Mecka AI Nears $500 Million Valuation in Sequoia Deal

Mecka AI is in talks to secure a new investment round led by Sequoia Capital that would value the startup at approximately $500 million. This potential deal comes just three months after the company announced a $60 million funding round led by Framework Ventures, which also saw participation from Menlo Ventures, SV Angel, and Kindred Ventures. The exact size of the upcoming Sequoia-led round has not been finalized, and the terms of the transaction could still change.

Founded in 2024 by Josh Gao, Mogen Cheng, Jason Chong, and Duy Nguyen, Mecka AI aims to solve the data bottleneck in physical artificial intelligence. Although the founders lack traditional robotics backgrounds, they recognized that a shortage of real-world interaction data was holding back general-purpose and humanoid robots. To address this, the startup pays individuals to record themselves performing everyday activities, such as fixing cars or making coffee, using smartphones and wearable body sensors.

The company, which draws its name from the fictional human-controlled giant robots known as mecha, is positioning itself to do for physical AI what Scale AI, Mercor, and Surge did for large language models. As of early June, co-founder Josh Gao projected that Mecka AI would reach an annual run rate of $100 million by the end of 2026. The startup operates in an increasingly competitive landscape that includes XDOF, which recently neared a $1.2 billion valuation, as well as Scale AI and Micro1.

For AI practitioners and robotics developers, Mecka AI's rapid valuation growth underscores the critical shift toward egocentric data collection. As developers move beyond simulated environments and teleoperation, access to diverse, real-world human motion datasets becomes essential for training robust humanoid models. This influx of capital suggests that high-quality, crowdsourced human physical data will become more readily available, potentially accelerating the deployment of general-purpose robots in real-world settings.