Taskonomics: Cost per Task Is Missing a Denominator

AI cost measurement has evolved from tracking token consumption, through token-based pricing analysis, to evaluating cost per completed task, but the passage argues that cost per task can be misleading when agents produce convincing yet incorrect results. It proposes cost per correct task, which divides total workload expense by only verified correct outcomes, as a more meaningful measure of AI efficiency. A CData Labs benchmark of 22 models found that none could correctly combine live CRM, warehouse, and ITSM data when given raw system access, despite many returning plausible account lists, while curated data-access tools containing predefined business logic enabled models across pricing tiers to produce identical correct answers. The findings suggest that AI economics depend not only on model selection but also on data architecture, since placing repeatable logic in the access layer can improve reliability and allow organizations to use lower-cost models; in the benchmark, equivalent correct results varied in cost by as much as 178 times.

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