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

Training Data

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