AGI Economy
= "From Population to Computing: A Fundamental Shift in Growth Drivers".
Description
Suyeon Kim:
"Professor Restrepo defines AGI as “a state in which all economically valuable work currently performed by humans can be accomplished using computational resources.” AGI thus represents more than technological superiority in specific domains—it marks a critical inflection point where algorithms and computing power combine to replace production activities across the entire economy. Restrepo projects that the drivers of economic growth will shift from population expansion and labor inputs to the rate at which computational resources scale.
This transformation transcends mere technological progress—it constitutes a fundamental economic restructuring. While industrial-era productivity gains emerged from labor force expansion, AGI- era growth derives from processing larger datasets and scaling computational capacity. As long as computational capacity keeps expanding, growth can continue in spite of population decline."
(Taejae Future Consensus Institute <World Research Trend>)
Characteristics
Suyeon Kim:
"Five Defining Characteristics of the AGI Economy
According to Restrepo’s analysis, an AGI economy with sufficient computational resources has five
structural features:
1 Computing Power Becomes the Primary Growth Engine
Growth originates from computational resources rather than population. Population decline is no longer a constraint on growth; what matters is the speed at which computational capacity expands. Economic scale also depends on computational capacity, not raw demographic power.
2 Wages Shift to Reflect AI Replication Costs
Wages no longer reflect human productivity but the cost of performing identical work with AI. The determining question becomes “What would it cost to replace this person with AI?” Human expertise and experience no longer serve as absolute wage determinants, and even highly refined skills cannot command a value above the cost ceiling set by AI replication.
3 Human Labor Concentrates in Supplementary Functions
Once all bottleneck work is automated, economies become bottleneck-free, and human labor becomes increasingly concentrated in supplementary domains. Activities that do not directly drive growth— creativity, caregiving work, or artistic production—become the primary remaining human economic functions.
Labor’s share of total income converges toward zero. Wages as a proportion of total economic output vanish, with wealth concentrated among those who own computational infrastructure. Income generation shifts from labor to the ownership of computational resources.
For reference, Korea’s 2024 employee compensation ratio (covering wage workers) stood at 67.9%. While Professor Restrepo’s concept of labor income share encompasses a broader definition that includes the self-employed, even when wage workers are considered in isolation, the current figure of approximately 68% would steadily decline toward zero in the AGI era.
5 The Social Meaning of Work Erodes
Human work continues, but undergoes a fundamental redefinition. Traditional economics positioned labor as an avenue for social recognition and self-actualization, but this function progressively weakens in AGI economies. As the economic value of labor fades, a fundamental question emerges: What constitutes work in a world where human labor holds diminished value? This moment calls for a philosophical reexamination of the essence of labor and human identity.
This leads to the interesting paradox that average wages may actually rise in the short term. As AGI replaces low-value work, human labor becomes increasingly concentrated in comparatively high-value activities. However, this wage increase fails to offset distributional inequality. While economies expand, most new wealth flows to those who control computational infrastructure."
(Taejae Future Consensus Institute <World Research Trend>)
Discussion
AI Replication Costs Set Wage Ceilings: The AGI Economy Demands a New Social Contract
Suyeon Kim:
Yale’s Restrepo Mathematically Proves “Economic Growth Without Labor” in his NBER article, “We Won’t Be Missed: Work and Growth in the AGI World”.
This economic realignment fundamentally transforms the determination of wages. Compensation no longer reflects human productivity but rather the cost of replication—the expense of performing identical work with artificial intelligence. Any given occupation must prove that it is difficult to replace with AI in order to retain substantial economic value. Consequently, income increasingly flows to those who own computational resources rather than those who provide labor.
Professor Restrepo categorizes the multitude of tasks that comprise economic activity into two fundamental types. “Bottleneck work” refers to critical infrastructure—core tasks that must function together for the broader system to operate. This kind of work has an interdependent structure in that economic activity as a whole grinds to a halt if even one bottleneck fails. In contrast, “supplementary work” occupies a complementary rather than foundational tier. The system operates without it, although its presence enhances quality of life and societal diversity.
This distinction shapes automation priorities. For economies to grow, the first thing that needs to improve is bottleneck efficiency. As computational resources expand, societies will increasingly delegate their most critical functions to AI systems. This includes energy generation, infrastructure maintenance, and scientific research. Supplementary work—the performing arts, design, or counseling services—is more likely to remain in human hands since it is not essential for growth. The critical constraint is that wages in these domains face a structural ceiling. Even where human labor remains necessary, its economic value cannot exceed the cost of replacing it with AI."
More information
- Pascual Restrepo, “We Won’t Be Missed: Work and Growth in the AGI World,” in Ajay K. Agrawal, Anton Korinek, and Erik Brynjolfsson
(eds.), The Economics of Transformative AI (University of Chicago Press, 2025), chap. 9. This research was presented at the NBER “The Economics of Transformative AI” conference in September 2025, subsequently published as NBER Working Paper No. 34423 in October of the same year, and is scheduled for inclusion as Chapter 9 in The Economics of Transformative AI to be published by University of Chicago Press.