The AI “Revolution in Military Affairs”: What Would it Really Look Like?

To some defense professionals and officials, the phrase “revolutions in military affairs” may seemingly belong in the 1990s, along with talk about how reconnaissance-strike and other high-tech capabilities would “lift the fog of war.” This understanding, however, reflects a misinterpretation of a concept that still holds significant analytical power for assessing defense applications of emerging technologies. The revolutions in military affairs (RMA) framework—a mental model evaluating technology’s effect on warfare—can be extremely helpful in addressing this distorted understanding, particularly for thinking through the impact of artificial intelligence (AI) on national security. For example, and as I have described in a longer paper, the RMA framework can help policymakers consider AI’s influence on defense amid the U.S.-China technological and strategic competition by illustrating the current limitations of AI’s military impact and highlighting areas where technological and intellectual progress could one day spark revolutionary changes. It also highlights that AI’s military impact could be limited in the near term without critical and careful thinking about how the technology is applied. 

An RMA is a new combination of operational innovation and organizational change, often driven by technology. This combination is so transformative that it renders previous means of military operations obsolete. Andrew Marshall, the late director synonymous with the Defense Department’s Office of Net Assessment, adapted his understanding of the idea from Soviet writings about the “Military-Technical Revolution” from the 1980s. Soviet theorists believed that American advances in reconnaissance-strike capabilities—combining high-tech intelligence, reconnaissance, and surveillance for targeting and precision-strike weapons—were fundamentally changing the nature of warfare by undercutting foundational principles of Soviet operations against NATO, namely superiority in troop numbers and offensive force echelonment in Europe. Marshall and his colleagues adapted the Soviet interpretation of the Military-Technical Revolution to try to think more systematically about the impact of new technologies on conflict, drawing insights from historical military advancements that could be considered revolutions in military affairs, like blitzkrieg warfare or nuclear weapons.

The decisive performance of reconnaissance-strike technologies against Saddam Hussein’s forces in the Gulf War in the 1990s, particularly precision-guided munitions, drew wider attention to the RMA concept. It led some practitioners outside of Net Assessment to erroneously think of reconnaissance-strike as “the” RMA, rather than one in a historical series. The Gulf War also fueled analysis that conventional warfare had turned a corner and that technology would usher in combat operations that were more precise and seamless, reducing much of the “fog and friction” of war. Misunderstanding and misapplication of the RMA framework, along with two decades of challenging counterinsurgency operations in Iraq and Afghanistan, drew the broader U.S. defense community’s attention away from RMA discourse and military-technological competition with near-peers writ large and toward addressing terrorist and insurgent threats.

Despite these misunderstandings and misapplications, the RMA framework remains a powerful analytical tool for thinking about the relationships among military technologies, operations, and organization. It can help policymakers and analysts think specifically and systematically about the ways technology impacts warfare. Notably, the framework also crucially acknowledges that technology alone is insufficient to drive revolutionary military change. It emphasizes the importance of creative thinking about the applications of military technology, not just the technology itself. As Marshall put it in one memorandum, “Technology makes possible the revolution, but the revolution itself takes place only when new concepts of operation develop and, in many cases, new military organizations are created.” Without corresponding innovative changes to military operations, processes, and structures stemming from experimentation, AI’s military impact could remain limited, even as its applications become more sophisticated.

Artificial Intelligence and the RMA

This reasoning makes the RMA framework highly useful for assessing AI’s present and potential future effects on warfare and the criteria under which it may be militarily transformative. Analysis of AI’s potential impact on national security and military affairs—already challenging given the broad range of definitions and applications that may be considered “artificial intelligence”—can be highly variable, veering from wildly optimistic to gravely precautionary. However, viewing AI through the lens of the four core components of RMAs—technological change, military systems evolution, operational innovation, and organizational adaptation—can help analysts gauge whether revolutionary technological and intellectual development is occurring. This analysis suggests that AI appears unlikely to fundamentally reshape military affairs in the near term.

Technological change driven by AI is highly dynamic but uneven. AI, broadly defined as the constellation of technologies that enable computer systems to perform tasks requiring human intelligence, historically comprises a range of decision-making systems but often denotes machine learning systems using deep neural networks. In the past decade, advances in certain AI techniques driven by neural networks have been rapid and impressive in areas like language processing and generation, computer vision, and decision support. Defense establishments have taken note, but the shortcomings of these AI applications give justified reason for pause: They can be brittle outside of training environments and vulnerable to adversarial manipulation, or they simply perform poorly in dynamic settings. Computing power, data sets, and training resources can all be expensive and difficult to come by, and integrating human-machines teams for certain AI applications is difficult and even potentially dangerous. AI development has also historically been prone to winters of minimal research progress. 

Similarly, AI is sparking military systems evolution in the United States and China, two countries that lead in global AI development by a number of different markers, but not yet in a militarily transformative way. Military systems evolution refers to the ways that new technologies are incorporated into new or existing military capabilities and applications. AI applications have advanced in autonomy and robotics, decision support, predictive maintenance and logistics, cyber, and modeling and simulation, yet are subject to many of the weaknesses mentioned above; progress is generally broader than it is deep. Many higher-profile AI-enabled programs in the unclassified realm across different types of applications, such as Joint All-Domain Command and Control (JADC2) or Loyal Wingman, are comparatively nascent. Military access to the useful, clean, and comprehensive data sets necessary to train AI applications can be limited.

As such, AI has done little to spark either operational innovation or organizational adaptation in the United States and China. Operational innovation stems from critical thinking about how militaries can capitalize on new technologies and systems to achieve their objectives; organizational adaptation connotes changes to the structure of the fighting force to exploit new systems and patterns of operations. For example, German blitzkrieg warfare centered new operations around speed and mechanized (tank) maneuvers, leading to the organizational adaptation of separating tank units from soldiers moving on foot. 

In the United States today, current and forward-looking joint and service operational concepts continue to emphasize the importance of traditional elements like long-range fires, maneuver, information dominance, and joint force projection as augmented but not transformed by AI. In China, AI similarly does not appear poised to transform the informatization concept of warfare, which emphasizes dominating the information environment as part of an asymmetric strategy to counter U.S. comparative advantages. Intriguingly, though, the future “intelligentization” concept discussed by some Chinese strategists, envisions AI as helping dominate the “cognitive” domain—including an adversary’s psychology, perception of information, and even brain function—and represents longer-term thinking about disruptive operations. Regardless, though, with military AI applications largely too immature to dictate shifts in operations, neither the United States nor China has altered its military organizations based on AI.

U.S.-China AI Competition Through the RMA Lens

So, what can policymakers do with this information? How can insights gleaned from the RMA framework inform U.S. policy approaches to tech competition with China? Why should defense analysts, technologists, or the public care if AI is not sparking imminent revolutionary changes to military affairs?

For one, thinking in RMA terms helps to evaluate mental models for AI’s impact on security and defense along an “evolutionary to revolutionary” spectrum. AI might spark evolutionary near-term change in the next decade or so, augmenting reconnaissance-strike capabilities without overturning dominant patterns of operations. In the United States, discussion about JADC2 points to this possibility—connecting “sensors to shooters” has been a joint goal since the Gulf War. AI could continue advancing autonomous capabilities, fulfilling noncombat functions like predictive maintenance, or augmenting cyber capabilities. However, analysis of seemingly less revolutionary change, including careful analysis of technological developments that may seem less obviously militarily applicable, must accompany evolutionary framing. With certain AI applications and methods advancing rapidly, it is important to acknowledge AI’s game-changing potential and how unforeseen technological applications could generate new, revolutionary military systems and operations. Analysts and policymakers who offhandedly dismiss even seemingly militarily irrelevant applications of AI do so at the risk of ceding advantages to creative adversaries. It is better to be safe than sorry. 

Policymakers can also use the RMA framework to identify indicators of AI-driven transformative change. Open-source technology monitoring could uncover potentially disruptive academic or private-sector tech progress. Specific AI signposts could include advanced development of enabling computing technologies, new, less compute-heavy learning methods, or progress in explainability that could help warfighters trust previously opaque capabilities. Advanced government signposts could include AI assurance cases and dependable test and evaluation frameworks that suggest a need and ability to assess application performance at scale, as well as new doctrinal guidance incorporating AI that informs future training and force development. For example, examining new Chinese doctrine will help determine the importance the People’s Liberation Army sees for AI applications in its future force. Passing these early- and late-stage signposts would indicate that AI technology may be making disruptions to military paradigms increasingly likely.

Finally, and crucially, the RMA framework can help identify the missing pieces of the intellectual puzzle when it comes to military applications of emerging technology. To date, the United States and China have made little progress on operational innovation and organizational adaptation with AI. A future AI RMA instigated by the United States or China will therefore require more grappling with how AI might change patterns of operations, thinking, and experimentation that historical case studies illustrate—such as the carrier aviation RMA and China’s writing on intelligentization—can be done even before new technological applications become operational. In the case of the carrier RMA, both the U.S. and Japanese navies possessed cutting-edge (for the time period) carrier and aviation technologies. However, the unique way the United States came up with operational patterns and structures using the new capabilities transformed naval warfare from being centered around battleships and thus provided a decisive advantage compared to the Japanese. Without critical thinking and experimentation around how new technologies will be applied, their impact on military affairs is not guaranteed to be revolutionary. 

Conclusion

AI may not yet or even imminently spark a revolution in military affairs. Nonetheless, using the RMA analytical framework to delve into why it is not doing so, and how it might in the future, can help us think systematically about AI’s potential impact on future warfare. AI’s impact could one day be transformative enough in areas like cyberwarfare or even nuclear deterrence without over-hyping nascent capabilities. To ensure that militaries use AI-enabled capabilities thoughtfully and responsibly, military futures analysis should be as rational and grounded as possible.