Contemporary digital transformation brings new opportunities for companies, lately especially in the form of virtual and augmented reality solutions (VR/AR). While the technologies are developing fast, knowledge about them and their possibilities is difficult to locate and reach. Cross-organizational knowledge networks that share knowledge about technology and its applications are needed. This paper analyzes cross-organizational knowledge sharing networks that operate behind the scenes of virtual and augmented reality. To understand the knowledge networking processes, this paper presents a case study of a regional VR/AR community. The aim of the paper is to understand how knowledge sharing networks naturally operate in the VR/AR context and what kind of processes and tools organizations harness. The paper presents a description on how the interviewed organizations and individuals utilize their knowledge network in VR/AR knowledge acquisition and creation. The distinct characteristics of the VR/AR field are discussed in light of existing literature on knowledge sharing and knowledge networks. In the findings, the need fora more systematic way of utilizing the network is identified. Knowledge networks provide the best value for their members when the network is actively harnessed, and there are network actors who focus on systematically spreading knowledge across the network. While the case study shows that the network members feel that they gain knowledge from the network, the use of the network varies between organizations and individuals. The network shows signs of movement toward more systematic knowledge sharing, and the knowledge network literature suggests that this development will improve the benefits of network participation for all actors in the network. Further studies on a larger scale in similar types of networks are suggested to allow better understanding of knowledge sharing in knowledge networks, and the challenges and benefits that are connected to it. VR/AR development as a rapidly evolving field lends itself to be an interesting context for studying knowledge networks.