FieldAI, an Irvine, California-based physical-AI startup, is reportedly raising $700 million at a $10 billion valuation, up sharply from its reported $2 billion valuation in 2025. The company is building general-purpose AI models designed to operate across different robot platforms and complex real-world environments.
FieldAI is betting that the future of robotics will not be won only by companies manufacturing better robots—but by the companies building the intelligence that can run across many different machines.
Founded in 2023, FieldAI develops what it calls Field Foundation Models (FFMs): robotics-focused AI systems designed around physical-world constraints, uncertainty and risk. Unlike simply adapting language or vision models for robots, FieldAI says its models are designed specifically for embodied intelligence.
The company's software is designed to work across quadrupeds, humanoids, wheeled robots and other platforms. Its approach is hardware-agnostic, meaning the same underlying intelligence can potentially be deployed across different robot manufacturers and form factors.
In August 2025, FieldAI announced more than $400 million in funding, including a $314 million round, at a reported $2 billion valuation. Investors included Bezos Expeditions, Khosla Ventures, NVentures, Temasek, Intel Capital and Emerson Collective.
Now, according to an October 2026 report, the company has signed a term sheet for a potential $700 million financing at a $10 billion valuation. Because the transaction was reported as not yet formally closed, the $10 billion figure should be treated as a reported prospective valuation rather than a completed financing valuation.
FieldAI's commercial strategy is particularly interesting: instead of manufacturing every robot itself, it focuses on the autonomy layer—the software "brain" that can potentially make different physical machines more capable.
That creates a potentially scalable business model: build the intelligence once, then deploy it across multiple hardware categories and industrial environments.
The bigger question is whether FieldAI can turn this technical approach into a widely deployed robotics platform. If it succeeds, its opportunity extends beyond a single robot or use case and into the broader infrastructure layer of physical AI.
DREAMS TIme
