The transition from startup to scaleup is crucial but challenging. Much of the literature has focused on how the timing of this transition impacts subsequent firm performance outcomes. Yet, we know considerably less about the determinants of the timing of scaling and how these determinants may condition the effects of the timing of scaling on subsequent firm performance. A key issue is the trade-off between accelerating resource development to scale sooner and the increased costs of early scaling due to time compression diseconomies. This study investigates whether and how firms can effectively reduce the time to start scaling while managing the challenges that stem from time compression diseconomies. Looking at US digital firms and leveraging the launch of the open-source machine learning platform (TensorFlow) by Google, this study argues and finds evidence on how access to open-source Software can enable firms to shorten resource development time and, thus, start scaling sooner without compromising performance.
