Europe’s Competitiveness Challenge in the AI Era: Leveraging Strong Institutions for a Decade Long of Strategic Renewal

1. Introduction

The last decade has been characterized by the profound transformation of global economic order driven by advances in computing power, leading to the convergence and adoption of artificial intelligence (AI), the rise of geopolitical fragmentation, shifting centres of technological power and capital accumulation. The AI era is not merely a technological transition but a fundamental reorganisation of how value is created, captured, and distributed across nations. In this new landscape, technological resilience defined as the capacity to develop, deploy, and govern critical technologies autonomously, has become a defining feature of national competitiveness and economic dominance. Recent scholarly analysis frames AI as a transformative general-purpose technology capable of deeply reshaping economies by boosting total factor productivity and reversing decades of sluggish growth relative to global competitors (Filippucci et al., 2024; Misch et al., 2025). Empirical modeling across 30 European nations demonstrates a definitive, positive correlation between AI integration and long-term macroeconomic development, with linear autoregressive distributed lag (ARDL) estimations showing that incremental advancements in AI capabilities yield a 0.217% lift in sustained economic growth (Kalai et al., 2024).

Despite possessing world-class research institutions, a highly skilled workforce of 1.8 million active researchers, and leadership in AI and quantum patent filings (European Patent Office, 2026), the European Union captures only 6% of global AI venture capital investment compared to 75% for the United States (OECD, 2026). Labour productivity in the EU languishes at 77.8% of US levels, while GDP per capita has fallen to 71.5% of the American benchmark, a gap that has widened steadily over the past decade (European Commission, 2025a; IMF, 2025). The ICT sector’s share of market capitalisation remains frozen at approximately 9% in Europe, compared to 45% in the United States, and only four of the world’s 50 largest technology companies are headquartered in the EU (European Commission, 2025a). Meanwhile, the continent’s promising startups face persistent scale-up barriers: 40 of 147 European unicorns have relocated their headquarters abroad since 2008, and European firms depend on US venture capital for 42% of their funding (Revoltella, Weiss, & Wolski, 2026). This institutional paradox characterized by strong knowledge creation but weak commercial execution, reflects a fundamental failure of institutional velocity, fragmented regulatory frameworks across 27 Member States and lengthy bureaucratic process in Brussels, create transaction costs that advantage larger, US-based competitors, while policy implementation lags significantly relative to comparators. The demographic dividend of Europe’s 63 million young people remains largely untapped, with youth unemployment averaging 14.5% and exceeding 20% in southern Member States (Eurostat, 2025).

Drawing on institutional economics and comparative analysis of Northeast Asian models, Japan’s J-Startup programme and South Korea’s AI G3 strategy, this policy brief diagnoses the structural bottlenecks in Europe’s innovation ecosystem and proposes a strategic renewal agenda anchored in the Knowledge, Institutions, Execution, Markets, Human Welfare framework. The objective is to chart a pathway for transforming Europe’s institutional assets from sources of inertia into engines of competitive advantage, ensuring that the continent harness its multifold potentials to emerge as a strong and rapid mover in the AI era rather than ceding technological sovereignty to the United States and China.

To conduct this work, I followed an institutional economics framework, drawing on the contributions of North (1990), Coase (1937, 1960), and Acemoglu and Robinson (2012) to analyse the relationship between institutional quality, transaction costs, and innovation performance. The analysis integrates quantitative data from multiple authoritative sources, the European Patent Office (2026), Eurostat (2025), the OECD (2025a, 2025c, 2026a), the International Monetary Fund (2025), and the European Commission (2025), covering labour productivity, GDP per capita, patent filings, venture capital investment, youth unemployment, and technology sector market capitalisation across the EU27, United States, China, Japan, and South Korea for the period 2015-2025. Comparative case study analysis used in this report, examines Northeast Asian institutional models—Japan’s J-Startup programme (Kato & Ikeuchi, 2026; METI, 2025) and South Korea’s AI G3 strategy (MSIT, 2024; Kim, 2025), to identify transferable and benchmarking institutional mechanisms for accelerating startup growth and technological deployment.

2. Demographic, Economic and ICT Dynamics Among Big Global Economies

Europe’s demographic profile presents both opportunities and challenges for AI-era competitiveness. As of 2025, the European Union’s population stands at approximately 449.2 million, making it the third-largest population centre globally, after China counting nearly 1.41 billion population and India with 1.44 billion people, and comparable to the United States estimating to have about 335 million population (Eurostat, 2025; World Bank, 2025). However, the demographic dynamics differ markedly across these jurisdictions, with profound implications for innovation capacity and long-term growth trajectories. Evidence shows that, AI led transformation is highly sensitive negative market or policy shocks, alongside labor market frictions that require proactive workforce upskilling and regional policy interventions to mitigate structural automation risks (Kalai et al., 2024a). Table 1 indicates the demographic dynamics of the major global economies.

The demographic divergence is stark and consequential among big economic powerhouses. Europe’s median age of 44.9 years ranks among the oldest globally, exceeded only by Japan (48.8 years) among major economies. While the United States maintains a younger profile (median age 38.5) supported by sustained immigration, Europe faces the dual challenge of an ageing workforce and declining birth rates (Eurostat, 2025). The old-age dependency ratio, the number of people aged 65 and over relative to the working-age population, stands at 36.2% in the EU, projected to reach 43.5% by 2040 (European Commission, 2024). However, the youth demographic, approximately 63 million Europeans aged 15-29, represents a significant asset to drive the European economy over the next two decades. This cohort is the most digitally native generation in history, with 98% of EU youth aged 16-24 using the internet daily, and 72% possessing advanced digital skills exceeding the general population average (Eurostat, 2025). Yet the potential of this demographic dividend remains constrained by structural barriers: youth unemployment averaged 14.5% across the EU in 2025, ranging from 5.2% in Germany to 24.8% in Spain and 22.3% in Greece as it indicated the Eurostat report of 2025. In addition, among young tertiary graduates, unemployment remains equally elevated at 12.3%, suggesting persistent skills mismatch and labour market rigidity (Eurostat, 2025).

The growth data reveal a clear and widening divergence. Global Gross Domestic Product –(GDP) growth is forecast to moderate to 2.6% in 2026, with the eurozone projected to see muted growth at just 0.9% before a mild rebound to 1.6% in 2027 (Atradius, 2025). Also, the International Monetary Fund (IMF) projects EU GDP growth at 1.2% for 2025, with medium-term projections “pointing to weak growth due to persistent structural challenges that constrain Europe’s economic dynamism”. By contrast, the United States maintained robust AI-driven investment growth at 2.8% in 2025, while Emerging Asia continues to lead globally at 5.1% (IMF, 2025; World Bank, 2025).

Table 2. Comparative Growth Indicators (2025).

Indicator	EU27	United States	China	Japan	South Korea
GDP growth (%)	1.2%	2.8%	4.8%	1.1%	2.3%
GDP per capita (USD, PPP)	59,200	82,800	25,400	52,100	63,700
GDP per capita as % of US	71.5%	100%	30.7%	62.9%	76.9%
Labour productivity (US=100)	77.8%	100%	26.5%	68.2%	72.5%
Real household income growth (%)	0.7%	2.1%	4.2%	0.3%	1.8%
ICT sector market cap (%)	9%	45%	22%	12%	18%

Sources: IMF (2025a), OECD (2025a), European Commission (2025a), Atradius (2025).

Table 2. Comparative Growth Indicators (2025).

Indicator	EU27	United States	China	Japan	South Korea
GDP growth (%)	1.2%	2.8%	4.8%	1.1%	2.3%
GDP per capita (USD, PPP)	59,200	82,800	25,400	52,100	63,700
GDP per capita as % of US	71.5%	100%	30.7%	62.9%	76.9%
Labour productivity (US=100)	77.8%	100%	26.5%	68.2%	72.5%
Real household income growth (%)	0.7%	2.1%	4.2%	0.3%	1.8%
ICT sector market cap (%)	9%	45%	22%	12%	18%

Sources: IMF (2025a), OECD (2025a), European Commission (2025a), Atradius (2025).

The data from the Organization for Economic Co-operation and Development (OECD) (2025), show that the Labour productivity in the EU stands at 77.8% of US levels in 2023, with only marginal improvement from 74.2% in 2022. This productivity gap represents the single most significant factor explaining the EU-US income divergence, accounting for approximately 85% of the difference in GDP per capita (European Commission, 2025). Real household income per capita in Germany declined in the first quarter of 2025, marking the second consecutive quarter of decrease, while the United States continued to grow at 0.5% (OECD, 2025). The ICT sector’s share of market capitalisation reached 45% in the United States and 22% in China by 2025, but remained at approximately 9% in Europe, showing that it has unchanged from a decade earlier report (European Commission, 2025).

Furthermore, Europe’s patenting performance reveals a nuanced picture of strength and vulnerability. In 2025, the European Patent Office (EPO) received a record 201,974 patent applications, reflecting a 1.4% increase from the previous year (European Patent Office [EPO], 2026). Computer technology remained the leading field with 17,844 applications (+6.1%), boosted by a 9.5% increase in AI-related patent applications and a remarkable 37.9% surge in quantum technologies. Crucially, European innovators held the largest share of both AI and quantum patent filings, and increased filings in these sub-fields by 2.6% and 22.1% respectively (EPO, 2026).

Table 3. Patent Performance by Technology Field (2025).

Technology Field	EU27	United States	China	Japan	South Korea
Total EPO patent applications	201,974	--	--	--	--
AI-related patent applications	17,844	--	--	--	--
AI patent filings (% global share)	28%	35%	22%	8%	5%
Quantum technology filings (% global share)	31%	30%	25%	7%	4%
Patent-to-product conversion rate	12%	28%	18%	15%	14%

Sources: EPO (2026), European Commission (2025a).

Table 3. Patent Performance by Technology Field (2025).

Technology Field	EU27	United States	China	Japan	South Korea
Total EPO patent applications	201,974	--	--	--	--
AI-related patent applications	17,844	--	--	--	--
AI patent filings (% global share)	28%	35%	22%	8%	5%
Quantum technology filings (% global share)	31%	30%	25%	7%	4%
Patent-to-product conversion rate	12%	28%	18%	15%	14%

Sources: EPO (2026), European Commission (2025a).

The patent data confirm that Europe’s research engine remains formidable. Europe’s AI-related patent filings increased by 9.5% year-on-year, while quantum filings surged by 37.9% (EPO, 2026). This positions Europe competitively in key enabling technologies. However, the patent-to-product conversion rate, defined as the share of patents that generate commercial products or services, stands at just 12% in Europe, compared to 28% in the United States and 18% in China (European Commission, 2025). This conversion gap represents the fundamental execution challenge as the European research excellence yields patents, but these patents yield commercial and financial products elsewhere.

3. Learning from Northeast Asian Models of Technological Resilience

Japan and South Korea offer compelling models of technological resilience. Both nations have positioned startups not merely as engines of economic growth but as pillars of national security, embedding them into open innovation ecosystems anchored by large industrial firms. Normative reality is that, Japan facing severe demographic pressures, with a median age of 48.8 and a shrinking population, views AI through the lens of “Society 5.0,” treating technology as a productivity tool to combat labour shortages while preserving cultural distinctiveness (Access Partnership, 2026). The demographic imperative has driven Japan to aggressively invest in automation and AI, with government spending on AI and robotics reaching 0.8% of GDP in 2025, compared to the EU’s 0.3% (OECD, 2025).

Japan’s J-Startup programme and South Korea’s Centres for Creative Economy & Innovation demonstrate how institutionalised support for startups can endure leadership changes, reflecting a bipartisan, long-term national priority (Klingler-Vidra & Pacheco Pardo, 2025). South Korea has articulated an aggressive “AI G3” strategy, leveraging state-backed industrial policy and national champion firms (Naver, LG) to build a domestic ecosystem independent of US or Chinese reliance (Access Partnership, 2026).

To be more explicit, Japan’s J-Startup programme, launched in 2018 by the Ministry of Economy, Trade and Industry (METI), operates through a tripartite framework of (i) Select (rigorous external review by private-sector experts), (ii) Connect (community-building with corporate and investor supporters), and (iii) Go Global (international expansion assistance via JETRO), the programme has, until the end of 2025, certified 272 companies across five cohorts, with leading sectors including bio/healthcare (56 firms), manufacturing (22), and an emerging aerospace cluster (Iwasaki, 2025; METI, 2025). Evidence shows that programme participation significantly enhances employment and sales growth, with effects “particularly pronounced for younger firms” where government certification has greater marginal value in overcoming information asymmetries (Kato & Ikeuchi, 2026).

In addition, the South Korea’s “AI G3” strategy represents a comprehensive, state-directed initiative to secure the nation’s position among the world’s top three artificial intelligence powers, combining massive infrastructure investment, regulatory reform, and a distinctive emphasis on safety as a competitive differentiator (Ministry of Science and ICT [MSIT], 2024). Formally launched in September 2024 under President Yoon Suk Yeol’s administration, the strategy is anchored by four national flagship projects: establishing a National AI Computing Center with graphics processing unit (GPU) capacity expanded fifteen-fold by 2030, securing KRW 65 trillion (approximately USD 47 billion) in private sector AI investment between 2024 and 2027, achieving 70% AI adoption in industry and 95% in the public sector by 2030, and leading global AI governance through robust safety and security capabilities (MSIT, 2024)

The institutional architecture of Northeast Asian innovation systems differs fundamentally from Europe’s approach. In Japan and South Korea, startups are embedded in the national security infrastructure, contributing to both economic growth and strategic autonomy (Klingler-Vidra & Pacheco Pardo, 2025). Their design features include but not limited to:

• Institutionalised Startup Support: Programmes like Japan’s J-Startup and South Korea’s Centres for Creative Economy & Innovation endure leadership changes, reflecting bipartisan commitment
• Complementarity, not disruption: Startups complement the industrial base of large firms (Samsung, Toyota, LG) by injecting agility and novel ideas into mature sectors
• Dual-use approach: Startups contribute to both the economy and explicit national security objectives, strengthening supply chain autonomy and national manufacturing capabilities
• Cultural safeguarding: South Korea champions models like HyperCLOVA X, which claims higher performance in Korean culture and language than GPT-4
• Demographic urgency: Labour shortages driven by ageing populations have accelerated automation adoption, creating a positive feedback loop between AI investment and economic necessity.

These applicable models demonstrate how institutional design can accelerate technological transitions by embedding startups into national innovation capabilities, linking research directly to industrial deployment, and treating technology as a matter of national security. The demographic constraints facing both countries have paradoxically accelerated their AI adoption, as labour shortages become existential threats to economic stability and prosperity now and in the future.

4. Understanding Europe Position

In contrast, Europe’s position is increasingly precarious. In 2025, European Central Bank President Christine Lagarde has warned that delaying AI adoption could “jeopardise Europe’s future,” noting that Europe has “already missed the opportunity to be a first mover in AI” (Lagarde, 2025, para 3). The investment gap is staggering: more than €350 billion were invested in software in the United States in 2024, compared to €110 billion in the EU and €60 billion in China (European Commission, 2025). In venture capital, US firms attracted about 75% (USD 194 billion) of global AI VC deal value in 2025, followed by the EU27 at just 6% (USD 15.8 billion) and China at 5% (USD 13.9 billion) (OECD, 2026a).

Furthermore, only 4 out of the 50 largest tech companies are based in the EU, and none of the EU’s most valued companies have been created from the ground up in the last 50 years (European Commission, 2025a). Between 2008 and 2021, 40 of the 147 European unicorns moved their headquarters abroad, the bulk to the United States (European Commission, 2025a). The loss of these high-growth firms represents not only foregone economic value but also the dissipation of institutional learning and innovation capacity (Table 4).

The competitiveness challenge is not merely about investment levels but about institutional architecture. Europe’s strength lies in its trusted institutions, industrial know-how, and regulatory frameworks. Yet these same institutions, designed for stability and protection, have become barriers to the speed and scale required in the AI era, characterized by digital speed. The question facing European policymakers is whether strong institutions can be leveraged as assets for strategic renewal rather than liabilities that perpetuate inertia.

If the current situation should be characterized as a late mover in AI, historical experience suggests that late movers can succeed through strategic institutional adaptation. As Gerschenkron (1962) demonstrated in his study of European industrialisation, latecomer advantages, the ability to leapfrog technological generations, avoid early mistakes, and adapt institutions for rapid adoption, can offset first-mover disadvantages. Europe’s position is not necessarily a fatal handicap; it can become a strategic opportunity if institutions are adapted for speed and scale. To achieve the rapid speed required to guarantee strategic dominance, of AI for Europe renewal over the next decade, the next section outline, theoretical foundations anchored on institutions as the powerful engine for growth.

5. Theoretical Framework: Institutions as the Engine of Growth

The theoretical foundations of this analysis rest on the canonical contributions of institutional economics, which provide the analytical lens for understanding why some nations convert knowledge into prosperity more effectively than others. Douglass C. North’s seminal work in early 1990s establishes institutions as the “rules of the game in a society or, more formally, the humanly devised constraints that shape human interaction” (North, 1990, p. 3). North’s framework distinguishes between formal institutions (constitutions, laws, property rights) and informal institutions (norms, conventions, codes of conduct), arguing that the interaction between these dimensions determines economic performance (North, 1990; North, 2005).

The foundational insight is that institutions reduce uncertainty and enable investment. This construct remains central to understanding the AI competitiveness challenge. In his Nobel lecture (1993), North argued that “the inability of societies to develop effective, low-cost enforcement of contracts is the most important source of both historical stagnation and contemporary underdevelopment in the Third World”. While for Europe, the institutional challenge is not the absence of enforcement but rather, the excessive and fragmented enforcement that raises transaction costs and slows adaptation to technological change, Wayne, 1996; Kraemer, Xu and Zhu, 2002; Biresselioglu, Kaplan and Yilmaz, 2018).

Ronald H. Coase’s transaction cost economics provides the micro-foundations for understanding why Europe’s institutional architecture creates friction. Coase demonstrated that firms exist because they reduce the transaction costs of market coordination defined as costs of search, negotiation, contracting, and enforcement. The optimal institutional arrangement minimises these costs (Medema, 1995). Applied to Europe’s competitiveness challenge, the question is whether European institutions, with their multiplicity of regulatory authorities, divergent national implementations, and complex compliance requirements, raise or lower transaction costs for AI innovation and deployment

The evidence suggests they raise the costs significantly. European firms face compliance costs for AI regulation estimated at €2.3-4.5 billion annually, with small and medium enterprises (SMEs) bearing disproportionately higher per-unit costs. This regulatory burden, combined with fragmented national implementations of EU directives, creates transaction costs that advantage larger, US-based firms with specialised compliance capabilities (Acemoglu & Robinson, 2012; (European Commission, 2025). Table 5 shows the estimated transactions costs by big economies

Daron Acemoglu and James A. Robinson’s (2012) work on extractive versus inclusive institutions provides a crucial framework for understanding Europe’s position. Inclusive institutions, which protect property rights, enable broad participation, and encourage innovation, have been the engine of modern economic growth and extractive institutions, which concentrate power and rents, produce stagnation and poverty (Acemoglu, Johnson and Robinson, 2001; Acemoglu and Robinson, 2012). Europe’s inclusive institutions, developed through centuries of political and economic evolution, represent a significant competitive advantage in an era where, trust and legitimacy matter for technology adoption.

However, when the benefits of existing institutional arrangements accrue to incumbent groups, these groups resist institutional change, even when that change is necessary for long-term prosperity. This “creative destruction” barrier is evident in Europe’s AI competitiveness challenge: existing industries, regulatory frameworks, and national champions resist the disruption that AI-enabled innovation requires, while emerging firms lack the institutional voice to advocate for rapid change aligned with the institutional decision speed required.

Albeit long time academic thought, this insight connects directly to Olson’s (1965) “logic of collective action”: concentrated interests (incumbent firms, national regulators, established labour unions) are better organised and more influential than diffuse interests (startups, young entrepreneurs, consumers). The result is “institutional sclerosis”, a condition where accumulated regulations and distributional coalitions slow adaptation and impede innovation (Olson, 1982).

Acemoglu’s more recent work on AI and shared prosperity is equally directly relevant. He argues that “the path of AI is not predetermined” and that institutional choices, particularly regarding labour, competition, and regulation, will determine whether AI produces shared prosperity or rising inequality (Acemoglu, 2021). Europe’s institutional tradition of social protection and stakeholder capitalism positions it to choose a more inclusive AI path, but only if it can overcome the execution deficit that prevents its research strengths from reaching the market (Acemoglu and Johnson, 2023).

The transition to a knowledge economy requires institutional adaptation that is fundamentally different from the transition to industrial capitalism (Powell & Snellman, 2004). In the knowledge economy, the production and distribution of knowledge, rather than land, labour, or capital, becomes the primary driver of value creation (Bell, 1973). This transition has profound institutional implications, as knowledge, unlike physical capital, is non-rival and characterised by increasing returns to scale (Romer, 1990).

This again takes us to the thinking of endogenous growth theory which demonstrates that knowledge accumulation drives long-run growth, but only when institutions enable research, protect intellectual property, and facilitate knowledge spillovers. The patent data confirm that Europe excels in knowledge creation thus paving the way to leaping the benefits of AI in terms of products and services at scale, should institutions accelerate their play in the game. However, as Europe produces knowledge at a rate comparable to the United States, in some good case, leading in some key fields, yet the bloc captures only a fraction of the economic value. This may lead to thinking of knowledge paradox which should harnessed by examining the knowledge value chain: production, codification, diffusion, commercialisation, and scaling. The available data show that Europe excels at the first two nodes (production and codification -patent filings-) but struggles at the last three (diffusion, commercialisation, scaling) (Powell & Snellman, 2004).

5.1. Institutional Velocity and the Knowledge-Execution Stance

Building on the institutional economics literature, we propose the concept of institutional velocity to diagnose Europe’s AI competitiveness challenge. Institutional velocity captures the speed with which institutions adapt, coordinate, and translate policy into execution. Institutional quality alone is insufficient; the velocity of institutional response determines whether research excellence becomes commercial advantage or remains academic curiosity.

This concept draws on North’s (1990) observation that “institutional change is overwhelmingly incremental” rather than revolutionary, but “the incremental changes are not neutral in their consequences”. When the pace of technological change exceeds the pace of institutional adaptation, societies experience “institutional lag”, a condition that penalises economies with complex, multi-level governance structures (Rodrik, 2000, North, 2005;).

Europe’s institutional assets are substantial, yet they suffer from low institutional velocity relative to comparators, the USA, China, Japan and South Korea. Research flows into publications rather than products. Regulation operates as a wall rather than a bridge. National systems protect their own small corners instead of building continental strength.

Europe boasts strong research activity and an ample talent pool, but businesses struggle to scale up, R&D expenditure remains below peers at 2.2% of GDP (compared to 3.4% in the US and 3.1% in South Korea), and digitalisation progresses too slowly (OECD, 2025). The gap between knowledge inputs and innovation outputs is widening, with Europe’s “innovation conversion ratio”, the share of R&D expenditure that translates into revenue growth, declining from 12% to 7.8% between 2010 and 2025 (European Commission, 2025). The result of that architecture is a knowledge-execution gap explained by the fact that Europe educates talent, then watches others profit from it; brilliant ideas born in European laboratories end up funded, developed, and owned elsewhere (Lagarde, 2025).

Table 6 shows that the investment gap between Europe and its competitors is wide, reflecting fundamental differences in institutional arrangements for risk capital. In 2025, the Venture Capital investments in AI globally reached USD 258.7 billion, representing 61% of all VC investment (OECD, 2026). US AI firms attracted approximately USD 194 billion representing 75% of global AI VC deal value, followed by the EU27 at USD 15.8 billion (6%) and China at USD 13.9 billion (5%). In terms of outgoing VC investments, US investors represented about 56% (USD 124 billion) of worldwide AI VC value, EU27 investors just 7% (USD 14.5 billion), and China 8% (USD 17.2 billion). Furthemore, mega deals exceeding USD 100 million now account for about 73% of total AI investment value, with deals above USD 1 billion representing roughly half of total AI investment value. For EU startups, financing remains critically dependent on US venture capital, which has supplied 42% of total VC funding for EU firms over the past decade (Revoltella, Weiss, & Wolski, 2026).

Table 7. AI Venture Capital Investment (2025).

Indicator	EU27	United States	China	Japan	South Korea
AI VC investment (USD billions)	15.8	194.0	13.9	4.2	3.8
Global share of AI VC (%)	6%	75%	5%	1.6%	1.5%
AI VC as % of GDP	0.09%	0.68%	0.10%	0.10%	0.28%
Mega deals (>USD 100m) share (%)	45%	78%	52%	35%	42%
Share of funding from domestic sources (%)	42%	92%	78%	65%	58%
VC investment per AI company (USD millions)	2.2	10.5	1.6	2.0	2.1

Sources: OECD (2026a), European Commission (2025a), Revoltella et al. (2026).

Table 7. AI Venture Capital Investment (2025).

Indicator	EU27	United States	China	Japan	South Korea
AI VC investment (USD billions)	15.8	194.0	13.9	4.2	3.8
Global share of AI VC (%)	6%	75%	5%	1.6%	1.5%
AI VC as % of GDP	0.09%	0.68%	0.10%	0.10%	0.28%
Mega deals (>USD 100m) share (%)	45%	78%	52%	35%	42%
Share of funding from domestic sources (%)	42%	92%	78%	65%	58%
VC investment per AI company (USD millions)	2.2	10.5	1.6	2.0	2.1

Sources: OECD (2026a), European Commission (2025a), Revoltella et al. (2026).

6. A New Framework to Maximize the Institutional Dividend in the Rapidly Growing and Competitive Worlds

Building on the strong foundations while attempting to address the institutional velocity challenges, I propose a framework that connects the full innovation chain, drawing on the institutional economics tradition and the knowledge economy literature:

KNOWLEDGE → INSTITUTIONS → EXECUTION → MARKETS → HUMAN WELFARE

Knowledge represents research excellence, scientific capability, and human capital, areas where Europe demonstrates clear strength, as evidenced by its patent leadership in AI and quantum technologies (EPO, 2026). However, as Mokyr (2002) argues that, knowledge must be “useful”, capable of application to production and welfare improvement. Data indicate that Europe’s research base includes approximately 1.8 million active researchers, representing 22% of the global total (European Commission, 2025).

Institutions encompass the regulatory frameworks, governance structures, and coordination mechanisms that channel knowledge toward productive ends. North (1990), Acemoglu and Robinson (2012), and Coase (1960) all emphasise that institutions shape incentives, reduce transaction costs, and determine which knowledge is pursued and how it is deployed. Europe’s institutional architecture—with 27 national innovation systems, multiple funding streams, and fragmented regulatory authorities, creates high coordination costs.

Execution captures the translation of knowledge into products, services, and scalable solutions, the critical bottleneck in the European system. This node reflects the managerial capabilities, risk capital, and entrepreneurial culture that turn research into revenue (Powell & Snellman, 2004). Execution deficits manifest in the 12% patent-to-product conversion rate in Europe, compared to 28% in the United States.

Markets reflect the commercialisation, diffusion, and scaling of innovations. As Romer (1990) demonstrates, large, integrated markets enable knowledge spillovers and increasing returns to scale. Europe’s fragmented markets still characterised by national regulatory variations limit the scaling of AI innovations.

Human Welfare represents the ultimate objective: improving living standards, protecting rights, and ensuring inclusive growth. This final node reflects the normative commitments of European social democracy and the institutional economics tradition of assessing institutions by their consequences for human flourishing.

7. Toward Strategic Renewal and Recommendations

The AI era is not a distant future but an unfolding present, with technology moving into hospitals, banks, factories, schools, public services, and defence. The countries and companies that control these systems will shape not only markets but the global economy, decisions, habits, jobs, and public trust.

The institutional velocity required for strategic renewal demands that at Europewide:

• Accept that the pace of movement has been slow relative to peer comparators while leveraging its strengths in trusted institutions and regulated sectors. By doing so, it’s high time to Launch an “AI Execution Agenda” moving beyond the “Apply AI Strategy” to establish concrete, time-bound targets for AI deployment across key sectors (manufacturing, healthcare, energy, transport). Establish clear success indicators and investment pathways.
• Shift from regulation-first to execution-first thinking, treating the AI Act as infrastructure rather than a constraint.
• Adopt a “whole of system” approach that integrates startups, large firms, and national security objectives
• Invest at scale in computing infrastructure, talent retention, and growth-stage financing
• Unlock youth innovation potential by reducing barriers to youth entrepreneurship and embedding young people in innovation ecosystems. This should also include, the creation of European Sovereign AI Fund aiming to address the financing gap that sees EU firms depend on other countries including the US Venture Capitals of their funding
• Introduce in their performance metrics that are time sensitive and capture institutional velocity and youth innovation absorption.
• European Commission to establish a “European AI Competitiveness Dashboard” that reports on performance progress on periodic basis along with formalizing high level the forum for self-reflection in holistic advances for AI.
• Create a “European Youth AI Corps and strengthen Youth Innovation Vouchers”: Establish a two-year rotational programme placing young AI talent in startups, research institutes, and public sector organisations across multiple Member States and provide young entrepreneurs (under 30) with vouchers for accessing computing resources, legal compliance, and business development support as well as aggressive reduction of the time (months) of regulatory adaptation for youth-led startups -fast-track” regulatory pathway for youth-led AI ventures.
• Integrate AI and entrepreneurship into university curricula across all EU member states, establishing/strengthening AI literacy as a core requirement for all levels of education including basic education. Also expand partnerships with leading US and Asian institutions to ensure curriculum quality and international connectivity.

EU member states should capitalize on their unique positioning and maximize gains

Germany: With GDP of nearly €4.1 trillion and AI investment totaling to €13.16 billion; youth unemployment standing at 5.2% and population of 84.4 million, the country needs to leverage its industrial base to lead in “Industry 4.0 with AI through acceleration of the Franco-German AI roadmap, focusing on manufacturing, automotive, and energy applications. In addition, the country should address the fragmentation of AI funding across federal states by creating a national AI coordination body with budgetary authority.

France: With the GDP: of nearly €2.8 trillion and AI investment of €16.39 billion; youth unemployment standing at 17.8% (high) and the population of 68.5 million, the country should build on its strengths in research and startup creation (Mistral AI). Prioritise sovereign AI models for public services and defence applications. France’s elevated youth unemployment represents both a challenge and an opportunity, the country need to launch a “Youth AI Services” initiative targeting a a significant portion of young graduates in AI-enabled public service roles. The country also presents a competitive edge to establish a “French AI Diaspora” fund to repatriate French AI talent from the United States and United Kingdom.

Italy with its GDP of around €2.1 trillion, youth unemployment of 22.3% (high) and the population of 58.8 million, the country needs to focus on AI applications for tourism, manufacturing with a special focus on SMEs, and public administration. Use the Draghi report as a mandate for reforming the innovation ecosystem, improving access to venture capital, and reducing bureaucratic barriers. Also, Italy’s high youth unemployment of more than 1 in 5 young people bein unemployed, demands urgent youth-focused intervention which include establishing a “Youth AI for Tourism” programme potential to creating several thousands of AI-enabled roles in that sector.

Spain sitting on the GDP of €1.5 trillion, youth unemployment of 24.8% and population: of 47.5 million, the country needs to capitalise on its strengths in renewable energy to lead in AI for energy grid optimisation. Address the dualisation of its labour market by investing in AI skills and digital literacy programmes. Spain’s youth unemployment is the highest among large EU economies; the country has to launch a national “AI Apprenticeship” programme targeting young people and establish a “Green AI” initiative linking Spain’s renewable energy leadership to AI-powered grid management.

Netherlands with its GDP of €1.1 trillion and AI investment nearing €4.47 billion, youth unemployment: 8.2% (relatively low) and the population of 17.5 million), the country needs to leverage its position as Europe’s tech hub to lead in AI hardware and semiconductor applications. In addition, it has to use its strong data infrastructure to develop sovereign cloud and data centre capabilities as well as address energy cost challenges for data centres. Finally, the country will need to establish a “European AI Hardware Consortium” with Dutch leadership.