<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hype on tigger.dev</title><link>https://tigger.dev/tags/hype/</link><description>Recent content in Hype on tigger.dev</description><generator>Hugo</generator><language>en-gb-oed</language><copyright>© Tadhg O'Brien</copyright><lastBuildDate>Thu, 29 Jan 2026 14:59:50 +0000</lastBuildDate><atom:link href="https://tigger.dev/tags/hype/index.xml" rel="self" type="application/rss+xml"/><item><title>The Golden Age Eating Itself</title><link>https://tigger.dev/2026-01-29-dark-optimism-ai/</link><pubDate>Thu, 29 Jan 2026 14:59:50 +0000</pubDate><guid>https://tigger.dev/2026-01-29-dark-optimism-ai/</guid><description>&lt;div id="outline-container-headline-1" class="outline-2"&gt;
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AI Hype, Model Collapse, and the Dead Internet
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I&amp;#39;ve been watching AI models dream about my &lt;a href="https://tigger.dev/2025-03-01-genai-eating-itself/"&gt;French Bulldog&lt;/a&gt; lately. The images are charming at first: stumpy legs, bat ears, that distinctive underbite. But look closer. The church steeple behind him has melted. The cobblestones have become scales. And sometimes, in the background, there&amp;#39;s a figure that might be human, might be furniture, might be nothing at all.&lt;/p&gt;
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This aesthetic glitch mirrors something far more troubling happening inside the models themselves. Researchers at Oxford and Cambridge found that when AI models train on AI-generated content, they don&amp;#39;t just get worse. &lt;a href="https://www.tigger.dev/posts/2025-03-01-genai-eating-itself/"&gt;They collapse.&lt;/a&gt;&lt;sup class="footnote-reference"&gt;&lt;a id="footnote-reference-1" href="#footnote-1"&gt;1&lt;/a&gt;&lt;/sup&gt; In one striking example, a model trained iteratively on its own outputs devolved from coherent text about medieval architecture into rambling nonsense about &amp;#34;jack-tailed rabbits&amp;#34; and &amp;#34;yellow-skinned&amp;#34; creatures that existed nowhere but in its own degrading probability distributions.&lt;/p&gt;</description></item></channel></rss>