LO
Receptionist / front desk — 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.
A high, early climb that mostly predates generative models: visitor kiosks are a solved product because registering a compliant visitor is a fixed sequence with a checkable outcome, and call routing has been automated in stages for thirty years — menu trees, then directories, now models that handle a caller who cannot name the department. The flattening is the exception: the contractor not on the list who is expected, the person unwell in the lobby. Two cautions on reading the height. Most people with this title spend more hours on the office administration around the desk than at the door, so the visible half is a poor guide to the post; and the likely change is the title being merged into facilities or office management rather than removed.
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
- 3 evidence-backed · 1 platform inference · 0 not enough evidence
- Verified events
- 2