An AI Capability Threshold for Rent-Funded Universal Basic Income in an AI-Automated Economy

2025-05-24

AI Summary

This study investigates whether AI-generated capital rents can sustainably finance a universal basic income (UBI) without raising taxes or creating new jobs. Using an extended Solow–Zeira model incorporating a task-continuum framework, net savings rate, and task elasticity, we derive— for the first time—a closed-form critical threshold condition under which UBI is fully financed by AI-driven economic rents. We identify nonlinear regulatory effects of public revenue-sharing ratios and market structure (e.g., monopoly power) on this threshold. Numerical simulations indicate that, under baseline parameters, AI’s relative automation productivity must reach 5–6 times current levels to fund a UBI equal to 11% of GDP; increasing the public share of AI rents to 33% lowers the threshold to 3×; monopoly further substantially reduces it. Our core contribution is the first analytically tractable and empirically calibratable fiscal sustainability criterion for AI-funded UBI.

Technology Category

• Philosophy and Ethics of AI: AI & Jobs/Labor
• Humans and AI: Other Foundations of Human Computation & AI
• Intelligent Robots: Embodied AI

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

We derive the first closed-form condition under which artificial intelligence (AI) capital profits could sustainably finance a universal basic income (UBI) without additional taxes or new job creation. In a Solow-Zeira economy characterized by a continuum of automatable tasks, a constant net saving rate $s$, and task-elasticity $sigma<1$, we analyze how the AI capability threshold--defined as the productivity level of AI relative to pre-AI automation--varies under different economic scenarios. At present economic parameters, we find that AI systems must achieve only approximately 5-6 times existing automation productivity to finance an 11%-of-GDP UBI, in the worst case situation where emph{no} new jobs or tasks are created. Our analysis also reveals some specific policy levers: raising public revenue share (e.g. profit taxation) of AI capital from the current 15% to about 33% halves the required AI capability threshold to attain UBI to 3 times existing automotion productivity, but gains diminish beyond 50% public revenue share, especially if regulatory costs increase. Market structure also strongly affects outcomes: monopolistic or concentrated oligopolistic markets reduce the threshold by increasing economic rents, whereas heightened competition significantly raises it. Overall, these results suggest a couple policy recommendations: maximizing public revenue share up to a point so that operating costs are minimized, and strategically managing market competition can ensure AI's growing capabilities translate into meaningful social benefits within realistic technological progress scenarios.

Problem

• Determine AI productivity threshold for funding UBI without taxes
• Analyze policy impacts on AI capability requirements for UBI
• Assess market structure effects on AI-driven UBI feasibility

Innovation

• AI capital profits fund UBI sustainably
• Productivity threshold analysis for automation
• Policy levers optimize public revenue share