Will AI take Embedded Software Jobs in 2024?

When you open a news feed, your inbox, or any other type of media source, you will probably see a headline regarding generative artificial intelligence.

To this day, there exist applications for generative artificial intelligence that can create graphics, social postings, blogs, and essays, in addition to reading, writing, and debugging code, to name just a few of its capabilities.

As generative AI models continue their rapid development, many creative people are beginning to wonder whether or not their jobs may eventually be taken over by software.

Even if embedded software developers consider themselves to be the only ones of their kind in the software market, will AI end up taking their jobs?

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Know about Will AI take Embedded Software Jobs?

Is it true that embedded software is excessively specialized?

The developers who work on embedded software may be tempted to believe that artificial intelligence cannot replace their jobs.

Embedded software, on the other hand, is a specialized subfield within the software business that calls for developers to collaborate closely with the underlying hardware.

You are required to know electronics as well as the ability to architect, build, and debug software.

Because performance is so important, it may appear improbable that a program could complete the task successfully because it requires optimizing a system for speed while also striking a balance between power usage and a million other factors.

If you’ve ever experimented with a generative AI model such as ChatGPT, you might have discovered that this isn’t always the case.

I was able to improve the performance of embedded software that a human wrote by using ChatGPT to optimize it.

The improvement was greater than thirty percent. Among other things, I’ve used it to assist in optimizing the organization of projects and git repos, construct makefiles for Docker containers, and improve style guidelines.

I’ve also used it to optimize the build system. Tools based on generative artificial intelligence are capable of producing hardware abstraction layers, low-level drivers, and test cases!

Even though embedded software may appear to be one of a kind, this does not mean that generative models are unable to account for it.

Generative artificial intelligence is able to develop embedded software

The process of developing embedded software can benefit from the use of generative artificial intelligence by teams. Even though they are still in their infancy, the tools are able to write code of a satisfactory quality.

An AI model would, in the vast majority of instances that I have tried, come up with a solution that is comparable to what I would have developed. At other times, it will include something that will make me question why it hadn’t occurred to me to do that!

After gaining some experience, I’ve realized that I can use the tools to generate what I need much more quickly than I could have developed it myself if I had done it using traditional methods. You could start to question whether an AI application could ever take my place.

Now, I realize that you might be thinking that I must not be that good at writing code (which is something that I wouldn’t completely rule out), but it is extremely important that we put this into context.

Since I was 14 years old, I’ve been working as a writer for embedded software. Since I started working professionally about 20 years ago, I’ve been writing embedded software.

I’ve worked on something close to 200 different projects, coached dozens of teams, instructed dozens of students, and analyzed a significant amount of code.

The quality of the results produced by the AI model is entirely dependent on the prompts and instructions provided by the user.

Do you remember the rule “Garbage in, garbage out” from the first year of software engineering school?

The artificial intelligence models are rather amazing; yet, in order to inform the model what is required, review what was generated, and then steer the AI to a viable answer, expertise is required.

 You can’t just give AI the task of developing embedded software for an Internet of Things weather station and expect it to write the application for you. It needs to be directed toward the appropriate final solution, much like an entry-level engineer does.

Generative AI will not replace embedded software developers, but it will provide teams with a new tool that enhances efficiency and quality for those who know how to use it!

Which would you prefer, a hammer or a nail gun?

It is essential to concentrate on that very last item. The term “generative AI” refers to a tool. It is not intended to serve as a replacement for skilled and knowledgeable developers.

AI has the potential to be a useful and powerful tool for accelerating development when it is used by someone who is experienced in their field.

For instance, I just came across some code that was constructing a straightforward filter. However, the junior engineers who created it did it inefficiently, despite the fact that it is something that is done in almost every project.

The compiler did not come to the rescue and optimize the program, as some would have expected. Because it took the filter just enough time to run, the system’s real-time performance was negatively impacted.

Rewriting the code and putting it through its paces of testing likely took an hour. It is not a major concern.

On the other hand, after doing something a million times in a row, you start to become bored with it and don’t want to do it again; thus, in order to rewrite the code, we use a generative AI model.

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It took approximately a minute and a half to rewrite it. I spent two minutes checking to make sure that it looked like what I imagined it ought to look like. After a few more minutes of testing, it was determined that the system was operating in a deterministic manner.

Artificial intelligence will not replace humans any time soon. It’s a device to use. If you are framing a house, you can either use a hammer or a nail gun, the latter of which will allow you to complete the task 10 times more quickly.

The outcome is the same, and it may even be improved. A nail gun is able to install nails consistently, whereas a person hammering nails in by hand will not provide the same results every time.

The usage of the nail gun does not diminish the value of the individual who is building the house. It raises the bar for the overall quality of their work while simultaneously boosting their productivity.

The analogy I’ll use is a nail gun for generative AI. It does not imply that you do not require engineers; rather, it indicates that the engineers you already have can work more efficiently and produce code of a higher quality.

Concluding Remarks

Will AI replace people who work in embedded software? It doesn’t seem likely to me. Technology and the furtherance of technology are highly sought after in the society in which we live.

There is an existing and growing shortage of embedded software engineers. Generative artificial intelligence may be the technology that helps level the playing field for the demand for our abilities.

You’ll not see AI take over our employment very soon; instead, you’ll see us getting faster and better at what we do.