LO
Statistician — 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
● 6 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 16 because statistical software, automated editing and rule-based coding were standard before this chart begins, while design, estimation and quality checks were done by statisticians. It climbs as statistics offices put machine-learning and language-model coding into production and test models that question respondents, and as AI tools enter analysis. It stays in the lower half because AI has not reached routine production of official statistics, trial regulators require AI analyses to be fixed in advance, and statisticians set the thresholds and check the automated output.
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
- 2026-10-01
- Basis of the task judgements
- 5 evidence-backed · 0 platform inference · 0 not enough evidence
- Verified events
- 7