We see a lot of interest in artificial intelligence (AI) as a learning tool. New programmers want to use AI to help them learn Python, JavaScript, Java or some other programming language. Traditionally, tech companies look for programmers who’re skilled in one or more programming languages. However, AI has changed the base requirement of skills for programmers — even for entry-level jobs.

Using AI to help with the execution of programs has become “table stakes.” In many cases, new hires are expected to be educated in code generation tools like GitHub Copilot, Tabnine and Claude, in addition to the preferred programming languages of the company.

In 2023, GitHub said that over 92% of programmers were using AI tools. And that percentage certainly hasn’t decreased.

AI skills today are considered must-haves that companies expect in programmers. This applies to both entry-level and seasoned programmers, no matter how long they’ve been in the industry. This is why companies must offer AI skills training as a requirement on their technology teams — since they are most likely already experimenting with it (whatever the official policy may be).

And this goes beyond training programmers on simple prompting, like for example, “write a function that sorts a list.” Instead, this also incorporates tackling the technical skills programmers will need. This article reviews three technical skills every programmer needs to effectively work alongside AI.

3 Technical Skills Programmers Need for AI

Reading programming languages.

Some programmers may be under the impression that with generative AI, they won’t have to spend as much time learning the details of a programming language. AI’s ability to look up a function you need to recall is a great time saver, but it’s only a time saver. We still cannot reject learning opportunities and rely on AI for all the answers. It’s important to not let AI become a crutch. AI can be a help, but don’t underestimate the importance of human understanding. Kernighan’s Law says that it’s twice as hard to debug code as it is to write it, so if you write code that’s as clever as you can possibly be, it’s too clever for you to debug. What does that mean when AI writes the code? Are you twice as clever as the AI?

Testing and debugging.

As the use of Gen AI becomes more widespread, we can expect skills like testing and debugging to become an in-demand skill for all developers. If AI is writing more and more of the code, technology teams will need much more comprehensive test suites to ensure efficacy. While AI can help write tests, it remains the developer’s job to know what to test. Programmers should ask questions like: Where are the corner cases, vulnerabilities and failure modes? Does the software respond cleanly to unexpected or incorrect input? When tests fail, how do you debug?

Reading code.

Code is the set of instructions written in the programming language that tells the computer how to complete a task. The ability to read code may become more important for the same reasons. Most of a programmer’s work is maintaining legacy code — code that has been written by an earlier generation of developers. If AI is generating the code, it’s “legacy” as soon as it’s written: Anyone who needs to work with the code in a non-trivial way will need to understand it. The ability to read code will be at a premium.

Secure coding has always been important but with AI, the task becomes even more important. You need to secure code that you didn’t write and that you may not understand.  For instance, GitHub Copilot was trained on source code in public repositories. How secure is that code? If AI is just parroting code that’s already out there, you really have no right to expect the results to be secure. If you ship insecure code, you won’t be able to blame the AI.

Wrapping Up

It’s important to make continuous training accessible to your tech teams — or the business could risk falling behind. In most cases, the only people who stand the chance to lose their jobs to AI are the ones who don’t know how to use AI. This doesn’t necessarily mean training on the tool itself, but instead, the skills programmers will need to fill the gaps. For example, testing, debugging and code-reading skills aren’t new to the tech industry. However, with the introduction of AI, programmers have a heightened expectation to adopt these skills in an AI-driven business.

During AI training, be specific on how and when to utilize the tool to dissuade programmers from using it as a crutch. Ensure them that they cannot slide by with AI, but instead must continue to learn new skills so they can effectively work alongside AI. With ongoing learning opportunities, you can upgrade your teams’ AI skills sets.

All in all, AI skills training should not only teach programmers how to integrate the tool into work processes, but most importantly, the in-demand skills they’ll need to keep abreast of ongoing changes in a digitally driven era.