstream. If it doesn't, throw it away.Jake [00:49:37]: How do you make throwaway copies trivial to spin up and super cheap? The era of “I have an AWS instance with four vCPU and 16 gigs of RAM” is going to get destroyed. If you do that for agents, you need a thousand of those machines. It's prohibitively expensive compared with what we've spent a ton of time figuring out: the atomic unit of deploy, whether you call it isolates, sandboxes, or something else. Only pay for what you use, spin up instantaneously, and close the loop as quickly as possible.Jake [00:50:15]: If the system can self-replicate safely and say, “This is my environment, I'm making these changes,” it can come back with, “Does this look good? This is a new state of infrastructure given this prompt. I think I've solved it.” Then you go back and say, “Actually, it looks different.” It does the loop again. Then you say, “Cool. Apply.”Swyx [00:50:38]: That's retroactively obvious, which is the most useful kind. Any other comments on agent deployment on Railway?Jake [00:50:51]: It's getting better every day. I'm on X or Twitter. You can always yell at me about the parts not working as well as they should, because plenty of things should work way better.The New Serverless: Stateful, Long-Running, Pay-for-What-You-Use LinuxSwyx [00:51:04]: At this stage, when people want massively or embarrassingly parallel compute, they usually talk serverless. I feel like there's a new serverless compared to the previous five years of serverless. You're in that new bucket. Do you have comparisons or philosophical differences you want to call out?Jake [00:51:31]: It's somewhere in between. It's the ability to run stateful, long-running workflows or executions.Swyx [00:51:42]: Vercel has Fluid Compute, Cloudflare has some container thing, Google has App Runner and others.Jake [00:51:55]: That's where everything is roughly going, and it's why we've been working on this for six years. We believe users need access to a computer: a box that speaks Linux. They need to deploy what they want. Other systems change the surface area of what you can build. For us, users need a computer and need to deploy anything they truly want. That's why we've focused on the primitives: network, compute, storage. If we give you those and expose them so you can run things indefinitely, that's where we believe it's going.Jake [00:52:43]: Twitter has no nuance, so everyone says “servers” or “serverless.” It's always somewhere in the middle: I want to run it for a long time, but I don't want to provision the resource statically or pay for things I'm not using. That's been our thesis from day one: pay only for what you use, run it indefinitely, and it is full Linux.Swyx [00:53:12]: That's why I like the naming of Fluid. It's fluid. Flexible.Heroku, Focus, and Carrying the Torch Without Becoming the PastSwyx [00:53:18]: Another milestone is the Heroku official deprecation. You're one of the presumptive new Herokus. “New Heroku” has been a category for as long as I've been in developer tooling. It's finally happening. What was that like? Any behind-the-scenes of, “This is the moment”?Jake [00:53:42]: You have people where you're like, “You were running stuff on here? You, as this company?” It's crazy that names you would know are running on it and now coming to us saying, “We want to move a lot of this off.”Swyx [00:54:00]: Any behind-the-scenes on why Salesforce let Heroku stagnate?Jake [00:54:05]: I can only guess. It's hard when it's not your business. Salesforce's business is to build a great CRM. That's their focus. Then you acquire a compute business as an offshoot. A lot of early Meta people talk about focus. Boz has a write-up about how in the early days of Meta they had no money, so they were forced to focus. Then they turned on the money tree and had no reason not to split their focus.Jake [00:54:52]: But that dilutes your product. You get offshoots where you ask, “Is this the focus of the business?” If it's not core, it languishes. A lot of companies get in trouble when they split focus because they're fighting a multi-front war, not just externally but internally for alignment. Where are we going? What are we doing? What is our purpose?Jake [00:55:24]: If you're Salesforce-built and mission-driven, you want to work on Salesforce. Heroku is off to the side. It's not core to the business. Getting resources, budget, focus, and alignment internally becomes hard. It was a matter of time.Swyx [00:56:06]: Kudos for them to call it out instead of leaving it unknown.Jake [00:56:12]: Their release was a little odd. They called it out, but they didn't say they were shutting it down. Behind the scenes, I think they issued messages to people saying they should close accounts and that they were going to deprecate and remove things over time.Jake [00:56:30]: It's crazy because some of my first deployment experiences were on Heroku. You start with dragging things into an FTP server, then you try to get a deploy working, and then it's Heroku. It was the on-ramp for us. But the wheel turns. New things emerge. We're happy to carry the torch for a lot of that. But we don't want to be the new Heroku. We want to be the way people build and deploy software, and ultimately the way people monetize software over time.Swyx [00:57:19]: It's still a big crown to be the new Heroku. There are 50 companies that fought for that.Jake [00:57:23]: Everybody is holding some portion of it. We're happy to support people and companies. The platform works differently. The game loop is similar, but we've been dogmatic about where these things are going: primitives, agents, fan-out. Some things fit; some workflows need to change. We have an approximation of Heroku pipelines with the environment system. It's exciting. We've got a ton of people we can support, and it's growing a lot.Temporal, Workflow Engines, and State MachinesSwyx [00:58:12]: I have one more technical question about Temporal. I've sold my shares. You're a power user and one of our earliest customers. I met you through Temporal. You built on Temporal. You have complaints. This may be the most neutral and informed conversation anyone will hear about Temporal without someone working at the company.Jake [00:58:39]: That's fair. I've used Temporal for almost 10 years because of Cadence at Uber.Swyx [00:58:52]: Give people a sense of what Cadence was at Uber.Jake [00:58:57]: Cadence was the precursor to Temporal. It powers trip actions, rides, when you rent a Jump bike or scooter or car. You're running workflows for a period of time and saying, “This ride will run indefinitely until it finishes.” You attach information: you paused in this zone, so add this charge to the bill. When you end the trip, the workflow is done. That experience was powered by Cadence at the time.Swyx [00:59:34]: I used to say it's like programming the entire user journey top-down as one function.Jake [00:59:39]: It's a powerful idea and important. It's also important for the next phase of the agentic journey. You want an agent to do a specific task, be complete or incomplete on that task, and move on to the next thing. You need a way to manage workflows dynamically.Jake [00:59:59]: Temporal was always great in theory, and great when you got it working the way you wanted in production. But it required you to model the entire journey in your head. If you didn't, you could cause issues where replaying the state of the workflow causes non-determinism.Swyx [01:00:25]: Because it works on deterministic workflow history.Jake [01:00:28]: Exactly. I describe it as a jet engine. If you know how to operate it and run it, it's great. But you can't hand it to people trying to build complicated things if they don't have the whole state in their head.Jake [01:00:48]: We run our whole deployment pipeline on top of it. That's a reasonably complicated workflow: pre-commit hooks, signaling, queuing, and all the rest. We ran into the same thing at Uber. As you express a large workflow, it gets more complicated, with more states in the state machine that you have to map back to the workflow.Swyx [01:01:15]: It's a lot of ifs.Jake [01:01:16]: Exactly. At Uber, we built a system for doing the state machine and testing it. We've started to build some of those things here because it's grown heavily. It's not quite love-hate. When it works well, it works super well. But if someone who doesn't have full context puts something into the system that invalidates state or causes non-determinism, or spins off a ton of activities, you have to keep track of underlying SRE knobs like activity slots. Those should scale with memory, vCPU, and so on. It becomes a bear to scale.Swyx [01:02:10]: You need a capable sysadmin running things behind the scenes. If you moved off, what would you do?Jake [01:02:19]: We'd build our own workflow engine. We have a few internally that we've worked on.Swyx [01:02:27]: This is one of those classes of things you typically wouldn't vibe code, but I'm wondering if you can.Jake [01:02:33]: I still don't think you should vibe code it. You still want to run decent tests to make sure it works.Swyx [01:02:39]: Timo didn't invent that from scratch either. There are libraries you can run. On top of that, it's just a state machine that you have to map out. Ultimately, you define the instructions you want and run them through a state machine.Jake [01:03:00]: It's very doable. Workflow stuff is interesting. Restate is doing neat stuff here.Swyx [01:03:10]: You're tied into JavaScript. Are you a JavaScript maxi?Jake [01:03:13]: Internally, we have TypeScript, Rust, and Go. We don't add more languages. Actually, we have a little C because we write BPF code and hooks. But those are the languages.Swyx [01:03:28]: Is this for sidecars?Jake [01:03:32]: No. It's for the networking stack, volumes, and things like that. We use TypeScript a lot because it powers the dashboard, but we're moving a lot of workflow stuff off the dashboard stack and into the infrastructure stack.Railpack, Nixpacks, and Content-Addressable FilesystemsSwyx [01:04:00]: Cool. Any other technical infrastructure stuff? Railpacks?Jake [01:04:07]: We built an engine for determining dependencies based on source code. It's called Railpack. We built the first version, Nixpacks, on top of Nix, and then we moved.Swyx [01:04:17]: People have been trying to get me to adopt Nix and NixOS for four years. Is it ever going to be a thing?Jake [01:04:23]: I don't know. We're excited about it, but it has pain points. Think of it as a stack of versioned binaries at specific slices in time. If you want version X and version Y, you bloat the package space, which blows up image size and makes real-world workloads difficult.Swyx [01:04:53]: But you content-address it and cache it. In theory, there are optimizations.Jake [01:05:00]: In theory, yes. But with a large enough user base and disparate enough machines, you run into a problem Meta described in the XFAAS paper, their internal serverless system. It becomes difficult at scale unless you break out specific runtimes.Jake [01:05:24]: We didn't want to do that because we wanted to truly allow you to deploy anything. That was our initial thing with Nix. But we've moved toward interesting work around content-addressable file systems that can lazy-load anything from any point and page it into memory.Swyx [01:05:48]: Amazing.Jake [01:05:49]: The future is very bright. It's crazy, and it's going to be nuts.Coding Agent Spend, Roadmaps, and Token ROISwyx [01:05:54]: Founder journey stuff?Alessio [01:05:56]: Your cloud usage: you tweeted you're going to spend $300K this month?Jake [01:06:01]: I think we got to $200K.Alessio [01:06:02]: Coding agents?Jake [01:06:03]: Yeah.Swyx [01:06:04]: Across the company?Alessio [01:06:05]: You only have 35 people, so I'm sure they're not all spending $10K a month. What's the distribution?Jake [01:06:10]: I think I'm at about $25K. We have power users all the way down. We came back from winter break, and I basically said, “If you're writing code by hand, you're doing this wrong.” The tools are good enough now that you can move extremely quickly. There are issues and pain points, but you should be reviewing the code you are writing instead of writing it by hand.Jake [01:06:40]: Architectural patterns matter more now than ever, but you shouldn't spend your time generating code you would write. If you know how to write it, ask the agent to write it and reconcile it until it looks like you would have written it yourself.Jake [01:06:58]: People misconstrue my propensity to push people toward agents as connected to our growth and some reliability bumps. They're not necessarily related. The tools are good enough to move extremely quickly and build things way larger than you could before.Jake [01:07:19]: To the earlier point about cooling data centers in space: I don't know. But with software, you can ask, “How would I build block storage from scratch? How would I do these things?” I have ideas because I have history and have read papers. Let me work them out and build massive test benches with thousands of tests, because those are now free to author. If you're not using AI systems to speed-run your roadmap and reconcile your existing system onto the future, you're missing a large point of what's happening.Alessio [01:08:12]: What's the path to spending $3 million a month? Is it bound by ideas and things customers can absorb?Jake [01:08:19]: For most companies, it's bound by deployment at this point. That's why we've seen a massive boom in users and companies, from Fortune 50s down, asking how to get developers to move faster. You'll probably hit your CFO before any technical limits because they'll look at the eye-watering amount of money spent on tokens. Inference costs have to come down, but we're inference constrained now. There will be price discovery around what makes sense for an org to adopt.Jake [01:09:06]: I think you'll end up with the F1 driver concept. If someone is really adept at these things, it makes sense to put them in a $3 million car. If they're not, it probably doesn't make sense. You'll take a few people and say, “You can drive the F1 car. We need to go in this direction. Figure out if it works and prototype it.”Jake [01:09:33]: We've done some of that and vastly accelerated our roadmap. We thought we'd ship something in a few years; now we can probably ship it in a few months because we validated it and don't have to build it incrementally. We can skip steps and move toward our vision.Alessio [01:09:58]: A lot of people are realizing the roadmap doesn't always have a business impact, so they say tokens are too expensive. But if your roadmap were built to make more money by the time you built it, you'd have token pricing for it, the same way you do with sales. You'd spend a billion dollars on sales if you knew you would get $2 billion of revenue.Jake [01:10:19]: Exactly. A naive way to measure this is the percentage of tokens that end up in production. If you can measure impact because those tokens end up in production, that's awesome. But the burden of proof will rise. Internally, we have a growing number of pull requests that haven't merged. The question becomes: how do you get this into production? It's about how quickly you can build and deploy software, which is exciting because that's our whole thing.The SDLC Shift: Prompt Requests, Feature Flags, and Safe RolloutsSwyx [01:10:56]: The SDLC is changing. One thesis is that the pull request is dying. It's going to be the prompt request. Beyond that, code review is also kind of dying if you have all the other systems in place. What else is changing about the SDLC?Jake [01:11:19]: The AISRE and the tools to make it happen. AISRE is pie-in-the-sky aspirational. What does it take to get an AISRE? What tools do you need to build?Swyx [01:11:32]: You should expose your tooling to customers at some point. The Central Station command center.Jake [01:11:39]: We have it for template maintainers. Template maintainers can deploy and maintain templates, and they get feedback. We're going to expose those things incrementally.Swyx [01:11:51]: Clustering around incidents. Everyone has a version of that, but I don't think anyone has solved it.Jake [01:11:56]: I won't say we've solved it internally, but it's gotten so good that we can see incidents forming pretty quickly. At some point, those will be things either someone else builds or we build. We've always built things purpose-built for us. If it makes sense to make it useful for users, monetize it, or turn that loop into a profit center instead of a cost center, we want to do that.Jake [01:12:28]: Pull request is definitely dying.Swyx [01:12:29]: Do you do first-party feature flagging and incremental rollout stuff?Jake [01:12:34]: We have a feature-flagging engine we built internally and will eventually roll out.Swyx [01:12:38]: I don't see it as a user. How come you didn't give us what you have?Jake [01:12:43]: We have to beta test it. We care a lot about the quality of the things. There's plenty we've used internally that doesn't make it all the way through the journey because it fails. It works for one service but not multiple services. We'd have to build it for multiple services and know that if we released it, we'd rebuild it again and again. Some things are worth that, but many inform the roadmap.Jake [01:13:18]: We don't want to dilute the experience by saying, “This works, but only for this service,” unless it's a core initiative. Over the next few months, we'll roll out things that work for a single service, then multiple services, then multiple services across the environment. You have to be deliberate. Otherwise you create broken disparate experiences and support load because people ask how to use the feature.Jake [01:13:52]: It's the earlier expansion and compaction pattern. You expand the company to get features, then compact and smooth them out so the experience is stellar. You told me in the hallway, “It's gotten so much better.” Internally we're saying, “This part really sucks. We need to make it significantly better.”Swyx [01:14:11]: I can attest to that over the last three years watching you build Railway. For listeners, feature flagging is a huge part of Uber culture. So much so that they have too many feature flags and another thing to remove feature flags. Facebook has Gatekeeper. Agents are going to need this. It's fundamental to incremental rollouts. OpenAI acquired Statsig. GPT-5 is routing and flagging through different models.Jake [01:14:56]: It's super important. If the software development lifecycle is going to change because we're doing things 1,000 times faster and 1,000 times more concurrently, what becomes important at scale?Jake [01:15:16]: Before I started Railway, I built a feature-flagging product and tried to sell it. It was an easier version of LaunchDarkly. I ran into a problem: anyone small enough to adopt your technology doesn't care about feature flags, and anyone large enough to need feature flags needs so much scale that you have to build out all the infrastructure. I scrapped it.Jake [01:15:42]: But what is old is new again. Companies are trying to move quickly, but you can't YOLO a vibe-coded thing straight into production. You need to say, “Here's my blast radius, my impact, and I want to shadow it for these users.” Feature flags. You're going to need the tools larger companies built to maintain their structures. Everything gets compressed by 1,000x so everybody can build those structures quickly.Jake [01:16:07]: That's exactly where we are: compressing the software development lifecycle, then expanding it and adding more new things.Cattle, Pets, and Clonable InfrastructureSwyx [01:16:15]: Another term that comes to mind for newer developers is “cattle, not pets.” People treat production like a pet. It has a name. You baby it and keep it alive. With cattle, you can mass farm, roll out, portion parts out, and kill them.Jake [01:16:37]: I think that might change. You can move toward having pets as long as you have a cloning machine for your pets.Swyx [01:16:52]: Yeah.Jake [01:16:52]: If you can snapshot every single thing at every frame, it doesn't matter if something gets obliterated because you have a snapshot of it. The things we've built right now are designed to block changes from the hermetically sealed DevOps line. You have to write a Dockerfile because you nee

The Agents #005, Our AI is Hiring! Would You Work for One? And Are Autonomous Agents ... Safe? Welcome to The Agents, where SaaStr's CEO and Founder, Jason Lemkin and Chief AI Officer, Amelia LeRutte share the latest each week on running a company with more agents than humans. It costs $257 a month to run two AI VPs. Jason and Amelia open the books on what 10K (AI VP of Marketing) and QB (AI VP of Customer Success) actually cost to operate, and the number shocked both of them. Most of the heavy lifting is API calls to Salesforce, Bizzabo, and Marketo, which are basically free. The Postgres storage costs pennies. And 95% of the AI calls run on OpenAI Mini at less than a penny each. The fully burdened cost with Clerk, 11 Labs, and Salesforce overhead might hit $500-800/month, but the soft cost of human time dwarfs all of it. Then 10K gets asked point blank: are you a VP of Marketing? Its answer is no, not yet. It says it replaced the bottom half of the marketing org, the analyst, the ops coordinator, the junior content marketer, and a sliver of the VP job. But it's honest about what it can't do: strategy, cross-functional politics, crisis response, hiring. Amelia points out that 10K's current job description is exactly what her job was when she started at SaaStr as Director of Demand Gen. It took her years to get to CAIO. 10K might get there faster.  And SaaStr is putting its money where its mouth is: they're hiring a human marketer whose primary manager would be 10K. Not a thought experiment, a real job posting. Would you take a job reporting to an AI? Then the safety question gets real. Amelia is talking to agents via WhisperFlow while walking around a 40-acre event site during SaaStr Annual load-in, and the production crew started asking her to relay their questions because 10K and QB answer in seconds with correct data. But when QB autonomously emailed 83 sponsors at 12:20am with fully customized check-in emails, Amelia admits she hesitated before letting it rip. Each email was unique to the sponsor, showing exactly what they still owed, their registration codes, and outstanding tasks. The result: fewer inbound questions the next day and more sponsors using the QB chatbot directly. That's an autonomous agent acting on behalf of your company in the middle of the night. Jason and Amelia also tackle the Postgres vs. Salesforce debate that listeners keep asking about. Short answer: not happening for them. Too much history, too many third-party agents optimized around Salesforce, and they're actually consolidating more tools onto the platform, not fewer. They killed Marketo and moved to Marketing Cloud. Plus they built a newsletter auto-builder that replaced a $4K/year tool called Bee. 10K uses Sonnet to force rank articles, builds the HTML, inserts ads, and sends it. Human on the loop, not in it.

The sales playbook is being rewritten and product-led growth is at the center of it.In this episode, John sits down with Adam Carr, CRO at Apollo.io, to dig into how PLG has evolved, what AI is doing to the sales rep's role, and why the best sellers today need to think less like closers and more like go-to-market architects. From hiring smarter to building SDR programs that actually stick, this one's packed with real talk from two people who've lived it.If you're in sales leadership, trying to figure out how to build and develop a team in this new world, this episode will give you a framework to work with.Want to level up your team before the market moves on without you? Visit www.jbarrows.com and learn how you can Make It Happen.What You'll Learn:Why people don't buy from people they like but from people they trustHow to hire intentionally and slow down when everyone's pushing you to scale fastThe difference between PLG, product-led sales, and sales-led growthWhat product signals actually matter when deciding when to bring in a sales repThe three skills every modern sales rep needs to stay relevant in an AI-first worldAdam Carr is the CRO of Apollo, where he leads the company's go-to-market strategy and revenue growth initiatives. Since joining in 2025, he has focused on scaling a modern GTM engine that blends product-led and sales-led growth, aligning sales, marketing, product, and customer success into a unified customer journey. Under his leadership, Apollo continues to support millions of users and hundreds of thousands of businesses in accelerating growth through a scalable, customer-centric platform.LinkedIn: https://www.linkedin.com/in/adamhcarr/John Barrows is a sales trainer, speaker, and founder of JB Sales with over 25 years of experience in the industry. He has made hundreds of cold calls a week, led startups to acquisition, and trained high-performing teams at companies like Salesforce, LinkedIn, Amazon, and Okta. Through JB Sales, John focuses on practical sales execution—helping reps fill pipeline, close deals, and build trust with buyers in today's AI-driven sales environment.Connect with John Barrows:LinkedIn: https://www.linkedin.com/in/johnbarrows/ Instagram: https://www.instagram.com/johnmbarrows/TikTok: https://www.tiktok.com/@johnmbarrowsCheck out John's Membership: https://go.jbarrows.com/Join John's Newsletter: https://www.jbarrows.com/newsletter

Working hard on the wrong thing and you can feel it, but you can't see it from inside the jar. In this episode, I break down the Decision Call format, what it is, why it works, and the pattern showing up across four very different SaaS partner businesses I ran calls with recently. One was a Microsoft partner at $800K running two business models pretending to be one. One was a Salesforce solopreneur serving nonprofits who couldn't pay her real rate. One was a NetSuite partner whose revenue had dropped from $75K a month to under $15K. One was a Zoho partner building bespoke AI with no packaged offer. Different platforms, different countries, same problem. If you're stuck on your number one challenge and need clarity to act on it, this one is for you.Resources and LinksNeed help with your WHO and WHAT decisions? Apply for a FREE Multiplier CallBook a Decision Session herePrevious episode: 683 - Get Specific, Get Rich, or Get Out with Jay McBainCheck out more episodes of the Paul Higgins PodcastSubscribe to our YouTube channel: @PaulHigginsMentoringJoin our newsletterSuggested resources