LO
Tax preparer / tax 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
● 1 verified event for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
Starts high, at 46, because in Singapore the tax authority was already pre-filling returns from employers' data long before this chart begins, and tax software had done the arithmetic for years. It rises as the authority moves from pre-filled returns to sending about a million people a computed tax bill with no return to file, and as language tools start drafting answers to tax questions — the simple return and the paperwork. It stops short of the top because complicated returns, advice before decisions and disputes with the authority still need someone who knows the rules, and because the authority itself leaves the checking to people.
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
- 1