India’s artificial intelligence ambitions have entered a crucial phase. The country is positioning itself as a major global AI hub, attracting billions of dollars in proposed investments while expanding domestic computing capacity. Yet beneath the optimism lies a harder question: Can India become an AI powerhouse without controlling enough of the infrastructure that powers artificial intelligence?
The question became particularly relevant in February 2026, when Indian IT stocks came under severe pressure amid growing fears that AI automation could disrupt traditional technology-services businesses. Reuters reported that the Nifty IT index fell about 21% during February, with the sector losing roughly $68.5 billion in market value.
At the same time, India was showcasing its AI ambitions at the India AI Impact Summit 2026. The government announced plans to add 20,000 GPUs to an existing pool of 38,000, highlighting the importance of domestic compute infrastructure.
This contrast exposes an important distinction: AI adoption is not the same as AI sovereignty.
Five Gates of AI Power
India is already strong at the application layer, benefiting from a huge digital population, software workforce and expanding AI adoption. But the deeper layers present tougher challenges.
The second gate is foundation models, where frontier development remains dominated by companies in the United States and China. India is making progress with indigenous models and multilingual AI, but competing at the frontier requires enormous computing resources and capital.
The third gate is data centres and cloud infrastructure. India is attracting major investments from domestic conglomerates and global technology companies. Reliance and Adani, for instance, announced combined AI and data-infrastructure investment plans worth around $210 billion.
Then come GPUs and advanced processors, followed by the most difficult gate: semiconductor fabrication. India’s semiconductor push is strategically important, but building a globally competitive advanced-node manufacturing ecosystem requires years of engineering experience, supply-chain depth and massive capital.