Abstract

In an era where the global data revolution is in full swing, data has emerged as a core element reshaping cognition and driving decision-making. Modern statistics, as a pivotal underpinning for harnessing this revolution, sees its innovative development and practical application directly determine the full unleashing of data value. Artificial intelligence (AI), a highly potential core engine amid the data revolution, serves not only as an important carrier for the innovative application of modern statistics but also as a critical force propelling industrial transformation and empowering social development. In today’s world, AI, acting as a new engine for the productivity revolution, is accelerating the optimization and upgrading of industries. It exerts an immeasurable driving effect on developing a modern industrial system, unlocking the potential of new productive forces, and fostering new advantages in international competition. Establishing a statistical monitoring system for the AI industry, clarifying its statistical scope and constructing monitoring indicators, therefore stands as a crucial measure to provide solid statistical support for advancing the high-quality development of the industry.
From an industrial perspective, we define the core AI industry as goods and services formed with data as the key production factor, computing power as the supporting foundation, and algorithmic models such as machine learning and deep learning as the core technologies. The core AI industry is further divided into four types: computing power services, data services, algorithmic models, and intelligent terminals. We adopts a statistical methodology of ‘key industries + key enterprises’ to measure the scale of the core AI industry. First, we formulates a statistical classification directory for the core AI industry. For sectors fully falling within the scope of the core AI industry, the industry-based approach is applied for statistics, meaning all enterprises in these sectors are included in the statistical monitoring of the core AI industry. Second, enterprises in other industries that engage in artificial intelligence–related production activities are incorporated into the corresponding statistical classification catalogue of the core AI industry through intelligent screening and departmental identification. On this basis, pilot calculations are conducted at the provincial level.
This statistical monitoring system will not only enable a clear understanding of the development scale, structural characteristics and operational dynamics of China’s (provincial) core AI industry, providing a basis for the precise formulation and adjustment of industrial policies and facilitating the high-quality and sustainable development of the AI industry, but also offer replicable practical experience for the statistical monitoring of AI industry development worldwide.