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The big picture Think of a large-language model (LLM) as a huge spreadsheet full of dials (its billions of weights).Two very different activities happen with that spreadsheet: Activity Everyday analogy Training Teaching a class of thousands of students all day, taking notes on everything they say, then rewriting the textbook each night. Inference (using the…

1 Introduction Matrix multiplication and its elemental dot products sit at the heart of every deep-learning operation—from the Q · Kᵀ attention scores in a Transformer to the weight–activation products in a convolution. Modern AI accelerators therefore live or die by the speed with which they can turn thousands of tiny A × B +…

AbstractThe last decade has seen two intellectual revolutions converge: (i) a renaissance in nonequilibrium thermodynamics that reframes life as a dissipative, entropy-exporting phenomenon, and (ii) the meteoric rise of large language models (LLMs) whose training and deployment now rival small nations in energy demand. Building on the schematic presented by Frank Schmidt (2025) that maps…

Below is a node-by-node “translation” of the diagram’s logic into the domain of large-scale language models (LLMs) such as GPT-4-class systems. I keep the original labels so you can see exactly where the parallels arise. Diagram node (biology) What it means in living systems LLM analogue How the analogy works (and where it breaks) LIFE…

Abstract Modern evolutionary theory is anchored in two intertwined but conceptually distinct economies: an information economy in which genomes store, shuffle, and refine heritable signals, and an energy economy in which organisms capture low-entropy free energy to power survival and reproduction. The former is naturally quantified by Shannon entropy, the latter by Boltzmann entropy. Neo-Darwinism—variation…