Since 2017 the automotive industry has developed a high demand for ground truth data. Without this data, the ambitious goal of producing fully autonomous vehicles will remain out of reach. The self-driving car depends on so-called self-learning algorithms, which require large amounts of “training data”. The production of this training data or “ground truth data” requires vast amounts of manual labour in data annotation, performed by crowdworkers across the globe. The crowdworkers both train AI systems and are trained by AI systems. Humans and machines work together in ever more complex structures.

An end of this work is not in sight; according to interviews with experts conducted for the study, the demand for this type of labour will continue to grow rapidly in the foreseeable future. However, as the study also shows, while this type of labour creates a new class of skilled crowdworkers, the precariousness of this work remains high because individual tasks are continuously under threat either of being automated or of being further outsourced to an even cheaper region in the world. As the study shows, 2018 saw an influx of hundreds of thousands of crowdworkers from Venezuela specialising in these tasks. On some new platforms, this group now makes up 75 per cent of the workforce. These recent geographical shifts in the supply of labour are a symptom of deeper structural changes within the crowdsourcing industry.