Global AI Computing Power Shortage and Industry Responses

A severe shortage of computing power has emerged as AI inference workloads surge beyond existing hardware capacity. The gap is driven not only by chip-fabrication limits and power-grid constraints, but also by a shift in AI usage: models now act as autonomous agents that write code, organize files, and integrate across software platforms, increasing per-task resource consumption by several orders of magnitude.

China Daily

Three interrelated trends amplify demand. First, AI has moved from “helpful assistant” functions to “autonomous agent” operations, requiring background analysis, reasoning, and verification. Second, developers of leading models use inference-time scaling, pausing to assess and refine outputs before responding, which can extend a request to hours of processing and consume millions of tokens. Third, commercial adoption has crossed a tipping point; finance, healthcare, and core business operations now rely on AI for tangible returns, turning experimental trials into essential services.

China Daily

Shen Jianguang, chief economist of e-commerce platform JD.com, highlighted the issue in a People’s Daily commentary. Chinese firms are pursuing higher output efficiency per unit of computing power through innovation and planning long-term capacity expansion. Globally, major tech companies accelerate data-center construction, secure high-end chips, and adopt more efficient model architectures, next-generation storage, and interconnect systems to mitigate pressure.

China Daily

The commentary stresses short-term optimization of resource allocation toward revenue-generating segments, coupled with long-term planning that aligns technology upgrades with capacity building. Chinese industry leaders argue that balancing these approaches is essential to sustain growth and preserve a competitive edge on the world stage.

China Daily

In the near term, AI providers are reallocating compute to business units that deliver direct revenue, while exploring software-level efficiencies such as model pruning and quantization. Over the longer horizon, investments in new data-center sites, advanced semiconductor procurement, and research into low-power AI architectures are expected to expand the overall supply of compute. Sources agree that computing power will become a strategic resource comparable to water or electricity in the AI era.

China Daily

• “[TANG YANJUN/CHINA NEWS SERVICE] Editor's note: Increasing artificial intelligence inference has led to greater demand for computing power than can be supplied.” — Editor’s note, China Daily

China Daily

• “First, AI is evolving from a "helpful assistant" into an "autonomous agent".” — China Daily analysis

China Daily

• “In the AI era, computing power has become a foundational strategic resource, much like water or electricity.” — Conclusion, China Daily

China Daily

• “Only by balancing short-term optimization with long-term planning, and aligning tech upgrades with capacity building, can the industry ensure steady growth and maintain a competitive edge on the global stage.” — China Daily conclusion

China Daily