LO
Retail salesperson / shop assistant — 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
● 1 verified event for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
A moderate climb driven by one task leaving rather than by anything arriving on the shop floor: knowing what is in the back used to require a person, because the inventory system was unreliable and the assistant compensated. As those systems became accurate enough to show customers directly, that compensating knowledge stopped being scarce — and it was the errand that started most conversations. The curve flattens from 2025 because what remains is advisory work anchored to physical stock, which no recommender receives as an input. Read the height as the loss of a reason to approach someone, not as a machine doing the selling — and note the change this curve cannot show at all, which is shifts cut to the half hour against a footfall forecast.
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
- 1