{"id":5854,"date":"2025-11-20T13:45:37","date_gmt":"2025-11-20T13:45:37","guid":{"rendered":"https:\/\/www.stanventures.com\/news\/?p=5854"},"modified":"2025-11-20T13:45:37","modified_gmt":"2025-11-20T13:45:37","slug":"ai-citation-vs-google-ranking-gap-study","status":"publish","type":"post","link":"https:\/\/www.stanventures.com\/news\/ai-citation-vs-google-ranking-gap-study-5854\/","title":{"rendered":"New Data Finds Sharp Gap Between Google Rankings &#038; LLM Citations"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">A new research report has revealed something many SEO professionals have suspected but could never quantify clearly: large language models (LLMs) and Google Search do not cite the web in the same way.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The study compared a massive dataset of 18,377 matched queries analyzed by Search Atlas reveals a growing disconnect between traditional SEO visibility and AI citation visibility.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">So what exactly did the study uncover? And what does it mean for brands trying to stay visible in the AI-first era?<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_83 counter-hierarchy ez-toc-counter ez-toc-transparent ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\"><\/p>\n<span class=\"ez-toc-title-toggle\"><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.stanventures.com\/news\/ai-citation-vs-google-ranking-gap-study-5854\/#why-did-search-atlas-compare-google-serps-with-ai-citations\" >Why Did Search Atlas Compare Google SERPs with AI Citations?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.stanventures.com\/news\/ai-citation-vs-google-ranking-gap-study-5854\/#how-was-the-study-designed\" >How Was the Study Designed?\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.stanventures.com\/news\/ai-citation-vs-google-ranking-gap-study-5854\/#what-did-the-study-find-about-perplexitys-alignment-with-google\" >What Did the Study Find About Perplexity\u2019s Alignment With Google?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.stanventures.com\/news\/ai-citation-vs-google-ranking-gap-study-5854\/#why-do-chatgpts-citations-differ-so-much-from-google\" >Why Do ChatGPT\u2019s Citations Differ So Much From Google?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.stanventures.com\/news\/ai-citation-vs-google-ranking-gap-study-5854\/#why-is-geminis-behavior-so-inconsistent\" >Why Is Gemini\u2019s Behavior So Inconsistent?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.stanventures.com\/news\/ai-citation-vs-google-ranking-gap-study-5854\/#does-ranking-well-on-google-help-you-get-cited-by-ai-models\" >Does Ranking Well on Google Help You Get Cited by AI Models?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.stanventures.com\/news\/ai-citation-vs-google-ranking-gap-study-5854\/#how-should-we-interpret-domain-vs-url-overlap\" >How Should We Interpret Domain vs URL Overlap?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.stanventures.com\/news\/ai-citation-vs-google-ranking-gap-study-5854\/#what-limitations-should-readers-consider\" >What Limitations Should Readers Consider?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.stanventures.com\/news\/ai-citation-vs-google-ranking-gap-study-5854\/#what-does-this-mean-for-the-future-of-seo-and-ai-discovery\" >What Does This Mean for the Future of SEO and AI Discovery?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.stanventures.com\/news\/ai-citation-vs-google-ranking-gap-study-5854\/#tl-dr\" >TL, DR<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"why-did-search-atlas-compare-google-serps-with-ai-citations\"><\/span><b>Why Did Search Atlas Compare Google SERPs with AI Citations?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The <a href=\"https:\/\/searchatlas.com\/research\/how-gpt-results-differ-from-search-engine-results\/\">purpose of the research<\/a> was to understand whether the web domains that appear on Google Search Engine Results Pages (SERPs) also appear as cited sources in responses generated by large language models.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">As AI has increasingly become a primary information gateway, the question is not optional anymore. It is necessary.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The study set out to measure overlap between:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Google SERP URLs and domains<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">LLM-generated citations from ChatGPT (GPT), Perplexity and Gemini<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">To avoid superficial similarities, the researchers used text embeddings and cosine similarity to pair LLM queries with <a href=\"https:\/\/www.stanventures.com\/blog\/types-of-serp-features\/\">SERP keywords<\/a> only when the two expressed 82% or higher semantic similarity.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This ensured that comparisons were made between queries with almost identical intent.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Once matched, each pair was evaluated for domain-level and URL-level overlap. This produced a detailed map of how closely each LLM tracks the current web.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"how-was-the-study-designed\"><\/span><b>How Was the Study Designed?\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Let\u2019s see exactly how the research team approached this.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The SERP dataset was collected across September and October 2025, capturing keyword-level search results. Each record included URLs and domains.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Meanwhile, the LLM dataset, gathered in October 2025 contained GPT, Gemini and Perplexity responses with complete citation lists.\u00a0<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-5857\" src=\"https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2025\/11\/st-of-20-sample-rows-after-applying-the-semantic-similarity-filter.jpg\" alt=\"St Of 20 Sample Rows After Applying The Semantic Similarity Filter\" width=\"831\" height=\"316\" srcset=\"https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2025\/11\/st-of-20-sample-rows-after-applying-the-semantic-similarity-filter.jpg 831w, https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2025\/11\/st-of-20-sample-rows-after-applying-the-semantic-similarity-filter-300x114.jpg 300w\" sizes=\"auto, (max-width: 831px) 100vw, 831px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Every SERP entry was parsed from JSON, normalized and cleaned. Domains were standardized using tldextract, while URLs were converted into consistent lowercase formats with unnecessary parameters removed.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For the LLM side, responses were grouped by query response ID. Each response preserved its platform, query title, timestamps and complete cited source list.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The key step was embedding which means both SERP keywords and <a href=\"https:\/\/www.stanventures.com\/news\/adobes-llm-optimizer-is-transforming-seo-3459\/\">LLM query titles<\/a> were converted into vectors using OpenAI\u2019s text-embedding-3-small model.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Each LLM query was matched with its closest SERP keyword using cosine similarity computed on GPU. Only pairs exceeding the 0.82 similarity threshold were retained.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This filtering resulted in 18,377 high-confidence LLM\u2013SERP matched pairs, which then formed the basis for overlap measurement\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Domain overlap measured shared domains referenced by both Google and the LLM for the same query. URL overlap measures exact page-level matches, more stringent and much harder to achieve. These overlap percentages were then aggregated across platforms and query intents<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"what-did-the-study-find-about-perplexitys-alignment-with-google\"><\/span><b>What Did the Study Find About Perplexity\u2019s Alignment With Google?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Among all platforms evaluated, Perplexity showed the strongest alignment with Google. This is not surprising, considering Perplexity performs live web retrieval.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-5859\" src=\"https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2025\/11\/Perplexity.jpg\" alt=\"Perplexity\" width=\"757\" height=\"373\" srcset=\"https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2025\/11\/Perplexity.jpg 757w, https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2025\/11\/Perplexity-300x148.jpg 300w\" sizes=\"auto, (max-width: 757px) 100vw, 757px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Across the dataset:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Perplexity\u2019s median domain overlap ranged between 25% and 30%, meaning a significant portion of the domains it cites also appear in Google\u2019s search results.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">URL overlap was also noticeably higher than the others, hovering near 20%.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">In total numbers, Perplexity shared 18,549 domains with Google, about 43% of all domains it cited.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This suggests Perplexity functions almost like a search engine in terms of source selection.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The model actively retrieves new, trending and authoritative sources, reflecting real-time SERP patterns. For businesses that already enjoy high Google rankings, Perplexity becomes an additional channel where visibility is likely to follow.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"why-do-chatgpts-citations-differ-so-much-from-google\"><\/span><b>Why Do ChatGPT\u2019s Citations Differ So Much From Google?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">ChatGPT, tested without WebSearch enabled, demonstrated far lower alignment with Google. The results showed:<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-5860\" src=\"https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2025\/11\/Open-AI-results.jpg\" alt=\"Open AI Results\" width=\"751\" height=\"364\" srcset=\"https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2025\/11\/Open-AI-results.jpg 751w, https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2025\/11\/Open-AI-results-300x145.jpg 300w\" sizes=\"auto, (max-width: 751px) 100vw, 751px\" \/><\/p>\n<ul>\n<li><span style=\"font-weight: 400;\">Median domain overlap: 10\u201315%<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Total shared domains: 1,503<\/span><\/li>\n<li><span style=\"font-weight: 400;\">That\u2019s 21% of ChatGPT\u2019s cited domains<\/span><\/li>\n<li><span style=\"font-weight: 400;\">URL overlap: typically under 10%<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The takeaway? ChatGPT behaves like a reasoning engine, not a retrieval engine. <\/span><span style=\"font-weight: 400;\">It relies on:<\/span><\/p>\n<ul>\n<li><span style=\"font-weight: 400;\">Pre-trained knowledge<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Distilled information<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Internal representations<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Summarization over citation<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Which means even if your site ranks #1 today, ChatGPT may still reference an entirely different domain if it believes the information is conceptually \u201cknown\u201d from training.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This is a fundamental shift: AI visibility is not the same as <a href=\"https:\/\/www.stanventures.com\/ai-seo-services\/\">AI SEO visibility<\/a>.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"why-is-geminis-behavior-so-inconsistent\"><\/span><b>Why Is Gemini\u2019s Behavior So Inconsistent?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Gemini\u2019s results were the most unpredictable of the three systems.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-5861\" src=\"https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2025\/11\/Gemini-search-results.jpg\" alt=\"Gemini Search Results\" width=\"759\" height=\"373\" srcset=\"https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2025\/11\/Gemini-search-results.jpg 759w, https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2025\/11\/Gemini-search-results-300x147.jpg 300w\" sizes=\"auto, (max-width: 759px) 100vw, 759px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Although Gemini shared 28% of its cited domains with Google, this number is misleading.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In absolute terms, Gemini matched only 160 domains with SERP results, just 4% of all domains appearing in Google\u2019s dataset.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Gemini\u2019s overlap varied dramatically across queries. Some responses had nearly zero overlap with SERP sources.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Others matched closely. Unlike Perplexity, Gemini appears to use a selective and filtered retrieval strategy, pulling from a narrower or more curated pool of sources.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This is particularly notable because Gemini is a Google product, yet its citation patterns differ significantly from Google Search.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"does-ranking-well-on-google-help-you-get-cited-by-ai-models\"><\/span><b>Does Ranking Well on Google Help You Get Cited by AI Models?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Here comes the big question the SEO world cares about. The<\/span><span style=\"font-weight: 400;\">\u00a0truth based on the data:<\/span><\/p>\n<ol>\n<li><b>Perplexity<\/b><span style=\"font-weight: 400;\">: Yes, ranking helps. Its retrieval model means SERP-visible domains are more likely to be cited.<\/span><\/li>\n<li><b>ChatGPT<\/b><span style=\"font-weight: 400;\">: Not necessarily. Even high-ranking sites may not appear.<\/span><\/li>\n<li><b>Gemini<\/b><span style=\"font-weight: 400;\">: Very little correlation. Google search dominance doesn\u2019t guarantee Gemini citations.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">So the larger point is: <a href=\"https:\/\/www.stanventures.com\/news\/llms-txt-for-ai-seo-do-you-really-need-it-for-ai-visibility-4572\/\">SEO and LLM<\/a> visibility are NOT the same game anymore. Brands need to optimize for both fields independently.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"how-should-we-interpret-domain-vs-url-overlap\"><\/span><b>How Should We Interpret Domain vs URL Overlap?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Domain-level overlap reflects topical consistency both Google and the LLM recognize that certain websites are authoritative on a subject.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-5856\" src=\"https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2025\/11\/Distribution-of-match-queries-by-platform.jpg\" alt=\"Distribution Of Match Queries By Platform\" width=\"588\" height=\"384\" srcset=\"https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2025\/11\/Distribution-of-match-queries-by-platform.jpg 588w, https:\/\/www.stanventures.com\/news\/wp-content\/uploads\/2025\/11\/Distribution-of-match-queries-by-platform-300x196.jpg 300w\" sizes=\"auto, (max-width: 588px) 100vw, 588px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">URL-level overlap is stricter and reflects retrieval precision. Only Perplexity demonstrated strong URL-level matching, even achieving near-perfect alignment for some queries.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">ChatGPT and Gemini routinely addressed the same topics while citing different pages or entirely different websites.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This shows that AI models rarely replicate search engine behavior at the exact page level. They operate from broader conceptual knowledge rather than strict URL retrieval.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"what-limitations-should-readers-consider\"><\/span><b>What Limitations Should Readers Consider?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The study acknowledges several constraints:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The analysis was limited to a two-month window (Sept\u2013Oct 2025).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Query intent distribution may not be perfectly uniform.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Semantic similarity (0.82) captures closeness, not identical intent.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Architectural differences between retrieval and non-retrieval models inherently affect outcomes.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Despite these limitations, the study\u2019s scale and methodology provide one of the clearest insights to date on the divergence between search engines and AI systems.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"what-does-this-mean-for-the-future-of-seo-and-ai-discovery\"><\/span><b>What Does This Mean for the Future of SEO and AI Discovery?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">If one thing is certain, it\u2019s that AI is reshaping how information is discovered. The study shows that traditional SEO visibility is no longer the sole determinant of online presence.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A brand can rank highly on Google yet fail to appear in AI-generated answers. Conversely, retrieval-focused platforms like Perplexity may carry that visibility over.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This signals a future where:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SEO strategies must adapt to LLM behaviors<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI citation optimization becomes a new discipline<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Businesses must balance search visibility with AI visibility<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Retrieval-augmented systems play a growing role in information access<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">For now, the strongest alignment lies with Perplexity.\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"tl-dr\"><\/span><b>TL, DR<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Google ranking \u2260 LLM citation.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Perplexity aligns closest with SERPs due to live web retrieval.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">ChatGPT cites from training, not rankings \u2192 low overlap.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Gemini is selective and inconsistent despite being Google\u2019s own model.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Domain overlap is moderate, URL overlap is very low across all models.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI visibility is now separate from SEO visibility.<\/span><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>A new research report has revealed something many SEO professionals have suspected but could never quantify clearly: large language models (LLMs) and Google Search do not cite the web in the same way.\u00a0 The study compared a massive dataset of 18,377 matched queries analyzed by Search Atlas reveals a growing disconnect between traditional SEO visibility [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":5858,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[15,14],"tags":[],"class_list":["post-5854","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-google"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI Citation vs Google Ranking Gap 2025 Study<\/title>\n<meta name=\"description\" content=\"Discover how AI Citation vs Google Ranking diverge as LLMs cite different sources. Learn why AI visibility requires new strategies.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.stanventures.com\/news\/ai-citation-vs-google-ranking-gap-study-5854\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Citation vs Google Ranking Gap 2025 Study\" \/>\n<meta property=\"og:description\" content=\"Discover how AI Citation vs Google Ranking diverge as LLMs cite different sources. 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