Robot Dividends: Private Sector AI Investment Dynamics in the US

Abstract

Throughout human history, there have been several breakthrough general-purpose technologies that have momentous implications for society and greatly expand the realm of what human beings can accomplish. These general-purpose technologies include inventions like farming, the factory system, the development of materials like iron and bronze, printing presses, electricity and the internet. However, in aggregate, there have not been that many general-purpose technology breakthroughs with one major study pegging the number at twenty-four (Suleyman et al., 2023). Strong evidence suggests that Artificial Intelligence (“AI”) is a general-purpose technology. Although the term Artificial Intelligence was first coined in 1956 and there have been several periods with reduced funding in AI research, notably the AI winters of 1974 – 1980 (Muthukrishnan et. Al, 2020) and 1987 – 1994 (Werner, 2024), over the past few years AI has become a critical area of development for corporate and state actors. This rapid development of AI, especially in the US, is due in part to the current technological backdrop. Key characteristics of this backdrop include broad digitization, increased computing power and the ability to create new solutions by combining existing technologies (Brynjolfsson et al, 2014). As a result, AI can now recognize faces, drive vehicles, compose music, interact with customers, write computer code and ultimately solve complex problems while teaching itself new skills. Much of the funding for AI development is coming from Big Tech corporations given their vast resources and considering the unprecedented pace of AI advancements, these corporations are under intense competitive pressures to invest in AI in order to protect their market positioning. This competitive tension between corporations extends to the state level with an increasing focus on geopolitical considerations being associated with AI development, which effectively translates to subsidies for Big Tech company investment into AI. An example of Big Tech subsidies resulting from perceived geopolitical tensions is the CHIPS and Science Act where US semiconductor companies received ~$53 billion of government funding starting in 2022 (White House Fact Sheet, 2024). Competitive pressures with regards to AI development have contributed to the deprioritization of human capital and AI investment has often come at the expense of employment within the US. In addition to escalating competitive pressures, several factors including current tax and fiscal policy associated with a concentration of power within the US (derived from structural considerations) along with a lack of an overall federal framework contribute to inertia within the US government to address the possible negative impact on the labor market resulting from private sector AI investment. This thesis analyzes recent data to explore the link between private sector AI investment and employment stability in the US and argues that the private sector in the US is not incentivized to invest in AI to help promote stable employment and consequently proposes potential structural reforms to address this dynamic.