Labor and Expanding AI
The Austrian School
The Austrian School approaches labor as an expression of human choice rooted in subjective value. Their framework, praxeology, treats economic behavior as the outcome of individuals weighing trade-offs, rather than responding to purely objective forces. In this view, labor is fundamentally a sacrifice of leisure. A person chooses to work for wages that exceed, in their own internal calculus, the value they place on their time. Because of this, Austrians argue that wages should emerge entirely from voluntary agreements between workers and employers, without external interference. Policies such as minimum wage laws or union interventions are seen as distortions that impose artificial costs on the market. In their reasoning, these distortions disrupt the natural coordination between supply and demand, often resulting in unintended consequences like unemployment, particularly among lower-skilled workers who are effectively priced out of participation.
How AI Breaks It
The entire model assumes that value emerges from individuals deciding whether to work based on their personal utility curves. AI breaks this by introducing non-human actors that produce without leisure, preference, or sacrifice. An AI agent does not choose between work and rest, nor does it require a wage to act. As a result, the core Austrian mechanism—voluntary exchange between labor supplier and employer—begins to lose relevance in sectors where production is handled autonomously. The “price of labor” becomes ambiguous when the marginal supplier of labor is not human at all. In that world, markets still exist, but they are no longer coordinating human action in the way praxeology assumes; they are coordinating capital and computation. The Austrian emphasis on individual sovereignty actually survives conceptually, but it shifts away from labor participation toward ownership and control of productive AI systems.
Reaganomics
Reaganomics, or supply-side economics, shifts the focus toward producers and the conditions necessary for economic expansion. Within this framework, labor is primarily viewed as a cost of production, one of several inputs that businesses must manage in order to grow. High labor costs, including wages, benefits, and payroll taxes, are seen as frictions that can discourage investment. The central idea is that reducing these frictions will stimulate production, which in turn drives broader economic growth. A key lever in this approach is taxation. By lowering income taxes, proponents argue that workers retain more of their earnings, increasing their incentive to work, while businesses benefit from lower costs, encouraging them to hire and expand. The emphasis on “flexible” labor markets reflects a preference for minimizing regulatory constraints, particularly those that add non-wage costs for employers, such as mandated benefits or strict labor protections. The underlying belief is that by optimizing conditions for producers, the benefits will eventually flow through the economy in the form of job creation and increased prosperity.
How AI Breaks It
While AI initially appears as the perfect realization of supply-side logic. It reduces labor costs, increases efficiency, and removes frictions that limit expansion. However, this creates a second-order contradiction. If the optimization of production systematically eliminates wage income, it erodes the consumer base that ultimately absorbs that production. Supply-side theory implicitly relies on a functioning demand layer, even if it does not center it analytically. AI accelerates the supply side to a point where it can outpace demand formation, creating overcapacity relative to purchasing power. Additionally, the benefits of this optimization tend to concentrate among those who own or control AI infrastructure, rather than diffusing broadly through wage gains. This concentration challenges the assumption that growth alone will translate into prosperity.
The Chicago School
The Chicago School reframes labor through the concept of human capital, offering a algorithmic and quantitative lens. Thinkers like Milton Friedman treated individuals almost as firms unto themselves, capable of investing in their own productive capacity. Education, training, and skill acquisition become forms of stable value, and capital expenditure, undertaken with the expectation of future returns in the form of higher wages. Within this framework, wages are closely tied to the marginal product of labor, meaning that what a workers earnings are a reflection of the value they generate in output or productivity. The implication is straightforward: increasing one’s productivity should lead to higher compensation. Like the Austrians, Chicago economists are skeptical of minimum wage laws, viewing them as blunt instruments that interfere with market signals. By imposing a wage floor above the productivity level of some workers, they argue, such policies can exclude those individuals from employment altogether, limiting access to entry-level opportunities that might otherwise serve as stepping stones.
How AI Breaks It
AI destabilizes both sides of that equation. First, it compresses or outright replaces the marginal product of many forms of human labor. If an AI system can perform a task at near-zero marginal cost and at equal or higher quality, then the human contribution to output trends toward zero, regardless of prior investment in education or training. Second, it weakens the incentive structure behind human capital formation. The premise that education reliably increases earnings depends on a relatively stable mapping between skill and market demand. AI introduces volatility into that mapping, where entire skill classes can be devalued rapidly. In effect, the “return on human capital” becomes uncertain or even negative in some domains. The Chicago framework still functions in areas where humans retain comparative advantage, but its predictive power erodes as AI expands into cognitive domains that were considered defensible.
Keynesian Economics
Keynesian economics takes a markedly different approach by focusing more on system-wide dynamics, particularly during periods of economic instability. A central concept is the idea that wages are “sticky,” meaning they do not adjust downward easily when economic conditions worsen. This stickiness arises from a combination of contracts, institutional norms, and psychological factors like morale. In this context, labor is not just a cost or a choice, but a primary driver of aggregate demand. When workers lose income or face wage stagnation, their reduced spending ripples through the economy, lowering demand for goods and services. Businesses, facing declining revenues, may respond by cutting jobs, which further suppresses demand in a self-reinforcing cycle often described as a downward spiral. Keynesians therefore emphasize the role of government in stabilizing the economy, particularly when unemployment rises. Through fiscal policy, such as increased public spending, governments can inject demand back into the system. They also tend to support mechanisms like unions and minimum wage laws, arguing that higher incomes for workers translate directly into higher consumption, which sustains economic activity.
How AI Breaks It
AI introduces a break in that loop by enabling production without corresponding wage distribution. If output increases while labor income decreases, aggregate demand weakens, not because goods are scarce, but because purchasing power is no longer broadly distributed. This creates a structural version of the demand shortfall Keynes originally diagnosed during cyclical downturns. The difference is that this is not a temporary shock but a persistent condition driven by technology. Traditional Keynesian tools, such as fiscal stimulus, can offset this temporarily by redistributing income, but they assume that employment will eventually recover as the economy stabilizes. In an AI-driven economy, that assumption may not hold. The system can remain highly productive while structurally underemploying human labor, forcing Keynesianism to confront the possibility that wages are no longer the primary transmission mechanism for demand.
Why The U.S. Economy Inevitably Breaks Apart
In a neutral reality, all predominant U.S. economic theories are tethered to the concept of human labor as “primary manipulator value”; whether it is John Maynard Keynes’s focus on the marginal propensity to consume or Milton Friedman’s Monetarist view of human capital, the system assumes a human being at the center of the inertia loop. However, the observed breaking point in the age of AI lies in the decoupling of productivity from human effort. In his work The End of Work, Jeremy Rifkin signaled that as the marginal product of labor tends toward zero due to automation, the circular flow of the economy begins to collapse, as inertia rooted in human activity evaporates. This creates a fundamental contradiction: Supply-Side economics, as outlined by Arthur Laffer and Robert Mundell, incentivizes the total removal of labor friction through AI, yet the resulting loss of wages undermines the aggregate demand necessary for Keynesian stability.
Additionally, current technological capabilities have already rendered the traditional brokerage model of banking and payment systems technically redundant. As Niall Ferguson notes in The Ascent of Money, the history of finance is a move toward abstraction, and we have reached a point where decentralized storage, orbital internet mesh networks, and peer-to-peer digital ledgers can execute the functions of a central reserve without the institutional overhead. The push for Central Bank Digital Currencies (DBDC) can be interpreted through the lens of Friedrich Hayek’s The Denationalization of Money, though in the opposite direction; rather than a free market of private currencies, CBDC systems represent an attempt by institutions to maintain a form of macroeconomic control over a digital economy that is naturally trending toward decentralization and unbundling. This technological shift creates a reality where the storage function of a bank is replaced by secure hardware, and the transfer function of a clearinghouse is replaced by protocol-level settlement.
So what can be done before the train stops moving? The techno-optimist route offers significant advantages, primarily the democratization of the means of production via personal AI agents, which aligns with the Austrian School’s ideal of individual sovereignty and the removal of rent-seeking intermediaries. However, serious concerns remain regarding what can be described as a ghost economy. If the system moves toward the technological unemployment once anticipated by Keynes in his 1930 essay Economic Possibilities for our Grandchildren, the social contract begins to erode. Without a mechanism to distribute human inertia, as value, produced by autonomous AI… wealth accumulates at the protocol-owner level, leading to what some theorists describe as algorithmic feudalism. The neutral observation is that while our technological capabilities now allow for the disintermediation of financial institutions and the automation of labor, our dominant economic frameworks remain anchored in a structure that depends on human wages to sustain the consumption required for system stability.
Conclusion
Re-Think the meaning and potential underpinings of of economics, through a physics philosophy lens.
“Economic inertia” is not a formal construct in modern economics, but perhaps it should be. It’s a term I use to describe a system-level property that governs how activity persists and is experienced relative to the rate of change in value, productivity, wages, and output within a macro economic system. In physics, relativity shows that time is not absolute; it is experienced differently depending on velocity. As an object accelerates, its relationship to time shifts, not because time itself changes universally, but because the observer’s frame of reference changes. A similar dynamic begins to emerge in economic systems under conditions of extreme acceleration driven by AI.
A second failure mode emerges at the structural level, one better described through aerodynamics than relativity. In aeroelastic flutter, forces that previously canceled each other out, reach a critical velocity, causing the surface to tear itself apart. What once stabilized the structure now amplifies its instability. Each deviation amplifies the next. The structure was designed for a range of speeds at which its internal tensions were self-correcting. Above the flutter threshold, those same tensions become self-destructive. A similar dynamic may govern economic systems under AI-driven acceleration. Supply-side optimization, demand-side stability, and institutional response have historically functioned as counterweights. At sufficient velocity, they begin competing for the same diminishing mass of human economic participation — amplifying instability.
