📊 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 centralized planning and renewable energy infrastructure to deploy AI data centers at gigawatt scale, surpassing US efforts constrained by grid and regulatory bottlenecks. The US remains dominant in chip performance but faces structural limits at the power delivery layer.
China is deploying AI data centers at gigawatt-scale by leveraging its centralized planning, extensive renewable energy buildout, and ultra-high-voltage transmission infrastructure, giving it a structural advantage over the United States, which faces grid and regulatory bottlenecks.
According to recent analysis by Thorsten Meyer, China’s approach involves routing eastern AI demand to western renewable hubs via 45 ultra-high-voltage transmission projects totaling over 40,000 kilometers, with a capacity of 340 GW. In 2025, China added over 430 GW of wind and solar, significantly surpassing US renewable additions.
While Chinese AI chips, such as Huawei’s Ascend 910C, lag behind US counterparts like NVIDIA’s H100 in raw inference performance, China compensates by substituting raw power throughput for chip-level efficiency. This system-level asymmetry allows China to deploy less-performant chips across a vast, renewable-powered grid, effectively closing the system-level gap.
The US maintains dominance in chip performance and AI model development but is constrained at the physical infrastructure layer due to fragmented jurisdiction, permitting delays, and transmission constraints. This creates a structural bottleneck that could limit large-scale AI deployment unless addressed through policy reforms or technological efficiency gains.
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 Gigawatt-Scale Infrastructure Race
The differing approaches to AI infrastructure reflect fundamental structural advantages: China’s centralized, renewable-powered grid enables deployment at gigawatt scale, potentially shifting global AI leadership. The US’s reliance on fragmented infrastructure and regulatory hurdles could impose a ceiling on its AI capacity growth, influencing international competitiveness and technological leadership in the coming years.large-scale AI data center cooling systems
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Structural Differences in US and Chinese AI Infrastructure Strategies
The US leads in chip innovation, model development, and AI applications, but faces constraints at the physical power delivery layer. Its data centers are evolving towards gigawatt-scale facilities, but grid limitations, permitting processes, and regional fragmentation impede large-scale deployment.
China’s strategy centers on centralized planning, extensive renewable energy projects, and a vast ultra-high-voltage transmission network, enabling it to deploy less-performant chips across a high-capacity power infrastructure. This approach leverages China’s constitutional advantages in infrastructure planning, contrasting with the US’s federal–state–local fragmentation.
Historically, the US has focused on optimizing performance-per-watt in chips and models, while China’s approach emphasizes raw wattage and transmission capacity, effectively substituting power for chip-level efficiency. This divergence is shaping the global AI infrastructure landscape in 2026.
“The gigawatt-scale capacity requirements of frontier AI deployments are fundamentally changing how we think about infrastructure. China’s centralized renewable buildout and transmission network enable deployment at a scale that the US cannot match due to regulatory and grid constraints.”
— Thorsten Meyer
Smart Grids
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Unresolved Questions on Future Infrastructure Dynamics
It remains unclear whether US policy reforms, technological efficiency gains, or new infrastructure investments will close the gigawatt gap. The long-term impact of China’s centralized approach versus US decentralization on global AI leadership is still developing.
Additionally, the precise pace at which the US can overcome grid and permitting constraints through policy or technology remains uncertain, as does the potential for China’s renewable and transmission expansion to sustain its advantage.
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Next Steps in AI Infrastructure Competition
Over the next 24 months, both countries are expected to accelerate infrastructure projects—China’s renewable and transmission expansion, and US policy efforts to streamline permitting and increase efficiency. Monitoring these developments will clarify whether the gigawatt gap narrows or persists, shaping global AI leadership.
Further technological innovations in chip efficiency and power management could also influence the structural dynamics, but policy and infrastructure investments will likely be decisive.
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Key Questions
Why is the gigawatt scale important for AI data centers?
Gigawatt-scale capacity is necessary to support the power demands of frontier AI models, which require massive energy throughput for training and inference at scale.
How does China’s centralized infrastructure strategy differ from the US?
China’s approach leverages centralized planning, extensive renewable energy projects, and ultra-high-voltage transmission to deploy large-scale AI data centers, while the US relies on fragmented jurisdiction, permitting delays, and off-grid solutions.
Will the US be able to close the gigawatt gap?
It is uncertain. Success depends on policy reforms, technological efficiency gains, and infrastructure investments. The structural constraints may impose a ceiling unless addressed.
Does chip performance still matter for AI deployment?
Yes, but at the system level, power throughput and infrastructure capacity are increasingly critical. Chinese chips are less performant but are compensated by vast power and transmission capacity.
What are the implications for global AI leadership?
China’s ability to deploy AI at gigawatt scale supported by renewable infrastructure could shift the balance of AI capabilities, challenging US dominance if structural constraints persist.
Source: ThorstenMeyerAI.com
