The MAD Podcast with Matt Turck

• Dashboards Are Dead: Sigma’s BI Revolution for Trillion-Row Data

  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

  --------  

  41:32
• Glean’s Breakthrough: CEO Arvind Jain on Scaling AI Agents & Search

  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

  --------  

  52:11
• Box’s Big AI Leap: Aaron Levie on Agents & the Future of Work

  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

  --------  

  1:15:46
• Snowflake CEO on Winning the AI Arms Race

  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

  --------  

  1:23:41
• Chasing Real AGI: Inside ARC Prize 2025 with Chollet & Knoop

  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

  --------  

  1:00:45

Altri podcast di Tecnologia

Podcast di tendenza in Tecnologia

Su The MAD Podcast with Matt Turck