There Is No Certification for This
AI compresses knowledge. It does not compress experience. The junior PM in your next interview has access to the same tools you do. What separates you now is judgment. There is no course for that. There never was.
Stop Looking for Unicorns
Healthcare AI job postings keep asking for unicorns: 14 years of experience in a field that is three years old, deep AI expertise, deep clinical expertise, deep product expertise. That person does not exist. And even if they did, they would not fix the real problem.
The Context Is the Product
In healthcare, AI can already predict. The hard problem is reasoning, and reasoning depends entirely on context. The physician who takes a complete history, examines carefully, and aggregates everything from the EHR to the nursing home fax is not being thorough. They are building the product.
AI Is Not a Product
People don't buy a drill. They buy a hole. AI is the drill. Every major technology wave produces the same confusion. SQL became infrastructure. Java became infrastructure. Cloud became a checkbox. AI is doing the same thing. We are in the expensive middle of that arc right now.
The North Star for AI Isn't Silicon Valley. It's the Hospital.
Healthcare is slow, regulated, fragmented, and over-documented. That's exactly why it's the best AI proving ground in the world. The field that everyone called a laggard has been running the most rigorous AI experiment for decades. The rest of us are just starting to catch up.
Listen to the patient.
We evaluated AI health tools by removing the very mechanism that makes diagnosis work, measured what happened, and called it a safety problem. It is not just a safety problem. It is a design problem. Medicine knew this in 1975
Not a Revolution. A Diagnosis
Two studies this week, one from Google and one from Microsoft, are being celebrated as evidence that healthcare AI has arrived. Read them together and they reveal something more uncomfortable: AI is filling a gap that healthcare created long before any of us started building AI for health.
What Do We Do With the Frameworks?
Twenty years of customer interviews, workshops, and journey maps. Then agentic AI arrived, and every framework I trusted turned out to share one assumption I had stopped noticing: that the human is always smarter than the tool. Here's what breaks when that stops being true.
The 3,000-Year-Old Argument in Healthcare: What AI Actually Changes
The clinical reasoning pattern hasn't changed since Hippocrates. The vessels carrying it have, from paper charts to cloud EHRs. For fifty years, every vessel moved the data without understanding it. AI breaks that pattern for the first time. Not a better vessel. One that thinks.
From Research to Reality: What It Takes to Scale Clinical AI Beyond the Lab
I still read NEJM and BMJ cover to cover. Lately they are filled with elegant AI breakthroughs: models that promise to transform diagnosis, prediction, resource allocation. The deeper I build enterprise software, the more critical I get. A paper is a demo. A scaled deployment is a different problem.
The Blueprint Was Already There
This week I read three papers that made me happy. JAMA. NEJM. Nature Medicine. All randomized trials. All showing AI outperforming standard care. Then I read the methodology. None of them LLMs. The AI winning in top journals in 2026 was built before the hype cycle. The blueprint was always there.
The 60-Point Gap: Why We're Measuring the Wrong Customer
A Nature study shows LLMs achieve 94.9% accuracy on benchmarks but only 34.5% when laypeople use LLMs on physician-created scenarios. The gap reveals something deeper: We measure the model in isolation. We deploy to a human in distress. The system fails at the intersection.