Cooking with OpenAI’s Research Chief: AGI, o1, Evals, and Scaling Laws , Mark Chen
OpenAI's Chief Research Officer Mark Chen discusses the lab's core research strategy in a wide-ranging conversation covering scaling laws, the o1 reasoning model bet, and the evaluation crisis facing the field. Chen addresses why pre-training remains viable despite recent reasoning advances, how OpenAI allocates compute across competing research directions, and the gap between published benchmarks and real-world model performance. The discussion reveals internal thinking on long-horizon reasoning, research taste development, and how AI could reshape the research process itself, offering rare insight into frontier-lab prioritization during a period of shifting model architectures.
Modelwire context
Analyst takeThe most underreported element here is Chen's framing of the evaluation crisis: the gap between benchmark performance and real-world utility is not a measurement inconvenience but a structural problem that affects how OpenAI itself decides which research bets are working. That admission from a sitting CRO carries more weight than any published leaderboard number.
Modelwire has no prior coverage to anchor this to directly, so this story stands largely on its own in our archive. It belongs to a broader thread running through frontier-lab strategy coverage generally: the tension between scaling pre-training compute and investing in post-training reasoning techniques like those behind o1. Chen's defense of continued pre-training investment is a direct counter-signal to the narrative, common in the wider press, that reasoning models have made raw scale less important. That framing is worth tracking as a hypothesis.
Watch whether OpenAI publishes a formal evals methodology update in the next two quarters. If they do, it would suggest the internal concern Chen describes is moving from acknowledged problem to institutional response, which would be a meaningful signal about how the lab plans to communicate capability claims going forward.
This analysis is generated by Modelwire’s editorial layer from our archive and the summary above. It is not a substitute for the original reporting. How we write it.
MentionsOpenAI · Mark Chen · o1 · Latent Space
Modelwire Editorial
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