We build a mechanism design framework where a platform designs GenAI models to screen users who obtain instrumental value from the generated conversation and privately differ in their preference for latency. We show that the revenue-optimal mechanism is simple: deploy a single aligned (user-optimal) model and use token cap as the only instrument to screen the user. The design decouples model training from pricing, is readily implemented with token metering, and mitigates misalignment pressures.

翻译：我们构建了一个机制设计框架，其中平台通过设计生成式人工智能模型来筛选用户，这些用户从生成的对话中获得工具性价值，并在延迟偏好上存在私有差异。我们证明，收益最优的机制是简单的：部署单一对齐（用户最优）模型，并以代币限额作为筛选用户的唯一工具。该设计将模型训练与定价解耦，可通过代币计量轻松实现，并能缓解对齐压力。