What actually counts as '10x content' for AI search? The old SEO playbook doesn't seem to work anymore

You’ve identified something crucial. 10x for AI is different from 10x for Google.

Here’s the fundamental shift:

Traditional 10x (Google):

• Be 10x better than competitors
• Longer, more comprehensive, better designed
• Win by having the best overall page

10x for AI:

• Be the definitive source for extractable answers
• Clear, structured, fact-dense
• Win by having snippets AI can confidently cite

Why your ultimate guides might not get cited:

AI doesn’t consume pages like humans do. It scans for specific passages that answer questions. A 5,000-word article with great information buried in flowing prose is harder to extract from than a 2,000-word article with:

• Direct answer in the first sentence of each section
• Clear H2 headings that match common questions
• Bulleted lists of key points
• Tables with comparative data
• FAQ schema at the end

The new 10x framework for AI:

Your content should be 10x better at:

1. Directly answering questions - First sentence of each section should be a complete answer
2. Structured extraction - Information organized so AI can pull specific facts
3. Authoritative signals - Author credentials, citations, expert quotes
4. Comprehensive coverage - Still cover everything, but in clearly bounded sections
5. Freshness - Updated dates, recent data, current examples

After doing SEO since 2012, here’s my take:

10x content is still relevant, but the definition needs updating.

Original 10x focused on:

• Design and presentation
• Comprehensiveness
• User experience

AI-era 10x needs to add:

• Structured extractability
• Direct answer formatting
• Factual density (stat every 150-200 words)
• Schema markup for machine understanding

What I’ve learned:

You can optimize the SAME content for both. Take your existing 5,000-word guide and:

1. Add a TL;DR at the top with direct answers
2. Restructure H2s as questions
3. Put the answer in the first sentence of each section
4. Add FAQ schema
5. Create comparison tables where relevant
6. Include a “Key Facts” summary table

We did this for a client’s top 10 guides. Google rankings stayed the same (actually improved slightly). AI citations increased from 2 to 23.

It’s not either/or. It’s restructuring for both.

Real data from analyzing 500+ pieces of content across AI citations:

What correlates with AI citations:

• FAQ schema present: 3.2x more likely to be cited
• Clear H2 question headings: 2.8x
• Statistics every 150-200 words: 2.4x
• Tables present: 2.1x
• Author bio with credentials: 1.9x
• Updated in last 6 months: 1.7x

What doesn’t correlate like you’d expect:

• Word count: No linear relationship. Sweet spot seems to be 2,000-4,000 words
• Backlinks: Weak correlation (0.3) vs. strong for Google (0.8)
• Page speed: Minimal impact on AI citations
• Keyword density: Irrelevant for AI

The pattern is clear:

AI systems care about extractability and authority, not the traditional SEO signals. A well-structured 2,500-word article with stats and FAQ schema beats a 6,000-word narrative masterpiece.

10x for AI = 10x better at being extracted and cited, not 10x longer or more comprehensive.

We’ve completely changed how we write 10x content for clients.

Old brief structure:

• Topic
• Target keywords
• Word count target
• Competitor content to beat

New brief structure:

• Topic
• Questions this content must answer (verbatim)
• Facts/stats to include with sources
• Table/comparison requirements
• FAQ questions to address
• Schema markup requirements
• “Direct answer” requirements for each section

The difference in AI citations has been dramatic. Our new format content gets cited 4-5x more than old format, even when old format had higher word count and more backlinks.

The key insight:

10x content for AI is about answering questions definitively, not creating an impressive reading experience.

Something nobody’s mentioned: Original insights and data.

I work at a SaaS company. Our most-cited content isn’t our longest guides - it’s shorter pieces that include:

• Original research we conducted
• Customer data (anonymized) with real numbers
• Case studies with specific metrics
• Expert quotes we gathered ourselves

AI systems seem to prioritize content that has information available nowhere else. If you’re just synthesizing information from other sources (even really well), you’re competing with everyone else doing the same thing.

But if you publish “We surveyed 1,000 marketers and found X” or “Our customers saw an average Y% improvement,” that’s 10x because it’s information only you have.

Original data = unfair advantage for AI citations.

Practical restructuring example that worked for us:

Before (ranked #1 on Google, zero AI citations): “The Evolution of Content Marketing: A Comprehensive History and Future Outlook”

• 4,500 words
• Narrative structure flowing through history
• Beautiful design with timelines
• Information spread throughout

After restructure (still ranks #1, now gets AI citations):

• Added 200-word TL;DR at top with key facts
• Changed H2s to questions: “What is content marketing?” “When did content marketing begin?” “What are the key content marketing strategies?”
• First sentence of each section directly answers the heading question
• Added comparison table of content marketing eras
• Added FAQ schema with 8 questions
• Added “Key Statistics” callout box
• Kept total length roughly the same

Same content, restructured for extractability. AI citations went from 0 to regular mentions within 2 months.

Small company perspective:

We can’t compete with enterprise content budgets. Our “10x” strategy is different:

• Focus on niche topics that big players ignore
• Be more specific than general guides
• Include real implementation examples, not theory

What works for us:

Instead of “The Ultimate Guide to Email Marketing” (competing with everyone), we publish “Email Automation for SaaS Free Trial Conversion” (specific niche where we have expertise).

These specific guides get cited by AI when people ask specific questions. We’ll never rank for “email marketing” but we get cited for niche questions that big players don’t specifically address.

10x for small players = 10x more specific and practical than generic guides.

Let me add the measurement angle:

How to know if your content is “10x” for AI:

Use Am I Cited or manual testing to check:

1. Does your content appear in AI responses for relevant questions?
2. Is it cited as a source?
3. What specific passages are being extracted?

What we’ve found testing hundreds of pieces:

AI tends to extract:

• The first 2-3 sentences of sections with question-based headings
• Bulleted lists of key points
• Data from tables
• FAQ question/answer pairs
• Definitions and explanations in first-person “is” statements

AI tends to ignore:

• Narrative paragraphs without clear structure
• Opinions without supporting data
• Introductory fluff before getting to the point
• Conclusions that just summarize previous sections

Practical test:

Take your best content and ask: “If AI could only extract one sentence from each section, would that sentence be a useful, complete answer?” If not, restructure.

This thread has completely reframed how I think about 10x content. Key takeaways:

10x for AI = Extractable + Authoritative + Comprehensive

My action plan:

1. Audit top 20 guides - Check AI citations with Am I Cited
2. Restructure for extraction:
   • Add TL;DR summaries
   • Change headings to questions
   • Put direct answers first in each section
   • Add tables where relevant
   • Implement FAQ schema
3. Add original elements:
   • Include customer data/case studies
   • Add expert quotes
   • Publish original research when possible
4. Maintain what works:
   • Keep comprehensive coverage
   • Keep quality design
   • Keep depth and nuance
5. Test and iterate:
   • Track AI citations before/after restructuring
   • Double down on what gets cited

The core insight: 10x for AI isn’t about being 10x better as a whole. It’s about being 10x easier to extract, cite, and trust.

Thanks everyone for the frameworks and real examples.