Introduction
The term “broken rung” comes from McKinsey’s corporate leadership research. It’s the bottleneck where entry-level positions disappear, blocking the first step of career advancement. And right now, it’s reshaping how developers build their careers.
Here’s what’s happening. Employment for software developers aged 22-25 dropped nearly 20% from late 2022 to July 2025. Tech internship postings fell 30%. Early-career engineers are facing a 7.4% unemployment rate—nearly double the national average. And those “entry-level” job postings? They’re asking for 2-5 years of experience.
AI coding assistants are automating the exact tasks that used to train juniors: boilerplate generation, basic CRUD operations, debugging, refactoring. Meanwhile, senior developers using these same tools are showing 2.5× greater productivity, leveraging their decades of pattern recognition and architectural know-how.
So here’s the paradox. As Baby Boomers retire in massive numbers, you need to fill senior roles. But by eliminating entry-level positions, you’re ensuring there’s no junior pipeline to develop future seniors. Today’s missing juniors become tomorrow’s missing seniors.
In this article we’re going to look at why AI creates uneven impact across experience levels, what’s changing in hiring and career structures, and give you actionable frameworks for keeping your talent pipelines healthy. This career disruption is part of a broader transformation reshaping what it means to be a developer, with identity shifts affecting how we define career advancement criteria.
What Is the Broken Rung Problem in Developer Career Progression?
The broken rung is the structural elimination of entry-level positions that used to serve as the first step on the developer career ladder. Just like a ladder with a missing first rung is unclimbable, a career pathway with no entry-level positions is inaccessible to newcomers.
Historically, juniors handled routine coding tasks and bug fixes under senior supervision. This served dual purpose: it delivered business value while building skills progressively.
AI coding assistants now automate these exact tasks. When Copilot can generate boilerplate faster than a junior can type it, the economic calculus shifts.
The data confirms what you’re probably already seeing. Employment for developers aged 22-25 declined 20% from late 2022 to July 2025. New graduates now make up just 7% of Big Tech hires, down 25% from 2023. Those “entry-level” jobs? They’re requiring 2-5 years of experience.
Here’s why this matters. With Baby Boomers retiring in the largest wave modern tech has seen, eliminating the junior pipeline today guarantees a succession crisis tomorrow. You’re not just making hiring decisions—you’re determining whether your organisation will have the senior technical leadership it needs five, seven, ten years from now.
How Has AI Disrupted the Junior Developer Pipeline?
Fastly’s research shows senior developers ship 2.5× more AI-generated code than juniors using identical tools. About a third of seniors report over half their shipped code is AI-generated. Juniors? They manage only 13%.
Why the gap? Seniors have pattern recognition from thousands of debugging sessions. They spot when AI-generated validation logic misses an edge case or when AI picks an algorithm that’s correct but slow at scale.
Juniors lack the foundational understanding to verify AI outputs critically. When AI-generated code breaks, they can’t debug it because they don’t understand what AI wrote.
The internship collapse tells the clearest story. Tech internship postings dropped 30% since 2023. Seventy percent of hiring managers believe AI can perform intern-level work. And the sentiment is stark: 37% of employers would rather “hire” AI than recent graduates.
Remote work compounds the problem. Traditional junior development relied on osmotic learning—overhearing architectural discussions, observing how seniors debug. The combo of remote work plus AI automation means fewer organic learning opportunities.
This creates a self-fulfilling prophecy. Juniors get fewer opportunities to develop the expertise that enables AI effectiveness, which then justifies further reductions in junior hiring.
Why Are Entry-Level Developer Jobs Declining Despite Overall Tech Growth?
Workers aged 22-25 in AI-exposed occupations saw 6% employment decline between late 2022 and July 2025. Older workers in identical occupations? They saw 6-9% increases.
The economic calculation is straightforward. If a senior with AI is significantly more productive than a junior with AI, hiring one senior at 2× the junior salary delivers better ROI than hiring two juniors.
Risk perception amplifies this. Junior-generated code needs extensive review and often requires rework. When 66% of developers cite “solutions that are almost right, but not quite” as their primary AI frustration, and juniors can’t catch those “almost right” outputs, the quality risk makes juniors expensive.
The trust deficit runs deep. Fifty-seven percent of hiring managers now trust AI more than recent graduates. Traditional coding interviews test syntax knowledge—precisely the skills AI has commoditised. Without clear criteria for “good junior in an AI world,” organisations default to hiring only seniors.
The feedback loop is concerning. Fewer junior hires means fewer seniors with experience developing juniors. This declining capability justifies further reductions. The cycle feeds itself.
What Are Senior vs Junior Developer AI Productivity Gains?
Senior developers ship 2.5× more AI-generated code than juniors using identical tools. This gap persists across different platforms. The limiting factor is human expertise, not tool capabilities.
Seniors have accumulated pattern libraries from thousands of debugging sessions. Pattern matching happens almost instantly—they recognise anti-patterns, spot missing error handling, identify security vulnerabilities. This is built intuition.
Architectural knowledge provides another multiplier. Seniors evaluate AI suggestions not just for correctness but for system fit. Does this create coupling? Is this the right abstraction level? Will this scale? Juniors often can’t even formulate these questions.
The “trust but verify” difference is fundamental. Seniors know what to verify and how. Juniors often accept AI outputs uncritically.
Prompt engineering creates a compounding advantage. Seniors articulate problems precisely. Better prompts yield better outputs, which need less editing. Juniors write vague prompts and get less useful outputs.
When AI-generated code breaks, seniors diagnose issues quickly using mental models. Juniors struggle because they don’t understand the code AI wrote.
The strategic implication? The gap isn’t tool quality. It’s human expertise enabling tool use.
How Do I Hire Developers Who Work Effectively with AI?
Shift from assessing code generation to code verification. Can candidates read, understand, critique, and improve AI-generated code?
Test debugging mastery. Present candidates with broken AI-generated code. Strong candidates demonstrate systematic diagnostic processes. The fix matters less than the methodology.
Evaluate prompt engineering through observation. Give candidates an ambiguous problem and watch how they structure requests to AI tools.
Assess the “trust but verify” mindset. You want candidates who leverage AI speed but maintain healthy scepticism. Understanding validation competencies and quality control approaches is critical here.
Probe foundational knowledge. Candidates don’t need to write quicksort from scratch, but they must understand time complexity and why different algorithmic choices matter. The four essential AI-era skills—context articulation, pattern recognition, strategic review, and system orchestration—provide a strong framework for evaluation.
Test system thinking. Present scenarios where a change in one component might affect others. Strong candidates trace how systems interact.
Evaluate learning agility over current stack knowledge. The ability to learn fast matters more than whether candidates already know your specific technologies.
Look for “double literacy”—candidates who understand both AI capabilities and their own human strengths.
Redesign your coding challenges. Move from “implement this algorithm” to “evaluate this AI-generated solution, identify issues, and improve it.”
How Do I Structure Career Paths for an AI-Augmented Team?
Base advancement on ability to handle complex problems and make sound architectural decisions, not lines of code written.
Design “hybrid roles” that combine AI leverage with human judgement:
Junior I (0-1 years): Learning fundamentals with supervised AI use. Build foundational understanding through manual implementation first, then learn AI tools. Success criteria: can explain code they’ve written, can debug with guidance, understands when to ask for help.
Junior II (1-2 years): Independent AI use with peer review mastery. Use AI independently but excel at code review. Success criteria: produces production-ready code efficiently, identifies issues in code review, articulates trade-offs.
Mid-Level (2-5 years): Architectural decisions with mentoring on verification. Make sound design choices for subsystems, mentor juniors on verifying AI outputs.
Senior (5+ years): System architecture, cross-team influence, developing others. Architect systems spanning multiple teams and develop the next generation of engineers. Know when to leverage AI versus when problems require deep human thought.
Implement mandatory “trust but verify” training at every level. Teach systematic code review processes for AI outputs, debugging workflows for code you didn’t write, test-driven approaches that verify AI-generated implementations.
Create explicit knowledge transfer moments. Schedule pair programming sessions where seniors demonstrate how they evaluate AI suggestions. Conduct architecture reviews where decision-making is made visible.
Establish AI governance standards as team norms. When should developers use AI? When should they code manually? How thoroughly must AI outputs be verified?
Build competency matrices that make skill requirements explicit at each level. Emphasise validation, debugging, system thinking, architectural decision-making—not code generation. As you develop these frameworks, consider how to implement career paths organisationally at scale to ensure your advancement structures support the entire team.
How Do I Prevent Junior Talent Pipeline Collapse in My Organisation?
Treat junior hiring as strategic investment, not cost. The business case is simple: organisations save around $20,000 per employee by developing skills internally.
Set targets. A certain percentage of hires should be junior-level, even when senior-only hiring appears more efficient. Track this as a KPI.
Implement deliberate onboarding programmes. The first 90 days should include AI tool training (how to use them, how to verify outputs, when not to use them), code review processes, debugging methodology, and mentorship pairing.
Create “safe spaces” for learning. Assign juniors to internal tools, non-critical features, or sandbox environments where they can experiment with AI and learn from failures without production risk.
Establish mentorship as an explicit part of senior role expectations. Research shows 38% of developers report AI tools have reduced direct mentoring—counter this deliberately.
What does good mentoring look like? One approach: “See, I prompted it with a descriptive function name and a comment, it generated this snippet. Now, watch how I evaluate it—I write a quick unit test to verify behaviour, check error handling, consider edge cases.”
Partner with universities and boot camps to shape curricula toward AI-era skills: verification, debugging, system thinking.
Track pipeline metrics rigorously. Monitor junior hiring rates, promotion velocity, time-to-productivity, retention at each level.
Resist the “seniors-only” temptation. Short-term productivity gains are real. But this creates long-term succession crisis.
Is It Still Worth Learning to Code (or Hiring Juniors to Learn)?
Yes, learning to code remains valuable. But what “learning to code” means has changed. The focus shifts from syntax memorisation to understanding systems, from writing every line manually to verifying outputs critically.
The fundamental skills remain relevant. Understanding data structures, algorithms, time and space complexity, system design, debugging methodology—these aren’t going away. AI can’t replace understanding why code works.
The skill atrophy risk is genuine. Juniors who over-rely on AI without building foundational understanding construct “houses of cards” code they can’t debug. The struggle to solve problems—the debugging, the trial and error, the frustration that yields understanding—that’s where learning happens.
The long-term advantage belongs to developers with solid fundamentals. They adapt to new tools faster. AI is the current tool, but the foundation enables using whatever comes next.
Engineers involved in designing or implementing AI solutions earn 17.7% higher salaries than non-AI peers. Organisations will always need people who can architect systems, make trade-off decisions, debug complex issues, and evaluate AI outputs.
The opportunity for juniors is broader than before. AI raises the ceiling. Juniors who master verification, system thinking, and AI leverage can contribute at levels previously requiring years of experience.
Organisations that stop developing juniors lose succession planning capability, institutional knowledge transfer, and innovation from fresh perspectives.
For individuals: learn fundamentals deeply using traditional methods first. Build projects manually to understand how components work together, why certain patterns exist, how to debug when things break. Then learn AI tools to multiply that effectiveness.
For organisations: invest in junior development with updated approaches. Teach verification alongside generation, debugging alongside AI-assisted coding, architecture thinking alongside feature implementation. As one researcher put it: “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.”
The broken rung is just one dimension of how AI is reshaping developer careers and hiring practices. Understanding the full context helps you make strategic decisions that sustain your talent pipeline while capturing AI’s productivity benefits.
FAQ Section
What does “broken rung” mean in developer careers?
The broken rung is the structural elimination of entry-level developer positions that serve as the first step on the career ladder. Borrowed from McKinsey’s corporate leadership research, it describes how removing this first advancement step makes the entire pathway inaccessible to newcomers.
Why are tech companies reducing junior developer positions?
AI coding assistants automate the routine tasks that historically provided training for juniors. When you combine this with the significant productivity advantages that senior developers show with AI tools, junior hiring becomes economically questionable in the short term—despite creating long-term succession planning problems.
How has AI changed what companies look for in developers?
Hiring criteria have shifted from code generation ability to code verification competency. Debugging mastery, prompt engineering, system thinking, and “trust but verify” mindset now matter more than syntax memorisation.
Will AI replace junior developers entirely?
No. Organisations still need people who can verify AI outputs, debug complex systems, and develop into future senior engineers. The junior role is transforming toward validation and learning, not disappearing.
Should junior developers be allowed to use AI tools while learning?
Research suggests learning fundamentals first, then introducing AI tools. The “trust but verify” mindset requires foundational knowledge to know what to verify.
How do senior and junior developers differ in AI productivity?
Seniors ship significantly more AI-generated code because they have pattern recognition from thousands of debugging sessions, architectural knowledge to evaluate suggestions, and domain expertise to catch subtle errors. The gap is human expertise, not tool access.
What skills should developer candidates demonstrate in interviews?
Evaluate code verification, debugging methodology, prompt engineering, system thinking, and foundational knowledge. Look for “trust but verify” mindset and learning agility over current stack knowledge.
Why is the broken rung a long-term organisational risk?
Eliminating junior positions today means no seniors tomorrow. This creates succession planning crisis, knowledge transfer failure, and loss of organisational capability to develop talent internally.
How can organisations maintain junior pipelines despite AI disruption?
Commit to junior hiring as strategic investment. Implement structured onboarding with AI training. Create safe learning spaces. Establish mentorship requirements. Track pipeline metrics. Resist seniors-only hiring. Partner with educational institutions. Invest in internal development.
Is learning to code still worth it in the AI era?
Yes. Understanding systems, verifying outputs, debugging effectively, and making architectural decisions matter more than syntax memorisation. Fundamental skills remain relevant because AI can’t replace understanding why code works.
What are “hybrid roles” in AI-augmented career paths?
Hybrid roles combine AI leverage with human judgement at each level. Junior I learns fundamentals with supervised AI use. Junior II uses AI independently while mastering peer review. Mid-Level makes architectural decisions while mentoring. Progression is based on problem complexity handled, not code volume.
