India Losing Out in the Generative AI Race
Published on 1/16/2025 | 4 min read
Published on 1/16/2025 | 4 min read
In 2024, India’s tech ecosystem attracted investments totaling $11.3 billion—a stark contrast to the West’s $184 billion. This disparity raises concerns about India’s ability to compete globally in generative AI (GenAI). While global tech giants such as OpenAI, Google, and Anthropic lead the charge, India faces significant barriers due to limited resources and foundational research.
One of India’s advantages lies in its affordable talent pool and lower product development costs. For instance, HCLTech has announced plans to integrate AI services for 100 clients by 2026. According to CEO Vijayakumar C.:
Generative AI is becoming real. The cost of using an LLM or conversational model has dropped by over 85% since early 2023, making more use cases viable.
Despite these developments, revolutionary breakthroughs from India remain elusive, primarily due to inadequate funding and limited research.
Developing GenAI and quantum computing technologies demands immense capital. Countries like the United States and China are advancing in foundational AI research, but India lags behind.
A student from a premier Indian institute observed:
China’s research output in the last two years alone has placed them decades ahead of India.
India’s private sector has shown reluctance to invest in long-term AI research, prioritizing short-term returns instead. Vedant Maheshwari, CEO of quso.ai, explained:
Foundational AI requires significant capital and patience, which are harder to secure in India. Funding here is mostly application-focused rather than foundational.
Data from top AI research conferences underscores this gap:
A quantum computing researcher revealed the meager support for advanced research, sharing that they were offered only ₹20,000 for their work—a testament to India’s underwhelming commitment to foundational AI innovation.
Industry leaders suggest that India should focus on becoming a global hub for AI use cases rather than developing large language models (LLMs). TCS CEO K Krithivasan remarked:
There is no huge advantage in building our own LLMs in India since there are already so many available.
Similarly, Nandan Nilekani, co-founder of Infosys, has advocated for India to establish itself as the “AI use case capital of the world.”
The lack of funding remains a significant obstacle. Mohandas Pai, head of Aarin Capital and former CFO of Infosys, stated:
Who will give $200 million to a startup in India to build an LLM? Creating such a model requires large capital, time, a huge computing facility, and a market. All of which India does not have.
Although startups like Sarvam AI and Krutrim have raised funding rounds of $41 million and $50 million, respectively, their impact is negligible compared to global players like Anthropic, which aims to raise $2 billion, bringing its valuation to $60 billion. This funding gap highlights India’s struggle to compete globally.
The Indian government’s budget for innovation is alarmingly low. Out of its ₹90 lakh crore annual expenditure, only ₹3,000 to ₹4,000 crore is allocated for innovation. Mohandas Pai has called for a ₹50,000 crore investment in AI to help India catch up. However, this level of funding may come too late to close the gap.
Even elite academic institutions like the Indian Institute of Science (IISc) suffer from limited budgets. The IISc’s entire budget is around ₹1,000 crore, making it difficult to compete with globally renowned research institutions.
India’s tech sector prioritizes short-term profits from outsourced IT services rather than investing in globally competitive, innovative products. Many startups are preoccupied with creating API wrappers for SaaS solutions instead of pushing the boundaries of core research.
Amit Sheth, founding director of the Artificial Intelligence Institute of South Carolina, has criticized India’s academic framework for emphasizing quantity over quality in research publications:
India’s research often lacks originality, and only a select few universities contribute to meaningful advancements.
In contrast, institutions in the United States and China focus on advancing state-of-the-art research, enabling them to dominate the AI landscape.
Predictions indicate that India could have around 100 AI unicorns within the next decade. However, this optimism must be tempered by realistic assessments of the challenges ahead.
To remain competitive, India must:
Without substantial financial backing and long-term commitment, India risks being relegated to the sidelines in the global AI race. However, with the right policy interventions and increased collaboration between the government, academia, and the private sector, India can still carve out a unique space in the AI ecosystem.