Why India Must Build Its Own Foundational AI Models

Policymakers and tech leaders in India have claimed that India should continue to focus on building the application layer of the AI stack as opposed to spending resources and capital on building the foundational models that drive these AI applications. 

This view has gained further momentum after the Chinese company Deepseek changed AI economics by open-sourcing foundational AI capabilities, thereby commoditising this layer of the AI stack.

India’s aspiration to become the “AI use-case capital of the world” by focusing on application-layer innovations is a commendable vision, but it risks the country’s long-term strategic, cultural, and technological interests.

While leveraging AI to address India-specific challenges is important, deprioritising foundational model development could leave India vulnerable to external dependencies and reduce its ability to shape its own future in an AI-driven world. 

Here’s why India must invest in building its own foundational AI models rather than solely focusing on applications:

Cultural Sensitivity And Educational Sovereignty

Foundational AI models are increasingly becoming the “holy truth” in education, content creation, and research. Models developed in Western or Chinese contexts often carry implicit cultural biases that may not align with Indian values or linguistic diversity.

For instance, studies have shown that existing large language models (LLMs) exhibit biases rooted in the cultural norms of their creators. In an educational context, these biases could lead to intellectual homogenisation, eroding India’s rich cultural heritage and diversity.

By developing its own foundational models trained on Indian datasets, India can embed its ethos, values, and cultural sensitivities into these systems.

This approach ensures that future generations receive education and content aligned with Indian realities rather than foreign ideologies.

Data Sovereignty And National Security

AI systems are fundamentally reliant on data. India generates one of the largest pools of digital data globally, spanning healthcare, financial transactions, agriculture, and more.

Relying on foreign foundational models—built by entities governed by other nations’ policies—raises significant concerns about data privacy, security, and potential misuse.

Sensitive national data could be exposed to surveillance or exploitation, jeopardising India’s economic and strategic interests. Developing indigenous foundational models ensures that India retains control over its data and aligns AI systems with national laws and values. This autonomy is critical for deploying AI in sensitive areas like defense, governance, and cybersecurity.

Strategic Autonomy In Global AI Governance

AI is poised to become a cornerstone of global power dynamics. Countries that control foundational AI technologies will wield disproportionate influence over global decision- making processes.

Dependence on foreign models could render India vulnerable to geopolitical pressures or restrictions on critical AI applications. For example, export controls on advanced computing chips or proprietary AI model weights by foreign governments could hinder India’s access to cutting-edge technology.

Building indigenous foundational models allows India to maintain strategic autonomy and participate as an equal player in shaping global AI governance frameworks.

Economic Growth And Innovation Ecosystem

Developing foundational models can catalyse a thriving domestic AI ecosystem. It can spur innovation across academia, startups, and industries by providing a robust base for domain- specific applications.

Indigenous models tailored to India’s unique challenges—such as regional language processing or rural healthcare—can democratise AI access and drive economic growth.

Moreover, building foundational models fosters local expertise in advanced AI research areas like neural architecture design and optimisation techniques.

This expertise can position India as a global leader in AI innovation rather than merely a consumer of foreign technologies.

Cost-Efficiency Is Achievable

Critics often argue that developing foundational models is prohibitively expensive and resource-intensive. However, advancements like algorithmic efficiency and modular architectures have significantly reduced the compute requirements for training large-scale models.

For instance, China’s DeepSeek demonstrated how cost-effective foundational model development can be achieved with fewer GPUs.

India has already made strides in creating affordable compute infrastructure under the IndiaAI Mission. With GPUs available at subsidised rates (often less than $1 per hour), the cost barrier is no longer insurmountable. Strategic public-private partnerships can further distribute costs while fostering innovation.

The Risks Of Over-Reliance On Applications

Focusing exclusively on application-layer innovations risks relegating India to a secondary role in the global AI value chain. Applications built atop foreign foundational models are inherently constrained by the capabilities and limitations of those models.

This dependency could stifle innovation and restrict India’s ability to address unique challenges effectively.

Additionally, applications are more susceptible to commoditisation as global competition intensifies. Foundational models, by contrast, represent the core intellectual property driving long-term value creation.

The Path Forward: A Balanced Approach

India does not need to abandon its focus on application-layer innovations entirely; rather, it should adopt a dual strategy that balances short-term gains with long-term sovereignty:

1. Invest in Foundational Models: Allocate resources toward developing indigenous LLMs and multimodal models tailored to India’s linguistic diversity and socio-economic realities.
2. Leverage Open-Source Models: Collaborate with open-source initiatives like Meta’s LLaMA while building proprietary capabilities for critical sectors.
3. Strengthen Data Infrastructure: Establish robust datasets reflecting India’s cultural and linguistic diversity through initiatives like the India Dataset Platform.
4. Foster Talent Development: Build academic-industry pipelines to cultivate expertise in foundational AI research.
5. Encourage Public-Private Partnerships: Collaborate with startups and enterprises to share costs and risks associated with foundational model development.

Maintaining Balance Is key

India’s ambition to democratise AI through application-layer innovations is laudable but insufficient for securing its long-term interests. Foundational models will drive critical aspects of society—from education to governance—and must reflect India’s unique values and priorities.

By investing in sovereign foundational AI capabilities today, India can ensure its place as a global leader in the AI revolution while safeguarding its cultural identity, strategic autonomy, and economic future. The choice is clear: build for independence or risk perpetual dependence.