The Looming AI Valuation Correction: China's Strategic Advancements and U.S. Structural Weaknesses

The global AI race is entering a critical inflection point, marked by China's accelerating technological self-sufficiency and the U.S. sector's growing structural vulnerabilities. While the U.S. has long dominated AI innovation, its reliance on advanced semiconductors, energy-intensive infrastructure, and a privatized ecosystem is being challenged by China's state-backed strategies in semiconductors, energy efficiency, and open-source innovation. For investors, this dynamic suggests a looming valuation correction in the U.S. AI sector as China closes the gap-and potentially overtakes the West in key areas.

Semiconductor Self-Reliance: China's Capacity Surge

China's semiconductor industry has made significant strides in recent years, driven by state-backed policies and private investment. By 2025, Chinese firms accounted for 33% of global wafer production capacity for foundational node logic chips, up from 19% in 2015 . Semiconductor Manufacturing International Corporation (SMIC), China's largest chipmaker, plans to double its 7nm chip production capacity by 2026, a critical step toward reducing reliance on U.S. and Dutch equipment .

While U.S. export controls have limited China's access to cutting-edge tools like ASML's extreme ultraviolet (EUV) lithography machines, the country is compensating by scaling mature-node chip production. These chips, though less advanced, are essential for automotive and medical devices, where cost efficiency trumps marginal performance gains . Meanwhile, China's AI chip output is tripling, supported by new fabrication plants and a strategic pivot to domestic alternatives like Huawei's Ascend series .

The U.S. response-tightening export controls and forming alliances with Japan and the Netherlands-has created a fragmented global supply chain. However, these measures are increasingly transactional. For instance, the Trump administration recently allowed conditional exports of Nvidia's H200 chips with a 25% fee, signaling a shift from outright bans to revenue-generating restrictions . Such policies, while aimed at preserving U.S. dominance, risk eroding the sector's long-term competitiveness by stifling collaboration and innovation.

Energy Efficiency: China's Renewable Edge

The energy demands of AI training and inference are becoming a critical bottleneck for global leaders. Modern large language models (LLMs) require vast computational power, with GPT-4 estimated to consume 51,773–62,319 MWh during training-equivalent to the annual electricity use of 4,700–5,700 U.S. households . Here, China's renewable energy infrastructure provides a decisive advantage.

By 2025, China added 373 GW of renewable energy capacity, including solar and wind, enabling data centers to operate at 30–50% lower electricity costs than in the U.S. . This energy surplus allows Chinese firms to deploy massive chip clusters, such as Huawei's CloudMatrix 384 system, which links 384 Ascend 910C chips to achieve performance comparable to Nvidia's 72-GPU GB200 NVL72 system . While Huawei's approach is less energy-efficient, China's cheap power and state subsidies for domestic-chip data centers offset this inefficiency.

In contrast, the U.S. faces rising energy costs and regulatory hurdles that slow renewable energy deployment. Without a scalable solution to its energy bottleneck, the U.S. risks losing its edge in AI infrastructure-a sector where cost and scale are increasingly decisive.

Open-Source Innovation: Democratizing AI

China's open-source AI initiatives are further narrowing the gap with the U.S. Startups like DeepSeek have released top-tier models and research for free, challenging the U.S. model dominated by a few private giants . This strategy democratizes access to cutting-edge AI, fostering a broader ecosystem of innovation while reducing reliance on U.S. technology.

The U.S. model, by contrast, is concentrated in a handful of firms like Nvidia and Google, which control both hardware and software. While this has driven rapid advancements, it also creates vulnerabilities. For example, U.S. export controls have forced Chinese firms to optimize midrange chips for near-frontier performance, as seen with DeepSeek's AI models . Over time, this could erode the U.S. sector's pricing power and market share.

U.S. Structural Weaknesses and the Case for Short-Selling

The U.S. AI sector's dominance is increasingly precarious. Its reliance on advanced semiconductors, energy-intensive infrastructure, and a privatized ecosystem leaves it exposed to China's state-backed strategies. Export controls, while intended to preserve U.S. leadership, are creating a fragmented global supply chain and reducing the sector's ability to scale. Meanwhile, China's energy advantages and open-source initiatives are enabling a more resilient and cost-effective AI ecosystem.

For investors, the implications are clear. The U.S. AI sector's valuation multiples-built on assumptions of sustained dominance-are at risk of correction as China closes the gap. Short-sellers should focus on firms heavily exposed to U.S. export controls (e.g., Nvidia) and those lacking energy-efficient infrastructure. Conversely, long-term investors may want to overweight Chinese firms and renewable energy plays, which are better positioned to capitalize on the next phase of the AI race.

[1] China to Triple AI Chip Output by 2025 Amid US Export Curbs [https://mexicobusiness.news/cloudanddata/news/china-triple-ai-chip-output-2025-amid-us-export-curbs]
[2] U.S.–China Chip Tensions Renew Focus on AI Controls as... [https://www.fintechweekly.com/magazine/articles/us-china-chip-controls-nvidia-h200-conditional-export-policy]
[3] Made in China 2025: Evaluating China's Performance [https://www.uscc.gov/research/made-china-2025-evaluating-chinas-performance]
[9] The AI Race Shifts from Chips to Energy for Data Centers [https://medium.com/@jannadikhemais/the-ai-race-shifts-from-chips-to-energy-for-data-centers-cc623a13c7f4]
[10] China's strategy in AI race with US - big chip clusters and cheap energy [https://www.cnbc.com/2025/11/07/chinas-strategy-in-ai-race-with-us-big-chip-clusters-cheap-energy.html]
[11] AI race comes down to power and data centres [https://finance.yahoo.com/news/ai-race-comes-down-power-093000350.html]