Artificial Intelligence (AI), as a core driver propelling socioeconomic development, is triggering a dual paradigm shift in scientific research (AI for Science, AI4S) and engineering technology (AI for Engineering, AI4E). This paper systematically elaborates on the driving forces, mechanisms, and practical pathways for the paradigm shift in digital ecosystem network development driven by AI4E. It points out that the traditional development paradigm of digital ecosystem networks faces a fundamental conflict between "rigid architectures and diversified scenarios", necessitating reconstruction with the goals of being "hyper-converged, highly trustworthy, and integrated". The paper introduces the critical foundations, technological underpinnings, and operational mechanisms for this AI4E-driven paradigm shift in digital ecosystem networks. It delineates the main characteristics of the new paradigm from perspectives including mindset, methodology, practical norms, and developmental pathways. Furthermore, it presents practical explorations of AI4E-empowered transformation: proposing the Polymorphic Intelligent Network Environment (PINE) based on Generative AI to forge the "second curve" of network technology systems; introducing On-Wafer Generative Vari-Structure Computing to foster new "chip species" of intelligent computing power; promoting endogenous safety and security (ESS) to empower the resilience engineering of digital system networks, thereby enhancing the endogenous security of AI application systems; and advocating for the construction of the "Hyper-Converged Networks and Intelligent Computing Testbed" as a major scientific facility. This testbed will validate the scientific conjecture that "structure determines efficiency/security/diversity", providing support for building an independent knowledge system, advancing independent sci-tech innovation, and deepening reforms in self-reliant talent training. The study provides both a theoretical framework and technological pathways for the paradigm evolution of digital ecosystem networks in the AI era.
WU Jiangxing
,
ZOU Hong
,
ZHANG Fan
,
LIU Qinrang
,
GAO Yanzhao
,
SHANG Yuting
,
QI Xiaofeng
. AI for Engineering: Driving a new paradigm for digital ecosystem network development[J]. Science & Technology Review, 2025
, 43(12)
: 19
-28
.
DOI: 10.3981/j.issn.1000-7857.2025.04.00041
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