Economy & Business26 Oct 2026 · about 6 min

India's AI financing demand to shift towards data centres, but GPU, regulatory risks persist: Report - The Economic Times

The brief

India’s AI growth will require large physical assets, especially data centres, GPU clusters and other computing infrastructure. These assets need substantial capital before they generate revenue. Debt funding can spread that cost over time, making it useful for established infrastructure projects with predictable financing needs. For example, GPU infrastructure may be financed through structured debt, sale-leaseback arrangements or hyperscaler funding. A lender can finance equipment while the operator repays the amount over an agreed period. This differs from early-stage funding, where investors often accept greater uncertainty in exchange for potential ownership and future returns. The Union Bank of India report expects demand to shift toward debt funding over the next three to five years. It recommends structured and equipment-financing products for GPU clusters. This reflects India’s strong AI adoption prospects, but also the need to manage hardware obsolescence, uncertain revenue models and evolving governance rules.

Why is demand for AI financing in India expected to shift toward debt funding for data centres and computing infrastructure over the next three to five years?

For example, GPU infrastructure may be financed through structured debt, sale-leaseback arrangements or hyperscaler funding. A lender can finance equipment while the operator repays the amount over an agreed period. This differs from early-stage funding, where investors often accept greater uncertainty in exchange for potential ownership and future returns.

The Union Bank of India report expects demand to shift toward debt funding over the next three to five years. It recommends structured and equipment-financing products for GPU clusters. This reflects India’s strong AI adoption prospects, but also the need to manage hardware obsolescence, uncertain revenue models and evolving governance rules.

What is debt financing, and how is it different from venture capital, government grants, and corporate investment?

Debt financing is borrowed money that a company must repay, usually with interest, over an agreed period. The lender generally does not receive ownership. This makes debt different from venture capital, which normally provides money in exchange for an equity stake and depends on the company’s future growth for returns.

Government grants usually support public, strategic or early-stage work without requiring normal repayment. Corporate investment comes from a company funding its own research or investing in another business for strategic or commercial reasons. In the article, foundational models can use venture capital, private equity, government grants and corporate R&D funding.

The financing choice depends on the activity’s risk and capital needs. GPU infrastructure can use structured debt because it involves expensive equipment. AI applications may attract venture capital, venture debt and revenue-based financing. AI skilling and Centres of Excellence mainly use grants, CSR funding and public-private partnerships.

How large is India’s potential AI market, measured by its internet users, smartphone users, and enterprise adoption of generative AI?

India’s potential AI market is large because it combines a vast connected population with growing business adoption. The country has around 880-900 million internet users and aims to reach 1.2 billion by 2030. Its smartphone user base is approximately 700-750 million. These users generate activity and data that can support AI development and adoption.

Enterprise demand is also accelerating. Generative AI is now part of over 60 per cent of large IT deals. India’s share of global mobile data traffic is among the highest in the world. Together, these figures show both a large consumer base and strong commercial interest in AI-enabled services.

The numbers describe market potential, not a guaranteed market size or revenue total. India still lacks sufficient indigenous computing infrastructure and foundational models. Growth will therefore depend on investment in data centres, GPUs, talent, applications and governance as adoption expands toward 2030.

What are GPUs and data centres, and why do they require such large upfront investments for AI?

Graphics processing units, or GPUs, are specialised computing chips widely used for demanding AI calculations. Data centres are facilities that house servers, storage, networking equipment and related systems. AI companies use them to train models, process data and deliver applications at scale.

The article identifies GPU and computing infrastructure as areas requiring high capital investment. A data-centre operator may need many GPUs, supporting equipment and a suitable facility before customers can use the computing capacity. This creates a large upfront cost. The report therefore points to structured debt, sale-leaseback arrangements and hyperscaler funding as possible financing methods.

These investments also carry a timing risk. The report highlights rapid global AI hardware obsolescence, with cycles of 18-24 months. India has limited domestic manufacturing capacity for computing infrastructure and GPUs. Building this capacity, while financing upgrades, will be important as AI adoption grows over the next three to five years.

What happens to companies and lenders when AI hardware becomes obsolete after only 18 to 24 months?

When AI hardware becomes obsolete quickly, companies may need to upgrade or replace GPUs before they have fully recovered their original investment. This can raise operating costs and reduce the resale value of older equipment. It also makes long-term planning harder because the useful life of an asset is uncertain.

For example, a company might borrow to build a GPU cluster, expecting its equipment to support revenue for several years. If newer hardware arrives within 18-24 months, customers may prefer faster systems. The company could then need fresh financing while still repaying its original debt. Sale-leaseback structures or equipment financing may help manage ownership and replacement needs.

Lenders face added risk because collateral can lose value quickly and revenue models may remain uncertain. The Union Bank report identifies rapid hardware obsolescence as a major threat. Financing products will need to reflect shorter technology cycles and the need for regular upgrades.

Why does India have major advantages in AI talent, internet users, and IT services but still lag in domestic computing infrastructure and foundational AI models?

India’s strengths come from scale and capability. The report highlights the world’s largest tech talent pool, a massive internet and smartphone user base, valuable data and strong IT services. These advantages help enterprises adopt and implement AI solutions. They also create a broad base of workers, users and business demand.

The gap is in the deeper infrastructure and research layers. India has limited domestic manufacturing capacity for computing infrastructure and GPUs. Its capabilities in developing foundational AI models are still at an early stage compared with the United States and China. This means India can provide talent and services while relying more heavily on external computing and model capabilities.

The sector also faces talent poaching to US and Gulf AI labs. Data privacy and AI governance frameworks are still evolving. India’s opportunity is therefore substantial, but progress depends on building indigenous compute infrastructure, strengthening foundational models and reducing regulatory uncertainty.

How do foundational AI models, AI applications, and AI services fit together in the technology stack, and why does each attract different kinds of financing?

Foundational AI models are broad, underlying systems that can support many uses. AI applications build specific tools or products on top of those capabilities. AI services help organisations adopt, implement or access those tools. Together, they form a stack that moves from core technology to practical business use.

The financing differs because the activities have different costs and uncertainty. Foundational model development can require long research cycles and attracts venture capital, private equity, government grants and corporate R&D funding. AI SaaS and applications can generate customer revenue, so they attract venture capital, venture debt and revenue-based financing. Computing infrastructure supporting the stack needs structured debt and equipment financing.

Lower-investment activities such as AI skilling and Centres of Excellence mainly use government grants, CSR funding and public-private partnerships. This mix shows that India’s AI economy is not financed through one route. Capital follows the project’s maturity, asset needs, risk and potential revenue model.

This brief was written by AI from the original reporting and checked by other models. Names, figures and quotes come from the source; read it for full context.