<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>AI@X on MacWorks</title><link>https://macworks.dev/docs/month/ai@x/</link><description>Recent content in AI@X on MacWorks</description><generator>Hugo</generator><language>en</language><atom:link href="https://macworks.dev/docs/month/ai@x/index.xml" rel="self" type="application/rss+xml"/><item><title>Week 13 Summary</title><link>https://macworks.dev/docs/month/ai@x/weekly-2026-W13/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://macworks.dev/docs/month/ai@x/weekly-2026-W13/</guid><description>&lt;h1 id="aix--week-of-2026-03-20-to-2026-03-26"&gt;AI@X — Week of 2026-03-20 to 2026-03-26&lt;a class="anchor" href="#aix--week-of-2026-03-20-to-2026-03-26"&gt;#&lt;/a&gt;&lt;/h1&gt;
&lt;h2 id="the-buzz"&gt;The Buzz&lt;a class="anchor" href="#the-buzz"&gt;#&lt;/a&gt;&lt;/h2&gt;
&lt;p&gt;The AI ecosystem is undergoing a massive architectural paradigm shift, transitioning away from pure text-based LLMs toward spatial &amp;ldquo;World Models&amp;rdquo;. Catalyzed by Meta&amp;rsquo;s V-JEPA 2.1 demonstrating zero-shot physics comprehension and AMI Labs&amp;rsquo; staggering $1.03 billion seed round, leading researchers are increasingly viewing pure language models as a &amp;ldquo;seductive trap&amp;rdquo;. This marks a profound pivot from brute-force text benchmark chasing to predictive representation learning that organically understands the physical world.&lt;/p&gt;</description></item><item><title>Week 14 Summary</title><link>https://macworks.dev/docs/month/ai@x/weekly-2026-W14/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://macworks.dev/docs/month/ai@x/weekly-2026-W14/</guid><description>&lt;h1 id="aix--week-of-2026-03-28-to-2026-04-03"&gt;AI@X — Week of 2026-03-28 to 2026-04-03&lt;a class="anchor" href="#aix--week-of-2026-03-28-to-2026-04-03"&gt;#&lt;/a&gt;&lt;/h1&gt;
&lt;h2 id="the-buzz"&gt;The Buzz&lt;a class="anchor" href="#the-buzz"&gt;#&lt;/a&gt;&lt;/h2&gt;
&lt;p&gt;The most signal-rich development this week is the collective realization that agentic AI does not eliminate work; it fundamentally mutates it into high-anxiety cognitive orchestration. The ecosystem is rapidly moving past the theoretical magic of frontier models to confront the exhausting, messy realities of production, recognizing that human working memory and legacy corporate infrastructure are the ultimate bottlenecks to automation.&lt;/p&gt;
&lt;h2 id="key-discussions"&gt;Key Discussions&lt;a class="anchor" href="#key-discussions"&gt;#&lt;/a&gt;&lt;/h2&gt;
&lt;p&gt;&lt;strong&gt;The Cognitive Wall of Agent Orchestration&lt;/strong&gt;
Operating parallel AI agents is proving to be immensely mentally taxing, exposing a massive gap between perceived and actual productivity as heavy context-switching wipes out efficiency gains. Leaders like Claire Vo and Aaron Levie argue that unlocking true ROI requires treating agents as autonomous employees needing progressive trust and intense oversight, predicting a surge in dedicated &amp;ldquo;AI Manager&amp;rdquo; roles.&lt;/p&gt;</description></item></channel></rss>