India’s Distributed Role in the Global AI Infrastructure Buildout

TL;DR

• India has entered the top-4 emerging markets by market capitalization (~$2.1 trillion), alongside Taiwan, China, and South Korea but through a distributed network of mid-to-large category leaders rather than one dominant company.
• India’s installed data center capacity is on track to reach roughly 1,900 MW by FY26, up from 75 MW in FY07, with market size projected to grow from ~$1.7 billion to ~$6.8 billion by FY30.
• Construction and power costs in Indian hubs like Vizag and Mumbai run at roughly half the cost of Singapore or Tokyo, reinforcing India’s position as a low-cost buildout location for AI-ready capacity.
• Only 25-30% of India’s existing data center stock can be retrofitted for AI-density workloads, meaning most new capacity is being built AI-ready by default.
• For private allocators, the relevant takeaway is structural: India’s value sits across multiple nodes of the compute stack (EMS, OSAT, construction, colocation), which changes how concentration risk should be underwritten relative to single-champion markets.

Quick Answer

India’s role in the global AI infrastructure buildout is defined by depth rather than concentration. Where Taiwan’s position rests on TSMC and South Korea’s on Samsung and SK Hynix, India’s roughly $2.1 trillion market-cap tier is built from a distributed set of category leaders spanning electronics manufacturing, semiconductor testing and assembly, and data center construction and operations. This structure matters for private allocators because it spreads company-specific risk across multiple points of the supply chain instead of concentrating it in one node, the same dynamic that makes broad sector ETFs a weak proxy for India exposure and bottom-up, direct underwriting the more precise tool.

Why India’s Market-Cap Tier Looks Different From Its Peers

India’s emergence as a top-4 emerging market by size is often read as a simple scale story. It isn’t. Taiwan’s roughly comparable market cap is anchored by a single foundry giant; South Korea’s by two memory manufacturers. India reaches the same tier through breadth: a network of specialized, mid-to-large operators each capturing a discrete segment of the global technology stack.

Dixon Technologies, India’s largest electronics manufacturing services (EMS) player by revenue, has moved beyond consumer electronics into data center infrastructure and telecom equipment. Kaynes Technology, through its semiconductor arm, is building out outsourced assembly and test (OSAT) capacity, with profitability from that expansion expected from FY27. Tata Electronics runs Apple’s iPhone assembly lines in Hosur and Pune, contributing to India’s roughly 25% share of global iPhone production and a stated trajectory toward $500 billion in annual electronics exports by FY30. Larsen & Toubro is building sovereign AI compute clusters directly with NVIDIA, including a 30MW GPU cluster in Chennai and a 40MW AI-ready data center in Mumbai. Around these anchors sits a broader EMS market including Syrma SGS, Amber Enterprises, and PG Electroplast valued at $40-45 billion in 2025 and projected to reach $150 billion by 2035.

Call this the Node Depth Premium: when a market’s capital-markets weight is distributed across many operationally distinct nodes of a supply chain rather than concentrated in one dominant company, a single company’s re-rating, execution stumble, or competitive loss has a proportionally smaller effect on the market’s overall structural position. Taiwan’s AI-hardware narrative is a single-company story with national consequences if that company falters. India’s is a portfolio of stories, which is a different and for allocators, more diversifiable risk profile.

What the Data Center Buildout Actually Looks Like

India’s installed data center capacity has moved from 75 MW in FY07 to a projected 1,900 MW by FY26, with more than 500 MW added in the past year alone. Market size is projected to roughly quadruple from ~$1.7 billion (₹14,000 crore) in FY26 to ~$6.8 billion (₹61,000 crore) by FY30, doubling India’s share of global data center capacity to roughly 5%.

Four distinct pools of capital are funding this expansion, each with a different strategy and footprint:

A structural detail worth underwriting closely: only 25-30% of India’s existing data center capacity can be retrofitted to meet AI-density and cooling requirements. That constraint means the large majority of new capacity coming online is greenfield and AI-ready by design, a tailwind for developers building new capacity rather than owners of older colocation stock.

Why the Cost Advantage Is More Than a Talking Point

India’s cost structure is a genuine differentiator, not a marginal one. Construction costs run at roughly $6.5-6.6 per watt in Vizag and Mumbai, versus $14.5-15.2 per watt in Singapore and Tokyo. Power tariffs average $0.078-0.080 per kWh in those same Indian hubs, against $0.12-0.15 per kWh in Singapore and Tokyo. Software engineering talent costs roughly $20,000 annually on average, around a quarter of comparable salaries in Japan or Australia. Together, these gaps compound: a data center built and staffed in India can carry a materially lower total cost base than the same facility in a developed APAC hub, which is a large part of why global capital allocators keep raising India’s share of planned spend rather than treating it as a one-off arbitrage.

Downstream Value Creation Across the Real Economy

The infrastructure buildout is not an end in itself; it is the substrate for AI adoption across the broader economy. By 2035, AI adoption across five core sectors in India is projected to unlock $550-600+ billion in nominal value, accounting for up to 40% of incremental sectoral growth in some categories:

Energy and utilities show the highest share of growth attributable to AI, though manufacturing carries the largest absolute value pool, a distinction that matters for allocators sizing thematic exposure versus absolute return potential.

What This Means for Private Market Allocators

Record dispersion within technology and semiconductor stocks means broad sector ETFs are an increasingly weak proxy for capturing India’s AI-infrastructure exposure; outcomes are concentrated in specific operational winners, which argues for bottom-up underwriting on unit economics, margin resilience, and cash flow rather than index-level beta. Private structures also offer a degree of insulation from the daily sentiment swings, supply-shock headlines, and rate re-pricing that move public markets, letting performance track operational growth over a longer horizon. Companies spanning B2B manufacturing (Zetwerk, Infra.Market) to enterprise tech and analytics (Fractal Analytics, InMobi) sit at different points along the same supply chain the data center numbers above describe which is the practical expression of the Node Depth Premium: multiple entry points into one structural theme, rather than a single-company bet.

Chief Brand & Investor Relations Officer

Sneha Dadrwal

Frequently Asked Questions

Q: Is India now one of the largest emerging markets by market capitalization?

A: Yes. India sits in a top-4 emerging market tier by market cap, at roughly $2.1 trillion, alongside Taiwan, China, and South Korea.

Q: How is India's position in the AI supply chain different from Taiwan's or South Korea's?

A: Taiwan's position is concentrated in foundry leadership (led by TSMC) and South Korea's in memory manufacturing (Samsung, SK Hynix). India's position is distributed across EMS, OSAT, electronics assembly, and data center construction and operations, with no single dominant company.

Q: How much data center capacity does India currently have, and how fast is it growing?

A: India's installed data center capacity is projected to reach approximately 1,900 MW by FY26, up from 75 MW in FY07, with market size projected to grow roughly fourfold to ~$6.8 billion by FY30.

Q: Why can't existing Indian data centers simply be upgraded for AI workloads?

A: Only an estimated 25-30% of existing capacity meets the density and cooling requirements AI workloads need, meaning most new AI-ready capacity has to be built from the ground up rather than retrofitted.

Q: Why does India's data center cost advantage matter for investors?

A: Construction and power costs in Indian hubs run at roughly 40-55% of costs in Singapore or Tokyo, which materially improves the return profile on new-build data center investment relative to developed APAC markets.

Q: What does India's distributed supply-chain structure mean for how investors should approach exposure?

A: Because value is spread across multiple operationally distinct companies rather than one national champion, broad index exposure captures the theme poorly; direct, bottom-up underwriting of individual operators along the supply chain is a more precise way to access the opportunity.

Sources

KPMG

PWC