📊 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, offsetting lower chip performance compared to the US. The US faces constraints at the physical power delivery layer, risking a structural bottleneck in AI deployment.
China’s AI infrastructure is rapidly scaling through centralized planning and extensive renewable energy deployment, enabling it to operate AI data centers at gigawatt capacity, despite lower chip performance levels than the US.
Recent developments reveal that Chinese authorities have routed AI demand to western renewable energy hubs via 45 ultra-high-voltage transmission projects covering over 40,000 kilometers, reaching a capacity of 340 GW. In 2025, China added over 430 GW of wind and solar capacity—eight times the US increase—pushing total renewable capacity above 1.8 TW and overall installed capacity to 3.89 TW. These energy resources support Chinese AI chips, such as Huawei’s Ascend 910C, which perform at about 60% of NVIDIA’s H100 inference levels, but benefit from the country’s ability to transmit large amounts of power over extensive UHV grids, effectively substituting raw power for per-chip performance.
In contrast, the US’s AI buildout is constrained by regulatory, permitting, and transmission bottlenecks, leading to a reliance on off-grid solutions like gas turbines and nuclear contracts to meet gigawatt-scale demands. The US’s infrastructure limitations mean that, despite superior chip performance, its capacity to deliver power at scale is lagging behind China’s centralized, renewable-based approach. This structural difference fundamentally alters the landscape of AI deployment at scale, shifting the focus from chip performance to power throughput.
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 structural divergence in infrastructure strategies could determine global AI dominance. China’s ability to leverage centralized planning and renewable energy to support gigawatt-scale data centers may allow it to deploy AI at larger scales more efficiently than the US, which faces regulatory and grid constraints. The evolving landscape suggests that physical power delivery, not chip performance, could become the critical bottleneck in AI progress, impacting future competitiveness and technological leadership.
gigawatt scale AI data center power supplies
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US and China Approaches to AI Infrastructure Development
The US leads in AI chips, models, and applications but is constrained at the physical power delivery layer due to fragmented jurisdictional structure, permitting delays, and grid limitations. Major US data centers now require 100 MW to 2 GW capacities, with some projects targeting 12 GW, but face bottlenecks in siting and grid access. Meanwhile, China’s approach involves centralized planning, with the NDRC’s Eastern Data Western Compute initiative channeling AI demand to renewable energy hubs across extensive UHV transmission networks. In 2025, China’s renewable capacity expansion and transmission infrastructure have outpaced US growth, enabling a different model of large-scale AI deployment based on raw power availability rather than chip efficiency.
“The gigawatt-scale capacity requirements of frontier AI deployments are being met through fundamentally different infrastructure strategies in China and the US.”
— Thorsten Meyer
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Uncertainties Around Future Infrastructure and Policy Changes
It remains unclear whether the US can overcome its physical infrastructure constraints through efficiency gains, policy reform, or new technologies. The extent to which China’s renewable buildout and transmission capacity can continue to scale at the current pace is also uncertain. Additionally, whether these structural advantages will translate into sustained AI leadership is still to be seen, as geopolitical and regulatory factors evolve.
Protection Technologies of Ultra-High-Voltage AC Transmission Systems
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Next Steps in Monitoring US and China AI Infrastructure Strategies
In the coming 24 months, attention will focus on US efforts to reform permitting processes, expand grid capacity, and develop off-grid solutions. Simultaneously, China’s continued renewable expansion and grid enhancements will be closely watched to assess whether its centralized infrastructure approach maintains its advantage. The evolution of global AI deployment will hinge on these developments, shaping the future landscape of AI capability at scale.
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Key Questions
Why does power infrastructure matter more than chip performance for AI scaling?
Because AI data centers require enormous amounts of electricity at gigawatt scale, the ability to physically deliver power reliably and efficiently becomes the key constraint. Without sufficient power infrastructure, even the most advanced chips cannot operate at large scale.
How does China’s centralized planning benefit its AI infrastructure buildout?
Centralized planning allows China to coordinate large renewable projects and extensive transmission networks without the permitting delays and jurisdictional fragmentation faced by the US, enabling the deployment of gigawatt-scale data centers supported by renewable energy.
Could the US catch up in infrastructure to match China’s gigawatt-scale deployments?
It is uncertain. The US can potentially expand grid capacity and reform permitting processes, but structural and regulatory hurdles may slow progress. Whether these efforts will be sufficient to close the gigawatt gap remains an open question.
What does this mean for global AI leadership?
If China’s infrastructure approach proves sustainable and scalable, it could enable it to deploy AI at larger scales more cost-effectively, challenging US dominance despite chip-level advantages.
Source: ThorstenMeyerAI.com
