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Who Loses from China’s Export Gains?
If China's exports kept rising at the same rate they are today, large trading economies with export profiles similar to China like the United States, Germany, Indonesia, and France stand to lose the most.
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1.
Our baseline scenario that serves as our point of comparison holds all countries’ share of global exports fixed at their present value through 2030 while prices evolve. In this scenario, China’s prices are declining steeply, while its volume growth remains modest, rising only slightly as world export volume increases. This means that China’s total export value in 2030 is less than its export value in 2025 in our baseline comparison ($3.6T vs. $3.1T; in our scenario where China’s export share continues to grow, its total export value increases by $681 billion annually in 2030). This has the further implication that in recent years, if China’s share of global exports had not been growing, its total export value would have otherwise declined, given the steep price declines in many sectors (and indeed, total export values did decrease in some HS 2-digit categories where volume growth was muted between 2022 and 2025).
2.
For methodological details see Francois de Soyres et al., “The Sectoral Evolution of China’s Trade,” Board of Governors of the Federal Reserve System, February 28, 2025. https://www.federalreserve.gov/econres/notes/feds-notes/the-sectoral-evolution-of-chinas-trade-20250228.html.
3.
Free cash flow here refers to operating cash flow minus capex. Items like Tencent’s media content and lease liability payments are excluded within this calculation.
4.
Hyperscalers and Telecoms here refer to Alibaba, Baidu, Huawei, China Mobile, China Telecom, and China Unicom. Tencent and ByteDance are excluded because they do not disclose their cloud service revenues.
5.
According to Artificial Analysis, cost per task is: “weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.” The Artificial Analysis Intelligence Index v4.3 includes: AA-Briefcase, GDPval-AA v2, AutomationBench-AA, Terminal-Bench v4.0, SciCode, Humanity’s Last Exam, GDP.pdf, CritPt, AA-Omniscience, and AA-LCR v1.1.
6.
For consistency in comparison, we used Zhipu’s MaaS ARR for ratio calculation, excluding revenues of its on-premise deployment and other types of business.
7.
Estimated by plugging in 3-month SOFR rate of 4% and CAS rate of 0.3%.
8.
For the purposes of this study, we group the US and its partners into a “G7+” bloc comprising the G7 (US, Canada, United Kingdom, Japan, all EU) plus South Korea, Taiwan, Norway, Australia, Mexico, and Israel.
9.
Rovjok material flows analysis of germanium, 2024.
10.
Unless otherwise noted we assume current US hardware when stating GWs of data center capacity.
11.
YMTC responded to its US entity listing by leaning heavily into partnerships with local SME suppliers and has reportedly achieved over 50% localization of its tool base. Crucially, 3D NAND production does not rely heavily on advanced lithography and is more dependent on advanced etch and deposition tools—areas where local alternatives to G7+ products from emerging suppliers like Naura and AMEC are relatively more developed.
12.
Epoch AI, database of AI Models-notable AI models, last updated on January 12, 2026, https://epoch.ai/data/ai-models. According to Epoch AI, a notable model meets any of the following criteria: (i) state-of-the-art improvement on a recognized benchmark; (ii) highly cited (over 1000 citations); (iii) historical relevance; (iv) significant use. A model is attributed to a country if at least one author of the introducing paper is affiliated with an institution based there. Models with authors from multiple countries may therefore be double counted.
13.
Note that Liang refers to active parameters, not total. Several Chinese models released since have far larger total counts, including Kimi K3 with 2.8 trillion parameters. But these are mixture-of-experts architectures activating only a fraction per token; Kimi K3 activates an estimated 50–100 billion parameters.
14.
Adam Jonas et al., “The Humanoid 100: Mapping the Humanoid Robot Value Chain,” Morgan Stanley Research, February 6, 2025.