India's AI Financing Demand to Shift Towards Data Centres, but GPU Infrastructure and Regulatory Risks Persist: UBI Report

New Delhi, Oct 22 (KNN) India’s artificial intelligence (AI) sector is expected to see rising demand for debt financing to fund data centres and computing infrastructure over the next three to five years, according to a report by Union Bank of India (UBI).

The report said the sector offers significant growth opportunities, supported by India’s expanding internet and smartphone user base and increasing enterprise adoption of generative AI. However, it also faces risks from rapid hardware obsolescence of graphics processing units (GPUs), uncertain revenue models and evolving regulatory frameworks.

India has around 880-900 million internet users, with the number expected to reach 1.2 billion by 2030. Its smartphone user base is estimated at 700-750 million. Generative AI is now part of more than 60 per cent of large IT deals, further supporting adoption.

“India brings a huge talent pool and one of the world's largest internet user bases to this race, but still lags in indigenous compute infrastructure and foundational models,” ANI reported, citing the report.

Infrastructure And Regulatory Challenges

The lender identified India's large technology talent pool, extensive digital user base and established IT services capabilities as key strengths. However, limited domestic manufacturing of computing infrastructure and GPUs, along with early-stage capabilities in foundational AI models, remain major constraints.

The report also flagged evolving data privacy and AI governance regulations, rapid global AI hardware obsolescence, typically over 18-24-month cycles, and the migration of skilled professionals to AI laboratories in the US and Gulf countries as key risks.

Financing Opportunities

The report outlined different funding requirements across the AI ecosystem. GPU and computing infrastructure require substantial capital and can be financed through structured debt, sale-and-leaseback arrangements and hyperscaler funding.

Foundational AI model development relies largely on venture capital, private equity, government grants and corporate research and development funding. AI software-as-a-service (SaaS) and application companies can access venture capital, venture debt and revenue-based financing.

AI skilling and Centres of Excellence, which require relatively lower investment, are primarily supported through government grants, corporate social responsibility funding and public-private partnerships.

Union Bank recommended developing structured and equipment-financing products for GPU clusters and expanding venture-debt partnerships to support AI application companies. It expects financing demand to increasingly shift towards debt-funded data centres and computing infrastructure over the next three to five years.

(KNN bureau)