[2603.29121] Economics of Human and AI Collaboration: When is Partial Automation More Attractive than Full Automation?

Economics > General Economics arXiv:2603.29121 (econ) [Submitted on 31 Mar 2026] Title:Economics of Human and AI Collaboration: When is Partial Automation More Attractive than Full Automation? Authors:Wensu Li, Atin Aboutorabi, Harry Lyu, Kaizhi Qian, Martin Fleming, Brian C. Goehring, Neil Thompson View a PDF of the paper titled Economics of Human and AI Collaboration: When is Partial Automation More Attractive than Full Automation?, by Wensu Li and 6 other authors View PDF HTML (experimental) Abstract:This paper develops a unified framework for evaluating the optimal degree of task automation. Moving beyond binary automate-or-not assessments, we model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation. On the supply side, we estimate an AI production function via scaling-law experiments linking performance to data, compute, and model size. Because AI systems exhibit predictable but diminishing returns to these inputs, the cost of higher accuracy is convex: good performance may be inexpensive, but near-perfect accuracy is disproportionately costly. Full automation is therefore often not cost-minimizing; partial automation, where firms retain human workers for residual tasks, frequently emerges as the equilibrium. On the demand side, we introduce an entropy-based measure of task complexity that maps model accuracy into a labor substitution ...