Intuitive psychology is a pillar of common-sense reasoning. The replication of this reasoning in machine intelligence is an important stepping-stone on the way
to human-like artificial intelligence. Several recent tasks and benchmarks for examining this reasoning in Large-Large Models have focused in particular on
belief attribution in Theory-of-Mind tasks. These tasks have shown both successes and failures. We consider in particular a recent purported success case, and
show that small variations that maintain the principles of ToM turn the results on their head. We argue that in general, the zero-hypothesis for model evaluation in
intuitive psychology should be skeptical, and that outlying failure cases should outweigh average success rates. We also consider what possible future successes
on Theory-of-Mind tasks by more powerful LLMs would mean for ToM tasks with people.