AI's Abundance: Will it Break Our Economic Model and End Money?
The advent of artificial intelligence (AI) is widely hailed as the defining technological shift of our era, promising a future of unprecedented material abundance and solutions to long-standing global challenges. Yet, beneath this techno-optimistic veneer lies a critical question: even if AI delivers on its promise of an economy overflowing with goods and services, how will that abundance be distributed? This tension is already palpable on a smaller scale, as evidenced by Australia’s food economy. Annually, the nation collectively discards an estimated 7.6 million tonnes of food, equating to approximately 312 kilograms per person. Simultaneously, a staggering one in eight Australians grapple with food insecurity, primarily due to insufficient funds to purchase necessary provisions. This stark paradox raises fundamental questions about our capacity to equitably share the bounty that an AI-driven revolution might yield.
Modern market economics, as articulated by economist Lionel Robbins, is fundamentally the study of how societies allocate scarce resources to satisfy seemingly limitless human wants. Markets are designed to ration these limited resources, with scarcity influencing prices and, consequently, compelling most individuals to work to earn money and produce more goods and services. The profound promise of AI – to generate abundance and resolve complex medical, engineering, and social dilemmas – directly challenges this foundational market logic. This tension is inextricably linked to growing anxieties that advanced technology will render millions of workers redundant, prompting the critical question: without paid employment, how will individuals earn a living, and how can a market economy continue to function?
It is crucial to recognize that technological advancement is not the sole cause of unemployment or widespread deprivation. A peculiar characteristic of market economies is their inherent capacity to generate mass want amidst apparent plenty. As economist John Maynard Keynes demonstrated, recessions and depressions can be intrinsic failures of the market system itself, leaving vast numbers in poverty even as raw materials, factories, and labor lie idle. Australia’s most recent economic downturn, though triggered by the public health crisis of the pandemic rather than a market failure, inadvertently illuminated a potential pathway for managing the economic implications of technology-fueled abundance. Adjustments to government benefits – including increased payments, the removal of activity tests, and eased means-testing – dramatically curtailed poverty and food insecurity, even as the nation’s productive capacity temporarily diminished. Similar policies were implemented globally, with cash payments introduced in over 200 countries. This widespread experience during the pandemic significantly bolstered burgeoning calls for integrating technological progress with the implementation of a “universal basic income” (UBI).
The concept of a universal basic income, where every individual receives a guaranteed income sufficient to cover basic necessities, is posited as a mechanism to help market economies navigate the transition to an AI-driven future, ensuring that technology’s promises are broadly shared. However, discussions around UBI necessitate clarity regarding its underlying philosophy. Some proposals, while beneficial, might still perpetuate significant wealth inequalities. Researchers like Elise Klein and James Ferguson advocate for a UBI framed not merely as welfare, but as a “rightful share.” They contend that the wealth generated through technological advancements and societal cooperation is a collective human endeavor and should therefore be enjoyed equally by all, akin to how a nation’s natural resources are considered the collective property of its populace. Debates surrounding UBI predate the current AI discourse, with similar surges of interest occurring in early 20th-century Britain when industrialization and automation boosted economic growth but simultaneously threatened jobs without eradicating poverty. Historically, groups like the Luddites even sought to destroy new machinery that they believed drove down wages, illustrating how market competition, while fostering innovation, often distributes the risks and rewards of technological change highly unevenly.
Rather than resisting the march of AI, an alternative approach involves fundamentally altering the social and economic systems responsible for distributing its gains. Some radical visions, such as “fully automated luxury communism” proposed by UK author Aaron Bastani, welcome technological progress, envisioning a future with more leisure and higher living standards. Bastani, however, favors “universal basic services” over a universal basic income. Under this model, essential services such as public transport, healthcare, education, and energy would be provided directly to citizens for free, rather than giving people money to purchase them. This approach would necessitate a significant shift in how AI and other technologies are applied, effectively socializing their use to ensure they directly meet collective needs.
Ultimately, proposals for universal basic income or services underscore that AI, by itself, is unlikely to usher in a utopia. As some analysts suggest, the interplay of technological advancement and ecological pressures could lead to vastly different futures, not only in terms of collective productive capacity but also in the political determination of who benefits and under what conditions. The immense power concentrated in the hands of tech billionaires and their companies raises concerns about a potential shift towards what some describe as “technofeudalism,” where control over technology and online platforms supplants traditional markets and democratic processes with new forms of authoritarianism. Waiting idly for a technological “nirvana” risks overlooking the immediate possibilities. The reality is that humanity already possesses the means to feed everyone and to end poverty; we do not require AI to illuminate these existing solutions.