Capital surge: Physical AI firms have obtained more than $23 billion in venture capital in 2026 so far, with expectations high in North America.
- Landmark deals: Waymo closed a $16 billion Series D; Skild AI and NEURA Robotics each raised $1.4 billion Series C rounds; Physical Intelligence secured $1 billion in funding.
- Technology shift: Robotics developers have used AI for years, but recent years have brought a fundamental shift in how AI and robotics work together.
- Data center demand: AI is both creating and serving hyperscale data center demand, with robots seen as potential assistants in building and maintaining facilities.
- Compute pressure: Simulation, fleet orchestration, and reasoning needs are pushing compute in both the cloud and the edge, while physical AI promises flexibility to address labor shortages and precision requirements.
Robotics developers have used artificial intelligence for years, but a fundamental shift in how the two technologies work together is now underway. According to a new report from The Robot Report, physical AI is enabling the future of automation in factories, warehouses, and beyond — and investors are backing that thesis with serious capital.
The money is following the machines
Physical AI firms have obtained more than $23 billion in venture capital in 2026 so far. The biggest deals underscore where the bets are being placed: Waymo's $16 billion Series D leads the pack, followed by 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 running particularly high in North America. Much of that optimism centers on what physical AI is expected to deliver: robots with the flexibility to address labor shortages and the need for precision across a variety of markets.
Data centers create a new demand loop
AI is both creating and serving the demand for hyperscale data centers — and robots are seen as potential assistants in building and maintaining these facilities. That relationship cuts both ways. The need for simulation, fleet orchestration, and reasoning is pushing compute requirements in both the cloud and the edge, meaning the infrastructure demands of physical AI extend well beyond the robot itself.
For manufacturing professionals, the implication is practical: deploying physical AI at scale is as much a compute and networking problem as it is a mechanical one.
What this means for automation planning
The shift from years of conventional AI use in robotics to today's more integrated physical AI approach changes how engineers should evaluate automation investments. Flexibility — not just repeatability — is becoming the selling point, driven by persistent labor shortages and tightening precision requirements.
The report is sponsored by Blackberry Ltd, through its QNX business, and Vention.
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