The Ministry of Electronics and Information Technology (MeitY) said in a Rajya Sabha reply on the progress of the IndiaAI Mission that it has released only a fraction of the approved outlay so far. On funds releases, the government said it had released Rs 21.79 crore in 2024–25 against revised estimates (RE) of Rs 173 crore, and Rs 379.15 crore in 2025–26 against RE of Rs 800 crore, as of February 9, 2026. It has not yet released any funds for 2026–27 against the budget estimate (BE) of Rs 1,000 crore.
Allocation across the pillars
The government has allocated Rs 10,371.92 crore for the IndiaAI Mission over five years, distributed across seven key pillars. Compute Capacity has received the highest allocation at Rs 4,563.36 crore, followed by Foundation Models at Rs 1,971.37 crore and Startup Financing at Rs 1,942.5 crore. At the lower end, Safe & Trusted AI has been allocated Rs 20.46 crore, about 5 times lower than the Rs 102.69 crore earmarked for overhead and contingencies.
Government says the IndiaAI Mission has:
- Onboarded more than 38,000 GPUs for a common compute facility accessible at subsidised rates.
- Empanelled 14 AI service providers offering cloud-based GPU access.
- Shortlisted 12 teams to develop indigenous foundational models or large language models.
- Approved 30 applications for India-specific AI use cases.
- Supported over 8,000 undergraduates, 5,000 postgraduates, and 500 PhD scholars for talent development.
- Established 27 India Data and AI Labs, with 543 more identified.
- Enabled access to compute resources for 114 academic researchers, 47 startups and MSMEs, 36 early-stage startups, 10 early-stage researchers, 32 students, 8 IndiaAI Fellows, and 58 government entities.
Why this matters: The slow pace of fund releases raises questions about the government’s ability to operationalise the IndiaAI Mission at scale, even as it emphasises indigenous model development. Notably, global technology firms such as Amazon and Microsoft have committed to investments of over $50 billion in India’s cloud and AI infrastructure, far exceeding public spending. Importantly, this gap could shape who ultimately controls compute capacity and innovation pipelines. At the same time, the limited allocation to Safe & Trusted AI is shockingly lower than the budget for overheads and contingencies, signalling weak prioritisation of safeguards, even as system deployments accelerate.
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