Large Language Models Reduce Agency Costs

11 Pages Posted: 14 May 2023

See all articles by Darcy W E Allen

Darcy W E Allen

RMIT University

Chris Berg

RMIT University

Nataliya Ilyushina

RMIT University - Blockchain Innovation Hub; RMIT University - ARC Centre of Excellence for Automated Decision-Making and Society

Jason Potts

RMIT University

Date Written: May 4, 2023

Abstract

Large Language Models (LLMs) or generative AI have emerged as a new general-purpose technology in applied machine learning. These models are increasingly employed within firms to support a range of economic tasks. This paper investigates the economic value generated by the adoption and use of LLMs, which often occurs on an experimental basis, through two main channels. The first channel, already explored in the literature (e.g. Eloundou et al. 2023, Noy and Wang 2023), involves LLMs providing productive support akin to other capital investments or tools. The second, less examined channel concerns the reduction or elimination of agency costs in economic organisation due to the enhanced ability of economic actors to insource more tasks. This is particularly relevant for tasks that previously required contracting within or outside a firm. With LLMs enabling workers to perform tasks in which they had less specialisation, the costs associated with managing relationships and contracts decrease. This paper focuses on this second path of value creation through adoption of this innovative new general purpose technology. Furthermore, we examine the wider implications of the lower agency costs pathway on innovation, entrepreneurship and competition.

Keywords: generativeAI, ChatGPT, agency costs, theory of the firm, innovation

JEL Classification: D23, D24, D83, L22, L23, O33

Suggested Citation

Allen, Darcy W E and Berg, Chris and Ilyushina, Nataliya and Potts, Jason, Large Language Models Reduce Agency Costs (May 4, 2023). Available at SSRN: https://ssrn.com/abstract=4437679 or http://dx.doi.org/10.2139/ssrn.4437679

Darcy W E Allen

RMIT University ( email )

440 Elizabeth Street
Melbourne, 3000
Australia

Chris Berg

RMIT University ( email )

124 La Trobe Street
Melbourne, 3000
Australia

Nataliya Ilyushina

RMIT University - Blockchain Innovation Hub ( email )

124 La Trobe Street
Melbourne, 3000
Australia

RMIT University - ARC Centre of Excellence for Automated Decision-Making and Society ( email )

Building 97, RMIT University
106-108 Victoria Street
Carlton, 3053
Australia

Jason Potts (Contact Author)

RMIT University ( email )

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