Every day there is a new AI model, a new tool, a new framework, or a new company promising to automate yet another piece of work.
Many engineers are watching this happen and asking themselves a difficult question:
“Will AI replace me?”
I think that’s the wrong question.
A better question is:
“As AI automates more things, which skills become more valuable?”
Let’s look at where AI is headed.
AI on the Edge
For years, AI lived primarily in the cloud. You sent data to a server somewhere in the world, it processed it, and sent a response back. But that’s changing. The next wave of AI is moving to the edge.
Your car cannot wait for a cloud response before applying the brakes. A robot cannot depend on an internet connection to make decisions. An industrial machine cannot stop production because a cloud service is unavailable.
The future of AI requires decisions to be made locally, on the device itself. And that’s exactly why AI is moving to the edge.
Now comes the interesting part.
What powers most of these edge devices?
Not AI. Embedded Linux it is.
Whether it’s automotive systems, industrial automation, networking equipment, robotics, smart cameras, medical devices, or IoT gateways, there is a very high chance that Embedded Linux is sitting underneath.
The AI application gets all the attention. The Linux platform quietly does all the heavy lifting.
Without these layers, AI is just another application that cannot run. And this is where a fascinating pattern emerges.
Business-end of your skills
As technology becomes more automated, fewer engineers understand what is happening underneath. Twenty years ago, many engineers understood operating systems and system internals. Today, most engineers are trained to use frameworks. Tomorrow, many may simply use AI.
But when something breaks, somebody still has to understand the layers beneath.
The solution isn’t found in a prompt. The solution comes from engineers who understand the platform. This creates what I call the Automation Paradox.
The higher the automation, the rarer the engineers who understand the layer beneath it.
And rarity creates value. Thousands of engineers can use AI tools only fewer can debug a Linux driver. Thousands can deploy applications only fewer can bring up a new hardware platform from scratch. Thousands can consume technology only fewer can build the foundation that technology depends on.
The industry is not struggling to find people who can use tools, it is struggling to find people who can solve problems. The kind of problems that stop products from shipping.
This is precisely why Embedded Linux continues to remain one of the most valuable skills in the industry.
If you’re looking for skills that will remain relevant even as automation grows, don’t just learn how to use the latest tools, learn the layer beneath them. Because the future will always need engineers who understand how the system really works.
Be one of them.
One of the most valuable skills an engineer can develop is also one of the least discussed: the ability to properly define a problem before attempting to solve it.
Yet this is precisely where many debugging efforts begin to go wrong.
The software industry is witnessing one of its biggest transformations. AI can now generate functions, classes, scripts, and even entire applications in seconds. Tasks that once took hours can now be completed with a prompt.
But there is one thing AI cannot do reliably:
The Embedded Linux ecosystem continues to evolve rapidly, driven by advancements in kernel development, the growing adoption of RISC-V, and the increasing demand for Edge AI and IoT solutions. Here’s a quick roundup of some of the key developments shaping the Linux and embedded systems landscape this month.
Every day there is a new AI model, a new tool, a new framework, or a new company promising to automate yet another piece of work. Most engineers are watching this happen and asking themselves a difficult question: