Updated 1 September 2026. The original version of this post described six ways a source can come to exist. Those six describe how media coverage comes to exist, and applying them to a citation set was the wrong shape, because most cited sources are not media. A reader pointed this out the day after publication and was right. The framework below is the corrected version: seven origins, with the original six sitting inside one of them. Our own published Proof Center scan disproves the original in five rows and we did not check it before publishing. The original text is preserved in the section marked What this post said first.
A source about your brand can come to exist in at least six different ways, and no AI visibility report currently distinguishes between them.
That gap did not matter much in June. It matters now, and this post explains why.
The seven origins
Owned. You published it. Your site, documentation, help centre, pricing page.
Registry. You submitted it to a structured listing. Directories, review marketplaces, Wikidata, Crunchbase.
Community. Somebody wrote it in public without being asked. Forums, threads, reviews, Q&A.
Media. Somebody at a publication was involved. Within that there are six meaningfully different sub-states: earned, editorially selected after a pitch, contributed, placed, sponsored, paid.
Syndicated. One item replicated across many domains. A wire release appearing on forty sites is one item, not forty placements.
Institutional. Research bodies, standards organisations, analysts and government. Access is sometimes paid. Publication is independent.
Obligated. It exists because a regulator or a court required it. Filings, records, notices.
None of these is automatically illegitimate. A placed article can go through real editorial scrutiny. Contributed columns have been normal in trade publishing for decades.
A single domain can produce sources in more than one category. G2 is a registry when the citation is a listing and a publisher when the citation is one of its editorial articles. Classification belongs to the page, not the domain.
Media is probably your smallest categoryThis is the part the original version missed entirely.
Look at the sources cited in AI answers about your category. For most B2B companies that list is dominated by review marketplaces, community threads, documentation and the company’s own site. Editorial coverage is present and it is usually a minority.
That lines up with data Featured published on 25 August, covering 22,881 AI citations across 405 audits, which reported 34.5 percent coming from sites with Domain Authority under 40. We have not verified the dataset and it is their finding rather than ours. But registry and community sources skew heavily sub-DA-40, and that is a plausible read of where a third of citations originate.
Which changes the budget question. A public relations programme aimed at editorial coverage is working on the smallest of the seven categories, and the most expensive one. Registry accuracy and community presence respond to different work, most of it cheaper.
The scan sees the publication, not how the coverage got there
When ChatGPT, Claude, Gemini or Perplexity cites a domain in an answer about your category, your visibility tool records a citation. It logs the domain, sometimes the URL, sometimes the sentence.
What it does not log is how that page came into existence.
An earned feature and a placed article arrive identically. Same domain. Same authority signal. Same increment to your number.
You can test this on your own report in about ten minutes. Take the source list, and for each one write a single word: earned, pitched, contributed, placed, sponsored, or paid.
If you cannot answer for a source, that is the answer.
The state most brands have the most of
Contributed content is the one worth thinking about hardest, because most B2B brands have far more of it than they realise.
A bylined column in a trade publication is your words, published under your name, on somebody else’s domain. It is not deceptive. It is a normal and long-standing part of trade publishing. Readers understand the convention.
But an AI system reading that page sees a page on an authoritative domain discussing your category and naming your company. Structurally it looks a great deal like a journalist writing about you. The convention that a reader understands is not encoded anywhere the retrieval system can see.
That is not an argument against contributed content. It is an argument for knowing how much of your evidence base is made of it, because a position built largely on your own bylines is a position built on one source of judgment wearing several mastheads.
Why this stopped being theoretical in August 2026
Until recently, improving your position in AI answers has been a diffuse activity. Publish more. Get mentioned more. Hope some of it lands.
On 25 August 2026, Featured launched a product called GEO Audit that identifies which publications AI engines are actually citing for a given category and ranks those publications as PR targets. It will draft the pitches. Their launch data covers 22,881 citations across 405 audits.
That is a legitimate product doing a legitimate thing. Working out which outlets influence AI answers and then pitching them is public relations with better targeting than guesswork.
But once the five sources that move your score have names, the work stops being diffuse and becomes a target list. Marketers will pursue those domains. That is not corruption. It is marketing responding rationally to a channel that has become measurable.
The consequence lands on the measurement rather than on the marketers. The population of sources stops being a natural sample and starts being an optimized one. A metric built on the assumption of a natural sample does not tell you what it used to tell you.
The same distinction just appeared in European law
On 2 August 2026, the transparency obligations in Article 50 of the EU AI Act became applicable. Providers of generative systems must mark synthetic output in a machine-readable format so it can be detected as artificially generated. Systems already on the market before that date have until 2 December 2026 to comply.
There is a carve-out worth reading closely. AI-generated text published to inform the public on matters of public interest does not require disclosure if it has been subject to human review and editorial control, with somebody identifiable holding editorial responsibility for it.
So in the disclosure regime, the line between labelled and unlabelled is editorial responsibility.
In the provenance question, the line between earned and placed is also editorial responsibility.
Same hinge, two regimes, and neither is visible to a system that counts domains.
What to do about it now
Three things, none of which require a new tool.
Answer the one-word question for every source in your report. Earned, pitched, contributed, placed, sponsored, paid. Keep the list.
Check the section, not just the domain. Many publications run branded content under a section name that reads like editorial. Brand voice, partner content, corporate news. A scan sees the domain.
Ask the question that actually decides things. If a competitor bought the same five placements next month, would your report be able to tell you why both of your numbers moved?
If the answer is no, then the metric is partly measuring spend, and it is measuring it in a currency nobody has priced.
FAQs
What is source provenance in AI visibility?
Provenance describes how a citation came to exist rather than where it appears. There are at least seven origins: owned, registry, community, media, syndicated, institutional and obligated. Editorial coverage is only one of them and is usually a minority of a brand’s cited sources.
Is placed or sponsored coverage bad for AI visibility?
Not inherently. Placed and sponsored coverage can be accurate and can pass editorial review. The problem is that measurement tools record it identically to independently earned coverage, so the number cannot tell you which kind of position you hold.
Can an AI visibility tool detect whether coverage was paid?
Not reliably. Most provenance states cannot be determined from a URL, and for several of them nobody outside the publication and the client has the information at all.
What did the EU AI Act change on 2 August 2026?
Article 50 transparency obligations became applicable, requiring machine-readable marking of AI-generated output. AI-written text informing the public is exempt from disclosure where a human has reviewed it under editorial control and holds responsibility for it.
How do I audit my own citation provenance?
Take the source list from your visibility report and assign one word to each source: earned, pitched, contributed, placed, sponsored, or paid. Sources you cannot classify are themselves a finding.
Why does provenance matter more now than it did last year?
Tools launched in 2026 can identify the specific publications AI engines cite for a category, which makes source acquisition targetable. Once sources can be acquired deliberately, a citation count stops being a neutral sample of what the world says about you.
ABOUT AXIS SUITE
Axis Suite by TrendAxis is the independent intelligence layer that explains what AI believes about your brand, why it believes it, and what decision that belief ultimately drives.
Most tools count citations. Axis Suite tells you which of those citations are actually evidence about you, how many separate parties they represent, and what would remain if your largest source disappeared.
Run a scan at axissuite.ai or read the methodology in the Proof Center.
What this post said first
The original version listed six origins: earned, editorially selected after a pitch, contributed, placed, sponsored and paid. Those six now appear as sub-states of Media above.
The correction came from a reader in r/GEO_optimization the day after publication. The specific failure was that we built the framework from a media relations context and published it without testing it against a citation set we already had. Our own Proof Center scan lists four registry sources and one institutional source, none of which the original framework could classify.