Developments in AI are proving beneficial for semiconductor manufacturers, both by creating new marketing opportunities and by facilitating manufacturing process improvements.
Semiconductors are materials which have a conductivity between conductors (generally metals) and nonconductors or insulators (such as most ceramics). Semiconductors can be pure elements, such as silicon or germanium, or compounds such as gallium arsenide or cadmium selenide.
McKinsey argues that AI will drive a large portion of semiconductor revenues for data centers and the edge.
The AI technology stack will open many opportunities for semiconductor companiesAI has made significant advances since its emergence in the 1950s, but some of the most important developments have occurred recently as developers created sophisticated machine-learning (ML) algorithms that can process large data sets, “learn” from experience, and improve over time. The greatest leaps came in the 2010s because of advances in deep learning (DL), a type of ML that can process a wider range of data, requires less data preprocessing by human operators, and often produces more accurate results.
AI will drive a large portion of semiconductor revenues for data centers and the edgeWith hardware serving as a differentiator in AI, semiconductor companies will find greater demand for their existing chips, but they could also profit by developing novel technologies, such as workload-specific AI accelerators (Exhibit 2). We created a model to estimate how these AI opportunities would affect revenues and to determine whether AI-related chips would constitute a significant portion of future demand.