The Target Story Isn't About Coupons. It's About Healthcare AI.

Twelve years ago, a Target statistician built a model that could predict pregnancy from 25 shopping items. The story usually gets told as a privacy parable. I'm telling it differently — as a preview of how healthcare AI is going to work for the rest of our lives.Your smartwatch can already flag atrial fibrillation days before a cardiologist would. It can detect depression weeks before clinical scoring catches it. It can spot cognitive decline six to twelve months before you notice. The science isn't the question anymore. The question is who gets to see what comes out of the model — and whether we build the governance before the surveillance economy locks in.In this episode I get into:• Why Andrew Pole's 2012 Target model was a dry run for what's coming in clinical AI• What the wearable accuracy numbers (70–95%) actually mean — and where they get softer than the headlines suggest• The function-creep economy that's already running: CGM data sold to ad partners, period-tracking app subpoenas, life insurers bidding on de-identified wearable sets• The 99.98% problem — why "de-identified" data isn't• Habit-disruption windows: the real case for early-detection surveillance in healthcare• Four policy moves that would change the data-broker incentive structure overnightFull written companion with sources and citations: danmccoymd.substack.comWatch on YouTube: https://youtu.be/LbE6TbGIzIYI'm Dan McCoy. Ignition by RocketTools is the podcast for healthcare executives, physicians, and AI builders trying to think clearly about where this is all going. New episode every week.