AI is Moving to the Edge. The Edge Runs Embedded Linux.

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.

Raghu Bharadwaj

Known for his unique ability to turn complex concepts into deep, practical insights. His thought-provoking writings challenge readers to look beyond the obvious, helping them not just understand technology but truly think differently about it.

His writing style encourages curiosity and helps readers discover fresh perspectives that stick with them long after reading

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.

The boot process.
The drivers.
The networking stack.
The memory management.
The communication with hardware.
The performance optimization.
The debugging.
The reliability.

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.

When the WiFi driver crashes.
When the system randomly hangs.
When memory usage grows uncontrollably.
When boot time exceeds the target.
When hardware refuses to initialize.
When the AI accelerator is underperforming.

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.

Don’t jump to the solution

June 13, 2026 No Comments

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.

AI Writes the Code. You Make It Work.

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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:

Linux Kernel & Embedded Systems Digest

June 10, 2026 No Comments

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.

AI is Moving to the Edge. The Edge Runs Embedded Linux.

June 5, 2026 No Comments

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:

Why Most Engineers Fail in Self-Learning Advanced Topics Like Linux Drivers — And How to Overcome It

Every year, thousands of engineers decide, “I’m going to learn Linux Kernel / Drivers on my own.” They start:

Scouting for books
Bookmark tutorials
Watch videos
Use AI…

For a few days, they feel productive, feel they have got all the required information

And then? Slowly they start to see:

Redundant information
Out-of-context explanations
Un-structured contents
Lack of foundational knowledge.

This is where confusion kicks in as they get overwhelmed at the situation and eventually they stop.

Have you experienced this?…

But let us tell you: the Problem Is Not Your Effort and Intelligence — It’s the Approach

Raghu Bharadwaj

Let’s get one thing straight: Learning Linux drivers or any core concepts like Kernel programming or Linux systems programming is not like learning a set of APIs or understanding some random concepts, it is much more than what you see and learn at the surface.

Complex code, abstract concepts and systems you can’t “see” can simply tire and confuse you and this is exactly where most engineers give up.

The real question you should be asking yourself is:

What are the necessary foundational skills that I should know before I start learning?
Do I have the necessary mindset and approach to understand these deeper aspects?
More than what I read/listened/watched today, should I be worrying about what exactly I added to my current understanding?
Did I build on my existing knowledge or is this completely new?

Remember that advanced systems cannot be rushed, there is a particular roadmap: punctuated with understanding, reflecting and relearning aspects which will help you build a concrete understanding

Another key aspect is for you to have a feedback loop, like when you’re stuck, there’s someone like a mentor to guide you through and accelerate your learning. You should have some source to:

Correct your thinking,
Tell you what you’re missing
Give you the right advice

This support from some authority will help you stay on track and not wander away from the path

Here are few tips to help you stay on course:

Slow Down to Speed Up

Stop trying to cover topics quickly and randomly

Instead:

Take one concept
Break it down
Revisit it multiple times
Practically explore the concept to build a clear understanding

Depth beats speed—every single time.

2. Think in Systems, Not Topics

Most of these concepts are connected and unless you start thinking about it as a whole system and not as individual topics you will not get the macro level perspective

3. Ask Better Questions

Every time you get confused or run into some error, ask:

Is this concept new, or do I lack the necessary understanding to learn this?
Am I guessing here?
Why did this program fail, let me trail the execution path and see?

These questions train your mind to think like an engineer.

4. Build a Daily Learning Habit

Not motivation. Not bursts. Just pure Consistency. Even 60–90 minutes of focused effort daily is enough—if done right.

5. Embrace Struggle as Progress

This is important. If you feel: Confused, Slow and Stuck you’re not failing. You’re finally learning something real.

6. Learn With Guidance (Not in Isolation)

Self-learning doesn’t mean learning alone. Having a structured path, a mentor and real-world explanation can reduce months of confusion into clarity.

Final Thought

Most engineers don’t fail because Linux drivers are too hard. They fail because: they try to learn it the same way they learned everything else, but this is different. This requires patience, depth and systems thinking and once you cross that barrier you don’t just learn Linux, you start thinking like a systems engineer.

If you wish, we can guide you with a structured and well-established learning process to help you gain a deeper understanding of Linux device drivers

Keep Learning,

Team TECH VEDA