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Chinese military’s AI attack chain lags foreign models: researchers

China’s military AI models trail their foreign equivalents in performance, concentrating on support functions rather than combat, according to a study by Chinese researchers. The findings appeared in a paper by researchers affiliated with the Armed Police Force Shandong Corps and the Engineering University of People’s Armed Police, published in the journal Defence Industry Conversion in China in…

Chinese military’s AI attack chain lags foreign models: researchers

A recent study conducted by Chinese researchers reveals that China's military artificial intelligence (AI) models significantly lag behind their foreign counterparts in performance. The research, published in the journal Defence Industry Conversion in China in July, indicates that the Chinese military primarily utilizes AI in auxiliary support functions, such as intelligent question-answering and content generation.

Performance-wise, the military's AI applications remain considerably inferior to foreign large language models, with limited progress in offensive combat applications. This study arrives amid an escalating AI-driven military competition between the United States and China, as both nations strive to leverage AI to bolster command and control capabilities.

The US military is actively promoting AI as a foundational element of its armed forces, with the Pentagon championing an "AI-first" approach to warfare. This push for faster deployment of AI technologies across various domains, including combat, intelligence, and decision-making, contrasts with China's more cautious approach. Instead, China is focused on integrating AI into the People's Liberation Army (PLA) to create a more interconnected force capable of seamless collaboration among multiple platforms and weaponry systems.

In August, state broadcaster CCTV announced that the Chinese air force had implemented an AI-enabled system designed to aid commanders and pilots in developing battle plans for operations involving over 100 units. The system, dubbed the "intelligent-strike planning system," was reported to have been utilized multiple times and aimed at prioritizing targets, coordinating attack waves, and assigning specific tasks to various units.

The Chinese researchers' findings closely align with an earlier American military analysis published in Military Review in early 2025. Authored by officers from the Virginia Army National Guard, the article examined the integration of AI with cyber and information operations, drawing upon previous US military operations and state-led exercises as case studies.

The Chinese researchers similarly concluded that the US military's use of AI showcased three key capabilities essential for future offensive warfare: the expansion of AI applications from information processing to direct attacks; the ability to inflict physical facility damage and cognitive effects through cyberattacks; and the seamless integration of AI into military command and decision-making processes.

The authors urged China to develop an "autonomous and controllable AI attack chain" capable of identifying and striking battlefield targets, generating code to attack adversary networks and command chains, and conducting strikes in the cognitive domain.

However, the study highlights a significant gap between China's rapid civilian AI advancements and their practical implementation in military settings. Despite the State Council's development plan to make China the world's leading AI power by 2030, released in 2017, the PLA has yet to establish a sizable, systematically-trained AI and big data workforce.

Specialists recruited from the civilian sector often lack military background and face limitations in operational environments. To bridge this gap, the researchers propose sending serving personnel to civilian universities for specialized training and integrating civilian experts into military exercises and routine combat readiness duties.

Written by urgent.news from South China Morning Post's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

Also reported by 1 other outlet

Read the original at scmp.com →

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