A new government document has laid out how India plans to make artificial intelligence infrastructure more open and accessible across the country.
The Office of the Principal Scientific Adviser to the Government of India has released a white paper titled “Democratising Access to AI Infrastructure”, prepared with inputs from domain experts and stakeholders. Published on Monday, the paper reviews India’s current AI capacities and explores ways to expand fair access to computing power, datasets and model ecosystems.
Sharing the document on X, the office said, “For India, democratising access means treating AI infrastructure as a shared national resource, empowering innovators across regions to build local-language tools, adapt assistive technologies, and create solutions aligned with India’s diverse needs.”
The paper defines democratised AI access as making core building blocks such as compute, data and model toolchains affordable and available to a broader range of users. It stresses that these resources should not remain limited to a few global companies or major urban centres, but instead support innovation across regions and institutions.
The document separates AI infrastructure into physical and digital layers. Physical infrastructure includes data centres, GPUs, TPUs and specialised processors needed to train and deploy large AI models. While India generates nearly 20 percent of global data, it accounts for only 3 percent of global data centre capacity. To address this gap, the paper highlights plans under the IndiaAI Mission, including a secure GPU cluster of 30,000 next generation units for sovereign and strategic use.
It also notes that data centres are heavily concentrated in Mumbai and Navi Mumbai, followed by Chennai, Bengaluru, Hyderabad, Delhi NCR, Pune and Kolkata. Along with hardware, the paper emphasises the importance of accessible, high quality datasets and strong model ecosystems.
A central idea in the paper is the digital public infrastructure approach. This treats AI systems as digital public goods, allowing users to access data, compute and models without being physically co located. Instead of a single large platform, the paper suggests a modular system of public good enablers.
In the early stage, the focus should be on lighter tools such as directories, metadata standards, access protocols and registries. Over time, this should expand to consent based data sharing systems and a coordinated compute exchange mechanism.
The white paper does not propose immediate policy changes. Instead, it presents a long term vision to shape India’s AI infrastructure early, so that future scaling can include non urban regions and individual innovators without major structural changes.
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