📊 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 infrastructure and renewable energy capacity to deploy gigawatt-scale AI data centers, while the US faces structural grid bottlenecks. This difference may influence global AI leadership.
China’s approach to powering AI data centers differs fundamentally from the United States, with China leveraging its centralized planning and extensive renewable energy infrastructure to operate at gigawatt-scale capacities, while the US faces significant grid and permitting constraints that limit its ability to scale similarly.
Current frontier AI data centers require 100 megawatts to start and up to 2 gigawatts at full buildout. The US infrastructure stack has responded by creating workaround solutions, such as off-grid gas turbines and regulatory arbitrage, to bypass grid bottlenecks. In contrast, China’s ‘Eastern Data Western Compute’ initiative routes demand across 45 ultra-high-voltage (UHV) transmission projects spanning over 40,000 kilometers, enabling large-scale renewable deployment and power transfer.
In 2025, China added over 430 GW of wind and solar capacity—roughly eight times the US’s additions—pushing its renewable capacity above 1.8 TW. Chinese AI chips, like Huawei’s Ascend 910C, are less performant than US chips but are deployed across a power infrastructure that operates without the US’s regulatory constraints. The Chinese model substitutes raw power throughput for chip performance, leveraging the scale of renewable generation and transmission to compensate for lower chip efficiency.
This structural difference stems from the US’s fragmented federal system, which complicates infrastructure siting and permitting, versus China’s centralized planning, enabling rapid deployment and integration of large-scale renewable projects. Whether the US can close the performance-per-watt gap through efficiency improvements or reform remains uncertain, but the structural advantage in infrastructure scale favors China’s approach.
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 Power Infrastructure on Global AI Leadership
This divergence in infrastructure strategy could determine which country maintains or gains AI dominance. China’s ability to deploy less efficient chips at scale, powered by a vast renewable grid, may offset the US’s technological edge in chip performance. The outcome hinges on whether the US can reform its permitting and grid constraints or whether China’s centralized infrastructure approach creates a sustained advantage in AI deployment at scale.
gigawatt-scale AI data center equipment
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Structural Foundations of US and Chinese AI Infrastructure Strategies
The US leads in AI chip design, models, and applications but is constrained at the physical infrastructure layer—specifically, the power delivery and grid integration required for gigawatt-scale data centers. Its infrastructure is fragmented, with regulatory and transmission bottlenecks causing delays and limiting capacity. Meanwhile, China’s centralized government directs large-scale renewable buildout and transmits power through an extensive UHV grid, enabling the deployment of large, gigawatt-scale AI data centers that operate efficiently without the same permitting hurdles.
In 2025, China’s renewable capacity expansion outpaced the US significantly, and its transmission network allows for the flexible routing of power to meet AI data center demands. Chinese chips, while less performant, are deployed across this robust power infrastructure, creating a system-level asymmetry that favors China’s approach to scaling AI infrastructure.
“The American AI buildout is constrained at the layer where physical infrastructure has to be permitted, sited, and energized. China is not constrained at that layer.”
— Thorsten Meyer
Advanced Concepts for Renewable Energy Supply of Data Centres
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Uncertainties in US Infrastructure Reform and Technological Advances
It remains unclear whether the US will implement regulatory reforms or technological efficiencies sufficient to close the gigawatt-scale infrastructure gap. The long-term impact of China’s centralized approach versus potential US policy changes is still developing and uncertain.
Extruded Cables for High-Voltage Direct-Current Transmission: Advances in Research and Development (IEEE Press Series on Power and Energy Systems)
Used Book in Good Condition
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Next Steps in Infrastructure and Policy Developments
In the coming 24 months, focus will be on US policy reforms aimed at streamlining grid permitting and expanding capacity, alongside technological advances in chip efficiency. Simultaneously, China’s continued renewable expansion and infrastructure investments will be monitored to assess how their approach influences global AI deployment and competitiveness.
How AI Uses Our Water: When Machines Get Thirst: Cooling Systems, Data Centres, and the Infrastructure Behind Artificial Intelligence
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Key Questions
Why is power infrastructure so critical for AI scaling?
AI data centers require massive amounts of power, especially at gigawatt-scale capacities. The ability to deliver reliable, large-scale power directly impacts the scale and speed of AI deployment.
How does China’s centralized planning give it an advantage?
China’s government can coordinate large-scale renewable projects and transmission infrastructure rapidly, bypassing the permitting delays common in the US, enabling faster deployment of large AI data centers.
Will chip performance improvements close the power gap?
While chip efficiency gains will help, the fundamental difference lies in infrastructure scale. Unless the US reforms its power grid constraints, the structural advantage may persist.
Could the US adopt China’s approach to infrastructure?
It is uncertain. The US’s federal system complicates centralized planning, but policy reforms and technological advances could mitigate some constraints over time.
Source: ThorstenMeyerAI.com
