This article is published in the “Bulletin of the Chinese Academy of Sciences” 2026, Issue 3, “Policy and Management Research”

Artificial Intelligence (AI) is the core driving force of a new round of industrial revolution and a strategic pillar for enhancing national competitiveness. The cultivation of AI talent is a decisive factor. This article systematically compares the AI talent cultivation systems of China and the United States from three dimensions: strategic planning, formal education, and practical fields. The study finds that the US continuously and systematically promotes AI talent cultivation at the national level, with strong autonomy in schools and deep cooperation between academia and industry, alongside a well-developed environment for AI innovation and entrepreneurship. Therefore, it is suggested that China should strengthen systematic deployment and responsibility at the national level, grant more autonomy to universities, and transform the AI talent cultivation system from being led by the education department to multi-entity collaboration and scenario-driven development, while solidifying the support of computing power, data, and application scenarios to build a high-quality talent cultivation system that supports AI technology innovation and industrial development.

AI is profoundly reshaping the global economic structure and technological competition landscape. To promote the sustainable development of this strategic technology and form a competitive advantage, it relies on high-quality talent support. China has repeatedly emphasized the advancement of AI education across all levels and the strengthening of AI talent cultivation, revealing the foundational, strategic, and pioneering position of AI talent cultivation in AI development.

In recent years, the scale of AI talent in China has grown rapidly, with a compound annual growth rate of 28.7% from 2015 to 2024, reaching a total of 53,000 by 2024, closely following the US’s 63,000, forming a “dual strong” pattern. An analysis by the US think tank MacroPolo of authors at the Neural Information Processing Systems (NeurIPS 2022) conference shows that among the top 20% of global AI talent in 2022, 47% received their undergraduate education in China, while 18% were educated in the US; 28% work in China, compared to 42% in the US. Data from the Outlook Weekly indicates that by 2024, the distribution of talent in China’s AI field across foundational, technical, and application layers will be 17.1%, 28.6%, and 54.3%, respectively, with a relative shortage of foundational talent. These structural and hierarchical differences in AI talent between China and the US reflect the deep heterogeneity of the two countries’ AI talent cultivation ecosystems. To trace and analyze these differences, a comparative study of the AI talent cultivation systems in China and the US is necessary.

In this article, “AI talent” specifically refers to high-level professionals engaged in basic theoretical research, core algorithm development, model architecture design, and AI technology innovation, typically possessing a master’s or doctoral degree. The growth of AI talent is essentially a long-term process influenced by foundational capabilities, research training, engineering practice, real-world scenario iteration, and ecological support. Therefore, AI talent cultivation must be promoted in a coordinated manner within the overall framework of national strategy, formal education, and practical fields.

1. Strategic Planning: The US Strengthens Talent Cultivation Through Iterative AI Strategies, While China Progresses Gradually with Overall Planning

1. The US Government Continuously Issues National Strategic Plans to Systematically Design AI Talent Cultivation Systems.

The last four US administrations have continuously improved the strategic layout for AI talent. The Obama administration laid the policy foundation by releasing the first version of the National Artificial Intelligence Research and Development Strategic Plan in 2016, which included “better understanding the national AI R&D workforce needs” as one of seven core strategies, initiating the national strategic layout for AI talent cultivation. During Trump’s first term, the competition for AI talent was elevated to a national security level. In February 2019, Trump signed an executive order to maintain American leadership in AI, launching the American Artificial Intelligence Initiative, prioritizing AI R&D as a federal investment area, and building the US AI workforce through STEM education, workforce AI skills training, and the cultivation of researchers. In 2021, the National Artificial Intelligence Initiative Act came into effect, legalizing the aforementioned policy framework. The Biden administration, while continuing previous policies, focused on attracting global talent and responsible AI development. The updated 2023 National AI R&D Strategic Plan proposed the “AI Talent Surge” initiative, reforming H-1B and O-1 visa processes to attract top overseas talent while increasing domestic STEM education investment. Trump’s second term policy shifted towards strengthening coordination and prioritizing domestic talent. The America’s AI Action Plan released in July 2025 established policy anchors for AI development in the US. In August 2025, the Departments of Labor, Commerce, and Education jointly issued the America’s Talent Strategy, prioritizing the promotion of AI literacy and skills development, creating a talent pathway from K-12 to industry.

2. A Relatively Complete Talent Cultivation System Has Been Established.

The US AI talent cultivation strategy includes providing educational resources, building a faculty team, and offering scholarships to undergraduate and graduate students, as well as strengthening the integrated deployment of education, technology, and talent. For example, the National Artificial Intelligence Initiative Act requires the National Science Foundation (NSF) to fund universities to establish graduate internship programs, providing financial support and internship opportunities for master’s or doctoral students in AI, emphasizing public-private cooperation to enhance AI education and training capabilities, and reinforcing collaboration between government departments and the private sector through the establishment of the National AI Initiative Office. In April 2025, Trump signed an executive order to advance AI education for American youth, systematically deploying youth AI education to stimulate local students’ interest in learning at the K-12 level.

China, Guided by AI Development Planning, Promotes the Construction of AI Talent Cultivation Systems Through Collaboration Among Multiple Departments.

In 2017, the State Council issued the New Generation Artificial Intelligence Development Plan, prioritizing the cultivation of high-end AI talent, encouraging universities to establish AI colleges, expand AI master’s and doctoral enrollment, and promote the “AI + X” interdisciplinary training model, encouraging collaboration among universities, research institutes, and enterprises in AI discipline construction to attract top global talent. To implement the planning requirements, the Ministry of Education has made significant adjustments to the higher education system, issuing the AI Innovation Action Plan in 2018, incorporating AI disciplines into the “New Engineering” project, and launching the International Talent Cultivation Program for AI in Chinese Universities, emphasizing improvements in discipline layout, professional construction, textbook development, innovation and entrepreneurship, and international exchange and cooperation in AI talent cultivation. Additionally, efforts have been made to strengthen graduate education in AI, with the Ministry of Education, the National Development and Reform Commission, and the Ministry of Finance jointly issuing opinions on promoting interdisciplinary integration and accelerating graduate education in AI in 2020, formulating the Guiding Plan for Graduate Education in AI (Trial), encouraging universities to explore new models for graduate education in the field of AI. To strengthen comprehensive support for AI talent, in April 2024, nine departments, including the Ministry of Human Resources and Social Security, issued the Action Plan for Accelerating Digital Talent Cultivation to Support Digital Economic Development (2024-2026), deploying a full-chain strategy for nurturing, attracting, retaining, and utilizing digital talent, marking a shift in talent strategy from “quantity expansion” to “structural optimization.” In January 2025, the Central Committee of the Communist Party of China and the State Council issued the Outline of the Education Power Construction Plan (2024-2035), further proposing to build a high-quality education system covering all levels, types, and populations through systematic reform and digital transformation.

In comparison, the last four US administrations have maintained a high degree of consensus on the strategic goal of “maintaining American global leadership in AI,” guided by continuously updated strategic planning, forming an integrated deployment of education, technology, and talent through congressional legislation and presidential executive orders. China’s strategic planning reflects strong overall characteristics and clear implementation paths, focusing on adjustments to the higher education system and directly intervening in the scale and structure of talent supply. However, China’s national-level AI development planning has only issued a relatively comprehensive version in 2017, with no subsequent updates or adjustments based on the development of AI technology and changes in international technological competition.

2. Formal Education: Both China and the US Promote AI Education Across All Levels, with the US Emphasizing Decentralized Collaboration and China Focusing on Centralized Planning

Basic Education: The US Builds an Open Cultivation System Through Multi-Party Collaboration, While China Designs a Nationwide AI Education Ecosystem Through Centralized Planning

The US Constructs an Open Cultivation System Through Multi-Party Collaboration.

1. Professional organizations lead the development of AI education frameworks, with states implementing them autonomously. In 2018, the American Association for Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) jointly launched the AI4K12 initiative, funded by the NSF; this initiative proposed a framework of five core concepts for AI education (perception, representation and reasoning, machine learning, natural interaction, and societal impact) and is currently developing national guidelines for K-12 AI education to guide states and school districts in designing curricula based on their resources.

2. Top universities develop free and open-source courses. For example, in 2021, the Massachusetts Institute of Technology and Harvard University jointly established the RAISE (AI for Social Empowerment and Education) center, launching a modular “Day of AI” curriculum for grades K-12, covering topics such as machine learning, chatbots, large models, and AI ethics. Launched in 2022, it has reached over 500,000 students in more than 100 countries by 2024, with course resources freely available under a creative commons license, providing teacher training and teaching support.

3. Companies and non-profit organizations strongly support the AI education ecosystem. Companies like Code.org, Google, Microsoft, and Amazon provide AI courses, visual programming platforms, generative AI tools, and teacher training resources for K-12 schools. For instance, Google’s Teachable Machine is a no-code model training web platform that lowers the technical barrier, making it easier for teachers and students to learn AI.

China Centralizes Planning to Build a Nationwide AI Education Ecosystem for Primary and Secondary Schools.

1. AI is incorporated into the national curriculum standards. In March 2022, the Ministry of Education issued the Standards for Information Technology Curriculum in Compulsory Education, listing AI as one of six logical mainlines. In November 2024, the Ministry of Education issued a notice to strengthen AI education in primary and secondary schools, setting a goal to basically popularize AI education by 2030 and proposing to build a curriculum system from perception experience and understanding application to project creation and cutting-edge application by school stage. To implement the requirements of the Outline of the Education Power Construction Plan (2024-2035) and the notice on strengthening AI education in primary and secondary schools, in May 2025, the Ministry of Education issued the General Education Guidelines for AI in Primary and Secondary Schools (2025 Edition), constructing an AI literacy framework from four dimensions: knowledge, skills, thinking, and values.

2. Local governments develop AI textbooks and resources. In 2020, the Guangzhou Institute of Education Research compiled the first provincial-level approved AI textbook for primary and secondary schools in the country. In 2025, the Beijing Municipal Education Commission issued the Beijing Plan for Promoting AI Education in Primary and Secondary Schools (2025-2027), proposing to build a “Beijing Basic Education AI Application Supermarket” that includes course packages, model libraries, and other resources.

3. Promote multi-party access to AI resources for primary and secondary schools. For example, Beijing Technology and Business University’s “Support for Science Education in Fangshan District” project opened AI laboratories and other resources. Nanshan District in Shenzhen utilized local industrial resources to create 26 AI education practice bases, including the Tencent Binhai Building exhibition hall and UBTECH Robotics exhibition hall. Meanwhile, the Ministry of Education actively promotes the construction of AI education bases in primary and secondary schools; as of November 2025, it has publicly announced two batches of 509 AI education bases covering provincial capitals, cities, and counties, forming a replicable demonstration system.

Higher Education Professional Development: US Universities Flexibly Adjust Programs Based on Market Orientation, While Chinese Universities Build According to Ministry of Education Guidance

US Universities Rapidly Adjust Program Settings, Forming a Mature Collaboration Model with Industry.

1. US educational systems allow universities to autonomously assess market demand and available resources to determine program offerings. For example, Carnegie Mellon University established the first undergraduate AI program in the US in 2018, and Stanford University set up the Human-Centered AI Institute (HAI) in 2019.

2. Interdisciplinary course design with diverse teaching methods. For instance, Stanford’s AI Lab (SAIL) emphasizes multidisciplinary support to cultivate students’ independent inquiry and innovation abilities; it employs top faculty or team teaching models, with the CS224N course in Spring 2024 led by Chris Manning and a diverse teaching team of 25. It also combines high-level lectures, seminars, after-class guidance, and extracurricular salons to create a diverse and open comprehensive teaching platform.

3. Companies actively participate in course system construction. For example, top tech companies provide substantial resource support for AI research at SAIL, including funding, large datasets, and high-performance computing resources. Researchers also participate in developing and teaching Stanford’s AI courses, bringing real-world industry problems into the classroom to cultivate AI talent with industry perspectives and innovative capabilities.

Chinese Universities Establish AI Programs with Ministry of Education Support, Actively Exploring AI Talent Cultivation.

1. Build a multidisciplinary and interdisciplinary professional system. Since 2018, 621 ordinary universities in China have established undergraduate AI programs, accounting for 45.5% of domestic undergraduate institutions, setting a record for the speed of construction in China’s higher education. By 2025, 26 universities plan to add undergraduate programs in “Artificial Intelligence Education” to enhance AI education capabilities. Some universities have established AI colleges or research institutes, focusing on creating interdisciplinary talent cultivation platforms. For example, Tsinghua University established an AI college, primarily cultivating talent in AI foundational theory while also considering the cultivation of “AI + X” interdisciplinary talents.

2. Research-oriented universities innovate interdisciplinary training for AI talent. Tsinghua University, Peking University, Shanghai Jiao Tong University, and Xi’an Jiao Tong University leverage top talent cultivation programs and examination selection mechanisms, forming elite AI talent classes that adopt integrated master’s and doctoral programs or small class systems with mentorship, encouraging students to take interdisciplinary courses and engage in research practices, such as Tsinghua’s “Smart Class” and Shanghai Jiao Tong University’s “Wu Class”; they also recruit leading talents for key industries (including AI) through targeted enrollment programs, such as Tsinghua’s “Innovative Leadership Engineering Doctorate” program, Peking University’s “Frontier Engineering Doctorate Professional Degree” program, and Xi’an Jiao Tong University’s “Innovative Engineering Leadership Talent Cultivation Plan”.

3. Enterprises explore participation in program construction, promoting the integration of AI education and industrial practice. Huawei collaborates with Peking University to host a “New Engineering Experimental Class” and has developed several industry-education integrated courses, with Huawei technical experts serving as mentors and regularly providing technical guidance in the classroom; Tsinghua University’s Department of Electronic Engineering and the Shanghai AI Lab jointly designed and built a co-constructed course on “Artificial Intelligence,” taught by a team of experts from both academia and industry, aimed at engineering master’s and doctoral students; Baidu provides AI Studio educational services based on its PaddlePaddle deep learning platform and collaborates with the Ministry of Education and the New Engineering Industry-Academia-Research Alliance to offer “Deep Learning Teacher Training Courses” nationwide.

Platforms and Practices: US Government-Industry-Academia-Research Collaboration Deepens Talent Cultivation, While Chinese Government Supports Joint Exploration by Schools and Enterprises

The US Deepens Talent Cultivation Cooperation Through Platforms and Projects.

1. Multi-party co-construction of platforms and carriers. For example, the NSF, in collaboration with other federal agencies and industry partners, launched the National AI Research Institute Initiative in 2020, which is the largest public-private investment in AI research to date, establishing 29 national AI research institutes. This initiative constitutes a distributed research network covering over 500 universities, research institutions, and collaborative units across the US, promoting AI scientific research and technological innovation while cultivating future-oriented AI talent. Each research institute is led by a university and collaborates with government departments, enterprises, and industry associations to form interdisciplinary teams responsible for research, education, training, and industry collaboration, promoting nationwide collaborative innovation and talent cultivation.

2. Actively establish student practice projects. Research universities in the US emphasize a “connectivist” training model in practical processes, integrating information, resources, knowledge, and connections from research institutes, industry enterprises, and other parties to form a connective network to solve real-world problems. Their AI practice projects mainly fall into three categories: first, academic research. These are primarily research projects from universities or research institutions, such as the University of Texas at Austin’s Moncrief Summer Internship Program, where students participate in interdisciplinary research projects. Second, industrial practice. These are mainly university-industry collaboration projects, enterprise projects, or innovation and entrepreneurship projects where students learn in real AI projects. Most research universities in the US have close ties with top tech companies, such as Amazon establishing a joint science center at MIT, where funded students must intern at Amazon. Universities also provide incubation support, encouraging student innovation and entrepreneurship, such as UC Berkeley’s AI lab, which offers entrepreneurial support and research resources, allowing students to conduct innovative research related to AI in the lab and transform it into entrepreneurial projects through Berkeley’s incubation platform. Third, government tasks. These are AI research projects proposed by various government departments based on their needs, funding university faculty and student teams to conduct research, such as the practice projects funded by NASA’s Langley Research Center (LaRC) Data Science Team (DST) to develop machine learning models.

China Actively Promotes Platform and Carrier Construction, Broadening Talent Cultivation Pathways.

1. The government supports joint construction of platforms by schools and enterprises. This involves attracting relevant enterprises, universities, research institutes, and service organizations to participate, achieving resource sharing, joint development, and risk-sharing, and promoting collaborative innovation. For instance, in 2017, the Ministry of Education and the Ministry of Industry and Information Technology jointly guided the establishment of the New Engineering Industry-Academia-Research Alliance, led by Beijing Institute of Technology, which promotes industry-academia cooperation through a working committee mechanism. The first established “AI Collaborative Education Working Committee” is led by the University of Science and Technology of China and Baidu, with multiple universities jointly initiating efforts to enhance AI education and teaching practices, providing teaching resources, services, and practice projects characterized by industry-academia cooperation. In the same year, the Chinese Academy of Sciences established the AI Industry-Academia-Research Innovation Alliance, with enterprises as the main body, building a platform for industry-academia-research cooperation, undertaking national major tasks multiple times, and promoting the implementation of industry-academia-research cooperation and technology transfer. The Ministry of Education explicitly proposed to support the establishment of industry-academia-research alliances for AI in the AI Innovation Action Plan for Higher Education Institutions.

2. Schools and enterprises jointly explore innovative carriers. On one hand, schools and enterprises co-build AI laboratories or innovation centers. For example, Tsinghua University co-established the Intelligent Interaction Joint Research Center with Huawei and the Tsinghua University-Tencent Technology (Shenzhen) Co., Ltd. Internet Innovation Technology Joint Laboratory; Peking University and Huawei established the Peking University Kunpeng Ascend Science and Education Innovation Excellence Center, and Shenzhen University and Softbank Technology Group established the Joint Innovation Center for Artificial Intelligence Technology. On the other hand, schools and enterprises collaborate to offer AI classes. For instance, Shenzhen University and Tencent co-built an AI specialty class (“Teng Class”) to cultivate undergraduate, master’s, and doctoral talents; Xi’an Jiao Tong University established the “HarmonyOS Elite Class” and “ADN Launch Plan” in collaboration with Huawei, as well as the “Baidu Big Data AI Elite Class” in collaboration with Baidu.

The Differences in Formal Education Systems Between China and the US Reflect a “Centralized Coordination” Approach in China and a “Decentralized Collaboration” Approach in the US.

In the basic education stage, the US framework is set by professional associations with states implementing it autonomously, while China has a unified curriculum standard set by the Ministry of Education, with local development of textbooks. In higher education, both China and the US actively promote professional development and practical platform construction through government-industry-academia-research cooperation. However, US higher education is predominantly led by state governments and the private sector, with universities enjoying high autonomy, making program settings more flexible and collaboration with enterprises deeper. To meet social needs and the development of higher education, almost all US universities update and formulate new strategic plans every 5-10 years. In contrast, China’s higher education system is guided by the government, which, while leveraging the advantages of centralized management for rapid AI program construction, has led to a rough development of discipline construction, resulting in “bubble-like” AI talent cultivation, where training models do not align with market demand, AI knowledge systems do not match industrial development, and there is a shortage of high-level faculty and practical teaching resources. Additionally, the source of AI talent in higher education in the US heavily relies on international students, increasing uncertainty and vulnerability in talent supply.

3. Practical Fields: The US’s Rich Resources and China’s Vast Market Support AI Talent Growth

AI technology is characterized by high complexity, rapid iteration, and strong engineering dependence, requiring large-scale data and computing power, interdisciplinary knowledge, and continuous experimentation in real-world scenarios. Therefore, the growth of AI talent relies more on the training and re-cultivation of diverse entities such as governments, enterprises, and research institutions in practical environments.

In comparison, the US has abundant innovation resources, providing a solid foundation for the growth of AI talent.

1. The US government and private investment have long led in providing more research and entrepreneurial opportunities for AI talent. The non-defense AI R&D budget of the US government increased from $1.6 billion in 2021 to $1.8 billion in 2023, with multiple departments suggesting it could reach $32 billion by 2026. In contrast, China proposed a forward-looking layout for AI technology projects in the New Generation Artificial Intelligence Development Plan, allocating over 2.4 billion yuan in national funding from 2018 to 2022 to accelerate AI industrial upgrading and talent cultivation. Despite growth in AI research investment, there remains a significant gap in both funding scale and growth rate compared to the US. Additionally, the US has consistently been the preferred destination for private investment in AI and a hub for new AI companies. A report from Stanford University indicates that in 2024, total private investment in AI in the US reached $109.08 billion, approximately 12 times that of China ($9.29 billion), and the number of new AI companies is nearly 11 times that of China.

2. The US’s leading computing power and data resources provide a foundation for the technical realization of AI talent. Computing power and data are essential resources driving AI development. According to data from the China Academy of Information and Communications Technology, in 2023, the US and China ranked first and second globally in computing power, accounting for 32% and 26%, respectively; China’s data production volume is expected to reach 41.06 ZB in 2024, accounting for 26.67% of the global total, second only to North America.

3. The US’s developed open-source ecosystem provides AI talent with extensive learning and collaboration platforms. The US has a rich open-source culture, with numerous platforms like Hugging Face and GitHub supporting knowledge sharing and collaborative innovation, gathering a large number of open-source AI projects and developers, promoting the dissemination and application of AI technology.

4. The US encourages innovation and has a culture that embraces failure, providing an ideal growth environment for AI talent. Venture capital firms generally prioritize the learning and adaptability of founding teams over mere success records, viewing failure as a valuable experience rather than a blemish. This cultural atmosphere encourages AI talent to take risks and attempt breakthrough technological routes.

5. The US focuses on strengthening cooperation in talent cultivation between public and private sectors, promoting two-way talent flow. In December 2025, the US launched the U.S. Tech Force program, recruiting around 1,000 software engineers, data scientists, and AI talents for a two-year project, with top tech companies like OpenAI, Google, and Nvidia able to recommend employees to participate, providing mentorship, training, and technical support. After the project ends, participants may prioritize employment with partnering companies.

Although China’s innovation resources are slightly inferior to those of the US, it has formed an application-oriented environment, where rich application scenarios and a large market provide unique practical platforms and rapid growth paths for AI talent. As early as 2018, about 32% of enterprises in China used AI-related technologies, far exceeding the US’s 22% and the EU’s 18%. In recent years, China’s AI technology has been widely implemented and rapidly developed in areas such as intelligent transportation, smart cities, smart healthcare, and intelligent manufacturing, allowing AI talents to validate technical solutions through massive feedback data, shortening the cycle for accumulating critical experience.

In summary, both China and the US have their unique characteristics in the practical field of AI: the US’s abundant resource support, open collaboration, and risk-tolerant culture favor the cultivation of AI talents with theoretical breakthrough capabilities and international influence; while China’s relatively backward computing power and data resources are rapidly developing, with rich application scenarios enabling talents to quickly validate technical solutions, forming continuous feedback from demand to realization, achieving technological breakthroughs in specific fields, and cultivating AI talents with practical transformation capabilities and scenario innovation abilities.

4. Main Conclusions and Related Suggestions

Main Conclusions

This article compares the AI talent cultivation systems of China and the US from the perspectives of strategic planning, formal education, and practical fields, leading to the following main conclusions.

1. The US continuously conducts strategic layout for AI talent cultivation at the national level, characterized by systematic, flexible, and closely market-aligned features; China’s AI talent cultivation strategic planning mainly relies on the Ministry of Education, with limited participation from the Ministry of Science and Technology, the Ministry of Industry and Information Technology, and other departments. The cross-departmental coordination mechanism is still not well-established, and the dispersion of responsibilities leads to insufficient policy enforcement.

2. Both China and the US promote AI education across all levels in their formal education systems. The US features decentralized, diverse, and open collaboration, with deep integration between universities and industry; China primarily focuses on centralized coordination and systematic advancement, with the Ministry of Education leading curriculum standards and professional development while managing overall coordination. China’s approach ensures efficient allocation of educational resources and standardization of AI talent cultivation, but it may lead to an assessment-oriented approach in AI talent cultivation, insufficient consideration of industry demand, limited enterprise participation, and disconnection between educational goals and industry needs. Currently, a suitable school-enterprise cooperation model for China’s national conditions is still being actively explored.

3. The US leads in investment, computing power, data, and other important resources in the AI field, possessing an open, collaborative, and risk-tolerant culture; China has a vast market with rich application scenarios, demonstrating efficient industrial transformation capabilities and extensive engineering practice experience. An ideal environment for AI talent innovation and entrepreneurship should combine resource support and application-driven advantages, continuously advancing foundational and cutting-edge research while accelerating technological innovation and iteration.

Related Suggestions

Based on the above research and drawing on the US experience, this article suggests that China’s AI talent cultivation should shift from an education system-led approach to a multi-entity collaborative, scenario-driven, and clearly responsible talent cultivation system.

1. Strengthen systematic deployment of talent cultivation and improve the collaborative implementation mechanism of strategic planning.

   • Formulate an AI talent cultivation strategic plan, systematically planning the construction of AI talent tiers, clarifying short-term and long-term goals for talent cultivation, attraction, and utilization, and dynamically adjusting according to AI technology development, industry demand, and the global AI talent landscape, establishing a regular evaluation and updating mechanism.
   • Improve the cross-departmental coordination mechanism by establishing a cross-departmental AI talent working group, strengthening collaboration among the Ministry of Education, the Ministry of Science and Technology, the Ministry of Human Resources and Social Security, the National Foreign Experts Bureau, the Ministry of Industry and Information Technology, and other departments to jointly promote the organizational implementation of the AI talent cultivation strategic plan, ensuring efficient resource allocation and coordinated policy implementation, and promoting deep integration of education, technology, talent, and industry.

2. Leverage application scenario advantages and support diverse entities in talent cultivation.

   • In line with the characteristic of AI R&D relying heavily on real-world problems and engineering practice, the government, universities, enterprises, and research institutions should jointly construct a talent cultivation system.
   • Promote the construction of application scenarios and AI talent cultivation in tandem. Support leading enterprises to take the lead, with universities and research institutions participating in building typical application scenarios in fields such as smart healthcare, intelligent connected vehicles, smart city governance, and intelligent manufacturing, while cultivating AI talent in the process.
   • Expand universities’ autonomy in program settings and cultivation models. Support universities in flexibly establishing AI-related programs based on real-world needs, promoting deep interdisciplinary integration of AI with advantageous disciplines. Encourage universities to incorporate typical industry scenarios and real engineering cases into their curriculum systems, build high-level faculty teams, and enhance talent cultivation quality through international cooperation. At the same time, support universities in offering AI general education courses to all students, expanding the foundational talent pool for AI and facilitating the flow of students from interdisciplinary backgrounds into the AI field, laying the groundwork for cultivating AI talents with unique disciplinary perspectives in interdisciplinary fields.
   • Support enterprises in deeply participating in the entire process of AI talent cultivation. Multi-department collaboration should support universities, research institutions, and AI enterprises in co-constructing interdisciplinary innovation platforms to attract and gather global AI talent, driven by real application scenarios; guide universities and research institutions to jointly formulate AI internship project plans, establishing AI intern positions in enterprises with corresponding policy support; establish an enterprise-led AI talent certification system, conduct competency certification, and complement higher education with vocational education; explore mechanisms for joint degree awarding by enterprises and universities, appropriately relax the restrictions on graduate program quotas related to AI, allowing enterprises to determine the number of graduate students needed, implementing a dual-mentorship system of “enterprise mentor + university mentor” to cultivate AI talent through a cost-sharing approach.

3. Strengthen service support systems to create an open and inclusive talent development ecosystem.

   • Accelerate the cultivation of open-source communities. Build and improve domestic open-source platforms, encourage enterprises to actively participate in open-source projects, establish transparent and democratic community governance mechanisms, advocate for an open and collaborative open-source culture, and promote communication and cooperation among AI talents.
   • Improve the international AI talent service guarantee system. Further enhance policies and legal systems related to visas, permanent residency, immigration, taxation, finance, and social security, creating an open and inclusive social integration environment to attract outstanding global AI talent.
   • Build a diversified investment and financing system. Increase government investment in AI R&D to provide stable support for talent; improve market-oriented investment and financing mechanisms to encourage social capital to flow into foundational research and original technologies, developing venture capital funds focused on cutting-edge AI technologies.
   • Improve infrastructure to lower the barriers for AI talent innovation and entrepreneurship. Address bottlenecks in computing power and data, establish relevant systems for the coordinated scheduling of computing resources and reasonable utilization of data, accelerate the construction of a national integrated computing power network, and promote the effective use of computing and data resources.