Cheaper per token. Ruinous in aggregate.
Your AI pilot looked cheap. Then it shipped, and the bill kept climbing every single month. You approved the model. You did not approve what it costs to run, and now Finance is asking why the efficiency project became the fastest-growing line in the budget.
Here is the part the hype skipped: training is no longer where the money goes. Running the model is. The 15x Problem treats that shift as what it really is, a finance problem, and hands the people who sign the budget the AI economics the demos never gave them. Training was the down payment. Inference is the mortgage, and the mortgage compounds with every agent you deploy.
The finance leader's operating manual for agentic AI spend, no code required:
Read it and you will know where the money actually goes, judge any AI proposal on its running bill instead of its demo, set spending ceilings that hold, and walk into the next budget review with an AI strategy you can defend line by line.
This is AI for finance leaders, not engineers: CFOs, founders, board members, and anyone who owns the budget for anything with AI in it. Part of the Lead, Govern & Apply AI series, in The Verifier's Library.
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