在生成式ＡＩ引爆全球科技竞赛之后，算力已成为攸关国家战略、产业主导权与企业生死存亡的核心 军备。从ChatGPT掀起第一波浪潮，到各国政府、科技巨头竞相投入大型语言模型（ＬＬＭ）与加速 运算架构，随着投入金额与能源消耗快速攀升，市场也意识到，真正决定ＡＩ效能上限与成本结构 的，并非单纯堆叠通用型ＧＰＵ，而是能否打造「为特定工作量而生」的专用芯片。ASIC正是在这 场算力决战中，市场正着眼的焦点所在。 ASIC重塑算力版图 ASIC的核心在于以高度客制化的硬体设计，换取远高于通用芯片的效能功耗比与长期成本优势。不 同于ＧＰＵ的广泛适用性，从架构设计阶段即深度绑定目标工作负载，能精准配置运算单元、记忆体 层级与资料通道，最大程度降低无效运算与能源浪费，这使ASIC在大规模、长时间运行的ＡＩ训练 与推论场景中，能显著拉开与ＧＰＵ的成本差距。因而成为云端服务商（ＣＳＰ）与大型科技公司在 追求极致能效比与总拥有成本（ＴＣＯ）优化下的必然选择。 公众号记得加星标⭐️，第一时间看推送不会错过。 真正决定AI效能上限与成本结构的，并非单纯堆叠通用型GPU，而是能否打造为特定工作量而生的 专用芯片。 AI模型规模与应用场景不断 ...

1月26日，市场研究机构Counterpoint Research在最新研报中预测，非GPU服务器AI芯片——AI ASIC阵 营在近期将经历高速增长，到2027年出货数量将达到2024年的三倍，2028年则有望以1500余万颗的规模 反超GPU。 报告显示，这一爆炸性增长的背后，是对谷歌TPU基础设施的强劲需求、AWS Trainium集群的持续扩 展，以及Meta(MTIA)和微软(Maia)随着其内部芯片产品组合的扩展而带来的产能提升。 就人工智能超大规模数据中心的出货量而言，谷歌预计将在2027年之前保持市场领先地位，这主要得益 于其Gemini生态系统的爆炸式增长。 针对谷歌的统治地位，Counterpoint Research研究员David Wu强调："尽管由于市场规模不断扩大，以及 竞争对手(如博通、Marvell和Alchip等设计公司)纷纷采用自研芯片，预计谷歌的市场份额将在2027年下 降至52%，但其TPU集群仍将是无可争议的行业核心和标杆。这一基准的支撑源于训练和运行下一代 Gemini模型所需的庞大且持续的计算能力，而这又需要谷歌持续、积极地扩充其内部芯片基础设施。" 该机构认为， ...

Core Viewpoint - The article emphasizes that 2026 is expected to be a year of significant harvest for domestic hard technology in the electronics industry, driven by advancements in AI and a consensus on performance trends within the AI industry chain [3][7]. Group 1: AI Industry Trends - The AI industry is transitioning from divergence to consensus in performance trends, with a notable recovery since the second half of 2023, marked by the return of Huawei's Mate series [3][7]. - The electronics sector has experienced a significant valuation expansion, aided by the rapid growth of passive funds and the resonance of macro policy, inventory cycles, and AI innovation cycles [3][7]. - As of December 16, 2025, the electronics sector has risen by 40.22%, ranking third among all industries [7][16]. Group 2: AI Model Evolution - The evolution of AI models is characterized by innovations in architecture, such as the mixture of experts (MoE) framework, which enhances efficiency by reducing computational load [27]. - The emergence of large models, like OpenAI's GPT-4, showcases the correlation between model size and performance, leading to significant advancements in understanding and reasoning capabilities [27]. - The demand for improved model efficiency has led to innovations in attention mechanisms, which lower computational complexity and memory requirements [27][28]. Group 3: Computing Power and Storage - The domestic chip industry is actively updating and iterating, with companies like Huawei planning to launch new chips in 2026, while the storage sector is expected to face shortages and price increases throughout the year [9]. - The demand for AI-driven storage solutions is projected to increase, with DRAM bit demand expected to rise by 26% year-on-year in 2026, driven by AI applications [9]. Group 4: Power and Connectivity - The optimization of data transfer and communication within servers is becoming a critical breakthrough for enhancing computing power, with the global high-speed interconnect chip market expected to reach $21.2 billion by 2030 [11]. - The increasing power consumption of data center chips necessitates advancements in power supply architectures, with a shift towards high-density power solutions [11]. Group 5: Semiconductor Industry - The semiconductor sector is anticipated to benefit from a recovery in demand, with a focus on domestic manufacturing and the rise of analog chips, which are expected to see increased adoption due to their potential for localization [12]. - The global semiconductor market is projected to achieve double-digit growth for three consecutive years from 2024 to 2026, driven by advancements in AI and domestic chip design [12][14].

Summary of Key Points from the Conference Call Industry Overview - The report focuses on the ASIC (Application-Specific Integrated Circuit) market, particularly in relation to AI (Artificial Intelligence) chips and servers, highlighting the increasing demand and customization in this sector [1][11][22]. Core Insights and Arguments - **ASIC Market Growth**: ASIC chips are expected to play a significant role in AI server solutions, with projections indicating that ASICs will contribute 40% of total AI chips by 2026 and 45% by 2027 [11][22]. - **Demand Projections**: The demand for AI chips is forecasted to reach 10 million, 14 million, and 17 million units from 2025 to 2027, with ASIC shipments contributing 38%, 40%, and 45% respectively [1]. - **Revenue Growth**: The global server total addressable market (TAM) is expected to grow by 42%, 32%, and 19% year-over-year, reaching $359 billion, $474 billion, and $563 billion from 2025 to 2027 [13]. - **Customization Benefits**: ASIC solutions provide higher gross margins for suppliers due to their customization, which allows for better performance and energy efficiency compared to general-purpose GPUs [15][22]. Company-Specific Highlights - **Wiwynn**: Expected to have the largest ASIC exposure among ODMs by 2026, with significant partnerships with Amazon and Meta. The company has reported over 100% year-over-year growth in revenue for the first three quarters of 2025 [6][27]. - **Hon Hai**: Anticipated to expand its ASIC customer base significantly by 2026, benefiting from its role as a supplier for Google TPU servers [23]. - **Innolight**: Positioned as a key supplier of optical transceivers, with expected revenue growth of 104% year-over-year in 2026 from 800G optical modules [24][25]. - **LandMark**: Expected to see a revenue increase from 71% in 2025 to 85% in 2026 due to the demand for high-speed optical transceivers [26]. - **EMC**: Anticipated to maintain a strong market position with over 50% market share in the ASIC AI server supply chain, expecting solid revenue growth [28]. - **TSMC**: Expected to manufacture next-generation TPUs, with projections indicating that TPU revenue will account for less than 5% of TSMC's total revenue through 2026 [29]. Additional Important Insights - **Market Dynamics**: The shift towards ASICs is driven by major AI model suppliers developing in-house ASIC platforms to optimize performance and reduce costs [22]. - **Investment Trends**: Amazon plans to invest up to $50 billion in AI infrastructure, which will utilize in-house Trainium chips and Nvidia GPUs [24]. - **Emerging Partnerships**: OpenAI's collaboration with Broadcom to design in-house AI accelerators is expected to enhance the capabilities of AI systems by 2029 [24]. This summary encapsulates the key points from the conference call, providing insights into the ASIC market's growth, company-specific developments, and broader industry trends.

Core Insights - The AI infrastructure buildout is primarily driven by the transition from CPUs to GPUs, which are significantly more efficient for AI training tasks [1][2] - The energy implications of data centers are profound, as they evolve from passive storage facilities to active, energy-intensive industrial engines [4][5] - The demand for data centers is expected to grow exponentially, with electricity consumption for accelerated servers projected to increase by 30% annually, contrasting with a modest 9% growth for conventional servers [16][30] Group 1: Energy Consumption and Infrastructure - Data centers currently consume approximately 415 terawatt-hours (TWh) of electricity, representing about 1.5% of global electricity consumption [28] - By 2030, global electricity consumption for data centers is projected to double, reaching roughly 945 TWh, which would account for nearly 3% of the world's total electricity [30] - The shift to high-performance computing has led to a tenfold increase in power density, necessitating advanced cooling solutions such as liquid cooling [7][20] Group 2: Energy Mix and Carbon Footprint - Data centers are heavily reliant on coal, which currently accounts for about 30% of their electricity supply, particularly in regions like China [41][43] - Natural gas meets 26% of global data center demand and is expected to be a primary energy source due to its reliability [44][46] - Renewables currently supply about 27% of data center electricity, with projections indicating that this could rise to nearly 50% by 2030 [47][48] Group 3: Regional Dynamics and Geopolitical Implications - The United States is the leading market for data centers, with per-capita consumption projected to increase from 540 kilowatt-hours (kWh) in 2024 to over 1,200 kWh by 2030 [53] - China is expected to see a 170% increase in data center electricity consumption by 2030, driven by a shift in computing hubs to western provinces rich in renewable resources [56][58] - Europe is experiencing steady growth in data center demand, with a projected increase of 45 TWh (up 70%) by 2030, influenced by stringent regulatory environments [59][60] Group 4: Supply Chain and Infrastructure Risks - The construction of data centers faces significant delays due to mismatched timelines with grid upgrades, potentially delaying 20% of planned global capacity by 2030 [68] - Data centers require vast quantities of critical minerals, creating vulnerabilities in supply chains, particularly with reliance on China for rare earth elements [70][71] - The shortage of power transformers is a critical bottleneck, with lead times extending from 12 months to over 3 years, limiting the pace of AI infrastructure deployment [75] Group 5: Efficiency and Future Outlook - The digital economy is decoupling from past energy efficiency trends, with energy consumption scaling linearly with digital ambitions [35][38] - AI technologies may provide significant carbon offsets by optimizing energy use in other sectors, potentially reducing global CO2 emissions by 3.2 to 5.4 billion tonnes annually by 2035 [80][82] - The future of data centers will be shaped by the availability of gigawatt-scale power connections, influencing economic power dynamics globally [88][89]

Summary of Key Points from the Conference Call Industry Overview - The conference call focused on the **semiconductor industry**, particularly the **AI semiconductor** segment, with insights from **Morgan Stanley** regarding the **cloud capital expenditure (capex)** and the **supply chain dynamics** in Taiwan [6][10]. Core Insights and Arguments - **Cloud Capex Growth**: Major cloud service providers (CSPs) are projected to spend nearly **US$582 billion** on cloud capex in **2026**, with estimates from Nvidia suggesting global cloud capex could reach **US$1 trillion** by **2028** [13][15]. - **AI Semiconductor Market Size**: The global semiconductor market size is expected to reach **US$1 trillion** by **2030**, with the AI semiconductor total addressable market (TAM) projected to grow to **US$235 billion** by **2025** [25]. - **Nvidia's Rack Output**: Post second-quarter earnings, expectations for **GB200/300 rack output** have become more bullish, with projections of approximately **34,000 racks** for **2025** and at least **60,000 racks** for **2026** [49]. - **Nvidia's GPU Supply**: TSMC is anticipated to produce **5.1 million** chips in **2025**, while NVL72 shipments are expected to reach **30,000** [42]. - **AI Semiconductor Demand Drivers**: The primary growth driver for AI semiconductors is attributed to **cloud AI**, with a significant focus on inference versus training AI semiconductors [27][71]. Additional Important Insights - **Capex to EBITDA Ratio**: The capex to EBITDA ratio has surged since **2024**, indicating increased capex intensity [21]. - **Custom AI Chips**: Custom AI chips are expected to outpace general-purpose chips, with a projected market size of approximately **US$21 billion** in **2025** [139]. - **TSMC's Capacity Expansion**: TSMC plans to expand its CoWoS capacity significantly, with projections of **93k wafers per month** by **2026** to meet the growing demand for AI chips [105][110]. - **China's AI Semiconductor Demand**: The demand for AI semiconductors in China is expected to grow, with local GPUs projected to fulfill only **39%** of the country's AI demand by **2027** [178][181]. Conclusion - The semiconductor industry, particularly in the AI segment, is poised for substantial growth driven by cloud computing and AI applications. Companies like Nvidia and TSMC are at the forefront of this expansion, with significant investments and capacity enhancements planned for the coming years.

Investment Rating - The report maintains an "Overweight" (OW) rating on several companies in the AI ASIC supply chain, including Accton, Wiwynn, Bizlink, and King Slide in downstream systems, as well as TSMC, Broadcom, Alchip, MediaTek, Advantest, KYEC, Aspeed, and ASE in upstream semiconductors [1][11]. Core Insights - The AI ASIC market is expected to grow significantly, with NVIDIA outpacing the ASIC market in 2025, generating enthusiasm for ASIC vendors. Asian design service providers like Alchip and MediaTek are anticipated to gain market share due to their efficient operations and quality services [2][21]. - The global semiconductor market is projected to reach $1 trillion by 2030, with AI semiconductors being a major growth driver, estimated to reach $480 billion, comprising $340 billion from cloud AI semiconductors and $120 billion from edge AI semiconductors [21][22]. Summary by Sections AI ASIC Market Developments - AWS Trainium: Alchip has taped out the Trainium3 design, with wafers already produced. Alchip is expected to have a strong chance of winning the 2nm Trainium4 project [3][15]. - Google TPU: Broadcom is expected to tape out a new 3nm TPU after the Ironwood (TPU v7p) enters mass production in 1H25, while MediaTek is also preparing for a 3nm TPU tape-out [4][18]. - Meta MTIA: Preliminary volume forecasts for MTIAv3 are expected in July, with considerations for larger packaging for MTIAv4 [5]. Downstream and Upstream Suppliers - Downstream suppliers for AWS Trainium2 include Gold Circuit for PCB boards, King Slide for rail kits, and Bizlink for active electrical cables. Wiwynn is expected to see 30-35% of its total revenue from Trainium2 servers in 2025 [6][11]. - Key upstream suppliers include TSMC for foundry services, Broadcom for IP and design services, and Alchip for back-end design services [11][10]. Market Size and Growth Projections - The AI semiconductor market is projected to grow to $50 billion by 2030, representing 15% of cloud AI semiconductors. This indicates a significant opportunity for AI ASIC vendors despite NVIDIA's dominance in the AI GPU market [21][24]. - The report estimates that the global AI capex total addressable market (TAM) for 2025 could reach around $199 billion, driven by major cloud service providers [26][58]. Financial Implications - Alchip's revenue from Trainium3 chips is estimated to be $1.5 billion in 2026, with expectations of continued growth in the AI ASIC market [18][21]. - MediaTek's revenue from TPU projects is projected to grow significantly, with estimates of $1 billion in 2026 and potential growth to $2-3 billion in 2027 [19][21].