Gartner has released a forecast projecting that semiconductors optimized for executing artificial intelligence (AI) tasks will generate approximately $53.4 billion in revenue for the semiconductor industry in 2023. This reflects a substantial growth of 20.9% compared to the previous year.
The rising prevalence of generative AI and the expanding utilization of diverse AI-based applications across data centers, edge infrastructure, and endpoint devices are prompting the demand for high-performance graphics processing units (GPUs) and specialized semiconductor devices. Alan Priestley, VP Analyst at Gartner, noted that this surge is spurring the manufacturing and deployment of AI chips.
Anticipated growth in AI semiconductor revenue remains robust, with projections indicating a 25.6% expansion to $67.1 billion in 2024. The AI chips market is projected to double its 2023 size by 2027, reaching a substantial $119.4 billion.
AI Semiconductors Revenue Forecast, Worldwide, 2022-2024 (Millions of $)
| 2022 | 2023 | 2024 | |
| Revenue ($M) | 44,220 | 53,445 | 67,148 |
As AI matures in enterprise applications, an increasing array of industries and IT organizations will incorporate AI chips into their systems. For instance, Gartner analysts predict that the value of AI-enabled application processors used in consumer electronics will rise to $1.2 billion by the end of 2023, a notable surge from $558 million in 2022.
The necessity for the cost-effective execution of AI tasks has paved the way for optimized designs that support efficient AI-based workload processing. This trend is expected to lead to a surge in the deployment of custom-designed AI chips. “Custom AI chips are set to replace the current prevalent chip architecture, such as discrete GPUs, for a wide spectrum of AI-based workloads, particularly those reliant on generative AI methods,” explained Priestley.
Generative AI’s increasing adoption also propels the demand for high-performance computing systems for development and deployment. Numerous vendors are already providing GPU-based systems tailored for high performance, and networking equipment is witnessing substantial short-term advantages. Over the long term, Gartner envisions hyperscale enterprises seeking cost-effective avenues for deploying these applications, which is likely to drive greater adoption of custom-designed AI chips.
