LO
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
● 7 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 telephone and video remote interpreting had already changed how interpreting is delivered before this chart begins, while the interpreting itself was done by people. It rises with the releases that could translate speech in real time and support interpreters' preparation, and stays below the middle because courts and health services require qualified interpreters by law, and clinical studies still find machine interpreting makes far more errors than qualified 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
- 2026-09-30
- Basis of the task judgements
- 2 evidence-backed · 3 platform inference · 0 not enough evidence
- Verified events
- 11