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

econ.GNMar 31, 2026

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

Organizations: Massachusetts Institute of Technology · École Polytechnique Fédérale de Lausanne · IBM Research · IBM’s Institute for Business Value

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.

Figures & tables

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.

Parameter	Estimate	Std. Error
$A_{0}$	-1.448	0.046
$A_{1}$	0.752	0.050
$a_{0}$	-0.034	0.005
$a_{1}$	0.077	0.003
$B_{0}$	1.474	0.080
$B_{1}$	1.049	0.000

Table 1: Estimated Parameters for the Scaling Law

Number of Classes	$\alpha$	a	$\beta$	b	$\sigma$	c	G	k
2	0.40	0.02	9.03	0.40	71.35	0.59	-0.30	-0.10
5	0.79	0.09	23.62	0.42	94.61	0.55	-0.30	-0.24
10	1.33	0.14	48.87	0.43	117.13	0.52	-0.30	-0.35
50	4.46	0.27	264.34	0.46	192.26	0.46	-0.30	-0.59
100	7.52	0.32	546.91	0.48	238.01	0.43	-0.30	-0.69
500	25.24	0.44	2958.46	0.51	390.69	0.36	-0.30	-0.93

Table 2: Scaling Law Parameters under Varying Number of Classes (n)

Scenario	Performance	Data	Training Steps	Model Size	Total
	( $r$ )	( $\epsilon_{D}$ )	( $\epsilon_{T}$ )	( $\epsilon_{M}$ )	Elasticity
2-Class Classification Task
Small Scale (I)	0.804	0.010	0.046	0.046	0.102
Medium Scale (II)	0.911	0.009	0.021	0.007	0.037
500-Class Classification Task
Small Scale (I)	0.351	0.023	0.544	0.283	0.849

Table 3: Performance Elasticity

Number of classes	Elasticity of substitution
Number of classes	$\sigma_{DT}$	$\sigma_{DM}$	$\sigma_{TM}$
2	0.982	0.982	0.716
5	0.918	0.918	0.706
10	0.875	0.875	0.698
50	0.790	0.790	0.685
100	0.758	0.758	0.694

Table 4: Pairwise elasticities of substitution by task complexity

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

Figure 6: Fraction of Computer Vision Task Compensation Economically Attractive to Automate.

Figure 7: Fraction of the U.S. businesses (by business counts) over different deployment scales.

Figure 8: Counts of deployed computer vision models at industry group (4d) deployment scale by partial automation rates.

Figure 9: Top 20 Automation Rates across Occupations at Industry Group (4d) Deployment Scale. See Appendix Figure J.1 for all occupations.

Occupation / Task / DWA	Task Automation	Full	Partial	Time Proportion	#Task	#Class
Transportation Security Screeners Task: Inspect carry-on items using x-ray equipment to determine whether items contain objects that warrant further investigation. DWA: Inspect cargo to identify potential hazards.	2.80%	2.72%	0.08%	2.81%	2	10
Transportation Security Screeners Task: Inspect checked baggage for signs of tampering. DWA: Inspect cargo to identify potential hazards.	3.24%	3.24%	0.00%	3.24%	2	7
Transportation Security Screeners Task: Locate suspicious bags pictured in printouts sent from remote monitoring areas, and set these bags aside for inspection. DWA: Locate suspicious objects or vehicles.	4.09%	4.09%	0.00%	4.09%	2	3
Transportation Security Screeners Task: Monitor passenger flow through screening checkpoints to ensure order and efficiency. DWA: Monitor access or flow of people to prevent problems.	3.05%	3.05%	0.00%	3.05%	2	3
Transportation Security Screeners Task: Record information about any baggage that sets off alarms in monitoring equipment. DWA: Record information about suspicious objects.	2.79%	2.71%	0.08%	2.79%	13	3
Transportation Security Screeners Task: Send checked baggage through automated screening machines, and set bags aside for searching or rescreening as indicated by equipment. DWA: Inspect cargo to identify potential hazards.	3.08%	2.99%	0.09%	3.09%	3	7

Table 5: Task-Level Automation Profiles

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Symbol	Value	Description	Source
$\kappa_{\text{init}}$	1000	Number of initial fine-tuning rounds for algorithmic development	Kadra et al. (2023) , Wistuba et al. (2022) , Zimmer et al. (2021)
$\kappa_{\text{recur}}$	6	Retraining frequency per year	Thompson et al. (2024)
$d_{\text{GPU}}$	0.22	GPU-specific annual price discount rate	Hobbhahn and Besiroglu (2022)
$d$	0.05	Discount rate applied across the economy	Sullivan (2023)
$L$	5	System operational lifespan in years	Assets (2003)
$p_{\text{data}}$	dynamic	Cost of collecting and labeling one datum	Thompson et al. (2024) , Singh et al. (2024) , Russakovsky et al. (2015) , Lin et al. (2015)

Appendix

Table B.1: Cost Function Parameters

Coeff.	Expression	Value (USD)
$c_{F}$	$\displaystyle C_{\text{impl}}+\frac{1-\left(1/(1+d)\right)^{L}}{1-1/(1+d)}\,C_{\text{maint}}$	$3{,}486{,}090$
$c_{D}$	$\displaystyle m\left[1+\kappa_{\text{recur}}\frac{1-\left(1/(1+d)\right)^{L}}{1-1/(1+d)}\right]p_{\text{data}}$	$6.19$
$c_{T}$	$\displaystyle 6\,m\left[\kappa_{\text{init}}+\kappa_{\text{recur}}\frac{1-\left(\frac{1}{(1+d)(1+d_{\text{GPU}})}\right)^{L}}{1-\frac{1}{(1+d)(1+d_{\text{GPU}})}}\right]\frac{p_{\text{GPU}}\,Z_{\text{input}}}{r_{\text{FLOP}}\,U_{\text{GPU}}}$	$3.83\times 10^{-6}$
$c_{I}$	$\displaystyle m\left[\frac{1-\left(\frac{1}{(1+d)(1+d_{\text{GPU}})}\right)^{L}}{1-\frac{1}{(1+d)(1+d_{\text{GPU}})}}\right]\frac{p_{\text{GPU}}}{U_{\text{GPU}}}\frac{40\times 50\,\delta_{i}N_{i}}{\tau/\tau_{\mathrm{GPU}}}$	$1.29\times 10^{-8}$

Appendix

Table B.2: Input prices under default parameter values (2024 USD).

	$\beta_{0}$	$\beta_{1}$	$\beta_{2}$	$\beta_{3}$	$\gamma_{1}$	$\gamma_{2}$	$\delta_{1}$	$\delta_{2}$	$\delta_{3}$	$R^{2}_{\mathrm{fitted}}$
Coefficient	2.86	14.22	-27.10	10.48	11.99	-1.82	-0.70	0.61	-0.62	0.98
Std. Error	0.06	0.82	1.74	0.96	0.52	0.29	0.01	0.01	0.05

Appendix

Table E.1: Fitted Coefficients for Mapping Accuracy to Cross-Entropy Loss.

Scenario	Performance	Data	Training Steps	Model Size	Total
	( $r$ )	( $\epsilon_{D}$ )	( $\epsilon_{S}$ )	( $\epsilon_{M}$ )	Elasticity
2-Class Classification Task
Small Scale (I)	0.804	0.010	0.046	0.046	0.102
Medium Scale (II)	0.911	0.009	0.021	0.007	0.037
5-Class Classification Task
Small Scale (I)	0.866	0.016	0.035	0.032	0.083

Appendix

Table I.1: Performance Elasticity Analysis Results

Figure J.1: Automation rates across occupations at industry group (4d) deployment scale.

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