A design methodology where product features are constrained or enabled by the specific cost and latency profile of the AI inference required to run them.
Most designers dream of infinite intelligence, but reality is bound by the cost of every token generated. Inference-Driven Product Design forces you to ask if a feature is worth the inference tax before you even open a design tool.
This approach shifts the focus from what is technically possible to what is economically viable. It forces product teams to optimize for 'intelligence density', ensuring that every millisecond of compute time directly correlates to a measurable increase in user value.
We launched an AI summary feature that was technically brilliant but cost us five dollars per user per month. We had to redesign the UI to trigger inference only on demand, dropping our costs by 90 percent.
I tell my product managers that every prompt is a line item on the P&L. If you cannot justify the inference cost, you cannot build the feature. It is not about being cheap, it is about being sustainable.
Our NPC dialogue system was too slow because we were using a massive model for simple greetings. We switched to a tiered inference model, using a tiny model for small talk and the big one only for complex quests.
We added an AI tutor that generated long-form essays for every student query. The compute bill arrived, and it was higher than our total subscription revenue for the quarter.
