📊 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 for AI power deployment due to its extensive renewable buildout and centralized transmission grid, allowing it to substitute power throughput for chip performance. The US faces constraints at the power infrastructure layer, risking a structural ceiling in AI capacity.
China’s centralized planning and extensive renewable infrastructure enable it to operate AI data centers at gigawatt-scale, giving it a structural advantage over the United States, which faces grid and permitting constraints at the power delivery layer.
In 2025, China added over 430 GW of wind and solar capacity, surpassing the US in renewable buildout and creating a cross-regional transmission capacity of 340 GW via ultra-high-voltage (UHV) lines. This infrastructure supports deploying lower-performance chips across vast power networks, effectively substituting raw wattage for chip performance.
Meanwhile, the US relies on fragmented grid systems, off-grid gas turbines, and regulatory arbitrage, which limit the scale at which AI data centers can operate—typically at 100 MW to 2 GW, with many projects constrained by grid bottlenecks and permitting delays. The US’s interconnection queue exceeds 2,300 GW, with an average wait time of five years, hampering large-scale deployment.
Chinese AI chips, like Huawei’s Ascend 910C, perform at roughly 60% of NVIDIA’s H100 inference levels and lack native FP8/FP4 support. However, due to the country’s ability to scale power infrastructure rapidly, the system-level capacity for AI deployment is expanding faster than the chip performance gap suggests, effectively compensating for lower chip efficiency.
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 Deployment Differences
This divergence in infrastructure fundamentally alters the landscape of AI deployment. China’s ability to transmit vast amounts of renewable-generated power across an extensive grid allows it to operate AI data centers at gigawatt scale, effectively bypassing the US’s regulatory and grid constraints. This could lead to a structural shift in global AI capacity, with China gaining a significant advantage in scaling AI infrastructure, regardless of chip performance disparities.
For the US, the bottleneck at the power layer could become a ceiling to AI growth unless regulatory reforms, grid expansion, or efficiency gains in hardware and models close the gap. The outcome will influence global AI leadership and economic competitiveness over the next two years.
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US and China AI Infrastructure Development Compared
The US leads in AI chip design, software, and applications but faces infrastructural constraints at the physical layer of power delivery. Its grid system is fragmented, with regulatory and permitting hurdles delaying large-scale data center deployment. The interconnection queue exceeds 2,300 GW, with an average wait time of five years, limiting the ability to scale AI infrastructure rapidly.
China, by contrast, has centralized planning under the NDRC and NEA, enabling rapid deployment of renewable energy and extensive transmission infrastructure. The Eastern Data Western Compute initiative channels eastern AI demand to western renewable hubs, leveraging 45 ultra-high-voltage transmission projects spanning over 40,000 km. This infrastructure supports deploying lower-performance chips at a system level that surpasses the US in overall AI capacity.
“The US AI infrastructure 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
On-site operation technology of ultra-high voltage transmission lines(Chinese Edition)
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Unresolved Questions About Future AI Infrastructure Growth
It remains unclear whether the US can overcome its infrastructural constraints through regulatory reform, technological efficiency gains, or grid expansion within the next two years. The long-term impact of China’s infrastructure-led approach on global AI leadership is also still developing.
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Next Steps in Monitoring US and China AI Infrastructure Strategies
Monitoring policy reforms, grid expansion projects, and hardware efficiency improvements in both countries over the coming months will clarify whether the US can close the gigawatt gap or if China’s infrastructural advantage becomes a sustained structural lead. Key milestones include US regulatory changes and Chinese renewable deployment rates.
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Key Questions
Why does power infrastructure matter more than chip performance for AI scaling?
Because large AI data centers require vast amounts of reliable, high-capacity power, and current infrastructure constraints limit the size and number of data centers that can be built, affecting overall AI capacity more than chip performance alone.
Can the US overcome its grid and permitting bottlenecks to compete with China?
This depends on policy reforms, grid modernization, and technological advances. The next two years will be critical to see if these measures can close the infrastructural gap.
Does China’s lower-performance chips limit its AI capabilities?
While Chinese chips currently lag in raw performance, their deployment across extensive, renewable-powered infrastructure compensates at the system level, enabling large-scale AI deployment.
What are the risks if the US cannot resolve its infrastructure constraints?
The US risks losing AI leadership and economic competitiveness if it cannot expand and modernize its power infrastructure to match China’s system-level capacity.
Source: ThorstenMeyerAI.com
