LO
Childcare worker / early-years educator — 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
It starts at 10 because sign-in, billing and parent-messaging software was already in wide use before language models; that is real automation, but of the smallest share of the work. It rises slowly from 2023h2, first as chat assistants began drafting activity plans and then as childcare apps added drafted observations and daily summaries, reaching 16. It flattens there because the core of the job (supervision, feeding, changing, comforting) has seen neither capability nor adoption move, and ratio rules hold the number of adults in the room fixed.
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
- 2026-10-25
- Basis of the task judgements
- 0 evidence-backed · 6 platform inference · 0 not enough evidence
- Verified events
- 0