人机协作中的缩放悖论
The Scaling Paradox in Human-AI Collaboration
AI总结：
本研究构建分析模型，发现人机协作中存在缩放悖论：人类高估AI能力时，更大的AI规模会降低系统性能、加剧企业利润损失，企业可通过成本内部化等策略管理偏差，AI缩放需结合人机交互管理。
AI中文摘要：
缩放定律的发现凸显了AI系统的巨大潜力，其呈现出引人注目的经验模式：随着AI系统规模扩大，其能力往往可预测地提升。然而在现实应用中，AI极少单独运行，而是常与人类协同工作，这引发了一个问题：这些增益在人机协作中是否依然存在。本研究构建了一个分析模型，以探究AI的经验缩放收益何时能转化为人机联合系统性能的提升。研究表明，人机系统的性能可随AI规模扩大而正向缩放——前提是人类对AI的能力有准确感知。但人类的感知偏差会从根本上改变这一关系：其一，当人类高估AI能力时，可能出现缩放悖论，即更大的AI规模会降低整体系统性能，并加剧企业层面的利润损失；其二，当人类低估AI能力时，性能仍会随规模提升，但速度显著放缓。研究进一步显示，企业可通过成本内部化、感知对齐等运营策略主动管理这些偏差，其有效性取决于AI部署的经济性及人类感知偏差的方向。这些发现表明，组织从人机交互界面管理中获得的收益可能多于单纯投资更大、更昂贵的AI系统；更广泛而言，AI缩放不仅应被视为技术挑战，也应被视为行为与运营挑战，警示人们不应认为更大的AI系统会自动带来更优的运营结果，AI缩放是否创造价值最终取决于提升的AI能力如何塑造人类信念与协作努力。
英文摘要：
The discovery of scaling laws has highlighted the extraordinary potential of AI systems with a striking empirical pattern: as AI systems scale, their capabilities tend to improve predictably. Yet, in real-world applications, AI rarely operates in isolation; instead, it often works alongside humans, raising the question of whether these gains persist in human-AI collaboration. In this work, we develop an analytical model to examine when the empirical scaling benefits of AI translate into improved human-AI joint system performance. We demonstrate that the performance of a human-AI system can scale positively as the AI scales up-provided that humans have an accurate perception of the AI's capabilities. Human misperception, however, can fundamentally alter this relationship: i) when humans over-perceive the AI's capabilities, a scaling paradox may arise, in which greater AI scale reduces overall system performance and amplifies firm-level profit losses, and (ii) when humans under-perceive the AI's capabilities, performance still improves with scale but at a substantially slower rate. We further show that firms can actively manage these distortions through operational policies such as cost internalization and perception alignment, whose effectiveness depends on the economics of AI deployment and the direction of human misperception. These findings suggest that organizations may benefit more from managing the human-AI interface than from simply investing in larger, more expensive AI systems. More broadly, our results suggest that AI scaling should be viewed not only as a technological challenge, but also as a behavioral and operational one, and caution against the view that larger AI systems will automatically lead to better operational outcomes. Whether AI scaling creates value ultimately depends on how increased AI capabilities shape human beliefs and collaborative efforts.
