Donghyun Suh

This paper develops a model of hierarchical production organizations to study the effects of technological change on income distribution, focusing on top labor incomes. The model features workers with different skill levels who interact with machines. Machine complexity determines how machines are organized inside the hierarchy and, through that channel, whether they augment or substitute for workers. Two main findings emerge. First, if machines only perform sufficiently simple tasks, they augment low-skilled workers and attenuate the ``superstar effect'' by flattening the upper tail of the income distribution. Second, if machines become sufficiently complex, then they substitute for low-skilled workers and augment high-skilled workers, strengthening the superstar effect. Lastly, I examine future AI systems that automate managerial tasks performed by high-skilled workers. AI managers reduce inequality within and across occupations, with the largest gains for the least skilled. However, this equalizing effect need not survive superintelligence; once machines surpass all humans and supervise everyone, further advances can widen inequality. The results highlight the importance of machine complexity and supervision costs for understanding the distributional effects of technology.

We analyze how output and wages behave under different scenarios for technological progress that may culminate in Artificial General Intelligence (AGI), defined as the ability of AI systems to perform all tasks that humans can perform. We assume that human work can be decomposed into atomistic tasks that differ in their complexity. Advances in technology make ever more complex tasks amenable to automation. The effects on wages depend on a race between automation and capital accumulation. If automation proceeds sufficiently slowly, then there is always enough work for humans, and wages may rise forever. By contrast, if the complexity of tasks that humans can perform is bounded and full automation is reached, then wages collapse. But declines may occur even before if large-scale automation outpaces capital accumulation and makes labor too abundant. Automating productivity growth may lead to broad-based gains in the returns to all factors. By contrast, bottlenecks to growth from irreproducible scarce factors may exacerbate the decline in wages.

Using a representative survey of Korean workers, we provide evidence on the adoption of Generative AI (GenAI) and how GenAI reallocates time at work. We find that 51.8% of workers use GenAI for work and GenAI  reduces working time by 3.8%. However, these gains may not materialize in aggregate productivity statistics yet: the correlation between time savings and output changes is near zero. We show this disconnect arises because workers capture efficiency gains primarily as on-the-job leisure, rather than increasing their output. These findings suggest that standard productivity measures may understate AI’s impact by missing non-pecuniary welfare channels.

This paper studies whether automation can mitigate the labor-market effects of population decline and how this mitigation differs across occupations. Korea’s working-age population is projected to fall by about 11 percent between 2024 and 2034. Holding current occupational demand fixed, we first project occupation-level labor-supply imbalances using Korean population projections and age-by-occupation employment data. We then estimate occupation-level automation probabilities by applying a large language model (LLM) to O*NET task statements and mapping the resulting task-level measures to Korean occupations. Automation can substantially reduce the aggregate labor shortfall, but it does not eliminate occupational imbalance because automation potential is only weakly aligned with projected demographic shortages. The main implication is therefore compositional: population decline changes the distribution of labor scarcity, while automation changes where shortages persist and where surplus pressure emerges. With sufficiently rapid AI progress, the same framework implies that the policy problem may shift from aggregate labor shortage to the distribution of labor surplus across occupations.