The Deepening Dependency of the "Compute South" and South Korea's Dual Response Strategy
Executive Summary
As the AI value chain reorganizes around the United States—which has preemptively secured data centers, semiconductors, and power—small developing countries like Nepal and Palau are becoming locked in as mere consumers of AI capabilities. The structural burden of simultaneously suffering from the outflow of service fees and the costs of automation-driven job losses is particularly acute for developing nations with narrow fiscal bases, directly linking to the core issue of widening international inequality in emerging technology sectors. While South Korea, alongside Taiwan, has been classified as a beneficiary of the AI investment boom, it also faces recent signals of cooling investment sentiment and the risk of being reintegrated into a US-led technical standard and infrastructure system. Consequently, South Korea stands as a middle power that must navigate hedging between the US and China, rather than as a victim of the "Compute South." Therefore, policy responses should be designed on separate tracks: one focusing on ODA and digital divide cooperation to support developing countries, and the other on enhancing domestic infrastructure self-reliance by diversifying semiconductor and cloud procurement. Even when participating in multilateral digital cooperation channels, South Korea must preemptively assess whether such involvement will ultimately lead to its integration into US technical standards.
I. Analysis of the Current Issue
Concerns over the Deepening Dependency of the "Compute South": Analysis of the Current Issue
1. Background and Progress of the Issue
○ Nepal's The Kathmandu Post characterized the country as "a consumer, rather than a producer, of AI capabilities" [1].
○ It raised structural concerns, noting that "while the costs of AI services flow abroad, the domestic market bears the burden of reduced labor demand caused by automation" [1].
○ Nepal is a small developing country with a narrow fiscal base.
- It relies heavily on tax revenues from public sector employment and labor income in the service sector. - The core concern of local columns is that if automation reduces employment, the very foundation of national public finance could be shaken [1].
○ In a separate article, the same media outlet highlighted disruptions in education.
- It pointed out the bewilderment of teachers and the growing reliance of students on AI (plagiarism) [10]. - It reported on how traditional exam evaluation systems are failing to keep pace with the rapid spread of AI [10].
○ Similar concerns are being raised in Pacific Island countries.
- A Palauan media outlet cited a local activist stating that "the Pacific must not become mere consumers of AI systems built elsewhere" [15]. - The need to develop indigenous AI tools tailored to Pacific languages and cultures was raised [15].
○ This trend aligns with broader discussions on the digital divide in developing countries.
- Some view this as an extension of the North-South divide debate that arose during the early adoption of the internet and smartphones [3]. - The impact of the post-COVID-19 expansion of automation and smart factories on the development of developing countries is evaluated as mixed, with both positive and negative effects [3].
2. Current Situation
○ A structure where infrastructure gaps translate directly into AI capability gaps is becoming entrenched.
- The United States maintains an overwhelming lead, with 5,427 data centers and $335.2 billion in cumulative private AI investment [9]. - The focus of AI competition has shifted from model performance to securing data centers, semiconductors, and power [9].
○ Counter-intuitive approaches are also being attempted in Africa.
- Initiatives are underway to leverage the massive power demand of AI data centers as "anchor demand" for rural electrification [14]. - This represents a local attempt to turn exclusion from computing infrastructure into an opportunity to expand power infrastructure [14].
○ Investment concentration is also pronounced among emerging Asian economies.
- While Taiwan and South Korea have been the primary beneficiaries of the AI investment boom, recent signals indicate a cooling of investment sentiment [17]. - India has reportedly seen relatively limited investment inflows [17].
○ India views "diffusion and managing side effects" as a more immediate task than competing at the AI frontier.
- While calls to slow down AI development are spreading globally, India is operating under a different calculus [4]. - Analysis suggests that for India, which does not lead the frontier, managing the side effects of widespread adoption is a more urgent priority [4].
○ Within China, concerns are also being raised about the widening wealth gap driven by automation.
- Alongside the overheating valuations of humanoid robot companies, the potential impact on ordinary workers has become a subject of debate in academic economics [7].
3. Key Actors and Positions
○ Local media and intellectuals in Nepal, Palau, and elsewhere: Stakeholders and whistleblowers regarding the dependency structure.
- They simultaneously worry about the erosion of their national fiscal bases and the loss of cultural and linguistic sovereignty [1][15]. - They call for building domestic AI capabilities and securing control over their own data [15].
○ United States: The largest holder of computing infrastructure and the de facto standard-setter.
- Leveraging its overwhelming dominance in data centers and private investment, it encourages allies and partners to integrate into its technical standards [9]. - Domestically, local opposition over the power demands of data centers has become a political issue [14].
○ China: Seeking an alternative axis by building an independent ecosystem.
- It is pursuing an AI-driven economic transformation domestically, but faces concerns over overheating humanoid valuations and widening inequality [7].
○ Middle powers (Germany, India, Japan, the United Kingdom, etc.): Seeking a "third way" that avoids both direct competition and dependency.
- They lack the capability to build frontier AI independently, but are also wary of deepening dependency on the US and China [8]. - The Carnegie Endowment for International Peace has proposed a middle-power strategy focused on securing "breakout capabilities" [8].
○ International economic organizations (PIIE, IMF, etc.): Formally putting the risk of unequal distribution on the agenda.
- The Peterson Institute for International Economics (PIIE) assessed that securing AI benefits depends on each country's access to computing infrastructure, technology, and investment [2]. - The International Monetary Fund (IMF) has warned that even within the EU, the benefits and costs of AI will be distributed unequally across countries and social classes [6].
4. Key Issues
○ Asymmetry between cost outflow and job loss.
- A structure where service fees flow out to foreign platforms, while the costs of job losses due to automation are borne entirely by the local economy [1].
○ Concerns over the erosion of fiscal sovereignty.
- Observers point out that in developing countries reliant on tax revenues from labor income, the spread of automation could threaten the very foundation of national public finance [1].
○ Potential entrenchment of the infrastructure gap.
- Countries lagging behind in the race to secure data centers, power, and semiconductors will find it difficult to close the AI capability gap in the short term [9].
○ Structural concerns over being "locked in as consumers."
- Both the Nepalese and Palauan cases warn of the possibility that their countries will be permanently locked in as consumers rather than producers of AI [1][15].
○ Limitations of middle-power response strategies.
- Multilateral alternatives, such as a "Compute Coalition," also carry the structural risk of ultimately being reintegrated into the technical standards of specific major powers [9][13].
II. In-Depth Analysis of the Issue
Concerns over the Deepening Dependency of the "Compute South": In-Depth Analysis of the Issue
1. Root Cause Analysis
○ The physical structure of the AI value chain itself is winner-take-all.
- The focus of AI competition has shifted from model performance to securing data centers, semiconductors, and power [9]. - The economics of data centers dictate that the region that can bring chip clusters online the fastest gains a decisive advantage [13]. - The United States has widened the gap, boasting 5,427 data centers and $335.2 billion in cumulative private AI investment [9].
○ This asymmetry assumes a consumer nation from the design stage.
- Nepal has been positioned as "a consumer, rather than a producer, of AI capabilities" [1]. - While payments for AI services flow abroad, the costs of job losses due to automation remain domestic [1]. - The geographical separation of value creation and cost burden is the core of this structure [1].
○ The smaller the developing country and the narrower its fiscal base, the more direct the impact.
- For economies like Nepal's, which rely heavily on tax revenues from public sector and service industry labor income, the core concern of local columns is that job losses from automation directly weaken the national fiscal base [1]. - This issue is particularly acute not for developing countries in general, but for specific types of economies with narrow tax bases.
○ The delayed response of education and workforce training systems is also one of the root causes.
- Schools in Nepal are simultaneously experiencing teacher bewilderment and a growing student reliance on AI (plagiarism) [10]. - Traditional examination and evaluation systems are failing to keep pace with the rapid spread of AI, undermining the very pathways for capacity building [10].
2. Structural Context
2-1. Economic Structure: Concentration of Capital, Power, and Infrastructure
○ International organizations also share the outlook that AI investment and the distribution of its benefits will not be equal across countries [2].
- Whether a country benefits depends on how quickly its economy adopts the technology and whether it possesses computing infrastructure, skills, investment, and international connectivity [2]. - Countries lacking all of these conditions are structurally funneled into the "Compute South."
○ Within Asia, the uneven concentration of investment presents contrasting realities.
- While Taiwan and South Korea have been the primary beneficiaries of the AI investment boom, signs of cooling investment sentiment have recently emerged [17]. - India is assessed to have received relatively limited investment inflows [17]. - This suggests that countries unable to participate in the frontier competition risk being excluded from the benefits even during investment booms.
○ Power infrastructure has emerged as a new variable.
- In Africa, attempts are being made to leverage the massive power demand of AI data centers as an "anchor demand" for rural electrification [14]. - While this represents a local response to turn compute exclusion into an opportunity for expanding power infrastructure, the limitation remains that hosting data centers still depends on capital from advanced economies [14].
2-2. Political Structure: Hedging by Middle Powers and Exclusion of Developing Countries
○ Under the US-China bipolar system, even middle powers find it difficult to secure autonomy.
- Leveraging its dominance in data centers, semiconductors, and power, the United States is pursuing a "Pax Silica" initiative that effectively demands allies choose a side [9]. - China is simultaneously building its own ecosystem led by WAICO and promoting a discourse of cooperation within the G20 [9]. - The "Compute Coalition" initiative proposed by the Carnegie Endowment for International Peace also carries the structural risk of ultimately being reintegrated into US technical standards during its implementation phase [9][13].
○ The position of middle powers such as Germany, India, Japan, and the United Kingdom is also precarious.
- These countries cannot build frontier AI independently, yet they face the risk of decoupling if they rely on either the United States or China [8]. - The response of middle powers is limited to seeking a "middle path between the race and dependency" [8]. - With even middle powers facing such constraints, the bargaining power of small developing nations like Nepal and Pacific Island countries is virtually nonexistent.
○ The EU case demonstrates that even regional integration cannot resolve dependency risks.
- The IMF warned that while AI could boost European productivity by about 1% over the next five years, the benefits and costs will be unevenly distributed [6]. - A structural pattern is hardening where Europe lags behind the United States and China in the competition for data centers and power grids [6]. - The dichotomy pointed out by ECB President Christine Lagarde—"losing growth if technology is rejected, or becoming dependent on the US and China if accepted"—is a dilemma that even advanced economies find difficult to escape [6]. - Even the solution of completing integration across the 27 member states faces structural constraints due to the fragmentation of fiscal sovereignty [6].
2-3. Social Structure: Asymmetric Distribution of Automation-Driven Employment Shocks
○ Concerns over "jobless growth" are also being raised in advanced economies.
- Concerns about a structure where the economy continues to grow but the demand for new employment does not increase have been raised by Singaporean media [12]. - In Australia, a Treasury report pointed out that the "lay-off trap" poses a more realistic threat than the existential risks of AI [16].
○ In China as well, the widening wealth gap driven by robotics and automation is a subject of debate within academic economics.
- Along with the overheated valuation of humanoid robot companies, the potential impact on ordinary workers is being discussed [7]. - With concerns of this scale arising even in advanced and middle-income countries, the capacity of small developing nations with weak social safety nets to absorb such shocks is bound to be far more limited.
3. Historical Precedents and Comparative Cases
○ The current situation is widely assessed as a replay of the North-South digital divide debate from the information age.
- Debates surrounding the impact of internet and smartphone proliferation on the North-South divide in the early 21st century have accumulated over a long period [3]. - The impact of the post-COVID-19 spread of automation, smart factories, and e-commerce on the development of developing countries is assessed as a complex mix of positive and negative outcomes [3]. - This suggests that discussions on the digital divide are a recurring structural pattern at every technological transition.
○ Analogies are also drawn to the first round of globalization (manufacturing offshoring).
- Calls for pacing by leading companies (such as the CEO of Anthropic) and the immediate reactions of political leaders and communist parties worldwide show a pattern similar to the debates over the realignment of winners and losers during past phases of globalization [5]. - However, while past manufacturing offshoring served as a pathway for transferring production bases and jobs to developing countries, AI automation presents a reverse structure, keeping production in advanced economies while shifting only the costs (job losses) to developing nations.
○ Warnings from Pacific Island countries against becoming "consumers of externally designed systems" echo past debates over dependency on telecommunications and media infrastructure.
- A local activist in Palau warned that "the Pacific must not become mere consumers of AI systems designed from the outside," urging the development of regionally led AI tools, linguistic and cultural safeguards, and data sovereignty [15]. - This is an extension of the same concerns Pacific Island countries experienced regarding external dependence in the past fields of telecommunications satellites and broadcast content [15].
4. Key Variables Shaping Future Developments
○ Whether there will be a geographical reallocation of investment in data centers and power infrastructure.
- Whether the African "anchor demand" model translates into actual electrification outcomes will determine whether an alternative pathway can be established for compute-excluded regions [14].
○ Whether multilateral initiatives like the "Compute Coalition" can secure substantive autonomy.
- The watershed moment will be whether the multilateral coalition of middle powers is ultimately reintegrated into US technical standards or succeeds in securing independent bargaining power [9][13].
○ The level of risk management consultations between major powers, such as the US-China AI Safety Dialogue.
- Even if the US-China AI Safety Dialogue scheduled for September takes place, it is highly likely to be limited to selective decoupling restricted to security-linked items [9]. - In this scenario, Compute South countries will merely be integrated into the regulatory framework as a byproduct of major power consultations, without gaining a meaningful voice.
○ The pace of tax structure reform within developing countries.
- Whether fiscal models reliant on labor income taxation can be redesigned for the era of automation is a key variable determining the fiscal sustainability of small developing nations like Nepal [1].
○ The pace of restructuring education and workforce training systems.
- Whether current educational evaluation systems, currently hindered by the spread of plagiarism and dependency, can transition toward fostering AI utilization capabilities will determine the long-term potential of becoming producer nations [10].
III. Recommended Final Response Measures
Concerns over the Deepening Dependency Structure of the "Compute South": Comprehensive Assessment and Response Measures
1. Comprehensive Assessment
○ The "Compute South" phenomenon is not a temporary gap but a structural consequence of the AI value chain.
- An economic logic operates where a handful of countries possessing the trifecta of data centers, semiconductors, and power hold a decisive advantage [13]. - The gap represented by the United States' 5,427 data centers and $335.2 billion in cumulative private investment cannot be closed in the short term [9]. - Warnings from Nepal and Palau are signals emerging from the very bottom of this structure [1][15].
○ This issue directly intersects with a core problem in the domain of emerging technologies (AI), namely the widening of international inequality.
- The key monitoring points of emerging technology development trends and changes in the international regulatory regime apply directly. - At the same time, it must be addressed as a structural gap between major powers and developing countries within the domain of international competition over AI.
○ The starting point for implications is that South Korea occupies a dual position in this structure.
- While classified alongside Taiwan as a primary beneficiary of the AI investment boom, signs of cooling investment sentiment have recently been detected [17]. - At the same time, it faces the middle-power risk of dependency, potentially being reintegrated into US-centric infrastructure and technical standards [9]. - It belongs to the category of middle powers contemplating hedging between the US and China, rather than being a victim of the Compute South.
○ Therefore, South Korea's response needs to be designed along two separate tracks: "supporting the Compute South" and "national hedging strategies."
- Support for developing countries should be approached through the frameworks of Official Development Assistance (ODA) and cooperation to bridge the digital divide. - Managing the national position should be approached by enhancing infrastructure self-reliance within the domain of AI technology and national competitiveness.
2. Short-Term Action Plan (Within 6 Months)
○ Formally assess the status of the "compute gap" through a joint initiative by the Ministry of Science and ICT and the Ministry of Foreign Affairs.
- Examine the reality of signals indicating a slowdown in domestic AI investment and analyze comparative cases such as Taiwan [17]. - Quantify the level of reliance on the United States for semiconductor and cloud procurement.
○ Strengthen South Korea's participation in the "middle-power digital divide" agenda within digital cooperation discussions under the G20 and the UN.
- Preemptively review the conditions and limitations of the "Compute Coalition" initiative proposed by the Carnegie Endowment for International Peace [9][13]. - Assess in advance the risk that participating in multilateral channels may ultimately result in reintegration into US technical standards [9].
○ Prioritize the review of AI literacy and education sectors within ODA projects targeting developing countries.
- Pay close attention to patterns where confusion in educational evaluation systems hinders capacity building itself, as seen in the case of Nepal [10]. - Consider whether to include the redesign of AI utilization curricula in existing science and technology ODA projects.
3. Medium-Term Action Plan (1 to 2 Years)
○ Promote infrastructure self-reliance by diversifying semiconductor and cloud procurement.
- Design a strategy for selective participation rather than full integration into the US "Pax Silica" initiative [9]. - Continuously compare the pace and scale of domestic investment in expanding data centers and power infrastructure against those of the United States and China.
○ Examine a cooperation platform to "bridge the digital divide" for developing countries in the Asia-Pacific.
- Pay attention to the demand for "indigenous AI tools and data sovereignty" raised by Pacific Island countries, including Palau [15].
- Examine cases that turn infrastructure marginalization into regional development opportunities, such as Africa's model linking data centers with rural electrification [14].
- This can be utilized as a diplomatic asset for cooperation with ASEAN and other developing countries in the region.
○ Preemptively estimate domestic job losses driven by automation and their impact on the tax base.
- Reexamine the logic of "if automation reduces employment, the state's fiscal foundation itself will be shaken"—the core concern in the Nepal case—in the context of South Korea [1].
- While South Korea has a broad tax base, making the direct impact limited, separate analyses are required for scenarios involving concentrated job losses in specific sectors.
4. Long-term Action Plan (3 Years or More)
○ Establish the agenda of "bridging the compute divide" as a permanent item in the process of shaping international AI norms.
- Seek channels to reflect the perspectives of middle powers and developing countries in great-power-centric discussions, such as the US-China AI Safety Dialogue [9].
- Cooperate to ensure that developing countries' concerns about "becoming permanently locked in as consumer nations" are not excluded from international norm discussions.
○ Propose a regional sharing model for sovereign compute capabilities to the international community.
- Examine alternative models to address the limitations of initiatives like the "Compute Coalition" among middle powers, which ultimately result in reintegration into US standards [9][13].
- However, since this presupposes significant capital and technological investment, its feasibility is assessed as limited.
5. Monitoring Indicators and Trigger Points
○ Investment Indicator: Whether the trend of AI investment inflows into South Korea and Taiwan reverses [17].
- If the investment cooling becomes a trend, it could translate into a risk of a domestic infrastructure investment gap.
○ Norms Indicator: Progress in the US-China AI Safety Dialogue and G20 AI cooperation discussions [9].
- If great-power-centric agreements solidify into a structure that excludes developing countries and middle powers, South Korea will need to revise its multilateral channel strategy.
○ Infrastructure Indicator: Whether the gap in US data centers and private investment widens further [9].
- If the gap widens further, the urgency for South Korea to diversify its semiconductor and cloud sectors will increase.
○ Social Indicator: The pace of automation-driven job losses in specific domestic sectors.
- Periodically reevaluate the applicability of the Nepal-style fiscal shock scenario to South Korea [1].
○ Regional Indicator: Whether demands for 'data sovereignty' are spreading among developing countries in the Pacific and Asia [15].
- If they spread, they should be reflected in South Korea's ODA and digital cooperation strategies.
6. Summary and Conclusion
○ Although the 'Compute South' issue began with warnings of fiscal and employment shocks in small developing countries like Nepal and Palau, its structural roots lie in the concentration of AI infrastructure centered around the United States [1][15][9].
○ South Korea is not a direct victim of this structure, but rather occupies the position of a middle power that simultaneously enjoys investment benefits and faces the risk of dependence on technological standards [17][9].
○ Therefore, it is rational to pursue a two-track response that separates support for developing countries (digital cooperation and ODA) from domestic hedging (infrastructure independence and procurement diversification).
○ The response should be phased: in the short term, assessing the current status and participating in multilateral channels; in the medium term, diversifying infrastructure and establishing regional cooperation platforms; and in the long term, participating in the formation of international norms.
○ A flexible approach is required to constantly monitor indicators across five pillars—investment, norms, infrastructure, society, and region—and immediately adjust strategies when triggers occur.
References
[1] [The Kathmandu Post] If AI automates work, how will Nepal fund the state?
[8] [Carnegie Endowment] AI Breakout Capacity: A Middle-Power Path Between Racing and Dependence
[10] [The Kathmandu Post] Are Nepali schools ready for the AI takeover?
[11] [Carnegie Endowment] The AI Labor Debate: Three Views on the Future of Work
[12] [Channel News Asia] Big Read: AI is creating an economy that can grow without needing more workers. Should we be worried? - https://channelnewsasia.com/today/big-read-ai-jobless-growth-economy-employment-6407706
[13] [Carnegie Endowment] The Compute Coalition: How to Build the Future of AI in the Free World
[15] [Island Times (PW)] Pacific must not become consumers of AI: Wahuzue
[16] [Australian Financial Review] Forget rogue robots; AI’s biggest threat is the ‘lay-off trap’
[17] [Mint] AI trade cools in Taiwan, Korea. Why India isn’t seeing the moneyhttps://www.livemint.com/market/stock-market-news/ai-investment-taiwan-south-korea-india-foreign-investor-inflows-11790788767893.html
[18] [DW (Deutsche Welle)] Backlash over data centers tests Europe's AI ambitions
[20] [The Kathmandu Post] AI can imitate our songs. Can it preserve them?
[21] [Ámbito Financiero] How the AI boom could aggravate the fiscal crisis of the rich world
[22] [The News International] AI boom could trigger severe market shocks, Bank of England warns
[23] [DigiTimes Asia] South Korea AI compute squeeze hits researchers and startups
[25] [Island Times (PW)] Pacific media leaders warn of AI and misinformation risks to public trust
*This text is an AI translation of an original written in Korean. Some translations or nuances may be inaccurate.
This report is an in-depth analysis planned by an EAI researcher, grounded in sophisticated AI-assisted research, and finalized by the EAI researcher.
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