中国 AI 热潮从模型价格转向电费与折旧

NextFin News — Ulanqab, a northern city hundreds of kilometers from Beijing, has become one of China’s densest clusters of data-center construction. Local authorities have signed dozens of projects. Alibaba, Huawei, Kuaishou and specialist operators have all taken space. One abandoned school is being converted into a computing facility. The attraction is not software talent. It is relatively cheap electricity, available land and a climate that eases cooling loads.

The building boom coincides with a sharp decline in model prices. In May, DeepSeek made permanent a 75 percent cut on its V4-Pro API rates. By October, a Token price index compiled by Silicon Data and cited by the Financial Times showed composite prices more than 52 percent below their May peak. Not every model fell equally; some premium offerings held steadier. The direction, however, is clear: intelligence sold by the Token is getting cheaper.

Yet the companies that sell compute are spending more, not less. Alibaba’s capital expenditure for the quarter ended June 30 reached 67.678 billion yuan, up 75 percent year on year, driven largely by AI infrastructure. In the same period, its AI cloud and computing services revenue rose 45 percent to 48.437 billion yuan. The two figures capture the industry’s new tension. Model providers race to lower list prices. Cloud operators must finance facilities whose payback stretches over years.

Power is the most visible line item. A hypothetical 10-megawatt IT load running continuously at a power-usage effectiveness of 1.2 would generate an annual electricity-cost gap of roughly 42 million yuan if the unit price differed by 0.40 yuan per kilowatt-hour. Over five years the cumulative difference exceeds 200 million yuan. Real contracts vary with utilization, tariffs and cooling design, but the sensitivity is large enough to pull site selection into the boardroom. Regions that can offer firm power, direct connections to generation, and lower ambient temperatures become strategic assets.

Location still involves trade-offs. Training clusters favor dense, high-bandwidth fabrics. Latency-sensitive inference prefers proximity to users and network hubs. Bulk training, batch jobs and cold storage can migrate toward cheap power; interactive products cannot ignore delay. Operators therefore split workloads rather than chase a single ideal site.

Ownership structure adds another layer of risk. A firm that buys its own GPUs absorbs utilization risk and depreciation. A firm that rents from a cloud provider trades higher long-run unit costs for flexibility. GPUs complicate the calculation further: physical life can outlast economic life. A newer chip generation or a more efficient model architecture can strand earlier purchases even if the hardware still functions. Cheap electricity improves the operating margin; it does not erase technological obsolescence.

Price cuts on the model side do not map one-to-one onto lower task costs. API list prices reflect competitive positioning as well as underlying efficiency. Customers increasingly route routine classification, summarization and support work to cheaper models and reserve expensive ones for harder reasoning. Agents amplify the effect. A single user request can trigger dozens of model calls, tool uses and retries. Lower Token prices expand the set of viable jobs; they do not automatically shrink the total compute consumed per completed workflow.

Alibaba’s results illustrate the split inside a single group. AI cloud and computing services delivered rising revenue and sharply higher adjusted EBITA. The AI laboratory and applications segment, which includes consumer products such as Qwen, posted a large adjusted EBITA loss as the company absorbed capability investment and higher inference costs. The same kilowatt-hour can appear as revenue on a cloud ledger and as cost on a model ledger. Chip vendors book sales when servers ship. Cloud operators recover capital only through years of utilization. Model companies recover it only if paying customers arrive in volume.

That sequencing is the core management problem. Model prices can fall by three-quarters in months. A data center’s depreciation schedule runs for years. Demand growth can fill capacity—or leave operators competing on price with assets that no longer set the cost frontier. Progress in algorithms and hardware raises the ceiling on what AI can do; it also shortens the comfortable payback window for any given generation of equipment.

China’s AI industry spent the last two years proving it could train competitive models and secure chips. It is now learning a more traditional discipline: how to site, power, finance and keep busy a heavy capital base while the product sold on top of that base keeps getting cheaper. Ulanqab’s rising skyline of server halls is one answer to the power question. The harder answer—whether those halls earn their keep after the next round of efficiency gains—will be written in utilization rates and free cash flow, not in Token price lists.

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