For generations, the path to becoming a senior software engineer followed a relatively predictable pattern. First, you started at the bottom. Then, you got to work.

You fixed bugs. You wrote tests. You maintained existing code. You built small features. You updated documentation. You investigated why something broke at 2 a.m. You made mistakes, received code reviews, learned from more experienced engineers, and gradually took on bigger and more complicated problems.

The work was not always glamorous. But it was how you learned.

Now AI has changed that equation. Generative AI can write boilerplate code, generate tests, explain unfamiliar code, suggest fixes, produce documentation, and increasingly assist with debugging and implementation. AI coding agents are also moving beyond individual suggestions toward completing increasingly substantial development tasks.

That creates an intriguing problem for companies building software teams. What happens when the work traditionally assigned to junior engineers is increasingly performed by AI?

The answer may determine not only how companies hire entry-level engineers, but whether they have enough experienced engineers to hire five or ten years from now.

The Junior Work Was Never Just “Junior Work”

It’s tempting to think of entry-level engineering tasks as work that companies would gladly automate. After all, nobody wants an expensive engineer spending half a day writing boilerplate code or updating documentation.

But there is a catch.

Those tasks were never valuable solely because of what they produced. They were valuable because of what they taught. A junior engineer who fixed a bug learned something about debugging. A developer who wrote tests learned something about how software fails. Someone who read an unfamiliar codebase began to understand how large systems were constructed. A developer who made a poor architectural decision and then saw its consequences learned something that a textbook couldn’t easily teach.

The work produced software. It also produced experienced engineers with well-earned battle scars.

And that second output may be the one AI threatens to disrupt.

Recent research on generative AI and software engineering describes this concern directly, arguing that AI may be absorbing parts of the pathway through which junior engineers traditionally developed expertise.

AI Could Create a “Missing Middle” in Engineering

AI may make experienced engineers dramatically more productive while simultaneously making it harder for inexperienced engineers to acquire the experience they need to become experienced.

Imagine a junior engineer encountering a difficult bug. In the traditional model, they investigate. They read logs. Search the codebase. Ask a colleague. Try a fix. Break something else. Try again. Eventually, they understand the underlying problem.

In an AI-assisted environment, they might simply ask an AI coding tool to identify the problem and propose a solution. The immediate result may be better. The learning process starts to disappear.

Multiply that across hundreds of small engineering tasks and an uncomfortable question emerges. Are we optimizing away the apprenticeship? And learning?

LinkedIn’s 2026 U.S. Software Engineer Talent Landscape already pointed to a tightening entry pathway. The report found a smaller share of computer science graduates were beginning their careers in traditional software engineering, even as the skills demanded of software engineers increasingly included AI-related capabilities and cloud platforms.

The challenge, then, is not simply getting the first job. It is preserving a viable path from the first job to the tenth year of an engineering career.

The Answer Isn’t to Stop Using AI

None of this means companies should tell junior engineers to put away their AI tools. That would be like asking accountants to stop using spreadsheets because spreadsheets might prevent them from learning arithmetic.

AI is part of the software development environment. Junior engineers need to learn how to use it. The better model is to ask, “How do we use AI to accelerate learning and production rather than eliminate it?”

That distinction could become central to engineering management. A junior engineer might use AI to generate a first draft of a function, for example. But instead of simply accepting the output, the engineer should be expected to explain it, test it, challenge its assumptions, identify its weaknesses, and modify it.

The goal is not to prevent AI from doing the work. Rather, the goal is to ensure the human is still doing the learning. That said, promoting learning is not a budget item or a concern at most companies. Reducing costs of an engineering team is.

The New Junior Engineer May Need Different Assignments

If AI handles more of the mechanical work, companies may need to redesign entry-level engineering roles around activities that develop judgment. That could mean giving junior engineers greater exposure to:

  • System architecture
  • Code reviews
  • Debugging and root-cause analysis
  • Product requirements
  • Customer problems
  • Technical tradeoffs
  • Testing and validation
  • Security and reliability
  • Observability and production systems
  • Collaboration with product and business teams

Some of this may make junior engineering more interesting. Instead of spending most of the day translating specifications into repetitive code, an early-career engineer might spend more time understanding why the software exists, how its components interact, and whether an AI-generated solution is appropriate.

That requires a different kind of mentorship. Oh, and a different definition of productivity too.

Companies May Need to Measure Learning, Not Just Output

AI creates a tempting management metric by leading CTOs to consider, “How much more code can the team produce and how fast?” But those are the wrong questions when developing junior engineers.

A better question might be, “How much more capable is this engineer than they were six months ago?” Or, “How can we speed up the learning so this junior engineer can be on par with the senior engineer manager so they can move in to the senior role when the senior person leaves the company?

That means managers may need to pay attention to things that are harder to quantify, driving to answers to questions like:

  • Can the engineer explain why a solution works?
  • Can they identify when an AI-generated answer is wrong?
  • Can they debug without immediately asking AI for the answer?
  • Can they break a vague business problem into technical requirements?
  • Can they recognize a tradeoff between speed, scalability, reliability and maintainability?
  • Can they increasingly work without supervision?

Those are signals of engineering growth. And they become even more important when AI can make almost anyone appear more productive.

The New Junior Engineer Will Need AI Fluency

There is another side to the equation. Companies shouldn’t simply preserve yesterday’s junior engineering role while adding an AI tool to it.

The engineers entering the workforce now will need to become fluent in AI-assisted development. That means knowing how to prompt effectively, but also knowing how to evaluate AI output, validate generated code, understand model limitations, protect sensitive information, and recognize when AI is confidently wrong.

Research into the future of software engineering increasingly emphasizes verification and validation as AI agents take on more implementation work. In other words, the junior engineer of the future may write less code manually while needing to understand more about the code being produced.

That is not a lesser engineering role. It may be a more demanding one.

Apprenticeship May Become More Deliberate

Historically, engineering apprenticeship happened almost organically. You worked alongside experienced people. You inherited their code. You watched how they solved problems. You made mistakes. They corrected you. Eventually, someone gave you something important enough to make you nervous.

AI disrupts that organic process. So, companies may need to make apprenticeships intentional.

That could mean structured code-review sessions, architecture discussions, debugging exercises, AI-free problem-solving periods, rotating assignments, paired development, and deliberate opportunities to own increasingly complex systems.

LinkedIn’s own REACH apprenticeship program offers one example of this more intentional approach, combining real business projects with mentorship, cohort learning and dedicated time for professional development.

The larger lesson is that junior engineers cannot simply be left to “learn on the job” if AI is simultaneously changing what the job looks like. The learning itself may need to become part of the job design.

Don’t Automate the Ladder You’re Trying to Climb

There is an important distinction between automating work and automating development.

Companies should absolutely automate repetitive work. They should use AI to eliminate unnecessary toil. They should give engineers tools that make them faster and more capable.

But they should be careful not to eliminate every opportunity for less-experienced engineers to wrestle with meaningful problems.

Because today’s junior engineer is tomorrow’s senior engineer. And today’s senior engineer is tomorrow’s architect, engineering leader, technical founder or principal engineer. If the industry removes too many of the steps along that journey, it may eventually discover that it has optimized away its own talent pipeline.

The solution isn’t to keep junior engineers busy doing work AI can do better. It is to rethink what junior engineers should be learning.

The best companies may increasingly treat AI not as a replacement for the engineering apprenticeship, but as part of a new apprenticeship model. One where AI handles more of the mechanical work while humans spend more time developing judgment, reasoning, curiosity, communication, and technical ownership.

The real question isn’t whether AI can do junior engineering work. It increasingly can. The real question is whether we can design the next generation of engineering careers so that humans still learn how to become senior engineers. For companies building technology teams for the next decade, that’s a talent strategy worth thinking about now.

Have Some Companies Stopped Hiring Junior Engineers?

Yes, they have. Some companies stopped hiring junior engineers over the past year. Those jobs have already been replaced with AI.

In a future article, I will discuss the quantifiable value that companies are getting with this approach. And the tradeoffs.