📊 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 AI infrastructure strategy centers on large-scale renewable energy and centralized planning, enabling gigawatt-scale data centers. The US leads in chip performance but faces constraints at the power delivery layer. This structural difference could influence global AI leadership.
China is building a gigawatt-scale AI infrastructure network driven by extensive renewable energy and centralized planning, positioning itself differently from the US, which faces constraints in physical power delivery. This structural advantage could reshape global AI deployment and leadership. This structural advantage could reshape global AI deployment and leadership.
Current AI data centers at the frontier require 100 megawatts to start and up to 2 gigawatts at full buildout, with the US relying on complex, fragmented power grids that face regulatory and transmission bottlenecks. In contrast, China has developed a system of ultra-high-voltage (UHV) transmission projects spanning over 40,000 kilometers, capable of transmitting up to 340 gigawatts, linking renewable energy hubs in the west with data centers in the east. In 2025, China added over 430 gigawatts of wind and solar capacity, roughly eight times the US increase, pushing total installed renewable capacity above 1.8 terawatts.
Chinese AI chips, such as Huawei’s Ascend 910C, perform at about 60% of US NVIDIA H100 inference levels and lack native FP8/FP4 support. However, the Chinese system compensates for lower chip performance by substituting raw power throughput, enabled by their renewable buildout and extensive transmission infrastructure. This approach is rooted in China’s centralized planning and state-controlled energy sector, contrasting with the US’s federal–state fragmentation, which constrains physical infrastructure development.
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 Power Differences for AI Leadership
This structural divergence means China can deploy AI infrastructure at a gigawatt scale more rapidly and flexibly than the US, which faces regulatory and grid constraints. See the China capability gap update for more details. While US chip performance remains superior, the ability to transmit large amounts of renewable energy across extensive UHV grids allows China to substitute raw power for chip-level performance. This could alter the global AI race, emphasizing infrastructure and energy policy over raw silicon innovation, and potentially giving China a strategic edge in deploying AI at scale.
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US and China AI Infrastructure Strategies Compared
The US dominates in AI chip performance, software, and applications, but faces a bottleneck at the physical power delivery layer due to decentralized grids and regulatory hurdles. Major US data center projects, such as Meta’s Hyperion and OpenAI’s Stargate, operate at 1–2 GW capacity, constrained by grid permitting and transmission issues.
China, on the other hand, has prioritized centralized infrastructure, building a vast network of ultra-high-voltage transmission lines to connect renewable energy sources with data centers. This enables the deployment of lower-performance chips at a system level that compensates for individual chip limitations. China’s renewable capacity growth outpaces that of the US, providing a substantial and scalable power base for AI infrastructure.
“The US 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 Future AI Infrastructure Developments
It remains unclear whether the US can overcome its power grid constraints through efficiency gains, regulatory reform, or technological innovation. The extent to which US chip performance improvements will close the system-level gap is also uncertain. Additionally, the long-term impact of China’s centralized infrastructure on global AI leadership depends on future policy and technological developments.
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Next Steps in AI Infrastructure Competition
In the coming 24 months, monitoring US efforts to reform energy permitting, expand renewable capacity, and improve power transmission will be critical. Simultaneously, China’s continued expansion of renewable infrastructure and UHV grid projects will determine if its structural advantage persists. The evolution of chip performance and energy efficiency will also influence the broader AI infrastructure landscape.
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Key Questions
Why does China focus on renewable energy for AI infrastructure?
China’s large-scale renewable buildout and centralized planning enable it to transmit vast amounts of power efficiently, supporting gigawatt-scale AI data centers without the regulatory constraints faced by the US.
Will US chip performance improvements close the power gap?
While US chips are currently more capable, the power infrastructure bottleneck may limit their deployment at scale unless significant reforms or technological breakthroughs occur.
How does China’s centralized system give it an advantage?
Centralized planning allows China to develop extensive transmission infrastructure and renewable capacity rapidly, enabling large-scale deployment of AI infrastructure that is less dependent on chip performance.
What are the risks for China in this approach?
Heavy reliance on centralized infrastructure and renewable energy expansion could face challenges from regulatory changes, technological limitations, or environmental constraints.
Could the US adopt similar infrastructure strategies?
Potentially, but it would require significant policy reforms, investment, and overcoming existing regulatory hurdles, which may take years to realize at scale.
Source: ThorstenMeyerAI.com
