Summary of OpenAI Research Leadership Interview
This summary covers key discussions with Jacob, Chief Scientist at OpenAI, and Mark, Chief Research Officer at OpenAI, focusing on the direction of AI research, the launch of GPT-5, evaluation methods, and building a resilient research culture.
Focus on Automated Research and GPT-5
The primary long-term research goal for OpenAI is producing an automated researcher capable of discovering new ideas, including automating their own ML research and advancing progress in other sciences [0:07, 7:13].
- GPT-5 Launch: The launch of GPT-5 was primarily an effort to bring reasoning into the mainstream by default [1:17].
- It aims to unify previous instant-response models (GPT series) and long-thinking models (O series) by determining the right amount of thinking required for any prompt [1:33].
- This step is seen as delivering reasoning and more agentic behavior by default [2:02].
- Impact on Science and Math: A significant capability of GPT-5 is its frontier-pushing performance in very hard sciences, surprising researchers by generating non-trivial new mathematics or solving problems that might take a student months [5:18, 6:10].
Evals and Measuring Progress
The team acknowledges that traditional evaluations (evals) are becoming saturated (e.g., inching from 96% to 98% is less significant) [2:34, 3:04].
- New Evaluation Focus: The focus is shifting toward actual marks of the model being able to discover new things [4:06].
- Economic Relevance: Future milestones involve movement on things that are economically relevant [0:16, 4:22].
- Reasoning Horizon: Progress is also measured by extending the time horizon models can reason autonomously, currently estimated around one to five hours for complex reasoning tasks [8:10].
Agency, Coding, and Culture
The discussion touched on agency trade-offs and the impact of advanced tools like CodeX.
- Agency Trade-off: There is an observed trade-off between model stability and depth when models utilize many tools or planning hops. The leaders suggest that the ability to maintain depth is directly related to consistency over long horizons (reasoning) [8:47, 9:47].
- CodeX Advancement: The CodeX team focuses on making reasoning models useful for messy, real-world coding, including handling style and proactivity [16:19]. Previous CodeX models often spent too little time on hard problems.
- "Vibe Coding" to "Vibe Researching": The ease of using tools like CodeX has shifted the default for high schoolers to "vibe coding." The hope is that this progresses to "vibe researching" [0:23, 21:43].
Research Culture and Talent Retention
Maintaining a world-leading research organization requires specific cultural elements:
- Protecting Fundamental Research: Ensuring researchers are not pulled entirely toward short-term product demands [32:05].
- Mission Clarity: People are motivated by the mission to discover new things about the deep learning stack, not just copying competitors [28:01].
- Hiring Attributes: They look for strong technical fundamentals coupled with the intent to work on ambitious problems and stick with them (persistence) [30:02]. Experience helps in learning the right problem horizon [23:21].
- Conviction vs. Truth-Seeking: Researchers need conviction in their idea but must also be maximally truth-seeking about when it is failing [24:06].
Key Takeaways
- OpenAI's North Star is creating an automated researcher [7:13].
- Reasoning and agentic behavior are central to the latest models, exemplified by GPT-5 [2:02].
- The future of measurement lies in real-world discovery and economic relevance, not just saturated benchmarks [4:22].
- The research culture prioritizes fundamental advancement over iterative product competition, supported by strong leadership chemistry and trust [28:01, 49:20].
- Compute remains a critical bottleneck; the belief that the field is shifting to being purely data-constrained is disputed [40:16].