AI_dev Europe 2024

Recent decisions by leading AI labs to either open-source their models or to restrict access have sparked debate about whether and how increasingly capable AI models should be shared. Open-sourcing in AI typically means making model architecture and weights freely and publicly accessible for anyone to modify, study, build on, and use. This offers advantages such as enabling external oversight, accelerating progress, and decentralizing control over AI development and use. However, it also presents a growing potential for misuse and unintended consequences. This paper examines the risks and benefits of open-sourcing highly capable foundation models. While open-sourcing has historically provided substantial net benefits for most software and AI development processes, I argue that for some highly capable foundation models likely to be developed in the near future; open-sourcing may pose sufficiently extreme risks to outweigh the benefits. In such a case, highly capable foundation models should not be open-sourced, at least not initially. Alternative strategies, including non-open-source model-sharing options, are explored.