AI Chips and the Economics of Computer

Wenqiang Lu

doi:10.70393/6a69656173.333733

Authors

Wenqiang Lu Nanchang Institute of Technology

DOI:

https://doi.org/10.70393/6a69656173.333733

ARK:

https://n2t.net/ark:/40704/JIEAS.v4n1a03

Disciplines:

Artificial Intelligence Technology

Subjects:

Machine Learning

References:

24

Keywords:

AI Accelerators, Semiconductor Value Chain, Compute Cost, Industrial Policy, Market Concentration

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.

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Author Biography

Wenqiang Lu, Nanchang Institute of Technology

School of Compuer Science, Nanchang Institute of Technology, China.

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