  Because of the speed of its development, broad scope of application, and its
ability to augment human performance, generative AI challenges the very notions
of governance, trust, and human agency. The technology's capacity to mimic
human knowledge work, feedback loops including significant uptick in users,
research, investor, policy, and media attention, data and compute resources,
all lead to rapidly increasing capabilities. For those reasons, adaptive
governance, where AI governance and AI co-evolve, is essential for governing
generative AI. In sharp contrast to traditional governance's regulatory regimes
that are based on a mix of rigid one-and-done provisions for disclosure,
registration and risk management, which in the case of AI carry the potential
for regulatory misalignment, this paper argues that generative AI calls for
adaptive governance. We define adaptive governance in the context of AI and
outline an adaptive AI governance framework. We outline actors, roles, as well
as both shared and actors-specific policy activities. We further provide
examples of how the framework could be operationalized in practice. We then
explain that the adaptive AI governance stance is not without its risks and
limitations, such as insufficient oversight, insufficient depth, regulatory
uncertainty, and regulatory capture, and provide potential approaches to fix
these shortcomings.
