Navigating the Deployment Dilemma and Innovation Paradox: Open-Source v.s. Closed-source Models

Yanxuan WU, Haihan Duan, Xitong Li, Xiping Hu

Track: Economics, online markets and human computation

Keywords: Deployment dilemma; open-source; close-source; foundation model

Abstract: Recent advances in Artificial Intelligence (AI) have introduced a new paradigm in Machine Learning (ML) model development: pre-training of foundation model and domain adaptation. Two groups lead in developing foundation model: closed-source developers and open-source community. As open-source community becomes increasingly engaged, the performance open-source models are catching up with closed-source models. However, this leaves domain deployers into a dilemma: use closed-source models via API access or host open-source models on proprietary hardware. Using closed-source models incurs recurring costs, while hosting open-source models incurs substantial hardware investments and potentially lagging advancements. This paper presents a game-theoretical model to examine the economic incentives behind the deployment choice and the impact of open-source engagement strategy on technology innovation. We find that the deployer consistently opts for closed-source APIs when the open-source community engages in the market reactively by maintaining a fixed performance ratio relative to closed-source advancements. However, open-source models can be favored when a proactive open-source community produces high-performance models independently. Also, we identify conditions under which engagement and competitiveness of the open-source community can foster or inhibit technological progress. These insights offer valuable implications for market regulation and the future of AI model innovation.

Submission Number: 2909