We are in the midst of a transformation in the way that biodiversity is observed on the planet. The approach of direct human observation, combining efforts of both professional and citizen scientists, has recently generated unprecedented amounts of data on species distributions and populations. Within just a few years, however, we believe that these data will be swamped by indirect biodiversity observations that are generated by autonomous sensors and machine learning classification models. In this commentary, we discuss three important elements of this shift towards indirect, technology driven observations. First, we note that the biodiversity data sets available today cover a very small fraction of all places and times that could potentially be observed, which suggests the necessity of developing new approaches that can gather such data at even larger scales, with lower costs. Second, we highlight existing tools and efforts that are already available today to demonstrate the promise of automated methods to radically increase biodiversity data collection. Finally, we discuss one specific outstanding challenge in automated biodiversity survey methods, which is how to extract useful knowledge from observations that are uncertain in nature. Throughout, we focus on one particular type of biodiversity data - point occurrence records - that are frequently produced by citizen science projects, museum records and systematic biodiversity surveys. As indirect observation methods increase the spatiotemporal scope of these point occurrence records, ecologists and conservation biologists will be better able to predict shifting species distributions, track changes to populations over time and understand the drivers of biodiversity occurrence.