India is not short of money for AI. What it is short of is compute.
According to the Economic Survey 2025-26, India’s ability to build large AI data centres is not limited by demand or funds alone. It is more constrained by access to GPUs and supporting infrastructure.
The survey said that AI should not be seen only as a new tech but more as a strategic priority that possesses the ability to shape India’s infrastructure, labour market, foreign policy and even culture.
Yet, when it comes to execution, the country’s AI ambitions are running into a hard physical limit.
The Survey flags that, to train advanced AI models, India needs a lot of compute power, mainly GPUs. But, GPUs are becoming more expensive as global demand is on the rise, leading to big players cornering GPU supply. Besides, the supply is also limited due to shortages in high-bandwidth memory and storage chips.
This is pushing up costs and creating uncertainty around AI expansion plans. In simple terms, even when money is available, AI projects cannot move forward unless hardware is secured.
To understand this better, the Economic Survey ran an agent-based simulation to study how AI compute capacity expands over time. The model allowed financing, power availability and hardware access to interact with each other.
In the early stages, the agent found that access to finance does slow expansion. Some data centre operators struggle to raise money before revenues kick in, a bottleneck which eases over time as cash flows stabilise. Under normal conditions, finance does not remain a long-term constraint.
However, as the simulation progresses, the dominant bottleneck shifts decisively to hardware availability. Even when India’s share of global GPU demand is assumed to be modest, long lead times and uncertain access leave a growing number of operators stuck waiting for GPUs. This hardware constraint lasts far longer than financing or power issues and ends up setting the pace of expansion.
Even when the model assumes high domestic demand and easy access to capital, the outcome barely changes. With financing constraints relaxed, projects reach the execution stage faster, only to hit the same GPU wall. Capital accelerates the journey to the bottleneck, but it does not remove it.
(The story will be updated soon)
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