AI Won't Kill Junior Devs - But Your Hiring Strategy Might

No juniors today means no seniors tomorrow: rethinking talent development

Junior developers remain essential in an industry increasingly using AI for coding, but their role is evolving rather than disappearing.

tl;dr: Rather than writing boilerplate code (which AI now handles), juniors must focus on higher-level skills like debugging, system design, and effective collaboration. Companies that cut junior positions entirely risk their future talent pipeline. The most successful junior developers will use AI as a learning tool rather than a crutch, verifying its output and understanding the "why" behind solutions. They should make sure they build up the ability to read and comprehend code.

Despite doomsaying that AI will kill off entry-level programming jobs, the need for junior engineers isn't going away. Industry leaders have realized that completely bypassing the junior level is shortsighted – after all, every senior engineer once started as a junior. As Camille Fournier bluntly put it, many tech managers who shifted to "senior-only" hiring are asking for trouble: "How do people ever become 'senior engineers' if they don't start out as junior ones?" according to Fournier.

Indeed, companies that try to staff only with experienced devs face a pipeline problem. Without juniors today, there are no seniors tomorrow. And beyond pipeline, junior developers bring fresh perspectives and growth potential that organizations can cultivate.

Leaders playing the "senior only" game risk cannibalizing their future talent base.

As Charity Majors argues, believing AI means you can stop training new engineers is a grave mistake – "By not hiring and training up junior engineers, we are cannibalizing our own future. We need to stop doing that." Majors emphasized on Stack Overflow. Smart engineering organizations are recognizing that junior devs still add value; the difference is that their value proposition is shifting.

Rather than cranking out boilerplate code, juniors in the AI era contribute by learning fast, adapting to new tools, and becoming the next generation of well-rounded engineers under guided mentorship. As Fournier notes, you ignore "early career" engineers at your peril:

"Don't underestimate the value early career engineers can bring to a team; unless we're a dying profession, their continued existence and success is the basis for our future."

In short, junior developers remain a critical investment – but how and where they add value is evolving.

AI coding assistants are reshaping junior developers' daily work, automating tedious tasks and raising expectations for higher-level contributions.

The day-to-day life of a junior engineer looks different now that tools like Cursor, Cline and Copilot are in the mix. Traditionally, new developers cut their teeth on small, repetitive tasks – fixing simple bugs, writing unit tests, churning through minor feature tweaks. These tasks were mundane but crucial for skill-building. Now, a lot of that grunt work can be handled by generative AI.

In many teams, an AI pair programmer can autocomplete code or generate the first draft of a function in seconds. This means a junior might spend less time writing boilerplate from scratch and more time reviewing or tweaking AI-suggested code. It sounds great – the boring parts are done, so humans can focus on more "interesting" problems. Indeed, developers report productivity boosts using AI to handle rote coding so they can tackle higher-level design and integration issues. But this shift also raises the bar for juniors. If an AI can fix a trivial bug or scaffold a component with minimal human input, why pay a junior to do it instead?

As Sourcegraph CEO Quinn Slack noted, many companies are asking,

"If AI can fix small bugs or add small features with little human intervention, then why pay a junior dev to do that instead?" Slack points out.

The result is that junior engineers are expected to contribute in ways that go beyond what an AI can do – things like understanding requirements, verifying correctness, and injecting creativity. Rather than being the person who cranks out boilerplate, a junior might be asked to serve as the "editor" of AI-produced code, or to focus on tasks requiring human insight.

Anna Demeo, a former head of engineering, describes this new dynamic: coders are becoming more like editors, needing to "understand the content and who the reader [customer] is" to guide AI-generated output. In practice, a junior dev might use Copilot to generate code, but they're responsible for reviewing it, running it against edge cases, and molding it to fit the project's context. AI is closing some skill gaps between junior and senior devs by accelerating how quickly code can be written, but it's also creating new expectations: juniors are expected to supervise the AI's work, not just accept it blindly. This is a big change in the apprenticeship model – more autonomy and higher-level thinking from day one, with AI handling the easy stuff.

Today's junior engineers are learning differently than past generations, relying on AI tools and self-guided exploration instead of exclusively on grunt work and in-person mentorship.

In decades past, a junior developer's learning journey was fairly structured: you'd join a team, pair up with a more experienced engineer, and spend months gaining proficiency by slogging through basic tasks. You learned by doing (and sometimes struggling), gradually absorbing best practices from code reviews and mentorship. Fast forward to 2025, and that journey has accelerated – and become more self-service. Modern juniors have a wealth of AI-powered help at their fingertips. Need to understand a new codebase? An AI can explain the code to you in plain language. Stuck on a bug? AI can suggest possible fixes in seconds. In fact, a recent survey found about 75% of developers use some kind of AI tool for coding or learning, so new engineers are almost expected to leverage these aids. The upside is a junior can ramp up faster on new technologies, using AI as a tutor and a guide.

The downside is the learning can be superficial if they're not careful. As one tech blogger lamented,

"Every junior dev I talk to has Copilot or GPT running 24/7. They're shipping code faster than ever. But when I dig deeper…that's where things get concerning." according to one report

Newcomers may solve problems quickly with AI's help, but skip learning the "why" behind the solution. Namanyay Goel, after conversations with junior devs, observed that many couldn't explain how their AI-generated code works or handle follow-up questions about edge cases – "foundational knowledge is missing as junior developers are not learning from scratch," he warns. In earlier generations, learning through struggle (e.g. scouring Stack Overflow threads) was slower but built deeper understanding; by contrast, "AI gives you answers, but the knowledge you gain is shallow", Goel notes, whereas reading expert discussions taught not just what worked but why. Another shift in the learning journey is the reduced face-time with mentors.

With remote work and AI assistance, some juniors have fewer opportunities to casually ask a senior developer for help. Jeff Watkins points out that recent cohorts were "forced to learn how to do the job without face-to-face mentors" due to the pandemic and now rely on AI assistants in that vacuum. This independence can be empowering but also isolating – juniors might lean on AI for answers in situations where previous generations would have received guided coaching. The terminology of "junior" vs "senior" is also evolving. Some companies now refer to "early career" engineers instead of "junior", with the expectation that even entry-level hires come in more skilled and independent from day one.

Per Camille Fournier, "Better get busy with those side projects and internships, college students!" she notes – in other words, new grads today are expected to have a stronger base coming in, possibly because they've had AI and online resources to learn from. The learning path for a junior developer now is less about slowly climbing a ladder of difficulty under close supervision, and more about taking initiative to learn continuously, often by collaborating with AI. Successful juniors treat AI as a learning tool – using it to prototype solutions and then studying the generated code to deepen their understanding – rather than a magic shortcut.

Those who approach it with patience (inspecting how and why the AI's code works, experimenting with it) can build up a strong foundation quickly. Those who don't may find themselves with gaps in their knowledge. In summary, today's juniors have unprecedented tools to learn faster, but they must be proactive in using them to truly absorb software craftsmanship, much like apprentices in any craft need to practice deliberately even if power tools are available.

Fundamental skills like debugging, code reading, system design, and communication have become even more critical for juniors as AI automates the easy parts.

There's a saying echoing in engineering circles: "Writing code is the easy part of software engineering – the hard part is what comes after." This is even more true in the age of AI. Generative AI can churn out code in seconds, but it can't ensure that code actually works in your complex system, meets your users' needs, or can be maintained over time. The upshot is that junior developers need to double-down on the foundational skills that AI can't replace. Debugging is a prime example.

When an AI-generated solution fails or behaves unexpectedly (a common occurrence), it falls to the developer to diagnose and fix it. Junior engineers who have relied on an assistant for code may struggle here – but learning to debug is non-negotiable. In fact, leaders have noticed that some juniors "struggle to debug AI-generated code" and end up with fragile systems they don't fully understand. Being able to read a stack trace, pinpoint a defect, and systematically resolve an issue is a skill that remains entirely human. Similarly, reading code (especially code you didn't write yourself) is a critical skill. AI might generate a chunk of code for you, but you must be able to read through it and grasp its logic. Is it doing what you intended? Are there hidden assumptions or errors? Juniors who build the habit of reading code critically – whether AI-produced or written by teammates – will stand out.

This ties into code review and quality mindset: treating AI output with the same scrutiny as you would a colleague's code. Importantly, system design and architectural thinking are now expected earlier in a developer's career. Since AI can help assemble building blocks, junior devs are increasingly freed to think about how those blocks fit together. Kesha Williams, an engineering leader, notes that thanks to AI automation her team's developers (including juniors) have more time to focus on "strategy and system design and creative problem-solving," and it even "seems to help them move faster into architecture." according to Williams

In other words, junior engineers are getting exposure to high-level design considerations sooner. But to excel in that, they need a solid grasp of fundamental concepts (like how web requests flow through a backend, or why one database schema might be better than another) that no AI can simply bestow upon them.

Communication and collaboration skills have also come to the forefront. When routine coding is less of a bottleneck, a junior's ability to communicate – to ask good questions, to explain their thinking, to understand product requirements – becomes a bigger part of their value. AI is not going to hop on a Zoom call and discuss trade-offs with your design team; a junior developer will. And in an environment where AI is involved, communication includes articulating what you need from the AI ("prompt engineering") and reporting back on what it did.

As Anna Demeo pointed out, the remaining developers in AI-heavy teams must be "critical thinkers who understand the business needs and can work in cross-functional teams" she emphasized. That means juniors should practice translating technical details for non-engineers, writing clear documentation, and collaborating across disciplines – skills that ensure they're not just coders, but engineers.

It's telling that the panel of developers convened by Business Insider emphasized that even with AI, "the basics of software engineering…would remain important" they concluded – understanding programming languages, how to scale systems, how to handle data, etc. These core competencies form the bedrock that allows an engineer to use AI effectively. In short, as a junior dev you should still invest time in learning computer science fundamentals, design principles, and debugging techniques.

AI can automate typing out a function, but it can't debug a production outage at 2 AM – that's where your fundamental engineering intuition is irreplaceable. And paradoxically, because AI makes it easy to generate lots of code quickly, the ability to manage complexity (through good design and clear communication) is even more crucial to prevent the codebase from spiraling out of control. The senior engineers of the future – today's juniors – will be defined less by how fast they can write code and more by how well they can understand, critique, and evolve code in a complex socio-technical system.

Over-reliance on AI poses serious risks: junior developers may see their core skills and engineering intuition atrophy if they treat AI as a crutch.

While generative AI is a powerful assistant, it can become a double-edged sword for an inexperienced developer. One risk is the emergence of what I call "house of cards code" – solutions that look correct on the surface but collapse under real-world conditions. This happens when a junior blindly accepts AI-generated code without fully understanding it or verifying its correctness. The immediate result might be a quick fix or a new feature that "works on my machine," but because the junior skipped the critical thinking step, the code may be full of hidden bugs, security flaws, or performance issues that only become apparent later.

A pattern noted across the industry is that some new developers are becoming too dependent on AI suggestions, pasting in code that they don't truly comprehend.

Over time, this dependence can erode the development of their own skills. If every time you encounter a problem, you turn to AI for an answer, you might never learn how to solve problems yourself. As one tech veteran observed, "we're trading deep understanding for quick fixes, and while it feels great in the moment, we're going to pay for this later." industry experts warn

The "payment" he refers to is the accumulation of knowledge debt – gaps in a developer's understanding that might not hurt today but will definitely hurt in the long run. Juniors who don't practice writing code unaided or who don't dissect AI outputs to learn from them can find themselves in a precarious position: they might have a portfolio of completed tasks, but struggle when faced with a novel problem or when the AI's suggestion is wrong.

Loss of debugging skills is a concrete example of skill atrophy. Debugging is like a muscle – it strengthens with use. If a junior leans on AI to even diagnose issues ("Hey AI, why is this code not working?"), they might not develop the methodical troubleshooting approach that seasoned developers have. This is dangerous because when the AI inevitably gives an incorrect or irrelevant answer (which happens often, as it has no real understanding of the code's intent), a junior could be left completely stumped.

In essence, over-reliance can short-circuit the feedback loop that builds intuition. Experienced engineers often talk about "gut feeling" or intuition about code – that sense of smell for where a bug might lurk or why a design doesn't feel right. Those instincts come from experience, from many hours of trial and error. If juniors skip directly to the solution every time via AI, they miss the errors and lessons that form that intuition. This concern is echoed by engineering leaders who caution that if AI handles all the easy bugs and tasks, junior devs might not get enough practice to build their instincts. Even worse, they might develop a false sense of confidence.

Imagine consistently getting 70% of a solution from an AI and thinking that's the whole job – you might never learn to do the remaining 30% properly.

I’ve describes this as the "knowledge paradox": "Seniors use AI to accelerate what they already know how to do; juniors try to use AI to learn what to do… The results differ dramatically." as I’ve explained In practice, senior devs guide the AI and then apply their judgment to fix or improve the output, whereas juniors may accept the AI's output uncritically. The result? Code that is brittle and half-understood by its author. Another subtle risk is losing the habit of rigorous learning.

In the past, a junior encountering something new might read documentation, experiment, maybe ask a teammate – all actions that reinforce learning. If that same junior instead just asks an AI every time, they get answers spoon-fed. The learning still can happen, but it's easy to become passive. "AI handles complexity on your behalf, which can actually impede learning," I’ve noted. When solutions "just appear" without the struggle, a junior might not develop resilience or curiosity to dig deeper.

Moreover, there's the risk of misinformation and outdated practices. AI models sometimes produce outdated code or suggest insecure practices because they were trained on data that includes bad or old code. A junior who doesn't have the experience to recognize this can introduce bugs or vulnerabilities unknowingly. I’ve observed that juniors often "accept incorrect or outdated solutions" from AI and "miss critical security and performance considerations." according to research Without a skeptical eye, a junior could happily implement a suggestion that works in the narrow case but fails tragically in production.

In short, unchecked reliance on AI can lead to skill rot. It's like relying on a calculator without ever learning arithmetic – fine for quick answers, but you'll be in trouble if the calculator is wrong or if you need to understand the equation. The key is balance: juniors (and all devs) should use AI as a tool – a powerful one – but also continuously challenge themselves to work through problems manually, verify AI's answers, and seek understanding. Many teams are now explicitly warning: don't let "vibe coding" with AI replace solid engineering practices experts caution.

The goal is to leverage AI's speed without losing the engineering rigor that ensures code is robust. Organizations that notice juniors leaning too hard on AI are responding by emphasizing training on fundamentals and requiring thorough code reviews to catch AI-induced mistakes. For juniors themselves, awareness is the first step: if you find you couldn't implement something without the AI, that's a sign to go back and study that area. Treat over-reliance as an engineering smell and correct it early – your future self (and team) will thank you.

To thrive, junior devs must cultivate new AI-era skills – from prompt engineering and verifying AI output to understanding AI's ethical implications and limits.

The rise of AI in software development doesn't just add tools to a developer's belt; it also demands new competencies. One of those is prompt engineering – the art of communicating effectively with AI. Crafting a good prompt (be it in natural language or a comment) can be the difference between an AI helper producing garbage vs. gold. As one engineering head noted, "having the skills to clearly articulate what you want to an LLM is a very important skill" for developers now. This means juniors should practice describing problems clearly and experimenting with rephrasing queries to get better results from AI. In a sense, it's an extension of problem decomposition – a skill we already value in coding – now applied to querying an AI. Alongside writing good prompts, juniors need skill in evaluating AI-generated code critically.

Think of it as quality control or AI output literacy. This involves testing the AI's code, checking edge cases, and comparing against requirements. Instead of assuming the AI is always right (it isn't – it often makes mistakes or oversimplifications), a strong junior developer treats its output with healthy skepticism. You might ask: Does this code handle invalid input? Is it following our team's conventions? Is the algorithm choice reasonable? Learning to ask these questions routinely is a new skill that distinguishes the best AI-augmented engineers.

In fact, many teams adopt a "trust, but verify" mindset: use the AI's speed, but always review and verify the results before merging.

As a junior, you can practice this by intentionally scrutinizing every AI suggestion – almost like you're doing a code review on the AI. Another emerging skill is understanding the basics of AI and machine learning itself. No, you don't need a PhD in ML to be a web developer – but having some knowledge of how these models work (and where they fail) is useful. Panelists have noted that developers should "understand machine-learning concepts and how AI models work, not necessarily how to build them from scratch" industry experts recommend. For example, knowing that large language models have no true understanding and can "hallucinate" incorrect answers gives you context to always double-check critical code. Juniors should learn about concepts like model training data, bias, and the idea that correlation is not causation, so they aren't mystified by the AI's behavior.

Soft skills in an AI context are also key. For instance, learning how to pair program with an AI. This involves a rhythm of iteratively asking the AI for help, assessing its responses, and guiding it with further detail. It's a bit like mentoring an extremely fast but sometimes erratic intern (one that happens to be made of silicon).

I suggest treating AI as "a very eager junior developer on your team" that is super fast but "needs constant supervision and correction". That's a skill to practice: how do you supervise and steer an AI assistant? It might mean learning commands or settings of these tools to make them more effective, or knowing when to turn it off and solve it yourself. Being adept at using AI-based tooling (whether it's VS Code's Copilot, Cursor or a CLI tool, or a code-generation API) will be a valuable skill set in job interviews and on the job. In fact, some companies now list "experience with AI coding tools" as a desirable skill for new hires.

We're also seeing the birth of specialized roles like "AI engineer" or "prompt engineer" that didn't exist a few years ago.

These roles often involve integrating AI into products or refining how AI is used by teams. A junior developer today might choose to steer their career towards this intersection of software and AI. If that interests you, start building skills in working with AI APIs, data prompt tuning, and evaluating model outputs – these could open up a high-demand career path. To summarize, junior developers should expand their skill set to include:

• Prompting skills – communicate with AI clearly and effectively (provide context, specify the task, iterate on prompts).

• Critical evaluation – never accept AI output at face value; test it, debug it, and make sure you understand it.

• AI tool proficiency – get comfortable with AI Tools or others in your stack; learn their features to use them optimally.

• Basic ML knowledge – understand what AI can and cannot do (e.g., it's great at pattern matching, bad at logical planning), and the concepts of training, bias, etc.

• Ethical and security awareness – be cautious about data privacy (don't feed customer data into a public AI), licensing issues, and biased outputs.

• Adaptability – keep learning as AI tools evolve. The AI of 2025 is not the same as 2022's – stay curious and be willing to adjust your workflows.

Developing these skills will ensure that instead of being outshined by AI, junior developers become effective AI-empowered engineers who can do things neither AI nor conventional devs could do alone.

Mentorship and onboarding need an update in the AI age, emphasizing guided use of AI tools, active learning, and "trust but verify" practices for juniors.

As generative AI becomes part of the developer toolkit, the way we mentor newcomers must adapt. In the past, a lot of mentorship for junior engineers involved teaching them how to do the rote tasks – how to write a loop, how to use Git, how to not crash the production server with a small change. Now, juniors might arrive already armed with AI assistance that handles some of those basics, but they need mentorship in areas that are arguably more subtle: how to use these tools wisely and how to develop the skills that the tools can't. Good mentors will explicitly coach juniors on how to integrate AI into their development workflow without becoming over-dependent.

For example, a mentor might pair program with a junior and show them how they use Copilot: "See, I prompted it with a descriptive function name and a comment, it generated this snippet. Now, watch how I evaluate that snippet – I write a quick unit test to make sure it covers edge cases, and I adjust it to fit our style." By making their thinking process visible, the mentor teaches the junior how to guide the AI and how to double-check it. A key mentoring point is to instill a mindset of verification. Juniors should learn from day one that AI is a partner, not an authority.

One practical approach is requiring juniors to explain any AI-generated code during code reviews.

For instance, if a junior used AI to generate a function, the mentor might ask: "Can you walk me through what this code is doing and why you believe it's correct?" If the junior struggles, it's a teaching moment – they might realize they need to dig deeper next time. This practice encourages juniors to use AI with understanding. Teams are also adapting their onboarding documentation. An example might be an "AI Assistance Guide" included in the new-hire packet, explaining which AI tools are approved, common pitfalls, and tips for effective use.

It could include guidelines like "Always run generated code through our test suite" experts recommend or "Don't use AI for sensitive code sections (security critical stuff) without a thorough review." Setting these expectations early normalizes responsible AI use.

Onboarding projects for juniors might evolve as well. Instead of the classic starter project (like a trivial bug fix), an onboarding task could be designed to teach both the codebase and how to use the AI tools in context. For example, a new hire might be asked to implement a small feature with the help of an AI assistant, and then write a short reflection on what the AI did well and where manual changes were needed. This not only ramps them up on the codebase but forces them to actively think about the AI's role and limitations.

Mentors should also be aware of the emotional aspect: Many junior developers are anxious that using AI might make them look like they're "cheating" or conversely, that not using AI means they're slower than their peers.

A good mentor will reassure them that using AI is not only acceptable but expected – as long as it's done prudently. In a recent roundtable, Kesha Williams mentioned working to help her team "not see AI as a threat but more as a partner" and setting up learning paths so people can embrace AI without fear. This kind of leadership is vital. Mentors and team leads should create an environment where juniors feel comfortable discussing AI openly – asking "The AI suggested this approach, is it okay?" or even sharing when the AI was wrong.

Mentorship in the AI era also means doubling down on teaching the fundamentals and filling gaps that AI might hide.

If you're a senior engineer mentoring a junior, you might notice that the junior hasn't yet learned a certain algorithm or design pattern because the AI often handles it. It's now part of your job to point them to that knowledge: "Hey, the code suggestion you got is using dynamic programming – do you understand that concept? If not, let's talk it through or find a resource." Essentially, mentors need to be proactive in preventing the "skill atrophy" we discussed. This could even mean occasionally asking juniors to do things without the AI as a learning exercise. For instance, "Try implementing this small module on your own first, and then we'll see what Copilot suggests and compare." Such exercises can highlight differences and teach why the AI's way is or isn't ideal.

Team culture around asking for help also needs attention. With AI as an ever-present assistant, some juniors might hesitate to ask human teammates for help, thinking "I should just figure it out with the AI."

Mentors should remind them that asking questions is still encouraged. In fact, having a junior explain their problem to a human can often surface insights even the best AI hint might not provide. Charity Majors pointed out that bringing juniors onto a team can "create an environment where asking questions is normalized and encouraged", which is healthy for the whole team. That shouldn't get lost in an AI-rich environment. If anything, mentors should lead by example, showing that even seniors ask each other for input and don't solely rely on a machine.

Finally, consider mentorship pairings: we might see a model where one senior is responsible for guiding both a new junior developer and the proper use of an AI tool for that junior. In this "triad", the senior oversees the learning of the junior with an eye on how AI is aiding or misleading them. Some companies are formalizing this by training seniors in "AI mentorship" – effectively becoming the go-to person on the team for questions like "When should I trust Copilot here?" or "Why is the AI suggestion suboptimal?".

In summary, effective mentorship now includes: teaching AI literacy, reinforcing fundamentals to counterbalance AI's shortcuts, and fostering a team culture where juniors use AI as one tool among many and still benefit from human guidance. The goal is to produce engineers who are both AI-savvy and deeply knowledgeable, rather than one or the other.

Traditional metrics of junior performance are shifting – evaluation now rewards code understanding, problem-solving, and effective use of AI more than raw output.

With AI in the mix, engineering managers are rethinking how they assess junior developers. It used to be that a manager might look at how many features a new engineer delivered in their first months, or how many bugs they fixed. But if an AI assistant is helping generate a lot of that code, those raw numbers are less indicative of the junior's personal skill. After all, cranking out 10 CRUD endpoints with AI assistance might not be as impressive (or valuable) as it once was.

Quality and comprehension are becoming the yardsticks. Can the junior developer explain the code they contributed? Do they understand the edge cases and caveats? When reviewing their work, do they catch errors and correct them, or do they just pass along AI outputs hoping for the best? Managers are placing more weight on code reviews and design discussions to gauge a junior's growth.

For example, a junior might be evaluated on how well they incorporate feedback – if a code review pointed out a flaw in an AI-suggested approach and the junior learns from it and doesn't repeat it, that's a positive sign. Another key performance indicator in an AI-heavy environment is problem-solving approach. Does the junior know when to leverage the AI and when to rely on their own thinking? An astute junior will, for instance, use the AI to generate ideas or boilerplate, but then diligently test and refine the result.

If they encounter a completely new problem, do they attempt to break it down logically or just throw the whole thing at AI and pray?

Managers might discuss during one-on-ones: "Tell me how you arrived at this solution." If the answer is "Copilot did it, I just clicked accept," that might be a red flag unless followed by "…and then I wrote these tests and refactored it because I realized it needed improvement." Essentially, the process matters as much as the end result. In performance reviews, we might hear new questions like: Is the developer using AI to enhance their productivity in a sensible way? Are they learning from AI suggestions over time (showing growth), or making the same mistakes repeatedly?

If a junior consistently produces code that passes CI and meets requirements, even if aided by AI, that's great – but if they can't fix something when it breaks or can't work without the AI holding their hand, that will come out in evaluation through scenario-based discussions or troubleshooting sessions.

Teams are also considering pair programming exercises or take-home projects for evaluation that explicitly involve AI. For instance, some interview processes now allow (or even encourage) candidates to use AI on a coding challenge, precisely to see how they use it. Similarly, a junior developer's trial project in a company might involve them building something with AI assist and then discussing their choices. This can reveal a lot: did they just accept the first answer the AI gave, or did they guide it thoughtfully?

One concrete metric could be the number of iterations a junior goes through with an AI tool to get to the final code. If they prompt once and copy-paste, versus prompt, test, refine prompt, and so on – the latter indicates a more thoughtful approach. In daily work, a manager might notice these patterns and give feedback accordingly.

Managers might not explicitly say that, but practically, they care about the junior's learning curve and problem-solving journey, not just the final commit. Are they developing good habits with these new tools? For senior engineers or tech leads evaluating juniors, one tip is to pay attention to failure modes: What does the junior do when the AI gives a wrong answer? That often separates a future star from someone who's struggling. A strong junior will identify something's off and seek another solution (different prompt, ask a colleague, consult docs). A weaker junior might be stuck or not even realize the solution is wrong. During evaluations or check-ins, discussing a time when the AI was wrong can be very illuminating. It's worth noting that companies haven't fully figured this out yet – this is all new territory.

But consensus is forming that core engineering thinking is what needs to be measured. Speed of coding is less impressive now (everyone's fast with AI); understanding and sound judgment are the differentiators. As one CTO wryly observed, "Why should we pay $100k for a junior engineer to slow things down, when we could pay $200k for a senior engineer to speed things up?" one leader notes. That sentiment might sound harsh, but what it really implies is that juniors must prove they're not causing slowdowns – i.e. not introducing lots of bugs or technical debt that seniors must clean up.

Therefore, a junior who, with the aid of AI, can deliver features with solid quality and minimal issues will justify their value. In performance terms, that means tracking things like the defect rate of their code, their responsiveness to feedback, and their growth in independence. In summary, expect your performance as a junior to be measured more on wisdom than velocity: your ability to use tools smartly, solve problems, learn quickly, and collaborate. Those who excel in these areas will find that AI is actually an asset in showcasing their strengths, while those who might have skated by on repetitive coding skills will need to step up their game.

The junior-to-mid-level career path is accelerating and transforming, with AI enabling faster growth but also requiring deliberate skill-building to avoid gaps.

Historically, it might take a few years for a junior engineer to confidently transition to a mid-level role, having accumulated enough experience through various projects and challenges. Now, we're seeing signs that this progression can happen quicker – but it's not an automatic freebie from AI; it depends on how the junior leverages the technology and fills in their own learning. On the optimistic side, generative AI can act like "career accelerant." A motivated junior developer today can gain exposure to a wide range of scenarios in a short time, partly because AI tools let them attempt things that would normally be above their pay grade.

For example, a junior can try scaffolding an entire microservice (something usually a mid-level might do) using an AI assistant, and in the process learn about all the pieces involved. They might fail or need help, but even that failure is a learning opportunity that might have only come after a promotion in the past. Some tech leads observe that juniors using AI effectively are "upleveling fast and not worrying about AI replacing them." Slack observed These juniors treat AI as a means to take on tasks that stretch their abilities – effectively practicing mid-level work earlier.

Also, because AI can handle a lot of coding grunt work, juniors can spend more time on understanding system architecture or participating in design discussions which previously might have been considered too advanced for them. Kesha Williams noted her developers move faster into architecture considerations, since AI frees them from some lower-level tasks. This suggests that a junior could build the competencies of a mid-level (like system thinking and design trade-offs) sooner than the traditional timeline.

We already see that being called a "mid-level" or even "senior" engineer might carry new meaning – it's less about years of manual coding and more about the ability to handle complexity and take ownership. Camille Fournier mentioned she started using "early career" and "senior" not just by years but by independence and judgment. Those qualities still take time and experience to build, AI or not.

One big unknown is how the lack of traditional apprenticeship affects long-term growth. If many companies reduce junior hiring (as some trends indicate), there could be a gap where in a few years, there aren't enough mid-level engineers because not enough juniors were trained. One IT leader warned that with fewer junior dev jobs, "there won't be a natural apprenticeship to more senior roles." some worry. In a sense, the industry at large needs to keep that funnel healthy. If an individual junior finds themselves in a place that isn't investing in their development (thinking the AI can replace that), they might need to seek out mentorship elsewhere (online communities, open-source projects, etc.) to grow. On the flipside, some predict that in a few years, teams will be smaller and more efficient, meaning fewer total roles including mid-level ones according to some predictions. If that happens, competition to get promoted might increase.

The juniors who will advance are those who prove they can handle the responsibilities of a mid-level – not just coding, but taking initiative, solving problems, and perhaps mentoring even newer folks (or "mentoring" AI models). We might also see a bifurcation in junior roles: some will become more like "software generalists with AI", and others might become highly specialized in, say, data engineering or DevOps, because AI doesn't eliminate the need for deep expertise in those areas.

A broad prediction for 3-5 years out is that a junior joining the workforce in the AI era might reach a productive, semi-autonomous state in, say, 1 year what used to take 2 years – if they and their team emphasize real learning alongside AI use. That could mean earlier promotions or more quickly taking on critical projects. However, it also means the distinction between a "junior" and "mid-level" engineer will blur sooner. A junior might be contributing at a mid-level scope early, but still be catching up on corner-case knowledge.

Engineering leaders are actively monitoring this transition. Some are optimistic:

"The end of junior devs as we know it is coming for sure… the role and expectations for a junior dev will look very different in a couple years." some predict

What likely will happen is not that the junior title vanishes, but that the early career period is more intense and rich. You'll be expected to handle what used to be mid-level tasks (with AI help) in your first couple of years. The positive side is you get to do more exciting work and prove yourself faster. The challenge is you have to constantly ensure you're actually leveling up and not just being propped up by tools. If you do it right, you might find yourself essentially operating as a mid-level engineer in, say, two years – capable of designing moderately complex features, guiding AI assistants effectively, and maybe mentoring the next wave of juniors in how to use them.

But if you do it wrong (coasting on AI without growth), you could hit a wall where you can't progress to mid-level because your fundamental skills are too shaky when confronted with a truly novel problem that AI hasn't seen before either.

In summary, the junior-to-mid journey is compressing for some and detouring for others. The main advice: embrace the acceleration that AI offers but be deliberate in filling the learning gaps. Seek out experiences that build the intuition and resilience you'll need as a mid-level and beyond. The first few years of your career might pack in a decade's worth of tool evolution and changes – stay adaptable. The ones who navigate this well could become very strong engineers relatively early, which is an exciting prospect.

In the next 3-5 years, the junior developer role will continue to evolve – potentially featuring smaller teams, new "AI engineer" roles, and a greater focus on adaptability.

Looking ahead, we can sketch a few plausible trends for the near future of junior programmers. First, many expect that software teams will become more efficient and possibly leaner thanks to AI. Studies already show high adoption of AI coding tools (97% of developers in some surveys use them at work), and as these tools improve, a handful of engineers might do what used to require a larger team. Ed Watal, a tech consultant, predicts that in a few years three engineers will deliver what five or six did before. If that holds, it implies fewer total junior hires per team. A project that might once have had two junior devs, two mid, and two senior might streamline to one junior, two senior, for example.

Another change on the horizon is how entry-level talent is trained. If companies hire fewer juniors directly, we might see more internship-to-hire pipelines or apprenticeship programs to ensure a supply of future talent. Large tech firms could invest in structured programs where juniors rotate through departments, learn how to work with AI tools responsibly, and build a portfolio of experiences. The end of the "trial by fire fixing bugs for a year" approach could give way to a more curated training program. Already, forward-looking engineering leaders like those at Microsoft and Google are exploring internal training on prompt engineering and AI literacy for all developers, including new hires. We can anticipate that in 3-5 years, AI-specific onboarding will be standard.

Of course, not everything will be rosy or radically different. There's a strong chance that three years from now we'll find that many challenges remain the same. Software projects will still get behind schedule, bugs will still creep in. AI might handle a lot of routine work, but the messy complexity of real-world systems will still demand human problem-solving.

In fact, as Charity Majors pointed out, AI making coding faster doesn't remove the hardest parts of engineering – understanding and evolving systems – and it "has not done a thing to aid in managing or operating that code. If anything, it has made the hard jobs harder." Majors writes. That suggests that junior devs in 3-5 years will be thrown into tackling those "hard parts" sooner, because the easy parts are done. So we may see junior developers who are quite savvy in operational thinking or site reliability concerns earlier in their career.

Long-term, some experts believe demand for strong engineers will actually increase despite AI, as software projects multiply and become more ambitious. I and others note that historically, automation in software (from assembly to high-level languages, from on-prem to cloud) didn't kill developer jobs – it expanded them, but shifted the skill emphasis.

By 2030, we might have even more people writing code (or "writing code via AI"), including non-traditional developers, but seasoned engineers (today's juniors grown up) will be overseeing and integrating the work of perhaps dozens of AI agents and citizen devs. In that scenario, the junior dev role might morph into something like "AI Wrangler" or "Software Conductor" – someone who knows enough coding and enough toolsmithing to orchestrate complex software creation, with AIs doing the mechanical work.

It's a bit futuristic, but not far-fetched given the trajectory. In summary, expect the junior dev role of the near future to involve: working on smaller, more multidisciplinary teams; using advanced AI tools from day one; possibly stepping into specialized "AI x Software" roles; and needing to learn faster than ever. Adaptability will be the keyword – the juniors who succeed will be those who can continuously update their skills as the tools and expectations change. As one engineering VP mused, "Everyone needs to be constantly adapting… This was true before AI, and it's true in a world with AI." leaders agree That's doubly true for those at the start of their careers.

Engineering leaders should strategically invest in junior developers by hiring and training them alongside AI – it's crucial for long-term team health and innovation.

For tech managers and team leads, the advent of AI doesn't spell the end of juniors – it changes how you hire, onboard, and grow them. Here are some recommendations to navigate this new landscape:

• Don't halt junior hiring – refine it. It might be tempting to freeze junior hiring in favor of senior experts plus AI, but that's a shortsighted strategy. As discussed, you risk an experience gap down the line if you have no pipeline of early-career talent. Even in an AI-rich environment, juniors bring fresh ideas, energy, and the chance to mold future senior engineers in your organization's culture. As Camille Fournier advises leaders, reconsider any "all-senior" stance: the success of early-career engineers "is the basis for our future". Instead of not hiring juniors, focus on hiring those with adaptability and a growth mindset – people eager to learn and comfortable with new tools. Look for evidence of self-learning (projects, courses, even AI tool usage in school) as a sign they'll thrive with AI on the team.

• Update your onboarding and training programs. Incorporate AI literacy into your new-hire training. Ensure that juniors joining the team get guidance on how to use the sanctioned AI tools effectively and responsibly. This might include walkthroughs of successful AI-assisted workflows and clear guidelines on things like not exposing sensitive data to public AI services. Pair new juniors with a "buddy" or mentor who can specifically coach them on not just the codebase, but also our AI practices. Consider creating an internal playbook (if you haven't already) for AI-assisted development – juniors (and frankly all engineers) should read docs that say, for example, "We treat AI suggestions like junior developer submissions: review them thoroughly" or "When debugging, use AI for hints but verify the fix with a test." Setting these norms early prevents bad habits.

• Foster a culture of "trust but verify" and continuous learning. Make it explicit that using AI is encouraged, but unchecked reliance is not. Encourage seniors to share stories in engineering meetings about times AI was wrong and how they caught it – this normalizes the fact that everyone has to stay sharp. Similarly, celebrate when a junior uses AI in a clever way to solve a problem and can explain the solution. That reinforces the behavior you want. Leaders should also provide learning opportunities that fill in fundamentals: maybe sponsor a weekly study group or lunch & learn on core topics (one week on debugging techniques, another on system design basics) so juniors build knowledge that AI might otherwise shortcut. Remember, the goal is to integrate AI and keep strengthening human skills.

• Set new performance expectations and feedback loops. Revise what success looks like for a junior engineer on your team. Instead of only metrics like "completed X tickets," include things like "demonstrates understanding of AI-produced code" or "asks for help or clarification when needed (doesn't hide behind AI)." During one-on-ones, ask juniors not just about what they delivered, but what they learned. Questions like "What's something you had to correct in an AI suggestion this sprint?" or "Is there anything the AI did that you didn't understand?" can prompt reflection and signal that learning is valued over sheer output. Provide timely feedback if you observe a junior overusing the AI without growth. It's better to course-correct early ("I noticed you copied that solution without checking edge cases; next time, think about X and Y") than to let poor practices ossify.

• Encourage mentorship and reverse-mentorship. Mentorship remains key. Ensure each junior has a go-to person for questions – someone who will not just give answers but also model how to approach problems (with and without AI). At the same time, recognize that juniors coming in now might be quite savvy with AI tools themselves. They might discover new plugins or prompt techniques. Let them share these findings with the team. This "reverse mentorship" where juniors educate others on AI tricks can be empowering for them and beneficial for everyone. It also affirms that proficiency with these new tools is an asset, not something to hide. One could even set up an internal forum or chat channel for AI-assisted coding tips, and juniors often enthusiastically contribute there.

• Diversify the experience junior devs get. Rotate junior engineers through different types of tasks: some where AI shines (e.g. writing boilerplate), and some where AI might not help much (e.g. debugging a tricky race condition, or doing customer support triage of bugs). The variety will ensure they don't develop tunnel vision. If your team is smaller now (fewer juniors overall), you might consider giving each junior exposure to multiple areas of the codebase or multiple stages of development (front-end, back-end, testing, ops) early on. This can accelerate their learning and also reveal where AI is most and least helpful, which is good insight for the team.

• Monitor workload and avoid "pseudo-senior" burnout. One risk with capable juniors and powerful tools is to overburden young engineers with too much responsibility too soon. Just because a new grad can, with AI help, take on what feels like a mid-level task doesn't mean they have the experience to handle the stress if things go wrong. Leaders need to strike a balance: give stretch opportunities but also be ready to support if the junior gets in over their head. Don't fall into the trap of assuming AI makes a junior "as good as" a senior – remember the many nuances that come with experience. Be mindful of their hours and stress levels; learning and working at an accelerated pace can be tiring. Keep an eye out for signs of struggle that might be hidden behind completed tasks.

• Plan for long-term career development. It's wise to revisit your engineering career ladder and see if it needs tweaks for the AI era. For instance, you might add competencies around AI tool usage, or explicitly state that a mid-level engineer in your org should be able to mentor a junior + AI pair. Ensure juniors know what they need to grow into mid-level roles: maybe more emphasis on system thinking and less on lines of code produced. You might also think about creating a dual track where someone can grow as a specialist (like AI engineering or data engineering) versus a product engineer, depending on their strengths and interests. Giving juniors a vision of how they can advance in 3-5 years, and what skills to cultivate, will motivate them and guide their use of AI tools for learning rather than shortcuts.

• Maintain a human-centric team spirit. Last but not least, reinforce the value of human qualities in your team. Creativity, curiosity, teamwork, empathy – these are things AI can't provide and which great engineers embody. Junior developers often bring a lot of curiosity and fresh perspective. Encourage them to question how things are done and to propose new ideas (perhaps inspired by something an AI suggested!). Having an AI in the loop can sometimes make work feel impersonal or transactional; counter that by organizing team hackathons, pair programming sessions, or post-mortems that focus on human learning. Camille Fournier noted that some of the best engineers grew in environments that were messy and ambiguous – they learned to navigate real-world complexity. So ensure juniors get that human context and don't work in a vacuum with just an AI.

In conclusion, generative AI is undeniably changing the software engineering landscape, but it doesn't eliminate the need for junior developers – it reshapes it.

Junior engineers are still the future seniors and tech leaders in the making. The organizations that recognize this will use AI as a force-multiplier for their junior talent, not as a replacement. By adjusting hiring, mentoring, and evaluation practices, we can create an environment where juniors flourish with AI rather than flounder because of it.

Those juniors, in turn, will become the adaptable, skilled engineers who keep pushing our industry forward. As I and others have emphasized, AI is a tool to amplify developers, not a substitute for them. Ensuring our early-career engineers develop in tandem with AI will safeguard our teams' creativity and capability for years to come.

The companies that blend human potential and AI assistance most effectively will lead – and well-supported junior developers will be a crucial part of that equation.

I’m excited to share I’m writing a new AI-assisted engineering book with O’Reilly. If you’ve enjoyed my writing here you may be interested in checking it out.