The MAD Podcast with Matt Turck

AI 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

What 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

What 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

What 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

What happens when you try to build the “General Electric of AI” with just 14 people? In this episode, Jeremy Howard reveals the radical inside story of Answer AI — a new kind of AI R&D lab that's not chasing AGI, but instead aims to ship thousands of real-world products, all while staying tiny, open, and mission-driven.Jeremy shares how open-source models like DeepSeek and Qwen are quietly outpacing closed-source giants, why the best new AI is coming out of China. You'll hear the surprising truth about the so-called “DeepSeek moment,” why efficiency and cost are the real battlegrounds in AI, and how Answer AI's “dialogue engineering” approach is already changing lives—sometimes literally.We go deep on the tools and systems powering Answer AI's insane product velocity, including Solve It (the platform that's helped users land jobs and launch startups), Shell Sage (AI in your terminal), and Fast HTML (a new way to build web apps in pure Python). Jeremy also opens up about his unconventional path from philosophy major and computer game enthusiast to world-class AI scientist, and why he believes the future belongs to small, nimble teams who build for societal benefit, not just profit.Fast.aiWebsite - https://www.fast.aiX/Twitter - https://twitter.com/fastdotaiAnswer.aiWebsite - https://www.answer.ai/X/Twitter - https://x.com/answerdotaiJeremy HowardLinkedIn - https://linkedin.com/in/howardjeremyX/Twitter - https://x.com/jeremyphowardFIRSTMARKWebsite - 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 (01:39) Highlights and takeaways from ICLR Singapore (02:39) Current state of open-source AI (03:45) Thoughts on Microsoft Phi and open source moves (05:41) Responding to OpenAI's open source announcements (06:29) The real impact of the Deepseek ‘moment' (09:02) Progress and promise in test-time compute (10:53) Where we really stand on AGI and ASI (15:05) Jeremy's journey from philosophy to AI (20:07) Becoming a Kaggle champion and starting Fast.ai (23:04) Answer.ai mission and unique vision (28:15) Answer.ai's business model and early monetization (29:33) How a small team at Answer.ai ships so fast (30:25) Why Devin AI agent isn't that great (33:10) The future of autonomous agents in AI development (34:43) Dialogue Engineering and Solve It (43:54) How Answer.ai decides which projects to build (49:47) Future of Answer.ai: staying small while scaling impact

InfluxDB just dropped its biggest update ever — InfluxDB 3.0 — and in this episode, we go deep with the team behind the world's most popular open-source time series database. You'll hear the inside story of how InfluxDB grew from 3,000 users in 2015 to over 1.3 million today, and why the company decided to rewrite its entire architecture from scratch in Rust, ditching Go and moving to object storage on S3.We break down the real technical challenges that forced this radical shift: the “cardinality problem” that choked performance, the pain of linking compute and storage, and why their custom query language (Flux) failed to catch on, leading to a humbling embrace of SQL as the industry standard. You'll learn how InfluxDB is positioning itself in a world dominated by Databricks and Snowflake, and the hard lessons learned about monetization when 1.3 million users only yield 2,600 paying customers.InfluxDataWebsite - https://www.influxdata.comX/Twitter - https://twitter.com/InfluxDBEvan KaplanLinkedIn - https://www.linkedin.com/in/kaplanevanX/Twitter - https://x.com/evankaplanFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturckFoursquare: Website - https://foursquare.comX/Twitter - https://x.com/Foursquare IG - instagram.com/foursquare (00:00) Intro (02:22) The InfluxDB origin story and why time series matters (06:59) The cardinality crisis and why Influx rebuilt in Rust (09:26) Why SQL won (and Flux lost) (16:34) Why UnfluxData bets on FDAP (22:51) IoT, Tesla Powerwalls, and real-time control systems (27:54) Competing with Databricks, Snowflake, and the “lakehouse” world (31:50) Open Source lessons, monetization, & what's next

Sigma Computing recently hit $100M in ARR — planning on doubling revenue again this year— and in this episode, CEO Mike Palmer reveals exactly how they did it by throwing out the old BI playbook. We open with the provocative claim that “the world did not need another BI tool,” and dig into why the last 20 years of business intelligence have been “boring.” He explains how Sigma's spreadsheet-like interface lets anyone analyze billions of rows in seconds, and lives on top of Snowflake and Databricks, with no SQL required and no data extractions.Mike shares the inside story of Sigma's journey: why they shut down their original product to rebuild from scratch, how Sutter Hill Ventures' unique incubation model shaped the company, what it took to go from $2M to $100M ARR in just three years and raise a $200M round — even as the growth stage VC market dried up. We get into the technical details behind Sigma's architecture: no caching, no federated queries, and real-time, Google Sheets-style collaboration at massive scale—features that have convinced giants like JP Morgan and ExxonMobil to ditch legacy dashboards for good.We also tackle the future of BI and the modern data stack: why 99.99% of enterprise data is never touched, what's about to happen as the stack consolidates, and why Mike thinks “text-to-SQL” AI is a “terrible idea.” This episode is full of "spicey takes" - Mike shares his thoughts on how Google missed the zeitgeist, the reality behind Microsoft Fabric, when engineering hubris leads to failure, and many more. SigmaWebsite - https://www.sigmacomputing.comX/Twitter - https://x.com/sigmacomputingMike PalmerLinkedIn - https://www.linkedin.com/in/mike-palmer-51a154FIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturckFoursquare: Website - https://foursquare.comX/Twitter - https://x.com/Foursquare IG - instagram.com/foursquare (00:00) Intro (01:46) Why traditional BI is boring (04:15) What is business intelligence? (06:03) Classic BI roles and frustrations (07:09) Sigma's origin story: Sutter Hill & the Snowflake echo (09:02) The spreadsheet problem: why nothing changed since 1985 (14:04) Rebooting the product during lockdown (16:14) Building a spreadsheet UX on top of Snowflake/Databricks (18:55) No caching, no federation: Sigma's architectural choices (20:28) Spreadsheet interface at scale (21:32) Collaboration and real-time data workflows (24:15) Semantic layers, data governance & trillion-row performance (25:57) The modern data stack: fragmentation and consolidation (28:38) Democratizing data (29:36) Will hyperscalers own the data stack? (34:12) AI, natural language, and the limits of text-to-SQL

A week after OpenAI's o3/o4-mini volleyed with Google's Gemini 2.5 Flash, I sat down with Arvind Jain— ex-Google search luminary, Rubrik co-founder, and now CEO of Glean —just as his company released its agentic reasoning platform and swirled with rumors of a new round at a $7 billion valuation. We open on that whirlwind: why the model race is accelerating, why enterprises still gravitate to closed models, and when open-source variants finally take over. Arvind argues that LLMs should “fade into the background,” leaving application builders to pick the right engine for each task.From there, we trace Glean's three-act arc—enterprise search powered by transformers (2019), retrieval-augmented chat the moment ChatGPT hit, and now agents that have already logged 50 million real actions inside Glean enterprise customers. Arvind lifts the hood on permission-aware ranking, tool-use orchestration, and the routing layer that swaps Gemini for GPT on the fly. Along the way, he answers the hard questions: Do agents really double efficiency? Where's the moat when every startup promises the same? Why are humans still in the review loop, and for how long?The conversation crescendos with a vision of work where every employee is flanked by a team of proactive AI coworkers—all drawing from a horizontal knowledge layer that knows the firm's language better than any newcomer. If you want to know what's actually working with AI in the enterprise, how to build agents that deliver ROI, and what the next era of work will look like, this episode is packed with specifics, technical insights, and bold predictions from one of the sharpest minds in the space.GleanWebsite - https://www.glean.comX/Twitter - https://x.com/gleanaiArvind Jain LinkedIn - https://www.linkedin.com/in/jain-arvindX/Twitter - https://x.com/jainarvindFIRSTMARKWebsite - 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 & Glean's $7B valuation rumor (02:01) The AI model explosion: open vs. closed in the enterprise (06:19) Why enterprises choose open source AI (and when) (10:33) The agent era: what are AI agents and why now? (12:41) Automating business processes: real-world agent use cases (16:46) Are we there yet? The reality of AI agents in 2025 (19:24) Glean's origin story: reinventing enterprise search (26:38) Glean agents: from apps to agentic platforms (31:22) Horizontal vs. vertical: Glean's strategic platform choice (34:14) How Glean's enterprise search works (39:34) Staying LLM-agnostic: integrating new AI models (42:11) The architecture of Glean agents: tool use and beyond (43:50) Data flywheels and personalization in Glean (47:06) Moats, competition, and the future of work with AI agents

In this episode, we sit down with Aaron Levie, CEO and co-founder of Box, for a wide-ranging conversation that's equal parts insightful, technical, and fun. We kick things off with a candid discussion about what it's like to be a public company CEO during times of volatility, and then rewind to the early days of Box — from dorm room experiments to cold emailing Mark Cuban and dropping out of college.From there, we dive deep into how AI is transforming the enterprise. Aaron shares how Box is layering AI agents, RAG systems, and model orchestration on top of decades of enterprise content infrastructure — and why “95% of enterprise data is underutilized.”We explore what's actually working with AI in production, what's still breaking, and how companies can avoid common pitfalls. From building hubs for document-specific RAG to thinking through agent-to-agent interoperability, Aaron unpacks the architecture of Box's AI platform — and why they're staying out of the model training wars entirely. We also dig into AI culture inside large organizations, the trade-offs of going public, and why Levie believes every enterprise interface is about to change.Whether you're a founder, engineer, enterprise buyer, or just trying to figure out how AI agents will reshape knowledge work, this conversation is full of practical insights and candid takes from one of the sharpest minds in tech.BoxWebsite - https://www.box.comX/Twitter - https://twitter.com/BoxAaron LevieLinkedIn - https://www.linkedin.com/in/boxaaronX/Twitter - https://x.com/levieFIRSTMARKWebsite - 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(01:51) Navigating uncertainty as a public company CEO(14:48) The Box origin story: college, cold emails, and Mark Cuban(23:39) Cloud transformation vs. the AI wave(30:15) The reality of AI in the enterprise: proof of concept vs. deployment(34:37) Inside Box's AI platform: Hubs, agents, and more(44:15) Why Box won't build its own model (and the dangers of fine-tuning)(51:51) What's working — and what's not — with AI agents(1:04:42) Building an AI culture at Box(1:13:22) The future of enterprise software and Box's roadmap

In this episode, we sit down with Sridhar Ramaswamy, CEO of Snowflake, for an in-depth conversation about the company's transformation from a cloud analytics platform into a comprehensive AI data cloud. Sridhar shares insights on Snowflake's shift toward open formats like Apache Iceberg and why monetizing storage was, in his view, a strategic misstep.We also dive into Snowflake's growing AI capabilities, including tools like Cortex Analyst and Cortex Search, and discuss how the company scaled AI deployments at an impressive pace. Sridhar reflects on lessons from his previous startup, Neeva, and offers candid thoughts on the search landscape, the future of BI tools, real-time analytics, and why partnering with OpenAI and Anthropic made more sense than building Snowflake's own foundation models.SnowflakeWebsite - https://www.snowflake.comX/Twitter - https://x.com/snowflakedbSridhar RamaswamyLinkedIn - https://www.linkedin.com/in/sridhar-ramaswamyX/Twitter - https://x.com/RamaswmySridharFIRSTMARKWebsite - 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 and current market tumult(02:48) The evolution of Snowflake from IPO to Today(07:22) Why Snowflake's earliest adopters came from financial services(15:33) Resistance to change and the philosophical gap between structured data and AI(17:12) What is the AI Data Cloud?(23:15) Snowflake's AI agents: Cortex Search and Cortex Analyst(25:03) How did Sridhar's experience at Google and Neeva shape his product vision?(29:43) Was Neeva simply ahead of its time?(38:37) The Epiphany mafia(40:08) The current state of search and Google's conundrum(46:45) “There's no AI strategy without a data strategy”(56:49) Embracing Open Data Formats with Iceberg(01:01:45) The Modern Data Stack and the future of BI(01:08:22) The role of real-time data(01:11:44) Current state of enterprise AI: from PoCs to production(01:17:54) Building your own models vs. using foundation models(01:19:47) Deepseek and open source AI(01:21:17) Snowflake's 1M Minds program(01:21:51) Snowflake AI Hub

In this fascinating episode, we dive deep into the race towards true AI intelligence, AGI benchmarks, test-time adaptation, and program synthesis with star AI researcher (and philosopher) Francois Chollet, creator of Keras and the ARC AGI benchmark, and Mike Knoop, co-founder of Zapier and now co-founder with Francois of both the ARC Prize and the research lab Ndea. With the launch of ARC Prize 2025 and ARC-AGI 2, they explain why existing LLMs fall short on true intelligence tests, how new models like O3 mark a step change in capabilities, and what it will really take to reach AGI.We cover everything from the technical evolution of ARC 1 to ARC 2, the shift toward test-time reasoning, and the role of program synthesis as a foundation for more general intelligence. The conversation also explores the philosophical underpinnings of intelligence, the structure of the ARC Prize, and the motivation behind launching Ndea — a ew AGI research lab that aims to build a "factory for rapid scientific advancement." Whether you're deep in the AI research trenches or just fascinated by where this is all headed, this episode offers clarity and inspiration.NdeaWebsite - https://ndea.comX/Twitter - https://x.com/ndeaARC PrizeWebsite - https://arcprize.orgX/Twitter - https://x.com/arcprizeFrançois CholletLinkedIn - https://www.linkedin.com/in/fcholletX/Twitter - https://x.com/fcholletMike KnoopX/Twitter - https://x.com/mikeknoopFIRSTMARKWebsite - 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 (01:05) Introduction to ARC Prize 2025 and ARC-AGI 2 (02:07) What is ARC and how it differs from other AI benchmarks (02:54) Why current models struggle with fluid intelligence (03:52) Shift from static LLMs to test-time adaptation (04:19) What ARC measures vs. traditional benchmarks (07:52) Limitations of brute-force scaling in LLMs (13:31) Defining intelligence: adaptation and efficiency (16:19) How O3 achieved a massive leap in ARC performance (20:35) Speculation on O3's architecture and test-time search (22:48) Program synthesis: what it is and why it matters (28:28) Combining LLMs with search and synthesis techniques (34:57) The ARC Prize structure: efficiency track, private vs. public (42:03) Open source as a requirement for progress (44:59) What's new in ARC-AGI 2 and human benchmark testing (48:14) Capabilities ARC-AGI 2 is designed to test (49:21) When will ARC-AGI 2 be saturated? AGI timelines (52:25) Founding of NDEA and why now (54:19) Vision beyond AGI: a factory for scientific advancement (56:40) What NDEA is building and why it's different from LLM labs (58:32) Hiring and remote-first culture at NDEA (59:52) Closing thoughts and the future of AI research

In 2022, Lin Qiao decided to leave Meta, where she was managing several hundred engineers, to start Fireworks AI. In this episode, we sit down with Lin for a deep dive on her work, starting with her leadership on PyTorch, now one of the most influential machine learning frameworks in the industry, powering research and production at scale across the AI industry. Now at the helm of Fireworks AI, Lin is leading a new wave in generative AI infrastructure, simplifying model deployment and optimizing performance to empower all developers building with Gen AI technologies.We dive into the technical core of Fireworks AI, uncovering their innovative strategies for model optimization, Function Calling in agentic development, and low-level breakthroughs at the GPU and CUDA layers.Fireworks AIWebsite - https://fireworks.aiX/Twitter - https://twitter.com/FireworksAI_HQLin QiaoLinkedIn - https://www.linkedin.com/in/lin-qiao-22248b4X/Twitter - https://twitter.com/lqiaoFIRSTMARKWebsite - 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(01:20) What is Fireworks AI?(02:47) What is PyTorch?(12:50) Traditional ML vs GenAI(14:54) AI's enterprise transformation(16:16) From Meta to Fireworks(19:39) Simplifying AI infrastructure(20:41) How Fireworks clients use GenAI(22:02) How many models are powered by Fireworks(30:09) LLM partitioning(34:43) Real-time vs pre-set search(36:56) Reinforcement learning(38:56) Function calling(44:23) Low-level architecture overview(45:47) Cloud GPUs & hardware support(47:16) VPC vs on-prem vs local deployment(49:50) Decreasing inference costs and its business implications(52:46) Fireworks roadmap(55:03) AI future predictions

Retrieval-Augmented Generation (RAG) has become a dominant architecture in modern AI deployments, and in this episode, we sit down with Douwe Kiela, who co-authored the original RAG paper in 2020. Douwe is now the founder and CEO of Contextual AI, a startup focusing on helping enterprises deploy RAG as an agentic system. We start the conversation with Douwe's thoughts on the very latest advancements in Generative AI, including GPT 4.5, DeepSeek and the exciting paradigm shift towards test time compute, as well as the US-China rivalry in AI. We then dive into RAG: definition, origin story and core architecture. Douwe explains the evolution of RAG into RAG 2.0 and Agentic RAG, emphasizing the importance of self-learning systems over individual models and the role of synthetic data. We close with the challenges and opportunities of deploying AI in real-world enterprise, discussing the balance between accuracy and the inherent inaccuracies of AI systems.Contextual AIWebsite - https://contextual.aiX/Twitter - https://x.com/ContextualAIDouwe KielaLinkedIn - https://www.linkedin.com/in/douwekielaX/Twitter - https://x.com/douwekielaFIRSTMARKWebsite - 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(01:57) Thoughts on the latest AI models: GPT-4.5, Sonnet 3.7, Grok 3(04:50) The test time compute paradigm shift(06:47) Unsupervised learning vs reasoning: a false dichotomy(07:30) The significance of DeepSeek(10:29) USA vs. China: is the AI war overblown?(12:19) Controlling AI hallucinations at the model level(13:51) RAG: definition and origin story(18:46) Why the Transformers paper initially felt underwhelming(20:41) The core architecture of RAG(26:06) RAG vs. fine-tuning vs. long context windows(30:53) RAG 2.0: Thinking in systems and not models(31:28) Data extraction and data curation for RAG(35:59) Contextual Language Models (CLMs)(38:04) Finetuning and alignment techniques: GRIT, KTO, LENS(40:40) Agentic RAG(41:36) General vs. specialized RAG agents(44:35) Synthetic data in AI(45:51) Deploying AI in the enterprise(48:07) How tolerant are enterprises to AI hallucinations?(49:35) The future of Contextual AI

In this episode, we dive into how AI is transforming video editing with Gaurav Misra, the CEO of Captions. Launched in New York in 2021, Captions already empowers over 10 million creators worldwide, leveraging AI to make video production as simple as clicking a button.Discover the strategic framework that led to the inception of Captions, and learn how the founders identified societal changes and technological advancements to build a groundbreaking company. We explore the challenges and opportunities of building an AI product for video editing, including how Captions is outpacing traditional content production workflows.Gaurav shares insights into the future of video editing, the role of AI in democratizing video production, and the unique approach Captions takes to differentiate itself from industry giants like Adobe and Capcut. CaptionsWebsite - https://www.captions.aiX/Twitter - https://x.com/getcaptionsappGaurav MisraLinkedIn - https://www.linkedin.com/in/gamisra1X/Twitter - https://x.com/gmharharFIRSTMARKWebsite - 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(01:30) What is Captions?(03:43) How did Captions start?(08:25) The strategy behind launching Captions(12:32) How is Captions different from other editing tools?(14:13) How does it compare to CapCut?(18:22) Who is the typical Captions user?(20:13) Why ‘Captions'?(23:47) Captions' product suite for production and editing(26:37) AI models powering Captions(36:22) AI lipsync(38:49) Personalized fine-tuned models for creators?(39:38) Building models vs. building wrappers(43:09) Cloud AI vs. Local AI(45:19) Optimizing for low latency(48:07) AI/ML stack at Captions(51:10) “Hallucinations are a feature, not a bug”(53:19) Prompt engineering(54:12) Have we passed the uncanny valley for AI avatars?(01:01:47) The impact of deepfakes(01:04:33) CapCut ban and its effects(01:05:05) Evolving from paid to freemium(01:07:42) Building a company on foundation models(01:09:01) Running an AI company in New York

AI customer service agents are quickly replacing the often clunky AI chatbots of years past, and revolutionizing how we all interact with customer service. In this episode, we dive into this rapid transformation with Mike Murchison, CEO of Ada, a fast-growing leader in the space.Mike shares how harnessing the power of several Generative AI models enables Ada to automate up to 83% of customer interactions, providing a seamless and empathetic service that rivals, and will soon surpass, human agents. We explore the challenges and triumphs of deploying AI in customer service in this new era, from the intricacies of model orchestration to the importance of resolution and empathy. Mike also teases the future of agentic AI in the enterprise, where AI agents collaborate across departments to innovate and improve products.AdaWebsite - https://www.ada.cxX/Twitter - https://x.com/ada_cxMike MurchisonLinkedIn - https://www.linkedin.com/in/mikemurchisonX/Twitter - https://x.com/mimurchisonFIRSTMARKWebsite - 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(02:27) Why is customer service a perfect use case for AI?(03:36) Why didn't foundation models replace AI “thin wrappers” out of the box?(05:27) What is Ada?(10:41) Reasoning engine, model orchestration, instruction following, routing(15:45) Hybrid systems, finetuning, customization(18:28) Prompt engineering, observability, self-improvement(22:07) RAG (Retrieval-Augmented Generation) and AI as a judge(23:06) Guardrails and security(24:33) Should we expect perfection from AI?(26:14) Measuring “resolution”(29:29) What actions can Ada AI Agents take?(32:12) Authentication and personalization(35:09) Handoff vs human delegation(38:12) ACX (AI Customer Experience) and the future of customer service professionals(42:13) Leveraging analytics and customer support data(45:54) AI agents for cross-selling and upselling(48:25) Traditional AI chatbots vs the new generation of AI Agents(51:24) Emotion, empathy, personality(54:56) Transparency and AI improvement(57:58) Managing AI: the measure-coach-improve loop(1:00:15) Ada Voice and Email(1:06:25) Future predictions for AI(1:07:56) Multi-agent collaboration

Replit is one of the most visible and exciting companies reshaping how we approach software and application development in the Generative AI era. In this episode, we sit down with its CEO, Amjad Masad, for an in-depth discussion on all things AI, agents, and software. Amjad shares the journey of building Replit, from its humble beginnings as a student side project to becoming a major player in Generative AI today. We also discuss the challenges of launching a startup, the multiple attempts to get into Y Combinator, the pivotal moment when Paul Graham recognized Replit's potential, and the early bet on integrating AI and machine learning into the core of Replit. Amjad dives into the evolving landscape of AI and machine learning, sharing how these technologies are reshaping software development. We explore the concept of coding agents and the impact of Replit's latest innovation, Replit Agent, on the software creation process. Additionally, Amjad reflects on his time at Codecademy and Facebook, where he worked on groundbreaking projects like React Native, and how those experiences shaped his entrepreneurial journey. We end with Amjad's view on techno-optimism and his belief in an energized Silicon Valley. Replit Website - https://replit.com X/Twitter - https://x.com/Replit Amjad Masad LinkedIn - https://www.linkedin.com/in/amjadmasad X/Twitter - https://x.com/amasad FIRSTMARK Website - https://firstmark.com X/Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ X/Twitter - https://twitter.com/mattturck (00:00) Intro (01:36) The origins of Replit (15:54) Amjad's decision to restart Replit (19:00) Joining Y Combinator (30:06) AI and ML at Replit (32:31) Explain Code (39:09) Replit Agent (52:10) Balancing usability for both developers and non-technical users (53:22) Sonnet 3.5 stack (58:43) The challenge of AI evaluation (01:00:02) ACI vs. HCI (01:05:02) Will AI replace software development? (01:10:15) If anyone can build an app with Replit, what's the next bottleneck? (01:14:31) The future of SaaS in an AI-driven world (01:18:37) Why Amjad embraces techno-optimism (01:20:36) Defining civilizationism (01:23:11) Amjad's perspective on government's role

In this episode, we explore the cutting-edge world of data infrastructure with Justin Borgman, CEO of Starburst — a company transforming data analytics through its open-source project, Trino, and empowering industry giants like Netflix, Airbnb, and LinkedIn. Justin takes us through Starburst's journey from a Yale University spin-out to a leading force in data innovation, discussing the shift from data lakes to lakehouses, the rise of open formats like Iceberg as the future of data storage, and the role of AI in modern data applications. We also dive into how Starburst is staying ahead by balancing on-prem and cloud offerings while emphasizing the value of optionality in a rapidly evolving, data-driven landscape. Starburst Data Website - https://www.starburst.io X/Twitter - https://x.com/starburstdata Justin Borgman LinkedIn - https://www.linkedin.com/in/justinborgman X/Twitter - https://x.com/justinborgman FIRSTMARK Website - https://firstmark.com X/Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ X/Twitter - https://twitter.com/mattturck (00:00) Intro (01:32) What is Starburst? (02:32) Understanding the data layer (05:06) Justin Borgman's story before Starburst (10:41) The evolution of Presto into Trino (13:20) Lakehouse vs. data lake vs. data warehouse (22:06) Why Starburst backed the lakehouse from the start (23:20) Starburst Enterprise (27:31) Cloud vs. on-prem (29:10) Starburst Galaxy (31:23) Dell Data Lakehouse (32:13) Starburst's data architecture explained (38:30) The rise of data apps (38:54) Starburst AML (40:41) “We actually built the Galaxy twice” (43:13) Managing multiple products at scale (45:14) “We founded the company on the idea of optionality” (47:20) Iceberg (48:01) How open-source acquisitions work (51:39) Why Snowflake embraced Iceberg (53:15) Data mesh (55:31) AI at Starburst (57:16) Key takeaways from go-to-market strategies (01:01:18) Lessons from the Dell partnership (01:04:40) Predictions for 2025

As AI takes over the world, data is more than ever “the new oil”, and data engineering is the discipline that makes data usable behind the scenes. In this episode, we dive deep into the present and future of data engineering with Ben Rogojan, also known as the Seattle Data Guy. A seasoned data engineering consultant, Ben has built a big brand and reputation in the field with over 100k followers on platforms like YouTube and Substack. We started the conversation with a deep dive into data engineering as a profession: what do data engineers actually do? What is the career path, and what should aspiring data engineers learn? We then explored some of the biggest stories of 2024 (including the rise of Iceberg) and went into some predictions for 2025, as a way to discuss some key topics everyone should be familiar with in data engineering, including the integration of AI in data workflows, the potential for automation, and why SQL isn't going anywhere. Discover how companies are navigating the complexities of data infrastructure, the rise of open table formats like Iceberg, and the ongoing battle between data giants like Snowflake and Databricks. Ben Rogojan Website - https://www.theseattledataguy.com Newsletter - https://seattledataguy.substack.com LinkedIn - https://www.linkedin.com/company/seattle-data-guy X/Twitter - https://x.com/seattledataguy FIRSTMARK Website - https://firstmark.com X/Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ X/Twitter - https://twitter.com/mattturck (00:00) Intro (01:20) Why 2025 will be huge for data engineering (02:55) The story of the Seattle Data Guy (06:51) What exactly is data engineering? (07:41) Data, AI, and ML: where do they overlap? (09:23) Data analyst vs. data engineer vs. data scientist: what's the difference? (11:20) A day in the life of a data engineer (12:58) Data engineering: Silicon Valley vs. everywhere else (15:27) How to become an AI engineer (28:46) Will AI replace AI engineers? (33:42) Why is the data world so complex? (36:53) The functional consolidation of the data world (38:34) Big data stories from 2024 (39:28) Why Iceberg is a game-changer (46:02) How startups manage data in their early days (48:44) Seattle Data Guy's favorite tools (50:09) Bold predictions for 2025

In this episode, we dive deep into the world of AI engineering with Chip Huyen, author of the excellent, newly released book "AI Engineering: Building Applications with Foundation Models". We explore the nuances of AI engineering, distinguishing it from traditional machine learning, discuss how foundational models make it possible for anyone to build AI applications and cover many other topics including the challenges of AI evaluation, the intricacies of the generative AI stack, why prompt engineering is underrated, why the rumors of the death of RAG are greatly exaggerated, and the latest progress in AI agents. Book: https://www.oreilly.com/library/view/ai-engineering/9781098166298/ Chip Huyen Website - https://huyenchip.com LinkedIn - https://www.linkedin.com/in/chiphuyen Twitter/X - https://x.com/chipro FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (02:45) What is new about AI engineering? (06:11) The product-first approach to building AI applications (07:38) Are AI engineering and ML engineering two separate professions? (11:00) The Generative AI stack (13:00) Why are language models able to scale? (14:45) Auto-regressive vs. masked models (16:46) Supervised vs. unsupervised vs. self-supervised (18:56) Why does model scale matter? (20:40) Mixture of Experts (24:20) Pre-training vs. post-training (28:43) Sampling (32:14) Evaluation as a key to AI adoption (36:03) Entropy (40:05) Evaluating AI systems (43:21) AI as a judge (46:49) Why prompt engineering is underrated (49:38) In-context learning (51:46) Few-shot learning and zero-shot learning (52:57) Defensive prompt engineering (55:29) User prompt vs. system prompt (57:07) Why RAG is here to stay (01:00:31) Defining AI agents (01:04:04) AI agent planning (01:08:32) Training data as a bottleneck to agent planning

In this episode, we sit down with Florian Douetteau, co-founder and CEO of Dataiku, a global category leader in enterprise AI and a fixture on the Forbes Cloud 100 list and in the Gartner Leader Quadrant. Florian shares his journey from a Parisian student fascinated by functional programming to leading a global enterprise software company. We discuss how Dataiku bridges the gap between technical and business teams to democratize AI in the enterprise, the challenges of selling to enterprise clients, and how Dataiku acts as an orchestration layer for Generative AI, helping businesses manage complex data processes and control AI, so they can build more with AI. Dataiku Website - https://www.dataiku.com/ X/Twitter - https://twitter.com/dataiku Florian Douetteau LinkedIn - https://www.linkedin.com/in/fdouetteau X/Twitter - https://twitter.com/fdouetteau FIRSTMARK Website - https://firstmark.com X/Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ X/Twitter - https://twitter.com/mattturck (00:00) Intro (02:08) Florian's life before Dataiku (06:58) Creation of Dataiku (12:08) Secret behind the Dataiku's name (12:47) How does Dataiku stay insightful about the future? (14:46) Building a platform, not just a tool (17:26) How to sell to the enterprise from the beginning (20:09) Dataiku platform today (26:55) Data is always the problem (28:50) LLM Mesh (36:02) Will Gen AI replace ML? (39:41) Managing Gen AI and traditional AI on one platform (40:37) Gen AI deployment in the enterprise (48:33) Dataiku's roadmap (50:28) What has changed with the company's growth?

In this episode, we dive into the world of generative AI with May Habib, co-founder of Writer, a platform transforming enterprise AI use. May shares her journey from Qordoba to Writer, emphasizing the impact of transformers in AI. We explore Writer's graph-based RAG approach, and their AI Studio for building custom applications. We also discuss Writer's Autonomous Action functionality, set to revolutionize AI workflows by enabling systems to act autonomously, highlighting AI's potential to accelerate product development and market entry with significant increases in capacity and capability. Writer Website - https://writer.com X/Twitter - https://x.com/get_writer  May Habib LinkedIn - https://www.linkedin.com/in/may-habib X/Twitter - https://x.com/may_habib FIRSTMARK Website - https://firstmark.com X/Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ X/Twitter - https://twitter.com/mattturck This session was recorded live at a recent Data Driven NYC, our in-person, monthly event series, hosted at Ramp's beautiful HQ. If you are ever in New York, you can join the upcoming events here: https://www.eventbrite.com/o/firstmark-capital-2215570183 (00:00) Intro (01:47) What is Writer? (02:52) Writer's founding story (06:54) Writer is a full-stack company. Why? (07:57) Writer's enterprise use cases (10:51) Knowledge Graph (17:59) Guardrails (20:17) AI Studio (23:16) Palmyra X 004 (27:18) Current state of the AI adoption in enterprises (28:57) Writer's sales approach (31:25) What May Habib is excited about in AI (33:14) Autonomous Action use cases

Nathan Benaich, founder and GP at VC firm Air Street Capital, publishes every year "State of AI", one of the most widely-read and comprehensive reports on all things AI across research, industry, and policy. In this episode, we sit down with Nathan to discuss some of the highlights of the 2024 edition of the report, including the "vibes" shift in the industry from existential risk concerns last year to the current monetization race, the financial success of the foundation model labs, how a generative AI app could top the Apple Store charts in 2025, and the challenges facing humanoid robotics. State of AI 2024 report: https://www.stateof.ai/2024-report-launch State of AI 2024 video: https://youtu.be/EVMbnPOuUl0 Air Street Capital Website - https://www.airstreet.com X/Twitter - https://x.com/airstreet Nathan Benaich LinkedIn - https://www.linkedin.com/in/nathanbenaich X/Twitter - https://x.com/nathanbenaich FirstMark Website - https://firstmark.com X/Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ X/Twitter - https://twitter.com/mattturck (01:08) Who is Nathan Benaich? (04:57) "Vibe" shift in AI (09:13) Current state of the foundation models (22:01) AI companies vs. SaaS (23:31) AI consumer apps (25:49) AI applications from a VC's perspective (29:25) "You don't need to be an AI engineer to build an AI company" (30:46) AI in robotics (34:36) AI regulations in Europe (40:55) Predictions on the future of AI (49:30) Nathan Benaich's favorite sources of information

In this special episode of the MAD Podcast, Matt Turck and Aman Kabeer from FirstMark delve into the AI market from a venture investor perspective, in the final weeks of an incredibly packed and exciting 2024. They comment on their favorite news stories, such as OpenAI's record-breaking $6.6 billion funding round and the massive $200B investments in AI infrastructure by Meta, Google, and Amazon. They tackle the latest trends in funding and valuations in both public and private markets, debate the critical question of whether we're in an AI bubble, examine the current state of AI demand, the potential of scaling laws, and the future of AI-driven innovation. They then discuss where they see opportunities for startups and investors across AI hardware, compute, foundation models, AI tooling, and both consumer and enterprise AI applications. FIRSTMARK Website - https://firstmark.com X/Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ X/Twitter - https://twitter.com/mattturck Aman Kabeer (Investor) LinkedIn - https://www.linkedin.com/in/aman-kabeer/ X/Twitter - https://x.com/AmanKabeer11 (00:00) Intro (02:20) The Year of Record-Breaking Evaluations and Investments (05:23) AI's Environmental Impact and Nuclear Revival (06:48) AI Valuations and Market Dynamics (17:01) Are We in an AI Bubble? (25:01) AI Progress and Demand (35:06) AI's Role in Consumer Applications (41:02) AI's Influence on SaaS and Business Models (50:55) AI's Role in Enterprise Transformation (01:04:00) The Future of AI: Apps and Agents

Before he founded Modal, Erik Bernhardsson created Spotify's music recommendation system. Today he's bringing a consumer app approach to radically simplifying developer experience for data and AI projects on the Modal platform. In this episode, we dive into the broader AI compute landscape, discussing the roles of hyperscalers, GPU clouds, inference platforms, and the emergence of alternative AI cloud providers. Erik gives us a product tour of the Modal platform, provides insights into the AI industry's shift from training to inference as the primary use case, and speculates on the future of AI-native consumer applications. Learn about Modal's commitment to fast feedback loops, their cloud maximalist approach, their dedication to building a product that developers truly love, as well as founder lessons Erik learned along the way. Erik's blog: https://erikbern.com "It's hard to write code for humans": https://erikbern.com/2024/09/27/its-hard-to-write-code-for-humans Modal Website - https://modal.com Twitter - https://x.com/modal_labs Erik Bernhardsson LinkedIn - https://www.linkedin.com/in/erikbern Twitter - https://x.com/bernhardsson FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:35) What is Modal? (02:18) Current state of AI compute space (09:54) Erik's path to starting Modal (13:57) Core elements of the Modal platform (28:52) Is serverless the right level of abstraction for AI compute? (33:35) Balancing costs: GPU vendor fees vs. customer pricing (37:56) Designing products for humans (42:43) Modal's early go-to-market motion (45:32) Managing early engineering team (48:26) The only correct way to add a new function to the company (50:07) Building company in NYC (52:05) Modal's roadmap (54:04) Erik's predictions on AI

A founding engineer on Google BigQuery and now at the helm of MotherDuck, Jordan Tigani challenges the decade-long dominance of Big Data and introduces a compelling alternative that could change how companies handle data. Jordan discusses why Big Data technologies are an overkill for most companies, how MotherDuck and DuckDB offer fast analytical queries, and lessons learned as a technical founder building his first startup. Watch the episode with Tomasz Tunguz: https://youtu.be/gU6dGmZzmvI Website - https://motherduck.com Twitter - https://x.com/motherduck Jordan Tigani LinkedIn - https://www.linkedin.com/in/jordantigani Twitter - https://x.com/jrdntgn FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (00:56) What is the Small Data? (06:56) Marketing strategy of MotherDuck (08:39) Processing Small Data with Big Data stack (15:30) DuckDB (17:21) Creation of DuckDB (18:48) Founding story of MotherDuck (24:08) MotherDuck's community (25:25) MotherDuck of today ($100M raised) (33:15) Why MotherDuck and DuckDB are so fast? (39:08) The limitations and the future of MotherDuck's platform (39:49) Small Models (42:37) Small Data and the Modern Data Stack (46:47) Making things simpler with a shift from Big Data to Small Data (50:04) Jordan Tigani's entrepreneurial journey (58:31) Outro

With a $4.5B valuation, 5M AI builders and 1M public AI models, Hugging Face has emerged as the key collaboration platform for AI, and the heart of the global open source AI community. In this episode of The MAD Podcast, we sit down with Clément Delangue, its co-founder and CEO, and delve deep into Hugging Face's journey from a fun chatbot to a central hub for AI innovation, the impact of open-source AI and the importance of community-driven development, and discuss the shift from text to other AI modalities like audio, video, chemistry, and biology. We also cover the evolution of Hugging Face's business model, and the different approach to company culture that the founders have implemented over the years. Hugging Face Website - https://huggingface.co Twitter - https://x.com/huggingface Clem Delangue LinkedIn - https://www.linkedin.com/in/clementdelangue Twitter - https://x.com/clemdelangue FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:46) Miami vs. New York vs. San Francisco (03:25) Current state of open source AI (11:12) Government regulation of AI (13:18) What is open source AI? (15:21) Open source AI: China vs U.S. (18:32) LLMs vs. SLMs (22:01) Are commercial LLMs just 'Training Wheels' for enterprises? (24:26) Software 2.0: built with AI (28:03) Hugging Face founding story (37:03) Are there any competitors? (44:06) Most interesting models on Hugging Face (50:35) Shifting focus in enterprise solutions (55:06) Bloom & Idefix (58:44) The culture of Hugging Face (01:04:44) The future of Hugging Face

This episode is a captivating conversation with Richard Socher, serial entrepreneur, investor, and AI researcher. Richard elaborates on why he likens the impact of AI to the Industrial Revolution, the Enlightenment, and the Renaissance, discusses important current issues in AI, such as scaling laws and agents, provides a behind-the-scenes tour of YOU.com and its evolving business model, and finally describes his current investment strategy in AI startups. You.com Website - https://you.com/business Twitter - https://x.com/youdotcom Richard Socher LinkedIn - https://www.linkedin.com/in/richardsocher Twitter - https://x.com/richardsocher FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (02:00) "AI era is the Industrial Revolution, Renaissance, and the Enlightenment combined" (07:49) Top-performers in the Age of AI (11:15) Comeback of the Renaissance Person (13:05) People tried to stop Richard from doing deep learning research. Why? (14:34) Jevons paradox of intelligence (17:08) Scaling Laws in Deep Learning (23:23) Can Deep Learning and Rule-Based AI coexist? (25:42) Post-transformers AI Architecture (28:20) Achieving AGI and ASI (36:43) AI for everyday tasks: how far is it? (44:50) AI Agents (55:45) Evolution of You.com (01:02:11) Technical side of You.com (01:06:46) Is AI getting cheaper? (01:13:05) What is AIX Ventures? (01:16:36) VC landscape of 2024 (01:24:31) Research vs Entrepreneurship (01:26:12) OpenAI's transformation and its impact on the industry

In this episode, we sit down with Tobie Morgan Hitchcock, the founder of SurrealDB, to dive deep into the evolving world of databases and the future of data storage, querying, and real-time analytics. SurrealDB isn't just another database — it's a multi-model database that merges document, graph, and time-series data, making it easier for developers to consolidate their backend without sacrificing performance. You'll learn how SurrealDB separates storage from compute for scalability, its innovative take on graph databases, and the radical decision to rewrite the entire platform in Rust. Tobie also shares how SurrealDB is designed to handle real-time analytics and integrate AI/ML models directly inside the database. If you're curious about the future of databases, this episode is packed with insights you won't want to miss. SurrealDB Website - https://surrealdb.com Twitter - https://x.com/SurrealDB Tobie Morgan Hitchcock: LinkedIn - https://www.linkedin.com/in/tobiemorganhitchcock Twitter - https://x.com/tobiemh FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro(02:03) What is SurrealDB?(02:53) How did SurrealDB get started?(09:10) The Challenges of Building a Database from Scratch(10:36) Why SurrealDB Chose Rust(12:54) A Deep Dive into SurrealDB's Unique Features(19:30) Why Now?(26:32) What Sets SurrealDB Apart from Other Databases(30:01) SurrealDB's Role in the Future of AI and Machine Learning(32:45) Why Developers Are Choosing SurrealDB(36:14) What's New in SurrealDB 2.0?(40:10) SurrealDB Cloud: Scalability Meets Simplicity(42:21) How SurrealDB Fits into the Competitive Database Landscape(45:37) Early Lessons from Building SurrealDB(48:34) Co-Founding SurrealDB with His Brother

In this episode, we dive deep into the story of how Datadog evolved from a single product to a multi-billion dollar observability platform with its co-founder, Olivier Pomel. Olivier shares exclusive insights on Datadog's unique approach to product development—why they avoid the "Apple approach" of building in secret and instead work closely with customers from day one. You'll hear about the early days when Paul Graham of Y Combinator turned down Datadog, questioning their lack of a first product. Olivier also reveals the strategies behind their iterative product launches and why they insist on charging early to ensure they're delivering real value. The second half of the conversation is focused on all things AI and data at Datadog - the company's initial reluctance to use AI in its products, how Generative AI changed everything, and Datadog's current AI efforts including Watchdog, Bits AI and Toto, their new time series foundational model. We close the episode by asking Olivier about his thoughts on the topic du jour: founder mode! ▶️ Listen to 2020 Data Driven NYC episode with Oliver Pomel: https://www.youtube.com/watch?v=oXKEFHeEvMs DATADOG Website - https://www.datadoghq.com Twitter - https://x.com/datadoghq Olivier Pomel LinkedIn - https://www.linkedin.com/in/olivierpomel Twitter - https://x.com/oliveur FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck

In this episode, we sit down with Ali Dasdan, CTO of ZoomInfo, a titan in the B2B sector, who harnesses vast datasets and advanced AI to redefine sales and marketing for over 35,000 global customers with $21.2 billion in annualized revenue. We delve deep into ZoomInfo's AI initiatives, including their transformative 'Copilot,' explore sophisticated data management, and discuss their dual platforms catering to internal and customer-facing needs. ZoomInfo Website - https://www.zoominfo.com Twitter - https://x.com/zoominfo Ali Dasdan LinkedIn - https://www.linkedin.com/in/dasdan Twitter - https://x.com/alidasdan FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (02:03) What is ZoomInfo (04:47) Data as service (06:15) Ali Dasdan's story (07:31) Organization of ZoomInfo (10:48) ZoomInfo Data Platform (21:02) Lessons from building a data platform (23:19) AI application at ZoomInfo (27:58) ZoomInfo's Copilot (37:43) ZoomInfo AI toolstack (39:30) Working with small vs. big companies in the AI business (43:39) Using data and AI for internal productivity

In this episode, we sit down with Eric Glyman, co-founder of Ramp, the company that revolutionized finance management to become a powerhouse valued at $7.6 billion. Eric shares the tradition of counting the days since Ramp's founding and how it fosters a sense of urgency and productivity, explains the use of AI to automate expense management and fraud detection, and gives an inside look at Ramp's cutting-edge AI products, including the Ramp Intelligence Suite and experimental agentic AI use cases. Ramp Website - https://www.ramp.com Twitter - https://x.com/tryramp Eric Glyman LinkedIn - https://www.linkedin.com/in/eglyman Twitter - https://x.com/eglyman FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:49) What is Ramp? (04:25) How did the company start? (09:18) Technical aspects of Ramp infrastructure (12:17) "We can tell you if you're paying too much" (14:20) Data privacy at Ramp (16:13) Data infrastructure tools used at Ramp (17:58) Traditional AI use cases (24:51) GenAI use cases (27:47) AI/human interaction (33:32) Ramp Intelligence Suite (39:38) How Ramp keeps high product release and product velocity (42:37) How did Ramp get to product-market fit? (45:54) Eric's perspective on building a company in NYC

In this episode, we reconnect with Sharon Zhou, co-founder and CEO of Lamini, to dive deep into the ever-evolving world of enterprise AI. We discuss how the AI hype is evolving and what enterprises are doing to stay ahead, break down the different players in the Inference market, explore how Memory Tuning is reducing hallucinations in AI models, the role of agents in enterprise AI, and the challenges of making them real-time and reliable. Lamini Website - https://www.lamini.ai Twitter - https://x.com/laminiai Sharon Zhou LinkedIn - https://www.linkedin.com/in/zhousharon Twitter - https://x.com/realsharonzhou FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (02:18) The state of the AI market in July, 2024 (10:51) What is Lamini? (11:43) What is Inference? (15:36) GPU shortage in the enterprise (18:06) AMD vs Nvidia (22:10) What is Lamini's final product? (25:30) What is Memory Tuning? (29:01) What is LoRA? (32:39) More on Memory Tuning (35:51) Sharon's perspective on AI agents (40:01) What is next for Lamini? (41:54) Reasoning vs pure compute in AI

In this episode, we sit down with Jeremy Kahn, the AI Editor at Fortune Magazine, who has recently published a book called "Mastering AI: A Survival Guide to Our Superpowered Future". Jeremy shares his unique insights on AI's potential risks and transformative benefits, including the importance of UI design in maximizing AI's utility, the potential for AI to create a "winner takes most" economy, and the need for thoughtful AI regulation to mitigate risks without stifling innovation. Book: https://www.amazon.com/Mastering-AI-Survival-Superpowered-Future/dp/1668053322 Jeremy Kahn LinkedIn - https://www.linkedin.com/in/jeremy-kahn-01100462 Twitter - https://x.com/jeremyakahn FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:43) Why the UI design is important for AI? (04:32) The book is called "Mastering AI". Why? (12:03) Automation Bias vs Automation Surprise (20:16) The role of AI in the future of science and art (25:32) "I think mass unemployment is a red herring, but we might see a lot of disruption" (34:19) Jeremy's perspective on Agentic AI (36:29) Does AI development need to be regulated? (38:56) Should we worry about the AGI and Superintelligence? (42:18) Who provided the most thoughtful conversation for the book? (43:57) "I didn't use AI for the book at all" (46:20) Jeremy's work at Fortune

In this episode, we sit down with Azeem Azhar, an expert on AI and technologies, whose weekly newsletter "The Exponential View" (www.exponentialview.co) is read by nearly two hundred thousand people from around the world. We delve into the nuances of AI adoption, discussing how LLM's are reshaping industries and what this means for corporate leaders, the dynamics between the U.S., China, and Europe in the AI race, and the concept of sovereign AI. Azeem Azhar Website - https://www.exponentialview.co Twitter - https://x.com/azeem FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (02:05) What does the "Exponential" really mean? (05:43) "Moore's law has not died" (11:52) Claude is the Macintosh of AI. What does it mean? (25:57) How does AI affect the enterprise? (34:06) Asia is more optimistic about AI than the West. Why? (38:42) Azeem's perspective on the sovereign AI (45:19) AI in the modern warfare (48:47) What is the Exponential asymmetry? (51:59) Energy transition and the influence of AI on it (55:21) Big Oil vs Chinese Solar: who's going to win? (59:18) AI opens new possibilities for everyone. How?

In this episode, we sat down with Aaron Katz, the CEO of ClickHouse, a company that went from an open-source analytical database into a highly successful cloud service, utilized by Spotify, Netflix, Disney, and many more. Aaron Katz provides intriguing insights into the challenges of transitioning an open-source project into a thriving business, ClickHouse's go-to-market strategy, the role of technical support in pre-sales, and the strategic decision to avoid traditional SDR and CSM roles. CLICKHOUSE Website - https://clickhouse.com/ Twitter - https://x.com/clickhousedb Aaron Katz LinkedIn - https://www.linkedin.com/in/aaron-katz-5762094 Twitter - https://x.com/ceo_clickhouse FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro(00:56) What is ClickHouse?(04:28) What are the use cases for ClickHouse?(06:17) Reducing the latency: why the world shifts to real-time(09:05) How did ClickHouse evolve from an open-source to a cloud product?(15:01) "Open source is the future of software"(17:27) Self-hosted deployments(18:45) ClickHouse's roadmap(20:51) Is there a real-time data stack?(22:25) ClickHouse partners in data ingestion(24:32) Who are ClickHouse's main competitors?(27:35) ClickHouse's sales process(36:44) Is partnerships a good go-to-market strategy?(37:44) When is the right time for startups to start partnering?(38:22) Aaron's story of becoming the CEO(43:50) Team and culture when working on two continents(46:15) What's next?

In this episode, we sit down with Daniel Dines, the co-founder and CEO of UiPath. From a small rented apartment in Bucharest to $1.3 billion in revenue, UiPath's story is one of perseverance, innovation, and strategic pivots. Daniel shares his insights on the pivotal moments that shaped UiPath, how to build a robust go-to-market strategy, the role of partnerships, and the lessons learned in hiring and managing a sales organization. UIPath Website - https://www.uipath.com/ Twitter - https://x.com/UiPath Daniel Dines LinkedIn - https://www.linkedin.com/in/danieldines Twitter - https://x.com/danieldines FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:38) UiPath was founded in an apartment in Bucharest. How did it all start? (08:05) Building a global product (11:26) The growth stage. (18:50) "We were AI from the beginning" (20:10) Raising the first round of funding. (23:48) Working with the board. (25:11) How did UiPath expand from the Romanian to the global market? (35:00) Process Mining, Task Mining, and Communications Mining. (41:41) The Automation Layer explained. (45:28) The use cases for using AI in UiPath's automations (56:22) UiPath's strategy for Gen AI adoption. (58:27) The team. (59:42) How important are partnerships for enterprise (01:02:48) Recruiting the best salespeople in the industry (01:07:10) Scaling from a software engineer to the CEO of a large company.

In this episode, we sit down with Howie Liu, co-founder and CEO of Airtable, to explore the incredible journey of Airtable from its early days to becoming a powerhouse in the enterprise software space. Howie provides a candid look at the challenges and learnings from transitioning Airtable from a PLG product to an enterprise platform, how companies are transforming their marketing operations with AI, and the transformative potential of AI in automating workflows and enhancing business processes. AIRTABLE Website - https://www.airtable.com/ Twitter - https://x.com/airtable Howie Liu LinkedIn - https://www.linkedin.com/in/howieliu/ Twitter - https://x.com/howietl FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro(02:40) What is Airtable in 2024?(05:35) How does Airtable apply AI to its products?(11:56) What are the AI use cases in Airtable?(18:35) The tech behind Airtable's AI capabilities(22:22) Is Airtable going to become an AI-first company?(25:15) Will AI kill programming as we know it?(29:24) How do big enterprises think about AI?(34:46) How did Airtable go from PLG to a large enterprise product?(41:00) AI Categories(47:47) "We definitely had our hiccups"(51:20) Was PLG a ZIRP-era phenomenon?(56:29) Howie's journey as a CEO

In this episode, we sat down with Tomasz Tunguz (https://twitter.com/ttunguz), the founder of Theory Ventures and a leading voice in the tech investment space. We discussed the transformative potential of Ethereum as a database company, the importance of data security in a decentralized world, and the evolving landscape of AI technologies from foundational models to AI-native applications.

In this episode, we sat down with Renen Hallak, founder and CEO of VAST Data, a $9 billion company that's shaking the foundations of data storage, databases and compute functionality. Through the conversation, we explore VAST's perspective on AI infrastructure, the process of selling over a billion dollars worth of software, and the technical innovations behind disaggregated, shared-everything architecture. VAST Data Website - https://www.vastdata.com/ Twitter - https://twitter.com/VAST_Data Renen Hallak LinkedIn - https://www.linkedin.com/in/renenh/ FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro(01:40) What is VAST Data?(02:56) The company was started in stealth mode. Why?(03:42) Did VAST get lucky with the gen AI explosion?(04:27) VAST Data founding story(05:57) How does the company work across 2 continents?(06:48) What made you think that you can disrupt the market?(09:23) VAST architecture explained(23:08) Moving from data storage to databases(25:01) What was the hardest thing to build?(26:32) How does VAST work with open source(26:54) A glimpse into the future products(28:22) The world without VAST: how it would've looked like(29:45) Who were VAST's first customers?(30:56) How do hedge funds use VAST?(32:08) VAST's sales strategy(34:04) Renen's transition from technical founder to CEO(36:01) How do you hire great people?(37:07) What was the hardest thing on your journey as a CEO?(38:43) $9B CEO daily routine(40:17) Difference between offices in NY and Israel(42:07) Renen's learnings from sales

In this episode, we sat down with Morgan McGuire, Chief Scientist of Roblox, and the mind behind the magic of the virtual universe. Together we explore the spectrum of creativity on Roblox, from no-code experiences to professional game development, dive deep into the cutting-edge AI tools Roblox is deploying, and how these tools are democratizing game development. Tune in to embark on a journey into the heart of creativity, technology, and community with Roblox. This is not just about playing games; it's about creating the future, one experience at a time. ROBLOX Website - https://www.roblox.com Twitter - https://twitter.com/Roblox Morgan McGuire LinkedIn - https://www.linkedin.com/in/morgan-mcguire-660120210 Twitter - https://twitter.com/casualeffects FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:05) Roblox is not a game, but a platform (10:03) How does Roblox leverage Gen AI? (13:34) How did the company start working on AI? (21:26) AI Code Assist (26:30) AI Material Generator (32:07) ControlNet (38:36) StarCoder (43:40) Who works at Roblox?

In this episode, we sat down with Benedict Evans, a leading voice in the tech industry and a former partner at Andreessen Horowitz. Known for his sharp insights and forward-thinking analysis, Benedict shares his expert perspective on what generative AI means for the future of technology, business, and society at large. Specifically, we dive deep into the evolving landscapes of generative AI, augmented and virtual reality, and the critical issue of AI bias. Join us as Benedict Evans provides a nuanced analysis of cutting-edge tech and shares his insights and perspectives on the road ahead. BENEDICT EVANS LinkedIn - https://www.linkedin.com/in/benedictevans/ Threads - https://www.threads.net/@benedictevans FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:06) The AI platform shift in 2024 (05:54) Gen AI in 2024 vs. PC-boom in the 80-s (13:24) Until AGI happens, there will be vertical-specific apps (15:12) Should companies have an AI strategy? (21:04) Platform shift OR paradigm shift? (23:55) How should we think about AGI in 2024? (34:08) Is gen AI grossly overhyped? (36:27) AI bias and the hidden problems in data (44:56) Apple Vision Pro and the future of AR/VR

In this episode, we sit down with Gary Little, CEO of Foursquare, to discuss Foursquare's remarkable evolution from a social app to a leader in location intelligence. Gary discusses how Foursquare uses smartphone ubiquity to create a global map through crowdsourcing, covering 190 countries and over 200 million points of interest. Learn about the challenges of managing complex, real-time datasets and how Foursquare employs machine learning and knowledge graphs to analyze foot traffic and device movements. The conversation also covers the critical role of privacy and data security in location tracking, especially in light of recent regulatory changes. Gary explains Foursquare's platform strategy, drawing parallels with Amazon's AWS, to enable customers to process and utilize location data for their applications. Foursquare Website - https://location.foursquare.com Twitter - https://twitter.com/Foursquare Gary Little (CEO) LinkedIn - https://www.linkedin.com/in/gary-little-0670ba4 Twitter - https://twitter.com/garylittlefsq FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:10) Brief history of Foursquare (03:07) What makes Foursquare's location data unique? (05:17) Foursquare Platform. What is it? (08:07) A glimpse into the future of Foursquare (10:00) More customers want to process the data themselves. Why? (13:42) Data privacy of today vs 10 years ago. What has changed? (16:41) Foursquare Graph: what does it do? (19:17) How is Foursquare utilizing AI? (22:17) How will AR/VR influence location intelligence?

In this episode, we dive into the fascinating world of AI art with Cris Valenzuela, CEO of Runway. Runway is a generative AI startup that co-invented Stable Diffusion, the deep learning technology that has captured the attention of the creative industry, including luminaries such as ASAP Rocky and Madonna's teams, by pushing the boundaries of digital creativity. We explore how generative AI tools empower visual artists to unleash their imaginations without the need for Hollywood-size budgets. We also discuss the effect of AI on the entire creative industry, similar to how the camera changed things back in the day. Join us for a glimpse into the future of creativity. RUNWAY Website - https://runwayml.com Twitter - https://twitter.com/runwayml Cris Valenzuela (Co-founder & CEO) LinkedIn - https://www.linkedin.com/in/cvalenzuelab Twitter - https://twitter.com/c_valenzuelab FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck Foursquare Website - https://location.foursquare.com Twitter - https://twitter.com/Foursquare (00:00) Intro (00:55) What is Runway? (03:09) Runway started before the GenAI boom. How? (04:41) What do people get wrong about GenAI? (07:18) How AI is going to change creative software? (08:44) What is Gen-2? (12:02) Runway's role in creating Stable Diffusion (14:25) Gen-1: a model or a product? (15:11) Runway's evolution from image generation to video (18:18) Runway partnered with Getty. Why? (19:52) How has the AI video generation ecosystem evolved? (21:58) Adoption cyсle for AI video generation. Where are we now? (24:45) Challenges of building a research-focused company (26:25) How to build and maintain a soul in a startup? (28:27) "It's like an invention of new art form" -

Join us in this exciting episode as we dive into the world of enterprise AI with Florian Douetteau, co-founder and CEO of Dataiku, the leading enterprise AI platform targeting Global 2000 companies. Since its founding in 2013, Dataiku has been at the forefront of democratizing AI in the enterprise. We'll explore the current state of deployment of AI in businesses around the world, dive deep into the differences between generative AI and traditional AI, explore emerging Generative AI uses cases in the enterprise, and get a sneak peek into Dataiku's latest breakthrough, the LLM Mesh, aimed at simplifying the use of multiple Generative AI models for companies. We'll also tackle the big challenges companies face when adopting AI, from managing costs to dealing with the uncertainties of Generative AI. This episode was recorded live at a recent Data Driven NYC, the monthly in-person event organized by FirstMark since 2011, hosted this month by our partners at Foursquare, the location intelligence company, at their beautiful headquarters. Dataiku Website - https://www.dataiku.com/ Twitter - https://twitter.com/dataiku Florian Douetteau LinkedIn - https://www.linkedin.com/in/fdouetteau Twitter - https://twitter.com/fdouetteau FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck Twitter - https://twitter.com/mattturck Foursquare Website - https://location.foursquare.com Twitter - https://twitter.com/Foursquare (00:00) Intro (01:09) What is Dataiku? (02:03) Is the market ready for AI? (04:33) Traditional AI vs Generative AI (08:33) What a company should know before diving into Generative AI? (10:18) Cost of Generative AI adoption (12:10) What blocks the AI adoption? (14:31) Dataiku product tour (16:34) How to build one product for different audiences (17:45) LLM Mesh: what is it? (21:10) Evolution of platform building with Gen AI (22:17) Enterprise AI motion in 2024 (23:28) Dataiku's partnerships (24:24) Being platform-first as a startup

In this episode, we sit down with Bob Moore, the CEO of Crossbeam, who turned a $2.6 billion mistake into a masterclass on Ecosystem-Led Growth (ELG). Fresh off publishing his new book, Bob shares why ELG is the future of business growth, challenging traditional strategies with data-driven insights and partnerships. Bob reveals how Crossbeam can help companies of any size leverage ELG to achieve remarkable growth. He dives into the role of data in ELG, the impact of AI on marketing, and practical steps for implementing ELG in your own company. From discussing the "slow heat death" of traditional growth strategies to unveiling the potential of data-driven partnerships, this episode is packed with eye-opening revelations. Bob also tackles the practical steps companies can take to implement ELG, making this a must-watch for CEOs, leaders, and entrepreneurs aiming to catapult their businesses into a new era of growth. Book: https://www.amazon.com/Ecosystem-Led-Growth-Blueprint-Marketing-Partnerships/dp/1394226837 Crossbeam Website - https://www.crossbeam.com Twitter - https://twitter.com/crossbeam Bob Moore LinkedIn - https://www.linkedin.com/in/robertjmoore/ Twitter - https://twitter.com/robertjmoore FirstMark Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (00:43) Bob recently wrote a book. Why did he do that as a CEO? (03:20) Bob's $2.6 billion mistake (12:15) What is ELG? (17:30) How does Crossbeam work? (20:51) Why do we need another type of go-to-market motion? (25:00) AI is killing inbound/outbound marketing (31:50) Applying ELG to your company (36:13) When should you do ELG and partnerships? (43:34) Outro

In this episode, we sat down with Emi Gal, founder and CEO of Ezra, a startup that leverages AI to detect cancer early and inexpensively. Emi provides insights into the landscape of the healthcare sector and talks about the differences between building an AI startup in healthcare versus SaaS. Turns out that "(In AI skills)... are not that transferable." EZRA Website - https://ezra.com Twitter - https://twitter.com/ezrainc Emi Gal LinkedIn - https://www.linkedin.com/in/emigal Twitter - https://twitter.com/emigal FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:50) Ezra raised $21 million in series B round (02:55) The origin of Ezra (06:06) Sourcing AI talent (06:52) Building a proof of concept (09:05) The tipping point for the product market fit (10:57) Y Combinator wants more MRI startups. Why? (11:37) Ezra's vision for MRI (13:25) Is it covered by insurance? (16:15) Full stack vs Software only (20:00) Training AI (22:55) Building an MRI database (25:45) Will radiologists get replaced by AI? (27:52) Creating reports with Generative AI (30:50) Can we trust AI in healthcare? (33:44) What are the specific challenges of building an AI startup? (39:01) Healthcare entrepreneurship (43:59) Staying fit as a CEO: Emi's mental and physical health routine (48:28) Plans for 2024

In this episode, we sat down with Des Traynor, co-founder of Intercom, to explore the seismic shift towards Artificial Intelligence in customer service software. Intercom has gone all-in to embrace AI as people's expectations of what chatbots can do started growing with the release of ChatGPT. Des shares the pivotal moments and strategic decisions that led to this transition, highlighting the urgency and vision that propelled Intercom to integrate AI into their core offerings. Des also delves into the challenges of building a bicontinental startup and the strategic pivot towards becoming an AI-first company. Tune in for an enlightening discussion on the strategy and journey of adapting AI. INTERCOM Website - https://www.intercom.com Twitter - https://twitter.com/intercom Des Traynor LinkedIn - https://www.linkedin.com/in/destraynor/ Twitter - https://twitter.com/destraynor FIRSTMARK Website - https://firstmark.com Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ Twitter - https://twitter.com/mattturck (00:00) Intro (01:16) How did Intercom make a transition to a generative AI product (Fin)? (05:34) Did the Intercom manifesto play a role in the transition? (07:16) What was the Intercom before Fin? (09:01) How much development effort did you spend on AI? (12:31) UX (15:20) People used to hate chatbots (17:51) GPT and building layers around it (20:50) The future of customer service (23:57) GPT-4/Llama/Mistral/Claude (25:58) Are multimodal AI-bots the future? (27:08) AI-hallucination (30:11) Customization (34:34) Will Fin get a voice? (36:26) Customer support cost and impact on profitability (39:58) How much should you charge? (45:26) AI-bot resolution rate (46:43) Can bots take action? (48:40) AI-adoption (51:14) How the Intercom team evolve (53:38) How did 4 Irish guys create a bi-continental startup? (56:17) Work distribution (58:38) Tech in Europe vs tech in the US

In this episode, we explore the dynamic world of modern analytics with Tristan Handy, CEO of dbt Labs (https://twitter.com/jthandy). DBT, which helps more than 30,000 enterprises ship trusted data products faster, has raised more than $400 million dollars, most recently at a $4B valuation.We discuss how dbt has revolutionized analytics engineering, enabling seamless data transformation and orchestration in the cloud. This innovation fosters greater collaboration among data teams and integrates software engineering principles into data analytics workflows.We also talk about dbt's Semantic Layer, a game-changer that streamlines data operations by standardizing key business metrics for consistent use across various analytical tools.In this conversation, we tackle pressing questions about the current state and future of data management and analytics. Is the "modern data stack" becoming obsolete? What's next for data engineering? And how is AI reshaping the analytics landscape?Tune in to discover our insights.

In this episode, we sat down with Bob van Luijt (https://twitter.com/bobvanluijt), the CEO of Weaviate, diving into the cutting-edge world of vector databases and their role in the AI revolution.Weaviate is an open source, AI-native vector database that helps developers create intuitive and reliable AI-powered applications. Weaviate sets itself apart with its vector search engine that integrates machine learning directly into its core, enabling more nuanced and context-aware search capabilities for AI-driven applications.This conversation explores vector databases (the core infrastructure behind generative models), the role of Retrieval-Augmented Generation (RAG), and how open source is driving commercial use cases.WEAVIATEWebsite - https://weaviate.ioTwitter - https://twitter.com/weaviate_ioBob van Luijt (Co-Founder & Co-CEO):LinkedIn - https://www.linkedin.com/in/bobvanluijtTwitter - https://twitter.com/bobvanluijtMatt Turck:LinkedIn - https://www.linkedin.com/in/turck/Twitter - https://twitter.com/mattturckDATA DRIVEN NYCThis episode of the MAD Podcast was recorded live at Data Driven NYC, an event series organized by FirstMark Capital. The events are free and held monthly in New York, currently with the support of Foursquare.If you wish to attend and be notified of future events, please follow FirstMark on Eventbrite at https://www.eventbrite.com/o/firstmark-capital-221557018301:00 What is RAG?06:20 Why is embedding models is such a hot topic right now?08:06 What is your assessment of RAG?09:53 Generative feedback loops11:46 What is Hybrid Search?15:15 What makes Weaviate special?16:53 What about security?17:45 Does RAG accelerated the need for real-time data?19:27 How to define good vector database? 22:11 What do you think about general purpose databases entering the field of vector-based databases?23:47 Interesting use cases of Weaviate25:27 What's your sense of the current state of the market?26:53 Open source vs commercial product on Weaviate29:23 How did it all get started?

Last week, we sat down with Alex Rinke (https://twitter.com/alexanderrinke), Co-founder & Co-CEO of Celonis, to explore how AI and automation are transforming business operations at large enterprises. Celonis is the pioneer of "process mining" - the technology that uses graph databases, AI, and automation to analyze processes, find inefficiencies and their root causes, and solve them.Most recently valued at $13B, Celonis is one of the most valuable startups globally. But Alexander and his two co-founders started Celonis while still in college on a $15,000 budget. In this conversation, we talked about the early days of Celonis, how Alex acquired his first enterprise clients without inside industry connections, how Celonis navigates go-to-market for a product with an expansive scope, and much more.CELONISWebsite - https://www.celonis.comTwitter - https://twitter.com/CelonisAlex Rinke (Co-Founder & Co-CEO):Twitter: https://twitter.com/alexanderrinkeLinkedIn: https://www.linkedin.com/in/alexander-rinke-10733061/DATA DRIVEN NYCThis episode of the MAD Podcast was recorded live at Data Driven NYC, an event series organized by FirstMark Capital. The events are free and held monthly in New York, currently with the support of Foursquare. If you wish to attend and be notified of future events, please follow FirstMark on Eventbrite at https://www.eventbrite.com/o/firstmark-capital-221557018300:00 - Intro02:02 - What is Process Mining?05:20 - How Celonis got started07:42 - “We had our first prototype in three weeks”09:36 - Pivotal partnership with ACP12:12 - How did Celonis find product-market-people fit?14:14 - Penetrating the global market16:19 - Technical deep dive into the Celonis' product19:29 - Celonis finds process gaps completely automatically21:15 - Who is the average user of Celonis inside companies?22:11 - How Celonis uses Generative AI 24:54 - Acquisition of Symbio25:56 - How to keep the fire of innovation inside the team?27:49 - How to bring a very horizontal product to market?32:24 - Scaling yourself as a leader34:15 - Glimpse into the future of Celonis35:37 - Outro