  As AI technologies are rolled out into healthcare, academia, human resources,
law, and a multitude of other domains, they become de-facto arbiters of truth.
But truth is highly contested, with many different definitions and approaches.
This article discusses the struggle for truth in AI systems and the general
responses to date. It then investigates the production of truth in InstructGPT,
a large language model, highlighting how data harvesting, model architectures,
and social feedback mechanisms weave together disparate understandings of
veracity. It conceptualizes this performance as an operationalization of truth,
where distinct, often conflicting claims are smoothly synthesized and
confidently presented into truth-statements. We argue that these same logics
and inconsistencies play out in Instruct's successor, ChatGPT, reiterating
truth as a non-trivial problem. We suggest that enriching sociality and
thickening "reality" are two promising vectors for enhancing the
truth-evaluating capacities of future language models. We conclude, however, by
stepping back to consider AI truth-telling as a social practice: what kind of
"truth" do we as listeners desire?
