March 23, 2026 03:49 PM GMT Investor Presentation | Asia Pacific Greater China Technology Hardware: Will Macro Uncertainties Weigh On AI Hardware Outlook? | Downloaded by Neil.Wang@troweprice.com M | Not for redistribution without written consent of Morgan Stanley Equity Analyst | | | --- | --- | --- | | | | Foundation | | March 23, 2026 03:49 PM GMT | | | | Investor Presentation Asia Pacific | Morgan Stanley Taiwan Limited+ | | | | Sharon Shih | | | | Sharon.Shih@morganstanley.com | +886 2 2730-2865 | | Gr ...

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Core Insights - The global wafer foundry industry is expected to grow significantly in 2026, driven by demand from North American cloud service providers (CSPs) and AI startups, with an anticipated annual growth rate of 24.8%, reaching approximately $21.88 billion [2] - TSMC is projected to experience the highest growth among major players, with an expected annual increase of 32% in 2026 [2] Advanced Process Demand - The demand for advanced processes in 2026 will be propelled by AI GPUs from companies like NVIDIA and AMD, as well as self-developed AI chips from CSPs such as Google, AWS, and Meta, along with AI startups like OpenAI and Groq [4] - TSMC's capacity for 5/4 nm and below is expected to be fully loaded until the end of the year, with Samsung Foundry also seeing a significant increase in orders for similar processes [4] - TSMC has raised its foundry prices for 5/4 nm and below processes for 2026, with visibility of orders extending into 2027, indicating potential for consecutive price increases [4] Mature Process Segment - In the mature process segment, TSMC and Samsung are accelerating the reduction of eight-inch wafer production, while stable growth in AI power-related demand is expected to improve overall capacity utilization for the year [5] - The demand for eight-inch wafers is primarily driven by AI-related power products and domestic demand in China, with some support from early inventory preparations by PC/Notebook ODMs due to concerns over rising IC costs [5] - Despite improvements in eight-inch production lines, full capacity utilization is not anticipated, and there are concerns about the consumer supply chain in the second half of the year, leading to mixed utilization rates [5] - For twelve-inch wafers, the expansion of 28 nm and above mature processes is expected to continue in 2026, but limited visibility on orders is anticipated due to the impact of high storage prices on consumer end products [5]

NVIDIA Corporation (NASDAQ:NVDA) is one of the AI Stocks That Will Skyrocket. Cantor Fitzgerald discussed NVIDIA Corporation (NASDAQ:NVDA)’s shares on March 12th as it reiterated an Overweight rating and set a $300 share price target. The firm has been in the news for several reasons over the past couple of weeks. For instance, Bloomberg reported on March 6th that the US government was considering rules that would require NVIDIA Corporation (NASDAQ:NVDA) and other AI chip manufacturers to secure the govern ...

智通财经获悉，英伟达CEO黄仁勋在北京时间3月17日凌晨的GTC大会上展现出英伟达在AI算力基础设 施领域的"前所未有AI算力创收超级宏图"，他告知全球投资者们，在Blackwell架构GPU算力强劲需求以 及即将量产的Vera Rubin架构AI算力体系更加炸裂式强劲需求推动之下，其在人工智能芯片领域的未来 营收规模到2027年可能至少达到1万亿美元，远远高于上一次GTC大会抛出的到2026年实现5000亿美元 AI算力基础设施蓝图。 在高盛、Wedbush以及摩根士丹利等看好英伟达股价前景的分析师们看来，在比预期更加强劲的营收增 长前景推动之下，英伟达市值即将继去年10月之后再度突破5万亿美元超级大关并且非常有望奔向比当 前高得多的历史新高点位。 对于英伟达股价而言，可能不久后将再创历史新高且带动全球AI算力产业链迈向新一轮上行轨迹，并 且英伟达抛出的万亿美元超级AI算力宏图，竭尽全力撑起"AI牛市叙事"这条资本市场主线。就华尔街 分析师们的平均目标价而言，意味着英伟达市值未来12个月内将突破6万亿美元市值，华尔街最乐观预 期更是高达8.8万亿美元总市值 当模型规模、推理链路与多模态/代理式Agentic ...

智通财经APP获悉，英伟达CEO黄仁勋在北京时间3月17日凌晨的GTC大会上展现出英伟达在AI算力基础设施领域的"前所未有AI算力创收超级宏图"，他告 知全球投资者们，在Blackwell架构GPU算力强劲需求以及即将量产的Vera Rubin架构AI算力体系更加炸裂式强劲需求推动之下，其在人工智能芯片领域的未 来营收规模到2027年可能至少达到1万亿美元，远远高于上一次GTC大会抛出的到2026年实现5000亿美元AI算力基础设施蓝图。 在高盛、Wedbush以及摩根士丹利等看好英伟达股价前景的分析师们看来，在比预期更加强劲的营收增长前景推动之下，英伟达市值即将继去年10月之后再 度突破5万亿美元超级大关并且非常有望奔向比当前高得多的历史新高点位。 对于英伟达股价而言，可能不久后将再创历史新高且带动全球AI算力产业链迈向新一轮上行轨迹，并且英伟达抛出的万亿美元超级AI算力宏图，竭尽全力 撑起"AI牛市叙事"这条资本市场主线。就华尔街分析师们的平均目标价而言，意味着英伟达市值未来12个月内将突破6万亿美元市值，华尔街最乐观预期更 是高达8.8万亿美元总市值 当模型规模、推理链路与多模态/代理式Agentic ...

Summary of the Conference Call on China's AI GPU Outlook and TSMC's Capital Expenditure Industry Overview - The conference focused on the outlook for China's AI GPU market and the latest capital expenditure expectations from TSMC, highlighting the increasing importance of domestic chip production in the AI sector [1][2][3]. Key Points and Arguments 1. **AI Chip Ownership**: Companies like Alibaba are moving towards owning their chips to reduce reliance on third-party suppliers, similar to Google's strategy with TPU [5][6]. 2. **Customization and Flexibility**: Owning chips allows companies to tailor their products to specific applications and adjust capacity based on demand, which is crucial in the rapidly evolving AI landscape [7][8]. 3. **Performance Comparison**: Domestic chips are reportedly closing the performance gap with international counterparts, with some Chinese chips outperforming NVIDIA's A100 in inference tasks [9][10]. 4. **Market Positioning**: Alibaba is positioned as a leading player in the AI space due to its comprehensive supply chain, including its chip production (Pingtouge) and cloud services [12][13]. 5. **Demand Projections**: The demand for AI chips in China is expected to grow significantly, with projections estimating a market size of $67 billion by 2030, driven primarily by internet companies [14][15]. 6. **Supply Chain Dynamics**: The supply of AI chips is anticipated to increase, with domestic foundries like SMIC and Hua Hong playing key roles in supporting production [27][28]. 7. **Self-Sufficiency Goals**: The self-sufficiency rate of domestic AI chips is projected to rise from 33% in 2024 to 76% by 2030, indicating a strong push towards local production [27][28]. 8. **Valuation Insights**: Valuations for companies like Kunlun and Pingtouge were discussed, with estimates suggesting a market cap range of $20 billion to $61 billion for Kunlun based on a price-to-sales ratio of 26x [19][20]. Additional Important Insights - **Investment Recommendations**: Alibaba was highlighted as a preferred investment due to its strong position in the AI ecosystem and expected performance in upcoming earnings reports [21][22]. - **Global Competitive Landscape**: The conference noted that while domestic companies are gaining ground, competition remains fierce, particularly from state-owned enterprises like Huawei and Cambricon [16][17]. - **Technological Advancements**: The discussion included the importance of advancements in chip technology and packaging, with Chinese firms catching up in areas like 2.5D packaging and advanced process nodes [29][30]. - **Market Consolidation**: The AI chip market is expected to undergo consolidation, with a few key players dominating the landscape, which may lead to reduced margins for new entrants [17][37]. This summary encapsulates the critical insights from the conference call, emphasizing the strategic shifts in China's AI chip industry and the implications for investment and market dynamics.

Summary of the Conference Call on China's AI GPU Industry Industry Overview - The report focuses on the **Chinese AI GPU industry**, highlighting its development and the competitive landscape in comparison to the US market [2][3][4]. Key Insights 1. **Significant Progress in Domestic AI GPU Supply**: - China's AI GPU development has advanced significantly despite restrictions on acquiring advanced AI chips from the US due to export controls. The domestic industry has made substantial progress in alleviating equipment and foundry bottlenecks over the past 12 months [4][5]. - By around 2028, domestic foundry capacity and chip supply are expected to meet core "sovereign demand" [4]. 2. **Policy Support and Economic Viability**: - Continuous policy support has accelerated early development, but long-term value will depend on the commercial competitiveness of Chinese AI GPU manufacturers. They must demonstrate attractive economics to achieve sustainable growth post-2028 [4][5]. - The total cost of ownership (TCO) for Chinese AI data centers is competitive due to lower chip prices and electricity costs, enhancing the attractiveness of domestic solutions [4]. 3. **Market Dynamics and Investment Outlook**: - The localization strategy in China is progressing, with efforts to expand chip, foundry, and equipment scales to compensate for process disadvantages. The optimistic scenario suggests that domestic GPUs could expand into training workloads and potentially achieve overseas applications [5]. - While no direct investment ratings for AI GPU stocks were provided, a positive outlook on the Chinese semiconductor supply chain was expressed, including companies like SMIC (foundry), NAURA (equipment), and ASM Pacific Technology (advanced packaging) [5]. Market Projections - The total addressable market (TAM) for Chinese AI chips is projected to grow to **$67 billion by 2030**, primarily driven by sovereign sectors and state-owned enterprises, with commercialization becoming key for long-term growth [11][12]. - The self-sufficiency ratio for AI chips in China is expected to rise to **76% by 2030**, although there may be a trend towards homogenization in the industry [20]. Competitive Landscape - The performance and cost comparison between Chinese AI GPUs and Nvidia's mainstream chips shows that the gap is narrowing, particularly with the upcoming H200 chip [15][31]. - The report emphasizes the importance of system-level performance and cost efficiency, suggesting that while there is still a performance gap at the chip level, Chinese solutions are becoming increasingly competitive in terms of overall system economics [31]. Risks and Challenges - The industry faces risks of homogenization as large clients may prefer to support sovereign GPU manufacturers, which could limit market access for independent third-party vendors [36]. - The potential for declining profit margins in the medium to long term due to increased competition and consolidation in the industry was noted [36]. Conclusion - The Chinese AI GPU industry is rapidly evolving, with significant advancements in domestic supply capabilities and a strong policy backing. However, the long-term success will hinge on the ability of local manufacturers to maintain competitive advantages in performance and cost while navigating the risks of market homogenization and potential profit margin pressures [24][37].

Summary of Key Points from the Investor Presentation on Global and China AI GPU Industry Industry Overview - The presentation focuses on the **AI GPU industry**, particularly the competitive landscape between **China** and the **US** in AI semiconductor production and demand [1][4][98]. Core Insights - **Long-term Demand Drivers**: - The AI semiconductor market is expected to grow significantly, with **cloud AI** being a major growth driver, potentially reaching a total addressable market (TAM) of **US$235 billion by 2025** [12][18]. - **China's AI chip TAM** is projected to grow to **US$67 billion by 2030**, with self-sufficiency expected to reach **76%** [109][111]. - **Market Dynamics**: - The **cloud capital expenditure (capex)** is robust, with estimates of nearly **US$632 billion** in 2026 from the top 10 global cloud service providers (CSPs) [12]. - **Nvidia's CEO** estimates global cloud capex could reach **US$1 trillion by 2028**, including sovereign AI [14]. - **Supply Chain Challenges**: - The semiconductor supply chain is prioritizing AI semiconductors over non-AI semiconductors, leading to potential shortages in other areas [10]. - **Tech inflation** is expected to impact demand for tech products, with rising costs for wafers, OSAT, and memory creating margin pressures for chip designers in 2026 [10]. - **China's AI GPU Development**: - The presentation raises critical questions about whether China can supply competitive AI GPUs at scale and the potential size of the domestic AI GPU market [100]. - The **local AI chip market** is expected to surpass US chips in value by **2027**, with **Huawei** projected to maintain over **50%** market share in local AI chips from 2026 to 2030 [148] [150]. Important Data Points - **NVIDIA's Production Estimates**: - TSMC is expected to produce **7-8 million GPU chips in 2025**, with NVIDIA's server rack chip consumption projected to reach **60,000-70,000** units [64][66]. - **AI Semiconductor Consumption**: - AI computing wafer consumption could reach **US$26 billion in 2026**, with NVIDIA accounting for the majority of this demand [56]. - **TSMC's Capacity Expansion**: - TSMC plans to expand its CoWoS capacity to **125k wafers per month by 2026** due to strong AI demand [47][52]. Other Notable Insights - **Geopolitical Risks**: - The presentation discusses potential geopolitical risks affecting the supply chain, including restrictions on foreign foundries and export controls on critical technologies [154]. - **Inference Economics**: - Domestic chips in China are reported to have lower total cost of ownership (TCO) and comparable costs per token for AI inference compared to NVIDIA's processors [158]. - **Strategic Responses**: - Recommendations for overcoming wafer process constraints include packaging more dies into a single chip and expanding manufacturing capacity [130]. This summary encapsulates the critical insights and data points from the investor presentation, highlighting the competitive landscape and future outlook for the AI GPU industry, particularly in the context of China and its efforts to close the technological gap with the US.

Summary of the Conference Call on China's AI GPU Sector Industry Overview - The focus is on the **China AI GPU ecosystem**, which is rapidly evolving due to high capital expenditure (capex) in AI and sustained policy support, aiming to close the technological gap with the US [2][24] - The report emphasizes the importance of **AI chips** as the foundation of AI infrastructure in China, assessing demand, supply constraints, and competitive landscape [3][26] Key Insights Domestic AI GPU Supply - China has made significant progress in developing local AI GPUs since 2020, overcoming initial constraints from US export controls [4] - By 2028, domestic foundry capacity and chip supply are expected to meet core sovereign needs, with local supply projected to reach around **US$30 billion** by 2027 [4][30] Commercial Viability - Long-term growth of China's AI GPU vendors depends on demonstrating compelling economics, with a competitive total cost of ownership (TCO) supported by lower chip prices and cheaper power [5] - The report suggests that for inference workloads, cost per token is more critical than peak performance, enhancing the competitiveness of domestic solutions [5] Market Dynamics - The total addressable market (TAM) for China's AI chips is estimated to grow to **US$67 billion** by 2030, driven initially by sovereign and state-owned enterprises (SOEs) [10][30] - The market is expected to remain supply-driven through 2027 due to foundry capacity constraints, with strong demand from cloud service providers and government-led AI investments [30] Competitive Landscape - China's localization strategy is gaining traction, with domestic GPUs expected to extend into training workloads and potentially see overseas adoption [6] - Major players in the AI semiconductor supply chain include **SMIC** (foundry), **NAURA** (equipment), and **ASM Pacific** (advanced packaging) [6] Risks and Challenges - The report highlights risks of commoditization and consolidation in the AI GPU sector, as large customers may favor sovereign-backed vendors, limiting the market for independent third-party vendors [42] - The ongoing debate centers around whether China can supply competitive AI GPUs at scale, with challenges in advanced chip design and manufacturing persisting [44][73] Valuation Insights - China's AI semiconductor design houses trade at significantly higher price-to-sales (P/S) multiples compared to global peers, reflecting expectations for rapid domestic AI substitution [47] - Specific companies like **Cambricon** and **Hygon** are highlighted for their high P/S ratios, indicating elevated market expectations despite smaller revenue bases [54] Future Outlook - The report outlines three scenarios for the future of China's AI chip market: a base case of gradual progress under constraints, a bull case of accelerated domestic capability, and a bear case of weaker supply and reduced substitution pressure [66][70] - The overall sentiment is constructive on China's AI semiconductor supply chain, with expectations for continued growth and development in the coming years [6][30]