AI Future Warfare: How Artificial Intelligence Could Reshape Four Essential Military Competitions

RAND’s Framework for AI in Future Warfare

How will advances in artificial intelligence reshape the way nations fight and win wars? This question, among the most consequential in contemporary defense policy, receives rigorous analytical treatment in a landmark 2026 report from the RAND Corporation, authored by researchers at the Center for the Geopolitics of Artificial General Intelligence. The study — titled “How Artificial Intelligence Could Reshape Four Essential Competitions in Future Warfare” — offers a systematic framework for evaluating AI’s potential to disrupt military operations across four fundamental dimensions of conflict.

Rather than making sweeping claims about an impending AI revolution in military affairs, the RAND researchers adopt a methodologically disciplined approach. They assume that AI ultimately delivers on its goal of removing the limits of human intelligence as a constraint on military operations, then rigorously examine the consequences of this assumption across the four “building block” competitions that constitute the foundations of modern warfare. The result is one of the most nuanced and analytically grounded assessments of AI’s military implications published to date, with implications that extend far beyond the U.S. Department of Defense to any nation seeking to understand how AI could reshape the character of armed conflict. For complementary insights into how AI is transforming other domains, see our analysis of the International AI Safety Report 2026.

Building Block Competitions: A New Analytical Approach

The RAND report’s most significant methodological contribution is its “building block” conceptual framework, which decomposes warfare into four component competitions that can be analyzed individually and then recombined to generate broader insights. These four competitions are: quantity versus quality of military assets, hiding versus finding on the battlefield, centralized versus decentralized command and control, and cyber offense versus cyber defense. Each competition represents an enduring tension in military strategy that AI has the potential to shift in fundamental ways.

This analytical approach has several important advantages over more common approaches to assessing AI’s military impact. By breaking down the complex phenomenon of warfare into discrete, well-defined competitions, the framework enables precise analysis of how AI affects specific mechanisms and trade-offs rather than generating vague assertions about transformation. It also allows the researchers to identify areas where AI’s impact is relatively clear and areas where significant uncertainty remains, producing a more honest and useful assessment than either alarmist or dismissive accounts of AI’s military potential.

The researchers draw on historical precedents, theoretical analysis, and ongoing developments in AI technology to evaluate each competition. They deliberately avoid projecting specific timelines for AI breakthroughs, instead focusing on the directional implications of continued progress in autonomy, machine learning, and robotic systems. This approach yields findings that are robust across a range of scenarios for AI advancement, making them more useful for long-term defense planning than predictions tied to specific technology milestones.

AI Future Warfare: Quantity Versus Quality of Military Assets

The first and perhaps most consequential building block competition examined by RAND is the enduring tension between quantity and quality in military force structure. Throughout modern military history, advanced militaries — particularly the United States — have favored quality over quantity, investing in small numbers of exquisite, highly capable platforms: stealth fighters, nuclear-powered submarines, precision-guided munitions, and other systems that achieve decisive advantages through technological superiority rather than numerical mass.

The RAND analysis concludes that AI could significantly shift this balance in favor of quantity. The core mechanism is the rapidly declining cost of autonomous uncrewed systems relative to crewed platforms. As AI-enabled autonomy improves, it becomes feasible to field large numbers of relatively simple, inexpensive platforms that can collectively match or exceed the combat effectiveness of small numbers of expensive, exquisite systems. The combination of what the report calls “more precise robotic mass and affordable robotic mass” could create a cost advantage that fundamentally challenges the traditional quality-centric approach to force design.

However, the researchers are careful to note that this shift is not absolute. Exquisite platforms will continue to have important roles in future warfare, particularly for missions requiring specialized capabilities that cannot be replicated by simpler autonomous systems. The question is not whether quality or quantity prevails entirely, but rather what the optimal mix becomes in a world where AI dramatically reduces the costs and increases the capabilities of mass. Research published by the U.S. Department of Defense on the Replicator initiative confirms that this shift toward autonomous mass is already underway.

The Rise of Attritable Uncrewed Systems

Central to RAND’s analysis of the quantity-quality balance is the concept of attritable uncrewed systems — autonomous platforms designed to be inexpensive enough that they can be lost in combat without critically degrading overall force capability. This concept represents a fundamental departure from traditional defense procurement, which focuses on maximizing the survivability and longevity of individual platforms. Attritable systems instead optimize for aggregate combat effectiveness across large numbers of expendable units.

The report identifies several categories of attritable systems that are already in development or early deployment: autonomous aerial vehicles for surveillance and strike, unmanned surface and undersea vessels for maritime operations, and ground-based robotic systems for logistics and reconnaissance. The key insight is that AI autonomy enables these systems to operate with minimal human supervision, dramatically reducing the personnel costs that traditionally represent the largest expense in military operations.

The implications for industrial base and defense production are profound. Fielding mass quantities of attritable systems requires manufacturing approaches more akin to consumer electronics production than traditional defense manufacturing, with its emphasis on low volumes of highly customized, extensively tested platforms. The RAND researchers argue that the United States needs to invest in new production capabilities that can deliver autonomous systems at scale, while also developing the operational concepts and organizational structures needed to employ mass effectively on the battlefield.

AI Future Warfare and the Hiding Versus Finding Balance

The second building block competition — hiding versus finding — addresses one of the most discussed aspects of AI’s military impact: its potential to transform intelligence, surveillance, and reconnaissance. AI’s ability to rapidly fuse and analyze data from proliferated sensors has led many analysts to conclude that the battlefield is becoming “transparent,” with increasingly few places for military forces to hide. The RAND report challenges this narrative as overly simplistic.

While acknowledging that AI will substantially improve finding capabilities — enabling militaries to process satellite imagery, signals intelligence, and sensor data at unprecedented speed and scale — the report argues that analysts frequently overstate these advantages while overlooking the countermeasures available to the hider. The hiding-finding competition is not a one-way street in which AI makes everything visible; it is a dynamic measure-countermeasure race in which both sides can leverage AI to improve their positions.

The report identifies three primary factors that determine the relative advantage between hiders and finders in any given scenario: the kind of information being sought, the domain and physical operating environment, and which side can better bring mass to bear in fielding sensors or countermeasures. In some environments — such as open ocean or clear-sky conditions — finders may gain significant advantages. In others — such as dense urban areas, forested terrain, or electromagnetic-dense environments — hiders retain substantial advantages even against AI-enhanced surveillance. For a broader perspective on AI’s dual-use implications across sectors, explore our analysis of AI-driven economic productivity.

AI Fog of War Machines and Autonomous Deception

Perhaps the most provocative concept introduced in the RAND report is the “AI fog of war machine” — an integrated system of AI-controlled decoys, electronic countermeasures, and deception operations designed to create confusion and uncertainty in an adversary’s intelligence picture. This concept represents a fundamental rethinking of military deception for the AI age, moving from relatively simple techniques like camouflage and radio silence to sophisticated, adaptive deception campaigns that operate at machine speed.

The AI fog of war machine would leverage large numbers of autonomous decoys — including aerial, maritime, and ground-based systems — that can mimic the signatures of real military assets. These decoys would be orchestrated by AI systems capable of generating and adapting deception plans in real time, responding to the adversary’s intelligence collection activities and adjusting the deception narrative to maintain plausibility. The result would be a battlefield environment in which an adversary’s AI-powered sensor fusion systems are overwhelmed with false targets, dramatically reducing the reliability of their intelligence picture.

The report argues that investment in AI-enabled deception represents one of the most cost-effective responses to AI-enhanced surveillance and targeting. Creating false targets is generally far cheaper than creating real military capabilities, and AI can multiply the effectiveness of deception by managing complex, multi-domain deception campaigns that would be impossible for human planners to coordinate. The RAND researchers recommend that the United States place deception and counter-reconnaissance at the center of its operational concepts rather than treating them as secondary capabilities.

Command and Control in an AI-Enabled Military

The third building block competition addresses a question that many defense commentators have debated: should AI enable more centralized or more decentralized command and control? Some argue that AI’s ability to process vast amounts of information and communicate it rapidly favors centralized command, in which senior commanders use AI to maintain detailed situational awareness and issue precise directives. Others contend that AI should empower distributed decision-making, enabling small units to operate autonomously with AI-augmented judgment.

The RAND report takes a nuanced position, arguing that mission command — a hybrid approach that combines top-down intent with bottom-up initiative — will remain the optimal model even with AI. The fundamental insight is that the limiting factor in wartime command is not cognitive capacity but information access. Even the most advanced AI systems cannot overcome the fog of war, communications disruptions, and intelligence gaps that characterize real combat environments. Mission command’s advantage lies not in processing power but in distributing decision authority to the level that has the best situational awareness for time-sensitive decisions.

The researchers acknowledge that AI could enhance both centralized and decentralized elements of mission command. At the centralized level, AI can help commanders develop better plans, process intelligence more rapidly, and coordinate complex operations across multiple domains. At the decentralized level, AI can augment the judgment of small-unit leaders, automate routine decisions, and maintain coordination even when communications are degraded. The key is that AI enhances the existing mission command framework rather than replacing it with a fundamentally different paradigm.

Cyber Offense Versus Defense in AI Warfare

The fourth building block competition examines the cyber domain, where AI’s potential to transform military operations is both significant and uncertain. The RAND analysis identifies a structural asymmetry in current cyber operations: offense holds a significant advantage because finding and exploiting vulnerabilities is generally easier than defending the vast attack surface of modern military networks. The question is whether AI shifts this balance and, if so, in which direction.

The report’s analysis is guardedly optimistic about AI’s potential to strengthen cyber defenses in the long term. AI-powered defensive systems can address three fundamental challenges that currently limit cyber defense: scale (monitoring the enormous volume of network activity across military systems), speed (detecting and responding to attacks in real time rather than after the fact), and effectiveness (distinguishing genuine threats from false positives with greater accuracy). These improvements could eventually make military battle networks significantly more resilient against cyberattacks.

However, the researchers caution that the short-term picture is more favorable to cyber offense. AI enables attackers to automate reconnaissance, generate sophisticated exploits, and adapt their tactics in real time. The offense will always retain some ability to penetrate networks because the complexity of modern software ensures that new vulnerabilities will continue to be discovered. The resulting competition is characterized by the report as an intensifying AI-enabled arms race, with both sides leveraging machine learning and autonomous systems to gain temporary advantages. The Cybersecurity and Infrastructure Security Agency has identified this dynamic as a priority concern for national security.

Strategic Implications for U.S. Defense Policy

The RAND report’s strategic implications section delivers a sobering message for U.S. defense planners: the country’s traditional approach to military superiority — based on small numbers of technologically superior platforms operated by highly trained personnel — may be increasingly vulnerable in an AI-enabled world. Relying on small force structures of exquisite capabilities, the report argues, is “quickly becoming a liability rather than an asset” as AI advances make mass and deception more important.

Three key recommendations emerge from the analysis. First, the United States should begin investing now in mass and new capabilities for deception, including the development of attritable uncrewed systems and AI-powered deception tools. Many of the opportunities to capitalize on the trends identified in the report are available today, even if AI technology is still maturing. Second, the United States needs to allocate scarce resources under the assumption that it will face sophisticated and adaptive adversaries, rather than hoping for an unassailable AI first-mover advantage that allows it to defy the identified trends.

Third, and perhaps most importantly, the United States needs a comprehensive plan to manage the transition to an AI-enabled force. This includes addressing critical implementation questions: what the right balance between exquisite capabilities and robotic mass looks like, how AI tools can be fielded to operational units in ways that build trust and reliability, and how human personnel and AI systems can be trained together in symbiotic ways that reinforce strengths and offset weaknesses. The RAND researchers emphasize that exploiting AI’s military potential is as much an organizational challenge as a technological one, requiring changes to doctrine, training, acquisition, and organizational structure.

Preparing for the Transition to AI-Enabled Force Structures

The concluding section of the RAND report addresses what the authors consider the most critical challenge: managing the transition from current force structures to AI-enabled ones. This transition requires not just new technologies but fundamental changes to how militaries organize, train, equip, and employ their forces. Historical precedents suggest that militaries that embrace disruptive changes to operational concepts and force structures gain significant advantages over those that simply use new technologies to make existing approaches incrementally better.

The report identifies several specific areas where transition planning is urgently needed. Defense production and sustainment must be reimagined to support the manufacture of large quantities of autonomous systems at price points orders of magnitude below current major weapons programs. Training and education systems must prepare personnel to operate effectively alongside AI systems, making decisions about when to trust AI recommendations and when to override them. Acquisition processes must be streamlined to match the pace of AI development, which operates on timescales of months rather than the years or decades typical of major defense programs.

The report also addresses the international dimensions of the AI warfare transition. Because AI technology is widely available and the barriers to developing military AI applications are lower than those for traditional advanced weapons systems, many nations will pursue AI-enabled military capabilities simultaneously. The resulting multi-polar competition adds complexity to defense planning and creates risks of miscalculation during crisis situations. International dialogue on norms and risk reduction measures for AI in warfare is identified as an important complement to national military modernization efforts. The comprehensive nature of RAND’s analysis provides an essential foundation for the informed policy debate that these challenges demand, and organizations can leverage platforms like Libertify to make such research more accessible through interactive experiences.