Sequoia Capital is leading a new financing round for Mecka AI, pushing the startup’s valuation to approximately $500 million. This deal arrives just three months after the company closed a $60 million raise led by Framework Ventures. While the exact size of this latest injection remains unclear and terms are not yet final, the rapid re-rating signals intense investor appetite for infrastructure that bridges the gap between digital intelligence and physical action.
Mecka tackles a specific bottleneck in robotics: the scarcity of high-quality human motion data. Co-founded in 2024 by Josh Gao, Mogen Cheng, Jason Chong, and Duy Nguyen, the team lacks traditional robotics expertise but identified that general-purpose robots stall without real-world interaction examples. They pay individuals to record everyday tasks, such as brewing coffee or repairing vehicles, using body sensors and smartphones. This “egocentric” data collection mirrors the strategy Scale AI used to fuel large language models, now applied to the physical domain.
The financial trajectory appears aggressive. Gao previously told Fortune that Mecka projects hitting a $100 million annual run rate by the end of 2026. Although the startup has not disclosed its customer roster, the demand for such datasets is growing among AI labs and robotics firms struggling to train humanoid models. Competitors like XDOF are also seeing massive valuations, with reports suggesting a $1.2 billion mark, while established players like Micro1 expand their services beyond text-based AI training.
This swift valuation jump from an undisclosed post-money figure on a $60 million round to $500 million suggests investors are betting heavily on data being the new oil for embodied AI. However, the lack of public customer names leaves some questions about actual revenue traction versus projected growth. If Mecka can deliver on its 2026 targets, it will have successfully positioned itself as the essential pipe for physical world intelligence, but the clock is ticking to prove that paid human recordings scale efficiently enough to meet those ambitious financial goals.
