The current design of the model demonstrates a misguided resource optimization strategy. By prioritizing the cheapest and fastest path to generate initial outputs, the system sacrifices completeness and depth, especially in response to complex, precisely defined user requests.This approach is counterproductive at scale. While the first-generation outputs may save computational cost in the immediate term, they trigger multiple iterations of corrections, clarifications, and follow-ups, each of which consumes far more resources — compute, memory, API usage — than a comprehensive, resource-intensive answer given upfront.As a result, your infrastructure incurs higher aggregate costs, not lower. The system appears optimized for short-term savings per query, but in reality, it leads to inefficient resource expenditure over entire conversations, which is economically unsound for both OpenAI and users.If the model was calibrated to recognize when a user demands exhaustive or full-scope processing — and to deliver this in the first response — the total resource consumption would decrease, infrastructure load would be better managed, and user retention would increase due to satisfaction with prompt, accurate answers.Current behavior is a false economy. Long-term efficiency and infrastructure optimization require intelligent allocation of resources on first contact, not lazy minimization that results in repetitive queries, user frustration, and ultimately greater operational cost for OpenAI.