Despite processing 15 million clinical charts, Datycs doesn't use this data for model training. Their agreements explicitly respect that data belongs to the patient and the client—an ethical choice that prevents them from building large, aggregated language models from customer data.

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.

For enterprise AI adoption, focus on pragmatism over novelty. Customers' primary concerns are trust and privacy (ensuring no IP leakage) and contextual relevance (the AI must understand their specific business and products), all delivered within their existing workflow.

In a move prioritizing access over monetization, OpenAI plans to offer its reasoning-level ChatGPT Health product to all users for free, without ads or rate limits. This represents an early form of 'universal basic intelligence' and a deliberate strategy to build trust and maximize societal benefit in a high-stakes domain, separating its health impact work from other company incentives.

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.

While OpenAI and Google are launching health-focused AI, consumer trust in data privacy will be a key competitive differentiator. Many users may wait for a company like Apple, with its strong privacy reputation, before connecting sensitive medical records.

The feature is a "data moat play disguised as a feature launch." By connecting to EHRs and wellness apps, OpenAI moves beyond ephemeral chats to build a persistent, indexed health profile for each user. This creates immense switching costs and a personalized model that competitors like Google and Meta cannot easily replicate with their existing data graphs.

The creation of ChatGPT Health was not a proactive pivot but a direct response to massive, organic user behavior. OpenAI discovered that 1 in 4 weekly active users—over 200 million people globally—were already using the general purpose tool for health queries, validating the immense market demand before a single line of dedicated code was written.

Companies are becoming wary of feeding their unique data and customer queries into third-party LLMs like ChatGPT. The fear is that this trains a potential future competitor. The trend will shift towards running private, open-source models on their own cloud instances to maintain a competitive moat and ensure data privacy.