Workers and researchers say data annotation jobs that many Venezuelans relied on have become scarce and poorly paid with the rise of generative AI
Laura Rodríguez Salamanca / Rest of World : Forums: Beehaw Forums: Alyaza To / Beehaw : Venezuelan migrants relied on clickwork to survive. Now AI is replacing them
Context & Ripple Effects
Venezuela had already been positioned as a low-cost hub for AI training work: earlier coverage described data-labeling firms drawing on labor in crisis-hit countries as demand for labeled data expanded. Microtask platforms also built a Global South workforce around creating and editing datasets for autonomous-driving systems through task-based remote work.
This report matters because it shows that the same labor pool used to supply AI’s inputs is now exposed to automation by its outputs. It also extends warnings that freelancers adopting generative AI could still be among those most vulnerable to displacement.
First-order effects
- Venezuelan migrants who depended on clickwork face fewer available annotation assignments and lower pay for the work that remains.
- Income from a previously accessible remote-work channel becomes less dependable for workers with limited alternatives.
Second-order effects
- Annotation platforms and their buyers can shift more routine work toward generative tools, reducing their need to source large volumes of low-cost human labor.
- Competition for remaining human-review and higher-skill task work is likely to intensify among the broader global tasker workforce.
Third-order effects
- If this pattern persists, AI labor markets may polarize: fewer routine labeling roles alongside a smaller set of more specialized human-evaluation tasks.
- The case highlights a recurring distributional tension in AI development: regions supplying low-cost training labor may capture less of the value as automation advances.
The trend: Generative AI is shifting human AI work from high-volume data production toward scarcer, more specialized oversight and evaluation roles.