As countries compete to become global AI hubs, India’s strengths in infrastructure, talent, and scale contrast with a persistent gap in private investment and frontier AI development
India’s position in the global AI race differs across the AI ecosystem. On data centre infrastructure, it competes on cost and scale that even wealthier markets struggle to match. In semiconductors, the mission has moved from policy to execution, albeit at mature technology nodes. But when it comes to AI capital, the venture and private funding that underpins frontier model development, AI companies and computing infrastructure, India remains a tier below the global leaders.
This divergence reflects where AI investment is flowing globally. While the United States continues to dominate private AI funding and frontier innovation, countries such as the UAE and Singapore have positioned themselves as regional AI hubs through infrastructure investment, policy support and sovereign capital. India offers a large domestic market, engineering talent, and cost competitiveness; however, it still trails the leading AI economies in attracting the private capital that finances frontier models, advanced compute infrastructure, and the next generation of AI companies.
India’s position in the global AI race differs across the AI ecosystem. On data centre infrastructure, it competes on cost and scale that even wealthier markets struggle to match. In semiconductors, the mission has moved from policy to execution, albeit at mature technology nodes. But when it comes to AI capital, the venture and private funding that underpins frontier model development, AI companies and computing infrastructure, India remains a tier below the global leaders.
This divergence reflects where AI investment is flowing globally. While the United States continues to dominate private AI funding and frontier innovation, countries such as the UAE and Singapore have positioned themselves as regional AI hubs through infrastructure investment, policy support and sovereign capital. India offers a large domestic market, engineering talent, and cost competitiveness; however, it still trails the leading AI economies in attracting the private capital that finances frontier models, advanced compute infrastructure, and the next generation of AI companies.
Capital: A Large, Unresolved Gap
US private AI investment reached USD 285.9 billion in 2025, more than 23 times China’s USD 12.4 billion, according to Stanford HAI’s 2026 AI Index. India remains far from that scale. Indian AI startups raised USD 1.48 billion in the first quarter of 2026, with Neysa’s USD 1.2 billion Series B led by Blackstone accounting for roughly 81 per cent of the total.
The United States remains the dominant destination for AI capital because it combines leading research ecosystems, deep venture and private capital markets, hyperscale cloud infrastructure, advanced semiconductor capabilities and large enterprise demand, says Biswajeet Mahapatra, Principal Analyst at Forrester. “India has moved beyond being viewed solely as a technology services hub and is increasingly becoming an important destination for AI investment,” he says, citing the country’s large engineering talent pool, expanding digital economy and growing enterprise technology spending.
However, Mahapatra places India a tier below the US in ecosystem maturity, frontier innovation capacity and large-scale AI infrastructure. Compared with smaller AI hubs such as Singapore and the UAE, India offers a significantly larger market opportunity and talent pool, although infrastructure availability and compute capacity are still developing. Continued investment in data centres, cloud infrastructure, governance and advanced AI capabilities, he notes, will be critical if India is to narrow the gap with global leaders.
Abhishek Srivastava, General Partner at Kae Capital, breaks that gap down by layer. India is “genuinely competitive at the application layer,” he says, citing strong unit economics, capital efficient founders and a technical talent base that is difficult to match. “The gap in runway between an early stage Indian AI startup and a comparable US company is real, and it shows up in hiring, speed of iteration and infrastructure access,” he says. “The application layer is reasonably funded. The urgent gap is in compute, infrastructure and foundation model development. Sarvam’s fundraise is an important proof point, but it is still an exception. Closing that gap is the work of the next few years.”
Srivastava also notes that investor confidence ultimately depends on founder depth, global ambition and paying enterprise customers. “The best Indian AI companies aren’t building for India and planning to expand later. They’re building something global that starts here,” he says. “Underlying all of this is capital efficiency. Indian founders are often genuinely better at doing more with less, and in the current market that’s a real edge.”
Infrastructure: India’s Advantage
If capital remains India’s biggest challenge, infrastructure is increasingly its strongest advantage.
Data centre construction in India costs roughly USD 5.5 million to USD 6.5 million per MW, comparable to Shanghai and significantly below Singapore’s USD 14.5 million, Tokyo’s USD 15.2 million and Zurich’s USD 14.2 million, according to Deloitte and Turner & Townsend. Cumulative committed data centre investment in India reached USD 126 billion by the end of 2025 and is projected to cross USD 180 billion in 2026, according to CBRE.
The momentum is also being driven by hyperscaler investment. Amazon Web Services has committed to investing about USD 12.7 billion in cloud infrastructure in India by 2030, underscoring the country’s growing role as a regional AI and cloud infrastructure hub.
“India stands to be a massive gainer because of its low cost and large consumption base,” says Anjali Kumar, Partner at Deloitte India. While the US leads with around 53.7 GW of installed data centre capacity, growing civil society opposition to new facilities is encouraging companies to consider investments beyond their home market, making India one of the biggest potential beneficiaries.
Compared with the UAE, home to the 5 GW Stargate AI Campus in Abu Dhabi, Kumar says India’s advantage lies in its market size and engineering talent. The UAE, however, benefits from sovereign capital, low cost energy and faster state led execution. Compared with Singapore, she says India competes on scale, cost, power availability and demand depth, while Singapore’s advantages lie in regulatory predictability and investor confidence.
The constraints, according to Kumar, are less about capital than execution. A single data centre project can require more than 40 approvals covering land, power, fire, environmental and water clearances. India will also need around 45 million square feet of data centre space by 2030, up from about 13 million square feet today. While power availability is not a national constraint, she says transmission infrastructure around key data centre locations will need to be strengthened.
Semiconductors: Progress, With Realistic Expectations
India has approved 13 semiconductor projects under the India Semiconductor Mission as pf mid 2026. The anchor project, the Tata Electronics and PSMC wafer fabrication plant at Dholera, is backed by an equipment agreement with ASML and is targeting first silicon by December 2026. The facility is designed to manufacture chips using mature 28 to 90 nanometre process nodes rather than leading edge logic. India currently has no sub 7 nanometre fabrication capability, and most industry timelines do not expect one before 2030.
Radhika Viswanathan, Chief Operating Officer at Applied Materials India, says India should focus on building differentiated capabilities rather than trying to replicate every part of the global semiconductor value chain. “India should be intentional about where it builds differentiated capabilities within the semiconductor value chain,” she says. “The objective is to focus on areas where India can create distinctive and lasting value rather than try to participate in every segment.”
Viswanathan points to what the industry describes as the “missing middle”, including manufacturing technologies, materials engineering, semiconductor equipment, advanced packaging, supplier ecosystems and research and development. Building depth across these areas, along with continued investment in infrastructure, talent development and industry academia collaboration, will be critical if India is to become a long term contributor to the global semiconductor ecosystem.
While India’s semiconductor ecosystem is still developing, Viswanathan says the country already has significant strengths in engineering talent, chip design and digital innovation. The next phase, she says, will be translating those strengths into a broader manufacturing and innovation ecosystem that can support long term AI growth.
Enterprise Adoption: Closing The Gap Faster Than Funding
India’s enterprise AI adoption is progressing faster than its capital flows suggest.
Kishan Sundar, Chief Technology Officer at Maveric Systems, says India has “largely caught up with Europe in enterprise AI maturity”, based on the company’s work with global banks. “In India, the biggest challenge isn’t governance. It’s fragmented enterprise data,” Sundar says. “Once that foundation improves, India has the potential to move faster than many mature markets.”
Sundar notes that India’s engineering capabilities and experience in executing large-scale technology transformations remain key strengths. The bigger challenge is improving data quality by addressing fragmented, siloed, and inconsistent enterprise data that AI systems struggle to use effectively.
Leadership in enterprise AI, according to Sundar, will ultimately depend on building stronger data foundations. It is also essential to create practical native AI governance without the burden of legacy Model Risk Management processes or heavy regulatory overhead. “If it gets both data and governance right, India can move from catching up to leading enterprise AI adoption,” he says.
The Bottom Line
India’s position in the global AI race depends on which part of the ecosystem is being measured.
On infrastructure, India’s cost advantage, expanding hyperscaler investments and growing domestic demand already make it one of the world’s most competitive destinations for AI capacity expansion. In semiconductors, the country has moved beyond policy announcements to projects under construction, although its ambitions remain focused on mature node manufacturing rather than frontier chips.
Capital, however, remains the defining gap. India’s infrastructure story is strengthening, but the country still trails the United States in attracting the private investment that funds frontier AI models, advanced compute infrastructure and the next generation of AI companies.
For now, India’s strongest case in the global AI race lies not in matching the capital pools of the US, but in becoming one of the world’s most competitive destinations for building, deploying and scaling AI. Whether that eventually translates into larger pools of private AI capital will depend on how quickly it can strengthen its infrastructure, semiconductor ecosystem and innovation capacity over the coming decade.
Originally published in Businessworld
