As the field of generative AI continues to grow, many are wondering if artificial intelligence could one day replace software engineers. According to a report by Grand View Research, the AI market is expected to expand at an impressive rate of 36.6% per year between 2024 and 2030, with AI applications in software development being a major area of investment.

As the CEO of a software development company, I often hear comments joking that AI tools, like ChatGPT, could easily replace my team. While there is a growing role for AI in software development, I firmly believe that AI will not replace software engineers — and here’s why.

AI is already helping software developers write code more quickly and efficiently. According to a survey by GitHub, 70% of developers use AI tools to streamline their work. These tools can assist with everything from writing code snippets to automating repetitive tasks. However, using AI tools to assist with coding is very different from relying on AI to write code independently.

While generative AI tools can produce functional code for simpler applications — such as a basic CRUD (Create, Read, Update, Delete) application — they still fall short when it comes to writing complex, secure, and human-readable code. A study by Stanford University revealed that code written with the help of AI tools tends to be less secure than code written by human programmers. Researchers from Bilkent University found that about 30.5% of the code produced by GitHub’s Copilot had issues, with some of the errors leading to security vulnerabilities, outdated libraries, or poor coding practices.

This is because AI tools are trained on publicly available code, which often includes outdated or incorrect solutions. Unlike human developers, AI doesn’t truly “understand” the context in which the code is written or the broader problem being solved. Instead, it draws on patterns and data from existing codebases, which limits its ability to evaluate whether a solution is valid or optimal.

While AI tools have their limitations, they can significantly empower skilled developers. The way we code has evolved considerably over the years. From the early days of punched cards to the widespread use of version control and syntax highlighting, every advancement has made development faster, easier, and more efficient. AI tools are simply the latest in this evolution, augmenting developers’ capabilities rather than replacing them entirely.

For example, developers can use AI tools to quickly generate code examples or first drafts, which they can then refine and improve. AI can also assist with mundane tasks like debugging, automating code reviews, or generating summaries of existing code. By handling these repetitive activities, AI allows developers to focus on more complex and creative aspects of their work.

The key here is that AI tools still require human oversight. Developers must review and improve the code generated by AI to ensure it meets quality, security, and performance standards. AI can suggest solutions, but only skilled developers can provide the context and expertise to make those solutions reliable and secure.

Another advantage of AI tools is their potential to help non-developers participate in software development. With AI-powered tools offering more flexibility than traditional low-code/no-code platforms, business users or process owners can create prototypes and communicate their ideas to developers. However, this could also introduce risks, particularly if non-technical users deploy AI-generated code for customer-facing systems without proper oversight.

The rise of “shadow IT,” where non-developers build and deploy applications without IT approval, is a real concern. If AI tools are used by non-developers to create systems handling sensitive customer data or interacting with external systems, there could be serious security and compliance risks. This is why it’s essential for companies to integrate AI tools into a structured, controlled environment, with guidance from skilled developers who understand industry regulations and security best practices.

To successfully integrate AI into software development, companies must invest in developing AI skills within their teams. There is a growing demand for developers who not only understand AI technologies but also know how to use them to enhance their existing work. Organizations that invest in AI training and talent development will be well-positioned to lead in the evolving technological landscape.

Equally important is the establishment of clear AI policies that define the appropriate uses of AI tools. A pragmatic approach to adopting AI can reduce potential risks and help avoid over-reliance on algorithms that could lead to poor decision-making or vulnerabilities.

While it’s clear that AI tools have the potential to transform software development, I believe they will complement, not replace, skilled software engineers. Used correctly, AI can dramatically speed up prototyping, reduce error rates, and free developers from repetitive tasks. However, it’s important to treat AI as a tool, not a crutch — something that augments human expertise and creativity, rather than replacing it.

In my company, we ensure that AI is used as an aid, not a substitute for talent and experience. The best developers will be the ones who know how to make AI work for them — combining their deep understanding of code with the power of AI to create faster, more efficient, and more secure software.

By Impact Lab