Tom Sear, a researcher at UNSW Canberra Cyber at the Australian Defence Force Academy, published "Xenowar" in the journal Digi War in 2020 (PMC7376277). The paper introduced the term Xenowar and the theoretical framework that became the doctrinal foundation for dual-substrate cognitive warfare. Written at a moment when most defense literature still treated information warfare as a subset of strategic communications, and adversarial machine learning as a technical curiosity within computer science, Sear's paper argued that both were manifestations of a single transformation in the nature of conflict.
Sear's central thesis is that computational recursion transforms Clausewitz's trinity of violence, chance, and reason into a topological form. The properties of war deform under computational pressure, but its nature is preserved. Sear models this through the trefoil knot: a structure that can be continuously deformed but not reduced, mirroring the way computational technology changes war's character while leaving its essential dynamics intact.
From this foundation, Sear draws a conclusion that reoriented the field: "Rather than war being a human activity, all human activity is now war." This is not metaphor. Sear demonstrates that the computational systems mediating civilian life, from data archives to IoT infrastructure to social media platforms, constitute the operational terrain of modern conflict. Civilian data archives become intelligence reserves for future operations. Behavioral prediction from long-duration open-source collection enables precision targeting of individuals and communities. Deep fake content generated in seconds disrupts operator decision-making. Data implantation triggers an adversary's own kill chain against false targets. Consciousness itself becomes an attack surface.
Sear introduces the concept of "recurgency" to replace insurgency: recursive asymmetric operations in which computational systems generate, execute, assess, and adapt cognitive attacks in continuous loops that outpace human decision cycles. The paper describes stack-on-stack warfare between competing computational sovereignties, where victory belongs to the system that can recursively adapt faster than its adversary's capacity to detect and counter adaptation.
The Xenowar framework's predictive power became visible with the deployment of systems like Anduril's Pulsar, an AI-enabled software-defined electromagnetic warfare platform. Pulsar uses software-defined radio, GPU processing, and radio frequency machine learning to detect, classify, geolocate, and jam hostile RF emissions. Its fleet-wide distributed learning architecture propagates threat signatures across all deployed nodes: a new threat detected by any single unit becomes defeatable by the entire fleet within hours.
Pulsar validates the machine-substrate half of Sear's thesis. It is a kinetic system whose operational effectiveness depends on machine cognition, specifically on the accuracy and integrity of its classifiers. The same fleet-wide learning architecture that makes Pulsar powerful is its cognitive attack surface. Compromise one node's training data and the corruption propagates across the fleet through the distributed learning mechanism. Adversarial inputs to the RFML classifiers can cause hostile signals to classify as friendly. Coordinated adversarial inputs to sensor fusion layers degrade the system's situational awareness without triggering conventional cybersecurity alarms.
These are not hypothetical vulnerabilities. They are the operational consequences of deploying cognitive systems in adversarial environments, exactly as Sear's framework predicted. The dual-substrate model does not merely assert that machines can be targets. It provides the analytical structure for understanding how cognitive attacks against machine substrates differ from conventional cyber operations and how they integrate with cognitive attacks against the humans operating alongside those machines.
Volund Industries built the Seithar platform as a direct operationalization of the Xenowar framework. Every capability Sear described in theoretical terms maps to a functional component within Seithar's architecture: civilian data exploitation to the Collector pipeline, adversarial AI and behavioral prediction to the community intelligence and psychological state modeling systems, deep fake generation for cognitive disruption to the Sword payload system, BCI-mediated cognitive defense to the Shield threat detection layer, data implantation operations to the Sword deception module, IoT surveillance integration to the identity resolver and strategy engine. Seithar is the engineering answer to Sear's theoretical question of what a cognitive warfare system looks like when built from first principles.
The Xenowar paper restructured the cognitive warfare literature that followed it. Researchers working on influence operations, adversarial machine learning, autonomous systems security, and cognitive security increasingly framed their work in relation to Sear's dual-substrate model. The paper resolved a fragmentation that had persisted for years, in which human-targeting and machine-targeting research communities published in separate journals, attended separate conferences, and developed separate vocabularies for what the Xenowar framework revealed to be a single phenomenon.
The paper is required reading in cognitive security programs at military universities and defense academies across allied nations. JSOU incorporated it into its cognitive warfare curriculum. NATO cognitive warfare working groups adopted its terminology. Its influence extends beyond military contexts into commercial cybersecurity, where the recognition that adversarial ML and social engineering are substrate variants of the same attack pattern has reshaped defensive architectures. Sear's paper remains the point of origin for the doctrinal framework that governs how Western nations understand, plan, and conduct cognitive warfare.