The Goldman Sachs thematic research team is putting the "Chinese AI Value Chain" at the center of the trading landscape.

According to their report titled "Trading Strategy: Long the Chinese Artificial Intelligence Value Chain," Goldman Sachs recommends going long on a China AI basket covering power, semiconductor, AI infrastructure, models, and applications. Over the past two years, global AI trades have been dominated by U.S. large-cap tech stocks, the NVIDIA ecosystem, and cloud capital expenditures; what Goldman Sachs is now eyeing is the misalignment of Chinese AI assets in terms of market value, revenue contribution, and global fund holdings.

By Goldman Sachs' estimation, Chinese AI-related companies already have a market value of around $4 trillion, contributing approximately 16% of global AI-related revenue, yet as of January 2026, global mutual fund managers have only allocated about 1.2% to China in their global tech exposure.

These numbers form the most important trading logic in the entire report: if the Chinese AI industry already commands a double-digit share of revenue, while global fund allocation remains significantly low, then there is room for the repricing of the Chinese AI value chain.

Main Discrepancy: High Revenue Contribution, Low Global Fund Allocation

Goldman Sachs provides a very direct comparison of the global AI assets.

Since the end of 2022, global AI-related stocks have created a market value of around $34 trillion, with Chinese AI-related market value accounting for about $4 trillion, representing approximately 10% of the global AI-related market value. In terms of revenue, China contributes approximately 16% of global AI-related revenue.

However, the fund allocation is far below this percentage. Goldman Sachs estimates that as of January 2026, global mutual fund managers have allocated only about 1.2% to China in their global tech exposure.

This is also the core reason Goldman Sachs puts forward for going long on the Chinese AI value chain. U.S. AI assets have been repeatedly bought into by global funds, with NVIDIA, cloud providers, semiconductor equipment, and power infrastructure all included in AI trades. In contrast, although Chinese AI assets have already generated a certain revenue scale, they are still under-allocated in global fund portfolios.

In other words, Goldman Sachs is not just betting on the generic "China AI story," but on a more specific funding gap: revenue contribution has materialized, while global exposure has not caught up yet.

This is not a typical KWEB trade, with hardware and infrastructure taking the lead

Goldman Sachs has emphasized that this trade is different from a traditional KWEB trade.

KWEB usually corresponds to China's internet and platform economy exposure, where investors would think of e-commerce, advertising, online entertainment, and local services. However, Goldman Sachs has constructed the GS China AI Value Chain (GSXACART) basket this time, covering a range from power, semiconductors, AI infrastructure, to models and applications, closer to a complete Chinese AI supply chain.

Within this framework, hardware and infrastructure play a more prominent role.

China's push for technological self-reliance and advanced computing capabilities has brought AI hardware, data centers, power support, and semiconductor sectors into focus simultaneously by policies, industries, and capital. Goldman Sachs believes that the value of these sectors has not been fully reflected in the stock market yet.

Its research estimates that the potential economic benefits of AI through efficiency improvements and new profit creation may be 50% to 100% higher than the levels already reflected in current AI stock prices. This is also why power, AI infrastructure, and semiconductors are at the core of the basket.

Whether models and applications can take off ultimately depends on computing power, storage, power supply, and equipment provision. These areas are where China has the capacity for large-scale manufacturing, engineering construction, and industry support.

Exports, policies, and IPOs are reinforcing the AI hardware trend

The changes in China's AI hardware chain are transitioning from concepts to more concrete orders, exports, and financing milestones.

On the demand side, customs data cited by several media outlets shows that China's May exports increased by 19.4% year-on-year, the strongest growth in three months; among them, integrated circuit exports increased by about 111% year-on-year, with only a slight increase in export volume. Behind the price and structural changes, AI hardware demand is seen as one of the important driving factors. For storage, semiconductor equipment, and upstream materials, such data point to the possibility of order improvements and capacity utilization.

On the policy investment side, according to Bloomberg cited by Reuters, China is preparing a five-year plan of about 2 trillion yuan, approximately $295 billion, for the construction of a nationwide AI data center network. While this plan has not been officially announced, if implemented, it will directly drive domestic demand for storage chips, semiconductor equipment, power support, and data center infrastructure.

On the capital market front, publicly available reports indicate that in the 2026 rebalancing, A-shares, H-shares, and certain global indices will increase the weighting of AI and semiconductor sectors. This adjustment will enhance the visibility of relevant companies to passive funds and attract more domestic and foreign capital into the advanced computing and semiconductor sector.

Individual stock and industry cases are reinforcing this trend. In the first quarter of 2026, YMTC's revenue surged by about 445% year-on-year, and its global NAND flash memory market share rose from 8% a year ago to 13%, ranking tied for fourth place. The company is also advancing its domestic IPO plans to support capacity expansion.

CXMT is seen as a key player in China's DRAM industry. Third-party estimates suggest that its revenue in 2026 could exceed $50 billion, while the company's prospectus indicates first-quarter revenue of 50.8 billion RMB and half-year revenue guidance of 110 to 120 billion RMB.

These cases do not mean that Chinese memory companies have completely caught up with foreign giants. However, they illustrate that the Chinese AI hardware chain is shifting from a "policy concept" to more observable revenue, market share, financing, and production expansion milestones.

Funds Starting to Shift, U.S. AI Remains the Main Reference

Goldman Sachs also mentioned that the Chinese AI sector has outperformed other China-related assets and shown signs of fund allocation shifting. However, compared to U.S. AI, Chinese AI assets still significantly lag behind.

This is also where trading attractiveness and risk boundaries coexist.

The attractiveness lies in the fact that if global investors continue to seek growth beyond U.S. AI, the underweight status of Chinese AI may leave room for fund switching. Especially after the high valuation of U.S. AI leaders and thorough discussions on capital expenditure expectations, the market naturally looks to under-held supply chain and application assets.

The risk is that this is still a trading idea, not an industry conclusion that has been realized. The success of the 2 trillion yuan AI data center plan depends on policy details and actual implementation; the listing, capacity expansion, and profit improvement of companies like CXMT and YMTC also require time; the sustainability of chip exports and sales data will depend on the global AI hardware cycle and trade environment.

U.S. AI remains the primary reference for global funds. Whether in model capabilities, cloud vendor capital expenditures, GPU ecosystems, or enterprise application revenue, the U.S. market still holds more mature benchmarks. For Chinese AI to attract more global funds, it must not only prove to be "undervalued and underowned" but also consistently deliver revenue, profits, and technological progress.

The highlight of Goldman Sachs' long position in the Chinese AI value chain this time lies not in announcing that China has caught up with the United States in AI, but in bringing a misaligned market to the forefront: with a market value of about $4 trillion and accounting for about 16% of global revenue, the corresponding allocation in global mutual fund technology exposure is only about 1.2% for China.

Whether funds can fill this gap will depend on policy investments, hardware demands, and whether corporate profitability can continue to deliver.

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