  This paper investigates the ontological characterization of Large Language
Models (LLMs) like ChatGPT. Between inflationary and deflationary accounts, we
pay special attention to their status as agents. This requires explaining in
detail the architecture, processing, and training procedures that enable LLMs
to display their capacities, and the extensions used to turn LLMs into
agent-like systems. After a systematic analysis we conclude that a LLM fails to
meet necessary and sufficient conditions for autonomous agency in the light of
embodied theories of mind: the individuality condition (it is not the product
of its own activity, it is not even directly affected by it), the normativity
condition (it does not generate its own norms or goals), and, partially the
interactional asymmetry condition (it is not the origin and sustained source of
its interaction with the environment). If not agents, then ... what are LLMs?
We argue that ChatGPT should be characterized as an interlocutor or linguistic
automaton, a library-that-talks, devoid of (autonomous) agency, but capable to
engage performatively on non-purposeful yet purpose-structured and
purpose-bounded tasks. When interacting with humans, a "ghostly" component of
the human-machine interaction makes it possible to enact genuine conversational
experiences with LLMs. Despite their lack of sensorimotor and biological
embodiment, LLMs textual embodiment (the training corpus) and resource-hungry
computational embodiment, significantly transform existing forms of human
agency. Beyond assisted and extended agency, the LLM-human coupling can produce
midtended forms of agency, closer to the production of intentional agency than
to the extended instrumentality of any previous technologies.
