In a working paper released by researchers Konrad Kording and Ioana Marinescu from the University of Pennsylvania, a new framework for analyzing artificial intelligence (AI) and its implications for the labor market is proposed. The paper was published in the context of a broader discussion at the Brookings Institution, focusing on how AI is often treated in macroeconomic models as mere capital, neglecting the unique rapid scaling characteristics of AI compared to traditional physical capital and labor. This paper attempts to bridge that gap by using a constant elasticity of substitution (CES) production function that distinguishes between physical and intelligence sectors. The research identifies a key aspect of the relationship between AI and human labor. As AI capabilities scale faster than traditional physical capital, there is a potential shift in employment dynamics, with human labor increasingly drawn to physical sector jobs as intelligence tasks are automated. The paper explains the complementary nature of physical and intelligence capabilities by discussing how each type allows for different types of efficiencies; one allows humans to act effectively, while the other enables them to do so efficiently. This framework helps to highlight the complexities of automation's impact on wages and employment structures. The authors note that the implications of automation on wages remain unclear. While the automation of intelligence tasks could lead to a decrease in human labor demand within that sector, it may also increase overall productivity, thus raising wages. In scenarios of high automation, wages tend to initially rise before declining as automation further permeates the labor market. This conclusion suggests that while AI may reduce demands for certain types of employment, it could also enhance overall economic productivity. The projected outcomes can vary significantly depending on the substitutability between outputs from physical and intelligence sectors. Ultimately, the paper provides a comprehensive analytical framework for policymakers and researchers to evaluate the effects of AI on the economy and labor force. It encourages evidence-based discussions about potential future trajectories of wage and employment outcomes in light of ongoing advancements in AI technology. The authors aim to facilitate a better understanding of the distinct challenges and opportunities presented by the integration of AI into the labor market, highlighting the importance of tailored policy responses that consider both economic and technological advancements.