A soldier who fires on a target must be able to reason “why” afterward. Similarly, a commander who authorizes a strike must be able to justify that decision to superiors, at times to lawyers, and possibly to the public. If an algorithm makes these decisions, how is the reasoning understood when it is not understood by the engineers who built it? Judging from how military technology has advanced, this is not hypothetical. It is the situation many militaries already face as artificial intelligence (AI) takes on a growing role in identifying threats, and even recommending or engaging targets. The usual conversation about military AI focuses on speed, power, and who can compute faster or strike first. But a quieter, more important question emerges alongside it. Which systems can justify their decisions when lives, laws, and legitimacy are on the line? Quantum AI, an unlikely and still unproven contender, is being explored as a possible answer. 

What Quantum AI Actually Is 

Quantum computing provides a fundamentally different approach to processing information than classical computer methods. Rather than relying solely on classical computational methods, quantum computers exploit quantum mechanical phenomena such as superposition, entanglement, and interference to solve certain classes of problems more efficiently than conventional computers. Where Artificial intelligence is the set of processes that let computers recognize patterns and make predictions with limited human guidance. Quantum AI combines the two, using quantum computers to conduct calculations while classical AI systems manage the rest of the process. 

Militaries are interested in this combination for several reasons, including faster logistics planning and better analysis of satellite imagery. Much of the focus on quantum AI has been on speed, optimization, and solving problems that challenge classical computers. But researchers are increasingly focused on a different possibility, one that has nothing to do with how fast a system can think and everything to do with how it decides and whether one can trust its decisions. 

The Problem With Black Boxes 

Most modern AI systems, particularly the neural network models that power image recognition and predictive analysis, function as what experts call a black box. The system takes in information and produces an answer, but its internal reasoning is buried in millions of numerical values that even its designers cannot fully interpret. For everyday uses like recommending a movie or predicting traffic, this opacity is harmless. In a military context, it is not. If an AI system flags an object in satellite imagery as a probable weapon launcher or recommends that a target meets the legal criteria for engagement, someone must eventually explain the decision reasoning. This is because militaries operate under rules of engagement and international humanitarian law, both of which require distinguishing combatants from civilians and justifying the use of force. If an investigation follows a strike, whether from a military tribunal, a human rights body, or a domestic court, “the algorithm decided” may not be an acceptable answer. Accountability requires a trail of reasoning and legal justification that a human being can follow and defend. 

This is not a distant, theoretical scenario. Militaries around the world are already integrating AI into surveillance, target prioritization, and threat classification systems. As these tools take on more responsibility, the gap between what they decide and what humans can explain grows wider. That gap creates legal exposure, operational risk, and serious human accountability problems for any military that wants to claim its use of force is lawful and proportionate. 

Why Quantum AI Might Offer a Way Out 

This is where quantum AI enters the conversation, not for its speed, but potentially to provide insight into AI decision making. Some researchers argue that certain quantum AI models, particularly a type known as gate-based models, organize information in ways that more closely mirror the actual structure of the problem being solved. Rather than compressing logic into an impenetrable web of numerical weights, these models preserve the structure that allows one to trace back to a specific input or condition that led to the result. In theory, this would allow investigators to follow a clearer path from data to decision, something today’s AI engines cannot offer. 

If this promise holds up, it will matter. A military that can show step-by-step why a system chose a target is in a far stronger position to defend that decision legally and ethically. It also strengthens public trust, both domestically and internationally, at a time when the use of autonomous and AI assisted weapons is under growing scrutiny from human rights organizations and international bodies. In this framing, the race for military AI dominance is not only about who can build the fastest or most powerful system. It becomes a race to build systems that are defensible. These systems must withstand the scrutiny of a courtroom, a parliamentary inquiry, or an international tribunal. 

A Promising Idea, Not a Proven One 

It would be a mistake to overstate that this capability is currently accessible. The idea that quantum AI offers built-in decision recovery is still unproven, not an established capability. Techniques used to explain classical AI decisions, tools such as LIME and SHAP, are only beginning to be adapted for quantum models. Early results are encouraging in narrow research settings, but no military or independent certification body has yet accepted quantum-generated decision recovery as sufficient evidence in a legal or operational context. The honest way to describe this field today is as a promising hypothesis worth serious investment, not a solved problem ready for deployment. Overselling it risks the same trap that has plagued classical military AI, where systems are fielded faster than the frameworks needed to hold them accountable. 

Conclusions 

The common denominator in military technology has been about speed. It is about who can evaluate fastest, be more decisive, and often strike first. Computers systems help drive these and AI plays a key role. Quantum AI’s most valuable military application may ultimately have little to do with either. If it can genuinely offer clearer, more transparent reasoning behind life-and-death decisions, it would address one of the most serious gaps in how militaries currently use it. This deserves considerable research attention and scrutiny in equal measures.  

The technology that ultimately proves most valuable on future battlefields may not be the one that thinks the fastest. It may be the one that can provide an answer when someone finally asks why.  

Maheen Butt is a Lahore-based researcher and strategic communications professional conducting research primarily on foreign affairs. She is a Mphil graduate of International Relations from Kinnaird College for Women, and her work focuses on global affairs, emerging technologies mainly on increasing use of AI in military affairs, and defense studies. The Author’s opinions are their own.

Explainability, Not Speed, Will Define the Next Military AI Race was originally published on Global Security Review.

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.