Modeling the AI-Driven Age of Abundance: Applying the Human-to-AI Leverage Ratio (HAILR) to Post-Labor Economics (original) (raw)

This paper explores the transformative impact of AI on automating knowledge work leading to the anticipated 'Age of Abundance' in a post-labor society where work is performed by machines rather than human labor. Through a detailed model incorporating variables such as cost of computing, AI model efficiency, and human-equivalent production output (derived from the human-to-AI leverage ratio, or HAILR), we provide a nuanced albeit tentative analysis of future productivity trends and economic realities. The model, integrating conservative estimates like a 30% annual improvement in AI model efficiency, projects a substantial increase in productivity; by 2044 it indicates that just four hours of productive human labor could yield as much as 636 years of equivalent output. The model is not intended as a precise prediction, rather a framework to allow scientists and laypersons to visualize the inevitability of the coming Age of Abundance. The assumptions are incidental. If work is automated at scale, one may reasonably change the assumptions in the model and still arrive at the same conclusion: extreme abundance. This research also critically examines the potential job displacement in knowledge and office work sectors, suggesting a loss of 9 out of 10 jobs by 2044 due to AI automation. The model also shows how the remaining workers will be empowered with their efforts "leveraged" by AI technologies. We highlight the economic and societal implications of these findings, including the need for proactive public policy and corporate strategy to navigate the challenges and opportunities presented by AI-driven transformations. The study underscores the criticality of grasping these shifts in timely ways for future workforce planning and societal adaptation. Although the model will certainly need to be revised to accommodate technological, political, and social changes, we believe that its simplicity, flexibility, and clarity can earn it a significant role in policy discourse. 1 Electronic copy available at: https://ssrn.com/abstract=4663704 Literature Review The notion of 'knowledge worker' was developed by Peter Drucker in his 1959 book, The Landmarks of Tomorrow (1). Drucker was a pioneer in contrasting knowledge work with manual labor in his managerial analyses. In our paper, the phrase "knowledge work" includes cognitive work generally performed on a computer. This includes the efforts of programmers, scientists, writers, and engineers who produce and handle information. (2) Office work (from simple clerical tasks to complex, multi-stage efforts) is especially amenable to AI automation. AI capabilities are also making many knowledge work efforts involving high-level thinking (such as medical and legal jobs) amenable to automation. Many early efforts to analyze and understand the dimensions of knowledge work from economic perspectives were inspired by the 1970s writings of Marc Porat (3) , following the lead of Fritz Machlup (4) in the 1960s. Mapping the impact of particular technologies such as AI on the productivity of workers has been an activity of many researchers in the decades that followed, as outlined in the se ctions to come. The modeling effort described in this paper is intended to use straightforward terms and common-sense concepts in ways that make the models usable in public policy deliberations as well as in community outreach or business planning. Providing clear yet powerful data visualizations and conceptual tools in these forums will focus these discussions and stimulate the production of useful insights.