China has proposed taking a leading role in creating a BRICS open-source AI community, supporting collaboration on large language models and AI training. At the 18th BRICS Summit in New Delhi on September 13, Xi Jinping also proposed a digital cloud platform, cooperation on smart factories and manufacturing standards, an engineer cultivation alliance and technology-focused youth exchanges. These were related but separate initiatives in his speech.
Together, the proposals indicate an ambition to compete across an international ecosystem covering technology, computing, skills, industrial applications and standards. The announcement does not yet explain how that ecosystem would be funded, governed or implemented.
For India, the strategic question is whether it can help develop an AI architecture for the Global South without becoming excessively dependent on any one foreign technology ecosystem.
From Individual Models To An International Ecosystem
Much public discussion of AI competition focuses on individual companies and models. But an AI ecosystem needs more than a model. It requires electricity, data centres, computing hardware, cloud services, data, developers, applications, skills, capital and standards.”
A nation managing only one of these stages may still achieve commercial success. One connecting several might exert much greater strategic influence.
China’s proposal fits a wider policy direction. Its international AI cooperation action plan, released on July 17, calls for more inclusive computing services and broader sharing of open-source AI ecosystems. It covers eight areas, including data, industrial applications, talent, standards, governance and ethics.
There is also an institutional dimension. Representatives of 29 countries signed an agreement in Shanghai on July 16 to establish the World Artificial Intelligence Cooperation Organisation, or WAICO. Malaysia’s foreign ministry identifies the country as one of the founding signatories. This is evidence of institution-building, but it does not, by itself, establish what the organisation can deliver.
The strategic inference is that Beijing wants a larger role in how emerging economies develop AI capabilities. If Chinese companies supply models, computing equipment, cloud services, industrial systems, and technical expertise, they could build relationships that go beyond software exports.
BRICS offers a multilateral platform for this ambition. Its members face common challenges in farming productivity, healthcare access, education, multilingual public services, logistics and energy management. These create opportunities for cooperation, though shared needs do not automatically produce a single market or common technology architecture.
Why Manufacturing Matters
The manufacturing component deserves particular attention because it connects AI to the physical economy.
A developing country looking to modernise its industrial base needs machinery, sensors, industrial software, telecommunications, engineering expertise and training. AI is useful when it works with these systems to improve quality control, forecast maintenance needs, manage energy use or help factories plan production.
China’s manufacturing and technology industries could offer advantages by combining these capabilities. If AI systems are deployed alongside equipment and engineering support, the resulting economic relationship could be far broader than a software sale.
This is one of the most consequential possibilities in the proposal. It is conditional: countries would still make their own procurement decisions, and suppliers must demonstrate performance, affordability and reliability.
For India, the manufacturing dimension raises a practical question: can it combine its software capabilities with its manufacturing ambitions quickly enough?
AI increasingly intersects with energy, telecommunications, education, trade, infrastructure and industrial policy.
AI capability is becoming part of national productive capacity.”
India’s response must connect technology development with the needs of factories, farms, hospitals and public services.
India’s Opportunity Is Already Taking Shape
India is not starting from scratch. Its 2026 BRICS chairmanship has emphasised resilience, innovation, cooperation and sustainability. The New Delhi declaration also records existing cooperation on AI and industrial competencies, alongside a BRICS Network on Digital Agriculture.
At the summit, Narendra Modi warned against weaponising technology and critical minerals. He also described linking AI, geospatial technology and digital public infrastructure to farmers’ needs through the digital agriculture network.
These priorities offer a basis for India’s contribution, but the distinction between Indian and Chinese approaches should not be exaggerated. The IndiaAI Mission spans computing, base models, datasets, applications, skills, startup financing and safe and trusted AI. India is pursuing capabilities across several layers of the ecosystem.
Within that wider effort, India could become an important partner for developing economies in AI applications, skills and digital public infrastructure.
Its development experience suggests several areas of opportunity: crop intelligence and irrigation management; primary-care assistance and multilingual health information; teacher support and vocational training; citizen services and administrative workflows; and industrial quality control and predictive maintenance.
The opportunity is to turn that experience into systems other countries can adapt to their own needs. It would require local partnerships, testing, suitable data and viable business models. An application that works in India will not automatically work elsewhere.”
Language is a relevant area. India’s multilingual challenge creates a reason to develop better speech, translation and local-language systems. The methods and technical capabilities developed could be useful to other multilingual economies, though each would require its own language resources and evaluation.
Skills could become equally important. AI adoption requires people who understand both technology and industries. India could help train implementation teams and professionals who use AI effectively in healthcare, education, manufacturing and public administration.
That would turn domestic experience into a wider commercial and development opportunity.
Open Source Does Not Automatically Mean Open Power
Open models can change the economics of AI adoption. Depending on licences and technical requirements, they let countries and companies deploy systems locally, adapt them to domestic languages, and build applications using their own data.
But “open source” needs cautious handling. Releasing model weights does not necessarily make an AI system fully open source. The Open Source Initiative’s definition also requires access to relevant code and information about training data. The proposed BRICS community’s actual licensing arrangements remain to be assessed.
Even a fully open model does not remove dependence elsewhere. A country may still rely on external suppliers for chips, cloud services, industrial software or technical support. Each dependency may seem manageable; together, they can cause vulnerabilities, especially in critical infrastructure.
The practical objective should be the ability to choose suppliers, move between systems and retain control over crucial data and applications. International cooperation and domestic capability can support each other if the arrangements preserve that flexibility.
Data governance will also matter. Countries collaborating on AI will need to decide which data can be shared, under what safeguards and for which purposes. Restrictive requirements can increase costs, while inadequate safeguards can undermine privacy, security and trust. Cooperation will need to handle these concerns rather than assume a single approach across BRICS.
The Test Is Implementation
Several outcomes remain possible. Cooperation could remain largely confined to declarations, conferences and training. It could produce useful partnerships around models and applications. Broader infrastructure and industrial integration would necessitate sustained investment, shared technical arrangements and political agreement.
The key questions are therefore concrete: who will fund the proposed programmes, who will govern them, what licences and standards will apply, and can participants change suppliers without losing access to their data or applications?
For India, the response must combine domestic capability with practical international cooperation. Compute, models, datasets and AI talent need sustained investment, with priorities chosen according to key strategic importance. India should also pursue joint applications in agriculture, healthcare, education, climate and public services, where participating countries can define shared problems and assess results.
Its digital public infrastructure experience and training capabilities may support those programmes. Its technology choices should preserve interoperability and avoid excessive dependence on any one ecosystem.
China’s proposal opens a more extensive discussion about how AI will become embedded in economic development. India’s opportunity is to help shape an open, interoperable, development-oriented ecosystem that combines software and engineering capabilities, multilingual systems, functional applications and human capital.
That opportunity will depend on what India builds and delivers. It requires treating AI as economic infrastructure rather than simply as a technology product.