How AI data-labeling companies like Appen profit from cheap labor in crisis-ridden countries like Venezuela to meet exploding demand
It was meant to be a temporary side job—a way to earn some extra money. Oskarina Fuentes Anaya signed up for Appen, an AI data-labeling platform … Tweets: @miad , @akurmanaev , @skinosian , and @charlottejee Tweets: @miad : :-/ “On one hand, it's been a lifeline for those without any other options. On the other, it's left them vulnerable to exploitation as corporations have lowered their pay, suspended their accounts... in an ongoing race to offer increasingly low-cost services to Silicon Valley.” https://twitter.com/... @akurmanaev : “By mid-2018, an estimated 200,000 Venezuelans had registered for [AI labeling firms] Hive Micro and Spare5, making up 75% of their respective workforces.” https://twitter.com/... Sarah Kinosian / @skinosian : “As the demand for data labeling exploded, an economic catastrophe turned Venezuela into ground zero for a new model of labor exploitation.” Must read about AI from @_KarenHao and @andreapaolahg_ https://twitter.com/... Charlotte Jee / @charlottejee : Brilliant, sad, poignant story from @_KarenHao and @andreapaolahg_ about the people who toil away, often for very low pay and in stressful conditions, to label the data used to train the AI that makes big tech companies billions https://www.technologyreview.com/ ...
Context & Ripple Effects
The story sits at the start of an arc that the later coverage keeps extending. In 2018, Samasource's Nairobi office was still the template — impact-sourcing pitched as opportunity for low-income workers training data for Google and Microsoft. Appen and microtask platforms like Hive Micro and Spare5 pushed the model somewhere darker: by mid-2018 an estimated 200,000 Venezuelans had registered on Hive Micro and Spare5, about 75% of their workforces, drawn in because a collapsing economy left them with no alternatives — a lifeline that also made them easy to underpay and easy to cut off, as the tweets from Charlotte Jee and others note about suspended accounts and falling pay.
The follow-on reporting shows the pattern generalizing rather than correcting: a hidden "tasker" underclass including subject-matter experts working for firms like Scale AI, Chinese labelers hired by Baidu, Alibaba, and JD.com running internships that exploit vocational school students, and opaque WhatsApp-based middleman networks hiring Kenyan annotators. The 2025 coda is the sharpest: annotation work Venezuelans relied on has become scarce and poorly paid as generative AI reshapes demand.
First-order effects
- Venezuelan workers like Oskarina Fuentes Anaya face the immediate squeeze documented in the piece — corporations lowering pay and suspending accounts in a race to offer Silicon Valley ever-cheaper labeling services, leaving workers with little recourse precisely because they have no other options.
- Appen and the microtask platforms get their cost advantage directly from crisis economics: the same dependency that makes Venezuelan labor cheap makes the platforms' pricing power structural, not incidental.
Second-order effects
- Competitors respond by sourcing the next cheapest labor pool rather than competing on quality — Chinese firms hiring Baidu, Alibaba, and JD.com work through vocational-school internships, and Kenyan annotators are reached through middlemen networks designed to avoid accountability.
- The intermediation layer thickens: opaque subcontracting and WhatsApp hiring groups push liability down the chain, so the brand-name AI buyers stay one step removed from the labor conditions their training data depends on.
Third-order effects
- If the pattern holds, AI's training-data supply chain consolidates into a globally distributed, deliberately invisible labor market where crisis economies are the sourcing frontier — with pay and protections ratcheting down as demand shifts, as the 2025 reporting on scarce Venezuelan annotation work suggests is already happening.
- Countermodels emerge from the same dynamic: Karya's structure, where workers keep ownership of the data they create and capture the profit, tests whether fair-trade labeling can compete with extraction-priced labor.
The trend: AI's data-labeling supply chain is becoming a hidden global labor market that sources from crisis economies, with accountability thinning at every intermediate layer even as worker-owned alternatives test the model.