TL;DR
· Goldman Sachs recommends buying 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 about $4 trillion, contributing approximately 16% of global AI-related revenue, yet global mutual funds' tech exposure to China is only about 1.2%.
· The core of this trade is not a single AI application explosion, but a revaluation opportunity driven by underweight capital, policy investment, and hardware demand.
· Risks are that data center investment, storage capacity expansion, IPO financing, and AI hardware exports still need to be realized.

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 China AI Value Chain," Goldman Sachs recommends going long on a Chinese AI basket covering power, semiconductors, AI infrastructure, models, and applications. Over the past two years, global AI trades have been dominated by large-cap US tech stocks, the NVIDIA supply chain, and cloud capital expenditures; Goldman Sachs now sees a mismatch between Chinese AI assets' market cap, revenue contribution, and global fund holdings.

Based on Goldman Sachs' estimates, China's AI-related companies already have a market cap of approximately $4 trillion, contributing about 16% of global AI-related revenue, but as of January 2026, global mutual fund managers allocate only about 1.2% to China within their global tech exposure.

This set of numbers forms the most important trading logic of the entire report: if China's AI industry already holds a double-digit share of revenue, while global capital allocation remains significantly underweight, then the China AI value chain has room for repricing.

Biggest Contrast: Revenue Contribution Is Not Low, Global Fund Allocation Is Very Low

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

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

Capital allocation is far below this ratio. Goldman Sachs estimates that as of January 2026, global mutual fund managers allocate only about 1.2% to China within their global tech exposure.

This is the core reason Goldman Sachs proposes going long on the China AI value chain. US AI assets have been repeatedly bought by global funds; NVIDIA, cloud vendors, semiconductor equipment, and power infrastructure have all been included in the AI trade mainstream. In contrast, although Chinese AI assets have formed a certain scale of revenue, they remain underweight in global fund positions.

In other words, Goldman Sachs is betting not on a simple "China AI narrative," but on a more specific capital allocation gap: revenue contribution has emerged, but global holdings have yet to catch up.

This Is Not a Traditional KWEB Trade; Hardware and Infrastructure Are More Front-Loaded

Goldman Sachs particularly emphasizes that this trade is different from the traditional KWEB trade.

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

Under this framework, hardware and infrastructure are positioned more forward.

China's push for technological self-reliance and advanced computing capability development has brought AI hardware, data centers, power support, and semiconductors under simultaneous attention from policy, industry, and capital. Goldman Sachs believes that the value of these segments has not yet been fully reflected in the stock market.

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

Whether models and applications explode ultimately depends on computing power, storage, electricity, and equipment supply. And these are precisely areas where China has capabilities in large-scale manufacturing, engineering construction, and industrial supporting.

Exports, Policy, and IPOs Are Strengthening AI Hardware Clues

Changes in China's AI hardware chain are moving from concepts to more specific orders, exports, and financing milestones.

On the demand side, customs data cited by multiple media outlets shows that China's exports in May grew 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 small increase in volume. Behind the price and structural changes, AI hardware demand is seen as an important driving factor. For storage, semiconductor equipment, and upstream materials, such data points to the possibility of improvements in orders and capacity utilization.

On the policy investment side, according to Reuters citing Bloomberg, China is preparing a five-year plan of about 2 trillion yuan, approximately $295 billion, to build a nationwide AI data center network. The plan has not yet been officially announced, but if implemented, it would directly drive demand for domestic 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 have increased AI and semiconductor weights in their 2026 adjustments. This will enhance the passive fund visibility of related companies and channel more domestic and foreign capital toward advanced computing and semiconductors.

Individual stocks and industry cases are also strengthening this clue. YMTC's Q1 2026 revenue surged about 445% year-on-year, with its global NAND flash memory market share rising from 8% a year ago to 13%, jumping to tied for fourth place, and advancing its domestic IPO plan to support capacity expansion.

CXMT is regarded as an important company in China's DRAM industry. According to third-party research estimates, its 2026 revenue could exceed $50 billion; the company's prospectus shows Q1 revenue of 50.8 billion yuan, with an H1 revenue guidance of 110 billion to 120 billion yuan.

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

Funds Are Starting to Rotate; US AI Remains the Main Benchmark

Goldman Sachs also notes that the Chinese AI sector has outperformed other China-related assets, and there are signs of capital allocation rotation. However, compared to US AI, Chinese AI assets still significantly lag.

This is where both the attractiveness and risk boundaries of the trade lie.

The attractiveness is that if global investors continue to look for growth lines outside of US AI, China's underweight status may leave room for capital rotation. Especially after US AI leaders have relatively high valuations and capital expenditure expectations have been 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 recommendation, not a realized industry conclusion. The 2 trillion yuan AI data center plan depends on policy details and actual execution; the listings, capacity expansions, and earnings improvements of companies like CXMT and YMTC will take time; whether chip export and sales data can be sustained depends on the global AI hardware cycle and trade environment.

US AI remains the main benchmark for global capital. Whether in model capabilities, cloud vendor capital expenditure, GPU ecosystem, or enterprise application revenue, the US market still has more mature benchmarks. For China AI to attract more global capital, it cannot just prove it is "cheap and underweight," but must continue to deliver revenue, profit, and technological progress.

The highlight of Goldman Sachs' long China AI value chain trade is not declaring that China AI has caught up with the US, but rather laying out a market mismatch: about $4 trillion market cap, about 16% of global revenue contribution, corresponding to only about 1.2% China allocation in global mutual funds' tech exposure.

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

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