The U.S. must set dominance of global AI sector as a strategic economic and national security priority. If the PRC becomes the dominant provider of AI infrastructure and LLM model development, this will have dire consequences for the U.S. and allow the PRC to embed itself in many foreign countries, especially across developing economies. Developing economies, which have a more positive view of AI than their more developed counterparts, face increased risks and less ability to counter them.
The People’s Republic of China (PRC) is actively working to shape technological choices, including those related to AI through its Digital Silk Road. While the stated purpose of the program is to enhance global connectivity, it carries strings. The program embeds Chinese tech standards and companies on participants, which expands Beijing’s economic, geopolitical, and directive influence in cyberspace. These investments include hardware and software infrastructure that can lead to an effective long-term lockout of other competitors in this space. The U.S cannot let this happen.
U.S. failure to lead risks both the economic advantages, including market access, of the largest AI ecosystem and the ability to shape global standards, governance, norms and values (including privacy). In the absence of U.S. leadership, this space is likely to be filled by a nation with differing standards, norms, and values. When the U.S. engages, it can offer transparent partnership with auditable open models, reducing the chance that partners turn to less safe alternatives, and strengthening U.S. partnerships with those countries going forward.
If Beijing’s systems become the default, it may not only provide data to improve their own AI capabilities, but also potentially exposes the purchasing country, and U.S. interests in that country, to disruption or espionage risk. These risks grow exponentially if these either adversarial or compromised AI systems are used in ports, grids, communications or other critical systems.
AI is not physical infrastructure, but when widely embedded it becomes infrastructural — hard to replace, a key foundation for economic activity, and subject to resilience, security and lock-in dynamics that give the provider geopolitical leverage.
This combination of risks and opportunities makes U.S. leadership in developing-economy AI investments essential, not only for American economic interests, but also for the long-term national security and sovereignty of developing economies themselves. Converting those advantages into durable influence in the developing world requires deliberate engagement, including efforts like Pax Silica. The window for trusted U.S. partnerships on secure infrastructure, open and auditable models, and capacity building is open but narrowing.
Notable trends in AI investments in the Developing World
Across the world, governments are seeking to leverage AI for public and private sector gains, but funding sources and the types of AI models employed diverge significantly. These choices have large short- and long-term impacts. Between 2017 and 2025 more than 80 countries published national AI strategies. As of 2023 developing countries were less than half as likely (just 30 percent) to have these strategies as developed countries (more than 60 percent).
While national AI strategies are expanding across developing economies, follow-through including funding – varies. For example, India’s IndiaAI Mission has committed substantial resources to infrastructure and ecosystem development aimed at sovereign capabilities. Kenya has adopted a National AI Strategy (2025–2030) alongside skilling initiatives and public-private AI hubs. The African Union’s Continental Artificial Intelligence Strategy provides a regional framework, while countries such as Nigeria and Rwanda are advancing targeted investments in AI centers and applications.
Many of these efforts are public-private partnerships with both domestic and foreign funding. A PRC company has positioned itself as Rwanda’s digital transformation infrastructure partner on projects including but not limited to AI. The PRC is also building key parts of Kenya’s AI infrastructure and providing funding to support it through loans.
A key trend is the choice between open-source, open-weight and closed, or proprietary models. Open-source and open-weight offer customizability, including linguistically, increased data control and sovereignty, and the ability to self-host, meaning the owner controls the infrastructure and can better control the costs. Fully open-source models go further by also releasing training data and code, allowing deeper inspection. Open-weight models require less upfront costs than open-source, which requires training from scratch.
Closed or proprietary models usually deliver out-of-the-box performance, require less infrastructure and come with providers who support continuous updates. However, they offer far less customizability and higher, as well as less predictable costs driven by usage volume. They can also create dependence on the provider for access, security, and future capabilities. These choices will shape data flows, as well as technological and geopolitical dependencies, for decades. Proactive U.S. leadership can help ensure AI becomes a source of shared prosperity and security rather than risking a new avenue of vulnerability and influence.
Unique challenges of AI in the developing world
Most low- and lower-middle-income countries face challenges in finding talent to support AI and other ICT needs, limiting local ability to design or train models. Developing markets also face private sector hesitancy to finance large-scale AI infrastructure given low demand and less reliable power which reduce guarantees that returns will justify the capital outlay. This makes government investment and use of AI for services a potentially critical anchor for AI infrastructure and adoption by creating the initial demand that later attracts broader private investment.
Developing countries are also more likely to have weaker legal and regulatory frameworks, creating uncertainty for AI investment and implementation. Lack of clarity around liability and enforcement raise costs and uncertainty which deters investments, since risk is more challenging to reliably assess or manage. Weak frameworks can also lead to AI exacerbating underlying issues by making processes, such as authoritarian surveillance, more efficient.
AI usage presents privacy risks that are heightened in lower capacity environments common in the developing world. AI models require massive amounts of data often including sensitive personal information such as location, biometrics, and communications to support pattern recognition that lets them serve users. Uses such as healthcare, smart cities, and surveillance infrastructure, require collecting and leveraging huge amounts of highly sensitive data. AI models also require auditing including for bias, which exposes the data and may let vendors or governments repurpose it.
Whenever large amounts of data are collected, privacy is at risk, making it important to minimize inadvertent exposure. Strong cybersecurity standards are essential for protecting this data. This makes it essential that privacy is included by design – from the outset – in AI efforts. These principles must be coupled with broader secure-by-design cybersecurity principles and leveraged into frameworks that are incorporated into all implementation.
For example, many countries lack comprehensive data protection laws, and enforcement mechanisms may be especially weak in developing economies, making it difficult to impose meaningful constraints on data collection, use, or cross-border transfer. Countries with weaker institutions and legal foundations face increased risks. These factors make privacy principles both more essential and more challenging to implement in developing countries.
Different Model Types as Developing Countries Adopt AI at the Government Level
Open-source and open-weight models are the primary ones being adopted by developing economies at the government level, chosen to support sovereignty and reduce dependency risk. Closed or proprietary models often involve data flowing outside the sovereign country, risking access by another state and passage into jurisdictions with different safeguards. But localized data alone does solve for data security, and even with self-hosting, limited local cybersecurity remains a risk.
Data centers being located physically in country does not necessarily give control over the technology, data, or strategic assets. For example, across Southeast Asia especially Malaysia and Indonesia the rapid expansion has been driven largely by foreign hyperscalers, but the underlying technology and decision rights are outside the host-country’s control. This can also create risk of export-control diversion, for example Singapore’s 2025 prosecution around misrepresentation of the end-users of servers containing U.S.-controlled Nvidia chips.
It is likely that open-source and open-weight models will remain the primary choice for high-volume and self-hosted use driven by cost and return on investment. Open models achieve approximately 90 percent of the performance of closed models at release and quickly make up the difference. This performance gap is continuing to narrow, and costs for comparative open models can be 17 percent that of the alternatives.
Across developing economies, AI models from the PRC are gaining traction more quickly than Western models. DeepSeek is being adopted across Africa at a rate two to four times that of Western alternatives, likely because of the cost and openness, key factors in resource constrained environments. Of note, Australia, Taiwan and South Korea have all issued public warnings around the security risks of DeepSeek, including banning its use on government devices. Some U.S. models are competing, such as Meta’s open-source Llama which is used for purposes including to deliver agricultural advisory services in Kenya, Nigeria, and India. But AI adoption and development across the world are evolving, leaving opportunity for market capture still in flux.
Current Impacts of PRC Investment in AI in Developing Countries
The PRC has been focused on advancing its global digital infrastructure footprint for more than a decade through a Belt and Road linked Digital Silk Road (DSR). The DSR covers a broad range of investments from surveillance technology to cloud computing and AI, from infrastructure to LLMs. Infrastructure support of any kind is uniquely difficult to reverse, locking in relationships between the two countries even if the geopolitical landscape shifts. This can open the door to coercive leverage should the providing country wish to exert it.
The PRC has already built significant parts of digital infrastructure across the Global South, especially across Africa and Asia, including telecommunications networks, fiber-optic cables, data centers, cloud services, and smart cities. By building this infrastructure, providing software and setting operating standards, the PRC creates systems that their state actors understand completely posing significant risk for penetration and influence. PRC’s national security laws require Chinese firms to cooperate with the state, meaning that the use of even supposedly private-sector technology from the PRC presents government surveillance risks. This poses a risk as the U.S. attempts to engage with these countries across sectors.
AI is likely to be foundational for the global economy over the next century. When countries adopt an AI product in partnership with another country they at least informally adhere to the standards of the producer. PRC leadership on AI opens the door for Beijing to set global standards on detecting bias and training models that are likely to think about the world the way the CCP does. The U.S. not leading in this space does not just risk U.S. market share but also the U.S. ability to set the operational and cultural standards for AI. If a U.S. adversary captures this market and sets these standards the U.S. will face long-term national security challenges.
How to Win
The U.S. does not need to match the PRC dollar for dollar but must arrive with solutions and credible long-term funding packages to the global south. The PRC’s own neighborhood, the Indo-Pacific, can offer a model for how this might function. In 2022, Solomon Islands took a PRC concessional loan to build 161 Huawei telecommunications towers. Yet when Australia funded the Coral Sea Cable in 2018 in place of a Huawei proposal to connect Papua New Guinea and Solomon Islands, and when Australia, Japan and the U.S. committed to jointly finance the East Micronesia Cable, allies showed that a trusted, competitive alternative can win when it is financed and offered early.
The U.S. can work with allies and partners already in these markets, particularly Australia, Japan, South Korea, India and Singapore in the Indo-Pacific, to co-finance infrastructure. These partnerships can and should also include security assistance that supports model evaluation and guidance on secure hosting. Such partnerships offer governments across the Global South a strong alternative without asking them to choose between great powers, a choice many are reticent to make.
