China’s Artificial Intelligence Innovation : A Top-Down National Command Approach?

Zeng, Jinghan (2021) China’s Artificial Intelligence Innovation : A Top-Down National Command Approach? Global Policy, 12 (3). pp. 399-409. ISSN 1758-5880

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China’s open embracing of the age of artificial intelligence (AI) has attracted considerable academic and media attention. Many argue that China has taken advantage of its national approach to contest for AI supremacy and geopolitical dominance. The relevant analyses assume China’s AI plans as being Beijing’s coherent top‐down geopolitically driven national strategy, reflecting Chinese leaders’ global ambitions. This article argues that these views are mistaken. It argues that China’s AI plans are primarily driven by contestation and the struggle for resources among domestic stakeholders who are economically motivated and have little awareness of the bigger geopolitical picture. Instead of a top‐down command approach, China’s national AI plan is an upgrade of existing local AI initiatives to the national level, reflecting a bottom‐up development. This article suggests that the existing analyses vastly exaggerate: (1) Beijing’s capacity to coordinate domestic capital and actors towards a unified, specific strategic objective; and (2) the extent of China’s AI advancement and its geopolitical threat, triggering unnecessary anxiety among China’s near competitors.

Item Type:
Journal Article
Journal or Publication Title:
Global Policy
Additional Information:
This is the peer reviewed version of the following article: Zeng, J. (2021), China’s Artificial Intelligence Innovation: A Top‐Down National Command Approach?. Global Policy. doi: 10.1111/1758-5899.12914 which has been published in final form at This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
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?? lawglobal and planetary changeeconomics and econometricspolitical science and international relationsmanagement, monitoring, policy and law ??
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Deposited On:
01 Dec 2020 12:27
Last Modified:
22 May 2024 00:53