<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Compute on Uni Matrix Zero</title><link>https://unimatrixz.com/tags/compute/</link><description>Recent content in Compute on Uni Matrix Zero</description><generator>Hugo</generator><language>en</language><copyright>Stephan Froede</copyright><lastBuildDate>Sat, 08 Aug 2026 23:13:39 +0200</lastBuildDate><atom:link href="https://unimatrixz.com/tags/compute/index.xml" rel="self" type="application/rss+xml"/><item><title>The Entropy Budget of Intelligence</title><link>https://unimatrixz.com/blog/ai-philosophy/the-entropy-budget-of-intelligence/</link><pubDate>Sat, 08 Aug 2026 00:00:00 +0000</pubDate><guid>https://unimatrixz.com/blog/ai-philosophy/the-entropy-budget-of-intelligence/</guid><description>&lt;p&gt;Generative AI has changed the economics of possibility. A model can produce another hypothesis, image, design or implementation almost instantly. What was once scarce—variation—can now be generated in abundance.&lt;/p&gt;
&lt;p&gt;But abundance does not remove uncertainty. It creates more candidates about which we can be uncertain.&lt;/p&gt;
&lt;p&gt;That is where entropy becomes useful. It provides a language for connecting the expansion of a possibility space to the work required to make that space navigable. Generation distributes probability across alternatives. Selection uses constraints, evidence and evaluation to concentrate attention on what survives.&lt;/p&gt;
&lt;p&gt;The important question is no longer only how many possibilities a system can create. It is how much relevant uncertainty it can remove—and what that reduction costs.&lt;/p&gt;</description></item><item><title>The Machine That Makes Its Own Infrastructure Obsolete</title><link>https://unimatrixz.com/blog/ai-philosophy/the-machine-that-makes-its-own-infrastructure-obsolete/</link><pubDate>Sat, 08 Aug 2026 00:00:00 +0000</pubDate><guid>https://unimatrixz.com/blog/ai-philosophy/the-machine-that-makes-its-own-infrastructure-obsolete/</guid><description>&lt;p&gt;The most interesting question about the AI investment boom is not whether it is a bubble. It is whether the physical architecture on which the boom is being built will remain economically relevant for as long as the assets being financed.&lt;/p&gt;
&lt;p&gt;Hundreds of billions of dollars are flowing into accelerators, power generation, transmission, cooling, buildings, chip fabrication, and grid connections. The International Energy Agency expects global data-center electricity consumption to more than double by 2030, to roughly 945 TWh, with AI the largest driver. In the United States, data centers could account for almost half of electricity-demand growth through the end of the decade.&lt;/p&gt;
&lt;p&gt;These forecasts may be reasonable. But they are not forecasts of demand alone. Embedded inside them is a second forecast that is discussed much less often: that the present relationship between useful computation, hardware, and energy will persist long enough to justify the infrastructure being built around it.&lt;/p&gt;
&lt;p&gt;That is a much more fragile assumption.&lt;/p&gt;</description></item></channel></rss>