Conference Agenda

It is acknowledged that great powers inevitably aspire to dominate different global topics. Topics like Artificial Intelligence (AI) have consolidated new perspectives on national and foreign policy strategies. The race for AI supremacy began several years ago. However, as AI advancements continue to transform industries, organizations, and strategies, there is a tendency to overlook the geopolitical implications of this ecosystem and its supply chains.

Some aspects of the AI ecosystem, such as data, algorithms, hardware, raw materials, and energy, are already defining several issues on the national security and diplomatic agendas of countries. These AI components define the implications of its development, usage, and diffusion.

A supply chain is a network that transforms inputs into outputs. This network conceptualization implies a relational approach where nodes and links interact, and several dimensions could be included. There is a growing interest in supply chains because price hikes, shortages of several goods, and tariff imposition affect economic growth and how businesses conduct operations. These phenomena, however, are a consequence of geopolitics. On the contrary, the supply chain of AI (the network that transforms minerals, data, microchips, energy, and work for developing and deploying AI) is a cause of global competition. However, there are no studies about the global implications of the AI supply chain to date.

So, in the age of AI, questions arise: What are the strategic supply chains? What does it mean for lagging countries and their organizations? To address these questions, we aim to analyze whether the race for AI supremacy is shaping two aspects: first, an AI gap among countries and their organizations, and second, an AI ecosystem, especially, hardware, energy, knowledge, and data centers. By empirically demonstrating the existence of the AI gap and its increase over time, this paper sheds new light on the implications of the AI ecosystem for different countries and their organizations. As a result, we provide evidence for the AI capabilities gap. It implies that only a few countries possess systemic AI capabilities, while many others will need to leverage them. Thus, AI methods are at the center of the AI ecosystem, serving as a tool for the advancement of organizations but also as a tool for power.

In this paper, we analyze four networks from different domains: the materials layer, the semiconductors layer, the energy layer, and the AI knowledge layer. However, the results obtained here also hold for a wide spectrum of layers that we can integrate into an AI ecosystem or another technological ecosystem that works with a supply chain pipeline. We first describe their network structure, namely, ordered graphs with the same vertices and similar degree definitions. Thus, each layer $L$ has the same number of nodes, $N$, as all countries are represented in each layer.