{"id":338688,"date":"2025-06-20T03:43:31","date_gmt":"2025-06-20T10:43:31","guid":{"rendered":"https:\/\/cms-articles.softonic.io\/en\/?p=338688"},"modified":"2025-07-01T14:20:32","modified_gmt":"2025-07-01T21:20:32","slug":"if-you-ask-an-ai-to-pick-a-number-between-1-and-50-it-often-chooses-27-why","status":"publish","type":"post","link":"https:\/\/cms-articles.softonic.io\/en\/if-you-ask-an-ai-to-pick-a-number-between-1-and-50-it-often-chooses-27-why\/","title":{"rendered":"If you ask an AI to pick a number between 1 and 50, it often chooses 27: Why?"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">When we ask AI models like ChatGPT, Claude, Gemini or Perplexity to&nbsp;<strong>pick a random number between 1 and 50<\/strong>, a surprising trend emerges\u2014many of them answer with the number 27. This&nbsp;<strong>unexpected consistency across models<\/strong>&nbsp;has sparked curiosity about how randomness works in large language models (LLMs).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why do AIs keep picking 27?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">At first glance, 27 might seem like a truly random choice. However,&nbsp;<strong>LLMs are not generating numbers at random<\/strong>\u2014they\u2019re selecting outputs based on patterns learned from human-generated data. According to AI expert Andrej Karpathy,&nbsp;<strong>most LLMs tend to &#8220;sound the same&#8221;<\/strong>, especially when asked simple or open-ended questions. This reflects a deeper issue:&nbsp;<strong>they replicate human biases<\/strong>&nbsp;learned from the data they were trained on.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">One Reddit user pointed out that&nbsp;<strong>27 feels &#8220;human-random&#8221;<\/strong>\u2014it&#8217;s not too low, not too high, and it&#8217;s mathematically interesting (3\u00b3). Another theory suggests that 27 serves as an optimal midpoint in a decision tree, following principles from game theory.&nbsp;<strong>In games that simulate random number selection<\/strong>, some models might default to 27 because it&#8217;s central and &#8220;safe.&#8221;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Another contributing factor is the&nbsp;<strong>pervasive popularity of the digit 7<\/strong>. In multiple human-based studies where people were asked to choose numbers at random,&nbsp;<strong>numbers ending in 7\u2014like 7, 27 or 77\u2014appeared disproportionately often<\/strong>. LLMs echo this tendency, as they&#8217;ve been trained on millions of human examples with the same patterns.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Not all models give the same answer\u2014some, like Grok, reportedly favor 42. But&nbsp;<strong>the dominance of 27 is a clear sign that AI randomness is often just human-like predictability<\/strong>&nbsp;in disguise.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>When we ask AI models like ChatGPT, Claude, Gemini or Perplexity to&nbsp;pick a random number between 1 and 50, a surprising trend emerges\u2014many of them answer with the number 27. This&nbsp;unexpected consistency across models&nbsp;has sparked curiosity about how randomness works in large language models (LLMs). Why do AIs keep picking 27? At first glance, 27 &hellip; <a href=\"https:\/\/cms-articles.softonic.io\/en\/if-you-ask-an-ai-to-pick-a-number-between-1-and-50-it-often-chooses-27-why\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;If you ask an AI to pick a number between 1 and 50, it often chooses 27: Why?&#8221;<\/span><\/a><\/p>\n","protected":false},"author":9317,"featured_media":338689,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","wpcf-pageviews":0},"categories":[1015],"tags":[],"usertag":[],"vertical":[],"content-category":[],"class_list":["post-338688","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/posts\/338688","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/users\/9317"}],"replies":[{"embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/comments?post=338688"}],"version-history":[{"count":1,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/posts\/338688\/revisions"}],"predecessor-version":[{"id":338690,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/posts\/338688\/revisions\/338690"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/media\/338689"}],"wp:attachment":[{"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/media?parent=338688"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/categories?post=338688"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/tags?post=338688"},{"taxonomy":"usertag","embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/usertag?post=338688"},{"taxonomy":"vertical","embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/vertical?post=338688"},{"taxonomy":"content-category","embeddable":true,"href":"https:\/\/cms-articles.softonic.io\/en\/wp-json\/wp\/v2\/content-category?post=338688"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}