The Junior Development Pipeline is Broken… And AI Broke It

The traditional junior development pipeline (as we previously knew it, at least) has been under immense pressure for years at this point. And now, AI has pushed it to breaking point. For decades, the overwhelming majority of software companies relied on a familiar progression route: Graduates entered as junior developers and learned via code reviews and production work, before eventually taking on more complicated tasks and moving up the ladder to become mid-level engineers.

 

The fact that junior developers needed time in the oven to hone their skills was broadly accepted. Because, in return, employers gained a consistent supply of experienced engineers who understood the business, its systems, and its customers, and, therefore, could confidently complete their work with high levels of productivity, as well as troubleshoot during emergencies.

 

This brings us back to AI. When LLM’s went mainstream, software engineering was forever changed. Tools like coding assistants, AI agents, and code generation models are, oftentimes, more than capable of handling a large proportion of the work that has traditionally been passed down to junior developers. In many ways, this is great for experienced developers, who can now burn through their work much more efficiently.

 

But it creates a big problem. Sure, AI makes experienced developers more productive, but this comes at the steep cost of removing the early career work of junior developers. As a result, junior developers become less necessary in many software businesses, leading to fewer jobs and – in the future – far fewer experienced devs available who understand the business and its systems.

 

Junior Work Has Always Been an Essential Training Ground

 

Let’s face it, junior development was never really fundamentally about writing code and contributing in a major way to the business’ output. It’s mainly a way to show them how the software they may have learned about at university (or on an equivalent educational pathway) works in the real world.

 

They’re bound to encounter confusing legacy systems, failed deployments, and unexpected customer behaviours, among many other things, that become valuable lessons in architecture and judgement. But the problem is that these are also the exact tasks that AI exceeds in.

 

In its research on Copilot, GitHub reported that developers who used the AI tool were able to complete a controlled coding task much more quickly than those who didn’t use it. What’s more, Microsoft and GitHub both reported that developers who used Copilot were also more satisfied and productive, across the board, than those who didn’t.

 

So, put yourself in the shoes of a software business leader at the moment. Why not just give a smaller number of experienced developers AI tools and have them also do the work that was previously passed down to several junior developers? The financial argument for hiring, at the very least, the same number of junior developers becomes harder to make.

 

We can shout from the hills about the dangerous ripple effect that this will have on the industry and economy as a whole (and will do soon in this very piece), but for companies whose focus is on a lower bottom line and increased profits year-on-year, it’s seen as a quick win.

 

Why AI Has Changed the Economics of Entry-Level Hiring

 

The tech industry, like many others, is no stranger to entry-level job droughts during times of economic uncertainty; just look at the 2008 financial crisis, for example. But now, things are a bit different because AI muddies the waters by also increasing the amount of output from experienced engineers.

 

The World Economic Forum’s ‘Future of Jobs Report 2025’ identified AI as a major influence on our employment market, and it also expects tech jobs to stay among the fastest-growing job categories, while still emphasising a skills disruption.

 

This is because AI is increasing the demand for jobs in software, while still changing the number of/type of people who are needed to produce it. Entry-level engineers are the most vulnerable because their tasks, as we’ve discussed already, are areas in which AI exceeds, and also because AI-supported development necessitates supervision from someone who really understands whether the generated code is actually right for the system; this job needs to be done by an experienced developer with intimate knowledge of the business’s systems.

 

The Case For Hiring Juniors

 

You’ve probably heard the phrase ‘organisational knowledge’ before. It refers to the total collection of knowledge, skills, and experience within an organisation, which it uses to achieve its goals. It’s something that every company vitally needs.

 

Training junior developers who are early in their careers creates organisational knowledge, as they will become familiar with the business, its product, systems, and customers. Investing in them means investing in a mid-level engineer who is fully confident and intimately familiar with the business.

 

On top of that, if a business, for instance, a design agency in Leeds, chooses not to hire junior developers, they are arguably just shifting their costs over to more expensive external recruits, via more expensive recruitment processes. Gambling on candidates who already lack that organisational knowledge, while also carrying other risks, like uncertainty around commute, salary package, and competition from other employers.

 

But the main issue here is a macro one, which is why it’s possible that it might not be fixed. The root cause of the junior developer hiring drought is lower costs for individual businesses. But this problem is bigger than an individual business; there will be a collective price for this short-sighted cost-cutting, in the form of a diminished future workforce incapable of satisfying the demands of both the industry and the world at large.

 

How the Role of the Junior Can Change

 

But the answer doesn’t just lie in hiring all of the juniors back and preserving all of their former tasks. It’s too late for that. Junior roles need to evolve. They need to spend less time writing boilerplate, and more time investigating production issues, helping to validate AI code, and improving tests.

 

This will require companies to restructure their workflows, redefining what they consider a meaningful development path. So what is a meaningful development path? Well, let’s take a look at experienced engineers and work our way back.

 

If their tasks have adapted to become more judgment-focused and strategic, then we can discern that those are the areas we need to develop in junior developers. They need to be apprentices in engineering judgement.