The global artificial intelligence landscape is rapidly consolidating, with an estimated 87% of advanced AI models originating from just two countries—the United States and China. This growing concentration of technological power is reshaping not only innovation and economic competitiveness but also the future of governance, digital sovereignty, and global development. For the Global South, the moment represents a critical inflection point: remain primarily consumers of AI technologies or actively participate in shaping the standards, safety frameworks, and policy architectures that will define the next digital era.
A New Axis of Technological Power
The dominance of the US and China in AI development is not accidental. It is the result of years of sustained investment in research ecosystems, semiconductor infrastructure, cloud computing, elite talent pipelines, and access to vast datasets. Leading technology companies, top-tier universities, and strong venture capital networks have enabled both nations to build and deploy increasingly sophisticated foundation models.
These models are not just technological milestones; they are strategic assets. They influence productivity, military capability, financial systems, healthcare innovation, and information flows. Countries that control AI infrastructure and model development are increasingly positioned to shape global economic rules and digital norms.
For the rest of the world, this concentration creates structural dependencies. Many nations rely on AI systems developed elsewhere for critical services, from language processing and digital governance to agriculture and climate modelling. While these tools offer immediate benefits, they also raise concerns about long-term autonomy, data ownership, cultural representation, and economic value capture.
The Consumer Trap
A growing number of countries in the Global South risk falling into what analysts describe as the “AI consumer trap.” In this model, nations import AI technologies, pay for access through cloud subscriptions or licensing, and generate data that further strengthens foreign platforms—without developing significant domestic capabilities.
This pattern mirrors earlier phases of the digital economy, where value creation was concentrated in a few regions while others remained dependent on external technology providers. The result is a widening innovation gap, limited local industry growth, and reduced influence over global regulatory frameworks.
Moreover, AI systems trained primarily on data from the Global North often fail to adequately represent the linguistic, cultural, and socio-economic realities of emerging markets. This leads to biased outputs, reduced effectiveness in local contexts, and the marginalisation of entire populations in digital decision-making systems.
Why Governance Matters
AI is not just about innovation; it is about governance. The rules being written today—on safety, ethics, data flows, intellectual property, and accountability—will shape the global digital order for decades.
If the Global South remains absent from these discussions, it risks operating under standards that do not reflect its priorities. Issues such as inclusive datasets, equitable access, multilingual AI systems, and development-focused deployment may receive insufficient attention in global policy frameworks dominated by a handful of technologically advanced economies.
Participation in governance does not require matching the US or China in compute capacity. It requires coordinated diplomacy, regional alliances, investment in regulatory expertise, and the creation of institutions capable of influencing global norms.
The Opportunity to Leapfrog
Despite the concentration of AI power, the current moment also presents a rare opportunity. Many Global South countries have large, young, and digitally connected populations. They are generating massive volumes of mobile-first data, adopting digital public infrastructure, and experimenting with innovative service delivery models.
These strengths can be leveraged to build domain-specific AI systems tailored to local needs. Instead of competing directly in the race for the largest foundation models, emerging economies can focus on applied AI in sectors such as agriculture, climate resilience, healthcare diagnostics, education, and financial inclusion.
Open-source AI models are another potential equaliser. They lower entry barriers, enable local customisation, and allow researchers and startups to innovate without the massive capital expenditure required for proprietary systems.
The Role of Regional Collaboration
No single country in the Global South is likely to match the scale of US or Chinese AI ecosystems on its own. However, regional cooperation can create meaningful alternatives.
Shared compute infrastructure, cross-border research networks, common regulatory frameworks, and pooled funding mechanisms can significantly increase collective capacity. Regional data-sharing agreements—designed with strong privacy protections—can also help create more representative and diverse training datasets.
Such collaboration would not only strengthen technological capabilities but also enhance bargaining power in global negotiations on AI standards and trade.
Talent as the Decisive Factor
One of the most critical battlegrounds in AI is human capital. The US and China have built strong ecosystems that attract and retain top researchers, engineers, and entrepreneurs. For the Global South, preventing talent drain and creating opportunities for advanced research and innovation will be essential.
This requires sustained investment in higher education, interdisciplinary AI programs, startup ecosystems, and public-sector innovation labs. It also involves creating career pathways that allow skilled professionals to work on globally relevant problems without leaving their home countries.
Balancing Innovation and Inclusion
The debate is not about technological rivalry alone; it is about ensuring that AI development is inclusive and aligned with diverse human needs. If AI systems are designed primarily by and for a narrow set of economies, they risk reinforcing existing global inequalities.
By actively participating in AI governance and development, countries in the Global South can advocate for models that prioritise public good applications, affordable access, and ethical deployment in low-resource environments.
A Strategic Choice, Not a Technological Fate
The concentration of AI model development in the US and China is a reality of the current moment—but it is not an irreversible outcome. The direction the Global South takes over the next decade will determine whether it becomes a passive market for imported intelligence or an active contributor to the global AI ecosystem.
The path forward lies in coordinated policy, targeted investment, regional cooperation, talent development, and a strong voice in international governance forums.
AI is often described as the defining technology of the 21st century. For much of the world, the question is no longer whether to adopt it, but whether to help shape it.
The answer will determine not just technological competitiveness, but the balance of economic power, cultural representation, and digital sovereignty in the decades to come.
