CHEAPUNO guide

How to stop measuring AI ROI by code lines or token counts?

Track verified business outcomes and total human review effort instead of raw technical metrics to reveal true AI value.

CHEAPUNO EditorialSources checked 10 10 月, 2026

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What you are probably seeing

You notice that your team generates huge amounts of code or text in short sessions, yet the final deliverables still take hours to clean up and debug.

Your costs for model calls keep rising while the number of completed, accepted tasks remains flat or grows slowly.

Why it happens

Raw metrics like lines of code ignore the high failure rate of AI outputs, which forces humans to spend extra time correcting errors and re-running tasks.

Focusing on input volume creates a false sense of productivity that hides the real friction caused by low confidence in AI results.

What changed recently

Step by step

1. Define one clear, verifiable outcome metric that represents a finished unit of value for your customer or team.

2. Calculate the total cost per outcome by adding up all model calls, including retries and failed attempts.

3. Time the human review effort required to move an AI output from draft to accepted state.

4. Compare the new total cost per outcome against your previous baseline to identify where waste actually occurs.

5. Set a target for total cost per outcome that includes both machine time and human labor hours.

Ways to go further

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