AI is solving a big pain point: software development is expensive, time-consuming, and often repetitive. For years, companies have wanted faster delivery cycles and reduced costs. Now with AI, that vision feels within reach.
But every optimization has a trade-off. And one that’s starting to surface is the erosion of engineering depth. When we treat software engineering as just a sequence of task completions, we forget that good software isn’t just built, it’s understood, maintained, and evolved.
What makes someone an architect or a tech lead isn’t the number of tickets they’ve closed. It’s their accumulated experience, successes, mistakes, and everything in between; that allows them to see systems holistically. AI doesn’t gain that kind of experience, and if junior engineers don’t either, then who will step up in the future?
AI’s Capabilities vs. Developer Growth
AI Is Getting Really Good But Only at the Execution Layer
Right now, AI can write functions, generate documentation, scaffold projects, and even suggest design patterns. Tools like GPT-4, Claude, and Perplexity can analyze logs, write tests, and produce bug fixes on command. That’s not hypothetical, that’s today.
And while that’s exciting, it’s also deceptive. AI isn’t “thinking.” It’s responding to patterns it’s seen before. It doesn’t understand context beyond what you give it. It doesn’t ask follow-up questions. It doesn’t say, “Wait, that architecture doesn’t scale” or “This library will be a nightmare to maintain in two years.”
That’s what junior engineers learn; by watching, asking, failing, and being mentored.
Junior Engineers Aren’t Just Code Generators
The early stages of an engineering career are where people build foundational knowledge: How does this API work? What are the tradeoffs of this framework? Why is this data structure better here? You don’t get those answers from AI output. You get them from digging in, making mistakes, and being part of a team that reviews your work and explains the “why” behind decisions.
Removing junior roles means removing that path to mastery.
The Evolving Role of Senior Engineers in an AI-First World
As AI offloads more and more of the hands-on coding work, senior engineers are moving from being problem-solvers to becoming problem framers. Their new role isn’t just writing code; it’s about defining the constraints, setting the architectural vision, and ensuring that the AI-generated solutions align with long-term goals.
Think of it like this: if AI is a chef that can cook any recipe, senior engineers are now the ones who decide the menu, source the ingredients, and ensure the kitchen doesn’t burn down.
Architecture Is More Than Just Blueprints
Architecture in software isn’t just about drawing diagrams. It’s about deeply understanding the trade-offs of one approach versus another; scaling bottlenecks, security implications, deployment complexity, team skillsets, and long-term maintainability.
No AI can fully own that. These decisions are nuanced and often political. They depend on business needs, technical debt, legacy systems, and human factors. This is where experienced engineers shine. But again, where will the next wave of architects come from if no one’s gaining experience?
The Apprenticeship Model: Still the Best Way to Build Great Engineers
Historically, software engineering followed an apprenticeship model. Juniors learned by doing, asking questions, reviewing code, and observing decisions made by senior peers. Over time, they absorbed patterns, recognized anti-patterns, and built the judgment necessary to operate independently.
AI disrupts that rhythm. If juniors are now prompting AI for solutions and copy-pasting code into production, they’re skipping the reflection stage, the why behind the code. It’s like learning to play music by pressing the demo button on a keyboard. Sure, it sounds great, but you didn’t learn how to play.
The Risk of Hollow Expertise
A new type of engineer is emerging: one who can ship fast using AI, but lacks depth. They might know how to use the tools, but they don’t understand the codebase they’re working in, or why certain decisions were made. They rely on stack traces and AI for fixes, but what happens when the problem is novel, architectural, or fundamentally human?
We’re seeing this now in teams using AI as their main productivity driver. Some developers ship more, yes, but the bugs are harder to debug, the PRs are harder to review, and the overall system coherence starts to fray.
Real-World Patterns: What Teams Are Seeing
- Startups skipping junior hires: Startups with lean teams are opting to avoid hiring entry-level talent entirely. Instead, they use AI to fill the gap, leaning heavily on one or two seniors to review and merge code. It works, for now, but creates brittle knowledge silos and slows onboarding.
- AI as a crutch, not a teacher: Some junior devs admit that they rarely understand the code they’re submitting. “I just ask ChatGPT and tweak what it gives me,” one told me. That’s efficient in the short-term, but it stunts growth.
- Burnout among senior engineers: With fewer juniors, seniors are becoming the bottleneck. They’re not writing code, they’re reviewing dozens of AI-generated pull requests, trying to fix subtle bugs and ensure architectural consistency. It’s draining.
The Long-Term Risk: The Experience Deficit
We don’t feel it yet, but a silent crisis is brewing in software engineering: the experience deficit.
As AI tools take over junior tasks and organizations reduce or eliminate entry-level hiring, we’re cutting off the bottom rungs of the career ladder. This might not hurt us in the next 2–3 years, but push the timeline out a decade, and things get messy.
Think about the institutional knowledge that lives in senior engineers’ heads, how certain systems were designed, why that service was split, why we avoid a certain library, what trade-offs we made under time pressure, and how we’ve historically handled outages. This kind of tribal knowledge is rarely well-documented, and AI can’t synthesize what isn’t recorded.
Without new engineers being groomed into these roles, there’s no one to inherit that knowledge. No one to challenge, reinterpret, or modernize it. We’re not just losing workers, we’re losing context.
What Happens When Senior Engineers Retire?
It’s already starting. Teams lose a key architect or tech lead, and nobody left knows why the core platform was built the way it was. Junior and mid-level devs are left maintaining black boxes with no institutional memory. AI might help patch things, but it can’t tell you why the box was built in the first place.
Multiply that by thousands of companies, and the consequences are enormous:
- Legacy systems rot faster because no one knows how to evolve them intelligently.
- AI-generated code lacks direction, leading to fragmentation and inconsistency.
- Security holes emerge because no one remembers the policies and assumptions systems were built around.
- Technical debt piles up, not because teams are lazy, but because they lack the judgment to know what’s debt and what’s just “fast delivery.”
This is how brittle tech ecosystems form, not from bad intent, but from a slow erosion of human insight.
Reimagining Engineering Culture in the Age of AI
If we want to use AI responsibly and sustainably in engineering, we need to rethink how we structure teams and grow talent. Here are a few ideas to start:
1. Redesign Junior Roles, Don’t Eliminate Them
The solution isn’t to stop hiring juniors, it’s to evolve their job descriptions. Let AI handle the grunt work, but use that saved time for deeper learning: pair programming, code reviews, architectural deep-dives, post-mortem participation. Make education explicit, not accidental.
2. Establish AI Mentorship Tracks
Just like we have career ladders, we should build AI-adapted mentorship paths. Not “how to prompt ChatGPT,” but “how to evaluate AI output, spot bad patterns, and ask smarter questions.” This should be a formal part of onboarding.
3. Build for Knowledge Transfer
We need to bake documentation, architecture notes, and decision logs into our development culture. Not as afterthoughts, but as critical artifacts that AI and future engineers can learn from. Tools like Docusaurus, Backstage, or even internal wikis should be maintained religiously.
4. Expand the Role of the Staff+ Engineer
Staff and Principal engineers should see themselves not just as technical leaders, but as stewards of cultural continuity. They should actively shape how junior engineers interact with AI and take responsibility for mentoring, not just reviewing.
Actionable Steps for Engineering Leaders
1. Keep a Hiring Pipeline for Early-Career Talent
Even if AI allows you to move faster, don’t let short-term efficiency erase long-term resilience. Keep hiring junior engineers but be intentional about how you support them. Create AI-assisted onboarding paths and pair them with mentors who help them grow judgment, not just speed.
2. Shift Performance Metrics
Measure engineers not just by output, but by how they contribute to system understanding. Recognize those who write great documentation, review code with mentoring in mind, and contribute to architectural clarity. These “invisible” contributions are what make AI-generated code usable and maintainable.
3. Create AI Code Review Protocols
AI-generated code still needs human review; ideally by someone who understands the surrounding system and can check for scalability, security, and integration pitfalls. Develop checklists or rubrics to guide code reviews of AI-written features, so the review process becomes structured and repeatable.
4. Treat System Understanding as a First-Class Skill
Encourage engineers to regularly rotate through parts of the codebase they didn’t write. Run architecture deep dives. Host “why we built it this way” sessions. Make sure the knowledge doesn’t die with a few key engineers or get assumed into AI prompts.
Actionable Steps for Individual Engineers
1. Don’t Just Prompt. Question
Use AI as a starting point, not the final answer. Ask: Why did the AI choose this approach? What would I do differently? What assumptions is it making? These questions turn passive consumption into active learning.
2. Study the Classics
There’s still incredible value in understanding fundamentals: data structures, distributed systems, software design patterns. These aren’t replaced by AI; they’re the filters through which you can interpret its output.
3. Be the Glue
In every team, there’s someone who sees how the parts fit together. Aim to be that person. Understand the bigger picture, the product context, the user needs. That’s the kind of insight AI can’t replicate.
Build for the Next Generation, Not Just the Next Release
The speed of AI is seductive. It promises a future where things get built faster, cheaper, and with fewer people. But software isn’t just built, it’s grown, nurtured, and evolved. That takes experience, curiosity, and community.
If we cut junior engineers out of the equation, we don’t just lose cheap labor; we lose our future architects, our future mentors, our future stewards. And when the current generation retires, we’ll be left with powerful tools but no pilots to fly the plane.
So let’s not fall for the illusion of permanent acceleration. Let’s build teams, cultures, and systems that invest in experience. Let’s use AI to amplify human judgment, not replace it. Let’s keep the ladder intact for everyone climbing it now, and for those who’ll need it tomorrow.
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