MediumProductProduct case10 min to answer

How would you estimate the unit economics of an AI feature before launch?

1Give yourself 10 minutes

2Answer out loud, not in your head

3Then compare with the answer below

Stuck? Show a way to structure it+

01Clarify the objective, user, constraints, and acceptable failure modes.
02Structure the response around usage distribution, token cost, gross margin, sensitivity.
03Compare credible alternatives and state the decision criteria.
04Finish with validation, monitoring, and what would change the decision.

Reference answer

strong answer

I would first define the outcome and acceptable error boundary. I would explicitly cover usage distribution, token cost, gross margin, sensitivity. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

usage distribution
token cost
gross margin
sensitivity

Must be adapted to the interviewer’s scenario
Would benefit from concrete numbers and personal examples

alternative answer

A second valid approach is to begin with the highest-risk assumption and test it cheaply. I would explicitly cover usage distribution, token cost, gross margin, sensitivity. I would select an approach only after comparing quality, latency, cost, safety, and operational complexity, then validate it with representative cases and production monitoring.

usage distribution
token cost
gross margin
sensitivity

Must be adapted to the interviewer’s scenario
Would benefit from concrete numbers and personal examples

Grade yourself

usage distribution

weight 30%

StrongExplains usage distribution concretely and ties it to a decision or measurable outcome.

WeakMentions usage distribution without implications, trade-offs, or evidence.

token cost

weight 25%

StrongExplains token cost concretely and ties it to a decision or measurable outcome.

WeakMentions token cost without implications, trade-offs, or evidence.

gross margin

weight 25%

StrongExplains gross margin concretely and ties it to a decision or measurable outcome.

WeakMentions gross margin without implications, trade-offs, or evidence.

sensitivity

weight 20%

StrongExplains sensitivity concretely and ties it to a decision or measurable outcome.

WeakMentions sensitivity without implications, trade-offs, or evidence.

Then expect these follow-ups

What assumption is most fragile?
Tests: depth
What alternative did you reject and why?
Tests: adaptability
How would you validate this in production?
Tests: adaptability

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