Frameworks
The essays argue. These are the models the arguments leave behind: compact, visual, and built to be reused on your own problems. Take them. Attribution appreciated, never required.
Regulated-market value does not climb a slope. It climbs a staircase: long flat treads of invisible work punctuated by sharp risers when a milestone lands. Judge a company by which riser is coming, not which tread it stands on.
From the essay at VerAvanti Insights →A dataset is a snapshot that decays. A moat is a proprietary input nobody else can capture, feeding a loop that compounds with every use. Volume helps and uniqueness helps, but only compounding widens the gap on its own.
Read →If your team stopped touching the product for ninety days, would it still get better on its own? Yes means a flywheel. No means a dataset with good marketing. Run the test before an investor runs it for you.
Read →A 510(k) predicate is not paperwork. It decides the codes, bundles, and payer precedent a device steps into on day one of clearance. Regulatory strategy is reimbursement strategy wearing a different hat.
Read →You cannot buy your way past a sales motion you have not figured out. Prove the motion with a small team on real cases, then deploy capital hard behind what works. Patience earns the right to aggression.
Read →There is no single procedure room. The cath lab buys rhythm, the office-based lab buys economics, the high-acuity suite buys control. One product can be right for one room and actively wrong for another.
Read →See it. Win the room. Build the moat. Scale the enterprise. Every tool in the book belongs to one of the four moves of the Outsider’s Arc, and each one tells you what it is for, how to run it, and how to know when you are failing it.
These models are the domain layer, the shapes the thinking takes in MedTech and AI. The universal layer, the method they all descend from, is the Outsider’s Arc: see, win, build, scale. Same system, two altitudes.