Physical Intelligence closed a $600 million Series B in November 2025 led by CapitalG at a $5.6 billion post-money valuation, up from a $2 billion valuation in November 2024.

The company raised a $400 million Series A in November 2024 at a $2 billion valuation, with participation from Jeff Bezos, OpenAI, Thrive Capital, Lux Capital, and Bond Capital. Earlier, Physical Intelligence raised a $70 million seed round in March 2024.

Other investors across rounds include Index Ventures and T. Rowe Price. In total, Physical Intelligence has raised approximately $1.07 billion in primary equity funding since its founding.

Physical Intelligence builds Vision-Language-Action foundation models that act as control policies for robots. The system ingests raw camera feeds and natural language instructions, then outputs real-time action commands that drive robot joints and actuators.

A typical workflow starts when a developer streams RGB-D camera images from any robot to Physical Intelligence's runtime. The system tokenizes this visual stream along with the robot's movement history and feeds it to a 3-5 billion parameter transformer model. Users can provide plain-language goals like "make a flat white" or "pack chocolates into this box."

The transformer predicts the next 50-step action sequence in roughly 100 milliseconds using the company's real-time chunking algorithm, which allows robots to continue moving while the model processes the next set of commands. A hardware abstraction layer then converts these action tokens into robot-specific joint commands, with safety wrappers monitoring force and velocity limits.

Physical Intelligence has released several model iterations, starting with π0 in October 2024 that could control 7 different robot types across 50+ tasks. The π*0.6 model incorporates reinforcement learning from both autonomous experience and human corrections, doubling task completion rates on complex manipulation tasks like espresso-making and laundry-folding.

The company has open-sourced its π0 model weights, allowing developers to fine-tune the foundation model with as little as 1-20 hours of their own robot data using common platforms like ALOHA and DROID simulators.

Physical Intelligence runs a B2B software-as-a-service model for robotics companies, manufacturers, and automation integrators. Pricing is a $300 monthly subscription per connected robot, yielding recurring revenue that scales with fleet deployments.

Rather than manufacturing hardware, Physical Intelligence provides hardware-agnostic AI models that work across different robot embodiments, described as an "Android for robots." This asset-light approach avoids the capital intensity and manufacturing complexity of vertically integrated robotics companies.

The model seeks network effects via an open-source strategy. By releasing model weights and allowing fine-tuning, Physical Intelligence attracts third-party developers who contribute training data and use cases back to the platform. These contributions improve model performance across diverse robot applications.

Revenue comes from direct enterprise subscriptions and API access for developers. Costs are primarily cloud infrastructure for model inference and training, plus data licensing from various sources, resulting in gross margins similar to software despite some pass-through costs.

The subscription model expands with customer deployments as fleets grow or as customers upgrade to more capable model versions. Enterprise customers can also run models on-premises for latency-sensitive applications, creating additional licensing options.

Figure AI is an alternative approach, raising over $1.5 billion to build both humanoid hardware and AI software in-house. The company recently ended its OpenAI partnership to develop proprietary models, aiming to tightly couple hardware and AI for performance and unit economics.

Tesla's Optimus program follows a similar vertical integration strategy, though production has faced delays with supply chain redesigns pushing 2025 volume targets at risk. Agility Robotics produces 10,000 Digit robots annually for warehouse applications, and currently relies on conventional motion planning rather than foundation models.

1X Technologies, backed by OpenAI, has deployed wheeled EVE robots commercially while developing bipedal NEO humanoids. Apptronik and Neura Robotics have raised $350 million and €1 billion, respectively, to mass-produce Apollo and MAiRA humanoids, increasing competitive pressure on AI-only providers.

Covariant and Skild AI pursue similar hardware-agnostic strategies, building general-purpose robot foundation models that integrate with multiple robot platforms. These companies compete directly on model performance, training efficiency, and developer ecosystem adoption.

Big tech labs including DeepMind's Gemini Robotics, Meta's robotics research, and OpenAI maintain mostly closed development programs. While these players have resources and talent, their closed ecosystems limit third-party adoption compared to Physical Intelligence's open-source approach.

Established industrial automation companies like ABB, KUKA, and Fanuc provide robot programming and control software for manufacturing applications. These solutions lack the generalization capabilities of foundation models, and offer reliability and integration with existing factory systems.

Newer entrants like Jacobi Robotics and Path Robotics focus on specific automation tasks like welding and material handling, potentially competing in vertical applications where Physical Intelligence's general-purpose models may be overengineered.

Physical Intelligence can expand beyond basic robot control into comprehensive robotics development platforms. The company's research on skill libraries and simulation tools creates opportunities for pre-built task packages that customers can license for specific applications like assembly, inspection, or material handling.

Edge inference modules represent another product expansion vector. By packaging models with safety runtimes and GPU microservers, Physical Intelligence can capture per-unit royalties from robot manufacturers who integrate the stack at build time rather than paying ongoing subscriptions.

API and developer tools create additional monetization opportunities as the robotics ecosystem matures. Physical Intelligence can offer pay-as-you-go inference services, synthetic data generation, and fine-tuning platforms that serve the long tail of robotics developers and researchers.

Industrial automation represents a massive expansion opportunity as manufacturers face labor shortages and seek flexible automation solutions. Companies like Foxconn, Mercedes-Benz, and Amazon are piloting humanoid robots for tasks that require dexterity and adaptability beyond traditional industrial robots.

Service and healthcare robotics markets offer significant growth potential, particularly for applications requiring natural language interaction. Physical Intelligence's language-grounded models are well-suited for elder care, hospitality, and clinical support robots where human-robot communication is essential.

The defense and aerospace sectors present high-value expansion opportunities, with government agencies seeking autonomous systems for logistics, maintenance, and support roles. Physical Intelligence's hardware-agnostic approach allows integration with specialized military and aerospace platforms.

International markets offer substantial growth potential as robotics adoption accelerates globally. European manufacturers are investing heavily in automation to address demographic challenges, while Asian markets like Japan and South Korea have strong robotics ecosystems and aging populations driving service robot demand.

Emerging markets present longer-term opportunities as manufacturing shifts to lower-cost regions that still require automation solutions. Physical Intelligence's software-centric model allows global deployment without establishing local manufacturing or hardware support infrastructure.

Strategic partnerships with international system integrators and robot manufacturers can accelerate market entry while leveraging local expertise and customer relationships. This approach reduces go-to-market costs compared to building direct sales operations in each geography.

Model commoditization: As foundation models for robotics mature, open-source alternatives and big tech offerings could commoditize the core technology, pressuring Physical Intelligence's pricing power and shifting competition toward cost rather than capability differentiation.

Hardware fragmentation: The robotics industry's lack of standardization requires Physical Intelligence to adapt its models to new robot embodiments and control systems, adding ongoing engineering overhead that could constrain scalability and raise customer acquisition costs.

Safety liability: Real-world robot deployments carry inherent safety risks, and any accidents involving Physical Intelligence's models could result in liability exposure, regulatory scrutiny, and customer reluctance to deploy autonomous systems in human-occupied environments.