📊 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 centralized planning and extensive renewable energy to deploy gigawatt-scale AI data centers, closing the system-level gap with the US. The US remains ahead technologically but faces structural constraints at the power infrastructure level.
China’s strategic focus on large-scale renewable energy and centralized transmission infrastructure is enabling the country to deploy AI data centers at gigawatt-scale, challenging the US’s dominance in AI infrastructure development. This development reflects a fundamental shift in how AI capacity is built and operated, with China leveraging structural advantages that bypass US regulatory and grid constraints, as discussed in the China Sphere Capability Gap report.
China has rapidly expanded its renewable capacity, adding over 430 GW of wind and solar in 2025 alone, which now exceeds 1.8 TW of renewable capacity. Its Eastern Data Western Compute initiative routes eastern AI demand to western renewable hubs via an extensive ultra-high-voltage (UHV) transmission network spanning more than 40,000 kilometers. This infrastructure allows China to operate AI data centers at gigawatt-scale, with some sites reaching 5 GW, without the regulatory bottlenecks faced by US projects.
In contrast, the US’s AI buildout is constrained by fragmented jurisdictional layers, permitting delays, and transmission bottlenecks. US data centers tend to operate at smaller scales (around 100 MW to 2 GW), relying on off-grid gas turbines, nuclear contracts, and deregulated grid arbitrage to meet power demands. While American chips outperform Chinese chips in raw silicon performance, China’s approach substitutes raw power throughput, enabled by its extensive renewable infrastructure, for chip-level performance, thus closing the system-level 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 Structural Differences in AI Power Infrastructure
This development indicates that, beyond technological advances, the political and infrastructural frameworks of a country significantly influence AI deployment capacity. China’s centralized planning and renewable energy expansion give it a structural edge in deploying gigawatt-scale AI data centers, potentially altering global AI competitiveness. The US’s fragmented system risks hitting a structural ceiling if it cannot overcome permitting and transmission hurdles, which could slow its AI progress despite technological leadership.
gigawatt-scale data center cooling systems
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China’s Renewable and Transmission Infrastructure Growth
China’s aggressive renewable energy expansion in 2025, totaling over 430 GW of new capacity, is part of its broader strategy to support large-scale AI infrastructure growth. Its UHV transmission network, the largest in the world, enables the transfer of power from renewable-rich western regions to eastern demand centers. This contrasts with the US, where grid fragmentation and regulatory complexity limit the scale of AI data centers and their power sources.
While Chinese AI chips lag in raw performance compared to US counterparts, their deployment across a vast, centrally managed power grid allows for a different approach—prioritizing raw throughput over chip-level efficiency. This shift reflects a fundamental difference in how each country approaches AI infrastructure development, rooted in their constitutional and regulatory frameworks.
“The Chinese approach substitutes raw watts for chip performance, enabled by extensive renewable infrastructure and centralized planning, closing the system-level gap with the US.”
— Thorsten Meyer
Advanced Concepts for Renewable Energy Supply of Data Centres
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Unresolved Questions on Future Infrastructure Dynamics
It remains unclear whether US efforts to improve efficiency, reform permitting processes, or expand renewable infrastructure can close the gigawatt gap. The impact of potential technological breakthroughs in chip performance or energy efficiency on this structural dynamic is also uncertain. Additionally, how geopolitical factors and international supply chains will influence the evolution of AI infrastructure remains to be seen.
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Next Steps in Global AI Infrastructure Competition
Over the next 24 months, both countries will likely continue expanding their respective infrastructure capacities. The US may attempt statutory reforms and technological improvements to overcome grid bottlenecks, while China will further integrate renewable buildout with centralized transmission. Monitoring policy developments, infrastructure investments, and technological advances will be key to assessing whether the gigawatt gap persists or begins to close.
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Key Questions
Why does China’s centralized infrastructure matter for AI deployment?
It allows China to deploy large-scale AI data centers at gigawatt capacity without the permitting and transmission delays faced by the US, giving it a structural advantage in AI infrastructure deployment.
Does chip performance still matter in AI infrastructure?
Yes, but at the system level, power throughput and infrastructure capacity are now more critical for deploying AI at scale, especially given China’s strategy of substituting raw power for chip-level performance.
Can the US close the gigawatt gap?
It is uncertain. Success depends on whether the US can reform permitting, expand renewable energy, and improve efficiency fast enough to overcome structural constraints.
What role does renewable energy play in China’s AI strategy?
Renewable energy provides the massive, scalable power needed for gigawatt-scale AI data centers, supported by China’s extensive UHV transmission network.
How might geopolitical factors influence this infrastructure dynamic?
International trade, supply chain security, and geopolitical tensions could impact access to technology, materials, and cooperation, influencing each country’s infrastructure development.
Source: ThorstenMeyerAI.com
