According to a report by Union Bank of India, the country’s artificial intelligence (AI) sector is expected to generate greater demand for debt financing for data centres and computing infrastructure over the next three to five years. However, the sector faces challenges from rapid hardware obsolescence, uncertain revenue models and evolving regulations governing data and AI.
The report attributed the expected growth to India’s expanding digital user base and rising enterprise adoption of generative AI. The country currently has around 880-900 million internet users, with the number projected to reach 1.2 billion by 2030. Its smartphone user base is estimated at 700-750 million. Generative AI is also being incorporated into more than 60 per cent of large IT deals, while India’s high share of global mobile data traffic supports AI development and adoption.
India’s strengths include a large technology talent pool, extensive internet and smartphone usage that generates data, and an established IT services industry capable of implementing AI solutions across enterprises. However, domestic manufacturing capacity for computing infrastructure and graphics processing units (GPUs) remains limited. The country’s capabilities in developing foundational AI models are also at an early stage compared with the US and China, while data privacy and AI governance frameworks continue to evolve.
The report identified rapid global AI hardware obsolescence, with GPU technology cycles of around 18-24 months, as a major risk for investors. Competition for skilled professionals from AI laboratories in the US and Gulf countries, along with regulatory uncertainty, could also affect the sector’s development.
Financing requirements vary across the AI ecosystem. Data centres and GPU infrastructure require substantial capital and could be supported through structured debt, sale-and-leaseback arrangements and funding from hyperscalers. Foundational AI model development is more dependent on venture capital, private equity, government grants and corporate research and development funding. AI software-as-a-service (SaaS) and applications can attract venture capital, venture debt and revenue-based financing.
AI skilling programmes and centres of excellence require comparatively lower investment and are largely suited to government grants, corporate social responsibility funding and public-private partnerships.
To address these requirements, the report recommended developing structured lending and equipment-financing products for GPU clusters, alongside expanding venture-debt partnerships to support AI application companies. The report expects financing demand to increasingly favour debt funding for data centres and computing infrastructure over the next three to five years.
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