当前，AI领域的顶尖科学家仍然多集中在欧美国家和地区。《全球人工智能科研态势报告（2015— 2024）》显示，中美两国汇聚全球近六成的AI研究人员，形成"双强并立"格局。美国以超6.3万的研究 人员数量居全球首位；中国研究人员数量则从2015年的不足万人跃升至2024年的5.2万人，年复合增长 率高达28.7%，展现出强劲发展势头。 但我国AI人才短缺也正在成为制约产业高质量发展的主要障碍。脉脉高聘人才智库《2024年度人才迁 徙报告》显示，AI相关岗位人才最为稀缺，供需比不足1.0，其中云计算、深度学习等技术岗位供需比 甚至低至0.27，"一人难求"的局面凸显。多家平台数据也表明，今年以来AI人才持续供不应求，除了当 前市场急需的算法工程师、大模型工程师、机器学习工程师之外，全产业链均存在显著人才缺口，这些 人才也成为企业竞相争夺的对象。 党的二十届四中全会明确提出，推动科技创新和产业创新深度融合，一体推进教育科技人才发展，深入 推进数字中国建设。近来，随着以DeepSeek为代表的生成式人工智能技术持续迭代升级，"AI+"正在加 速渗透各行各业，由此催生出对于AI人才的巨大需求。 AI被视为影响大国博弈 ...

Core Insights - The 22nd China Computer Conference (CNCC 2025) is being held in Harbin, focusing on the theme "Digital Intelligence Empowerment, Infinite Possibilities" and gathering top scholars, industry leaders, and representatives from international organizations in the computer field [1] Group 1: Development of AI Models - Baidu's Chief Technology Officer, Wang Haifeng, discussed the development paths of general models and scenario models, stating that they are complementary rather than separate [1][3] - General models serve as a foundation, with their robust data, computing power, and algorithm capabilities continuously raising the technological ceiling [3] - As general models become more powerful, their ability to solve scenario-specific problems will also improve, while scenario models focus on understanding industry and application contexts to meet constraints like time, resources, and environment [1][3] Group 2: Computing Power and Talent Development - Wang emphasized that computing power is fundamental, noting the rapid development of domestic computing power, with Baidu's Kunlun chip forming a self-developed cluster of 30,000 cards [3] - The growth of other domestic chip manufacturers is also highlighted, indicating a thriving ecosystem [3] - Regarding talent development in the AI era, Wang pointed out that talent is a key support for the development of large models, with young individuals quickly mastering core technologies [3] - He observed that talented individuals prioritize not only salary but also research platforms, computing support, and growth opportunities when choosing employers, suggesting that companies must provide comprehensive support to attract AI talent [3]

Core Insights - A paper co-authored by Fuli Luo from Peking University and Xiaomi's large model team was published on arXiv, indicating a significant connection between Luo and Xiaomi [1][3] - Luo's previous work included leading the development of the DeepSeek-V2 model, and her talent has attracted attention from industry leaders, including Xiaomi's founder Lei Jun, who publicly offered her a substantial salary to join the company [3][6] - Despite not officially joining Xiaomi, Luo has become a key figure in the company's research, raising questions about her role and the nature of her collaboration with Xiaomi [5][6] Group 1 - The paper's publication marks a clear signal of Fuli Luo's association with Xiaomi, despite her not being officially listed as part of the company [1][5] - The tech industry is highly competitive for AI talent, especially as companies like Xiaomi seek to enhance their capabilities in AI and multi-modal interactions [6][8] - The public's fascination with Luo has led to a phenomenon of "cult of personality," overshadowing her research contributions and focusing instead on her personal narrative [8] Group 2 - Luo's academic background and previous achievements, including multiple publications at top conferences, serve as a strong endorsement of her capabilities [5][6] - The attention on Luo reflects broader trends in the tech industry, where the recruitment of top talent is crucial for maintaining competitive advantage [6][8] - Ultimately, Luo's choice to maintain an independent identity may allow her to collaborate with companies like Xiaomi while focusing on her research without corporate constraints [8]

Core Viewpoint - The discussion between Luo Yonghao and Zhou Hongyi highlights the trend of companies aggressively recruiting AI talent, which Zhou describes as tactical rather than strategic [1] Group 1: AI Talent Acquisition - Zhou Hongyi believes that the current practice of "high-priced talent acquisition" is primarily tactical, aimed at gaining immediate expertise rather than long-term strategic development [1] - Zhou explains that many aspects of developing transformer models are publicly available, including algorithms and open-source resources, making the knowledge accessible [1] - The real challenge lies in the engineering aspects of building large-scale models, such as a trillion-parameter model, which requires significant infrastructure and experience to navigate effectively [1] Group 2: Importance of Experience - Companies often recruit experienced individuals not just for their knowledge but to avoid common pitfalls in the engineering process, thus "buying experience" and critical know-how [1]

Group 1: AI Talent Demand - AI-related positions are increasingly popular in campus recruitment, with a notable rise in demand for roles such as algorithm engineers, machine vision engineers, and machine learning engineers [2][3] - Companies are eager to secure candidates with "future productivity" skills through early recruitment, driven by the rapid evolution of technology and the scarcity of AI talent [2][3] - OPPO emphasizes the need for AI talent, particularly in areas like AI model research and personalized service algorithms, highlighting the appeal of international students [2][3] Group 2: Diverse Employment Preferences - Job seekers, particularly those from the post-2000 generation, are prioritizing growth opportunities, alignment of company values with personal beliefs, and work-life balance over just salary [5][6] - There is a growing trend of returning to hometowns for employment, with candidates valuing job stability and quality of life alongside salary [5][6] - Companies are encouraged to offer additional benefits beyond salary to attract talent, as candidates increasingly seek roles that provide personal and professional development [5][6] Group 3: Characteristics of Post-2000 Job Seekers - The new generation of job seekers is more open to cross-disciplinary opportunities and values a company's culture of openness and diversity [5][6] - They exhibit confidence and flexibility in communication, often engaging directly with executives and expressing their ideas without hesitation [5][6] - Continuous learning and the development of hybrid skills are deemed essential for maintaining competitiveness in a rapidly changing job market [5][6]

Core Insights - The competition for AI talent among major internet companies has intensified as they launch campus recruitment for the 2026 graduating class, with a significant focus on AI-related positions [1][2][3] - There exists a stark contrast in the AI job market, where top-tier companies are aggressively seeking talent while small and medium enterprises struggle to attract qualified candidates [1][3][4] - The rapid expansion of AI undergraduate programs in China has not resolved the underlying structural issues in talent supply and demand [1][8][9] Recruitment Trends - Baidu initiated its 2026 campus recruitment on July 8, aiming to issue over 4,000 offers, with more than 90% of positions related to AI [2] - ByteDance, Alibaba, and Tencent have also launched their recruitment efforts, with significant increases in the number of AI-related job openings [2][3] - The demand for AI talent is reflected in the recruitment strategies of these companies, which prioritize positions such as algorithm engineers and machine learning specialists [2][3] Talent Supply and Demand Dynamics - The AI job market is characterized by a "pyramid" structure, where high-end talent is scarce while competition for lower-tier positions is fierce [4][5][7] - A report indicates that the AI talent gap in China has exceeded 5 million, highlighting a critical supply-demand imbalance [5] - Many job seekers, despite being highly qualified, face intense competition, with some reporting that they are unable to secure desirable positions [6][7] Educational Challenges - The rapid establishment of AI programs in 621 universities has not aligned with industry needs, leading to a mismatch between graduates' skills and employers' expectations [8][9] - Experts suggest that educational institutions need to enhance their resources and teaching quality to better prepare students for the evolving job market [8][9] - There is a call for improved collaboration between academia and industry to bridge the gap between education and practical application [9][10]

Core Insights - The article highlights a fierce "AI talent war" in Silicon Valley, with companies like Meta and Google offering exorbitant salaries to attract top AI talent, reflecting the scarcity and importance of such talent in the AI-driven technological revolution [1][2] Group 1: AI Talent Competition - Meta has offered over $200 million in total compensation to recruit the head of Apple's AI foundational model team, surpassing the salary of Apple's CEO Tim Cook for 2024 and approaching the annual income of soccer star Cristiano Ronaldo [1] - Google has spent $2.4 billion to acquire the technology and core R&D talent from AI programming company Windsurf, indicating a trend of "talent acquisition" in the tech industry [1] - The competition for AI talent is likened to a modern "gold rush," with companies recognizing that talent is now the most critical production factor in the AI era, replacing traditional factors like capital and land [1] Group 2: Implications for China's AI Industry - China faces a significant AI talent gap, with over 5 million positions unfilled, particularly in foundational areas like AI chips, algorithm research, and underlying architecture systems [2] - There is a need for increased investment in AI education to cultivate local talent while also attracting high-end overseas talent to create a favorable development environment [2] - Companies in China should offer competitive salaries, sufficient computing resources, and broader development opportunities to attract and retain top AI talent [2] - The article warns against the irrational bidding for AI talent that could lead to a bubble, emphasizing the importance of rational valuation of AI talent to ensure that technological dividends drive industrial upgrades and social progress [2]

Core Insights - The competition in the AI sector emphasizes the importance of talent, data, and computing power, with a significant focus on talent acquisition as a primary driver of success [1] - The recruitment landscape is shifting, with companies increasingly valuing practical skills and experience over formal educational qualifications [22][23] Talent Acquisition Trends - A recent analysis of AI job postings in major cities revealed that many companies are moving away from strict educational requirements, with some only requiring a bachelor's degree for high-paying AI positions [2][23] - Companies are prioritizing engineering and academic capabilities, emphasizing the need for candidates with practical research experience and problem-solving skills [3][24] Evolving Job Requirements - The demand for composite skill sets is rising, particularly for roles like AI product managers, who must balance user needs with technical model understanding [4][34] - The role of AI product managers is becoming more complex, requiring a deep understanding of technology and the ability to adapt to rapid advancements in AI [34][35] High-Demand Positions - Algorithm engineers are emerging as a "golden profession" in the AI era, with a significant percentage of high-paying AI roles focusing on algorithm-related positions [5][19] - Companies like DeepSeek and others are offering competitive salaries for algorithm engineers, with entry-level positions starting at around 10-20k per month and experienced roles reaching up to 110k per month [6][19] Salary Insights - High-paying AI positions are prevalent, with companies like Huawei and Ideal offering salaries ranging from 50k to 90k per month for various AI roles [8][11] - The average salary for positions in the large model field exceeds 6.75k per month, indicating a strong demand for talent in this area [22] Industry Dynamics - The automotive sector is becoming a major player in AI talent recruitment, with companies like Ideal and Geely actively seeking algorithm engineers [15][19] - The overall supply-demand ratio for AI talent is uneven, with high-tech roles like cloud computing showing a significant talent shortage [22]