# In the Shadow of Smith’s Invisible Hand: Risks to Economic Stability and
Social Wellbeing in the Age of Intelligence

Jo-An Occhipintia,b,c,* William Hynesb,d,e Ante Prodana,b,c,f Harris A.
Eyreb,g,h,i Roy Greenj Sharan Burrowk Marcel Tannerl,m John Buchanann Goran
Ujdura,b,c Frederic Destrebecqo Christine Songa,b Steven Carnevalep,q Ian B.
Hickiea,b Mark Heffernanr

###### Abstract

Work is fundamental to societal prosperity and mental health, providing
financial security, identity, purpose, and social integration. The emergence
of generative artificial intelligence (AI) has catalysed debate on job
displacement. 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. 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 governments, civic
organisations, and business to work together to ensure a smooth transition to
an AI-dominated economy to safeguard the Mental Wealth of nations.

###### keywords:

artificial intelligence, recession, economic policy, wellbeing, system
dynamics

††journal: …

[first]Brain and Mind Centre, University of Sydney, Camperdown, Australia
Mental Wealth Initiative, University of Sydney, Camperdown, Australia Computer
Simulation & Advanced Research Technologies (CSART), Sydney, Australia

World Bank, Paris, France Santa Fe Institute, Santa Fe, New Mexico, USA

School of Computer, Data and Mathematical Sciences, Western Sydney University,
Sydney, Australia

Brain Capital Alliance, San Francisco, California, USA Baker Institute for
Public Policy, Rice University, Houston, Texas, USA Meadows Mental Health
Policy Institute, Dallas, Texas, USA

University of Technology Sydney, Broadway, Sydney, Australia

Visiting Professor in Practice, London School of Economics Grantham Institute,
London, UK

Swiss Academies of Arts and Sciences, Bern, Switzerland Swiss Tropical and
Public Health Institute & University of Basel, Switzerland

Business School, University of Sydney, Sydney, Australia

European Brain Council, Brussels, Belgium

Point Cypress Ventures, San Francisco, California, USA Mental Health Services
Oversight & Accountability Commission, Sacramento, California, USA

Dynamic Operations, Sydney, Australia

## Introduction

Quality work has long been recognized as a fundamental pillar of prosperity
and mental health, providing not just financial security and a sense of
identity and purpose, but fostering social connection and integration – all of
which are fundamental to a thriving society [1, 2, 3]. However, the fabric of
work has been undergoing significant change over the past decades with a
distinct shift towards more precarious employment along with work
intensification [4, 5], raising concerns about the impact of this shift on
mental health and societal wellbeing [6, 7]. Particularly affected by these
changes are young people. The International Labour Organization reported in
2022 that those aged 15-24 years faced significant challenges in securing and
retaining quality employment, experienced an unemployment rate three times
that of adults, and more than one in five were not engaged in education,
employment or training [8]. The shift towards greater labour market
flexibility and job insecurity over the last four decades has been largely
tolerated and justified as necessary for continued economic growth and
prosperity. However, the adverse impacts of this shift on mental health,
especially among young people, is a significant concern. Financial hardship,
job quality and job insecurity are well-documented risk factors for mental
health issues [9, 10, 11, 12]. Against a backdrop of declining productivity
growth, and evidence that reduced job security and increasing demands for
worker productivity paradoxically undermines productivity [6], efforts to
safeguard worker wellbeing have moved into focus. This focus has served as a
counterweight against further deterioration in job security and quality.
However, the emergence of generative artificial intelligence (AI) poses a
substantial threat to this balancing force.

Generative AI is not simply automation, it is an intelligence; it stands apart
from previous technological advancements in its ability to undertake routine
and non-routine cognitive tasks across a broad range of disciplines and is on
a trajectory to achieving beyond human capabilities. This capability, while
currently in its infancy, has the potential to substantially deepen the
decoupling of economic productivity from labour input [13, 14], putting at
risk a large proportion of jobs once considered safe from automation, and
profoundly reshaping labour market dynamics [15, 16]. Past technological
advancements have led to the automation of routine manual and cognitive tasks,
which has decreased the demand for work requiring these skills. Concurrently,
there has been an expansion of opportunities for non-routine cognitive tasks
(Figure 1), typically associated with higher wages. This shift has contributed
to the growth of the middle class and has helped sustain economic demand.
However, it is precisely this substantial segment of non-routine cognitive or
professional jobs that is now facing potential disruption from the
capabilities of generative AI. While most agree on the likelihood of short-
term disruption as generative AI is adopted across the economy, opinions
diverge on its overall impact on job displacement. Some argue that generative
AI will create new jobs and industries (requiring human labour) that will
offset displaced jobs consistent with the experiences with previous
technological developments, and bring with it advances in healthcare, cheaper
goods and services, more efficient allocation of resources, educational and
reskilling opportunities, innovation, and new investments, products and
services [17]. Others warn that the scale and nature of job displacement will
be unprecedented, that generative AI is cheaper and more productive than human
capital, it can be ‘upskilled’ far more efficiently than humans, and that the
resulting labour underutilisation, downward pressure on wages, and middle
class contraction will bring forth social and economic instability [15]. We
explore through dynamic modelling the conditions under which both may be true.

The capital-to-labour ratio is an economic indicator that measures the amount
of capital (such as physical infrastructure, machinery, or technology) that is
available per worker in the production process. It serves as a key determinant
of productivity, providing insights into labour efficiency and the capital
intensity of an economy or industry [18]. However, in the Age of Intelligence,
while significant investments in AI-capital (increasing the numerator) is
similarly expected to drive up productivity, it is also likely to drive down
the need for labour hours (decreasing the denominator) with significant
intensity, thereby increasing the capital-to-labour ratio and precipitating
increases in labour underutilisation. We posit the existence of an AI-capital-
to-labour ratio threshold beyond which a self-reinforcing cycle of
recessionary pressures could be triggered by reductions in disposable income
and consumption brought on by too great an increase in labour
underutilisation, and requiring sustained government intervention to maintain
stability [15]. Employing a system dynamics model, the aim of this research
was to answer three primary research questions: (i) What are the possible
trajectories for per capita disposable income and consumption / demand in the
economy over the longer term in response to varying degrees of shifts in the
capital-to-labour ratio; (ii) Could a capital-to-labour ratio threshold exist
in the context of plausible parameter estimates, beyond which the economy may
enter a self-reinforcing cycle of recessionary pressures, and if so, what are
the conditions under which this might occur; and (iii) what level of new job
creation would be required to prevent an overall reduction in consumption
across the economy in the advent of the speculated substitution of 25% of
current work by generative AI [19, 20].

Figure 1: Share of employment by type of occupation, Australia, 1986 – 2022
(August) (reprinted with permission. Source: Borland, J. & Coelli, M. The
Australian labour market and IT-enabled technological change. Working Paper
No. 01/23. Melbourne Institute of Applied Economic & Social Research,
Melbourne, Australia, 2023).

## Method

Model structure and assumptions

Figure 2: A system dynamics model of the interaction between the capital-to-
labour ratio, labour underutilization, disposable income, and relative prices,
and their impact on consumption. Symbols, their values, and data sources are
defined in Appendix 1.

Figure 2 presents the system dynamics model used for the analyses. The model
consists of four stocks (or state variables); $P,U,O$ and $K$ corresponding to
population, the number of people who are unemployed or underemployed in the
population (underutilisation), the labour underutilisation onset rate, and the
capital-labour ratio (K-L ratio) respectively. People are added to the total
population, $P$, at a constant fraction $g$ per year representing a net
population increase (births plus immigration minus deaths and emigration)
consistent with current population projections. Those becoming underutilised
flow into the stock, $U$, at a rate (per year) equal to
$\frac{P\frac{i}{\mu}O-U}{d}$, where $i$ is the number of people in the labour
force in 2023.5, $\mu$ is the population in 2023.5, with $\frac{i}{\mu}$
giving the proportion of the population in the labour force, and $d$ is the
delay in changes to the underutilisation onset rate as a result of changes to
the K-L ratio trend. For simplicity, we assume that the proportion of the
population in the labour force (participating in or seeking work) remains
constant (i.e. this model does not account for major demographic shifts that
would alter the proportion in the labour force within the model time horizon
(mid-2050). People flow out of the underutilisation stock at a rate equal to
$P\frac{i}{\mu}\lambda$, where $\lambda$ is the new job creation rate per
capita (which assumes that these new jobs provide sufficient hours to move
individuals out of a state of being unemployed or underemployed). All-cause
mortality reduces the number of people unemployed and underemployed
(underutilised persons) at a rate equal to $Um$, where $m$ is the per capita
mortality rate for those of working age.

Labour underutilisation onset increases over time at a rate equal to
$O\beta\eta$, where $\beta$ is the base rate of change in the labour
underutilisation onset rate, and $\eta$ is the per unit change in the
underutilisation onset rate as a result of changes to the K-L ratio (dependent
on a graphical converter, see Appendix 1). The K-L ratio increases at a rate
per year equal to $K\alpha$, where $\alpha$ is the percent increase in the K-L
ratio based on the historic average. Scenarios exploring AI-related increases
to the K-L ratio (capital deepening) occur through changes to the value of
$\alpha$. Multifactor productivity (MFP) changes at a rate equal to
$\nu\delta$, where $\nu$ is the base MFP rate and $\delta$ is the per unit
change in the MFP rate as a result of changes to the K-L ratio (dependent on a
graphical converter, Appendix 1). Prices of goods and services, $\rho$,
decrease as MFP increases (dependent on a graphical converter, Appendix 1).
The impact of labour underutilisation on aggregate disposable income,
${\psi}$, is

$\psi=(P\frac{i}{\mu}-U+\frac{r}{1+r})U\times 0.77\times\theta\tau$

where $r$ is the ratio of underemployed to unemployed, $\theta$ is the per
unit change in disposable income as the proportion of the population
underutilised changes (i.e. as the proportion of the population that is
unemployed and underemployed increases, it is assumed to exert a downward
pressure on wages and hence disposable income - dependent on a graphical
converter, Appendix 1), and $\tau$ is the scaling factor derived by
calibration that ensures the model’s initial conditions broadly reflect real
world disposable income (namely, Real Net National Disposable Income, a
measure of real income available across the economy to spend or save). The
scaling factor takes account of the disposable income that is unlikely to be
affected by downward pressure on wages (e.g. social security benefits,
investment income, etc.). The per capita disposable income is given by
$\frac{\psi}{P}$.

The consumption index, $C$ is the per capita ability to purchase goods and
services in the economy that accounts for productivity-related price decreases
due to generative AI, and is equal to

$\frac{\psi/P}{\frac{\rho}{INIT(\rho)}\omega+(1-\omega)}$

where $\omega$ is the proportion of disposable income affected by
productivity-related price decreases (it is assumed, for example, that
increased productivity in the economy will not decrease the cost of housing,
education, insurance, etc), and ‘INIT’ refers to the initial value of a
parameter at the start of a simulation (so $\rho/INIT(\rho)$ is the ratio of
current prices to initial prices and assesses how much prices change over time
as a proportion of the original value). Therefore, the consumption index
reflects the competing forces of productivity-related price reductions (which
increase real disposable income) and reductions in disposable income due to
increased underutilisation and downward pressure on wages.

## Model analysis

Australian data was used to derive plausible parameter estimates for the model
as a demonstration of the feasibility of the hypothesis of recessionary
pressures associated with scaled uptake of generative AI across the economy.
The potential impacts of generative-AI were modelled through changes to
$\alpha$ (the percent increase in the K-L ratio, also known as capital
deepening). The baseline scenario reflects the average increase in $\alpha$ of
1.8% per annum derived from data from the Australian Bureau of Statistics
(ABS) over the period 1995 to 2023. To address research question (i), a range
of possible intensities of capital deepening through uptake of generative AI
across the economy (i.e. increases to the AI-capital-to-labour-ratio) were
modelled through increases in the value of $\alpha$ to 4%, 7% and 10% per
annum (scenarios a, b, and c respectively). These K-L ratio scenarios are
conservative as the 10% increase per annum is projected to increase
underutilization by 23.8% by mid-2050 (assuming no government action), which
is below the estimated 25% of work/jobs that will be affected across the
economy as reported by OpenAI researchers, Goldman Sachs, and others [19, 20,
21]. Impacts on several key outcomes were explored at the end of the
simulation time horizon (mid-2050); per capita disposable income (expressed in
US dollars) and the consumption index (proxies for demand in the economy) and
labour underutilisation. Sensitivity analyses were performed to assess the
impact of uncertainty in estimates of the new job creation rate, the
proportion of disposable income subject to productivity-related price
decreases, the base labour underutilisation onset rate increase per year, and
the ratio of underemployed to unemployed on the simulation results. We used
Latin hypercube sampling to draw 200 sets of values for the selected
parameters from a uniform joint distribution (the sample space for each
parameter is provided in Appendix 1). Given that new job creation above the
baseline rate (whether occurring through new industries emerging from the
generative AI-related technical disruption or through government stimulus) is
unlikely to occur instantaneously, a 2-year delay was implemented with an
s-shaped onset over time. Differences in projected outcomes between the
baseline and scenario runs were calculated for each set of parameter values
and summarised using simple descriptive statistics. Model construction and
sensitivity analyses were performed using Stella Architect ver. 3.4
(www.iseesystems.com). Microsoft Excel, R, and Python were used for post
dynamic modelling summary statistics and graphical presentation of results.

To address research question (ii), a sensitivity analysis was performed using
all possible combinations of the annual K-L ratio percent increase (ranging
from 2% to 10%; increments of 0.28) and new job creation rate (ranging from a
0.5- to 12-fold increase in the historic average of the new job creation rate;
increments of 0.4), drawing 900 sets of values from a joint distribution
incrementally evenly distributed. A heatmap was generated to visualise the
conditions under which the simulated consumption index would fall below the
baseline trajectory. To address research question (iii), the K-L ratio was set
to an 11% increase per annum which approximates the speculated substitution of
25% of current work/jobs by generative AI. A range of values of an increase in
the new job creation rate were explored (i.e. between a 1- to 12-fold
increase) to understand the extent to which new jobs would need to be created
to prevent a decline in the consumption index compared to the baseline over
the next 20 years (2025-2045) in the absence of government intervention.

Figure 3: Simulation results. Disposable income per capita in US dollars and
underutilised persons for alternative scenarios between 2023.5 and 2050.5,
compared with the baseline. The baseline and scenario’s a, b, and c reflect an
average increase in the AI-capital-to-labour ratio of 1.8%, 4%, 7%, and 10%
per annum respectively. The solid line indicates the simulated median values
over time, darker shading represents 50% uncertainty interval, and lighter
shading indicates the 95% uncertainty interval derived from the sensitivity
analysis. Declining disposable income per capita in the baseline scenario
reflects the increasing trend in the underutilisation rate.

## Results

Regarding research question (i), Table 1 shows simulation results for
alternative scenarios of generative AI-related capital deepening on per capita
disposable income, the consumption index, and underutilised persons against
the baseline over the period 2023.5 to 2050.5. Scenario b which signifies a
moderate increase in the historic average annual increase in the K-L ratio due
to generative AI, is projected to result in a mean 99.76% (95% uncertainty
interval, 70.61% – 137.52%) increase in underutilisation, a 21.2% (95%
interval, 13.6% - 28.3%) decrease in the consumption index, and a 26% (95%
interval, 20.6% - 31.8%) decrease in the disposable income per capita (note
that all intervals reported are derived from the distributions of model
outputs calculated in the sensitivity analyses; they provide an indication of
the effect of uncertainty in selected parameter estimates but should not be
interpreted as confidence intervals). The simulated trajectories provided in
Figure 3 highlight that the intensity of generative AI-related capital
deepening is likely to influence the timing and extent of the increase in
labour underutilisation and declines in disposable income per capita over the
longer term. Regarding research question (ii), Figure 4 demonstrates the
values of the consumption index (the ability to purchase goods and services in
the economy) as a function of the AI-capital-to-labour ratio and rate of new
job creation (either through innovation in the market or government stimulus).
The figure demonstrates a threshold beyond which increases in the AI-capital-
to-labour ratio would result in values of the consumption index that fall
below the baseline over the longer term, and that the threshold depends on the
rate of new job creation. At low levels of increase in the AI-capital-to-
labour ratio, increases in the new job creation rate is sufficient to prevent
downturns in the consumption index. However, as demonstrated in Figure 5, at
moderate to high levels of the AI-capital-to-labour ratio, while initially the
consumption index increases against the baseline, even high rates of growth in
the new job creation rate are insufficient to offset labour underutilisation
and associated declines in disposable income per capita. This suggests that
the AI-capital-to-labour ratio represents a potential economic marker for
threats to national wellbeing that could be closely monitored. Regarding
research question (iii), the model estimates (in this example) that at least a
10.8-fold increase in the current rate of new job creation would be needed to
prevent a decline in the consumption index compared to the baseline over the
next 20 years (2025-2045) in the absence of government intervention (recalling
that the model assumes that the new jobs will provide sufficient hours to move
individuals out of a state of being unemployed or underemployed). However, a
far higher growth rate in new jobs would be needed to achieve and sustain
growth in demand above the baseline.

Figure 4: Simulation results. The heatmap shows the impacts of the AI-capital-
to-labour ratio and new job creation rate on the consumption index over the
period 2023.5 to 2050.5. The pale shading corresponds to increases in the
consumption index against the baseline, the lighter red shading indicates
smaller percentage reductions against the baseline, and dark red shading to
larger percentage reductions. Figure 5: Simulation results. The figure shows
the non-linear change in consumption index between 2023.5 and 2050.5 under a
scenario of a moderate rate of AI-capital deepening (K-L ratio increase per
annum of 7%) and high rate of jobs growth (a 6-fold increase in the baseline
new job creation rate per annum) compared to the baseline. The solid line
indicates the simulated consumption index over time, darker shading represents
50% uncertainty interval, and lighter shading indicates the 95% uncertainty
interval derived from the sensitivity analysis.

## Discussion

The aim of this study was to assess the merit of the hypothesis that
recessionary pressures might be associated with expanded implementation of
generative AI across the economy, employing plausible parameter estimates. The
findings suggest that without government intervention, generative AI has the
potential to significantly disrupt labour market dynamics and demand in an
economy emphasizing the need for proactive policy measures to mitigate adverse
impacts. Projections highlight the risk of substantial increases in labour
underutilization even from moderate levels of AI-capital deepening, leading to
declines in per capita disposable income and consumption despite increased
consumer purchasing power related to productivity growth and lowering of
prices. Furthermore, our study suggests the existence of an AI-capital-to-
labour ratio threshold beyond which the economy may enter a self-reinforcing
cycle of recessionary pressures. Such a scenario could pose formidable
challenges to economic stability and necessitate sustained government
intervention to prevent social and economic instability. The AI-capital-to-
labour ratio therefore represents a potential economic marker for threats to
economic and social wellbeing in the Age of Intelligence. The findings
underscore the urgency of developing strategies to manage the transition
towards an AI-dominated economy while safeguarding Mental Wealth and social
prosperity.

While these analyses were conducted within a specific context (Australia), and
not designed to precisely determine where the threshold in the AI-capital-to-
labour ratio lies, the results indicate that such exploration should be
conducted across a variety of contexts. Whether a threshold exists and where
it lies will depend on the features of each economy including industry
composition, occupational structures, tax structures, the regulatory
environment, government policies and incentives, and social acceptability and
environmental considerations of the integration of generative AI in workplaces
and everyday life. Conducting risk mapping based on AI-capital-to-labour ratio
and other indicators across different economy archetypes may allow for the
creation of tailored strategies to manage the transition to an AI-augmented
economic landscape, while recognizing that these thresholds may be dynamic as
the features of economies evolve. Advanced economies with a high degree of
automation and digital infrastructure may face different challenges compared
to emerging markets where labour-intensive industries still predominate and
there is a shortage of skilled workforces to capitalize on the advantages of
generative AI [22]. Similarly, economies with robust social institutions and
safety nets may have different risk profiles compared to those with less
comprehensive support systems [23]. Economic inter-dependencies and global
trade dynamics can also shape the trajectory of AI-capital deepening.
Economies that rely heavily on exports or are integrated into global supply
chains may face pressure to adopt generative AI technologies to remain
competitive, potentially accelerating the approach to the threshold. Further
empirical work is clearly needed; however, by closely tracking changes in this
ratio, both within sectors and across the economy, governments can better
anticipate and respond to emerging challenges, thereby enhancing their
capacity to ensure economic resilience and social cohesion.

Pre-distribution: The role of governments & labour unions

While beyond the scope of this aggregate-level analysis, increases in labour
underutilisation are likely to bring labour market reshaping, along with the
risk of further deepening wage polarization and inequality [15, 24, 25]. As
generative AI technologies advance, enabling greater productivity with less
human input in non-routine cognitive or professional jobs, the distribution of
economic gains will become further skewed and the disproportionate accrual of
economic benefits to the owners of capital is likely to aggravate existing
wealth disparities across generations [26, 27, 28, 29]. Young people entering
the workforce at this time will face significant challenges. The automation of
entry-level professional positions—the bedrock for skill-building and early
career development—threatens to undermine young workers’ ability to establish
themselves economically. The absence of these foundational roles may impede
the accumulation of wealth for this generation, potentially leading to a
widening intergenerational wealth gap as they face a labour market with fewer
quality jobs and stagnant wages [28]. This shift not only affects their
immediate employment prospects but also has repercussions for long-term
financial stability and prospects for wealth creation. The erosion of stable
employment and career progression, fundamental to young adults’ aspirations
and well-being, increases the likelihood that young people will go on to
experience high psychological distress and mental disorder, increasing the
risk of suicidal behaviours and undermining social cohesion over the longer
term [30, 31]. This further underscores the need for proactive measures to
support all workers, but particularly young workers, and ensure that the path
to prosperity remains accessible and inclusive in the Age of Intelligence.

In light of these challenges, redistribution measures such as taxation and
progressive social policy have been proposed as a means to addressing widening
inequalities associated with generative AI-associated job scarcity,
displacement, and downward pressure on wages [15, 32]. However, while
redistribution efforts can play a role in alleviating adverse impacts of
generative AI, such measures are reactive and retain systemic issues already
in place [33]. Additionally, history has demonstrated that even in the
presence of abundance, perceived scarcity persists, and the struggle for
redistribution of wealth can incite conflict and division [16, 28]. A more
forward-looking strategy lies in the concept of pre-distribution, which seeks
to address the root causes of inequality by adjusting the mechanisms of wealth
generation before inequalities become entrenched [34]. Pre-distribution
policies focus on ensuring that the economic structures and market forces
themselves generate equitable outcomes, rather than relying solely on
redistribution.

Governments will need to play a crucial role in shaping the landscape for pre-
distribution in the Age of Intelligence. Strategies could include investing in
the foundational economy and underfunded sectors with strong job creation
potential, such as health, social care, science, arts and culture, and the
green economy. Exploring public policies such as reduced workdays with
maintained salaries to ensure that the benefits of AI-induced productivity are
translated into widespread well-being [32]. Additionally, governments can
encourage private sector compliance with ethical AI adoption standards,
through incentivizing businesses to comply with initiatives like an AI
Fairwork Pledge [32]. Innovative mechanisms such as an AI Displacement
Insurance Fund [32] are another avenue for government action, where public-
private collaboration would see companies that automate jobs contribute to a
pool of resources dedicated to easing labour market transitions and supporting
workers affected by AI-driven changes [32]. Through multifaceted strategies,
governments, in partnership with civic organisations can lay the foundation
for a more inclusive economy, where the transformative potential of AI is
harnessed for the good of all, rather than exacerbating existing divides. By
prioritizing pre-distribution, policymakers can address disparities at their
source, fostering a society that is resilient, cohesive, and prepared for the
future.

Similarly, labour unions have a critical role to play in this rapidly evolving
economic landscape shaped by generative AI, in promoting fair labour
practices, negotiating job security, and lobbying for pre-distribution
policies that ensure the gains from AI-driven productivity are equitably
shared. Unions are in a unique position to influence policy discussions around
generative AI, safeguarding the interests of a new generation of workers and
preventing their marginalisation. Tripartite planning involving unions,
government, and employers can facilitate solutions that respect the dignity of
labour, support workers’ mental health, and preserve the social fabric of
nations. To fulfill this role, unions could work with government and employer
representatives to develop an international labour standard on ‘Decent work in
the Age of Intelligence,’ ensuring that AI-related jobs maintain high-quality
standards and are incorporated into national legislation [32]. Establishing
such labour standards will assist in negotiating for AI-related work that
augments, rather than substitutes, human intelligence (brain capital),
creativity, and innovation, pushing for clauses in employment contracts or
industry-wide agreements that set ethical standards for AI use, include
protections against unjustified job displacement and ensure fair compensation
for AI-generated work. Additionally, unions could advocate for the
establishment of regulatory frameworks for data-knowledge ownership (e.g.,
data cooperatives or trusts [35, 36]) that ensure individuals are fairly
compensated and retain control over their data and intellectual contributions
to the development and refinement of generative AI. Through such actions,
unions can position workers at the centre of technological governance to
positively shape labour market evolution and avoid the destructive trajectory
of technological determinism.

The recent triumph of the Writers Guild of America (WGA) over the studios’
attempted use of generative AI to automate scriptwriting serves as a beacon
for labour unions navigating the complexities of generative AI in the
workplace. This victory, however, does not guarantee future success as
generative AI becomes more subtly woven into the fabric of work. Unions are
now operating in a terrain vastly different from the one in which they
originated, necessitating a fundamental rethink of their strategies. As unions
like the WGA demonstrate, there is significant power in collective resistance
against the exploitative use of generative AI. However, in this new labour
market landscape, unions must adapt and forge a balanced approach that fosters
innovation and business opportunities while upholding hard-won workers’ rights
and incomes. To achieve this equilibrium, unions must actively engage in AI
governance, ensuring representation in critical discussions with employers,
policymakers, and technologists, on the adoption of AI in the workplace, and
push for transparency and ethical standards. A comprehensive mapping of
professions and sectors, including supply chain and distribution networks,
could equip labour unions to engage in strategic, pre-emptive measures to
safeguard vulnerable jobs. Additionally, educational partnerships will be
crucial, ensuring young workers have access to training programs that equip
them with the skills needed for AI-augmented jobs. Transition to a generative-
AI powered economy presents a unique opportunity to reevaluate and renew the
social contract between employers, employees, government, and broader society.
This period of transition offers a significant opportunity for labour unions
to demonstrate their enduring value as an essential pillar for work justice
and human rights. This subtle yet pivotal moment could mark a renaissance for
the union movement, affirming its role in a fair and forward-looking labour
market.

## Study limitations

It is crucial to recognize the inherent uncertainties and constraints
associated with any modelling effort. The complexities of the economy and the
interactions among myriad factors pose a challenge to comprehensively
encapsulate all elements within a single model. Nonetheless, striving for an
exhaustive model is neither necessary nor particularly beneficial. A
simplified, abstract, and transparent systems model can provide profound
insights into the dominant forces shaping complex system behaviours. A
limitation of our current approach is its focus on macroeconomic indicators
and does not have the capacity to explore the differential impacts across
various demographic groups, sectors, economies, or income levels. For example,
while the model projects a decline in disposable income, it does not delineate
how this decline may disproportionately impact different income strata.
Additionally, it does not make a distinction between high-skilled, well-
compensated jobs and lower-skilled less remunerative ones in simulating
alternative rates of new job creation. This may obscure the full extent of the
impact of generative AI on labour underutilisation, disposable income, and
overall economic demand. However, it is important to reiterate the study’s
intention which was to demonstrate the plausibility of a threshold in the AI-
capital-to-labour ratio existing, thereby validating the hypothesis. Future
research should aim to refine and broaden the modelling framework to account
for a wider array of factors and nuances. This includes examining the varied
impacts of generative AI adoption across different industries and in diverse
global economic settings, as well as the influence of policy measures in
determining these outcomes. It is also important to consider potential
feedback loops that intertwine economic and social variables. Empirical
studies that support risk mapping, validate model predictions, and scrutinize
the effects of real-world policy applications will be instrumental in
fostering evidence-based policy making, especially as we navigate the
implications of this major technological advance.

Table 1: Parameter values used in the model analysis and data sources. Parameter | Symbol | Value | Data source
---|---|---|---
Underutilised persons initial | $U_{0}$ | 1,445,000 |  Unemployed plus underemployed persons in Australia as of 30th June 2023 – seasonally adjusted ABS, 6202.0 Labour Force, Australia, Table 22. https://www.abs.gov.au/statistics/ labour/employment-and-unemployment/labour-force-australia/latest-release)
Ratio of underemployed to unemployed | r | 1.6 |  Derived from ABS, 6202.0 Labour Force, Australia, Table 22. https://www.abs.gov.au/statistics/labour/employment-and-unemployment/ labour-force-australia/latest-release. Average value over the 10-year period, Dec 2013 – Nov 2023. Varied by +/-10% in sensitivity analyses.
Ratio of disposable income of an underemployed to fully employed person | - | 77% |  Derived from Table 25 of: Campbell I., Parkinson, S. and Wood, G. (2014) Underemployment and housing insecurity: an empirical analysis of HILDA data, AHURI Final Report No.230. Melbourne: Australian Housing and UrbanResearch Institute.
Labour underutilisation onset rate initial | $O_{0}$ | 9.9% |  Derived from ABS, 6202.0 Labour Force, Australia, Table 22 https://www.abs.gov.au/statistics/labour/employment-and-unemployent/ labour-force-australia/latest-release
Base labour underutilisation onset rate increase per year | $\beta$ | 0.0015 |  Reasoned estimate based on plausible forward projections of labour underutilisation to 2050.5. Varied by +/-10% in sensitivity analyses.
Labour force initial | i | 14,585,316 |  Labour force total persons as of 30th June 2023. ABS, 6202.0 Labour Force, Australia, Table 22: https://www.abs.gov.au/statistics/labour/employment-and- unemployment/labour-force-australia/latest-release.
Capital-to-labour ratio (K-L ratio) initial | $K_{0}$ | 94.6 |  ABS, Australian National Accounts, 2022-23, Cat. no. 5204.0, Table 13: https://www.abs.gov.au /statistics/economy/national-accounts/australian-system-national-accounts/ latest-release. Quality adjusted hours worked and indexed (2021=100)
K-L ratio percent increase per annum | $\alpha$ | 1.8% |  ABS, Australian National Accounts, 2022-23, Cat. no. 5204.0, Table 13: https://www.abs.gov.au/statistics/economy/national-accounts/australian-system -national-accounts/latest-release. Average annual increase in the K-L ratio over the period 1995 to 2023 derived from regression model.
Initial population | $P_{0}$ | 26,638,544 |  The population of Australia as of 30th June 2023 (https://www.abs.gov.au/statistics/people/population)
Population increase percent per annum | $g$ | 1.1 |  Population projections, Australia:https://www.abs.gov.au/statistics/people /population/population-projections-australia/2022-base-2071. This approximates medium series by 2050.5.
Mortality rate | m | 0.0015 |  Derived from Australian Bureau of Statistics. Table 1.9, Life Tables, Australia, 2020-2022 https://www.abs.gov.au/statistics/people/population/life- expectancy/latest-release and population data for each age.
New job creation rate per capita | $\lambda$ | 0.0021 |  To align the model‘s output with the June 2023 Real Net National Disposable Income (RNNDI) per capita in Australia, a scaling factor of 86,985 was applied. This calibration ensures the model‘s initial conditions broadly reflect a real world economic indicator of disposable income (data: Table 1: https://www.abs.gov.au/statistics/economy/national-accounts/australian -national-accounts-national-income-expenditure-and-product/latest-release#data-downloads)
Delay in change to underutilisation onset rate | d | 5 |  5 years – an estimate. Delay in changes to the underutilisation onset rate as a result of changes to the K-L ratio.
Base Multi-Factor Productivity (MFP) rate | $v$ | 0.56298 |  Derived from MFP data, OECD, Australia, 1985-2022: https://data.oecd.org/lprdty/multifactor-productivity.htm
Scaling factor | $\tau$ | 86,985 |  Derived from MFP data, OECD, Australia, 1985-2022: https://data.oecd.org/lprdty/multifactor-productivity.htm
Proportion of disposable income affected by productivity-related price decreases | $\omega$ | 50% |  Reasoned estimate. Varied between 30% and 70% in sensitivity analyses.
The per unit change in average disposable income as the proportion of the population underutilised changes | $\theta$ | - |  Graphical converter: Points (underutilisation, disposable income): (0.000, 1.359), (0.500, 1.152), (1.000, 1.000), (1.500, 0.876), (2.000, 0.796), (2.500, 0.748), (3.000, 0.705), (3.500, 0.676), (4.000, 0.648), (4.500, 0.631), (5.000, 0.612). Value of 1 is reference point (i.e. current values of underutilisation and average disposable income). Highlighted example says that if underutilisation was double the current rate, then average disposable income (wages) would be 79.6% the current level (or 20.4% less than the current level). Approximates Phillips Curve provided by the Reserve Bank of Australia (Figure 3 in https://www.rba.gov.au/publications/rdp/2021/pdf/rdp2021-09.pdf)
The per unit change in the underutilisation onset rate as a result of changes to the K-L ratio due to generative AI | $\eta$ | - |  Graphical converter: Points (K-L ratio, labour underutilisation onset rate): (0.000, 0.000), (0.500, 0.291), (1.000, 1.000), (1.500, 1.990), (2.000, 3.083), (2.500, 4.029), (3.000, 4.636), (3.500, 4.976), (4.000, 5.000), (4.500, 5.000), (5.000, 5.000).
The per unit change in the MFP rate as a result of changes to the K-L ratio due to generative AI | $\gamma$ | - |  Graphical converter: Points (K-L ratio, MFP): (0.000, 0.000), (0.500, 0.415), (1.000, 1.000), (1.500, 1.524), (2.000, 2.214), (2.500, 3.135), (3.000, 4.093), (3.500, 4.667), (4.000, 4.929), (4.500, 5.000), (5.000, 5.000)
The per unit change in prices as MFP increases | $\rho$ | - |  Graphical converter (MFP, prices): Points (MFP, Prices): (0.000, 1.3890), (0.500, 1.1500), (1.000, 1.0000), (1.500, 0.9223), (2.000, 0.8870), (2.500, 0.8560), (3.000, 0.8332), (3.500, 0.8083), (4.000, 0.8000), (4.500, 0.8000), (5.000, 0.8000)

## Conclusion

This study reveals the paradox of generative AI’s impact on prosperity: while
it holds the promise of increased productivity and wealth creation, it also
poses a substantial risk to economic stability and societal well-being. Our
results suggest the existence of a threshold in the AI-capital-to-labour ratio
beyond which the pace of new job creation would likely be insufficient to
offset the adverse effects of labour displacement. This threshold serves as a
novel economic marker, a warning signal for policymakers to monitor closely as
AI continues to advance. This study serves as a platform for a broader
dialogue on the role of governments, industry, and unions in shaping a future
where technological advancements are harnessed for the collective good. It is
a call to action for strategic, informed, and coordinated efforts to manage a
just transition toward an AI-augmented economy, ensuring that prosperity is
not only preserved but also equitably shared, and that the Mental Wealth of
nations is protected.

### Funding and acknowledgements:

This work was undertaken under the Mental Wealth Initiative supported by seed
funding and philanthropic gifts provided to the Brain and Mind Centre,
University of Sydney.

### Author contribution:

Manuscript concept and drafting: JO & AP; Critical revision of manuscript and
contribution of important intellectual content: all authors.

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