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If AI Takes Most of Our Jobs, Money as We Know It Will be Over. What Then?

Accepted submission by upstart at 2025-08-20 00:45:29
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If AI takes most of our jobs, money as we know it will be over. What then? [theconversation.com]:

Disclosure statement

Ben Spies-Butcher is co-director of the Australian Basic Income Lab, a research collaboration between Macquarie University, University of Sydney and Australian National University.

Partners

Macquarie University [theconversation.com] provides funding as a member of The Conversation AU.

View all partners [theconversation.com]

It’s the defining technology of an era. But just how artificial intelligence (AI) will end up shaping our future remains a controversial question.

For techno-optimists, who see the technology improving our lives, it heralds a future of material abundance [theconversation.com].

That outcome is far from guaranteed. But even if AI’s technical promise is realised – and with it, once intractable problems are solved – how will that abundance be used?

We can already see this tension on a smaller scale in Australia’s food economy. According to the Australian government, we collectively waste around 7.6 million tonnes [dcceew.gov.au] of food a year. That’s about 312 kilograms per person.

At the same time, as many as one in eight Australians [theconversation.com] are food-insecure, mostly because they do not have enough money to pay for the food they need.

What does that say about our ability to fairly distribute the promised abundance from the AI revolution?

AI could break our economic model

As economist Lionel Robbins articulated [www.socialscience.international] when he was establishing the foundations of modern market economics, economics is the study of a relationship between ends (what we want) and scarce means (what we have) which have alternative uses.

Markets are understood to work by rationing scarce resources towards endless wants [sparkl.me]. Scarcity affects prices – what people are willing to pay for goods and services. And the need to pay for life’s necessities requires (most of) us to work to earn money and produce more goods and services.

This article is part of The Conversation’s series on jobs in the age of AI [theconversation.com]. Leading experts examine what AI means for workers at different career stages, how AI is reshaping our economy – and what you can do to prepare.

The promise of AI [theconversation.com] bringing abundance and solving complex medical, engineering and social problems sits uncomfortably against this market logic.

It is also directly connected to concerns [theconversation.com] that technology will make millions of workers redundant. And without paid work, how do people earn money or markets function?

Meeting our wants and needs

It is not only technology, though, that causes unemployment. A relatively unique feature of market economies is their ability to produce mass want, through unemployment or low wages, amid apparent plenty.

As economist John Maynard Keynes revealed [imf.org], recessions and depressions can be the result of the market system itself, leaving many in poverty even as raw materials, factories and workers lay idle.

In Australia, our most recent experience of economic downturn wasn’t caused by a market failure. It stemmed from the public health crisis of the pandemic. Yet it still revealed a potential solution to the economic challenge of technology-fuelled abundance.

Changes to government benefits – to increase payments, remove activity tests and ease means-testing – radically reduced poverty and food insecurity [acoss.org.au], even as the productive capacity of the economy declined.

Similar policies were enacted globally [worldbank.org], with cash payments introduced in more than 200 countries. This experience of the pandemic reinforced growing calls [rollingstone.com] to combine technological advances with a “universal basic income”.

This is a research focus of the Australian Basic Income Lab [ausbasicincome.org], a collaboration between Macquarie University, the University of Sydney and the Australian National University.

If everyone had a guaranteed income high enough to cover necessities, then market economies might be able to manage the transition, and the promises of technology might be broadly shared.

Welfare, or rightful share?

When we talk about universal basic income, we have to be clear about what we mean. Some versions of the idea would still leave huge wealth inequalities.

My Australian Basic Income Lab colleague, Elise Klein, along with Stanford Professor James Ferguson, have called instead for a universal basic income designed not as welfare, but as a “rightful share”.

They argue [abc.net.au] the wealth created through technological advances and social cooperation is the collective work of humanity and should be enjoyed equally by all, as a basic human right. Just as we think of a country’s natural resources as the collective property of its people.

These debates over universal basic income are much older than the current questions raised by AI. A similar upsurge of interest in the concept occurred in early 20th-century Britain [springer.com], when industrialisation and automation boosted growth without abolishing poverty, instead threatening jobs.

Even earlier, Luddites [nationalarchives.gov.uk] sought to smash new machines used to drive down wages. Market competition might produce incentives to innovate, but it also spreads the risks and rewards of technological change very unevenly.

Universal basic services

Rather than resisting AI, another solution is to change the social and economic system that distributes its gains. UK author Aaron Bastani offers a radical vision of “fully automated luxury communism [google.com.au]”.

He welcomes technological advances, believing this should allow more leisure alongside rising living standards. It is a radical version of the more modest ambitions outlined by the Labor government’s new favourite book – Abundance [theconversation.com].

Bastani’s preferred solution [newstatesman.com] is not a universal basic income. Rather, he favours universal basic services.

Instead of giving people money to buy what they need, why not provide necessities directly – as free health, care, transport, education, energy and so on?

Of course, this would mean changing how AI and other technologies are applied – effectively socialising their use to ensure they meet collective needs.

No guarantee of utopia

Proposals for universal basic income or services highlight that, even on optimistic readings, by itself AI is unlikely to bring about utopia.

Instead, as Peter Frase outlines [jacobin.com], the combination of technological advance and ecological collapse can create very different futures, not only in how much we collectively can produce, but in how we politically determine who gets what and on what terms.

The enormous power of tech companies run by billionaires may suggest something closer to what former Greek finance minister Yanis Varoufakis calls “technofeudalism [australiainstitute.org.au]”, where control of technology and online platforms replaces markets and democracy with a new authoritarianism.

Waiting for a technological “nirvana” misses the real possibilities of today. We already have enough food for everyone. We already know how to end poverty. We don’t need AI to tell us.

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Will AI pull the career ladder up out of reach – or just change what it looks like? [theconversation.com]:

Disclosure statement

Rachael Hains-Wesson does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Partners

RMIT University [theconversation.com] provides funding as a strategic partner of The Conversation AU.

View all partners [theconversation.com]

Once, a university degree was widely seen as a “ticket” to securing high-paying jobs and social mobility.

Now, as artificial intelligence (AI) promises to revolutionise the labour market, it’s university students and recent graduates who face some of the greatest uncertainty.

How do you pick a major or a career when it isn’t obvious what jobs will even exist in 10 years’ time?

Back in May, the chief executive of the AI company Anthropic, Dario Amodei, claimed [axios.com] AI could eradicate half of all entry-level white collar jobs over the next five years.

At this stage, it’s still up for debate [cnbc.com] whether AI will lead to such a mass wipe-out of graduate roles, or just change what these jobs look like.

Either way, we have a collective duty to prepare young people for an AI-driven world. Students, educators, employers and the government all have a role to play.

First foot on the ladder

In many white collar or “knowledge work” careers, the “lower rungs” of the career ladder have traditionally consisted of entry-level roles that centre on tasks such as data entry, routine report writing, document review or basic analysis.

These jobs were not only a rite of passage [weforum.org], but also a critical training ground for developing industry-specific skills, professional judgement and workplace confidence.

Many of these tasks are now at risk of being disrupted by generative AI.

This article is part of The Conversation’s series on jobs in the age of AI [theconversation.com]. Leading experts examine what AI means for workers at different career stages, how AI is reshaping our economy – and what you can do to prepare.

In the United States, the unemployment rate for recent college graduates is now higher than [newyorkfed.org] the broader unemployment rate.

Experts say [apnews.com] this is due to economic uncertainty, high competition for jobs, and the slowing of white-collar job growth. But some argue [apnews.com] the impacts of AI are also a factor, especially in fields like IT.

The International Labour Organization has published a list of “exposure indices” ranking [ilo.org] a range of occupations from those deemed “not exposed” to generative AI to those that are highly exposed.

You can search yours below:

Replaced – or enhanced?

To help unpack some of AI’s impacts, it is helpful to quickly differentiate between the idea of “automation” AI, where jobs are replaced, and “augmentation” AI, which improves the output of existing workers.

Findings from a recent study [doi.org] suggest different kinds of work may differ in their exposure to these kinds of disruptions.

The study found in low-skilled occupations, automation AI could negatively impact new work, employment and wages. In high-skilled occupations, augmentation AI may raise wages and help create new work.

The study’s author, David Marguerit, suggests [doi.org] this could have negative implications for wage inequality.

Not the first time

AI is not the first technology to threaten the automation of young workers and early career tasks. From the introduction of calculators and computers to email and communication systems, technological innovations have steadily reshaped the nature of working roles.

Each of these innovations removed or transformed certain routine duties, often sparking fears of job losses, but also creating space for new responsibilities and skills [cfg.eu]. What makes the current wave of AI distinct is the breadth of cognitive and creative functions it can perform, and the speed at which these capabilities can be deployed across industries.

A 2022 study [doi.org] explored the potential risks of job automation for young Australians as they entered the workforce between 2009 and 2019.

Interestingly, its findings suggest young Australians often began in jobs at high risk of automation but reduced this risk by gaining qualifications, changing roles, or avoiding part-time or casual work.

Fewer options existed for avoiding jobs at high risk of change, such as data entry. Successful strategies for doing so were influenced by parental wealth, computer access, and ability to apply knowledge in new contexts.

This repositions the AI debate. Rather than predicting which jobs will last, we should tackle socio-economic divides by ensuring equal access to technology at home and in education, promoting the developmental use of AI and fostering critical reflection. For example, we could do this through structured classroom discussions about AI’s ethical and social impacts.

We also need to build a labour market that protects entry-level workers from soon-to-be automated roles to augmentation AI roles. In other words, getting them on the ladder.

What we all can do

How can we prepare for an AI-driven future? For those new to choosing career pathways, it’s worth looking at which industries are growing and which skills are hard to automate [ilo.org].

Jobs that require empathy, creativity and complex critical thinking are at less risk of AI automation, such as health care, education, creative arts, renewable energy and construction.

Policymakers and educators can also enhance the value of on-the-job training, such as internships and industry-linked projects [acen.edu.au], which have been shown to bridge theory and practice [ijwil.org] and improve career learning [youtube.com].

Recent research [tandfonline.com] showcases a critical gap between the support students expect during placements and what they actually receive from workplace supervisors.

This means an investment in targeted upskilling, relevant AI-focused internships, AI-informed learning and teaching, as well as prioritising career learning [cica.org.au] as a core graduate outcome.

Want to write?

Write an article and join a growing community of more than 209,200 academics and researchers from 5,283 institutions.

Register now [theconversation.com]

Journal Reference:
Marguerit, David. Augmenting or Automating Labor? The Effect of AI Development on New Work, Employment, and Wages, (DOI: 10.2139/ssrn.5169611 [doi.org])
Marguerit, David. Augmenting or Automating Labor? The Effect of AI Development on New Work, Employment, and Wages, (DOI: 10.2139/ssrn.5169611 [doi.org])
Just a moment..., (DOI: 10.1080/13676261.2022.2112161 [doi.org])
Just a moment..., (DOI: https://www.tandfonline.com/doi/abs/10.1080/07294360.2025.2486178 [doi.org])

See also:

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