NexCOBOT on Big Tech's robotics acquisitions and scaling AI

NexCOBOT is seeing increased demand from developers of legged and mobile manipulator robots as Big Tech acquires startups. General Manager Jenny Shern told The Robot Report that companies like Mobileye, Amazon, and Meta have made major acquisitions to fast-track innovation in physical AI.

NexCOBOT, which spun out from NEXCOM Group in 2018, provides motion controllers, safety controllers, and consulting services to robotics manufacturers. Based in New Taipei City, Taiwan, the company is now focused on scaling its open, functional safety robotic controllers to meet a strong global order backlog as it heads into mass production.

Big Tech uses acquisitions for faster innovation

Acquisitions of robotics firms by large technology companies are set to continue. Shern stated that smaller companies possess strong expertise in areas like robot learning and autonomous control, while larger firms have the computing infrastructure, data, and capital needed to scale those innovations.

As AI models improve, acquiring these companies offers a faster path for Big Tech to simplify development and establish a position in intelligent systems. Shern noted that due to company policy, NexCOBOT does not disclose specific internal finances, but affirmed the company is in a highly stable financial position.

Proving reliability remains a critical hurdle

While U.S. Funding for physical AI has increased, overall venture capital for early-stage companies has reportedly fallen. Shern said this creates a more selective funding environment, where capital is still available for firms demonstrating strong technical differentiation and a clear commercialization path.

Early-stage startups may face greater pressure to validate their business models sooner. This could slow new entrants but may encourage more focused innovation aimed at solving real operational challenges. Shern also predicted increased collaboration between startups, industrial companies, and larger tech firms as developers seek alternative paths to scale.

Shern defined "AI-native robots" as systems originally structured to incorporate AI as a core part of how they perceive, decide, and interact. She reported rapid progress in perception, motion planning, and human-robot interaction. However, for industrial applications, reliability and safety remain critical, so adoption will advance in stages as the technology proves itself in real-world environments.

Open platforms and software key to scaling

Shern explained that Big Tech can bridge the speed disparity between fast-moving AI software and slower robotics hardware by separating software innovation from hardware development where possible. AI models can be updated continuously, while hardware requires longer design and testing cycles for reliability.

By building on modular platforms and standardized interfaces, new AI capabilities can be deployed onto existing robotic systems without waiting for new hardware generations. This allows developers to use AI advancements while maintaining the stability industrial applications demand.

Open ecosystems benefit both suppliers and users, Shern said. She cited the growing adoption of open robot control platforms, which let manufacturers integrate components from different vendors instead of relying on a single proprietary system. This gives users flexibility and reduces integration complexity.

For a recent client, deploying NexCOBOT's certified functional safety controller based on an open system helped shorten the development cycle from an estimated three to five years down to just two years. For suppliers, open ecosystems expand market opportunities because products can work across a wider range of platforms and industries.

Physical AI expansion into established and new fields

Shern expects Big Tech activity in both established industries and new applications. Established sectors like manufacturing, logistics, and warehousing have clear business use cases for automation, making them attractive for scaling physical AI.

Advances in AI are also opening possibilities in less structured environments where robotics traditionally struggle, including service applications and healthcare support. The most immediate adoption will likely happen where there is already strong demand, but the long-term impact could extend far beyond traditional industrial settings.

Robots that generate large amounts of operational data and benefit directly from AI advances are likely to attract the most Big Tech attention. Humanoid robots, mobile robots, and systems for dynamic environments align closely with large technology companies' strengths in AI, computing, and software.

Highly specialized robots built for a narrow industrial process may be less attractive unless they provide unique intellectual property or address a large market. Companies will look for robotics technologies that can scale across multiple applications and create long-term business advantages.