  Observers and practitioners of artificial intelligence (AI) have proposed an
FDA-style licensing regime for the most advanced AI models, or 'frontier'
models. In this paper, we explore the applicability of approval regulation --
that is, regulation of a product that combines experimental minima with
government licensure conditioned partially or fully upon that experimentation
-- to the regulation of frontier AI. There are a number of reasons to believe
that approval regulation, simplistically applied, would be inapposite for
frontier AI risks. Domains of weak fit include the difficulty of defining the
regulated product, the presence of Knightian uncertainty or deep ambiguity
about harms from AI, the potentially transmissible nature of risks, and
distributed activities among actors involved in the AI lifecycle. We conclude
by highlighting the role of policy learning and experimentation in regulatory
development, describing how learning from other forms of AI regulation and
improvements in evaluation and testing methods can help to overcome some of the
challenges we identify.
