摘要
新一代人工智能浪潮不断袭来,而数字化、智能化快速扩张却难以显著推高全要素生产率,由此形成新“索洛悖论”,其关键在于技术范式跃迁与制度变迁错位。通过梳理“科学—技术—产业—生产率—制度”的演进链条,分析生成式人工智能对知识生产和产业组织的深刻影响,可以发现当前我国数字经济发展存在数据要素流动不畅、算法黑箱、科技金融与数字产业脱节等结构性约束。在此基础上,应当重构数据、算力与算法等新型要素制度,完善科技金融支持与容错安排,构建技术与制度协同的智能化治理框架,以实现人工智能时代的生产率释放与高质量发展。
The rapid expansion of digitalization and intelligent technologies has not yet translated into a significant rise in total factor productivity,giving rise to a new“Solow paradox”.The core of this paradox lies in the misalignment between the technological paradigm shift ushered in by next-generation artificial intelligence and the slower pace of institutional change.By tracing the evolution from science to technology,industry,productivity,and ultimately institutional transformation,this study examines how generative AI reshapes knowledge production and industrial organization.The analysis reveals several structural constraints within China’s digital economy,including impeded data factor flows,opaque algorithmic processes,and a persistent disconnect between science-technology finance and the digital industrial ecosystem.Building on these insights,the paper argues for the reconstruction of institutional arrangements governing data,computational power,and algorithms;the improvement of financial support mechanisms and tolerance for innovation risks;and the establishment of a governance framework that enables synergistic interaction between technology and institutions.Such reforms are essential for unlocking productivity gains and achieving high-quality development in the age of artificial intelligence.
作者
张叶东
Zhang Yedong(School of Law,Shenzhen University)
基金
广州市法学会2025年度法学研究课题粤港澳大湾区数据跨境流动规则衔接机制对接研究(项目编号:892007915)资助。
关键词
新一代人工智能
新“索洛悖论”
全要素生产率
制度变迁
Next-generation artificial intelligence
New“Solow paradox”
Total factor productivity
Institutional change
