Epic calls itself a platform. And with the show of force at UGM last week, that’s exactly how the company now describes itself: inviting vendors to “network with others working on the Epic platform,” marketing a “cloud‑powered platform” for healthcare intelligence, and selling a “Payer Platform” to connect plans and providers. Even customer stories celebrate moving to “a single Epic platform.”
But is Epic really a platform? The TL/DR is no.
Ben Thompson from Stratechery uses Bill Gates’s test to define a platform:
A platform is when the economic value of everybody that uses it exceeds the value of the company that creates it. Then it’s a platform
In Thompson’s framing, platforms facilitate third-party relationships and externalize network effects. During my time at Apple, the role of products as platforms–enabling developers to build their own experiences–was never lost on anyone. Apple’s success with the App Store wasn’t just about building great devices, it was about cultivating a marketplace where developers could thrive. To me, this is what it looks like to clear the Gates line.
In contrast, while Epic has captured significant value as the dominant vertical system of record, it does not pass the Bill Gates test for a platform, at least if “outside ecosystem” means independent developers and vendors. If anything, several UGM highlights overlapped with startup offerings, reinforcing Epic’s suite-first posture.
Beyond platforms, Thompson describes aggregators as internet-scale winners that have three concurrent properties: 1) a direct relationship with end users; 2) zero or near‑zero marginal cost to serve the next user because the product and distribution are digital; and 3) demand‑driven multi‑sided networks where growing consumer attention lowers future acquisition costs and compels suppliers to meet the aggregator’s rules.
Healthcare has lacked the internet physics that make either archetype inevitable. Patients rarely choose the software, employers and payers do. Much of care is physical and local, so marginal cost does not collapse at the point of service. Data has historically been locked behind site‑specific builds and business rules.
The policy landscape is shifting in a way that could finally make internet-style economics possible in healthcare. The national data-sharing network, TEFCA went live in late 2023 with the first Qualified Health Information Networks designated, including Epic’s own Nexus. The next milestone matters more for consumers: Individual Access Services (IAS). IAS creates a standardized, enforceable way for people to pull their health records through apps of their choice across participating networks, not just within a single portal. That means a person could authorize a service like ChatGPT, Amazon, or Apple Health to fetch their data across systems. Layer that onto ONC’s new transparency rules for AI and the White House’s push for clear governance, and the long-standing frictions that protected incumbents begin to fall away. Policy doesn’t create consumer demand by itself, but it clears the path. With IAS on the horizon, the conditions could be in place for true platforms to form on top of the data, and for the first genuine aggregators in healthcare to emerge.
Viewed through Thompson’s tests, Epic is neither a Thompson‑style aggregator nor a Gates‑line platform. Epic sells to enterprises, implementations take quarters and years, and its ecosystem is curated to reinforce the suite rather than to externalize network effects. Even its most aggregator‑looking asset, Cosmos, aggregates de‑identified data inside the Epic community to strengthen Epic’s own products, not to intermediate an open, multi‑sided market. UGM reinforced that direction with native AI charting on the way, an expanded AI slate, and a push to embed intelligence deeper into Epic’s own workflows. These are rational choices for reliability, liability, and speed inside the walls. They are not the choices of a company trying to own consumer demand across suppliers.
AI is the first credible force that can bend healthcare toward aggregation because it directly addresses Thompson’s three conditions. A high‑quality AI assistant can own the user relationship across employers, plans, and providers, the marginal cost to serve the next interaction is close to zero once deployed, and the product improves with every conversation, which lowers acquisition costs in a compounding loop. If that assistant can read with permission on national rails, reason over longitudinal data, coordinate benefits, and route to appropriate suppliers, demand begins to concentrate at the assistant’s front door. Suppliers then modularize on the assistant’s terms because that is where users start. That is Aggregation Theory applied to triage, chronic condition management, and navigation. The habit is forming at the consumer edge. With millions of Americans using ChatGPT, the flywheel is no longer theoretical.
It is worth being explicit about the one candidate aggregator that already exists at internet scale. With mass-market reach and daily use, ChatGPT could plausibly become a demand controller in health once the IAS pathway standardizes consumer-authorized data flows across QHINs. The building blocks are there in a way they never were for personal health records a decade ago: IAS rules now spell out how an app verifies identity and pulls data on behalf of a consumer, QHINs are live and interconnected, Epic Nexus alone covers more than a thousand hospitals, and HTI-1 is codifying transparency for AI-mediated decision support. If a consumer agent like ChatGPT could fetch records under IAS, explain benefits and prices, assemble prior authorization packets, book care, and learn from outcomes to improve routing, it would check Thompson’s boxes as an aggregator: owning the user relationship, facing near-zero marginal costs per additional user, and compelling suppliers to meet its terms. But there are complicating factors. HIPAA and liability rules may require ChatGPT to operate under strict business associate agreements, consumer trust in an AI holding intimate health data is far from guaranteed, and regulators could constrain or slow the extent to which a general-purpose model is allowed to intermediate medical decisions. Even so, the policy rails make such a role technically feasible, and ChatGPT’s usage base gives it a head start if it can navigate those hurdles.
Demand‑side pressure makes this shift more likely rather than less. Employer medical cost trend is projected to remain elevated through 2026 after hitting the highest levels in more than a decade, and pharmacy trend is outpacing medical trend, driven in part by the consumer‑adjacent GLP‑1 category. KFF’s employer survey shows a two‑year, mid‑to‑high single digit premium rise with specific focus on GLP‑1 coverage policies, and multiple employer surveys now estimate that GLP‑1 drugs account for a high single digit to low double digit share of total claims, with a sizable minority of employers reporting more than fifteen percent. As more of that cost shifts to households through premiums and deductibles, consumers gravitate to services that compress time to care and make prices legible. Amazon is training Prime members to expect five‑dollar generics with RxPass and a low‑friction primary care membership via One Medical, and Hims & Hers has demonstrated that millions will subscribe to vertically packaged services, now including weight‑management programs built around GLP‑1s. These behaviors teach consumers to start outside the hospital portal. Coupled with a trusted AI, they are the ingredients for real demand control.
None of this diminishes Epic’s role. If anything, the rise of a consumer aggregator makes a reliable clinical system of record more valuable. The most likely outcome is layered. Epic remains the operating system for care delivery, increasingly infused with its own AI. A neutral services tier above the EHR transforms heterogeneous clinical and payer data into reusable primitives for builders. And at the consumer edge, one or two AI assistants earn the right to be the first stop, finally importing internet economics into the information-heavy, logistics-light parts of care. That is a more precise reading of Thompson’s theory: aggregators win by owning demand, not supply. Healthcare never allowed them to own demand, but interoperability and AI agents change that. With IAS about to make personal data portable, the shape of the winning aggregator starts to look less like a portal and more like a personal health record—an agent that follows the consumer, not the institution. Julie Yoo’s “Health 2.0 Redux” makes the case that many of these ideas are not new. What is new is that, for the first time, the rails and the models are real enough to let a PHR evolve into the aggregator that healthcare has been missing.
Be Curious, Not Judgmental 