Mentoring Developers When AI Writes Half Their Code

How AI tools create a mentorship paradox that demands new approaches to developing engineering talent

The developer beside you types a brief prompt into GitHub Copilot and watches as perfect React components materialise on screen. Within minutes, they’ve built a feature that would have taken hours just two years ago. They submit their pull request with satisfaction, having delivered exactly what was requested.

But here’s the uncomfortable question: are they actually becoming a better developer?

This scenario plays out across engineering teams worldwide. AI coding assistants promise to accelerate development and reduce routine work, freeing developers to focus on higher-level problems. Yet many senior engineers report a troubling pattern: junior developers who can ship features rapidly but struggle to debug issues, explain their architectural choices, or adapt when requirements change.

More concerning still, organisations are promoting these developers based on their AI-assisted productivity. I’ve worked with companies that elevate developers from associate to senior roles in 18 months based purely on feature velocity, only to discover these “senior” developers can’t architect systems, mentor others, or debug complex issues without AI assistance.

We’re facing a mentorship paradox. The very tools designed to make developers more productive may be inhibiting the fundamental skill development that creates strong engineers. Rather than making mentorship easier, AI has made it more complex and more crucial than ever.

When Efficiency Becomes the Enemy of Learning

Let me share what happened at a fintech startup I recently advised. Their junior developer, Sarah, had been shipping features at remarkable speed using GitHub Copilot. Her velocity charts looked impressive. Management was already discussing her promotion to a mid-level role after just fourteen months.

Then came the day when a critical payment processing bug emerged in production. The AI-generated code was failing under specific edge conditions that hadn’t appeared in testing. Sarah spent eight hours attempting to debug code she hadn’t actually written, making random changes and hoping something would work. Eventually, a senior engineer had to step in and rewrite the entire module.

This incident revealed a fundamental issue that most engineering leaders miss: those “tedious” tasks that AI handles often provide essential learning opportunities. Moreover, the metrics used to justify Sarah’s rapid career progression told only part of the story.

The traditional career progression for software engineers follows a well-established timeline that many organisations have forgotten in their rush to retain talent:

• Associate/Junior Developer (0-2 years): Learning fundamentals, working on defined tasks with clear requirements
• Mid-Level Developer (2-4 years): Taking ownership of features, understanding system interactions, beginning to mentor others
• Senior Developer (4-7 years): Designing systems, making architectural decisions, leading technical initiatives
• Staff/Principal Engineer (7+ years): Setting technical direction, mentoring teams, solving organisation-wide technical challenges

This progression exists for good reason: each level requires not just technical skills, but judgement, communication abilities, and system thinking that develop only through experience. However, the shift from traditional job titles to level-based systems (L3, L4, L5) has obscured these expectations for many engineers. Combined with AI-assisted productivity, this creates a dangerous illusion that developers can advance rapidly through levels based solely on output metrics.

When a junior developer encounters an error in code they’ve written line by line, they understand the context, logic flow, and potential failure points. They develop mental models about how systems behave and break. But when AI generates the initial implementation, debugging becomes archaeological work: reverse-engineering someone else’s thought process to understand why it’s failing.

Cognitive scientists have identified this as the “illusion of knowledge” phenomenon. When we observe or copy solutions, our brains create the sensation of understanding without actually building the neural pathways that enable flexible application.

In programming terms, this manifests as several concerning patterns:

• Surface-level pattern matching: Junior developers learn to recognise what “looks right” without understanding underlying principles. They can modify existing AI-generated code but struggle to architect solutions from scratch.

• Fragile understanding: When problems arise that don’t match familiar patterns, they lack the mental models to diagnose root causes or generate alternative approaches. Their knowledge is like a house of cards: impressive when everything goes right, but fragile when faced with unexpected challenges.

• Context switching difficulties: Moving between different codebases, frameworks, or problem domains becomes challenging because their knowledge is tied to specific AI-generated patterns rather than transferable principles.

• Dependency formation: Rather than developing problem-solving skills, they develop prompt-engineering skills. This creates a dangerous career bottleneck: as they advance, they need architectural thinking and technical leadership capabilities that prompt engineering cannot provide.

This becomes particularly problematic when developers are promoted rapidly based on AI-assisted output rather than demonstrated competency in the skills their new role requires.

Learning to Code with AI

To understand effective AI integration, consider two different onboarding experiences I observed at similar companies:

Team A’s Approach: Full AI Immersion

Alex, a bootcamp graduate, joined a fast-moving startup where productivity was paramount. From day one, he used GitHub Copilot for everything. His first month looked successful: he shipped three features and impressed everyone with his velocity.

After six months, they promoted him to a mid-level position, citing his exceptional productivity. But cracks appeared quickly. When asked to explain his implementation choices during code review, Alex struggled. When a bug emerged in production, he couldn’t trace through his own logic. Most critically, when asked to mentor a new junior developer, Alex couldn’t explain fundamental concepts because he’d never truly learned them himself.

Two years later, Alex remained stuck at mid-level despite his continued high velocity. He couldn’t design systems independently and avoided legacy codebases where AI assistance was less effective. His rapid early promotion had created a career ceiling that would take years to overcome.

Team B’s Approach: Scaffolded Integration

Emma joined a consulting firm that had developed a structured approach to AI-assisted learning. Her first month involved implementing fundamental algorithms manually. Only after demonstrating competency was she introduced to Copilot for boilerplate generation.

Emma’s initial velocity was lower than Alex’s, but her manager explained the traditional progression timeline and how each level built upon previous skills. By month three, Emma was using AI for routine tasks whilst maintaining understanding of underlying principles. When she reached the 18-month mark, she possessed the analytical and communication skills necessary for mid-level responsibilities.

Three years later, Emma was promoted to senior developer with genuine system design capabilities and strong mentoring skills. Her career trajectory looked fundamentally different: she was developing the analytical and leadership abilities needed for staff and principal roles.

The difference wasn’t intelligence or motivation. It was the learning structure that surrounded AI tool adoption and a clear understanding of what each career level actually required.

Building Knowledge Brick by Brick

Effective mentorship in the AI era requires scaffolded learning: carefully structured approaches that introduce AI assistance without creating dependency, whilst maintaining clear expectations about the skills needed for career progression.

• Foundation First: Before introducing AI assistants, ensure junior developers can implement basic functionality manually. They should understand loops, conditionals, data structures, and common algorithms without assistance.

• Progressive Enhancement: Introduce AI tools in stages, starting with specific, bounded tasks. Rather than using Copilot for entire functions, begin with code completion for routine syntax or boilerplate generation.

• Deliberate Practice Sessions: Create exercises where junior developers must explain AI-generated code line by line, identify potential issues, or modify implementations to meet changing requirements.

• Context-Rich Challenges: Design problems that require understanding the broader system, not just isolated code generation.

• Pair Programming with Purpose: During pair programming sessions, alternate between AI-assisted and manual implementation. Have junior developers drive whilst you provide guidance.

I recommend what I call the “Algorithm Thursday” approach: dedicate time each week to implementing fundamental algorithms from scratch. The goal isn’t memorising implementations but understanding problem-solving patterns that transfer across domains.

Another effective technique is “code archaeology”, where we present junior developers with AI-generated solutions and ask them to reverse-engineer the requirements. What problem was this solving? What assumptions did the AI make?

Crucially, this scaffolded approach must be tied to clear progression criteria. Junior developers need to understand that moving from Level 2 to Level 3 requires demonstrating specific competencies beyond feature velocity: system thinking, debugging complex issues, adopting core engineering principles (such as SRP, KISS, and DRY amongst others), explaining architectural decisions, and beginning to mentor others.

The Art of Questioning: Developing Critical Thinking

Watch this code review conversation between Maya, a senior engineer, and David, a junior developer who just submitted an AI-generated authentication system:

David: “Here’s the login function. Copilot generated it and all the tests pass.”

Maya: “Looks clean. Walk me through what happens when someone enters the wrong password five times in a row.”

David: “Uh… it would keep checking the database each time?”

Maya: “Right. And with a million users, what might that mean for our database?”

David: “Oh. It could get overwhelmed with failed login attempts.”

This conversation illustrates effective mentorship. Instead of pointing out the missing rate limiting, Maya guided David to discover the vulnerability himself. This questioning approach becomes particularly important when mentoring developers who appear productive but may lack foundational understanding.

Questions That Build Analytical Thinking

The most effective mentoring questions follow predictable patterns:

• “What happens when…” - Expose edge cases and failure modes
• “How might this behave with…” - Reveal scalability and performance implications
• “What assumptions…” - Uncover hidden dependencies and constraints
• “If we needed to…” - Develop architectural thinking

This type of critical thinking becomes crucial as developers advance through career levels. A mid-level developer might implement a solution correctly, but a senior developer must evaluate multiple approaches, consider long-term implications, and communicate trade-offs to stakeholders.

Beyond Happy Path Thinking

AI excels at generating solutions that work perfectly under ideal conditions. After reviewing AI-generated code, I recommend the “Evil User Exercise”: spend ten minutes trying to break the implementation. This develops the paranoid mindset that distinguishes reliable systems from fragile ones.

The Tournament Method

Generate multiple solutions to the same problem using different AI tools, then compare approaches. This develops understanding of trade-offs and design choices whilst demonstrating the type of comparative analysis that senior developers must possess.

Common AI Mentorship Pitfalls

After working with dozens of teams adopting AI tools, I’ve identified recurring anti-patterns that sabotage junior developer growth and create false signals for promotion:

• The Velocity Trap: Management promotes developers based on increased story points and faster feature delivery. But velocity metrics can mask underlying skill atrophy. I worked with one team where junior developers were completing tickets 300% faster than before AI adoption, leading to rapid promotions. When they attempted to modify legacy code without AI assistance, productivity plummeted.

• The Copy-Paste Syndrome: Junior developers learn to recognise “good enough” AI output without developing judgement about code quality. Their code reviews look sophisticated and implementations work correctly, but they cannot explain architectural decisions or adapt designs when requirements change.

• The Prompt Engineering Obsession: Some developers become experts in AI interaction but lack foundational knowledge needed for senior-level responsibilities. I’ve seen developers who could generate impressive components through clever prompting but couldn’t explain basic concepts like state management.

• The Context Collapse: AI tools work best with isolated problems, but real software development requires understanding system interactions. Developers who rely heavily on AI often struggle with integration challenges that become critical at senior levels.

Recovery Strategies

When teams recognise these patterns:

• Implement AI-free zones: Designate projects where AI assistance is prohibited
• Pair programming intensives: Increase structured pairing sessions where developers must explain reasoning
• Architectural reviews: Regularly discuss system design decisions that AI tools cannot make
• Legacy code rotation: Ensure developers work with older codebases that predate AI assistance

Archaeological Debugging

When junior developers didn’t write the original implementation, they must develop detective skills to understand unfamiliar code before they can fix it. This skill becomes increasingly important as developers advance: while an associate developer might receive debugging guidance, mid-level and senior developers are expected to diagnose complex issues independently.

The CSI Approach to Code Investigation

• Crime Scene Analysis: Before changing anything, understand what the code is supposed to do
• Evidence Collection: Use logging and debugging tools extensively to gather data about actual versus expected behaviour
• Hypothesis Formation: Form explicit theories about potential causes to prevent random code changes
• Controlled Experiments: Test hypotheses systematically by modifying one element at a time

Building Debugging Intuition

The most effective debugging skill is developing intuition about where problems typically occur:

• Common AI Code Pitfalls: Inadequate error handling, missing edge case validation, assumptions about data structure consistency
• System Integration Points: Problems frequently occur at boundaries between components
• Performance Blind Spots: AI optimises for correctness over performance, creating solutions that work in development but fail under production load

This capability becomes essential as developers progress to senior roles where they’re expected to solve complex technical problems independently.

Assessment in an AI World

Traditional assessment methods become problematic when AI tools are involved. We need approaches that focus on skills that remain fundamentally human whilst aligning with realistic career progression expectations.

When developers are advanced from associate to mid-level roles in 12-18 months instead of the traditional 2-3 years, assessment methods must accurately evaluate whether they possess the skills for their new responsibilities.

Level-Appropriate Assessment

• Associate/Junior (Level 2): Focus on implementation quality, learning velocity, and ability to work with guidance. AI assistance is acceptable and beneficial.

• Mid-Level (Level 3): Expect independent problem-solving, system understanding, code review participation, and beginning mentorship capabilities. Core competencies must be demonstrated without AI assistance.

• Senior (Level 4): Require system design abilities, architectural decision-making, effective mentoring, and technical leadership. AI assistance for implementation is fine, but strategic decisions must be human-driven.

Key Assessment Areas

• Architecture and Design Thinking: Ability to design systems and explain architectural decisions
• Problem Decomposition Skills: Breaking complex problems into manageable components
• Code Review Participation: Quality of feedback provided to peers
• Technical Communication: Explaining concepts to non-technical stakeholders
• Cross-Domain Knowledge Transfer: Applying concepts across different technologies

The Future of Career Progression

The ultimate test of mentorship effectiveness is whether junior developers develop skills needed for senior roles whilst maintaining realistic expectations about career advancement.

Understanding Traditional Progression

Many developers today lack clear understanding of what career progression traditionally looks like. The shift from descriptive job titles to numerical levels has obscured the skills and experience that distinguish each career stage.

Traditional timelines aren’t arbitrary: they reflect the time needed to develop genuine expertise. Each transition requires new capabilities that go beyond technical implementation:

• Associate to Mid-Level: Developing product thinking, understanding user requirements, making technical trade-offs independently

• Mid-Level to Senior: System-level thinking, technical leadership, mentoring capabilities

• Senior to Staff: Business context understanding, cross-functional collaboration, organisation-wide technical thinking

Managing Progression Expectations

Effective mentorship must help developers understand realistic progression timelines and why rapid promotion might not serve their long-term career interests. This means having honest conversations about:

• How skills required for senior roles develop through experience, not just output
• The difference between being capable of senior-level work and being ready for senior-level responsibility
• How premature promotion can create career ceilings that are difficult to overcome

The goal is developing engineers who can progress sustainably through their careers, building the experience and judgement needed for each level rather than advancing based on short-term productivity metrics.

Organisational Implementation

Organisations need systematic approaches to implementing AI-aware mentorship whilst resisting pressure to accelerate career progression unrealistically.

Promotion Timeline Reform

Perhaps most challenging, organisations must resist market pressure to accelerate promotion timelines:

• Market Reality Check: While competitors might promote developers rapidly, this often creates organisational debt: engineers in senior roles without senior capabilities.

• Level Expectations Clarity: Clearly define what capabilities are required at each level, beyond just technical implementation.

• Alternative Recognition: Create ways to recognise high-performing junior developers without premature promotion through financial incentives, learning opportunities, or project leadership roles.

• Long-Term Perspective: Help stakeholders understand that sustainable technical leadership requires experience and judgement that develop over time.

Integration with Performance Management

• Include mentorship outcomes in performance reviews
• Make team development a requirement for advancement to staff/principal levels
• Ensure managers understand the long-term costs of premature promotion
• Build systems that support sustainable career development rather than optimising for short-term metrics

The Strategic Imperative

Many organisations are unknowingly creating a leadership pipeline crisis. If junior developers don’t develop analytical, communication, and strategic thinking skills, and if they’re promoted before developing these capabilities, companies will face shortages of effective technical leadership in 3-5 years.

This problem is invisible today because AI-assisted junior developers are productive in implementation roles, and rapid promotion satisfies short-term retention goals. But organisations will find themselves with “senior” engineers who cannot make architectural decisions and “staff” engineers who cannot influence across teams.

The Economic Reality

The cost of premature promotion extends beyond individual careers. Teams with inappropriately leveled engineers face increased technical debt, reduced innovation, and decreased effectiveness in complex problem-solving. The short-term retention benefits of rapid promotion are overwhelmed by long-term capability deficits.

As AI capabilities expand, the value of uniquely human skills increases. The teams that develop these capabilities systematically, whilst maintaining realistic progression standards, will have sustainable competitive advantages.

Conclusion: The Future of Engineering Excellence

The integration of AI tools into software development isn’t making mentorship easier: it’s making it more sophisticated and more crucial. We can choose the path of short-term efficiency and rapid promotion that creates AI-dependent developers with inflated titles, or we can choose the more challenging path of developing adaptive expertise whilst respecting the experience requirements of senior roles.

Effective mentorship in the AI era requires new frameworks, new skills, and new assessment approaches. Senior engineers must evolve from technical teachers to learning coaches, helping junior developers develop both AI collaboration skills and robust foundational capabilities that enable sustainable career progression.

The evidence is clear: junior developers who learn to leverage AI while maintaining deep understanding of fundamental principles, and who advance through career levels at appropriate speeds, outperform those who become dependent on AI assistance or are promoted beyond their capabilities.

The organisations that recognise this imperative and invest in structured mentorship programmes whilst maintaining realistic progression standards will build competitive advantages in talent attraction, retention, and long-term capability. Those that assume AI tools automatically improve outcomes, or that rapid promotion creates genuine senior talent, may find their technical organisations weakened.

The question isn’t whether AI will transform software development: it already has. The question is whether we’ll adapt our mentorship approaches and career progression practices to ensure the next generation of engineers develops the full range of capabilities they’ll need to thrive, whilst advancing at speeds that allow genuine expertise to develop.

The future belongs to engineers who can think creatively, lead effectively, and adapt continuously. Our responsibility as mentors is ensuring we develop these capabilities, not just AI proficiency, whilst helping engineers understand what sustainable career progression requires. The stakes couldn’t be higher, and the opportunity couldn’t be greater.

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This tension between AI efficiency and human development creates significant opportunities for structured coaching and leadership development. Whether you’re an engineering leader looking to develop more effective approaches to team development in an AI world, or an individual engineer seeking to navigate career progression and skill development alongside AI tools, I’d welcome a conversation about how these frameworks might apply to your situation. I provide both organisational consulting for teams and managers, as well as personal coaching for engineers at all levels who want to build sustainable careers and develop genuine senior-level capabilities.

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About The Author

Tim Huegdon is the founder of Wyrd Technology, a consultancy focused on helping engineering teams achieve operational excellence and strategic AI adoption. With over 25 years of experience in software engineering and technical leadership, Tim helps organisations and individuals navigate the hidden costs of technology adoption and build sustainable competitive advantage through human-AI collaboration rather than replacement.