The global artificial intelligence (AI) in semiconductor market size was exhibited at USD 56.42 billion in 2024, and is projected to hit around USD 232.85 billion by 2034, representing at a solid CAGR of 15.23% from 2024 to 2034.
The Artificial Intelligence (AI) in Semiconductor Market is experiencing rapid growth, driven by the rising demand for intelligent computing across various industries such as automotive, consumer electronics, healthcare, and manufacturing. AI technologies require high-performance and energy-efficient hardware, prompting semiconductor manufacturers to develop specialized chips such as AI accelerators, GPUs, FPGAs, and ASICs optimized for machine learning and deep learning applications. The market is also benefiting from increasing investments in AI-driven data centers, smart devices, and autonomous systems. Key players are focusing on integrating AI capabilities directly into chipsets to enable edge computing, reduce latency, and enhance real-time processing. Additionally, partnerships between AI software firms and semiconductor companies are fostering innovation and expanding application areas. North America currently leads the market due to strong R&D activities and a robust technological infrastructure, while Asia-Pacific is expected to witness significant growth, supported by the proliferation of consumer electronics and government initiatives promoting AI adoption. Overall, the market is poised for sustained expansion, underpinned by technological advancements and the growing ubiquity of AI across multiple sectors.
Recent Trends in Artificial Intelligence (AI) in Semiconductor Market
- Surge in AI Chip Demand: The AI chips market was valued at $73.27 billion in 2024 and is projected to reach around USD 927.76 billion in 2034, with a remarkable CAGR 28.90% from 2024 to 2034.
- Generative AI Driving Growth: The rise of generative AI applications has significantly increased demand for semiconductors, prompting the industry to innovate and produce more capable and efficient chips.
- High-Performance Computing (HPC) Expansion: The global demand for AI and HPC is expected to grow by over 15% in 2025, leading to upgrades in major application markets and signalling a new boom for the semiconductor industry.
- AI Integration in Smartphones: Companies like Taiwan Semiconductor Manufacturing Company (TSMC) anticipate that AI-powered features in smartphones will drive future demand, with TSMC reporting record net sales of $26.3 billion in a recent quarter.
- Investment in AI Chip Production: Intel unveiled the Gaudi3 AI chip for generative AI software, aiming to compete with Nvidia and AMD, with a launch planned for 2024.
- Tariff Impacts on AI Infrastructure: Despite semiconductor tariff exemptions, building AI infrastructure is becoming more expensive due to tariffs on key components like accelerator boards and data centre equipment.
- Supply Chain Adjustments: Companies are adapting to tariff challenges by considering shifts in manufacturing and assembly locations to mitigate costs and maintain competitiveness in the AI semiconductor market.
Scope Of Artificial Intelligence (AI) in Semiconductor Market
| Report Attribute | Key Statistics |
| Market Size by 2034 | USD 232.85 Billion |
| Market Size in 2023 | USD 48.96 Billion |
| Market Size in 2024 | USD 56.42 Billion |
| Market Growth Rate from 2024 to 2034 | CAGR of 15.23% |
| Largest Market | Asia Pacific |
| Base Year | 2023 |
| Forecast Period | 2024 to 2034 |
| Segments Covered | By Chip Type, By Application, and By End-use |
| Regions Covered | North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa |
Artificial Intelligence (AI) in Semiconductor Market Top Companies
- Nvidia Corporation
- Intel Corporation
- Advanced Micro Devices, Inc.
- Xilinx, Inc.
- Google Inc. (Alphabet Inc.)
- Qualcomm Incorporated
- IBM Corporation
- Samsung Electronics Co., Ltd.
- Huawei Technologies Co., Ltd.
- Amazon Web Services, Inc.
Artificial Intelligence (AI) in Semiconductor Market Recent Activities
TSMC’s Revenue Surge: Taiwan Semiconductor Manufacturing Company (TSMC) reported a 42% increase in first-quarter revenue for 2025, reaching T$839.3 billion (approximately $25.6 billion). This growth is largely attributed to the rising demand for AI technologies, offsetting declines in consumer electronics.
Broadcom’s Share Buyback: Broadcom announced a new share buyback program worth up to $10 billion, reflecting confidence in its semiconductor and infrastructure software divisions, particularly concerning AI investments.
FuriosaAI’s Rejection of Meta’s Acquisition: South Korean AI hardware startup FuriosaAI declined an $800 million acquisition offer from Meta, citing differing visions for the company’s future. FuriosaAI specializes in high-performance AI inference chips and plans to continue its independent growth trajectory.
Horizon Robotics’ IPO and Expansion: Chinese AI chipmaker Horizon Robotics filed for an IPO in Hong Kong, aiming to raise $500 million. The company focuses on AI chips for self-driving cars and advanced driver assistance systems, reflecting the growing integration of AI in automotive semiconductors
U.S. Tariffs and Semiconductor Industry: The implementation of new U.S. tariffs, including a 104% duty on Chinese imports, has raised concerns about increased costs and potential demand destruction in the electronics and semiconductor sectors. While some semiconductors are exempt, key AI components and related imports remain affected, potentially hindering AI development and adoption.
China’s Investment in Semiconductor Industry: The Chinese government announced a $65 billion plan to support its domestic chip industry, with significant funding allocated to companies like Rapidus. This move aims to bolster China’s position in the global semiconductor market amid ongoing trade tensions.
Artificial Intelligence (AI) in Semiconductor Market Segmentation
Chip Type Outlook
- Central Processing Units (CPUs)
- Graphics Processing Units (GPUs)
- Field-Programmable Gate Arrays (FPGAs)
- Application-Specific Integrated Circuits (ASICs)
- Tensor Processing Units (TPUs)
The AI in semiconductor market is segmented by chip type into Central Processing Units (CPUs), Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), and Tensor Processing Units (TPUs). CPUs, being general-purpose processors, play a foundational role in managing AI system operations and coordinating tasks. However, their sequential processing nature limits performance in intensive AI workloads, which has led to increased reliance on more specialized chips. GPUs, with their highly parallel architecture, have become essential for AI training, particularly in deep learning and neural networks. Companies like NVIDIA dominate this space, pushing continuous innovations in AI-optimized GPU architecture.
FPGAs offer the advantage of reconfigurability, making them suitable for edge computing scenarios where low latency and adaptability are crucial. Their use is rising in sectors like telecommunications and automotive. ASICs, on the other hand, are customized for specific AI tasks, delivering superior performance and energy efficiency. These chips are widely adopted in large-scale applications, such as autonomous driving and data center operations. TPUs, developed by Google, are designed specifically for accelerating tensor computations in AI models. Though limited in accessibility compared to GPUs or CPUs, TPUs are increasingly influencing AI chip architecture development across the industry.
Application Outlook
- AI Training
- AI Inference
- Edge AI
- Cloud AI
- Others
In terms of application, the AI semiconductor market is segmented into AI training, AI inference, Edge AI, Cloud AI, and others. AI training involves feeding vast datasets into models to help them learn and make predictions. This process requires extremely high computational power, typically provided by GPUs and specialized ASICs. As AI models grow more complex, there’s a noticeable trend toward more energy-efficient training solutions and distributed computing architectures. AI inference, which is the deployment of trained models for real-time decision-making, is gaining significant traction across industries such as retail, finance, and consumer tech. This stage demands chips that are optimized for quick, efficient responses.
Edge AI refers to performing AI computations on devices at the network’s edge rather than in centralized data centers. It supports real-time processing and enhances privacy by keeping data local, which is particularly useful in applications like autonomous vehicles and IoT devices. Cloud AI remains dominant in enterprise environments, allowing businesses to leverage vast computing resources for large-scale AI processing. However, there is a growing trend toward hybrid models that combine the scalability of cloud with the responsiveness of edge computing. The “others” category includes emerging applications such as robotics, smart manufacturing, and AI-enhanced cybersecurity systems.
End-Use Outlook
- Healthcare
- Automotive
- Consumer Electronics
- Industrial Automation
- Banking and Finance
- Others
From an end-use perspective, the AI semiconductor market serves a wide range of industries including healthcare, automotive, consumer electronics, industrial automation, and banking and finance. In healthcare, AI-powered tools are revolutionizing diagnostics, imaging, and patient monitoring. The demand for semiconductors that can process high-resolution medical data efficiently is rising rapidly. The automotive sector is leveraging AI for advanced driver-assistance systems (ADAS) and fully autonomous driving. This requires chips capable of real-time, fail-safe performance — often achieved through FPGAs or ASICs.
Consumer electronics is another rapidly growing area, with AI now integrated into smartphones, smart speakers, wearables, and smart TVs. Companies are increasingly designing custom AI chips, like Apple’s Neural Engine, to deliver on-device intelligence for better speed and privacy. In industrial automation, AI is driving predictive maintenance, robotics, and intelligent supply chains. Edge AI plays a vital role here by reducing latency and ensuring operational continuity. Lastly, in the banking and finance sector, AI chips are used for fraud detection, algorithmic trading, and real-time data analysis. These applications require both speed and security, leading to strong adoption of high-performance semiconductor solutions.
Immediate Delivery Available | Buy This Premium Research Report@ https://www.precedenceresearch.com/checkout/3999
 in semiconductor market size was exhibited at USD 56.42 billion in 2024, and is projected.){kind=link}