GIFT

In future warfare, long range precision weapons and cheap remote and mobile sensors will practically eliminate the traditional large ground-unit tactical maneuver against “near-peer” enemies. In its place, tactics and maneuver will consist of decentralized and sometimes isolated tactical small-units (TSU) – characterized as squad and smaller units. These TSUs will have to fend for themselves, have greater responsibility, and greater ability to survive, sustain and fight as self-contained entities (Scales, 2019).

Historically 90% of all military force casualties occurred in TSU close-combat (Roper, 2018). In addition, in the last modern ground wars, TSU extreme violence close-combat often resulted in “fair fights” against relatively inferior, ill-equipped opposing forces (Roper, 2018). What this means is in future warfare, TSUs will be challenged to gain or sustain overmatch with enemy forces in a prolonged ground war without improving how we train those forces. Such close-combat lethality “matching” should never happen with the degree of investment in synthetic training technology being developed for infantry TSUs today. Unfortunately, it may if new synthetic training technology is used to train TSUs the way they’ve always been trained over the last several decades which resulted in the outcomes above. The future synthetic training model will be a critical technology and learning model to build and sustain overmatch in the future warfare US Army and Marine Corp TSUs. This new model will include the ability to rapidly develop and sustain combat team cohesion, maturity and individual combat competency through experiencing multiple synthetic “bloodless battles.” New architectures, engineering practices, competency structures, live-data based stimulus and assessment, machine learning (ML) and artificial intelligence (AI), and new organizational changes to unit level training will both increase the development rate and quality of future TSU overmatch. The ideas presented in this paper are considered as necessary changes to achieve that goal.