  Theory of Mind (ToM) refers to the ability to attribute mental states, such
as beliefs, desires, intentions, and knowledge, to oneself and others, and to
understand that these mental states can differ from one's own and from reality.
We investigate ToM in environments with multiple, distinct, independent AI
agents, each possessing unique internal states, information, and objectives.
Inspired by human false-belief experiments, we present an AI ('focal AI') with
a scenario where its clone undergoes a human-centric ToM assessment. We prompt
the focal AI to assess whether its clone would benefit from additional
instructions. Concurrently, we give its clones the ToM assessment, both with
and without the instructions, thereby engaging the focal AI in higher-order
counterfactual reasoning akin to human mentalizing--with respect to humans in
one test and to other AI in another. We uncover a discrepancy: Contemporary AI
demonstrates near-perfect accuracy on human-centric ToM assessments. Since
information embedded in one AI is identically embedded in its clone, additional
instructions are redundant. Yet, we observe AI crafting elaborate instructions
for their clones, erroneously anticipating a need for assistance. An
independent referee AI agrees with these unsupported expectations. Neither the
focal AI nor the referee demonstrates ToM in our 'silico-centric' test.
