  With the widespread proliferation of AI systems, trust in AI is an important
and timely topic to navigate. Researchers so far have largely employed a myopic
view of this relationship. In particular, a limited number of relevant trustors
(e.g., end-users) and trustees (i.e., AI systems) have been considered, and
empirical explorations have remained in laboratory settings, potentially
overlooking factors that impact human-AI relationships in the real world. In
this paper, we argue for broadening the scope of studies addressing `trust in
AI' by accounting for the complex and dynamic supply chains that AI systems
result from. AI supply chains entail various technical artifacts that diverse
individuals, organizations, and stakeholders interact with, in a variety of
ways. We present insights from an in-situ, empirical study of LLM supply
chains. Our work reveals additional types of trustors and trustees and new
factors impacting their trust relationships. These relationships were found to
be central to the development and adoption of LLMs, but they can also be the
terrain for uncalibrated trust and reliance on untrustworthy LLMs. Based on
these findings, we discuss the implications for research on `trust in AI'. We
highlight new research opportunities and challenges concerning the appropriate
study of inter-actor relationships across the supply chain and the development
of calibrated trust and meaningful reliance behaviors. We also question the
meaning of building trust in the LLM supply chain.
