What Makes Writing Writing
Chapter 01·the premise
What was asked
- 01How exposed is each occupation, and does that track pay the way we assume?
- 02If a job is built from abilities, does predicting its exposure from those abilities agree with the score the dataset publishes for the job itself?
- 03Which occupation disagrees most between the two — and is that claim robust to how the comparison is scaled?
- 04What kind of variable would explain the direction of the errors?
What it found
A job is made of abilities, so a job's exposure ought to be predictable from the exposure of its parts. It is not: the two layers of this dataset run in opposite directions (−0.367), sharing only 13.5% of their variance, and exposure rises with pay rather than falling with it. Exactly one of the 21 abilities scores negative — Originality, the capacity for ideas nobody has had — and it is the ability writers rank highest on. Yet writing is rated the 4th most exposed occupation in the country, and moves further between the two orderings than any other job in the file. The variable the ability model is missing is not an ability. It is the medium the work comes out as: words, or objects.
Of the 271 occupations measured in this dataset, writing ranks most exposed to artificial intelligence.
Only three sit above it: survey researchers, translators, and public relations specialists. Below it sits almost every job we have spent a decade imagining would go first. Janitors are down there, scoring : twenty-eight times lower than writing.
That is not the finding. That is only the premise — and it is already enough to turn one old assumption over. In this data, exposure to AI rises with pay rather than falling with it . The jobs the dataset calls most exposed are, on the whole, the better-paid ones.
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