Core Viewpoint - The article discusses the supply-demand imbalance of NVIDIA's H20 AI chips in the Chinese market following the lifting of export restrictions, highlighting the significant demand from Chinese tech companies and the challenges in meeting this demand due to geopolitical factors and supply chain constraints [2][3][4]. Supply and Demand Dynamics - After the lifting of export restrictions, there is a strong demand for H20 AI chips in China, with NVIDIA placing an order for 300,000 units from TSMC due to this demand [2]. - Jefferies estimates that the total demand for H20 chips in the Chinese market is around 1.8 million units, while NVIDIA's inventory is projected to be only between 600,000 to 900,000 units by early 2025, indicating a significant supply gap [2]. - Chinese tech giants have already placed pre-orders worth over $12 billion for H20 chips to secure future supply [3]. Geopolitical Influence - The H20 chip is not a standard product but rather a result of geopolitical influences, particularly U.S. export control policies that have fluctuated, impacting its availability in China [4]. - The U.S. government updated its export control list in April 2025, leading to a temporary halt in sales to China, which resulted in NVIDIA writing down inventory values by $4.5 billion to $5.5 billion in its Q1 2026 financial report [6]. Production and Supply Chain Challenges - NVIDIA's CEO announced in July 2025 that the company received U.S. government approval to resume shipments of H20 chips to China, but the supply chain is unable to respond immediately due to prior adjustments [7]. - TSMC faces challenges in fulfilling NVIDIA's orders, with a potential nine-month timeline required to restart the supply chain for H20 chips, which may not meet the immediate needs of Chinese customers [10][11]. Market Positioning and Competitiveness - The H20 chip is positioned to provide a legal and compatible high-performance computing solution for Chinese tech companies, especially in AI inference and large model training, amidst restrictions on advanced AI chips from the U.S. [5]. - Despite the H20's limitations in performance compared to the H100, its specifications, such as 96GB HBM3 memory and 4TB/s bandwidth, make it suitable for many applications in the Chinese market [12][13]. Challenges for Domestic Alternatives - Domestic AI chips struggle to replace the H20 due to the extensive reliance on NVIDIA's CUDA platform, which requires significant code rewriting and adaptation for alternative frameworks [12]. - The pricing strategy of the H20 chip is designed to create a competitive edge, being priced between $6,500 and $8,000, which is strategically positioned above the government procurement price of Huawei's Ascend 910B [14]. - The current state of domestic AI chips is at a critical point, requiring ongoing technological innovation and policy support to achieve broader market acceptance [15].

Core Viewpoint - TSMC's upcoming earnings report is expected to reflect a dual drive of "demand differentiation + technology premium," with Morgan Stanley setting a target price of NT$1,288, implying a 17.6% upside and maintaining a "buy" rating [1] Revenue Forecast - Morgan Stanley identifies three scenarios for TSMC's revenue growth in 2025: - A 5% quarter-on-quarter increase (over 30% year-on-year) could lead to an annual growth rate exceeding 30% - A 0-3% quarter-on-quarter increase (around 20% year-on-year) would result in a 20% annual growth rate - A 1-3% quarter-on-quarter decline would maintain the annual growth rate around 20% [1] - The demand structure in the semiconductor industry is highlighted, with strong demand for AI server chips contrasted against weak consumption in smartphones and PCs [1] Gross Margin Resilience - Morgan Stanley predicts a gross margin of 53%-58% for Q3, with a minimum of 53% even in a pessimistic scenario, supported by: - TSMC's strategy of "technology premium + capacity binding" with a 20%-25% price increase for 3nm processes compared to 5nm - Over 95% utilization of CoWoS advanced packaging capacity, leading to continued cost dilution [2] - There is a potential for wafer price increases in 2026 if AI demand remains strong, allowing TSMC to further solidify its profit margins [2] Demand Drivers - The upward risks for TSMC's performance are concentrated on three factors: - AI chip demand exceeding expectations, with current H100/H200 chip orders booked until 2026 - Intel's CPU outsourcing business accelerating from 2025-2027, expected to contribute 5%-8% to revenue growth - A recovery in the cryptocurrency market boosting ASIC chip production [3] - Downward risks include: - Ongoing inventory adjustments in consumer electronics until the end of 2025 - Slower-than-expected customer expansion for processes below 3nm - Overruns in operational costs for U.S./European factories [3] - TSMC's technological lead in advanced processes (20% lower yield than Samsung's 2nm) is emphasized as a core competitive advantage [3] Investment Focus - Investors are advised to monitor three key signals from TSMC's earnings call on July 17: - Whether the full-year revenue guidance is revised upwards to the 30% range - Clear statements regarding wafer pricing strategy for 2026 - Quantitative assessments of the sustainability of AI demand and the recovery pace of non-AI applications [4] - Positive developments in these areas could push TSMC's valuation above a 25x dynamic P/E ratio, while negative outcomes could signal a risk of falling to a 15x P/E ratio [4] - The impact of a 6% import tariff on TSMC's U.S. clients is noted, but the company's technological irreplaceability is expected to mitigate tariff impacts [4] Conclusion - TSMC's unique positioning as a bellwether for the semiconductor cycle and a beneficiary of the AI revolution is highlighted, with its performance elasticity serving as a key indicator of whether "technology premium can transcend cycles" [5] - For investors, understanding the key indicators from the earnings call may become crucial for semiconductor investment strategies in the second half of 2025 [6]

Core Insights - Nvidia has achieved a market capitalization of $4 trillion, becoming the first company to reach this milestone, reflecting a significant shift in the AI industry from model training to practical application [2][3] - The rapid growth of Nvidia's market value from $1 trillion to $4 trillion in just two years highlights its pivotal role in the AI revolution and infrastructure [5][6] - The demand for AI capabilities has surged, with Nvidia's GPUs dominating the high-end AI chip market, as evidenced by their use in approximately 76% of the world's top 500 supercomputers [6][12] Company Performance - Nvidia's stock price reached a historic high of $164.10 on July 10, 2023, contributing to its rapid market value increase [2][3] - The company's revenue for the fiscal year 2024 is projected to exceed $120 billion, with a remarkable net profit margin of 58% [6] - Analysts predict that Nvidia's market capitalization could reach $5 trillion within the next 18 months, indicating strong future growth potential [5] AI Market Dynamics - The AI landscape is shifting towards practical applications, with a significant increase in AI usage among enterprises, rising from 20% in 2017 to 78% in 2024 [15] - Companies are seeing substantial returns on AI investments, with an average return of $3.7 for every dollar spent [15] - Major tech companies are heavily investing in AI infrastructure, with an estimated total investment of $320 billion in 2024 [24] Consumer and Industrial Applications - AI applications are rapidly integrating into daily life and industrial processes, leading to a massive demand for computational power [13][21] - In the consumer sector, AI tools like ChatGPT are gaining traction, with 225 million daily active users, while Google maintains a significant user base of 2 billion [17][18] - In industrial applications, companies like Airbus and Nike are leveraging AI for efficiency improvements, such as reducing component weight and optimizing designs [21] Future Trends - The focus of AI development is shifting from model training to inference, necessitating advancements in computational efficiency and energy consumption [22][24] - Major tech firms are exploring renewable energy solutions to support their AI operations, with significant investments in solar, wind, and nuclear energy [25] - The competitive landscape is evolving, with a potential "supplier lock-in" effect as users become accustomed to specific AI platforms, solidifying the dominance of leading companies like Nvidia [27]