From “World’s Factory” vs “World’s Back Office” to Competing Tech Powers
Historically, China and India took very different routes into the global technology economy. China poured capital into manufacturing, electronics, telecom equipment and infrastructure, building dense industrial clusters and deep supply chains. India, by contrast, rode the wave of IT services, software, and back-office work rather than heavy hardware.
That divergence still shapes today’s AI race. China enters the AI era with:
Large domestic electronics and semiconductor manufacturing capacity, even if still behind the US and allies in cutting-edge chips.
A “core AI sector” already worth nearly 600 billion yuan, projected to grow over 15% annually to exceed 1 trillion yuan by 2030.
India, on the other hand, enters with:
A massive pool of software talent and one of the world’s most active AI developer communities, including being a leading contributor to AI projects on GitHub.
Deep integration into global IT services and cloud-management work, but relatively shallow domestic hardware and chip fabrication capacity.
So the technology gap is not simply that “China is ahead”: China is more integrated in physical infrastructure and manufacturing, while India is stronger in human capital and services. The AI era demands both.
The AI Race: Compute, Models and Policy
China set an explicit goal in 2017 to become the world leader in AI by 2030, with “iconic advances” by 2020. The explosion of generative AI from US firms in 2022–23 briefly put Beijing on the back foot, but the response has been rapid: a proliferation of large models, heavy state support, and a race to optimize scarce chips.
Export controls from the US have cut China off from top-tier GPUs and advanced semiconductor equipment. In theory this should cripple China’s ambitions; in practice, it has pushed Chinese firms to innovate under constraints:
Frontier models like DeepSeek have demonstrated that Chinese firms can build competitive large models with limited access to the best GPUs, using highly efficient algorithms and careful optimization.
Chinese tech giants and chipmakers are pushing software platforms such as Huawei’s Flex:ai to squeeze more performance out of each chip, slicing accelerators into virtual units and improving cluster utilisation.
In short: China’s AI gap with the US is heavily hardware-driven, but it is compensating with scale, policy push and aggressive optimisation.
India’s AI strategy looks very different. The IndiaAI Mission, approved in March 2024 with an outlay of over ₹10,300 crore (about US$1.25 billion), aims to build a “comprehensive AI ecosystem” – compute, data, skilling, startups and governance. Key elements include:
Establishing a national AI compute infrastructure to give startups and researchers access to affordable GPUs.
Curating high-quality public datasets.
Funding AI startups and academic research.
Developing AI governance and safety guidelines.
India’s IT minister has openly praised DeepSeek’s low-cost, high-efficiency approach as a model of “frugal innovation” that fits India’s own philosophy: doing more with less capital. That is both a strength and a warning: a frugal strategy can succeed only if it is paired with hard investments in domestic compute capacity, cloud infrastructure and chips.
Critical gap here:
China is constrained by geopolitics but has already built vast domestic AI industrial structure. India has political room and global goodwill, but its physical AI infrastructure is still emerging. If India under-invests in sovereign compute and hardware for too long, the talent advantage may translate into value for foreign clouds and platforms rather than for Indian firms.
Underwater Data Centres: Where China is Already in the Water, India is Still on the Shore
Underwater data centres sound like science fiction, but they are rapidly becoming a symbol of the next infrastructure race.
Globally, the idea took off with Microsoft’s Project Natick, which submerged a test data centre off Scotland. The experiment showed very high reliability and improved energy efficiency but was wrapped up in 2024 without moving to full commercial deployment.
China has gone several steps further and turned the concept into large-scale commercial projects:
Off Hainan island, a subsea data centre launched its first phase in 2022 and is now in full commercial use, with cabins of around 1,300 tonnes housing hundreds of servers each.
New projects near Shanghai and Hainan claim to be the world’s first wind-powered underwater data centres, using offshore wind plus seawater cooling to target very low power usage effectiveness and minimal land use.
These underwater centres are not just engineering stunts. They address three strategic constraints:
1. Energy and cooling costs – Seawater provides natural cooling, cutting energy use and eliminating the need for fresh water.
2. Land scarcity – Coastal megacities can expand compute without sacrificing valuable land.
3. AI compute demand – As AI workloads grow, the marginal cost of energy and cooling becomes a central competitive factor in hosting large models.
India, by contrast, is in a different stage of the infrastructure cycle. Its data-centre market is growing rapidly – from around US$4 billion in 2021 to an estimated US$11 billion in 2024 – driven by cloud growth and digitalisation. There is aggressive investment in traditional data centres and submarine fibre cables:
New subsea cable systems linking India to global networks are coming online between 2024 and 2025.
Projects like the Kochi–Lakshadweep submarine cable enhance domestic connectivity and resilience.
However, there is no evidence yet of India moving seriously into underwater data centres. Policy discussions and telecom recommendations focus on submarine cables and green data centres, but not subsea server pods.
This exposes a qualitative technology gap:
China is experimenting at the frontier of physical compute infrastructure – combining offshore wind, subsea cooling and AI workloads.
India is strengthening more conventional, land-based data centres and cables – essential, but not yet frontier-shaping.
If underwater or similarly radical cooling/compute architectures become mainstream in 2030–2040, China and a handful of pioneers will set standards and control patents, while India risks being a late adopter and price-taker.
Governance, Regulation and Trust Gaps
Technology gaps are not only about hardware; they are also about rules.
China has moved fast to regulate and shape AI domestically:
It has issued content rules, security standards and draft technical norms for generative AI, trying to keep systems aligned with political and social controls while supporting growth.
This gives Beijing tight control over domestic models, but may limit open experimentation and can make Chinese AI less trusted in open, global contexts.
India, on the other hand, is positioning itself as a champion of “safe and trusted AI.” The IndiaAI mission sits alongside emerging governance guidelines aiming for responsible AI deployment. The democratic, multi-stakeholder environment is messy and slower but potentially more aligned with global norms on privacy, transparency and rights.
The paradox:
China moves faster on physical infrastructure and industrial scaling, but faces geopolitical suspicion and domestic censorship constraints. India moves slower on hardware and large-scale platforms, but can build a reputation for trust, openness and third-party verification.
In underwater data centres, for example, long-term concerns include environmental impact on marine ecosystems, cybersecurity of critical subsea infrastructure, and jurisdiction over data stored offshore. If India can’t match China on speed, it could differentiate on standards – becoming the place where global firms pilot “green, audited, rights-respecting” data-centre models rather than purely cost-optimised ones.
Looking Ahead to 2035: Possible Futures for the India–China Tech Gap
Projecting forward a decade, a few scenarios emerge.
1. China as the hardware and infra giant of the AI ocean
China continues to scale underwater data centres, offshore wind-powered compute parks and domestic AI chips, partially bypassing US hardware dominance and shaping new standards for data-centre design in Asia. Under this scenario, many Asian and African countries might lean on Chinese infra and cloud, even if Western markets stay cautious.
2. India as the trusted AI services and governance hub
India leverages the IndiaAI mission to build strong domestic models for health, agriculture, language and governance, and becomes a global hub for AI services, safety audits and regulatory sandboxes. It may not host the largest clusters, but becomes an essential partner in deploying AI ethically in the Global South.
3. Or a missed opportunity
There is also a darker scenario: if India under-invests in physical compute, domestic chip ecosystem and experimental infrastructures (including underwater or other ultra-efficient data centres), its talent and startups may mostly end up serving foreign platforms. China, constrained by export controls, could still secure a lead in regional cloud and infra because it built things while India debated.
A Critical Agenda for India: Closing the Structural Gaps
If India wants to narrow the technology gap with China in a serious way, the agenda needs to be sharper and more uncomfortable:
Move from “frugal innovation” to “frugal but massive investment”
DeepSeek’s low-budget success is inspiring, but it was still built on large GPU clusters. India must not use frugality as an excuse to avoid large-scale investment in sovereign AI compute.
Experiment at the frontier, not just follow mature models
India should pilot its own extreme-efficiency data-centre concepts – submerged, barge-based, desert-cooled, or nuclear-adjacent – rather than waiting for Western or Chinese designs to stabilise. Today, underwater data centres look risky; by 2035, they could be the default for coastal megacities.
Build a hardware and semiconductor flank aligned with AI goals
India will not catch up overnight with China’s factories, but targeted investment in AI-relevant chips, advanced packaging, and photonics – combined with alliances with friendly chip powers – can reduce vulnerability.
Turn governance strength into a market advantage
If India can offer climate-audited, rights-respecting, geopolitically trusted data-centre and AI services, it could attract countries that are wary of both Chinese state control and Western surveillance capitalism.
The Next Gap is About Who Designs the “Ocean of Compute”
The real India–China technology gap in the coming decades will be about who shapes the architecture of global compute – the “ocean of servers, cables and energy” that AI runs on.
China has already lowered hardware into the literal ocean and is wiring it to offshore wind and domestic AI chips. India is strengthening its cables and land-based data centres and building an AI mission rooted in talent and ethics – but has not yet taken bold bets on radically new infrastructure.
If India wants a genuinely strategic position in the AI age, it must think beyond being the world’s coding shop and instead help design the next generation of compute: where it is built, how it is powered, how it is governed, and who it ultimately serves. The race with China is not yet lost, but it is no longer about catching up to yesterday’s factories – it is about helping to invent tomorrow’s oceans of intelligence.#AIInfrastructure
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