What ‘r you token about?

Tokens are the fundamental unit of consumption in large language models (LLMs) and other AI systems. They represent chunks of text—roughly sub-word pieces that models process for both input, (your prompt, context, documents), and output (the response). Why is this relevant now? Because the consumption of tokens is exploding, and they are creating their own economy. And it’s very likely that they will be one of the most important components of the global economy if AI delivers on its promise. How do we calculate the cost? Total cost = (Input tokens × input price) + (Output tokens × output price), usually billed per million tokens (MTok). Output tokens are usually more expensive because of the thinking involved. That obvious means more energy is required, therefore, the cost. How much? Like almost everything in life, you have tiers. Budget/light models (e.g., Gemini Flash variants, GPT-4o mini equivalents): Often $0.10–$0.60 input / $0.40–$2.50 output per MTok. But if you can fancy them, Frontier models (e.g., GPT-4o class, Claude Sonnet, Gemini Pro) can cost you $1–$5 input / $8–$25 output per MTok. What’s the dynamic? For AI providers (OpenAI, Anthropic, Google, xAI, etc.): Tokens are the primary revenue mechanism. Inference (serving queries) now dominates costs and revenue over training. Falling per-token prices haven’t reduced bills, because usage has exploded, driving higher total spend. Enterprises report AI as one of the fastest-growing budget items, sometimes 25–50% of IT spend. For the Broader economy: AI boosts productivity (coding, analysis, content) but introduces new costs and volatility. In terms of energy, there is a significant shortage to serve the demand AI is creating. In summary, tokens act like “AI fuel”, a granular, usage-based currency that makes compute costs transparent and scalable. Get used to it, it will be an important part of our daily lives.

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