Every time you ask ChatGPT to write an email, summarize a document, or generate code, you’re burning through tokens. Every time a corporation runs thousands of customer service queries through an AI model, it’s burning through a lot more of them. And now, economists are starting to pay very close attention to how many tokens the entire economy is consuming.

AI companies have long used tokens as their fundamental billing unit, the way a utility company charges for kilowatt-hours. But the metric is quietly graduating from corporate invoices to something much bigger: a macroeconomic indicator for tracking how fast AI is spreading through the economy.

Tokens as the new kilowatt-hour

Here’s how it works. In AI, a “token” is a chunk of text, roughly three-quarters of a word, that a language model processes. When OpenAI or Anthropic or Google charges enterprise customers, they meter usage in tokens. Input tokens for what you send the model, output tokens for what it sends back.

The parallel to earlier technological shifts is hard to miss. Economists once tracked electricity consumption to gauge industrialization. Then they tracked cloud computing spend to measure digital transformation. Token consumption is shaping up to be the equivalent metric for the AI era.

Why CFOs suddenly care about token counts

At the enterprise level, token-based metering has forced organizations to build entirely new analytics infrastructure. Companies deploying AI at scale need to understand not just whether the technology works, but how much each interaction costs and what kind of return they’re getting.

CFOs are now integrating token consumption directly into budgeting and ROI calculations. That’s a meaningful shift. It means AI usage isn’t just an engineering decision anymore. It’s a line item that finance teams scrutinize the same way they’d scrutinize headcount or software licensing fees.

This has spawned what some are calling “AI tokenomics,” a field focused on the economic dynamics of token generation and consumption within AI ecosystems. Enterprises are deploying dedicated analytics tools to track this consumption, improving both cost management and productivity assessment.

The macro picture

Token consumption data is increasingly being viewed as a standard macroeconomic indicator for AI adoption. The logic is intuitive. If token consumption in healthcare is growing at three times the rate of token consumption in manufacturing, that tells you something about where AI is actually being deployed versus where companies are just talking about it.

Token consumption, by contrast, is a direct proxy for AI work performed. More tokens consumed means more AI queries processed, more documents analyzed, more code generated, more customer interactions automated.