In the era of PageRank, “Link Juice” or Citation Flow flowed through hyperlinks (<a> tags). It was a directed graph where node A voted for node B. In the era of Large Language Models (LLMs), the graph is semantic, and the “juice” flows through Co-occurrence and Attribution.

LLMs do not navigate the web by clicking links. They “read” the web during training. If your brand name appears frequently alongside authoritative terms (“reliable,” “expert,” “secure”) in high-quality text, the model learns these associations.

We call this Semantic Citation Flow.

If the New York Times mentions your brand, even without a hyperlink, the model updates its internal weights to associate your entity with the authority of the NYT. The “link” is established in the neural network’s latent space, not in the DOM.

Traditional link building is often indistinguishable from spam. AI-focused citation building requires a different approach:

  1. Digital PR: The goal is mentions in “High-Weight” corpus sources. These are sources likely to be included in fine-tuning datasets (Wikipedia, major news, academic journals, GitHub).
  2. Stat-Bait: Creating primary data studies is the most effective way to get citations. LLMs are hungry for facts. If you provide the stat, you become the source of truth.
  3. Entity Association: getting your brand mentioned in the same paragraph as other known industry leaders helps “anchor” your entity in the correct vector cluster.

Google has long talked about “implied links” (unlinked mentions). For LLMs, all links are implied. The concept of a clickable pathway is irrelevant to a model that has already memorized the map.

In this environment, your brand name is your URL. Protect its reputation furiously, as negative sentiment in training data acts as “negative link juice,” poisoning your generated visibility.