India’s Essential Investment in Foundational AI Models

Focusing on foundational AI models is crucial for ensuring India’s prominence in the global AI landscape. Application-layer innovations alone may compromise India’s position and influence in the AI value chain. Models created in Western and Chinese contexts often have cultural biases that do not resonate with India’s diverse values and languages. By prioritising the development of sovereign foundational AI capabilities, India can secure its leadership role in the AI revolution.

Need for Sovereign Foundational AI Models

Indian policymakers and tech leaders have advocated for maintaining a focus on the application layer of AI technology instead of investing in foundational models that underpin these applications. This perspective gained traction following the actions of the Chinese company Deepseek, which redefined AI economics by offering foundational AI capabilities as open-source, thus commoditising this part of the stack. Although India aspires to be the “AI use-case capital of the world” by concentrating on application-layer innovations, this strategy jeopardises its long-term strategic, cultural, and technological interests.

Risk of External Dependencies

While utilising AI to tackle challenges specific to India is vital, neglecting the development of foundational models can leave the country overly reliant on external systems. This dependency could hinder India’s ability to influence its AI future.

Cultural Sensitivity and Educational Sovereignty

Foundational AI models are increasingly viewed as the standard in education, content creation, and research. Models shaped in Western or Chinese environments may carry hidden cultural biases that do not align with India’s distinct values or linguistic range. Research indicates that existing large language models (LLMs) display biases influenced by the cultural norms of their creators. In education, these biases could result in a loss of intellectual diversity, undermining India’s rich cultural legacy. By creating its own foundational models based on Indian datasets, the nation can integrate its values, ethos, and cultural sensitivities into these systems, ensuring future generations receive education and content reflective of Indian realities.

Data Sovereignty and National Security

Data is the backbone of AI systems, and India produces one of the largest repositories of digital data globally, encompassing healthcare, financial transactions, agriculture, and more. Relying on foreign foundational models, developed by entities influenced by other nations’ policies, raises significant data privacy and security concerns. Critical national data might be vulnerable to surveillance or misuse, threatening India’s strategic and economic interests. By focusing on the creation of homegrown foundational models, India can maintain control over its data and ensure alignment of AI systems with national values and laws, which is vital for sensitive sectors like defence and governance.

Strategic Autonomy in Global AI Governance

As AI becomes integral to global power structures, nations controlling foundational AI technologies will have greater influence over international decision-making processes. Dependence on foreign models might expose India to geopolitical pressures that could restrict access to essential AI applications. Events like export controls on advanced technology by foreign governments could hinder India’s technological progress. Developing indigenous foundational models empowers India to maintain a strategic edge and collaborate as an equal entity in shaping global AI governance frameworks.

Economic Growth and Innovation Ecosystem

The establishment of foundational models can stimulate a vibrant domestic AI ecosystem. It can ignite innovation across academia, startups, and industries by providing a strong base for targeted applications. Models designed specifically for India’s unique challenges—such as processing regional languages or addressing rural healthcare—can democratise AI access and contribute to economic development. Furthermore, nurturing foundational models cultivates local expertise in sophisticated AI research areas, including neural architecture and optimisation techniques, positioning India as a leader in AI innovation rather than a mere consumer of foreign technologies.

Cost-Effectiveness of Foundational Models

Some critics assert that developing foundational models demands excessive resources and investment. However, advances in algorithmic efficiency and modular architectures have made training large-scale models far more manageable. For example, China’s DeepSeek has shown how foundational models can be developed cost-effectively with a limited number of GPUs. India has made progress in establishing affordable computing infrastructure through the IndiaAI Mission, and with GPUs available at subsidised rates, the financial barrier is no longer prohibitive. Strategic partnerships between public and private sectors can further mitigate costs while encouraging innovation.

The Dangers of Solely Focusing on Applications

Exclusively concentrating on application-layer innovations risks placing India in a peripheral role within the global AI value chain. Applications based on foreign foundational models are limited by the capabilities of those models. This reliance may stifle innovation and hamper India’s ability to address its specific needs. Additionally, as international competition intensifies, applications are more vulnerable to commoditisation. In contrast, foundational models constitute the core intellectual property that generates long-term value.

A Balanced Strategy Moving Forward

Instead of completely shifting away from application-layer innovations, India should embrace a dual strategy that aligns immediate benefits with long-term sovereignty:

• Invest in Foundational Models: Channel resources towards the development of indigenous large language models and multimodal frameworks that reflect India’s linguistic and socio-economic diversity.
• Utilise Open-Source Models: Engage in collaborations with open-source projects, such as Meta’s LLaMA, while advancing proprietary solutions for critical sectors.
• Fortify Data Infrastructure: Create comprehensive datasets that capture India’s cultural and linguistic diversity, such as through initiatives like the India Dataset Platform.
• Encourage Talent Development: Develop pipelines between academia and industry to nurture expertise in foundational AI research.
• Promote Public-Private Partnerships: Collaborate with startups and enterprises to share the financial burdens and risks associated with foundational model creation.

The Importance of Maintaining Balance

While India’s aim to democratise AI through application-layer innovations is admirable, it is insufficient for safeguarding long-term interests. Foundational models are essential for critical societal components—from education to governance—and must embody India’s unique values and priorities. Investing in sovereign foundational AI capabilities now will position India as a global leader in the AI revolution, protecting its cultural identity, strategic autonomy, and economic future. The decision to build for independence is crucial to avoiding ongoing dependence.