Summary
This article introduces a new method for measuring the true economics of AI workflows, focusing on "cost per outcome" rather than just total LLM spend. The author, founder of botanu, highlights that successful business events in AI systems often require multiple attempts, retries, and tool calls, making traditional individual model call tracking insufficient. The goal is to provide a clear understanding of the actual cost for a single successful AI-driven outcome.
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