  The recent wave of generative AI has sparked unprecedented global attention,
with both excitement and concern over potentially superhuman levels of
artificial intelligence: models now take only seconds to produce outputs that
would challenge or exceed the capabilities even of expert humans. At the same
time, models still show basic errors in understanding that would not be
expected even in non-expert humans. This presents us with an apparent paradox:
how do we reconcile seemingly superhuman capabilities with the persistence of
errors that few humans would make? In this work, we posit that this tension
reflects a divergence in the configuration of intelligence in today's
generative models relative to intelligence in humans. Specifically, we propose
and test the Generative AI Paradox hypothesis: generative models, having been
trained directly to reproduce expert-like outputs, acquire generative
capabilities that are not contingent upon -- and can therefore exceed -- their
ability to understand those same types of outputs. This contrasts with humans,
for whom basic understanding almost always precedes the ability to generate
expert-level outputs. We test this hypothesis through controlled experiments
analyzing generation vs. understanding in generative models, across both
language and image modalities. Our results show that although models can
outperform humans in generation, they consistently fall short of human
capabilities in measures of understanding, as well as weaker correlation
between generation and understanding performance, and more brittleness to
adversarial inputs. Our findings support the hypothesis that models' generative
capability may not be contingent upon understanding capability, and call for
caution in interpreting artificial intelligence by analogy to human
intelligence.
