For decades, India excelled at adopting technologies developed elsewhere. Software services, mobile internet and cloud computing created enormous opportunities, but the underlying platforms were largely built outside the country.
Artificial intelligence presents a different moment.
As governments around the world increasingly view AI as a strategic asset, countries are racing not only to use the technology but also to influence how it is built.
Against that backdrop, BharatGen’s participation in Project Tapestry represents something larger than another AI announcement. It signals India’s intention to help shape the architecture of future AI systems rather than merely consume them.
Sovereign AI Gains Momentum
The rise of generative AI has concentrated enormous technological power in a handful of companies, mostly based in the United States and China. That concentration has triggered growing interest in sovereign AI, a concept that seeks to give countries greater control over their data, infrastructure and AI capabilities.
In April 2026, the AI Alliance launched Project Tapestry, an initiative aimed at enabling collaborative development of open and sovereign AI systems. The alliance includes more than 200 organizations spanning technology companies, academic institutions and research bodies.
According to the AI Alliance, Project Tapestry is designed around a federated approach to AI development. The objective is to enable participants to collaborate while retaining flexibility over their own datasets, infrastructure and deployments.
That philosophy reflects a broader shift taking place worldwide.
The United Arab Emirates has invested heavily in the Falcon family of large language models. Singapore has backed the development of SEA-LION, designed specifically for Southeast Asian languages. European policymakers have increasingly emphasized digital sovereignty and open AI initiatives.
Rather than standing outside that movement, India is seeking to participate in it.
India’s AI Infrastructure Push
India’s ambitions are being backed by public investment.
In March 2024, the Union Cabinet approved the IndiaAI Mission with an outlay of ₹10,371.92 crore over five years. The programme seeks to support compute infrastructure, datasets, foundation models, startups and AI talent development.
A central pillar of the mission is providing access to more than 10,000 GPUs through public-private partnerships.
That focus on computing power reflects the realities of modern AI.
Training advanced foundation models requires enormous computational resources and access to large datasets. Without those capabilities, countries risk becoming dependent on foreign platforms for technologies that are increasingly expected to underpin industries ranging from healthcare and finance to education and manufacturing.
India has already begun supporting domestic AI projects. Reuters reported in June 2026 that Bengaluru-based SatSure secured a grant worth ₹246 million from the Indian National Space Promotion and Authorisation Centre to develop AI-powered earth observation foundation models.
These efforts suggest that India’s AI strategy is moving beyond policy announcements and into implementation.
BharatGen’s Distinctive Strength
Within that broader ecosystem, BharatGen occupies a unique position.
Led by IIT Bombay and supported under the National Mission on Interdisciplinary Cyber-Physical Systems, the initiative brings together nine institutions and more than 60 researchers working on multilingual and multimodal AI technologies.
Its ecosystem includes Param2, a large language model, along with speech recognition, text-to-speech and document understanding systems.
Param2, unveiled earlier this year, contains 17 billion parameters and supports all 22 scheduled Indian languages.
That capability addresses one of the most persistent challenges in artificial intelligence.
Most large language models have historically been dominated by English and a small number of high-resource languages. Building systems capable of understanding India’s linguistic diversity requires vast datasets and deep contextual knowledge.
What appears to be a complexity problem can also become a strategic advantage.
Why Global Models Alone May Not Be Enough
The emergence of powerful models from OpenAI, Google, Anthropic and Meta raises an obvious question.
Why do countries need their own AI ecosystems?
The answer increasingly revolves around control, customization and resilience.
Governments and enterprises often have regulatory requirements, language needs and data considerations that cannot always be addressed by generic models developed elsewhere.
Local languages remain underserved in many AI systems. Countries are also becoming more sensitive to issues surrounding data governance, intellectual property and long-term technological dependence.
This does not mean sovereign AI is about isolation.
Instead, it reflects a growing effort to strike a balance between international collaboration and domestic capability.
Project Tapestry itself embodies that approach.
According to the AI Alliance, the initiative seeks to foster collaborative AI development while allowing participants greater control over how their resources and systems are deployed.
Economic Stakes Are Rising
The significance of AI extends well beyond technology.
McKinsey has previously estimated that artificial intelligence could contribute between $450 billion and $500 billion to India’s economy by 2030, highlighting the scale of the opportunity.
At the same time, the country faces important constraints.
India possesses abundant engineering talent and a vibrant startup ecosystem, but relatively few companies currently operate at the frontier of foundation model development. Access to advanced chips and computing infrastructure remains a challenge, particularly as geopolitical tensions reshape global supply chains.
Building globally competitive AI systems will require sustained investments, research excellence and commercialization capabilities.
Those are long-term challenges rather than short-term fixes.
Pursuing A Different Path
India’s approach differs from the models pursued by the world’s two leading AI powers.
In the United States, private technology companies have driven much of the innovation. In China, state support and large domestic champions have played a central role.
India appears to be exploring a third path. It is investing in domestic capabilities while participating in international collaborations and open ecosystems.
That strategy could prove particularly important as countries seek alternatives to excessive dependence on a small number of AI providers. Success is far from guaranteed.
Building world-class AI capabilities requires patience, capital and sustained research. Yet BharatGen’s role in Project Tapestry suggests that India is seeking something larger than technological self-sufficiency.
It is seeking a seat at the table where the rules and foundations of the AI era are being defined.
Previous waves of technology largely made India an important participant. Artificial intelligence offers the possibility of becoming something more: a contributor to the architecture itself.
