  Artificial Intelligence (AI) is poised to enable a new leap in the creation
of scholarly content. New forms of engagement with AI systems, such as
collaborations with large language models like GPT-3, offer affordances that
will change the nature of both the scholarly process and the artifacts it
produces. This article articulates ways in which those artifacts can be
written, distributed, read, organized, and stored that are more dynamic, and
potentially more effective, than current academic practices. Specifically,
rather than the current "early-binding" process (that is, one in which ideas
are fully reduced to a final written form before they leave an author's desk),
we propose that there are substantial benefits to a "late-binding" process, in
which ideas are written dynamically at the moment of reading. In fact, the
paradigm of "binding" knowledge may transition to a new model in which
scholarship remains ever "unbound" and evolving. An alternative form for a
scholarly work could be encapsulated via several key components: a text
abstract of the work's core arguments; hyperlinks to a bibliography of relevant
related work; novel data that had been collected and metadata describing those
data; algorithms or processes necessary for analyzing those data; a reference
to a particular AI model that would serve as a "renderer" of the canonical
version of the text; and specified parameters that would allow for a precise,
word-for-word reconstruction of the canonical version. Such a form would enable
both the rendering of the canonical version, and also the possibility of
dynamic AI reimaginings of the text in light of future findings, scholarship
unknown to the original authors, alternative theories, and precise tailoring to
specific audiences (e.g., children, adults, professionals, amateurs).
