The Spread of Chinese AI Adoption Among African Businesses and the U.S.-China Tech Rivalry: Local Trends and South Korea's Response

Executive Summary

The adoption of AI by African businesses is not a choice of ideological alignment but a pragmatic outcome based on cost-performance calculations for specific business functions. A dual-track approach, exemplified by Nigeria's Curacel, is becoming the market standard: using Chinese models for high-frequency, repetitive tasks and Western models for complex reasoning. Huawei is pursuing a long-term strategy of gradually encroaching on the entire financial infrastructure, while China is expanding its market base through the free distribution of open-weight models from Alibaba's Qwen and DeepSeek. In contrast, U.S. Big Tech is concentrating its infrastructure investments in its own country and allied nations, retreating to a discourse of trust and partnership in Africa. Despite U.S. pressure for bloc formation in a 'Pax Silicana' style, the absence of a unified response framework at the African Union level suggests that pragmatic choices by individual companies will continue. As a third party not subordinate to either the U.S. or China, South Korea should prioritize multi-vendor integration services and strategies specializing in niche sectors, while also preemptively designing for supply chain bifurcation in security-linked areas like finance and telecommunications.

I. Situational Analysis

The Spread of Chinese AI Adoption Among African Businesses and the U.S.-China Tech Rivalry: An Analysis of Local Trends

1. Background and Developments

Until recently, discussions about AI adoption by African businesses converged on the issue of the infrastructure gap. Akua Gyekye, Microsoft's Head of Government Affairs for Africa, stated that Africa's AI challenge is no longer about "whether to adopt." Instead, she argued, the key is to build trust, infrastructure, and partnerships [10]. This diagnosis reflects the perspective of Western Big Tech, which still views the African market primarily as a target for infrastructure investment.

However, the logic of choice on the ground has unfolded differently. The Nigerian insurtech company Curacel is a case in point. CEO Henry Mascot revealed that when expanding the AI models to run the company's internal infrastructure, he did not rely solely on Silicon Valley [1]. The assessment was that for high-frequency tasks like coding, data extraction, classification, and customer service, Chinese models were comparable to Western ones in terms of cost-performance [1]. However, Mascot acknowledged that Western systems still hold an advantage in complex reasoning or tasks where reliability is critical [1]. This shows that the adoption of Chinese AI by African companies stems not from an ideological choice but from a task-specific calculation of cost versus performance.

This trend is not confined to individual companies. According to a RAND Corporation study, the regions with the largest increase in the use of Chinese AI models during the survey period were Russia, the Middle East, Africa, and South America [4]. The Council on Foreign Relations (CFR) analyzed this phenomenon, suggesting that the adoption of U.S. and Chinese models is diverging along geopolitical and economic development lines [4]. This interpretation suggests that Africa's choice is not an ideological alignment with a specific bloc, but rather the result of selecting technologies that fit its own stage of development and cost structure.

2. Current Situation

Chinese state-run media frames this trend as an achievement in bridging the digital divide. The Global Times reported that an African delegation attending a media forum responded positively to China's efforts to reduce the digital gap [9]. The same article cited a New York Times report that Chinese AI models are widely preferred among African developers because they are cheap and freely available, presenting this as a critical perspective from Western media [9]. This is a pattern where Chinese state media leverages Western reporting to highlight the success of its own open-source strategy.

At the corporate level, Huawei's push into the financial sector is notable. Jason Cao, CEO of Huawei's Digital Finance Business Unit, explained the company's approach to the AI transformation of the banking sector at the Huawei Connect 2026 event in Johannesburg, South Africa [14]. Huawei is leveraging its 16-year track record of supporting the modernization of African financial institutions' ICT infrastructure, data platforms, and mobile payment ecosystems [14]. This is not simply about selling models but is a market penetration strategy of gradually encroaching on the entire financial infrastructure over the long term.

Within China, the distribution of open-weight models has been elevated to a national strategy. Alibaba's Qwen model has reached 3 billion cumulative downloads, and purchasing delegations from 177 countries participated in WAIC 2026 [6]. DeepSeek also recently sent new shockwaves through the U.S. policy and tech communities by releasing its V4 Pro open-weight model, which is available for free download [7]. EAI's existing assessment is that this open-source proliferation strategy is leading to tangible commercial success in the Global South, including Africa [6].

This dynamic is not limited to Africa. Reports have emerged that in Southeast Asia, both the U.S. and China are pressuring regional countries to join their respective AI blocs. Beijing is offering exclusive benefits to attract partners in the Global South, while U.S.-led organizations are reportedly perceived as exclusive [11]. At the APEC Forum on Digital Technology and AI, regional countries adopted a statement supporting open-source AI models, which is considered a rare point of consensus in the U.S.-China zero-sum competition [5]. This suggests that Africa and Southeast Asia share a similar pattern: responding to pressure for bloc alignment with pragmatism.

3. Analysis of Key Actors

Local African businesses are the most proactive actors in this dynamic. As the Curacel case shows, they adopt a strategy of using both U.S. and Chinese models in parallel, depending on the business function [1]. Practical criteria such as cost, download availability, and linguistic flexibility—rather than ideology or geopolitical alignment—drive their adoption decisions. Their common interest is to keep their options open rather than becoming dependent on a single bloc.

The Chinese government and companies aim to preemptively capture the developer ecosystem by leveraging the free distribution of open-weight models. This involves a division of labor between model developers like DeepSeek and Alibaba's Qwen and infrastructure companies like Huawei [6][7][14]. With access to advanced semiconductors restricted, their strategy is to use the openness and low cost of the models themselves as a competitive advantage to expand their base in markets where the U.S. has not yet established a coherent response system [6]. State-run media, including the Global Times, frames this trend within a development cooperation narrative of bridging the digital divide [9].

The U.S. government and Big Tech employ a strategy of pressuring for bloc alignment, but this pressure lacks real binding power on the ground in Africa [1]. Microsoft emphasizes maintaining its edge through infrastructure and trust-building, but this is tantamount to an indirect admission that it is already losing on price competitiveness [10]. Big Tech firms like Alphabet, Amazon, Meta, Microsoft, and Oracle plan to invest over $700 billion in data centers and computing infrastructure in 2026 [3], but this investment is not translating into immediate price competitiveness in the African market.

U.S. think tanks like RAND and CFR are treating this phenomenon as a warning sign, interpreting it as geopolitical bloc formation [4]. This indicates a growing sense of crisis within the U.S. policy community about losing ground in African, Middle Eastern, and South American markets.

4. Key Issues

The first issue is one of interpretation: is Africa's choice a bloc shift or pragmatic parallelism? The Curacel case points to the latter [1], but the geopolitical framing by RAND and CFR tends to converge on the former [4]. This difference in interpretation is a variable that will shape the intensity of the future U.S. response.

The second issue is the differentiation of competitiveness by task domain. For high-frequency, standardized tasks, Chinese models have a firm cost advantage, but for complex reasoning and reliability-focused tasks, Western models maintain their superiority [1]. Whether this differentiation will persist or narrow as the performance of Chinese models improves will determine the future market landscape.

The third issue is the difference in the competitive axis: infrastructure versus model accessibility. The U.S. camp emphasizes its lead in infrastructure like data centers and semiconductors [2][3], while China employs a strategy of lowering the entry barrier itself with freely downloadable open-weight models [6][7]. In the infrastructure-poor African market, the latter approach is currently gaining the upper hand in immediate adoption rates.

The fourth issue is whether this dynamic is a phenomenon limited to Africa. Similar patterns of bilateral pressure and local non-aligned pragmatism are observed in Southeast Asia [5][11]. If this is a recurring pattern across the Global South, a reassessment of the effectiveness of the U.S. bloc pressure strategy itself is inevitable.

II. In-Depth Analysis

The Spread of Chinese AI Adoption by African Businesses: Root Causes and Structural Context

1. Root Cause: Mismatch in Cost Structures and Development Stages

The primary reason African businesses are choosing Chinese AI is not ideology but the bottom line. As the Curacel case demonstrates, for repetitive, high-frequency tasks like coding, data extraction, classification, and customer service, Chinese models deliver results similar to Western models at a much lower cost [1]. Most African startups have weak initial funding conditions. In a structure where per-token costs determine profitability, the appeal of low-cost models is absolute.

A second factor, downloadability, adds to this. The 3 billion cumulative downloads of Alibaba's Qwen model is not just a popularity metric [6]. It means that companies lacking the foreign currency to pay for cloud API fees or concerned about data sovereignty can download the model and run it on their own local servers. The free distribution of DeepSeek V4 Pro has similarly lowered the entry barrier for African developers [7][12].

Linguistic flexibility is a third axis. Africa is home to a wide range of languages, such as Swahili, Hausa, and Amharic, that are underrepresented in the training data of large Western models. While Chinese models may not have precisely targeted this gap, their open-weight structure allows local developers to fine-tune them, which has had the effect of bridging the language divide. Mascot's assessment that Western models maintain an edge in high-level reasoning and reliability [1] suggests that the choice in the African market is not a complete replacement but a task-specific bifurcation.

2. Structural Context: Three Tiers of Power Relations

Economic Tier: The Infrastructure Gap and Access to Capital

The scale of investment by Western Big Tech is overwhelming. Just five companies—Alphabet, Amazon, Meta, Microsoft, and Oracle—are projected to invest over $700 billion in data center expansion in 2026 [3]. However, this investment is concentrated on expanding data centers in the U.S. and allied countries, with Africa being a lower priority. Microsoft's reframing of Africa's AI challenge as a matter of "trust, infrastructure, and partnership" [10] is, paradoxically, evidence that it has already ceded the initiative to China in the initial adoption phase.

Huawei's approach is designed to fill this void. The 16-year track record in building financial ICT infrastructure that Jason Cao, CEO of Huawei Digital Finance, emphasized in South Africa [14] is part of a strategy to create a dependent ecosystem that encompasses not just a single model but also payment systems, data platforms, and the cloud. This is an AI version of the low-cost, full-package strategy that Huawei employed in the telecommunications equipment market.

Political Tier: Pressure for Bloc Alignment and Non-Aligned Pragmatism

Through its 'Pax Silicana' concept, the United States is effectively demanding that its allies choose a side [2]. This is supported by reports from Southeast Asia that both the U.S. and China are exerting pressure to incorporate countries into their respective AI frameworks [11]. However, a unified response mechanism or regional AI regulations at the African Union level have yet to be formed. This vacuum is being filled by pragmatic choices made at the level of individual nations and, furthermore, individual companies.

China is using this vacuum as an arena for narrative competition. The way the Global Times reported on the favorable reactions of the African delegation while simultaneously citing a critical New York Times article to reframe it as "concerns from Western media" [9] is a classic technique of reflexive justification. It is a narrative effort to reframe Africa's pragmatic choice as a success for China's open-source strategy.

Security Tier: Data Sovereignty and Dual-Use Risks

Many African nations are uncomfortable with their data being stored on Western cloud servers. Downloadable models are attractive because they can bypass this concern. However, this simultaneously creates a new dependency on Chinese technology standards and data flows. Considering that the Chinese government is moving to strengthen controls over the cross-border movement of its AI and data technologies [8], it remains uncertain what form of technological dependency the adoption of downloadable models by African companies will lead to in the long run.

3. Historical Precedent: A Repeat of the Telecommunications Infrastructure Competition

This dynamic is similar to the process in the African telecommunications infrastructure market from the late 2000s to the 2010s, where Huawei and ZTE displaced Ericsson and Nokia. At that time, Western companies promoted high-end specifications and reliability, but Chinese companies rapidly expanded their market share with a package combining low-cost equipment and government loans. As a result, the core telecommunications infrastructure of many African countries became dependent on Chinese equipment, which later acted as a structural constraint for the U.S. in persuading African nations during the 5G security debate.

A similar trajectory is being detected in the AI domain. Once a market is captured at the initial adoption stage through price and accessibility, data standards, API ecosystems, and developer communities become locked into that technology stack. The case of countries at the APEC Forum on Digital Technology and AI adopting a statement in support of open-source models [5] shows that this lock-in is also underway in Asia. Nikkei Asia noted that China's growing influence "stems not from a strategy to outmaneuver Washington, but because it aligns with the goals of each country" [5]. There is a strong possibility that the same interpretation applies to Africa.

However, there are also differences. Telecommunications infrastructure was procured through large-scale national projects, making inter-governmental negotiations and loans key variables. AI model adoption, on the other hand, occurs at the level of individual companies making API calls or downloads, resulting in a much lower entry barrier and relatively smaller switching costs. This also suggests that Africa's dependency on Chinese AI may not become as entrenched as its dependency on telecommunications infrastructure.

4. Key Variables for Future Developments

The first variable is the speed of Western Big Tech's localization investments in Africa. The key is whether the "trust and infrastructure" emphasized by Microsoft [10] will translate into actual data center construction or the development of local language models. So far, it has remained at a declarative level.

The second variable is the direction of regulations related to data sovereignty in African countries. If individual nations strengthen data localization requirements, the relative advantage of downloadable Chinese models could increase.

The third variable is the trend in the technology gap in the domain of complex reasoning. If the current advantage held by Western models [1] narrows, the possibility that the current task-specific bifurcation could shift to a full-scale dependency on Chinese models cannot be ruled out. Conversely, if the gap is maintained, the pragmatic dual-track strategy of African companies is expected to continue for a considerable period.

The fourth variable is the U.S. response. If the 'Pax Silicana' concept [2] continues to pressure Africa to choose a side without offering concrete incentives, the departure of pragmatic African companies is likely to accelerate.