India faces a critical decision about its AI future, but experts say the answer isn't choosing between open-source and proprietary models,it's using both strategically. As the United States and China compete fiercely for AI leadership, India must balance access and innovation with competitive advantage and national sovereignty, according to technology leaders shaping the country's AI ecosystem.

Why Is India's AI Strategy Suddenly So Important?

The debate over open versus closed artificial intelligence models has intensified globally, particularly as high-performing Chinese open-source models have emerged that rival leading US proprietary systems while costing a fraction to develop and deploy. India now faces a strategic crossroads: should it prioritize an open AI ecosystem that accelerates innovation and broadens access, invest heavily in proprietary frontier models, or pursue a hybrid approach that balances all three priorities.

The stakes are high because AI sovereignty has become a national security concern. Recent controversies surrounding the availability of Anthropic's Mythos and Fable models highlighted how countries can become dependent on foreign AI systems and infrastructure, making the choice between open and closed models increasingly viewed as a matter of national strategy.

What Do Leading AI Executives Recommend for India?

The consensus among India's technology leaders is clear: a one-size-fits-all approach won't work. Manish Gupta, Senior Director for India and Asia-Pacific at Google DeepMind, explained that India should leverage both open and proprietary AI models depending on the specific use case and strategic context.

"You need both closed frontier models and open-weight models. Closed frontier models power the advances, and they remain cutting-edge, and they will be ahead on the curve. Open-weight models can be industrialized in a much cheaper way, so you need both," said Ravi Kumar S.

Ravi Kumar S, CEO at Cognizant

Gupta emphasized a critical distinction: once open-weight models are released publicly, they remain available indefinitely for developers to build upon. However, he cautioned against assuming Chinese open-source models will remain permanently open. While Chinese developers have released powerful open-weight models that compete with proprietary systems, the Chinese government has begun placing restrictions on access as models become more capable.

"You cannot rule out those models becoming closed in the future," noted Manish Gupta.

Manish Gupta, Senior Director for India and APAC at Google DeepMind

How Should Indian Startups Choose Between Open and Proprietary Models?

• Complex Real-World Problems: Startups tackling sophisticated AI use cases should leverage frontier models like Google's Gemini, which are becoming increasingly cost-effective and can solve complex problems that simpler models cannot handle.
• Cost-Sensitive Applications: For use cases where speed and cost matter more than cutting-edge intelligence, or when deep customization is required, open-weight models offer a more economical path that can run inside private data centers.
• Strategic Infrastructure Needs: India should build sovereign capabilities in areas where strategic autonomy matters, while leveraging both open and proprietary models across different layers of the AI technology stack.

Marc Manara, OpenAI's global head of startups, reinforced this pragmatic approach, arguing that developers should use the "right tool for the right job." He noted that frontier models remain highly differentiated and that OpenAI's goal is to offer a range of models that can fulfill as many different jobs as possible while continuing to push the frontier of AI capability.

What's Happening With Google's Open-Source AI Models?

Google has invested significantly in open-source AI through its Gemma family, which is built using the same research and technology as its flagship Gemini models. The Gemma family has surpassed 900 million downloads in total, with Gemma 4 alone crossing 300 million downloads in about three months. The family includes specialized variants such as MedGemma for medical applications, ShieldGemma for safety, DiffusionGemma for image generation, and T5Gemma for text-to-text tasks, each targeting different use cases.

Google has also doubled down on releasing cost-efficient proprietary models in recent months, launching Gemini 3.6 Flash, Gemini 3.5 Flash-Lite, and 3.5 Flash Cyber to cater to price-sensitive customers. However, these models remain significantly more expensive than Chinese alternatives like DeepSeek V4 Flash, though DeepSeek is planning to introduce substantial price increases across its AI services.

What's Driving the Global Push for Open AI Models?

In July, a group of 25 leading US technology executives, spearheaded by Nvidia CEO Jensen Huang, released a letter arguing that open models are vital for innovation and national security. The letter contended that open models expand access to the AI economy while strengthening competition and helping keep the gains of AI broadly shared rather than concentrated in a few hands.

The initial signatories included Microsoft, Meta, IBM, and Palantir. The list quickly grew to over 150 signatories including Amazon, Google, OpenAI, and SpaceX. Anthropic remains the only prominent frontier AI lab that hasn't signed the letter. The letter was aimed at preventing potential restrictions on open-source AI as the Trump administration considered its response to Chinese AI models.

For infrastructure companies such as Nvidia, wider adoption of open models translates into greater demand for AI chips and computing infrastructure, meaning their commercial interests align with a more open AI ecosystem. Anthropic, meanwhile, has sought to strike a middle ground, calling for mandatory safety testing for both open and closed frontier models, tighter chip export controls, and a crackdown on "industrial-scale distillation" of proprietary models.

As India charts its own AI course, the evidence suggests that the most successful path forward involves playing across all layers of the technology stack, leveraging the permanence and accessibility of open models while harnessing the cutting-edge capabilities of proprietary frontier systems where they matter most.