Abstract: With the iterative breakthroughs in technological applications of generative artificial intelligence (GAI), an increasing number of researchers have begun utilizing GAI to assist scientific work. This new wave of "technological dividends" and its potential "intelligence gap" warrant close attention. Based on empirical survey data from researchers at multiple Chinese universities, this study employs cutting-edge machine learning algorithms to systematically examine the impacts of GAI on researchers' innovative behaviors, working hours, and their heterogeneous treatment effects. The findings reveal:First, while GAI significantly stimulates researchers' innovative behaviors, it simultaneously leads to a corresponding increase in working hours, indicating that the current "technological dividends" of GAI are primarily manifested in the aspect of innovation incentives.Second, the effects of GAI demonstrate significant heterogeneity among researchers, with research output, age, and gender being critical factors influencing these differential treatment effects. Specifically, male researchers and high-output individuals exhibit more pronounced improvements in innovative behaviors under GAI's influence, while experiencing relatively limited negative impacts on working hours. Further verification confirms that high-performing researchers characterized by superior productivity constitute the main beneficiaries of GAI's "technological dividends" at the current stage.

摘要： 摘要：随着生成式人工智能（GAI）在技术应用上的迭代突破，越来越多的科研人员开始使用GAI来辅助科研工作，新一轮“技术红利”及其潜在的“智能鸿沟”需要予以重视。基于对国内多所高校科研人员的实证调查数据，运用前沿机器学习算法实证检验了GAI对科研人员创新行为与工作时间的影响及其异质性处理效应。研究发现，第一，使用GAI在显著激发科研人员创新行为的同时，还会导致其工作时间同步增加，即当下GAI的“技术红利”主要体现在创新激励一个方面。第二，使用GAI对不同科研人员的影响存在显著差异，科研产出、年龄与性别是影响异质性处理效应的关键因素。一方面，男性及高科研产出的个体在创新行为上受GAI的积极影响，提升效果更为明显，另一方面，他们在工作时间上受GAI的消极影响也十分有限，进一步验证发现，以“高产”为特征的优势科研人员是现阶段GAI“技术红利”的主要受益者。