{"id":190577,"date":"2025-08-11T08:04:52","date_gmt":"2025-08-11T13:04:52","guid":{"rendered":"https:\/\/ahrefs.com\/blog\/?p=190577"},"modified":"2025-08-26T09:46:16","modified_gmt":"2025-08-26T14:46:16","slug":"how-to-detect-ai-generated-content","status":"publish","type":"post","link":"https:\/\/ahrefs.com\/blog\/how-to-detect-ai-generated-content\/","title":{"rendered":"How to Detect AI-Generated Content"},"content":{"rendered":"<div class=\"intro-txt\">In April 2025, we analyzed 900,000 newly created web pages and discovered that 74.2% contained AI-generated content.<\/div>\n<p>With the rapid growth of generative AI, businesses, educators, and publishers are asking a critical question: how can we tell what\u2019s written by humans and what\u2019s produced by machines?<\/p>\n<p>The answer: it\u2019s possible, but not foolproof. Here\u2019s how to approach AI detection effectively, the limitations you need to understand, and a better way to get more reliable results.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1600\" height=\"1993\" class=\"wp-image-190578\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-1.jpg\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-1.jpg 1600w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-1-341x425.jpg 341w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-1-768x957.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-1-1233x1536.jpg 1233w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\"><\/p>\n<p>Learn more in our study: <a href=\"https:\/\/ahrefs.com\/blog\/what-percentage-of-new-content-is-ai-generated\/\">74% of New Webpages Include AI Content (Study of 900k&nbsp;Pages)<\/a><\/p>\n<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\" data-anchor=\"Yes, it\u2019s possible to detect AI-generated content\" data-section=\"one\">\n<h2><a id=\"post-190577-_3bymhqi10oif\"><\/a>Yes, it\u2019s possible to detect AI-generated content<\/h2>\n<\/div><\/div>\n<p>Some people are skeptical that AI content detection is even possible. It is possible, but with some important caveats.<\/p>\n<p>AI-generated text tends to have distinctive statistical and stylistic patterns. These patterns are not always obvious to human readers, but they can often be detected by specially constructed detection models.<\/p>\n<p>In simple terms, all AI detectors work by comparing patterns in text against large collections of human-written and AI-generated examples.<\/p>\n<p>Traditionally, this was done with statistical detection: counting features like word and n-gram frequencies, common syntactic structures, stylistic choices, and even statistical measures like perplexity (predictability of word choice) and burstiness (variation in sentence length), then flagging anomalies.<\/p>\n\n<table id=\"tablepress-452\" class=\"tablepress tablepress-id-452 tablepress-responsive tablepress-ahrefs-width-full\">\n<thead>\n<tr class=\"row-1 odd\">\n\t<th class=\"column-1\">Feature type<\/th><th class=\"column-2\">Explanation<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"row-2 even\">\n\t<td class=\"column-1\">Word frequencies<\/td><td class=\"column-2\">Count how often words like \u201cthe\u201d or \u201ccat\u201d appear in a sample: the: 3, cat:&nbsp;2<\/td>\n<\/tr>\n<tr class=\"row-3 odd\">\n\t<td class=\"column-1\">N-gram frequencies<\/td><td class=\"column-2\">Measure sequences such as bigrams: \u201cthe cat\u201d appears twice, \u201ccat sat\u201d appears once<\/td>\n<\/tr>\n<tr class=\"row-4 even\">\n\t<td class=\"column-1\">Syntactic structures<\/td><td class=\"column-2\">Identify patterns like Subject\u2013Verb\u2013Object (SVO) structures, e.g., \u201cthe cat sat,\u201d \u201cthe cat yawned\u201d<\/td>\n<\/tr>\n<tr class=\"row-5 odd\">\n\t<td class=\"column-1\">Stylistic choices<\/td><td class=\"column-2\">Note tone, perspective, or formality; e.g., third-person, neutral tone<\/td>\n<\/tr>\n<tr class=\"row-6 even\">\n\t<td class=\"column-1\">Perplexity<\/td><td class=\"column-2\">Calculate the predictability of each word based on preceding context\u2014lower perplexity often means more predictable (and possibly machine-generated) text<\/td>\n<\/tr>\n<tr class=\"row-7 odd\">\n\t<td class=\"column-1\">Burstiness<\/td><td class=\"column-2\">Compare variation in sentence length; AI text may show consistent lengths while human text is more variable<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<!-- #tablepress-452 from cache -->\n<p>A third, less common approach is watermarking\u2014embedding hidden signals into AI-generated text at creation time.<\/p>\n<p>Like UV marks on currency, these signals can later be checked to confirm whether text came from a specific model, but this only works if the model owner chooses to implement it.<\/p>\n<p>As of now, no major LLM providers like OpenAI, Anthropic, or Google have confirmed that they use watermarking on their public-facing model outputs. (And why would they want to penalize their&nbsp;users?)<\/p>\n<p>Learn more: <a href=\"https:\/\/ahrefs.com\/blog\/how-do-ai-content-detectors-work\/\">How Do AI Content Detectors Work? Answers from a Data Scientist<\/a><\/p>\n<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\" data-anchor=\"How to use AI content detectors\" data-section=\"two\">\n<h2><a id=\"post-190577-_lk1nt8usmb6n\"><\/a>How to use AI content detectors<\/h2>\n<\/div><\/div>\n<p>There are lots of AI detection tools available, ranging from free browser-based checkers to enterprise-grade platforms with API integrations.<\/p>\n<p>If you\u2019re an Ahrefs user, you can run our AI Content Detector directly within <a href=\"https:\/\/app.ahrefs.com\/site-explorer\"><strong>Site Explorer<\/strong><\/a>\u2019s <strong>Page Inspect<\/strong> feature. Simply open <strong>Site Explorer,<\/strong> enter the URL you want to check, navigate to the <strong>Page Inspect<\/strong> report, and you can click the AI Detector tab to see an analysis, right alongside other key SEO metrics:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"2048\" height=\"1778\" class=\"wp-image-190579\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-2.jpg\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-2.jpg 2048w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-2-490x425.jpg 490w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-2-768x667.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-2-1536x1334.jpg 1536w\" sizes=\"auto, (max-width: 2048px) 100vw, 2048px\"><\/p>\n<p>Good detectors don\u2019t just give you a single yes-or-no verdict: they also break the text down and show you the likelihood that different passages are AI-generated, provide an overall article-level likelihood score, and in some cases even attempt to identify which models (such as GPT-4o) were likely used to create the content.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1602\" height=\"1952\" class=\"wp-image-190580\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-3.png\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-3.png 1602w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-3-349x425.png 349w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-3-768x936.png 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-3-1261x1536.png 1261w\" sizes=\"auto, (max-width: 1602px) 100vw, 1602px\"><\/p>\n<p>We ran a small-scale test comparing several of the most popular AI detectors to see how they perform in practice. The table below shows our results:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"2048\" height=\"1512\" class=\"wp-image-190581\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-4.jpg\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-4.jpg 2048w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-4-576x425.jpg 576w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-4-768x567.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-4-1536x1134.jpg 1536w\" sizes=\"auto, (max-width: 2048px) 100vw, 2048px\"><\/p>\n<p>Based on my testing, Ahrefs\u2019 AI detector and Copyleaks were the best-performing AI detectors, with GPTZero and Originality.ai close behind. At the other end of the scale, Grammarly and Writer performed the worst in my testing.<\/p>\n\n<table id=\"tablepress-453\" class=\"tablepress tablepress-id-453 tablepress-responsive tablepress-ahrefs-width-full\">\n<thead>\n<tr class=\"row-1 odd\">\n\t<th class=\"column-1\">AI content detector<\/th><th class=\"column-2\">Score<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr class=\"row-2 even\">\n\t<td class=\"column-1\">Ahrefs<\/td><td class=\"column-2\">13\/18<\/td>\n<\/tr>\n<tr class=\"row-3 odd\">\n\t<td class=\"column-1\">Copyleaks<\/td><td class=\"column-2\">13\/18<\/td>\n<\/tr>\n<tr class=\"row-4 even\">\n\t<td class=\"column-1\">GPTZero<\/td><td class=\"column-2\">12\/18<\/td>\n<\/tr>\n<tr class=\"row-5 odd\">\n\t<td class=\"column-1\">Originality.ai<\/td><td class=\"column-2\">12\/18<\/td>\n<\/tr>\n<tr class=\"row-6 even\">\n\t<td class=\"column-1\">Scribbr<\/td><td class=\"column-2\">10\/18<\/td>\n<\/tr>\n<tr class=\"row-7 odd\">\n\t<td class=\"column-1\">ZeroGPT<\/td><td class=\"column-2\">9\/18<\/td>\n<\/tr>\n<tr class=\"row-8 even\">\n\t<td class=\"column-1\">Grammarly<\/td><td class=\"column-2\">6\/18<\/td>\n<\/tr>\n<tr class=\"row-9 odd\">\n\t<td class=\"column-1\">Writer<\/td><td class=\"column-2\">4\/18<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<!-- #tablepress-453 from cache -->\n<p>Learn more in my full write-up: <a href=\"https:\/\/ahrefs.com\/blog\/best-ai-detectors-tested\/\">The 8 Best AI Detectors, Tested and Compared<\/a><\/p>\n<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\" data-anchor=\"No AI detector is perfect\" data-section=\"three\">\n<h2><a id=\"post-190577-_m8f69fpletyj\"><\/a>No AI detector is perfect<\/h2>\n<\/div><\/div>\n<p>Like LLMs, AI detectors are probabilistic\u2014they estimate likelihood, not certainty. They can be highly accurate, but false positives are inevitable. That\u2019s why you shouldn\u2019t base decisions on a single result. Run multiple checks, look for patterns, and combine findings with other evidence.<\/p>\n<p>All AI detectors share the same fundamental limitations, regardless of the tool or technology used.<\/p>\n<ul>\n<li><strong>Heavily edited or \u201chumanized\u201d AI text may evade detection. \u201c<\/strong>Post-processing\u2019 (things like rephrasing sentences, swapping in synonyms, rearranging paragraphs, or running the text through a grammar checker) can disrupt the statistical signals that detectors look for, reducing their accuracy.<\/li>\n<li><strong>Basic detectors may lack accuracy and advanced features. <\/strong>Detection tools require frequent updates to stay ahead of new AI models\u2014generative AI evolves quickly, and detectors need regular retraining to recognize the latest writing styles and avoidance techniques. At Ahrefs, our detector supports multiple leading models, including models from OpenAI, Anthropic, Meta, Mixtral, and Qwen, so you can check content against a broader range of likely sources.<\/li>\n<li><strong>Effectiveness varies by language, content type, and model.<\/strong> Detectors trained primarily on English prose may struggle with technical writing, poetry, or less common languages.<\/li>\n<li><strong>Ambiguous cases (like human text edited by AI) can blur results. <\/strong>These hybrid workflows create mixed signals that can confuse even advanced systems.<\/li>\n<li><strong>Even the best tools can produce false positives or negatives. <\/strong>Statistical detection is never infallible, and occasional misclassifications are inevitable because the patterns these systems rely on can overlap between human and AI writing, and subtle edits or atypical writing styles can easily blur the distinctions.<\/li>\n<\/ul>\n<p>Remember: false accusations based on incorrect AI detection results can seriously damage the reputation of individuals, companies, or academic institutions.<\/p>\n<p>With these limitations in mind, it\u2019s a good idea to corroborate any detector output with additional methods before drawing conclusions.<\/p>\n<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\" data-anchor=\"How to use human intuition\" data-section=\"four\">\n<h2><a id=\"post-190577-_8vs5ooyk462l\"><\/a>How to use human intuition<\/h2>\n<\/div><\/div>\n<p>Human judgment can be extremely helpful for adding context to results from AI detectors. By examining context\u2014such as patterns across multiple articles, a history of posts on social media, or the surrounding circumstances of publication\u2014you can better gauge the likelihood that AI was involved in the writing.<\/p>\n<p>Signs to look&nbsp;for:<\/p>\n<ul>\n<li><strong>Overly consistent tone with no subtle quirks. <\/strong>Human writing is inherently a bit messy and unpredictable, with small variations in style, rhythm, and word choice that reflect personality and context. AI-generated text can sometimes lack these imperfections, producing a uniform tone that feels slightly too polished or mechanical.<\/li>\n<li><strong>Verbosity. <\/strong>AI is very good at stretching simple ideas into long-winded explanations.<\/li>\n<li><strong>Lack of new information. <\/strong>AI outputs often read as generic or surface-level (this is particularly obvious on LinkedIn: many AI-generated comments simply restate the original author\u2019s idea in new words without adding any meaningful perspective or&nbsp;value).<\/li>\n<li><strong>Tell-tale word choices. <\/strong>AI has a preference for slightly \u201coff\u201d idioms like <em>\u201cever-evolving landscape\u201d<\/em>, formulaic hooks (<em>\u201cThis isn\u2019t X\u2026 it\u2019s Y\u201d<\/em>), or overuse of em dashes and emojis.<\/li>\n<li><strong>Incentives. <\/strong>Is there a clear motivation for the author to use AI content?<\/li>\n<\/ul>\n<div id=\"attachment_190582\" style=\"width: 1124px\" class=\"wp-caption alignnone\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-190582\" class=\"wp-image-190582\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-5.png\" alt width=\"1114\" height=\"388\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-5.png 1114w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-5-680x237.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2025\/08\/word-image-190577-5-768x267.png 768w\" sizes=\"auto, (max-width: 1114px) 100vw, 1114px\"><p id=\"caption-attachment-190582\" class=\"wp-caption-text\">I see you, ChatGPT.<\/p><\/div>\n<p>None of these signs offer definitive evidence for AI content, but they can add helpful context to other forms of evidence.<\/p>\n<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\" data-anchor=\"A better way to use AI content detectors\" data-section=\"five\">\n<h2><a id=\"post-190577-_lyoscn5phhw7\"><\/a>A better way to use AI content detectors<\/h2>\n<\/div><\/div>\n<p>If you run an AI detector on just one article, an unreliable result can be problematic. But that issue becomes less important when you look at results at scale. Running this process across many pages gives you a much clearer picture of how AI is used as part of the company\u2019s broader marketing strategy.<\/p>\n<p>With Ahrefs\u2019 <strong>Top pages<\/strong> report in <strong>Site Explorer,<\/strong> you can see an \u201cAI Content Level\u201d column for almost any website page. From there, you can even inspect any individual URL and get an idea of the AI models that were likely used in the page\u2019s creation.<\/p>\n<p>Here\u2019s a video talking through this process:<\/p>\n<p><iframe loading=\"lazy\" title=\"Embedded post\" src=\"https:\/\/www.linkedin.com\/embed\/feed\/update\/urn:li:ugcPost:7349016584494956547?compact=1\" width=\"504\" height=\"399\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<p>For a quick tip: use this report to spot top-ranking, heavily AI-generated content and consider creating your own AI version. If it\u2019s ranking, it\u2019s meeting search intent\u2014making it a potential opportunity for you, and your AI content workflow.<\/p>\n<div class=\"further-reading\"><div class=\"reading-title\">Further reading<\/div><div class=\"reading-content\">\n<ul>\n<li><a href=\"https:\/\/ahrefs.com\/blog\/ai-generated-content-does-not-hurt-your-google-rankings\/\">AI-Generated Content Does Not Hurt Your Google Rankings (600,000 Pages Analyzed)<\/a><\/li>\n<li><a href=\"https:\/\/ahrefs.com\/blog\/what-percentage-of-new-content-is-ai-generated\/\">74% of New Webpages Include AI Content (Study of 900k&nbsp;Pages)<\/a><\/li>\n<li><a href=\"https:\/\/ahrefs.com\/blog\/ai-overviews-cite-ai-generated-content-more-than-human-writing\/\">AI Overviews Cite AI-Generated Content More Than Human Writing<\/a><\/li>\n<\/ul>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>With the rapid growth of generative AI, businesses, educators, and publishers are asking a critical question: how can we tell what\u2019s written by humans and what\u2019s produced by machines? The answer: it\u2019s possible, but not foolproof. Here\u2019s how to approach<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":194,"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":[457],"class_list":["post-190577","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 v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How to Detect AI-Generated Content<\/title>\n<meta name=\"description\" content=\"Here\u2019s how to approach AI detection effectively, the limitations you need to understand, and a better way to get more useful results.\" \/>\n<meta name=\"robots\" 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