Generative AI may create a socioeconomic tipping point through labour displacement.

Journal: Scientific reports
Published Date:

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.

Authors

  • Jo-An Occhipinti
    Mental Wealth Initiative, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Level 4, Moore College CG2, 1 King Street, Newtown, NSW, 2042 Australia.
  • William Hynes
    World Bank, Paris, France.
  • Ante Prodan
    School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia.
  • Harris Eyre
  • Roy Green
    UTS Business School, University of Technology Sydney, Sydney, Australia.
  • Sharan Burrow
    London School of Economics Grantham Institute, London, England.
  • Marcel Tanner
    Swiss Academies of Arts and Sciences, Bern, Switzerland.
  • John Buchanan
    Business School, University of Sydney, Sydney, Australia.
  • Goran Ujdur
    Mental Wealth Initiative, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Level 4, Moore College CG2, 1 King Street, Newtown, NSW, 2042 Australia.
  • Frederic Destrebecq
    European Brain Council, Brussels, Belgium.
  • Christine Song
    Mental Wealth Initiative, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Building M02C, Level 4, 94 Mallet Street, Camperdown, NSW, Australia.
  • Steven Carnevale
    Point Cypress Ventures, San Francisco, CA, USA.
  • Ian B Hickie
    Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.
  • Mark Heffernan
    School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia.