AI Chips Statistics By Market, Revenue, Requirement And Utilization

Introduction

AI Chips Statistics: Artificial intelligence will play a significant role in technological innovations in the future. Accordingly, systems are being developed to control AI technologies so that they can be integrated into appropriate apparatus. The general purpose of AI software systems is inappropriate for target control because the computer hardware is insufficient to implement modern AI systems. This is where the need for AI chips came to fruition.

They generally focus on intensive data processing powered by faster and more efficient transistors. Such a design enables it to perform at high processing speeds and has lower energy consumption than traditional chips. It is interesting to get valuable insights into AI chips and how they have revolutionized the IT industry.

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• The AI chips market is expected to reach USD 71 billion in 2024, a 33% increase from 2023.
• AI chips revenue from compute electronics is projected to be USD 33.4 billion, making up 47% of the total AI chips market.
• Revenue from AI chips used in automotive electronics is expected to reach USD 7.1 billion in 2024.
• AI chips in consumer electronics will generate USD 1.8 billion in revenue.
• The value of AI accelerators used in servers is forecasted to total USD 21 billion in 2024 and increase to USD 33 billion by 2028.
• AI PCs, which include neural processing units, will account for 22% of total PC shipments in 2024.
• Leading companies in the AI chip market include Nvidia, IBM, Cerebras Systems, Graphcore, SambaNova Systems, and Qualcomm.
• Tech giants like AWS, Google, Meta, and Microsoft are investing in custom AI chips to improve operational efficiency and reduce costs.
• Companies like Mythic and Lightmatter are focusing on low-power AI processors for edge computing applications.
• Innovations such as IBM’s AIU for the Watson.x platform and Cerebras Systems’ Wafer-Scale Engine are pushing the boundaries of AI chip design.
• Qualcomm’s Snapdragon processors and Apple’s Neural Engine are significant contributors to the mobile AI chip market.
• Companies like Lightmatter are developing energy-efficient AI chips for cloud and edge AI applications.
• The AI semiconductor market is expected to continue double-digit growth through the forecast period, with 2024 experiencing the highest growth rate.
• Companies are heavily investing in AI chip research and development to meet the increasing demand for AI applications across various sectors.
• AI chips are being increasingly used in data centers, automotive, consumer electronics, and edge computing applications.

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What is Artificial Intelligence?

• Artificial intelligence is a field of computer science research focusing on intelligence based on computer systems.
• It also deals with methodologies that enable machines to get a holistic view of the environment to maximize their chances of achieving valuable goals.
• Web search engines such as Google, YouTube, and Amazon are among the most popular uses of AI.
• In 2-3 years, creative tools such as ChatGPT, Gemini, and Claude have become the most used websites.
• The AI chip revolution has enhanced the technology implementation
• The AI chip market is valued at 53.7 billion USD in 2023 and is set to grow by an impressive 30% in 2024. This rapid growth is a testament to the exciting potential of AI technology and the increasing demand for AI chips across various industries.
• The global Artificial Intelligence Chip Market has generated USD 20.28 billion in revenue in 2023 and is projected to have a revenue of 38.16% from 2024 to 2033
• AI chips are being integrated into a variety of smart devices, such as smartphones, laptops, and wearables. This widespread adoption ensures that there will be enormous public demand among users, reassuring us of the versatility and applicability of AI technology in our daily lives.

Working With AI Chips

• Computer chips are integrated circuit units manufactured on a microscopic level. Miniscule-level components such as transistors and tiny switches are embedded into chips to ensure the stable flow of electrical current within the circuit.
• These AI chips serve as the brain behind data processing.
• Depending upon hardware and functionality, chips can be classified in the following ways.

AI Chip Market Revenue Worldwide

(Reference: statista.com)

From the graph, it can be inferred that

• The AI chip market is growing steadily
• By 2025, it is expected to reach 91.96 billion USD
• It could be mainly attributed to the hype around the AI application continuing to grow, thus leading to strong positive forecasts for AI

(Reference: statista.com)

• With a compound annual growth rate (CAGR) of 30.3%, the artificial intelligence chip industry in the United States is expected to reach a size of USD 165 billion by the end of 2030, from its projected revenue share of USD 28 billion in 2023.
• With a market valuation of over USD 30.9 billion the same year, North America continued to lead the AI sector, followed by Asia Pacific and Europe.
• The market for AI chips is expected to grow by 60% by 2025, reaching a value of over USD 50 billion.
• NVIDIA is the world’s top manufacturer of AI chips; as of the end of July 2023, its total revenue had increased to USD 13.507 billion, a 101.485 percent rise from the previous year.

(Source: marketsandmarkets.com)

Market Forecast

• By 2028, the market for AI chipsets is expected to have grown from USD 44.3 billion in 2022 to USD 131.8 billion.
• Throughout the projected period, the market is anticipated to expand at a Compound Annual Growth Rate (CAGR) of 20.8%.
• North America is the region with the highest proportion, followed by Asia-Pacific, Europe, and the rest of the world, according to the bar chart.
• It is anticipated that the manufacturing and automotive sectors will generate significant growth prospects for the AI chipset industry.
• During the projection period, growing economies like India and other Asian nations, together with established nations like the US and China, are anticipated to be significant markets for AI chipsets.
• The availability of large data and the rising need for deep learning and neural networks are responsible for the market’s expansion.
• NVIDIA (US), Microsoft Corporation (US), IBM Corporation (US), Intel Corporation (US), and Qualcomm Inc. (US) are significant players in the AI chipset industry.

Use Of AI Chips

The current AI system wouldn’t be able to function effectively without using AI chips. Here are some ways in which they are being used.

Large Language models

• AI chips intensify the rate at which AI, machine learning, and deep learning algorithms are being trained and refined.
• They are instrumental in the development of large language models.
• They are used to optimize operations to enhance the performance of LLMs and, by extension, generative AI tools such as chatbots and AI assistants.

Autonomous Vehicles

• With the help of AI chips, it is possible to have driverless cars that contribute towards overall intelligence and safety.
• They can process and gain large amounts of data that vehicle cameras, LIDAR, and other sensors collect.
• Along with this, the vehicles’ parallel processing capabilities help enable real-time decision-making systems that allow the cars to autonomously navigate towards complex environments, detect obstacles, and respond to dynamic traffic controls.

Robotics

AI chip statistics reveal that AI chips profoundly impact the development of advanced robotic systems. They are helpful in significant robotics areas, from harvesting crops to humanoid robots that can serve as vital tools of assistance for human beings.

Advantages Of AI Chips Over Traditional Chips

Regarding the overall development and deployment of artificial intelligence, AI chips perform better than regular chips. According to AI chip statistics, here are the advantages of AI chips over traditional chips.

• Parallel Processing Capabilities

AI chips are effective due to their processing and method of computing. While general-purpose chips use sequential processing methods, AI chips utilize parallel processing technologies to ensure computation and calculation simultaneously. Hence, it ensures that machines can complete different tasks at once. This also means that significant, complex problems can now be easily broken into small ones and thus can be solved simultaneously, leading towards swift and more effective processing.

• Energy Efficiency

AI chips are designed to become more energy-efficient than conventional computer chips. They use techniques such as low-precision arithmetic to perform computations with fewer transistors, thus using less energy. Besides, as they work with parallel processing, they can distribute workloads more effectively than other chips. Therefore, they minimize energy consumption. Likewise, if we refer to AI chip statistics, the long-term use of AI chips can help ensure a significant reduction in carbon footprint, particularly in data centers.

• Data Accuracy

AI chips are customizable to meet the requirements of AI models or applications that allow specific AI models or applications that enable hardware to adapt to different tasks. The customizations include using the chip architecture to meet the particular AI workloads. Such flexibility is essential for advancing AI systems that enable developers to tailor their needs to meet their unique needs and accommodate variations of algorithms’ data types and computation requirements.

Cost Involving AI Chips

(Source: nextplatform.com)

• The cost of AI chips would depend on the overall revenue they can generate in return. Till 2027, the total income of AI chips is expected to touch USD 400 billion.
• If the AI chips represent the value of computer systems, it could be based on considerable revenue with time. If looking at the actual data of the chart from 2022 to estimating towards 2027, it can be seen that the overall value has been rising from 2023 to 2027.
• It implies a growth rate of 64.1% from 2022 to 2023.
• Furthermore, AI chip revenue is limited to XPU sales and the overall chip content used by AI systems in the data center and the edge.
• The AI chip sales are expected to rise by 70% to USD 117.5 billion.
• The IDC also reckons that even though revenue is high, it is still based on the aspect that they should not be considered as AI serves in the basic vocabulary.

AI Chips Requirement By Industry

(Reference: enterpriseappstoday.com)

• By 2023, AI chips will be utilized globally in a variety of healthcare-related fields. Hematology (2.9%), Radiology (75.2%), General and Plastic Surgery (1.3%), Cardiovascular (10.9%), Clinical Chemistry (1.2%), Microbiology (1%), and Neurology (2.7%), in order of the share of AI across various categories, are the following.
• In the field of telecommunication, artificial intelligence (AI) chips have aided in the development of self-optimizing networks (SONs), which enable precise anomaly detection and prediction.
• Currently, AI chips are being used by 63.5% of the telecom industry to enhance overall performance. This covers a wide range of typical problems as well, such as gathering fragmented data across several systems (23%), incorporating incomplete or unstructured data (19%), needing more technical know-how (18%), and tech integration (8%).

AI Chips Utilization By Companies

• As per AI chip statistics, Google and Apple are the two largest AI chip users
• Apple uses A11 and A12 bionic chips in their Apple devices which have led to them becoming 15% faster and consumer 50% less power
• Google uses AI chips for its cloud computing mobile devices and data centers.

Recent Updates on AI Chips

As of 2024, it is essential to understand recent AI chips.

Nvidia’s Blackwell GPU

• This unique chip uses two units packaged together to act like one giant chip.
• This allows the chip to achieve high performance; however, it also significantly increases the cost of manufacturing the chip.
• Nvidia had to lower the precision of the calculations performed by the chip from 8 bits to 4 bits to save memory and energy.

Cerebras Wafer Scale Engine 3

• This chip is massive and contains 43 trillion transistors.
• It is built on a 5-nanometer process node and uses a different approach than most other chips by using the entire wafer to create one giant chip.
• This can be more efficient than having many more minor chips on a single wafer because it reduces the need for complex interconnects between the chips.
• However, manufacturing large chips is also more complex, and the yield rate is lower.

In-Charge Analog Chip

• This chip is unique because it combines analog and digital techniques. It performs the multiplication operation digitally and then uses capacitors to accumulate the charge analogously.
• This approach is more energy-efficient than traditional analog chips.
• The chip is still under development, but the company plans to release a commercial product later this year.

(Source:  youtube.com)

The Global Chip Race – Powering AI’s Future And Reshaping Technological Dominance

Computer chips are essential components in modern devices, from smartphones to dishwashers. Following Moore’s Law, which states that computing power doubles every two years, their power has increased significantly. This trend has led to significant advancements in computing capabilities and decreased costs.

There is growing concern about the rising expense of developing cutting-edge chips for artificial intelligence (AI). Sam Altman, founder of OpenAI, calls for a USD 7 trillion investment in the industry to address this challenge.

The world heavily relies on Taiwan for chip manufacturing, and any disruption in this supply chain could severely impact the global economy. Several countries, including the United States, China, and the European Union, subsidize domestic chip production to ensure reliable supply and potentially gain an edge in the AI race.

AI has the potential to improve various sectors, including healthcare and education, significantly. Chris Miller, an expert in the field, argues that while AI has potential risks, the focus should be on harnessing its potential for positive change.

The development of computer chips plays a critical role in advancing AI technology, and there is a global race to secure a reliable supply of these powerful components.

(Source: youtube.com)

• The AI Chips market is expected to have a CAGR of 38.16%
• Based on the CPU segment (AI chips based), it accounted for a market share of 31.29% in 2023
• Market is valued at USD 20.28 billion in 2023

The Future Of AI Chips

• AI chips are fundamental to the rapid advancement of artificial intelligence, but the journey to meet the growing demand is fraught with significant challenges. Nvidia, a top AI hardware and software supplier, holds approximately 80% of the global market share in GPUs. However, Nvidia does not manufacture its chips. Instead, it depends on Taiwan Semiconductor Manufacturing Corporation (TSMC), which produces around 90% of the world’s advanced chips. TSMC’s chips are essential for various technologies, including Apple’s iPhones and Tesla’s electric vehicles. They are the sole manufacturers of Nvidia’s high-performance H100 and A100 processors used in most AI data centers.
• TSMC’s dominance in the market has led to severe bottlenecks in the global supply chain. With limited production capacity and resources, TSMC struggles to meet the escalating demand for AI chips. “The demand for these chips is far exceeding the supply,” stated Fist from the Center for a New American Security (CNAS). As a result, AI developers may face long wait times, possibly months or even years, to acquire the latest GPUs from Nvidia.
• However, these supply shortages are not expected to last indefinitely. TSMC is taking steps to address these bottlenecks. Its subsidiary, Japan Advanced Semiconductor Manufacturing (JASM), is constructing a factory in Kumamoto, which is anticipated to be fully operational by the end of 2024. Additionally, TSMC is building two state-of-the-art plants in Arizona, with the first set to begin chip production in 2025.
• In the interim, prominent AI developers like Microsoft, Google, and Amazon are designing custom AI chips to reduce reliance on Nvidia. The UXL Foundation, a consortium of companies, is also working to counter Nvidia’s dominance. They have developed an open-source alternative to Nvidia’s CUDA platform. Intel, with its Gaudi 3 chip, and AMD, with its processors for laptops and computers, are directly challenging Nvidia, signaling a future shift towards a less Nvidia-reliant semiconductor sector.

 A Fragile Geopolitical Landscape

• Taiwan’s pivotal role in the global supply of AI chips is complicated by its geopolitical situation. China views Taiwan as a rogue province rather than an independent nation, and some analysts foresee a potential Chinese invasion within the decade. Such an event would jeopardize TSMC’s ability to manufacture AI chips, putting the entire AI industry at risk.
• Amid rising U.S.-China tensions, President Joe Biden introduced stringent export controls in 2022. These controls significantly limit China’s access to AI chips, chip-making equipment, and chip design software, most of which are controlled by U.S. companies like Nvidia. While companies like Intel can still sell new AI chips in China, they must limit their performance to comply with U.S. regulations.
• Dohmen, a researcher focused on U.S.-China tech competition, emphasized that the restrictions aim to curb China’s military modernization and prevent the development of weapons of mass destruction using AI chips. However, these measures also reflect a desire for AI dominance. “We want to be the first; we want to be the best in tech and AI innovation,” Dohmen remarked.
• The United States is also working to reduce its dependence on East Asian chip fabrication facilities. Beyond facilitating the construction of TSMC plants in Arizona, the government secured a third TSMC site in Phoenix through the CHIPS and Science Act. Additionally, over USD 52 billion in federal funding and incentives have been allocated to support U.S. semiconductor manufacturing, research, and development.

Computational Constraints

• As developers create increasingly powerful AI models, computational demands soar, necessitating advancements in AI chips. “Chips need to keep up,” Fist noted, but AI chips have finite computational resources. The number of chips required to scale a state-of-the-art AI system is growing exponentially, increasing fourfold yearly. Conversely, the algorithmic efficiency of chips, or the ability to do more with fewer chips, is only doubling annually. Consequently, the requirements for AI chips are outpacing the industry’s current capabilities.
• Instead of merely increasing the number of chips, companies are exploring ways to enhance AI hardware. One promising area is in-memory computing, which eliminates the separation between data storage (memory) and data processing (logic) to accelerate operations. AI chip designers like Nvidia and AMD are incorporating AI algorithms to improve hardware performance and fabrication. These innovations are crucial to keep pace with the rapid advancements in AI.
• First, it highlighted the urgency of this endeavor: “There are all of these different exponential trends at play. So there’s this big rush to figure out how we build even more specialized chips for AI. Or, how do we innovate in other parts of the stack?”

Conclusion

The rise of AI chips marks a pivotal moment in computing technology, shifting from general-purpose processors to specialized hardware designed for artificial intelligence. AI chip statistics reveal the growing importance of these components, with projections indicating that AI chip revenue will reach USD 400 billion by 2027, underscoring their critical role in enabling and accelerating AI innovations across various sectors.

AI chips offer several key advantages over traditional processors, including superior parallel processing capabilities, enhanced energy efficiency, and improved data accuracy. These attributes make them particularly well-suited for handling complex computational tasks required by modern AI systems, from training large language models to powering autonomous vehicles and advanced robotics.

AI chip statistics demonstrate the rapid growth of this market, with a projected growth rate of 64.1% from 2022 to 2023. This surge is driven by the expanding applications of AI across diverse fields and the increasing sophistication of AI models, which demand ever-greater computational resources. By 2027, AI chip revenue is expected to rise by 70% to USD 117.5 billion, reflecting the industry’s robust expansion.

However, the AI chip industry faces significant challenges. The current landscape is dominated by a few key players, notably Nvidia in GPU production and TSMC in chip manufacturing. This concentration of resources and expertise has led to supply chain bottlenecks and geopolitical vulnerabilities, particularly given Taiwan’s central role in global chip production.

The geopolitical dimension of AI chip production adds another layer of complexity to the industry’s future. Tensions between major powers influence trade policies and technological development, and the global AI chip supply chain faces potential disruptions. This has spurred efforts by various countries to develop domestic chip production capabilities, aiming to reduce dependence on foreign suppliers and secure their positions in the AI race.

The AI chip industry is poised for further innovation and disruption. Emerging technologies such as in-memory computing and the integration of AI algorithms into chip design processes promise to push the boundaries of what’s possible in AI hardware. Meanwhile, major tech companies are increasingly investing in developing their custom AI chips, potentially reshaping the competitive landscape.

AI chip statistics highlight the cyclical relationship between chip technology and AI development. As AI models grow more complex and computationally intensive, the demand for more powerful and efficient chips continues to rise. This creates a feedback loop where advancements in chip technology enable more sophisticated AI applications, which in turn drive further innovations in chip design.

However, the industry must also grapple with the challenge of sustainability. AI chip statistics show that the number of chips required to scale a state-of-the-art AI system is growing exponentially, increasing fourfold yearly. Conversely, chips’ algorithmic efficiency is only doubling annually. This trend raises questions about the long-term viability of current approaches and underscores the need for breakthrough technologies to meet future demands.

In conclusion, AI chips stand at the forefront of technological innovation, playing a crucial role in shaping the future of artificial intelligence and the broader technological landscape. As the field evolves, it will be shaped by a complex interplay of technological advancements, market dynamics, geopolitical factors, and sustainability concerns. The ability to navigate these challenges while driving innovation will be vital to realizing the full potential of AI and ensuring its benefits are widely accessible. The compelling AI chip statistics presented here underscore the critical importance of this technology in our rapidly evolving digital world.

Ketaki Joshi

Ketaki Joshi is a professional medical writer with extensive experience in scientific research on illness, health, and healthcare. Her work includes creating feature articles for newsletters and websites, as well as research news stories for doctors and researchers. With a lifelong passion for reading, Ketaki transitioned from a career at a French multinational company to pursue writing professionally. Her dedication to the craft has culminated in the recent release of her first Amazon-published short story, "The Envelope That Changed Our Lives."

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