Power Play: Why China's Energy Strategy Is Giving It The Edge In The AI Race

Given its voracious appetite for energy, it's safe to say that AI innovation is not bottlenecked by silicon; rather, it's gridlocked by power. Two of the biggest players in the sector appear to be on opposite sides of the spectrum regarding the issue.

For a long time, the United States has remained entangled in fragmented energy regulation and a power grid increasingly incapable of supporting industrial-scale AI. Meanwhile, China is building the one thing AI truly needs to scale: energy infrastructure. 

The country has effectively tripled its renewable energy installations, transforming its grid at a speed unmatched anywhere else. In 2022 alone, data from Statista shows it added roughly as much solar capacity as the rest of the world combined. 

According to the International Energy Agency (IEA), in 2024, China invested more than $625 billion in clean energy. By the end of July that year, its installed wind and solar power capacity had reached 1.206 billion kilowatts, meeting a target it had set for 2030, six years ahead of schedule. 

At this pace, the economic powerhouse is on track to reach 1,000 GW just from solar power by 2026. Additionally, the IEA projects that the country will spend $88 billion on grid and storage investments in 2025, plus an additional $54 billion to improve coal generation. This shows the groundwork China is laying for a long-term advantage in AI deployment. The U.S., by contrast, is already hitting the grid ceiling.

Aging power grid holding back the U.S.

However, with concern rising and China pulling ahead, U.S. President Donald Trump has jumped into the conversation. In a major policy move, the head of state signed a "sweeping executive order" to fast-track federal permitting, streamline reviews, and hurry up the construction of all major AI infrastructure projects, including factories, data centers, and power plants.

"You’re gonna go so fast, you’re gonna say, ‘wait a minute, this is too fast. I didn't expect it to go this quickly," Trump said, adding that the initiative would bring in "tens of billions of dollars" and ensure the U.S. enjoys "total industrial technological supremacy."

This is the clearest sign yet that Washington is finally recognizing that the AI race is not just about smarter models but about who can build faster and scale harder.

For a long time, the narrative around artificial intelligence was that supremacy could be achieved primarily through model architecture and advanced GPUs. But this has played out. 

Compute capacity, the ability to train, fine-tune, and deploy large models at scale, will be key in determining who dominates the sector. However, it relies on access to vast amounts of stable energy, and on this front, the U.S. is woefully underprepared.

It's no secret that the country's power grid is plagued by bottlenecks and aging transmission lines, and is facing heavy strain from electrification trends and the proliferation of data centers. 

Reports indicate that utilities are already overwhelmed by demand from AI-linked facilities. Some locations are even facing multi-year delays for grid access. Things have gotten bad enough that America's largest data center markets, including Virginia and Texas, are now imposing moratoriums or rationing megawatt allocations.

Open-source momentum is shifting east

Since 2017, Beijing has outlined a national roadmap to lead the world in AI. Startups like DeepSeek and Eshallgo are examples of how this strategy is being operationalized. They have favored lean, fast-deploying models over huge, resource-intensive training runs. 

This shift reflects a broader ideological divergence: The U.S. is focused on optimizing for closed-source, centralized models with huge capital expenditures, while China is prioritizing efficiency and deployment over perfection.

Interestingly, even this centralized Chinese model is beginning to fragment, with open-source developers gaining steam. As American tech entrepreneur Balaji Srinivasan said:

"AI is decentralizing to Asia, too. Manus, DeepSeek, Qwen, Kimi. Interestingly, they are also decentralizing out of China. By going open source, and by physically moving out of China."

Case in point: in just two weeks since the release of Kimi K2, Alibaba's Qwen3-Coder has already surpassed it, despite being half the size and featuring double the context window. This shows that open-source development is rapidly reaching escape velocity, and deployment models are getting leaner and faster.

In enterprise environments, speed of integration beats theoretical model superiority. China understands this. Its AI push isn't about building the "best" foundation model; it's about embedding intelligence into economic infrastructure right now.

On the other hand, U.S. policymakers are still fixated on semiconductor choke points. While export bans on Nvidia and AMD chips might temporarily delay Chinese training runs, they do nothing to address the U.S.'s domestic infrastructure shortcomings. If anything, such bans will only force China to speed up investments in self-sufficiency, pushing companies toward custom chips optimized for specific use cases and energy-efficient deployment.

There's no denying the United States' edge in foundational research, elite talent, and venture capital. Still, this advantage will become increasingly irrelevant if the outputs of that ecosystem can't scale in the physical world. The collapse of GPU access is not the main constraint. The real pain is the inability to run those GPUs at full capacity without risking grid instability, skyrocketing energy costs, or political backlash.

Infrastructure paralysis is the true bottleneck

U.S. grid limitations are already manifesting in tangible ways. Energy costs are rising at double the rate of inflation, fueled not by generation expenses, but by transmission and distribution constraints. According to a recent Lawrence Berkeley Lab study, retail energy revenues have increased over 20% since 2019, despite flat consumption, a sign that grid strain is inflating costs across the board.

China's vertically integrated strategy, linking energy, compute, and enterprise software, gives it an asymmetrical advantage. And because deployment leads to data feedback loops, performance refinement, and long-tail monetization, first-mover status matters. Each AI solution embedded today creates a platform for expansion tomorrow.

Trump's executive order may mark a turning point, at least in tone. It acknowledges that building models alone isn't enough. Without fast-tracked permitting for data centers, energy infrastructure, and next-gen factories, the U.S. will remain mired in regulatory inertia.

To counter China's momentum, the U.S. must broaden its AI policy lens. Chips matter, but energy infrastructure matters just as much, if not more. Federal investment in AI-aligned power infrastructure, especially clean, high-density energy sources like nuclear and utility-scale solar, must become a policy priority.

Benzinga Disclaimer: This article is from an unpaid external contributor. It does not represent Benzinga’s reporting and has not been edited for content or accuracy.