The G7 And The Social-Network Logic Of The AI Divide – Analysis – Eurasia Review
The G7 And The Social-Network Logic Of The AI Divide – Analysis – Eurasia Review
Publish Date: 2026-06-23 12:44:00
Source Domain: www.eurasiareview.com
Using an unordered list, summarize the following article with between 4 and 8 key points.
For three years, discussions about AI have revolved around the same topics: Will AI take our jobs? Can governments regulate high-performance models? Are machines smarter than humans? Academic discussions and policy reports are full of these questions, but a more important shift is taking place in the shadows and is being overlooked.
The global AI race is no longer just about technological innovation and regulatory debates, but about the fight for access rights. This shift is clearly evident in the recent AI discussions at the G7 Summit. While public attention continues to focus on the use of “generative AI” that generates Chatbots and text generation, the political focus has shifted.
Who will have access to cutting-edge AI capabilities? Under what conditions? In what political and institutional framework? This represents a fundamental shift in the political economy of technology. Traditionally, technological revolutions have spread through the market steam engines, electricity, automobiles, computers, the internet-any country with the means was able to acquire them. Although timing and scope varied, the underlying principle was almost exclusively economic in nature. If something is useful and economically viable, it will spread over time.
Artificial Intelligence behaves slightly differently. However, state-of-the-art AI systems are increasingly dependent on resources that are not readily available and not easy to replicate. Examples include state-of-the-art semiconductors, high-performance computing infrastructure, dedicated cloud environments, enormous amounts of energy, and large numbers of highly specialized engineers. Therefore, today’s ecosystem is not defined by “abundance”, but characterized by “bottlenecks”. From the perspective of traditional economics, these bottlenecks appear as “supply chain problems”.
From the perspective of social network analysis, however, something more important becomes clear: the emergence of “strategic nodes” within global networks. Network theory teaches us that influence does not necessarily lie with the largest actors, it often lies with those who hold the most advantageous position within the network. Actors who control bridges, Gateways, and connection points have enormous influence on the overall outcome. This perspective explains why current AI discussions often revolve around semiconductors, data centers, cloud infrastructure and trusted partnerships. State-of-the-art semiconductor production is dependent on a few companies and countries. High-performance AI accelerators are dominated by a limited number of vendors.
The AI ecosystem (size by betweenness centrality)
The development of next-generation models is concentrated on relatively few companies, and large-scale Training and operation require enormous data center capacity and constant energy requirements. Taken alone, each of these components is merely an independent industry. However, when viewed as a tightly intertwined network, they form an indissoluble “System of dependency” interconnected in a single structure. Within this system, not only ownership, but also how these connections are made, determines about success or failure Here, the recent G7 discussions become significantly more interesting.
On the surface, as always, they emphasize responsible AI, security and international cooperation, but behind them lie more strategic questions: Should access to advanced AI capabilities be controlled by alliances? What if access to cutting-edge AI technology was reserved exclusively for “trusted partners”? This means a radical break with previous technological eras. State-of-the-art AI would no longer function just as a commodity, but would develop into strategic infrastructure.
A comparison with the energy industry illustrates this reality. In the 20th century, geopolitical influence was closely linked to control of energy resources and transportation routes. Oil-producing countries, Pipelines, sea lanes, maritime borders-these “bottlenecks” were of great national interest precisely because they connected producers and consumers.
Today, a similar logic is emerging in the field of artificial intelligence. The decisive resource is no longer oil alone, but computing power. Although computing power is often discussed as a technical concept, its geopolitical implications are becoming increasingly clear. Training and operating advanced AI systems requires access to computing resources at a scale that few organizations can currently provide.
These organizations, in turn, are dependent on semiconductor manufacturers, cloud providers, energy systems and regulatory frameworks. Viewed in terms of networking, computing power acts as a “bridge node” that connects technological capabilities with economic, military and social outcomes. This change has significant implications for how countries define their national competitiveness.
Many governments have responded reflexively to the AI Revolution by asking, How to develop their own cutting-edge models But that may not be the right question. More importantly, the question is whether they occupy a strategically important position within the broader AI ecosystem. Some countries can exert influence without developing the world’s most advanced models. Instead, they can gain prominence by operating critical data centers, providing energy infrastructure, developing specialized AI applications, providing cybersecurity expertise, or participating in the Governance frameworks that govern access.
This perspective is particularly important for medium-sized nations. Southeast Asian countries such as Malaysia, Indonesia and Vietnam are not expected to dominate the development of forward-looking models in the near future. Their importance at the infrastructure level of the AI economy could increase. However, regional investment in data centers, The expansion of cloud infrastructure and the rapid growth of digital connectivity suggest that Southeast Asia could become an important hub in the global AI network.
The strategic value of such a position should not be underestimated. Analyses of social networks repeatedly show that actors who build bridges often exert more influence than would be expected due to their size alone. The same principle applies to nations. Countries that connect regions, industries and technology ecosystems are often more important than traditional metrics of economic power suggest. The impact extends beyond the economy. Artificial intelligence is increasingly linked to cybersecurity, defense, government, healthcare, finance and critical infrastructure.
Decisions about access to advanced AI systems can therefore not only affect the competitiveness of companies, but also shape national resilience and strategic autonomy. This is precisely why the concept of “trusted partner” should be examined more closely. At first glance, trusted partnerships may be technical or organizational arrangements, but in reality they can represent mechanisms for distributing access to strategic technologies. Membership in such networks can determine which countries receive early access to advanced technologies and which continue to rely on external providers.
As a result, a new form of geopolitical stratification is emerging. The border may no longer run between developed and developing countries, nor necessarily between democracy and authoritarianism. Rather, it is likely to run increasingly between countries integrated into large AI networks, and those, those who operate on the margins of these networks This possibility should prompt policymakers to rethink their approach to AI strategy The discussion is all too often presented as a race for technological hegemony-a framework that assumes that the primary goal is to develop the most advanced systems.
However, network analysis lays a different perspective. Influence may depend less on developing breakthrough models and more on securing an advantageous position within the structures that support them. For policymakers, this means investing not only in research and development, but also in infrastructure, energy systems, talent development, international partnerships, cybersecurity and Governance mechanisms. These elements determine how countries integrate into the broader AI ecosystem. For companies, it means recognizing that competition. Competitive advantages increasingly depend on participation in networks and less on isolated skills
Access to data, computing resources, partnerships and ecosystems could become more important than any single technology The impact on each of us will be significant Decisions on AI Governance are not just technical challenges; It’s about who will benefit from the next wave of technology and who will simply follow the decisions of others.
The history of the Industrial Revolution clearly shows how technological change has transformed economic and political forces. Artificial intelligence is likely to be no exception. However, what is changing is the way that power is exercised. The 20th century was dominated by energy, transportation and trade networks. The 21st century will increasingly be dominated by networks of computing resources, data and artificial intelligence.
About Dr. Sameer Kumar
Dr. Sameer Kumar has a PhD in Social Networking and is presently working as Associate Professor at Asia-Europe Institute, University of Malaya. He has over two decades of work experience in the industry and academia and also holds a PMP® (Project Management Professional) certification from PMI, USA. Dr. Sameer has been consistently ranked in the World’s Top 2% Scientists list (released by Stanford University and Elsevier) from 2021 to 2025 (present) – Field: Social Sciences.
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