Efficiency gains in economic processes often do not deliver the projected overall savings. Irrespective of whether the increase in efficiency saves energy, resources, time or transaction costs, there are various mechanisms that spur additional consumption as a consequence. These mechanisms are generically called rebound effects, and they are problematic from a sustainability perspective as they decrease or outweigh the environmental benefits of efficiency gains. Since one of the overarching purposes of digitalization is to increase efficiency, rebound effects are bound to occur frequently in its wake. Rebound effects of digitalization have been ignored until recently, but they have been increasingly studied lately. One particular mechanism of digital rebound, however, has been largely disregarded so far: the digitalization-induced lowered skill requirements needed to perform a specific activity. As with other types of rebound effects, this leads to an increase in the activity in question. In this paper, we propose the term skill rebound to denote this mechanism. We use the example of self-driving cars to show how digitalization can lower the skill bar for operating a vehicle, and how this opens 'driving' a car to entirely new socio-demographic categories such as elderly, children or even pets, leading to increased use of the (transportation) service in question and thus to rebound effects. We finally argue that these unintended environmental effects of skill rebound must be better understood and taken into account in the design of new digital technologies.