The article explores the impact of generative AI (gen AI) applications on the semiconductor industry and suggests scenarios and models. It discusses the surge in demand for computational power and the challenges semiconductor executives face in meeting this demand.
McKinsey analysts have developed scenarios outlining the effects of gen AI on B2B and B2C markets, all of which foresee a significant increase in computing demand. The article emphasizes the need for expanded data centers and semiconductor fabrication plants to address this demand. It also discusses the estimated wafer demand for high-performance components and the necessary strategies for industry stakeholders to ensure scalability and sustainability in the face of growing compute power needs.
The increased adoption of gen AI in B2B applications results in a surge in compute demand. The article brings 6 use cases that require significant computational resources to operate effectively.
- Coding and software development.
- Creative content generation (e.g., marketing materials).
- Customer engagement (e.g., addressing customer inquiries via a chatbot).
- Innovation – generate products and materials for R&D processes (e.g., designing a candidate drug molecule).
- Simple concision – summarize and extract insights using structured data sets (e.g., generating standard financial reports).
- Complex concision – summarize and extract insights using unstructured or large data sets (e.g., synthesizing findings in clinical images such as MRI or CT scans).
B2C compute demand is driven by the number of consumers who engage with gen AI, their level of engagement, and its compute implication.
The article presents six scenarios that illustrate the potential outcomes of gen AI demand for both B2B and B2C applications. These scenarios are crucial for understanding the implications of gen AI adoption on various industries, including the semiconductor industry.
