LO
Farmer — 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.
Starts at 18 because mechanisation and crop-spraying drones were already part of farming before this chart begins, and rises slowly and almost independently of the model releases on the axis: what moves it is farmers buying drones and self-steering machinery, a capital decision made season by season and, in some countries, paid for by the state. The small steps are that equipment spreading. It stays low because the decisions about what to grow and the delicate hand work of picking are not what current machines do.
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
- 2026-09-23
- Basis of the task judgements
- 1 evidence-backed · 5 platform inference · 0 not enough evidence
- Verified events
- 2