There’s a peculiar irony unfolding across the software industry right now. AI coding assistants like Claude Code, GitHub Copilot, and their competitors have made it easier than ever to produce working code. Yet this very capability is quietly eroding the foundational skills that separate truly excellent engineering organizations from those that merely ship features.
Let me be direct: if you’re leading a technology organization and you’re not actively concerned about skills degradation among your engineering teams, you should be.
The Illusion of Productivity
AI tools are genuinely impressive. An engineer can describe what they want, and moments later they have functioning code. Entire features materialize in hours instead of days. From a pure output perspective, the productivity gains are undeniable.
But here’s what we’re seeing in organizations that have fully embraced AI-assisted development without maintaining rigor around technical fundamentals: they’re building software that works today but becomes increasingly difficult to maintain, extend, and debug tomorrow.
The problem isn’t the AI. The problem is what happens when engineers stop needing to understand what they’re building.
The Degradation Loop
When you don’t have to think through a solution yourself, you don’t develop the mental models that make you effective at understanding and modifying that solution later. This creates a compounding problem:
An engineer uses AI to generate a solution they don't fully understand.
There's no immediate feedback that anything is wrong, so the lack of understanding goes unnoticed.
When changes are needed, the engineer turns to AI again rather than building the understanding needed to proceed independently.
No one has the mental model to recognize the debt forming beneath the surface.
Eventually, even with AI assistance, the system resists change. What once moved fast is now expensive to modify at all.
Teams that could move fast six months ago are now struggling with changes that should be straightforward. The AI that helped them build quickly can’t help them understand why their system behaves unexpectedly under load, why their data model makes certain queries impossible to optimize, or why their architecture can’t accommodate new requirements without significant rework.
What AI Can’t Replace
AI coding assistants excel at generating code that matches patterns from their training data. They’re remarkably good at the “how” of implementation. But software engineering has never been primarily about the “how.”
The hard problems in software, the ones that determine whether your system will scale, whether it will be maintainable, whether it will meet evolving business needs, are problems of understanding:
Deep enough to model it correctly, not just implement what was asked.
Between different architectural approaches, and what each choice forecloses in the future.
Of the technologies you're using, and how they behave under conditions that don't show up in tests.
Of technical decisions on future optionality, including what you're making harder or impossible down the road.
AI can help you implement a caching layer. It cannot tell you whether caching is the right solution to your performance problem, or whether you’re introducing consistency issues that will manifest as subtle bugs six months from now.
The Brittleness Problem
Software built by engineers who don’t understand it tends to be brittle in specific, predictable ways:
AI-generated code often includes error handling that looks comprehensive but doesn't address the failure modes that matter. When something goes wrong in production, the logging is generic, recovery paths haven't been thought through, and debugging becomes archaeology.
Without a clear mental model, codebases accumulate unnecessary abstractions, redundant systems, and architectural inconsistencies. Each addition makes sense in isolation; the aggregate is a maze.
AI assistants learn from public codebases, reproducing common patterns -- including patterns that are overused or misapplied. Engineers who don't understand why a pattern exists can't recognize when it's inappropriate.
Systems designed without deep understanding of their dependencies tend to break in surprising ways when those dependencies change. The engineer who let AI handle the integration didn't build the mental model needed to anticipate edge cases.
What This Means for Technology Leaders
If you’re responsible for a technology organization, you have a choice to make. You can optimize for short-term output, encourage maximum AI usage, and deal with the consequences when your codebase becomes unmaintainable. Or you can take a more nuanced approach that preserves and develops the technical capabilities that will matter over the long term.
Here’s what I’d recommend:
When evaluating engineering work, ask not just "does it work?" but "do we understand why it works?" Code review should focus on mental models and reasoning, not just correctness.
Some problems should be solved without AI assistance, specifically because the struggle of solving them builds understanding. This is especially true for junior engineers who need to develop foundational skills.
The ability to diagnose and fix complex problems is a core engineering skill that AI struggles to replicate. Celebrate and develop this capability deliberately.
AI can help implement architectural decisions, but the decisions themselves -- and the reasoning behind them -- must come from engineers who understand the system holistically. Don't let architecture emerge from a series of AI-generated solutions.
In an era when anyone can generate code, the differentiator is understanding. Seek engineers who demonstrate genuine curiosity about how things work and why -- not just facility with tools.
How VergeOps Can Help
Building and maintaining deep technical capabilities across your engineering organization is challenging work, especially when you’re simultaneously trying to deliver on business commitments. This is where external expertise can make a significant difference.
At VergeOps, we work with technology organizations to strengthen their technical foundations while navigating the complexities of AI-assisted development. Our approach focuses on building lasting internal capability, not creating dependency on consultants.
Architectural guidance and professional services. We partner with your teams on critical projects, providing the architectural expertise and hands-on implementation support that ensures systems are built right from the start. Our consultants don’t just deliver solutions. They work alongside your engineers, transferring knowledge and building understanding as we go.
Training programs designed for the AI era. Our workshops address the specific skills gaps that AI-assisted development creates. We focus on the fundamentals that matter most: debugging complex systems, understanding architectural trade-offs, designing for maintainability, and building the mental models that separate effective engineers from those who merely generate code.
Coaching and mentoring for technical leaders. Developing technical talent requires more than training courses. We provide ongoing coaching relationships that help your senior engineers and architects grow into the leaders your organization needs: people who can guide teams toward technical excellence while leveraging AI tools appropriately.
Architectural assessments and governance frameworks. Sometimes you need an outside perspective on where you stand. We evaluate codebases, architectures, and engineering practices to identify skills gaps and technical debt before they become crises, then help you build the governance structures that prevent future problems.
The goal isn’t to slow down your use of AI. It’s to ensure your teams have the foundation to use these powerful tools responsibly, building software that’s not just functional today, but maintainable and evolvable for years to come.
The Path Forward
We’re not suggesting we abandon AI coding assistants. They’re powerful tools that, used thoughtfully, can genuinely enhance engineering effectiveness. But a power tool in the hands of someone who doesn’t understand the craft produces shoddy work quickly.
The organizations that will thrive in this era are those that recognize AI as an amplifier of capability, not a replacement for it. They’ll use AI to move faster on problems they understand deeply, while continuing to invest in the human expertise that makes that understanding possible.
Technical skills aren’t less important because AI can write code. They’re more important, because AI makes it possible to build complex systems without understanding them. That’s a recipe for failure that scales as fast as AI-generated code does.
The question isn’t whether your team can ship features with AI assistance. The question is whether they’ll be able to maintain, debug, and evolve what they’ve built a year from now.
Make sure your answer is yes.