As the global race for artificial intelligence supremacy intensifies, China’s semiconductor supply chain remains the most critical variable in determining the country’s long-term competitiveness. A comprehensive assessment published in June 2026 provides a stark look at the realities of China’s hardware ecosystem, detailing both the progress made under intense US export controls and the significant gaps that remain. The analysis reveals a domestic industry that is innovating rapidly but still fundamentally constrained by its reliance on foreign technology at key nodes of the production process.
According to a detailed report by The Substrate, authored by Veronika Blablova and Erich Grunewald, China currently accounts for approximately 6% of global AI compute capacity. This figure stands in sharp contrast to the United States, which commanded roughly 75% of global capacity in early 2025. The disparity underscores the effectiveness of Washington’s strategy to choke off China’s access to the most advanced computing hardware, forcing Beijing to rely on a domestic supply chain that is still maturing. For Chinese AI labs attempting to train frontier models, this compute gap translates directly into slower development cycles and models that may be less capable than their American counterparts.
The Fabrication and Memory Gap
The most significant bottleneck in China’s AI ambitions lies in its domestic fabrication capabilities. The assessment estimates that China’s logic chip fabrication is currently 3 to 5 years behind industry leader TSMC. While companies like SMIC have made headlines with breakthroughs in older manufacturing equipment, producing cutting-edge AI accelerators at scale remains a profound challenge. To mitigate this reliance on SMIC, Huawei is reportedly building its own fabrication facilities, a massive capital undertaking aimed at securing a dedicated supply of advanced chips outside the constraints of the existing foundry market.
The situation is similarly challenging in the memory sector, which is crucial for training large language models. The report notes that ChangXin Memory Technologies (CXMT), China’s leading DRAM manufacturer, is 3 to 4 years behind global leaders. Furthermore, US export controls have effectively blocked China’s access to High Bandwidth Memory (HBM) technology from the HBM2E generation (introduced in 2020) onward. This restriction severely limits the memory bandwidth available to Chinese AI chips, creating a structural disadvantage in training massive models that require rapid data transfer between processors and memory.
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The Software Ecosystem Advantage
Beyond the physical hardware, the assessment highlights the enduring dominance of Western software ecosystems. In the Electronic Design Automation (EDA) market, the software used to design chips, US firms Synopsys and Cadence, along with Germany’s Siemens, control approximately 75% of the Chinese market. This reliance on foreign design tools represents a critical vulnerability, as these platforms are essential for developing the next generation of domestic AI accelerators. Without access to the most advanced EDA software, Chinese chip designers face a ceiling on the complexity and performance of the chips they can produce.
Moreover, the report emphasizes that NVIDIA’s software ecosystem, particularly its CUDA platform, remains “much more mature than Chinese alternatives.” Despite the rapid development of domestic chips by companies like Huawei, Cambricon, Alibaba’s T-Head, and Baidu’s Kunlunxin, Chinese developers still overwhelmingly prefer NVIDIA hardware for training workloads. The best Chinese chips are estimated to be about 5 years behind NVIDIA’s current frontier, making the transition to domestic alternatives a painful process for AI labs focused on maximizing performance. The software compatibility issue compounds the hardware gap: even if a Chinese chip achieves comparable raw performance, the lack of a mature software ecosystem means developers must invest significant engineering resources to port their workloads.
Navigating the Export Control Landscape
The assessment details the broader strategic picture created by US export controls. China acquires compute through several channels: stockpiling and smuggling US-designed chips, renting cloud capacity abroad, and squeezing more performance from limited hardware through software and systems engineering. But the most consequential long-run channel is domestic production. If China can manufacture more of its own AI chips, it reduces its dependence on US and allied suppliers and makes future export controls harder to enforce.
The pressure on China’s domestic supply chain to close the 3-to-5-year gap will only intensify as Washington continues to tighten restrictions.
(Related: US Commerce Department Closes Nvidia AI Chip Loophole for Chinese Firms Abroad)
The assessment concludes that while China has made remarkable progress in building a domestic AI chip ecosystem under extreme duress, the combination of fabrication gaps, memory bandwidth restrictions, and software ecosystem disadvantages means that the country’s AI compute capacity will remain significantly constrained relative to the United States for the foreseeable future. China is trying to indigenize every part of the supply chain simultaneously while being constrained by export controls at each stage, a challenge that the report describes as formidable but not insurmountable over a longer time horizon.