LO
Technical writer / documentation engineer — 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.
The highest curve in the technology group and the steepest rise of any writing occupation on this site, for a reason specific to this job: the source of truth is machine-readable and the output format is fixed, so a reference can be produced from the code itself. Doc generators had taken part of it long before models; what models added was prose that reads as if a person wrote it, which removed the last reason to have a person write it. The flattening from 2025 is the part with no mechanism — following your own instructions on a clean machine and finding the step that is impossible. A model trained on the codebase inherits exactly the knowledge that must be absent for that test to work, so the value there comes from not knowing.
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
- 2026-09-14
- Basis of the task judgements
- 2 evidence-backed · 2 platform inference · 0 not enough evidence
- Verified events
- 2