Viqus Logo Viqus Logo
Home
Categories
Language Models Generative Imagery Hardware & Chips Business & Funding Ethics & Society Science & Robotics
Resources
AI Glossary Academy CLI Tool Labs
About Contact

AI Labs Tap Former Employees for Data Training, Valuing Mercor at $10B

AI Data Training Startups Mercor Scale AI TechCrunch Artificial Intelligence
October 29, 2025
Viqus Verdict Logo Viqus Verdict Logo 8
Knowledge is Power
Media Hype 7/10
Real Impact 8/10

Article Summary

AI labs are increasingly reliant on tapping into the knowledge and experience of former employees from industries such as investment banking, consulting, and law to enhance their AI models. Mercor, a startup led by 22-year-old Brendan Foody, is at the forefront of this trend, operating a marketplace that connects these former employees with AI labs seeking to automate workflows. The company pays contractors up to $200 an hour to provide training data, and has grown to over 10,000 contractors, generating approximately $500 million in annualized recurring revenue. This strategy addresses concerns within AI labs about directly accessing and utilizing proprietary data, a significant hurdle in the rapidly evolving field. The model highlights a shift from traditional data labeling services – like those offered by Scale AI – toward a more nuanced approach leveraging human expertise. Mercor’s rapid growth, fueled by investments and a shift in AI model training, underscores the demand for specialized knowledge in this sector. However, the practice raises questions regarding data security and potential conflicts of interest, despite Mercor’s efforts to prevent corporate espionage.

Key Points

  • AI labs are moving away from directly acquiring data and instead focusing on hiring former industry professionals to train AI models.
  • Mercor's marketplace connects these former employees with AI labs, offering a cost-effective solution to access specialized knowledge.
  • The startup’s rapid growth – fueled by a $10 billion valuation – demonstrates the increasing demand for human expertise in AI model training.

Why It Matters

This news is significant because it reveals a crucial adaptation within the AI landscape. The initial focus on readily available datasets is giving way to a recognition of the importance of domain-specific knowledge, particularly when it comes to automating complex industries like finance and law. This shift implies a greater need for expertise in workflow understanding and the ability to translate human processes into effective AI training data. For professionals in these sectors, it highlights the potential for new career opportunities and the evolving role of traditional industries in the age of AI. Furthermore, it demonstrates a broader trend of companies seeking to leverage their existing intellectual capital in novel ways.

You might also be interested in