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Industrial Policy for AI Competitiveness
Publication date Nov. 11, 2025
Summary
Amid growing expectations for productivity innovation through Generative AI, major advanced countries are pursuing industrial policies to strengthen AI competitiveness at the national strategy level. In this article, I compare AI industrial policies of the United States, China, and Europe to analyze each country's policy objectives and the role of capital markets in achieving them, and to derive implications for Korea's AI strategy.
The AI industry meets the necessary conditions for industrial policy in terms of economies of scale, externalities, and oligopolistic market structure. Instruments for the industrial policy can be divided into supply (technology and human resources), demand (market creation), and governance (institutions and norms) aspects, with the capital market functioning as a foundation connecting these elements to maximize policy effectiveness.
The United States, China, and Europe have established strategies centered on the private sector, government, and norms respectively, while sharing the common feature of gaining momentum for innovation through capital markets. In Korea, the role of private investment is expanding, but the scale of investment, particularly foreign investment attraction, remains somewhat limited. The government needs to present an efficient growth strategy centered on areas of strength in the AI value chain, while establishing a virtuous cycle of innovation in which R&D returns lead to reinvestment by activating private investment through capital markets.
The AI industry meets the necessary conditions for industrial policy in terms of economies of scale, externalities, and oligopolistic market structure. Instruments for the industrial policy can be divided into supply (technology and human resources), demand (market creation), and governance (institutions and norms) aspects, with the capital market functioning as a foundation connecting these elements to maximize policy effectiveness.
The United States, China, and Europe have established strategies centered on the private sector, government, and norms respectively, while sharing the common feature of gaining momentum for innovation through capital markets. In Korea, the role of private investment is expanding, but the scale of investment, particularly foreign investment attraction, remains somewhat limited. The government needs to present an efficient growth strategy centered on areas of strength in the AI value chain, while establishing a virtuous cycle of innovation in which R&D returns lead to reinvestment by activating private investment through capital markets.
Amid growing expectations for productivity innovation through Generative AI (GenAI), efforts to enhance competitiveness across the entire value chain related to Artificial Intelligence (AI) are ongoing. This has served as the backdrop for various forms of industrial policy aimed at improving the AI industry’s productivity, pursued by individual countries. This study analyzes the content, scope, and characteristics of industrial policies designed to enhance AI competitiveness, focusing on the three regions leading the AI industry--United States, China, and Europe. Based on this analysis, the role of capital markets in supporting AI industrial policy within the domestic environment is discussed.
The Meaning and Instruments of Industrial Policy
In a broad sense, industrial policy refers to the government’s “intentional” and “goal-oriented” support aimed at promoting specific economic activities or restructuring the industrial composition.1) The government’s intervention to foster domestic businesses with high growth potential has been discussed in various contexts throughout history. For instance, Alexander Hamilton, the first U.S. Secretary of the Treasury, advocated for strengthening the manufacturing competitiveness of the newly founded United States through financial support.2) British political economist John Stuart Mill emphasized the necessity of protecting infant industries with strong growth potential.3) More recently, Nobel laureates Paul Krugman and Daron Acemoglu have argued that industrial policy may be justified under conditions such as oligopolistic market structures or distortions in investment incentives.4)
This raises the question of whether policy support is necessary for the AI industry. Krugman proposed three conditions for successful industrial policy: the existence of market failure, specific industry structure, and the absence of alternative solutions. First, when the market fails, government support may be necessary for industry development and efficient resource allocation. Regarding the AI industry, particularly in the case of foundation models, R&D has economies of scale due to their increasing size, and externalities arise as a single high-performance model can be used across various application fields. Furthermore, from a structural perspective, the AI industry approximates an oligopolistic market, which is particularly evident in areas such as semiconductors and large-scale AI model development.
Capital market support is an essential element for the successful implementation of industrial policy. Industrial policy instruments can be broadly categorized into supply-side, demand-side, and governance aspects (Table 1). Active capital markets is one of the institutional frameworks that enables effective allocation of resources to innovative companies through the promotion of (private) investment. However, the role of capital markets does not stop there; they support the formation of policy funds, the effective implementation of public R&D and infrastructure investment, and are also linked to public procurement policy among demand-side policy elements. As such, capital markets serve as infrastructure for the successful implementation of industrial policy, and their importance can be specifically confirmed in the country-specific cases examined below.
Characteristics of AI Industry Policies by Country: United States, China, and Europe
While the United States, China, and Europe share the common trait of leading the global AI race, their respective industrial policies exhibit distinct characteristics. These differences stem not only from variations in policy objectives but also from disparities in available national or regional resources—particularly the differing conditions of capital markets that underpin and support industrial policy.
Looking at the United States, which leads the global AI industry, the creativity of the private sector and the dynamism of the capital markets serve as the driving forces that give it the highest level of competitiveness in all aspects of AI development and application. Outstanding R&D resources centered around higher education institutions and Silicon Valley form the foundation for innovation in AI technology and the development of the AI industry. The strong concentration of AI-related patent applications in the United States, which far exceeds the global average, attests to this foundation of innovation (Figure 1). The new technologies born from these excellent R&D resources can anticipate support from the capital markets. Over the past decade, U.S. venture capital (VC) investment in AI has increased annually by approximately 18%, with total funding exceeding USD 95 billion in 2024 (Figure 2).
While the advancement of the U.S. AI industry has been led by the private sector, China has been systematically developing its domestic AI industry under government leadership. In 2017, the State Council of China set the goal of becoming the global leader in AI by 2030.5) The key strategic pillars to achieve this goal include technological self-reliance, securing data sovereignty, and fostering a domestic AI ecosystem. The results of these strategies can be observed in the changes in the scale and scope of AI investment in China. Whereas investments were heavily concentrated in the autonomous driving sector in 2015, they have since grown substantially in scale and become more widely distributed across diverse sectors (Figure 3).
A closer look at China’s AI industry policy implementation structure for achieving such objectives shows a cooperative model in which the central government sets policy goals, local governments secure funding sources, and state-owned or private enterprises execute the projects. In particular, this centralized strategy demonstrates strengths in terms of funding efficiency. Alongside the central government’s recent establishment of an AI industry fund worth approximately USD 8.2 billion, local governments such as Hangzhou (around RMB 100 billion) and Beijing (around RMB 50 billion) have also secured large-scale resources to foster the AI industry.6) Utilizing these funds, China continues to make annual public investments totaling several billion dollars to internalize core technologies such as AI chips and deep learning frameworks.
In contrast to the market-driven approach of the United States and the state-led strategy of China, Europe distinguishes itself by pursuing a norm-based approach that seeks to secure investment resources through multi-layered cooperation. The establishment of 128 pieces of AI-related legislation across Europe over the past decade,7) in addition to comprehensive regulatory frameworks such as the GDPR and the EU AI Act, reflects this priority on institutional governance. This approach is expected to promote AI as a trusted technology, rather than merely a short-term growth engine. Furthermore, through initiatives such as InvestAI and the EU AI Champions Initiative, Europe is striving to narrow the investment gap with the United States and China by strengthening cooperation among member states and with private investors. As a result of these efforts, the number of AI firms successfully raising capital in Europe has steadily increased since 2022, in contrast to China, where the trend has been declining over the same period (see Figure 4).
Comparison of AI Industrial Policies and Implications for Korea
As discussed earlier, the United States, China, and Europe—leaders in the global AI race—employ policy approaches suited to their respective environments among the various tools of industrial policy (Table 2). The United States, with its strong R&D capabilities and highly developed capital markets, has established a virtuous cycle of innovation driven primarily by the private sector, while the government plays a relatively limited role. In contrast, China has adopted a government-led strategy aimed at enhancing competitiveness across the entire AI ecosystem and improving planning and procurement efficiency to achieve technological self-reliance. Europe, meanwhile, distinguishes itself from the other two by securing competitiveness through multilateral cooperation in terms of investment scale and guiding sustainable innovation by proactively establishing a regulatory framework.
When comparing these different AI development strategies, it is necessary to examine not only their advantages but also their limitations. The U.S. strategy, led by the private capital market, tends to concentrate investment in a small number of high-growth enterprises, which may lead to polarization and inefficient resource allocation across society, including reduced employment opportunities.8) In contrast, China’s government-led strategy risks inefficiency as limited resources are dispersed across too many sectors. Lastly, Europe’s relatively high regulatory barriers may hinder the emergence of innovative enterprises, making it difficult to accumulate growth momentum from the early stages.
The experiences of leading AI nations suggest that the role of the capital market is critical for the success of Korea’s industrial policy as a latecomer. The U.S.’s developed capital market enables a virtuous cycle by connecting venture capital with technology-innovative firms, accelerating the commercialization of research outcomes, and reinvesting the returns into further innovation. Korea, likewise, needs to enhance conditions that allow private capital to actively support innovation by having the government share initial risks and enabling autonomous private-sector investment. Meanwhile, the Chinese model, which involves setting long-term development strategies and mobilizing large-scale funds, implies that Korea can improve resource allocation efficiency by formulating a clear medium- to long-term strategy as a technological follower. However, to mitigate potential distortions arising from centralized investment decisions, the government should focus on laying the groundwork for innovation—such as infrastructure development and talent cultivation—while ensuring that private capital can make effective, expertise-based investments through a collaborative public-private framework. Finally, as in the European case, Korea should establish a clear AI regulatory framework to secure investor confidence and pursue sustainable innovation.
Although the Korean capital market remains limited in scale, its capacity to support the AI industry—centered on indirect investment—has been expanding, making it a crucial time to strengthen efforts toward sustainability. As of 2024, AI venture investment increased by 75.1% year-on-year,9) and as of the first quarter of 2025, 83.5% of newly established funds were financed by private capital,10) both of which are positive developments. However, it is important to note that the investment gap with the United States, China, and Europe remains substantial, and even compared to countries with similar quantitative characteristics, such as Israel and Canada, the proportion of foreign investment attracted has been relatively low (see Figure 5). This suggests that in order to secure additional growth drivers, Korea must actively pursue cooperation through mechanisms such as cross-border joint funds. Furthermore, private investment is a necessary condition for sustainable growth, as it can mitigate the crowding-out effect caused by policy funds while reinforcing strategic support for highly competitive industries. In conclusion, a clear government growth strategy and regulatory framework, along with coordinated public, private, and international investment through the capital market, could enhance Korea’s global competitiveness in AI.
1) For example, Juhasz, R., Lane, N.J., Oehlsen, E., Perez, V.C., 2025, Measuring Industrial Policy: A Text-based Approach, NBER Working Paper No. 33895. reviewed various documents stating the policy objectives to find common features of industrial policies to be the political goal and intentional interventions.
2) Hamilton, A., 1791, Report on the Subject of Manufactures. Washington DC.
3) Mill, J.S., 1848. Principles of political economy, In: Robson, J.M. (Ed.), Collected Works of John Stuart Mill, vol. III. University of Toronto Press, pp. 918–919.
4) Refer to Krugman, P.R., 1983, Targeted industrial policies: Theory and evidence. Industrial change and public policy, pp.123-155; Acemoglu, D., 2023, Distorted innovation: does the market get the direction of technology right?, AEA Papers and Proceedings, pp. 1-28.
5) 国务院, 2017, 新一代人工智能发展规划的通知, 国发〔2017〕35号. Translated by Webster, G., Creemers, R., Kania, E., 2017.8.1., Full Translation: China’s ‘New Generation Artificial Intelligence Development Plan’ (2017), DigiChina.
6) Chan et al., 2025, Full Stack: China’s Evolving Industrial Policy for AI, RAND Expert Insight.
7) Stanford HAI, 2025, Chapter 6: Policy and Governance, AI Index Report 2025.
8) Acemoglu, D., 2023, Distorted innovation: does the market get the direction of technology right?, AEA Papers and Proceedings, pp. 1-28.
9) Ministry of SMEs and Startups, 2025.4.8., Announcement of 2024 Top 10 DeepTech Venture Investment Trends, Press Release (in Korean).
10) Ministry of SMEs and Startups, 2025.5.20., Venture Investment Totals KRW 2.6 Trillion, Fund Formation Reaches KRW 3.1 Trillion in Q1 2025, Press Release (in Korean).
The Meaning and Instruments of Industrial Policy
In a broad sense, industrial policy refers to the government’s “intentional” and “goal-oriented” support aimed at promoting specific economic activities or restructuring the industrial composition.1) The government’s intervention to foster domestic businesses with high growth potential has been discussed in various contexts throughout history. For instance, Alexander Hamilton, the first U.S. Secretary of the Treasury, advocated for strengthening the manufacturing competitiveness of the newly founded United States through financial support.2) British political economist John Stuart Mill emphasized the necessity of protecting infant industries with strong growth potential.3) More recently, Nobel laureates Paul Krugman and Daron Acemoglu have argued that industrial policy may be justified under conditions such as oligopolistic market structures or distortions in investment incentives.4)
This raises the question of whether policy support is necessary for the AI industry. Krugman proposed three conditions for successful industrial policy: the existence of market failure, specific industry structure, and the absence of alternative solutions. First, when the market fails, government support may be necessary for industry development and efficient resource allocation. Regarding the AI industry, particularly in the case of foundation models, R&D has economies of scale due to their increasing size, and externalities arise as a single high-performance model can be used across various application fields. Furthermore, from a structural perspective, the AI industry approximates an oligopolistic market, which is particularly evident in areas such as semiconductors and large-scale AI model development.
Characteristics of AI Industry Policies by Country: United States, China, and Europe
While the United States, China, and Europe share the common trait of leading the global AI race, their respective industrial policies exhibit distinct characteristics. These differences stem not only from variations in policy objectives but also from disparities in available national or regional resources—particularly the differing conditions of capital markets that underpin and support industrial policy.
While the advancement of the U.S. AI industry has been led by the private sector, China has been systematically developing its domestic AI industry under government leadership. In 2017, the State Council of China set the goal of becoming the global leader in AI by 2030.5) The key strategic pillars to achieve this goal include technological self-reliance, securing data sovereignty, and fostering a domestic AI ecosystem. The results of these strategies can be observed in the changes in the scale and scope of AI investment in China. Whereas investments were heavily concentrated in the autonomous driving sector in 2015, they have since grown substantially in scale and become more widely distributed across diverse sectors (Figure 3).
In contrast to the market-driven approach of the United States and the state-led strategy of China, Europe distinguishes itself by pursuing a norm-based approach that seeks to secure investment resources through multi-layered cooperation. The establishment of 128 pieces of AI-related legislation across Europe over the past decade,7) in addition to comprehensive regulatory frameworks such as the GDPR and the EU AI Act, reflects this priority on institutional governance. This approach is expected to promote AI as a trusted technology, rather than merely a short-term growth engine. Furthermore, through initiatives such as InvestAI and the EU AI Champions Initiative, Europe is striving to narrow the investment gap with the United States and China by strengthening cooperation among member states and with private investors. As a result of these efforts, the number of AI firms successfully raising capital in Europe has steadily increased since 2022, in contrast to China, where the trend has been declining over the same period (see Figure 4).
Comparison of AI Industrial Policies and Implications for Korea
As discussed earlier, the United States, China, and Europe—leaders in the global AI race—employ policy approaches suited to their respective environments among the various tools of industrial policy (Table 2). The United States, with its strong R&D capabilities and highly developed capital markets, has established a virtuous cycle of innovation driven primarily by the private sector, while the government plays a relatively limited role. In contrast, China has adopted a government-led strategy aimed at enhancing competitiveness across the entire AI ecosystem and improving planning and procurement efficiency to achieve technological self-reliance. Europe, meanwhile, distinguishes itself from the other two by securing competitiveness through multilateral cooperation in terms of investment scale and guiding sustainable innovation by proactively establishing a regulatory framework.
The experiences of leading AI nations suggest that the role of the capital market is critical for the success of Korea’s industrial policy as a latecomer. The U.S.’s developed capital market enables a virtuous cycle by connecting venture capital with technology-innovative firms, accelerating the commercialization of research outcomes, and reinvesting the returns into further innovation. Korea, likewise, needs to enhance conditions that allow private capital to actively support innovation by having the government share initial risks and enabling autonomous private-sector investment. Meanwhile, the Chinese model, which involves setting long-term development strategies and mobilizing large-scale funds, implies that Korea can improve resource allocation efficiency by formulating a clear medium- to long-term strategy as a technological follower. However, to mitigate potential distortions arising from centralized investment decisions, the government should focus on laying the groundwork for innovation—such as infrastructure development and talent cultivation—while ensuring that private capital can make effective, expertise-based investments through a collaborative public-private framework. Finally, as in the European case, Korea should establish a clear AI regulatory framework to secure investor confidence and pursue sustainable innovation.
1) For example, Juhasz, R., Lane, N.J., Oehlsen, E., Perez, V.C., 2025, Measuring Industrial Policy: A Text-based Approach, NBER Working Paper No. 33895. reviewed various documents stating the policy objectives to find common features of industrial policies to be the political goal and intentional interventions.
2) Hamilton, A., 1791, Report on the Subject of Manufactures. Washington DC.
3) Mill, J.S., 1848. Principles of political economy, In: Robson, J.M. (Ed.), Collected Works of John Stuart Mill, vol. III. University of Toronto Press, pp. 918–919.
4) Refer to Krugman, P.R., 1983, Targeted industrial policies: Theory and evidence. Industrial change and public policy, pp.123-155; Acemoglu, D., 2023, Distorted innovation: does the market get the direction of technology right?, AEA Papers and Proceedings, pp. 1-28.
5) 国务院, 2017, 新一代人工智能发展规划的通知, 国发〔2017〕35号. Translated by Webster, G., Creemers, R., Kania, E., 2017.8.1., Full Translation: China’s ‘New Generation Artificial Intelligence Development Plan’ (2017), DigiChina.
6) Chan et al., 2025, Full Stack: China’s Evolving Industrial Policy for AI, RAND Expert Insight.
7) Stanford HAI, 2025, Chapter 6: Policy and Governance, AI Index Report 2025.
8) Acemoglu, D., 2023, Distorted innovation: does the market get the direction of technology right?, AEA Papers and Proceedings, pp. 1-28.
9) Ministry of SMEs and Startups, 2025.4.8., Announcement of 2024 Top 10 DeepTech Venture Investment Trends, Press Release (in Korean).
10) Ministry of SMEs and Startups, 2025.5.20., Venture Investment Totals KRW 2.6 Trillion, Fund Formation Reaches KRW 3.1 Trillion in Q1 2025, Press Release (in Korean).