What AI engines cite: a 36,268-citation study of the AEO category and what changed in August 2026

Workspace and time window

• Workspace: a continuous monitoring workspace SolCrys runs on its own category.
• Window: May 12 to June 11, 2026 (30 consecutive days).
• Tool: the SolCrys AEO platform's citation-extraction module.
• Earlier edition: a first version published May 22, 2026 used 22 prompts and 4 engines (April 22 to May 22). Because the prompt set doubled and Claude was added, counts from the two editions are not comparable, and this page does not compare them.

Prompt set

We used a fixed set of 44 buyer-comparison prompts written to reflect what a B2B marketer, agency lead, or VP of marketing would ask an AI engine while shortlisting AEO tools. The full set is available on request.

The examples below are paraphrases. The actual prompts vary in punctuation and qualifier wording, a deliberate choice: engines respond differently to small wording shifts, and a single-phrasing study would over-fit.

• "What are the best AEO tools for B2B marketing teams in 2026?"
• "Best AEO platforms for agencies managing multiple clients"
• "Best tools to track brand visibility in ChatGPT"
• "Top generative engine optimization platforms compared"

Responses and citation extraction

The window produced about 3,493 responses, fewer than a full 44 × 5 × 30 grid, because Claude joined mid-window and not every prompt ran on every engine every day.

From each response we extracted every URL the engine cited or linked as a source, reduced it to its registrable domain (for example, blog.hubspot.com/foo becomes hubspot.com), and counted it. Totals: 36,268 citations, 7,962 unique URLs, and 2,858 unique cited domains. Each domain was assigned a source type:

• Vendor sites: domains of the AEO, SEO, and marketing-software vendors tracked in the workspace (for example, Semrush, Profound, HubSpot, and Ahrefs), including the publisher's own domain.
• Editorial: trade press, news, analyst, reference, and academic sites (for example, TechRadar, Search Engine Land, Wikipedia, and arXiv).
• UGC: Reddit, YouTube, Quora, forums, and community wikis.
• Other: everything else, including long-tail blogs, personal sites, GitHub, niche industry pages, and vendors that are not on the tracked list.

Vendor bias disclosure

SolCrys is a vendor in the AEO category and publishes this study. Its own domain is counted in the vendor-sites total but left out of the named domain ranking, so the ranking describes the sources a buyer's answer draws on rather than the publisher's own standing. Every other cited URL is counted as captured, whoever owns it.

What this study can and cannot claim

It describes citation patterns for the AEO category specifically, in a 30-day window, on a 44-prompt sample across 5 engines. That is enough to characterize where engines drew their sources when answering buyer questions about AEO platforms in that window.

It is not enough to claim the same distribution holds for other categories, or that it is stable over time. The August follow-up below shows how quickly it can move. The full caveats are listed near the end of this report.

What this cross-section shows, and what it is

One engine broke and four did not. ChatGPT went from citing Reddit in roughly a quarter of its citations to effectively never. Google AI Overviews was unchanged. Perplexity and Gemini cited Reddit rarely on both sides (under 2%); Perplexity's dip from 1.5% to 0.2% is on too small a base to read as the same break. Claude sat at zero on both sides, which is not a retrieval story: Reddit sued Anthropic in June 2025 over unlicensed crawling, and reddit.com has not appeared in Claude's citations in any of our runs.

The before side is a flat line, not an average. Across 10 consecutive daily runs from July 10 to 19, ChatGPT's Reddit share landed between 22% and 25% every day, on about 8,500 ChatGPT citations. The next runs on this workspace, from Aug. 7, came back at 0.4%, then 0.8%, then 0.0%. A 10-day band that tight followed by a step that size is not sampling noise.

Stated plainly, because it matters for how much weight to put on it: this is one monitoring workspace in one category, on the 44-prompt set documented above, with fewer runs after the break than before. It is not a panel and is not offered as one. Our baseline is also far higher than the tracker figure reported in the press (23.4% versus 3.83%), most likely because the prompts are B2B buyer comparisons captured through the API rather than a general prompt mix; the direction is the same. Its job here is to answer a question the larger panels do not: whether the break was ChatGPT-specific. Under one method, across five engines, it was. The workspace also has a run gap from July 20 to Aug. 6, so it cannot date the break; that dating belongs to the reporting above.

The second change: fewer citations per answer

ChatGPT cut the number of citations it shows per answer by roughly three quarters over the same period, from about 33 to about 9 on this prompt set. That is a separate change from the Reddit one, and a tracker measuring share rather than count would not see it: if every answer carries fewer sources, each surviving source's share can hold steady while the absolute number of citations a brand earns falls sharply.

This matters for anyone reading a citation dashboard after August. A brand whose ChatGPT citation count fell did not necessarily lose ground; the denominator shrank for everyone. Citation share survived the change as a metric. Citation count did not, and any target set against a July count is measuring a different surface.

Naming a source is not the same as reading it

ChatGPT stopped naming Reddit. Whether it also stopped reading Reddit is a separate question that citation data cannot answer. Access and attribution are separate decisions: the licensing that lets an engine retrieve a source is independent of the product choice to show it underneath the answer. For a brand, that means a thread describing your product badly could still shape an answer while being invisible in every citation report you run. Our instrumentation records what an engine cites, not every page it consulted, so this study cannot settle the question either.

What took the share, in this category

On the same workspace and engine, the share moved toward vendor and official pages. Tracked vendors' own sites (not counting the publisher's) took a much larger share of ChatGPT's citations after the break, while user-generated sources' share collapsed. In a category whose vendors publish heavily, the engine reached for their sites once the forum threads were out. On this one engine, that reverses the owned-media picture from the primary window.

What this does not settle is the mechanism. "site:"-scoped retrieval is the leading explanation in the trade press, and it fits what we see, since the share moved to official domains. But correlation in one category is not a cause, and we have not tested it. The dating and the mechanism belong to the reporting linked above; the five-engine cross-section is ours.

Why a source's AI visibility is a licensing variable

Reddit's citation history across engines tracks its commercial and legal relationships at least as closely as anything about its content, which did not change:

• May 2024: OpenAI and Reddit announce a partnership that brings Reddit content into ChatGPT. By July 2026, Reddit accounted for roughly a quarter of ChatGPT's citations in this study's workspace.
• June 2025: Reddit sues Anthropic over unlicensed crawling. Claude has not cited Reddit in any of our runs.
• September 2025: an earlier, shorter dip in ChatGPT's Reddit citations that recovered within weeks. A recovery in 2025 is not evidence about 2026; the 2026 break had not recovered as of Sept. 17, 2026, in our data.
• July 2026: reports that Reddit is weighing whether to renew Google's AI content agreement. As of September 2026, Google AI Overviews is the largest engine still citing Reddit in our measurement.
• August 2026: ChatGPT's retrieval change; Reddit citations go to roughly zero there.

What this does and does not change for strategy

It does not make community work worthless. Google AI Overviews cited Reddit at about the same rate after August as before in our data, and buyers researching your category still read community threads directly. What it changes is the promise you can honestly make: community presence buys influence, and it buys citations on some engines, but no vendor can commit to a citation on a specific engine six months out. Any playbook that told you to invest two quarters in Reddit to earn ChatGPT citations, including earlier versions of ours, was quietly pricing in a licensing arrangement it did not control.

The durable reading of this study is the shape and its volatility, not the leaderboard. In the primary window, sources no single brand controls supplied most citations, the long tail was the largest bucket, and no vendor's own site passed 2.4%. On ChatGPT, one retrieval change then moved share from forums to vendor and official pages within weeks. Strategy that survives a week like Aug. 10, 2026 is diversified across source layers and measured per engine. Strategy concentrated on one platform for one engine lost nearly all of that engine's citations with no warning and no appeal.

Finding 1: Community and reference sources out-cited every vendor's own site

Reddit (2,882 citations) and Wikipedia (1,489) each drew more citations than Semrush (883), the most-cited vendor site in the category. The Reddit-to-top-vendor ratio is about 3.3:1; Wikipedia-to-top-vendor is about 1.7:1. TechRadar (1,133) also out-drew every vendor.

Implication: owned media is necessary but structurally insufficient. The highest-leverage incremental dollar in an AEO content budget is usually earned media: getting your brand named, quoted, or linked in the editorial coverage and community discussion engines already cite. That is a PR, community, and analyst-relations problem as much as a content-marketing one. Most B2B marketing teams are organized to ship blog posts; few are organized to land a trade-press roundup inclusion or earn unprompted community mentions.

A caveat on Wikipedia: this is not an instruction to create a Wikipedia page. Wikipedia's notability guidelines for organizations require multiple independent, non-routine secondary sources by unaffiliated parties; self-promotion and paid placements do not count. Conflict-of-interest rules discourage editing pages where you have a financial relationship, and a January 2026 Bureau of Investigative Journalism story on a London PR firm that hired middlemen to edit clients' Wikipedia pages shows how much scrutiny such edits attract. The implication is not "make a Wikipedia page"; it is "earn enough independent press that a Wikipedia entry becomes defensible on its own merit." Most vendors in this dataset are not at that bar.

Finding 2: Reddit was the most-cited domain, until ChatGPT stopped naming it

Reddit alone accounted for 7.9% of all citations in the window ending June 11, 2026, more than any vendor and more than any single editorial source. When a B2B buyer asked an answer engine for "the best AEO tool for an in-house team of five," the engine looked for lived buyer experience to anchor its answer, and threads in communities like r/SEO, r/marketing, r/SaaS, and r/bigseo were the most concentrated source of it on the open web. About two months later ChatGPT stopped citing those threads (see What changed in August 2026).

Implication: community visibility matters for B2B vendors whose buyers compare options before purchase, and it is not a substitute for owned and earned coverage. Authentic community participation is structurally different from content marketing: the communities engines cite have strong anti-self-promotion norms, moderator-enforced rules, and long memories. A brand that drops product links gets deleted or banned; a brand that contributes substantively over months earns unprompted mentions. Budget it as influence on what buyers and engines read, not as the purchase of citations on a named engine, which August showed no vendor controls.

We cover the tactical playbook for community-layer work in Reddit and G2 community sources for AEO.

Finding 3: The most-cited vendor site captured only 2.4% of category citations

Semrush (883 citations) and Profound (881) tied at 2.4%, with HubSpot at 2.0% and Ahrefs at 1.5%. Vendor sites together drew about a quarter of all citations, so engines were reading vendor content; they were not concentrating on any one vendor.

Implication: in this window, no vendor owned AI citation share for the category. That is an opening, and a narrow one, and August showed it is engine-specific: on ChatGPT, vendor sites as a class took nearly half of the citations after the break. The mix we would bet on across engines is (1) owned pages that answer long-tail buyer questions directly and accurately, (2) consistent earned coverage in trade publications, and (3) genuine participation in the communities and channels buyers already use. Source-layer matching framework: how to earn LLM citations.

What the shape does and does not explain

The shape is consistent with published research on retrieval-augmented generation, where designs such as Self-RAG and Corrective RAG retrieve from many sources and critique them rather than fixating on a few high-authority domains. How production engines implement retrieval is not public, so treat that research as context, not as the explanation for this table.

The strategic implication is uncomfortable for vendors planning around a few "pillar pieces" on their own site. The closer your source-layer presence resembles the underlying citation distribution (long, broad, and distributed), the better it fits how these engines retrieved in this window.

Owned layer: be retrievable and correct

In the primary window no single vendor's site passed 2.4% of category citations, so the owned layer is not where most citations come from across a category's buyer questions. It is the only correct source for deep-funnel questions about your own product, and on ChatGPT after August it gained share: vendor and official pages were what the engine reached for once forum threads were out.

Keep investing, with the job defined as being retrievable and correct rather than dominant. Quality beats volume: documentation that answers the questions buyers actually ask is a better bet than hundreds of generic marketing posts.

Earned layer: the highest-leverage single action

If a B2B vendor has one marginal marketing dollar, editorial coverage aimed at the domains engines already cite is the strongest bet in this data, with one caveat: which domains get named is engine-specific and moved in August, so check per engine before you target.

• TechRadar (1,133 citations): the most-cited trade publication in this window.
• Tier 2 trade press: Search Engine Land, Search Engine Journal, Marketing Brew, and MarTech.
• Analyst coverage: Gartner, Forrester, or G2-published research, when it is publicly indexable.
• Existing inclusions: guest essays or interviews on adjacent industry sites already in the long-tail bucket.

Community layer: large in this window, and engine-dependent

In the window measured here, Reddit alone was 7.9% of citations, more than any vendor site and more than any single editorial source. That figure belongs to the window: on ChatGPT it has since gone to roughly zero, while on Google AI Overviews it held. Treat the community layer as real and worth staffing, and treat any single engine's attribution of it as revocable.

The community-layer playbook is harder to staff and harder to fake than owned or earned. It requires (1) people who can credibly participate in technical communities over months without sounding like marketing, (2) willingness to be helpful in threads that do not mention your product, and (3) patience for a 6–12 month horizon. Underwrite it against the outcome August left standing: influence on what buyers and engines read, plus citations on the engines that still attribute community sources, not a guaranteed ChatGPT citation.

Extend community presence to G2, Capterra, and Software Advice, and to YouTube where Google's surfaces matter to your buyers. Operational playbook: Reddit and G2 community sources for AEO.