<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Career on Mark Chmarny</title><link>https://blog.chmarny.com/tags/career/</link><description>Recent content in Career on Mark Chmarny</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>Mark Chmarny</managingEditor><lastBuildDate>Mon, 08 Jun 2026 08:00:00 -0700</lastBuildDate><atom:link href="https://blog.chmarny.com/tags/career/index.xml" rel="self" type="application/rss+xml"/><item><title>Staying Relevant Through Inflection Points</title><link>https://blog.chmarny.com/posts/staying-relevant-through-inflection-points/</link><pubDate>Mon, 08 Jun 2026 08:00:00 -0700</pubDate><guid>https://blog.chmarny.com/posts/staying-relevant-through-inflection-points/</guid><description>&lt;p&gt;Working for the leading AI company, people tend to ask you what AI means for their careers. Usually, they want to hear one of two extremes: &amp;ldquo;this changes nothing&amp;rdquo; or &amp;ldquo;we&amp;rsquo;re all doomed&amp;rdquo;. To be honest, I appreciate both. A lot of what I have spent learning over two plus decades is now just a prompt away. Hard-earned debugging skills and deep technical knowledge can be generated by a model in seconds. It&amp;rsquo;s uncomfortable to watch. But having your knowledge become cheap doesn&amp;rsquo;t mean &lt;em&gt;you&lt;/em&gt; become irrelevant. There is a huge gap between those two statements.&lt;/p&gt;</description></item></channel></rss>