arXiv：cs.AI· Difang Huang, Shumin Yu, Fadong Chen, Weihua Zhou·· 16 小时前
生成式 AI 的“筛选时机”效应：迟到选项如何拉低团队创意
Too Late to Prune: Generative AI, the Screen, and Team Creative Decisions
AI 导读
一项预注册实验将 148 个三人团队随机分配到同一语言模型的五种使用时间表，在产品设计任务中把同样的 15 分钟访问从搜索阶段移到筛选阶段，专家评定的创意得分下降 0.62 个标准差，但各团队使用模型的频率相近。
正文
Abstract:Generative AI makes options cheap and shifts the bottleneck of creative work from generating options to screening them. We argue that AI's value depends on whether its options arrive before or after the team's screen. In our model, early options compete for a place in the portfolio; late options arrive after that competition and are kept unscreened. In a preregistered experiment, we randomly assigned 148 three-person teams to five schedules of access to the same language model during a product-design task with timed search and selection stages. Moving the same 15 minutes of access from search to selection lowered expert-rated creativity by 0.62 standard deviations, although teams used the model at similar rates. Platform logs locate the difference at the screen. Early access enlarged the pool of ideas fivefold, and teams pruned it. Late-access teams reached the screen with the same small pool as teams without AI and then searched again, devoting 29% of their selection-stage queries to generating new options, compared with 11% for teams with access throughout. Late access added breadth, not quality: these teams submitted 73% more proposals than did teams without AI. Language-model judges detected the benefit of access but not the cost of late timing.
| Subjects: | General Economics (econ.GN); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.14984 [econ.GN] |
| (or arXiv:2610.14984v1 [econ.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14984 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Difang Huang [view email]
[v1]
Sat, 10 Oct 2026 15:02:14 UTC (173 KB)
来源：arXiv：cs.AI · arxiv.org