LO
Construction worker — 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 second-lowest curve on this site and the shape is almost flat, because the constraint here is not dexterity but the absence of a fixed reference frame: a site changes hourly and no two corners are alike, which is the profile robotics has handled worst for forty years. What little rise there is belongs to setting out and checking — robotic total stations and scan-to-model comparison are among the most successful deployments in construction, because position against a model is measurable. Read the flatness carefully: the mechanism that is actually moving this work is not on the curve at all, because prefabrication moves the same task into a factory rather than automating it on site.
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
- 2