If open source is to win, it must go public

Joshua Z Tan, Nicholas Vincent, Katherine Elkins, Magnus Sahlgren

Keywords: public ai, open source, public goods

TL;DR: Open source AI alone will face challenges in producing public goods; we also need public AI infrastructure and institutions.

Abstract: Open source projects have made incredible progress in producing transparent and widely usable machine learning models and systems, but open source alone will face challenges in fully democratizing access to AI. Unlike software, AI models require substantial resources for activation—compute, post-training, deployment, and oversight—which only a few actors can currently provide. This paper argues that open source AI must be complemented by public AI: infrastructure and institutions that ensure models are accessible, sustainable, and governed in the public interest. To achieve the full promise of AI models as prosocial public goods, we need to build public infrastructure to power and deliver open source software and models.

Submission Number: 40