This chapter explores how the processes underpinning the development of Artificial Intelligence (AI) systems, particularly large language models, can conceal, entrench, and perpetuate hermeneutical injustice—a particular form of epistemic injustice. It examines how the processes are shaped by a range of problematic technical practices, including automated data collection (for example, OpenAI’s CLIP model), human evaluations (for example, Reinforcement Learning from Human Feedback) and automated benchmarking techniques (for example, toxicity assessments). These practices often mirror existing gaps in dominant knowledge frameworks, further entrenching hermeneutical injustice. As the epistemic authority of AI is validated over time, conceptions of the world that are tied to particular places at particular points in time become crystallised. Here, people are ‘made up’ in the digital space based on what socio-technical AI systems identify as salient, largely informed by an expansive range of automated practices that systematically project existing hermeneutical injustices onto the wider world. These systems have the power to contour and advance certain conceptions of what ought to be contested ideas and understandings. Here, future-making is shaped by the hermeneutically enriched, who will continue to benefit as the data extracted from our interactions with these systems shape the next generation of AI models (a ‘colonisation of the future’). Existing epistemic hierarchies are reinforced, making it increasingly difficult for marginalised perspectives to contest and reshape dominant knowledge frameworks. The chapter concludes by exploring strategies to strengthen the epistemic ‘infrastructure’ surrounding AI construction, enhance contestability, and ultimately mitigate hermeneutical injustice.