The three eras (Raymond’s diagram)
| Era | Who searches | What happens |
|---|---|---|
| Past | a human | types a query into Google |
| Now | a human via ChatGPT | ChatGPT fires its own queries at Google |
| Next | a human via an AI agent | the agent fans out many queries to Google |
“LLM turns a user query into fan-out queries which gets sent to search engines.”In the “Past” world you optimized for the human’s exact phrasing and fought for position #1. In the “Now” and “Next” worlds an LLM sits in the middle. It reformulates the human’s intent into many queries, reads a handful of pages, and cites a few. Ranking #1 for the original phrasing is neither necessary nor sufficient, because what you actually want is to be citable across the whole fan-out. You don’t have to guess at that fan-out. You can watch the machine’s actual queries. Synscribe ships tools for exactly this: a Chrome extension that reveals ChatGPT’s web-search queries, and Birdseye (a macOS app) that shows Claude Code’s queries. It’s the same move you’d make as a human: step into the shoes of a serious customer researching the space, and ask how they would find your product.
What machines actually cite
The GEO content strategy comes straight from citation-type data (“What ChatGPT Gobbles Up”, promptwatch.com):| Content type | Where it lands in citations |
|---|---|
| Listicles (“Top X”, “X Best”) | the obvious top of the list |
| Landing pages | the real winner, “taking both 2nd & 3rd position" |
| "How-to” articles | ”the next secret weapon” |
| Comparison / review pages | win “for B2B with long sales cycle and buying committees” |
best duty drawback pulls Zollback, hr intake automation pulls Jinba, how to set reminder linkedin dm pulls Kondo, and assent vs comply pro pulls Reglyr. Each one is a specific,
high-intent query answered by a matched page.
❓ [needs Raymond: referral tracking]. PLAN 1.2 says “why we track ChatGPT/Perplexity/Claude/ Gemini referrals,” but the Workshop Notes cover seeing queries (extension/Birdseye), not the referral-tracking rationale. Confirm the tracking philosophy (likely lives in the PostHog/Onboarding SOP).
Operationalize it
- Fan-out thinking when you find keywords: 2.2.1 — Keyword exploration.
- See and measure AI-engine traffic separately: 2.6.1 — PostHog dashboard setup and 1.10 — Attribution.
- Why the agent-in-the-middle changes how we operate, too: 1.12 — Agent-operated SEO.