2025
Key Trends in AI Semiconductor industry
written by Wonick Park, Journalist, TheMiilk
The most important company driving the AI semiconductor industry is Nvidia. Data released by BOND, an investment firm led by Mary Meeker, who is known for analyzing internet trends, shows this trend at its extreme.
The number of developers in the NVIDIA ecosystem worldwide has surged sixfold in the last seven years to more than 6 million, according to Bond. This growth is largely driven by CUDA, the language used to program NVIDIA GPUs. This developer ecosystem has allowed Nvidia GPUs to capture more than 90% of the market share in AI datacenters.
However, experts expect this absolute dominance to change over time. This is because companies in the AI semiconductor space are looking to reduce their reliance on Nvidia. The rapid evolution of AI technology is also influencing AI semiconductor trends.
Trend 1
AI Semiconductor Market Increasingly Competitive: Nvidia Rises to the Challenge
The AI semiconductor market for data centers, also known as AI accelerators, is dominated by Nvidia's GPUs, but competition is intensifying.
AMD is challenging Nvidia's dominance with its Instinct MI350 and MI400. The MI400, a next-generation semiconductor, offers up to 40FP4 petaflops of performance, 432GB of next-generation HBM4 memory, and 300GB/s of scale-out bandwidth. It will be available in 2026 on a 2-nanometer process (2nm). AMD claims it will be able to compete with Nvidia's latest AI chip, Blackwell.
While increased competition can have positive effects in terms of spurring technological innovation and stabilizing prices, software compatibility and ecosystem support are expected to be key factors in user choice. Nvidia's Cuda ecosystem is still considered to have a strong competitive advantage over other competitors such as AMD. This is because competition in the AI semiconductor market is not just about hardware performance, but also about software ecosystems. The process of developing and deploying AI models requires not only the absolute performance of hardware, but also ease of development, compatibility with existing code, and a robust software ecosystem that supports a variety of frameworks and libraries. NVIDIA's Cuda has been building a strong moat in these areas for many years.
It's also important to note that as AI models get larger and run for longer periods of time, the total cost of ownership (TCO) becomes increasingly important - not just CAPEX, but OPEX, which includes power consumption, cooling, maintenance, etc.
Competitors like AMD are aggressively touting their TCO advantages. For AI infrastructures operating in large data centers, power efficiency is not only important for OPEX savings, but also for carbon emissions, so environmental metrics such as “carbon emissions per prompt (requested answer to an AI model)” may become an important criteria for GPU selection in the future.
Trend 2
Big tech companies accelerate development of custom AI chips (ASICs)
It is also worth noting that major big tech companies operating large-scale AI services, such as Google (TPU), Amazon (Trainium, Inferentia), and Meta (MTIA), are actively developing custom AI chips (ASICs) optimized for their specific workloads.
The primary motivation for this move is to maximize performance for the specific AI computations required by their services. For example, Meta is developing a chip optimized for its content recommendation and advertising algorithms. It also aims to save money on external chip purchases, maximize power efficiency, and reduce data center operating costs. There are also strategic factors at play, such as gaining control over core technologies and accelerating innovation in AI by developing its own chips.
Google's TPUs have been the most prominent performers in this area. Google CEO Sundar Pichai introduced the seventh-generation TPU Ironwood during the Google Cloud Next 2025 keynote at the Mandalay Bay Convention Center in Las Vegas on April 9, saying, “Ironwood is the most powerful chip we've ever built.”
The next-generation TPU Ironwood is a high-performance chip with more than 10x the performance of the previous generation TPU (v5p). A “pod” of multiple TPUs can contain more than 9,000 chips and deliver 42.5 exaflops (one exaflop is 100 billion operations per second) of performance.
The production of customized chips could represent a new business opportunity for advanced foundries like TSMC and Samsung Electronics. At the same time, however, foundries are faced with the challenge of effectively responding to increasingly complex chip designs and the need to produce multiple varieties in low volume.
Trend 3
Next-Generation Memory (HBM4) and Advanced Packaging Technology Leap
To maximize AI semiconductor performance, high-performance memory that can rapidly process massive amounts of data along with powerful computational capabilities and advanced packaging technology that efficiently connects them are essential.
In particular, high-bandwidth memory (HBM) has emerged as a key partner for AI GPUs, and the race is on to preempt next-generation HBM technology. SK Hynix was the first in the world to deliver 12-stage HBM4 samples to Nvidia, and Micron has also recently delivered HBM4 samples. Samsung Electronics is also planning to mass-produce HBM4 in the second half of 2025.
The HBM4 stacks D-RAM vertically up to 12 layers to realize a peak capacity of 36GB, and offers more than 2TB/sec of data processing bandwidth. It is expected to deliver more than 60% performance improvement over the current flagship HBM3E. One of the key technologies of HBM4 is ‘hybrid bonding’. It directly connects chips with finer spacing than conventional methods to improve electrical properties, which is advantageous for stacking more D-RAM.
The evolution of packaging technology as a whole is also playing a crucial role in the development of AI semiconductors.
In order to realize the ultra-high performance of AI semiconductors, manufacturing (mainstream) and packaging (post-processing) technologies are being closely converged from the chip design stage.
Taiwan's TSMC, the No. 1 semiconductor foundry, is leading the way in advanced packaging technologies such as CoWoS (Chip on Wafer on Substrate) and Info-oS (Integrated Fan-Out on Substrate).
Furthermore, the company is actively developing processing-in-memory (PIM) and in-memory computing technologies that integrate computational functions directly inside the memory semiconductor, dramatically reducing the bottlenecks and power consumption associated with data movement. “New opportunities for the semiconductor industry in the era of generative AI will come from bridging the infrastructure gap,” said Henry Huang, Director of Investments at Micron Ventures. “Advancements in next-generation memory interconnect technology will be key to determining AI computational performance going forward.”
Trend 4
Geopolitical Risks Rise and Supply Chain Reorganization Accelerates
In addition to technology and market competition, the AI semiconductor industry faces a significant variable: geopolitical risks. In particular, the U.S. government continues to tighten export restrictions on high-performance AI semiconductors and related manufacturing equipment to the People's Republic of China in an effort to prevent the leakage of its technology to China and to curb China's growing AI technology and military capabilities.
These measures are broadly applied through the Foreign Direct Products Regulations (FDPR), which affect not only U.S. companies, but also third-country companies that produce semiconductors using U.S. technology or equipment.
In response to the U.S. sanctions, China has set a national goal of semiconductor technology independence and is investing heavily to build its own supply chain. In particular, it is attempting to reduce its dependence on U.S. technology by increasing investment in mature (legacy) process technologies and utilizing open architectures such as RISC-V, instead of advanced processes that are the focus of U.S. sanctions.
Rising geopolitical tensions and the vulnerability of semiconductor supply chains that are concentrated in certain regions have led governments and companies to step up efforts to stabilize and diversify their semiconductor supply chains. With the rapid growth and technological sophistication of the semiconductor industry, competition between countries and companies for skilled design, process, and packaging professionals is expected to intensify.
Trend 5
Future Outlook and Implications
The AI semiconductor market is expected to continue its long-term growth, driven by the continued evolution of AI technology and the explosive expansion of applications in areas such as autonomous driving, smart cities, healthcare, and robotics.
Some market research firms have even suggested that the AI semiconductor industry could grow to $1 trillion by 2030. Given the importance of the technology, the AI semiconductor industry, which is the foundation for AI model development and AI service delivery, is poised to become a key driver that will impact most industries.
There are also many challenges to overcome. In particular, the surge in power consumption as AI models get larger and data centers expand is making improving the energy efficiency of AI semiconductors a top priority. In the future, the AI semiconductor race will not only be about increasing computational speed, but also about developing low-power design techniques, discovering new semiconductor materials, and developing innovative architectures that deliver the same performance with less power.
As the realization that AI semiconductor technology supremacy is directly related to a country's economic security and future competitiveness spreads, governments are expected to increase investment and policy support to foster their domestic semiconductor industries. Global R&D competition is likely to intensify, and invisible wars between countries and companies to secure core technologies and high-level talent are likely to intensify.
© STK. All rights reserved. Unauthorized reproduction, distribution, modification, or commercial use of this content is strictly prohibited and may result in civil or criminal liability under applicable laws.
2025
Key Trends in AI Semiconductor industry
written by Wonick Park, Journalist, TheMiilk
The most important company driving the AI semiconductor industry is Nvidia. Data released by BOND, an investment firm led by Mary Meeker, who is known for analyzing internet trends, shows this trend at its extreme.
The number of developers in the NVIDIA ecosystem worldwide has surged sixfold in the last seven years to more than 6 million, according to Bond. This growth is largely driven by CUDA, the language used to program NVIDIA GPUs. This developer ecosystem has allowed Nvidia GPUs to capture more than 90% of the market share in AI datacenters.
However, experts expect this absolute dominance to change over time. This is because companies in the AI semiconductor space are looking to reduce their reliance on Nvidia. The rapid evolution of AI technology is also influencing AI semiconductor trends.
Trend 1
AI Semiconductor Market Increasingly Competitive: Nvidia Rises to the Challenge
The AI semiconductor market for data centers, also known as AI accelerators, is dominated by Nvidia's GPUs, but competition is intensifying.
AMD is challenging Nvidia's dominance with its Instinct MI350 and MI400. The MI400, a next-generation semiconductor, offers up to 40FP4 petaflops of performance, 432GB of next-generation HBM4 memory, and 300GB/s of scale-out bandwidth. It will be available in 2026 on a 2-nanometer process (2nm). AMD claims it will be able to compete with Nvidia's latest AI chip, Blackwell.
While increased competition can have positive effects in terms of spurring technological innovation and stabilizing prices, software compatibility and ecosystem support are expected to be key factors in user choice. Nvidia's Cuda ecosystem is still considered to have a strong competitive advantage over other competitors such as AMD. This is because competition in the AI semiconductor market is not just about hardware performance, but also about software ecosystems. The process of developing and deploying AI models requires not only the absolute performance of hardware, but also ease of development, compatibility with existing code, and a robust software ecosystem that supports a variety of frameworks and libraries. NVIDIA's Cuda has been building a strong moat in these areas for many years.
It's also important to note that as AI models get larger and run for longer periods of time, the total cost of ownership (TCO) becomes increasingly important - not just CAPEX, but OPEX, which includes power consumption, cooling, maintenance, etc.
Competitors like AMD are aggressively touting their TCO advantages. For AI infrastructures operating in large data centers, power efficiency is not only important for OPEX savings, but also for carbon emissions, so environmental metrics such as “carbon emissions per prompt (requested answer to an AI model)” may become an important criteria for GPU selection in the future.
Trend 2
Big tech companies accelerate development of custom AI chips (ASICs)
It is also worth noting that major big tech companies operating large-scale AI services, such as Google (TPU), Amazon (Trainium, Inferentia), and Meta (MTIA), are actively developing custom AI chips (ASICs) optimized for their specific workloads.
The primary motivation for this move is to maximize performance for the specific AI computations required by their services. For example, Meta is developing a chip optimized for its content recommendation and advertising algorithms. It also aims to save money on external chip purchases, maximize power efficiency, and reduce data center operating costs. There are also strategic factors at play, such as gaining control over core technologies and accelerating innovation in AI by developing its own chips.
Google's TPUs have been the most prominent performers in this area. Google CEO Sundar Pichai introduced the seventh-generation TPU Ironwood during the Google Cloud Next 2025 keynote at the Mandalay Bay Convention Center in Las Vegas on April 9, saying, “Ironwood is the most powerful chip we've ever built.”
The next-generation TPU Ironwood is a high-performance chip with more than 10x the performance of the previous generation TPU (v5p). A “pod” of multiple TPUs can contain more than 9,000 chips and deliver 42.5 exaflops (one exaflop is 100 billion operations per second) of performance.
The production of customized chips could represent a new business opportunity for advanced foundries like TSMC and Samsung Electronics. At the same time, however, foundries are faced with the challenge of effectively responding to increasingly complex chip designs and the need to produce multiple varieties in low volume.
Trend 3
Next-Generation Memory (HBM4) and Advanced Packaging Technology Leap
To maximize AI semiconductor performance, high-performance memory that can rapidly process massive amounts of data along with powerful computational capabilities and advanced packaging technology that efficiently connects them are essential.
In particular, high-bandwidth memory (HBM) has emerged as a key partner for AI GPUs, and the race is on to preempt next-generation HBM technology. SK Hynix was the first in the world to deliver 12-stage HBM4 samples to Nvidia, and Micron has also recently delivered HBM4 samples. Samsung Electronics is also planning to mass-produce HBM4 in the second half of 2025.
The HBM4 stacks D-RAM vertically up to 12 layers to realize a peak capacity of 36GB, and offers more than 2TB/sec of data processing bandwidth. It is expected to deliver more than 60% performance improvement over the current flagship HBM3E. One of the key technologies of HBM4 is ‘hybrid bonding’. It directly connects chips with finer spacing than conventional methods to improve electrical properties, which is advantageous for stacking more D-RAM.
The evolution of packaging technology as a whole is also playing a crucial role in the development of AI semiconductors.
In order to realize the ultra-high performance of AI semiconductors, manufacturing (mainstream) and packaging (post-processing) technologies are being closely converged from the chip design stage.
Taiwan's TSMC, the No. 1 semiconductor foundry, is leading the way in advanced packaging technologies such as CoWoS (Chip on Wafer on Substrate) and Info-oS (Integrated Fan-Out on Substrate).
Furthermore, the company is actively developing processing-in-memory (PIM) and in-memory computing technologies that integrate computational functions directly inside the memory semiconductor, dramatically reducing the bottlenecks and power consumption associated with data movement. “New opportunities for the semiconductor industry in the era of generative AI will come from bridging the infrastructure gap,” said Henry Huang, Director of Investments at Micron Ventures. “Advancements in next-generation memory interconnect technology will be key to determining AI computational performance going forward.”
Trend 4
Geopolitical Risks Rise and Supply Chain Reorganization Accelerates
In addition to technology and market competition, the AI semiconductor industry faces a significant variable: geopolitical risks. In particular, the U.S. government continues to tighten export restrictions on high-performance AI semiconductors and related manufacturing equipment to the People's Republic of China in an effort to prevent the leakage of its technology to China and to curb China's growing AI technology and military capabilities.
These measures are broadly applied through the Foreign Direct Products Regulations (FDPR), which affect not only U.S. companies, but also third-country companies that produce semiconductors using U.S. technology or equipment.
In response to the U.S. sanctions, China has set a national goal of semiconductor technology independence and is investing heavily to build its own supply chain. In particular, it is attempting to reduce its dependence on U.S. technology by increasing investment in mature (legacy) process technologies and utilizing open architectures such as RISC-V, instead of advanced processes that are the focus of U.S. sanctions.
Rising geopolitical tensions and the vulnerability of semiconductor supply chains that are concentrated in certain regions have led governments and companies to step up efforts to stabilize and diversify their semiconductor supply chains. With the rapid growth and technological sophistication of the semiconductor industry, competition between countries and companies for skilled design, process, and packaging professionals is expected to intensify.
Trend 5
Future Outlook and Implications
The AI semiconductor market is expected to continue its long-term growth, driven by the continued evolution of AI technology and the explosive expansion of applications in areas such as autonomous driving, smart cities, healthcare, and robotics.
Some market research firms have even suggested that the AI semiconductor industry could grow to $1 trillion by 2030. Given the importance of the technology, the AI semiconductor industry, which is the foundation for AI model development and AI service delivery, is poised to become a key driver that will impact most industries.
There are also many challenges to overcome. In particular, the surge in power consumption as AI models get larger and data centers expand is making improving the energy efficiency of AI semiconductors a top priority. In the future, the AI semiconductor race will not only be about increasing computational speed, but also about developing low-power design techniques, discovering new semiconductor materials, and developing innovative architectures that deliver the same performance with less power.
As the realization that AI semiconductor technology supremacy is directly related to a country's economic security and future competitiveness spreads, governments are expected to increase investment and policy support to foster their domestic semiconductor industries. Global R&D competition is likely to intensify, and invisible wars between countries and companies to secure core technologies and high-level talent are likely to intensify.
© STK. All rights reserved. Unauthorized reproduction, distribution, modification, or commercial use of this content is strictly prohibited and may result in civil or criminal liability under applicable laws.