{"id":5046,"date":"2025-09-09T17:13:10","date_gmt":"2025-09-09T17:13:10","guid":{"rendered":"https:\/\/autogenai.com\/apac\/?p=5046"},"modified":"2025-10-16T14:38:00","modified_gmt":"2025-10-16T14:38:00","slug":"the-day-the-vibe-died-why-gpt-5-fell-short-of-expectations","status":"publish","type":"post","link":"https:\/\/autogenai.com\/apac\/blog\/the-day-the-vibe-died-why-gpt-5-fell-short-of-expectations\/","title":{"rendered":"The Day The Vibe Died: Why GPT-5 Fell Short of Expectations"},"content":{"rendered":"\n<p class=\"has-text-align-center\"><strong>By: Archie Rowberry<\/strong><\/p>\n\n\n\n<p>On the 7th of August 2025, GPT-5 came out, the biggest, most expensive, most vaunted LLM ever developed. This was the model that would propel us beyond needing white-collar workers, developers, accountants, or engineers.<\/p>\n\n\n\n<p>On the 7th of August 2025, GPT-5 came out, the biggest, most expensive, most vaunted LLM ever developed. This was the model that would propel us beyond needing white-collar workers, developers, accountants, or engineers.<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/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:\/\/autogenai.com\/apac\/blog\/the-day-the-vibe-died-why-gpt-5-fell-short-of-expectations\/#Market_and_Media_Response\" >Market and Media Response<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/autogenai.com\/apac\/blog\/the-day-the-vibe-died-why-gpt-5-fell-short-of-expectations\/#The_Vibes_Were_Off_User_Reactions_to_GPT-5\" >The Vibes Were Off: User Reactions to GPT-5<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/autogenai.com\/apac\/blog\/the-day-the-vibe-died-why-gpt-5-fell-short-of-expectations\/#Super-Users_Felt_It_Most_A_Familiarity_Problem\" >Super-Users Felt It Most: A Familiarity Problem<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/autogenai.com\/apac\/blog\/the-day-the-vibe-died-why-gpt-5-fell-short-of-expectations\/#GPT-4_Familiarity_vs_GPT-5_Change\" >GPT-4 Familiarity vs. GPT-5 Change<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/autogenai.com\/apac\/blog\/the-day-the-vibe-died-why-gpt-5-fell-short-of-expectations\/#The_Chinese_Room_A_Lens_for_Understanding_LLM_Perception\" >The Chinese Room: A Lens for Understanding LLM Perception<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/autogenai.com\/apac\/blog\/the-day-the-vibe-died-why-gpt-5-fell-short-of-expectations\/#Trust_and_the_Human%E2%80%93AI_Interface\" >Trust and the Human\u2013AI Interface<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/autogenai.com\/apac\/blog\/the-day-the-vibe-died-why-gpt-5-fell-short-of-expectations\/#AutogenAIs_Approach_Aligning_AI_with_Real-World_Workflows\" >AutogenAI\u2019s Approach: Aligning AI with Real-World Workflows<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/autogenai.com\/apac\/blog\/the-day-the-vibe-died-why-gpt-5-fell-short-of-expectations\/#Turn_AI_Into_Your_Most_Reliable_Bid_Partner\" >Turn AI Into Your Most Reliable Bid Partner<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Market_and_Media_Response\"><\/span>Market and Media Response<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>And yet, when it was released it flopped. Markets and media had a <strong>distinctly flat reaction<\/strong> to its abilities and, for the time being at least, white-collar work is safe.<\/p>\n\n\n\n<p>There are a couple of reasons why GPT-5 is not the model people expected.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\"><strong>Plateauing Capabilities:<\/strong> <\/h3>\n\n\n\n<p>Experts have long predicted that models wouldn\u2019t become smarter indefinitely. A <strong>plateau<\/strong> in capabilities in the short to medium term was already expected and factored into valuations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\"><strong>Hype:<\/strong> <\/h3>\n\n\n\n<p>The entire world of AI users was making noise, and many CEOs predicted that <a href=\"https:\/\/x.com\/sama\/status\/1953264193890861114\" target=\"_blank\" rel=\"noreferrer noopener\">GPT-5 would be a \u201cplanet-killing\u201d model<\/a>. Expectations were for a leap in performance at least as great as that between GPT-3 and GPT-4.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-medium-font-size\"><strong>Competition:<\/strong> <\/h3>\n\n\n\n<p>Many competitors have now reached <strong>parity in performance<\/strong> with OpenAI. The ingredients for developing LLMs are well-known, and the competitive edge has eroded.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"2851\" height=\"1522\" src=\"https:\/\/autogenai.com\/apac\/wp-content\/uploads\/sites\/5\/2025\/09\/LLM-Performance.svg\" alt=\"\" class=\"wp-image-5048\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Vibes_Were_Off_User_Reactions_to_GPT-5\"><\/span>The Vibes Were Off: User Reactions to GPT-5<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Conflicting Feedback<\/h3>\n\n\n\n<p>The second, and arguably more interesting, reason is to do with <strong>how we\u2019ve come to use and what we\u2019ve come to expect from LLMs<\/strong>.<\/p>\n\n\n\n<p>People didn\u2019t like the vibes of the new model, and their issues were varied and often conflicting:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>It was too verbose<\/li>\n\n\n\n<li>It wasn\u2019t verbose enough<\/li>\n\n\n\n<li>It wouldn\u2019t understand them<\/li>\n\n\n\n<li>It followed instructions too obligingly<\/li>\n\n\n\n<li>It no longer felt warm and supportive<\/li>\n<\/ul>\n\n\n\n<p>Pulling out clear areas for improvement from this feedback seemed impossible, with no consistent pain points beyond the model being \u201cdifferent\u201d and \u201coff\u201d.<\/p>\n\n\n\n<p>GPT-5 <strong>could and should be the most advanced and capable model on the market<\/strong>, enabling users to perform tasks at a near postgraduate level \u2014 and yet people were not vibing with it.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Super-Users_Felt_It_Most_A_Familiarity_Problem\"><\/span>Super-Users Felt It Most: A Familiarity Problem<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Looking at Ourselves<\/h3>\n\n\n\n<p>Why was it that <strong>OpenAI\u2019s super-users<\/strong>, those who should theoretically be best at maximising LLM performance, had the most visceral reaction to GPT-5?<\/p>\n\n\n\n<p>To understand this, we need to <strong>look at how familiar we\u2019ve become with LLMs<\/strong> \u2014 and, perhaps, look at ourselves rather than the models.<\/p>\n\n\n\n<p>There are two brilliant things about humans:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>We love patterns.<\/strong> It\u2019s what made us great hunters and gatherers. We categorise and identify predators, prey, and edible foods through pattern-matching.<\/li>\n\n\n\n<li><strong>We are deeply social.<\/strong> We crave interaction and are highly efficient at recognising behavioural patterns in each other. This allows us to build bonds, collaborate, and empathise.<\/li>\n<\/ol>\n\n\n\n<p>These two strengths combined <strong>expose a flaw in LLMs<\/strong>: we\u2019ve <strong>imprinted personalities and behaviours<\/strong> onto these models. We\u2019ve <strong>anthropomorphised<\/strong> not only their capabilities but their flaws.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"GPT-4_Familiarity_vs_GPT-5_Change\"><\/span>GPT-4 Familiarity vs. GPT-5 Change<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Two Years of GPT-4 Shaped Expectations<\/h3>\n\n\n\n<p>The GPT-4 series of models all felt incredibly similar tonally because they were trained on similar data, using similar cost functions and alignment goals.<\/p>\n\n\n\n<p>This family has been around since <strong>March 2023<\/strong> \u2014 over two years for users to <strong>become accustomed to their quirks and idiosyncrasies<\/strong>. Just as we become familiar with a friend over time, we became familiar with GPT-4.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">GPT-5\u2019s Behavioural Shift<\/h3>\n\n\n\n<p>GPT-5, on the other hand, had:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A <strong>significantly updated set of training data<\/strong><\/li>\n\n\n\n<li>A <strong>different set of evaluation criteria<\/strong><\/li>\n\n\n\n<li><strong>New safety and alignment decisions<\/strong><\/li>\n<\/ul>\n\n\n\n<p>This resulted in a model that was <strong>materially different<\/strong> in its behaviour and reactions.<\/p>\n\n\n\n<p>When GPT-5 was released, people reacted <strong>as any of us might when a friend we\u2019ve known for two years suddenly changes personality<\/strong> \u2014 badly.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Chinese_Room_A_Lens_for_Understanding_LLM_Perception\"><\/span>The Chinese Room: A Lens for Understanding LLM Perception<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">John Searle\u2019s Thought Experiment<\/h3>\n\n\n\n<p>Social philosopher <strong>John Searle<\/strong> posed a thought experiment in 1980, called <strong>the Chinese Room<\/strong>.<\/p>\n\n\n\n<p>In this scenario:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>An English-speaking subject sits in a windowless room with two letterbox doors and a large book.<\/li>\n\n\n\n<li>The book contains all possible combinations of Mandarin phrases and their correct responses.<\/li>\n\n\n\n<li>The subject receives Mandarin inputs through one door, copies out the correct response from the book, and posts it through the other door.<\/li>\n\n\n\n<li>They have <strong>no understanding<\/strong> of what they\u2019re writing or the conversation.<\/li>\n<\/ul>\n\n\n\n<p>To the person outside, the subject seems <strong>fluent and intelligent<\/strong>, even though they\u2019re just following instructions.<\/p>\n\n\n\n<p>This mirrors our <strong>subconscious impressions of LLMs<\/strong>. Even when we rationally know they\u2019re trained on massive datasets, we <strong>imprint human behaviour onto them<\/strong>, shaping our interactions and expectations.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Trust_and_the_Human%E2%80%93AI_Interface\"><\/span>Trust and the Human\u2013AI Interface<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Value Lies in Application, Not Just Intelligence<\/h3>\n\n\n\n<p>What\u2019s becoming increasingly clear as we move into the AI age is that <strong>to deliver value with LLMs, businesses must focus on the users<\/strong>, not just the models.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Humans are the <strong>owners and operators<\/strong> of processes.<\/li>\n\n\n\n<li><strong>Trust<\/strong> in the models is essential for effective task offloading.<\/li>\n\n\n\n<li>The <strong>interface between AI and humans<\/strong> is where the most friction appears.<\/li>\n<\/ul>\n\n\n\n<p><strong>Raw intelligence is now secondary<\/strong> to an organisation\u2019s ability to use it effectively.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>The application layer is where companies can make or break the integration of AI.<\/p>\n<\/blockquote>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"AutogenAIs_Approach_Aligning_AI_with_Real-World_Workflows\"><\/span>AutogenAI\u2019s Approach: Aligning AI with Real-World Workflows<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Purpose-Built for Bid Writers<\/h3>\n\n\n\n<p>At <strong>AutogenAI<\/strong>, we\u2019ve built <strong>systems and workflows specifically for bid-writers<\/strong>, leveraging our understanding of the proposal writing process to align models\u2019 behaviours to <strong>a single, reliable persona<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Model Profiling and Task Matching<\/h3>\n\n\n\n<p>Our understanding of <strong>model strengths and weaknesses<\/strong>, along with intensive profiling, allows us to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Match specific models to specific tasks<\/strong> within the bid-writing process<\/li>\n\n\n\n<li><strong>Unify the AI generation pipeline<\/strong> to meet user expectations<\/li>\n\n\n\n<li>Deliver a <strong>consistent, seamless experience<\/strong><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Turn_AI_Into_Your_Most_Reliable_Bid_Partner\"><\/span>Turn AI Into Your Most Reliable Bid Partner<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><a href=\"https:\/\/autogenai.com\/apac\/book-a-demo\/\" rel=\"noreferrer noopener\" target=\"_blank\"><strong>Book a demo with AutogenAI<\/strong><\/a> to discover how to harness GPT technology effectively and integrate AI smoothly into your workflows.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>By: Archie Rowberry On the 7th of August 2025, GPT-5 came out, the biggest, most expensive, most vaunted LLM ever developed. This was the model that would propel us beyond needing white-collar workers, developers, accountants, or engineers. On the 7th of August 2025, GPT-5 came out, the biggest, most expensive, most vaunted LLM ever developed&#8230;.<\/p>\n","protected":false},"author":5,"featured_media":5047,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[10],"tags":[],"class_list":["post-5046","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-proposal-writing"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>GPT-5 Fell Short of Expectations | AutogenAI<\/title>\n<meta name=\"description\" content=\"Explore why GPT-5 didn\u2019t meet the hype \u2014 from subtle behavioural shifts to user trust &amp; expectations. 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