Hi [if:first_name]%%first_name%%,[else]there,[endif],
AI token usage is growing fast, and so is the pressure to make that spend pay off. However, as AI becomes more agentic, token usage increases with each retrieval, tool call, and intermediate output, making execution design matter for efficiency.
Engineering is offering a warning of what happens when AI scales faster than usage, with reports and anecdotes describing burning through annual budgets in four months after adopting an AI coding tool. This underscores a broader point about optimizing for economically efficient AI usage.
In this whitepaper, “The token economy: How enterprise AI architecture impacts cost and utility at scale”, we discuss how token efficiency becomes an AI architecture question and the role that context, intelligent routing, and harnesses play in reducing unnecessary reasoning and helping teams get more work done per token.
