China’s push to win influence in the Global South is increasingly being routed through artificial intelligence—less as a futuristic promise and more as a package of practical offers: compute, connectivity, training, surveillance-adjacent “public safety” tools, and the policy frameworks that make those systems easier to deploy. In recent months, President Xi Jinping’s government has framed AI cooperation as a development accelerator and a governance alternative, positioning Beijing not only as a supplier of technology but as an architect of rules for how emerging digital systems should be built and governed.
The strategy is ambitious in scope and unusually direct in its messaging. Rather than treating AI as a narrow sectoral issue—something to be negotiated between ministries of science or telecom regulators—China is presenting it as a diplomatic instrument that can bind governments together around shared infrastructure and shared standards. The pitch is tailored to countries that often feel excluded from Western-led technology ecosystems, whether because of export controls, procurement barriers, or the political conditions attached to aid and investment. For many leaders in Africa, Latin America, the Middle East, and parts of Asia, the appeal is straightforward: AI partnerships that come with tangible capacity-building and fewer strings attached.
But the deeper story is about order-making. China’s outreach is not simply trying to sell models; it is trying to shape the environment in which models are deployed—data flows, cloud sovereignty, procurement norms, and the institutional habits that determine who gets to set the terms. In that sense, AI diplomacy is becoming a lever for a broader vision: a world in which the Global South can pursue digital development without aligning itself fully with US or European regulatory approaches, and in which China’s preferred model of state-industry coordination becomes a template.
A diplomacy of deliverables, not declarations
One reason China’s AI diplomacy resonates is that it is packaged as deliverables. The rhetoric may be grand, but the operational focus is on what governments can actually implement: national AI strategies, public-sector pilot projects, government-to-government training programs, and the integration of AI into existing digital infrastructure such as e-government platforms, smart-city initiatives, and national broadband plans.
In many countries, the bottleneck is not the absence of ambition—it is the lack of compute capacity, technical talent, and procurement pathways that can move from concept to deployment. China’s offer tends to address those constraints simultaneously. It can provide hardware supply chains, cloud services or cloud-like infrastructure, software stacks, and training curricula, often through consortia that include Chinese state-linked firms and local partners. That bundling matters because it reduces the friction that typically slows AI adoption: governments do not have to stitch together multiple vendors, negotiate incompatible standards, or build entire ecosystems from scratch.
This is why the outreach is frequently described as “practical” rather than purely symbolic. China’s diplomats and industry representatives are not only discussing ethics or governance principles; they are proposing roadmaps that include implementation timelines, demonstration projects, and mechanisms for long-term maintenance. For leaders who face domestic pressure to show progress in digital transformation, the ability to point to working systems—however limited at first—can be politically valuable.
The Global South angle: building ties outside the traditional tech club
China’s AI diplomacy is also shaped by geography and network logic. The outreach is heavily oriented toward countries that sit outside the most influential Western policy and technology circles. These are states that may not have the leverage to demand concessions from major powers, but that can still become critical nodes in global supply chains and data infrastructure.
For Beijing, this is not just about goodwill. It is about creating a coalition of users and regulators who normalize Chinese approaches to AI deployment. When a country adopts Chinese cloud infrastructure, integrates Chinese AI tools into public services, or signs agreements that define data handling and system interoperability, it creates path dependency. Future upgrades and expansions become easier, and switching costs rise. Over time, that can translate into influence that is less visible than military or economic coercion but potentially more durable.
There is also a strategic communications dimension. China’s messaging often emphasizes sovereignty and development. It argues that AI governance should not be imposed by a small group of wealthy countries and that digital development should be judged by outcomes—jobs created, services improved, administrative efficiency gained—rather than by compliance with external standards that may be difficult to meet.
This framing is particularly effective where governments already distrust Western conditionality. Many Global South states have experienced years of negotiations in which technology access is tied to political alignment, human rights scrutiny, or regulatory requirements that are expensive to implement. China’s approach, by contrast, is presented as partnership without lectures: a model in which the recipient state retains control over its own development priorities while China provides the means.
An alternative global order, built through systems
The most consequential element of China’s AI diplomacy is its implicit claim to be building an alternative global order. That does not necessarily mean a formal replacement of existing institutions overnight. Instead, it works through standards, procurement practices, and the institutionalization of Chinese-led frameworks in partner countries.
AI governance is not only about laws; it is about the architecture of decision-making. Who audits systems? Who certifies models? How are risks assessed? What happens when a system fails? Which agencies have authority over data? How are cross-border data transfers handled? These questions determine whether AI becomes a tool of public accountability or a tool of administrative control.
China’s outreach tends to emphasize centralized coordination and state-led planning. In many partner countries, that aligns with existing governance structures. Where governments already rely on top-down digital transformation programs, Chinese AI solutions can be integrated into those programs with minimal institutional disruption. The result is a governance style that is easier to scale: a state sets priorities, a vendor supplies systems, and oversight is embedded within the same administrative ecosystem that deploys the technology.
That is why the “alternative order” idea is not merely rhetorical. If enough countries adopt similar approaches—similar procurement models, similar data governance habits, similar training pipelines—then the global baseline shifts. Even if international organizations continue to exist, the practical center of gravity for AI deployment could move toward a multi-polar arrangement in which China’s preferences carry more weight.
The trade-off: speed and capacity versus transparency and rights
For all the appeal of AI diplomacy, there is a trade-off that many observers are beginning to articulate more clearly: speed and capacity can come at the cost of transparency, independent oversight, and sometimes civil liberties.
AI systems deployed in public administration—whether for fraud detection, welfare targeting, border management, or “smart city” functions—can have profound impacts on individuals. When procurement is bundled and vendor relationships are long-term, governments may have less leverage to demand explainability, third-party auditing, or robust red-teaming. Even when a partner country wants stronger safeguards, the technical and institutional capacity to enforce them may be limited.
China’s model often assumes that governance can be achieved through internal regulation and state supervision rather than through external scrutiny. That can be attractive to governments that view external oversight as interference. But it can also create risks for citizens if accountability mechanisms are weak or if data governance is not sufficiently granular.
This is where the Global South context matters. Many countries are not choosing between perfect options; they are choosing between imperfect ones under resource constraints. If Western vendors refuse to sell certain capabilities or impose compliance burdens that are too costly, and if Chinese vendors offer workable systems quickly, the decision calculus changes. The outcome may be a patchwork of AI governance regimes—some more robust than others, some more opaque than citizens would prefer.
Still, it would be inaccurate to portray the entire strategy as purely extractive. Some partner governments genuinely want to modernize public services and build domestic capacity. Training programs and local partnerships can produce real skills. The question is whether those skills translate into independent capability or whether they primarily serve to deepen reliance on foreign vendors.
What “AI diplomacy” looks like on the ground
AI diplomacy is often discussed in abstract terms, but it manifests in concrete projects. In many partner countries, the early phase of AI adoption follows a familiar pattern: identify high-value administrative use cases, digitize relevant processes, deploy AI-assisted tools, and then expand once the systems prove useful.
Common entry points include:
1) Government service modernization: AI chatbots for citizen support, document processing for licensing and benefits, and predictive analytics for administrative workloads.
2) Smart-city and public safety: video analytics, traffic optimization, and incident detection. These systems can improve efficiency, but they also raise concerns about surveillance and due process.
3) Education and workforce training: AI tutoring pilots, language translation tools, and vocational training platforms.
4) Agriculture and climate resilience: image-based crop monitoring, weather forecasting support, and advisory systems for smallholders.
5) Financial inclusion: risk scoring for credit and fraud detection for mobile payments, which can expand access but also introduce bias risks if data quality and model governance are weak.
China’s advantage is that it can supply not only the AI tools but also the surrounding infrastructure—cloud, connectivity, and integration services. That makes it easier for governments to move from pilot to scale. It also means that the diplomatic relationship becomes embedded in the operational backbone of national digital systems.
The role of standards and training
Beyond deployments, China’s diplomacy also targets the “soft infrastructure” of AI: standards, training, and institutional partnerships. By shaping how partner countries define AI readiness—what counts as acceptable risk, how data should be categorized, how models should be evaluated—Beijing can influence the long-term trajectory of AI governance.
Training programs are particularly important because they create networks of officials and engineers who understand AI through a particular lens. When those trainees return to their home institutions, they can advocate for procurement choices, regulatory approaches, and technical architectures that align with Chinese offerings. Over time, that can reduce friction for future deals and increase the likelihood that Chinese systems become the default.
This is one reason AI diplomacy can be more effective than traditional technology diplomacy. It does not only create contracts; it creates communities of practice.
The geopolitical subtext: reducing dependence on Western chokepoints
Another driver is the desire to reduce dependence on Western chok