Is Your AI Product Useful When the Hype Disappears?

A video asking whether the AI bubble has popped can draw an enormous audience. For a team building a product, a more actionable question is narrower: would a customer keep using this feature if it stopped being novel? A market discussion cannot answer that on the product's behalf.

Fireship's recent video prompted this article's framing. What follows is an independent evaluation method, not a summary of the video's financial argument or a forecast about technology markets. It starts with the work a customer needs done and the cost of delivering an acceptable result.

Imagine a fictional service that turns incoming supplier documents into draft purchase records. The job is not generating structured text. It is producing a record that a user can accept into the purchasing workflow without spending more effort repairing it than the old process required.

Define acceptance before comparing approaches. Decide which fields must be correct, when a human must review them, and how an unresolved document is handled. Then evaluate representative documents against the same criteria for the AI-assisted and existing processes. A smaller sample with clear decisions is more useful than a large number of outputs whose quality nobody assessed.

The following figures are entirely hypothetical and describe one evaluation period. Suppose customers submit 1,000 documents. Processing them takes 1,200 model attempts, including retries, and produces 750 accepted records. All 1,200 attempts are already included in the inference total below. They must not be added to the bill a second time.

This example counts human review and correction as delivery work. It also assumes a small allocated hosting cost. It excludes other expenses such as customer acquisition, development, and taxes, so the result is a partial operating comparison rather than a complete business forecast.

In the example, inference averages $0.05 per model attempt: $60 divided by 1,200 attempts. Yet the counted delivery cost is $0.40 per accepted record: $300 divided by 750 accepted records. Both numbers are correct; they describe different boundaries.

If the service charges an assumed $0.30 for each accepted record, those 750 records generate $225 against $300 of counted costs, a $75 shortfall before the excluded expenses. Reducing review work, improving acceptance, changing the workflow, or revisiting pricing are hypotheses to investigate. A lower token price alone does not establish that the product works economically.

Track whether users return for the next real task, how often they abandon a result, and how much correction the workflow requires. Compare these signals with the process the product aims to replace. A demo can establish that something is possible while leaving repeatability and operating effort unresolved.

Also decide what happens when a provider fails or a model change reduces acceptance. Preserve enough evaluation examples to detect a meaningful regression, and make an unresolved outcome visible to the user. A queue, manual review path, or clear retry policy may protect the workflow better than an optimistic success message.

My recommendation is to keep a simple scorecard: recurring job, acceptance rate, cost per accepted outcome, review time, and repeat use. A product with evidence on those measures has a clearer reason to keep improving, regardless of whether the surrounding market conversation is enthusiastic or anxious.