Report Industry Investment Rating - Not provided in the content Core Viewpoints of the Report - The rise of AI computing power has opened up new demand space for non - ferrous metals, and the pricing logic and demand landscape of commodities may be reconstructed. The new development in the AI field is becoming an indispensable new driving force for the growth of non - ferrous metal demand. At present, one should maintain sufficient imagination for the non - ferrous metal demand prospects brought by AI while closely tracking the micro - demand and high - frequency data of each variety [2][3][40] Summary by Relevant Catalogs 1. AI Industry from "Algorithm Economy" to "Industrial Integration" 1.1 AI Industry Development History Review - The AI industry has generally gone through six stages: start, reflection, application, depression, steady and booming development. Before 2011, it was in the exploration and accumulation stage focused on algorithm theory and early model verification. From the 1950s to the 1990s, it experienced two booms and troughs. After the 1990s, new technologies injected new life but had limited impact on hardware and materials [7] - From 2012 - 2023, AI entered the breakthrough and expansion periods driven by deep learning and computing power. It was mainly "algorithm - economy" centered, with capital and technology concentrated on software and chips, indirectly affecting the traditional industrial chain [8] - In 2024, it became a double - inflection point of technology and demand. The AI industry entered the acceleration period of industrial integration and infrastructure construction, with the growth focus shifting from "algorithm dividends" to "computing power bottlenecks", and from "virtual innovation" to "physical expansion" [10][11] 1.2 Current State of AI Commercialization - After the "computing power arms race" and capital frenzy, the AI industry has entered a new stage. Commercialization and verifiable profit models have become key factors, and the global commercialization path shows differences [12][13] - In terms of Sino - US development comparison, although the US still leads in some aspects, China is catching up rapidly. The decline in reasoning costs provides conditions for AI commercialization and large - scale application, and the financial reports of tech giants verify the entry into the second half of AI commercialization [14][20][24] - Tech giants are still increasing capital expenditure on computing power and data centers. It is expected that the total capital expenditure of tech giants will exceed $350 billion in 2025, $450 billion in 2026, and $500 billion in 2027 [26] 2. AI Industry Development Spurs New Demand for Non - Ferrous Metals 2.1 Panoramic Insight into the AI Industry Chain - The AI industry chain is a multi - level system composed of the basic layer, technology layer, and application layer, with non - ferrous metals running through it [28] - The basic layer provides computing power, with chips as the core carrier, and AI servers, optical modules, and data centers as important infrastructure, promoting the coordinated development of related industries [29] - The technology layer transforms basic resources into practical capabilities, with algorithm paradigms evolving, and multi - modal large models emerging. The application layer integrates AI with the real economy, promoting efficiency improvement and industrial transformation, and non - ferrous metals become the cornerstone of AI industrial implementation [30][31][32] 2.2 "AI + Non - Ferrous Metals" Leads the Industry to Higher - end and Intelligent Development - China's non - ferrous metal industry has developed rapidly but faces problems such as insufficient resource security and high - end supply. The "Work Plan" aims to promote high - quality development, emphasizing digital transformation and the "AI + non - ferrous metals" action [34][35][36] 3. Conclusion: Symbiosis of Non - Ferrous Metals and AI Industry, Key Reconstruction Period for the "Demand Map" - AI has opened up new demand space for non - ferrous metals, and the pricing logic and demand landscape may be reconstructed. Copper, aluminum, and tin have broad application prospects in the AI field due to their unique physical and chemical properties [40][41][42] - The next report will focus on evaluating the incremental demand for non - ferrous metals from the development of data centers (computing power centers) [43]

Core Insights - Edwin Chen, a 37-year-old MIT graduate, has made headlines by debuting on the Forbes American Billionaires list with a net worth of $18 billion, thanks to his company Surge AI, which has reached a valuation of $24 billion in the AI data annotation sector [1][4][7] - Surge AI and Scale AI are positioned as key players in the AI industry, providing essential "data fuel" for algorithms, which is crucial for the development of advanced AI models like ChatGPT and Claude3 [4][6] Company Overview - Surge AI was founded by Edwin Chen in 2020 after he identified a significant gap in the data annotation market, particularly after a failed outsourcing attempt at Facebook [5][6] - The company has achieved remarkable growth, generating eight-digit revenue within 12 months of launching its first product, and has since secured contracts with major tech firms like OpenAI, Google, and Microsoft [6][10] Market Dynamics - The AI industry is experiencing a wealth creation surge, with data annotation companies like Surge AI benefiting from their unique positioning as "pick-and-shovel" providers in the AI gold rush [4][9] - The valuation of Surge AI has led to significant wealth accumulation for its founder, who holds 75% of the company's shares, highlighting the lucrative nature of the AI sector [7] Technological Advancements - Surge AI is developing advanced intelligent annotation systems capable of recognizing cultural nuances in over 200 languages and achieving extremely low error rates in medical image annotation [10] - The company is also working on cognitive annotation to enhance data with philosophical and ethical dimensions, setting it apart from competitors focused on basic classification tasks [10][11] Future Outlook - Despite warnings of a potential AI bubble, Edwin Chen remains focused on building a pathway to Artificial General Intelligence (AGI) through innovative data annotation solutions [11] - Surge AI's contracts emphasize the commercial value of data usage rights, indicating a shift towards viewing data as a critical asset in the evolving digital landscape [11]

Core Viewpoint - Surge AI has surpassed Scale AI in revenue, achieving over $1 billion in 2024 compared to Scale AI's $870 million, despite Scale AI being founded earlier and having significant funding from major investors like Meta [2][4][6]. Group 1: Company Performance - Surge AI, founded in 2020, is projected to generate over $1 billion in revenue in 2024, while Scale AI, founded in 2016, is expected to generate $870 million [2]. - Surge AI has not raised any funding, whereas Scale AI has raised $17.4 billion from notable investors including Meta Platforms and Accel [2]. - The CEO of Scale AI, Alexandr Wang, was recently poached by Meta, which may indicate internal challenges within Scale AI [4]. Group 2: Market Insights - Reports suggest that Surge AI is not only larger but also perceived as a better service provider compared to Scale AI, despite Scale AI's media presence [5]. - Surge AI is initiating a funding round aiming to raise $1 billion, with a projected valuation of $15 billion, while Scale AI's valuation has recently surged to nearly $29 billion due to Meta's investment [6]. Group 3: Company Philosophy and Mission - Surge AI aims to drive the development of Artificial General Intelligence (AGI) through high-quality data, emphasizing that data quality determines the potential of AI [10][12]. - The company believes that human experiences shape the values of AI, paralleling how life experiences contribute to human creativity and intelligence [16][18]. - Surge AI's mission is to cultivate AGI that embodies human-like qualities such as curiosity and creativity, with a focus on making impactful contributions to society [20][21]. Group 4: Founder Background - Edwin Chen, the founder and CEO of Surge AI, has a background in mathematics, computer science, and linguistics from MIT, and has previously worked at major tech companies like Google and Facebook [23][27]. - Chen's entrepreneurial journey was inspired by the challenges he faced in obtaining reliable data annotation during his tenure at these tech giants [24][28]. - Surge AI has achieved significant growth, increasing its business tenfold within six months and improving machine learning model performance for clients by 50% through data re-annotation [30][31]. Group 5: Operational Strategy - Surge AI employs a technology-driven approach to product development, offering customizable data annotation templates and easy-to-use APIs for clients [33][34]. - The company utilizes a collaborative human/AI annotation infrastructure to enhance data quality and efficiency, participating in the training processes of major AI models like ChatGPT and Claude3 [36]. - Edwin Chen advocates for a startup approach that prioritizes engineering and founder-led direction over early hiring of data scientists or product managers, focusing on significant breakthroughs rather than incremental improvements [38][40].

Group 1: Founder Psychology and Roles - Eric Schmidt emphasizes the difference between founders and professional managers, stating that founders are visionaries while professional managers are "amplifiers" who help scale ideas [4][10] - Schmidt reflects on his experience at Google, noting that he was not a typical entrepreneur but rather a professional manager who contributed during the company's scaling phase [3][4] - He discusses the challenges of attracting talent, highlighting that many talented individuals often choose to start their own companies instead of joining established firms [3][5] Group 2: Market Dynamics and Startup Ecosystem - Schmidt points out that many startups are often acquired for their talent rather than their products, indicating a market structure that can be inefficient [6][7] - He notes that the majority of startups fail, with traditional venture capital experiences suggesting that 4 out of 10 will fail completely, and 5 will become "zombies" with no growth potential [7] - The conversation highlights the importance of competition for startups, suggesting that true leadership is demonstrated when facing challenges from larger companies [11][12] Group 3: AI and Future Trends - Schmidt believes that AI is currently underestimated rather than overhyped, citing the scaling laws that drive AI advancements [33][34] - He discusses the potential of AI to transform business processes and scientific breakthroughs, emphasizing the importance of understanding how humans will coexist with advanced AI systems [35][39] - The conversation touches on the competitive landscape between the U.S. and China in AI development, with China investing heavily in AI as a national strategy [41][42] Group 4: Talent Acquisition and Management - Schmidt stresses the importance of attracting top talent by creating an environment where individuals feel they are solving significant problems [18][20] - He differentiates between "rockstar" employees who drive change and "mediocre" employees who are self-serving, advocating for the retention of the former [21][22] - The discussion includes insights on how to identify and nurture high-potential talent within organizations [24][25] Group 5: Challenges in AI Development - Schmidt highlights the challenges of defining reward functions in reinforcement learning, which is crucial for AI's self-learning capabilities [51] - He warns about the potential pitfalls of over-investing in AI infrastructure without a clear path to profitability, suggesting that many companies may face economic traps [47][48] - The conversation concludes with a call for companies to focus on the most challenging problems in AI, as solving these will yield the most significant rewards [52][53]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The report highlights the significant advancements in AI technology and applications in both China and the United States, emphasizing the competitive landscape and the unique strengths of each country in various AI sectors [3][10][33] Summary by Sections AI Infrastructure Analysis - The United States leads in cloud computing technology, while China excels in localized service advantages [10][18] - American companies are at the forefront of algorithm innovation, whereas Chinese firms demonstrate strong application innovation capabilities [10][18] - China holds a substantial market share in data centers, accounting for one-fourth of the global market, with rapid growth potential [25] AI Technology Analysis - Chinese visual AI companies are showing robust momentum, establishing unique advantages in the market [33] - The United States has a deep accumulation of knowledge graph technology, while China leads in commercializing these technologies [33] - Chinese companies are rapidly iterating and innovating in AI model applications, gradually closing the gap with international standards [40] AI Application Analysis - Chinese humanoid robots are emerging as strong competitors, showcasing significant advancements in technology [58] - Chinese AI glasses are gaining market share, with domestic manufacturers pulling ahead of overseas competitors [58] - The AI smartphone market is being reshaped by Chinese manufacturers, who are innovating in various AI applications [58] - In smart home technology, the U.S. focuses on high-end solutions, while China emphasizes comprehensive smart home integration [58][62] Industry Solutions - In the financial sector, U.S. companies excel in payment solutions and investment platforms, while Chinese firms lead in mobile payments and AI healthcare applications [71][76] - The U.S. is at the forefront of autonomous driving technology, while Chinese companies are leveraging local market advantages for rapid application [77] - Chinese AI healthcare companies are making significant strides in medical imaging analysis, while U.S. firms lead in drug discovery and health management [82] - In retail, Chinese companies are innovating in e-commerce through AI, while U.S. firms focus on optimizing the entire shopping experience [83]