The half of AI visibility that is not SEO

Nobody won the GEO argument on r/SEO last week. Nobody could have.

The thread is called “GEO is just a Buzz Word Marketers Selling to a Fools”. Sixty comments. Several people who clearly do this for a living. Nobody moved an inch.

Most of them say GEO is SEO with a new label. roncraig gives the honest version: clients ask for it, so you sell it, because otherwise they buy it from someone worse. VillageHomeF takes the hard line. The internals are proprietary. Every tactic has been debunked. The category is sold to people who cannot tell.

Then SpaceUnicorny calls both sides wrong. Three claims, none of which fit the majority view. You need no site copy and no backlinks to get recommended. The free tier of ChatGPT often does not search at all. A brand can go from nothing to cited inside a day.

The thread reads that as a louder opinion. It is not an opinion. It describes a second system, one that owes nothing to crawling, ranking or links. The argument cannot resolve because the two sides were never discussing the same one.

We got this wrong too

Our query fan-out post says answer engines do not remember your brand, they search for it. One section is headed “the model is not an index”.

Half right. The missing half decides whether the search ever looks for you.

RESONEO, a French agency reverse engineering ChatGPT’s search stack in public, names the split. Most tools average the two halves together. Ours included.

Dynamic visibility is what the model finds. Live retrieval, fan-out queries, source selection. It behaves like SEO. It shifts by model and by day. It depends on whatever index sits underneath. Pinning down a number takes many runs.

Parametric visibility is what the model already knows. Brand salience, entity associations, authority priors, baked into the weights during training. No search happens. It holds steady for months and moves only when the knowledge cutoff moves. It costs very little to measure, because nothing browses.

The two run on different clocks. Measuring one tells you almost nothing about the other. Conflate them and you get sixty comments of people shouting past each other.

Parametric memory comes first

Say parametric memory only governed the search-off case. Then it would be a footnote. Most commercial queries trigger retrieval, and fan-out would be the whole story.

RESONEO argues it does not work that way. Parametric memory shapes retrieval at both ends.

Upstream, the model writes its own fan-out queries. It does not pull them from a neutral vocabulary. It reaches for entities and sources it already holds. A brand absent from memory never becomes a candidate. The search that would have found you never runs.

Downstream, the model picks between results. It favours sources sitting in its weights. Those get cited more often, placed higher, and framed better than equally relevant sources it has no prior on.

So parametric awareness is not a channel running beside retrieval. It gates it. Being unknown to the model is a different failure from ranking eleventh.

That rescues all three of SpaceUnicorny’s claims. A recommendation drawn from weights needs no site copy and no backlinks. A free tier that does not search is a tool-access difference between plans. A citation inside a day is easy once the constraint is being mentioned somewhere retrievable, not being ranked.

The evidence is uneven

This is one agency’s lens, and it deserves the scrutiny we would want.

The retrieval work holds up

RESONEO’s honeypot put a hashed, unlinkable page on their own domain. It logged full headers when a crawler hit it. It returned an encrypted sentinel, so they could check a participant’s pasted response against whether the fetch really happened.

That sentinel is the important part. It kills the failure mode that ruins most research here: a model confidently describing a page visit that never occurred. Their findings on plan tiers and model routing rest on server logs, not on what an assistant said about itself.

The parametric side is thinner

Its best demonstration measures rather than explains. Dan Petrovic at DEJAN AI asked Gemini 3 Flash to name a hundred brands at random, two hundred thousand times. Roughly twenty million mentions came back. He built an association graph of nearly three million nodes and ran Personalized PageRank over it.

The result is not a popularity list. Maison Margiela never gets recalled spontaneously. It still ranks as the best positioned non-seed brand, because it sits densely among brands that do. Centrality beats awareness.

That proves parametric memory has structure, and that the structure is measurable. It does not prove how much the gating effect is worth. Nobody outside the labs has isolated it. The mechanism is plausible and partly evidenced. Nobody has put a number on it.

One figure to stop repeating

Somewhere in that thread, Perplexity is said to draw sixty per cent of its answers from search results and forty per cent from its own database. sparkignitefire asked for a source and called the figure too precise for something that varies by query. No source arrived. We repeat it here only because we keep seeing it quoted, and it should stop.

Where the sceptics are right, and where they break

VillageHomeF makes the strongest argument in the thread. It deserved better than it got. If the internals are proprietary, if outputs move daily, and if no tactic has been isolated against a control, then a visibility number is unsupportable.

Against dynamic visibility, that mostly holds. We have argued a version of it ourselves.

marintkael answers it properly. Sixteen frozen questions, once a day, twenty-eight days. On the day of posting: OpenAI cited them in twenty-seven per cent of answers, Claude in nine, Gemini in none. Their measured standard deviation across the run was 2.8 points. They then refuse to interpret a seventeen against five split between two Claude tiers, because it sits too close to that band. That is more discipline than most published research in this field shows.

Against parametric visibility, the charge collapses. Nothing browses. No retrieval to depend on. No index underneath to drift. Ask the same model family the same questions and you get the same answers for months, until the cutoff moves.

The half sceptics call unmeasurable turns out to hold the field’s most reproducible measurement.

Measure your parametric half this afternoon

Call a model through the API with browsing and tools switched off. Nothing retrieves, so whatever comes back is weights only. Then ask:

• What do you know about [brand]?
• Who are [brand]‘s main competitors?
• What are the most frequent criticisms of [brand]?
• How would you position [brand] against [named rival]?
• Score these brands on perceived authority from one to ten: [list].

The last one earns its place. A score across a list exposes relative gaps. Asking about yourself alone returns a number with nothing to compare it against.

Name the geography every time. France, Europe and worldwide return three different answers. Leave it unstated and you inherit the model’s default, which skews Anglo-Saxon on an English-heavy corpus.

Run it against each model family your buyers actually use, because the weights differ between them. Then repeat the whole thing next quarter.

What you get is a perception map, not a score, and both kinds of finding are actionable. An angle the model has never heard of is a hole in your editorial coverage. A criticism it repeats unprompted already sits in the weights, and it will keep reaching every buyer who asks until enough authoritative material outweighs it.

The levers run on different clocks

Accept the split and the levers separate. Most GEO retainers bill for one set and promise results on the other.

Dynamic levers are tactical and fast. Technical work. Freshness signals. Snippet and passage quality. Mentions on sources that are retrievable today. Per-model monitoring. Feedback lands in days, and it moves enough that you need repetition to trust it.

Parametric levers are strategic and slow. Press and analyst relations. Editorial content on authoritative sites. Presence in the reference corpora that get ingested wholesale. Direct answers to criticisms already baked in. Feedback arrives in cutoffs, not weeks. The work you commission now aims at a snapshot nobody has taken yet.

RESONEO calls this the Google Dance of LLMs. Each cutoff redistributes the rankings held in the weights, so the strategic window sits between two of them. We would widen the error bar. The inference from how pre-training works is sensible, but nobody has watched enough cutoffs to confirm it. The duller version survives. Parametric work has a long lead time, so starting it this quarter because you noticed a problem this quarter is already late.

The honest position

Nobody in that thread is stupid. The majority is right that retrieval is mostly SEO aimed at a moving target. We have said so at length. The dissenter is right that a second system sits underneath, and it owes nothing to crawling, ranking or links. It reads as a fight because the field uses one word for two things, and the people selling chose the word.

Turn that suspicion here too. A framework that halves a contested field reads well and over-applies easily. The mechanism joining the halves is argued, not measured. And we profit from a story where AI visibility turns out to be bigger than SEO.

Voxoria measures the dynamic half properly. Your prompts run daily across ChatGPT, Perplexity, Gemini and Google AI Overviews. We capture the fan-out queries, who got mentioned, and which sources were retrieved and cited. That half is volatile, and daily tracking is the only thing that separates a real move from a 2.8-point standard deviation.

The parametric half needs a different instrument, run quarterly. We would rather sell you one honest half than average the two and call the result visibility.

Start tracking the dynamic half, then run the parametric test above this afternoon and put the next one in the calendar.