As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its core idea is still intriguing: Instead of sending all data and tasks from an end user's device to the cloud, possibly covering thousands of kilometers and introducing delays lower-bounded by propagation speed, edge servers deployed in close proximity to the user (e.g., at some base station) serve as proxy for the cloud. This is particularly interesting for upcoming machine-learning-based intelligent services, which require substantial computational and networking performance for continuous model training. However, this promising idea is hampered by the limited number of such edge servers. In this article, we discuss a way forward, namely the V-Edge concept. V-Edge helps bridge the gap between cloud, edge, and fog by virtualizing all available resources including the end users' devices and making these resources widely available. Thus, V-Edge acts as an enabler for novel microservices as well as cooperative computing solutions in next-generation networks. We introduce the general V-Edge architecture, and we characterize some of the key research challenges to overcome in order to enable wide-spread and intelligent edge services.