The Currency of AI: Understanding Tokens
Every time an employee uses an AI tool to ask a question, summarize a report, or generate code, a meter is running. That meter is measured in tokens. Tokens are the basic units of data that an AI model processes, whether it's a piece of a word, punctuation,
or a snippet of code. Think of them as the currency of AI; you pay for what you use.This pricing model is a significant departure from traditional software's flat-rate subscriptions. With AI, costs are variable and directly tied to consumption. Two types of tokens affect your bill: input tokens (the data you send to the model) and output tokens (the model's response). Output tokens are typically three to five times more expensive because generating a response requires significantly more computational power. This variable, usage-based pricing means that many companies are just now waking up to what they've signed up for, with nearly eight in ten IT leaders reporting surprise charges tied to AI consumption.
The Paradox of Model Pricing
One might assume that as AI technology matures, the price per token would steadily decrease, and in some ways, it has. Fierce competition among providers like OpenAI, Google, and Anthropic has led to significant price cuts for some models. However, overall corporate spending on AI is skyrocketing. Research shows that average business spending on AI tokens is 13 times higher than it was in early 2025.The paradox is explained by a simultaneous shift toward more powerful, and therefore more expensive, premium models. While a basic AI task might cost cents, a complex workflow can be many times more expensive. In April 2026, premium models accounted for over 55% of the total AI cost for businesses, despite representing a smaller share of the tokens used. This trend shows that while the unit price for some AI tasks is falling, companies are choosing to spend their budget on more capable, and costly, AI brains.
Why Your AI Needs (and Costs) Are Growing
The other major cost driver is the sheer volume and complexity of AI usage. Companies are moving beyond simple chatbots and into what are known as "agentic workflows." These are multi-step, automated processes where an AI agent might plan a task, use various tools, and validate its own work, triggering numerous model calls for a single user request. A simple query that cost a few cents in 2023 can now equate to over a dollar in an agentic workflow in 2026.This explosion in usage is staggering; from January 2025 to April 2026, token usage among businesses grew by over 1,000%. This growth is compounded by what experts call "token bloat," where users feed AI models excessive, unnecessary information, driving up both input and output costs. An inefficient prompt can easily lead to costs 200% higher than expected as the AI burns through tokens trying to make sense of the noise.
Strategies for Smart AI Spending
As AI becomes a formal line item on corporate budgets, managing its cost is shifting from a technical task to a strategic imperative. The first step is gaining visibility. Many organizations are hit with massive bills simply because they can't see where the spending is coming from. Experts recommend a proactive governance strategy rather than reactive cost tracking.One of the most effective strategies is model tiering, or dynamic routing. This involves automatically using smaller, cheaper models for simple tasks like text formatting, while reserving the expensive, high-powered models for complex reasoning. Another key tactic is optimizing prompts and context. Trimming unnecessary information from inputs can create compounding savings at enterprise scale.Furthermore, businesses are implementing technical solutions like prompt caching, where an AI stores and reuses answers to repeated queries at a lower cost. Setting firm spending limits and real-time alerts at the user, team, and application level can act as a crucial guardrail to prevent runaway costs before they spiral out of control.
