Big Tech Is Buying Its Way Into Physical AI — and Controller Makers Are Feeling the Pull

Jenny Shern, general manager of NexCOBOT, sees the trend accelerating.

"Acquisitions will certainly continue as robotics becomes increasingly important to major tech companies."

"Smaller robotics companies and startups have strong expertise in areas such as robot learning, perception, or autonomous control, while larger companies have the computing infrastructure, data resources and capital needed to scale those innovations."

Big Tech firms are acquiring smaller robotics companies to streamline development and establish a position in intelligent systems. For manufacturing professionals, the question is what this consolidation means for the systems they will eventually deploy on the plant floor.

Demand Is Shifting Toward Newer Robot Formats

NexCOBOT, based in New Taipei City, Taiwan, provides a range of controllers for robotics developers and manufacturers.

Shern said inbound interest has broadened well beyond conventional industrial arms.

"Beginning last year, our team got lots of requests from companies building legged robots, including quadrupeds and humanoids, as well as mobile manipulators."

"Our controllers support traditional robots and these newer kinds of systems."

NexCOBOT is in a highly stable financial position and is focused on scaling its open, functional safety robotic controllers.

AI-Native Does Not Mean Deployment-Ready

Shern draws a sharp line between conventional automation and what she calls AI-native robots.

"AI-native robots are robots that were originally structured to incorporate AI as a core part of how they perceive, make decisions, and interact with their environment."

"Traditional robots typically follow predefined instructions in structured settings, while AI-native robots are designed to adapt to changing conditions and learn from new inputs."

For engineers, the catch is maturity. AI models and software can be updated continuously, while robotics hardware requires longer design, testing, and deployment cycles. Shern's recommended architecture separates the two.

"Big Tech can help bridge this speed disparity while leveraging the strengths of both industries by separating software innovation from hardware development wherever possible."

"By building on modular platforms and standardized interfaces, new AI capabilities can be deployed onto existing robotic systems without waiting for entirely new hardware generations."

Open Platforms, Compressed Timelines

Open robot control platforms allow manufacturers to integrate components from different vendors, reducing integration complexity. NexCOBOT's own case is a concrete data point: the company's certified functional safety controller helped a client shorten its development cycle from three to five years down to just two years. Its ROBASafe functional safety offering is part of that product line.

Shern also noted the industry may see increased collaboration between startups, industrial companies, and larger technology firms as early-stage funding becomes more selective.

Where the Buyers Will Look Next

Established industries like manufacturing, logistics, and warehousing have clear business use cases for automation, making them the most immediate targets for scaled physical AI. As for which robot makers end up on acquisition lists, Shern's criterion is data.

"Robots that generate large amounts of operational data and can benefit directly from advances in AI are likely to attract the most attention from Big Tech."