AI exposure: Film and Video Editors

51.0% of this occupation's weighted task load is exposed, which puts Film and Video Editors at the 89th percentile of 923 occupations. The capability is largely there. Its average task scores 2.7 out of 4 on what a current system can produce, and the frictions that hold other jobs in place are comparatively weak here.

What holds the line here is context. Across this occupation's 22 tasks it averages 1.80 out of 3, the highest of the five friction dimensions. In plain terms, the work depends on knowledge the model cannot hold. Much of this job runs on things that were never written down: what this particular organisation does, what happened last week, what the person across the table actually meant. That context is the barrier, and it erodes as systems are given more access.

The most exposed thing this job does is Verify key numbers and time codes on materials, at 80.0%. The least is Mark frames where a particular shot or piece of sound is to begin or end, at 0.0%. A gap of 80.0% between two parts of the same job is the reason this index publishes at task level. An occupation-wide number would have hidden both.

Within arts, design, entertainment, sports and media occupations, this one is more exposed than most. The median across the 40 roles in the group is 36.4%, and only 8 of them score higher than this. Occupational families are not uniform, and the spread inside them is often wider than the gap between them.

What would move this score. Of 22 tasks, 18 are currently banded exposed, 0 assisted and 4 untouched. For that distribution to shift materially would take systems being given deeper access to the organisation's own records and history, which is already happening. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.