LO
Veterinarian — 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
Starts at 16 because digital radiography, laboratory analysers and practice software that sends reminders and invoices were already standard before this chart begins. It rises as software that reads animal radiographs and slides, and tools that draft the clinical note from a consultation, reach veterinary practices — the reading and the writing around the job. It stays low because the core is hand work on living animals and decisions made with their owners, and because in Singapore the law ties treating an animal to a licensed person.
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
- 2026-09-26
- Basis of the task judgements
- 1 evidence-backed · 5 platform inference · 0 not enough evidence
- Verified events
- 1