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How to test willingness to pay for AI code review tools with early customers
AI code review tools: Test willingness to pay through a concrete workflow, buyer, and offer.
Test willingness to pay through a concrete workflow, buyer, and offer. General enthusiasm about a category is weak evidence. Ask how the buyer evaluates alternatives and what process would turn interest into an approved purchase.
Start with the decision
Present a clearly described package and discuss the value, charging unit, implementation effort, and next commercial step. Record actual decisions and objections. Keep exploratory interviews distinct from commitments or guaranteed future revenue.
The illustrative scenario
This is an educational workbook. The company or fund and all numerical inputs below are fictional. They are not ADIN results, customer claims, market benchmarks, or a forecast.
The fictional service suggests review comments on pull requests for an engineering team. Maintainers remain responsible for accepting a finding and deciding whether a change is ready. Review traffic varies with release cycles.
Too many plausible but unhelpful comments can increase reviewer work. A severity label has little meaning unless the team agrees which findings require action.
A monthly cost bridge with visible assumptions
For this exercise, assume 320 completed units in a month, 19 minutes of human work per unit, and a fictional labor rate of $30 per hour. The labor rate, service cost, and revenue are invented inputs. They are not current vendor prices, customer results, or a suggested commercial offer. Too many plausible but unhelpful comments can increase reviewer work. A severity label has little meaning unless the team agrees which findings require action.
The bridge subtracts only the costs shown. The remainder is not an accounting gross margin or profit figure: other expenses, taxes, timing differences, and classifications are outside this simplified model. A negative result is useful information about the assumptions, rather than evidence that an entire product category cannot work.
Now change one input. If the workflow requires twice the review time, the human-work line doubles even when the provider's unit price stays constant. If volume rises, distinguish a genuinely fixed cost from a cost that will step up. Reconcile the model with observed delivery logs before using it to support a pricing or fundraising claim.
| Item | Illustration |
|---|---|
| Completed workflow units | 320 |
| Assumed revenue for this month | $1,500.00 |
| Variable service cost | 320 × $0.18 = $57.60 |
| Listed fixed delivery cost | $320.00 |
| Human work at an assumed $30/hour | $3,040.00 |
| Remainder after these listed costs | -$1,917.60 |
Use this worksheet
Answer the questions with a source, date, owner, and next action. Mark an unknown as unknown. Use public or permissioned information and involve a qualified specialist when a question requires expertise beyond the team’s competence.
- Who can approve the purchase?
- What alternative is funded today?
- Which package is being tested?
- What objection blocks a decision?
- What observable next step indicates progress?
Questions specific to this scenario
- What makes a finding actionable?
- Which changes are excluded?
- Who adjudicates reviewer disagreement?
Keep a labeled sample of true defects, false alarms, dismissed suggestions, and defects missed by both workflows. Record repository and change type so aggregate numbers remain interpretable.
A concrete next test
Ask two maintainers to label the same permissioned set independently before looking at the tool's findings. Investigate disagreements about severity and actionability. A shared rubric gives the next comparison more meaning than counting comments that nobody would act on.
Interpret the result
One accepted offer does not establish a universal price. Compare the buying situation and service burden before applying the result to a different segment, and preserve evidence from people who declined.
Compare actionable findings and reviewer effort on similar changes. A tool that produces more comments is not automatically finding more consequential problems.
Sources and method
These primary resources provide background for the concepts. The fictional scenario, calculations, and worksheet are ADIN educational examples; the sources do not report or validate those invented results. Reviewed October 11, 2026.