Mecka Series B: $60M to Teach Robots Human Motion

The Mecka Series B puts a $60 million bet on one of robotics' least glamorous problems: teaching machines how humans actually move. Mecka AI, a two-year-old startup that pays people to wear body sensors while performing everyday tasks, announced the funding round on October 7, 2026, with Sequoia Capital leading and an unusual cluster of hardware strategics joining in. The round arrives as humanoid robotics shifts from hardware showcases to a grueling data problem that no amount of web scraping can solve.

Who is backing the Mecka Series B

Sequoia Capital led the Mecka Series B, which values the company at roughly $500 million, according to TechCrunch. New money came from Nvidia, Microsoft's venture fund M12, Qualcomm Ventures, and Samsung, a roster that reads like a who's-who of companies building robotics hardware stacks. Returning backers include Framework Ventures, Kindred Ventures, and Neo, alongside prominent angels including DoorDash CEO Tony Xu, former Snowflake and ServiceNow CEO Frank Slootman, and Milan Kovac, the executive who previously ran Tesla's Optimus humanoid program, as reported by WebProNews.

The presence of Nvidia, Samsung, and Qualcomm as strategic investors is telling. According to AI Weekly, each treats Mecka's EgoVerse dataset as a component of their own robotics hardware efforts rather than as a distant financial bet. TechCrunch had previously reported in September that the startup was nearing a round at the $500 million mark, and the announced Mecka Series B confirms that range. The round brings Mecka's disclosed funding to roughly $128 million.

The timing places the Mecka Series B in a red-hot funding window for agent-adjacent companies. Just a day later, Butterfly Effect announced a round exceeding $500 million for its general AI agent Manus, covered here in Manus's post-Meta funding story, while Arena raised $200 million at a $3.1 billion valuation weeks earlier. The Mecka Series B fits the pattern: capital is flowing to the infrastructure layer behind autonomous systems.

Motion data is the new bottleneck

Mecka does not build robots. It plugs into other people's hardware and sells the human-demonstration data that trains them. The company's pitch, laid out in its own Series B announcement, is that motion, contact, force, and geometry simply are not on the internet, so they cannot be scraped, licensed, or bought. Mecka pays demonstrators to record themselves doing routine tasks like making coffee or repairing vehicles while wearing body sensors and using smartphones, then processes those recordings into structured training datasets.

The scale of the dataset is already substantial. According to AI Weekly, the EgoVerse dataset holds 1,362 hours of recorded demonstrations across 80,000 episodes and 2,087 unique demonstrators. TechCrunch notes that founders from fintech and crypto, with no robotics background, arrived at an egocentric capture approach that sidesteps the industry's traditional reliance on teleoperation, where workers pilot robots by remote control to generate training examples.

TechCrunch has framed Mecka as attempting to do for robotics what Scale AI, Mercor, and Surge did for large language models: build the human-data supply chain the industry runs on. Real-world data for robot training is also being chased by startups like XDOF, reportedly in Series B talks at a $1.2 billion valuation, and Micro1, according to TechCrunch's previous reporting. Human-data platforms that started with language models are expanding into robotics as well.

A two-year-old with nine-figure revenue

What sets the Mecka Series B apart from a typical robotics raise is the revenue story. According to WebProNews, the company says it crossed $100 million in annualized run-rate revenue in June 2026 and is targeting $300 million by year-end, all with a team of roughly 40 to 60 people. Nine-figure sales from a sub-60-person startup stands out in an industry still known more for prototypes than profits.

The customer claims are big but deliberately vague. In its announcement, the Mecka team wrote that it supplies several of the top frontier robotics labs and multiple large technology companies, without naming any of them. That reticence is standard for data vendors whose value depends on confidentiality, but it means the revenue figures come from the company rather than independent verification.

Research collaborations lend the dataset some academic credibility. Mecka has worked with researchers from Stanford, MIT, Georgia Tech, UC San Diego, ETH Zurich, and Meta on EgoVerse, a research project examining how human demonstrations can support robot learning, as reported by WebProNews.

The wider race to record the physical world

The Mecka Series B lands as the entire robotics industry scrambles to record human activity at scale. The Financial Times reported on October 10 that robotics labs are building so-called robot gyms, where workers use robotic arms to perform tasks, and sending recording equipment into homes, offices, and factories to film people doing everyday work, as reported by RuntimeWire. The goal is to capture movement, contact, and force in the settings robots will eventually have to handle themselves.

That context explains why hardware giants are investing in a data company rather than building collection operations alone. Recording real-world movement requires labor networks, sensor rigs, and processing pipelines that are hard to assemble inside a chip or cloud company. Mecka's human-data strategy, as described by TechCrunch, is a bet that this infrastructure can serve robotics companies as a shared layer, even as some labs build their own capture systems in parallel.

Whether the Mecka Series B pays off will depend on two questions the round's backers clearly have answers for: whether humanoid training can scale on demonstration data the way language models scaled on text, and whether Mecka's first-mover collection network becomes the default dataset or gets undercut by in-house efforts. For now, the strategic money behind the Mecka Series B has placed its bet, and the physical-AI data race has a new favorite to watch.