Podcast
The MAD Podcast with Matt Turck
Hosted by Matt Turck · EN
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.
40episodes
Episodes
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The Rise of Agentic Commerce — Emily Glassberg Sands (Stripe)
Jul 10, 202501:15:14Summary readyAgentic 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
SummaryAI Engineering Revolution: Winners, Chaos & What’s Next | FirstMark
Jul 3, 202500:49:53Summary readyWelcome to a special FirstMark Deep Dive edition of the MAD Podcast. In this episode, Matt Turck and David Waltcher unpack the explosive impact of generative AI on engineering — hands-down the biggest shift the field has seen in decades. You’ll get a front-row seat to the real numbers and stories behind the AI code revolution, including how companies like Cursor hit a $500M valuation in record time, and why GitHub Copilot now serves 15 million developers.Matt and David break down the six trends that shaped the last 20 years of developer tools, and reveal why coding is the #1 use case for generative AI (hint: it’s all about public data, structure, and ROI). You’ll hear how AI is making engineering teams 30-50% faster, but also why this speed is breaking traditional DevOps, overwhelming QA, and turning top engineers into full-time code reviewers.We get specific: 82% of engineers are already using AI to write code, but this surge is creating new security vulnerabilities, reliability issues, and a total rethink of team roles. You’ll learn why code review and prompt engineering are now the most valuable skills, and why computer science grads are suddenly facing some of the highest unemployment rates.We also draw wild historical parallels—from the Gutenberg Press to the Ford assembly line—to show how every productivity boom creates new problems and entire industries to solve them. Plus: what CTOs need to know about hiring, governance, and architecture in the AI era, and why being “AI native” can make a startup more credible than a 10-year-old giant.Matt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturckDavid WaltcherLinkedIn - https://www.linkedin.com/in/davidwaltcherX/Twitter - https://x.com/davidwaltcherFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCap(00:00) Intro & episode setup (01:50) The 6 waves that led to GenAI engineering (04:30) Why coding is such fertile ground for Generative AI (08:25) Break-out dev-tool winners: Cursor, Copilot, Replit, V0 (11:25) Early stats: Teams Are Shipping Code Faster with AI (13:32) Copilots vs Autonomous Agents: The Current Reality (14:14) Lessons from History: Every Tech Boom Creates New Problems (21:53) FirstMark Survey: The Headaches AI Is Creating for Developers (22:53) What’s Now Breaking: Security, CI/CD flakes, QA Overload (29:16) The New CTO Playbook to Adapt to the AI Revolution (33:23) What Happens to Engineering Orgs if Everyone is a Coder? (40:19) Founder opportunities & the dev-tool halo effect (44:24) The Built-in Credibility of AI-Native Startups (46:16) The Irony of Dev Tools As Biggest Winners in the AI Gold Rush (47:43) What’s Next for AI and Engineering?
SummaryGuillermo Rauch: Why Software Development Will Never Be the Same
Jun 26, 202501:45:40Summary readyIn 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
SummaryInside Canva’s $3B ARR AI Design Rocketship — CTO Brendan Humphreys on Magic Studio & Canva Code
Jun 20, 202500:56:38Summary readyCanva 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
SummaryGitHub CEO: The AI Coding Gold Rush, Vibe Coding & Cursor
Jun 12, 202501:04:46Summary readyAI coding is in full-blown gold-rush mode, and GitHub sits at the epicenter. In this episode, GitHub CEO Thomas Dohmke tells Matt Turck how a $7.5 B acquisition in 2018 became a $2 B ARR rocket ship, and reveals how Copilot was born from a secret AI strategy years before anyone else saw the opportunity.We dig into the dizzying pace of AI innovation: why developer tools are suddenly the fastest-growing startups in history, how GitHub’s multi-model approach (OpenAI, Anthropic Claude 4, Gemini 2.5, and even local LLMs) gives you more choice and speed, and why fine-tuning models might be overrated. Thomas explains how Copilot keeps you in the “magic flow state,” how even middle schoolers are using it to hack Minecraft. The conversation then zooms out to the competitive battlefield: Cursor’s $10 B valuation, Mistral’s new code model, and a wave of AI-native IDE forks vying for developer mind-share. We discuss why 2025’s “coding agents” could soon handle 90 % of the world’s code, the survival of SaaS and why the future of coding is about managing agents, not just writing code.GitHubWebsite - https://github.com/X/Twitter - https://x.com/githubThomas DohmkeLinkedIn - https://www.linkedin.com/in/ashtomX/Twitter - https://twitter.com/ashtomFIRSTMARKWebsite - 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 (01:50) Why AI Coding Is Ground Zero for Generative AI (02:40) The $7.5B GitHub Acquisition: Microsoft’s Strategic Play (06:21) GitHub’s Role in the Azure Cloud Ecosystem (10:25) How GitHub Copilot Beat Everyone to Market (16:09) Copilot & VS Code Explained for Non-Developers (21:02) GitHub Models: Multi-Model Choice and What It Means (25:31) The Reality of Fine-Tuning AI Models for Enterprise (29:13) The Dizzying Pace and Political Economy of AI Coding Tools (36:58) Competing and Partnering: Microsoft’s Unique AI Strategy (41:29) Does Microsoft Limit Copilot’s AI-Native Potential? (46:44) The Bull and Bear Case for AI-Native IDEs Like Cursor (52:09) Agent Mode: The Next Step for AI-Powered Coding (01:00:10) How AI Coding Will Change SaaS and Developer Skills
SummaryInside the Paper That Changed AI Forever - Cohere CEO Aidan Gomez on 2025 Agents
Jun 5, 202501:02:24Summary readyWhat really happened inside Google Brain when the “Attention is All You Need” paper was born? In this episode, Aidan Gomez — one of the eight co-authors of the Transformers paper and now CEO of Cohere — reveals the behind-the-scenes story of how a cold email and a lucky administrative mistake landed him at the center of the AI revolution.Aidan shares how a group of researchers, given total academic freedom, accidentally stumbled into one of the most important breakthroughs in AI history — and why the architecture they created still powers everything from ChatGPT to Google Search today.We dig into why synthetic data is now the secret sauce behind the world’s best AI models, and how Cohere is using it to build enterprise AI that’s more secure, private, and customizable than anything else on the market. Aidan explains why he’s not interested in “building God” or chasing AGI hype, and why he believes the real impact of AI will be in making work more productive, not replacing humans.You’ll also get a candid look at the realities of building an AI company for the enterprise: from deploying models on-prem and air-gapped for banks and telecoms, to the surprising demand for multimodal and multilingual AI in Japan and Korea, to the practical challenges of helping customers identify and execute on hundreds of use cases.CohereWebsite - https://cohere.comX/Twitter - https://x.com/cohereAidan GomezLinkedIn - https://ca.linkedin.com/in/aidangomezX/Twitter - https://x.com/aidangomezFIRSTMARKWebsite - 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 (02:00) The Story Behind the Transformers Paper (03:09) How a Cold Email Landed Aidan at Google Brain (10:39) The Initial Reception to the Transformers Breakthrough (11:13) Google’s Response to the Transformer Architecture (12:16) The Staying Power of Transformers in AI (13:55) Emerging Alternatives to Transformer Architectures (15:45) The Significance of Reasoning in Modern AI (18:09) The Untapped Potential of Reasoning Models (24:04) Aidan’s Path After the Transformers Paper and the Founding of Cohere (25:16) Choosing Enterprise AI Over AGI Labs (26:55) Aidan’s Perspective on AGI and Superintelligence (28:37) The Trajectory Toward Human-Level AI (30:58) Transitioning from Researcher to CEO (33:27) Cohere’s Product and Platform Architecture (37:16) The Role of Synthetic Data in AI (39:32) Custom vs. General AI Models at Cohere (42:23) The AYA Models and Cohere Labs Explained (44:11) Enterprise Demand for Multimodal AI (49:20) On-Prem vs. Cloud (50:31) Cohere’s North Platform (54:25) How Enterprises Identify and Implement AI Use Cases (57:49) The Competitive Edge of Early AI Adoption (01:00:08) Aidan’s Concerns About AI and Society (01:01:30) Cohere’s Vision for Success in the Next 3–5 Years
SummaryAI That Ends Busy Work — Hebbia CEO on “Agent Employees”
May 29, 202500:48:24Summary readyWhat if the smartest people in finance and law never had to do “stupid tasks” again? In this episode, we sit down with George Sivulka, founder of Hebbia, the AI company quietly powering 50% of the world’s largest asset managers and some of the fastest-growing law firms. George reveals how Hebbia’s Matrix platform is automating the equivalent of 50,000 years of human reading — every year — and why the future of work is hybrid teams of humans and AI “agent employees.” You’ll get the inside story on how Hebbia went from a stealth project at Stanford to a multinational company trusted by the Department of Defense, and why their spreadsheet-inspired interface is leaving chatbots in the dust. George breaks down the technical secrets behind Hebbia’s ISD architecture (and why they killed RAG), how they process billions of pages with near-zero hallucinations, and what it really takes to sell AI into the world’s most regulated industries.We also dive into the future of organizational design, why generalization beats specialization in AI, and how “prompting is the new management skill.” Plus: the real story behind AI hallucinations, the myth of job loss, and why naiveté might be the ultimate founder superpower.HebbiaWebsite - https://www.hebbia.comTwitter - https://x.com/HebbiaAIGeorge SivulkaLinkedIn - https://www.linkedin.com/in/sivulkaTwitter - https://x.com/gsivulkaFIRSTMARKWebsite - 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 (01:46) What is Hebbia (02:49) Evolving Hebbia’s mission (04:45) The founding story and Stanford's inspiration (09:45) The rise of agent employees and AI in organizations (12:36) The future of AI-powered work (15:17) AI research trends (19:49) Inside Matrix: Hebbia’s flagship AI platform (24:02) Why Hebbia isn’t just another chatbot (28:27) Moving beyond RAG: Hebbia’s unique architecture (34:10) Tackling hallucinations in high-stakes AI (35:59) Research culture and avoiding industry groupthink (39:40) Innovating go-to-market and enterprise sales (41:57) Real-world value: Cost savings and new revenue (43:49) How AI is changing junior roles (45:55) Leadership and perspective as a young founder (47:16) Hebbia’s roadmap: Success in the next 3 years
SummaryAI Eats the World: Benedict Evans on What Really Matters Now
May 22, 202501:15:09Summary readyWhat if the “AI revolution” is actually… stuck in the messy middle? In this episode, Benedict Evans returns to tackle the big question we left hanging a year ago: Is AI a true paradigm shift, or just another tech platform shift like mobile or cloud? One year later, the answer is more complicated — and more revealing — than anyone expected.Benedict pulls back the curtain on why, despite all the hype and model upgrades, the core LLMs are starting to look like commodities. We dig into the real battlegrounds: distribution, brand, and the race to build sticky applications. Why is ChatGPT still topping the App Store charts while Perplexity and Claude barely register outside Silicon Valley? Why did OpenAI just hire a CEO of Applications, and what does that signal about the future of AI products?We go deep on the “probabilistic” nature of LLMs, why error rates are still the elephant in the room, the future of consumer AI (is there a killer app beyond chatbots and image generators?), the impact of generative content on e-commerce and advertising, and whether “AI agents” are the next big thing — or just another overhyped demo.And, we ask: What happened to AI doomerism? Why did the existential risk debate suddenly vanish, and what risks should we actually care about?Benedict EvansLinkedIn - https://www.linkedin.com/in/benedictevansThreads - https://www.threads.net/@benedictevansFIRSTMARKWebsite - 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 (01:47) Is AI a Platform Shift or a Paradigm Shift? (07:21) Error Rates and Trust in AI (15:07) Adapting to AI’s Capabilities (19:18) Generational Shifts in AI Usage (22:10) The Commoditization of AI Models (27:02) Are Brand and Distribution the Real Moats in AI? (29:38) OpenAI: Research Lab or Application Company? (33:26) Big Tech’s AI Strategies: Apple, Google, Meta, AWS (39:00) AI and Search: Is ChatGPT a Search Engine? (42:41) Consumer AI Apps: Where’s the Breakout? (45:51) The Need for a GUI for AI (48:38) Generative AI in Social and Content (51:02) The Business Model of AI: Ads, Memory, and Moats (55:26) Enterprise AI: SaaS, Pilots, and Adoption (01:00:08) The Future of AI in Business (01:05:11) Infinite Content, Infinite SKUs: AI and E-commerce (01:09:42) Doomerism, Risks, and the Future of AI
SummaryNomic: Truly open AI
Feb 1, 2024Tap to summarizeWe are so excited today to be joined by Brandon Duderstadt, CEO + Cofounder, and Zach Nussbaum, Machine Learning Engineer, from Nomic AI. They discuss how Nomic AI is building tools like Atlas + GPT4all that enable everyone to interact with AI scale datasets and run models on consumer computers - and - stay tuned for an exciting announcement about their newest product release later in the podcast.Thanks for joining us for the first episode of Season 2 of the MAD Podcast. We will be back to our regular weekly schedule with new conversations with leaders in the Machine Learning, AI and data landscape. If you like this show, you can find the video recording of this episode -- along with many, many more -- on the Data Driven NYC channel on YouTube.NOMIC AIwww.nomic.aitwitter.com/nomic_aiwww.linkedin.com/in/bstadt/www.linkedin.com/in/zach-nussbaum/FIRSTMARKfirstmark.comtwitter.com/FirstMarkCapMatt Turck (Managing Director)www.linkedin.com/in/turck/twitter.com/mattturckData Driven NYC YouTube ChannelFirstMark Capital Eventbrite0:46 - What is Nomic AI & how it got started5:57 - Building GPT4ALL7:23 - Running LLMs on a personal computer16:00 - Nomic Atlas21:33 - Launching Nomic Embed28:10 The Importance of Data in AI31:10 - Benchmarking LLMs32:56 - The Future of Nomic AI36: 22 - Building an AI Startup in New York39:10 - Nomic AI is hiring
Transcribe →Weights & Biases: The Developer-First MLOps Platform with CEO Lukas Biewald
Aug 9, 2023Tap to summarizeToday we have the pleasure of talking to Lukas Biewald, CEO of Weights and Biases for a conversation about Lukas' entrepreneurial journey building two companies in the MLOps space, the current capabilities of the Weights & Biases platform, lessons learned on the Go to Market front, and more!
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