5 Ways to Protect Your Programming Job Against AI

Table of Contents
1. Master a Specialized Niche
2. Work on Complex Software Development Projects
3. Integrate AI-Augmented Software Development into Your Work
4. Document Your Code
5. Involve Yourself in Application Deployment and Management

Is AI-augmented software development coming for your programming job? The answer depends, in part, on exactly what your job is and how well generative AI tools can perform your work for you.

And fortunately, there are steps software developers can take to help protect their jobs against AI-augmented software development tools. Keep reading for a look at five actionable strategies for making your programming job AI-resistant.

1. Master a Specialized Niche

AI-augmented development tools work well when dealing with popular programming languages and domains. If your job is to develop web apps using JavaScript, for example, AI tools can likely handle a fairly large percentage of your workload.

But AI performs considerably more poorly when dealing with highly specialized domains. If you handle back-end development for legacy ERP systems, for instance, you are probably less replaceable by AI tools.

The point here is that the more specialized your development work is, and the more different you are from other programmers, the safer your job will be in an AI-dominated world.

Related:AI-Assisted Coding: What Software Developers Need to Know

2. Work on Complex Software Development Projects

Along similar lines, the more complex the projects you work on, the less likely it is that AI-augmented development tools can handle the bulk of your work.

The main reason why is that complex projects involve a lot of context. There may be hundreds of dependencies and environment-specific variables that programmers need to think about. Explaining that context to AI tools is hard, and getting them to generate code that works within unusual, bespoke configurations is even harder.

Programmers who write stateless, containerized apps probably have more to fear from AI-assisted development tools than do developers who work with complex, sprawling codebases.

3. Integrate AI-Augmented Software Development into Your Work

The more you use AI-augmented development tools in your daily work, the less likely it is that those tools will totally take over your work.

This idea might seem counterintuitive, but it makes sense when you realize that by embracing AI tools to help do parts of your job, you make it easy to identify and demonstrate the limits of those tools. This strategy also puts you in a position to say that you're already working as efficiently as possible by automating mundane tasks using AI, and that the value you bring to the table hinges on work that AI can't perform.

Related:Is Generative AI the Next Big Threat to Open Source Software?

Programmers who sit idly by, waiting for others to look for ways to insert AI into their jobs, are at greater risk of losing work to AI-augmented development tools than are developers who are already using those tools extensively.

4. Document Your Code

Here's another tip that may seem counterintuitive: The better you document your code, the harder it will be for AI to generate code in place of you.

Here again, the reason why has to do with the fact that the more clarity and definition you can bring to the work you're already doing, the easier it is to justify why you — and not an AI tool — need to continue doing that work. If your code is poorly documented, it's easier for your company to decide that it should just scrap it all and replace it with code produced by AI-augmented development tools. But if you have good, well-documented code, it makes less sense to replace it with AI-generated code, which will probably not be as good.

Plus, one thing that many AI-augmented development tools currently don't do well is document their code. So, by documenting your code well, you differentiate yourself from AI.

5. Involve Yourself in Application Deployment and Management

Programmers who embrace the DevOps revolution by thinking of themselves not just as coders, but also as stewards of software deployment and management, are less likely to be replaced by AI.

After all, AI has gotten pretty good at writing code, but not at deploying or managing applications within unique environments. To be sure, AI has a role to play in these latter domains (and that's why AIOps has become a thing). But until the tooling becomes so advanced that we achieve NoOps — which I doubt we ever will — there will always be strong demand for DevOps engineers who understand not just how to write code, but also how to deploy and manage it, because AI just isn't good enough to handle all of that work on its own.

Conclusion: Stop Worrying About AI-Augmented Software Development

There has been plenty of speculation about how AI-augmented development tools might render human coders obsolete, or at least less valuable. And that could happen, at least to coders who perform relatively simplistic, mundane work. But those who think outside the box have little to fear from AI-assisted development tools.