In the behavioral cloning approach to end-to-end driving, a dataset of expert driving is collected and the model learns to guess what the expert would do in different situations. Situations are summarized in observations and the outputs are low or mid-level commands (e.g. brake, throttle, and steering; or trajectories). The models learn to match observations at time T to actions recorded at T or as simultaneously as possible. However, when deploying the models to the real world (or to an asynchronous simulation), the action predicted based on observations at time T gets applied at T + $Δ$ T. In a variety of cases, $Δ$ T can be considerable and significantly influence performance. We first demonstrate that driving at two different speeds is effectively two different tasks. Delays partially cause this difference and linearly amplify it. Even without computational delays, actuator delays and slipping due to inertia result in the need to perform actions preemptively when driving fast. The function mapping observations to commands becomes different compared to slow driving. We experimentally show that models trained to drive fast cannot perform the seemingly easier task of driving slow and vice-versa. Good driving models may be judged to be poor due to testing them at "a safe low speed", a task they cannot perform. Secondly, we show how to counteract the effect of delays in end-to-end networks by changing the target labels. This is in contrast to the approaches attempting to minimize the delays, i.e. the cause, not the effect. To exemplify the problems and solutions in the real world, we use 1:10 scale minicars with limited computing power, using behavioral cloning for end-to-end driving. Some of the ideas discussed here may be transferable to the wider context of self-driving, to vehicles with more compute power and end-to-mid or modular approaches.