For the past two years, AI success has been measured in tokens. But that's an incomplete picture. Tokens only measure how much AI talks. They don’t measure how much work gets done. That’s why we introduced a new metric: Agentic Work Units (AWUs). An AWU is one discrete task completed by an AI agent. A prompt processed. A reasoning chain completed. Or a tool invoked. Simply put: AWUs = work. Tokens = compute. Both matter. But they measure different things: Tokens show our footprint in the global AI compute economy. AWUs show the work our platform actually completes for customers. Not knowing the difference will cost you. Because in LLMs, output tokens are up to 10× more expensive than input tokens. So the goal isn’t just using fewer tokens. It's making sure every output token produces real work. That’s what AWUs capture. Not AI chatter. Actual productivity.

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