The Turing Valley: How AI Capabilities Shape Labor Income

TL;DR

This paper examines how AI capabilities across different knowledge dimensions influence labor income, highlighting the importance of communication modes between humans and AI in shaping economic outcomes and the potential impacts of AGI development.

Contribution

It introduces a multidimensional framework analyzing AI's varying effects on labor income based on communication modes and AI strength across dimensions.

Findings

AI improvements in strong dimensions increase labor income.

Communication mode determines whether weak AI improvements raise or lower labor productivity.

Maximizing labor income depends on AI performance across all dimensions and communication properties.

Abstract

Current AI systems are better than humans in some knowledge dimensions but weaker in others. Guided by the long-standing vision of machine intelligence inspired by the Turing Test, AI developers increasingly seek to eliminate this "jagged" nature by pursuing Artificial General Intelligence (AGI) that surpasses human knowledge across domains. This pursuit has sparked an important debate, with leading economists arguing that AGI risks eroding the value of human capital. We contribute to this debate by showing how AI capabilities in different dimensions shape labor income in a multidimensional knowledge economy. AI improvements in dimensions where it is stronger than humans always increase labor income, but the effects of AI progress in dimensions where it is weaker than humans depend on the nature of human-AI communication. When communication allows the integration of partial solutions, improvements in AI's weak dimensions reduce the marginal product of labor, and labor income is maximized by a deliberately jagged form of AI. In contrast, when communication is limited to sharing full solutions, improvements in AI's weak dimensions can raise the marginal product of labor, and labor income can be maximized when AI achieves high performance across all dimensions. These results point to the importance of empirically assessing the additivity properties of human-AI communication for understanding the labor-market consequences of progress toward AGI.