  As increasingly powerful generative AI systems are developed, the release
method greatly varies. We propose a framework to assess six levels of access to
generative AI systems: fully closed; gradual or staged access; hosted access;
cloud-based or API access; downloadable access; and fully open. Each level,
from fully closed to fully open, can be viewed as an option along a gradient.
We outline key considerations across this gradient: release methods come with
tradeoffs, especially around the tension between concentrating power and
mitigating risks. Diverse and multidisciplinary perspectives are needed to
examine and mitigate risk in generative AI systems from conception to
deployment. We show trends in generative system release over time, noting
closedness among large companies for powerful systems and openness among
organizations founded on principles of openness. We also enumerate safety
controls and guardrails for generative systems and necessary investments to
improve future releases.
