Dubai, UAE – The global semiconductor industry is set to witness a significant revenue boost in the coming year, with artificial intelligence (AI) chips taking center stage. According to the latest report from Gartner, Inc., AI chip revenues are projected to reach a staggering $53.4 billion in 2023, marking a remarkable 20.9% increase from the previous year.
The rapid advancements in generative AI and the escalating utilization of various AI-based applications across data centers, edge infrastructure, and endpoint devices have necessitated the development and deployment of high-performance graphics processing units (GPUs) and finely-tuned semiconductor devices.
“The developments in generative AI and the increasing use of a wide range AI-based applications in data centers, edge infrastructure and endpoint devices require the deployment of high performance graphics processing units (GPUs) and optimized semiconductor devices,” said Alan Priestley, VP Analyst at Gartner. “This is driving the production and deployment of AI chips.”
This trend is predicted to persist as AI semiconductor revenue is anticipated to maintain double-digit growth, surging by 25.6% in 2024 to reach $67.1 billion. The outlook becomes even more astounding as the AI chips market is poised to more than double its 2023 value, projected to hit a remarkable $119.4 billion by 2027.
Industries spanning the spectrum and IT organizations are expected to incorporate AI chips into their systems, reflecting the maturity of AI-based workloads in the enterprise landscape.
Gartner analysts foresee a substantial rise in the value of AI-enabled application processors within consumer electronics. By the close of 2023, these processors are anticipated to contribute a substantial $1.2 billion to the market, a substantial increase from the $558 million recorded in 2022.
The push for enhanced efficiency and optimized designs to support cost-effective AI workload execution is anticipated to drive a surge in custom-designed AI chip deployments. Priestley noted that numerous organizations are poised to replace the prevailing chip architecture, primarily discrete GPUs, with custom AI chips across a wide range of AI-based workloads, particularly those rooted in generative AI techniques.
The drive for generative AI has also fueled demand for high-performance computing systems, leading to the introduction of GPU-based systems and networking equipment by various vendors. In the long term, hyperscale companies are anticipated to increasingly adopt custom-designed AI chips as they seek efficient and cost-effective solutions for deploying these applications.