Value-based care has finally found its software stack

The stack that lost

The first wave of VBC software — population health suites bolted onto EHRs — never delivered. It was reporting software sold as operations software.

The stack that is winning

Modern VBC operators are composing best-of-breed: a risk adjustment engine, a care management workflow tool, a network intelligence layer, and an actuarial finance system. Each is a venture-scale category.

The consolidation question

Will these four categories consolidate into a suite? Probably eventually. But the next five years belong to the specialists.

The virtual-first economics finally work

For most of the last decade, virtual-first models struggled to hit unit economics that a payer would underwrite at scale. That is changing. The combination of ambient documentation, asynchronous triage, and specialty-specific care pathways has pushed provider capacity up and cost-to-serve down enough that a well-run virtual practice is now defensibly cheaper than the in-person alternative for a growing set of conditions.

The winners are the teams that resisted the temptation to be everything to everyone and instead pointed the entire operation at a single population until the CAC-to-LTV curve was decisively positive.

Distribution is still the moat

Product beats no product. Distribution beats product. The digital health graveyard is full of clinically superior tools that could not get in front of the patients they were built for. The founders winning in 2026 are the ones who chose a distribution channel — an employer, a health plan, a specialty group, a condition-specific community — and built the product for that channel from day one.

Why the incumbent playbook keeps failing

The instinct of a large healthcare organization is to procure a platform, run a governance committee, and let the technology diffuse through mandate. That playbook worked for imaging PACS in the 2000s. It has failed, visibly and expensively, for the current wave of AI-native tooling. The reason is structural: modern systems have to be tuned to the institution's own data, workflow, and liability posture, and that tuning is a product-engineering exercise, not a procurement exercise.

Founders who understand this shape their commercial motion around a technical champion inside the health system — usually a CMIO, a service-line chief, or a director of quality — and treat every deployment as a co-development contract with clear evaluation gates.

The corollary is that the sales cycle is longer than any founder wants to admit, and the winners raise capital that lets them survive twelve months of pilot without a single dollar of expansion revenue.

What we are watching from the studio

Inside the venture studio, we are prioritizing three build areas connected to this thesis: the workflow substrate under clinical AI (consent, provenance, routing, human-in-the-loop review); the measurement layer that translates model behavior into a claim a payer or regulator can act on; and the operational tooling for the specialty clinics and virtual-first practices that will be the first commercial buyers of the next wave.

We are less interested in another general-purpose copilot. We are more interested in the boring infrastructure that makes ten specialized copilots safe to run at once.

The field notes

Across the last quarter we sat in on operating reviews with fourteen portfolio and prospective teams working adjacent problems. Three patterns kept surfacing. First, the teams that moved fastest were not the ones with the deepest research bench — they were the ones with the shortest feedback loop between a real clinical user and the roadmap. Second, the winners had unusually opinionated evaluation harnesses. Third, none of them treated regulatory strategy as a phase; they treated it as a running conversation with the product.

What follows is a longer look at what we saw, what we think it implies for founders, and where we are actively deploying capital and studio effort in the coming twelve months.