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Dual-trajectory Innovation Enabled by AI Large Models in Technology and Demand: Mechanisms and Practical Exploration
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Jiang YU1, 2, Jiayu NIE1, 2, Wanqing LI1, 2, Feng CHEN1, 2
Journal of Technology Economics | 2024, 43(12) : 9 - 22
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Journal of Technology Economics | 2024, 43(12): 9-22
AI Big Model Driven Innovation
Dual-trajectory Innovation Enabled by AI Large Models in Technology and Demand: Mechanisms and Practical Exploration
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Jiang YU1, 2, Jiayu NIE1, 2, Wanqing LI1, 2, Feng CHEN1, 2
Affiliations
  • 1 Institutes of Science and Development, Chinese Academy of Sciences Beijing 100190 China
  • 2 School of Public Policy and Management Chinese Academy of Sciences Beijing 100049 China
Published: 2024-12-10 doi: 10.12404/j.issn.1002-980X.J24101816
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The development of AI large models is reshaping the innovation model driven by technology-push and demand-pull, making the interaction mechanisms between the two more closely integrated. However, existing literature lacks a systematic discussion on the innovation process driven by the interaction between demand and technology under the influence of AI large models. For this reason, a case study of AI large model-empowered innovation in the Tmall Genie product was conducted, based on the perspectives of the technology track and market track. The pathways for technology-push, demand-pull, and dual-track interactive innovation enabled by AI large models were extracted. The findings indicate that traditional AI technologies contribute to technology-push innovation by participating in stages such as technology identification, market validation, and testing, while also embedding in demand-pull innovation through stages like user need acquisition, evaluation, and transformation, facilitating the discovery and realization of personalized demands. AI large models enable the synergistic evolution of technology and demand, and support industry upgrading by promoting innovation ideation, technological advancement, bidirectional interaction, iterative innovation, knowledge expansion, and transformation. Compared with the innovation diffusion under the weak coupling mode between technology and demand driven by traditional AI, AI large models, with their significant advantages in expanding "user attributes" "innovator roles" and "knowledge domains" promote innovation diffusion under the strong coupling mode between technology and demand. It provides theoretical foundations and practical insights for enterprise innovation management and industrial upgrading empowered by AI large models.

artificial intelligence  /  technological trajectory  /  market trajectory  /  innovation pathway  /  case study
Jiang YU, Jiayu NIE, Wanqing LI, Feng CHEN. Dual-trajectory Innovation Enabled by AI Large Models in Technology and Demand: Mechanisms and Practical Exploration[J]. Journal of Technology Economics, 2024 , 43 (12) : 9 -22 . DOI: 10.12404/j.issn.1002-980X.J24101816
Year 2024 volume 43 Issue 12
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doi: 10.12404/j.issn.1002-980X.J24101816
  • Receive Date:2024-10-10
  • Online Date:2025-07-19
  • Published:2024-12-10
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  • Received:2024-10-10
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    1 Institutes of Science and Development, Chinese Academy of Sciences Beijing 100190 China
    2 School of Public Policy and Management Chinese Academy of Sciences Beijing 100049 China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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