Training Data

• Building the "App Store" for Robots: Hugging Face's Thomas Wolf on Physical AI

  Thomas Wolf, co-founder and Chief Science Officer of Hugging Face, explains how his company is applying the same community-driven approach that made transformers accessible to everyone to the emerging field of robotics. Thomas discusses LeRobot, Hugging Face's ambitious project to democratize robotics through open-source tools, datasets, and affordable hardware. He shares his vision for turning millions of software developers into roboticists, the challenges of data scarcity in robotics versus language models, and why he believes we're at the same inflection point for physical AI that we were for LLMs just a few years ago. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

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• Deal Velocity, Not Billable Hours: How Crosby Uses AI to Redefine Legal Contracting

  Ryan Daniels and John Sarihan are reimagining legal services by building Crosby, an AI-powered law firm that focuses on contract negotiations to start. Rather than building legal software, they've structured their company as an actual law firm with lawyers and AI engineers working side-by-side to automate human negotiations. They've eliminated billable hours in favor of per-document pricing, achieving contract turnaround times under an hour. Ryan and John explain why the law firm structure enables faster innovation cycles, how they're using AI to predict negotiation outcomes, and their vision for agents that can simulate entire contract negotiations between parties. Hosted by Josephine Chen, Sequoia Capital Mentioned in this episode: Data processing agreement (DPA): GDPR-mandated contract between controllers and processors. Crosby handles DPAs as part of B2B contracting. Credence good: Economic term for services whose quality is hard to judge even after consumption. Used to explain why legal buyers value lawyers-in-the-loop and malpractice coverage.

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• n8n CEO Jan Oberhauser on Building the Universal AI Automation Layer

  When the AI wave hit, n8n founder Jan Oberhauser faced a critical choice: become irrelevant or become indispensable. He chose the latter, transforming n8n from a simple workflow tool into a comprehensive AI automation platform that lets users connect any LLM to any application. The result? Four times the revenue growth in eight months compared to the previous six years. Jan explains how n8n’s “connect everything to anything” philosophy, combined with a thriving open source community, positioned the company to ride the AI automation wave while avoiding vendor lock-in that plagues enterprise software. Hosted by George Robson and Pat Grady, Sequoia Capital Mentioned in this episode: Model Context Protocol (MCP): Open protocol that lets AI models safely use external tools and data that is used extensively by n8n for orchestration. Vector database: A database optimized for storing and searching embeddings. These “vector stores” can pair with LLMs for retrieval-augmented workflows. Granola: AI productivity tool mentioned by Jan as a recent favorite.  Her: A film that Jan says, “a few years ago, it was sci fi, and it’s now suddenly this thing that is just around the corner.”

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• Scaling the ‘Cursor for Slides’ to $50M ARR: Gamma founder Jon Noronha

  Before ChatGPT made AI mainstream, John Noronha was building Gamma with a simple insight: everyone hates making slides but needs visual communication for high-stakes ideas. His background at Optimizely proved crucial as Gamma became a testing laboratory for AI models, running hundreds of experiments to discover that Claude excels at creative taste, Gemini wins on cost efficiency, and reasoning models actually hurt creativity. John explains how solving their own blank page problem inadvertently solved it for millions of users, turning a near-failing startup into a cash flow positive platform with 250 million presentations created. He discusses competing with PowerPoint's 500 million users while expanding beyond slides into documents, websites and visual storytelling. Hosted by Sonya Huang, Sequoia Capital

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• Delphi’s Dara Ladjevardian: How AI Digital Minds Can Scale Human Connection

  Dara Ladjevardian, founder and CEO of Delphi, is creating digital minds that allow people to scale their thoughts and availability without replacing human connection. Inspired by Ray Kurzweil’s theory of mind as a hierarchy of pattern recognizers, Dara built an adaptive temporal knowledge graph that captures how people think and reason. From helping CEOs train new hires to enabling coaches to monetize their expertise 24/7, Delphi represents a new form of conversational media. Dara explains why authentic human representation matters, how digital minds actually increase desire for real human connection, and why he believes 2026 will be the tipping point for adoption for digital minds. Hosted by Sonya Huang and Jess Lee, Sequoia Capital Mentioned in this episode: How to Create a Mind: 2012 book by Ray Kurzweil that inspired Dara The Memoirs of Akbar Ladjevardian: 2008 book about Dara’s grandfather, an Iranian industrialist, that led him to create his first “digital mind” Build: 2022 book by Tony Fadell that refers to itself as “a mentor in a box”; another inspiration for Dara The 2 Sigma Problem: 1984 paper by Benjamin Bloom about how students that receive one-on-one tutoring perform two standard deviations better than students educated in a classroom environment

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