State of AI Citations 2026: What a Live Index Reveals About How AI Picks Sources

By the numbers: the live index today

These figures come straight from MentionRadar’s query–domain index and update on their own — as of July 2026, reflecting ChatGPT citations (Gemini and Grok are rolling in).

Citations are concentrated: the 15 most-cited domains alone account for roughly 17% of every citation in the index, across 1,075 tracked categories. The rest of the web shares the long tail.

The most-cited domains right now

The domains AI cites most across the whole index, by total recorded citations. Big community, editorial, and platform sites dominate — the structural reason “get mentioned where AI already reads” beats “publish and wait.”

Live from the index, July 2026. Your own domain’s standing is one lookup away — run the free AI Citation Checker.

How this report is compiled

Every finding comes from MentionRadar’s query–domain index: a background system that runs real buyer questions through AI assistants, extracts the domains each answer cites, and records those links so they can be compared over time. That design matters — a one-off prompt screenshot can’t tell you whether an answer is stable, but an index that re-queries and stores results can. The headline figures above are live aggregates; the structural findings below are directional patterns, and we say so where it matters.

For the mechanics of how an inverted index of AI answers is built, see the Reverse AI Search pillar. For how a figure becomes publishable — what gets sampled, over what window, and how every estimate or attributed third-party number is labelled — see our methodology.

Finding 1: Citations concentrate on a few domains per category

Within any given category, a small set of domains tends to absorb the majority of citations, while a long tail of sites is cited rarely or never — the same power-law shape as organic search. The index-wide concentration figure above is the aggregate version of this; inside a single category it is usually sharper still. This sets realistic expectations: being cited at all puts you ahead of most competitors, and displacing an incumbent is a multi-query campaign, not a single content fix. We publish per-category reference ranges in category share-of-voice benchmarks.

Finding 2: AI answers are volatile

Re-running the same query over time reveals that the cited source set is not fixed. For many questions, the domains an AI names this week differ from last week’s — sometimes because a page changed, often because the model’s retrieval or weighting shifted underneath an otherwise unchanged web. That has two implications. First, a single check is a snapshot, not a verdict — you need to monitor over time to know whether a win is real. Second, volatility is itself a signal: a steady citation is a stronger asset than a flickering one. We unpack measurement and what drives churn in AI citation volatility.

Finding 3: Format matters — some content types punch above their weight

Looking at what gets cited, certain formats appear far more often than their share of the web would predict: head-to-head comparisons and “best” lists, pages carrying original data, structured documentation, and review platforms. The common thread is extractability — content that gives a model a clean, self-contained, attributable answer to lift. The full breakdown is in which content types get cited most by AI, and the specific pull of review sites is examined in the review-platform effect.

Finding 4: Prompt volume is directional, not gospel

A popular idea in AI-search circles is that “prompt volume” — how often a question is asked of AI — can be measured precisely the way keyword search volume is. The honest read is that prompt-volume figures are inferred and noisy: useful for prioritisation, dangerous as a precise input. We make the case in does prompt volume mean anything. The report’s position: use it to rank, never to forecast.

Looking ahead: cross-model disagreement

The index is built to measure something single-model tools can’t: how much ChatGPT, Gemini and Grok disagree on which sources to cite for the same question. Early signs echo what others report — different training data, retrieval and recency produce materially different answer sets — which would mean a single-model visibility number under-counts your real exposure. As Gemini and Grok come online in the public index, this becomes a live figure here rather than an expectation. The strategic implication already holds: optimise for, and plan to measure across, all three models.

What this means for your 2026 strategy

• Get cited where AI already reads. The most-cited domains are communities, editorial and review platforms — earn mentions there, don’t just publish and wait.
• Pick queries by category reality. In a concentrated category, prioritise ruthlessly and expect a campaign, not a quick win.
• Monitor over time. Volatility means one check is a snapshot. Track the queries that matter so you see churn early.
• Write for extraction. Lead with a self-contained answer, structure for scanning, and publish the formats that earn citations.
• Plan for multi-model. Build for ChatGPT today, but structure your measurement so Gemini and Grok slot in without a rethink.

See your own slice of the data

This report is the aggregate view. Your own view is one lookup away: the free AI Citation Checker reads the same index backwards and returns the real queries AI already cites your domain on — no signup. Run yours, then a competitor’s, and you’ll see Finding 1 play out in your own category in seconds.