LO
Security guard — 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 climb is one task and it is the one humans are genuinely bad at: sustained visual vigilance collapses within about twenty minutes, and detection software does not get bored, so video analytics deployed widely on a poor human baseline. It flattens because detection is not response — the patrol robots in retail and transit are specified as observe-and-report and route alerts to a person, and the value of a guard at an incident is that somebody in uniform is standing there, which is a social fact rather than a technical one. The height is about watching. The change to the roster is not in this curve: sites keep response times while halving guards, because the cameras find things faster.
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