Goldman Sachs Calls for Long Position on China's AI: Behind a $4 Trillion Market Cap, Global Funds Have Allocated Only 1.2%

TL;DR

• Goldman Sachs recommends buying into the China AI value chain basket, covering power, semiconductors, AI infrastructure, models, and applications.
• Goldman Sachs estimates that China's AI-related market cap is around $4 trillion, contributing approximately 16% of global AI-related revenue, yet global mutual funds have only about 1.2% allocated to China within their tech exposure.
• The core of this trade is not a single AI application explosion, but a re-rating opportunity driven by underweight capital, policy investment, and hardware demand.
• Risks include that data center investments, memory production expansion, IPO funding, and AI hardware exports still need to be consistently delivered.

Goldman Sachs' thematic research team is pushing the "China AI Value Chain" to the center of trading focus.

According to its report titled "Trade Strategy: Long the China Artificial Intelligence Value Chain," Goldman Sachs recommends going long on a China AI basket covering power, semiconductors, AI infrastructure, models, and applications. Over the past two years, global AI trades have been dominated by large US tech stocks, the NVIDIA ecosystem, and cloud capital expenditure; Goldman Sachs is now eyeing the disconnect between China AI assets' market cap, revenue contribution, and global fund positioning.

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

This set of numbers forms the most important trading logic of the entire report: if the Chinese AI industry already holds a double-digit share on the revenue side, yet global fund allocation remains significantly underweight, then there is room for repricing within the China AI value chain.

Biggest Disparity: Significant Revenue Contribution, Minimal Global Fund Allocation

Goldman Sachs' breakdown of global AI assets provides a direct comparison.

Since late 2022, global AI-related stocks have created approximately $34 trillion in market cap, of which China's AI-related market cap is about $4 trillion, accounting for around 10% of the global AI market cap. On the revenue front, China contributes about 16% of global AI-related revenue.

Fund allocation, however, is far below this proportion. Goldman Sachs estimates that as of January 2026, global mutual fund managers had only allocated about 1.2% to China within their global tech exposure.

This is also the core reason Goldman Sachs proposes going long on the China AI value chain. US AI assets have been repeatedly bought by global capital; NVIDIA, cloud vendors, semiconductor equipment, and power infrastructure have all been incorporated into the AI trade narrative. In contrast, although Chinese AI assets have formed a certain revenue scale, they remain underweighted in global fund portfolios.

In other words, Goldman Sachs is betting not on a pure "China AI narrative," but on a more specific capital allocation gap: revenue contribution has already emerged, but global holdings haven't caught up yet.

This Is Not a Traditional KWEB Trade; Hardware and Infrastructure Are Prioritized

Goldman Sachs specifically emphasizes that this trade differs from the traditional KWEB trade.

KWEB typically corresponds to exposure to Chinese internet and platform economies, leading investors to think of e-commerce, advertising, online entertainment, and local services. However, this time Goldman Sachs has constructed the GS China AI Value Chain (GSXACART) basket, covering everything from power, semiconductors, and AI infrastructure to models and applications, more closely resembling a complete Chinese AI supply chain.

Within this framework, hardware and infrastructure are given greater priority.

China's push for technological self-reliance and advanced computing capability construction means that AI hardware, data centers, power support, and semiconductor segments are simultaneously attracting policy, industry, and capital attention. Goldman Sachs believes the value of these segments has not yet been fully reflected in stock prices.

Its research estimates that the potential economic benefits from AI through efficiency gains and new profit creation could be 50% to 100% higher than what is currently priced into AI stocks. This is why power, AI infrastructure, and semiconductors are placed at the core of the basket.

Ultimately, the potential explosion of models and applications depends on computing power, storage, electricity, and equipment supply. These are precisely areas where China possesses capabilities in large-scale manufacturing, engineering construction, and industrial support.

Exports, Policies, and IPOs Are Strengthening the AI Hardware Thesis

Changes in China's AI hardware chain are moving from a concept toward more concrete orders, exports, and financing milestones.

On the demand side, customs data cited by various media outlets shows China's May exports grew 19.4% year-on-year, the strongest increase in three months. Within this, integrated circuit export value surged approximately 111% year-on-year, while export volume only grew slightly. Behind these price and structural changes, AI hardware demand is seen as a key driving factor. For storage, semiconductor equipment, and upstream materials, such data points to the potential for improved orders and capacity utilization.

On the policy investment side, according to Bloomberg cited by Reuters, China is preparing a five-year plan of approximately 2 trillion yuan, or about $295 billion, for building a national AI data center network. The plan has not been officially announced, but if implemented, it would directly stimulate domestic demand for memory chips, semiconductor equipment, power support, and data center infrastructure.

On the capital market side, public reports indicate that A-shares, Hong Kong stocks, and some global indices are increasing the weight of AI and semiconductors during their 2026 rebalancing. This will enhance the passive fund visibility of related companies and channel more domestic and international capital toward advanced computing and semiconductors.

Individual company cases and industry examples are also reinforcing this narrative. YMTC saw its Q1 2026 revenue surge approximately 445% year-on-year, its global NAND flash market share rising from 8% a year ago to 13%, jumping to a tie for fourth place, and it is advancing its domestic IPO plans to support capacity expansion.

CXMT is regarded as a significant company in China's DRAM industry. Third-party research estimates its 2026 revenue could exceed $50 billion; the company's prospectus shows Q1 revenue of 50.8 billion RMB, with a first-half revenue guidance of 110 to 120 billion RMB.

These cases do not mean Chinese memory companies have fully caught up with overseas giants, but they indicate that China's AI hardware chain is transitioning from a "policy concept" to more observable milestones in revenue, market share, financing, and capacity expansion.

Funds Are Beginning to Rotate; US AI Remains the Primary Benchmark

Goldman Sachs also notes that the China AI sector has outperformed other China-related assets, showing signs of fund allocation rotation. However, compared to US AI, the performance of China AI assets still significantly lags behind.

This is where both the trade's attractiveness and its risk boundaries lie.

The attractiveness lies in the fact that if global investors continue to seek growth lines outside of US AI, the underweighted status of China's AI could leave room for capital rotation. Especially after US AI leaders have seen high valuations and their capital expenditure expectations are fully discussed, the market will naturally look for supply chain and application assets that are not yet fully held.

The risk is that this remains a trading suggestion, not an already realized industrial conclusion. The 2 trillion yuan AI data center plan depends on policy details and actual execution; the listings, capacity expansion, and profitability improvements of companies like CXMT and YMTC also require time; and the sustainability of chip exports and sales data depends on the global AI hardware cycle and trade environment.

US AI remains the primary benchmark for global capital. Whether it's model capabilities, cloud vendor capital expenditure, the GPU ecosystem, or enterprise application revenue, the US market still possesses more mature benchmarks. For Chinese AI to attract more global capital, it cannot merely prove it is "cheap and under-owned"; it must consistently deliver revenue, profits, and technological progress.

The key takeaway from Goldman Sachs' long China AI value chain call is not declaring that Chinese AI has caught up to the US, but rather highlighting a market dislocation: approximately $4 trillion in market cap and around 16% of global revenue contribution correspond to only about 1.2% allocation to China within global mutual funds' tech exposure.

Whether capital can bridge this gap will depend on whether policy investment, hardware demand, and corporate earnings can continue to materialize.