ChatGPT as a security threat: US–China security dilemma in the generative AI race
Bibliographic record
Abstract
The rise of AI has introduced new complexities into the traditional US–China security dilemma, with both sides viewing each other’s technological advances as potential threats. This article examines how ChatGPT has further complicated these dynamics. Its success triggered not only Chinese anxiety over losing ground in technological and strategic competition but also concerns about threats to ideological and regime security. In response, China implemented a series of measures aimed at regulating generative AI to preserve domestic stability while simultaneously advancing its own technological capabilities. These reactions, coupled with the technical success of China’s DeepSeek, reshaped the competitive landscape – this time triggering anxiety and strategic insecurity in the United States and propelling the US–China generative AI race into a new, more intense phase. This article focuses on China’s initial security concerns about ChatGPT, shedding light on the origins of the AI race and how AI has become deeply entangled in broader security dynamics.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".