A growing number of great powers are rapidly investing their resources and capital in the field of artificial intelligence (AI) technology and autonomous systems to derive military benefits from them at tactical, operational, and strategic levels. This rapid development of AI technology has led many analysts to think that it will improve international politics and the balance of power. But as far as the nuclear weapons or concept of nuclear deterrence is concerned, AI is quite a concern regarding nuclear weapons. To understand its impact on nuclear deterrence, one must look at the effect of AI on a state’s second-strike capability.
Nuclear deterrence depends upon the second-strike capability, which is, in case of a preemptive strike, a state having a survivable nuclear force to inflict a retaliatory strike on an adversary via a nuclear triad (land, air, and sea). This threat of retaliation keeps the state from carrying out a first strike, thus maintaining the equilibrium. For second-strike capability, states maintain their survivable nuclear force via ground-launched mobile missiles, strategic bombers, and submarines. And for a credible and assured second-strike capability, states go for nuclear submarines, which are quite difficult to detect.
But the second-strike capability is not invulnerable; states can detect and then eliminate these platforms, but the harder part lies in finding them. And for that, AI can come in handy by analyzing and processing great chunks of data. Russia and China’s second-strike platforms are mostly comprised of ground-launched mobile missiles. These platforms are usually hidden via camouflage, netting, or inside tunnels and are difficult to detect and target, but silos are comparatively easy to target and are more vulnerable.
AI empowers intelligence processing, making it easy to conduct operations by collecting data from various platforms like satellites, reconnaissance aircraft, drones, and ground-based sensors to find and track nuclear weapons. Integrating AI with sea-based sensing technologies will make the ocean transparent to detect nuclear submarines. AI integration with multiple sea-based sensors will track submarines by automating pattern recognition. It is quite a concern for the US and the UK, which keep most of their nuclear warheads on their submarines.
Besides destroying the adversary’s nuclear weapons and their platforms. AI can also undermine Command and Control (C2) of a state that is used to carry out or launch retaliatory strikes. C2 is responsible for detecting the launch of a nuclear weapon, reporting it to the higher authority, and then transmitting the retaliation order to nuclear forces. These systems can identify a wide array of missiles, conduct damage assessment, transmit short messages over long distances, and protect the leader responsible for nuclear decisions. Targeting command and control can be undertaken by a state, equal to carrying out a nuclear strike. Because that state will conceive it as an undermining of its nuclear deterrent, which will ultimately lead to preemptive retaliation.
Command and control are also vulnerable, like their components: radars, high-frequency transmitters, and satellites. These can easily be targeted by an adversary state. With advancements in AI, their integration with satellites can easily track land-based command posts and can accurately target airborne ones. Furthermore, these satellites can easily be targeted by AI-enhanced anti-satellite weapons, which would be responsible for detecting and tracking incoming nuclear projectiles. States with sophisticated cyber capabilities enhanced by AI can be employed against adversary C2 systems to undermine their early warning systems and disrupt order transmission. This threat is increasing gradually with states shifting from analog to digital systems.
A major fear for states is that AI will weaken nuclear deterrence by strengthening missile defense systems, which will undermine second-strike capability and will make first strike more tempting. These missile defense systems can detect the launch of a missile, track salvos of missiles, estimate their trajectories, and destroy them by interceptors, all within less than 30 minutes. AI would prove to be a force multiplier for this kind of capability. Machine learning algorithms will analyze data from various sensors to distinguish between warheads and decoys and speed up the decision-making process once an adversary launches its missiles. The software will help in predicting the trajectory of missiles in an easier and more efficient way. Furthermore, AI will also assist in material sciences in making agile, cheap, and maneuverable interceptors.
The integration of AI into nuclear deterrence showcases multiple and yet complex challenges. The undermining of second-strike capability, disruption of command and control systems, and enhancing the effectiveness of missile defense systems introduce unprecedented risks of miscalculation and inadvertent war. Its potential to revolutionize military capabilities cannot be ignored, but the destabilizing effect on nuclear deterrence must be addressed. States must pursue robust international dialogue and regulations. To preserve the balance between deterrence and AI technology, policymakers must prioritize cooperative frameworks, guardrails, and strategies to ensure that technological advancements do not outpace the upholding of strategic stability.
Zohaib Arif
Zohaib Arif is an undergraduate student of Strategic Studies at the National Defence University, Islamabad. His academic interests include Nuclear Strategy, Drone Warfare, and Geopolitics.
