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:
Yann Lecun’s Cake: An analogy Meta AI’s leader shared in his 2016 NIPS keynote
OpenAI’s Deep Research Team on Why Reinforcement Learning is the Future for AI Agents | Training Data podcast - Listen or read transcript on Metacast
