Will AI replace Software Engineers?

But the rapid adoption of AI tools has also led many to speculate about the future of the software engineering profession.

Despite the ominous headlines, software engineering isn’t going away anytime soon. This article provides a realistic overview of the current state of AI in software engineering, its benefits, and its limitations.

The emergence of ChatGPT and other AI tools has left many people asking whether AI can or will replace Software Engineers eventually. While we can’t predict the future, for right now at least, the consensus among most tech experts is “definitely not.”

First, let’s start with what AI can do. Current AI technologies, such as Large Language Models (LLMs) like ChatGPT, can assist human developers with a variety of software development tasks. For example, Software Engineers can use artificial intelligence for help with tasks like basic code generation and debugging flawed code. These use cases definitely boost Software Engineer productivity, freeing them up to focus on more creative or challenging responsibilities.

But artificial intelligence hasn’t advanced enough to replace software engineering professionals entirely — and it likely won’t reach that point in the near future without some sort of major technological leap toward artificial general intelligence (AGI). Current AI technology can’t be trusted to make complex decisions or create innovative solutions to problems. In many cases, AI tools also need help from human programmers to write accurate code.

In other words, AI is a valuable tool that supports but doesn’t replace human intelligence and expertise. That means companies will still need to carry a roster of their own engineering talent and decision-makers.

While it’s highly unlikely AI will replace programmers without some sort of shift in the status quo, that doesn’t mean it’s not impacting software engineering. Here are a few ways that Software Engineers can use this innovative technology in their daily workflows.

Software Engineers can use AI to automate tasks that don’t require critical thinking or complex decision-making.

Say, for example, you’re developing a software program that will generate API documentation for web applications. You can use a tool like Swagger(opens new window) to automatically generate this documentation without manual intervention.

Here are other examples of routine tasks you can automate with AI systems:

AI applications enhance productivity by helping Software Developers generate and review code.

Github Copilot(opens new window) offers suggestions in real time as users write code. You can start writing a function, and the AI will automatically complete it as you type. This feature allows you to write code faster and may reduce errors.

The application also uses natural language processing to interpret and respond to the user’s input. For example, you can ask Copilot to generate source code or convert your Python code to JavaScript. This simple interaction can significantly reduce the amount of time spent programming.

Software development teams can use AI technologies to debug code faster.

Suppose you’ve written a complex piece of code that causes your software program to crash. Manually debugging the program would require you to check thousands of lines of code carefully — and hope you spot the error the first time around.

Accelerate this process by using an AI debugging assistant like JamGPT(opens new window) or ChatDBG(opens new window). These applications automatically diagnose bugs in code snippets. They also offer suggestions to fix the code, saving developers time.

The latest AI technologies allow Software Engineers to test their software and detect problems before deployment.

For example, Software Developers can use Applitools(opens new window) to test user interfaces for accessibility and visual functionality. The AI-powered platform offers suggestions for remedying errors. For instance, the tool may recommend creating a mobile-responsive layout to allow users to access a website on their smartphones.

Software Engineers can also use artificial intelligence tools like Functionize(opens new window) to test and improve code quality. Functionize can perform localization testing to check if software is appropriate for a specific culture or region. Developers can also use the platform for regression testing, which makes sure that applications still function after coding updates.

As technology evolves, Software Engineers often need to learn new programming languages to stay relevant. AI tools like ChatGPT can speed up this process by:

AI can also help aspiring or beginner programmers who fear coding is too hard to learn. For example, a new coder can ask ChatGPT to explain complex aspects of a language in simple terms.

AI is a powerful tool, but it’s not infallible. Here are a few reasons you won’t see AI replacing Software Engineers completely.

AI algorithms can produce code that looks correct but has serious flaws. This code may contain bugs or use inefficient solutions that make it impossible to scale the program.

Additionally, artificial intelligence hallucinations(opens new window) can lead to inaccurate or nonsensical code. Hallucinations occur when large language models generate incorrect or false content. This issue often affects computer vision tools and chatbots.

Software Engineers must check AI-generated code carefully for hallucinations and other errors. These anomalies could cause the software to malfunction or expose sensitive data to security breaches.

AI and machine learning algorithms can use existing data and trends to solve problems but often lack the necessary context for true innovation.

For instance, AI platforms may use outdated data that doesn’t account for recent developments or unexpected events. These tools also can’t make nuanced decisions that require gut instincts, ethical judgments, or nuanced business context.

These limitations mean that AI is helpful for brainstorming solutions, but it’s not a replacement for human creativity and problem solving skills.

AI software engineering has advanced faster than the laws governing this technology. This gap raises several concerning legal questions.

For example, current intellectual property laws(opens new window) in the United States protect work that “contains sufficient original and creative authorship by a human author.” But this definition doesn’t specify what percentage of code must be created by human Developers to qualify for protections.

Additionally, Copilot and other AI models were trained with publicly available data. If they replicate copyrighted materials, these platforms could violate copyright laws, leading to legal issues for companies.

Discussions around AI often evoke worries of job displacement and income loss for tech professionals. But many experts insist that we won’t see AI replace programmers in the foreseeable future.

According to Kevin Dewalt(opens new window) — founder at Prolego, an AI transformation consulting firm — AI can’t replace the interpersonal skills of human programmers. As Dewalt writes in a Medium essay, “the essence of creating software lies in creativity, defining problems, breaking them down, troubleshooting, and effective communication. These are intricately human skills that AI is yet to replicate.”

These limitations mean the AI tools of the near future will continue to complement programming jobs, not eliminate them. Software Engineers can use this technology to create better software and improve productivity without fear that their careers will become obsolete.

Current and aspiring Software Engineers can use many strategies to gain AI expertise.

A college education is probably the most traditional pathway to a tech career. You can earn a bachelor’s degree in computer science, data analytics, and other fields. This avenue can enable you to learn foundational AI concepts, but colleges often fail to keep pace with the latest technical advancements. Additionally, college requires a significant financial investment.

You can also learn AI software engineering through online courses and certifications. For example, the United States Artificial Intelligence Institute offers the Certified Artificial Intelligence Engineer program(opens new window). This self-paced program teaches AI fundamentals and culminates in an exam. Participants pay approximately $700 for the course — much cheaper than college —but don’t gain explicit work experience through the program.

A better option? A paid apprenticeship with Multiverse. Our 15-month Software Engineering Apprenticeship allows career starters to gain hands-on experience working for a leading employer. Apprentices also study machine learning techniques, software development, and other essential skills.

Multiverse’s AI Jumpstart module also allows upskillers to learn new skills. This asynchronous course teaches apprentices foundational AI concepts and skills. Participants also strengthen their problem-solving skills and learn how to integrate AI ethically into their daily roles.

Artificial intelligence has shaken up the tech industry, but it won't replace Software Engineers in this decade. Instead, this technology will likely lead to exciting innovations and new career opportunities.

You can set yourself up for success in this field with a paid Multiverse apprenticeship. You’ll gain hands-on experience and get paid to learn code and other core AI skills.

Fill out our quick application(opens new window) today to start your AI adventure.