When the analyst who coined a term stops using it, that is usually a sign the underlying claim stopped being a differentiator.
Investors are quietly dropping "AI-powered" as a standalone selling point, and that shift says a lot about where startup diligence is heading next for every founder building on top of a model rather than around one.
A category label gets quietly retired
Rock Health has begun retiring "AI deal" as its own funding category, on the basis that AI is now embedded in close to every digital health product rather than describing a distinct type of startup.
Clinical workflow software, the largest funded category at roughly 48% of both deals and capital over the past year, is a major reason why: AI is simply assumed to be part of the offering, not a feature worth calling out on its own anymore, the same way "internet-enabled" stopped being a selling point once every product had it.
For scale, AI-enabled digital health companies captured 54% of all US venture funding in 2025, $14.2B total, according to Rock Health, meaning the majority of the sector's capital already assumes AI as a baseline feature rather than a premium claim, well before this year's formal retirement of the category label.
The moat moved somewhere harder to fake
When the firm that coined "AI deal" retires the label, that is a signal the claim has become table stakes rather than a premium worth paying for.
Durable advantage in 2026 is coming from domain depth, workflow ownership and network effects, not from having AI at all. That is a much harder story for a startup to tell in a pitch deck, because those moats take years to build and can't be shown off in a single demo the way a slick AI feature can.
It is also exactly why Abridge's named health-system results, described in the previous article, are outperforming a generic "AI-powered" pitch in the same category. The startups still leading with the AI feature itself are increasingly speaking to an audience that already assumes that part.
The messaging gap that's about to widen
Expect investor diligence to spend far more time probing workflow ownership and domain depth than probing the AI stack itself.
Startups that lead with a named deployment and a specific metric are already outperforming those still leading with "AI-powered," and that gap should keep widening through the rest of 2026 as more of the category's early winners publish comparable results, forcing the rest of the field to either match that evidence bar or fall further behind it.
