Edge AI software layer diagram. Source: Numurus Before Windows, only engineers and computer scientists could do much with computers. Windows changed that by giving everyone a user interface, built-in apps, and plug-and-play hardware capabilities that all worked together. The same shift is now arriving for robots. I remember when the first PCs came out.
I was just starting college to become a robotics engineer, and I was excited. PCs were powerful machines. Microprocessors were faster than anything most people had touched, and the capabilities they offered for solving mathematical problems and running complex engineering processes in minutes was exciting. But at the time, the usefulness of PCs was limited to a small group of people who had the skills and interest to learn how to use them.
To make a PC do something, you had to know how to work with command-line only operating system interfaces, learn complex hardware protocols, and write software from scratch. Like most of my friends and family at the time, the world looked at a PC and saw an expensive box that did not do much for them. That all changed when Windows hit the market and turned PCs from a niche engineering tool into a device usable by anyone in the world.
Today, there is a new and rapidly growing market of edge AI processors, embedded processors that run AI models in robotic and other automated systems from companies like NVIDIA , AMD , Qualcomm , Hailo , and others. These chips allow systems to rapidly analyze camera and other data and make split-second control decisions without needing to be connected to the internet.
They are fast enough, cheap, and power-efficient enough to run real AI workloads in the field. The hardware is past the inflection point. But the people who can actually use these processors are still a small group. While they typically come with a Linux operating system that has built-in applications, hardware support, and user interfaces similar to Windows and other desktop PC operating systems, the solution does little to support the needs of customers wanting to use these chips.
First off, robots need to interface with cameras , lasers, GPS, motors , and control systems, not mice, keyboards and printers. Robots also need software applications that can connect live sensor data to AI models to control motors, not word processing and spreadsheet applications. Finally, robots don’t typically have keyboards and displays connected to them; they need user interfaces that connect through web-browsers on network connected PCs.
Once again, these limitations mean that only a small group of experienced engineers and software developers are able to take advantage of the capabilities of these new edge AI processors make possible. For everyone else, an edge AI processor is the same kind of expensive box the PC was in 1981. Capable, but inaccessible. As a robotics and automation engineer, I quickly saw the potential these chips offered for solving many of the the challenges the industry had been struggling with for many years.
After using some of these edge AI processors on robotic and smart sensing projects, I also realized how difficult and time consuming it was to use these chips, even for teams of experienced engineers and developers. In 2020, my company Numurus pivoted from selling robotic smart sensors to developing an easy-to-use software platform called NEPI (Numurus Edge Platform Interface) that takes care of much of the under-the-hood software most robots require.
NEPI provides plug-and-play drivers for cameras, navigation sensors, motors, lights, and control systems. It also supports auto detection and orchestration of AI models, built-in automation applications, and an intuitive browser-based user interface (UI) for connecting from remote network connected PCs. NEPI installs and runs as a Docker container on top of the edge AI chip’s native operating system, allowing anyone to download and get working in minutes with no computer programming experience needed.
NEPI also includes a simple pull, deploy, and build system for downloading and customizing the source-code from the NEPI Github repository. What Windows did for the PC What unlocked the PC was not faster hardware. It was a software layer that handled the things most people did not want to learn how to do. Windows arrived with plug-and-play drivers.
Connect a printer, and the system found it and made it work. Connect a mouse, same thing. The user did not have to write a single line of code to interact with hardware they had not chosen in advance. Windows came with built-in applications. A word processor, a spreadsheet, a way to look at files. Most users did not need to write applications.
They needed applications to exist. Windows gave the PC a screen, a keyboard, and a mouse all working together through a UI that did not require a manual. Most users figured it out in an afternoon. After Windows, the PC was no longer just for specialists. It was for everyone. The hardware did not change. The access did. Submit your session idea for the 2026 RoboBusiness What edge AI processors need to become useful to more people Edge AI is waiting for the same shift.
The hardware is here. What is missing is a software layer that handles the things most people do not want to learn how to do. That layer needs plug-and-play hardware drivers. If a team wants to add a camera, a sonar, a lidar, an IMU, or a GPS module, they should be able to connect it and have the system recognize it. They should not have to write a driver for it.
It needs AI model management. Loading a model, versioning it, swapping it for a newer one, recovering when something fails. Most teams have a model. Few teams want to build the runtime that surrounds it. It needs built-in applications for the actual use cases. Robotics. Automation. Inspection . Sensor data processing. Event-driven action.
The most common needs in this space should be solved out of the box, not rebuilt every project. And it needs a UI that the operator can actually use. This is where edge AI has a wrinkle the original PC did not have. Most edge AI systems are robots, drones, vessels, or industrial equipment. They do not have a keyboard, a mouse, or a screen attached.
The UI has to come from somewhere else. The answer is a browser-based interface served from the device itself. Connect a laptop, point a browser at the device, and you have a UI. No specialized hardware. No specialized software. Anyone with a browser can interact with the system. Who benefits when edge AI becomes accessible The story of the PC is also the story of who got to use a computer.
Before Windows, computers were for programmers, researchers, and people willing to learn how to write code. After Windows, computers were for accountants, writers, students, kids, parents, and schools. The audience grew by orders of magnitude, and the applications that got built on top reflected the new audience. Edge AI is about to go through the same expansion.
Today, edge AI is mostly for teams that can afford embedded software experts. That usually means well-funded robotics startups, established OEMs, and defense contractors. Everyone else is locked out, not by hardware cost but by software complexity. Once edge AI becomes accessible, the audience changes. STEM programs can integrate AI-based automation without requiring every team member to be an embedded software expert.
Researchers in adjacent fields can prototype AI-enabled hardware without hiring a separate embedded team. Startups can ship the first version of their product in a few weeks instead of a year. OEMs can offer their customers AI capabilities the customers can actually configure themselves. This expansion is not just good for the people who get new access.
It is good for the industry. The PC ecosystem did not get bigger because programmers got more productive. It got bigger because people who were not programmers got to use computers. Edge AI is set up to follow the same path
