The label of the “first AI war” obscures the reality that Operation EPIC FURY is still a conflict in which human judgment remains central to targeting. Artificial intelligence (AI) has not replaced human operators, but it has redefined how human judgment functions. The contemporary battlefield is now shaped by rate of fire, targeting accuracy, AI-enhanced cognition, and the real transformation that machine learning has introduced into modern warfare. 

One of the most persistent misunderstandings about AI-assisted targeting is the claim that humans have somehow been removed from the loop, decisions are made solely on AI recommendations, or strikes are approved in seconds without meaningful review. The human factor has not disappeared. Humans remain indispensable to targeting. What has evolved is not the elimination of human involvement, but the rapid synthesis of intelligence with target acquisition. That distinction matters because many assume there is little or no review before a strike is done. However, no systems make decisions independently. They supplement human decision-making by sorting and ranking information to generate recommended outcomes that must still meet rigid criteria. Analysts verify intelligence, legal teams conduct reviews, commanders make the final decision, and human beings remain responsible for the outcome. 

Critics often point to ambiguity in strategic-level U.S. directives. The Department of War Directive 3000.09 attempted to regulate certain AI-enabled systems, though the technology at the time was far less sophisticated than it is today. Military doctrine undermines the myth of autonomous targeting as well. The Army’s FM 3-60 frames targeting as an iterative command process and states that commanders remain the final approval authority for targeting activities and acceptable levels of risk. Machines may assist with detection, but they do not inherit command responsibility. The result is that humans remain in the loop because targeting is still a command process, not an autonomous one. Military doctrine frames targeting as a cycle of deciding, detecting, delivering, and assessing, but commanders retain authority over acceptable risks. AI can compress and organize the data, but it cannot make strategic or moral judgments. 

AI models remain central to identity-based targeting and advanced decision support. Open-source reporting indicates that sophisticated models, such as Anthropic’s Claude, integrated into systems such as Palantir designed Maven Smart System, have enabled rapid conversion of vast amounts of intelligence, surveillance, and reconnaissance (ISR), signals intelligence, and behavioral data into target packages. 

Human productivity has also increased. Tasks that once required weeks and a large staff can now be completed in minutes with fewer personnel. However, speed and efficiency do not mean AI independence. It does not change what Clausewitz described as the “grammar of war.”  

The most consequential shift in conflict today is the compression of time within the targeting cycle and its integration into intelligence. In the past, high-value or high-payoff targets were often missed because manual processes relied heavily on human operators and overwhelming amounts of ISR data. Past conflicts reflected these limitations. During Operation Desert Storm, Iraqi mobile Scud launchers exploited delays by firing and relocating before U.S. forces could strike them. Kinetic precision still frequently exceeds intelligence fidelity. A munition could hit its coordinates perfectly while the underlying intelligence remained flawed. The use of intelligence to target enemy combatants predates modern technology. AI did not invent decapitation strategies; it made them more data-driven and less dependent on purely human intelligence sources. The Information Age once overwhelmed operators with data. AI now provides a way to navigate that environment. This is why cyber intelligence and persistent access are essential to modern targeting. Pattern-of-life targeting relies on multiple streams of surveillance and behavioral data. AI’s greatest strength is its ability to combine these streams on a scale that would overwhelm most military units. Yet the central question remains unresolved by algorithms alone: should the target be neutralized? That decision is legal, moral, political, and strategic. 

A clear example of AI-integrated intelligence limits came on the first day of the 2026 Iran War, when a U.S. missile struck an elementary school in Minab, Hormozgan province, killing civilians, including children, in one of the war’s deadliest civilian incidents. The incident underscored a basic truth: AI-enabled targeting is only as dependable as its data. Here, the system likely relied on outdated intelligence that missed the school’s proximity to an Islamic Revolutionary Guard Corps compound.  

AI was not the likely source of failure. More likely, flawed intelligence and the fog of war were to blame. Human operators still validated the strike with satellite imagery and intelligence reviews, even though the target was effectively co-located with the school.  

The episode showed both the limits of AI models and the need for human review. Systems like Maven Smart System’s Target Workbench sort, correlate, and reveal intelligence, but humans still approve of final actions. AI can aid target validation, but legal review and command authorization remain essential. 

CONCLUSION 

The effectiveness of any algorithm depends entirely on the intelligence architecture and data supporting it. AI does not create certainty; it produces probability. If the underlying data is manipulated, incomplete, stale, or inaccurate, the output will reflect those flaws. The greater danger is not the removal of the human in the loop, but the compression of human judgment into groupthink. AI-generated recommendations can create an aura of probabilistic certainty that encourages agreement instead of scrutiny. Human operators may still make the final call, but the risk is that they increasingly validate model logic rather than independently challenge it. 

Humans remain in the loop today, but intelligence is now sorted at machine speed while generative systems provide recommendations to reviewers within the targeting cycle. Doctrine should evolve to ensure that human judgment takes precedence over AI-generated recommendations. The defining feature of this so-called first AI war is therefore not the replacement of human agency, but the intensification of human responsibility to judge, restrain, and decide at machine speed. 

Lieutenant Colonel Matthew J. Fecteau is an information operations officer working with artificial intelligence, and a PhD researcher at King’s College London. The views expressed in this report are those of the author and do not necessarily reflect the official policy or position of the Department of the Army, the Department of War, or the US Government. 

Why the “First AI War” is Still a Human Struggle was originally published on Global Security Review.