AI has made drug candidates abundant. The industry now has more promising compounds than it can develop.
The bottleneck is not discovery. It is everything that comes after: the clinical networks, compliance frameworks, and data rails that determine whether a candidate ever reaches a patient. Every program builds these from scratch. When the program ends, the work disappears. The next program starts over.
Every industry that scaled did the same thing first: made the underlying work reusable. Fintech called it payment rails. Logistics called it shipping protocols.
Healthcare AI has not made that move. The models are ready. The candidates are ready. The infrastructure that connects them to patients, regulators, and clinical reality is still being assembled by hand, one program at a time.
That is the problem worth solving.
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7July 3, 2026 874 1
