Publication
Abstract
Because chips exhibit large differences in speed, power consumption, and cost across tasks, performance, energy use, and time often involve intricate trade-offs under heterogeneous workloads. As frontier models continue to scale, compute is increasingly becoming a binding constraint, yet the economic meaning of “one additional unit of compute” remains insufficiently well defined. This paper links chip specialization to cost functions, market structure, and industrial policy, and explicitly incorporates the system-level division of labor between training and inference. It further emphasizes that leading-edge process capacity is scarce, so compute tends to concentrate in a small number of firms and countries, raising entry barriers in the compute–chip ecosystem and strengthening the extent to which industrial policy instruments such as subsidies, tax incentives, and export controls can generate salient shocks to the supply of compute.
Keywords
AI Accelerators , Semiconductor Value Chain , Compute Cost , Industrial Policy , Market Concentration .
Metadata
Disciplines: Artificial Intelligence Technology
Subjects: Machine Learning
Cite This Article
APA Style
Lu, W. (2026). Ai chips and the economics of computer. Journal of Industrial Engineering and Applied Science, 4(1), 19-26. https://doi.org/10.70393/6a69656173.333733
Acknowledgments
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.
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DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
INFORMED CONSENT STATEMENT
CONFLICT OF INTEREST
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
AUTHOR CONTRIBUTIONS
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