发表机构
Carnegie Mellon University; Amazon（卡内基梅隆大学; 亚马逊）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出结果最大化目标与OutcomeShare机制，通过三层模拟证明其能提升AI辅助生产效率，表明AI生产力取决于使用目标而非仅模型能力。
AI 中文摘要
生成式人工智能（AI）模型能够执行日益复杂的任务，然而更多的AI使用并不一定转化为相应的生产力提升。我们将token-max（令牌最大化）视为这种低效的一个来源：当令牌消耗被视为生产性努力时，智能体被鼓励过度使用并消耗超出必要的计算量。我们转而提出outcome-max（结果最大化），它奖励每单位成本下经独立验证的任务完成情况，并引出一种有原则的停止规则。接着，为了研究这些目标，我们开发了一个三层模拟框架，涵盖即时交互、长期行为适应和组织协作。在所有三个层面上，outcome-max提高了AI辅助生产的效率，同时在很大程度上保持了已验证的任务性能。为了进一步使这些激励与outcome-max保持一致，我们引入了OutcomeShare（结果共享），一种激励机制。理论和模拟表明，OutcomeShare能够诱导参与，同时为员工、企业和LLM提供商创造共享收益。总之，我们的结果表明，AI生产力不仅取决于模型能力，还取决于如何构建管理AI使用的目标。
英文摘要
Generative artificial intelligence (AI) models can perform increasingly complex tasks, yet greater AI usage does not necessarily translate into proportional productivity gains. We identify token-max as one source of this inefficiency: when token consumption is treated as productive effort, agents are encouraged to over-exert and expend computation beyond what is necessary. We instead propose outcome-max, which rewards independently verified task completion per unit cost and induces a principled stopping rule. Then, to study these objectives, we develop a three-level simulation framework spanning immediate interaction, long-run behavioral adaptation, and organizational collaboration. Across all three levels, outcome-max improves the efficiency of AI-assisted production while largely preserving verified task performance. To further align these incentives with outcome-max, we introduce OutcomeShare, an incentive mechanism. Theory and simulation show that OutcomeShare can induce participation while generating shared gains for employees, firms, and LLM providers. Together, our results suggest that AI productivity not only depends on model capability, but also on how to construct the objectives governing AI use.
