AI demands more software developers, not less

It’s time we officially bury the idea that AI means we won’t need software developers. For years, we’ve heard that generative AI and large language models (LLMs) are coming for our jobs. In software development, that supposedly means fewer humans writing code. That’s not what the data suggests. Rather than shoving developers aside, AI coding tools like GitHub Copilot are spurring organizations to build more software, faster—a classic case of the Jevons paradox. In economics, the Jevons paradox describes how gains in efficiency lead to greater consumption of a resource, not less. Here, that “resource” is developer time and effort.

As AI tools make coding cheaper and easier, the demand for code explodes—and so does the need for skilled developers. In fact, that’s the only real question left: What role will junior developers play in AI-driven software development?

Faster coding, greater output

Consider GitHub Copilot. One internal study split 95 engineers into two groups: those with GitHub Copilot and those without. 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 aren’t trivial improvements.

Look beyond small tests, and you’ll find a consistent story. In 2023 GitHub said Copilot generated close to 46% of all code in files where it’s enabled—and sometimes more than 60%, depending on the language. That includes Java, one of the world’s most used enterprise languages. That number has almost certainly climbed in the past two years. Indeed, Microsoft, ZoomInfo, and others more recently reported time savings of 40% to 50% on coding tasks, including trickier projects that normally eat up valuable developer hours.

The result? Engineers get more done, feel less frustrated, and can take on projects that previously languished in backlog purgatory. Studies show that AI-assisted teams also 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.

More AI, more people

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. That’s exactly the Jevons paradox effect: Making coding more efficient drives organizations to expand—tackling bigger, more diverse software initiatives.

Here are a few reasons we’ll see skilled developers become more important, not less, with AI:

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. That “nice-to-have” internal dashboard? Suddenly feasible. That experimental customer-facing feature? It’s worth a shot now that prototyping is faster. LLM assistance makes tackling this backlog economically viable.

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. If anything, the speed of generation might initially lead to more code needing careful review, refactoring, and ongoing support. Skilled human developers become critical to keeping all this new code running reliably and securely for years.

Complexity: LLMs excel at well-defined, localized tasks based on patterns learned from vast data sets. They are statistically brilliant mimics. 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. These remain fundamentally human domains that require creativity, critical thinking, and strategic oversight.

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? Human developers are essential validators, editors, and quality controllers. They bridge the gap between generated code and production-ready, reliable software.

So that’s why we’ll still need people. As a reminder of why we’ll need more AI, too, IBM’s research suggests 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.

You keep using that word ‘skilled’

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.

Across all skill levels, companies are scrambling to upskill developers in AI and machine learning. A late-2023 survey in the United States and United Kingdom showed 56% of organizations listed prowess in AI/ML as their top hiring priority for the coming year. Gartner predicts that by 2027, roughly 80% of developers will need at least a fundamental AI skill set. How will this play out? Well, junior devs might start their careers using AI tools to handle routine coding tasks (learning from the suggestions as they go) while quickly taking on more complex tasks. Meanwhile, senior engineers become coaches, reviewers, and orchestrators, ensuring AI-generated code fits business requirements and meets security standards.

TL;DR? Hire more developers

The net-net for executives: AI will not replace developers. Instead it will make them more efficient, and that efficiency can spur companies to undertake even more (and bigger) projects. We’ve seen this movie before, whether we’re talking about compilers, open source frameworks, or cloud platforms. Every time development gets a productivity bump, the market seizes on it to build more software. LLMs are just the latest and most dramatic iteration of this pattern.

Rather than slashing development budgets, a company’s smarter move is to invest in upskilling. Offer training in AI-driven development and reward those who learn to direct AI effectively. The key to successfully navigating this transformative period lies in adapting training and mentoring practices to equip junior developers with the essential skills needed to thrive in an AI-driven environment. This includes a strong emphasis on code evaluation, problem-solving, and a commitment to continuous learning.

This should shrink the backlogs of “someday” projects and grow the pipelines of real, revenue-driving features. By accelerating developer efficiency, including for junior developers, AI should yield more (and better) software and an unending need for top-notch talent.