Does page authority work differently for AI search? My high-DA pages aren't getting cited

This is one of the most important shifts to understand. Yes, authority works VERY differently.

The data point that proves it:

Only 12% of URLs cited by ChatGPT, Perplexity, and Copilot rank in Google’s top 10.

80% of AI citations don’t appear in Google’s top 100.

This isn’t a correlation issue - it’s fundamentally different evaluation systems.

Traditional authority (SEO):

AI authority:

Why the difference:

AI systems stake their reputation on citations. If ChatGPT cites unreliable sources, users stop trusting ChatGPT.

AI needs to evaluate content quality directly, not use proxy metrics like backlinks.

The topic authority shift is massive.

Domain authority assumption (traditional):

“Our DA 72 site should rank for anything we write about.”

Topic authority reality (AI):

A specialist site focused on one topic can have higher AI authority than a generalist high-DA site.

Example:

Query: “Best practices for Kubernetes security”

High-DA generalist site:

• DA 80
• 1 article on Kubernetes
• Written by marketing team
• Last updated 2023

Specialist blog:

• DA 25
• 50 articles on Kubernetes
• Written by certified experts
• Updated monthly

Who gets cited?

The specialist. Every time.

Why:

AI systems recognize topic depth. They see:

• Consistent expertise across related content
• Author credentials matching the topic
• Recent, maintained information
• Comprehensive coverage

The strategic implication:

Build topic clusters, not just pages. Establish deep authority in specific areas rather than shallow coverage of everything.

For your high-DA site:

Where are you genuinely expert? Double down there. Don’t expect AI authority in areas you cover superficially.

Author credentials matter way more than I expected.

Our test:

Same content, different author attribution:

Version A:

• “Written by Staff Writer”
• No author page
• No credentials shown

Version B:

• Named author with photo
• Author page with credentials
• Links to LinkedIn, publications
• Person schema implemented

Result:

Version B got cited 4x more often in AI responses over 3 months.

Why it works:

AI systems verify author authority. They check:

• Does this person exist elsewhere?
• Are their credentials verifiable?
• Do they write about this topic consistently?
• Are they recognized in the field?

The implementation:

1. Create detailed author pages:

   • Professional bio
   • Credentials and certifications
   • Publications and speaking
   • Social profiles

2. Implement Person schema:

   • Job title
   • Expertise areas
   • SameAs links to profiles
   • Works for organization

3. Consistent author presence:

   • Same author on related topics
   • Regular publishing cadence
   • Expert voice throughout

The bottom line:

Anonymous content struggles in AI search. Credited expert content wins.

Content recency is the biggest authority signal shift.

The observation:

Our comprehensive guide from 2021, ranking #1 in Google, wasn’t getting AI citations.

A competitor’s less detailed article from 2025? Cited frequently.

What we did:

Updated our guide with:

• Current information (2025/2026 data)
• Recent examples and case studies
• Updated statistics
• Clear “Last updated” date

Results:

AI citations increased 340% within 6 weeks.

Why recency matters so much:

AI systems are trained to provide current, accurate information. Outdated content is a liability - if AI cites outdated info, it damages their credibility.

The practical approach:

1. Add update dates - Visible “Last updated” on all content
2. Regular refresh schedule - Quarterly review of key content
3. Current examples - Replace dated examples with recent ones
4. Fresh statistics - Update data points regularly

The maintenance cost:

Yes, it’s ongoing work. But it’s the price of AI authority.

Monitor and prioritize:

Use Am I Cited to track which pages get cited. Prioritize updating those for maximum impact.

Schema markup is disproportionately important for AI authority.

Why:

AI systems rely on structured data to understand:

• Content type and purpose
• Author credentials
• Publication and update dates
• Entity relationships
• Topic categorization

Critical schema types:

Article schema:

{
  "@type": "Article",
  "headline": "...",
  "author": {...},
  "datePublished": "...",
  "dateModified": "...",
  "publisher": {...}
}

Person schema (for authors):

{
  "@type": "Person",
  "name": "...",
  "jobTitle": "...",
  "sameAs": ["linkedin", "twitter"]
}

Organization schema:

{
  "@type": "Organization",
  "name": "...",
  "foundingDate": "...",
  "description": "..."
}

FAQ and HowTo schema: Direct AI response optimization.

The impact:

Pages with comprehensive schema implementation get cited more reliably. AI can verify information more easily.

Your audit:

Check schema implementation on your key pages. Missing schema = missing authority signals.

Entity recognition is an underappreciated authority signal.

What it means:

AI systems recognize entities - people, organizations, concepts - and evaluate authority based on entity relationships.

How to build entity authority:

1. Establish clear entity definition:

   • Your organization as a recognized entity
   • Key people as recognized entities
   • Products/services as entities

2. Connect entities:

   • Author works for Organization
   • Organization created Product
   • Product relates to Topic

3. Cross-reference signals:

   • Wikipedia mention (if notable enough)
   • Wikidata entry
   • Knowledge Panel presence
   • Consistent information across sources

The verification loop:

AI systems cross-reference entity information. If your organization appears consistently as an authority on Topic X across multiple trusted sources, your content on Topic X has higher authority.

Building entity authority:

• PR coverage mentioning your expertise
• Industry directory listings
• Association memberships
• Speaking engagements
• Published research

These feed AI’s understanding of who you are and what you’re expert in.

Analyzing competitor authority in AI.

What I learned:

Small competitors beating us had:

• More recent content
• Clearer author credentials
• Better schema implementation
• Deeper topic focus

They didn’t have:

• Higher domain authority
• More backlinks
• Longer history

The audit process:

For each competitor getting cited:

1. Check their content update date
2. Review author credentials displayed
3. Analyze schema implementation
4. Assess topic depth

The pattern:

Winners in AI authority had optimized for AI-specific signals, not traditional SEO signals.

Our response:

Stopped focusing on link building for these pages. Instead:

• Updated content with current information
• Added author expertise signals
• Implemented missing schema
• Created supporting topic cluster content

Results after 4 months:

Went from 15% to 38% share of voice in AI responses for our key topics.

The lesson:

Audit competitors in AI responses, not just Google rankings. Different game, different strategy.

How to measure authority in AI search.

Traditional authority metrics (less useful):

• Domain Authority (Moz)
• Domain Rating (Ahrefs)
• Trust Flow (Majestic)

AI authority metrics (more useful):

• Citation frequency in AI responses
• Share of voice vs. competitors
• Topic coverage breadth
• Entity recognition signals

What to track:

Using Am I Cited or similar:

1. Citation rate - How often your content appears
2. Position - Are you cited first, or buried?
3. Context - How are you described/positioned?
4. Trend - Is your authority growing or declining?

The correlation analysis:

We tracked changes in:

• Content recency → Citation increase
• Author credential additions → Citation increase
• Schema implementation → Citation increase
• Backlink acquisition → No correlation

The measurement shift:

Stop measuring what traditional SEO measures. Measure what AI actually rewards.

Your dashboard should include:

• AI citation frequency
• Topic-specific authority
• Author-level performance
• Competitive share of voice

This thread fundamentally changed my understanding.

My key insights:

1. Different signals entirely - DA/backlinks matter little for AI
2. Recency is critical - Updated content beats established pages
3. Author credentials matter - Expert attribution drives citations
4. Topic depth beats breadth - Specialist sites win
5. Schema enables verification - AI needs structured data to trust

My action plan:

Immediate:

• Audit schema on top 20 pages
• Update dates and examples on key content
• Add author credentials and Person schema

Short-term:

• Start Am I Cited monitoring
• Identify topic areas to deepen
• Build author authority pages

Strategic shift:

• Stop measuring success by DA growth
• Focus on AI citation metrics instead
• Build topic clusters, not individual pages

The mental model change:

Traditional authority = votes from other sites AI authority = demonstrated expertise verified by AI

Thanks for the comprehensive perspective on this!