Open Sourcing GPTs: Economics of Open Sourcing Advanced AI Models

TL;DR

This paper analyzes the economic factors influencing open sourcing of advanced large language models, revealing how firm size, performance, and research incentives affect open source decisions and ecosystem growth.

Contribution

It provides empirical insights and a theoretical model explaining the strategic trade-offs firms face when open sourcing advanced AI models.

Findings

Open sourcing likelihood decreases with performance edge over rivals.

Large tech companies are more likely to open source LLMs.

Open sourcing increases research activities related to the models.

Abstract

This paper explores the economic underpinnings of open sourcing advanced large language models (LLMs) by for-profit companies. Empirical analysis reveals that: (1) LLMs are compatible with R&D portfolios of numerous technologically differentiated firms; (2) open-sourcing likelihood decreases with an LLM's performance edge over rivals, but increases for models from large tech companies; and (3) open-sourcing an advanced LLM led to an increase in research-related activities. Motivated by these findings, a theoretical framework is developed to examine factors influencing a profit-maximizing firm's open-sourcing decision. The analysis frames this decision as a trade-off between accelerating technology growth and securing immediate financial returns. A key prediction from the theoretical analysis is an inverted-U-shaped relationship between the owner's size, measured by its share of LLM-compatible applications, and its propensity to open source the LLM. This finding suggests that moderate market concentration may be beneficial to the open source ecosystems of multi-purpose software technologies.