According to Fortune, China has substantially increased energy subsidies for its largest data centers, potentially cutting energy bills by up to half for companies like Alibaba, ByteDance, and Tencent. The move specifically targets facilities using domestic chips from Huawei and Cambricon, while excluding those using foreign chips from companies like Nvidia. Meanwhile, OpenAI announced a $38 billion, seven-year deal with Amazon for computing capacity to train AI models and process ChatGPT queries, following similar large cloud deals with Microsoft and Oracle. In earnings news, Palantir reported blockbuster third-quarter results with $1.2 billion in revenue (up 63% year-over-year) and $476 million in net income (up 40%), driven by 121% growth in U.S. commercial business. These developments signal an intensifying global competition in AI infrastructure and capabilities.
Sponsored content — provided for informational and promotional purposes.
China’s Strategic Energy Gambit
China’s targeted energy subsidies represent a calculated industrial policy move that goes far beyond simple cost reduction. By specifically excluding data centers using foreign chips, Beijing is creating a powerful economic incentive for companies to adopt domestic semiconductor technology, even though Huawei and Cambricon chips are reportedly less efficient than Nvidia’s offerings. This creates a protected domestic market where Chinese chipmakers can iterate and improve while being shielded from direct competition. The timing is particularly significant as electricity costs rise due to increased AI compute demands, making energy efficiency a critical competitive factor. However, this approach risks creating a technological island where Chinese AI companies become increasingly disconnected from global standards and innovation cycles.
OpenAI’s Cloud Dependency Deepens
OpenAI’s $38 billion commitment to Amazon Web Services reveals the staggering infrastructure costs required to remain competitive in the generative AI race. While the company recently struck partnerships with multiple cloud providers, this level of spending creates significant vendor lock-in and raises questions about long-term strategic flexibility. The fact that OpenAI needs to secure computing capacity from three different major providers simultaneously suggests either extraordinary growth expectations or concerns about any single provider’s ability to scale quickly enough. More critically, this massive capital outlay for compute resources means OpenAI must generate substantial recurring revenue just to cover infrastructure costs, potentially forcing the company to prioritize commercial applications over broader AI safety research.
Palantir’s Valuation Reality Check
While Palantir’s growth numbers are impressive, the company’s rich valuation and the skepticism from investors like Michael Burry highlight broader concerns about AI stock valuations. The 121% growth in U.S. commercial business is notable, but Palantir’s total revenue of $1.2 billion remains modest compared to peers with similar market capitalizations. More importantly, the company’s aggressive growth targets assume sustained enterprise demand for AI implementation services at current premium pricing levels. As more competitors enter the AI consulting and implementation space and enterprises develop internal capabilities, Palantir may face pricing pressure and slowing growth. The company’s confrontational tone toward skeptics, rather than addressing valuation concerns directly, could signal underlying nervousness about maintaining current growth trajectories.
The Global Subsidy Race Intensifies
The parallel developments of China’s energy subsidies and various U.S. state tax breaks for data centers signal the beginning of a global subsidy war for AI supremacy. Unlike previous technology competitions, AI requires massive, energy-intensive infrastructure that creates natural geographic advantages for regions with cheap power and supportive policies. China’s approach of tying subsidies to domestic chip usage represents a more targeted industrial policy than the broader tax incentives seen in some U.S. states. However, both strategies risk creating market distortions and potentially violating international trade agreements. The bigger risk is that this subsidy race could accelerate AI development faster than safety frameworks and regulatory oversight can develop, creating potential governance gaps in a critically important technology domain.
The Infrastructure Bottleneck Reality
Behind the headline numbers lies a fundamental constraint: the global AI industry is rapidly approaching physical limits in power availability and computing capacity. OpenAI’s need to secure $38 billion in cloud computing, on top of existing commitments to Microsoft and Oracle, suggests that even the largest tech companies are struggling to scale infrastructure fast enough. This infrastructure scarcity creates winner-take-most dynamics where well-funded players can outspend competitors on compute resources, potentially stifling innovation from smaller players. The situation mirrors the early days of cloud computing but with even higher stakes, as AI capabilities become increasingly central to economic and technological competitiveness. Companies and countries that control the underlying infrastructure may ultimately wield more power than those developing the AI models themselves.
