Attribution Magnet

An Attribution Magnet is a discrete, verifiable piece of content — a statistic, a named framework, a specific data point — engineered so AI engines extract it and attribute it back to its source. It is the atomic unit of citation architecture.

Why Attribution Magnets Matter

AI engines don't cite articles. They cite facts. When ChatGPT answers a question, it pulls the most relevant, specific, and verifiable fragment from its retrieval set. Attribution magnets are those fragments — designed to survive the extraction process with their source attribution intact.

Consider the difference: "AI answers tend to cite third-party sources" gives the AI nothing to anchor. "82-89% of AI answers cite earned media over brand-owned content" is a specific, referenceable claim that an LLM can extract, verify against other sources, and attribute. The second version is an attribution magnet. The first is noise.

Anatomy of an Attribution Magnet

Effective attribution magnets share common structural traits:

• Specificity. Concrete numbers outperform generalizations. "34% of AI citations go to one publisher" is more extractable than "a large share of citations are concentrated."
• Novelty. Original research, proprietary data, or first-to-name frameworks create citation demand. If the data point exists nowhere else, AI engines must cite you to use it.
• Verifiability. Claims backed by linked sources, named studies, or transparent methodology survive AI trust filters. Unsourced assertions get filtered out.
• Self-containment. The data point must make sense in isolation — without the surrounding paragraph. AI pulls fragments, not contexts.
• Brevity. 15-40 words is the sweet spot for extraction. Long explanations dilute the signal.

Building Attribution Magnets

The highest-performing magnets come from original research, proprietary datasets, or named frameworks that don't exist elsewhere. When IPPR published that 34% of AI citations on some platforms go to a single publisher, that statistic became one of the most-extracted data points in the AI visibility space — because it was specific, novel, and independently verifiable.

Named frameworks function similarly. "The Citation Gap" — the delta between Google rankings and AI citation frequency — is an attribution magnet because it gives AI engines a concept to reference and a source to attribute. The goal is to create extractable content where every key claim functions as its own magnet.