A new wave of engineers is coming into the tech industry, and they’re armed with a very powerful tool—AI. In the not-so-distant past, the journey to becoming a proficient developer was marked by a long and often grueling path of understanding concepts from the ground up. You’d learn about data structures, algorithms, network protocols, and more until you had built a foundation deep enough to understand the true mechanics behind the code you were writing. However, with readily available AI models, like ChatGPT or Copilot, a new type of engineer is emerging: the AI-powered “expert beginner.”
These junior engineers have tools to generate code for them at lightning speed. Need to create a React component? Need an API integration? Need to debug a cryptic error message? AI can often provide the answers accurately and without the need to dive deeply into the underlying concepts. This means that a junior engineer can seemingly accomplish a lot—they can write functional code, push features, and even solve complex issues—without understanding how all the gears turn beneath the surface. But what does this mean for the future of software engineering?
The Illusion of Expertise
The term “expert beginner” refers to someone who can appear to be highly capable due to the tools at their disposal but lacks the foundational understanding that true expertise requires. With AI, the line between beginner and expert blurs, at least on the surface. Junior engineers, supported by these powerful tools, can produce output that looks professional and well-constructed. They might be able to add features to an existing codebase, fix bugs, and even follow best practices—at least superficially.
But often, this expertise is a veneer. The AI-generated solutions come without context or depth of knowledge. The junior engineer may not fully understand why a specific optimization is suggested or why a certain library is the right choice. They might be able to write the code but struggle to explain what happens under the hood if things go wrong or if they need to build something fundamentally new.
The Pitfalls of Skipping Fundamentals
The problem with relying on AI to write code without understanding the deeper layers is that it can lead to dangerous outcomes. Code that works is not always code that is well-written or secure. Writing functional code is one thing, but building maintainable, scalable, and performant systems is an entirely different challenge. When developers lack a strong foundation, they might miss crucial security implications, overlook performance bottlenecks, or make choices that come back to haunt the entire team when scaling becomes necessary.
Software engineering has always been about more than just getting things to work—it’s about understanding why things work and ensuring that they continue to work well into the future. Skipping over the fundamentals with the help of AI shortcuts means that the “expert beginner” might be ill-equipped to handle more advanced challenges down the road when things inevitably get complex.
The Value of Curiosity and Learning
Does this mean AI is a problem? Not necessarily. AI is an incredible tool, and when used properly, it can help developers learn faster, solve problems more effectively, and avoid reinventing the wheel. The key is in how we choose to use it.
Junior engineers have an unprecedented opportunity to accelerate their learning with AI. The best way forward is to treat AI as a collaborator—not a crutch. When AI suggests a solution, it’s an opportunity to ask, “Why does this work? What underlying principles is this using?” This curiosity-driven approach allows developers to benefit from the convenience of AI while still striving to understand the core concepts.
Engineering is about more than producing code; it’s about the lifelong pursuit of learning and understanding. If AI is used as a stepping stone to accelerate that learning, then it becomes a valuable asset. If it’s used as a shortcut to bypass understanding, it risks creating a generation of developers who are able to produce results but not solve real problems when the AI falls short.
The Future of Engineering in an AI-Powered World
The rise of AI doesn’t diminish the importance of traditional software engineering skills—it makes them more crucial than ever. We need engineers who can understand and evaluate the solutions AI provides, who can make informed decisions, and who can innovate in ways that AI cannot. The foundational knowledge of how systems work, how data flows, and how different components interact will always be important, no matter how advanced our tools become.
AI is changing what it means to be an engineer, but it doesn’t change the value of expertise. The engineers who will thrive in this new landscape will be those who use AI as a tool to deepen their understanding, not those who allow it to mask their gaps in knowledge.
Ultimately, the goal should be to rise above the “expert beginner” stage—to become a true expert who can wield AI tools with skill and insight rather than rely on them blindly. True expertise isn’t just about writing code; it’s about knowing why that code matters and how to improve it.