📊 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’s centralized infrastructure and renewable energy buildout enable it to deploy AI data centers at gigawatt scale, bypassing US grid constraints. This structural advantage could shift global AI leadership if it persists.
China is building a gigawatt-scale AI infrastructure that leverages its extensive renewable energy and ultra-high-voltage transmission network, positioning itself differently from the United States, which faces significant grid and permitting constraints. This structural difference could influence global AI leadership in the coming years, and understanding the China Sphere Capability Gap, Q2 2026 Update: Five Labs, Five Strategies, One Narrowing Frontier is crucial for assessing future developments.
While US AI infrastructure remains dominant in chip design, models, and software applications, its physical power delivery layer is constrained by complex permitting, siting, and transmission bottlenecks. US data centers now require 100 MW to 2 GW of power, with projects like Meta’s Hyperion reaching 5 GW. However, the US relies on off-grid gas turbines, nuclear contracts, and regulatory arbitrage to meet these demands.
China, on the other hand, has developed a different approach. Through the NDRC’s Eastern Data Western Compute initiative, it routes eastern AI demand to western renewable hubs via over 40,000 km of ultra-high-voltage (UHV) transmission lines, with a capacity of 340 GW. In 2025, China added over 430 GW of wind and solar, more than eight times the US addition, pushing its renewable capacity above 1.8 TW and total capacity to nearly 4 TW.
Although Chinese AI chips like Huawei’s Ascend 910C are less capable per chip than US counterparts, the Chinese system compensates by substituting raw power for chip performance. This system-level asymmetry allows China to deploy less efficient chips over vast renewable-powered grids, effectively closing the system-level performance gap faster than chip-level improvements can. This is rooted in China’s centralized planning and large-scale renewable buildout, contrasting with the US’s fragmented federal system.
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 Power Gap in AI Deployment
This structural difference in infrastructure positioning could determine global AI dominance. China’s ability to scale AI data centers through renewable energy and extensive transmission infrastructure may allow it to bypass US grid constraints, potentially enabling faster deployment at gigawatt scale. Conversely, the US’s constraints could limit its ability to scale AI infrastructure at the same pace unless policy reforms or technological efficiency gains close the gap.
Understanding this fundamental divide is critical for policymakers, industry leaders, and investors, as it shapes the future landscape of AI development and deployment, with potential shifts in technological leadership and economic influence.
large-scale renewable energy data center equipment
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US vs. China: Divergent Infrastructure Strategies for AI
The US has led in AI chip design, models, and applications, but faces physical infrastructure constraints that limit the scale of its data centers. Its reliance on off-grid power sources and regulatory arbitrage has allowed some expansion, but bottlenecks remain at the grid and permitting levels.
China’s approach, driven by centralized planning, large-scale renewable energy projects, and an extensive UHV transmission network, enables it to deploy AI infrastructure at a gigawatt scale more readily. Its renewable capacity grew rapidly in 2025, and the transmission network allows it to transmit power across vast distances with minimal losses, circumventing the US’s regulatory hurdles.
This divergence is rooted in constitutional differences: the US’s federal fragmentation versus China’s centralized authority, which facilitates large-scale infrastructure projects aligned with national strategic goals. For more insights, see the China Sphere Capability Gap report.
“The gigawatt-scale capacity requirements of frontier AI deployments are fundamentally changing how infrastructure is built and operated, especially in China with its centralized planning and renewable buildout.”
— Thorsten Meyer
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Uncertainties in Future Infrastructure Developments
It remains unclear whether the US can overcome its grid and permitting constraints through policy reforms, technological efficiency gains, or new infrastructure initiatives. Similarly, China’s continued renewable expansion and transmission capacity growth are subject to policy, economic, and environmental factors that could influence their trajectory.
Additionally, whether the efficiency improvements in chips, racks, and models will narrow the performance gap or whether the system-level asymmetry will persist is still uncertain. The impact of these developments on global AI leadership remains to be seen.
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Next Steps in Global AI Infrastructure Competition
Over the coming 24 months, focus will be on policy reforms in the US aimed at easing grid constraints and permitting processes, as well as on technological advancements that improve power efficiency. Meanwhile, China’s renewable capacity expansion and transmission infrastructure development will continue to be monitored for their impact on AI deployment scale.
Industry and government stakeholders will likely evaluate whether infrastructure constraints can be alleviated or whether the current system-level asymmetries will persist, shaping future global AI competitiveness and strategic positioning.
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Key Questions
Why does the US face more constraints in building AI data centers?
The US faces regulatory, permitting, and transmission bottlenecks at the federal, state, and local levels, which complicate siting and energizing large-scale power infrastructure needed for gigawatt-scale AI data centers.
How does China’s renewable energy strategy support its AI infrastructure?
China’s rapid expansion of wind and solar capacity, combined with its extensive ultra-high-voltage transmission network, allows it to transmit large amounts of renewable power across regions, enabling large-scale AI data center deployment without the same constraints faced by the US.
Could US efficiency gains close the gigawatt gap?
Potentially, yes. Improvements in chip performance per watt, policy reforms, and new grid infrastructure could reduce the gap. However, whether these measures can fully offset the systemic constraints remains uncertain.
What does this mean for global AI leadership?
If China maintains its infrastructure advantage, it could accelerate AI deployment at scale, challenging US dominance. The outcome depends on policy, technological, and infrastructural developments over the next two years.
Source: ThorstenMeyerAI.com
