The 52% figure is the one most likely to be misread. It is the adjusted difference in posted fixed-price project budgets between AI-related and other postings in the same subcategory and posting month.2 It is not realized pay, earnings or a wage premium, and it says nothing about whether total demand rose. A fixed-price budget bundles value, scope and the labor needed into one number, so a larger budget could reflect a larger project rather than a higher price for the same work.
The hourly data offer a partial check, not a decomposition. Posted hourly rates, which price worker time, differ by about 1.5% after adjustment, so the posted price of an hour is close across the two groups. Hours worked in the first 90 days are about 41% higher for AI-related work, but that comparison covers only hired hourly contracts with positive recorded hours. The authors conclude that AI-related work is associated with more labor and larger projects rather than a higher price per hour, and the announcement says the higher budgets 'may partly reflect' broader scope.1 Using hourly-contract evidence to interpret the fixed-price gap is an inference: the two contract types are different postings, and hours are observed only for hired contracts with recorded hours. The report offers possible mechanisms, such as clients experimenting more or workers owning larger bundles, but does not test them.
The 1.5% rate gap and the 41% hours gap come from different eligible populations (posted hourly ranges versus hired contracts with positive recorded hours), so they should be read separately. They should not be multiplied into a cost or pay estimate, and the report does not offer one. Nor does it claim the hourly and fixed-price gaps should match.
All three are associations from OLS regressions with subcategory and month controls. The report states they do not identify a causal effect of the AI-related label, and that label itself comes from an LLM classifier.