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.

Artificial intelligence is quietly redrawing the front lines of national security, and the United States’ Defense Industrial Base (DIB) now sits in the crosshairs of a new style of conflict. What once looked like routine cyber incidents now appears as a persistent campaign that fuses digital intrusion, supply chain manipulation, and targeted information operations into a single pressure system aimed at the factories, laboratories, and logistics networks that keep American forces armed and ready.

When most Americans imagine national defense, they focus on the uniformed force and the platforms that dominate headlines. Behind that visible edge of power stands a sprawling, largely private ecosystem of designers, manufacturers, software firms, logistics providers, depots, and research institutions that convert national intent into fielded combat capability. Federal regulation describes this DIB as the combined government and private sector industrial complex that can research, develop, produce, and sustain weapon systems and their components at the scale required for military operations.

That chain is far more diverse than the marquee names on major defense contracts. It includes niche machine shops, regional logistics firms, university laboratories, software providers, and government facilities that share technical data and workflow information through a constant flow of digital exchanges. Design files, bills of material, maintenance records, and operational telemetry form the nervous system of this industrial organism. When the system is healthy, the nation can surge production and sustain readiness under stress. When it is strained, deterrence becomes much harder to demonstrate in a crisis.

This industrial nervous system is absorbing AI at high speed, often with limited guardrails. Across the DIB, machine learning supports predictive maintenance, defect detection, demand forecasting, scheduling, and supply chain visibility. Corporate leadership often has only a surface-level understanding of how thinking machines can erode corporate identity, trust, and reputation quietly, yet catastrophically.

For the strategist, this adaptation expands the terrain adversaries can probe. Industry surveys in the World Economic Forum’s Global Cybersecurity Outlook 2026 report show that executives see AI as the primary driver of change in cybersecurity and regard AI-related vulnerabilities as the fastest-growing category of cyber risk. Organizations recognize that their attack surface is expanding as AI tools move into sensitive workflows, yet governance practices and security controls often lag deployment.

Hybrid campaigns against the DIB rarely announce themselves through spectacular outages. Security officials describe a patient pattern that begins with access and aims for endurance rather than immediate disruption. Once inside a network or supplier ecosystem, adversary teams map production rhythms, supplier dependencies, identity systems, and critical decision points where even modest delays can ripple across schedules and inventories. AI has become a force multiplier

for this reconnaissance, mining engineering files, and communications at machine speed and surfacing patterns that would take human analysts far longer to identify.

The contest is unfolding below the threshold of open conflict. Strategic competitors apply pressure through cyber intrusion, intellectual property theft, data manipulation, counterfeit insertion, and narrative operations designed to erode confidence in the U.S. capacity to arm itself and its allies. Hybrid pressure often manifests as subtle slowdown, rising cost, and creeping doubt rather than overt sabotage.

The stakes for deterrence are significant. Modern deterrence depends not only on visible platforms and war plans but also on an adversary’s belief that the United States can sustain forces and generate combat power at speed. If competitors conclude that AI-enabled systems in the DIB are brittle or easily manipulated, the credibility of deterrence weakens. The 2026 National Defense Strategy emphasizes strengthening the industrial base as a pillar of national power, tying cyber resilience directly to the ability to deter and wage war.

Pentagon planners increasingly describe cyber activity against the DIB as a persistent campaign environment. Adversaries can pursue national security effects through intrusions into private networks and business processes that support critical programs.

The technical attack surface is broad because the DIB operates as a tightly coupled system. Engineering teams collaborate across companies, moving design data through shared tools and platforms. Components flow through multiple tiers of suppliers before the final assembly. A single compromised vendor can offer adversaries access to multiple programs. Hybrid campaigns exploit these seams.

The effect of these operations extends beyond physical outcomes. Manipulated or stolen data can become material for influence narratives that frame U.S. production as unreliable or compromised. Production delays caused by cyber incidents can be amplified to sow doubt among allies about American capacity.

Adversary teams also benefit from repetition. They have treated the DIB as a learning environment, refining tradecraft through continuous operations. Hybrid coercion in this context is deliberate and calibrated, remaining below the threshold that would trigger a national response while still generating uncertainty and doubt.

Emerging evidence suggests that agentic AI systems are beginning to automate portions of this intrusion lifecycle. Automated chains reduce the cost of probing many suppliers and narrow the window for defenders to respond. For smaller firms with limited security resources, the pressure can quickly become unmanageable.

Security experts argue that defending against such campaigns will require disciplined governance and architecture. Pre-deployment AI security assessment and continuous validation must become standard practice, covering model selection, data provenance, access control, logging, and periodic reevaluation.

Architectural choices will also play a critical role. Zero trust principles and micro segmentation can reduce the impact of intrusions by limiting movement across networks. For DIB operators, this means treating segmentation and strong identity controls as the default architecture.

The industrial context around these cyber efforts is already volatile. The aerospace and defense sector is managing supply chain disruptions and resource constraints while trying to scale production. This environment amplifies the effectiveness of cyber sabotage and influence operations because attacks land in systems that are already stressed.

The strategic logic behind adversary focus on the DIB is clear. By degrading confidence and throughput, competitors can undercut deterrence without open conflict. When intrusions are contained and production continues under pressure, the calculus of hybrid aggression shifts.

Hybrid warfare against the DIB aims to bend trust in the systems that generate combat power. If those trust systems are distorted through advanced technologies and coordinated information operations, the result is slower decisions, higher costs, delayed output, and a quieter form of coercion.

Mr. Greg Sharpe is a Fellow and the director of Communications and Marketing for the National Institute for Deterrence Studies and the Managing Design Editor for the Global Security Review. Greg has over 35 years of military, federal civilian, and defense contractor experience in the fields of database development, digital marketing & analytics, technology use case exploration and assessment, and as a USAF doctrine outreach and engagement analyst. The views of the author are his own.

Quiet Warfare: Bending Data and Perceptions in the Defense Industrial Base was originally published on Global Security Review.