Over the course of 2025 the People’s Liberation Army moved beyond pilot projects and into a pattern of integrating artificial intelligence across training, maintenance, sensors and unmanned platforms. That trend is best understood as a series of incremental integrations rather than a single, sweeping leap to autonomous combat. Taken together the publicly disclosed cases from 2025 show a force experimenting with AI to raise operational tempo, lower human workload on routine technical tasks, and accelerate decision cycles at echelon.
Concrete examples matter. In May 2025 state media and reporting described the PLA Navy’s first operational use of an AI-assisted decision system for emergency ship degaussing. The reported result was a roughly 60 percent improvement in efficiency for the degaussing sequence, a technical maintenance task that directly affects a vessel’s magnetic signature and its susceptibility to magnetic mines and certain passive detection systems. That sort of gains is not trivial. It is a targeted application of machine learning to optimize a narrowly defined physical model where data-driven parameter tuning yields fast returns.
A second vein of activity exposed in open sources is the use of large AI models and engineering platforms in logistics, personnel management and medical support inside military institutions. Reporting in 2025 identified DeepSeek and similar Chinese models being trialed for non-combat duties in military hospitals and mobilization planning, with officials and affiliated entities describing broader ambitions to expand such systems’ roles. These deployments reveal an operational logic: start with high-value, low-irreversibility tasks that generate training data and confidence before moving AI closer to kinetic decision loops.
At the same time Chinese industry and research centers openly demonstrated embodied AI on the path toward more ambitious robotic platforms. Coverage of R&D programs and commercial showcases highlighted humanoid and legged platforms that combine 3D vision, advanced IMUs and high-throughput inference hardware. Those demonstrations are relevant because they show the integration of perception stacks, motion control and onboard inference that would be necessary for fielded autonomous support vehicles or logistics robots. But demonstration capability is not the same as fielded, doctrine-backed autonomous lethal systems. Public materials emphasize human oversight even as integration proceeds.
A critical enabling factor is compute and the supply chain. Reporting in 2025 documented PLA-linked projects referencing Western accelerators such as Nvidia A100 and H100 in research papers and patents, while procurement signals and public pressure pushed a parallel move to domestic AI accelerators like Huawei Ascend family chips. That mixed sourcing has operational implications. On the one hand domestic hardware can reduce external choke points; on the other hand domestic chip ecosystems still lag Western offerings in some maturity metrics, affecting training speed, model size and power consumption tradeoffs for deployed edge inference. The net result is pragmatic workarounds and a heterogeneous hardware base inside PLA-affiliated programs.
How does this show up in exercises and regional signaling? The PLA continued conventional large-scale drills around Taiwan and in adjacent seas during 2025, combining live-fire and joint-service maneuvers. Public footage and official releases occasionally included AI-enabled elements or AI-themed messaging, from AI-assisted logistics drills to simulation-assisted command support at training events. Those insertions are consistent with a learning curve approach: use exercises to stress-test integration points, generate operational data and normalize new human-machine workflows in the force.
Operational benefits are straightforward to enumerate. AI can compress sensing to decision timelines, automate routine maintenance to increase sortie rates, and coordinate large numbers of platforms in ways that outstrip unaided human bandwidth. The degaussing example highlights a more prosaic but strategically relevant effect: improving sustainment and recoverability under contested conditions increases fleet resilience without changing the basic balance of weapons.
Risks and limits are equally important. First, the integration problem is hard. The PLA must marry modern, data-hungry AI systems to legacy C2 architectures, fielded sensors with varied fidelity, and uneven communications backbones. That mismatch creates brittle edges where automation can fail in degraded electromagnetic or cyber-contested environments. Second, automation bias and overreliance on model outputs can produce dangerous failure modes when training data does not reflect adversary deception or rare contingencies. Third, the black-box nature of many modern models complicates forensic analysis of mistakes and undermines command assurance in fast-moving engagements. These technical risks translate directly into doctrinal and escalation risks if decision authorities misjudge AI system confidence under pressure.
From a policy perspective there are three pragmatic priorities for outside observers and allied planners. One, systematically monitor where AI is being applied inside PLA force elements by tracking procurement notices, patent filings and state media demonstrations. That intelligence is imperfect but it is the best early-warning filter for capability shifts. Two, invest in rigorous red teaming and adversarial testing of AI-enabled behaviors under contested-spectrum, degraded-sensor and spoofing conditions. Laboratory performance does not guarantee battlefield reliability. Three, update allied C2 doctrine and standards for human-machine interaction so that automated support functions have defined failure modes, explicit handoff rules, and auditable decision logs. Those changes will be essential to avoid unintended escalatory dynamics when both sides operate accelerated decision cycles.
Where does this leave us at the start of 2026? The publicly observable pattern through 2025 is one of incremental operationalization. China is not presenting a single revolutionary AI weapon overnight. Instead it is layering AI into maintenance, training, logistics, sensor processing and unmanned platforms to raise tempo and reduce human workload. That approach will produce tangible operational advantages over time, especially in high-rate tasks and massed unmanned operations. But the same path also exposes familiar systemic vulnerabilities: supply chain constraints, integration brittleness, and the potential for miscalculation when automation compresses decision timelines. Understanding this duality is essential for realistic contingency planning and for crafting norms that constrain the riskiest forms of autonomous escalation while allowing predictable, auditable uses of AI in logistics and training.