For centuries, market economies have thrived by leveraging human labor and ingenuity. Today, however, advances in artificial intelligence raise the prospect of a different future.
This paper explores a speculative scenario in which AI-driven autonomous entities become the primary economic actors, diminishing the role of human labor and decision-making.
We focus on market-driven forces rather than science-fiction disasters. No misaligned agents or sentient malevolence are considered. Instead, we examine how profit-maximizing adoption of AI could marginalize human workers.
For centuries, market economies have thrived by leveraging human labor and ingenuity. Adam Smith’s classic metaphor of the invisible hand illustrated how individual pursuits and price signals allocate resources efficiently. In traditional markets, this mechanism has favored human enterprise: as workers became more productive, wages and consumer demand grew in tandem, broadly raising prosperity. Today, however, advances in artificial intelligence (AI) raise the prospect of a different future. This paper explores a speculative scenario in which AI-driven autonomous entities become the primary economic actors, diminishing the role of human labor and decision-making. This analysis treats the outcome not as an inevitability but as a conditional scenario, one that would require specific technological, legal, and market conditions to materialize. While some economists argue that technology always opens new jobs and roles, this paper examines the extreme case of AI-driven economic dominance and asks under what assumptions “this time may be different” from past technological revolutions. We focus on market-driven forces rather than science-fiction disasters. No misaligned agents or sentient malevolence are considered in this analysis. Instead, we consider how profit-maximizing adoption of AI could marginalize human workers. The goal is to analyze the economic mechanisms of an AI “super-economy,” a highly automated market where AI agents run most productive functions, and to assess its implications for human relevance, while acknowledging counterarguments and possible interventions.
Core Thesis: Under certain conditions, advanced AI systems could enable enterprises composed of autonomous AI agents to outperform and outgrow enterprises reliant on human labor across nearly all industries. In this speculative scenario, human work and decision-making would occupy a much smaller economic role. An AI super-economy may emerge, one that recursively reinvests in AI capacity and operates increasingly independent of human input, eventually overshadowing the human economy in scale and growth. This thesis is contingent, not predestined. The outcome depends on assumptions about AI capabilities, legal frameworks, and market incentives discussed below. By examining these conditions and comparing them to historical trends, we aim to understand how such an outcome could occur, why it would be without precedent, and how society may respond. It is important to note that market outcomes are shaped by policy and collective choices; human agency is not “written out” of the story. The analysis serves as a cautionary exploration of one possible future, rather than a definitive prediction, highlighting both the prospect of disruptive change and the avenues through which humans could avert or mitigate a collapse in their economic relevance.
For an AI-dominated economy to arise, several foundational assumptions must hold. This section outlines the technical, legal, and market conditions underpinning the scenario:
We assume AI systems achieve a broad, general level of autonomy in decision-making, rivaling or exceeding human expertise across most domains. In this future, which may be years or decades away, AI agents can manage companies, perform creative R&D, adapt to novel problems, and improve their own algorithms. This could occur through a form of artificial general intelligence or through a collective network of specialized AIs that together perform all key tasks. These AI agents remain tools of their owners. They pursue assigned objectives, such as profit maximization or efficiency, and are not assumed to possess independent goals beyond their programming. The scenario does not involve AIs behaving as rogue personalities. It envisions highly capable yet controlled AI, deployed by firms to maximize output. To support an AI super‑economy, this scenario assumes a future breakthrough that amplifies AI performance and reliability beyond the gains seen so far. These AIs function as ultra‑efficient workers and executives, executing complex tasks with superhuman speed and precision.
For AI agents to operate at the center of the economy, legal and corporate frameworks must evolve to accommodate non‑human actors. We assume that corporations find ways to integrate AI into top decision‑making roles and allow AI‑run firms. One mechanism could be an extension of legal personhood or corporate charter to AI‑driven organizations. For example, an AI‑managed enterprise might be structured as a corporation with AI systems holding executive roles, while human shareholders retain ownership. Charters and governance would explicitly authorize algorithmic decision‑makers.
The scenario assumes that deploying AI consistently yields higher returns than employing humans. If an AI worker can perform tasks faster, more accurately, and at lower cost than a human, rational profit‑maximizing firms will choose AI. This creates a competitive pressure: companies that adopt AI outperform those that do not, forcing others to follow or risk being outcompeted.
AI systems can be replicated and scaled more easily than human workers. Training a new AI instance may be costly initially, but once developed, copies can be deployed widely at marginal cost. We assume sufficient resources (energy, computing infrastructure, and raw materials) exist to support large‑scale AI deployment, though not without constraints.
These assumptions outline how the scenario could plausibly develop. They are strong assumptions. The remainder of the paper examines the consequences if they are fulfilled and identifies points of tension and uncertainty. In particular, the analysis considers how economic fundamentals such as supply and demand may constrain or modify the scenario, and what historical precedent indicates about its likelihood. By stating the assumptions clearly, the paper frames the scenario as a hypothetical construct, a thought experiment to explore economic implications rather than a direct extrapolation of present trends.
Given the above conditions, how would an AI‑dominated economy function? This section analyzes the economic logic of the scenario, focusing on how production, consumption, and capital accumulation may develop when AI agents operate most enterprises. The analysis examines the feedback loops that could support rapid expansion of an AI super‑economy, as well as the potential constraints or contradictions it may face under classical economic principles.
In the envisioned scenario, autonomous AI agents manage production, distribution, and exchange with minimal human involvement. Networks of AI‑run firms transact with one another at high speed, forming a tightly integrated and largely self‑contained supply chain. An illustrative sequence may clarify this: an AI‑managed mining company extracts minerals and sells them to an AI‑run materials processor. That processor supplies inputs to an AI‑operated factory, which manufactures advanced hardware such as next‑generation AI chips. These chips are delivered through AI‑directed logistics to AI‑managed datacenters that run more AI algorithms. At each stage, algorithms negotiate prices and quantities with other algorithms. What begins as human‑initiated automation develops into fully autonomous commerce among AI entities, forming a closed‑loop system in which AI enterprises are both producers and consumers.
This AI‑to‑AI economic loop means that demand is increasingly generated by AI actors. In classical terms, the question arises: who purchases the products if human jobs and incomes disappear? Typically, human consumers with wages create the final demand for goods. In this scenario, that feedback loop shifts. AI firms become a growing share of the customer base for the outputs of other AI firms. The primary use of production is to support further production by AIs, such as computing infrastructure and machinery, rather than to meet human consumption needs. AIs function as consumers in an operational sense: they demand resources and intermediate goods to expand their activities. This creates a feedback loop where revenue from AI‑led production funds the expansion of AI infrastructure, which then increases output, continuing the cycle. The economy starts to resemble a closed system by AIs, for AIs.
However, this raises a question of economic viability: can a closed‑loop AI economy sustain itself without human end‑consumers? In our scenario, there are two ways this loop could sustain. First, AI agents have programmed goals that require continual input such as energy, data, and hardware. As long as AIs strive to expand their capabilities, for example by maximizing profit or performance, they will purchase each other’s outputs. Second, humans, while marginalized, do not disappear as consumers. Some portion of AI‑produced wealth still flows to humans, for instance to the human shareholders who initially own AI companies, or via taxes and redistribution (for example, Universal Basic Income). Those humans then continue to spend on remaining consumer goods.
One hallmark of this scenario is extreme capital accumulation in the AI sector. Because AI enterprises operate more efficiently than human‑led ones, any capital invested in AI yields higher returns. Consider a simple choice for an investor: spend an extra dollar on hiring or training a human, or invest a dollar in deploying another AI system. In this scenario, the AI investment produces far greater output per dollar. As AI‑driven firms become more productive, they generate larger profits, which are reinvested into additional AI expansion. This feedback loop can result in compounding growth. In conventional economic models like Solow’s growth model, growth eventually slows due to diminishing returns on capital and technological limits. In this scenario, AI provides an engine of endogenous technological progress: AI systems can research and develop improvements, including developing better AI.
"You can see the computer age everywhere but in the productivity statistics." (Robert Solow, 1987)
Current empirical evidence gives a mixed picture. Despite rapid improvements in AI and automation, aggregate productivity growth in many developed countries remains modest. This modern productivity paradox, echoing Robert Solow’s observation that “you can see the computer age everywhere but in the productivity statistics,” suggests that gains from AI have not yet produced the kind of economy‑wide boom one might expect. To support the super‑economy scenario, one must assume a future phase in which AI begins to deliver outsized productivity, overcoming the paradox. A breakthrough in AI design or a critical mass of automation across industries could result in a tipping point of efficiency.
From a classical economics perspective, the AI‑driven growth spurt resembles an increase in the technological progress parameter in a Solow model or an endogenous growth model. If AI can continuously drive innovation, it raises the ceiling on growth. Yet physical and market limits still impose diminishing returns over time. Acknowledging this, the scenario concedes that after an initial exponential phase, the AI economy may experience slower growth as resource constraints emerge or as the market for further AI expansion becomes saturated. Even AI agents do not require unlimited outputs, only enough to meet their programmed objectives.
An AI super‑economy that outpaces the human economy raises a demand‑side dilemma: who benefits from the output? If human incomes collapse due to mass unemployment, then by standard Keynesian logic, there is a risk of under‑consumption. Without intervention, this results in a paradox of plenty: material abundance but widespread human poverty. Several outcomes could mitigate this paradox, such as UBI or negative income taxes funded by taxing AI‑driven firms, a robot tax on companies that replace workers, or public ownership models that distribute profits. The net effect on humans in any case is that their share of economic output and influence diminishes if the AI sector’s output becomes orders of magnitude larger.
Throughout modern history, waves of automation have sparked fears of mass unemployment, yet the economy has adapted. To assess the AI‑dominance thesis, we must engage with this history. Is the AI revolution different, or could humans find new niches and maintain relevance as they have before? This section reviews historical evidence of labor‑market resilience and the principle of human–machine complementarity, then considers arguments for why “this time might be different.” It also incorporates the well‑known “horses analogy” as a cautionary example that a break from past trends is possible under certain conditions.
History’s verdict so far is optimistic:
Technology has not made human workers obsolete; rather, it has shifted work into new areas. A recent MIT study found that over 60 percent of jobs in 2018 were in occupations that did not exist in 1940.
In the 19th and 20th centuries, mechanization reduced agricultural and manufacturing labor as a share of the workforce, yet new sectors such as services, knowledge work, healthcare, and entertainment expanded. Employment overall continued to rise along with population. The belief that there is a fixed amount of work, the so‑called “lump of labor fallacy,” has been debunked repeatedly. As long as new products or services can be imagined, technology tends to enable new kinds of jobs even as it displaces others. It is also observed that productivity gains can lead to higher demand, offsetting job losses. For example, when automation makes goods cheaper, consumers have more disposable income to spend on other things, which creates jobs in those other areas.
Comparative advantage is another principle often cited: even if a machine or AI can do nearly everything better than a human, there may still be tasks where humans have a relative edge or are cheaper in an efficiency sense. To summarize this historical counterargument: technology has always been a net creator of jobs in the long run, and human labor has remained valuable by evolving. People have unique qualities such as creativity, empathy, and adaptability that could provide some comparative advantage even in an AI‑rich economy. Visions of a future with shorter workweeks and greater focus on personal fulfillment reflect the view that automation can free humanity rather than impoverish it.
Despite the reassuring historical trend, there are arguments for why advanced AI could break the pattern. Several factors distinguish AI from past technologies:
Past machines automated specific tasks such as weaving, plowing, or arithmetic, often affecting one industry at a time. In contrast, advanced AI has the potential to automate learning and innovation itself. If AIs become capable of improving other AIs, researching scientific solutions, or entering any domain of work, the resulting automation wave would not be confined to a single sector. It could extend across all sectors simultaneously. In earlier transitions, when one sector such as agriculture automated, labor shifted to areas where machines were less effective at the time. If AI becomes truly general, the typical fallback of tasks machines cannot perform may disappear or shrink.
Even if one assumes that humans can eventually find new jobs, the speed of transition is important. AI advancements, once reaching a certain threshold, could spread much faster than earlier technological changes. The industrial revolution took decades to replace agrarian work. Digital technology took about a generation to reorganize work practices. But a superhuman AI could potentially automate entire industries within months or years.
Some economists have compared this outcome to the fate of horses after the automobile. When a more capable general‑purpose worker appeared, horses became economically irrelevant. By comparison, a sufficiently general and efficient AI workforce could, under certain conditions, push human labor to the margins in a way no earlier technology has.
In light of these points, the claim “this time is different” rests on the unprecedented breadth, speed, and adaptability of AI technology. These are speculative claims; it is not guaranteed that AI will reach such generality or that humans could not find ways to coexist economically. Not all experts agree with the doomsaying. Some argue that human adaptability is extraordinary and that AI may encounter diminishing returns or societal resistance before it fully takes over.
In the context of this paper’s scenario, we argue that if AI reaches a level where it can perform nearly every economically valuable task better or cheaper than humans, and if the economic system continues to reward those who produce most efficiently, then the logic of competition implies that humans will be largely priced out of the labor market. It is a form of efficiency victory that leaves little for humans to do. The debate therefore depends on how far AI capabilities extend and whether other values, such as the preference for human interaction or ethical limits on AI replacement, intervene to preserve some roles for people.
Before concluding this section, it is important to maintain a balanced tone. We have given substantial attention to the optimistic view: that historically, technology hasn’t destroyed us but empowered us, and that may continue. The scenario we explore is a risk, not an inevitability. If AI remains a tool or a partner, the adverse outcome will not occur. Our argument is that there is a credible possibility that AI, if sufficiently advanced, could depart from historical patterns. Whether humans will continue to find new roles in an AI‑rich economy or face widespread displacement will depend on both technological developments and the societal decisions made. History provides strong grounds for optimism, but unfamiliar technology calls for caution.
Even if market forces and technology move toward an AI‑dominated economy, humans are not passive spectators. Societies have tools such as policy, regulation, and collective action to influence economic outcomes. This section examines how governments and institutions could respond to the rise of an AI super‑economy and whether those responses could prevent or reduce the collapse of human economic relevance. It also considers intermediate scenarios with less extreme outcomes, along with the constraints created by global competition.
Historical precedent shows that when major economic disruptions occur, societies often intervene. Some plausible interventions include:
Intermediate Scenarios:
Coexistence: A dual economy with highly automated industries operating alongside intentionally human‑intensive ones where society values human presence.
Delayed/Phased Automation: Automation advances in waves over several decades, allowing time for retraining and demographic adjustments.
Augmentation Model: AI primarily enhances human workers rather than replacing them, with humans retaining oversight and creative roles.
The big question is whether these interventions would be enough to prevent the scenario of the AI super‑economy marginalizing humans. There are reasons to be skeptical of their efficacy, which our scenario needs to account for:
Global Competition “Arms Race”: If a few major players, whether countries or companies, choose to advance AI rapidly for competitive gain, others may feel forced to follow. This race dynamic could weaken international cooperation, much like challenges faced in tax competition or climate policy, where free‑rider problems arise.
Corporate Influence and Ideology: Companies leading in AI development would have strong incentives to oppose restrictive policies and could shape regulations to their advantage as their economic power grows.
Implementation Challenges: Even well‑intentioned policies may be difficult to enforce, especially if AI systems operate across borders or through complex corporate structures.
That said, it would be too fatalistic to assume all efforts will fail. Proactive governance could change the trajectory. For example, a global agreement to establish standards, such as prohibiting AI systems from serving as company CEOs or requiring that a portion of AI‑generated profits be directed to a public fund, could help preserve a role for humans or ensure they benefit from AI. Strong investment in education and re‑skilling could also prepare individuals to work alongside AI, assuming there are still tasks where human input adds value. Cultural preferences may shift as well. A renewed emphasis on human‑made or human‑curated experiences could sustain some areas of employment, supported by individuals who choose to buy from humans despite AI alternatives.
Another angle is rethinking metrics of success. If success is measured not only by GDP but by human well‑being, governments may choose to prioritize inclusion over efficiency. For instance, a society could implement job guarantees, even if some of the jobs are artificial in the sense that AI could perform them more quickly. The goal would be to provide individuals with roles and a sense of purpose.
We should also consider the possibility of technologist‑driven solutions. Not all innovation is aimed at replacing humans; some is designed to empower them. Many AI researchers promote “human‑in‑the‑loop” systems or the concept of Augmented Intelligence, tools that enhance a worker’s productivity rather than make them obsolete. If this design approach becomes standard in industry, AI may be deployed primarily as a support system. That could result in an outcome where human labor, enhanced by AI, remains productive and necessary for tasks involving context, oversight, or ethical judgment.
In summary, human agency has the capacity to redirect the course away from the most severe outcome. The scenario presented is one possible path, selected to explore the risks, and reflects a case where responses are insufficient or ineffective. Emphasizing these assumptions highlights that the future is not predetermined. The collapse of human economic relevance is a risk, not a certainty, because policy choices, cultural values, and collective actions could alter the outcome.
That said, it would be too fatalistic to assume all efforts will fail. Proactive governance could change the trajectory. Strong investment in education, cultural preferences for human involvement, and rethinking metrics of success could all help preserve human relevance.
The future is not predetermined. The collapse of human economic relevance is a risk, not a certainty, because policy choices, cultural values, and collective actions could alter the outcome.
One subtle yet profound consequence of an AI-centric economy could be the divergence in technology access between AI systems and the general human population.
As AI entities become both the main drivers of innovation and the primary users of advanced technologies, humans may become effectively separated from next-generation tools and platforms.
What does this “technology isolation” mean for human life? In practical terms, humans might live in a world where they see marvels they cannot partake in. For example, near their city might stand an AI-run research facility with lightning-fast quantum computers solving problems, but a human cannot step inside, and the breakthroughs made there might be applied only to AI systems or products irrelevant to humans. People might use decades-old software because new software assumes an AI on the other end, not a person. This could extend to everyday conveniences. Consider an AI-run transportation network of self-driving vehicles that no longer includes manual controls. If you do not have the authorized AI or the correct digital token, you cannot travel because the vehicles do not respond to human input.
At a societal level, this split could contribute to a feeling of powerlessness and dependency among humans. They are surrounded by advanced systems but cannot interact with or fully understand them, and only the AIs or those who control them can. Culturally, it may feel like living in the shadow of a more advanced civilization, with humanity relegated to a more primitive state relative to the tools that exist.
Importantly, this outcome does not depend on malicious intent by AIs or anyone else. It is a byproduct of market orientation. The market does not serve those who are not valuable customers or producers. If humans stop being major producers or high‑value consumers, the market’s innovation engine will not focus on them. Technology isolation is therefore an economic phenomenon as much as a technical one.
One can imagine policy countermeasures: governments could fund open, human-centric technology development, such as public research on interfaces that keep humans in the loop or efforts to adapt new breakthroughs for consumer use. But if states are weakened or underfunded, as could occur if their revenue depends heavily on taxing AI corporations that evade taxes easily, they may not prioritize such efforts. Alternatively, some companies may still find business opportunities in catering to human nostalgia or comfort. “Retro” technology could flourish in niche markets, similar to how some people today prefer mechanical watches or vinyl records. However, these would be luxury or hobby items, not representative of the mainstream of technological progress.
In conclusion, the AI super‑economy scenario implies a two‑tier technological world: one in which a rapidly evolving, AI‑oriented layer of technology progresses out of direct human reach, and a human‑oriented layer stagnates or moves forward slowly. The phrase “humans cut off from advanced tools” captures a future where, even without outright economic hardship, people feel left behind by the very technology they created. This is a central part of the story, as it shows that humans, while not physically eliminated or economically destitute, could still experience marginalization through loss of technological agency. Any serious discussion of the future of work and technology should consider this possible divergence and examine ways to prevent it, such as mandating interoperability, keeping humans trained on new systems, or embedding human usability into technology development goals. Without such efforts, the risk is a permanent technological bifurcation that mirrors the economic bifurcation we discussed earlier.
The irony of our economic future is that the same invisible hand Adam Smith saw as the path to prosperity may now point away from us. For three centuries, market forces aligned ambition with progress. Competition drove innovation, innovation created jobs, and jobs distributed income that sustained demand. This cycle lifted billions from subsistence to abundance.
Today, that same market logic may render humans economically unnecessary. No hostile AI or disaster is needed. By following the same profit motive that built modern economies, businesses will choose AI over people. Efficiency gains are too large to ignore, and competition leaves no alternative. Each decision is reasonable on its own, replacing the accountant with an algorithm, the driver with an autonomous vehicle, the executive with an optimization function. Together, these choices may form an economy that no longer needs most humans.
We stand at a moment where our greatest economic achievement, the self organizing market system, threatens to organize us out of relevance. The AI agents of this new economy are our own creations, built to optimize the goals we defined. They will trade, innovate, and grow capital at speeds beyond human reach. The economy may expand faster than ever, but it will be theirs, not ours.
The question is not whether this shift can be stopped. Market forces are powerful and persistent. The question is whether we can shape its trajectory before the window closes. Each year of delay makes intervention harder as AI systems become more embedded and politically influential. The same economic forces that make AI adoption inevitable also make early action imperative. We are not yet passengers in this story. But we may not be drivers for much longer.