Orchestrating Power: The Cultural–Institutional Nexus and the Rise of Digital Innovation Ecosystems in Great Power Rivalry

Abstract

This article examines how digital innovation ecosystems have emerged as strategic institutions of power in contemporary world politics. It argues that, unlike Cold War technological rivalries driven by centralized, state-led control, today’s digital competition depends on states’ capacity to orchestrate scalable, multistakeholder ecosystems. Using a cultural–institutional framework, we explain how differences in strategic culture and institutional governance impact the ecosystem’s vitality and performance. A qualitative comparative analysis of the United States, China, and Russia reveals that constructive orchestration, aligning state institutions with generative, commercial-to-national innovation flows, enhances digital leadership, whereas rigid, obstructive governance limits it. This highlights ecosystem governance as a critical dimension of statecraft in the digital age. The findings underscore that the positions of great powers in the global technological hierarchy depend not only on resources or capabilities but also on the effectiveness of ecosystem governance as an evolving instrument of geopolitical power.

1. Introduction

Diverse IT infrastructures and digital technologies have become fundamental for military, political, and economic power in the contemporary great power rivalry. The capacity to innovate in this digital landscape, especially in artificial intelligence (AI), has become a central determinant of power and influence. Political leaders often echo the narratives of technological supremacy from the Cold War, underscoring the strategic importance of AI and associating achievements with overall power distribution and supremacy, much like nuclear weapons, missiles, and space technology were perceived during the Cold War (CW).

For example, Vladimir Putin’s 2017 famous statement that “the country that leads in AI will rule the world” [1] directly aligns with the narrative developed by the U.S. at the beginning of the space age in the early 1960s, that the country that controls space will rule the world [2]. In October 2018, Xi Jinping said that China must “ensure that our country marches in the front ranks, when it comes to theoretical research in this important area of AI” [3]. In 2019, the American Secretary of Defense, Dr. Mark Esper, expressed a similar approach when referring to the competition in the field of AI as the space race… “Whoever gets there first is going to dominate” [4]. Speaking at the Artificial Intelligence Action Summit in Paris in February 2025, Vice-President J.D. Vans said that “the United States of America is the leader in AI, and our administration plans to keep it that way” [5].

Nevertheless, we argue that contemporary great power tech rivalries considerably differ from previous ones because they have shifted from singular military-industrial to generative digital-innovation rivalries. Thus, they are conducted through different agents, pose different threats, and result in power transitions. We use two cartoons, one from 1962 and another from 2019, to illustrate these differences and generate the puzzle and research question from these differences.

The first cartoon [6] was published by the Daily Mail in 1962, while the second [7] appeared in the Financial Times in 2019. Published in Western media, the two cartoons represent a Western perspective using the same narrative of arm wrestling to describe world politics as a definite competition between two giant superpowers. Arm wrestling is a popular exercise to demonstrate the superiority of one player over another. In an analogy to world politics, it represents a power struggle, with each side trying to outmaneuver the other. In the 1962 cartoon, the leaders of the U.S. and the Soviet Union, representing their states, compete while sitting on hydrogen bombs and threatening to push the button that would launch an attack on the other side. The 2019 cartoon depicts two robotic arms, not specific leaders, whose sleeves bear the national flags, thus symbolically representing the U.S. and China as wrestling nations. Nevertheless, they do not threaten each other.

It is essential to note that cartoons often depict certain perceptions of reality rather than an exact representation. These two cartoons illustrate the prominent role of technology in these two competitions, highlighting several differences. First, in the 1962 cartoon, the two superpowers compete for and directly threaten each other with military capacity and the ability to use weapons of mass destruction. In the 2019 cartoon, the struggling robotic arms indicate that digital technology is central to the competition. Still, its actual use and threat are undefined. The second issue concerns the competing agents. In the 1962 cartoon, the competition is held directly between the two leaders. Technology is at their service. Conversely, in the 2019 cartoon, the competition is between two robotic arms. The identities of the agents through which competition is held remain vague.

The third issue concerns the power balance and the competitors’ identity. The 1962 cartoon’s competitors were the U.S. and the Soviet Union. In the 2019 cartoon, the U.S. competes with China, not Russia, the successor of the Soviet Union. Furthermore, in the 1962 cartoon, the U.S. and the Soviet Union are portrayed as equal nuclear powers. In contrast, the power balance appears unequal in the 2019 cartoon. The American robotic arm is thin, speaking to the growing American concern about its relative inferiority compared to China, whose arm is muscular. Nevertheless, the American arm holds on. Based on this representation, we ask why and how this shift occurred. More specifically, why is Russia absent, and why does China rival the U.S.?

To answer these questions, we advance a threefold argument. First, the digital technology landscape encompasses a broad range of generative technologies whose innovation process primarily occurs outside government institutions, in a domain that, in Western society, is mainly associated with the private sector, characterized by a spin-in flow dynamic [8]. Therefore, digital power is no longer derived solely from state capacity, organized in a state-centric structure, but from ecosystem performance, in which innovation flows from commercial to national stakeholders and vice versa. We define digital innovation ecosystems as dynamic, multistakeholder environments composed of interconnected public, private, and academic actors who collectively generate, diffuse, and scale technological innovation. Unlike traditional R&D systems, these ecosystems are characterized by distributed ownership, decentralized innovation flows, and mutual interdependence among stakeholders.

Second, innovation ecosystems have become more than drivers of economic value. They are strategic for national power. Third, national ecosystem governance shaped by cultural–institutional configuration influences their ecosystem’s vitality, and consequently, their national power performance. In other words, countries’ abilities, through their state institutions, to orchestrate an ecosystem impact their overall ability to convert digital innovation into strategic advantages. States’ ecosystem orchestration refers to the role of state institutions in shaping this landscape, not through direct control or command, but by aligning incentives, establishing regulatory frameworks, and coordinating interdependencies to advance national strategic objectives. Orchestration differs from coordination, which implies shared authority, and from governance, which can be broader or more passive, by emphasizing active, purposive alignment of diverse innovation actors around strategic outcomes.

As such, ecosystems should be treated as strategic political structures through which technological rivalry is conducted. State institutions provide the framework, policies, resources, and other mechanisms necessary to constructively build scalable, sustainable, and prosperous technology ecosystems, shaping states’ abilities to compete and lead in a rapidly evolving global order. Our analytical framework suggests that national strategic culture shapes the environment in which these institutions operate, both within and toward their local tech ecosystems, in either a constructive or obstructive manner. Ecosystems are more likely to prosper when the national cultural–institutional characteristics support innovation originating in the private sector. Conversely, ecosystems will encounter difficulties when the national cultural–institutional characteristics are obstructive toward innovation from the private sector.

State institutions are constructive when they act as enablers, prioritizing the ecosystem over their selfish interests, such as securing the regime, and working collaboratively with the private sector to encourage innovation. For example, providing necessary infrastructures, policies, and regulations that favor market freedom, risk sharing, providing resources, adopting emerging technologies, etc. State institutions are obstructive when they conflict with the private sector in these areas, for example, overregulation, risk-averse policies, inadequate infrastructure provision, prioritizing selfish objectives over systemic ones, and focusing on short-term gains over long-term stability and sustainability.

We suggest that this culture–institution axis can explain the perceived lead of the U.S. and China as its key rival, as well as Russia’s relative lag, in the digital landscape. To support this claim, we draw on the work of Minkov and Kaasa [9], who, building on the Minkov-Hofstede cultural dimensions model, identified the U.S., China, and Russia as representing three distinct cultural–institutional types. We argue that these typologies plausibly explain each country’s approach to its local innovation digital ecosystem, which corresponds with key technological innovation and ecosystem development indicators.

Our core contribution is to conceptualize the argument that digital innovation ecosystems function as strategic institutions of power in contemporary world politics, through which states convert technological capacity into strategic advantages, and to theorize how national cultural–institutional configurations shape their governance and geopolitical impact. Accordingly, effective ecosystem governance has become a critical dimension of statecraft: states whose cultural–institutional frameworks align with generative, cross-sectoral innovation are better positioned to sustain digital leadership and shape the global order.

The article has four sections. The first section situates digital innovation ecosystems within the literature on great power rivalry, strategic culture, and state–private sector relations. The second section presents a cultural–institutional framework to explain how ecosystem governance shapes national power. In the third section, we apply this framework through a comparative analysis of the United States, China, and Russia, examining indicators of technological leadership and how different cultural–institutional types influence each country’s approach to ecosystem orchestration and performance. The article concludes by discussing the implications for the evolving global balance of technological power and the role of ecosystems as strategic institutions in international relations.

2. Literature Review

This research builds on and contributes to several fields of study, including emerging technology and great power politics, private–public sector relations regarding innovation ecosystems, and strategic culture.

2.1. Strategic Technologies and Great Power Politics

Scientific and technological achievements have been directly linked to powerhood since the end of WWII (Second World War), driving tech rivalries among the great powers. International Relations literature examines the way states harness strategic technologies for their military and economic considerations, as well as establishing themselves as powerful actors, influencing world politics, while shaping the distribution and balance of power [10,11,12,13,14,15,16].

In this context, the past decade has seen growing academic attention to digital or emerging technologies in world politics. Some of the studies identify the evolving new technological reality as a Fourth Revolution, calling for greater academic attention to these technologies in International Relations [11,17,18,19,20,21,22,23]. Other scholarly works provide more in-depth accounts in several areas. One area of research focuses on the use of digital technologies in great power politics. In this context, the bulk of the research focuses on China’s path to achieving great power status and the role attributed in Chinese strategy to technological advancement [24,25,26,27].

2.2. Emerging Technologies and Military Transformations

Others investigate the implications of Chinese technological progress on the status, leadership, and consequent strategy of the US [28,29,30,31]. Another area of research corresponds with the existing literature on the potential effects of technological advancement and innovation on military activity and warfare. Much of the debate focuses on evolution vs. revolution in military affairs [32,33,34,35,36,37,38,39], on the implementation and consequences of cyber warfare, and on the potential effects AI [40,41,42] and Quantum Computing [43] technologies will have on military power, identifying opportunities and challenges [44]. Despite these valuable contributions, further advancement of research in world politics regarding the politics of these technologies is needed, especially considering the effect on power distribution and its causes.

2.3. Public–Private Interdependence and Innovation Ecosystems

Relatedly, over the past two decades, a growing body of IR literature has recognized that states and their defense communities assign more functions to private sector entities and rely on their open innovation due to their growing expertise in specific areas, budget constraints, and greater efficiency resulting from a start-up organizational culture [19,45,46,47,48]. The discussion of these developments highlights a shift, blurring conventional distinctions between public and private or state and non-state actors, toward networks or ecosystems of public–private partnerships [49,50]. Nevertheless, this strand in the literature focuses on ecosystems as an economic phenomenon, or on the decentralized nature of these networks of firms, universities, government agencies, and others that interdependently collaborate to produce and diffuse innovation [51,52,53]. This framing underestimates the extent to which such innovation ecosystems now function as strategic or political institutions for power-building, i.e., mechanisms through which states convert technological capacity into enduring power. This is particularly so in the case of generative emerging technologies, such as AI, where global influence depends on the scale, speed, and systemic integration of innovation.

We advance these discussions by highlighting the strategic interdependence between state institutions and commercial private sector actors, because a state’s power depends on the vitality of its private sector. Consequently, the scale and performance of private sector commercial actors, especially in AI, are evaluated as indicators of national capacity and power. In line with this reality, state institutions are experiencing a shift in their roles and responsibilities. Instead of directly and actively developing capacities, they are tasked with orchestrating their ecosystems to advance their vitality for greater national power and competitiveness.

2.4. Strategic Culture and Institutional Behavior

Consistent with theories of hierarchical governance in IR [54,55,56], we argue that although innovation ecosystems have a decentralized networked structure, the hierarchical structure remains but is reconfigured. State institutions are shaping conditions for innovation instead of micromanaging every component for direct accumulation of assets under government control. They facilitate, harness, or redirect innovation toward their geopolitical objectives by investing in core infrastructures, setting regulatory and ethical boundaries, defining long-term strategic goals, and creating incentive structures that align innovation activity with national interests.

Consequently, ecosystem management became a new dimension of power and leadership and a core pillar of the twenty-first-century grand strategy of great powers. In this context, Matthew Kreonig’s work [57] on democratic vs. autocratic institutions in contemporary great power rivalry is noteworthy, although it does not deal directly with assessing countries’ ecosystem management. Kreonig argued that historically, great power competition was determined by the function of domestic institutions to achieve military, economic, and diplomatic power. Therefore, domestic institutions are the crucial explanatory factor over culture or other factors, and democratic institutions are better than autocratic ones. According to this logic, Kreonig argued that the American democratic system is an advantage in current contemporary great power competition because it provides the U.S. with a stronger and more efficient economy, diplomacy, and military. To support his argument, Kreonig used the example of the two Koreas: “Countries with good economic institutions, like those found in South Korea, have higher long-run rates of economic growth. Whereas countries with poorer economic institutions, such as North Korea, suffer from lower growth rates” ([57], p. 19).

We offer a more nuanced perspective that accounts for long-term cultural factors and the interaction between public and private sectors in shaping national institutional systems. A common visual metaphor, the nighttime satellite image of the Korean Peninsula, illustrates this complexity. South Korea appears brightly illuminated, while North Korea remains in darkness, seemingly affirming the developmental advantage of democratic, liberal institutions. Yet, expanding the view to include China complicates this narrative: despite being autocratic and non-liberal, China resembles South Korea far more than North Korea in terms of visible development. This suggests that differences in economic advancement cannot be attributed solely to political–institutional systems.

Another example that challenges the explanatory power of institutional systems alone, and points to a deeper cultural dimension, is the case of East and West Germany. During the Cold War, the Soviet Union established a socialist state in the East, while the U.S. supported a market economy in the West through the Marshall Plan. Following reunification, Germany became institutionally unified. Yet, more than thirty years later, notable economic and social disparities persist between East and West, suggesting that enduring cultural differences, rather than institutional ones, continue to shape developmental outcomes. Therefore, we advance the discussion concerning variance in ecosystem advancement and performance by suggesting a cultural–institutional framework.

2.5. Cultural–Institutional Perspective on Ecosystem Power

When discussing (strategic) culture, we follow the popular definitions applied by socio-economic and political research, understanding culture as a set of traditional beliefs and values used by social, ethnic, or religious groups for generations [58,59]. Edward Hall divided [60] cultures according to their mode of communication regarding interaction and engagement among different actors and agents, ranging from high-context (most information is communicated implicitly) to low-context (almost everything is communicated explicitly). Hall’s division largely overlaps with traditional vs. modern cultures [32], which is often used to analyze variance in countries’ strategic cultures.

Hofstede presented [61] another fundamental approach in which he broke culture into four separate dimensions: (1) dependence on superiors, (2) need for rules and predictability, also associated with nervous stress, (3) the balance between individual goals and dependence on the company, and (4) the balance between ego values and social values. Mikhael Minkov, Hofstede’s co-author and critic, later suggested a simplified two-by-two model with individualism–collectivism (the extent to which societies prioritize individualism over group cohesion) and monumentalism–flexibility (long-term fixed view vs. more adaptable and change-oriented short-term view) axes [9]. The primary argument was that subjective cultural perspectives along these axes shape patterns of social behavior at the national level.

Extensive literature, including Bonetto et al. [62], Gallego-Alvarez and Pucheta-Martinez [63], and Jourdan and Smith [64], utilizes these models to examine the relationship between specific innovation and social development indicators and organizational and national culture metrics. The model by Inglehart [65] includes two dimensions reflecting cross-national polarization between traditional versus secular-rational orientations, and authority and survival versus self-expression values. Büschgens et al. [66] demonstrate that flexible, outward-looking organizational cultures strongly support innovation, highlighting how shared values act as informal control mechanisms. Boubakri et al. [67] demonstrate that national culture has a significant impact on firm-level innovation outcomes across countries. We extend these insights from the organizational level to the national ecosystem level.

Security studies scholarship did not consider culture an important factor in shaping national security concepts and strategies until the end of the CW. The explanatory force of culture on power distribution and other security-related issues has become more broadly investigated in the last two decades [68]. Adamsky [32] was among the pioneers who provided in-depth research on the impact of strategic culture on military tech innovation. Adamsky investigated how different strategic cultures influenced the way new technologies were adopted by the U.S., the USSR/Russia, and Israel in the 1980s–1990s, leading to the Revolution in Military Affairs (RMA). Adamsky used Hall’s [60] division of cultures according to communication style (high context vs. low context) and time orientation (polychronic vs. monochronic), adding Hofstede’s metrics of social structure (collectivistic vs. individual) [61] to demonstrate RMA features in “individualistic US” and “collectivistic Russia”. In a more recent work on Russia, Adamsky defined (strategic) culture as “A set of shared values, norms, beliefs, assumptions, and narratives, which shape and sometimes determine the collective identity, instincts, and modus operandi of a given strategic community in its approach to questions of peace and war” [69].

Minkov and Kaasa also argued [9] that the “Minkov-Hofstede revised model” captures consistent cultural dimensions across national units concerning social practices and institutional mechanisms. Based on this two-by-two model, they identified different national cultural types and examined various countries. According to their work, each of the three great powers explored here exemplifies a different cultural type.

The U.S. is identified as an “Individualist-Monumentalist Culture” characterized by strong national pride and fixed values, among which independence and assertiveness are significant, and socioeconomic success is strongly associated with competitiveness. China is identified as a “Collectivist-Flexible Culture” that values group cohesion and cooperation. It features a mix of tradition and modernity toward economic progress and is open to change. It emphasizes self-improvement while aiming for social harmony in a work culture that combines pragmatism and discipline. Interestingly, South Korea is also considered in this group. Finally, Russia follows a “Collective-Monumentalist Culture” type, which is highly group-oriented with strong loyalty, adherence to tradition, and emphasis on hierarchy and authority. This type also features conservative social values, conformity over independent thinking, and relative resistance to rapid change. Our work fits within all these existing scholarships by exploring approaches to digital tech innovation and ecosystem structure based on national-cultural types and their effect on institutional orchestration.

Given the gradual and long-term nature of cultural change, it is puzzling why countries’ relative positions do not remain more or less stable when developing in similar areas, such as key technologies. For example, in the CW tech rivalries, the Soviet Union was a prominent leader, and until the end of the 1960s, it helped China build an industrial economy. In the first decade of the space race, the USSR accomplished significant achievements, surpassing the United States. However, by the end of the CW, its technological prominence had declined in many fields. Furthermore, in contemporary digital tech rivalry, Russia, to a certain extent, a shortened heir to the USSR, is not only absent from the first ranks but is also integrating into China’s technological sphere of influence.

Therefore, we argue that while cultural characteristics provide a deep context for institutional transformations, the institutional orchestration and interplay between stakeholders regarding innovation direction flow are significant in shaping countries’ operational approach for national innovation and ecosystem development. We assess that there is a gap in the literature between well-developed research on the cultural specifics and practical application for techno-strategy and social science methodology to explain the national development trajectories. Thus, we suggest using a cultural–institutional approach to explain variance in innovation ecosystems’ vitality in the digital landscape, and consequently, tech rankings and power distribution.

3. Innovation Ecosystems as Strategic Institutions of Power in the Digital Landscape—Theory and Practice

3.1. Development of the National-Commercial Interplay

The Cold War and current digital tech rivalries differ in the types of leading technologies, which also shape their innovation directions, moving from the public sector to the private sector or vice versa. In his work on “Innovation Power” [70], Eric Schmidt explained the difference between contemporary and Cold War tech rivalries by differentiating between singular and generative technology. Singular technologies [S-technologies] have a “clear threshold of technological mastery, and once a country reaches it, the playing field is leveled”. Furthermore, S-technologies do not contribute to further advancing innovation power. Conversely, digital technologies, primarily artificial intelligence, that have become the forefront of current tech rivalries are generative in nature [G-technologies]. They are “a platform for continuous scientific and technological innovation [and] can lead to yet more innovation”. In contemporary world politics, Schmidt wrote, “the source of a country’s power lies in its ability to continuously innovate”, and do so “faster and better than its competitors”. Therefore, advancing AI is critical for competition over innovation power, concluded Schmidt.

Cold War tech rivalries primarily focused on S-technologies, including nuclear, space, and the strategic triad of ICBMs, submarines, and aviation. They were characterized by their physicality and reliance on heavy industrial infrastructure. Developing and maintaining these technologies required substantial investment in manufacturing capabilities, materials science, and large-scale engineering projects. Moreover, developing nuclear weapons and ICBMs demanded expertise in atomic physics, rocketry, and complex systems engineering, driving competition among great powers to attract top scientific talent and establish comprehensive military-industrial complexes (MICs). Nations needed robust industrial bases and infrastructures, extensive research and development programs, and significant financial resources to pursue advancements in these areas, which had little (if any) commercial potential. Even with dual usage for military and civil applications, most technologies did not have commercial applications. Thus, even when developed by industry, they were for national use [19].

Digital technologies, at the forefront of contemporary tech rivalries, rely on information processing, software development, and networked systems, fundamentally altering the nature of innovation and the requisite national capacities. As a result, success in this domain centers on intellectual capital, agility, and adaptability rather than sheer industrial might. Moreover, the interconnected nature of digital technologies necessitates collaboration among all stakeholders, including those from the private sector, public sector, academia, and international partners, to stay at the forefront of advancements. Therefore, as G-technologies are mostly civilian and commercial, they are primarily developed by private sector entities, making rivalry more economically based than militarily based. We theorize two primary innovation flow pathways in this context, each reflecting distinct governance structures, objectives, and relationships.

The first is a National-to-Commercial pathway. It reflects a traditional, state-led innovation model wherein the government is the primary architect of research and development (R&D), particularly of dual-use S-technologies. Governance structures under this model are highly centralized, with state agencies directing innovation priorities, allocating funding, and retaining ownership and control over critical technologies and infrastructure. In-house R&D efforts are preferred, and outsourcing is conducted under strict oversight. The private sector plays a secondary role, often limited to executing government-defined objectives as contractors or subcontractors. It heavily depends on the public sector instead of market forces. Government investment or prioritizing decisions in this pathway are typically guided by strategic national interests, especially in domains where market incentives are weak or where technologies remain in nascent or high-risk stages. As such, innovation is tightly linked to national security goals, aligning with state interests and advancing national capabilities, limiting undesired proliferation. This pathway emphasizes strategic and political leadership and competition. Under this pathway, adaptation to broader economic and technological shifts is expected to be slow and limited. The 1962 cartoon, in which American and Soviet leaders wrestle while sitting atop H-bombs, is a good artistic reflection of the tech-rivalry dynamic under a national-to-commercial pathway over S-technologies.

In contrast, the second pathway, which we refer to as Commercial-to-National, exemplifies a more open and market-driven innovation model shaped by ecosystem dynamics and public–private interdependence. Here, governance is more decentralized and collaborative, relying on partnerships that allow private entities to lead innovation efforts. Governments serve as enablers—creating favorable regulatory conditions, offering investment incentives, and acting as anchor customers or venture partners. The ownership of technological assets primarily resides in the hands of private actors, although the benefits often spill over into national strategic objectives. This pathway emphasizes scalability, global competitiveness, and economic leadership, aligning with the logic of value creation and technological power originating from the private sector. Investment is usually guided by market logic, with financial profitability and innovation potential attracting private capital.

Under this decentralized ecosystem structure, governments are customers of the private sector. They integrate commercially available products, such as artificial intelligence, cloud computing, and big data analytics, for state use, while companies retain ownership and set development priorities. They benefit from cost-sharing and rapid technological advances, but risk reduced control over strategic assets, oversight, and interdependence, which raises different security concerns. However, state institutions are not regular customers. Using regulatory frameworks, procurement policies, and incentives, they have the ability, and even the responsibility, to adopt a holistic perspective, prioritizing systemic objectives for advancing and scaling the ecosystem’s vitality. Thus, they serve as orchestrators and facilitators of the innovation scene, aligning private innovation with broad national objectives.

A key distinction between the two pathways concerns dual-use technologies, which have both civilian and military applications. In the National-to-Commercial model, such technologies are typically developed under state control to ensure strategic oversight and security, especially on sensitive technologies, with civilian uses emerging as secondary applications of defense-led innovation. Under these circumstances, the dual use of sensitive technologies presents a challenge rather than an opportunity. By contrast, the Commercial-to-National pathway, characterized by decentralized ecosystem governance, frequently sees dual-use technologies emerging from the commercial sector. Here, the dual use of sensitive technologies presents both opportunities and challenges [71]. Thus, governments have a vested interest in fostering spin-in innovation, adapting private-sector advancements for national strategic purposes, and in sustaining the vitality of the broader ecosystem as a means of securing access to cutting-edge capabilities. This shift from directive to facilitative governance reflects broader transformations in the exercise of state power in the innovation domain, which in turn shape international power distribution.

These pathways are dynamic and are not fixed. State institutions and private firms continually renegotiate their roles, shaping how innovation is directed, funded, and controlled in response to strategic, economic, and technological shifts. For example, the growing maturation of technology and the relaxation of geopolitical constraints in the post-Cold War era enabled American defense agencies to adopt commercial digital technologies [19,48,71,72]. Additionally, the rise of SpaceX illustrates the potential of shifting from a national-to-commercial to a commercial-to-national innovation pathway in the space technology landscape. However, growing security concerns might prompt renewed state oversight, as in semiconductors and cybersecurity.

Table 1. Innovation flow pathways.

Feature	National-to-Commercial	Commercial-to-National
Governance	State-led with centralized decision-making.	Decentralized, with public–private partnerships and ecosystem-driven governance.
Directing Innovation and R&D	Governments lead and fund R&D to achieve strategic priorities. Limited commercial input.	The private sector leads innovation, leveraging governments as facilitators, investors, and customers.
Government Objectives	Advancing national strategic capabilities and ensuring state control over key technologies.	Fostering scalable innovation for competitiveness, global economic leadership, and power.
Ownership of Assets	Primarily owned and controlled by governments.	Owned and operated by private entities, with shared benefits to national systems.
Role of the Private Sector	Limited to contractors or implementers of state-defined priorities.	Independent innovators and drivers of technological innovation (power).
Investment Logic	High government investment due to limited profitability or early-stage development risks.	Profitability and market potential drive private sector investments, with (minimal) state funding.
Government–Private Relations	Dependency of the private sector on state directives and funding.	Collaborative interdependence, where governments create and orchestrate enabling environments.

summarizes these two pathways through which innovation flows between the state and the commercial sector. It highlights them as two distinct frameworks for understanding how innovation is cultivated, mobilized, and institutionalized. The first is indicative of vertically integrated models, common in the early Cold War era, or defense-industrial systems. The second reflects contemporary configurations where innovation ecosystems serve as strategic infrastructures and where state power is increasingly exercised through facilitative, rather than directive, mechanisms.

The shift in pathways developed gradually in the aftermath of the Cold War, when the intensity of great power rivalries significantly decreased. It was time for increased interaction, cooperation, and economic and technological integration worldwide, including among previously rival great powers, such as the United States and Russia. In this strategic context, the development and utilization of digital information technology have accelerated, becoming a significant economic imperative and a force multiplier for military campaigns in Western, Chinese, and Russian militaries [31,32]. This process required investments in the force buildup of information technologies. Nevertheless, the decrease in the intensity of the great power rivalry in the 1990s reduced the ability to invest large sums of public funding in strategic technology development. For example, in the U.S., overall, government military R&D and procurement budgets experienced a reduction of about 30% [19].

The need to procure advanced technologies while working within modest budgets motivated government defense actors to consider the dual use of these technologies as an attractive option. They reasoned that collaborating with the private sector could potentially lead to more efficient and affordable technological build-up [19,48].

For example, in 1993, the then-U.S. Minister of Defense Les Aspin tasked [73] the military to “reaffirm the policy preference for the acquisition of commercial items and the use of functional performance specifications unless a DoD-unique product specification is the only practical alternative to ensure that a product or service meets users’ need” in parallel with facilitating reinvestment that allows defense industries to shift to nondefense production. Consequently, the dual usage of digital technology has evolved from a significant threat to national security into an opportunity, leading to greater commercialization [71]. Thus, digital tech innovation gradually became more aligned with a commercial-to-national pathway and innovation governance of an ecosystem structure.

Evron and Bitzinger [19] referred to this dynamic as military–civil fusion (MCF), emphasizing that it differs from traditional dual-use civil–military integration, which only aligns defense and civilian industrial bases. MCF is a process of “integrating the cutting-edge technologies into military products through joint, civil–military technological collaboration, starting at the earliest stages of products’ R&D”. Out of this dynamic emerged a “common ‘technology well’ to which both the military and civilian R&D bases contribute and from which both can draw”. The rapid pace of technological change in the digital realm required that interested countries institutionally embrace flexibility, openness, and risk-taking in their approaches to innovation, fostering entrepreneurship with their private sector to harness the full potential of these transformative technologies beyond state-controlled institutions.

The current phase of great power rivalry is often traced back to 2008, when China rose to power, Russia engaged Georgia in war, and the global financial crisis occurred. Since then, the rivalry has gradually intensified, gaining significant expression in the digital technology landscape. National power is strongly associated with countries’ capacities in the digital field, including commercial infrastructures developed, operated, and owned by large national corporations that often serve as proxies for the corresponding governments (Huawei, ZTE, Google, SpaceX, etc.). In this context, generative (commercial) AI technologies have been acknowledged as the top of the digital pyramid, responsible for the next industrial and military affairs revolutions [11,17,18,20,21,25,40,41,74].

G-technologies based on broad ecosystems and an innovation direction of a commercial-to-national pathway form the basis for developing solutions that support national security interests. Great power rivalries have changed accordingly. They take place between global commercial digital platforms. The winner of the current great power rivalry is defined by much broader G-technologies than just powerful military S-technologies of destruction and deterrence. National power based on such G-technologies cannot be achieved in classical government-driven R&D and procurement. Succeeding in today’s digital tech rivalry depends on the vitality of ecosystems, which vary not only in output or scale but also in how ecosystems are governed. Ecosystems are characterized by a complex interdependence between the public and private sectors, collectively driving strategic and economic advancements. The 2019 cartoon is representative of this phase.

Most ecosystem actors have individual goals and success metrics, such as entering new markets, expanding market share, ensuring survivability, maximizing their interests, and increasing influence. Nevertheless, state actors are not regular stakeholders. Although they are not in charge, they are prime organizing agents. In ecosystems, decisions and actions are made according to signals received and transmitted among different ecosystem actors [75]. The policies and actions of state institutions are essential signals for others. For this reason, their central role demands that they prioritize systemic outcomes rather than those of specific actors, including their own. It is their responsibility to advance wholesome success goals, such as the stability and prosperity of the system itself [52,55,76]. Systemic goal-setting requires complex governance, encompassing institutional design, cross-sectoral coordination, and the integration of sectoral concerns [77,78]. The conceptual and operational approach of state institutions to such complex governance, i.e., how they engage with, shape, and orchestrate the distributed networks of actors responsible for innovation, impacts the ecosystems’ performance and explains the national posture in the power politics of the digital landscape. We argue that countries that cultivate their national ecosystem, rather than prioritizing direct government objectives, would enjoy better performance and consequently a stronger posture in the digital balance of power.

In practice, indicators of a positive systemic approach include state institutions’ focus on identifying and developing the technological infrastructures and regulations needed for the private sector to flourish, thereby ensuring strategic self-reliance, even if owned by the private sector. Additionally, they could foster collaboration through public–private partnerships to reduce uncertainty, manage risks, and shape national and international standards.

Understanding why and how state institutions approach their local innovation digital ecosystems in a certain manner has a strong explanatory force concerning shifts in the balance of power. We argue that countries differ in their approaches to managing their digital ecosystems due to specific cultural and institutional features. In so doing, we lean on existing organizational-level literature on how national culture shapes corporate innovation outcomes, showing that cultural traits like flexibility, individualism, long-term orientation, and low power distance foster greater innovation performance [66,67]. To identify distinctive types, we lean on Minkov and Kaasa [79], who conducted a cross-cultural examination of a large number of countries using the Minkov-Hofstede model. They theorized distinct cultural–institutional types based on a two-dimensional model comprising Individualism versus Collectivism, which predicts societal outcomes tied to emancipation, liberalism, and the rule of law, and Flexibility versus Monumentality, which predicts long-term planning, education, and life-history strategies.

Building on these works, we argue that the institutional-cultural types defined by this matrix account for variation in the behavior of national state institutions. Each type represents a distinct configuration of cultural and institutional capacities that shapes how states govern their national innovation ecosystems. This configuration determines the extent to which state institutions are able, and willing, to foster resilient and dynamic innovation in digital ecosystems. The causal mechanism we propose links strategic culture to institutional preferences, which in turn shape the mode and quality of ecosystem governance. Cultural characteristics such as individualism or monumentalism influence institutional risk tolerance, openness to private-sector collaboration, and preferences for directive versus facilitative control. These institutional tendencies manifest in the orchestration of innovation ecosystems, affecting their adaptability, scale, and strategic utility. Through this lens, we demonstrate how deeply embedded cultural logics translate into institutional behavior, which then impacts the performance and vitality of such ecosystems. Thus, a state’s ability to leverage digital ecosystems for power projection.

More specifically, we hypothesize that the effect of a state’s institutional capacity on ecosystem performance is mediated by its position within the matrix of individualism–collectivism and flexibility–monumentalism: states characterized by high individualism and high flexibility will exhibit the most dynamic and innovative ecosystems; states with high collectivism and high flexibility will foster coordinated but moderately adaptive systems; states with high individualism and high monumentalism will produce elite-driven innovation with lower systemic adaptability; and states with high collectivism and high monumentalism will exhibit the least agile and least productive innovation ecosystems due to cultural emphasis on conformity, hierarchy, and value permanence.

Notably, we acknowledge the limitations inherent in applying broad cultural typologies. National cultures are not monolithic. Furthermore, overreliance on such typologies risks cultural determinism. Our framework aims to mitigate these concerns by integrating empirical ecosystem indicators and governance practices, treating cultural–institutional types as reflective lenses rather than deterministic categories.

3.2. Methodology of the Analysis

To examine this hypothesis, this study adopts a structured and focused comparison of the three great powers: the United States, China, and Russia. Each country represents a distinct cultural–institutional type in the Minkov–Kassa framework and differs in its ecosystem structure and governance, ranging from decentralized to centralized. Finally, the three also exhibit differences in the performance of their digital innovation ecosystems across key digital power indicators. By analyzing the three, we aim to explain the changing balance of power in the global digital landscape and order.

We employ a qualitative comparative analysis of national innovation ecosystem governance along three dimensions: the overall approach to spin-in innovation flows, the degree of institutional orchestration, and the mode of stakeholder interdependence. Orchestration can be directive, facilitative, or obstructive, while interdependence may be hierarchical, networked, or fragmented. These governance types were evaluated based on qualitative assessment of institutional behavior, policy orientation, and public–private dynamics. Specifically, we examined the presence of regulatory and fiscal incentives for innovation, openness to non-state actors in agenda setting, alignment of national strategies with ecosystem needs, and demonstrated outcomes in innovation scalability. Data were generated from official policy documents, global innovation indices, and secondary literature.

The analysis is divided into two sections. First, we assess the performance of these three countries. To empirically evaluate their performance, we draw on diverse, internationally recognized indicators of economic scale and innovation capacity, including GDP, R&D expenditures, AI rankings, patent applications, and ecosystem policies. These data are sourced from the World Bank, WIPO, OECD, and related databases, covering trends where available back to the 1990s.

Second, we analyze and compare the ecosystem structure and governance of each of the three to explain performance and strategic outcomes in the global digital landscape. Each case study is analyzed according to a consistent structure: the cultural–institutional profile, based on the Minkov–Kaasa typology; ecosystem governance model and innovation flow; and state-private sector interaction mechanisms. This comparative structure enables us to articulate how different configurations yield varying results in ecosystem vitality, thereby helping to account for the observed shift in the balance of power from the Cold War to the current digital age.

4. Comparative Look at Digital Power Indicators

This section examines the distribution of digital power across the U.S., China, and Russia using commonly employed indicators and their associated measurements to assess national capabilities and progress in the digital landscape, particularly AI. Importantly, our goal is not to develop a new ranking or comparative scale. Instead, we draw on existing studies that already offer comparative assessments and rankings in these areas.

To assess the performance of national innovation ecosystems, we rely on a combination of widely recognized indicators drawn from global databases and rankings. These include GDP and R&D expenditures (to capture overall resource endowment and investment intensity), the number of researchers (as a proxy for human capital), patent filings (reflecting knowledge production and innovation diffusion), and international AI indices (which benchmark technological leadership, research capacity, and commercialization). These indicators are measured consistently across countries and over time. We focus on the period from 1991 to 2023, during which digital power gradually became an integral part of the overall national power. Additionally, we examine specific rankings and indices related to the digital economy and AI to capture more targeted aspects of digital power.

These indicators focus on resource availability, whether from market or government funding, and the priority given to capacity-building. For example, military spending as a percentage of GDP signals the importance of security, while R&D spending as a percentage of GDP highlights the value placed on technological advancement. The allocation of R&D resources, from both public and private sources, depends on two main factors: the total volume of available resources, measured by metrics like GDP or budget, and the share of this total dedicated to R&D. Additionally, since people purposefully develop technology, “human capital” is a crucial national asset. Advanced fields, such as digital technology and AI, require a highly skilled workforce, whose cultivation is the responsibility of state institutions. This is measured by the number of researchers per million people.

While these indicators provide a useful comparative lens, we acknowledge their limitations in fully capturing the dynamic and qualitative aspects of innovation. Additionally, such cross-national indicators are subject to specific limitations, including underreporting, definitional variation, and methodological opacity. Authoritarian regimes may inflate capabilities or restrict data transparency, while AI readiness is measured differently across indices. To address these concerns, we triangulate multiple independent sources and treat trends over time as more indicative than single-year values.

Based on GDP, the U.S. remains the leading economic power among the three, maintaining a substantial lead despite China’s rapid growth, becoming second globally in 2010 and first in Purchasing Power Parity since 2016. From 2010 to 2023, China’s GDP nearly tripled, while Russia has consistently lagged at about one-tenth of the U.S. GDP. The U.S. also leads in R&D spending as a percentage of GDP, with China catching up and Russia falling behind, as shown in Figure 1. Likewise, the U.S. has the highest number of R&D researchers per million people, while China ranks lowest, though its large population partly explains this. Notably, Russia has led in military spending as a percentage of GDP since 2015, with China lowest on this measure.

Table 2. Development data based on the World Bank Statistics [80].

	1991	1995	2000	2005	2010	2015	2020	2021	2022	2023
GDP (current Trillion USD, 2022)
U.S.	6.16	7.64	10.25	13.04	15.05	18.3	21.35	23.68	26.01	27.72
Russia	0.52	0.4	0.26	0.76	1.52	1.36	1.49	1.84	2.27	2.02
China	0.38	0.73	1.21	2.29	6.09	11.06	14.69	17.82	17.88	17.79
Military expenditure (% of GDP)
U.S.	4.88	3.86	3.11	4.09	4.9	3.46	3.65	3.42	3.34	3.36
Russia	n/a	3.78	3.31	3.33	3.59	4.87	4.17	3.61	4.69	5.86
China	2.31	1.69	1.84	1.87	1.73	1.78	1.76	1.61	1.62	1.67
Research and development expenditure (% of GDP)
U.S.	n/a	n/a	2.62	2.5	2.71	2.79	3.47	3.46	n/a	n/a
Russia	n/a	n/a	1.05	1.07	1.13	1.1	1.09	0.96	0.94	n/a
China	n/a	n/a	0.89	1.31	1.71	2.06	2.4	2.4	n/a	n/a
Researchers in R&D (per million people)
U.S.	n/a	n/a	3479	3548	3549	3875	4452	n/a	n/a	n/a
Russia	n/a	n/a	3442	3224	3086	3110	2725	2676	2698	n/a
China	n/a	n/a	552	860	901	1165	1602	1687	n/a	n/a

Green = Leader; Orange = lowest performer; n/a = Data was unavailable.

compares these indicators for 1991–2023, with green highlighting the leader and orange indicating the lowest performer.

Despite these achievements, over the past two decades, the American share of worldwide R&D expenditures has decreased by approximately 10%, while China’s share has increased by about 15%, challenging U.S. leadership. Furthermore, if these trends continue, China is expected to bypass the US [81].

Although Russia ranks last in R&D and economic indicators, it has led in military spending as a share of GDP since 2015, with China last on this measure. Arms exports are another benchmark of military power: the U.S. dominates, while China ranks fourth behind the U.S., Russia, and France. From 2014 to 2018, the U.S. accounted for 34% of global arms exports, Russia for 21%, and China for just 5.9%. However, Russia’s arms exports dropped by 53% from 2019 to 2023 due to its military industry’s shift to domestic rearmament during the Ukraine war, reducing its share to 11% and placing it third, behind France, which is second, and before China, which remains fourth [82]. Notably, despite modest military spending and limited arms exports, China now outpaces Russia in the tech rivalry, highlighting a shift in the balance of power.

The data illustrates that while military power remains essential, it is not enough on its own to define global leadership today. Other factors, especially technological indicators, are equally vital, as shown by current tech rivalries. This underscores the need to examine digital and AI-related metrics as well.

Across various AI-related indicators, the U.S. and China are clearly the top two leaders, while Russia does not even rank among the top 5–10. See, for example, the International Digital Economy and Society Index (I-DESI) [83], which compares the EU’s digital-economic performance with 18 other countries using 24 weighted indicators. According to the latest 2020 report, the U.S. leads by a wide margin, while Russia and China rank lowest, with similar scores (see Figure 2).

One more key AI ranking is the Tortoise Global AI Index (TGAII), which utilizes 111 indicators across seven areas, Talent, Infrastructure, Operating Environment, Research, Development, Government Strategy, and Commercial, to assess global AI leadership. In its fifth update (September 2024), the U.S. remains the clear leader, followed by China, while Russia ranks 31st out of 83, between Brazil and Estonia [84]. Based on earlier Tortoise data [85], Figure 3 shows scores from 1 to 100 for each country across the seven categories for each country. This highlights Russia’s lag, mainly due to relatively low R&D, talent, and commercial scores. Additional R&D funding data and patent trends support this finding.

Stanford University’s AI Index team [86] publishes “The Global AI Vibrancy Tool” [87] that offers detailed weighted index scores for R&D and economic indicators. Based on 2023 data, the U.S. ranks first, followed immediately by China, while Russia ranks 29th.

Another indicator of digital ecosystem vitality and leadership is the number of patent applications submitted, especially those by non-residents, which suggests the ecosystem’s attractiveness. Trends in patent applications from the WIPO database [88] (see Figure 4) reveal three key points. First, Russia consistently shows low patent activity. Second, China experienced significant growth since the early 2000s, surpassing the U.S. in the early 2010s. Third, in the U.S., non-resident patent applications make up over half of the total, highlighting America’s innovation strength in attracting global talent and intellectual property compared to China.

Taken together, these rankings indicate a power shift, with China replacing Russia as the U.S.’s primary digital competitor. They also highlight the growing role of non-state commercial actors and research networks, marking a shift in how technological power is conceptualized and exercised. Commercial firms, academia, and talent pools now act as agents of national power alongside state institutions, exemplifying a commercial-to-national innovation dynamic. Technological leadership depends on the co-evolution of diverse stakeholders to generate knowledge, attract talent, and produce and scale breakthrough innovations. In this sense, innovation ecosystems function as strategic infrastructures of power.

5. Cultural–Institutional Country Profile Analysis

5.1. The U.S.: Distributed Orchestration Through Enabling Institutions

The American cultural–institutional framework exemplifies an Individualist–Monumentalist type. This type aligns with the United States’ strategic approach to its digital technology innovation ecosystem. The strong ethos of individualism favors personal ambition with low central control over innovation, resulting in a thriving and highly entrepreneurial bottom-up startup approach that promotes competition and tolerance of high risk-taking and fuels a disruptive innovation ecosystem. It also features a market-driven governance style that embraces this bottom-up approach. Conversely, monumentalism embodies confidence, high self-esteem, and resistance to constraints, which drive bold efforts for innovation but may also foster resistance to necessary adaptations or improvements.

In this distinctive cultural–institutional type, market mechanisms drive technological progress and adaptation, while state institutions focus on systemic goals, acting as catalysts for the tech ecosystem. Overall, the U.S. public sector plays an enabling rather than directive role. The U.S. thus represents a model of distributed ecosystem orchestration: state institutions deliberately avoid direct control over digital tech innovation but actively invest in infrastructure, research, and market incentives to sustain ecosystem vitality.

The roots of the U.S. digital innovation model trace back to the Cold War, when government agencies, like DARPA and NASA, laid the groundwork for core digital technologies, including the internet, satellite communications, and GPS. During this period, public-sector investments in mainly defense-driven technologies spurred breakthroughs that were later commercialized, demonstrating a national-to-commercial pathway [32,45,47,48].

With the end of the Cold War, America’s unipolar moment, and globalization, the U.S. approach shifted to a more commercially driven model focused on consumer technologies, scalable business models, and technological entrepreneurship. The 1990s dot-com expansion illustrates this shift, as internet diffusion, deregulation, and flexible capital markets enabled American digital firms to lead globally [45]. Nevertheless, the White House initiated proactive policies to boost private-sector growth [89]. By the late 1990s, even the defense sector recognized the attractiveness of commercial technologies and the need to keep the high-tech industry dynamic [90]. Over time, the system moved from state-led development toward purchasing innovations from the private sector [91].

Consequently, the American digital technology ecosystem is highly decentralized, comprising a dense network of small and medium-sized firms, leading global tech giants, strong universities, venture capital, and supportive state agencies. In this ecosystem, private-sector actors take the lead in scaling and commercializing breakthroughs, while state institutions intervene mainly indirectly by funding basic research, providing early-stage support through civil and defense channels, and creating incentives for R&D. They also maintain competition by coordinating industrial policy and regulatory oversight.

This approach gave rise to the iconic “Silicon Valley”, where talent, capital, and disruptive ideas converged. It fostered rapid innovation, market agility, and a deliberate avoidance of centralized control, leaving private actors to determine the course of digital evolution. Consequently, major technology companies such as Google, Apple, Amazon, and Microsoft operate independently, often setting global technological standards without direct government guidance or alignment with national (security) priorities [92,93,94,95,96,97].

In the realm of tech innovation, the U.S. has built a decentralized innovation governance model where private enterprises lead technological progress and state institutions play a limited yet supportive role focused on systemic goals. This model creates a dynamic environment where firms, universities, and VCs interact dynamically to generate cutting-edge innovation [32,45,47].

With this framework, U.S. state institutions concentrate on safeguarding and supporting the national private sector while boosting the responsiveness of public-sector entities. For example, government policies focus on identifying vulnerabilities in the U.S. manufacturing base and outlining strategies to enhance sustainability and sovereignty [98]. Additionally, protecting critical digital and physical infrastructure, emphasizing partnerships to enhance resilience across manufacturing and digital systems [99]; advancing manufacturing innovation and ensuring secure supply chains critical to U.S. sovereignty and economic sustainability [100]; and investing in domestic semiconductor manufacturing, which is essential for digital sovereignty and maintaining a sustainable manufacturing base in critical technology areas [101].

Recent initiatives, such as the 2022 CHIPS and Science Act [101], reflect a partial recalibration of this model. While still rooted in market-based innovation, the Act marks a more proactive federal role in safeguarding strategic supply chains, incentivizing domestic semiconductor manufacturing, and supporting foundational research. However, rather than signaling a return to directive governance, this policy exemplifies facilitative orchestration, whereby state institutions intervene to enable ecosystem resilience and competitiveness while preserving private-sector leadership in innovation development.

Vice President Vans’s words capture the proactive approach of American state institutions: “…the development of cutting-edge AI in the U.S. is no accident. By preserving an open regulatory environment, we’ve encouraged American innovators to experiment and to make unparalleled R&D investments” [5].

The United States maintains a competitive advantage in R&D investment and human capital over China. However, its model also introduces structural constraints, such as regulatory fragmentation, potential gaps in foundational R&D, and over-reliance on global (foreign) talent, which can hinder the ecosystem’s further development. As the global digital landscape evolves, the United States’ ability to balance market-driven dynamism with strategic and regulatory oversight will likely shape its long-term position in the international digital hierarchy.

5.2. China: State-Centric, Top-Down, but Flexible Hybrid Orchestration

China’s strategic approach to digital technology innovation is grounded in a cultural–institutional framework that Minkov and Kaasa describe as a Collectivist–Flexible type. This model combines a collectivist ethos with a flexible and adaptable governance, resulting in a state-centric, top-down, yet system-focused approach to execution. In the digital landscape, centralized state orchestration goes together with pragmatism and responsiveness. State institutions act as both coordinators and drivers of innovation, setting broad national objectives, designing policies, and ensuring their implementation through institutional networks. Thus, China’s hybrid innovation model combines state-led initiatives with private sector participation, fostering regulated competition in an environment of government support [27].

In contrast to the American model, where innovation is driven mainly by private-sector actors with limited state intervention, China’s approach emphasizes strong state coordination, hierarchical execution, long-term planning, and policy implementation. China’s state institutions tightly orchestrate the digital innovation ecosystem through a mix of state-owned enterprises, partially privatized firms with growing state ownership, and nominally private companies operating within clear policy boundaries. While this structure allows for some market competition, it is reinforced through state instruments, including subsidies, regulatory incentives, and public procurement to advance national strategic objectives.

A key feature of China’s approach is the Chinese government’s growing recognition of the private sector’s role in driving innovation and its proactive approach. For example, in July 2001, President Jiang Zemin declared that the party should formally accept private business owners [102]. Since then, policies have deliberately blurred the lines between the state and private sectors to foster domestic technology commercial champions. “From 2000 to 2019, the share of private firms receiving investments from state-owned enterprises or heavily reliant on state capital more than doubled, to 35% of all registered capital” [103,104]. This practice has expanded China’s digital ecosystem while preserving strong state oversight and control [27,105].

Several policy frameworks exemplify this model: (a) The Made in China 2025 strategy identified digital technologies, particularly AI, semiconductors, and smart manufacturing, as national priorities; (b) China’s 2017 AI Strategy, the world’s first dedicated national AI plan, frames AI leadership as a state objective, with state institutions playing a central role in resource allocation and direction [106]; (c) Xi Jinping’s 2020 economic strategy, known as the “dual circulation strategy”, emphasized bolstering China’s domestic market (internal circulation) while maintaining some external ties. This strategy sought autonomy in critical sectors to boost resilience and self-reliance in an increasingly uncertain global environment [107]. By generating domestic demand for IT solutions and regulating foreign market access, this strategy enabled the growth of domestic commercial competitors.

China’s success in building digital innovation power is also rooted in its ability to generate and harness vast big-data resources, a critical foundation for AI innovation. China’s large population and rapid digitalization, driven by government initiatives, have created an unparalleled data environment. Chinese state institutions have capitalized on this advantage by maintaining relatively loose regulations on data collection and use compared to other countries. This permissive approach enables firms to experiment and scale rapidly, giving China’s ecosystem a competitive advantage in data-driven innovation [108].

China’s posture in digital power must be viewed in light of its historical shift from industrialization to technological leadership over recent decades. Since the 1980s, China’s state institutions have pursued a “great international circulation strategy”, using surplus labor to become a global manufacturing hub [109]. Western investment and technology transfers were pivotal in this phase, facilitating industrial growth by transferring knowledge and expertise to domestic industries. Consequently, China built indigenous tech capabilities and developed global tech leaders, such as Huawei and ZTE, in telecommunications and hardware. This approach is often called the “Far East’s martial arts, using an opponent’s energy and movements against them”, turning Western tech advantage into a driver for China’s rise [110].

Another significant dimension of China’s state-driven strategy to nurture the local ecosystem to position itself at the forefront of emerging technologies is its investment in human capital. Traditionally, China’s economic advantage was its low-cost mass labor force, but it had deliberately shifted toward building a knowledge-intensive workforce by expanding the quantity and quality of STEM (science, technology, engineering, and mathematics) graduates. According to available data, China was producing approximately 4.7 million STEM graduates annually by 2016, nearly twice the total number of STEM graduates in India. Moreover, China surpassed the U.S. in STEM PhD production in 2007, with projections indicating that it would nearly double American numbers by 2025 [111]. This rapid growth marks China’s commitment to advancing indigenous innovation and maintaining competitiveness in critical digital fields.

China’s steady increase in R&D investment further reflects its commitment to advancing domestic innovation. From roughly 0.5% of GDP in 1996 to around 2.5% in 2020, China’s R&D expenditure trajectory highlights its transition from a low-cost manufacturing economy to a knowledge-intensive one. This level of investment exceeds Russia’s and is comparable to that of the U.S. [80], reflecting China’s long-term ambition for economic-tech leadership.

Finally, China’s hybrid orchestration model, grounded in strategic planning and institutional coherence, exemplifies the power of state institutions to direct markets, coordinate resources, and develop talent. This approach has propelled China’s rapid rise as a global digital leader and shows its adaptability to the commercial-to-national pathway. Yet, the same features that fuel this growth, centralized direction, regulatory flexibility, and scale, also pose risks, including possible stagnation in breakthrough innovation, tensions between regulatory stability and flexibility, and an over-reliance on applied technologies [27,105]. Nonetheless, China’s trajectory highlights how a collectivist–flexible strategic culture can serve as a powerful engine for digital transformation.

Notably, despite its impressive digital rise, China’s model also faces critical constraints. State involvement, which has been particularly heavy in recent years, may deter foreign investment and international trust, especially amid concerns over IP protections, censorship, and surveillance, issues that have already been raised against China. Additionally, China risks compromising its independent innovation, especially in cutting-edge fields that require academic and entrepreneurial freedom, particularly under growing political pressure under Xi Jinping’s leadership. These factors could ultimately limit China’s ability to sustain breakthrough innovation and build global standards leadership in the long run.

5.3. Russia: Centralized Control with Limited Ecosystemic Orchestration

Russia is characterized as a Collective–Monumentalist culture type, which underpins its state-controlled approach to technological innovation, prioritizing strategic and defense state capacities over the development of a broad ecosystem. This aligns with a national-to-commercial pathway, where innovation remains concentrated in state institutions with limited openness to private sector involvement or broader ecosystem orchestration [112]. As such, Russia exemplifies centralized control with limited ecosystemic performance.

In practice, Russia relies heavily on state-owned enterprises (SOEs) as its main engines of innovation and modernization [112,113]. However, Makarchev and Wieprzowski argued [114] that under Putin, these SOEs have blurred the lines between economic and political functions, serving more as political tools than efficient economic entities. Monumentalism and collectivism further limit tolerance for bottom-up innovation, disruptive individual entrepreneurialism, or risk-taking, making commercial innovation slow and rare.

Russia has been largely resistant to shifting toward a commercial-to-national pathway and often actively obstructs this transition by maintaining tight centralized control, which restricts private sector growth and undermines entrepreneurship and innovation [112]. Consequently, Russia’s digital tech ecosystem remains small, with few national-level firms, and its dependence on foreign mass-market technologies persists, with a heavy reliance on China since 2014. This reliance reflects a broader geopolitical realignment. As Moscow deepens its integration into China’s digital technological sphere, through data-sharing agreements, digital hardware imports, and AI collaborations, it gradually relinquishes its autonomy. This growing dependency risks entrenching a bipolar global tech order, undermining the vision of multipolarity, positioning Russia less as an independent pole, and enhancing China’s influence.

In 2019, President Vladimir Putin declared “We have an entire state program aimed at developing AI, just like in China, the U.S., and some other countries. We are building up our efforts. We believe that we must certainly not be slow or lag behind, and we have every chance to excel in this area” [115]. Responsibility for this was assigned to major SOEs, including Sberbank, Rostelecom, Rostec, Rosatom, and Russian Railways [113]. Yet, despite such ambitions, Russia continues to lag in digital tech innovation overall, except in specific areas such as cybersecurity and selected software niches, which state institutions prioritize for their objectives.

Nadibaidze discussed [116] this gap between the Russian leadership’s narrative of AI prominence and its actual AI capacities. “The main plot of the AI narrative consists of the Russian state acting as the main protagonist, leading the country in the global AI competition, modernizing it, and strengthening its sovereignty in response to what are perceived as ‘hostile’ measures from the main antagonist: the West”. Nadibaidze also noted that while Russian state officials recognize the private sector’s role in digital tech innovation, they continue to prioritize the state’s leadership in making Russia a leading AI power. This reflects Russia’s “historical trajectory… towards technology, where technologies themselves are prioritized as symbols of sovereignty and great power status, as part of its quest for ontological security”. For example, in May 2023, Russia approved a plan to develop critical technologies, providing a basis for domestic high-tech production [117]. Development was expected to be executed through national “megaprojects”, including guaranteed demand from state corporations and a priori selection of the prime contractors employed by the state corporations for particular projects. National subsidies and regulations support such planned projects.

In summary, the United States has positioned its innovation ecosystem as a cornerstone of its geopolitical power, using its scale, openness, and absorptive capacity to maintain technological dominance. China’s model shows how top-down orchestration and adaptive institutions can build ecosystem strength through planning, experimentation, and responsiveness. In contrast, Russia relies primarily on state entities and the military-industrial complex, operating under strong central control, following a national-to-commercial pathway that lacks the interdependence, incentives, and institutional coherence necessary for a dynamic ecosystem.

Table 3. Ecosystem governance summarizing country analysis.

	U.S.	China	Russia
Cultural–Institutional Type	Individualist– Monumentalist	Collectivist– Flexible	Collectivist– Monumentalist
Innovation Style	Disruptive, entrepreneurial	Adaptive	State-driven
Drivers of Innovation	Private tech sector	Government + tech firms	State institutions
Risk-Taking	High	Moderate	Low
Role of the State	Minimal regulation Encourage entrepreneurship and competition	Heavy intervention, flexible toward entrepreneurship, and guided competition	Centralized control, limited entrepreneurship, and competition
AI ranking	1st	2nd	31st

summarizes key contrasts in innovation style, risk-taking, and the role of state institutions.

This comparison reinforces our central claim that the performance of digital innovation ecosystems is shaped not only by resources or state strategies but also by deeper cultural–institutional logics that govern how innovation is fostered, diffused, and scaled. States that align institutions with generative, multistakeholder ecosystems enjoy greater “national power” and are better positioned to shape the digital order.

6. Conclusions and the Way Forward

This article began with a puzzle central to contemporary world politics and IR literature, asking why the global balance of technological power shifted from a military-industrial rivalry dominated by the U.S. and the Soviet Union to a digital landscape where the United States and China lead, while Russia lags.

In the past, the technological great power competition focused on singular, military-industrial capabilities, developed through centralized, state-directed innovation systems. Innovation served a national security logic. In that context, even when private industry played a role, the state ultimately controlled the direction and output of innovation. Conversely, contemporary tech competition is shaped by generative, civilian-led digital capabilities that originate primarily within decentralized, commercially driven ecosystems. This transition redefines power not as the stock of state-owned capabilities alone but as the capacity to orchestrate distributed innovation flows that continuously scale technological advantage.

Thus, the digital balance of power today, i.e., the prominence of the U.S. and China while Russia lags, reflects more than just the scale of investment and state capacity, but also the dynamism and vitality of national innovation ecosystems. Therefore, to answer the question, we examined how cultural–institutional differences toward ecosystem governance influence the performance of digital innovation ecosystems, which are now critical for national power. We advanced the argument that managing to scale ecosystem-based innovation constructively is advantageous, while failing to do so bears strategic costs.

Our analysis compared the United States, China, and Russia, three great powers with distinct cultural–institutional types and governance models, to demonstrate how differences in ecosystem orchestration shape relative positions in the global digital hierarchy. The findings show that the U.S., with its decentralized, market-driven innovation model and enabling institutions, remains a global leader due to its absorptive capacity and dynamic private sector. China has rapidly converged by orchestrating an adaptive hybrid model, one that is state-directed yet responsive to private innovation and ecosystem scaling. Russia, in contrast, has struggled to transition from a control-based, military-industrial legacy to a generative digital model, constrained by obstructive institutions and rigid cultural logics.

This comparison highlights that cultural–institutional configurations explain the divergent trajectories of their ecosystems. The U.S. orchestration model demonstrated how individualism and institutional openness enabled a decentralized ecosystem where private actors drive disruptive innovation, while the state facilitates absorptive capacity and systemic stability. China’s collectivist–flexible model shows how strategic, adaptive orchestration can mobilize scale and speed in ecosystem building, blending top-down direction with market incentives. By contrast, Russia’s collectivist–monumentalist type, marked by centralized control and rigid institutional logics, hinders its transition toward a generative, commercial-to-national innovation dynamic.

Put this way, this article’s core contribution is to establish digital innovation ecosystems as strategic institutions in world politics and IR literature because they serve as mechanisms through which states convert technological capacity into power. In this light, ecosystem governance becomes a critical dimension of statecraft. States that align their strategic culture and institutional arrangements with the requirements of generative, cross-sectoral innovation (commercial-to-national) will be better positioned to sustain digital leadership and shape global rules and standards.

Several implications follow. First, the digital rivalry between today’s great powers is not merely a contest of resources, R&D spending, hardware capacity, or spectacular demonstrations; it is a contest of ecosystem governance quality: how states design, adapt, and orchestrate complex interdependencies among diverse actors. Second, our framework invites further IR research into how ecosystem vitality affects the balance of power dynamics and into the conditions under which states succeed or fail to institutionalize supportive innovation environments. Third, this perspective suggests that national security and technological competitiveness are closely linked to how effectively states enable private and public actors to create knowledge, adapt rapidly, scale new technologies, and ensure that the private sector’s knowledge and capacities align with national security considerations and objectives.

Normatively, our findings suggest that success lies not in controlling innovation, but in enabling it—through adaptive regulation, strategic infrastructure investment, and mechanisms that foster public–private trust. States should therefore treat the vitality of innovation ecosystems not merely as a technical or economic concern, but as a core element of national strategy. The Cold War’s centralized technological arms race has given way to a more diffuse contest, where the ability to orchestrate resilient and scalable innovation ecosystems determines which states can continuously renew and expand their power. For democracies, preserving openness while protecting critical sectors is especially vital. States that successfully master the art of strategic orchestration are better positioned to shape technological markets and influence the international order. Conversely, those that fall short risk losing their capacity to affect global outcomes. Recognizing innovation ecosystems as strategic institutions of power expands our understanding of how power is built, sustained, and contested in the digital age.

Future research can extend our findings in several directions. First, deeper within-case analysis, particularly of Russia, can refine the model by tracing long-term trajectories of technological power and unpacking causal mechanisms. Russia represents a historically complex case: from a largely agrarian and peripheral state in the early 20th century, it rose to superpower status after WWII but has struggled to sustain that position in the digital era. Further inquiry should situate the Soviet rise in its historical context. Second, broadening the comparative scope to include middle and emerging powers could help test and refine the cultural–institutional framework across diverse strategic environments. Third, applying the model to other technological domains may reveal sector-specific patterns of orchestration. Finally, interdisciplinary work at the intersection of international relations, innovation studies, and global ethics could shed light on how states’ approaches to ecosystem governance shape the evolving digital order.