A recent working paper by Anton Korinek and Donghyun Suh explores various scenarios for the transition to Artificial General Intelligence (AGI). The paper analyzes the impact of technological progress on production and wages, considering the possibility of full automation and its impact on the labor market. The authors discuss the race between automation and capital accumulation, the effects on wage mobility, and the potential for widespread gains in productivity. This research provides valuable insights into the economic consequences of AGI development.
The transition to Artificial General Intelligence (AGI) has been the subject of much interest and speculation in recent years. Many researchers and industry leaders believe that AGI, which refers to AI systems that can perform all tasks at a human level, may soon become a reality. In a working paper titled “Scenarios for Transformation in AGI”, economists Anton Korinek and Donghyun Suh highlight the economic implications of AGI development.
The paper begins by examining the relationship between technological progress, output and wages. The authors propose a framework that decomposes human work into atomic tasks with different levels of complexity. He argues that progress occurs in technology Enable automation of increasingly complex tasks, potentially leading to automation of all tasks with the advent of AGI.
An important aspect analyzed in the paper is the race between automation and capital accumulation. If automation progresses slowly, there will always be enough work for humans, and wages can continue to rise. However, if the complexity of tasks that can be performed by humans is limited and full automation is achieved, salaries may decrease. The authors also consider the possibility of a decline in wages before full automation occurs if large-scale automation outpaces capital accumulation, leading to an oversupply of labor.
Research shows that automation of productivity growth can have widespread benefits in returns to all factors of production. On the other hand, disruptions to growth caused by rare, irreplaceable factors may exacerbate wage declines. The authors emphasize the importance of understanding the distribution of tasks in the complexity domain and its impact on economic outcomes.
While the paper provides valuable insights into the potential consequences of AGI development, it also acknowledges the uncertainties surrounding the transition. The authors highlight that the distribution of tasks across complexity plays an important role in determining economic outcomes. They consider both unbounded and bounded distributions, the latter reflecting the limited computational capabilities of the human brain.
Overall, Korinek and Suh’s research contributes to the ongoing discussion about the future of work in the age of AI and automation. By analyzing different scenarios for changes in AGI, the paper highlights the potential impacts on output, wages and human welfare. It serves as a valuable resource for policymakers, researchers, and industry leaders who want to understand the economic implications of AGI developments.
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