Every new LLM model release hits the job market like a tremor. You feel it. The layoff announcements. The hiring freezes. The LinkedIn posts from engineers with five years of experience suddenly searching for work. And more experienced people moving their profile to "Open to work" anticipating something bad going to happen soon.
Most embedded engineers are watching this and asking: is my job next?
Wrong question.
The right question is this — what exactly is dying, and what is not?
The Honeymoon is Over
There was a time when writing C for a microcontroller was a niche skill. Rare enough that companies paid well for it and asked few questions about depth. You configured a peripheral. You got the UART talking. You set up a timer interrupt. That was enough. Job is assured and the projects just need you writing C code.
That era is ending faster than most people want to admit.
AI started as a small icon in the corner of your IDE. A suggestion here, an autocomplete there. Then it got faster. Then it stopped being a tool and started being a category replacement.
Right now, AI can generate a working SPI driver from a one-line prompt. It can scaffold a FreeRTOS task structure, wire up a DMA transfer, produce a complete HAL layer — in seconds. Without complaining. Without a hike request.
That is not a productivity tool. That is a replacement.
What "10X Productivity" Actually Means
When a company says AI gives them 10X productivity, they are not talking about making their engineers faster.
They are talking about division, not multiplication.
Why pay ten developers when one developer with AI produces what ten used to?
The math is not subtle. The intention is not hidden. And in embedded systems this is playing out right now — quietly, in hiring decisions being made this quarter.
Here is what most embedded engineers are missing: AI-generated code is already better than what a two or three year experienced developer produces. Slightly below a seven or eight year developer. And for many companies, average code is a good enough start.
That phrase should stop you cold.
The Real Gap — And It Is Specific to Embedded
Here is where the embedded story diverges hard from the general software story.
Most people calling themselves embedded engineers are wrapper-covered software developers. They learned C at a mediocre level. They picked up an evaluation board. They followed a tutorial, got an LED blinking, configured a timer, called it firmware.
They can speak the words. PWM. Bare-metal. Register map. DMA. The vocabulary is there.
But ask them why a specific hardware decision was made, and they freeze. Ask them what happens to I2C signal integrity when you push a 400 kHz bus with the wrong pull-up resistor value, and they go quiet. Ask them to explain why a particular ISR design is a timing hazard waiting to happen — and they cannot answer.
They are operating entirely at the abstraction layer. Software assumptions. HAL convenience. Evaluation board forgiveness. They have never been forced to understand the physics underneath — because the framework hid it, the RTOS abstracted it, and the dev board made everything forgiving.
That abstraction layer is exactly what AI is best at replacing.
AI does not know why your sensor misbehaves at 3.2V when the datasheet says it operates down to 3.0V. AI does not detect that your PCB layout is coupling noise into the ADC reference line. AI does not feel the difference between a stable system and one that is three months from a field failure.
Physics does not negotiate with language models.
The gap between the abstraction layer and the physics layer — that is where real embedded engineering lives. And that gap is increasing in value every quarter.
The Middle Man Nobody Wants
There is a phrase that captures what is happening precisely.
The middle man someone wants to skip.
That is the position a large segment of people calling themselves embedded developers now occupy. They sit between the hardware and the software. They translate. They configure. They integrate what others designed. They are valuable as long as AI cannot do that translation faster and cheaper.
AI can.
The engineers who understood hardware at the physics level were never middle men. They were the ones making decisions AI cannot make — because those decisions require understanding failure modes that are not in any training dataset, timing constraints buried in datasheet footnotes, tradeoffs that only reveal themselves when a product comes back from the field.
That engineer is not being replaced. That engineer is becoming more scarce — and more valuable — every quarter.
Where the Real Engineering Happens Now
Here is the part nobody is saying out loud.
The big logos — the recognisable names everyone chases — are not where real embedded engineering happens right now. They are where features get churned and processes get followed. A bug fix means changing three conditional checks after four weeks of approval cycles. One bug. Weeks of process. Few lines of code.
The real engineering is happening in startups and SMEs.
Small teams. Full product ownership. No process buffer between your decision and the hardware it affects. When you call a microcontroller selection or a protocol choice, you live with that decision through prototyping, validation, and production. Nobody else catches your mistakes. The physics talks back to you directly.
These companies are hiring right now. Not for coders. Not for people who can prompt AI and ship average firmware. For engineers who understand why hardware behaves the way it does — and can take what AI generates and make it production-ready.
That is a specific skill. It is rare. And unlike the market for mediocre C programmers, it is not shrinking.
There is another reality here. At a startup or SME, you are not competing against five hundred identical resumes for the same role. You are a big fish. The engineering is real, the ownership is real, and the growth is faster — because you cannot hide behind a process that protects mediocrity.
The Only Question That Matters
Coding, as practised by the majority of people calling themselves embedded developers, is dying. Not eventually. Now.
The engineers who survive this — and do more than survive — are the ones who understand physics, who can read a datasheet and feel the constraints, who can take what AI generates and harden it for the real world.
That engineer is not replaceable by the current generation of AI. Or the next.
The question is which one you are building yourself to be.
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