Function's Longitudinal Data Moat

The real moat here is not the blood test itself, it is the repeat history of how markers move over time for the same person. Function already has more than 5 million test results, sells a recurring subscription with two lab draws per year, and is expanding into MRI, genomics, wearables, and health record uploads. That creates a compounding training set for models that can link changing biomarkers to scans, symptoms, and next actions.

• Most smaller testing startups sell a one time panel or a narrow niche panel. Function is built around repeat use, with about 200,000 subscribers on a $499 annual membership and two rounds of 100 plus biomarker testing per year, which makes the data more useful because it captures trends, not just snapshots.
  1 sacra 2 sacra
• The important comparison is Oura. Oura built a much larger business by collecting continuous sensor data and is now moving into blood panels to create one record across rings, labs, and hormones. Function is approaching the same convergence from the opposite side, starting with clinical lab data and layering in imaging and wearables.
  2 sacra 4 sacra
• Competitors like Superpower are building a broader care experience, with messaging, medical teams, wearables integrations, and commerce around supplements. Function has stayed lighter on treatment and heavier on aggregation and analysis, which keeps regulatory burden lower and makes the software layer itself the core product.
  1 sacra 5 sacra

This points toward a consumer health stack where the winning product is the one that becomes the default place people return to interpret every new signal. If Function keeps increasing test cadence and successfully pulls in imaging, wearables, and records, its dataset should get harder to match each year and make AI guidance the main value driver, not the lab panel.