📊 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 leverages its centralized renewable energy and extensive transmission grid to deploy gigawatt-scale AI data centers, offsetting lower chip performance. The US leads in chip tech but faces infrastructure bottlenecks. The next 24 months will determine if the US can close this power gap.
China’s centralized energy infrastructure and extensive ultra-high-voltage transmission grid are enabling the deployment of gigawatt-scale AI data centers, giving it a structural advantage over the United States, which faces significant power infrastructure constraints. This dynamic is discussed in detail in the China Sphere Capability Gap report. This shift could redefine global AI capacity leadership, highlighting the importance of infrastructure in AI infrastructure development.
While US AI leadership remains strong in chip performance, the physical layer of infrastructure—power delivery—has become a critical bottleneck, emphasizing the need for strategic infrastructure planning discussed in the China Sphere Capability Gap report. US data centers require large-scale, often off-grid power solutions, including gas turbines and nuclear contracts, to meet their gigawatt-scale needs. In contrast, China’s approach leverages its centralized planning and massive renewable buildout, with over 430 GW of wind and solar added in 2025 alone, and a network of 45 ultra-high-voltage transmission projects spanning over 40,000 kilometers, capable of transmitting 340 GW across regions.
Chinese chips, such as Huawei’s Ascend 910C, perform at roughly 60% of US NVIDIA H100 inference levels, but the country compensates through raw power capacity—substituting wattage for chip performance. This asymmetry results from China’s constitutional advantage in infrastructure planning, contrasting with the US’s fragmented regulatory environment, which hampers large-scale power siting and permitting. Consequently, China’s system-level capacity is expanding faster than US chip improvements can close the performance gap.
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 the Power Infrastructure Divide
This structural difference could influence global AI leadership by shifting the focus from chip performance to total system capacity. China’s ability to deploy lower-performance chips across vast, renewable-powered grids may enable faster, larger-scale AI deployments. Meanwhile, the US risks hitting a ceiling if it cannot reform or adapt its infrastructure permitting and grid management policies. The next two years will be critical in determining whether the US can innovate around these constraints or whether China’s infrastructure-led approach will redefine AI capacity standards.
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Recent Trends in US and Chinese AI Infrastructure
The US has dominated AI chip design, infrastructure, and model development but faces growing challenges in physical power delivery. Major US data centers now require 100 MW to 2 GW, with some projects reaching 12 GW, but siting and permitting delays limit expansion. Conversely, China’s AI infrastructure benefits from centralized planning, enabling large renewable capacity and extensive transmission networks that bypass many regulatory hurdles. This allows China to scale AI deployments through raw power, despite lower chip efficiency.
China’s renewable capacity surged in 2025, with wind and solar adding approximately eight times the US’s new capacity, supporting a system that transmits power over ultra-high-voltage lines. This infrastructure enables Chinese AI deployment to operate at a system level that compensates for lower chip performance, challenging the traditional performance-per-chip paradigm.
“The US is constrained at the layer where physical infrastructure has to be permitted, sited, and energized. China is not constrained at that layer, using its infrastructure advantage to substitute power throughput for chip performance.”
— Thorsten Meyer
Uncertain Outcomes of Infrastructure and Policy Reforms
It remains unclear whether the US will successfully reform its permitting and grid infrastructure to close the gigawatt gap or whether China’s centralized system will continue to outpace US capacity expansion. The impact of potential technological efficiency gains versus structural constraints is still being evaluated, and the next 24 months will be pivotal in this dynamic.
Next Steps in US-China AI Infrastructure Competition
US policymakers and industry leaders are likely to focus on regulatory reforms and grid modernization to overcome infrastructure bottlenecks. Simultaneously, China will continue expanding renewable capacity and transmission infrastructure, leveraging its central planning advantage. Monitoring these developments will be essential to understanding which country consolidates a leadership position in AI deployment capacity.
Key Questions
Why does power infrastructure matter more than chip performance for AI deployment?
AI data centers at frontier scale require gigawatt-level power capacity. Without sufficient, reliable power, deploying large-scale AI systems becomes infeasible regardless of chip performance. Infrastructure constraints can thus act as a bottleneck, limiting overall capacity growth.
Can the US overcome its infrastructure constraints to match China’s gigawatt-scale deployments?
It is uncertain. US efforts on permitting reform and grid modernization could help, but legislative and regulatory hurdles are significant. The next two years will be critical in determining if these reforms can accelerate US capacity expansion.
How does China’s renewable energy buildout support its AI infrastructure?
China’s rapid expansion of wind and solar capacity, combined with extensive ultra-high-voltage transmission lines, allows it to transmit large amounts of power across regions, enabling large-scale AI data centers to operate efficiently despite lower chip performance.
What are the risks for China relying on system-level power capacity instead of chip performance?
Lower chip efficiency could limit AI model performance or increase operational costs. However, the large-scale power infrastructure compensates at the system level, making raw throughput the dominant factor in deployment capacity.
Will technological improvements in chips or energy efficiency close the gigawatt gap?
Potentially, but current trends suggest that structural infrastructure advantages may be more decisive in the near term. The impact of efficiency gains remains uncertain and is a key area to watch.
Source: ThorstenMeyerAI.com
