The Economic Implications of Large Language Model Selection on Earnings and Return on Investment: A Decision Theoretic Model

TL;DR

This paper presents a decision-theoretic framework for evaluating the financial impact of different large language models in business, emphasizing earnings and ROI rather than just performance metrics.

Contribution

It introduces a novel framework that incorporates economic factors into LLM selection, aiding companies in aligning technology choices with financial objectives.

Findings

Superior accuracy of expensive models can justify higher investment under certain conditions.

Operational variables significantly influence the economics of LLM deployment.

Key factors like success probability and gain/loss impact model sensitivity.

Abstract

Selecting language models in business contexts requires a careful analysis of the final financial benefits of the investment. However, the emphasis of academia and industry analysis of LLM is solely on performance. This work introduces a framework to evaluate LLMs, focusing on the earnings and return on investment aspects that should be taken into account in business decision making. We use a decision-theoretic approach to compare the financial impact of different LLMs, considering variables such as the cost per token, the probability of success in the specific task, and the gain and losses associated with LLMs use. The study reveals how the superior accuracy of more expensive models can, under certain conditions, justify a greater investment through more significant earnings but not necessarily a larger RoI. This article provides a framework for companies looking to optimize their technology choices, ensuring that investment in cutting-edge technology aligns with strategic financial objectives. In addition, we discuss how changes in operational variables influence the economics of using LLMs, offering practical insights for enterprise settings, finding that the predicted gain and loss and the different probabilities of success and failure are the variables that most impact the sensitivity of the models.