Executive Summary
- Study Scope: Search Atlas analyzed 18,377 matched queries across GPT, Gemini, and Perplexity versus Google results
- Core Finding: Large language models cite sources fundamentally differently than Google ranks them
- Perplexity Performance: 25-30% domain overlap with Google, closest to traditional search behavior
- ChatGPT Gap: Only 10-15% domain overlap with Google, highly selective in source citations
- Gemini Inconsistency: Just 4% domain overlap, most unpredictable citation patterns
- Critical Insight: Ranking #1 in Google provides no guarantee of appearing in AI-generated answers
- Strategic Implication: SEOs must develop separate optimization strategies for traditional search versus AI platforms
- Platform Types: Retrieval-based systems (Perplexity) favor SEO signals; reasoning-focused models (ChatGPT, Gemini) don’t
The Visibility Disconnect SEOs Must Understand
In a groundbreaking analysis that challenges conventional SEO wisdom, new research reveals a significant gap between where your website ranks in Google and whether AI platforms will cite you in their answers.
Search Atlas, an SEO software company, compared citations from OpenAI’s GPT, Google’s Gemini, and Perplexity against Google search results. The analysis of 18,377 matched queries finds a gap between traditional search visibility and AI platform citations.
The fundamental shift:
Large language models cite sources differently than Google ranks them.
This single finding has profound implications for every business investing in search visibility. Your #1 ranking may be worthless in the AI-driven discovery channels where your customers are increasingly finding information.
Perplexity: The Bridge Between Search and AI
Among the AI platforms studied, Perplexity shows the strongest correlation with traditional search results—but even here, the overlap is surprisingly limited.
Perplexity performs live web retrieval, so its citations look more like search results. Across the dataset, Perplexity showed a median domain overlap of around 25–30% with Google results, with median URL overlap close to 20%.
The numbers in context:
In total, Perplexity shared 18,549 domains with Google, representing about 43% of the domains it cited.
This means that even for the AI platform most aligned with traditional search, over half of its cited sources come from domains that may not appear in Google’s top results for the same query.
Why Perplexity Differs
Perplexity’s architecture actively searches the web and its citation patterns more closely track traditional search rankings. If your site already ranks well in Google, you are more likely to see similar visibility in Perplexity answers.
For marketers, this suggests that traditional SEO efforts will have the most carryover effect on Perplexity visibility compared to other AI platforms.
ChatGPT: A Different Citation Philosophy
The gap widens dramatically when examining ChatGPT’s behavior.
Significantly lower overlap:
ChatGPT showed much lower overlap with Google. Its median domain overlap stayed around 10–15%. The model shared 1,503 domains with Google, accounting for about 21% of its cited domains. URL matches typically remained below 10%.
This represents a fundamental divergence from traditional search visibility. ChatGPT is accessing and prioritizing information in ways that have little relationship to Google’s ranking algorithms.
What This Means for Marketers
ChatGPT and Gemini rely more on pre-trained knowledge and selective retrieval. They cite a narrower set of sources and are less tied to current rankings. URL-level matches with Google are low for both.
The implication is clear: optimizing for Google won’t optimize for ChatGPT. These are separate visibility channels requiring distinct strategies.
Gemini: The Unpredictable Outlier
Google’s own AI platform shows the most inconsistent citation behavior—ironically having the least correlation with Google Search results.
The surprising disconnect:
Gemini behaved less consistently. Some responses had almost no overlap with search results, while others lined up more closely. Overall, Gemini shared just 160 domains with Google, representing about 4% of the domains that appeared in Google’s results, even though those domains made up 28% of Gemini’s citations.
This 4% overlap is stunning. Google’s AI platform is citing sources that barely appear in Google’s own search results for the same queries.
The Consistency Problem
The inconsistency adds another layer of complexity. Marketers cannot rely on predictable patterns with Gemini—sometimes it aligns with search results, often it doesn’t. This makes optimization particularly challenging.
The Death of “Rank and They Will Find You”
The core strategic implication:
Ranking in Google doesn’t guarantee LLM citations. This report suggests the systems draw from the web in different ways.
For decades, SEO strategy has been straightforward: rank high in Google, capture visibility. That singular focus is no longer sufficient.
Two Distinct Visibility Systems Emerge
The research suggests marketers must now think in terms of dual optimization:
- Traditional Search Optimization – For Google and search engines using ranking algorithms
- AI Citation Optimization – For LLMs using reasoning and selective retrieval
These are not the same thing, and strategies that work for one may not work for the other.
Platform-Specific Strategic Guidance
For Retrieval-Based Systems (Perplexity)
For retrieval-based systems like Perplexity, traditional SEO signals and overall domain strength are likely to matter more for visibility.
Recommended approach:
- Continue traditional SEO best practices
- Focus on domain authority building
- Maintain strong backlink profiles
- Optimize for featured snippets and quick answers
- Ensure content is crawlable and well-structured
For Reasoning-Focused Models (ChatGPT & Gemini)
For reasoning-focused models like ChatGPT and Gemini, those signals may have less direct influence on which sources appear in answers.
Recommended approach:
- Build brand authority and recognition
- Create authoritative, citable content
- Establish thought leadership positions
- Develop unique insights and original research
- Focus on E-E-A-T signals
- Ensure consistent entity definitions across platforms
Study Methodology and Limitations
Research Parameters
The study analyzed a substantial dataset, but researchers acknowledge important limitations.
The dataset heavily favored Perplexity, accounting for 89% of matched queries, with OpenAI at 8% and Gemini at 3%.
This distribution means conclusions about ChatGPT and Gemini are based on smaller sample sizes, though still significant.
Matching Methodology
Researchers matched queries using semantic similarity scoring with an 82% similarity threshold using OpenAI’s embedding model.
This approach means paired queries expressed similar information needs but were not identical searches, allowing for broader pattern recognition while maintaining relevance.
Temporal Considerations
Time window limitation:
The two-month window provides a recent snapshot only—longer timeframes would be needed to see whether the same overlap patterns hold over time.**
AI platform behaviors may evolve, meaning these patterns could shift. However, the fundamental differences between platform types are likely to persist.
What This Means for Your SEO Strategy
Immediate Action Items
- Audit AI Visibility: Test your brand’s appearance across all major AI platforms
- Diversify Optimization: Don’t rely solely on Google SEO for visibility
- Platform-Specific Content: Consider creating content optimized for AI citation, not just ranking
- Monitor New Metrics: Track brand mentions in AI answers, not just search rankings
- Build Authority Signals: Focus on E-E-A-T factors that work across both systems
Long-Term Strategic Shifts
Traditional SEO isn’t dead, but it’s insufficient.
Businesses must now maintain presence across multiple discovery systems:
- Traditional search engines (Google, Bing)
- Retrieval-based AI (Perplexity)
- Reasoning-based AI (ChatGPT, Gemini, Claude)
- Social platforms
- Direct traffic channels
Each requires different optimization approaches and measurement frameworks.
The Measurement Challenge
Current analytics tools weren’t designed for this fragmented visibility landscape. Brands are being discovered, evaluated, and chosen inside AI conversations that leave no referral trail.
Proxy metrics to watch:
- Branded search volume increases
- Direct traffic growth patterns
- Share of voice in AI platform testing
- Brand mention frequency across platforms
- Attribution gaps between awareness and conversion
Industry Expert Perspectives
While this study provides quantitative data, industry practitioners have been observing these patterns anecdotally for months. The research confirms what many SEOs suspected: AI visibility operates on fundamentally different principles than search rankings.
The platforms using live retrieval (Perplexity) show the most alignment with traditional SEO, while those relying on trained models (ChatGPT, Gemini) demonstrate citation patterns that seem almost independent of current search rankings.
Looking Ahead: The Fragmentation of Discovery
This research suggests we’re entering an era of fragmented discovery where multiple systems compete for attention, each with its own logic for surfacing information.
The new reality:
- Users will discover brands through multiple channels simultaneously
- Different audiences will prefer different discovery methods
- Visibility must be maintained across parallel systems
- Attribution becomes increasingly complex
- Success requires multi-channel optimization
Actionable Recommendations by Business Type
For Enterprise Brands
- Invest in presence across all platforms equally
- Build comprehensive knowledge graphs
- Maintain robust structured data implementation
- Monitor brand mentions across all AI platforms
- Develop platform-specific content strategies
For SMBs
- Focus on Perplexity optimization (closest to traditional SEO)
- Build strong local and niche authority
- Create unique, original content that stands out
- Leverage customer reviews and testimonials
- Ensure consistent NAP data across platforms
For Content Publishers
- Diversify traffic sources immediately
- Build direct audience relationships
- Create subscription/email strategies
- Develop authoritative voice in specific niches
- Monitor which content gets cited in AI answers
For E-commerce
- Optimize product data for AI parsing
- Build brand recognition beyond rankings
- Create comprehensive buying guides
- Develop unique product insights
- Track assisted conversions from AI exposure
The Bottom Line
A paradigm shift is underway:
Your Google ranking remains valuable for traditional search traffic, but it no longer guarantees comprehensive visibility. AI platforms—which are rapidly becoming primary discovery channels—use fundamentally different methods to select and cite sources.
Perplexity offers the closest parallel to traditional search, making it the natural starting point for AI optimization efforts. ChatGPT and Gemini represent a new frontier where domain authority, brand recognition, and content quality matter more than ranking algorithms.
The winners in this new landscape will be brands that recognize AI visibility as a separate discipline requiring dedicated strategy, measurement, and optimization—not merely an extension of existing SEO efforts.
Frequently Asked Questions (FAQ)
Q: Does this mean Google SEO is dead?
A: No. Traditional SEO remains essential for Google search visibility, which still drives massive traffic. However, ranking in Google doesn’t guarantee LLM citations, and the systems draw from the web in different ways. SEO is evolving, not dying—you now need both traditional search optimization and AI citation strategies.
Q: Which AI platform should I optimize for first?
A: Start with Perplexity. Perplexity’s architecture actively searches the web and its citation patterns more closely track traditional search rankings. If your site already ranks well in Google, you are more likely to see similar visibility in Perplexity answers. Your existing SEO efforts will have the most carryover effect here.
Q: Why is the overlap between Google and AI citations so low?
A: Different systems use different selection criteria. ChatGPT and Gemini rely more on pre-trained knowledge and selective retrieval. They cite a narrower set of sources and are less tied to current rankings. They’re optimizing for answer quality and trustworthiness, not ranking algorithms.
Q: What was the sample size for this study?
A: The analysis covered 18,377 matched queries across OpenAI’s GPT, Google’s Gemini, and Perplexity compared against Google search results. However, the dataset distribution was uneven, with Perplexity accounting for 89% of matched queries.
Q: What percentage of domains overlap between Google and each AI platform?
A: The overlap varies dramatically:
- Perplexity: Showed a median domain overlap of around 25–30% with Google results, with Perplexity sharing 18,549 domains with Google, representing about 43% of the domains it cited
- ChatGPT: Median domain overlap stayed around 10–15%, with the model sharing 1,503 domains with Google, accounting for about 21% of its cited domains
- Gemini: Shared just 160 domains with Google, representing about 4% of the domains that appeared in Google’s results
Q: Why does Google’s own AI (Gemini) have such low overlap with Google Search?
A: Gemini behaved less consistently, with some responses having almost no overlap with search results while others lined up more closely. Gemini appears to use reasoning-based selection rather than ranking-based retrieval, even though both are Google products serving different purposes.
Q: How were queries matched in this study?
A: Researchers matched queries using semantic similarity scoring with an 82% similarity threshold using OpenAI’s embedding model. This means queries expressed similar information needs but weren’t necessarily identical searches.
Q: What are the limitations of this research?
A: Three main limitations exist: The dataset heavily favored Perplexity (89% of matched queries) with OpenAI at 8% and Gemini at 3%. Paired queries were semantically similar but not identical. The two-month window provides only a recent snapshot—longer timeframes would be needed to see whether the same overlap patterns hold over time.
Q: Should I stop focusing on Google rankings?
A: Absolutely not. Google still drives significant traffic and remains the dominant search engine. However, you should now allocate resources to AI visibility strategies as well, treating them as complementary channels rather than assuming Google success automatically translates to AI visibility.
Q: What optimization approach works for ChatGPT and Gemini?
A: For reasoning-focused models like ChatGPT and Gemini, traditional SEO signals may have less direct influence on which sources appear in answers. Focus on building brand authority, creating authoritative and citable content, establishing E-E-A-T signals, and ensuring consistent entity definitions across platforms.
Q: What optimization approach works for Perplexity?
A: For retrieval-based systems like Perplexity, traditional SEO signals and overall domain strength are likely to matter more for visibility. Continue strong SEO practices including domain authority building, backlink development, structured data, and content optimization.
Q: How can I track my AI visibility if it doesn’t show in analytics?
A: Monitor proxy metrics including branded search volume increases, direct traffic pattern changes, manual testing of brand mentions across AI platforms, and attribution gaps between awareness channels and conversions. Several emerging tools now track brand appearance in AI answers.
Q: Will this gap between Google and AI citations continue?
A: Likely yes, though patterns may evolve. The fundamental difference is architectural: retrieval-based systems will always align more with search rankings, while reasoning-based models will continue to use selective, quality-based citation patterns independent of ranking algorithms.
Q: What’s the single most important takeaway from this research?
A: Ranking in Google doesn’t guarantee LLM citations. This report suggests the systems draw from the web in different ways. You need separate, dedicated strategies for traditional search and AI platforms—they are not the same visibility channel.
