Nufar Gaspar breaks down the unit economics of AI tokens in agentic workflows, where costs compound fast and most teams have no framework to tell productive spend from waste. The core argument: measuring cost per successful task, not raw token volume, is the only metric that matters when agents start spinning in loops.
The piece targets operators specifically. It covers why agentic systems generate what Gaspar calls 'tokens that spin', repetitive, circular model calls that consume budget without advancing a task, and how model selection decisions made early in a pipeline create cost multipliers that are hard to unwind later. The right model for the job is not always the most capable one.
Worth reading in full for the experimentation budget argument. Gaspar makes the case that teams cutting token costs indiscriminately often kill the exploratory runs that generate real capability gains. The tension between cost control and preserving productive experimentation is where the practical framework lives.
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