Capability determinism, energy, and AI labour substitution

Contemporary policy and media discourse on AI and labour follows a recurring pattern. A demonstration of model capability runs through an inference about workplace substitution to a distributional politics, with the variables that would decide the outcome treated as background. This paper names that pattern capability determinism, situates it within Wyatt's account of soft technological determinism, and reframes the substitution question as a unit-cost economics test. On the AI side, cost per task decomposes into a variable inference cost dominated by electricity and the supply chain that produces compute, an amortised integration cost recoverable across volume, and a residual supervision cost that regulatory and professional regimes will not let fall to zero. Once these three lines are loaded, the substitution discourse turns out to operate as a feedback structure: capability gain stresses the supporting systems faster than it improves the calculus on which substitution depends. The variables outside the AI debate, energy markets, integration economics, professional regulation, capital-market patience, and the geopolitics of compute supply chains, decide whether substitution clears in any given place at any given time. Substitution arrives selectively, configurationally, and at a more modest scale than the dominant discourse implies.