The transition to artificial general intelligence (AGI) has been a topic of great interest and speculation in recent years. Many researchers and industry leaders believe that AGI, or AI systems that can perform all tasks at a human level, may soon become a reality. In a working paper titled “AGI Transition Scenarios,” economists Anton Korinek and Donghyun Suh delve into the economic implications of AGI development.
The article begins by examining the relationship between technological progress, production and wages. The authors propose a framework that breaks down human work into atomistic tasks of varying levels of complexity. They claim that progress in technology they enable the automation of increasingly complicated tasks, potentially leading to the automation of all tasks with the advent of AGI.
One of the key aspects analyzed in the article is the race between automation and capital accumulation. If automation progresses slowly enough, there will always be enough work for people and wages can continue to rise. However, if the complexity of the tasks that humans can perform are reduced and full automation is achieved, wages could collapse. The authors also consider the possibility of wage declines before full automation occurs if large-scale automation outpaces capital accumulation, leading to an oversupply of labor.
The study suggests that automating productivity growth could result in widespread increases in profits across all factors of production. On the other hand, growth bottlenecks caused by scarce, irreproducible factors may deepen wage declines. The authors emphasize the importance of understanding the distribution of tasks in the complexity space and its impact on economic performance.
While the article provides valuable insights into the potential consequences of the development of AGI, it also acknowledges the uncertainties associated with this transformation. The authors emphasize that the distribution of tasks in the complexity space plays a key role in determining economic performance. They consider both unconstrained and constrained distributions, the latter reflecting the finite 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 artificial intelligence and automation. By analyzing different scenarios for the transition to AGI, the article sheds lithe on the possible impacts on production, wages and human well-being. It serves as a valuable resource for policymakers, researchers and industry leaders who want to understand the economic implications of AGI development.
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