Sequoia-led round values robot motion data startup Mecka AI near $500M

Mecka AI, a startup that pays people to record themselves performing everyday tasks so the motion data can be used to train robots, is nearing a new funding round led by Sequoia Capital at a valuation of about $500 million, according to TechCrunch. The outlet, citing two people with knowledge of the deal, reports that the precise size of the round is not yet known and that the terms are not final and could still change. Sequoia declined to comment, and Mecka did not respond to a request for comment, TechCrunch says.

A second round in three months

The Sequoia-led financing would arrive just three months after Mecka announced a $60 million round led by Framework Ventures, with participation from Menlo Ventures, SV Angel and Kindred Ventures, according to TechCrunch. A valuation near half a billion dollars on top of that would mark a sharp step up for a company founded only in 2024.

The startup also has revenue momentum to point to. As of early June, co-founder Josh Gao told Fortune that Mecka was projecting an annual run rate of $100 million by the end of 2026, TechCrunch notes. Measured against that projection, the reported valuation works out to roughly five times forward run rate, a multiple that suggests investors are paying a premium for exposure to the robotics data category.

Founders who saw a data gap

Mecka was co-founded in 2024 by four entrepreneurs: Canadians Josh Gao and Mogen Cheng, who previously built a restaurant fintech startup; Jason Chong, who joined Coinbase after it acquired his crypto exchange; and Duy Nguyen, the only non-Canadian on the team, who handles operations, TechCrunch reports.

None of them come from robotics backgrounds. According to TechCrunch, they instead identified a shortage of physical-world data and concluded that capturing real-world interactions was the primary bottleneck holding back general-purpose robots, including humanoids. The company's name derives from "mecha," a fictional giant robot controlled by humans.

The business model borrows directly from the human-data companies that grew up around large language models. TechCrunch reports that Mecka set out to do for robotics what Scale AI, Mercor and Surge did for LLMs, paying people to record tasks such as making coffee or fixing cars using body sensors and smartphones.

An increasingly crowded scramble

Mecka has not publicly disclosed its customer list, but TechCrunch reports that many robotics companies and AI labs rely on this kind of "egocentric" real-world capture, alongside other physical data collection methods such as teleoperation, to build their models.

It is not the only company chasing the opportunity. TechCrunch reported the previous week that XDOF, another startup collecting real-world data for robot training, was nearing a new round at a $1.2 billion valuation. Meanwhile, human-data platforms that built their businesses on LLMs, including Scale AI and Micro1, are expanding beyond text into physical data collection.

Why it matters

The Mecka deal is the latest signal that investors now view data, rather than hardware or model architecture, as the binding constraint in robotics. If general-purpose and humanoid robots are to move beyond demonstrations, they need large volumes of real human motion showing how tasks are actually performed, and startups that can supply that data are being valued accordingly.

The pace of the funding also matters. A Sequoia-led round at roughly $500 million, arriving only months after a $60 million raise, shows how quickly capital is rotating into the physical data layer as robotics heats up. For teams that can assemble networks of paid contributors, effectively applying the Scale AI playbook to bodies rather than text, Mecka's trajectory suggests the market believes that playbook transfers.