## 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 compares these indicators for 1991-2023, with green highlighting the leader and orange indicating the lowest performer. Figure 1: R&D in GDP Dynamics [80]. 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].