I’m trying to understand the “rules” for AI trust like we understand Google’s.
What we know about Google:
What I don’t understand about AI:
Questions:
Looking for actual mechanics, not guesses.
AI trust evaluation is both similar to and different from Google. Here’s what we know:
The four core trust signals:
How AI systems verify trust:
| Signal | How AI Evaluates | What You Control |
|---|---|---|
| Accuracy | Cross-reference with other sources | Cite sources, use data |
| Authority | Training data presence, citations | Build expertise signals |
| Transparency | Clear authorship, attribution | Author bios, dates, sources |
| Consistency | Historical content quality | Long-term content strategy |
The training data factor:
AI systems learned trust patterns from their training data. Sources that appeared frequently in reliable contexts (news, academic, industry publications) are inherently “trusted” by AI models.
The uncomfortable truth:
Major publications (NYT, Forbes, Wikipedia) have built-in trust from training data. New or smaller sites must prove trust through other signals.
But there’s hope:
AI evaluates contextually. A small expert in a niche can outrank major publications for specific queries if they demonstrate genuine expertise in that area.
Exactly. Here’s the nuance:
DA correlation data:
| Domain Authority Range | AI Overview Citation Rate |
|---|---|
| 80-95 (Major sites) | 27-49% of citations |
| 70-85 (Established) | 15-25% of citations |
| 60-75 (Industry experts) | 10-20% of citations |
| 40-60 (Growing sites) | 5-15% of citations |
| Under 40 | Under 5% of citations |
BUT context matters:
For the query “What is project management software?”:
For the query “Best Scrum practices for 5-person teams?”:
The contextual weighting:
AI adjusts trust based on query type:
Your opportunity:
Don’t try to compete with Forbes on “What is CRM?” Compete on “Best CRM for boutique marketing agencies” where your specific expertise matters more than broad authority.
E-E-A-T translation for AI systems:
How E-E-A-T maps to AI trust:
| E-E-A-T Component | Traditional SEO | AI Trust Equivalent |
|---|---|---|
| Experience | First-hand knowledge | Case studies, real examples |
| Expertise | Subject knowledge | Comprehensive, accurate content |
| Authoritativeness | Industry recognition | Citations from other sources |
| Trustworthiness | Reliability | Transparency, accuracy |
What AI systems look for:
Experience signals:
Expertise signals:
Authoritativeness signals:
Trustworthiness signals:
The AI verification:
AI systems cross-reference these signals across the web. Your about page says you’re an expert? AI checks if third parties confirm this.
Small site perspective - we DO get AI citations despite low DA:
Our situation:
What works for us:
Our citation patterns:
| Query Type | Citation Rate | Why |
|---|---|---|
| Broad (“What is HIPAA?”) | 5% | Too general |
| Medium (“HIPAA for clinics”) | 22% | Some expertise |
| Specific (“HIPAA for solo practices”) | 61% | Deep expertise |
The lesson:
We can’t compete on broad queries. But for our specific niche, we outperform sites with 2x our DA.
Our strategy:
Practical trust-building tactics:
Quick wins (1-3 months):
Medium-term (3-6 months):
Long-term (6-12 months):
The compound effect:
Trust builds over time. A single press mention helps, but 12 months of consistent expertise signals + third-party validation creates significant AI trust.
What doesn’t work:
This clarifies the AI trust landscape. Here’s my synthesis:
AI trust factors (ranked by impact):
What’s similar to traditional SEO:
What’s different:
My action plan:
The strategic insight:
Don’t try to build “AI domain authority.” Build genuine expertise in your niche that AI systems can verify through multiple sources.
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