The New Definition of Authority
In traditional SEO, authority is derived from backlink volume (PageRank). In GEO, authority is derived from Entity Association and Sentiment Vectors. An LLM doesn't just care that people link to you; it cares how and where people talk about you.
The Three Pillars of LLM Authority
To build authority that an AI model trusts, you must optimize for these three technical layers:
1. Sentiment Vector Management LLMs are trained to avoid hallucinating negative traits. If the web's collective sentiment about your brand (on Reddit, G2, Trustpilot) is a negative vector, the LLM will actively bypass your site for high-intent queries. Action: Audit your third-party sentiment every 30 days.
2. Co-occurrence Mapping LLMs learn by association. If your brand name constantly appears in the same sentence as "GEO experts" or "AI optimization," the model builds a strong semantic connection.
Tactic: Get your brand mentioned in comparison articles alongside established entities. Even if they aren't links, the co-occurrence reinforces your position in the model's Knowledge Graph.
3. Entity-Object Linking Use your "About Us" page to link your brand entity to the real-world profiles of your experts. Link to their LinkedIn, ResearchGate, or Wikipedia pages. This creates a chain of trust that the LLM can verify via its training data.
Authority Benchmarks for 2026
| Signal | SEO Value | GEO Value |
|---|---|---|
| Backlink | High (Link Equity) | Low (Unless it carries context) |
| Review Sentiment | Medium (Conversion) | Critical (Ranking Factor) |
| Wikipedia Mention | High (Trust) | Ultimate (Entity Validation) |
| Social Proof | Low (Secondary) | High (For Model Calibration) |