Why AI Won’t Replace Software Developers
In recent years, the idea that AI will revolutionize software development and render human coders obsolete has gained traction. The notion that generative AI and large language models (LLMs) will take over coding tasks and leave developers out of a job has been a persistent theme. However, a closer examination of the data suggests that this is not the case. Instead, AI coding tools like GitHub Copilot are spurring organizations to build more software, faster, leading to a classic example of the Jevons paradox.
The Jevons Paradox
The Jevons paradox describes how gains in efficiency lead to greater consumption of a resource, not less. In the context of software development, this means that the increased efficiency brought by AI coding tools will lead to more software being built, not less. This is precisely what is happening. As AI tools make coding cheaper and easier, the demand for code explodes, and so does the need for skilled developers.
GitHub Copilot: A Study in Productivity
One internal study split 95 engineers into two groups: those with GitHub Copilot and those without. The results showed that developers assisted by GitHub Copilot finished a coding task 55% faster, with a higher overall success rate (78% versus 70%). A separate experiment with nearly 2,000 developers at Microsoft and Accenture found a 13% to 22% boost in weekly pull requests among AI-assisted teams. These improvements are not trivial.
More AI, More Output
The numbers tell a consistent story. In 2023, GitHub reported that Copilot generated close to 46% of all code in files where it’s enabled—and sometimes more than 60%, depending on the language. This includes Java, one of the world’s most used enterprise languages. The number has almost certainly climbed in the past two years. Microsoft, ZoomInfo, and others have reported time savings of 40% to 50% on coding tasks, including trickier projects that normally eat up valuable developer hours.
The Result: More Done, Faster
The result of these improvements is that engineers get more done, feel less frustrated, and can take on projects that previously languished in backlog purgatory. Studies show that AI-assisted teams enjoy higher accuracy rates: In some tests, automated code had a 53% higher success rate on unit tests compared to code written manually. Software development becomes less about tedious implementation and more about problem-solving.
This productivity windfall leads to a fascinating consequence. When a team suddenly delivers on their to-do list in half the time, they don’t tell their engineers to take the rest of the year off. Instead, they start building the next wave of features. They focus on new business ideas. Rather than hire half as many developers, companies build twice as many things. This is exactly the Jevons paradox effect: Making coding more efficient drives organizations to expand—tackling bigger, more diverse software initiatives.
- Productivity: Every company has a backlog of desired features, internal tools, automation projects, and application ideas that remain unbuilt due to time and resource constraints. LLMs lower the activation energy required to start these projects.
- Maintenance: More software written means more software to test, debug, secure, maintain, update, and integrate. LLM-generated code isn’t magically bug-free or self-maintaining.
- Complexity: LLMs excel at well-defined, localized tasks based on patterns learned from vast data sets. They struggle with large-scale system architecture, novel problem-solving, deep understanding of business context, complex security considerations, performance optimization under unusual loads, and nuanced user experience design.
- Quality control: Related to the above, an LLM might generate code that looks plausible, but is it secure? Is it efficient? Does it handle edge cases correctly? Does it align with the overall system architecture and business goals?
So, what does this mean for developers? As a reminder of why we’ll need more AI, too, IBM’s research suggests that generative AI could translate into 15% to 20% more products or features rolled out by businesses, at a 10% to 15% faster time to market. That’s a huge competitive edge. Gartner notes that as AI-driven coding becomes standard, the appetite for software has no natural upper limit. In simpler terms, the world needs code for everything from mobile apps to blockchain platforms (well, maybe not so much blockchain), and demand is only rising. The U.S. Bureau of Labor Statistics still projects 25% growth in software developer jobs from 2022 to 2032—much faster than average. Some roles might shift or merge, but there’s no sign of a decline in overall developer need. If anything, we’ll need more skilled engineers to orchestrate these AI-driven workflows.
Entry-level software development will change in the face of AI, but it won’t go away. As LLMs increasingly handle routine coding tasks, the traditional responsibilities of entry-level developers—such as writing boilerplate code—are diminishing. Instead, their roles will evolve into AI supervisors; they’ll test outputs, manage data labeling, and integrate code into broader systems. This necessitates a deeper understanding of software architecture, business logic, and user needs. Doing this effectively requires a certain level of experience and, barring that, mentorship. The dynamic between junior and senior engineers is shifting. Seniors need to mentor junior developers in AI tool usage and code evaluation. Collaborative practices such as AI-assisted pair programming will also offer learning opportunities. Teams are increasingly co-creating with AI; this requires clear communication and shared responsibilities across experience levels. Such mentorship is essential to prevent more junior engineers from depending too heavily on AI, which results in shallow learning and a downward spiral of productivity loss.
