Competing in Artificial Intelligence Chips: China’s Challenge amid Technology War
Bibliographic record
Abstract
Drawing on field research conducted in 2019 in cooperation with Tsinghua University, this report assesses the challenges that China is facing in developing its AI chip industry amid unprecedented US technology export restrictions. Success in artificial intelligence (AI) is not limited to data and algorithms alone. The third component that determines success in research and applications are advanced specialized AI chips that provide increased computing power and storage , while decreasing energy consumption. Companies that have access to leading-edge AI chips are essentially in the fast lane, where improvements continue to be rapid and mutually reinforcing. China has relied almost solely on the United States to import such advanced AI chips, but the US-China technology war has abruptly disrupted China’s access to these critical sources of AI success. Will America’s unprecedented technology export restrictions cripple China’s AI ambitions? Or will it force China to race ahead on its own? Specifically, what realistic options does China have to substitute AI chip imports from the United States through local design and fabrication or through imports from other non-US sources? The report highlights China’s challenge of competing in AI, and contrasts America’s and China’s different AI development trajectories. Starting much later than the United States, Chinese universities and public research institutes have conducted a significant amount of AI research (some of it at the frontier), but knowledge exchange with industry remains limited. Drawing on deep integration with America’s AI innovation system, Chinese AI firms, in turn, have focused primarily on capturing the booming domestic mass markets for AI applications, investing too little in AI research. To find out what is happening today in China’s AI chip design, capabilities and challenges are assessed, both for the large players (Huawei, Alibaba and Baidu) and for a small group of AI chip “unicorns.” The report concludes with implications for China’s future AI chip development, considering the disruptive effects of the technology war and the global coronavirus pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".