Ist Domain Authority für die KI-Suche noch relevant, oder müssen wir völlig anders denken?

Your instinct is right. AI evaluates authority fundamentally differently than traditional search.

Traditional Search Authority:

• Primarily backlink-based
• Domain-level signal
• PageRank-derived metrics
• Links = votes of confidence

AI System Authority Evaluation:

• Multi-signal analysis
• Content + entity + context
• Training data associations
• Demonstrated expertise signals

The 5 Authority Signals AI Systems Actually Use:

DA still correlates (higher DA sites often have these signals too), but it’s not the PRIMARY driver for AI.

Topical authority is where smaller players can win:

The domain authority problem:

Building DA from 30 to 60 might take 3-5 years and massive investment.

The topical authority opportunity:

Becoming the authority on a SPECIFIC topic can happen in 6-12 months with focused effort.

Case study:

Client: Small fintech startup (DA: 25) Competitor: Major bank (DA: 80)

General query “best banking app” - Bank wins Specific query “banking app for freelancer taxes” - Our client wins

Why? Client created THE definitive resource on banking for freelancers:

• 30+ interconnected articles
• Original freelancer survey data
• Expert interviews with accountants
• Templates and tools

AI recognizes them as the topical authority for that specific intersection.

The strategy:

Don’t try to compete on broad authority. Own a narrow space completely.

Adding technical perspective on how AI crawlers evaluate your site:

What AI crawlers specifically look for:

1. Structured data presence - Schema.org markup
2. Content organization - Clear hierarchy, logical structure
3. Information freshness - Regular updates, timestamps
4. Source attribution - Citations, references, transparency
5. Author signals - Bylines, credentials, profiles

Technical authority signals:

• HTTPS (basic trust)
• Fast loading (can be crawled efficiently)
• Mobile-friendly (modern, maintained)
• Clean code (professional development)
• Sitemap and robots.txt (crawler-friendly)

Often overlooked:

Your llms.txt file. It’s like robots.txt for AI systems - tells them what content is most relevant for citation purposes.

Technical foundation matters. A brilliant article on a slow, messy site sends negative signals.

From a PR perspective, the kind of authority that matters has shifted:

What used to matter:

• Any link from authoritative site = good
• Volume of backlinks
• DA of linking sites

What matters now for AI:

1. Being cited as an expert source > mentioned in passing
2. Your data being referenced > your brand being named
3. Presence on AI-referenced platforms > random media mentions

Where AI systems actually source information:

PR strategy shift:

Focus on becoming a cited source on platforms AI actually references, not just any media coverage.

Startup perspective on competing without DA:

Our situation:

• DA: 18 (one year old site)
• Industry leaders: DA 60-80
• Needed AI visibility to compete

What we did:

1. Focused ruthlessly - Picked ONE topic to dominate
2. Built depth, not breadth - 100 articles on our topic, zero on anything else
3. Original research - Survey data nobody else had
4. Community presence - Active in relevant subreddits
5. Expert positioning - Founder did 30+ podcast interviews

Results after 8 months:

• Still DA 25 (barely moved)
• But ChatGPT cites us for our specific topic
• Perplexity includes us alongside industry giants
• Google AI Overview features our data

The lesson:

You can’t shortcut overall domain authority. But you can build topical authority and AI visibility without it.

Enterprise observation:

We have massive DA (80+), but that doesn’t automatically translate to AI citation.

What we’ve learned:

• DA opens doors but doesn’t guarantee citation
• Content quality matters as much as domain signals
• Topical organization affects AI understanding
• Old content with high DA can be ignored if outdated

What we changed:

1. Content consolidation - Combined thin pages into comprehensive resources
2. Topic hub architecture - Clear topical organization AI can parse
3. Freshness program - Systematic content updates with new data
4. Schema everywhere - Every page fully marked up

Even with strong DA, we had to optimize for AI-specific signals.

DA is a starting point, not a guarantee.

How to actually track AI-specific authority:

Metrics that matter:

What to watch:

• Citation rate trend over time (improving or declining?)
• Which content gets cited (pattern recognition)
• Competitive movement (who’s gaining/losing?)
• Platform differences (strong on ChatGPT, weak on Perplexity?)

DA is still one input, but it’s not the primary metric for AI visibility. Track what actually matters.

This has been an eye-opening thread. Here’s my synthesis:

Key paradigm shift:

Traditional SEO: Authority flows primarily from backlinks AI Search: Authority is multi-dimensional (entity, topical, content, trust)

New authority framework:

1. Entity authority - Is your brand clearly recognized and understood?
2. Topical authority - Are you the expert in specific domains?
3. Content authority - Is your content comprehensive, fresh, and well-structured?
4. Trust authority - Do you have expertise signals and third-party validation?

Action items:

1. Audit entity recognition (schema, knowledge graphs, consistency)
2. Identify topical authority opportunities where we can dominate
3. Restructure content into clear topic hubs
4. Build presence on AI-referenced platforms
5. Implement AI-specific tracking metrics

Mindset shift:

Stop asking “how do we increase DA?” Start asking “how do we become the obvious authority AI should cite for our topics?”

Thanks everyone. 15 years of SEO experience and I’m still learning.