LO
Auto mechanic / vehicle technician — 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 low, gentle climb, and the start is already high relative to the movement because the main automation in this trade happened decades ago: on-board diagnostics made reading a fault code a lookup long before anyone called it AI, and the recent extension is more of the same. It flattens because a code names a symptom and not a cause — the same code can mean a failed sensor, a chafed wire or a mouse — and because the repair itself is unstructured manipulation in a confined space on a machine that has been in service. The curve cannot see this trade's actual threat, which is a demand change rather than an automation one: electric drivetrains have far fewer serviceable parts, so fewer jobs arrive.
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