{"id":187017,"date":"2025-04-14T11:20:14","date_gmt":"2025-04-14T16:20:14","guid":{"rendered":"https:\/\/ahrefs.com\/blog\/?p=187017"},"modified":"2025-08-26T09:49:20","modified_gmt":"2025-08-26T14:49:20","slug":"llms-flatten-originality","status":"publish","type":"post","link":"https:\/\/ahrefs.com\/blog\/llms-flatten-originality\/","title":{"rendered":"LLMs Don\u2019t Reward Originality, They Flatten It"},"content":{"rendered":"<div class=\"intro-txt\">Originality is idealized, especially in tech and marketing.\n<p>We\u2019re told to \u201cthink different,\u201d to coin new terms, to pioneer ideas no one\u2019s heard before and share our thought leadership.&nbsp;<\/p><\/div>\n<p>But in the age of AI-driven search, originality is not the boon we think it is. It might even be a liability\u2026 or, at best, a long game with no guarantees.<\/p>\n<p>Because here\u2019s the uncomfortable truth: LLMs don\u2019t reward firsts. They reward consensus.<\/p>\n<p>If multiple sources don\u2019t already back a new idea, it may as well not exist. You can coin a concept, publish it, even rank #1 for it in Google\u2026 and still be invisible to large language models. Until others echo it, rephrase it, and spread it, your originality won\u2019t matter.<\/p>\n<p>In a world where AI summarizes rather than explores, originality needs a crowd before it earns a citation.<\/p>\n<h2><a id=\"post-187017-_3tv00a7trvtl\"><\/a><div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\"> The accidental experiment that sparked this epiphany&nbsp;<\/div><\/div><\/h2>\n<p>I didn\u2019t intentionally set out to test how LLMs handle original ideas, but curiosity struck late one night, and I ended up doing just&nbsp;that.<\/p>\n<p>While writing a post about <a href=\"https:\/\/ahrefs.com\/blog\/multilingual-seo\/\">multilingual SEO<\/a>, I coined a new framework \u2014 something we called the <em>Ahrefs<\/em> <em>Multilingual SEO Matrix<\/em>.<\/p>\n<p>It is a net-new concept designed to add information gain to the article. We treated it as a piece of <a href=\"https:\/\/ahrefs.com\/blog\/how-to-become-a-thought-leader\/\">thought leadership<\/a> that has the potential to shape how people think about the topic in future. We also created a custom table and image of the matrix.<\/p>\n<p>Here\u2019s what it looks&nbsp;like:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1172\" height=\"1813\" class=\"wp-image-187018\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/example-of-ahrefs-multilingual-seo-matrix-used-to.png\" alt=\"Example of Ahrefs' Multilingual SEO Matrix used to show how variations in regional and language targeting lead to different SEO opportunities.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/example-of-ahrefs-multilingual-seo-matrix-used-to.png 1172w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/example-of-ahrefs-multilingual-seo-matrix-used-to-275x425.png 275w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/example-of-ahrefs-multilingual-seo-matrix-used-to-768x1188.png 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/example-of-ahrefs-multilingual-seo-matrix-used-to-993x1536.png 993w\" sizes=\"auto, (max-width: 1172px) 100vw, 1172px\"><\/p>\n<p>The article ranked first for \u201cmultilingual SEO matrix\u201d. The image showed up in Google\u2019s AI Overview. We were cited, linked, and visually featured \u2014 exactly the kind of SEO performance you\u2019d expect from original, useful content (especially when searching for an exact match keyword).<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1397\" height=\"466\" class=\"wp-image-187019\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/ahrefs-multilingual-seo-matrix-cited-by-google-in.png\" alt=\"Ahrefs' Multilingual SEO Matrix cited by Google in AI Overviews as a framework used to organize a website for multiple languages and regions.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/ahrefs-multilingual-seo-matrix-cited-by-google-in.png 1397w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/ahrefs-multilingual-seo-matrix-cited-by-google-in-680x227.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/ahrefs-multilingual-seo-matrix-cited-by-google-in-768x256.png 768w\" sizes=\"auto, (max-width: 1397px) 100vw, 1397px\"><\/p>\n<p>But, the AI-generated text response hallucinated a definition and went off-tangent because it used other sources that talk more generally about the parent topic, multilingual SEO.<\/p>\n<div class=\"recommendation\"><div class=\"recommendation-title\">Recommendation<\/div><div class=\"recommendation-content\">You can check out your visibility in AI overviews using Ahrefs\u2019 Brand Radar. Search your brand alongside core topics or compared to competitors and see how much visibility you\u2019re also getting:\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-187042\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/Ahrefs-Brand-Radar-for-AI-Overview-Visibility.jpg\" alt=\"Ahrefs' Brand Radar for AI Overview visibility tracking\" width=\"871\" height=\"781\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/Ahrefs-Brand-Radar-for-AI-Overview-Visibility.jpg 871w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/Ahrefs-Brand-Radar-for-AI-Overview-Visibility-474x425.jpg 474w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/Ahrefs-Brand-Radar-for-AI-Overview-Visibility-768x689.jpg 768w\" sizes=\"auto, (max-width: 871px) 100vw, 871px\"><\/p>\n<\/div><\/div>\n<p>Following my curiosity, I then prompted various LLMs, including ChatGPT (4o), GPT Search, and Perplexity, to see how much visibility this original concept might actually get.<\/p>\n<p>The general pattern I observed is that all&nbsp;LLMs:<\/p>\n<ul>\n<li>Had access to the article and&nbsp;image<\/li>\n<li>Had the capacity to cite it in their responses<\/li>\n<li>Included the exact term <em>multiple<\/em> times in responses<\/li>\n<li>Hallucinated a definition from generic information<\/li>\n<li>Never mentioned my name or Ahrefs, aka the creators<\/li>\n<li>When re-prompted, would frequently give us zero visibility<\/li>\n<\/ul>\n<p>Overall, it felt academically dishonest. Like our content was correctly cited in the footnotes (sometimes), but the original term we\u2019d coined was repeated in responses while paraphrasing other, unrelated sources (almost always).<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1180\" height=\"2048\" class=\"wp-image-187020\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/chatgpts-response-when-prompted-about-the-multili.jpg\" alt=\"ChatGPT's response when prompted about the Multilingual SEO Matrix, hallucinting a response despite citing Ahrefs' article as a reference.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/chatgpts-response-when-prompted-about-the-multili.jpg 1180w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/chatgpts-response-when-prompted-about-the-multili-245x425.jpg 245w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/chatgpts-response-when-prompted-about-the-multili-768x1333.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/chatgpts-response-when-prompted-about-the-multili-885x1536.jpg 885w\" sizes=\"auto, (max-width: 1180px) 100vw, 1180px\"><\/p>\n<p>It also felt like the concept was absorbed into the general definition of \u201cmultilingual SEO\u201d.<\/p>\n<p>That moment is what sparked the epiphany: LLMs don\u2019t reward originality. They flatten it.<\/p>\n<p>This wasn\u2019t a rigorous experiment \u2014 more like a curious follow-up. Especially since I made some errors in the original post that likely made it difficult for LLMs to latch onto an explicit definition.<\/p>\n<p>However, it exposed something interesting that made me reconsider how easy it might be to earn mentions in LLM responses. It\u2019s what I think of as \u201cLLM flattening\u201d.<\/p>\n<h2><a id=\"post-187017-_v652o0afy1xp\"><\/a><div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\"> The problem of \u201cLLM flattening\u201d&nbsp;<\/div><\/div><\/h2>\n<p>LLM flattening is what happens when large language models bypass nuance, originality, and innovative insights in favor of simplified, consensus-based summaries. In doing so, they compress distinct voices and new ideas into the safest, most statistically reinforced version of a&nbsp;topic.<\/p>\n<p>This can happen at a micro and macro&nbsp;level.<\/p>\n<h3><a id=\"post-187017-_wtlmygbkba0c\"><\/a>Micro LLM flattening<\/h3>\n<p>Micro LLM flattening occurs at a topic level where LLMs reshape and synthesize knowledge in their responses to fit the consensus or most authoritative pattern about that&nbsp;topic.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1800\" height=\"928\" class=\"wp-image-187021\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/how-llms-flatten-originality-at-a-micro-level-for.png\" alt=\"How LLMs flatten originality at a micro level for individual topics.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/how-llms-flatten-originality-at-a-micro-level-for.png 1800w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/how-llms-flatten-originality-at-a-micro-level-for-680x351.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/how-llms-flatten-originality-at-a-micro-level-for-768x396.png 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/how-llms-flatten-originality-at-a-micro-level-for-1536x792.png 1536w\" sizes=\"auto, (max-width: 1800px) 100vw, 1800px\"><\/p>\n<p>There are edge cases where this does not occur, and of course, you can prompt LLMs for more nuanced responses.<\/p>\n<p>However, given what we know about how LLMs work, they will likely continue to struggle to connect a concept with a distinct source accurately. <a href=\"https:\/\/help.openai.com\/en\/articles\/7842364-how-chatgpt-and-our-foundation-models-are-developed\">OpenAI explains this<\/a> using the example of a teacher who knows a lot about their subject matter but cannot accurately recall where they learned each distinct piece of information.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1828\" height=\"514\" class=\"wp-image-187022\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/screenshot-of-open-ais-example-of-how-chatgpt-use.jpg\" alt=\"Screenshot of Open AI's example of how ChatGPT uses training data in responses.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/screenshot-of-open-ais-example-of-how-chatgpt-use.jpg 1828w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/screenshot-of-open-ais-example-of-how-chatgpt-use-680x191.jpg 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/screenshot-of-open-ais-example-of-how-chatgpt-use-768x216.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/screenshot-of-open-ais-example-of-how-chatgpt-use-1536x432.jpg 1536w\" sizes=\"auto, (max-width: 1828px) 100vw, 1828px\">So, in many cases, new ideas are simply absorbed into the LLM\u2019s general pool of knowledge.<\/p>\n<p>Since LLMs work semantically (based on meaning, not exact word matches), even if you search for an exact concept (as I did for \u201cmultilingual SEO matrix\u201d), they will struggle to connect that concept to a specific person or brand that originated it.<\/p>\n<p>That\u2019s why original ideas tend to either be smoothed out so they fit into the consensus about a topic or not included at&nbsp;all.<\/p>\n<h3><a id=\"post-187017-_nib0hte390p2\"><\/a>Macro LLM flattening<\/h3>\n<p>Macro LLM flattening can occur over time as new ideas struggle to surface in LLM responses, \u201cflattening\u201d our exposure to innovation and explorations of new ideas about a&nbsp;topic.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1600\" height=\"1418\" class=\"wp-image-187023\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/how-llm-flattening-occurs-at-a-macro-level-reducin.png\" alt=\"How LLM flattening occurs at a macro level reducing the visibility of new idea in responses over time.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/how-llm-flattening-occurs-at-a-macro-level-reducin.png 1600w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/how-llm-flattening-occurs-at-a-macro-level-reducin-480x425.png 480w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/how-llm-flattening-occurs-at-a-macro-level-reducin-768x681.png 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/how-llm-flattening-occurs-at-a-macro-level-reducin-1536x1361.png 1536w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\"><\/p>\n<p>This concept applies across the board, covering all new ideas people create and share. Because of the flattening that can occur at a topic level, it means that LLMs could surface fewer new ideas over time, trending towards repeating the most dominant information or viewpoints about a&nbsp;topic.<\/p>\n<p>This happens not because new ideas stop accumulating but rather because LLMs re-write and summarize knowledge, often hallucinating their responses.<\/p>\n<p>In that process, they have the potential to shape our exposure to knowledge in ways other technologies (like search engines) cannot.<\/p>\n<p>As the visibility of original ideas or new concepts flattens out, that means many newer or smaller creators and brands may struggle to be seen in LLM responses.<\/p>\n<h2><div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\"> How is this different from the pre-LLM status quo?&nbsp;<\/div><\/div><\/h2>\n<p>The pre-LLM status quo was how Google surfaced information.<\/p>\n<p>Normally, if the content was in Google\u2019s index, you could see it in search results instantly anytime you searched for it. Especially when searching for a unique phrase only your content used.<\/p>\n<p>Your brand\u2019s listing in search results would display the parts of your content that match the query verbatim:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"998\" height=\"223\" class=\"wp-image-187024\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/screenshot-of-a-google-search-listing-result-for-a.png\" alt=\"Screenshot of a Google search listing result for Ahrefs' multilingual SEO post.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/screenshot-of-a-google-search-listing-result-for-a.png 998w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/screenshot-of-a-google-search-listing-result-for-a-680x152.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/screenshot-of-a-google-search-listing-result-for-a-768x172.png 768w\" sizes=\"auto, (max-width: 998px) 100vw, 998px\"><\/p>\n<p>That\u2019s thanks to the \u201clexical\u201d part of Google\u2019s search engine that still works based on matching word strings.<\/p>\n<p>But now, even when an idea is correct, even when it\u2019s useful, even when it ranks #1 in search \u2014 if it hasn\u2019t been repeated enough across sources, LLMs often won\u2019t surface it. It may also not appear in Google\u2019s AI Overviews despite ranking #1 organically.<\/p>\n<p>Even if you search for a unique term only your content uses, as I did for the \u201cmultilingual SEO matrix\u201d, sometimes your content will show up in AI responses, and other times it won\u2019t.<\/p>\n<p>LLMs don\u2019t attribute. They don\u2019t trace knowledge back to its origin. They just summarize what\u2019s already been said, again and&nbsp;again.<\/p>\n<p>That\u2019s what flattening does:<\/p>\n<ul>\n<li>It rounds off originality<\/li>\n<li>It plateaus discoverability<\/li>\n<li>It makes innovation invisible<\/li>\n<\/ul>\n<p>That isn\u2019t a data issue. It\u2019s a <em>pattern<\/em> issue that skews toward consensus for most queries, even those where consensus makes no-sensus.<\/p>\n<p>LLMs don\u2019t match word strings; they match meaning, and meaning is inferred from repetition.<\/p>\n<p>That makes originality harder to find, and easier to forget.<\/p>\n<p>And if fewer original ideas get surfaced, fewer people repeat them. Which means fewer chances for LLMs to discover them and pick them up in the future.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1560\" height=\"1430\" class=\"wp-image-187025\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/the-catch-22-of-discovery-for-new-ideas-which-need.png\" alt=\"The catch-22 of discovery for new ideas which need repetition before they can be discovered.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/the-catch-22-of-discovery-for-new-ideas-which-need.png 1560w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/the-catch-22-of-discovery-for-new-ideas-which-need-464x425.png 464w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/the-catch-22-of-discovery-for-new-ideas-which-need-768x704.png 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/the-catch-22-of-discovery-for-new-ideas-which-need-1536x1408.png 1536w\" sizes=\"auto, (max-width: 1560px) 100vw, 1560px\"><\/p>\n<h3><a id=\"post-187017-_49663jgd4qrn\"><\/a>LLMs appear to know all, but aren\u2019t all-knowing. They\u2019re confidently wrong <em>a lot.<\/em><\/h3>\n<p>One of the biggest criticisms of AI-generated responses is that they are often completely inaccurate\u2026 well, this is why. If they\u2019re incapable of attributing an original concept to its creator, how else are they to calculate where else their interpretation of their knowledge is flawed?<\/p>\n<p>Large language models will increasingly have access to everything. But that doesn\u2019t mean they understand everything.<\/p>\n<p>They collect knowledge, they don\u2019t question it.<br>\nThey collapse nuance into narrative.<br>\nAnd they treat repetition as&nbsp;truth.<\/p>\n<p>And here\u2019s what\u2019s new: they say it all with confidence. LLMs possess no capacity for reasoning (yet) or judgment. But they feel like they do and will outright, confidently tell you they&nbsp;do.<\/p>\n<p>Case in point, ChatGPT being a pal and reinforcing this concept that LLMs <em>simulate judgment convincingly<\/em>:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1061\" height=\"1694\" class=\"wp-image-187026\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/example-of-chatgpts-ability-to-simulate-judgement.png\" alt=\"Example of ChatGPT's ability to simulate judgement convincingly.\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/example-of-chatgpts-ability-to-simulate-judgement.png 1061w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/example-of-chatgpts-ability-to-simulate-judgement-266x425.png 266w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/example-of-chatgpts-ability-to-simulate-judgement-768x1226.png 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/04\/example-of-chatgpts-ability-to-simulate-judgement-962x1536.png 962w\" sizes=\"auto, (max-width: 1061px) 100vw, 1061px\"><\/p>\n<p>How meta is it that despite having no real way of <em>knowing<\/em> these things about itself, ChatGPT convincingly responded as though it does, in fact,&nbsp;know?<\/p>\n<p>Unlike search engines, which act as maps, LLMs present answers.<\/p>\n<p>They don\u2019t just retrieve information, they synthesize it into fluent, authoritative-sounding prose. But that fluency is an illusion of judgment. The model isn\u2019t weighing ideas. It isn\u2019t evaluating originality.<\/p>\n<p>It\u2019s just pattern-matching, repeating the shape of what\u2019s already been&nbsp;said.<\/p>\n<p>Without a pattern to anchor a new idea, LLMs don\u2019t know what to do with it, or where to place it in the fabric of humanity\u2019s collective knowledge.<\/p>\n<p>This isn\u2019t a new problem. We\u2019ve always struggled with how information is filtered, surfaced, and distributed. But this is the first time those limitations have been disguised so&nbsp;well.<\/p>\n<h2><div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\"> How to get your ideas included in more LLM responses&nbsp;<\/div><\/div><\/h2>\n<p>So, what do we do with all of this? If originality isn\u2019t rewarded until it\u2019s repeated, and credit fades once it becomes part of the consensus, what\u2019s the strategy?<\/p>\n<p>It\u2019s a question worth asking, especially as we rethink what visibility actually looks like in the AI-first search landscape.<\/p>\n<p>Some practical shifts worth considering as we move forward:<\/p>\n<ul>\n<li><strong>Label your ideas clearly<\/strong>: Give them a name. Make them easy to reference and search. If it sounds like something people can repeat, they&nbsp;might.<\/li>\n<li><strong>Add your brand<\/strong>: Including your brand as part of the idea\u2019s label helps you earn credit when others mention the idea. The more your brand gets repeated alongside the idea, the higher the chance LLMs will also mention your&nbsp;brand.<\/li>\n<li><strong>Define your ideas explicitly<\/strong>: Add a \u201cWhat is [your concept]?\u201d section directly in your content. Spell it out in plain language. Make it legible to both readers and machines.<\/li>\n<li><strong>Self-reference with purpose<\/strong>: Don\u2019t just drop the term in an image caption or alt text \u2014 use it in your body copy, in headings, in internal links. Make it obvious you\u2019re the origin.<\/li>\n<li><strong>Distribute it widely<\/strong>: Don\u2019t rely on one blog post. Repost to LinkedIn. Talk about it on podcasts. Drop it into newsletters. Give the idea more than one place to live so others can talk about it&nbsp;too.<\/li>\n<li><strong>Invite others in<\/strong>: Ask collaborators, colleagues, or your community to mention the idea in their own work. Visibility takes a network. Speaking of which, feel free to share the ideas of \u201cLLM flattening\u201d and the \u201cMultilingual SEO Matrix\u201d with anyone, anytime&nbsp;\ud83d\ude09<\/li>\n<li><strong>Play the long game<\/strong>: If originality has a place in AI search, it\u2019s as a seed, not a shortcut. Assume it\u2019ll take time, and treat early traction as bonus, not baseline.<\/li>\n<\/ul>\n<p>And finally, decide what kind of recognition matters to&nbsp;you.<\/p>\n<p>Not every idea needs to be cited to be influential. Sometimes, the biggest win is watching your thinking shape the conversation, even if your name never appears beside it.<\/p>\n<h2><a id=\"post-187017-_132yomf8qjcd\"><\/a>Final thoughts<\/h2>\n<p>Originality still matters, just not in the way we were taught.<\/p>\n<p>It\u2019s not a growth hack. It\u2019s not a guaranteed differentiator. It\u2019s not even enough to get you cited these&nbsp;days.<\/p>\n<p>But it is how consensus begins. It\u2019s the moment before the pattern forms. The spark that (if repeated enough) becomes the signal LLMs eventually learn to&nbsp;trust.<\/p>\n<p>So, create the new idea anyway.<\/p>\n<p>Just don\u2019t expect it to speak for itself. Not in this current search landscape.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>But in the age of AI-driven search, originality is not the boon we think it is. It might even be a liability\u2026 or, at best, a long game with no guarantees. Because here\u2019s the uncomfortable truth: LLMs don\u2019t reward firsts.<span class=\"ellipsis\">\u2026<\/span><\/p>\n<div class=\"read-more\">Read more \u203a<\/div>\n<p><!-- end of .read-more --><\/p>\n","protected":false},"author":195,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"wp_typography_post_enhancements_disabled":false,"footnotes":""},"categories":[469,390,335],"tags":[],"coauthors":[458],"class_list":["post-187017","post","type-post","status-publish","format-standard","hentry","category-ai-search","category-marketing","category-general-seo","odd"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>LLMs Don\u2019t Reward Originality, They Flatten It<\/title>\n<meta name=\"description\" content=\"In the age of AI-driven search, originality is not the boon we think it is. It might even be a liability\u2026 or, at best, a long game with no guarantees.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ahrefs.com\/blog\/llms-flatten-originality\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LLMs Don\u2019t Reward Originality, They Flatten It\" \/>\n<meta property=\"og:description\" content=\"In the age of AI-driven search, originality is not the boon we think it is. 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