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

Rajesh P. Narayanan and R. Kelley Pace

Papers from arXiv.org

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.

Date: 2025-03
References: Add references at CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2503.05816 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2503.05816

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().