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The MAD Podcast with Matt Turck

Matt Turck
The MAD Podcast with Matt Turck
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  • Goodbye Excel? AI Agents for Self-Driving Finance – Pigment CEO
    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
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  • AI Video’s Wild Year – Runway CEO on What’s Next
    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
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  • How to Build a Beloved AI Product - Granola CEO Chris Pedregal
    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?
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  • Anthropic's Surprise Hit: How Claude Code Became an AI Coding Powerhouse
    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
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  • Ex‑DeepMind Researcher Misha Laskin on Enterprise Super‑Intelligence | Reflection AI
    What if your company had a digital brain that never forgot, always knew the answer, and could instantly tap the knowledge of your best engineers, even after they left? Superintelligence can feel like a hand‑wavy pipe‑dream— yet, as Misha Laskin argues, it becomes a tractable engineering problem once you scope it to the enterprise level. Former DeepMind researcher Laskin is betting on an oracle‑like AI that grasps every repo, Jira ticket and hallway aside as deeply as your principal engineer—and he’s building it at Reflection AI.In this wide‑ranging conversation, Misha explains why coding is the fastest on‑ramp to superintelligence, how “organizational” beats “general” when real work is on the line, and why today’s retrieval‑augmented generation (RAG) feels like “exploring a jungle with a flashlight.” He walks us through Asimov, Reflection’s newly unveiled code‑research agent that fuses long‑context search, team‑wide memory and multi‑agent planning so developers spend less time spelunking for context and more time shipping.We also rewind his unlikely journey—from physics prodigy in a Manhattan‑Project desert town, to Berkeley’s AI crucible, to leading RLHF for Google Gemini—before he left big‑lab comfort to chase a sharper vision of enterprise super‑intelligence. Along the way: the four breakthroughs that unlocked modern AI, why capital efficiency still matters in the GPU arms‑race, and how small teams can lure top talent away from nine‑figure offers.If you’re curious about the next phase of AI agents, the future of developer tooling, or the gritty realities of scaling a frontier‑level startup—this episode is your blueprint.Reflection AIWebsite - https://reflection.aiLinkedIn - https://www.linkedin.com/company/reflectionaiMisha LaskinLinkedIn - https://www.linkedin.com/in/mishalaskinX/Twitter - https://x.com/mishalaskinFIRSTMARKWebsite - 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 (01:42) Reflection AI: Company Origins and Mission (04:14) Making Superintelligence Concrete (06:04) Superintelligence vs. AGI: Why the Goalposts Moved (07:55) Organizational Superintelligence as an Oracle (12:05) Coding as the Shortcut: Hands, Legs & Brain for AI (16:00) Building the Context Engine (20:55) Capturing Tribal Knowledge in Organizations (26:31) Introducing Asimov: A Deep Code Research Agent (28:44) Team-Wide Memory: Preserving Institutional Knowledge (33:07) Multi-Agent Design for Deep Code Understanding (34:48) Data Retrieval and Integration in Asimov (38:13) Enterprise-Ready: VPC and On-Prem Deployments (39:41) Reinforcement Learning in Asimov's Development (41:04) Misha's Journey: From Physics to AI (42:06) Growing Up in a Science-Driven Desert Town (53:03) Building General Agents at DeepMind (56:57) Founding Reflection AI After DeepMind (58:54) Product-Driven Superintelligence: Why It Matters (01:02:22) The State of Autonomous Coding Agents (01:04:26) What's Next for Reflection AI
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About The MAD Podcast with Matt Turck

The MAD Podcast with Matt Turck, is a series of conversations with leaders from across the Machine Learning, AI, & Data landscape hosted by leading AI & data investor and Partner at FirstMark Capital, Matt Turck.
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