📊 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 structurally positioned to deploy AI at gigawatt-scale data centers due to its extensive renewable energy and transmission infrastructure. The US, despite leading in chip performance, faces constraints at the power delivery layer, risking a structural gap in AI deployment capacity.
China’s centralised energy infrastructure, supported by its extensive renewable capacity and ultra-high-voltage transmission network, now enables gigawatt-scale AI data centers, contrasting with the United States’ grid constraints that limit its ability to deploy similarly large facilities at scale. See the China Sphere Capability Gap report for more details.
Recent analysis by Thorsten Meyer highlights that China’s approach to AI infrastructure relies on a coordinated, top-down strategy leveraging its large-scale renewable energy buildout and a vast UHV transmission network spanning over 40,000 kilometers. This allows China to route renewable power efficiently to AI data centers, enabling deployments of 1–2 gigawatts per site, with some projects exceeding 5 GW in total capacity.
In contrast, the US dominates in chip performance, AI models, and infrastructure but faces significant challenges in physically delivering sufficient power to large-scale AI data centers. The US grid is fragmented, with permitting, siting, and transmission bottlenecks creating a de facto ceiling on gigawatt-scale deployment, despite having the technological capacity to build more powerful chips and models.
Chinese chips, such as Huawei’s Ascend 910C, perform at roughly 60% of NVIDIA’s H100 inference levels but are deployed across a system that substitutes raw power throughput for chip-level performance. This asymmetric approach is enabled by China’s centralized planning and extensive renewable infrastructure, which allows for a different definition of “AI capability at scale.”
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 Structural Gigawatt Gap in AI Infrastructure
This divergence in infrastructure strategy could determine the future global leadership in AI deployment. While the US maintains a technological edge in chip performance, its grid constraints threaten to limit large-scale AI expansion, potentially ceding ground to China’s more integrated, renewable-powered approach. For a deeper analysis, see the China Sphere Capability Gap report.
gigawatt-scale AI data center power supply
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China’s Centralized Planning and Renewable Expansion Drive AI Infrastructure
China’s government-led initiatives, such as the NDRC’s Eastern Data Western Compute program, aim to direct eastern AI demand to western renewable hubs via over 40,000 km of ultra-high-voltage transmission lines. In 2025, China added over 430 GW of wind and solar capacity—eight times the US’s additions—raising total renewable capacity to over 1.8 TW. This large-scale renewable buildout, coupled with centralized planning, enables China to bypass the US’s grid and permitting constraints, deploying AI data centers that operate at gigawatt-scale.
Meanwhile, the US’s approach relies heavily on off-grid gas turbines, nuclear contracts, and complex permitting processes, which limit the rapid deployment of large-scale AI infrastructure despite its technological leadership in chips and models.
“The gigawatt-scale capacity requirements of frontier AI deployments are now met more readily in China through centralized planning and renewable infrastructure than in the US, which faces structural constraints.”
— Thorsten Meyer
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Uncertain Impact of Efficiency Gains and Policy Reforms
It remains unclear whether technological improvements in chip efficiency, regulatory reforms, or policy changes in the US will close the gigawatt gap. The pace at which the US can overcome grid and permitting constraints through efficiency or reform is still uncertain, and the potential for China’s infrastructure advantage to widen remains a key question.
Renewable Energy in Power Systems
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Next Steps in Infrastructure Policy and Deployment Strategies
In the coming 24 months, both countries will likely pursue efforts to expand renewable capacity, streamline permitting, and optimize infrastructure. The US may focus on regulatory reform and efficiency gains, while China continues to leverage its centralized planning to expand gigawatt-scale data centers. Monitoring these developments will clarify whether the structural gap persists or narrows.
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Key Questions
Why does China’s renewable buildout matter for AI deployment?
China’s large-scale renewable energy capacity and extensive transmission grid enable it to power gigawatt-scale AI data centers, bypassing the US’s grid constraints and accelerating large-scale AI deployment.
Will US technological leadership in chips be enough to compensate for infrastructure constraints?
While US chips outperform Chinese alternatives, the physical delivery of power remains a bottleneck. Without addressing grid and permitting issues, the US risks limiting the scale of its AI deployments regardless of chip performance.
Could policy reforms in the US close the gigawatt gap?
Potentially, yes. Reforms that streamline permitting, expand renewable capacity, and improve grid infrastructure could help the US deploy larger AI data centers, but the timeline and feasibility are still uncertain.
Is China’s approach sustainable long-term?
China’s reliance on centralized planning and renewable infrastructure is a strategic advantage now, but long-term sustainability will depend on maintaining renewable growth, managing grid complexity, and technological innovation.
Source: ThorstenMeyerAI.com
