KJCKorea
Journal Central

This study examines China’s national artificial intelligence (AI) development strategy through the analytical lens of policy network theory, focusing on the pivotal role of the National Development and Reform Commission (NDRC). While previous research has emphasized China’s rapid AI rise and the Sino–U.S. technological rivalry, this paper investigates the internal dynamics of policy formation and implementation. China’s policymaking operates through a dual structure combining “top-level design” and “fragmented authoritarianism.” Within this framework, the NDRC acts as a central orchestrator that designs overarching strategies, allocates critical resources such as computing power (“East Data, West Computing”) and capital (“AI Vouchers”), and coordinates inter-ministerial and central–local interactions. Conceptualizing this structure as a “centrally-managed network,” the study demonstrates that the NDRC functions as both architect and regulator of China’s techno-industrial complex, revealing how state-led coordination drives AI governance under Xi Jinping.

The purpose of this study is to analyze in-depth China's policy to cultivate semiconductor talents, which has emerged as a national task under the competition for technological hegemony between the U.S. and China. this paper tried to track the process of policy changes over the past 20 years through a multi-layered analysis framework called 'Changtip (elite)-waist (engineer)-bottom (skilled manpower)' and to identify key strategies and dynamics that operate underneath it. As a result of the study, it was found that China's talent training system was completed through three key characteristics. First, this system is not a completed 'design' but a product of 'reactive evolution' that has evolved in response to failures (data). Second, the core strategy of this evolutionary process is the 'binary tweezers strategy', which is summarized as a two-stage approach of 'pre-separation and post-fusion'. In particular, the three-stage convergence means a transition to a "matrix-type convergence design" in which heads (R&D elites) and bases (multi-layered talented people) are woven around the field (company). Third, the key driving force behind this 'matrix design' is the qualitative change from 'Incentive 1.0' to 'Incentive 2.0'. As talent training was upgraded to the 'national survival' issue, the mandatory 'new vocational education law' and the 'four major packages (finance + finance + land + credit)' were mobilized, and the timing of linking resources directly related to the survival of companies was forced as an 'unselectable proposal'. In conclusion, this study found that China's semiconductor talent strategy has been completed as a 'state-led talent independence ecosystem' that completes the 'matrix-style convergence' design through 'reactive evolution' and implements it as a 'compulsory driving force'. This is a powerful system that goes beyond market logic, suggesting that it will become a key variable in the future competition for technological hegemony.

With the revision of the three data laws in 2020, various data utilization strategies are being actively discussed across various industries. Even the medical data sector, which has previously been subject to significant restrictions due to various regulations, is exploring new possibilities with the introduction of the concept of "MyData" in healthcare. While the implementation of the MyData service in healthcare starting in March 2025 offers positive aspects, such as allowing individuals to easily access and utilize their medical data at any time, there is controversy over the effectiveness of this service. The medical community, in particular, has a negative view of the service, citing concerns about data leaks and the privatization of healthcare. Furthermore, concerns have been raised about the ambiguity of the "Medical MyData" concept and the liability for medical data leaks. While it may seem desirable for individuals to store and manage their own medical data, it is questionable whether this alone will significantly benefit the data subject. There is also skepticism that this could prioritize the interests of large medical institutions, insurance companies, and healthcare data platform companies. This issue is expected to remain a contentious issue, and resolving it is necessary to implement MyData projects. This paper examines the current state of "smart healthcare," a topic of much discussion in China, along with government and regional policies and legislation. We examine various issues and solutions related to the utilization of medical data in China, and propose recommendations for the advancement of our own legal system.