War has always been shaped by technology. From gunpowder to nuclear weapons, each technological change has changed not only the battlefield but also the moral and political calculations governing conflict.
But the integration of artificial intelligence into modern warfare could represent a far more profound transformation than previous military revolutions. The conflict involving Iran, the United States, and Israel is increasingly being described as the world’s first full-scale AI warfare, a conflict in which machine learning systems, algorithmic targeting tools, and autonomous weapons are deeply integrated into operational decision-making. If this characterization proves accurate, its impact will extend far beyond the Middle East.
At the heart of this new paradigm is the expanded use of AI systems to accelerate and automate key military functions. Advanced machine learning platforms are currently being used for operational planning, target identification, intelligence analysis, logistics coordination, and battlefield simulation. Tools such as Claude AI, introduced for military operational planning, demonstrate how commercial AI platforms are becoming integrated into defense applications. Originally designed for business analysis and natural language processing, AI models are now supporting operational decision-making in complex military environments. This shift blurs the lines between civilian technology ecosystems and military infrastructure in ways that policymakers are only beginning to understand.
Even more important is the continued expansion of Project Maven, a machine learning platform developed by the US Department of Defense to automate aspects of the military “kill chain.” Project Maven was originally deployed to process surveillance footage and identify potential targets faster than human analysts. However, over time, its functionality has expanded significantly. The system was developed in collaboration with leading technology companies, including Palantir Technologies, Amazon Web Services, Microsoft, and satellite intelligence provider Maxar Technologies, and is currently contributing to target recognition, operational planning, and intelligence integration. Effectively, it shortens the time between identifying a potential target and carrying out an attack, significantly changing the tempo of modern warfare.
On the battlefield itself, AI is increasingly being integrated into weapon platforms. A striking example is the LUCAS drone, which is a reverse-engineered adaptation of the Shahed-136 design widely used in Iran. Unlike traditional drones that rely heavily on remote operators, AI-enabled systems like LUCAS can operate with significant autonomy, coordinate with other drones in swarms, and adapt to battlefield conditions in real time. Autonomous swarm capabilities represent a dramatic change in warfare. Instead of a single expensive aircraft piloted by a human operator, militaries can deploy dozens or hundreds of AI-driven drones that work together algorithmically to overwhelm defenses.
Israel is also at the forefront of incorporating AI into military targeting operations. Systems such as Habsora and Lavender can help identify potential attack targets and determine the operational value of an attack. These systems are designed to analyze large amounts of data, communication intercepts, satellite imagery, behavioral patterns, and social network analysis to generate lists of individuals and infrastructure that are deemed to have legitimate military purposes. Automating such processes would allow the military to create target lists on an unprecedented scale.
But the efficiency that makes AI attractive to military planners also poses serious ethical risks. Specific targeting systems incorporate algorithmic calculations that weigh the expected number of civilian casualties against the perceived strategic value of eliminating a specific target. In some scenarios, tens or even hundreds of civilian losses could be considered acceptable if the algorithm assigns a sufficiently high value to the target. When decisions about life and death are partially delegated to machines operating on statistical models rather than human judgment, the moral lines of war become dangerously blurred.
Iran appears to understand the implications of this technological innovation. One of its strategic responses is an attack on the region’s digital infrastructure, particularly data centers linked to cloud computing providers. Attacks on facilities associated with companies such as Amazon in the United Arab Emirates (UAE) and Bahrain have disrupted cloud services in the region, highlighting a new dimension of modern conflict: the targeting of digital infrastructure that supports AI-powered warfare. In an era where military algorithms rely on massive computational resources and real-time data processing, cloud infrastructure has effectively become part of the battlefield. Data centres, satellite links and digital networks are now as strategically important as air bases and military ports.
The pervasive danger of AI-driven warfare lies in a phenomenon known as decision compression. Traditionally, military planning involves a series of deliberative stages: intelligence gathering, analysis, strategic discussion, and command authority. AI dramatically accelerates these processes. Algorithms can analyze thousands of potential targets in minutes, simulate combat outcomes in real time, and recommend operational responses faster than human analysts can review them. This speed is tactically advantageous, but it also reduces opportunities for self-reflection and self-restraint. In conflict environments, faster decision-making often comes with less scrutiny and fewer safeguards against mistakes.
This shortened decision timeline also changes the role of human operators. In theory, humans would remain “in the loop” with responsibility for approving AI-generated recommendations before any action is taken. In practice, however, the complexity and speed of algorithmic systems create a tendency for human operators to rely heavily on machine recommendations. If an AI system flags a target with a high probability score and recommends immediate action, operators may feel pressured to quickly approve the attack rather than challenge the algorithm’s assessment. Over time, this dynamic risks turning human oversight into a mere procedural formality rather than a meaningful safeguard.
Another big concern is the opacity of AI systems. Many modern machine learning models operate as black boxes. This means that even the designer cannot fully explain how a particular conclusion is reached. When such systems are used for targeting and operational planning, military commanders may find themselves relying on recommendations that they do not fully understand. This creates a paradoxical situation. Decision makers are expected to take responsibility for actions guided by systems whose internal logic remains largely opaque.
Beyond the battlefield, there are also worrying implications for domestic governance and civil liberties. Technology companies involved in military AI development will have access to vast datasets generated during conflict operations. These datasets include surveillance imagery, behavioral analysis, and communications metadata, which is exactly the kind of information that can be used to train algorithms for policing and population surveillance. Once developed, such tools could easily transition from military use overseas to domestic applications such as counterinsurgency, predictive policing, and counterinsurgency operations against domestic opposition.
Recent research highlights how unpredictable AI decision-making can be in strategic scenarios. A study by researchers at King’s College London investigated how advanced AI models perform in an environment simulating a geopolitical war game. The findings of this study were deeply disturbing. In approximately 95% of scenarios, the AI system chose to escalate the conflict to nuclear conflict if such an option was available. Algorithms operating according to strategic optimization logic concluded that early escalation offers the highest chance of “winning.” These simulations do not mean that AI systems will automatically cause nuclear war, but they highlight how algorithmic decision-making can produce outcomes that are fundamentally at odds with human concepts of restraint and deterrence.
The evolution of AI warfare did not begin with the Iran conflict. The Ukraine war served as a laboratory for both sides to test AI-assisted reconnaissance, drone swarms, and automated battlefield analysis. Meanwhile, a large-scale military operation utilizing AI was carried out in the Gaza Strip. What is currently emerging in the conflict over Iran appears to be the next stage in the industrial-scale integration of AI across nearly every aspect of the war.
If this trajectory continues, future battlefields may begin to resemble automated industrial production more than traditional combat. Algorithms identify targets, autonomous drones carry out attacks, and machine learning systems analyze the results and generate the next set of recommendations. All of this takes place within a compressed time frame that leaves little room for human review. The danger is that AI may not only make wars more efficient, but also easier to wage and harder to control.
For the international community, the implications are profound. International law, humanitarian norms, and military doctrine were developed for a time when human decision-makers remained central to the conduct of war. AI challenges that assumption. Without clear rules governing the deployment of autonomous weapons, algorithmic targeting, and military AI, the world could soon face conflicts in which machines operate at speeds and scales that exceed human oversight.
The author is a trade facilitation expert and works with the federal government of Pakistan.
Disclaimer: The views expressed in this article are the writer’s own and do not necessarily reflect Geo.tv’s editorial policy.
Originally published in News
