Contested vs Settled: Your Most Volatile AI Queries Are the Open Slots

Questions this guide answers

• Why do my AI citations change every week?
• Which AI queries should I target first for GEO?
• Are volatile AI-citation queries worth optimizing?
• What is a contested vs settled AI query?
• How do you win an unstable AI-citation query?

Direct answer

AI-citation volatility is a signal about the query, not a flaw in your tracking. Some queries reshuffle their cited sources run after run; others sit rock-stable for weeks. The volatile ones are not a maintenance burden to be quieted down. In our reading they are the open slots: queries where no source has yet won the canonical position an engine reaches for, so the answer keeps drawing from a shifting handful of candidates.

We split the spectrum into two regimes. A contested query is unbranded discovery work, the "best X for Y" and feature-comparison questions where the engine has no settled go-to source and the cited set churns. A settled query is branded, factual, or definitional, where one canonical source has won and the answer barely moves. The reframe that follows from this is the whole point of the page: a high-value query that looks volatile is usually a slot still up for grabs, not a write-off.

You earn your way into a contested slot by treating it as a process, not a single reading. Measure presence as a rate over weeks instead of reacting to one run. Separate ordinary sampling run-variance from genuine week-over-week drift. Then feed consistent, fresh, human-approved signals across the independent sources each engine samples, and re-measure to see whether the rate is climbing. The regime split and the settling mechanism below are our labeled interpretation of how this works. The run-variance discipline and the one rock-stable branded example are what our own measured data shows, and we keep those two kinds of claim clearly separated throughout.

Why citations move at all

Before treating variance as a selection signal, it helps to know why an AI-citation number moves even when nothing you control has changed. We will not re-derive that here. The mechanics live in why your AI visibility score moves: engines are non-deterministic by design, models get updated, competitors publish, third-party citations shift, freshness windows turn over, and sampling cadence introduces its own jitter. A score is a statistical estimate, not a deterministic count, so some movement is normal noise.

That page draws one conclusion: the moving score is a symptom, and the diagnostic job is to ask what new content was published and which citations changed, rather than panic at the number. This page makes the opposite move on the same raw phenomenon. There, variance is something to diagnose and often to ignore. Here, variance is a selection signal: a map of which queries are still contested and therefore still winnable. Same noise, inverted use. Read the score-moves page for the rigor on what is and is not real movement; read this one for what to do with the queries that move the most.

The two regimes, and how to tell them apart

Contested and settled are the two ends of a spectrum, not a hard binary, and most prompts sit somewhere between. The diagnostic is not a single threshold; it is a pattern you read over several runs. A settled query shows high, stable presence with a small, recurring set of cited sources. A contested query shows low or middling presence with a cited set that reshuffles run to run, often pulling in a different domain each time.

Our own data gives a clean illustration of each end. The settled example is a single branded self-lookup, the prompt "is SolCrys AI a legit AEO provider?", which sits at 100% presence (45 of 45 responses across all five engines), a citation rate around 49%, and sentiment of 20 positive, 25 neutral, 0 negative. That is what a slot with a won canonical source looks like: rock-stable, because the engines already have a place to go. This is one branded prompt illustrating a settled slot. It is not a measured finding that branded or factual queries are stable as a class.

The contested end is the discovery cohort: "closed loop AI visibility" around 18% presence, "CMS integration" around 9%, "cover Amazon Rufus" around 7%, "tracking brand visibility in ChatGPT/Perplexity" around 2%, and roughly sixteen other discovery prompts at 0%. These are low and noisy, the signature of slots no source has locked. The practical tells for sorting a query yourself:

• Presence level: settled queries cluster near the top (consistently high presence); contested queries sit low or in the middle and bounce.
• Source stability: settled queries cite the same one or two sources every run; contested queries pull a different domain into the answer from run to run.
• Query type as a prior, not a rule: branded, factual, and definitional questions skew settled; unbranded "best X for Y" and feature questions skew contested. Treat type as a hint to check, never as the verdict.
• Read it over runs, not once: a single run cannot tell the two apart, because one run of a contested query can look deceptively stable by luck. The regime shows up in the pattern across several runs.

The reframe: a volatile high-value query is an open slot, not a write-off

Here is the inversion, stated plainly as our interpretation. Most teams see a high-value query that swings between runs and conclude it is unstable, unreliable, or not worth the effort, and they redirect attention to queries that already read steady. That instinct quietly selects for slots someone else has already won, where your upside is smallest.

We read the same volatility the other way. A contested slot is churning precisely because the position is unclaimed. The engines are still shopping among candidate sources because none has earned the canonical spot. That is the moment a new source can become one of the corroborating few. A settled slot, by contrast, is stable because the contest already ended, and entering it means dislodging an incumbent that the engines have learned to trust. So variance flips from a symptom you suppress into a selection signal you act on: the volatile, high-value queries are the ones to prioritize, not the ones to write off.

This is an interpretation, not a measurement, and we want to be exact about the boundary. We are not claiming our data proves that prioritizing volatile queries produces citations. We are claiming the logic is sound and worth testing on your own prompt set: target the high-value queries that are still contested, because those are the slots still open. The disciplined way to act on that without fooling yourself is the subject of the rest of this page, and the broader "design a test you cannot fool yourself with" frame lives in the testable GEO playbook.

Run-variance vs non-stationarity: the discipline we actually own

The reframe only works if you can tell a query that is genuinely moving from one that is merely sampling noisily. This is the part our data supports most firmly, and it is the discipline we hold ourselves to. The strongest grounded fact we have, as of the 7-day window ending 2026-06-21, is a stability test: nine consecutive runs of the same prompt set, with no content change between them, produced these overall mention rates, in percent:

• 7.95, 5.45, 5.0, 5.0, 8.86, 5.0, 4.55, 6.36, 7.05
• No monotonic trend. The series oscillates inside a band and goes nowhere, even though nothing changed between runs.
• At roughly n=45 responses per prompt, a single mention appearing or vanishing moves a prompt's presence by about two percentage points on its own.

How a contested slot settles: per-engine corroboration

If a contested slot can settle, what does the settling look like? Our working hypothesis, and we are labeling it a hypothesis on purpose, is that a slot settles per engine when several independent sources corroborate the same claim, giving the engine a stable place to reach. We hand the full mechanism to AI cites consensus, not authority, which makes the case that engines repeat the claim corroborated across the most independent sources rather than ranking a single authoritative page. "Agreement settles the slot" is the one-line version of that idea, and it is our hypothesis, not a measured result.

Our landscape data is consistent with this picture without proving it. Across the window we looked at 13,377 citations spanning 1,507 domains, with no single domain dominating: reddit.com is #1 at about 6.4%, wikipedia is #2 at about 3.6%, and nothing sits above that 6.4% ceiling. The engines also barely agree with each other. Per-engine mention rates differ sharply, with Claude strongest around 9.6% and Perplexity weakest around 4.5%, which points to low cross-engine source overlap. That is exactly what you would expect if each engine samples a different handful of sources, and a slot has to settle separately inside each one as its sampled sources start to corroborate. We say "consistent with," not "proven by," because nothing in this landscape snapshot demonstrates the causal step from agreement to a settled slot. It is a hypothesis the data does not contradict.

Two consequences follow from taking the per-engine view seriously. First, there is no blanket "AI engines do X" here; a slot can be settled in Claude and still contested in Perplexity, so you read and act per engine. Second, settling is not instant. New material has to be crawled, indexed, and corroborated, which is a process measured in days, not minutes. We say daily, not real-time, about that clock.

How to earn a contested slot, governed

Earning into a contested slot is the same governed loop SolCrys runs on everything else: measure, diagnose, execute, verify. Nothing about it is automatic, and the execute step stays human-approved. We do not claim the engine publishes for you, and we do not promise a slot; you earn your way in, and the engines decide.

The loop applied to a contested query looks like this:

• Measure the rate. Track presence for the query as a rate over weeks of runs, not from one reading. A single run cannot tell you whether a slot is contested or settled, and a two-run difference is usually just run-variance.
• Diagnose contested vs settled. Use the tells above: presence level, source stability across runs, and query type as a prior. Confirm the high-value queries you care about are genuinely contested (low and churning), which is what makes them open rather than already lost.
• Execute consistent fresh signals across sampled sources, human-approved. Because each engine samples a different handful of independent sources, the work is getting your claim corroborated across several of them over time, not perfecting one authoritative page. Every piece is reviewed and approved by a person before it ships. The corroboration mechanism is detailed in AI cites consensus, not authority.
• Verify on a re-run. Re-measure over subsequent runs and look for a sustained climb that survives many readings, the non-stationarity signal rather than a one-run blip. If the rate holds its rise across weeks, the slot is settling for you; if it wobbles inside its band, nothing real has moved yet.

What we don't claim

To keep the method honest, here is the boundary of what this page does and does not assert.

The discipline below is what our data actually shows. The interpretation above it is ours, offered as a frame to test on your own prompt set, not as a proven law.

• We do not claim branded or factual queries are stable as a class. Our settled example is a single branded self-lookup at 100% presence. It illustrates what a settled slot looks like. It is one prompt, not a measured cohort, and we do not generalize the regime from n=1.
• We do not publish the contested cohort's small run-to-run moves as slots being won or lost. Those plus-or-minus-1-to-3-point changes are one mention flipping at n=45, which is sampling run-variance. The only clean trend statement our data supports for that cohort is the negative one: no monotonic drift.
• We do not claim a slot settles because sources agree. That is a hypothesis our landscape is consistent with, not a measurement. The causal step from corroboration to a settled slot is unproven, and we label it a hypothesis everywhere it appears.
• We did not run a study ranking many query types by citation stability. The regime framing is a logical reading of how citations behave, generalized as method. We are not implying SolCrys measured a stability ranking across query types, and there is no such study behind this page.
• We do not promise a slot, or claim the engine publishes for you. Contested slots are up for grabs; you earn your way in with human-approved content, and the engines decide. The settling clock is daily, not real-time, because new material must be crawled and corroborated over days.
• We measure per engine, not as one blob. The five engines barely overlap in the sources they cite, so a query can be settled in one and contested in another. There is no blanket "AI engines do X" claim here.

Sources

• SolCrys citation, visibility, and per-prompt measurement, workspace solcysai-aeo, 7-day window ending 2026-06-21, five engines (ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude); 22 prompts, 9 runs, 13,377 citations
• SolCrys 36,268-citation AEO category study — canonical owned-baseline (rank and owned-citation share)

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