Pratyusha Duvvuri describes competing definitions of token economics and the different needs of engineers, managers and finance teams. She discusses the trade-off between model cost and quality, and explains how varied provider agreements complicate comparisons.
She presents an approach to consolidating usage dataToken volume is the total number of input, output, cached, or reasoning tokens an AI workload processes over a defined period or task. and mapping it to organisational and financial hierarchiesA data pipeline moves information through ordered collection, transformation and delivery steps for use by software or AI.. A demonstration of her team’s product illustrates cost and adoption views, while she distinguishes reporting lines from finance ownership.
Duvvuri closes by discussing the challenges of a rapidly changing product, different audiences and reconciling data that providers may revise over time.
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