AI Agents as New Economic Actors
The rise of autonomous AI agents marks a pivotal new chapter in economic history, comparable in significance to the emergence of the modern corporation or the advent of wage labor. Unlike traditional software tools, these AI-driven agents possess autonomy, goal-orientation, and adaptive learning, enabling them to make independent economic decisions in production, exchange, negotiation, and investment. In essence, they constitute a new class of economic participant rather than mere extensions of human operators. This introduction of a non-human actor into capitalism challenges classical assumptions of the system, which since the days of Adam Smith have been predicated on human behavior, incentives, and limitations. Rather than simply accelerating existing trends, AI agents are poised to reconfigure the internal logic of capitalism, giving rise to what has been dubbed the “Agentic Economy.” This paradigm shift implies that economic processes – from micro-level labor and consumption patterns to macro-level market structures and wealth distribution – will be fundamentally transformed by the widespread participation of AI agents.
At their core, AI agents are software entities that perceive their environment, learn, and act toward defined goals with minimal human intervention. Their defining features (e.g. operating without continuous human input and learning from experience) mean that they can function as rational economic actors in their own right. Advanced agents, especially those powered by large language models or similar AI, can carry out complex multi-step tasks and even collaborate in multi-agent systems, exhibiting emergent behaviors beyond any single agent’s programming. This capability elevates them to first-class economic actors, engaging in value exchange, strategy, and market coordination with only minimal human oversight. In summary, AI agents stepping onto the world stage as autonomous decision-makers signals a transformation as profound as previous economic revolutions – heralding an era where non-human agents actively shape market outcomes and economic dynamics.
General-Purpose Autonomy and Historical Parallels
AI agents represent not just another labor-saving tool, but a general-purpose technology (GPT) with broad, economy-wide impact. Throughout history, a handful of transformative innovations – the steam engine, electrification, the internal combustion engine, and computing – have earned the GPT label by fundamentally reshaping industries and boosting overall productivity and growth. Generative AI and autonomous agents are widely seen as the next entrants to this elite class of technologies, potentially driving change even faster than prior revolutions due to the rapid diffusion and adaptability of AI software. As a GPT, AI’s influence is projected to span virtually every sector, prompting new investments in skills and reorganization of business processes while altering the very nature of work itself. In effect, AI’s general-purpose autonomy – its ability to learn and perform an open-ended range of cognitive tasks – parallels the role of past industrial revolution technologies in enabling a cascade of complementary innovations and productivity gains across the entire economy.
Historical parallels provide context for the scale of change underway. Just as the First Industrial Revolution mechanized production and the Second harnessed electricity for mass production, the current AI-driven transformation (sometimes dubbed a “Fourth Industrial Revolution”) could dramatically augment cognitive labor and decision-making. Past GPTs often induced productivity booms after an adaptation period, and AI may follow a similar pattern. Indeed, economists note that AI and machine learning are reshaping firm behavior by enhancing prediction and decision-making across industries, much like earlier GPTs reorganized production and management practices. A useful analogy cited by researchers is that “the [AI] Business Revolution is changing the office, the store, and the market, just as the Industrial Revolution changed the factory”. In other words, AI’s general-purpose nature means its impact won’t be confined to a few niches – it will be pervasive, touching everything from how goods are produced, to how services are delivered, to how organizations are structured. This sets the stage for transformative economic shifts reminiscent of the most significant technological upheavals in history.
Feedback Loops in Growth – Insights from the GATE Model
A key question is how the rise of AI agents might translate into macroeconomic change. Insights can be drawn from the Growth and AI Transition Endogenous (GATE) model, an integrated economic model that takes a compute-centric view of AI development. In GATE’s framework, increasing investments in computing power and algorithmic innovation directly translate into more capable AI systems, which in turn automate a broader set of tasks in the economy. As more tasks become automated by these AI “workers,” overall economic productivity climbs – and a portion of the gains is reinvested back into further AI research and computing infrastructure. This creates a self-reinforcing feedback loop between AI advancement and economic growth. In essence, compute → better AI → more automation → faster growth → resources for even more AI becomes a virtuous cycle.
The GATE model formalizes this cycle through three core components: Compute, Automation, and Production. Investments in compute (hardware, data centers, R&D) expand the capacity to train and deploy advanced AI. As compute and algorithmic efficiency grow, AI systems can handle progressively more complex tasks, pushing the frontier of Automation outward (more tasks across the economy become feasible to automate). Those AIs deployed in the workforce – essentially “digital workers” operating at scale – contribute to Production alongside human labor and traditional capital, boosting output and productivity. Crucially, part of the increased output gets plowed back into further expanding compute resources and AI development, closing the loop. The GATE model highlights how this dynamic could lead to accelerating returns: each incremental improvement in AI yields economic gains that fund the next round of improvements. Such endogenous feedback means AI could become a powerful engine of growth, continually fueling itself as higher growth enables more investment into even more capable AI.
Accelerated Growth and Productivity Booms
If AI agents do indeed become widespread economic actors, one expected outcome is a dramatic acceleration in productivity and economic growth. Traditional economic growth in advanced economies has averaged on the order of 2–3% per year in recent decades. AI-driven automation has the potential to shatter these norms. For instance, simulations from the GATE model project that as AI automates an increasing share of tasks, growth rates could be elevated to anywhere from 2× to 20× the historical average during the automation boom period. In practical terms, this suggests the possibility of annual global growth surging to the high single digits or even double-digit percentages, far above the ~3% post-WWII benchmark. Such a productivity bonanza would be unprecedented in the modern era – essentially a productivity boom fueled by armies of digital labor working alongside (or in place of) humans.
Indeed, some economic models even countenance the possibility of “explosive” growth, meaning annual growth >30%, if AI reaches a point of massively self-replicating capability. Optimistic analysts argue that because AI labor can be replicated and scaled much faster than human labor, deploying advanced AI could increase the effective labor force exponentially and drive growth into the range of 20-30% or more per year. By contrast, more conservative economists urge caution: for example, Daron Acemoglu and others project that AI might only add negligible growth (on the order of <2% GDP increase over a decade) if its adoption remains slow and fraught with inefficiencies. These wide-ranging scenarios illustrate the high uncertainty in forecasting AI’s macro impact. The reality could lie between these extremes – substantial yet not limitless acceleration. Recent analyses suggest significant growth accelerations are plausible (even when accounting for bottlenecks like the Baumol effect that slows growth when some tasks resist automation). At the same time, there are practical limits and lags (e.g. the time to diffuse AI across industries, or constraints in complementary factors like physical capital) that might temper the most explosive outcomes. In summary, AI offers the enticing prospect of a major productivity boom – perhaps lifting growth to multiples of its historical rate – but the scale of this boom will depend on how broadly and quickly AI is adopted across the economy, and how society navigates the transition.
Agentic Capital vs. Human Labor – Rethinking Factors of Production
The advent of AI agents blurs the once-stark line between labor and capital, forcing economists to rethink these fundamental categories. In classical economics, labor has meant human effort (physical or cognitive), and capital has meant man-made assets (machines, tools, etc.) employed in production. AI agents confound this dichotomy because they are capital that performs labor. They are software programs – owned assets – yet they autonomously execute cognitive work traditionally done by humans. This hybrid nature has prompted analysts to label advanced AI agents as “agentic capital” or “digital labor,” a new factor of production that combines attributes of both capital and labor. The implications of this shift are profound. If a significant and growing portion of productive work (especially in services and knowledge sectors) is done by AI agents that can be replicated at near-zero marginal cost, then the traditional relationship between wages and profits is upended. In a human-only economy, labor earns wages and capital earns profits, and the two shares tend to move within certain ranges. But in an agentic economy, the returns to productive activity may increasingly accrue to the owners of AI (capital owners) rather than workers, simply because the “workers” are now owned machines.
This raises critical questions for income distribution and policy. A world with abundant AI labor could see labor’s share of income decline sharply while capital’s share (profits, rents) rises, potentially exacerbating inequality if left unchecked. Policymakers are already debating how to adapt: for example, whether tax systems should be adjusted so that capital (or the use of AI/robots) is taxed more heavily relative to human labor, to offset the loss of wage-based tax revenue and to fund social safety nets. Some experts propose “robot taxes” or AI usage fees to redistribute the gains, while others emphasize removing tax biases that currently favor capital investment over hiring workers. Additionally, there is a call for new definitions of employment and work – if AI agents perform jobs, should we redefine what constitutes a “job” or consider new mechanisms of compensating humans (such as data dividends or universal basic income)? The notion of agentic capital forces a reexamination of how we define productive contributions: the classic factors of production may need updating to include AI-agent labor as a distinct category, and economic models must account for a scenario where labor is owned and infinitely replicable. In short, the balance between labor and capital – a cornerstone of economic theory and policy (from wage laws to tax codes) – will need to be recalibrated for the age of AI agents.
Global Economic Order and Wealth Distribution
Widespread AI automation doesn’t only challenge firm-level or national economics – it could also reshape the global distribution of wealth and power. One concern is that AI’s benefits might concentrate in the hands of those who develop and own these systems (typically large tech firms or advanced economies), leading to greater concentration of wealth. If “agentic capital” replaces a large portion of human labor, the economic returns from this hyper-productive new factor will flow primarily to its owners. Given that today the cutting-edge AI capabilities are controlled by a few big corporations (and by extension their shareholders), this trend could produce unprecedented wealth inequality. The traditional mechanism for broad distribution of income – wages paid to human workers – becomes much less effective when human labor is no longer the engine of production. Thus, within countries, we might see a world of plenty (high GDP and productivity) that paradoxically coexists with a large segment of the population struggling to earn income, a scenario some have termed a “under-consumption crisis” in an age of abundance. This is the societal paradox of the agentic economy: immense aggregate wealth but skewed distribution, requiring new mechanisms (beyond wage labor) to distribute purchasing power in order to sustain demand and social stability.
Globally, the deployment of AI could reorder the economic hierarchy among nations. Advanced countries with substantial AI investments and digital infrastructure are poised to reap outsized gains, potentially widening the gap between tech-rich and tech-poor nations. For example, wealthy nations can leverage AI to dominate high-value industries (finance, pharma, advanced manufacturing, defense), making it even harder for developing economies to catch up. Simultaneously, AI-driven automation of manufacturing and services may erode the traditional development pathways of poorer countries by undercutting the cost advantage of cheap labor. A scenario in which a handful of countries (or corporations) control the lion’s share of AI resources and capabilities would likely concentrate geopolitical power, akin to how control of oil or industrial might did in past eras – but potentially on an even greater scale given AI’s broad applicability.
Yet, this future is not preordained to be dystopian. There are also paths toward broader prosperity in an AI-rich world, if society deliberately chooses them. One optimistic vision is that AI could democratize access to services and knowledge: for instance, cheap or free AI-driven tutors, healthcare assistants, and financial advisors could become available to billions, raising overall living standards even if income inequality persists. Another idea is to adopt new ownership models that spread the wealth generated by AI. For example, decentralized autonomous organizations (DAOs) run by AI agents could distribute profits to a broad base of token-holders rather than traditional shareholders. In such a model, an AI system could operate a productive enterprise and automatically share its gains among many owners around the world, offering a structural alternative to the extreme concentration of wealth. Implementing this at scale, however, faces technical and governance challenges – from preventing AI entities from colluding or gaming the system, to verifying human beneficiaries (“proof of humanity”) in these networks.
Ultimately, whether the agentic economy leads to widely shared prosperity or exacerbates inequality will depend on the policy and governance choices we make in the coming years. It’s increasingly clear that proactive measures – such as updated tax policies, strengthened social safety nets like universal basic income, massive investments in reskilling, and perhaps new legal frameworks for AI ownership – will be needed to steer the economy toward inclusive outcomes. The global economic order in the era of AI is at a crossroads. With wise intervention, we could harness AI to create abundance and even explore post-scarcity scenarios of widely distributed wealth. If we fail to adapt, we risk a future of extreme concentration – a high-tech feudalism dominated by those who control the algorithms and compute. As one analysis concluded, the shape of the future is “not predetermined by the technology itself” – it will be determined by how we manage this transition. The dawn of the agentic economy thus presents both unparalleled opportunities for prosperity and urgent challenges for ensuring that prosperity is broadly shared across humanity.
