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 He shares insights on their rapid development process, why today’s written prompts will become outdated and how AI is transforming everything from video generation to computer control.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:

Hosted by: Ravi Gupta and Sonya Huang, Sequoia Capital 

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

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: 

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:

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:

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:

Stef Corazza leads generative AI development at Roblox after previously building Adobe’s 3D and AR platforms. His technical expertise, combined with Roblox’s unique relationship with its users, has led to the infusion of AI into its creation tools. Roblox has assembled the world’s largest multimodal dataset. Stef previews the Roblox Assistant and the company’s new 3D foundation model, while emphasizing the importance of maintaining positive experiences and civility on the platform. 

Mentioned in this episode:

Hosted by: Konstantine Buhler and Sonya Huang, Sequoia Capital

Ioannis Antonoglou, founding engineer at DeepMind and co-founder of ReflectionAI, has seen the triumphs of reinforcement learning firsthand. From AlphaGo to AlphaZero and MuZero, Ioannis has built the most powerful agents in the world. Ioannis breaks down key moments in AlphaGo's game against Lee Sodol (Moves 37 and 78), the importance of self-play and the impact of scale, reliability, planning and in-context learning as core factors that will unlock the next level of progress in AI.

Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital

Mentioned in this episode:

Hema Raghavan is co-founder of Kumo, a company that makes graph neural networks accessible to enterprises by connecting to their relational data stored in Snowflake and Databricks. Hema talks about how running GNNs on GPUs has led to breakthroughs in performance as well as the query language Kumo developed to help companies predict future data points. Although approachable for non-technical users, the product provides full control for data scientists who use Kumo to automate time-consuming feature engineering pipelines.

Mentioned in this episode:

Hosted by: Konstantine Buhler and Sonya Huang, Sequoia Capital

Berkeley professor Ion Stoica, co-founder of Databricks and Anyscale, transformed the open source projects Spark and Ray into successful AI infrastructure companies. He talks about what mattered most for Databricks' success -- the focus on making Spark win and making Databricks the best place to run Spark. He highlights the importance of striking key partnerships -- the Microsoft partnership in particular that accelerated Databricks' growth and contributed to Spark's dominance among data scientists and AI engineers. He also shares his perspective on finding new problems to work on, which holds lessons for aspiring founders and builders: 1) building systems in new areas that, if widely adopted, put you in the best position to understand the new problem space, and 2) focusing on a problem that is more important tomorrow than today.

Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital

Mentioned in this episode: