Skip to main navigation Skip to search Skip to main content

Generative AI may create a socioeconomic tipping point through labour displacement

  • Jo An Occhipinti
  • , William Hynes
  • , Ante Prodan
  • , Harris Eyre
  • , Roy Green
  • , Sharan Burrow
  • , Marcel Tanner
  • , John Buchanan
  • , Goran Ujdur
  • , Frederic Destrebecq
  • , Christine Song
  • , Steven Carnevale
  • , Ian B. Hickie
  • , Mark Heffernan
  • The University of Sydney
  • Computer Simulation & Advanced Research Technologies (CSART)
  • World Bank
  • Santa Fe Institute
  • Rice University
  • Brain Capital Alliance
  • University of Technology Sydney
  • The London School of Economics and Political Science
  • Swiss Academies of Arts and Sciences
  • Swiss Tropical and Public Health Institute
  • European Brain Council
  • Point Cypress Ventures San Francisco California
  • Mental Health Services Oversight & Accountability Commission
  • Dynamic Operations

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)
74 Downloads (Pure)

Abstract

Work is fundamental to societal prosperity and mental health, providing financial security, a sense of identity and purpose, and social integration. Job insecurity, underemployment and unemployment are well-documented risk factors for mental health issues and suicide. The emergence of generative artificial intelligence (AI) has catalysed debate on job displacement and its corollary impacts on individual and social wellbeing. Some argue that many new jobs and industries will emerge to offset the displacement, while others foresee a widespread decoupling of economic productivity from human input threatening jobs on an unprecedented scale. This study explores the conditions under which both may be true and examines the potential for a self-reinforcing cycle of recessionary pressures that would necessitate sustained government intervention to maintain job security and economic stability. A system dynamics model was developed to undertake ex ante analysis of the effect of AI-capital deepening on labour underutilisation and demand in the economy using Australian data as a case study. Results indicate that even a moderate increase in the AI-capital-to-labour ratio could increase labour underutilisation to double its current level, decrease per capita disposable income by 26% (95% interval, 20.6–31.8%), and decrease the consumption index by 21% (95% interval, 13.6–28.3%) by mid-2050. To prevent a reduction in per capita disposable income due to the estimated increase in underutilization, at least a 10.8-fold increase in the new job creation rate would be necessary. Results demonstrate the feasibility of an AI-capital-to-labour ratio threshold beyond which even high rates of new job creation cannot prevent declines in consumption. The precise threshold will vary across economies, emphasizing the urgent need for empirical research tailored to specific contexts. This study underscores the need for cross-sectoral government measures to ensure a smooth transition to an AI-dominated economy to safeguard the Mental Wealth of nations.

Original languageEnglish
Article number26050
Number of pages11
JournalScientific Reports
Volume15
Issue number1
DOIs
Publication statusPublished - Dec 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 8 - Decent Work and Economic Growth
    SDG 8 Decent Work and Economic Growth
  3. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  4. SDG 10 - Reduced Inequalities
    SDG 10 Reduced Inequalities

Keywords

  • Artificial intelligence
  • Economic policy
  • Recession
  • System dynamics
  • Wellbeing

Fingerprint

Dive into the research topics of 'Generative AI may create a socioeconomic tipping point through labour displacement'. Together they form a unique fingerprint.

Cite this