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White-collar professionals are earning up to $200 an hour training AI models, but workers say the reality falls short of expectations. Mercor, a San Francisco startup selling training data to AI labs, pays its 30,000 contractors upward of $4 million daily and was reportedly valued near $20 billion in early talks last July. Whilst some roles offer $350 an hour for psychiatry experts or up to $1,000 for venture capital partners, most positions average around $105 an hour. Workers report tight deadlines, flat-rate projects consuming more time than paid for, and inconsistent feedback. A deeper issue emerges as AI models quickly learn from expert corrections, causing work in specific specialties to dry up within weeks. Several contractors have sued data-training firms over alleged misclassification and underpayment. Economists debate whether this represents sustainable employment or a temporary phenomenon that will shrink as systems improve.

Mercor competitor Deccan AI raises $25M, sources experts from India. As demand grows for training and refining AI models, Deccan AI - a startup supplying post-training data and evaluation work - has raised $25 million in its first major funding round, with much of that work carried out by an India-based workforce of experts. The all-equity Series A round was led by A91 Partners, with participation from Susquehanna International Group and Prosus Ventures. While frontier AI labs including OpenAI and Anthropic build core models in-house, much of the post-training work - from data generation to evaluation and reinforcement learning - is increasingly being outsourced as companies push to make systems reliable in real-world use. Deccan is emerging as one of a new set of startups serving that demand. Founded in October 2024, Deccan provides services ranging from helping models improve coding and agent capabilities to training systems to interact with external tools such as application programming interfaces (APIs), which connect AI models to software systems. The startup works with frontier labs on tasks such as generating expert feedback, running evaluations and building reinforcement learning environments, while also serving enterprises through products including its evaluation suite, Helix, and an operations automation platform. The work is also evolving as models move beyond text into so-called "world models" that better understand physical environments, including robotics and vision systems. Deccan's customers include Google DeepMind and Snowflake, according to the company. It has onboarded about 10 customers and runs a couple of dozen active projects at any given time, founder Rukesh Reddy (pictured above) said in an interview. The startup, headquartered in the San Francisco Bay Area with a large operations team in Hyderabad, employs about 125 people and relies on a network of more than 1 million contributors, including students, domain experts, and PhDs. Around 5,000 to 10,000 contributors are active in a typical month, Reddy told TechCrunch. Techcrunch event San Francisco, CA | October 13-15, 2026 About 10% of Deccan's contributor base has advanced degrees such as master's and PhDs, though the share is higher among active contributors depending on project requirements, Reddy said. The market for AI training services has expanded rapidly alongside the rise of large language models, with companies such as Meta-owned Scale AI and its rival Surge AI, as well as startups Turing and Mercor competing to provide data labeling, evaluation, and reinforcement learning services. "Quality remains an unsolved problem," Reddy said, adding that tolerance for errors in post-training is "close to zero" as mistakes can directly affect model performance in production. That makes post-training more complex than earlier stages, requiring highly accurate, domain-specific data that is harder to scale. The work is also highly time-sensitive, he said, with AI labs sometimes requiring large volumes of high-quality data within days, making it difficult to balance speed with accuracy. The sector has faced criticism over working conditions and pay, with large pools of gig workers often used to generate training data. Reddy said earnings on Deccan's platform range from about $10 to $700 per hour, with top contributors earning up to $7,000 a month. India emerges as a hub for AI training talent. Even as its customers are largely U.S.-based AI labs, most of Deccan's contributors are based in India. Competitors such as Turing and Mercor also source contractors from the country, but operate across a broader set of emerging markets. Deccan chose to concentrate much of its workforce in India to better manage quality, Reddy said. "Many of our competitors go to 100-plus countries to find the experts," he said. "If you have operations in just one country, it becomes far easier to maintain quality." That approach highlights India's current position in the global AI value chain - as a supplier of talent and training data rather than a developer of frontier models, which remain concentrated among a handful of U.S. companies and a few players in China. However, Reddy said Deccan has begun sourcing talent from a few other markets, including the U.S., for niche expertise in geospatial data and semiconductor design. Reddy said Deccan was built as a "born GenAI" company, in contrast to traditional data labeling firms that began with computer vision tasks. This means it has focused on higher-skill work from the outset. Deccan grew 10x over the past year and is now at a double-digit million-dollar revenue run rate, Reddy said, declining to share specifics. About 80% of its revenue comes from its top five customers, reflecting the concentrated nature of the frontier AI market, he added.