The Economics of Autonomous Research

Autonomous research AI agents are becoming remarkably capable, but running long investigations can be expensive—and more computation does not always produce better results. How can we make autonomous research both affordable and productive?
This talk explores an approach that combines smaller AI models, selective frontier guidance, and resource-aware research orchestration. Drawing on experiments and a recorded demonstration, we’ll examine where these systems spend their effort, what helps them make meaningful progress, and when additional model capability earns its cost.

Attendees will gain a framework for evaluating the trade-offs between capability, cost, and research quality, along with lessons for building autonomous workflows that use limited resources effectively. We’ll close with the open challenges that stand between promising demonstrations and scalable autonomous research.