LO
Actuary — 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
● 2 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 at 30 because actuarial data work was already heavily tooled before this chart begins, and rises with each release that could write code and analysis — the preparation layer of the job. It flattens rather than climbs because what sits above that layer is placed on a named function by regulation: the curve reflects how much of the work beneath the signature a machine now reaches, not a signature becoming optional. The regulatory steps in 2024 and 2025 are governance duties, which add work as much as they remove it.
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.
Method and sources#
- Assessment date
- 2026-09-23
- Basis of the task judgements
- 4 evidence-backed · 2 platform inference · 0 not enough evidence
- Verified events
- 2