The ubiquity of Big Data and machine learning in society evinces the need of
further investigation of their fundamental limitations. In this paper, we
extend the
``too-much-information-tends-to-behave-like-very-little-information''
phenomenon to formal knowledge about lawlike universes and arbitrary
collections of computably generated datasets. This gives rise to the simplicity
bubble problem, which refers to a learning algorithm equipped with a formal
theory that can be deceived by a dataset to find a locally optimal model which
it deems to be the global one. However, the actual high-complexity globally
optimal model unpredictably diverges from the found low-complexity local
optimum. Zemblanity is defined by an undesirable but expected finding that
reveals an underlying problem or negative consequence in a given model or
theory, which is in principle predictable in case the formal theory contains
sufficient information. Therefore, we argue that there is a ceiling above which
formal knowledge cannot further decrease the probability of zemblanitous
findings, should the randomly generated data made available to the learning
algorithm and formal theory be sufficiently large in comparison to their joint
complexity