VCs Discuss Modular AI Harnesses With Swappable Models

Yohei Nakajima proposed small harnesses allowing swappable models with learnings stored outside the system. Martin Casado praised the modular design. Vijay Pande suggested shared file systems as an alternative while favoring robust harnesses that ease model swaps like updating a home. Yangqing Jia stressed task-focused harnesses to avoid brittleness and enable long-horizon success. Yam Peleg advocated minimal harnesses. Aarthi Ramamurthy and others weighed lightweight options against general-purpose ones, noting trained harnesses may blur into neural architectures for better generalization.