Field notes · Topic
Economics.
The price of a token is the smallest number in most AI business cases. These notes cover the whole bill, from build and running costs to review time and the cost of errors, and how to show value against a baseline.
7 notes
Notes on this topic
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The cost of a token vs. the cost of a mistake.
Model prices keep falling; mistakes don’t get cheaper. We compare cost per task with the cost of an error — and often end up with a small model for most cases and a person for the rest.
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How to write an AI business case that finance will approve.
A credible AI business case prices the whole workflow, including running costs, review time and errors, against a measured baseline. It writes down the assumptions finance can check, and names the point at which you stop.
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Hours saved are not money saved: measuring the ROI of an AI system.
Time saved becomes value only when someone decides what the time is for. Measure against the baseline, attribute results carefully, and count the quality, speed and risk gains the business already puts a price on.
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What drives the cost of an AI project, and how to keep it predictable.
The model is rarely the expensive part. Cost follows the systems you connect, the state of the data, the review design, the languages, the security reviews and how much routines change. A fixed scope per phase keeps it predictable.
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What is a token? The unit behind AI costs and limits, explained.
Tokens are how language models read, write and bill. They explain context limits, why long documents cost more, why output is priced differently from input, and why the same text can cost differently across languages and models.
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How to forecast LLM running costs before the first invoice arrives.
Forecast from the task, not from a price list: calls per task, tokens per call, retries, volume peaks and review minutes, all measured in the pilot. Then set a ceiling with alerts before launch.
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How to cut LLM costs without quietly cutting quality.
Most savings come from sending less, and sending it to the right place. Every change is checked against the evaluation set, because a cheaper system that needs more review is not cheaper.