AI Video’s Wild Year – Runway CEO on What’s Next

The most successful enterprises are about to become autonomous — and Eléonore Crespo, Co-CEO of Pigment, is building the nervous system that makes it possible. In this conversation, Eléonore reveals how her $400 million AI platform is already running supply chains for Coca-Cola, powering finance for the hottest newly public companies like Figma and Klarna, and processing thousands of financial scenarios for Uber and Snowflake faster and more accurately than any human team ever could.Eléonore predicts Excel will outlive most AI companies (but maybe only as a user interface, not a calculation engine) explains why she deliberately chose to build from Paris instead of Silicon Valley, and shares her contrarian take on why the AI revolution will create more CFOs, not fewer.You'll discover why Pigment's three-agent system (Analyst, Modeler, Planner) avoids the hallucination problems plaguing other AI companies, how they achieved human-level accuracy in financial analysis, and the accelerating timeline for fully autonomous enterprise planning that will make your current workforce obsolete.PigmentWebsite - https://www.pigment.comTwitter - https://x.com/gopigmentEléonore CrespoLinkedIn - linkedin.com/in/eleonorecrespoFIRSTMARKWebsite - https://firstmark.comTwitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/Twitter - https://twitter.com/mattturck(00:00) Intro (01:22) Building Pigment: 500 Employees, $400M Raised, 60% US Revenue (03:20) From Quantum Physics to Google to Index Ventures (06:56) Why Being a VC Was the Perfect Founder Training Ground (11:35) The Impatience Factor: What Makes Great Founders (13:27) Hiring for AI Fluency in the Modern Enterprise (14:54) Pigment's Internal AI Strategy: Committees and Guardrails (17:30) The Three AI Agents: Analyst, Modeler, and Planner (22:15) Why Three Agents Instead of One: Technical Architecture (24:10) Agent Coordination: How the Supervisor Agent Works (24:46) Real Example: Budget Variance Analysis Across 50 Products (27:15) The Human-in-the-Loop Approach: Recommendations Not Actions (27:36) Solving Hallucination: Why Structured Data Changes Everything (30:08) Behind the Scenes: Verification Agents and Audit Trails (31:57) Beyond Accuracy: Enabling the Impossible at Scale (36:21) Will AI Finally Kill Excel? Eleanor's Contrarian Take (38:23) The Vision: Fully Autonomous Enterprise Planning (40:55) Real-Time Supply Chain Adaptation: The Ukraine Example (42:20) Multi-LLM Strategy: OpenAI, Anthropic, and Partner Integration (44:32) Token Economics: Why Pigment Isn't Token-Intensive (48:30) Customer Adoption: Excitement vs. Change Management Challenges (50:51) Top-Down AI Demand vs. Bottom-Up Implementation Reality (53:08) The Reskilling Challenge: Everyone Becomes a Mini CFO (57:38) Building a Global Company from Europe During COVID (01:00:02) Managing a US Executive Team from Paris (01:01:14) SI Partner Strategy: Why Boutique Firms Come Before Deloitte (01:03:28) The $100 Billion Vision: Beyond Performance Management (01:05:08) Success Metrics: Innovation Over Revenue

2025 has been a breakthrough year for AI video. In this episode of the MAD Podcast, Matt Turck sits down with Cristóbal Valenzuela, CEO & Co-Founder of Runway, to explore how AI is reshaping the future of filmmaking, advertising, and storytelling - faster, cheaper, and in ways that were unimaginable even a year ago.Cris and Matt discuss:* How AI went from memes and spaghetti clips to IMAX film festivals.* Why Gen-4 and Aleph are game-changing models for professionals.* How Hollywood, advertisers, and creators are adopting AI video at scale.* The future of storytelling: what happens to human taste, craft, and creativity when anyone can conjure movies on demand?* Runway’s journey from 2018 skeptics to today’s cutting-edge research lab.If you want to understand the future of filmmaking, media, and creativity in the AI age, this is the episode. RunwayWebsite - https://runwayml.comX/Twitter - https://x.com/runwaymlCristóbal ValenzuelaLinkedIn - https://www.linkedin.com/in/cvalenzuelabX/Twitter - https://x.com/c_valenzuelab FIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro – AI Video's Wild Year (01:48) Runway's AI Film Festival Goes from Chinatown to IMAX (04:02) Hollywood's Shift: From Ignoring AI to Adopting It at Scale (06:38) How Runway Saves VFX Artists' Weekends of Work (07:31) Inside Gen-4 and Aleph: Why These Models Are Game-Changers (08:21) From Editing Tools to a "New Kind of Camera" (10:00) Beyond Film: Gaming, Architecture, E-Commerce & Robotics Use Cases (10:55) Why Advertising Is Adopting AI Video Faster Than Anyone Else (11:38) How Creatives Adapt When Iteration Becomes Real-Time (14:12) What Makes Someone Great at AI Video (Hint: No Preconceptions) (15:28) The Early Days: Building Runway Before Generative AI Was "Real" (20:27) Finding Early Product-Market Fit (21:51) Balancing Research and Product Inside Runway (24:23) Comparing Aleph vs. Gen-4, and the Future of Generalist Models (30:36) New Input Modalities: Editing with Video + Annotations, Not Just Text (33:46) Managing Expectations: Twitter Demos vs. Real Creative Work (47:09) The Future: Real-Time AI Video and Fully Explorable 3D Worlds (52:02) Runway's Business Model: From Indie Creators to Disney & Lionsgate (57:26) Competing with the Big Labs (Sora, Google, etc.) (59:58) Hyper-Personalized Content? Why It May Not Replace Film (01:01:13) Advice to Founders: Treat Your Company Like a Model — Always Learning (01:03:06) The Next 5 Years of Runway: Changing Creativity Forever

Granola is the rare AI startup that slipped into one of tech’s most crowded niches — meeting notes — and still managed to become the product founders and VCs rave about. In this episode, MAD Podcast host Matt Turck sits down with Granola co-founder & CEO Chris Pedregal to unpack how a two-person team in London turned a simple “second brain” idea into Silicon Valley’s favorite AI tool. Chris recounts a year in stealth onboarding users one by one, the 50 % feature-cut that unlocked simplicity, and why they refused to deploy a meeting bot or store audio even when investors said they were crazy.We go deep on the craft of building a beloved AI product: choosing meetings (not email) as the data wedge, designing calendar-triggered habit loops, and obsessing over privacy so users trust the tool enough to outsource memory. Chris opens the hood on Granola’s tech stack — real-time ASR from Deepgram & Assembly, echo cancellation on-device, and dynamic routing across OpenAI, Anthropic and Google models — and explains why transcription, not LLM tokens, is the biggest cost driver today. He also reveals how internal eval tooling lets the team swap models overnight without breaking the “Granola voice.”Looking ahead, Chris shares a roadmap that moves beyond notes toward a true “tool for thought”: cross-meeting insights in seconds, dynamic documents that update themselves, and eventually an AI coach that flags blind spots in your work. Whether you’re an engineer, designer, or founder figuring out your own AI strategy, this conversation is a masterclass in nailing product-market fit, trimming complexity, and future-proofing for the rapid advances still to come. Hit play, like, and subscribe if you’re ready to learn how to build AI products people can’t live without.GranolaWebsite - https://www.granola.aiX/Twitter - https://x.com/meetgranolaChris PedregalLinkedIn - https://www.linkedin.com/in/pedregalX/Twitter - https://x.com/cjpedregalFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Introduction: The Granola Story (01:41) Building a "Life-Changing" Product (04:31) The "Second Brain" Vision (06:28) Augmentation Philosophy (Engelbart), Tools That Shape Us (09:02) Late to a Crowded Market: Why it Worked (13:43) Two Product Founders, Zero ML PhDs (16:01) London vs. SF: Building Outside the Valley (19:51) One Year in Stealth: Learning Before Launch (22:40) "Building For Us" & Finding First Users (25:41) Key Design Choices: No Meeting Bot, No Stored Audio (29:24) Simplicity is Hard: Cutting 50% of Features (32:54) Intuition vs. Data in Making Product Decisions (36:25) Continuous User Conversations: 4–6 Calls/Week (38:06) Prioritizing the Future: Build for Tomorrow's Workflows (40:17) Tech Stack Tour: Model Routing & Evals (42:29) Context Windows, Costs & Inference Economics (45:03) Audio Stack: Transcription, Noise Cancellation & Diarization Limits (48:27) Guardrails & Citations: Building Trust in AI (50:00) Growth Loops Without Virality Hacks (54:54) Enterprise Compliance, Data Footprint & Liability Risk (57:07) Retention & Habit Formation: The "500 Millisecond Window" (58:43) Competing with OpenAI and Legacy Suites (01:01:27) The Future: Deep Research Across Meetings & Roadmap (01:04:41) Granola as Career Coach?

What happens when an internal hack turns into a $400 million AI rocket ship? In this episode, Matt Turck sits down with Boris Cherny, the creator of Claude Code at Anthropic, to unpack the wild story behind the fastest-growing AI coding tool on the planet.Boris reveals how Claude Code started as a personal productivity tool, only to become Anthropic’s secret weapon — now used by nearly every engineer at the company and rapidly spreading across the industry. You’ll hear how Claude Code’s “agentic” approach lets AI not just suggest code, but actually plan, edit, debug, and even manage entire projects—sometimes with a whole fleet of subagents working in parallel.We go deep on why Claude Code runs in the terminal (and why that’s a feature, not a bug), how its Claude.md memory files let teams build a living, shareable knowledge base, and why safety and human-in-the-loop controls are baked into every action. Boris shares real stories of onboarding times dropping from weeks to days, and how even non-coders are hacking Cloud Code for everything from note-taking to business metrics.AnthropicWebsite - https://www.anthropic.comX/Twitter - https://x.com/AnthropicAIBoris ChernyLinkedIn - https://www.linkedin.com/in/bchernyX/Twitter - https://x.com/bchernyFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro (01:15) Did You Expect Claude Code’s Success? (04:22) How Claude Code Works and Origins (08:05) Command Line vs IDE: Why Start Claude Code in the Terminal? (11:31) The Evolution of Programming: From Punch Cards to Agents (13:20) Product Follows Model: Simple Interfaces and Fast Evolution (15:17) Who Is Claude Code For? (Engineers, Designers, PMs & More) (17:46) What Can Claude Code Actually Do? (Actions & Capabilities) (21:14) Agentic Actions, Subagents, and Workflows (25:30) Claude Code’s Awareness, Memory, and Knowledge Sharing (33:28) Model Context Protocol (MCP) and Customization (35:30) Safety, Human Oversight, and Enterprise Considerations (38:10) UX/UI: Making Claude Code Useful and Enjoyable (40:44) Pricing for Power Users and Subscription Models (43:36) Real-World Use Cases: Debugging, Testing, and More (46:44) How Does Claude Code Transform Onboarding? (49:36) The Future of Coding: Agents, Teams, and Collaboration (54:11) The AI Coding Wars: Competition & Ecosystem (57:27) The Future of Coding as a Profession (58:41) What’s Next for Claude Code

Agentic commerce is no longer science fiction — it’s arriving in your browser, your development IDE, and soon, your bank statement. In this episode of The MAD Podcast, Matt Turck sits down with Emily Glassberg Sands, Stripe’s Head of Information, to explore how autonomous “buying bots” and the Model Context Protocol (MCP) are reshaping the very mechanics of online transactions. Emily explains why intent, not clicks, will become the primary interface for shopping and how Stripe’s rails are adapting for tokens, one-time virtual cards, and real-time risk scoring that can tell good bots from bad ones in milliseconds.We also go deep into Stripe's strategic AI choices. Drawing on $1.4 trillion in annual payment flow—1.3 percent of global GDP—Stripe decided to train its own payments foundation model, turning tens of billions of historical charges into embeddings that boost fraud-catch recall from 59 percent to 97 percent. Emily walks us through the tech: why they chose a BERT encoder over GPT-style decoders, how three MLEs in a “research bubble” birthed the model, and what it takes to run it in production with five-nines reliability and tight latency budgets.We zoom out to Stripe’s unique vantage point on the broader AI economy. Their data shows the top AI startups hitting $30 million in ARR three times faster than the fastest SaaS companies did a decade ago, with more than half of that revenue already coming from overseas markets. Emily unpacks the new billing playbook—usage-based pricing today, outcome-based pricing tomorrow—and explains why tiny teams of 20–30 people can now build global, vertically focused AI businesses almost overnight.StripeWebsite - https://stripe.comX/Twitter - https://x.com/stripe?Emily Glassberg SandsLinkedIn - https://www.linkedin.com/in/egsandsX/Twitter - https://x.com/emilygsandsFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro (01:45) How Big Is Stripe? Latest Stats Revealed (04:06) What Does “Head of Information” at Stripe Actually Do? (05:43) From Harvard to Stripe: Emily’s Unusual Journey (08:54) Why Stripe Built Its Own Foundation Model (13:19) Cracking the Code: How Stripe Handles Complex Payment Data (16:25) Foundation Model vs. Traditional ML: What’s Winning? (20:09) Inside Stripe’s Foundation Model: How It Was Built (24:35) How Stripe Makes AI Decisions Transparent (28:38) Where Stripe Uses AI (And Where It Doesn’t) (34:10) How Stripe’s AI Drives Revenue for Businesses (41:22) Real-Time Fraud Detection: Stripe’s Secret Sauce (42:51) The Future of Shopping: AI Agents & Agentic Commerce (46:20) How Agentic Commerce Is Changing Stripe (49:36) Stripe’s Vision for a World of AI-Powered Buyers (55:46) What Is MCP? Stripe’s Take on Agent-to-Agent Protocols (59:31) Stripe’s Data on AI Startups Monetizing 3× Faster (01:03:03) How AI Companies Go Global — From Day One (01:07:48) The New Rules: Billing & Pricing for AI Startups (01:10:57) How Stripe Builds AI Literacy Across the Company (01:14:05) Roadmap: Risk-as-a-Service, Order Intent, and Beyond

In this episode, Vercel CEO Guillermo Rauch goes deep on how V0, their text-to-app platform, has already generated over 100 million applications and doubled Vercel’s user base in under a year.Guillermo reveals how a tiny SWAT team inside Vercel built V0 from scratch, why “vibe coding” is making software creation accessible to everyone (not just engineers), and how the AI Cloud is automating DevOps, making cloud infrastructure self-healing, and letting companies expose their data to AI agents in just five lines of code.You’ll hear why “every company will have to rethink itself as a token factory,” how Vercel’s Next.js went from a conference joke to powering Walmart, Nike, and Midjourney, and why the next billion app creators might not write a single line of code. Guillermo breaks down the difference between vibe coding and agentic engineering, shares wild stories of users building apps from napkin sketches, and explains how Vercel is infusing “taste” and best practices directly into their AI models.We also dig into the business side: how Vercel’s AI-powered products are driving explosive growth, why retention and margins are strong, and how the company is adapting to a new wave of non-technical users. Plus: the future of MCP servers, the security challenges of agent-to-agent communication, and why prompting and AI literacy are now must-have skills.VercelWebsite - https://vercel.comX/Twitter - https://x.com/vercelGuillermo RauchLinkedIn - https://www.linkedin.com/in/rauchgX/Twitter - https://x.com/rauchgFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro (02:08) What Is V0 and Why Did It Take Off So Fast? (04:10) How Did a Tiny Team Build V0 So Quickly? (07:51) V0 vs Other AI Coding Tools (10:35) What is Vibe Coding? (17:05) Is V0 Just Frontend? Moving Toward Full Stack and Integrations (19:40) What Skills Make a Great Vibe Coder? (23:35) Vibe Coding as the GUI for AI: The Future of Interfaces (29:46) Developer Love = Agent Love (33:41) Having Taste as Developer (39:10) MCP Servers: The New Protocol for AI-to-AI Communication (43:11) Security, Observability, and the Risks of Agentic Web (45:25) Are Enterprises Ready for the Agentic Future? (49:42) Closing the Feedback Loop: Customer Service and Product Evolution (56:06) The Vercel AI Cloud: From Pixels to Tokens (01:10:14) How Vercel Adapts to the ICP Change? (01:13:47) Retention, Margins, and the Business of AI Products (01:16:51) The Secret Behind Vercel Last Year Growth (01:24:15) The Importance of Online Presence (01:30:49) Everything, Everywhere, All at Once: Being CEO 101 (01:34:59) Guillermo's Advice to Younger Self

Canva just announced $3 billion in ARR, 230 million monthly active users, and 24 million paying subscribers—including 95% of the Fortune 500. Even more impressive? They’ve been profitable for seven years while growing at 40–50% per year. In this episode, Canva’s Head of Engineering, Brendan Humphreys, reveals how he went from employee #12 to leading 2,300 engineers across continents, and why Canva’s “pragmatic excellence” lets them ship AI features at breakneck speed—like launching Canva Code to 100 million users in just three months.Brendan shares the story of Canva’s AI journey: building an in-house ML team back in 2017, acquiring visual AI startups like Kaleido and Leonardo AI, and why they use a hybrid of OpenAI, Anthropic, Google, and their own foundation models. He explains how Canva’s App Store gives niche AI startups instant access to millions, and why their $200M Creator Fund is designed to reward contributors in the AI era. You’ll also hear how AI tools like Copilot are making Canva’s senior engineers 30% more productive, why “vibe coding” isn’t ready for prime time, and the unique challenges of onboarding junior engineers in an AI-driven world.We also dig into Canva’s approach to technical debt, scaling from 12 to 5,000 employees, and why empathy is a core engineering skill at Canva. CanvaWebsite - https://www.canva.comX/Twitter - https://x.com/canvaBrendan HumphreysLinkedIn - https://www.linkedin.com/in/brendanhumphreysX/Twitter - https://x.com/brendanhFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro (01:14) Canva’s Mind-Blowing Growth and Profitable Journey (03:41) Why Brendan Left Atlassian to Join a Tiny Startup (06:17) What Being a Founder Taught Brendan About Leadership (07:24) Growing with Canva: From 12 Employees to 2,300 Engineers (10:02) How Canva Runs a Global Team from Sydney to Europe (13:16) Is AI a Threat or a Superpower for Canva? (15:22) The Real Story Behind Canva’s AI and Machine Learning Team (17:23) How Canva Ships New AI Features So Fast (19:19) A Tour of Canva’s Latest AI-Powered Products (21:03) From Design Tool to All-in-One Productivity Platform (26:21) Keeping Up the Pace: How Canva Moves So Quickly (30:22) The Future: AI Agents, Copilots, and Smarter Workflows (33:14) How AI Tools Are Changing the Way Engineers Work (35:47) Rethinking Hiring and Training in the Age of AI (37:01) Why Empathy Matters in Engineering at Canva (39:41) Building vs. Buying: How Canva Chooses Its AI Tech (41:23) Lessons Learned: Technical Debt and Scaling Pains (51:18) Shipping Fast Without Breaking Things (53:08) What’s Next: AI Video, New Features, and Big Ambitions