ARTIFICIAL INTELLIGENCE AND THE STRUCTURAL TRANSFORMATION OF THE SEMICONDUCTOR INDUSTRY: A THEMATIC REVIEW OF DEMAND DYNAMICS, TECHNOLOGY SPECIALIZATION, AND SUPPLY CHAIN RECONFIGURATION
DOI: https://doi.org/10.5281/zenodo.22829671
Thomas Rayen XR, Dr. Shivananda R Koteshwar, Dr. M R Jhansi Rani
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Keywords:
Artificial Intelligence; Semiconductor Industry Transformation; AI Hardware Accelerators; Supply Chain Reconfiguration; Technology Specialization.
Abstract:
The semiconductor industry is undergoing a significant structural transformation driven by the rapid expansion of artificial intelligence (AI) and the growing demand for advanced computational infrastructure. While existing studies have examined semiconductor evolution, AI hardware innovation, and supply-chain dynamics independently, an integrated understanding of how these forces collectively reshape industry structure remains limited. This study addresses that gap through a secondary-data analytical review employing thematic synthesis of peer-reviewed academic literature, technology trend analyses, and industry intelligence reports. The findings reveal four interconnected structural patterns: AI acting as a demand multiplier, increasing semiconductor intensification across industrial sectors, technological transition toward specialized architectures, and strategic realignment of global supply chains. The analysis suggests that the semiconductor industry is moving beyond cyclical market behavior toward an infrastructure-centric model in which AI-driven computing requirements redefine innovation pathways and competitive logic. A conceptual framework is proposed demonstrating how AI expansion generates demand shocks that accelerate technology specialization, trigger supply-chain reconfiguration, and ultimately lead to structural industry transformation. The study extends Dynamic Capability Theory, Resource Dependence Theory, and Technological Paradigm Shift perspectives by positioning semiconductors as strategic infrastructure within the digital economy. Managerial implications highlight the need for R&D portfolio reconfiguration, ecosystem partnerships, and alignment with data-center-driven competition, while policy implications emphasize semiconductor sovereignty, strategic investment, and supply-chain diversification. The study offers a unified theoretical and strategic interpretation of AI-driven semiconductor transformation and provides a foundation for future empirical and ecosystem-level research.