White-Collar Workers Now Training AI to Replace Themselves

White-collar professionals are facing a paradox: some are now being paid to train artificial intelligence (AI) systems that could eventually take over their own roles. Mercor, a San Francisco-based startup, has made headlines for hiring thousands of highly skilled contractors to teach AI models how to perform expert-level tasks. Questions around job security, AI ethics, and the future of professional employment are sparking widespread debate.

The company targets fields that traditionally offer stable careers, including psychology, creative writing, industrial engineering, and even astronomy. The goal is simple but unsettling: use human expertise to make AI more capable—potentially reducing the need for humans in these roles over time.

Mercor’s Rapid Expansion in the Gig Economy

Mercor hired over 30,000 contractors in 2025 alone, working with major AI developers such as OpenAI and Anthropic, according to the Wall Street Journal. The startup operates in the growing white-collar gig economy, offering flexible work but also introducing uncertainty. While many see the work as a temporary lifeline for professionals struggling to find stable positions, the broader implications for career longevity are significant.

Pay rates vary widely depending on expertise. For instance, social media marketers writing video captions can earn $45 an hour, while specialized roles such as astronomers or psychologists may receive much higher compensation. This pay structure highlights both the value of human expertise and the precariousness of relying on gig work tied to AI development.

High-Skill Roles on the Frontline of AI Training

Mercor isn’t just targeting low-skill or repetitive tasks—it’s enlisting experts in areas that traditionally require years of study and experience. Creative writers craft prompts and narratives that train AI to produce human-like content, while industrial engineers guide AI in process optimization. Psychologists help teach AI to understand human behavior, emotions, and decision-making processes.

This approach demonstrates the dual-edged nature of AI adoption: human knowledge makes AI more powerful, but it also accelerates the automation of highly skilled roles. Many workers are grappling with the reality that their expertise is now a tool to make themselves obsolete.

Ethical and Economic Concerns

Critics argue that Mercor’s business model raises ethical questions. Paying workers to teach AI that may replace them could exacerbate inequality and destabilize traditional career paths. Labor experts warn that this could set a precedent for “self-replacement” in professional industries, shifting risk entirely onto human contractors.

At the same time, supporters say this model provides valuable income for underemployed experts while helping AI companies develop safer, more reliable systems. The debate reflects broader societal concerns about the pace of automation and the responsibilities of tech companies toward the workforce.

The Future of White-Collar Work

As AI capabilities expand, the line between human and machine expertise continues to blur. Mercor’s model offers a glimpse into a future where even highly skilled jobs may become temporary stepping stones for training AI. Workers are caught in a cycle of using their knowledge to ensure AI can eventually function independently, highlighting a new form of career uncertainty.

Experts suggest that adaptability, continuous learning, and diversification of skills will become essential for professionals who want to remain relevant in an AI-driven economy. Some industries may evolve alongside AI, while others may face radical transformations or even obsolescence.

Workers Respond to AI Training Roles

Reactions among contractors vary. Some embrace the opportunity as a way to stay employed and engage with cutting-edge technology. Others worry about long-term consequences, fearing that their contributions will accelerate job loss. Conversations on social media and professional forums reveal a mix of curiosity, excitement, and anxiety.

Mercor’s approach underscores a fundamental shift in labor dynamics: expertise is no longer just a career asset—it’s also a resource for machines. For white-collar professionals, the challenge now is balancing immediate opportunities with long-term career sustainability in an AI-driven future.