Will AI Replace Programmers and Software Engineers?

Advancements in artificial intelligence (AI) and developing generative AI (GenAI) technologies are already changing the way we do many things. Text-generating chatbots like ChatGPT and Bard have made it easier for everyday users to do tasks like brainstorming ideas, writing, and synthesizing information. These same chatbots are also trained in programming languages, like Python, Java, and C++.

What does this mean for the future of coding jobs, like programmers and software engineers? While there are likely to be some changes to these job roles, a complete elimination of these roles is less likely to happen—at least for quite some time. In fact, programmers and software engineers are driving many of the AI advancements we enjoy today.

In this article, we’ll explore how AI stands to change the way programmers and software engineers work, current limitations of AI in programming, and offer some suggestions for future-proofing your job skills as AI becomes increasingly relevant.

Will AI replace programmers?

As of this writing, AI is not equipped to replace programmers and software engineers. Current GenAI tools have several limitations when it comes to coding, making human intervention crucial throughout the development process.

Before AI is advanced enough to replace programmers, developers will need to master artificial general intelligence (AGI), which is the ability for machines to do any intellectual task a human can do. Learn more about AGI in the video below from DeepLearning.AI’s course, Generative AI for Everyone.

However, current tools are advanced enough to start impacting the way programmers work, particularly when it comes to entry-level and software engineering job requirements.

Here’s what ChatGPT-4 has to say about whether AI will replace programmers:

Let’s take a closer look at AI's impact on programming roles and its limitations.

How AI is impacting programming and software engineering

There are certain tasks that GenAI tools are equipped to handle, specifically more rote technical tasks. Think about it: GenAI tools are large language models (LLMs), which essentially work by continuously predicting the most likely next word (or set of characters) based on the data it’s been trained on. These tools were built to replicate routines.

Some examples of how AI is impacting programming roles are:

• Automation: AI is well-positioned to automate repetitive tasks, enabling increased efficiency and productivity.

• Writing assistance: AI can help write code in common programming languages. It can also explain a line of code and format it as comments to include in your code without impacting the output.

• Bug detection and fixing: If you run into issues with your code or want to check your work before deploying, AI can identify where bugs are originating and offer solutions to fix it.

• Project forecasting: This is perhaps more of a project management task. When starting a new project, AI can use historical data to predict how long programming tasks will take, helping create project timelines.

Programmers and software engineers can also use these tools early in their careers as they learn how to code and build their skills. If you’re having trouble reading, writing, or understanding a line of code, try asking ChatGPT for an explanation.

AI limitations in programming

Alongside the benefits of using AI come several limitations, which means it’s important for humans to continue playing a strong role in programming and software engineering tasks.

Here are some limitations of AI:

• Lack of innovation: AI is unable to think critically and generate new ideas—it can only repeat ideas based on the data it’s been trained on. Critical thinking and problem-solving are important programming skills that AI cannot replicate.

• Inaccurate code: AI is prone to hallucinations, so an AI tool may produce inaccurate code. This can be particularly true with more complex requests.

• Security risks: AI may learn from user inputs and store data, using that data to improve future outputs. Before using any AI, it’s important to be aware of the system’s data storage and usage to prevent security issues.

• Copyright and intellectual property concerns: Just as AI can store and use your inputs as data, it may also learn from other users’ copyrighted data. If you’re using AI for business programming tasks, it’s worth knowing what the AI has been trained on to avoid accidental infringement.

When will AI replace programmers?

AI is not in a position to replace programmers now, but as a developing technology, its current limitations may become less limiting over time. However, even then, replacing programmers with AI will face another obstacle: human comfort.

Programmers and software engineers develop products that deeply impact society. In order for AI to completely replace these job roles, people in society will need to be comfortable relying on these technologies to create programs that analyze medical records, handle financial systems, fly airplanes, control nuclear power plants, and manage military defense systems.

Because some software engineers work on highly sensitive programs, confidence in AI’s programming capabilities will have to be very high before AI is in a position to replace programmers completely—and reaching this level of confidence will likely take time.

Another important point to remember when you’re trying to forecast when AI will replace programmers: Human programmers are crucial participants in AI development. Even as the technology becomes more advanced, AI programmers and AI software engineers are working on these tools to guide and supervise those advancements.

The future of programming

Although it seems unlikely that AI will replace programmers in the near future, programmers must expand their skills to incorporate AI into their typical workstreams. Of course, anyone who works in the computer science field is accustomed to the need to learn new technologies.

Here are some growing and in-demand AI skills, concepts, and frameworks for programmers and software engineers:

• Applying and deploying application programming interfaces (APIs)

• Prompt engineering

• Machine learning

• Deep learning

• Cloud platforms

• Natural language processing (NPL)

• AI ethics

Build your AI skills.

Enhance your programming capabilities by learning new AI skills. You can take several paths to start using and learning about these technologies. Here are some popular course options on Coursera:

• For a beginner-level course, start with the IBM Applied AI Professional Certificate. You’ll start with an introduction to GenAI. Then, you’ll learn to use Python to build and deploy your AI applications.

• For an intermediate course, try the IBM AI Engineering Professional Certificate. Here, you’ll deepen your understanding of machine learning and deep learning fundamentals, and you’ll apply those concepts to deploy algorithms and build models and neural networks.

• To practice working with GenAI, explore Vanderbilt University’s Prompt Engineering Specialization. You’ll learn how to use ChatGPT to increase your efficiency in your daily life.