  How do humans assess the performance of Artificial Intelligence (AI) across
different tasks? AI has been noted for its surprising ability to accomplish
very complex tasks while failing seemingly trivial ones. We show that humans
engage in ``performance anthropomorphism'' when assessing AI capabilities: they
project onto AI the ability model that they use to assess humans. In this
model, observing an agent fail an easy task is highly diagnostic of a low
ability, making them unlikely to succeed at any harder task. Conversely, a
success on a hard task makes successes on any easier task likely. We
experimentally show that humans project this model onto AI. Both prior beliefs
and belief updating about AI performance on standardized math questions appear
consistent with the human ability model. This contrasts with actual AI
performance, which is uncorrelated with human difficulty in our context, and
makes such beliefs misspecified. Embedding our framework into an adoption
model, we show that patterns of under- and over-adoption can be sustained in an
equilibrium with anthropomorphic beliefs.
