What we measured about it: 8,100 times a month of demand across four names for the same discipline, defined everywhere and measured almost nowhere.
Run on your market, a read shows: where AI answers exist on your buyers' questions, which are open or figure-poor, and your real search demand under every phrasing, which AI answers in your market are open, partial or closed (with the evidence), what your competitors run in the public ad archives, and your break-even numbers computed from your own economics.
Book the 20-minute callor read the measured pieces first →"Ai search engine optimization" is searched about 8,100 times a month in the US at difficulty 41 of 100, with advertisers already paying $37.91 for the top slot. The practitioner phrasings are climbing beneath it: "llm seo" 880 searches a month, "chatgpt seo" 390 searches a month, "generative engine optimization" 4,400 searches a month. Same discipline, four names, and almost nobody defining any of them shows a measurement. measured
What optimizing for AI search actually means
Three surfaces, one job: be the source the generated answer cites. That requires knowing where answers exist (measure, twice: 7 answers changed between our pulls across three markets), what the existing answers lack (usually decision figures), and publishing the missing substance on a crawlable domain with provenance a model can trust.
The uncomfortable measured truth
Most of what ranks for this phrase is repackaged classic SEO advice. The measurable difference is the unit of competition: rankings are a list you can climb gradually; citations are slots you either hold or do not. That binary is why the doorway classification matters: spending on a closed answer is spending on a wall.
One read: your demand, your winnable AI answers, every competitor's ads, and your site's catch rate. Price and scope on a short call, plainly.
Book a call or see what a read contains →