LO
Cleaner / janitor — 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 steady low climb that belongs almost entirely to floor-scrubbing machines, which are among the most successfully deployed service robots anywhere — not because of any recent advance but because the task's shape suits them: flat surface, repeatable route, sparse obstacles, and failure means stopping. The curve flattens where the easy hectares run out, because edges, stairs, bathrooms and today's spill are unstructured manipulation in a space that changes daily. What the curve cannot show is the change that has actually reached this occupation: occupancy sensors and scan points deciding the route, which removes discretion without removing a single task.
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