AI systems depend on large amounts of data, and recent reporting describes a growing layer of work dedicated to labelling that data for training and testing. Workers are said to classify and tag content such as text, images, audio and video, and to carry out related moderation and quality checks as part of AI data pipelines.
Both sources frame data labelling as often repetitive and time-limited, linked to short-term project needs rather than long-term employment. The Conversation adds that interviews with data workers in China and Australia portray the work as part of a broader gig economy, with concerns about precariousness and exploitative conditions. Business Line focuses more on what the work actually involves—categorising, labelling, testing and moderating—while providing less detail on labour conditions.
Taken together, the articles suggest that while the “AI job boom” narrative highlights new opportunities, the day-to-day reality for many roles may be temporary, task-based and centred on large-scale annotation tasks that support AI development.
