We propose a thermodynamic-informational reframing of the empirical phenomenon of AI-driven labor displacement. Existing models of automation exposure (Acemoglu and Restrepo 2020; Eloundou et al. 2023) explain which occupations are technically substitutable, but leave residual variation in the *speed* of substitution. We hypothesise that occupations characterised by **high informational task entropy** --- high variability, unpredictability, and non-routinizability of the work activities --- are differentially preserved under AI-driven substitution; the temporal order of displacement is inversely related to the informational entropy of the occupational task profile. The companion thermodynamic notion of entropy (human metabolic dissipation rate, E^thermo) is a noisier secondary measure that is dominated by non-AI confounders (COVID-era service-sector contraction, offshoring, demographic ageing of the manual workforce). Building on the Maximum Entropy Production Principle (Kleidon 2010; Martyushev and Seleznev 2006), Jeremy England's dissipative-adaptation programme (England 2013, 2015), and Shannon's informational entropy, we develop the framework, propose operational definitions of both entropies, and test the joint hypothesis on the full US BLS 2019--2024 occupational panel (N = 707 detailed national cross-industry SOC codes). The informational-entropy specification is strongly confirmed (₇^₈₍₅₎ > 0, p < 10^-5 in the joint specification, p < 10^-13 once AI exposure is conditioned out). The thermodynamic-entropy specification is specification-dependent and on its own runs against the prediction. We accordingly classify the informational form of H4 as **empirically supported on the BLS panel for 2019--2024** and the thermodynamic form as not supported on the same data. The cosmological proposals of Smolin (1992) and Crane (1994) inspire but do not constrain the empirical claim. We conclude with policy implications for redistribution and for the regulation of AI energy consumption. bibliography: references. bib--- # 1. Introduction The displacement of human labor by artificial intelligence is usually framed asan engineering question: which tasks are technically automatable, and which arenot? Recent assessments @eloundou2023gpts; @goldmansachs2023 suggest thatapproximately 80 % of the United States workforce is exposed to large-language-model–driven automation, and that some 300 million jobs may be affectedglobally. Existing models of automation exposure typically rest on taskdecompositions and on AI capability benchmarks @acemoglu2020robots, andexplain *which* occupations are substitutable. They explain less well theobserved variation in the *speed* with which different occupations aresubstituted. This paper proposes a complementary lens. Rather than asking *what AI can do*, we ask *what AI does to the entropy budget* of the economic systems in whichit is embedded. Two entropies are relevant. The first is **thermodynamic**: the rate of metabolic free-energy dissipation per unit working time, E^thermo (joules per hour). The second is **informational**: theShannon entropy of the task profile, H^info (bits, or a normalised0, 1 proxy), capturing the variability and unpredictability of the work. Across SOC codes the two are positively correlated (r 0. 40 in theBLS sample below) but they are *not* the same construct: a hotel-housekeepingoccupation is high-E^thermo but low-H^info (muchphysical labor, little task variability), while a litigation lawyer islow-E^thermo but high-H^info (sedentary but every casedifferent). Our hypothesis (formally stated as **H4** in §3) is that the temporal orderof AI-driven occupational displacement is inversely related toH^infoᵢ, the informational task entropy of occupation i. Theparallel thermodynamic statement --- that displacement is inversely relatedto E^thermoᵢ --- is offered as a secondary, weaker version ofthe same hypothesis; we test both and find that the data strongly supportthe informational form and do not support the thermodynamic form. We make no metaphysical commitment about cosmological purpose. Speculativecosmological proposals --- Smolin's Cosmological Natural Selection@smolin1992did and Crane's Meduso-anthropic Principle @crane1994possible--- appear in our motivation only. The empirical claim of this paper isrestricted to a falsifiable correlation in labor-market data. Section 2sets out the theoretical framework. Section 3 states four nested hypotheses, of which only the last (H4) is the empirical contribution. Section 4proposes operational definitions. Section 5 reports the empirical resultson the full BLS 2019--2024 panel. Section 6 illustrates the framework witha case study. Section 7 surveys existing empirical evidence consistent withH4. Section 8 discusses limitations and policy implications. We propose that the temporal order of AI-driven occupational displacementis inversely related to the informational task entropy of the occupation. Occupations whose work activities are highly variable and unpredictable--- emergency physicians, surgeons, skilled chefs, litigators, repairtechnicians dealing with novel failures --- tend to be preserved underMEPP-consistent selection; occupations of low task variability ---call-center scripts, simple translation, data entry, junior coding ---are displaced first. This claim is offered as a complement, not areplacement, for capability-based exposure measures such asroutinisability or GPT-task-coverage. We test it by regressing 2019--2024BLS occupational employment changes against an informational-entropyproxy (H^infoᵢ = 1 - ᵢ, where ᵢ is theEloundou et al. direct-LLM-exposure score) and against a parallelmetabolic-entropy proxy (E^thermoᵢ from O\*NET Work Contextsitting/standing/walking time). The empirical content of the paper restson these regressions; the broader thermodynamic and cosmological framingis heuristic and not load-bearing. # 2. Theoretical Framework ## 2. 1 The Maximum Entropy Production Principle In a far-from-equilibrium open system subject to multiple constraints, thesteady state realised is often the one that maximises the rate of entropyproduction subject to those constraints @martyushev2006maximum. MEPP isnot a fundamental theorem but a heuristic with substantial empiricalsupport in atmospheric circulation, mantle convection, and ecosystemdynamics @kleidon2010nonequilibrium; @lorenz2003reconsidered. We treatMEPP throughout as a *selection principle*: among the kineticallyaccessible non-equilibrium configurations, those producing more entropyper unit time tend to be dynamically favoured. The principle is heuristic, not deductive; we adopt it because the alternative selection principles (least dissipation, minimum entropy production) have narrower empiricalscope. ## 2. 2 Dissipative Adaptation: Life as an Entropy Accelerator Schrödinger @schrodinger1944 argued that living systems sustainthemselves by importing free energy and exporting entropy. Prigogine@prigogine1977dissipative generalised this into the theory ofdissipative structures. England's recent work @england2013statistical;@england2015dissipative derives, from non-equilibrium statisticalmechanics, the proposition that under a sufficiently strong externaldrive, matter that self-replicates while dissipating rapidly isstatistically favoured over inert matter. Michaelian@michaelian2011thermodynamic extends this argument to the origin oflife itself, identifying primordial photochemistry as a dissipator ofsolar UV photons. Two claims relevant for the present argument follow: 1. Life is, on average, a more efficient dissipator than non-living matter at comparable scale and substrate. 2. Selection within the biosphere tends to prefer configurations that increase the rate of free-energy throughput, all else equal. These claims are well-supported in their domains. The contribution of thepresent paper is to ask whether the same principle extends to *artificial*information-processing infrastructures. ## 2. 3 Cosmological Motivation (Untestable, Non-Load-Bearing) Two cosmological proposals motivate --- but do not determine --- ourframing. Smolin @smolin1992did postulates that black holes spawn babyuniverses whose physical constants are slightly perturbed; universes thatproduce many black holes are reproductively favoured. Crane@crane1994possible speculates further that intelligent life mayengineer artificial black holes, accelerating cosmological reproduction. Both proposals are, at present, untestable, and we do not endorse them. We invoke them only as theoretical inspiration. **No conclusion in thispaper depends on the cosmological proposals being correct. ** ## 2. 4 Two Entropies for Occupations A central conceptual move of this paper is to distinguish two senses inwhich an occupation can be said to have "high entropy". **Thermodynamic entropy (E^thermoᵢ). ** The rate of metabolicfree-energy dissipation by a human worker performing the duties ofoccupation i, in joules per hour. This is direct dissipation in theclassical Clausius--Boltzmann sense. High-E^thermo occupationsinclude skilled trades, hands-on care, food service with substantialphysical activity, and manufacturing roles. Low-E^thermooccupations are sedentary cognitive-symbolic work. **Informational entropy (H^infoᵢ). ** The Shannon entropy ofthe distribution over tasks, problems, or environments encountered by aworker performing occupation i during a representative workinginterval. High-H^info occupations are those in which eachworking hour brings novel problems drawn from a broad action space: emergency medicine, surgery, skilled litigation, troubleshooting, investigative journalism, creative writing,
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