  Substantial scholarship has estimated the susceptibility of jobs to
automation, but little has examined how job contents evolve in the information
age as new technologies substitute for tasks, shifting required skills rather
than eliminating entire jobs. Here we explore patterns and consequences of
changes in occupational skill and characterize occupations and workers subject
to the greatest re-skilling pressure. Recent work found that changing skill
requirements are greatest for STEM occupations. Nevertheless, analyzing 167
million online job posts covering 727 occupations over the last decade, we find
that re-skilling pressure is greatest for low-skilled occupations when
accounting for distance between skills. We further investigate the differences
in skill change across employer and market size, as well as social demographic
groups, and find that these differences tend to widen the economic divide. Jobs
from large employers and markets experienced less change relative to small
employers and markets, and non-white workers in low-skilled jobs are most
demographically vulnerable. We conclude by showcasing our model's potential to
precisely chart job evolution towards machine-interface integration using skill
embedding spaces.
