The Visual Cortex of Physical AI with Chris Matthieu
What happens when AI agents stop living entirely on screens and start controlling cameras, wheels, motors, and eventually full robotic bodies?
Chris Matthieu, VP of Developer Ecosystem at RealSense, joins Greg to explore the rapidly converging worlds of AI agents, computer vision, robotics, and physical intelligence. A lifelong hacker who started programming on a Commodore VIC-20 as a kid, Chris has spent more than four decades building technology, including five startup exits, before joining Intel as an entrepreneur in residence and eventually helping grow the RealSense developer ecosystem.
Chris shares how his fascination with connected machines evolved from IoT platforms and decentralized computing into his latest project, AgenticROS, an experimental system that gives AI agents direct access to ROS and allows them to perceive and control physical robots. His robot Bob can already use camera data to identify where Chris is, calculate his position, control its wheels, and follow him through a room without Chris explicitly programming every behavior.
Greg and Chris also dig into RealSense’s evolution from an Intel incubator project into an independent company, why depth perception is becoming foundational infrastructure for physical AI, and how open source software, cheaper hardware, and AI coding tools are dramatically lowering the barrier to building real robotic systems.
Highlights:
Chris’s journey from hacking on Commodore computers as a child to building and exiting five technology startups before moving into Intel and RealSense. - Why builders who avoid ROS can end up recreating infrastructure that already exists, and why the broader ROS ecosystem can dramatically accelerate robotics development.
How AgenticROS connects tools such as Claude, Codex, Gemini, and other AI agents directly to ROS, giving them access to cameras, perception data, and robot controls.
Why Chris believes increasingly capable AI models may eventually control robotic bodies without developers having to explicitly program every movement or create a predefined skill for every task.
The startup lesson Chris learned at Intel: stop building technology in search of a market and instead start with painful customer problems people are willing to pay to solve.
Why timing played such an important role in spinning RealSense out of Intel, especially as generative AI, physical AI, and robotics began accelerating simultaneously.
How RealSense depth cameras give robots an RGBD view of the world by calculating depth at every pixel, while increasingly moving AI and perception processing directly onto the camera itself.
How open source SDKs and AI code generation are shrinking prototype timelines from weeks or months to hours, allowing developers to validate ideas far more quickly.
Why side projects, GitHub activity, curiosity, and building in public can be some of the strongest signals for engineers looking to break into robotics and physical AI.
How one experimental LinkedIn post about Chris’s robot generated roughly 180,000 impressions and led to NVIDIA webinars, conference talks, and unexpected opportunities.
Why individual developers can sometimes validate ideas faster than large companies, especially when AI coding tools, inexpensive cameras, Raspberry Pis, and NVIDIA Jetsons make functional robotics prototypes accessible for hundreds rather than tens of thousands of dollars.
Chris’s philosophy of failing fast, testing the market early, and recognizing when either the technology or the customer demand is not ready yet.
Why he believes the enormous funding rounds flowing into physical AI may reflect the scale of the potential market, even while competing approaches such as VLAs, world models, and skill-based systems are still evolving.
His advice for anyone entering robotics: start with an inexpensive desktop robot, experiment with it, and remember that complex robots are ultimately systems of smaller components working together.
Where Chris hopes physical AI ultimately leads: robots that understand the people around them, safely operate in human environments, and perhaps finally bring him coffee in the morning and a beer in the evening.
For developers, founders, students, and anyone curious about where AI goes after the chatbot, this conversation offers a hands-on look at how rapidly robotics is becoming easier to prototype, program, and experiment with, and why the next wave of AI may increasingly have cameras, wheels, arms, and a physical presence.
Learn more about RealSense: https://www.realsenseai.com/
Learn more about AgenticROS: https://agenticros.com/
Connect with Chris Matthieu on LinkedIn: https://www.linkedin.com/in/chrismatthieu/
Connect with Greg Toroosian: https://www.linkedin.com/in/gregtoroosian/