数据、算法、算力要素赋能智能经济的理论逻辑与实践路径

冯科

（北京大学经济学院，北京 100871）

摘要：智能经济作为引领全球产业变革和经济增长的关键力量，其发展有赖于数据、算法、算力三大新生产要素的协同赋能。本文系统分析了数据、算法、算力逐步成为智能经济基本生产要素的历史必然性，阐述了三大要素通过资本化释放内生增长动力，协同发挥作用重塑生产方式并推动新质生产力跃升的理论逻辑。针对当前智能经济中的数据悖论、算法异化、算力分布失衡等难题，尝试从多个角度研提政策建议和实施路径，为政策制定者通过优化新生产要素配置助力新质生产力发展提供理论支持和决策依据。

基金项目：国家社会科学基金重点项目“数字经济推动农村地区共同富裕的机制与路径研究”（22AJL008)

关键词：智能经济；数据；算力；算法；要素化；新质生产力

作者简介：冯科，北京大学经济学院教授、博士生导师。

引用格式：冯科.数据、算法、算力要素赋能智能经济的理论逻辑与实践路径[J].首都经济贸易大学学报,2026,28(2):16-28.

The Theoretical Logic and Practical Path of Data, Algorithm, and Computing Power Factors to Empower the Intelligent Economy

FENG Ke

(Peking University, Beijing 100871)

Abstract: As an advanced form of digital economy, the intelligent economy is becoming a key engine for high-quality economic development. Its core driving force comes from the collaborative empowerment of three new production factors: data, algorithm, and computing power. Based on the analytical framework of historical materialism and endogenous growth theory, this paper systematically demonstrates the historical inevitability of the evolution of the three major factors into basic production factors, and constructs the theoretical logic of the three-dimensional coupling of "data (value carrier)-algorithm (decision engine)-computing power (basic support)". This paper holds that the three major factors realize the release of endogenous growth momentum through the capitalization process, and the data experience the butterfly process of "resource-asset-capitalization". The algorithm forms technical barriers and competitive advantages through the intellectual property system, and the computing power constructs the infrastructure ecology through economies of scale and financial innovation. The three jointly reshape the production function and promote the leap of new quality productive forces. The internal mechanism is reflected in the positive feedback cycle and multiplier amplification effect. Data provides fuel for algorithm training, and computing power provides support for algorithm operation. The algorithm creates new knowledge output by mining data value and improving computing power efficiency, thus forming a new resource allocation mode of "data traction, algorithm regulation, and computing power drive".

This paper further reveals that the three major factors still face systematic challenges in the process of capitalization accumulation, such as the governance paradox between data non-rivalry and capital exclusivity, the alienation of labor process caused by the black box of algorithms, the global power imbalance in the distribution of computing resources, and the impact of the organic composition of digital capital on the traditional employment structure. Given the above problems, this paper puts forward the following policy implications and practical paths: (1) deeply cultivating intelligent ecology, and building an independent R&D system covering key technology breakthroughs, systematic innovation, and open-source ecological construction; (2) deepening institutional innovation to improve the governance framework of data property rights definition, algorithm responsibility identification, and computing power resource allocation; (3) deepening policy coordination to coordinate technological innovation, industrial development, personnel training and regional coordination policies; (4) grasping the integration of human and machine, and innovating the knowledge education system and labor protection mechanism. By clarifying the internal mechanism and value transformation path of the collaborative empowerment of the three factors, this paper provides more targeted theoretical support and decision-making basis for policymakers to optimize the factor allocation and cultivate new quality productive forces.

Keywords: intelligent economy; data; computing power; algorithm; factorization; new quality productive forces

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