The last type of deductive bottleneck to be exemplified is the paradigm shift. The main feature of this case is that a model might be trained in one set of circumstances or governing paradigm, but fail under another paradigm. Critically, the paradigm shift may not be marked by a significant shift in the distribution of x or y features (e.g., as might be seen from a clustering), but nevertheless the mapping will fail.