The Medtech Moat AI Can't Easily Break

Article 2 of 5 in the Pharma vs. Medtech Series

A pacemaker company and a surgical robot company both keep customers without a patent — so does AI strengthen that edge, or hand a rival a way around it?

Prepared by Richstorm.co

Key Takeaways

• Abbott's device business — heavy in heart devices — earned a 33.7% profit margin in 2025 versus an 18.2% company-wide average, without a single patent doing the work.

• Intuitive Surgical's moat works the same way, for a different device: over 3.1 million da Vinci procedures performed in 2025 alone, and a $217,034 average cost (a 2008 academic study, approximately $336,000 in 2026 dollars) to retrain a single surgeon on a new platform.

• AI is now being built directly into both companies' own devices, giving doctors and hospitals one more reason to stay put.

• In both cases, a separate kind of AI company is doing the opposite — pulling data across every manufacturer's devices into one shared system, chipping away at part of the moat.

• In both cases, that erosion hits only the monitoring or data-tracking layer wrapped around the device — not the implant, the robot, or the trained relationship, which is where the real money is.

Two Moats, Same Pattern

Most of medtech is a tough business to make real money in. A competitor can usually build a similar product, sell it for less, and win the account — hospitals buy in bulk and negotiate hard, and there's rarely a legal reason stopping a rival from catching up. Two corners of medtech are the exception, and they got there the same way.

Table: Two different devices, the same non-patent mechanism.

Cardiovascular: AI Is Making the Moat Stickier

Table: Abbott's device segment margin versus its company-wide average, 2023–2025.

Every major device maker is building AI directly into its own hardware — tools that help plan a procedure, size an implant correctly, or guide a surgical robot more precisely. Because these tools are built for one company's specific devices, they don't transfer to a competitor's equipment. That gives a hospital and a trained physician a second reason to stay put: not just "we already know this system," but "the smart features only work on this system too."

A different kind of AI company does the opposite: it builds AI that works across every manufacturer's devices, gathering a patient's ongoing heart-device data into one shared system regardless of brand. Cardiology's own professional society has said the lack of any shared, cross-company system is one of the field's biggest everyday headaches — exactly the gap these tools are stepping into. That chips away at part of the moat: the part where doctors feel locked into one company's software just to track their patients.

Surgical Robotics: The Same Split, a Different Device

The same two forces are showing up in surgical robots, and one real company proves it. Each robot maker is building AI features tied to its own machine — Intuitive's own case-data analytics, Medtronic's Hugo integrating an AI video-analysis tool it now owns outright. Those tools make a hospital's existing robot smarter, but only that one — a hospital can't take Intuitive's analytics and run them on a Medtronic machine.

Working the opposite direction is a company called Caresyntax, which built a vendor-neutral platform that uses AI to pull data from surgical devices, video, and hospital records across every manufacturer — not just one. It does for surgical robots what the cross-brand cardiac monitoring tools do for heart devices: it makes the layer of software wrapped around the machine less dependent on which company built the machine.

Same Pattern, Twice

Table: The same two-layer split shows up in both moats, independently.

This is not a coincidence, and that's what makes it worth trusting. Two different devices, two different companies, two different diseases — and in both cases, AI is reinforcing the layer that actually makes the money (the implant, the robot, the trained relationship) while a separate, vendor-neutral AI tool erodes the software wrapped around it. In neither case has the erosion reached the core relationship yet.

Where This Leaves Both Moats

AI is making it harder to lose the core relationship in both cardiovascular and surgical robotics, while making it easier to lose the software wrapped around each. Whether that smaller crack eventually grows large enough to threaten the core relationship too is the open question in both cases — and it's the one the next article in this series takes further, using it to test the broader claim that AI is more likely to widen the gap between pharma and medtech than close it.

Read the full breakdown here: AI Won't Save Medtech From Pharma — It Might Make the Gap Worse

Article 3 of 5 in the Pharma vs. Medtech Series