According to TechCrunch, Mercor CEO Brendan Foody revealed at TechCrunch Disrupt 2025 that AI labs including OpenAI, Anthropic, and Meta are bypassing traditional data contracts by hiring former senior employees from investment banks, consulting firms, and law firms through his marketplace platform. The 22-year-old CEO explained that companies like Goldman Sachs resist sharing data that could automate their value chains, creating demand for contractors who understand proprietary workflows. Mercor pays industry experts up to $200 hourly and distributes over $1.5 million daily to tens of thousands of contractors, achieving $500 million in annual recurring revenue and a $10 billion valuation in under three years. This emerging approach signals a fundamental shift in how artificial intelligence companies access specialized knowledge while navigating corporate resistance to automation.
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The Knowledge Economy Reimagined
What Mercor represents is the maturation of AI’s data acquisition strategy from simple data labeling to sophisticated knowledge extraction. Early AI training relied heavily on low-cost labor in developing countries performing basic categorization tasks. The current approach recognizes that automating complex professional services requires deep institutional knowledge that can’t be captured through public datasets or simple web scraping. This evolution mirrors how technology adoption typically progresses – starting with easily accessible data before moving to more valuable, protected information sources.
The economic model here is particularly fascinating. At $200 per hour, Mercor is essentially creating a premium gig economy for white-collar professionals, fundamentally different from ride-sharing or food delivery platforms. These contractors aren’t performing simple tasks but rather transferring years of accumulated expertise that would otherwise remain locked within corporate walls. The scale – $1.5 million distributed daily – suggests this isn’t a niche experiment but a substantial new labor market.
Legal and Ethical Quandaries
The most immediate concern revolves around intellectual property boundaries. Foody’s assertion that “knowledge in an employee’s head belongs to the employee” represents an extremely generous interpretation that would likely face legal challenges. Most employment contracts include clauses protecting trade secrets and confidential information, and the line between general industry knowledge and proprietary processes can be dangerously blurry.
The platform’s job posting seeking startup CTOs who “can authorize access to a substantial, production codebase” raises particularly troubling questions. This essentially invites executives to monetize their companies’ most valuable assets – their codebases – for personal gain. While Mercor claims to prevent corporate espionage, the incentive structure inherently encourages pushing ethical boundaries, especially when contractors can earn life-changing amounts for sharing knowledge they developed while employed elsewhere.
Competitive Landscape Shifts
Mercor’s rapid growth to $10 billion valuation reflects how quickly the AI data market is evolving. The platform benefited significantly from Meta Platforms‘ investment in Scale AI, which caused many AI labs to seek alternative data providers. However, Mercor remains smaller than competitors Surge and Scale AI, both valued above $20 billion, indicating this market is still in its early stages with room for multiple winners.
The emergence of specialized data marketplaces represents a new layer in the AI infrastructure stack. Just as cloud providers abstracted hardware management and ML platforms simplified model development, these knowledge marketplaces are abstracting data acquisition. This specialization allows AI labs to focus on model architecture and application development while outsourcing the complex challenge of sourcing high-quality training data.
Industry Implications and Resistance
Foody’s prediction that “ChatGPT will be better than the best consulting firm, investment bank, and law firm” points to the existential threat driving corporate resistance. Industries facing automation have every reason to protect their proprietary methodologies, as their entire business models depend on maintaining information asymmetry between experts and clients.
The divide between companies embracing this change and those resisting it will likely define competitive dynamics across professional services. Early adopters might leverage these platforms to accelerate their own digital transformation, while resisters risk being disrupted by more agile competitors. However, the long-term viability of relying on former employees as primary data sources remains uncertain, as corporations will inevitably strengthen legal protections and employment agreements.
Future Outlook and Risks
As this startup model scales, several risks emerge. Regulatory scrutiny seems inevitable, particularly around data privacy, intellectual property, and labor classification. The current legal framework wasn’t designed for this type of knowledge transfer, and we can expect significant litigation that will shape the boundaries of what constitutes acceptable knowledge sharing.
The concentration risk is another concern – with most revenue coming from a few AI labs, Mercor’s business model depends heavily on continued demand from major players. Any shift in AI development strategies or the emergence of alternative data acquisition methods could quickly undermine their value proposition. Additionally, as AI models become more capable of learning from smaller datasets or generating their own training data, the need for human knowledge transfer might diminish over time.
Ultimately, Mercor’s success highlights a fundamental tension in the AI revolution: the conflict between rapid technological progress and established business interests. How this tension resolves will determine not just Mercor’s future, but the pace and direction of AI adoption across the global economy.
