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

Wensu Li, Atin Aboutorabi, Harry Lyu, Kaizhi Qian, Martin Fleming, Brian C. Goehring, Neil Thompson

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 ratio, quantifying human labor displacement at each accuracy level. We calibrate the framework with O*NET task data, a survey of 3,778 domain experts, and GPT-4o-derived task decompositions, implementing it in computer vision. Task complexity shapes substitution: low-complexity tasks see high substitution, while high-complexity tasks favor limited partial automation. Scale of deployment is a key determinant: AI-as-a-Service and AI agents spread fixed costs across users, sharply expanding economically viable tasks. At the firm level, cost-effective automation captures approximately 11% of computer-vision-exposed labor compensation; under economy-wide deployment, this share rises sharply. Since other AI systems exhibit similar scaling-law economics, our mechanisms extend beyond computer vision, reinforcing that partial automation is often the economically rational long-run outcome, not merely a transitional phase.

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

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 ratio, quantifying human labor displacement at each accuracy level. We calibrate the framework with O*NET task data, a survey of 3,778 domain experts, and GPT-4o-derived task decompositions, implementing it in computer vision. Task complexity shapes substitution: low-complexity tasks see high substitution, while high-complexity tasks favor limited partial automation. Scale of deployment is a key determinant: AI-as-a-Service and AI agents spread fixed costs across users, sharply expanding economically viable tasks. At the firm level, cost-effective automation captures approximately 11% of computer-vision-exposed labor compensation; under economy-wide deployment, this share rises sharply. Since other AI systems exhibit similar scaling-law economics, our mechanisms extend beyond computer vision, reinforcing that partial automation is often the economically rational long-run outcome, not merely a transitional phase.

Paper Structure

This paper contains 64 sections, 102 equations, 10 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. From Technical Feasibility to Economic Viability
  4. Reconceptualizing Automation
  5. Scaling Laws and the Economics of AI Quality
  6. Integrating Micro-Foundations with Macro-Employment Effects
  7. Positioning Our Contribution
  8. Theory
  9. Model Set Up
  10. Assumptions and Scope
  11. Supply of AI: In Terms of Both Quality and Quantity
  12. Scaling Law: The Production Function for AI Quality
  13. Cost Minimization for AI Systems
  14. Cost Components in AI System Development and Deployment
  15. Demand for AI Quality
  16. ...and 49 more sections

Figures (10)

  • Figure 1: Breakdown of occupation compensation by task-level automation type: different cases of partial equilibrium, ranked from most to least benefit from AI automation.
  • Figure 2: Quantifying Human-AI Work Allocation via Entropy and Cross-Entropy Loss
  • Figure 3: Highlighted empirical evidence linking task entropy to processing time across different time scales.
  • Figure 4: Pairwise Isoquant Contours Illustrating Substitutability among Data, Training Steps, and Model Size across Task Complexity (2-Class vs. 500-Class Classification Tasks)
  • Figure 5: Composition of Full and Partial Automation of Vision-Task Labor Compensation Across Deployment Scales
  • ...and 5 more figures