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Episode Details
EPISODE INFO
- Released
- April 24, 2025
- Duration
- 30m
- Channel
- No Priors
- Watch on YouTube
- ▶ Open ↗
EPISODE DESCRIPTION
On this episode of No Priors, Sarah sits down with Isa Fulford, one of the masterminds behind deep research. They unpack how the initiative began, the role of human expert data, and what it takes to build agents with real-world capability and even taste. Isa shares the differences between deep research and OpenAI’s o3 model, the challenges around latency, and how she sees agent capabilities evolving. Plus, OpenAI has announced that deep research is free for all US users starting today. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @IsaFulf Show Notes: 0:00 Deep research’s inception & evolution 6:12 Data creation 7:20 Reinforcement fine-tuning 9:05 Why human expert data matters 11:23 Failure modes of agents 13:55 The roadmap ahead for Deep Research 18:32 How do agents develop taste? 19:29 Experience and path to building a broadly capable agent 22:03 Deep research vs. o3 25:55 Latency 27:56 Predictions for agent capabilities
SPEAKERS
Sarah Guo
hostIsa Fulford
guestNarrator
other
EPISODE SUMMARY
In this episode of No Priors, featuring Sarah Guo and Isa Fulford, No Priors Ep. 112 | With OpenAI Deep Research, Isa Fulford explores Inside OpenAI Deep Research: Building a Broadly Capable Research Agent The episode features Isa Fulford discussing Deep Research, OpenAI’s agentic product that uses reinforcement learning, web browsing, and tools to perform complex, multi-step research tasks. She explains how the team moved from an internal demo to a production system by designing new datasets, tools, and evaluations grounded in real-world knowledge work. The conversation covers when reinforcement fine-tuning (RFT) is worth doing, how human experts and synthetic data shaped the model’s capabilities, and how Deep Research is already used across domains from science to fashion and travel. Fulford also outlines the path toward unified, trustworthy agents that can both research and take actions, along with the safety, memory, and UX challenges that must be solved first.
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