China’s AI advances mask widening gap with U.S. in private capital investment

The performance gap between leading Chinese and U.S. AI models has narrowed to less than 3%, yet U.S. private AI investment is still 20 times higher than China’s

By Lee Shih Ta

Judging solely by model rankings, the AI race between China and the U.S. appears to be close to parity. Since DeepSeek burst onto the scene last year, Chinese models including Alibaba’s Qwen have continued to improve, and U.S. companies no longer enjoy the overwhelming lead they held just a few years ago. Stanford University’s 2026 AI Index shows that as of March, the performance gap between leading U.S. and Chinese models had narrowed to just 2.7%, with models from the two countries taking the lead in different rankings.

But the capital picture tells a very different story.

The same report shows that U.S. private AI investment reached $285.9 billion in 2025, compared with just $12.4 billion in China, a difference of 23 times. In other words, even as the performance gap between the two countries’ top models fell below 3%, the gap in private capital spending behind them remained huge.

Chinese AI startups raised more than 110 billion yuan ($16.2 billion) in the first quarter of this year, up 185% year on year. But PitchBook data shows global AI venture funding reached $255.5 billion over the same period, with U.S.-based OpenAI, Anthropic and xAI accounting for 67.3%, or about $172 billion. Those three companies alone raised more than 10 times as much as all Chinese AI startups combined during the period. While the two datasets use different methodologies, they still point to the heavy concentration of global AI venture funding in a small group of leading U.S. companies.

The picture is similar when the focus shifts from fundraising to actual infrastructure investment. Goldman Sachs estimated late last year that China’s leading internet companies would invest more than $70 billion in AI-related areas in 2026, equivalent to just 15% to 20% of the amount U.S. hyperscalers were forecast to spend. Capital Group, meanwhile, estimates that capital spending by major U.S. technology companies will rise from $425 billion in 2025 to $791 billion this year, compared with an increase from $57 billion to $118 billion for their Chinese counterparts. That would narrow the gap only slightly, from about 7.5 times to 6.7 times.

Private investment, however, is only part of China’s AI capital picture. In 2025, China launched its 60 billion yuan National Artificial Intelligence Industry Investment Fund. This year, authorities were also reportedly considering a plan to invest around 2 trillion yuan over five years in a nationwide network of AI data centers, though details of the proposal are still a work in progress.

The U.S. government is also increasing its AI spending. Brookings data on federal AI contracts through March this year show that about $7.2 billion in funding had been allocated. The figures aren’t directly comparable, but they still point to a clear difference in the structure of capital spending: U.S. AI expansion is more heavily driven by technology companies and private capital, while the government and state-owned enterprises play a more prominent role in China.

Can state capital bring in private investment?

If China already has large amounts of state capital flowing into AI, why has private investment failed to expand at anything close to the scale seen in the U.S.? The answer starts with commercial returns. Amazon, Microsoft and Google all have enormous global cloud businesses. Adding data centers and GPUs is not simply a bet on the future of AI; those investments can also be monetized directly by selling computing power, models and software services to companies around the world.

China’s cloud market, by comparison, remains much more domestically focused, while intense price competition among large language models has put pressure on monetization. Technological capabilities may be improving rapidly, but that progress does not necessarily translate into revenue and cash flow at the same pace. Goldman Sachs notes that Chinese hyperscalers still derive 90% to 95% of their revenue domestically, while paid usage of leading Chinese chatbots remains limited.

Profit pressure at China’s largest technology companies also constrains their ability to invest. Alibaba’s (BABA.US; 9988.HK) capital expenditure jumped 75% year on year to 67.68 billion yuan in the April-June quarter, while its net profit fell 75%. The company subsequently raised about $10.2 billion through a share placement to support further AI development. Baidu’s (BIDU.US; 9888.HK) second-quarter revenue fell 4% year on year, while its net profit dropped to 2.3 billion yuan. For such companies, massive AI spending translates more directly into pressure on profits, cash flow and financing needs.

State capital can take on longer-term projects such as data center and power grid construction, domestic chip development and fundamental research. But it primarily addresses the question of who will provide the money. It cannot guarantee that customers will be willing to pay for AI services. If demand and profits fail to grow alongside investment, policy-driven supply could result in low utilization or inefficient capital allocation.

The real test, therefore, is whether state investment can be converted into commercial demand and profits, which in turn could attract more private capital.

That demand gap can also be seen in the revenue mix of Chinese AI infrastructure suppliers. High-speed optical module supplier InnoLight (3308.HK; 300308.SZ) saw revenue from overseas markets surge 209.9% to 39.62 billion yuan in the first half of this year, while revenue from Mainland China rose just 7.7% to 2.16 billion yuan. Overseas markets now account for nearly 95% of its total revenue.

Sitting at the heart of the AI data-center investment boom, InnoLight’s results offer a useful barometer for global AI capital spending. The company’s interim report noted that Alibaba, Tencent (0700.HK) and Baidu spent a combined 64.7 billion yuan in capital expenditure in this year’s first quarter, up 18% year-on-year. By comparison, Microsoft, Amazon, Meta and Google recorded combined capital expenditure of $164.9 billion in the second quarter, up about 86%. A leading Chinese AI infrastructure supplier is therefore seeing most of its incremental demand come from overseas, underscoring how Chinese companies have become important suppliers to the global AI infrastructure boom even as demand growth in their home market remains considerably weaker.

Yet lower private investment does not mean China’s AI capabilities must lag by the same proportion. Restrictions on advanced chips complicate the comparison further. Even if Chinese companies were prepared to spend the same amount as their U.S. counterparts, they would not necessarily be able to obtain the same computing power. That has forced them to improve model efficiency, lower inference costs and accelerate adaptation to domestically produced chips.

That helps explain, at least partly, how private investment in China can be so much lower even as the performance gap between leading Chinese and U.S. models is just 2.7%.

The big question is whether greater efficiency can continue to offset the large capital gap. AI is moving toward agents, multimodal systems and robotics, while large-scale inference will require more chips, electricity and data centers. If U.S. companies continue to outspend their Chinese counterparts by hundreds of billions of dollars each year, their greater computing resources and capacity for experimentation could eventually translate back into a wider technological advantage.

America’s spending spree also carries the risk of overinvestment. If massive AI infrastructure spending ultimately fails to generate sufficient revenue, today’s capital advantage could turn into tomorrow’s depreciation burden, debt and idle capacity. For China, how effectively state capital can turn computing power, chips and infrastructure into market demand, corporate profits and sustained private investment will determine how far its model can go.

China has already shown that lower levels of private capital aren’t preventing it from rapidly closing the technological gap in AI. But as AI enters its next phase, with greater dependence on computing power, electricity and large-scale inference, it remains unclear how long efficiency gains can continue to offset the capital gap. As the model gap narrows, the ability to turn capital into demand, profits and reinvestment could become a new dividing line in the U.S.-China AI race.

Lee Shih Ta is an editor at Bamboo Works.

You can contact him at shihtalee@thebambooworks.com

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