Hospitals are bleeding $260 billion a year to denied insurance claims — and AI is making it worse. One YC founder decided to fight back, using AI to beat insurers at their own game. In this episode, we dive deep into how Aegis, a Y Combinator startup, is using AI agents to help healthcare providers recover billions lost to claim denials. Founder Ong shares his journey from Calcutta to Carnegie Mellon to YC, the inside story of getting into YC at the last minute, and how his team is tackling one of healthcare’s most entrenched problems. What you’ll learn: - How YC companies are attacking trillion-dollar industries with AI - Why healthcare loses $260B a year to denied insurance claims - The hidden incentives driving insurers to deny payments - How Aegis built real traction in just 10 weeks - What YC really teaches founders beyond the playbook - The power of the YC network and why it still compounds after demo day 📌 Watch now and subscribe for more smart, fast, founder-first interviews. Chapters: 00:00 The YC Application Journey 00:48 Introducing Krishang from Aegis 01:21 Krishang's Entrepreneurial Background 02:30 The Birth of Aegis 04:53 The Power of YC Content 07:03 Getting into YC: The Application Process 10:00 The YC Batch Experience 14:25 Maintaining Momentum Post-YC 17:59 Advice for Aspiring YC Applicants 19:09 Tackling the Healthcare Industry 21:41 Targeting Medical Billing Companies 22:07 AI in Insurance: A Growing Challenge 22:21 The Impact on Hospitals 24:31 Investor Perspectives on AI in Healthcare 26:48 Strategies for Success in Healthcare Startups 33:12 The YC Advantage 33:49 The Power of the YC Network 38:14 Silicon Valley's Collaborative Ecosystem 39:58 Conclusion and Final Thoughts 🎧 Listen on the go: Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson
They applied to YC with 90 minutes on the clock—and got in. Then they pivoted into the most operationally gnarly corner of fintech: global payroll. Avi Konduru (Shor) breaks down how AI agents + stablecoins can vertically rebuild EOR, cut costs by an order of magnitude, and expand the market beyond today’s incumbents. In this episode, we go deep on YC as an ambition amplifier, pivot mechanics under real pressure, price vs. TAM strategy, and why launch videos (done right) are still YC’s most underrated distribution hack. You’ll learn: How a 90-minute YC application (and one-take demo) still cleared the barThe precise wedge: vertically owning entities + automating back office with AIWhy “someone else’s margin is your opportunity” actually maps to EORPricing strategy: undercut to expand TAM vs. match to maximize marginHow YC Launch video distribution compresses customer discovery into daysThe pitfalls: agent reliability, compliance debt, and scaling beyond the batch Chapters 00:00 The Last-Minute Application Rush 00:42 Welcome to Lobster Talks 01:07 Introducing Shor: Reinventing Global Payroll 02:38 The YC Experience: A Rollercoaster Journey 05:02 The Pivot: From Stablecoin Infra to EOR 10:29 Bootstrapping Challenges and Lessons Learned 18:00 The Unexpected Turn: Applying to YC Again 23:27 Competing with Deel and Rippling 27:07 Understanding Reseller Margins and Fees 27:27 Deel's Automation and Disruption in Entity Management 27:46 Setting Up Entities in High-Traffic Countries 28:49 Challenges and Regulatory Issues in Global Payroll 30:06 AI Agents Revolutionizing EOR Operations 32:33 Pricing Strategy and Market Expansion 40:11 The Power of Launch Videos in YC 44:07 The Role of Influencers in Marketing 48:07 Future Challenges and Customer Acquisition 51:31 Conclusion and Final Thoughts 📌 Watch now and subscribe for more smart, fast, founder-first interviews. 🎧 Listen on the go: Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson
We finally found a voice assistant that actually ships work. Not a demo, not a hype reel—April closes the loop on email and calendar while you’re driving, lifting, or walking to your next meeting. In this YC-insider conversation, Neha (co-founder of April) breaks down how a narrow, vertical agent can outperform “do-everything” assistants, why dogfooding—not retention dashboards—built their product moat, and what a screen-lite future means for founders and operators. We also cover YC batch dynamics in a crowded voice category, Demo Day strategy, and the roadmap to a true “voice OS.” What you’ll learn: - Why narrowing scope (email + calendar first) beats generalist agents for real outcomes - The dogfood standard: building to a founder’s own bar, then scaling - How YC treats multiple “competing” companies—and why that can help you ship faster - Demo Day tactics: being live, iterating weekly, and selling the founder, not the fantasy - Voice vs. screens: trust, closed-loop execution, and the path to screenless workflows - April’s roadmap: LinkedIn, WhatsApp, Slack, Notion—and verticalizing for sales & investors 📌 Watch now and subscribe for more smart, fast, founder-first interviews. Chapters 00:00 Introduction to April: The AI Voice Assistant 00:24 Welcome to Lobster Talks: Meet Neha from April 01:12 Diving into April's Features and Use Cases 02:46 The Journey of Building April 03:58 Challenges and Successes in the Voice AI Space 05:36 The Future of Voice AI and Investor Insights 07:37 YC Experience and Investor Reactions 08:47 The Competitive Landscape and Collaboration 12:25 The Role of YC and the Voice AI Market 20:08 The Vision for a Screenless Future 23:06 Preparing for YC Demo Day 25:38 The Importance of Execution in Business 26:16 Advice for Startups: Ship Quickly and Get Feedback 26:50 Future Integrations and Features 28:37 Challenges and Strategies During YC Journey 32:31 Maintaining Momentum Post-YC 33:20 Mental Resilience and Personal Well-being 38:10 Exciting Future Plans for April 40:17 Predictions and Insights on AI Assistants 41:09 Lessons from Zoho and Book Recommendations 43:23 Conclusion and Final Thoughts 🎧 Listen on the go: Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@lobstercap
A Netflix storage engineer walks into YC and ships an “infinite, shareable disk” on top of S3—30× faster and up to 90% cheaper—then dials GTM for the AI era. This is the file system’s comeback story. Today I sit down with Hunter Leath (ARL) to unpack how a decade inside AWS + Netflix revealed a gap the hyperscalers won’t close: developers want storage that feels local, scales like S3, and doesn’t nuke the budget. We get into: YC as confidence engine, moving a family to SF, rebuilding for speed, why AWS won’t copy this, and why the file system—not object storage—becomes AI’s universal interface. You’ll learn -Why the clouds won’t ship a product that cannibalizes billions in revenue -The architecture that makes ARL 30× faster and up to 90% cheaper -How to catch customers exactly when new AI workloads start (the real ICP) -Post-batch velocity: how SF energy kills the YC slump -Why “serverless everything” needs a serverless disk to persist state -The contrarian bet: the file system is the future data interface for AI 📌 Watch now and subscribe for more smart, fast, founder-first interviews. Chapters 00:00 The Risk of Leaving Big Tech 00:33 Introducing Hunter Leh and Aril 01:32 Hunter's Journey from AWS to Netflix 02:47 The Birth of Aril 06:55 Challenges and Insights from YC 07:26 The Solo Founder Experience 19:39 Building and Launching Aril 21:01 Go-to-Market Strategy and Customer Acquisition 24:04 The AI Industry's Growing Demand 24:49 Fundraising Journey and Investor Insights 26:35 AWS and Market Dynamics 29:17 Innovations in Data Storage 36:23 Maintaining Momentum Post-YC 38:27 Future Predictions in Data Infrastructure 42:22 Contrarian Views on AI and Data Storage 44:20 Conclusion and Contact Information 🎧 Listen on the go: Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson
YC just changed the rules—and the market is catching up. We break down why seed is the power position, how CVCs are reshaping Series A, and what YC’s new Early Decision really means for founders and investors. In this episode, we cover Lobster Capital updates (first Series A, first DPI), how we decide follow-ons from an insider vantage point, the rise of “seed-strapping,” Coinbase Ventures x YC’s RFS on Fintech 3.0, and why stablecoins + AI agents may be the next real on-chain wedge. We also unpack YC’s Early Decision—who it actually benefits—and what to expect heading into the next Demo Day. You’ll learn: Why seed has asymmetric leverage (and why top YC teams don’t optimize for dollars) How we evaluate follow-ons: revenue quality, NRR, churn, hiring, and real signal vs noise CVCs at Series A: when specialization beats the “Tier-1” logo “Seed-strapping”: profitability at seed, and why some teams skip A entirely Coinbase Ventures x YC’s Fintech 3.0 RFS and the stablecoin/AI-agent stack YC Early Decision: who it helps (hardware/bio) and how YC captures talent earlier 📌 Watch now and subscribe for more smart, fast, founder-first interviews. Chapters 00:00 Introduction to Investing Insights 00:23 Welcome to Lobster Talks 00:45 Lobster Capital Updates 03:16 Series A Graduation Rates 04:08 Fund Strategy and Follow-Ons 05:38 YC Companies and Profitability 10:33 Fundraising and Strategic Alliances 17:28 Crypto and FinTech 3.0 25:23 Global Currency Dynamics 26:20 The Rise of Stablecoins 27:27 AI and Crypto Synergy 32:03 Speculative Trading and Meme Coins 38:42 YC's Early Decision Program 44:01 The Future of YC and Startup Ecosystem 51:04 Conclusion and Upcoming Content 🎧 Listen on the go: Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson
A robotaxi playbook… for dirt. Flywheel AI is turning excavators into remotely operated, camera-first machines — collecting the data to make them autonomous next. In this YC-insider episode, we unpack Flywheel AI’s “Waymo for excavators” strategy: retrofit any machine in hours, deliver value with tele-op now, and use that profitably collected data to train autonomy later. We get into labor shortages, safety economics (OSHA penalties), competitor traps (drive-by-wire only), and how to actually do hardware at YC in 90 days without getting stuck in pilot hell. You’ll learn Why construction’s bottleneck is skilled operators — and how tele-op removes it The dangerous blind-spot reality on sites and the true cost of safety incidents Flywheel’s retrofit + single-screen UX that works on any excavator brand/size The autonomy roadmap: camera-only stack, data flywheel, edge-case capture How to win data rights on site (be the only retrofit, own the dataset) The YC hardware playbook: sell first, build last; parallelize to kill lead-time 📌 Watch now and subscribe for more smart, fast, founder-first interviews. Chapters: 00:00 From Sandbox to Real Excavator: The Journey Begins 00:20 Introducing Flywheel AI: Revolutionizing Excavators 01:49 The Labor Shortage Crisis in Construction 03:51 The Dangers of Operating Excavators 05:58 Teleoperation: Enhancing Safety and Efficiency 11:28 The Path to Autonomous Excavators 16:34 Competing in the Autonomous Excavator Market 22:26 Demo Day: Bringing an Excavator to YC 24:37 Returning the Excavator 24:47 Demo Day Setup and Reactions 26:04 Autonomy and Data Training 26:38 Joining YC and Initial Thoughts 28:03 YC's Impact on Hardware Startups 29:18 Building and Iterating Hardware 33:13 Advice for Hardware Startups 34:49 Final Thoughts and Reflections 45:03 Accelerating Iteration Cycles 48:38 Conclusion and Contact Information
A “cute idea” until it wasn’t: RealRoots walked into YC as an overlooked consumer play and walked out with $9.4M ARR and an oversubscribed round. Summary: In this YC-insider episode, Dorothy Li (RealRoots) breaks down how AI-powered friendship matchmaking turned into real traction across 80+ cities. We unpack the demo day shock, the stigma shift (friendship ≈ dating 10 years ago), and the manual-to-AI playbook that de-risks consumer. We also cover investor blind spots, GTM math (cold DMs → paid), and why cofounder fit is a “you’ll know in 10 seconds” decision. You’ll learn: -Why “consumer is back” at YC—and how RealRoots rode a stigma shift -The manual-first, AI-next method that actually finds PMF -How 2–3k cold DMs converted to 11% paid ($20) and seeded the funnel -The marketplace + AI stack behind curated IRL events at scale -Investor pattern errors: scar tissue vs. behavior/tech inflections -Co-founder tactics, hiring posture, and sustainable pace vs. 9-9-6 Chapters: 00:00 Introduction to RealRoots and Demo Day Success 00:48 Meet Dorothy Li: Founder of RealRoots 01:32 The Problem of Loneliness and Finding Community 02:43 How RealRoots Uses AI to Build Friendships 04:02 Expansion and Success of RealRoots 08:07 The YC Experience and Its Impact 17:39 Overcoming Stigma and Building for the Future 29:27 Investor Hesitations and Scar Tissue 30:12 Challenges in the Friendship App Space 30:59 The Concept of 'Targets' in Startups 32:04 The Importance of Consumer Behavior and Technology Changes 33:04 Co-Founder Story: Meeting Through RealRoots 36:16 Manual Efforts in Early Startup Stages 38:49 The Impact of a Co-Founder 39:45 Work-Life Balance in Startups 44:18 YC's Focus on Younger Founders 48:21 Validating Your Startup Idea 55:09 Customer Acquisition Strategies 56:30 Conclusion and Final Thoughts 📌 Watch now and subscribe for more smart, fast, founder-first interviews. 🎧 Listen on the go: Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson
A YC founder turns a manual, low-IQ grind into an AI agent that finds creators, negotiates terms, and scales UGC—sometimes a little too far. What starts as a viral local-model demo becomes Stormy AI’s end-to-end engine for influencer marketing. Fresh off YC Demo Day, Robert Lukoszko (Stormy AI) breaks down the pivot, the fundraising blitz, and how agencies are replacing hours of scrolling with autonomous outreach. We get into model-proof moats, why micro-creators beat celebrity accounts, and the coming wave of AI-generated influencers. You’ll learn: How a YC pivot formed around a founder’s own pain (and real demand) The playbook: sourcing, outreach, negotiation, and QA with AI agents Why “every better model makes us stronger” is the right moat test Micro vs. macro creators: what actually converts in 2025 The next act: AI-native UGC, personalization, and brand-owned AI faces Tactical Demo Day lessons: energy + social proof = signed checks Chapters: 00:00 Introduction and Cold Open 00:13 Welcome to Lobster Talks 00:37 Demo Day Insights 03:05 The Journey of Stormy AI 04:39 From Viral Demos to YC Acceptance 07:35 Pivoting to Stormy AI 09:41 Automating Influencer Marketing 18:39 The Future of Influencer Marketing 19:50 AI and UGC: The Future of Influencer Marketing 20:11 The Rise of AI Influencers 20:41 AI-Generated Content vs. Human Content 21:05 The Makeup Industry and AI Influencers 22:26 The Shift to Micro-Influencers 23:23 Emerging Platforms and the Decline of Meta 25:22 The Future of AI in Content Creation 28:58 Preparing for an AI-Driven Future 31:46 Building a Moat in the AI Industry 34:54 The Importance of Vision and Customer Interaction 38:16 Stormy AI: Current and Future Plans 39:20 Conclusion and Call to Action 📌 Watch now and subscribe for more smart, fast, founder-first interviews. 🎧 Listen on the go: Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson
AI won’t live in chat. The future is headless agents doing real work — and calling humans only when it matters. Dexter Horthy, cofounder of HumanLayer, explains how agent-driven software actually ships. In this fast, tactical deep-dive, we unpack HumanLayer’s origin story (from failed data tools to paid customers in a week), why frameworks lag real production apps, and the workflow that lets AI agents ship in complex codebases. We cover research-plan-implement loops, context engineering, team process, and how “specs become the new code.” You’ll learn - How HumanLayer emerged from a SQL “janitor” agent that needed human approvals - The YC grind as a solo founder and closing first revenue in a week - Why horizontal AI dev tools are hard — and how top 1% teams actually build - Context engineering 101: research → plan → implement, and why it beats vibe coding - How to review plans, not code, to scale quality across a team Where headless agents win first — and why culture, not models, is the bottleneck. Chapters 00:00 Introduction and Cold Open 00:21 Welcome to Lobster Talks 00:47 Guest Introduction and Background 01:12 Early Startup Journey 01:35 Building the AI Agent 02:18 Challenges and Pivots 03:52 Solo Founder Experience 05:28 The Importance of Data Tools 11:20 YC Experience and Revenue 13:59 Building for the 1% vs. 99% 23:50 Exploring New Ideas 25:18 Exploring Cloud Code SDK 25:34 Building Experiments with Claude 26:05 Challenges and Learnings 26:49 Insights from AI Engineering Talks 28:22 The Future of Coding with AI 30:22 Context Engineering and Workflow 32:19 Product Development and Customization 38:22 Scaling AI in Teams 48:10 Exciting Future Prospects 50:42 Conclusion and Farewell 📌 Watch now and subscribe for more smart, fast, founder-first interviews. Show Notes & Resources: • 14min Youtube video on wielding coding agents: https://hlyr.dev/ace • Blog post version - https://github.com/humanlayer/advanced-context-engineering-for-coding-agents/blob/main/ace-fca.md • Sign up for codelayer beta: https://humanlayer.dev/code 🎧 Listen on the go: Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson
Founders closing rounds before lunch. Investors making handshake commitments in the hallway. A startup with $9M+ ARR and another with $15M ARR lighting up the room. YC Demo Day isn’t a show; it’s a marketplace where speed and execution decide everything. In this fast, founder-first debrief, we break down what actually happened at the latest YC Demo Day: the subtle format changes (that matter), why the one-minute pitch is only the opener, and how deals really get done. We cover the batch’s AI/devtools tilt, the contrarian bets in defense and hardware, and why early traction remains the single best predictor at seed. We also unpack portfolio construction, conversion-rate dynamics inside YC, and what support looks like after the cameras stop. You’ll learn: - The real Demo Day mechanics: tranches, chat apps, long breaks for dealmaking - Why some hot rounds are already full and what to do about it - How to win YC deals: first-meeting decisions, 24–48h timelines, and prep work - The $9M+ ARR investment we made—and why traction beats narrative - Why we’re now backing deeptech/hardware (missiles vs drones, autonomous excavators) - Portfolio strategy: aiming for the fund returner, not spray-and-pray Chapters: 00:00 Introduction and Demo Day Overview 00:15 Behind the Scenes of Demo Day 01:03 Changes and Improvements at YC Demo Day 03:11 Investor Insights and Strategies 04:23 Engaging with YC Startups 06:35 The Importance of Early Traction 09:49 Demo Day Pitch Dynamics 13:42 Fundraising Conversations and Strategies 18:07 Lobster Capital's Investment Approach 21:54 Portfolio Construction and Future Prospects 27:02 AI and Dev Tools: A Crowded Space 27:39 Challenges in Identifying Winners 28:48 Customer Acquisition: The Key to Success 29:57 The Importance of Traction 33:32 Investing in Deep Tech and Hardware 39:24 Evaluating Flywheel's Potential 45:46 Supporting Startups Post-Demo Day 50:09 Looking Ahead: The Never-Ending Cycle 📌 Watch now and subscribe for more smart, fast, founder-first interviews. 🎧 Listen on the go: Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson
A surfer duct-tapes a phone to his board… and ends up saving enterprise couriers 32 seconds per stop. The last meter of delivery — not the last mile — is where the money is. In this Lobster Talks episode, Ingo Boegemann, co-founder/CEO of truemetrics, breaks down how sensor fusion + mission intelligence turn messy building entrances, courtyards, and wrong pins into precise, repeatable delivery actions. We go deep on Europe vs. US GTM, GDPR constraints (and why the US may unlock even more value), landing whales like GLS, and the unscalable POC that unlocked scale. You’ll learn Why “a generic geocode is just the starting point” — and how to map entrances that actually workThe POC → pilot → rollout playbook (and why Truemetrics charges for POCs)How to integrate via SDK without slowing ops — and show value before engineering lifts a fingerEurope vs. US: privacy ceilings, data linking, and why boots-on-the-ground still wins enterprise salesThe real driver bottleneck: pressure, compliance, and turning best drivers’ tacit knowledge into softwareThe long game: building a data moat for autonomous last-meter delivery Chapters 00:00 Introduction to Last Meter Delivery 01:07 Meet Ingo Boegemann: The Journey to Truemetrics 02:15 From Surfboards to Sensor Fusion 05:04 Challenges and Realizations in Delivery Solutions 06:47 European vs. US Market Dynamics 11:42 The Path to Scaling in North America 19:02 Innovative Solutions for Delivery Logistics 21:37 How Truemetrics Technology Works 26:36 Magnetic Field Intensity and Machine Learning Models 27:29 Challenges in Courier Data Integration 29:52 Sales Process and Proof of Concept 32:08 Logistics Industry Vulnerabilities 35:42 Future of Autonomous Deliveries 38:30 Data-Driven Delivery Solutions 48:31 Closing Remarks and Contact Information 📌 Watch now and subscribe for more smart, fast, founder-first interviews. 🎧 Listen on the go: Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson
What if you could simulate human reactions — and know exactly how your customers, investors, or audience will respond before you act? James He is building precisely that with Artificial Societies. In this episode, we dive deep with James, YC W25 founder of Artificial Societies — a wildly ambitious startup that simulates entire groups of humans using AI personas to predict how messages spread, markets react, and products succeed (or fail). From simulating 1,000 VCs to get into YC… to replacing $20,000 market research surveys, this conversation is a masterclass in founder execution, behavioral science, and AI-first GTM strategy. What you’ll learn: • How Artificial Societies works (and how founders are really using it today) • Why traditional A/B testing and market research are broken • Product-led growth lessons from 15,000+ activated users • The surprising YC tricks James used to get into the batch • Practical ways AI can upgrade your messaging, GTM, and PMF search • The future of simulating entire economies (and why we’re not there yet) 📌 Watch now and subscribe for more smart, fast, founder-first interviews. Chapters: 00:00 Simulating Investors: The Early Days 00:27 Introducing Artificial Societies 00:48 Journey to YC and Beyond 01:06 How Artificial Societies Works 03:02 The Science Behind Social Influence 05:27 Technical Insights and Applications 17:28 James' Personal Journey 22:29 Building and Growing the Startup 24:50 Using Internal Tools for Optimization 27:38 The Evolution of A/B Testing with AI 29:41 Challenges in Market Research 33:14 AI's Role in Market Research 34:40 Balancing Customer Feedback and Product Vision 42:55 Future Predictions and AI Capabilities 47:48 Closing Remarks and Final Thoughts 🎧 Listen on the go: Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@UCahCIZ9KfmeK4rLosGNYpdg My other YouTube Channel: https://www.youtube.com/@GJarrosson
YC just dropped its latest Requests for Startups — and they’re not just ideas, they’re roadmaps to the future. From AI-native enterprise software to 10-person $100B companies, these signals reveal where the smartest founders (and investors) should be looking. In this episode of Lobster Talks, Laurie and I break down Y Combinator’s newest RFS — from retraining workers for the AI economy to video generation as a computing primitive. We debate the real opportunities, the traps, and how these trends could reshape the startup ecosystem. What you’ll learn: - Why YC’s RFS is one of the best ways to predict upcoming demo day winners - The massive opportunity in retraining workers for the AI economy - Why video generation is shifting from an output to a primitive - How AI-native startups could disrupt giants like Salesforce - Whether 10-person $100B companies are actually possible - The risks, politics, and second-order effects of LLMs in government Chapters: 00:00 Introduction: The Value of Customer Money 00:26 Y Combinator's Request for Startups 01:14 AI and Worker Retraining 02:30 The Future of Semiconductors and Deep Tech 04:34 AR, VR, and AI in Training 09:02 Video Generation: The Next Frontier 16:12 The First 10-Person $100 Billion Company 22:31 The Future of Small Teams and Fundraising 24:03 Challenges and Opportunities in Multi-Agent Systems 28:12 AI Native Enterprise Software 34:39 Using AI to Revolutionize Government Consulting 41:24 Conclusion and Upcoming Events 📌 Watch now and subscribe for more smart, fast, founder-first interviews. 🎧 Listen on the go: Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson
What if you built a tool so powerful, it became the #1 repo on GitHub—twice? That’s exactly what Daniel Farrell did with Onlook, an open-source AI-powered visual editor for code. From crashing Chrome extensions to crashing Hacker News, Daniel takes us through how he and his co-founder built momentum, community, and virality from nothing. In this fast, founder-first conversation, we cover: • How Onlook went from prototype to #1 GitHub repo • Why Hacker News beat Product Hunt for traction • The real benefits and tradeoffs of going open source • How open source GTM can win enterprise customers • What most people misunderstand about AI and design• Why taste, story, and community still matter more than code 📌 Watch now and subscribe for more smart, fast, founder-first interviews. 00:00 The Most Trending Repo in the World 00:16 Meet Daniel Farrell: Co-Founder of Onlook 00:59 The Vision Behind Onlook 01:57 Building the Prototype and Early Challenges 04:55 Finding the Perfect Co-Founder 11:57 The Open Source Journey 14:49 The Hacker News Effect 16:47 Global Impact and Community Building 19:40 The Importance of Open Source 24:31 Challenges of Maintaining a Community 25:00 Open Source Success Stories 25:21 The Super Base Playbook 26:33 Advantages of Open Source for Enterprises 33:42 Impact of AI on Design and Marketing Teams 42:25 Future Bets and Bold Predictions 48:00 Where to Find Onlook and Final Thoughts Listen on the go: Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson
What if launching a tech startup was as easy as filling out a form? Ben Collins, co-founder of Woz, joins Gabriel to unpack how his AI platform helps anyone — even non-technical founders — build full-stack software businesses. From betting against vertical SaaS to designing an AI cofounder you can trust, this conversation is a tactical deep dive into where startup creation is headed. 🔍 In this episode: Why Woz pivoted away from a holding company model What most AI-first code tools get totally wrong How mobile-first enables higher quality standards A unique approach to betting on future AI capability curves The real reason most AI startups are failing users 📌 Watch now and subscribe for more smart, fast, founder-first interviews. ⏱ Chapters: 00:00 Introduction to Scaling and Bottlenecks 00:18 Introducing Ben Collins and Woz 01:04 The Vision Behind Woz 03:43 The Founding Story 06:06 The Shift to a Platform Approach 08:53 Navigating the Competitive Landscape 12:39 Philosophical and Strategic Insights 17:02 The Mobile-First Strategy 25:34 The Journey to Product-Market Fit 27:15 Navigating Rapid Technological Changes 32:21 The Impact of Y Combinator 35:00 AI's Role in Scaling Service Businesses 36:42 Challenges for Non-Technical Founders 39:13 Bold Predictions and Future Bets 45:02 Exciting Announcements and Closing Thoughts 🎧 Listen on the go: Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAP Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson
YC isn't just about AI anymore — it's quietly becoming America’s new industrial engine. In this episode, Gabriel and Laurie break down how Y Combinator is doubling down on hardtech, defense, and energy — and why this matters more than ever. From geopolitical risk to deep tech traction, we unpack how YC startups are shaping the future — and why top seed investors are scrambling to keep up. We also reveal how internal partner dynamics at YC shape which billion-dollar startups get funded. 🎙 This is your tactical, insider breakdown of what’s next for the world’s most important startup accelerator. What you’ll learn: * Why YC is going all-in on defense, energy, and hardware * How LOIs and demo day results work for hardtech startups * The “too late” moment for most seed investors trying to access YC deals * Why YC group partners matter — and how to track them * What the NextView “existential crisis” blog means for the seed market * Which partners are shaping the S25 batch behind the scenes 📌 Watch now and subscribe for more smart, fast, founder-first interviews. Chapters00:00 First Impressions and Initial Skepticism 00:36 Welcome to the Podcast 00:52 YC's New Request for Startups 01:57 YC's Hardware and Industrial Focus 07:08 The Importance of Energy and Defense 08:26 Political and Regulatory Considerations 14:27 Investing in Deep Tech and Hardware 20:15 Challenges in Seed Investing 21:25 The Impact of YC on Seed Investing 22:24 The YC Network and Its Influence 23:35 Challenges and Opportunities for Non-YC Investors 24:25 The Journey of Investing in YC 25:42 The Importance of Timing in YC Investments 29:39 Changes in YC Leadership 33:42 The Role of Group Partners in YC 37:35 Personal Experiences with YC Group Partners 39:30 Conclusion and Next Steps 🎧 Listen on the go:Spotify: https://open.spotify.com/show/1u1JyRKH8JYFjhBkvTUOAPApple: https://apple.co/4cZ8RMmMy other YouTube Channel: https://www.youtube.com/@GJarrosson
What happens when a college student builds an AI tool that goes viral overnight — and changes how thousands of students study? Summary: In this episode of Lobster Talks, Gabriel sits down with David Yu, co-founder of YouLearn — the YC-backed AI tutor that transforms PDFs, class recordings, and YouTube videos into quizzes, notes, and a personal tutor. From a dorm-room side project to a viral social growth engine, this episode is packed with tactics, conviction, and bold bets on the future of learning. You’ll learn: How YouLearn hit 10,000 users in 24 hours from a single post The exact tactics they used to blow up on Instagram and TikTok Why most AI tools in education fail — and how YouLearn avoids it The truth about retention, virality, and real product usage How David thinks about AI hallucinations and data safety What learning could look like in 2030 with Neuralink, glasses, and beyond 📌 Watch now and subscribe for more smart, fast, founder-first interviews. Chapters: 00:00 Viral Launch on Social Media 00:20 Introduction to YouLearn 00:47 Meet the Founder: David Yu 01:09 The Birth of YouLearn 02:37 Early Challenges and Successes 05:09 Going Viral Again 05:45 Handling Rapid Growth 08:14 Maintaining Motivation 10:53 The Importance of Co-Founders 12:00 Transition to Full-Time 15:30 Iterating on the Product 18:36 Joining Y Combinator 21:04 Investor Skepticism Towards YC 21:49 Misconceptions About Joining YC 22:33 Right and Wrong Reasons to Join YC 23:28 Maximizing YC Experience 25:08 Navigating YC with Existing Revenue 30:26 AI's Role in Education 31:48 Future of Learning with AI 34:05 Misconceptions About AI in Learning 35:10 The Evolution of Learning Tools 40:26 Final Thoughts and Future Prospects 🎧 Listen on the go: Spotify: https://spoti.fi/4m2K7ah Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson
What if your inbox didn’t just organize itself — it acted on your behalf? Nizar Abi Zaher, founder of Zero, is building the AI-native email client that reads, replies, filters, and organizes without you lifting a finger. In this tactical, fast-paced conversation, we explore his path from Cal.com engineer to YC founder — and how he's going head-to-head with Superhuman. From open-source strategies to voice-powered inboxes, Nizar breaks down how Zero is redefining email — and what it takes to ship fast, build in public, and stay top of mind. In this episode, you’ll learn: • Why Zero was built around what people hate • How YC pushed them to launch before they were ready • How being open-source accelerates product velocity • What Superhuman got wrong — and what Zero does better • How social presence became their biggest moat • What email looks like in 5 years (hint: voice + AI agents) 📌 Watch now and subscribe for more smart, fast, founder-first interviews. Chapters: 00:00 Introduction to Zero: The AI-Powered Email Client 00:42 Meet the Founder: Nazar's Journey 01:24 The Problem with Email and Zero's Solution 02:56 Differentiating from Competitors 05:21 Rapid Development and Open Source Benefits 11:08 The Importance of Social Media Presence 14:57 Strategies for Effective Social Media Use 21:26 The Value of Authenticity on Social Media 24:07 The Importance of Onboarding 27:02 Launching and Iterating Quickly 29:31 Voice and Email Innovations 32:42 The Future of Email 40:57 The YC Experience and Final Thoughts 🎧 Listen on the go: Spotify: https://spoti.fi/4m2K7ah Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson
They didn’t sell to insurance brokers — they replaced them. Dakotah Rice didn’t build AI tools for the industry. He built an AI-first insurance company — and now Harper is scaling faster than they can handle. In this episode, Dakotah shares how Harper went from kitchen-table experiments to a high-growth, fully autonomous insurance brokerage. He breaks down the real bottlenecks, the AI architecture behind their workflows, and the bold bets that could reshape an entire industry. What you’ll learn: Why Harper ditched SaaS and chose vertical integration How to embed engineers directly in operational workflows The underrated bottlenecks to AI-first company scaling Why relationships are overrated in insurtech How to build trust with real-world customers — without mentioning AI The real limits (and future) of fully autonomous firms 📌 Watch now and subscribe for more smart, fast, founder-first interviews. Chapters 00:00 Introduction to Harper and Dakota Rice 00:38 Dakota's Background and Harper's Mission 02:13 Challenges and Pivots in Building Harper 04:29 Entering Y Combinator and Scaling Up 06:19 The Importance of Workflow Understanding 13:17 Lessons from Past Failures 18:34 Vision for AI in the Future 21:51 Building Complex AI Systems 22:49 Human Element in AI Services 23:53 Customer Perception of AI 27:37 Scaling Challenges and Solutions 35:14 AI's Role in Future Business Models 37:58 Future Predictions and Industry Insights 42:46 Conclusion and Contact Information 🎧 Listen on the go: Spotify: https://spoti.fi/4m2K7ah Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson
What if a single AI voice agent could handle 100,000 calls a day and free up your entire support team? Episode Summary: In this episode of Lobster Talks, Gabriel sits down with Kevin Wu, founder of Leaping AI, to unpack how voice-powered AI is automating 50–70% of repetitive support calls, self-optimizing over time, and achieving 90% customer satisfaction. We dive into the origin story, technical stack, ROI metrics, human-in-the-loop safeguards, and bold predictions for 2030. You’ll learn: How Leaping AI began: from BCG ideation to the first major enterprise customer The differentiator: combining voice AI with evaluation and self-optimization modules Managing edge cases and human fallback to maintain 90% satisfaction Tech stack overview: orchestration layer, Twilio, multiple TTS/ASR providers, LLMs Calculating ROI and setting realistic metrics with weekly check-ins Future bets: AI vs. human empathy, integration challenges, market structure by 2030 Practical advice for CX leaders overwhelmed by AI hype Chapters: 00:00 Introduction to Leaping AI with Kevin Wu 00:59 Kevin Wu's Journey and Inspiration 02:10 Winning the First Customer 02:41 Leaping AI's Unique Selling Points 03:59 Self-Improving AI Agents 05:17 Handling Edge Cases and Human Involvement 06:32 Early Days and First AI Call 07:33 Challenges and Continuous Improvement 09:00 Market Position and Competitor Insights 13:47 Customer Metrics and ROI 17:18 Technical Stack and Partnerships 19:09 Sponsor Message and Text-to-Speech Choices 19:32 The Best AI Voice Provider: Does It Exist? 21:10 Challenges in Voice AI for Sales 23:31 Implementing AI in Customer Support 25:37 Customer Reactions to AI 29:32 Future Predictions for AI in Customer Service 37:45 Getting Started with AI in Customer Service 📌 Watch now and subscribe for more smart, fast, founder-first interviews. 🎧 Listen on the go: Spotify: https://spoti.fi/4m2K7ah Apple: https://apple.co/4cZ8RMm My other YouTube Channel: https://www.youtube.com/@GJarrosson