LO
Translator / Interpreter — 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
● 4 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 high because machine translation was already good before 2022 — translation memory and neural MT had been in the workflow for a decade. What changed after 2023 is that the draft became good enough that the paid step moved from translating to certifying. The flattening in late 2025 is the pushback: buyers who cut human review discovered the failure mode is invisible until it is expensive.
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
- 2026-09-09
- Basis of the task judgements
- 3 evidence-backed · 2 platform inference · 0 not enough evidence
- Verified events
- 5