Rather than relying on a small group of experts, OpenAI has built a three-tiered system involving over 260 physicians. This includes high-level strategic advisors, a large cohort for data operations like red-teaming and comparison tasks (communicating via Slack), and a core group of close advisors who translate this collective expertise into concrete evals and training data for researchers.

OpenAI's health division serves a dual purpose: delivering societal benefits and providing a real-world, high-stakes environment for AI safety research. Problems like scalable oversight (supervising superhuman AI) move from theoretical exercises to practical necessities when models outperform physicians on narrow tasks, creating concrete feedback loops that accelerate safety progress.

Singhal says OpenAI wants to help patients navigate scheduling, logistics, and care questions, but it will rely on startup and provider partners for the last mile. He describes ChatGPT as a quarterback for health rather than a replacement for care delivery. The goal is enabling the ecosystem.

Singhal says asking five physicians one question can yield eight answers, so OpenAI built a network of hundreds of experts across specialties, languages, and countries. That network shapes evaluations and training data instead of letting one or two clinicians adjudicate hard cases. It is a deliberate aggregation of expert judgment.

To lower the activation energy for user adoption, OpenAI deliberately will not use data connected to ChatGPT Health to train its foundation models. This strategic choice is designed to remove any tension between privacy and utility, assuring users their sensitive information is not being used for other purposes and building the trust necessary for scaled impact in the healthcare domain.

OpenAI's launch of ChatGPT Health, which integrates medical records, signals a clear strategy to move beyond general-purpose APIs. Foundation model companies are now building specialized, vertical-specific products, posing a direct threat to "wrapper" startups that rely on the underlying models' existing capabilities.

Initially, even OpenAI believed a single, ultimate 'model to rule them all' would emerge. This thinking has completely changed to favor a proliferation of specialized models, creating a healthier, less winner-take-all ecosystem where different models serve different needs.

Frontier AI models excel in medicine less because of their encyclopedic knowledge and more because of their ability to integrate huge amounts of context. They can synthesize a patient's entire medical history with the latest research—a task difficult for any single human. This highlights that the key to unlocking AI's value is feeding it comprehensive data, as context is the primary driver of superhuman performance.

Building small models exclusively on proprietary vertical data leaves them vulnerable to obsolescence whenever next-generation foundation models emerge. Post-training open-weight general models with domain-specific RL and alignment preserves broad intelligence while granting elite, PhD-level competence in regulated fields.