India's AI funding is set to shift toward debt financing for data centres and computing infrastructure over the next three to five years, according to a report by Union Bank of India covered on October 22, 2026 — a financing map that startup funding in India will increasingly have to follow.
How each AI layer gets funded
The report draws a clear line between AI's capital layers: GPU and computing infrastructure, requiring heavy capital, will lean on structured debt, sale-leaseback arrangements and hyperscaler funding. Foundational AI model development will rely on venture capital, private equity, government grants and corporate R&D, while AI SaaS and applications attract venture capital, venture debt and revenue-based financing. AI skilling and Centres of Excellence, needing lower investment, are primarily supported by government grants, CSR funding and public-private partnerships.
The risks flagged
Rapid global AI hardware obsolescence on 18–24 month cycles, talent poaching to US and Gulf AI labs, and regulatory uncertainty around data and AI governance remain the biggest threats to the sector. India's capabilities in developing foundational AI models are still at an early stage compared to the US and China, while data-privacy and AI-governance frameworks are still evolving.
The lender recommended developing structured and equipment-financing products for GPU clusters and expanding venture-debt partnerships to support AI application companies.
Why it matters for founders
If you are building AI infrastructure, start structuring for debt, not just equity — lenders are building products for your capex. If you are building applications, the venture-debt window is widening, but obsolescence cycles mean your hardware bets should be short-dated.