India's AI Moment: Why It's Not Just About Language, but Vision and R&D

While everyone talks about language barriers, I see something bigger, a chance for India to lead in AI through strategic R&D, sovereign infrastructure, and real-world impact in agriculture and healthcare. What do you think; can India’s AI story inspire the next paradigm? 

India is the home to the world’s largest digital identity system (Aadhaar), processes more real-time digital payments than the U.S. and Europe combined (via UPI), and now ranks 3rd globally in AI startup funding. In fact, the artificial intelligence (AI) market in India is projected to reach $8 billion by the end of 2025, almost 6% of the world’s share.

Isn’t it laudable?

I recently came across an insightful article¹ in MIT Technology Review, written by Shadma Shaikh, an independent technology journalist based in India. She covers internet platforms, the app economy, and digital culture, and is also the co-founder of FactorDaily, a social-impact focused newsroom. Her piece explores the rise of India's AI ecosystem in the wake of China's DeepSeek-R1 launch, sparking several reflections that I believe are worth sharing.

Here’s my perspective on the article and India's remarkable vision for R&D and AI.

When DeepSeek-R1, a Chinese foundation model, emerged as a powerful open-source alternative to Western models earlier this year, it sparked significant interest in the global AI community, particularly in India. 

For many of us, it was a moment of realisation; the disruptive potential of lean, capital-efficient innovation became evident. It challenged long-standing assumptions about scale, funding, and geography. 

It also inspired a growing cohort of Indian technologists to ask, "Why not us?”

Is a diverse language a real barrier for India's model?

However, amid the conversation about linguistic diversity and digital gaps, we must be cautious not to assume that language is the primary barrier for India in building competitive AI. 

It isn't.

International models from OpenAI to Mistral are already being fine-tuned across dozens of languages, and many multilingual open-source architectures are increasingly capable of handling non-English use cases. 

Language models today are inherently flexible, and with the correct training data and adaptation layers, they can support India's linguistic landscape. The challenge is more profound, systemic, and rooted in a chronic underinvestment in research and development (R&D), infrastructure, and commercialisation.

There is an opportunity for research and development to create synthetic datasets tailored explicitly for Indian language models. Alternatively, leveraging the Bhashini² model could help address some of the existing challenges. A stronger emphasis on research and innovation will be crucial to moving forward effectively in this area.

Reimagining R&D for impact

India's R&D spending has hovered around 0.65% of GDP³ for years, less than a fifth of what the United States or China spends. It isn't merely a funding issue. It's a vision issue. Most of India's R&D resides within isolated government agencies, and even their most cutting-edge innovations rarely translate into civilian or commercial technologies. 

There is an evident lack of connection between research and scalable product development. While bodies like DRDO and ISRO have achieved globally celebrated milestones, their impact in the broader innovation ecosystem remains limited.

It needs a new model, one that draws inspiration from institutions like DARPA⁴in the US, which connects ambitious research to high-risk, high-reward technological outcomes. Indian R&D must become more risk-tolerant, interdisciplinary, funded and incentivised to produce real-world impact.  

This change is not just necessary, it's inspiring.

Building meaningful infrastructure

India is now beginning to address a long-ignored gap in its computing infrastructure. MeitY's move to allocate 18,000 GPUs to foundational AI research is a crucial step, but it must be backed by sustained policy and private-sector momentum and encouraged by the government. 

Building meaningful infrastructure is not just about hardware; it's also about software. It's about access, openness, and scale.

Crucially, the infrastructure story must be about more than just GPUs. It must also be about access, openness, and scale. The centre and state governments should streamline bureaucratic processes and encourage Public-private partnerships to build hyperscale data centres designed not only for domestic use but also as regional AI hubs across the Global South. India has the cost advantage, talent pool, and geopolitical neutrality to play this role effectively.

Global companies, from Microsoft and Google to smaller AI labs should be invited to co-invest in these efforts, not just as vendors but as partners in long-term development. India can offer them a sandbox to test inclusive, multilingual AI and deploy models in markets that Western solutions have historically underserved.

A global AI paradigm shift

Rather than replicating the Silicon Valley playbook of building ever-larger foundation models, India has the opportunity to define a new paradigm. One focused on responsible AI use cases, lightweight models tailored to local realities, and sovereign infrastructure that prioritises serving citizens first.

This approach requires more than capital. It requires courage. A culture of deep-tech entrepreneurship must be nurtured, where small teams are empowered to solve complex problems with innovative engineering, not just scale. Models like Pragna-1B and Krutrim-2 demonstrate that it's possible.

To catalyse an actual AI moment, India and other nations must invest in:

• Open R&D ecosystems: Create funding models that incentivise open science, similar to Europe's Horizon programme.
• Equity for researchers: Provide startup-style equity and long-term financial upside to technologists, not just founders.
• Global knowledge alliances: Invite collaboration from top research labs, enabling Indian researchers to lead open-source AI that is tailored to India yet relevant to the world.
• Inclusive data policies: Build large, diverse, multilingual datasets that are ethically sourced and transparently shared.

Sovereignty through collaboration

Ultimately, AI sovereignty must not be confused with AI isolation. The best way for India or any country to shape the global AI landscape is to create tools that others want to use. Not because they're big, but because they're thoughtful, efficient, and inclusive. 

Models like DeepSeek-R1 are powerful not just because of their benchmarks but because of their availability. Openness breeds ecosystems. And ecosystems win.

India’s position in AI compared to its global counterparts

The information below has been compiled from various sources, including UNESCO⁵, McKinsey⁶, OECD⁷, LinkedIn⁸, NVIDIA⁹, the World Economic Forum¹⁰, the Chinese Government¹¹, the European Commission¹², UK Research and Innovation¹³, Tech Nation¹⁴, the IndiaAI Mission¹⁵, Alibaba¹⁶, Baidu¹⁷, Eurostat¹⁸, NASSCOM¹⁹, IIT²⁰, AIPRM²¹, Forbes²², TechCrunch²³, Reuters²⁴, The Wall Street Journal²⁵, BusinessWire²⁶, The Guardian²⁷, Financial Times²⁸, The Verge²⁹, and Analytics India Magazine³⁰.

India has made impressive progress in advancing its AI capabilities, particularly in talent development and establishing affordable infrastructure. To enhance its global competitiveness and sustain growth, India has valuable opportunities to capitalise on by increasing AI investments, building skills development, investing in research to prepare AI specialists, strengthening ethical and regulatory frameworks, and improving data availability and privacy standards. 

Additionally, scaling AI applications across various sectors will be crucial. By fostering collaboration between local and international AI companies and creating more accessible markets and job opportunities, India has the potential to emerge as a leading global hub for AI.

Conclusion

In AI, as in space, India has the chance to build something uniquely its own, lean, ambitious, and globally relevant. But to do so, we must invest in ideas, infrastructure, and individuals, not just to catch up, but to lead with purpose. India's potential in the global AI landscape is not just significant, it's something to be proud of.

I appreciate Shadma for encouraging me to explore potential solutions for addressing India's emergence. Let's work together to find a constructive path forward.

References: 

1. https://www.technologyreview.com/2025/07/04/1119705/inside-indias-scramble-for-ai-independence/?utm_source=flipboard&utm_content=other
2. https://bhashini.gov.in/ 
3. https://carnegieendowment.org/research/2025/02/the-missing-pieces-in-indias-ai-puzzle-talent-data-and-randd?lang=en 
4. https://www.darpa.mil/ 
5. https://www.unesco.org/en
6. https://www.mckinsey.com/
7. https://www.oecd.org/ 
8. https://economicgraph.linkedin.com/content/dam/me/economicgraph/en-us/PDF/AI-in-the-EU-Report.pdf 
9. https://analyzify.com/statsup/nvidia 
10. https://www.weforum.org/publications/ai-for-impact-artificial-intelligence-in-social-innovation/ 
11. https://www.statista.com/topics/8383/artificial-intelligence-in-china/#statisticChapter 
12. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises#:~:text=update%3A%20January%202026-,Highlights,the%20information%20and%20communication%20sector. 
13. https://www.gov.uk/government/publications/artificial-intelligence-sector-study-2023/artificial-intelligence-sector-study-2023
14. https://technation.io/uk-ai/#:~:text=Key%20findings%3A,of%20the%20past%20three%20years. 
15. https://indiaai.gov.in/article/top-ai-statistics-and-trends-of-2023 
16. https://www.alibabacloud.com/blog/alibabas-core-businesses-reignite-growth-as-ai-strategy-delivers-strong-results_602006#:~:text=AI%2DDriven%20Growth%20Accelerates,player%20in%20global%20cloud%20computing. 
17. https://www.statista.com/statistics/1079978/china-baidu-research-and-development-costs/ 
18. https://worlddata.ai/trends?q=%20Eurostat&sn=SCIENCE%20&%20TECHNOLOGY
19. https://nasscom.in/knowledge-center/publications/unlocking-value-data-and-ai-india-opportunity 
20. https://www.iit.ac.lk/course/bsc-artificial-intelligence-and-data-science/
21. https://www.aiprm.com/en-gb/ai-statistics/ 
22. https://www.forbes.com/uk/advisor/business/software/uk-artificial-intelligence-ai-statistics/#:~:text=The%20IT%20and%20telecommunications%20sector,rates%2C%20at%20around%2011.5%25. 
23. https://techcrunch.com/2025/02/11/ai-investments-surged-62-to-110-billion-in-2024-while-startup-funding-overall-declined-12-says-dealroom/ 
24. https://www.thomsonreuters.com/en/press-releases/2025/june/the-ai-adoption-reality-check-firms-with-ai-strategies-are-twice-as-likely-to-see-ai-driven-revenue-growth-those-without-risk-falling-behind 
25. https://www.wsj.com/tech/ai/artificial-intelligence-investing-charts-7b8e1a97
26. https://www.businesswire.com/news/home/20250508760017/en/Artificial-Intelligence-AI-Machine-Learning-Industry-Almanac-2025-Market-Research-Statistics-Trends-and-Leading-Companies---ResearchAndMarkets.com
27. https://www.theguardian.com/environment/2025/may/22/ai-data-centre-power-consumption
28. https://www.ft.com/partnercontent/coupa/the-future-of-finance-how-ai-and-data-can-boost-business-performance.html#:~:text=Finance%20leaders%20(31%20per%20cent,in%20cash%20and%20liquidity%20management.
29. https://www.theverge.com/ai-artificial-intelligence
30. https://analyticsindiamag.com/