Training Data

• How AI Breakout Harvey is Transforming Legal Services, with CEO Winston Weinberg

  Harvey CEO Winston Weinberg explains why success in legal AI requires more than just model capabilities—it demands deep process expertise that doesn’t exist online. He shares how Harvey balances rapid product development with earning trust from law firms through hyper-personalized demos and deep industry expertise. The discussion covers Harvey’s approach to product development—expanding specialized capabilities then collapsing them into unified workflows—and why focusing on complex work like international mergers creates the most defensible position in legal AI. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

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• The AI Product Going Viral With Doctors: OpenEvidence, with CEO Daniel Nadler

  OpenEvidence is transforming how doctors access medical knowledge at the point of care, from the biggest medical establishments to small practices serving rural communities. Founder Daniel Nadler explains his team’s insight that training smaller, specialized AI models on peer-reviewed literature outperforms large general models for medical applications. He discusses how making the platform freely available to all physicians led to widespread organic adoption and strategic partnerships with publishers like the New England Journal of Medicine. In an industry where organizations move glacially, 10-20% of all U.S. doctors began using OpenEvidence overnight to find information buried deep in the long tail of new medical studies, to validate edge cases and improve diagnoses. Nadler emphasizes the importance of accuracy and transparency in AI healthcare applications. Hosted by: Pat Grady, Sequoia Capital  Mentioned in this episode:  Do We Still Need Clinical Language Models?: Paper from OpenEvidence founders showing that small, specialized models outperformed large models for healthcare diagnostics Chinchilla paper: Seminal 2022 paper about scaling laws in large language models Understand: Ted Chiang sci-fi novella published in 1991

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• OpenAI’s Deep Research Team on Why Reinforcement Learning is the Future for AI Agents

  OpenAI’s Isa Fulford and Josh Tobin discuss how the company’s newest agent, Deep Research, represents a breakthrough in AI research capabilities by training models end-to-end rather than using hand-coded operational graphs. The product leads explain how high-quality training data and the o3 model’s reasoning abilities enable adaptable research strategies, and why OpenAI thinks Deep Research will capture a meaningful percentage of knowledge work. Key product decisions that build transparency and trust include citations and clarification flows. By compressing hours of work into minutes, Deep Research transforms what’s possible for many business and consumer use cases. Hosted by: Sonya Huang and Lauren Reeder, Sequoia Capital  Mentioned in this episode: Yann Lecun’s Cake: An analogy Meta AI’s leader shared in his 2016 NIPS keynote

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• Palo Alto Networks’ Nikesh Arora: AI, Security and the New World Order

  Palo Alto Networks’s CEO Nikesh Arora dispels DeepSeek hype by detailing all of the guardrails enterprises need to have in place to give AI agents “arms and legs.” No matter the model, deploying applications for precision-use cases means superimposing better controls. Arora emphasizes that the real challenge isn’t just blocking threats but matching the accelerated pace of AI-powered attacks, requiring a fundamental shift from prevention-focused to real-time detection and response systems. CISOs are risk managers, but legacy companies competing with more risk-tolerant startups need to move quickly and embrace change.  Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  Mentioned in this episode: Cortex XSIAM: Security operations and incident remediation platform from Palo Alto Networks

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• MongoDB’s Sahir Azam: Vector Databases and the Data Structure of AI

  MongoDB product leader Sahir Azam explains how vector databases have evolved from semantic search to become the essential memory and state layer for AI applications. He describes his view of how AI is transforming software development generally, and how combining vectors, graphs and traditional data structures enables high-quality retrieval needed for mission-critical enterprise AI use cases. Drawing from MongoDB's successful cloud transformation, Azam shares his vision for democratizing AI development by making sophisticated capabilities accessible to mainstream developers through integrated tools and abstractions. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital  Mentioned in this episode: Introducing ambient agents: Blog post by Langchain on a new UX pattern where AI agents can listen to an event stream and act on it  Google Gemini Deep Research: Sahir enjoys its amazing product experience Perplexity: AI search app that Sahir admires for its product craft Snipd: AI powered podcast app Sahir likes

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