China’s Ulanqab AI Revolution: What India Can Learn About Building the Next AI Hub

China’s artificial intelligence revolution is no longer confined to research laboratories, software companies, or technology districts. The country is rapidly emerging as a global AI powerhouse, and one of the most striking examples of this transformation can be found in Ulanqab, a city in Inner Mongolia in northern China.

Ulanqab was once better known for its livestock, vast grasslands and potato cultivation. Today, however, the landscape is increasingly defined by something very different: thousands of servers powering the digital economy.

The region has emerged as one of China’s major AI and computing hubs. By August 2026, its computing capacity had reached around 172,000 petaflops, reflecting the scale of China’s investment in distributed computing infrastructure.

But Ulanqab’s transformation is not simply a story of building massive data centres. It is a story of geography, energy, infrastructure and strategic planning coming together.

When Geography Becomes a Technology Advantage

One of Ulanqab’s biggest advantages is its climate. With an average temperature of around 4.3°C, the region offers favourable conditions for data-centre operations. Cooler temperatures can reduce the energy required to keep servers within optimal operating conditions, making large-scale computing more efficient.

The region also has significant potential for wind and solar power. This creates an important combination: abundant land, relatively cool weather and access to renewable energy.

China has leveraged these advantages through its “East Data, West Computing” strategy. The idea is to move computing workloads and data-centre infrastructure away from densely populated and expensive eastern cities towards regions where land and energy are more readily available.

This approach allows China to expand computing capacity without placing all of the infrastructure burden on major metropolitan centres such as Beijing.

More importantly, it links the growth of the digital economy with the development of renewable energy.

The Lesson for India

Ulanqab offers India both a warning and an opportunity.

India’s technology ecosystem remains heavily concentrated in metropolitan centres such as Bengaluru, Mumbai and Hyderabad. These cities have the talent, connectivity and established technology ecosystems required for AI development. But they also face high land prices, rising infrastructure costs, congestion and increasing pressure on electricity and water resources.

India therefore needs to think beyond the traditional technology-city model.

States and regions with cooler climates, abundant renewable energy and available land could potentially become important locations for the next generation of AI infrastructure. Rajasthan, Uttarakhand and Himachal Pradesh, for example, offer different combinations of renewable-energy potential, land availability and climatic advantages.

However, geography alone will not create an AI hub.

A successful AI infrastructure cluster requires far more than warehouses filled with servers. It needs high-speed connectivity, reliable electricity, large-scale energy storage, resilient transmission infrastructure, water and cooling systems, cybersecurity, skilled manpower and strong links with research and technology companies.

This is where India can learn an important lesson from China.

From Metro-Centric to Distributed AI Infrastructure

India should consider developing a network of AI infrastructure clusters beyond its major metropolitan cities.

Satellite cities, industrial corridors and strategically selected rural regions could host large-scale data centres and computing facilities, while major cities continue to serve as centres for research, software development, finance and entrepreneurship.

Such a model could create a distributed AI ecosystem.

Instead of concentrating computing power, talent and investment in a handful of cities, India could create multiple regional AI centres connected through high-speed digital networks.

This would have a broader economic impact as well. AI infrastructure could bring investment, skilled employment, supporting industries and improved connectivity to regions that have traditionally depended on agriculture or conventional industries.

The Renewable Energy-AI Connection

Perhaps the biggest opportunity lies in connecting India’s renewable-energy ambitions with its AI infrastructure strategy.

AI data centres are extremely energy-intensive. As demand for AI computing grows, electricity availability and cost will become increasingly important determinants of where computing infrastructure is built.

India can therefore think of renewable energy and AI infrastructure as two parts of the same strategic ecosystem.

Large solar and wind projects could be integrated with data-centre clusters, supported by battery storage and reliable grid connectivity. This could help reduce the operating cost and carbon footprint of AI computing while creating new demand for renewable power.

The objective should not simply be to build more data centres. It should be to build energy-efficient, geographically distributed and economically integrated AI infrastructure.

Economics of AI Infrastructure

AI data centres consume enormous amounts of electricity and require advanced cooling, reliable grids, high-speed connectivity and large land parcels. As land, power and operating costs rise in metropolitan cities, India can locate energy-intensive computing infrastructure in regions offering cheaper land, renewable energy and cooler climates. This could lower operating costs, improve energy efficiency and create a geographically distributed AI ecosystem, while allowing major cities to remain centres for talent, research, finance and innovation.

 India’s AI Infrastructure Gap

India is rapidly expanding its AI infrastructure through the IndiaAI Mission, GPU access and large private-sector data-centre investments. However, capacity remains concentrated in Mumbai, Chennai, Bengaluru, Hyderabad and Delhi-NCR. India needs to diversify this infrastructure by developing AI computing clusters in renewable-energy-rich regions. Such decentralisation could reduce pressure on metropolitan power and land resources, strengthen domestic computing capacity, support startups and research institutions, and promote more balanced regional economic development.

Ulanqab’s Bigger Message

The most important lesson from Ulanqab is that the future of technology does not necessarily belong only to the biggest cities.

The digital economy may be virtual, but the infrastructure that powers it is deeply physical. AI requires land, electricity, cooling, connectivity and increasingly large amounts of renewable energy.

That changes the equation for countries like India. A region once known primarily for agriculture can, with the right combination of infrastructure, energy and policy, become part of the backbone of the digital economy.

Ulanqab demonstrates what is possible when geography is treated not as a constraint, but as a strategic asset. For India, the opportunity is to build its own version of this model—not by copying China, but by adapting the principle to Indian conditions.

The next AI hub may not necessarily be Bengaluru or Mumbai. It could emerge from a region that today is better known for its farms, deserts, mountains or rural economy.

India’s AI revolution may ultimately depend not only on who develops the smartest models, but also on where the computing power to run them is built.