What does this equation mean?

That ceiling on n is the practical reason the labeller headcounts above are so low. At even the lower historical human-feedback price, filling a dataset an order of magnitude larger than InstructGPT’s would cost in the low hundreds of thousands of US dollars — a real but modest sum next to a frontier training budget, and yet one that competes for the same specialised recruiting pipeline, the same quality-control overhead, and the same limited pool of raters who can be trusted to agree with a house standard closely enough to be useful. Money alone does not remove the bottleneck; the InstructGPT and HH-RLHF datasets were both built by teams in the tens of people not because a larger budget was unavailable, but because vetting and retaining more reliable raters is the actual constraint, and it does not scale linearly with spend.