- Scale gap: Google still processes ~14 billion searches / day; ChatGPT hovers around ~37 million. You’re looking at 1 LLM query for every 400 Google queries.
- Intent gap: Only ≈ 15 % of ChatGPT prompts are classic “informational search.” ≈ 75 % are generative tasks (code, drafts, summaries) that were never clicks in the first place.
- Traffic gap: When Google shows an AI-Overview, downstream organic CTR drops ≈ 70 %. Dedicated AI engines send ≈ 96 % less referral traffic than classic Google results.
Implication: Chasing LLM citations is fine as a branding bonus, but it’s not a growth lever for most sites. Spend effort proportionally.
A pragmatic playbook (what actually moved the needle in my own tests)
1) Question-first headings – treat every H2/H3 like a unit-test: Q in, 40-60 word A out, then elaborate.
2) Answer-first, expand-later – inverted-pyramid writing is a killer for snippet extraction.
3) Chunk + schema – short paragraphs, lists, FAQ/HowTo markup. The clearer the structure, the higher the chance an LLM copies you verbatim.
4) Keep it fresh & sourced – stats with a 2025 timestamp and a citation get surfaced more often (recency + trust signals).
5) /llms.txt – a zero-cost “robots.txt for LLMs” to spotlight the pages you actually want crawled.