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

Aran Nayebi

TL;DR

The paper investigates whether rents generated by AI capital can sustainably fund a universal basic income without new taxation or job creation. It develops a closed-form solvency threshold within a Solow-Zeira-CES framework that includes an AI capability parameter $\gamma_t$, and analyzes how public ownership, operating costs, and market structure shift the required AI productivity. Calibrations with current US data suggest that funding an 11% of GDP UBI requires AI to be about 5–7 times pre-AI automation productivity, with earlier crossings possible under rapid AI growth or favorable policy conditions. The results provide a rigorous benchmark for assessing when advancing AI capabilities could finance social transfers and highlight policy levers—ownership, competition, and cost controls—that influence feasibility.

Abstract

We derive the first closed-form condition under which artificial intelligence (AI) capital profits could sustainably finance a universal basic income (UBI) without relying on new taxation or the creation of new jobs. In a Solow-Zeira task-automation economy with a CES aggregator $σ< 1$, we introduce an AI capability parameter that scales the productivity of automatable tasks and obtain a tractable expression for the AI capability threshold -- the minimum productivity of AI relative to pre-AI automation required for a balanced transfer. Using current U.S. economic parameters, we find that even in the conservative scenario where no new tasks or jobs emerge, AI systems would only need to reach only 5-7 times today's automation productivity to fund an 11%-of-GDP UBI. 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 automation 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. These results therefore offer a rigorous benchmark for assessing when advancing AI capabilities might sustainably finance social transfers in an increasingly automated economy.

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

TL;DR

The paper investigates whether rents generated by AI capital can sustainably fund a universal basic income without new taxation or job creation. It develops a closed-form solvency threshold within a Solow-Zeira-CES framework that includes an AI capability parameter , and analyzes how public ownership, operating costs, and market structure shift the required AI productivity. Calibrations with current US data suggest that funding an 11% of GDP UBI requires AI to be about 5–7 times pre-AI automation productivity, with earlier crossings possible under rapid AI growth or favorable policy conditions. The results provide a rigorous benchmark for assessing when advancing AI capabilities could finance social transfers and highlight policy levers—ownership, competition, and cost controls—that influence feasibility.

Abstract

We derive the first closed-form condition under which artificial intelligence (AI) capital profits could sustainably finance a universal basic income (UBI) without relying on new taxation or the creation of new jobs. In a Solow-Zeira task-automation economy with a CES aggregator , we introduce an AI capability parameter that scales the productivity of automatable tasks and obtain a tractable expression for the AI capability threshold -- the minimum productivity of AI relative to pre-AI automation required for a balanced transfer. Using current U.S. economic parameters, we find that even in the conservative scenario where no new tasks or jobs emerge, AI systems would only need to reach only 5-7 times today's automation productivity to fund an 11%-of-GDP UBI. 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 automation 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. These results therefore offer a rigorous benchmark for assessing when advancing AI capabilities might sustainably finance social transfers in an increasingly automated economy.

Paper Structure

This paper contains 10 sections, 4 theorems, 27 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Solow-Zeira Model with an AI-Automation Capability Parameter
  3. Baseline Zeira Technology
  4. AI Capability Shifter
  5. Results
  6. Simulation of AI-Powered Rent-Funded UBI Scenarios
  7. When Could AI Capabilities Reach the UBI Threshold?
  8. Competition and Market Structure Effects on $\gamma_t^{\star}$
  9. Public Revenue Share, Oversight Costs, and $\gamma_t^{\star}$ Trade-offs
  10. Discussion

Key Result

Proposition 1

In the economy described in Section sec:model, with CES given in eq:cesAI, a constant transfer $B$ is balanced in every period iff If $\gamma_t \ge \gamma_t^{\star}$ the competitive equilibrium converges linearly to the balanced-growth path (BGP); otherwise, the transfer is unsustainable.

Figures (3)

  • Figure 1: Projected AI capabilities ($\gamma_t$) vs. time-varying UBI AI capability threshold ($\gamma^{\star}_t$). The black dashed line is the required capability $\gamma_t^{\star}$ to fully fund a UBI that comprises 11% of the GDP (leading to a $\gamma_t^{\star}$ between 5-7$\times$ the pre-AI productivity on automated tasks, under current economic assumptions). Solid black and grey dash-dotted lines are the UBI threshold for low and high elasticity values. Under fast scaling (AI capability doubling every year), AI would cross the threshold by the late 2020s. Semi-fast scaling (doubling every 2 years) reaches the threshold in the early 2030s, whereas moderate (doubling every 5 years) and slow (doubling every 10 years) scenarios achieve $\gamma_t^{\star}$ by 2038 and 2052, respectively. The trajectories are illustrative, starting from a nominal, conservative 2025 capability level ($\gamma_0 \equiv 1$), which assumes AI currently delivers no boost beyond the pre-AI automation level in aggregate across all automated tasks.
  • Figure 2: Impact of competition on the required AI capability ($\gamma_t^{\star}$). Each curve traces the minimum capability $\gamma_{\text{oligo},t}^{\star}$ (defined in Proposition \ref{['prop:oligo']}) needed to fund a UBI at three evaluation horizons in Figure \ref{['fig:gamma']} (2028, 2038, 2052) as the number of symmetric AI firms $m$ rises (horizontal axis). Moving from monopoly ($m=1$) to a small oligopoly ($m\!\approx\!2$-$5$) raises the threshold sharply; beyond roughly ten firms the curve flattens and approaches the competitive benchmark $\gamma^{\star}_{\mathrm{comp},t}$, because pure profits vanish but rental income persists. The current AI market, dominated by only a few providers, lies on the steep, finite part of the curve.
  • Figure 3: Trade-off between public revenue share ($\Theta$) and operating cost ($c$) on the capability threshold $\gamma_t^{\star}$. The solid curves plot the 2025‐base‐year capability threshold required to fund an 11 %-of-GDP UBI when the public captures a share $\Theta$ of AI rents. Two operating-cost assumptions are shown: a low-cost regime ($c = 0.50$, blue) and a high-cost regime ($c = 0.75$, orange). The horizontal axis converts $\Theta$ to percentage points of AI capital; the vertical dashed line marks the current U.S. public stake ($\Theta \approx 14.5\%$). Model parameters match those in Figure \ref{['fig:gamma']}. These curves flatten and approach the quantity $C_t$ in Proposition \ref{['prop:twocountry']} as $\Theta \to 100\%$ public ownership.

Theorems & Definitions (8)

  • Proposition 1: Capability threshold for a rent-financed UBI
  • proof
  • Corollary 1: Comparative statics of the capability threshold
  • proof
  • Proposition 2: Oligopoly mark-ups and the capability threshold
  • proof
  • Proposition 3: Cross-country ownership advantage
  • proof