India's AI Ambition: Can It Bridge the Talent, Data, and Research Gaps?

The world is at a crossroads for the race to AI superpower and I was quite curious to know where India stands in this AI race. In the high-stakes global race for Artificial Intelligence (AI) leadership, can India truly compete and lead, or will it be relegated to a minor role? Then I came across this excellent paper, titled "The Missing Pieces in India’s AI Puzzle: Talent, Data, and R&D," which dives deep into this question, analyzing three crucial elements for AI success: talent, data, and research & development (R&D).

The paper highlights how the US and China are currently at the forefront of this AI race, while other nations, including India, are actively trying to establish their own competitive AI strategies. Early this year in January China sparked panic in the whole AI space of USA with the release of DeepSeek-R1, an open-source model developed by a Chinese AI start-up. The very fact that this model was built at a very low cost busted the myth that AI leaders in USA have been telling that AI infrastructure is a costly affair.

India also does not want to be falling behind in the race, launched its National AI Mission in 2024, named IndiaAI Mission. It has laid out a plan across seven elements of the “AI stack”: computing/AI infrastructure, data, talent, research and development (R&D), capital, algorithms, and applications. The paper takes the definition of AI stack from a report prepared by National Security Commission on Artificial Intelligence (NSCAI) for USA government which presents the strategy for winning the artificial intelligence era. Despite India's ambitions in contributing to AI space, the success in the AI race requires multiple pieces of the AI puzzle to be in place. India’s focus so far has been on only two elements- ensuring the availability of AI focussed compute systems and to some extent building Indic language models. Out of the 7 elements of AI stack, significant gaps in talent, data, and R&D need to be urgently addressed. Without filling these "missing pieces," India risks falling short of its goal to become a global AI leader.

The paper goes on to explore the challenges within these three key areas and proposes recommendations for India to strengthen its approach, emphasizing the need to boost AI talent, build accessible digital data, and foster cutting-edge research. Ultimately, it argues for a shift towards a "Competitiveness in AI" strategy to complement India's existing "AI for All" vision.

In 2024, India launched its national AI mission, committing significant funds (over $1.3 billion) over five years to become a leader in AI. The initial focus has been on building the essential infrastructure, particularly securing AI chips and boosting computing power (with a target of 10,000 GPUs).

This early emphasis on computing resources was a strategic move, recognizing global concerns around chip access. However, the paper suggests that this focus might have come at the expense of other crucial elements for a strong AI ecosystem. India's overall strategy for AI competitiveness is still evolving.

The paper argues that talent, data, and research now require significantly more attention and investment. The upcoming sections will explore the challenges India faces in these three key areas and propose solutions to bridge these gaps.

The Talent Puzzle: India's Potential and the Gaps to Fill

When we talk about India's strengths in the AI race, one of the first things that comes to mind is its vast pool of science, technology, engineering, and mathematics (STEM) graduates. On top of this India is currently experiencing a demographic dividend. This means India has a large and young workforce, which can boost productivity and drive economic growth and with creating an AI talent pool it’s a perfect opportunity to harness this innate potential.

With all these favorable resources in the country, on the surface, it seems like India should have a natural advantage in AI talent. However, the paper urges us to take a closer, more nuanced look at this. The reality is, India faces several key challenges when it comes to AI talent:

1. A Global Shortage, Amplified for India: The paper highlights that there's a worldwide shortage of AI professionals, affecting even leading nations like the US and China. As the demand for AI skills explodes, the gap between available talent and the need will only widen. This is particularly critical for India, as AI presents a massive opportunity for the country's growth. Without enough skilled people, India risks missing out on fully capitalizing on this potential, especially as globalization shifts towards services, where India has traditionally been strong.

2. The Brain Drain: One significant issue the paper points out is the migration of India's brightest AI minds. Interestingly, while government strategies focus on increasing AI courses, they don't adequately address why top Indian AI talent often leaves the country for opportunities abroad. If you see the below figure (an amazing illustration my MarcoPolo named Global AI Talent Tracker showing talent migration, check original figure for an animated visualization) showing that many talented Indian students, even those graduating from top institutions like the IITs, end up pursuing advanced degrees and careers in the US or Europe. Losing this high-potential research talent is a problem India needs to solve urgently.

   

3. Quality and the Need for Upskilling: While India boasts a large number of STEM graduates, employers often raise concerns about their job readiness. The paper emphasizes that improving the quality of education is crucial. If I share my recent experience taking a course on Linear Algebra in an AI related course, I strongly felt that it lacked the industrial applications of learned concepts in the course. The students only learn the application part in industry. There is a huge gap in academia and industry. Simultaneously, there's a massive need to up-skill India's existing IT workforce to become AI-proficient – a significant challenge for the tech and IT services industry.

4. An Imbalance in Talent Types: The paper breaks down AI talent into three categories: top-tier (cutting-edge researchers, data scientists), mid-tier (application developers, domain experts), and low-tier (project managers, implementers). For any country to become a true AI powerhouse, it needs a healthy mix of talent across all these levels, tailored to its specific AI strategy. While India has a growing number of developers in the AI/machine learning space (suggesting strength in the mid- and low-tiers, and being a major contributor to AI projects on platforms like GitHub which is a remarkable growth story for India), top-tier research talent remains concentrated in the US, China, and Europe. Building and retaining this high-level talent will be a significant challenge for India without a major push.

   

So, how can India bridge this talent gap and truly leverage its "talent nation" status in the AI era? The paper offers several key recommendations:

• Partner with Global Tech Giants: Microsoft CEO, Satya Nadella has committed to train over 10 million people in India over the next 5 years. Encourage more companies to undertake similar large-scale training initiatives within India. The goal should be to involve many such companies in training diverse types of AI talent.

• Strategically Build Different Talent Tiers: India needs a clear plan to cultivate talent at all levels: top-tier AI researchers and scientists, mid-tier AI developers and architects, and low-tier AI integrators who can apply AI in various industries.

• Address the Top-Tier Brain Drain: Creating a dynamic AI ecosystem is key to retaining top researchers. This includes fostering research opportunities, strong industry-academia collaboration, and supportive policies. The paper highlights the need for more Indian PhDs and scientists to focus on AI research. It suggests identifying 25-30 universities to train top-tier talent and investing in 4-6 centers of excellence in AI research with globally competitive salaries and infrastructure, potentially attracting back Indian talent currently abroad. Encouragingly, the paper notes that India's ability to retain top-tier AI talent has shown some improvement since 2019.

• Upskill the Existing Workforce: India's IT services firms need to aggressively upskill their current employees to ensure they remain relevant in an AI-driven world.

• Integrate AI into Education: The curriculum at all levels (K-12, graduate, postgraduate) needs to incorporate AI concepts. Collaboration between industry and academia is crucial to ensure graduates have the specific skills employers need.

• Develop Diverse AI Skills: Beyond just engineers, India needs to cultivate AI entrepreneurs, product managers, designers, researchers, and ethicists. Educational institutions across different disciplines should adapt their programs accordingly.

• Attract Global STEM Talent: India should create visa policies to attract top science and engineering talent from neighboring regions, capitalizing on brain drain in those countries. The recently announced G20 talent visa is a positive step in this direction, aiming to attract top researchers.

• Prepare for AI-Induced Job Changes: India needs to proactively address the potential for AI to cause job displacement by focusing on training and reskilling its workforce for new AI-related roles. Given the anticipated global shortage of AI professionals, India has a significant opportunity here.

The "talent puzzle" is complex, but by strategically addressing these challenges and implementing these recommendations, India can truly leverage its human capital to become a major player in the global AI landscape.

The Data Bottleneck: The "Oil" Missing from India's AI Engine

Artificial Intelligence thrives on data – it's the essential fuel that powers algorithms and models. While having lots of data isn't a guarantee of AI success, it's definitely a necessary starting point. Think of companies like OpenAI and Google in the US, and the tech giants in China; their AI models have been trained on massive amounts of data, giving them a significant head start.

The paper highlights a crucial challenge for India: a lack of readily available, high-quality, India-specific data in the volumes needed to train advanced AI models. Even well-funded Indian AI startups have pointed out this fundamental problem, sometimes resorting to using artificial ("synthetic") data to train their systems.

Why is India facing this "data disadvantage"? The paper breaks it down into several reasons:

1. Limited Access to Existing Big Data: Indian startups and researchers simply don't have the same access to the sheer volume of data that global tech giants like Google, Meta, and Microsoft possess just by virtue of them being big tech giants and their massive user bases and platforms globally. This data is often proprietary or too expensive to acquire.

2. Scarcity of Unique Indian Data: Unlike global platforms, Indian firms often lack access to unique data specific to Indian consumers or businesses. This could include data from systems like UPI (Unified Payments Interface) or data in Indian languages that isn't yet widely available online.

3. Data Silos and Poor Quality: While India generates a vast amount of digital data thanks to its growing digital infrastructure and user base, this data often sits isolated in different systems or is not well-organized or labeled, making it difficult to use effectively for AI.

4. Over-Reliance on Government Solutions: India's national AI mission seems to heavily depend on the government to create and manage a central data platform. The upcoming IndiaAI Datasets Platform aims to be a repository of data from both public and private sectors, similar in concept to the private platform Hugging Face. However, the paper argues that a single government-managed platform might struggle to meet the diverse and extensive data needs of AI research and development. Leading AI efforts show that data requirements are vast and varied, and a government platform might face challenges with data completeness, structure, and quality.

The paper emphasizes that more creative thinking is needed to solve this data challenge, potentially involving both government and private sector initiatives. The global market for AI datasets is rapidly expanding, and Indian startups and researchers need cost-effective ways to tap into this. Furthermore, India needs a clear, long-term strategy to gather and utilize the massive amounts of data in Indian languages to build tailored AI models for its own population and businesses.

So, how can India overcome this data bottleneck? The paper offers several key recommendations:

• Unlock Indian Consumer/Transaction Data: India needs to find ways to access and utilize the vast amounts of data generated by its large internet user base through telecom, e-commerce, fintech, and logistics firms, while respecting privacy. Learning from the success of UPI in fintech, the right regulations and market-based approaches could unlock this data for AI innovation.

• Develop Multiple Data Marketplaces: Instead of relying solely on a government platform, India should encourage the development of various data marketplaces where non-personal data can be shared (under appropriate privacy and security guidelines) with AI entrepreneurs and researchers. This could be a more scalable solution given the diversity of data across the country, similar to the data marketplaces operated by companies like SAP and AWS in the US.

• Free Up Government Data: Data held within various government departments (agriculture, health, transportation, etc.) should be made more accessible. Initiatives like the Integrated Geospatial Data-Sharing Interface (GDI) for sectors like agriculture and transportation offer a good model for unlocking sector-specific government data. The focus should be on making unstructured data usable for AI applications.

• Scale Up Current Government Efforts: While initiatives like the IndiaAI Datasets Platform and the Open Government Data Platform are a good start, they need significant scaling and improvement in terms of data quality, regular updates, and standardized formats. Appointing a Chief Data Officer for India could help streamline these efforts and ensure data quality, potentially learning from the EU's Metadata Quality Dashboard.

• Build Large Multilingual Data Repositories: Efforts like the Bhashini translation system to create open-source datasets in Indian languages are promising but need to be scaled up significantly. Exploring ways to leverage the vast amounts of multilingual data generated on telecom platforms and digitizing historical regional content could provide a major boost.

• Develop a DPI-Like Approach for Data Commons: India should consider applying the principles of its successful Digital Public Infrastructure (DPI) to data sharing. Creating data exchanges with appropriate privacy safeguards and consent mechanisms could prevent data from being monopolized by a few large firms and empower entrepreneurs to build innovative applications.

The "data dilemma" is a tough one, but the paper argues that with a long-term, creative, and strategic approach, India can turn its vast digital footprint into a powerful asset in the global AI race.

The Research & Development Imperative: India's Path to AI Innovation

The paper makes it clear that any country serious about leading in the global AI race cannot afford to neglect Research & Development (R&D). The United States and China, the current frontrunners, have both strategically prioritized AI R&D, with detailed plans and significant investments over the past decade.

Unfortunately, the paper points out that India hasn't yet undertaken such a long-term strategic exercise for building its AI R&D capabilities, nor has it invested nearly as much as these leading nations. A country's AI R&D strength can be seen in its research publications, patents, and the quality of its research talent.

Interestingly, the paper notes that India is making progress in terms of the number of AI-related research papers published, even catching up to the US (though not China). However, a more telling metric – the number of AI patents granted – reveals a significant gap between India and the global AI leaders. This suggests that while India's AI research output is growing, it's not translating into as many tangible innovations.

Just like with talent and data, the paper emphasizes the need to understand and address the specific challenges India faces in building a cutting-edge AI research ecosystem.

Breaking Down India's AI R&D Gap:

1. Low Public and Private R&D Spending: Compared to leading innovation-driven countries, India's overall R&D spending as a percentage of GDP is quite low. More concerningly, the private sector's investment in AI R&D is currently negligible. Globally, the commercial sector is generally more effective at turning R&D into practical products. While India's national AI mission has allocated funds, a significant portion is directed towards infrastructure and startups, with a comparatively smaller amount earmarked for core R&D through Centers of Excellence (CoEs). The paper argues that both public and private sectors need to substantially increase their AI R&D investments.

2. Limited Institutions Focused on AI R&D: India currently has a limited number of well-funded institutions dedicated to cutting-edge AI research. This contributes to the "brain drain" of talented researchers moving abroad. Notably, none of India's institutions rank among the top global AI research institutions, unlike China, which has several in the top ten for AI publications.

3. AI Patents Lagging Behind Research: The ratio of publications to patents in India suggests that its AI patent activity isn't keeping pace with its research output. Furthermore, when looking at research quality (measured by citations), India's ranking drops. The level of international collaboration in AI research is also lower in India compared to other top research-producing countries, which can impact research quality and impact.

4. Lack of Cutting-Edge AI Infrastructure: While India has made efforts to establish AI research platforms and increase GPU capacity, the paper suggests that Indian startups and CoEs will still lag behind their counterparts in the US and China, where governments and tech firms are also heavily investing in similar infrastructure. Recent restrictions on advanced GPU imports could further complicate India's efforts.

5. Inadequate Resourcing of CoEs: The existing and planned AI Centers of Excellence in India, while a positive step, may suffer from insufficient funding. The allocated budget per center might not be enough to attract top AI talent, afford necessary computing power, and acquire relevant datasets for groundbreaking research.

To strengthen India's AI R&D capabilities, the paper proposes several key recommendations:

• Boost Public-Private R&D Investment: The government should incentivize private companies to invest in AI R&D, either through their own centers or by partnering with university research parks.

• Foster Industry-Academia Partnerships: Encourage collaborations between global tech firms and Indian research institutions, similar to Nokia's partnership with IISc for 6G research. This model should be expanded across various sectors.

• Identify Clear Research Focus Areas: With limited resources, India needs to strategically define key areas where AI can have the most significant impact and direct research efforts accordingly. Analyzing existing AI patents and publications can help identify strengths and weaknesses. Areas like personalized medicine and optimized crop management are suggested as potential focus areas.

• Build a Robust Research Ecosystem: Continue and expand initiatives like the "One Nation One Subscription" for journal access and the PAIR initiative for research collaboration. Attracting back Indian AI scientists working abroad by providing adequate resources and a supportive environment is also crucial. Furthermore, creating better incentives for researchers to commercialize their work and launch startups should be encouraged.

• Empower AI Research Parks and CoEs: Provide AI parks and Centers of Excellence with autonomy, talent, funding, and strong IP protection to foster dynamic research ecosystems. Significantly increase funding for cutting-edge AI research in these institutions to match global competition. Learning from China's extensive network of AI tech parks and significant investment funds could be beneficial.

• Leverage Multinational R&D Centers in India: Systematically engage with the AI and ML talent within the R&D centers and Global Capability Centers (GCCs) of multinational corporations in India. Incentivize them to spin out startups and collaborate with Indian research institutions.

• Promote International Collaboration: Partner with friendly nations on AI R&D, focusing on ethical AI development, establishing global guardrails, and promoting open-source AI. Encourage joint research by providing incentives and support for collaborations between Indian and international AI researchers. Initiatives like AI R&D exchange programs and international fellowships can also strengthen India's research workforce.

The paper concludes that a concerted effort across both public and private sectors, with strategic focus and adequate investment, is crucial for India to transform its growing AI research output into tangible innovation and become a true leader in the global AI race.

Additionally, this is my personal opinion that inculcating research mindset in India’s youth requires more structures changes in our education system as well. Introducing students to research sector and related skills like reading and writing scientific papers, contributing to the sector through their writings should be encouraged at all levels of education.

Conclusion: Weaving Together "AI for All" and Global Competitiveness

The paper paints a clear picture of the challenging global AI landscape. A few powerful countries and companies currently hold significant advantages in the fundamental building blocks of AI – hardware, data, algorithms, talent, and funding. This creates an uneven playing field, where Indian startups and businesses might find it difficult to directly compete in foundational AI development in the short term.

The first-mover advantage held by the US and China, along with their Big Tech giants, is indeed formidable. Therefore, as this paper argues, India's path forward lies in building upon its own strengths and strategically addressing the critical gaps in its domestic AI ecosystem – specifically in talent, data, and research. Just as China's focus on nurturing talent and research has contributed to its recent AI advancements, plugging these "missing pieces" is crucial for boosting India's own AI competitiveness.

However, the paper emphasizes that India's AI strategy shouldn't solely focus on catching up. It also needs to continue its efforts to promote a more equitable global AI landscape. India has already taken steps in this direction through its presidency of the Global Partnership on Artificial Intelligence, working to bridge the divide between developed and developing nations in AI. This voice for the Global South needs to remain strong, preventing a future where the benefits of AI are concentrated in the hands of a few.

Furthermore, the paper suggests that India can leverage its success with Digital Public Infrastructure (DPI) on a global scale in the AI domain. This could involve building international collaborations around open AI cloud infrastructure and establishing global data commons. Extending India's DPI philosophy to AI could be a significant contribution to shaping global AI governance.

Finally, the paper stresses the importance of India actively participating in global AI standards-setting processes and forging alliances with like-minded nations, particularly around open-source AI development. Multi-stakeholder collaborations can foster broader innovation ecosystems.

Ultimately, the paper concludes that India's success in the competitive global AI arena hinges on finding the right balance between its existing "AI for All" vision (both domestically and internationally) and a focused "Competitiveness in AI" strategy. By strategically addressing its internal gaps while advocating for a more equitable global landscape, India can aim for a leadership position in the transformative world of Artificial Intelligence.