The rise of artificial intelligence is creating a new category of remote work opportunities in data annotation and labeling, offering flexible schedules and competitive hourly rates for workers willing to train AI models. Riley Willis, a contractor with DataAnnotation Tech, exemplifies this emerging workforce, earning approximately $25 per hour while working 30 hours weekly from home—all without speaking to a single colleague.
Willis’s role involves “fact-checking” AI model outputs and labeling information based on project-specific criteria. The work encompasses teaching artificial intelligence to improve its creative writing, coding skills, and factual accuracy. “You are literally going through and you’re labeling the information based on a series of criteria that differ between projects,” Willis explained. He focuses primarily on factual verification, ensuring AI models aren’t “lying or hallucinating information”—a critical concern as companies race to improve their AI systems.
The compensation structure is project-dependent, with hourly rates ranging from $20 to $30. Willis started at $20 per hour and now earns around $25, with rates varying based on task complexity. This income allows him to cover essential expenses including tuition for his computer science degree at the University of Florida’s online program and rent in Raleigh, North Carolina, though he acknowledges living frugally.
The job offers significant flexibility that appeals to introverts and those seeking work-life balance. Workers operate asynchronously with no set boss, no coworker contact, and no daily meetings. Willis can work at any hour, including 1 a.m. if he chooses. However, the position has notable drawbacks: workers are paid only for active work time, not breaks or research periods. Willis estimates he sometimes spends six to seven hours on research but gets compensated for only five hours of actual work.
The repetitive nature of data annotation can be “mind-numbing,” and unlike traditional employment, there are no paid lunch breaks or casual paid interactions with colleagues. Despite these limitations, Willis believes the arrangement is ideal for those with full-time jobs seeking side income, suggesting workers could earn an extra $25-50 daily working just one to two hours.
As AI companies continue pushing to enhance their models, the demand for data annotators and labelers is spiking, creating accessible entry points into the AI industry for workers without advanced technical degrees.
Key Quotes
You are literally going through and you’re labeling the information based on a series of criteria that differ between projects. I personally work on a lot of factual stuff, just because I just find the pay-to-effort ratio is nice. So there’s a lot of fact-checking responses, making sure the models aren’t lying or hallucinating information.
Riley Willis, an AI contractor with DataAnnotation Tech, describes his daily work training AI models. This quote is significant because it reveals the critical human role in preventing AI hallucinations—a major concern as AI systems are deployed more widely across industries.
It’s perfect for an introvert. I will personally say work-life balance is nice, in the sense that, obviously, it’s remote and I can work whenever I want. I can work at 1 o’clock in the morning if I really feel like it.
Willis highlights the flexibility that makes AI data annotation attractive to certain workers. This matters because it demonstrates how the AI industry is creating new work arrangements that differ significantly from traditional employment models.
Even in a high-paced job, I feel like, you know, there’s paid lunch breaks, stuff like that. Or, you know, you spend a second or two talking to someone, but you’re still on the clock getting paid. With this, you’re really not getting paid unless you are doing the work.
Willis identifies a key drawback of AI contract work—the lack of traditional employment benefits. This quote matters because it exposes potential labor concerns in the growing AI training industry, where workers bear more risk than traditional employees.
I think this would be really, really good for someone who has an actual job and is doing this, like, two hours a day. If you have an actual full-time job, and you just add the extra hour, hour and a half a day earning an extra $25 to $50 a day, just when you have nothing else to do, is definitely doable.
Willis suggests the ideal use case for AI annotation work is as supplemental income rather than primary employment. This perspective is important because it reveals how the AI industry may be creating a two-tier workforce—those with stable full-time employment who can benefit from gig work, versus those relying on it as their sole income source.
Our Take
The proliferation of AI data annotation jobs reveals a fundamental paradox in artificial intelligence development: systems marketed as autonomous and intelligent require extensive human labor to function properly. While tech companies promote AI as revolutionary automation, they’re simultaneously creating a shadow workforce of contractors who teach these systems basic accuracy and reasoning.
The $20-30 hourly rate and flexible scheduling are attractive, but the gig-work structure—no benefits, pay only for active work, isolation—represents a concerning trend. As AI becomes more central to the economy, we’re seeing the emergence of a new precarious labor class that’s essential to AI functionality but excluded from traditional employment protections.
Most significantly, the need for humans to constantly fact-check AI outputs and prevent hallucinations suggests current AI systems are far less capable than marketing suggests. This human-in-the-loop requirement will likely persist, making data annotation a growth sector even as AI capabilities advance. The question is whether this workforce will gain better protections and compensation as their importance becomes undeniable.
Why This Matters
This story illuminates a crucial but often invisible component of the AI revolution: the human workforce required to train and refine artificial intelligence systems. As companies invest billions in AI development, they’re creating an entirely new labor market for data annotation—work that’s essential for preventing AI hallucinations and improving model accuracy.
The emergence of flexible, remote AI training jobs represents a significant shift in how AI development is resourced. Unlike traditional tech jobs requiring advanced degrees, data annotation offers accessible entry points with competitive hourly rates, potentially democratizing participation in the AI economy. However, the gig-work nature—with pay only for active work time and no traditional benefits—raises important questions about labor practices in the AI industry.
For businesses, this highlights the ongoing human dependency of AI systems despite automation promises. The fact that AI models require constant human oversight and training reveals that artificial intelligence remains far from autonomous. For workers, especially students and those seeking supplemental income, this represents a growing opportunity sector, though one that requires careful consideration of the trade-offs between flexibility and job security in the evolving AI-powered economy.
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Source: https://www.businessinsider.com/ai-tutor-data-annotator-hourly-pay-fully-remote-contract-work-2025-2