4 Myths About AI Citations, Debunked
AI chooses what people see. As industry-leader ChatGPT surpasses 2.5 billion prompts per day—with Google’s AI Overview and an ever-growing list of competitors short on its heels—marketers must recognize that the information customers, prospects, and investors need to make decisions is passing through an entirely new filter.
Understanding that filter is of crucial importance, then. No one can navigate, manipulate, or use a marketing channel of which they do not have a firm grasp. The natural response to this vacuum has been a deluge of tip-sheets that explain how to win AI citations via simple hierarchies and five-step plans.
Unfortunately—but not surprisingly—the fact of the matter is far more nuanced than the majority of these sources are willing to admit. Becoming aware of the common misconceptions and the complex realities they conceal will likely prove far more instructive than the sources that claim to lay it all out in an infographic or a LinkedIn post.
Below are four of the most prevalent AI citation myths that are circulating the web, debunked with hard data and technical knowledge on how these systems actually work.
Myth #1: Producing Quality Content is Enough to Get Cited
This myth has a special ‘no-nonsense’ appeal to it, especially in light of all the other myths below. Why not just produce content that’s worth citing and stop trying to play the system? Indeed producing genuinely valuable content is exactly what every marketer should begin with. It is not, however, the full story.
AI systems cannot feasibly scan the entire web to find the article, video, or post with the highest quality answer to the user’s query. They instead have built-in biases for where to go looking in the first place. These preferences will vary from system to system; ChatGPT cites Wikipedia 27% of the time, while Google’s AI Overview cites Youtube in 78% of product comparison searches and is 3.1 times more likely to cite video content in general.
This means that the most relevant, informative content could struggle to get cited if its manner and format of distribution make it unlikely that AI will even consider it. It is also worth noting that what constitutes “quality” content is often determined by user intent, as discussed in myth no. 3 below. Thus, it is more accurate to say that AI cites high-quality, well-targeted, well-distributed content.
Myth #2: Paid Media Has No Influence on AI Citations
This is one of the most prolific myths about AI, likely because it’s an oversimplification of a generally true principle: paid advertisement is considered less credible than organic, earned content. Extending that principle to say that paid media has no influence on AI responses, however, is a gross and often costly error.
Paid content has a significant and demonstrable presence amid AI citations. Wire-distributed press releases are favored for early search windows due to recency bias—65% of citations come from material published in the preceding year—while sponsored content in reputable publications like Forbes, The Wall Street Journal, and The Verge is processed identically to earned editorial content.
More significant than whether a piece of content is paid, then, is whether it meets the same quality and relevance standards as the earned media in the publication. AI avoids unhelpful content, not paid content.
Myth #3: Static Platform Hierarchies Govern AI Citations
It is tempting to arrange the various channels at marketers’ disposal into a tidy hierarchy of AI preference. These blanket evaluations tend to read something like “news over social media over owned media” or “forums always outrank proprietary content.” Once again, reality is far more nuanced.
It is true that not all platforms are considered equally—per myth no. 1 above—but why and how certain platforms are favored largely stems from user intent. User-generated content on forums and social platforms generally constitute less than 4% of overall citations, but are often the default for guiding consumer purchase decisions. In a business-to-business (B2B) query about technical product specifications, meanwhile, vendor-owned content becomes the most authoritative source.
The key is therefore not to identify the most-preferred channels but to match the right content with the right query on the right platform. The only useful hierarchy is the one that matches the needs of the audience and the goals of the business at the time of distribution.
Myth #4: Optimization Approaches Work Universally
Finally, the prevalent notion that a single set of best practices can be applied across the board to optimize content for AI citation falls into the same bucket of oversimplification as the first three myths. While certain steps—like taking technical SEO measures to ensure basic visibility—are strictly necessary for being cited, many others depend yet again on the system, the user, and the query.
A very common example of such an overgeneralized approach is purging long-form narrative content in favor of simple, structured answers. In the example from the previous myth, a user seeking detailed technical information about a B2B product might require precisely the kind of in-depth content that a straightforward short-from answer cannot provide. Answers should always be clearly structured, of course, but they should also reflect their ultimate purpose.
Such nuance, in combination with the varied preferences of different systems, means that it is generally best to diversify across formats and channels, only targeting specific approaches if they specifically appeal to the target audience and their needs. The growing prevalence of models that generate multiple sub-queries for every user query makes a broad presence all the more crucial for visibility.
Bottom Line
The common thread throughout these myths is an assumption that AI citations are governed by rigid, universal rules. This is clearly not the case, and realizing the need for a nuanced and adaptive approach is the first step toward consistently winning AI citations. Testing, monitoring, iteration, and continuous self-education are non-negotiable; everything else can—and likely will—change just as rapidly as AI technology and the people who use it.
Be sure to check out our other blogs on useful and interesting public relations topics, like compliant communications for publicly-traded companies.
