Tata Consultancy Services (TCS) is in advanced discussions with multiple hyperscalers to construct additional AI data centres in India, following its agreement with OpenAI to develop facilities ranging from 100 megawatts to 1 gigawatt, as Bloomberg first reported. CEO K Krithivasan highlighted a projected national requirement of 10 gigawatts of AI data centre capacity by 2030, against only 5 to 6 gigawatts currently announced, creating substantial unmet demand that TCS aims to address through strategic infrastructure investments. “We are having discussions with multiple other hyperscalers. We are in advanced discussion with multiple players,” Krithivasan stated in the Bloomberg interview.
For infrastructure and technology leaders, TCS’s positioning signals a maturing domestic AI compute ecosystem capable of supporting hyperscale workloads while enabling sovereign data residency and reduced latency for Indian enterprises. The OpenAI partnership, announced at India’s AI Impact Summit, involves TCS’s HyperVault unit providing liquid-cooled, high-density racks optimised for GPU clusters, with Tata Sons Chairman N Chandrasekaran describing it as a milestone for India’s global AI leadership ambitions.
Financing and execution model for gigawatt-scale facilities
A full 1 gigawatt AI data centre typically demands investments of $35 billion to $50 billion, but TCS structures participation around physical infrastructure—racks, connectivity, power systems and cooling—estimated at $7 to $8 billion per project. TCS commits $1 billion alongside TPG’s matching investment, with the balance secured through debt financing, allowing the company to capture value in build-operate-transfer models without full funding exposure. This approach mirrors TCS’s historical success in cloud migration and now extends to AI infrastructure, where it can layer end-to-end services including model training, agentic deployment and application intelligence atop owned facilities.
Enterprises evaluating AI readiness will benefit from this hybrid model, gaining access to premium compute closer to data sources while leveraging TCS’s integration expertise to operationalise frontier models without geopolitical supply risks.
Strategic positioning in India’s AI infrastructure race
Krithivasan views AI as a natural evolution from prior waves in cloud and mobile, positioning TCS to differentiate through guaranteed infrastructure access and Nvidia GPU availability amid global shortages. The strategy addresses latent demand that hyperscalers alone cannot meet by 2030, particularly for green-powered facilities aligned with India’s sustainability mandates and PLI incentives for electronics manufacturing.
For CIOs planning sovereign AI stacks, TCS’s momentum offers a credible path to gigawatt-scale capacity, reducing reliance on overseas clouds and enabling custom fine-tuning on localised datasets.
Broader implications for enterprise AI adoption
This expansion reinforces India’s trajectory as an AI hub, with TCS bridging the supply-demand chasm through public-private collaborations that skill youth and empower industry transformation. Enterprises stand to gain from accelerated deployment cycles, lower egress costs and compliance with emerging data localisation rules under DPDP.
