Core Insights - ASML emphasizes the growing demand for chips driven by AI, particularly in mainstream chip markets, during the China International Import Expo [1][5] - The company showcases its holistic lithography solutions aimed at enhancing efficiency and reducing costs for chip manufacturers [3][4] Group 1: Company Overview - ASML has participated in the China International Import Expo for the seventh time, aiming to strengthen interactions with Chinese customers and partners [1] - The company has over 2,000 employees in China, with offices in 17 cities and multiple logistics and maintenance centers [4] Group 2: Technological Innovations - ASML's holistic lithography solutions integrate lithography machines, computational lithography, and measurement technologies to improve yield while lowering energy consumption and costs [3] - Key products showcased include the TWINSCAN XT:260, which enhances production efficiency by four times, and the TWINSCAN NXT:870B, capable of processing over 400 wafers per hour [4] Group 3: Market Trends - The rapid development of AI applications is driving significant growth in the semiconductor industry, particularly in areas like smart devices, electric vehicles, and industrial automation [4][5] - The importance of mainstream process chips is expected to rise, with China focusing heavily on this segment due to its extensive AI application scenarios [5]

Core Viewpoint - The article discusses the perceived dichotomy between value investing and technology investing, particularly in the context of the AI era, and explores the reasons behind this stereotype [4][5]. Group 1: Value vs. Technology Investing - The stereotype that value investing and technology investing are oppositional stems from the low proportion of technology investments made by well-known value investors [5]. - Value investors often struggle to share their technology investment cases, reinforcing the stereotype [5]. - A key principle of value investing is that value must be assessable, which is challenging in the rapidly changing technology sector [5][6]. Group 2: Characteristics of Technology Companies - Not all technology companies are unprofitable at inception; some, like Nvidia, can be profitable from the start [6]. - The financial losses of some tech companies do not negate their long-term value, as profitability can be established once business models and payment capabilities are confirmed [6][9]. - Understanding the business model of technology companies often requires specialized knowledge, creating a barrier for value investors [7][13]. Group 3: AI's Impact on Industries - The demand for computing power driven by AI has significantly enhanced the value of the semiconductor foundry industry, which has a stable business model and clear competitive advantages [16][18]. - AI has transformed the storage industry by increasing the demand for storage capacity and bandwidth, leading to supply shortages and rising prices [19][20]. - The shift from traditional computing to AI-driven models has created bottlenecks in storage, as the new computing methods require more frequent data storage [19]. Group 4: Semiconductor Industry Dynamics - The semiconductor industry is experiencing a slowdown in the pace of innovation, as evidenced by the observed deceleration of Moore's Law [25][28]. - Future advancements in semiconductor manufacturing may occur at a slower rate, with increased reliance on chip clusters to enhance computing power [29]. - The competitive landscape in semiconductor manufacturing is evolving, particularly in the context of national policies promoting self-sufficiency in technology [21][24].

Core Viewpoint - Baillie Gifford, established in 1908, has successfully navigated various economic cycles and has become a prominent investment giant by accurately betting on high-growth companies in the 21st century, such as Amazon, Tesla, and Nvidia [2][5]. Investment Philosophy - Baillie Gifford's investment philosophy emphasizes long-term growth potential, focusing on a framework called "10 Questions for Due Diligence" that assesses companies based on their competitive advantages, corporate culture, social contributions, growth potential, and capital allocation [7][16]. Due Diligence Framework - The "10 Questions" framework includes inquiries about revenue growth, future changes, competitive advantages, cultural uniqueness, user satisfaction, profitability, capital distribution, market valuation, and the reasons for potential undervaluation [10][16]. - This approach prioritizes long-term factors over short-term metrics, allowing for a deeper understanding of a company's future potential rather than relying solely on historical data [16][17]. Case Study: ASML - ASML, a leading photolithography equipment manufacturer, has a dominant market share of 70%-80% and is crucial for advancing semiconductor technology, particularly through its extreme ultraviolet (EUV) lithography machines [18][19]. - Baillie Gifford's investment in ASML began in 1996, and the company is viewed as essential for maintaining the momentum of Moore's Law, which predicts the doubling of transistors on a chip approximately every two years [29][30]. - ASML's corporate culture is characterized by a competitive spirit, aiming for leadership in the industry, and the company is expected to achieve significant revenue growth in the coming years [30][31]. Recent Developments - Despite ASML's strong historical performance, recent market conditions have led to a reduction in Baillie Gifford's holdings in the company, reflecting concerns over future growth amid changing industry dynamics and leadership transitions [31].

Core Insights - ASML has reported strong financial results for the first three quarters of 2025, with net sales of €7.5 billion, net lithography system sales of €5.6 billion, and a net profit of €2.1 billion, reflecting a gross margin of 51.6% [2] - The company anticipates a 15% growth in net sales for 2025 and expects sales in 2026 to be at least at the same level as 2025, with long-term revenue projections for 2030 ranging between €44 billion and €60 billion [2] - AI is identified as a key driver for growth in the global semiconductor industry, with increasing demand for advanced logic chips and DRAM chips benefiting ASML's broader customer base [2][5] Financial Performance - ASML's net sales for Q3 2025 reached €7.5 billion, with a significant portion coming from lithography systems [2] - The company achieved a net profit of €2.1 billion, indicating robust financial health despite the cyclical adjustments in the semiconductor industry [2] Market Outlook - The semiconductor industry is currently in an adjustment phase, gradually moving towards an upward cycle, with ASML maintaining an optimistic outlook on the overall industry prospects [2][11] - AI is expected to play a transformative role in the semiconductor market, with predictions that it will contribute approximately $10 trillion to global GDP by 2030 [6] AI's Impact on Semiconductor Demand - AI is seen as a major force driving the next wave of semiconductor market growth, with the potential to significantly influence chip demand once applications become widespread [5][8] - Current AI developments are still in the investment and construction phase, and the true demand for chip production capacity has yet to be fully realized [3][7] China Market Dynamics - ASML's sales in China have been high due to previously accumulated unfulfilled orders, but the company expects a normalization of business in the Chinese market in the coming years [3][11] - The Chinese market accounted for over 30% of ASML's global sales in recent years, but this is expected to revert to historical levels of 15%-20% as part of the normal cyclical adjustments [12][13] Technological Challenges and Innovations - The AI era presents challenges in terms of computational power and energy consumption, with demand for AI capabilities growing exponentially [9][10] - ASML is pursuing a dual-track strategy to address these challenges, focusing on both 2D scaling and advancing 3D integration technologies to enhance chip performance and efficiency [10]

Core Viewpoint - ASML has reported strong financial results for the first three quarters of 2025, with a net sales of €7.5 billion, net lithography system sales of €5.6 billion, and a net profit of €2.1 billion, reflecting a gross margin of 51.6% [1] Financial Performance - In Q3 2025, ASML achieved net sales of €7.5 billion and net profit of €2.1 billion, with a gross margin of 51.6% [1] - The company anticipates a 15% growth in net sales for 2025 and expects 2026 sales to be at least at the same level as 2025 [1] AI's Impact on Semiconductor Industry - AI is becoming a key driver for growth in the global semiconductor industry, with strong investments in AI leading to increased demand for advanced logic chips and DRAM chips [3] - The CEO of ASML noted that while AI is expected to benefit semiconductor equipment companies, the actual demand for equipment has not yet fully materialized [4] - The semiconductor industry recognizes AI as a major force for the next wave of market growth, with AI evolving into a fundamental part of social infrastructure [5] Challenges and Opportunities - The current phase of AI development is characterized by significant investment but limited immediate demand for chips, indicating that the true impact on semiconductor capacity is yet to be realized [4][7] - AI presents two major challenges: the exponential growth in computing power demand and energy consumption concerns [9] - ASML is pursuing a dual-track approach to address these challenges, focusing on both 2D scaling and advancing 3D integration technologies [10] Market Dynamics in China - ASML's sales in China have been significant, with the Chinese market accounting for over 30% of global sales in recent years [12] - The company expects a normalization of sales in China, with a return to historical levels of 15%-20% of global sales [13] - ASML continues to support local customers in China while adhering to export control regulations [13] Strategic Collaborations - ASML has formed a strategic partnership with Mistral AI, investing in the company to accelerate AI applications and integration [7] - The company has launched the TWINSCAN XT:260 lithography machine aimed at enhancing production efficiency in advanced packaging [10]

Core Insights - AI is identified as a new driving force for the future of the semiconductor industry, transitioning from traditional computing to AI applications, which are still in the foundational stage but will diversify significantly in the coming years [2][3] - The shift towards AI applications will challenge traditional chip design economies of scale, necessitating new approaches such as Chiplet architecture to manage development costs and enhance market flexibility [3] - Advanced packaging technologies are becoming critical for performance enhancement, with a focus on system design as a key area for future development in the semiconductor sector [3] Group 1 - AI is redefining the meaning of Moore's Law, moving from centralized data centers to edge computing and AIoT applications, which will include smart cars, robots, smart homes, and smart cities [2] - The cost of designing advanced chips, such as TSMC's products below 5nm, can reach approximately $2 billion, making it unfeasible for companies to invest heavily without guaranteed sales [3] - The Chiplet concept allows for modular design, enabling the reuse of high-performance modules across various products, thus distributing development costs and increasing market adaptability [3] Group 2 - The semiconductor industry is approaching physical limits of Moore's Law, with the pace of process miniaturization slowing down, which may challenge Taiwan's leading position in wafer foundry and packaging [3] - Advanced packaging technologies like CoWoS and InFO are becoming essential for improving chip integration efficiency, shifting the focus from merely cost control to performance enhancement [3] - System design is emphasized as a crucial area for future focus, as it will ultimately dictate the direction of industry development [3]

Group 1: Semiconductor Industry Research Methodology - The semiconductor industry is characterized by strong cyclical properties, with significant price fluctuations influenced by inventory levels, utilization rates, and expansion rhythms [5][19]. - The industry is driven by the "Moore's Law," which promotes technological and product iterations, alongside a trend of localization versus global division of labor [5][19]. - The growth of the semiconductor industry is intertwined with two cycles: the technology innovation cycle and the supply-demand cycle [15]. Group 2: Identifying High-Growth Trend Stocks - The Dividend Discount Model (DDM) serves as a theoretical foundation for asset pricing, focusing on company profitability and macroeconomic conditions [22]. - Relative valuation is essential in practice, relying on comparisons across international, industry, and company levels, with key metrics including capital expenditure, revenue, and profit [23]. - High-growth stocks are primarily driven by earnings per share (EPS) growth, which is critical for identifying potential investment opportunities [24]. Group 3: Specific Company Insights - Northern Huachuang is highlighted for its high technical barriers and clear competitive landscape, making it a leading player in the semiconductor sector [33]. - Luxshare Precision has demonstrated high performance in fulfilling product lines, significantly benefiting from major clients like Apple [42]. - Zhaoxin Microelectronics has seen substantial stock price increases due to its core RF module manufacturing capabilities, driven by the transition from 4G to 5G [45].

Core Insights - The public's perception of AI is significantly lagging behind its actual advancements, with a gap of at least one generation [2][5][41] - AI is evolving at an exponential rate, with predictions indicating that by mid-2026, AI models could autonomously complete tasks for up to 8 hours, potentially surpassing human experts in various fields by 2027 [9][33][43] Group 1: AI Progress and Public Perception - Researchers have observed that AI can now independently complete complex tasks for several hours, contrary to the public's focus on its mistakes [2][5] - Julian Schrittwieser, a key figure in AI development, argues that the current public discourse underestimates AI's capabilities and progress [5][41] - The METR study indicates that AI models are achieving a 50% success rate in software engineering tasks lasting about one hour, with an exponential growth trend observed every seven months [6][9] Group 2: Cross-Industry Evaluation - The OpenAI GDPval study assessed AI performance across 44 professions and 9 industries, revealing that AI models are nearing human-level performance [12][20] - Claude Opus 4.1 has shown superior performance compared to GPT-5 in various tasks, indicating that AI is not just a theoretical concept but is increasingly applicable in real-world scenarios [19][20] - The evaluation results suggest that AI is approaching the average level of human experts, with implications for various sectors including law, finance, and healthcare [20][25] Group 3: Future Predictions and Implications - By the end of 2026, it is anticipated that AI models will perform at the level of human experts in multiple industry tasks, with the potential to frequently exceed expert performance in specific areas by 2027 [33][39] - The envisioned future includes a collaborative environment where humans work alongside AI, enhancing productivity significantly rather than leading to mass unemployment [36][39] - The potential transformation of industries due to AI advancements is profound, with the possibility of AI becoming a powerful tool rather than a competitor [39][40]

Core Viewpoint - The rapid changes in the world, particularly in the stock market, are largely centered around artificial intelligence (AI), which is seen as a once-in-a-century opportunity akin to the discovery of electricity [1][10] - Value investing is not fundamentally opposed to benefiting from technological changes, as any field where value can be assessed falls within the scope of value investing, including technology [1][9] Semiconductor Industry - The demand growth in the semiconductor industry is expected to outpace the past decade due to AI development, and the current phase is just the beginning of this technological transformation [4] - The wafer foundry business exemplifies a "value assessable" sector, as the business model remains unchanged despite the arrival of AI, and the demand for high-performance computing is increasing [4][5] - Factors such as high minimum economic scale, high customer trial-and-error costs, and accumulated learning curves contribute to the creation of a competitive moat in the wafer foundry sector [5] Storage Industry - The storage industry is also benefiting from AI technology, with manufacturing and design characterized by high economies of scale and significant customer trial-and-error costs [6] - The relationship between computing and storage is changing due to AI advancements, leading to a faster growth in storage demand while the business model remains stable [6] Analog Chips - The analog chip sector is seen as both assessable in value and capable of sharing in the AI era's benefits, with high customer trial-and-error costs and a low share of downstream costs [8] - The development of AI is expected to increase the demand for analog chips, particularly in AI servers and various applications like robotics and smart glasses [8] Long-term Trends - The slowdown or peak of Moore's Law may help mitigate China's relative disadvantages in semiconductor manufacturing, as the progress in single-chip computing power may not meet growing computational demands [9] - Long-term certainty judgments can lead to the emergence of stable business models and companies with wide moats, aligning with traditional research methods in value investing [9] Investment Philosophy - Value investing does not reject progress or technology but adheres to the principle of assessing value to identify understandable stocks and earn within one's capability [10]

Core Viewpoint - The article emphasizes the importance of unifying instruction set architecture (ISA) for the development of China's computing chips, suggesting that RISC-V should be adopted as a standard to enhance innovation and resource efficiency in the semiconductor industry [5][30]. Group 1: Evolution of Computing Architecture - Over the past 40 years, the development of processor chips has followed a "negation of negation" spiral, oscillating between self-research and abandonment [4]. - The last five years have seen a resurgence of machine and platform manufacturers entering the "chip war," shifting from CPU-centric homogeneous computing systems to heterogeneous computing involving CPUs and xPUs [5]. - The computing evolution has transitioned from centralized processing to distributed systems, with the current core CPUs dominated by x86 and ARM architectures [9][10]. Group 2: Challenges in Architecture Innovation - The article discusses the difficulty of architecture innovation and the greater challenge of building an ecosystem, highlighting that software and collaboration barriers are significant [14]. - The dominance of x86 architecture is attributed to its ability to adapt and expand its instruction set to meet new application demands, while RISC architectures have struggled due to high costs and inability to disrupt existing ecosystems [11][13]. - The article notes that the software development costs significantly exceed hardware costs, making it challenging for new architectures to gain traction in the market [19]. Group 3: Future of RISC-V and ARM - RISC-V faces commercialization challenges despite its potential, with successful applications primarily in simple software scenarios like embedded systems [21]. - The article predicts that x86 CPUs will continue to dominate the server market for the foreseeable future, while ARM's success will depend on its ability to penetrate the x86-dominated landscape [20]. - The article suggests that the future of RISC-V in general-purpose computing will require overcoming significant hurdles, particularly in software and ecosystem development [24]. Group 4: Unified Instruction Set as a Key Pathway - The article advocates for a unified instruction set as a critical pathway for scaling China's computing chips, with cloud service providers being more successful in self-developing chips due to their control over the entire stack [25][26]. - It highlights that successful self-developed chips, like those from Apple, are not just about hardware but also about the integration of software and ecosystem capabilities [27][28]. - The call for RISC-V as a unified instruction system aims to avoid redundant efforts and resource wastage in chip development, promoting a more efficient innovation landscape [30].