LO
First-line manager / team supervisor — how we know
The page itself gives the judgements. This one gives what they rest on: which technologies bear on the work, how the estimate moved since language models reached the public, and the method behind both.
Which technologies matter here#
Four separate signals. They are deliberately not added together — a job exposed to two technologies is not twice as exposed.
How it got here#
The index is not a static number. This is where it would have sat at each capability checkpoint since ChatGPT — reconstructed, and labelled as such.
—— this stretch contains a verified event- - - no event in this stretch — reconstruction only0 = no task exposed, 100 = every task exposed
● 3 verified events for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
Starts higher than most office roles because the biggest task in this job — building the roster — had real software long before language models: workforce-management systems have been assigning shifts against availability, skill and cost since the 2000s. The gentle slope after 2023 is a second wave arriving on the writing side: weekly reports, then first drafts of performance reviews. It is a slope and not a jump because none of this removes a supervisor, it removes hours from one — and that shows up as more people per supervisor rather than fewer supervisors, which moves at the speed of reorganisations.
A flat line is not a forecast of safety. It says which tasks automation has reached so far — the occupations that moved least here are the ones where the constraint is physical or regulatory, and both of those can change.
Written about this#
These pieces argue from the same records this page holds, and each of their sections names what it rests on.
Method and sources#
- Assessment date
- 2026-09-11
- Basis of the task judgements
- 3 evidence-backed · 3 platform inference · 0 not enough evidence
- Verified events
- 3