Robotics data startup XDOF has reached a $1.2 billion valuation in its Series B funding round, led by 8VC. The company, which builds data pipelines for AI-powered robots, achieved this milestone just three months after its Series A round. As a private company, XDOF highlights the growing venture capital focus on the infrastructure needed to train physical AI systems.
XDOF, a startup focused on building the data infrastructure for robotics, has reportedly reached a $1.2 billion valuation in its latest Series B funding round. Led by venture capital firm 8VC, this valuation follows a rapid expansion phase, occurring just three months after the company successfully closed its $70 million Series A round in June 2026. Because XDOF is a privately held company, it is not listed on any stock exchange, and there is no public share price for investors to trade.
The core business model of XDOF centers on the scarcity of high-quality data for training robots. While artificial intelligence models for text and images have benefited from the massive amount of information available on the internet, robotics developers face a different challenge. They require detailed, real-world data—captured through sensors—to teach machines how to perform physical tasks like grasping objects, folding laundry, or navigating environments. XDOF positions itself as a specialized service provider that captures, annotates, and structures this data, essentially functioning as an outsourced supply chain for physical AI developers.
Founded in 2024 by researchers from the University of California, Berkeley, including Philipp Wu, Yide Shentu, and Nemo Jin, the company traces its origins to the GELLO project, which focused on low-cost teleoperation systems for robotic arms. The company is currently scaling a global workforce of human data collectors who record these physical tasks to create training sets for machine learning models. With an annualized revenue run rate reported near $50 million and a client base that includes prominent AI research laboratories, XDOF is attempting to replicate the role that specialized labeling firms like Scale AI played for the earlier generation of language models.
For venture capital investors, the rapid rise of XDOF points to the high demand for 'Physical AI' infrastructure. However, the sector comes with specific business risks that investors typically track in private, high-growth startups. The primary challenge is the high cost of scaling a global workforce to collect physical data manually. As the robotics field matures, the industry may see a shift toward synthetic data—where AI simulates physical environments to train other robots—potentially reducing the reliance on human-recorded datasets. Additionally, as a capital-intensive startup, XDOF remains dependent on continuous venture funding to fuel its operations and research, which could be impacted by changing market conditions or shifts in investor sentiment toward the broader artificial intelligence sector.
The company’s future growth will likely depend on its ability to maintain its technological lead and expand its customer base beyond research labs into commercial robotics manufacturing. Observers will track whether the firm can reduce its operational costs while proving that its human-collected data provides a tangible advantage over competing synthetic data models.