How we assess an occupation
Most automation coverage collapses three different questions into one number. We keep them apart, and we show you which of the three any given claim rests on.
The unit is the task, not the job
A job title is a bundle of tasks that happens to be bought together. Automation acts on tasks, so that is where we assess.
Three layers we never merge
The single most common error in automation coverage is treating "a machine can do this" as "this job will go away."
Can a machine do this task at all?
Would an employer actually adopt it here — cost, reliability, regulation, maintenance?
After adoption, did headcount or job scope actually change?
Four technology signals
Tracked separately. A job exposed to two of them is not twice as exposed — the risks do not add.
Evidence has stages
Not every piece of news is allowed to move an assessment. A demo and a layoff are not the same kind of fact, so they carry different weight — and only some of them can change a baseline at all.
Second-hand summary or incomplete information. Recorded, but never moves an assessment.
A demo, benchmark or paper shows the task can be done. Updates what the technology can do — not what employers will do.
Small-scale trial in a real setting. Tells us the deployment conditions are being tested, not that they hold.
An employer has put it into production. Can move the baseline — weighted by scale and how similar the setting is.
Verifiable change in hiring, headcount, hours or job scope. Highest weight — but causal attribution still has to be argued, not assumed.
Failure, rollback, regulation or cost is suppressing adoption. Can lower an assessment or widen its uncertainty.
Every claim is labelled
You should never have to guess whether something is a sourced fact or our reasoning.
Right now 37 verified events back 45 of 151 task judgements; the rest are labelled platform inference. Every record was read at the source and entered by hand — the automated monitoring pipeline is not connected yet. We would rather show an honest count than cite sources we have not verified.
What we refuse to do
- Add different technology risks together into one score.
- Call anything a probability of job loss.
- Let a model invent a plausible-looking percentage when we have no data.
- Generalise one company's case to a whole occupation worldwide.
- Treat the number of times a story was reposted as evidence strength.
- Attribute layoffs to AI without a credible causal argument.
- Advise "learn AI" or "keep upskilling" and call that a plan.
How much of this rests on evidence
The honest aggregate of the per-task labels you see on every occupation page.
30 occupations and 10 majors are researched. 37 verified events are attached across 30 occupations, which puts 45 of 151 task judgements on evidence; the remaining 106 are labelled platform inference, task by task, on the page itself.
Where this is going
Evidence now enters through a manual, audited path: each event is recorded with its source URL, date, stage, scope and the tasks it bears on; it is a candidate until a named person verifies it, and only verified records reach a page — where they also flip the linked tasks from platform inference to evidence-backed. The automated pipeline (scheduled retrieval, fact extraction, de-duplication, entity resolution) is still to be built; until then the list grows at the speed a human can verify.