Join us as we train our neural nets on the theme of the century: AI. Sonya Huang, Pat Grady and more Sequoia Capital partners host conversations with leading AI...
Josh Woodward: Google Labs is Rapidly Building AI Products from 0-to-1
As VP of Google Labs, Josh Woodward leads teams exploring the frontiers of AI applications. He shares insights on their rapid development process, why today’s written prompts will become outdated and how AI is transforming everything from video generation to computer control. He reveals that 25% of Google’s code is now written by AI and explains why coding could see major leaps forward this year. He emphasizes the importance of taste, design and human values in building AI tools that will shape how future generations work and create.
Mentioned in this episode:
Notebook LM: Personal research product based on Gemini 2 (previously discussed on Training Data.)
Veo 2: Google DeepMind’s new video generation model.
Paul Graham on X replying to Aaron Levie’s post that “One approach to take in building in AI is to do something that's too expensive to be reasonably practical right now, and just bet that the costs will drop by 10X or 100X over time. The cost curve is on your side.”
Where Good Ideas Come From: Book on the history of innovation by Steven Johnson.
Project Mariner: Google DeepMind’s research prototype exploring human-agent interaction starting with browser use.
Replit Agent: Josh’s favorite new AI app
The Lego Story: Book on the history of Lego.
Hosted by: Ravi Gupta and Sonya Huang, Sequoia Capital
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51:16
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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54:09
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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1:04:52
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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32:45
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
Join us as we train our neural nets on the theme of the century: AI. Sonya Huang, Pat Grady and more Sequoia Capital partners host conversations with leading AI builders and researchers to ask critical questions and develop a deeper understanding of the evolving technologies—and their implications for technology, business and society.
The content of this podcast does not constitute investment advice, an offer to provide investment advisory services, or an offer to sell or solicitation of an offer to buy an interest in any investment fund.
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