<![CDATA[The rapid evolution of artificial intelligence (AI) has triggered a transformative shift in global computing demand, particularly for graphics processing units (GPUs), which have become essential for handling large-scale parallel computations. This study explores how the accelerating development and adoption of AI contribute directly to rising GPU prices, emphasizing the interplay between technological growth, supply chain constraints, and semiconductor manufacturing limitations. By utilizing qualitative and literature-based approaches, the research examines academic publications, industry reports, and online datasets to evaluate pricing trends, production bottlenecks, and the broader economic implications of AI’s expansion. Findings indicate that AI-driven applications, such as generative models, predictive analytics, and automated decision-making systems, have significantly increased the demand for high-performance GPUs, placing additional pressure on semiconductor supply chains already limited by production capacity, geographical concentration, and fluctuating global market conditions. These constraints create persistent price inflation, highlighting a strong causal link between AI deployment and GPU market volatility. The study also identifies structural weaknesses in supply chain systems, including overdependence on a small number of manufacturers and delays in scaling fabrication technologies. The results offer a clearer understanding of how AI’s rapid growth reshapes hardware economics and complicates accessibility for education sectors, small businesses, and independent developers. Furthermore, the study underscores the need for sustainable strategies, such as diversifying semiconductor production, exploring alternative computing architectures, and improving forecasting models to support balanced technological advancement.]]>
