The 2026 World Artificial Intelligence Conference and High-Level Meeting on Global AI Governance concluded in Shanghai with a question that will shape the next phase of global development: will artificial intelligence become a shared instrument of progress or another source of inequality between technologically powerful countries and the rest of the world?

Held from July 17 to 20, the conference brought together representatives from more than 100 countries and international organizations, alongside scientists, policymakers and business leaders. More than 1,100 companies presented over 3,000 exhibits, reflecting the expanding scale of China’s AI ecosystem. Yet the significance of the Shanghai meeting extended beyond technological demonstrations. Its central message concerned access, governance and the right of developing countries to participate meaningfully in the AI revolution.

President Xi Jinping placed openness at the heart of this vision. He called for a just and equitable global AI governance system, greater international cooperation and wider access to technological innovation. China also announced plans to expand training, exchanges and cooperation centres with ASEAN, the African Union, the Arab League, the Community of Latin American and Caribbean States, BRICS and the Shanghai Cooperation Organization.

For the Global South, this approach addresses a practical problem. Many developing countries understand the transformative potential of AI, yet remain constrained by the high cost of advanced models, limited computing infrastructure, inadequate technical capacity and dependence on foreign platforms. Without affordable access, AI could deepen the development gap rather than reduce it.

China’s open-source AI ecosystem offers an important response. Open models allow universities, start-ups, public institutions and local developers to examine, adapt and deploy technology according to their own requirements. A Pakistani university can build an educational application in local languages. An African health authority can adapt a model for disease monitoring. A Southeast Asian agricultural institution can use AI to improve crop forecasting without surrendering complete control over its data and operations.

This is why the Western description of Chinese open-source AI as a potential “trap” deserves closer examination. A trap presupposes the existence of viable alternatives. For much of the Global South, no Western alternative currently offers a comparable combination of affordability, accessibility and adaptability. Proprietary systems may provide impressive capabilities, but access is frequently governed by commercial subscriptions, centralized application interfaces, export restrictions and corporate decisions made far from the societies affected by them.

The real choice facing many developing countries is therefore between accessible technology and technological exclusion. Presenting affordable Chinese models as inherently threatening while treating expensive closed systems as politically neutral reveals a clear double standard.

Security concerns still require serious attention. No country should adopt critical AI infrastructure without examining cybersecurity, data protection, model behavior and supply-chain risks. These principles should apply equally to Chinese, American, European and other systems. Technology should be evaluated through evidence, testing and transparent standards rather than geopolitical origin.

Open-source models provide a stronger foundation for such scrutiny. Independent researchers and security specialists can inspect their code, architecture or model components. Vulnerabilities can be identified, debated and corrected across a wider technical community. Closed models require users to trust the assurances of the company controlling the system while offering limited visibility into how it was trained, modified or governed.

Openness alone cannot guarantee security. Malicious code can be published openly, and model weights do not automatically reveal every risk embedded in training data or deployment infrastructure. Effective protection requires continuous auditing, secure hosting, data localization where appropriate, transparent documentation and independent evaluation. Even so, claims about hidden manipulation are more testable in open systems than in proprietary platforms protected by commercial secrecy.

Western policymakers should therefore apply the same questions to their own AI industry. Who controls the model? Who determines access? Where is user data processed? Can a country inspect the system it depends upon? Can prices or conditions change unilaterally? Can access be withdrawn during a political dispute?

If dependence on Chinese open-source technology is automatically described as a trap, dependence on closed, profit-driven Western platforms must also be examined. When a small group of corporations controls foundational models, computing infrastructure, technical standards and global data flows, the result risks becoming a form of technological colonialism. Developing countries become permanent consumers of intelligence produced elsewhere, paying for access while contributing data and receiving limited influence over governance.

China’s proposition is more attractive because it can support technological agency. Open-source access enables countries to build domestic skills, train engineers, create local applications and gradually develop their own AI ecosystems. This shifts cooperation from the sale of finished products towards shared capacity-building.

China must also respond constructively to legitimate concerns. It can strengthen confidence by supporting third-party security assessments, multilingual technical documentation, open safety benchmarks and international research partnerships. Developing countries should avoid replacing one form of dependence with another by investing in local computing infrastructure, sovereign data governance and domestic talent.

The creation of the World Artificial Intelligence Cooperation Organization, supported initially by 29 countries including Pakistan, gives Shanghai’s vision an institutional direction. Its credibility will depend on whether it provides developing countries with a genuine voice in setting standards, assessing risks and distributing the benefits of innovation.

AI governance cannot become the exclusive privilege of countries that reached the technological frontier first. Nor should security become a justification for preserving commercial dominance or restricting access to knowledge. The Global South requires partnerships that expand its choices, capabilities and decision-making authority.

Shanghai has placed the essential issue before the international community. The future of AI will be judged by more than the sophistication of its models. It will be judged by who can access them, who can understand them, who can shape their rules and whose development they ultimately serve. China’s open-source approach offers developing countries something increasingly valuable: the opportunity to become participants in the AI future rather than customers standing outside it.