Where are the gaps in funding and infrastructure, in talent and policy-with regard to what India needs to do to keep pace in a global race of AI-with respect to creating an AI hub in India like OpenAI or Gemini or DeepSeek?
In the continuum of AI development, OpenAI’s ChatGPT, Google’s Gemini, and DeepSeek emanate as embodiments of true-often contested-an innovation benchmark with which qualified benchmarks are represented and what would then be worthy of becoming co-written under meaningfulness and intelligibility. However, the question that often comes up during discussions of technology in India concerns why the country is not developing anything relative to these.
1. Investment in R&D
These investments have gone into research and development (R&D) during the grueling years of evolution of AI models like OpenAI or Gemini or DeepSeek. For example, if compared with the developed tech nations such as the US, where around 3 percent of GDP goes to R&D, India is hovering around 0.7. It is such paltry funding in India that hampers long-term and long-scale AI program, which requires constant financial support, and initial capital investment is essential with the constant fund.
Indian funding policies are usually regarding funds and projects getting paid or promising predictable returns rather than high-risk, high-reward investments such as any normal foundational AI research project. So this investment pattern again reflects more funding going into something that is enhancing existing technologies than completely new ones. Besides, this is much less speculative and oriented toward long terms, however very much needed in pioneering because the whole venture capital ecosystem is still immature in India.
India has deep pockets which are not sinking into AI like those of the US private persona inducements like Google and Microsoft, or China with its Baidu and Tencent. Here, both are deep and broad in capital and take a perspective of extensive strategic open-ended AI research obtaining. This has been one of the deterrents which have traveled a long way toward curbing India’s own growth of competitive models in AI; this now greatly requires a paradigm shift in public and private investment strategies to plug the funding gap.
2. Hardware Limitations and Data Centers
Yet, high-performing computing hardware, mostly general-purpose GPUs, have been instrumental in building up advanced artificial intelligence that is also used in training complex neural networks given the affordability of large data sets. This has not been documented as far as India is concerned on the high-end part of hardware. High duties and import costs together with logistical costs in the import of high-end GPUs make scaling-up AI research effort prohibitively expensive.
Besides the build-up of big data-industry development in India, the infrastructure for such data is far from that of the U.S. or China in both scales and efficiency. Data centers must not only be equipped with the most advanced hardware, but also be aware of cooling systems, as thousands of GPUs run simultaneously and generate heat within them. Operational costs are certainly elevated with the Indian weather being hot and naturally needing a lot of cooling.
The latest U.S. restrictions completely sort complicated affairs for India into a tiered category wherein acquisition of advanced chips is restricted due to a ban under the new rule from the U.S.; hence it greatly influences building up and expanding AI data centers. The announced plan for the build-up of data center capacities also has activities like the India AI mission, which aims at procuring over 10,000 GPUs for India, but the infrastructure at present is still far behind what is needed to be competitive on a global scale in AI model development.
This hardware and its challenge in terms of data centers remain highly influential in determining how fast India can innovate in AI and call for earnest efforts to improve local manufacturing as well as import strategies for such high-tech components.
3. Talent Migration and Educational Focus
Every year India produces copious amounts of IT professionals, yet a significant trend of talent migration is observed in which many qualified candidates leave the country to work abroad because of better research environments, funding, and salaries. The educational system in India is vast but lacks emphasis on high-end cutting-edge AI research or interdisciplinary learning or chance of seeing failure as a step toward innovation. This is why there are no shortages of talent, yet talent is not always directed toward cutting-edge AI research.
4. Entrepreneurial Ecosystem and Investment Culture
With a vibrant and dynamic startup ecosystem, investment in the deep-tech sectors, such as AI foundation models, would impart a degree of cautiousness. Indian investors’ tendency is to prefer projects that provide quicker and more tangible returns as opposed to those bearing long gestation periods to realize path-breaking results. Discussions on platforms like X have witnessed the debate that although India has a viva tech scene, the risk appetite in investing in high-risk AI research is not really there compared to Silicon Valley or tech hubs in China. It is this cultural and economic environment that directs and decides the flow of funds into the tech space.
5. Policy, Regulation, and Government Support
The state of AI model making in India is much dependent on the government’s support and, of course, a conducive policy environment. While there is intent to nurture AI through dominate AI projects such as IndiaAI and the rollout of plans pertaining to a national AI strategy, conversion of such policies into practical outcomes has been sluggish. The still-evolving AI law arrangements regarding data protection and IPR are breeding uncertainties in the minds of tech entrepreneurs and investors.
The Digital Personal Data Protection Bill intends partly to assuage some privacy concerns around AI; however, The general regulatory landscape related to data, especially for the use of data while training AI, is still to evolve. The absence of such an environment can thus kill a single project on a bigger scale; it will make companies sift through possible legal complications.
Political backing is growing but still pales in significance when compared to countries where defense and public sector contracts provide substantial demand for AI innovation. The Indian government’s approach has been much about providing an enabling environment through policy rather than direct procurement, which might not spur demand globally.
Moreover, the bureaucratic pathway to research grant application or public-private partnership establishment is long and arduous, hence strangling the momentum of innovation. To truly catalyze AI growth, India requires a more simplified policy framework, driving public funding more aggressively, and regulatory certainty that encourages rather than stifles AI innovation.
For India, the process of creating its own models of artificial intelligence akin to those created by OpenAI or DeepSeek is fraught with many challenges ranging from financial to infrastructural, educational, cultural and policy-related barriers. The story, however, is not just one of lag and deprivation, but also a tale of potential. With the mandate of recently announced government schemes initiating AI sovereignty, increased participation of the private sector, and growing emphasis on development of indigenous technology, it must now shift to how it can be done rather than focus on why India has not been able to do it yet, leveraging unique strengths with such broad features as linguistic diversity, large datasets and a burgeoning tech-savvy population.
The tale of India in AI is yet incomplete, and every step it takes in policy, investment and education arguably turns yet another leaf in the story where India doesn’t just use AI but also defines it globally.
