<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>AI ROI on No Semicolons</title><link>https://nosemicolons.com/tags/ai-roi/</link><description>Recent content in AI ROI on No Semicolons</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 09 Jun 2026 10:53:14 +0000</lastBuildDate><atom:link href="https://nosemicolons.com/tags/ai-roi/index.xml" rel="self" type="application/rss+xml"/><item><title>The AI Code Generation Metric That Actually Predicts Production Success (It's Not What You Think)</title><link>https://nosemicolons.com/posts/ai-code-generation-success-metrics/</link><pubDate>Tue, 09 Jun 2026 10:53:14 +0000</pubDate><guid>https://nosemicolons.com/posts/ai-code-generation-success-metrics/</guid><description>&lt;p>Ever wondered why some AI-generated code blocks sail through production while others crash and burn? I spent the last six months tracking every piece of AI-assisted code my team shipped, and the results completely flipped my assumptions about what makes generated code successful.&lt;/p>
&lt;p>Spoiler alert: It wasn&amp;rsquo;t cyclomatic complexity, test coverage, or any of the traditional metrics we&amp;rsquo;ve been obsessing over. The metric that actually predicted production success caught me completely off guard.&lt;/p></description></item></channel></rss>