What does the decline in O3 price in Openai mean for the future of AI in healthcare?
When Openai announced that it would reduce its state-of-the-art models by 80%, it focused on the developer and startup sense. But the real story is more than just code. It’s about infrastructure and industries that rely on it.
This change not only marks AI, but also a turning point in the healthcare system itself. One of the most powerful commercial reasoning engines is priced for the first time within the scope of institutions that need transformation the most – but there is often a lack of resources to use cutting-edge technologies to conduct meaningful experiments.
What once thought of as a luxury is now almost a utility – the price transfer is huge. The cost of investing tokens has dropped from $10 to $2, and the output token has dropped from $40 to $8. This is AI that is crucial to the system, not just a fluke. This is revolutionary.
In health care, this distinction is important.
While most of the conversation surrounding generating AI focuses on possible issues: writing notes, summarizing charts, answering patients’ questions, the cost is still the elephant in the room. Large models like O3 require huge computing resources, until recently, deploying them to any scale would mean burning through a pilot budget and hope for a slight ROI.
This price drop changes the equation. The elephant has left the room.
For health systems and healthcare providers, this allows advanced reasoning capabilities to be embedded directly into existing operations:
- Automatically mark inconsistencies in documents across systems
- Real-time support for clinicians browsing complex patient history
- Analyze patterns across roles and workflows to eliminate redundancy
- Reconcile data across islands to support smooth handover of care
These are not less efficient. They are the ancestry that slows down health care and thwarts clinical professionals and patients.
Historically, the problem is not a lack of interest or innovation. Healthcare is full of visionary leaders who understand what needs to change. This problem is structural: dispersed data, limited engineering bandwidth and high cost of scale experiments. This is especially true outside of large academic medical centers and corporate health systems.
By cutting cost barriers, Openai effectively opens the door to a wider organization, no matter how big or small – to actually test, deploy and iterate these tools. Not a one-time pilot, but in a real environment with real workflows and real users.
It should be clear that this does not mean that medical institutions should adopt LLMS tomorrow. But what has changed is that the main obstacle to large-scale costs is no longer the once immovable obstacle. For healthcare leaders who have been watching the AI wave, it’s a time to turn from curiosity to action.
Just like the early days of cloud computing or the rise of the Internet itself, organizations that first use the technology to move will define their trajectory and influence the future of the trajectory of healthcare. More importantly, they will set the standard for how it is used in healthcare, not only for productivity, but for better outcomes, safer systems, and a more humane experience for patients and clinicians.
Image source: Mr. Cole_photographer, Getty Images
Justin Liu is the co-founder and CEO of Charta Health, an AI-powered platform that transforms clinical charts by applying AI and large language models. In the deep technological context of Google and Rockset (acquired by OpenAI), Charta is redefining the surface insights of its providers, ensuring document accuracy and discovering hidden revenues on a large scale.
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