Is AI creating more productive but dull programmers?

AI coding tools boost software developer productivity, but originality and quality fall as output rises

Featured faculty: Kim Keongtae and Michael Zhang
Written by Pan Jingyi

AI-powered coding assistants are changing how software is built. These tools can be highly effective in helping programmers and software developers, even those with less experience, to write code in a programming language. Almost everyone can now make an app or software from scratch by simply asking an AI chatbot, a practice known as vibe coding.

OpenAI’s release of Codex in 2021 was the first AI model powerful enough to build entire programmes from simple text prompts. The company then partnered with GitHub, a platform where developers worldwide can store and collaborate on their software code, to launch GitHub Copilot as an autocomplete tool. In early 2023, Copilot was transformed into Copilot X, a full AI coding assistant.

Nowadays, Copilot X is not the only tool in the market. Other major AI companies, such as Anthropic and DeepSeek, as well as big tech like Google, Amazon, and Meta, have also launched their AI-assisted software development tools. As millions of developers start relying on AI, burning questions loom: have these tools changed how developers code, and can they actually boost productivity?

“AI technology may increase the developers’ productivity and push the limits of what developers feel able to attempt, but these gains come with trade-offs for code quality and originality.”

Professor Kim Keongtae

Kim Keongtae, Professor in the Department of Decisions, Operations and Technology at the Chinese University of Hong Kong (CUHK) Business School, collaborates with Michael Zhang, the Wei Lun Professor of Business AI in the same department, their PhD student Li Xinyu, and Francis Joseph Costello of Nova School of Business and Economics, to seek answers. In the study titled Exploring altered open source software development patterns in a time of generative AI, they examine 1,350 open-source software developers on GitHub before and after Copilot X was released.

The analysis finds that CopilotX significantly helps occasional contributors who were previously less active on GitHub. These casual developers participated in 48 per cent more open-source projects and became far more likely to experiment with new programming languages after using the AI tool.

Meanwhile, highly active or more seasoned developers who used Copilot X updated their code 12 per cent more often and contributed to six per cent more projects compared to those who didn’t use the tool. AI not only helps developers work faster but also spreads their effort across a larger set of projects and switches between different projects more easily.

Contributing to open-source projects sometimes requires developers to learn an unfamiliar codebase and figure out how the software is structured before making a small update. For occasional contributors, these barriers can be enough to stop them from participating at all.

“AI coding assistants can lower these barriers and make the first step easier,” says Professor Kim. “After conversational AI coding assistance became available, developers became more willing to explore new programming languages and projects.”

The hidden costs of easy coding

While productivity rose, a noticeable increase in copy-pasted code appeared. Although more projects are being started, they are not necessarily leading to more original work. “AI technology may increase the developers’ productivity and push the limits of what developers feel able to attempt, but these gains come with trade-offs for code quality and originality,” Professor Kim adds.

“Copied code is not always harmful, as some reuse is normal in software development. The concern is that if developers insert larger blocks of code without fully understanding how they interact with the rest of the project, the project may accumulate technical debt or a shaky foundation.”

The build-up of hidden problems could make software harder to maintain, debug, or improve later. It can also lead to compatibility issues, inconsistent coding styles, and more rework down the line. For project managers, this finding calls for treating AI-generated code as a draft, not as final output.

AI can boost careers

Open-source software development relies on voluntary effort, so developers have to contribute their personal time. While some programmers and software developers participate for their own interests, GitHub functions much like a public portfolio to showcase recent work and collaborations. Employers also often look at GitHub profiles to assess software developers’ skills and experience.

Therefore, to further examine whether changes in how developers worked with AI affected their careers, the researchers matched developers’ GitHub histories with their LinkedIn profiles and found encouraging signs. Developers with access to Copilot X were more likely to experience internal transitions and promotions in the following six to 12 months.

It appears that increases in open-source contributions and proficiency with new technologies are associated with positive career outcomes. “Visible output on GitHub can also signal employers that a developer is adapting to new tools and can leverage them to deliver more work,” Professor Kim says.

“However, the benefits of AI coding tools for career improvement are temporary. When only a few developers know how to use these tools really well, they’ll be noticeably more productive than others. As these tools become widely used, simply using AI won’t make a developer stand out as much anymore.”

How can developers stand out in the AI age?

Given the short-term benefits and the increasingly prevalent copied code, Professor Kim warns developers that AI should only be used as a learning partner and assistant. Although AI may accelerate software development, it cannot replace the developer’s responsibility to understand and evaluate the system. “Take the AI tool as a tutor rather than a shortcut. The human developer should remain responsible for the final judgement.”

This advice is especially crucial for beginners, as less experienced software developers are more likely to use code suggested by AI tools, even if the code is not very good or has mistakes.

RELATED ARTICLE

How retail investors use GenAI to navigate stock markets

As technologies evolve rapidly, AI tools can play a vital role in ensuring software quality and in helping spot critical issues during development, including reviewing updates that might introduce risks and identifying areas that need more testing. Highly skilled developers can leverage these tools to validate code outputs, establish clear coding rules, and integrate code into complex systems.

Ultimately, Professor Kim anticipates that the more sustainable career advantage will likely come from higher-level capabilities, such as understanding software’s underlying problems, evaluating AI output, designing maintainable systems, and coordinating human-AI workflows.

“The career advantage from knowing how to use AI coding tools will not vanish but shift towards those who can use it in more advanced, strategic, and complex ways.”