Why Being First to Publish on a Topic Increases LLM Citation Frequency

Being first can help a page get cited by language models, but not because models reward publication date. The citation studies discussed below, from Ahrefs and from Algaba and colleagues, do not test publication order directly. What the evidence suggests are mechanisms that can favor early, original work: early pieces collect links and references that later work builds on, models trained on that web inherit its citation patterns, and an early page faces little competition while a topic is new. The same evidence also shows a force pulling the other way. Some AI assistants favor recent content, which rewards whoever updated last, not whoever published first.

The useful conclusion is that being first is an advantage only if the work stays the best and most current version of its kind.

How early work gets a head start

These mechanisms are an inference. None of the studies cited here measures a first-publisher advantage directly; the account below combines evidence on how references accumulate, how training data was weighted and how models favor work that is already highly cited.

It collects references. The first credible piece on a new topic can become the one others link to and quote when they write about it later. Those links and mentions accumulate over time. A page that becomes the standard reference early can keep that position, because each new article that cites it adds to its visibility.

Models inherit that pattern. Research on references generated by language models from their training alone found that they reproduce existing citation habits and exaggerate them. In a study published in the Findings of NAACL 2025, Algaba and colleagues asked models to suggest scholarly references without searching the web. The suggestions closely resembled human citation patterns but showed “a more pronounced high citation bias,” favoring works that were already highly cited. The authors warn that this may amplify the “Matthew effect,” in which those who already receive attention receive more. If early work becomes the most cited work in its area, a model’s built-in knowledge is likely to favor it.

Training favors sources judged higher quality. OpenAI’s GPT-3 paper explains that its training datasets were “not sampled in proportion to their size, but rather datasets we view as higher-quality are sampled more frequently.” That paper describes a 2020 model, and companies’ current documentation does not describe training data in the same detail. The principle it shows, that some sources count more than others in training, makes widely referenced early work more likely to leave a mark.

Early on, there is little competition. Google has described what happens when a topic has few good sources. Liz Reid of Google, explaining AI Overview errors in May 2024, listed “not having a lot of great information available” as one reason AI Overviews go wrong, a situation Google calls a data void. For a new topic, a careful first page can be one of very few candidates any system can retrieve.

What works against being first

Freshness can beat priority. Four AI assistants in an Ahrefs study cited content 25.7% fresher than organic Google results on average, and for ChatGPT the gap reached 393 to 458 days. For an assistant that leans toward recent pages at retrieval time, a later article that covers the same topic with newer information may be cited ahead of the original.

Later work can be better. The first piece on a topic can have the least information to work with. If a later piece adds data, examples or corrections, it may become the reference others cite, and the first-mover advantage disappears.

The data void fills. The low-competition window closes as more pages are published. An early page that is never improved can fall behind pages that arrived later but go deeper.

What “first” needs to mean in practice

Being first helps most when it comes with originality. Google’s AI optimization guide contrasts commodity content, such as “7 Tips for First-Time Homebuyers,” with non-commodity content that offers first-hand, specific information. Being first with commodity content is a brief lead at best. Being first with original data, a new analysis or direct experience creates something others may need to reference.

How to use an early position

1. Publish original material, such as data you collected, a test you ran or a primary source you analyzed, not a summary of what others have said.
2. Make it easy to reference. A clear title, a stable URL, a dated publication and a plain statement of the main finding near the top make it simple for others to cite you correctly.
3. Keep it current. Update the page as the topic develops, and show the update date. This protects against later articles that would otherwise be both fresher and more complete.
4. Earn references deliberately. Share the work where people in the field will see it. Citations from other credible sources are what turn an early page into a standard reference.
5. Do not rely on timing alone. A first page that is thin, inaccurate or stale gains little and can be overtaken quickly.

Publish first if you can, but publish something worth citing, and then keep it the best version available. Early, original work can build a lead through links, references and the citation patterns models learn. Recency-favoring assistants and better later work can erase that lead if the page stops improving.