While robotics developers have used artificial intelligence for years, the past few years have seen a fundamental shift in how the technologies work together. Rather than rely solely on deterministic programming for things like navigation and object recognition, robots are increasingly able to perceive, comprehend, decide, and act with AI. Physical AI is not just a buzzword; it’s also enabling the future of automation in factories, warehouses, and beyond. In this free download, readers will learn from industry experts who are building and deploying physical AI. The commercial applications for the technology seem unlimited, but they depend on domain-specific data and different training approaches.
In addition, * The Robot Report *interviewees discuss whether embodied AI such as mobile manipulators and humanoids are leading to artificial general intelligence (AGI) and general-purpose robotics. Innovators and investors are betting on physical AI to give robots the flexibility to address labor shortages and the need for precision in a variety of markets.
Physical AI firms have already obtained more than $23 billion in venture capital in 2026 so far, according to The Wall Street Journal. Much of the focus has been on foundation models, improving industrial automation, and autonomous vehicle and humanoid form factors. The biggest deals include Waymo’s $16 billion Series D, Skild AI’s $1.4 billion Series C, NEURA Robotics’ $1.4 billion Series C, and Physical Intelligence’s $1 billion in funding. Expectations are high, particularly in North America.
AI is both creating and serving the demand for hyperscale data centers, as robots are seen as potential assistants in building and maintaining these facilities. The need for simulation, fleet orchestration, and reasoning is pushing compute in both the cloud and the edge.
In this report, you can read about:
- How industry is getting started with physical AI
- Where robotics and AI still need more work
- Examples of autonomous vehicles as AI agents
- The role of networks in scaling and securing physical AI
- Where to find the most valuable and useful data
- Why humans are still central to physical AI training
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