📊 Full opportunity report: The gigawatt gap. Why China is structurally positioned for AI power and the US is engineering around its grid. on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
China is leveraging its centralised infrastructure and renewable energy capacity to deploy gigawatt-scale AI data centers, giving it a structural advantage over the US, which faces grid and permitting constraints. The next 24 months will determine if the US can close this gap or if China’s approach becomes the new standard.
China is deploying AI data centers at gigawatt-scale capacity, a development that highlights a fundamental structural difference from the United States, which faces significant grid and permitting constraints. This shift impacts global AI infrastructure deployment and competitive positioning.
Recent reports indicate that Chinese infrastructure efforts, including the Eastern Data Western Compute initiative, are routing eastern demand to western renewable hubs via over 45 ultra-high-voltage transmission projects, totaling more than 340 GW capacity. In 2025, China added over 430 GW of wind and solar, surpassing US renewable additions by nearly eight times, and now has a total installed capacity of approximately 3.89 TW.
While Chinese AI chips, such as Huawei’s Ascend 910C, perform at about 60% of NVIDIA’s H100 inference levels and lack native FP8/FP4 support, China compensates by substituting raw power capacity for chip performance. This approach is enabled by the country’s centralized planning, extensive renewable infrastructure, and high-voltage transmission network, allowing deployment at gigawatt-scale data centers that operate on a system level rather than just chip performance.
In contrast, the US relies on a fragmented power system with complex permitting, off-grid gas turbines, and regulatory arbitrage, resulting in data centers typically operating at 100 MW to 2 GW scale, with some projects reaching 5 GW but constrained by grid access and siting issues. This structural difference means the US’s infrastructure buildout is limited at the physical delivery layer, whereas China’s centralized approach allows for larger-scale deployment.
The gigawatt gap.
Why China is structurally
positioned for AI power
and the US is engineering
around its grid.
power capacity end 2025
5-year average wait
45 projects · 340 GW capacity
vs. H100 · compensated by watts
interconnection queue
installed capacity
built by end-2024
on-site generation
DY 2024-25 → 2026-27
solar additions 2025
generation capacity
installed base
of capacity
add ratio
2025 alone
capacity end 2025
installed capacity
of capacity
Low watts
grid + transmission capacity
More watts
chip performance / FP precision
The US has perf-per-watt advantage. China has watts-without-bound advantage. These are asymmetric substitutes — not the same axis. When the perf-per-watt side is bounded by grid capacity and the watts-without-bound side is bounded by chip performance, the binding constraint differs.Thorsten Meyer · The Gigawatt Gap · Energy & Infrastructure 01
Implications of Structural Power Infrastructure Differences
This divergence in infrastructure strategies could reshape global AI competitiveness. China’s ability to deploy large-scale AI data centers supported by renewable energy and extensive transmission lines enables faster and more flexible scaling, potentially outpacing US efforts constrained by regulatory and grid bottlenecks. The outcome of this structural advantage may influence global AI leadership, technological innovation, and economic influence over the next decade.
gigawatt-scale AI data center equipment
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Background on US and Chinese AI Infrastructure Strategies
The US has historically led in AI chip design, models, and applications but faces physical limitations in power infrastructure. Its data centers are typically built at smaller scales due to grid permitting and siting challenges, relying on off-grid solutions and regulatory arbitrage. Meanwhile, China’s approach leverages centralized planning, large renewable capacity, and ultra-high-voltage transmission to deploy gigawatt-scale AI data centers, bypassing many US grid constraints. This difference reflects broader constitutional and policy distinctions: US federal fragmentation versus China’s centralized control.
In 2025, China’s renewable buildout and transmission infrastructure expanded rapidly, with over 430 GW of wind and solar added, positioning it to support large-scale AI deployment. US infrastructure buildout remains constrained by permitting delays and grid access issues, limiting the size and deployment speed of AI data centers despite advances in chip performance and model efficiency.
“The gigawatt-scale capacity requirements of frontier AI deployments are reshaping the infrastructure landscape, with China leveraging central planning and renewable energy to deploy at scale.”
— Thorsten Meyer
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Unresolved Questions on Future Infrastructure Trends
It remains unclear whether US efforts to improve efficiency, reform permitting processes, or develop new power solutions will close the gigawatt gap. Additionally, the long-term impact of China’s centralized approach on technological innovation and global competitiveness is still uncertain. The pace at which US policy and infrastructure can adapt to these structural differences is also an open question.
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Next Steps in AI Infrastructure Competition
Over the next 24 months, monitoring US policy reforms, renewable capacity expansion, and infrastructure projects will be critical. Simultaneously, observing China’s deployment scale, transmission expansion, and chip development progress will clarify whether the structural advantage persists or diminishes. Key milestones include new large-scale data center projects, renewable installations, and regulatory changes in the US.
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Key Questions
Why does the gigawatt scale matter for AI data centers?
Gigawatt-scale capacity is necessary for training and deploying frontier AI models at scale, enabling faster and more flexible AI infrastructure deployment.
How does China’s approach differ from the US in building AI infrastructure?
China leverages centralized planning, extensive renewable energy, and ultra-high-voltage transmission to deploy large-scale data centers, bypassing US grid permitting constraints. The US relies more on fragmented, off-grid solutions and smaller-scale data centers.
Can the US close the gigawatt gap through efficiency gains?
It is uncertain. While efficiency improvements in chips and models are ongoing, structural constraints in permitting and grid access may limit the US’s ability to scale data centers at the same pace as China.
What are the risks if China maintains its infrastructure advantage?
China could achieve faster and larger-scale AI deployment, potentially consolidating global AI leadership and influencing technological standards and economic power.
Will policy reforms in the US change the current landscape?
Reforms could help, but their effectiveness depends on how quickly and comprehensively permitting, grid expansion, and regulatory barriers are addressed, which remains uncertain.
Source: ThorstenMeyerAI.com
