LO
IT support specialist / helpdesk — 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.
The steepest curve in the technology group, and the mechanism is the cleanest on the site: the repeat half of a service-desk queue is a narrow problem with a known resolution sequence and a machine-checkable outcome — the account unlocks or it does not — so a system can retry unattended. The climb starts before generative models because self-service portals had already taken the password half. It flattens where the long tail begins: a user's description that is confident and wrong about a key detail gives a tool the same false premise it gives a person, and this occupation's whole skill is refusing it. Read the height as the easy tickets, and note what the curve cannot show — the hard half being left to a team sized on the volume that was automated.
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
- 3