Scenarios for the Transition to AGI

TL;DR

This paper explores how different technological progress scenarios leading to AGI affect wages and output, highlighting the importance of task complexity distribution and automation pace on economic outcomes.

Contribution

It introduces a model analyzing the impact of automation and task complexity on wages and output during the transition to AGI, emphasizing the role of task complexity distribution.

Findings

Wages may rise indefinitely if task complexity distribution has a thick tail.

Wages could collapse if full automation is reached and task complexity is bounded.

Automation pace relative to capital accumulation influences wage dynamics.

Abstract

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 the distribution of task complexity exhibits a sufficiently thick infinite tail, 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.