I remember sitting in a boardroom in early 2023 with a CTO who had just received their first six-figure bill from a leading LLM provider. They had deployed a simple customer service bot, but they hadn't accounted for the recursive loops in their prompt engineering. "— We thought it was cents per query," he told me, staring at a line item that suggested otherwise. It turned out their system was feeding entire PDF manuals into the context window for every single greeting, effectively burning twenty dollars per conversation.
This wasn't an isolated incident, but a classic case of infrastructure mismanagement. The problem wasn't the AI itself, but the lack of a caching layer and a total disregard for token economy. I’ve seen teams build massive vector databases without understanding the cost of high-dimensional search queries, leading to what I call the "Invoice Shock" phase of corporate AI adoption. To avoid this, leaders must treat tokens as a finite resource, much like server bandwidth or raw materials in a factory.