Researchers are constantly looking to train more expressive models more quickly. Any method which can converge + take large jumps will be chosen. You are sort of guaranteed to end up in a place where the sharpness is high but it somehow comes under control. If we weren't there.... we'd try a new architecture until we arrived there. So deep learning doesn't "do this", we do it using any method possible and it happened to be the various architectures that currently fit into "deep learning". Keep in mind many architectures which are deep do not converge - you see survivorship bias.
Researchers are constantly looking to train more expressive models more quickly. Any method which can converge + take large jumps will be chosen. You are sort of guaranteed to end up in a place where the sharpness is high but it somehow comes under control. If we weren't there.... we'd try a new architecture until we arrived there. So deep learning doesn't "do this", we do it using any method possible and it happened to be the various architectures that currently fit into "deep learning". Keep in mind many architectures which are deep do not converge - you see survivorship bias.