The Limits of Grant-Based Growth
For years, India's approach to nurturing its artificial intelligence ecosystem relied heavily on government grants and academic funding. Initiatives like the IndiaAI Mission, approved in March 2024 with an outlay of over ₹10,000 crore, were crucial for
building foundational capabilities. This included providing subsidised access to GPU compute power and funding for developing indigenous models. This strategy successfully kickstarted research and lowered entry barriers for early-stage projects. However, as the AI landscape matures, the limitations of this model have become apparent. Grants are excellent for exploration and creating proofs-of-concept, but they often fall short of providing the substantial, sustained capital needed to scale a company, commercialise a product, and compete with global giants. The cost of training cutting-edge AI models can be immense, potentially running over a billion dollars per training run by 2027, a figure far beyond the scope of traditional grants.
Enter Long-Term Risk Capital
The conversation in New Delhi and Bengaluru has now shifted decisively towards long-term risk capital. This refers to patient, high-stakes investment from venture capital (VC) funds and other private equity sources prepared to back ambitious projects over many years, from development to commercialisation. Unlike grants, risk capital is an equity investment; investors take a stake in the company, aligning their success with the startup's growth. This model is better suited for the capital-intensive nature of building 'frontier AI'—highly advanced models that push the boundaries of the technology. This pivot is already visible in the numbers, with Indian AI startups attracting significant VC funding in 2026, demonstrating renewed investor appetite.
A Proposed National AI Fund
To bridge this funding gap, the government is reportedly considering a major intervention. Discussions are underway for a proposed National Frontier AI & Compute Fund (NFAICF), which could see an anchor investment of ₹15,000 to ₹20,000 crore. According to recent reports from September 2026, this fund would operate as a SEBI-regulated Alternative Investment Fund, providing long-duration capital to companies developing frontier AI models and financing critical infrastructure like GPU clusters and specialised data centres. The goal is to create a dedicated domestic equity vehicle that can make the large, patient bets required for deep-tech AI development, which traditional VC and grant systems may find too risky or capital-intensive. The proposal, discussed in closed-door meetings with industry stakeholders, is still under consultation but signals a strategic intent to de-risk private investment and build sovereign capabilities.
Fueling the Next Wave of Startups
This strategic shift is a direct response to the needs of India's burgeoning AI startup ecosystem. While early efforts focused on application-layer companies, the new emphasis is on building core infrastructure and foundational models. Access to substantial risk capital is vital for these companies to move beyond experimentation and build scalable, commercially viable products. The IndiaAI Mission already includes a Startup Financing pillar aimed at supporting companies through their lifecycle, and the proposed NFAICF would complement this by providing the heavy-duty capital needed for the most ambitious projects. With this support, startups can afford the massive computing resources, attract top talent, and sustain the long R&D cycles needed to create truly innovative AI. By August 2026, Indian AI startups had already raised approximately $1.56 billion across over 200 deals, indicating that the move towards a more robust funding environment is already underway.
Challenges and the Road Ahead
Despite the optimism, the transition is not without challenges. India still faces a relative shortage of homegrown large-scale AI models and a dependency on foreign technology and hardware. Furthermore, fostering a mature risk capital market requires more than just government funds; it needs a robust ecosystem of experienced investors, clear regulatory frameworks, and a steady pipeline of high-quality, investable startups. The proposed fund is designed to attract private and potentially international co-investment, but its governance and investment strategy will be critical to its success. The debate is no longer about whether India should invest in AI, but how. Moving from the safety of grants to the high-stakes world of risk capital is a bold gamble, but it may be the only way for India to secure its place as a creator, not just a consumer, in the global AI landscape.
