Retrieval Floor
The Retrieval Floor is the authority threshold below which an AI engine's probability of retrieving a site is effectively zero, which multiplies every downstream optimization by zero.
A site below the floor cannot fix its citation problem with better formatting, denser statistics, or cleaner chunking, because the engine never fetches the page that carries those improvements. The industry describes this as a retrieval "bottleneck" or "gate". I prefer floor because it tells you what to do: get above it first (crawlability, indexation, being in the fan-out source pool), and only then spend effort on how pages read. On my own sites, the single largest citation step-change came from internal-linking and indexation work, not from any content tactic. That is what living near the floor looks like.
Citation Vacuum
A Citation Vacuum is a high-demand question that AI engines cannot crisply answer from their current sources, detectable because the model hedges. The first authoritative answer published into the vacuum tends to become the standing answer.
You can detect one directly: ask the engine and watch it hedge ("sources vary", "no definitive figure exists"). The hedge is a flare marking an uncontested citation slot. On a brand-new, essentially zero-authority domain I run, question-shaped study pages built this way were being cited by Perplexity at position one within two to four weeks of publishing. The vacuum is also the honest counterpoint to the Retrieval Floor: filling one is the fastest legitimate way I have measured for a site to get its first citations while it is still building the authority to compete anywhere else.
Fan-out Farming
Fan-out Farming is the strategy of systematically owning the low-competition sub-queries that answer engines fan a prompt out into, instead of fighting for the contested head term.
When you prompt a modern answer engine, it does not run one search. It explodes your question into a set of parallel sub-queries (the established term for this is query fan-out) and assembles the answer from what those sub-queries retrieve. The leaves are close to invisible in keyword tools, because most fan-out sub-queries show zero recorded search volume. That is precisely why they are cheap. Breadth of coverage within a topic surfaces you across more fan-out sets than ranking first for one phrase ever will.
Substrate Seeding
Substrate Seeding is placing true, distinctive information into the third-party sources an answer engine already retrieves from, so you ride into answers through material the engine trusted before you existed.
The substrate is the pool of sources an answer engine already trusts and retrieves from: Reddit, Wikipedia and Wikidata, YouTube transcripts, GitHub, the established publications in your niche. I should be clear that the play itself is not my discovery; agencies market a version of it as "LLM seeding". I use substrate because it is more precise about the mechanism. You are not seeding the model, you are seeding the corpus it draws from, and the distinction matters when you try to measure whether it worked. The ethical line also matters: seed correct, distinctive facts, or you are just polluting the well you drink from.
Live-Answer Shaping
Live-Answer Shaping is optimizing for the in-session fetch: the moment an AI assistant's agent retrieves a page live, mid-conversation, while a human waits, because what that fetch ingests shapes the answer being read right now.
Some AI fetches are not background crawling. When you ask ChatGPT something that needs fresh information, an in-session agent (it identifies as ChatGPT-User in server logs; Perplexity and Claude have equivalents) fetches pages while the human waits. Live-Answer Shaping means serving those user-agents fast, clean, server-rendered pages whose first screenful contains the extractable answer. Almost nobody instruments this separately from bot crawling, which is a mistake I only caught by reading my own server logs. The in-session fetch is the one moment where your page and a live reader are connected through the model in real time.
Citation Cliffhanger
A Citation Cliffhanger is a citable passage deliberately built with an honest information gap that requires the click to complete: the method without the worked example, the finding without the sortable table, the number without the interactive checker.
AI answers quote the passage; the gap is what converts the footnote into a visit. The line I hold: the passage must still be true, complete enough to deserve the citation, and the thing behind the click must actually exist. A cliffhanger is a reason to visit. Bait is a reason to distrust you, and answer engines increasingly re-read the pages they cite.
Unclicked Influence
Unclicked Influence is traffic that originates from an AI mention but arrives later as branded search or Direct, because the reader never clicked the footnote and the referrer was stripped anyway.
Most of the traffic AI answers send you will never look like AI traffic. The mention happens inside an answer, the human does not click the footnote, and they arrive hours or weeks later by typing your name into a search bar or address bar. In analytics that lands as branded search and Direct, and referrer stripping means the majority of even the clicked visits report as Direct too. The industry has adjacent umbrella terms ("dark traffic", the "AI dark funnel") for everything attribution cannot see. This term is narrower on purpose, because the mechanism is measurable if you name it: watch branded search and direct trend against your citation footprint, not your referral clicks.
The one I am deliberately not claiming: citation half-life
I use "citation half-life" constantly, for the time it takes a page's AI-citation rate to decay by half as the retrieval substrate drifts (roughly 4.5 weeks at the median, in the data I watch). I did not coin it and neither did any GEO writer. Bibliometrics has had "cited half-life" as a standardized journal metric for half a century, and several people writing about AI search reached for the same framing earlier in 2026 than I did publicly. It is the right term precisely because it was already the right term somewhere else. Use it freely; just do not let anyone sell it to you as their invention, me included.
Names are cheap to make and expensive to displace, which is why a young field's vocabulary settles fast and mostly by accident. GEO is settling right now; a July 2026 academic survey of 45 GEO studies noted outright that the field's terminology is still fragmented. These seven have survived a year of my own internal use, dozens of experiments, and one deliberately unclaimed eighth. If they are useful, take them; the words travelling matters more to me than the link, although section seven explains exactly why I can afford to say that.