The race to build artificial intelligence-driven economies is transforming how the global economy and societies are organized.<\/p>\n
Breakthroughs in generative AI and machine learning in recent years have accelerated a new industrial revolution\u2014one in which nations that can develop and deploy the most powerful computing infrastructure will be best positioned to reap the most economic, military and geopolitical advantages.<\/p>\n
A central part of this competition\u00a0is<\/a>\u00a0AI data centers, also called \u201cAI factories.\u201d Unlike traditional data centers, these sprawling facilities house a high density of specialized AI chips known as graphics processing units, or GPUs.<\/p>\n They require customized architectures optimized for AI workloads and consume vast amounts of energy. Building them demands massive capital, sustained government and private-sector investment, and institutional flexibility to adapt to the disruptive innovations of an emerging AI-driven economy.<\/p>\n While the United States and China are both racing to harness AI for economic growth and strategic leverage, their approaches to AI factories are diverging\u2013reflecting the distinct economic conditions and technological constraints each faces.<\/p>\n Washington is doubling down on capital-intensive buildouts, betting that private-sector investment and global partnerships will solidify American leadership in AI.<\/p>\n Beijing, meanwhile, is recalibrating\u2014scaling back its once-frenzied data center investment boom amid a slowing economy, industrial overcapacity and a fast-changing tech landscape.\u00a0<\/p>\n America\u2019s data center bet<\/p>\n The US has embraced an all-in strategy, as shown in President Donald Trump\u2019s recently unveiled\u00a0AI Action Plan<\/a>, which focuses on mobilizing private and foreign capital to scale infrastructure as quickly as possible. Tech giants like Microsoft, Google, Amazon and Meta are investing unprecedented sums to build next-generation data centers at home and abroad.<\/p>\n In 2025 alone, the \u201cMagnificent Seven\u201d tech firms are on track to\u00a0invest<\/a>\u00a0a record US$364 billion in data center construction and upgrades. This spending surge is now\u00a0contributing<\/a>\u00a0more to US GDP growth than\u00a0all consumer spending\u2014a historic first in America\u2019s consumption-driven economy.<\/p>\n Mega-projects like the $500 billion\u00a0Stargate<\/a>\u00a0supercomputing campus \u2013 a partnership between OpenAI, Oracle, and SoftBank \u2013 illustrate the ambition. AI capital spending is now\u00a0estimated<\/a>\u00a0to account for nearly 2% of US GDP, approaching the scale of the 19th-century railroad boom.<\/p>\n Abroad, Washington is leveraging its technological edge to deepen geopolitical ties, with Trump brokering multibillion-dollar data center deals with the\u00a0United Kingdom<\/a>\u00a0and the\u00a0United Arab Emirates<\/a>. US companies are also expanding partnerships with\u00a0Norway<\/a>\u00a0and\u00a0Japan<\/a>\u00a0to build a global AI infrastructure network anchored by an American tech stack.<\/p>\n However, this investment spree has raised concerns about a possible bubble. Analysts\u00a0warn<\/a>\u00a0that the sector\u2019s speculative momentum and unclear business models echo the dot-com and telecom bubbles of the past.<\/p>\n The dominance of hyperscalers\u2014cloud giants like Amazon Web Services, Google Cloud and Microsoft Azure\u2014has raised antitrust and security concerns, with policymakers\u00a0warning<\/a>\u00a0that their control of AI infrastructure creates systemic vulnerabilities.<\/p>\n The 2024 CrowdStrike update\u00a0glitch<\/a>\u00a0that paralyzed global operations by disrupting Microsoft\u2019s cloud services exposed the risks of such single points of failure. Additionally, these mega data centers are\u00a0exacerbating<\/a>\u00a0water insecurity issues in many local communities.\u00a0<\/p>\n Despite concerns over market concentration, security risks and environmental costs, America\u2019s AI infrastructure development shows no sign of slowing. Unlike traditional infrastructure, AI data centers\u00a0depreciate<\/a>\u00a0rapidly and require constant reinvestment to keep pace with advances in hardware and model design.<\/p>\n The result is an ecosystem heavily reliant on political backing, private capital and economies of scale\u2014one that reinforces hyperscalers\u2019 dominance and embodies America\u2019s techno-capitalist push.<\/p>\n China\u2019s cooling AI data center boom<\/p>\n In the wake of ChatGPT\u2019s 2022 debut, Beijing\u00a0made<\/a>\u00a0data centers a priority, prompting a wave of investment in \u201cnational computing hubs\u201d across the country.<\/p>\n By 2024, more than 500 projects had been\u00a0launched<\/a>, from Inner Mongolia to Guangdong. Many were part of the \u201cEastern Data, Western Computing\u201d initiative, which aimed to relocate data storage and processing from coastal cities to inland regions with cheaper land and more renewable energy.<\/p>\n For a time, AI infrastructure was seen as the next great economic driver, capable of offsetting a property market slump and slowing economic growth. But the release of DeepSeek\u00a0shifted<\/a>\u00a0industry focus from training large models to inference\u2013the process of deploying those trained models to generate results. China\u2019s new\u00a0\u201cAI+\u201d initiative\u00a0<\/a>reflects this pivot to inference, aiming to accelerate AI commercial applications.<\/p>\n The earlier building spree has left Beijing with a hangover. By 2025, as much as 80% of China\u2019s new computing capacity, built through the government\u2019s push for data centers in 2022, reportedly\u00a0sits<\/a>\u00a0idle.<\/p>\n President Xi Jinping has issued unusually blunt warnings against over-investment in AI, criticizing \u201cneijuan\u201d (\u5185\u5377) or involution\u2014a term describing wasteful, self-reinforcing competition and industrial overcapacity.<\/p>\n His statement appears to mark a significant change in China\u2019s stance. During a visit to France last year,\u00a0Xi insisted<\/a>\u00a0that China faced no overcapacity problem.<\/p>\n The pullback on AI data centers reflects a disconnect between top-down industrial policy and actual market demand, compounded by technical setbacks caused by US export controls on cutting-edge chips. Many of China\u2019s data centers in its western regions were built for model training just as the demand\u00a0shifted<\/a>\u00a0toward inference.<\/p>\n Conducting inference typically requires different hardware configurations \u2013 faster, more responsive chips that prioritize low latency and efficiency over sheer computational power. As a result, much of China\u2019s existing infrastructure is\u00a0ill-suited\u00a0<\/a>to the application-oriented needs of its major eastern cities.<\/p>\n To address this gap, Beijing has\u00a0announced<\/a>\u00a0plans to build an inference-focused data center in Wuhu, a southeastern prefecture, to serve major urban markets like Shanghai, Hangzhou, and Nanjing.<\/p>\n Hardware shortages remain one of Beijing\u2019s biggest obstacles. The US currently controls\u00a0over 70%<\/a>\u00a0of global computing capacity while using export controls to restrict China\u2019s access to advanced chips such as Nvidia\u2019s H100 and critical packaging technologies.<\/p>\n Domestic alternatives like Huawei\u2019s Ascend chips still\u00a0lag behind<\/a>\u00a0Western hardware in performance and energy efficiency. Moreover, because advanced AI clusters require not only chips but highly engineered interconnections across tens of thousands of processors, US firms continue to lead in systems-level design.<\/p>\n