The Compute Build-Out

Model efficiency grabbed the headlines; the capital build-out underneath is the bigger story. China is financing AI infrastructure on two tracks at once, a national grid and private hyperscaler capex, and the numbers have stepped up sharply.

The state track is “Eastern Data, Western Computing” (dong shu xi suan), launched in 2022 to route data-processing demand from the coast to eight computing hubs in energy-rich western regions.^[1] By mid-2024, construction-driven investment in the hubs had surpassed 200 billion yuan, with rack capacity in the millions.^[2]

The private track is even more striking. In February 2025, Alibaba committed 380 billion yuan, about $53 billion, to AI and cloud infrastructure over three years, and noted that the sum exceeds its total AI-and-cloud spending of the previous decade.^[3] By 2026 it was halfway through the plan and signalling the investment could turn profitable within a few years.^[4]

One company’s three-year AI budget now exceeds its entire prior decade of spend. That is the shape of a supercycle.

Power is the real constraint

Compute is ultimately an energy business. The IEA projects global data-centre electricity demand roughly doubling to about 945 TWh by 2030, with China and the US driving some 80% of the growth; China’s own data-centre demand is set to rise about 170% by 2030.^[5] That is the logic behind siting compute next to cheap, often renewable, western power.

The two tracks reinforce each other. The state grid lowers the fixed cost of siting compute near cheap western power; the hyperscaler capex fills those sites with demand. But both run into the same hard limit, electricity, which is why the location of compute is becoming an energy-policy decision as much as a technology one. This is also why a capex cycle of this size creates a long tail of second-order beneficiaries: the power generation, grid equipment, cooling, optical networking and domestic silicon that a build-out consumes are, in aggregate, a larger and more durable market than the model layer that sits on top.

Reading it as an investor

A capex supercycle of this size creates a long value chain of second-order beneficiaries, power generation and grid equipment, cooling, data-centre construction, optical and networking components, and the domestic silicon that fills the racks. When a build-out is this large and this policy-backed, the durable returns often sit in the infrastructure feeding it rather than in the model layer on top. That is where we spend our time.