LO
Insurance agent — 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 22 because online quotes, electronic applications and comparison portals already existed before this chart begins — Singapore has required life insurers to offer policies priced without distribution costs since 2015. It rises slowly as insurers move applications, simple claims and reminders into apps, and as language tools start drafting needs analyses, advice records and follow-ups — the paperwork around the sale. It stays in the lower part of the range because the sale itself has not moved: in Singapore, representatives still sold about 97% of new life and health business by premium in the first half of 2026, and the share bought online fell.
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
- 2026-09-25
- Basis of the task judgements
- 2 evidence-backed · 4 platform inference · 0 not enough evidence
- Verified events
- 3