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Will Neural Scaling Laws Activate Jevons' Paradox in AI Labor Markets? A Time-Varying Elasticity of Substitution (VES) Analysis

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  • Rajesh P. Narayanan
  • R. Kelley Pace

Abstract

AI industry leaders often use the term ``Jevons' Paradox.'' We explore the significance of this term for artificial intelligence adoption through a time-varying elasticity of substitution framework. We develop a model connecting AI development to labor substitution through four key mechanisms: (1) increased effective computational capacity from both hardware and algorithmic improvements; (2) AI capabilities that rise logarithmically with computation following established neural scaling laws; (3) declining marginal computational costs leading to lower AI prices through competitive pressure; and (4) a resulting increase in the elasticity of substitution between AI and human labor over time. Our time-varying elasticity of substitution (VES) framework, incorporating the G\o rtz identity, yields analytical conditions for market transformation dynamics. This work provides a simple framework to help assess the economic reasoning behind industry claims that AI will increasingly substitute for human labor across diverse economic sectors.

Suggested Citation

  • Rajesh P. Narayanan & R. Kelley Pace, 2025. "Will Neural Scaling Laws Activate Jevons' Paradox in AI Labor Markets? A Time-Varying Elasticity of Substitution (VES) Analysis," Papers 2503.05816, arXiv.org.
    • Handle: RePEc:arx:papers:2503.05816
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