Nufar Gaspar explains that AI tokens are chunks of text processed by models, with costs varying dramatically by task complexity. The industry has shifted from unrestricted 'token maxing' to a restrictive 'token anxious' mindset, but the goal should be 'token smart' spending. Agentic workflows consume 5-30x more tokens than simple chats due to multi-step loops, with 60% of costs going to checking and refining outputs. The true metric should be cost per successful task, not per token. Gaspar introduces a framework categorizing tokens into 'tokens that teach' (experimentation), 'tokens that produce' (deliverables), and 'tokens that spin' (waste). The strategy is to eliminate spinning tokens, optimize production costs, and protect teaching tokens. Practical advice includes auditing usage, killing idle automations, monitoring input-to-output ratios, and choosing the right model for each task. For organizations, making usage visible and allocating budgets based on workload is key to managing costs effectively.