By Susanthika S & Parul Oberoi
The challenges of sustaining purchasing power, the limitations of Universal Basic Income, and the need for complementary policy interventions in an automated economy.
Elon Musk said it without blinking: "Probably none of us will have a job." He framed it almost as liberation if you want to work, treat it like a hobby. The robots will handle the rest. However, tucked away in that Silicon Valley utopia is a question no billionaire has adequately answered: if no one earns a wage, who in the world is left to buy anything?
This is not a philosophical riddle. It is the central economic contradiction of our era, and its resolution will define whether the AI revolution becomes the greatest democratization of prosperity in human history or its most spectacular collapse.
The scale of the disruption now being forecast is genuinely unprecedented. Unlike the Industrial Revolution, which swept away manual labour but spawned entirely new cognitive industries, artificial intelligence threatens both simultaneously. A McKinsey Global Institute analysis suggests up to 45% of jobs in the United States could be affected by AI over the next two decades.
Bernie Sanders, as ranking member of the Senate Health, Education, Labour, and Pensions Committee, released a report finding that AI could replace nearly 100 million American jobs within a decade, among them 47% of truck drivers, 64% of accountants, and 89% of fast-food workers.
When Ford's CEO predicts that AI could eliminate literally half of all white-collar jobs, and Anthropic's own founder warns that entry-level white-collar roles could be wiped out within five years, we are no longer debating whether disruption is coming. We are debating who absorbs the blow.
The answer most economists are reluctantly converging on, in many cases, is Universal Basic Income (UBI). The idea is ancient: a guaranteed floor of income for every citizen, regardless of employment status. Andrew Yang brought it into the American mainstream during the 2020 primaries with his Freedom Dividend of $1,000 per month for every adult.
Alaska has operated a version of this for decades through its Petroleum Dividend, distributing between $1,000 and $2,000 annually to every resident, no questions asked. Pilot programs worldwide have consistently shown that guaranteed income does not, as critics fear, make people lazy.
Labour participation declines are marginal when payments do not replace full wages. What they do provide is a floor, a cushion against the vertigo of structural unemployment.
Nevertheless, here is where the economist wants to inject some cold arithmetic. UBI alone is not the same as preserved purchasing power. The AI economy will likely produce two contradictory price pressures at the same time. On one hand, AI-driven automation should dramatically deflate the cost of manufactured goods, logistics, food production, and many services, which make up the bulk of a working family's spending basket.
On the other hand, assets, particularly housing, are likely to inflate sharply, as concentrated wealth seeks stores of value and productive capacity is increasingly owned by a smaller group of capital holders.
The interactive model above illustrates this tension precisely. A ₹12,500 monthly UBI in India (analogous to Yang's $1,500 figure in US terms), combined with 20?flation in everyday goods, actually improves real purchasing power for basic consumption. However, a layer in 30% housing inflation is entirely plausible in Indian metros and American cities alike, and that nominal gain is swallowed.
The math makes the political conclusion inescapable: UBI without complementary housing policy, digital asset taxation, and wealth redistribution is a half-measure. This is where the policy imagination must stretch beyond simply mailing checks.
Senator Sanders' framework offers more structural tools: a 32-hour work week with no pay cut (American workers are already 400% more productive than in the 1940s), mandatory worker representation on corporate boards at 45%, profit-sharing schemes giving employees at least 20% equity in their companies, and a robot tax, a levy on large corporations replacing workers with automated systems, the proceeds funding displaced workers' transition.
These are not radical ideas imported from the fringe. They are operating a policy in Germany today. For economists and academics, the professional opportunity here is equally significant and equally urgent. The field must evolve from cataloguing the old economy to building the intellectual infrastructure of the new one.
Universal Basic Income needs serious modeling, not just of the cheque amount, but of its interaction with AI-driven deflation, asset inflation, digital taxation, and post-scarcity market dynamics. Digital asset taxation, capturing value from the automated capital that is rapidly concentrating in the hands of technology corporations, is an entire subdiscipline waiting to be written.
The question of how to tax a robot, a large language model, or an autonomous logistics system that generates trillions in value while employing no one is one of the defining fiscal puzzles of the coming decade.
The pedagogical implications are equally profound. Economists who teach the next generation cannot do so with curricula designed for a world of stable employment and marginal automation. Students must learn AI literacy as a core economic competency, not simply how to use these tools, but how to interrogate their outputs, identify their biases, and understand where algorithmic reasoning systematically fails.
The economists who will matter most in the next 20 years are those working at the intersection of money and meaning: behavioral economics, political economy, and the psychology of work and identity in a post-labor world. These are domains that AI cannot colonize, because they are fundamentally about what human beings value, something no model has yet learned to ask.
The danger is not that AI arrives. The danger is that it arrives in a world where we have no structural answer to the distribution question. When a small group of multi-billionaires controls the technologies that will shape the lives of billions and captures the overwhelming share of the productivity gains, the resulting concentration is not just an inequality problem.
It is a stability problem. An economy in which the top 1% control more than 40% of total wealth, as is currently the case in the United States, is one with a chronically shrinking consumer base. Even the billionaires need someone to sell to.
Musk's robots will build the cars. Yang's freedom dividend will help some people buy them. However, the real work of designing the tax structures, the ownership models, the labor rights frameworks, and the educational systems that make this transition humane rather than catastrophic is beyond the reach of AI.
It is, in the most literal sense, work that only humans can do. We had better get started.
(Susanthika S and Parul Oberoi are Assistant Professors at Christ (Deemed to be University), Delhi NCR.)
The views expressed are not necessarily those of The South Asian Times
