Generative AI - Generalist

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.

The CEO of $2 billion AI training startup says that humans will stay involved in data creation for decades. Shubhangi goel new follow authors and never miss a story! * Human feedback remains essential for AI training, says Invisible Technologies' CEO. * Synthetic data can't replace humans because there are too many kinds of tasks for AI to accomplish. * Data labeling startups continue to hire specialized workers as tech giants seek high-quality data. Artificial intelligence won't be training AI anytime soon, says the CEO of a data labeling startup. On an episode of the "20VC" podcast released last week, Matt Fitzpatrick, the CEO of Invisible Technologies, said that one of the biggest misconceptions in the AI training industry is that humans won't be needed in a few years. "When I first started this job, the main push back I always got was that synthetic data will take over and you just will not need human feedback two to three years from now," said Fitzpatrick, who joined the startup last year. "From first principles, that actually doesn't make very much sense." Synthetic data refers to data that is artificially created. It is used for training AI or machine learning models, mostly where real data is scarce or can't be used because of privacy concerns. Human feedback, on the other hand, asks real people to filter, rank, and train AI responses. On the podcast, Fitzpatrick said that there are too many kinds of tasks for AI to accomplish in the world, and it would take a long time to do them accurately with language and cultural context in mind. For example, the legal industry contains vast amounts of nonpublic information. Get its newsletter for the inside scoop on today's big stories. "On the GenAI side, you are going to need humans in the loop for decades to come," he said. "And I think that is something that most people are starting to realize." Fitzpatrick was previously a senior partner at McKinsey, where he led QuantumBlack Labs, the firm's AI research and software development arm. Invisible, which raised $100 million in September at a $2 billion valuation, competes with data labeling companies such as Scale AI and Surge AI. These startups have raised billions in the past year as tech giants race to secure the data needed to train their AI models. They hire millions of human contractors, who help teach the models math, science, coding, and characteristics such as humor and empathy. Fitzpatrick joins the CEOs of other data labeling startups in saying that the industry will continue to require human effort. In September, the CEO of Mercor, Brendan Foody, said that the most important aspect of the business was data quality and "having phenomenal people that you treat incredibly well." In July, the CEO of Handshake, a job platform that pivoted into AI training last year, said that humans will still be needed to train AI, but who makes the cut is changing. Garrett Lord said the data annotation industry is shifting from requiring generalists to highly specialized experts, including in math and science. "Now these models have kind of sucked up the entirety of the entire corpus of the internet and every book and video," Lord said on a podcast. "They've gotten good enough where, like, generalists are no longer needed." Read next. Business insider tells the innovative stories you want to know.

"Surge AI, a data-labeling firm that competes with Scale AI, has hired advisors to raise as much as $1 billion in the first capital raising in the firm's history," Reuters reported.