In this episode, I sit down with Saket Saurabh (CEO of Nexla) to discuss the fundamental shift happening in the AI landscape. The conversation is moving beyond the race to build the biggest foundational models and towards a new battleground: context. We explore what it means to be a "model company" versus a "context company" and how this changes everything for data strategy and enterprise AI.
Join us as we cover:
Model vs. Context Companies: The emerging divide between companies building models (like OpenAI) and those whose advantage lies in their unique data and integrations.
The Limits of Current Models: Why we might be hitting an asymptote with the current transformer architecture for solving complex, reliable business processes.
"Context Engineering": What this term really means, from RAG to stitching together tools, data, and memory to feed AI systems.
The Resurgence of Knowledge Graphs: Why graph databases are becoming critical for providing deterministic, reliable information to probabilistic AI models, moving beyond simple vector similarity.
AI's Impact on Tooling: How tools like Lovable and Cursor are changing workflows for prototyping and coding, and the risk of creating the "-10x engineer."
The Future of Data Engineering: How the field is expanding as AI becomes the primary consumer of data, requiring a new focus on architecture, semantics, and managing complexity at scale.
