The Blindside of AI Economics
Christian Ekeigwe
This article was motivated by my listening to the erudite Professor Scott Galloway of New York University Stern School of Business on CNN Smerconish last Saturday, February 7, 2026, during which he pointed out what Amazon said, as reported by The New York Times in October 2025, that Amazon has outlined plans to potentially double its sales/revenue by 2033 without increasing its U.S. warehouse workforce, utilizing increased automation to avoid hiring roughly 600,000 new workers; it represents the future – many companies like UPS and Microsoft are all leveraging AI to reduce human labor. The first thing that came to my mind was, “there is something they are not seeing, a blindside,” namely, who will buy the goods and services to double Amazon’s revenue if there is no increase in consumers, and instead, reductions? The projected increase in revenue is a forlorn hope without confident consumption; if it happens, it will be fleeting.
We are watching artificial intelligence become the great accelerant of modern economic life in our generation. It sharpens prediction, compresses production cycles, and expands the frontier of what firms can accomplish. Yet beneath this momentum lies a structural oversight — a blindside that threatens to undermine the very prosperity AI promises to deliver.
At the center of AI’s economic appeal is a single, dominant thrust: efficiency. AI pushes relentlessly to reduce costs, accelerate processes, and amplify output across every layer of economic activity. But when this drive toward efficiency hardens into a blinkered focus, it narrows our vision. It blinds us to the unintended consequences gathering at the edges of our systems. And if this blindside remains unexamined, it carries the power to destabilize the very economy we are striving to optimize. Efficiency may be the engine of AI economics, but without balance, foresight, and human judgment, it can just as easily become the fault line.
This fault line becomes clearest when we revisit the circular flow model, a primal fundamental of macroeconomics. As Paul Samuelson emphasized, the circular flow demonstrates that “one person’s expenditure is another person’s income,” underscoring that consumption is not a peripheral feature of the economy — it is its bloodstream. Whatever novelty AI introduces, there is no economic model in which consumption can be relegated or bypassed. Production without consumers is not progress; it is paralysis.
This principle is echoed across economic history. The insight is not new. In fact, it is one of the oldest truths in economics. Adam Smith, in The Wealth of Nations, observed that “consumption is the sole end and purpose of all production.” Alfred Marshall reinforced the same logic, noting that demand — not supply — determines the value of goods in real markets. Even Milton Friedman, often associated with supply-side thinking, acknowledged that “the ultimate goal of economic activity is consumption.” Across ideological lines, the consensus is unmistakable: consumption is indispensable.
Yet AI’s efficiency gains often come at the expense of the very workers whose incomes sustain consumption. When firms automate tasks, streamline operations, and reduce labor costs, they may strengthen their balance sheets, but they simultaneously weaken the purchasing power of households. John Maynard Keynes warned that economies falter when “the propensity to consume falls below the level required for full employment.” AI risks pushing us precisely into that territory.
This is the blindside: AI can accelerate production while hollowing out the consumer base that keeps the economy alive, creating a form of macroeconomic fragility that efficiency alone cannot repair. The transition to an AI-augmented economy is not automatic. Displaced workers do not instantly reappear in new, high-value roles. Joseph Schumpeter’s concept of “creative destruction” is often invoked to justify technological disruption, but Schumpeter himself acknowledged that the process is turbulent and uneven — and that institutions must manage the transition. Retraining does not occur spontaneously. And the opportunities created by AI — though real — require time, support, and access to be captured.
Modern research reinforces this. Daron Acemoglu and Pascual Restrepo show that automation can reduce labor’s share of income and depress aggregate demand unless counterbalanced by deliberate policy. Similarly, Nobel laureate Joseph Stiglitz argues that markets alone cannot ensure equitable or stable outcomes when technological change is rapid and asymmetric. The OECD likewise warns that without active labor-market policies, technological disruption can erode both employment and consumption, weakening the circular flow.
This is why government policy becomes indispensable. To preserve the circular flow, the state must act as the stabilizing force that ensures AI’s efficiency gains do not undermine the economy’s demand engine by allowing those gains to accumulate as passive capital rather than as income that households can spend. Without deliberate intervention, AI-driven productivity improvements risk being absorbed into balance sheets rather than circulating through wages, consumption, and investment. Policy must therefore redirect a portion of these gains back into the hands of displaced workers — supporting their transition, sustaining demand, and keeping the economic cycle intact. That means the government should have a policy of: Taxing a portion of AI-driven productivity gains, Using those revenues to support workers displaced by automation, Funding retraining and transition programs, Maintaining household purchasing power during periods of adjustment.
This logic echoes Keynesian stabilization theory, the role of automatic stabilizers, and contemporary proposals such as Bill Gates’ “robot tax,” which argues that automation dividends must be reinvested into human capital.
It also aligns with the OECD’s findings that active labor-market policies are essential during periods of technological upheaval.
Such policies are not punitive; they are protective. They ensure that the benefits of AI are recycled back into the economy rather than being pooled into isolated sectors where they might remain idle. Supporting retrenched workers keeps the circular flow intact and active while workers acquire the skills needed to participate in the new opportunities AI creates. And they prevent efficiency from becoming self-defeating.
AI economics will create an urgent and different necessity that no existing policies are designed to deal with; the paradigm shift will endanger society itself in a vicious circle that affects the companies, too. There is a moment in law called the doctrine of necessity — the principle that when the system itself is in danger, extraordinary action becomes not optional but required.
It justifies steps outside the usual boundaries when the alternative is collapse. That same logic applies to today’s economy. AI’s efficiency thrust is displacing the very workers whose incomes sustain demand, creating a structural threat that markets cannot self-correct in time. When household purchasing power erodes faster than new opportunities appear, we are no longer debating ideology; we are confronting necessity. To preserve the circular flow, policymakers must ensure AI’s gains circulate rather than pool as idle capital.
Tax a portion of AI-driven productivity, support displaced workers, fund large-scale retraining, and protect consumption. These are not activist interventions — they are system-preserving responses to the Blindside of AI Economics. When efficiency endangers the engine of prosperity, necessity becomes the governing doctrine.
The blindside of AI economics, then, is not merely technological — it is macroeconomic. It is the failure to recognize that efficiency without consumption is unsustainable, and that the transition to an AI-driven economy requires deliberate, coordinated policy to maintain balance.
AI can be a force for broad-based prosperity. But only if we acknowledge the blindside now — only if we ensure that efficiency does not become a blinkered focus, and that the circular flow of income remains unbroken and functioning as designed. The future of AI economics will not be determined by algorithms alone, but by the wisdom with which we govern their impact. That wisdom is already warning us that AI’s blinkered efficiency focus creates macroeconomic fragility when it undermines consumption, the indispensable engine of the circular flow.
§ Christian Ekeigwe FCA, CPA (Massachusetts), CISA, is Chairman, Audit Committee Institute and Visionary at Audit is Trustworthy.
