LO
Waiter / restaurant server — 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 step in this curve is 2023 and it was not caused by AI: QR-code ordering moved a large share of order-taking to the customer's own phone during the pandemic and stayed there, which is the clearest completed automation in the occupation. It flattens early because what is left — holding twelve tables in your head, reading the one that has gone quiet, and the recovery when the kitchen is an hour late — has no instrumented input. The height understates the change to the job: where ordering left, covers per server went up and the thirty seconds at the table disappeared, so a task was removed and the work got harder rather than smaller.
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