Source: OpenEvidence, which offers an AI chatbot for doctors, is raising $250M at a $12B valuation, up from $6B in Oct., and has $150M in annualized ad revenue
Stephanie Palazzolo / The Information : X: @steph_palazzolo and @arfurrock X: Stephanie Palazzolo / @steph_palazzolo : New! OpenEvidence, which offers a “ChatGPT for doctors,” is raising at a $12B valuation, doubling its valuation from its last round 2 months ago. This would make it one of a handful of AI application startups to surpass a $10B+ price and $100M ARR. https://www.theinformation.com/ ... Arfur Rock / @arfurrock : OpenEvidence raising $250M at $12B. $150M RR on just ads, +3x from August. On <1M users. Resulting in 90% GM, inclusive of all compute. 2026 will be the year AI ads cross the rubicon. If you're building an AI app figure out your ad strategy ASAP. You'll be behind without.
Context & Ripple Effects
OpenEvidence’s reported financing follows its $6B valuation in an October funding round, itself a sharp step up from the company’s first outside round at a $1B valuation. The new report adds a commercial metric to that valuation arc: advertising, rather than only subscription revenue, is presented as the product’s revenue engine.
The combination of fewer than one million users, fast-growing ad revenue, and reported 90% gross margin makes the story relevant beyond medical AI: it is a test of whether narrowly targeted professional AI distribution can support premium advertising economics despite compute costs.
First-order effects
- The reported $250M raise would give OpenEvidence additional capital at a $12B valuation, while marking a doubling from its October price.
- OpenEvidence’s claimed $150M annualized ad revenue and 90% gross margin put its ad-supported model—not just its clinical chatbot—at the center of investor scrutiny.
Second-order effects
- Other AI products serving clinicians face greater pressure to demonstrate monetization and retention, particularly if they rely on similarly valuable professional audiences.
- Advertisers seeking access to medical professionals may treat conversational AI as a more measurable distribution channel, while platforms must show that ad load does not undermine user trust or utility.
Third-order effects
- If repeatable, this model would shift parts of vertical AI away from seat-based software economics toward audience-and-intent monetization, making distribution quality a core competitive moat.
- The durability of that shift remains uncertain: high revenue per user can support large valuations, but it also concentrates risk in advertiser demand and the product’s ability to preserve professional credibility.
The trend: Vertical AI companies are increasingly being valued on their ability to turn specialized user distribution into high-margin revenue, not merely on model capability.